Safe operation system for hub cabin of wind generating set

Abstract

The invention relates to a safe operation system for a hub cabin of a wind generating set, and the system comprises an Internet of Things gateway which is configured to obtain the field operation information of the hub cabin, and judges the operation state of an operator in real time; the plurality of motion detection nodes, the motion detection unit and the body area network gateway are configured to collect inertial vector data of each node of the body of an operator; the body area network gateway is configured to collect and fuse inertial vector data of the plurality of motion detection units, and judge the posture change of the operator; and the Internet of Things gateway confirms the working state of the working personnel according to the early warning notification and sends alarm information to the working personnel or a remote server through an alarm module. According to the invention, by acquiring the detection data of the motion detection units of all parts of the body of the operator, the posture change of the operator is judged by the body area network gateway, power line interference does not exist, and an alarm can be given to the operator in the operation area in time when the operator falls down or rolls over.

Description

Technical Field

[0001] The present application relates to a maintenance operation system for a wind turbine generator set, belongs to the field of wind turbines, and particularly relates to a safe operation system for a hub compartment of a wind turbine generator set. Background Art

[0002] Wind turbines, also known as wind turbines, are referred to as wind turbines or wind turbines. They are devices that convert the kinetic energy of airflow into mechanical energy and are one of the necessary elements of a wind power plant. The wind engine drives the blades of the wind turbine to rotate through wind power, and then increases the speed of rotation through a speed increaser to enable the generator to generate electricity. The structures of existing wind turbines are roughly the same, such as Figure 1 As shown, it generally includes a tower base 10, a tower 1 and a nacelle 2 located at the top of the tower 1. The nacelle 2 is the core part of the wind turbine generator set and is located at the top of the tower 1. It contains major power generation equipment, such as generators, gearboxes, control systems, brake systems, etc. The nacelle 2 is the power source of the entire wind turbine generator and is responsible for converting the mechanical energy of the wind turbine blades 4 into electrical energy. The hub cabin 3 is located at the front end of the nacelle 2 and is directly connected to the wind turbine blades 4. The hub cabin 3 is the part that connects the wind turbine blades and the main shaft, and usually includes a structure for fixing the wind turbine blades 4. It also carries the rotational motion of the blades and transmits this power to the generator in the nacelle. The hub cabin 3 of the wind turbine generator set is a key component that connects the wind turbine blades and the main shaft (not shown), and contains important components such as a pitch control system and a generator. Therefore, regular maintenance of the hub cabin 3 is of positive significance to ensuring equipment reliability, extending equipment life, improving power generation efficiency, and reducing safety risks. During operation, the operator can enter through the tower entrance 11 of the tower base 10, and reach the nacelle 2 through the elevator, and then enter the hub compartment 3. However, since the wind turbine blades 4 are in a state of free rotation driven by wind power under normal working conditions, the hub compartment 3 is also in a rotating state at the same time. This leads to a high risk for the operation and maintenance personnel when inspecting the hub compartment 3. For this reason, a hub lock disk (not shown) is usually provided on the hub compartment 3 of the existing wind turbine generator set, and the hub lock disk can be plugged and matched with the hub lock pin (not shown) to put the hub compartment 3 in a locked state. However, there are still cases where the hub lock pin is not completely locked due to negligence or inadequate operation of the operation and maintenance personnel. Such irregular operation is very likely to cause safety accidents. Moreover, the biggest problem with the hub compartment is that the hub compartment is independent of other parts of the wind turbine, and sensors with power cords cannot be used. Without a power cord, the existing operation system has a very short continuous use time and it is very inconvenient to replace the battery. It can be seen that the current wind turbine hub compartment safety operation system is greatly affected by environmental factors. The existing system cannot accurately know the safety status of hub compartment operators and needs to be improved. Summary of the invention

[0003] In view of the above problems existing in the prior art, one aspect of the present invention is to provide a safe operation component deployed in a nacelle and a hub compartment of a wind turbine, specifically, a safe operation system for a hub compartment of a wind turbine.

[0004] In order to achieve the above-mentioned first object, the present invention provides a safe operation system for a hub compartment of a wind turbine generator set, wherein the wind turbine generator set comprises a tower and a nacelle located at the top of the tower, and a hub compartment is arranged on one side of the nacelle. The system comprises: An IoT gateway, which is deployed in the nacelle and / or hub compartment of the wind turbine generator set and is configured to obtain on-site operation information of the hub compartment and determine the operation status of the operator in real time; The motion detection unit and the body area network gateway are constructed as wearable devices and are respectively deployed on different parts of the operator's body. The motion detection unit is configured to collect inertial vector data of each node of the operator's body; the body area network gateway is configured to collect and fuse the inertial vector data of multiple motion detection units, extract inertial vector features from the multiple motion detection units and analyze the inertial vector features, judge the posture change of the operator according to the analysis result, and send an early warning notification if the posture of the operator is judged to be abnormal; The Internet of Things gateway confirms the operating status of the operator according to the early warning notification and sends an alarm message to the operator or the remote server through the alarm module.

[0005] In the present invention, the body area network is a human-centered wireless communication technology used for data exchange between devices around the human body. The body area network gateway in this application is a hardware device or software system that can aggregate data collected by various sensors in the body area network (such as heart rate, blood pressure, body temperature sensors, etc.) and transmit it to the cloud, remote server or local device through other communication networks (such as Wi-Fi, 4G / 5G, Bluetooth, ZigBee, etc.) for further processing.

[0006] In some embodiments, preferably, the early warning notification includes a vibration notification, a sound alarm and / or a light warning, wherein the vibration notification is issued by a wearable device deployed on the body of the operator, and the sound alarm or light warning is issued by an alarm module deployed in the working area, wherein the alarm module is communicatively connected to the body area network gateway via the Internet of Things gateway.

[0007] Specifically, in the present invention, the Internet of Things gateway specifically includes a video analysis unit, a posture confirmation unit and a communication unit. The video analysis unit is configured to confirm the physical state of the operator through the video stream information and audio information of the on-site operation collected by the video acquisition unit and the audio acquisition unit.

[0008] According to the above scheme, in some embodiments of the present invention, a speaker and a human-machine interface can also be deployed in the wheel hub compartment. When the IoT gateway confirms the physical condition of the operator, it can make a video call with the remote manager through the speaker and the human-machine interface. By obtaining the video information and sound information of the operation area in real time, the manager or medical staff can provide remote guidance to the operator.

[0009] In this application, a remote server is a server device or virtual environment that is accessed remotely over a network and can host applications, store data, or provide computing services. Users do not need to directly contact the physical device, but access and control the server through remote connection protocols (such as SSH, RDP, FTP, etc.).

[0010] The body area network gateway at least includes: A data acquisition module is configured to be communicatively connected to each motion detection unit, and based on a unified time synchronization mechanism, acquire inertial vector data of a plurality of the motion detection units and perform data fusion; a feature extraction module configured to extract an acceleration peak feature and an angular velocity change feature from the inertial vector data acquired by the data acquisition module, wherein the acceleration peak represents the impact force at the moment of falling, and the angular velocity change feature represents the rotational motion when a fall may occur; A feature analysis module, configured to obtain a posture change rate according to the acceleration peak value and the angular velocity change characteristics, wherein the posture change rate represents the posture transformation of the operator; The machine learning algorithm model is configured to judge the posture changes of the operator through a case library of the posture change rate of each body node through an SVM, decision tree, random forest or neural network algorithm model.

[0011] Preferably, when performing data fusion, the data acquisition module includes: Time-synchronize the data from different motion detection units to ensure the accuracy of fusion; The raw data of sensors of multiple motion detection units are stitched together to form a high-dimensional vector; After the raw data of the sensors of multiple motion detection units are aligned in time through the interpolation method, the noise is removed through filtering to ensure the smoothness of the data.

[0012] Preferably, when extracting the acceleration peak feature, the feature extraction module includes: The data of the acceleration sensor is processed by a low-pass filter to remove the high-frequency noise in the acceleration sensor and correct the gravity acceleration. The corrected acceleration value is represented by the acceleration value a in the x, y, and z directions. x、a y and a z ; The corrected acceleration value a x 、a y and a z The modulus a of the total acceleration vector is synthesized total ; The total acceleration vector is sampled in a specific time window, with a sampling frequency between 0.5 seconds and 2 seconds; the acceleration vector values ​​of multiple data sampling points are obtained; Set the acceleration threshold. When a certain acceleration vector value exceeds the acceleration threshold, it is considered that the acceleration peak occurs.

[0013] Preferably, when acquiring the angular velocity change feature, the feature extraction module includes: Get the angular velocity data of the x-axis, y-axis and z-axis collected by the angular velocity sensor, which are defined as w x 、w y and z ; The angular velocity data is integrated over time to obtain the angular velocity change value.

[0014] Preferably, the feature analysis module includes: Based on the change in angular velocity, the three Euler angles of body rotation are obtained: pitch angle (Pitch): represents the rotation of the torso around the horizontal axis (x-axis), such as leaning forward and backward; roll angle (Roll): represents the rotation of the torso around the longitudinal axis (y-axis), such as side tilt; yaw angle (Yaw): represents the rotation of the torso around the vertical axis (z-axis), that is, left and right rotation.

[0015] In other embodiments, the motion detection unit includes a first detection unit, a second detection unit, a third detection unit, a fourth detection unit and a fifth detection unit, each of which includes at least a processor, an inter-node communication unit and a posture sensor, the posture sensor includes at least an acceleration sensor and an angular velocity sensor, the acceleration sensor is configured to obtain the instantaneous acceleration of each body node in real time, and the angular velocity sensor is configured to obtain the instantaneous angular velocity of each body node in real time; the inter-node communication unit is configured to establish a communication connection between each motion detection unit and the body area network gateway; the processor is configured to pre-process the data collected by the posture sensor. Specifically, the first detection unit, the second detection unit, the third detection unit, the fourth detection unit and the fifth detection unit of the present invention are multiple posture sensors arranged at multiple locations on the operator's body, for example, the first detection unit can be a head posture sensor, the second detection unit can be a torso detection sensor, the third detection unit can be a waist detection sensor, the fourth detection unit can be a wrist detection sensor, and the fifth detection unit can be a foot detection sensor. Furthermore, in the above description, the head posture sensor, the body detection sensor, the waist detection sensor, the wrist detection sensor, and the foot detection sensor are actually the same posture sensors.

[0016] Preferably, when the processor pre-processes the data collected by the posture sensor, the processor includes: Filtering is used to remove high-frequency noise from the data collected by the attitude sensor; Moving average is used to smooth the data collected by the posture sensor and reduce sudden noise interference; and / or normalization processing is used to adjust the data magnitude so that the data collected by the posture sensors of each body node can be compared with each other. Among them, filtering processing is used to remove high-frequency noise in the data collected by the posture sensor and retain useful low-frequency signals (such as human body motion related signals). The purpose is to eliminate high-frequency interference signals and enhance the reliability and accuracy of the data. The method includes low-pass filtering, band-pass filtering or sliding window filtering, wherein the goal of low-pass filtering is to remove noise higher than the frequency by setting the cutoff frequency. Its implementation method includes using a discrete filter (such as an FIR or IIR filter). Band-pass filtering is to retain the frequency range of interest while filtering out noise in other frequency bands. In the present invention, moving average is used to smooth the data collected by the posture sensor and reduce sudden noise interference. The purpose is to reduce random fluctuations and enhance signal stability. Its implementation method is to take the average value of the current moment and several data points before and after it as the processing result at the current moment.

[0017] More specifically, as a preference, when performing data fusion, the data acquisition module includes: Time-synchronize the data from different motion detection units to ensure the accuracy of fusion; The raw data of sensors of multiple motion detection units are stitched together to form a high-dimensional vector; After the raw data of the sensors of multiple motion detection units are aligned in time through the interpolation method, the noise is removed through filtering to ensure the smoothness of the data.

[0018] Preferably, when extracting the acceleration peak feature, the feature extraction module includes: The data of the acceleration sensor is processed by a low-pass filter to remove the high-frequency noise in the acceleration sensor and correct the gravity acceleration. The corrected acceleration value is represented by the acceleration value a in the x, y, and z directions. x 、a y and a z ; The corrected acceleration value a x 、a y and a z The modulus a of the total acceleration vector is synthesized total ; The total acceleration vector is sampled in a specific time window, with a sampling frequency between 0.5 seconds and 2 seconds; the acceleration vector values ​​of multiple data sampling points are obtained; Set the acceleration threshold. When a certain acceleration vector value exceeds the acceleration threshold, it is considered that the acceleration peak occurs.

[0019] Preferably, when acquiring the angular velocity change feature, the feature extraction module includes: Get the angular velocity data of the x-axis, y-axis and z-axis collected by the angular velocity sensor, which are defined as w x 、w y and z ; The angular velocity data is integrated over time to obtain the angular velocity change value.

[0020] Preferably, the feature analysis module includes: Based on the change in angular velocity, the three Euler angles of body rotation are obtained: pitch angle (Pitch): represents the rotation of the torso around the horizontal axis (x-axis), such as leaning forward and backward; roll angle (Roll): represents the rotation of the torso around the longitudinal axis (y-axis), such as side tilt; yaw angle (Yaw): represents the rotation of the torso around the vertical axis (z-axis), that is, left and right rotation.

[0021] The wind turbine hub compartment safety operation system provided by the present invention forms a motion detection unit by acquiring wearable devices on the head, torso, waist, wrists, ankles and other parts of the operator. The motion detection unit communicates with a body area network gateway formed by the wearable devices to transmit sensor data in real time. The body area network gateway determines the posture changes of the operator without power line interference and the posture changes are accurately determined. When the operator falls or rolls, a warning can be issued in time to the operator in the operation area. At the same time, the remote server can be connected through the Internet of Things network, and remote management personnel or medical staff can provide remote assistance to determine the status and even injuries of the operator. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other objects, features and advantages of the present application will become more apparent.

[0023] Figure 1 A schematic diagram of the structure of a wind turbine is shown; Figure 2 The system structure block diagram of the wind turbine hub compartment safety operation system of the present invention is shown; Figure 3 An exemplary structural block diagram of a motion detection unit of the wind turbine hub compartment safety operation system of the present invention is shown.

[0024] Main reference numerals: 1-tower; 2-cabin; 3-hub compartment; 4-fan blades; 10-tower base; 11-tower entrance; 31-speaker; 32-human-machine interface; 33-video acquisition unit; 34-audio acquisition unit; 100-motion detection unit; 101-first detection unit; 102-second detection unit; 103-third detection unit; 104-fourth detection unit; 105-fifth detection unit; 1001-processor; 1002-inter-node communication unit; 1003-attitude sensor; 1004-acceleration sensor; 1005-angular velocity sensor; 200 - remote server; 300-medical staff; 400-management personnel; 500-alarm module; 600-IoT gateway; 601-video analysis unit; 602-gesture confirmation unit; 700 - body area network gateway; 701 - data acquisition module; 702 - feature extraction module; 703 - feature analysis module; 704 - neural network algorithm model. DETAILED DESCRIPTION

[0025] For the following, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0026] like Figures 1 to 3 As shown, the wind turbine hub cabin safety operation system provided by the present invention comprises: A plurality of motion detection units, which are constructed as wearable devices and are respectively deployed at different parts of the operator's body, are configured to collect inertial vector data of each node of the operator's body; the motion detection unit in the present invention may include a first detection unit, a second detection unit, a third detection unit, a fourth detection unit and a fifth detection unit, each of the motion detection units at least includes a processor, an inter-node communication unit and a posture sensor, the posture sensor at least includes an acceleration sensor and an angular velocity sensor, the acceleration sensor is configured to obtain the instantaneous acceleration of each body node in real time, and the angular velocity sensor is configured to obtain the instantaneous angular velocity of each body node in real time; the inter-node communication unit is configured to establish a communication connection between each motion detection unit and the body area network gateway; the processor is configured to pre-process the data collected by the posture sensor. In addition, in the present invention, the posture sensor may also include a gyroscope, and the gyroscope is configured to detect the angle change value when the operator's body rotates.

[0027] The system of the present invention also includes a body area network gateway, which is deployed on the body of the operator and can usually be a smart communication device such as a mobile phone. It is configured to collect and fuse the inertial vector data of multiple motion detection units, extract inertial vector features from the multiple motion detection units and analyze the inertial vector features, and judge the posture change of the operator based on the analysis results. If the posture of the operator is judged to be abnormal, an early warning notification is sent. The early warning notification may include a vibration notification, a sound alarm and / or a light alarm, wherein the vibration notification is issued by a wearable device deployed on the operator's body, such as a watch or a bracelet. The sound alarm or light alarm is issued by an alarm module deployed in the working area, wherein the alarm module and the body area network gateway are connected to each other through an Internet of Things gateway deployed in the wind turbine.

[0028] In addition, in the system of the present invention, the Internet of Things gateway network is connected to a remote server, and the remote server is configured to obtain real-time video and sound information of the operating area while receiving remote warning information, so that managers or medical staff can provide remote guidance to operators.

[0029] In some embodiments, the processor of the motion detection unit, when preprocessing the data collected by the gesture sensor, includes: Filtering is used to remove high-frequency noise from the data collected by the posture sensor; common filters such as first-order low-pass filters and Butterworth filters can be used for this filtering. Moving average is used to smooth the data collected by the posture sensor and reduce sudden noise interference; and / or normalization is used to adjust the data level so that the data collected by the posture sensors of each body node can be compared with each other.

[0030] Specifically, in the system of the present invention, the body area network gateway at least includes: a data acquisition module, which is configured to be communicatively connected to each motion detection unit, and based on a unified time synchronization mechanism, obtain the inertial vector data of multiple motion detection units and perform data fusion; a feature extraction module, which is configured to extract acceleration peak characteristics and angular velocity change characteristics from the inertial vector data obtained by the data acquisition module, the acceleration peak represents the impact force at the moment of falling, and the angular velocity change characteristics represent the rotational movement during a possible fall; a feature analysis module, which is configured to obtain the posture change rate based on the acceleration peak and the angular velocity change characteristics, and the posture change rate represents the posture transformation of the operator; a machine learning algorithm model, which is configured to judge the posture change of the operator through a case library of posture change rates of each body node through an SVM, decision tree, random forest or neural network algorithm model.

[0031] More specifically, when performing data fusion, the data acquisition module includes: Time-synchronize the data from different motion detection units to ensure the accuracy of fusion; The raw data of sensors of multiple motion detection units are stitched together to form a high-dimensional vector; After the raw data of the sensors of multiple motion detection units are aligned in time through the interpolation method, the noise is removed through filtering to ensure the smoothness of the data.

[0032] Preferably, when extracting the acceleration peak feature, the feature extraction module includes: The data of the acceleration sensor is processed by a low-pass filter to remove the high-frequency noise in the acceleration sensor and correct the gravity acceleration. The corrected acceleration value is represented by the acceleration value a in the x, y, and z directions. x 、a y and a z ; The corrected acceleration value a x 、a y and a z The modulus a of the total acceleration vector is synthesized total ; The formula for the resultant acceleration is as follows: .

[0033] The total acceleration vector is sampled in a specific time window, with a sampling frequency between 0.5 seconds and 2 seconds; the acceleration vector values ​​are obtained at multiple data sampling points; for example, for a sensor with a sampling frequency of 100 Hz, a 1 second time window can be selected, containing 100 data points. The peak acceleration value is calculated in each time window.

[0034] An acceleration threshold is set. When a certain acceleration vector value exceeds the acceleration threshold, it is considered that an acceleration peak has occurred. For example, the synthetic acceleration during a fall usually exceeds a certain threshold (such as 15 m / s²). As an alternative, in some embodiments, the local maximum value in each time window can also be detected as the acceleration peak. The local maximum value means that the current point is larger than the adjacent points before and after it. For example, if the acceleration at a certain time point is larger than the 10 data points before and after it, it can be considered as a peak value.

[0035] Similarly, in the present invention, when acquiring the angular velocity change feature, the feature extraction module includes: Get the angular velocity data of the x-axis, y-axis and z-axis collected by the angular velocity sensor, which are defined as w x 、w y and z ; The angular velocity data is integrated over time to obtain the angular velocity change value. The specific formula is as follows:

[0036] in, θ(t) is the Euler angle at a certain point in time.

[0037] Furthermore, the feature analysis module includes: Based on the angular velocity change value, the three Euler angles of body rotation are obtained: pitch angle (Pitch): represents the rotation of the torso around the horizontal axis (x-axis), such as forward and backward leaning; roll angle (Roll): represents the rotation of the torso around the vertical axis (y-axis), such as sideways tilt; yaw angle (Yaw): represents the rotation of the torso around the vertical axis (z-axis), that is, left and right rotation. Euler angles can help determine the following basic body postures: Upright state: Pitch and Roll angles are close to zero, and Yaw angle can be ignored or used as a reference according to actual application. Forward or backward leaning: When the Pitch angle is large (positive value indicates forward leaning, negative value indicates backward leaning), the trunk obviously tilts forward or backward. Lateral tilt: When the Roll angle is large, the trunk tilts to one side (positive value indicates rightward leaning, negative value indicates leftward leaning). Rotational posture: Yaw angle indicates the rotation of the body in the horizontal plane, such as left-right rotation of the body around the vertical axis. By jointly analyzing these three angles, the current posture state can be accurately determined. For example: Pre-fall sign: If the Pitch or Roll angle changes dramatically, it may mean that the human body is leaning forward or sideways, and there is a risk of falling. Post-fall posture: If the trunk maintains a certain angle for a long time after falling (such as Roll or Pitch angle close to 90°), it may indicate that the human body is on the ground and has not returned to the standing posture.

[0038] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0039] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0040] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A wind turbine hub cabin safety operation system, the wind turbine comprising a tower (1) and a nacelle (2) located at the top of the tower (1), a hub cabin (3) being arranged on one side of the nacelle (2), characterized in that: The system includes: An Internet of Things gateway (600) is deployed in a nacelle (2) and / or a hub compartment (3) of a wind turbine generator set and is configured to obtain on-site operation information of the hub compartment (3) and determine the operation status of an operator in real time; The motion detection unit (100) and the body area network gateway (700) are constructed as wearable devices and are respectively deployed on different parts of the body of an operator, the motion detection unit (100) being configured to collect inertial vector data of each node of the operator's body; the body area network gateway (700) being configured to collect and fuse inertial vector data of a plurality of the motion detection units (100), extract inertial vector features from the plurality of the motion detection units (100) and analyze the inertial vector features, and judge the posture change of the operator according to the analysis result, and send an early warning notification if it is judged that the posture of the operator is abnormal; The Internet of Things gateway (600) confirms the operating status of the operator according to the early warning notification and sends alarm information to the operator or the remote server through the alarm module.

2. The wind turbine hub compartment safety operation system according to claim 1, characterized in that: The early warning notification includes a vibration notification, a sound alarm and / or a light warning, wherein the vibration notification is issued by a wearable device deployed on the body of the operator, and the sound alarm or light warning is issued by an alarm module (500) deployed in the working area, wherein the alarm module (500) is communicatively connected to the body area network gateway (700) via the Internet of Things gateway (600).

3. The wind turbine hub compartment safety operation system according to claim 1, characterized in that: The Internet of Things gateway (600) specifically comprises a video analysis unit (601), a posture confirmation unit (602) and a communication unit (603); the video analysis unit (601) is configured to confirm the physical state of the operator through the video stream information and audio information of the on-site operation collected by the video acquisition unit (33) and the audio acquisition unit (34).

4. The wind turbine hub compartment safety operation system according to claim 1, characterized in that: A loudspeaker (31) and a human-machine interface (32) are arranged in the wheel hub compartment (3), and when confirming the physical condition of the operator, the Internet of Things gateway (600) can conduct a video call with a remote manager through the loudspeaker (31) and the human-machine interface (32).

5. The wind turbine hub compartment safety operation system according to claim 1, characterized in that: The body area network gateway (700) comprises at least: A data acquisition module (701) is configured to be communicatively connected to each motion detection unit (100), acquire inertial vector data of a plurality of the motion detection units (100) based on a unified time synchronization mechanism, and perform data fusion; A feature extraction module (702) is configured to extract an acceleration peak feature and an angular velocity change feature from the inertial vector data acquired by the data acquisition module (701), wherein the acceleration peak represents the impact force at the moment of falling, and the angular velocity change feature represents the rotational motion when a fall may occur; A feature analysis module (703) is configured to obtain a posture change rate according to the acceleration peak value and the angular velocity change characteristics, wherein the posture change rate represents the posture transformation of the operator; The machine learning algorithm model is configured to judge the posture change of the operator through a case library of posture change rates of each body node through a SVM, decision tree, random forest or neural network algorithm model (704).

6. The wind turbine hub compartment safety operation system according to claim 5, characterized in that: When performing data fusion, the data acquisition module (701) includes: Time-synchronizing data from different motion detection units (100) to ensure the accuracy of fusion; splicing raw data of sensors of multiple motion detection units (100) to form a high-dimensional vector; After the raw data of the sensors of the plurality of motion detection units (100) are aligned in time by an interpolation method, noise is removed by filtering to ensure the smoothness of the data.

7. The wind turbine hub compartment safety operation system according to claim 5, characterized in that: When extracting the acceleration peak feature, the feature extraction module includes: The data of the acceleration sensor (1004) is processed by a low-pass filter to remove high-frequency noise in the acceleration sensor (1004) and to correct the gravity acceleration; the corrected acceleration value is represented as the acceleration value a in the x, y, and z directions. x 、a y and a z ; The corrected acceleration value a x 、a y and a z The modulus a of the total acceleration vector is synthesized total ; The total acceleration vector is sampled in a specific time window, with a sampling frequency between 0.5 seconds and 2 seconds; the acceleration vector values ​​of multiple data sampling points are obtained; Set the acceleration threshold. When a certain acceleration vector value exceeds the acceleration threshold, it is considered that the acceleration peak occurs.

8. The wind turbine hub compartment safety operation system according to claim 5, characterized in that: When acquiring the angular velocity change feature, the feature extraction module (702) includes: Get the angular velocity data of the x-axis, y-axis and z-axis collected by the angular velocity sensor, which are defined as w x 、w y and z ; Integrate the angular velocity data over time to obtain the angular velocity change value; The feature analysis module (703) includes: Based on the change in angular velocity, the three Euler angles of body rotation are obtained: pitch angle, which represents the rotation of the torso around the x-axis; roll angle, which represents the rotation of the torso around the y-axis; and yaw angle, which represents the rotation of the torso around the z-axis.

9. The wind turbine hub compartment safety operation system according to claim 1, characterized in that: The motion detection unit (100) comprises a first detection unit (101), a second detection unit (102), a third detection unit (103), a fourth detection unit (104) and a fifth detection unit (105); each of the motion detection units (100) comprises at least a processor (1001), an inter-node communication unit (1002) and a posture sensor (1003); the posture sensor (1003) comprises at least an acceleration sensor (1004) and an angular velocity sensor (1005); the acceleration sensor (1004) is configured to obtain the instantaneous acceleration of each body node in real time; the angular velocity sensor (1005) is configured to obtain the instantaneous angular velocity of each body node in real time; the inter-node communication unit (1002) is configured to establish a communication connection between each motion detection unit (100) and the body area network gateway (700); and the processor (1001) is configured to pre-process the data collected by the posture sensor (1003).

10. The wind turbine hub cabin safety operation system according to claim 9, characterized in that: When the processor (1001) pre-processes the data collected by the posture sensor (1003), it includes: Filtering is used to remove high-frequency noise from the data collected by the attitude sensor; Moving average is used to smooth the data collected by the posture sensor (1003) to reduce sudden noise interference; and / or normalization processing is used to adjust the data level so that the data collected by the posture sensor (1003) of each body node can be compared with each other.

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