Ultra-high-speed elevator traveling cable attitude detection system and detection method

Abstract

The invention discloses a posture detection system and method for a traveling cable of an ultra-high-speed elevator. The posture detection system for the traveling cable of the ultra-high-speed elevator comprises a plurality of first MEMS nine-axis sensors and a second MEMS nine-axis sensor, wherein the first MEMS nine-axis sensors are located on the traveling cable and arranged in the length direction of the traveling cable, and the second MEMS nine-axis sensor is arranged at the bottom of a lift car. The posture detection method for the traveling cable of the ultra-high-speed elevator comprises the following steps that a car runs, the posture of a first MEMS nine-axis sensor changes, and corresponding data are collected; carrying out gravity removal and elevator operation data removal processing on the collected data, and carrying out corresponding filtering and noise reduction processing; performing secondary frequency domain integration on the data in each direction to calculate the speed and the shaking displacement; and fitting and restoring a three-dimensional moving track according to the shaking displacement data in each direction. The multi-direction shaking and posture change of the elevator traveling cable can be accurately detected, and therefore real-time health monitoring of the traveling cable in the elevator running process is achieved.

Description

Technical Field

[0001] This invention relates to a system and method for detecting the attitude of the traveling cable in an ultra-high-speed elevator, applicable to scenarios such as monitoring the swaying of the traveling cable during the operation of an ultra-high-speed elevator, and belongs to the field of elevator technology. Background Technology

[0002] The elevator traveling cable is a crucial flexible component connecting the elevator car and fixed equipment in the hoistway. It provides power, signal transmission, and a safety link to the car, making it one of the most critical operating components in the elevator system. One end of the traveling cable is fixed to the junction box at the bottom of the car, and the other end is fixed to a bracket on the hoistway wall or control cabinet side. Because both ends are constrained by mechanical structures, its degree of freedom of movement is mainly concentrated in the middle suspended section. Therefore, during elevator operation, the sway amplitude in the middle region is usually significantly greater than at the ends. With the car's acceleration, deceleration, braking, and aerodynamic disturbances, the traveling cable experiences significant swaying and torsion. Excessive swaying or abnormal torsion can cause the traveling cable to rub against or snag against the hoistway wall, guide rail brackets, or other structures, leading to safety risks such as insulation wear, signal interruption, or even cable breakage. Therefore, three-dimensional attitude monitoring of the traveling cable during high-speed operation is of great significance for ensuring the safe operation of the elevator.

[0003] A search revealed that Chinese patent CN112938693A discloses a method for detecting the swing amplitude of a traveling cable using multiple sets of infrared photocells. However, this method only installs detection devices on the left and right sides, and can only detect the swing in one direction, failing to detect or identify swings in other directions or torsional postures.

[0004] Chinese patent CN114383537A discloses the use of a photoelectric rangefinder to detect the bending radius of an elevator traveling cable and to assess its health status based on monitoring the bending of the traveling cable. However, assessing health status solely based on the bending radius is too simplistic and cannot accurately assess its posture.

[0005] Current technologies for monitoring accompanying cables mostly employ methods such as infrared beam scanning, laser ranging, or single-point accelerometers. These solutions generally suffer from the following shortcomings: (1) The detection dimensions are limited, and it can only monitor the lateral swing in the left and right directions, but cannot obtain the longitudinal swing or the torsional posture of the cable itself; (2) Most solutions only set up detection units in the local part of the cable, which cannot realize continuous attitude observation of the entire length of the traveling cable and is difficult to reflect the overall dynamic behavior of the cable in a long-stroke elevator. (3) The scheme based on optical projection or reflective patch is easily affected by insufficient well lighting, dust blockage, patch detachment, etc., and has poor long-term stability; (4) A single-point accelerometer cannot distinguish between the elevator's lifting acceleration and the cable's own sway, resulting in a large amount of coupling noise in the signal; Existing technologies struggle to comprehensively monitor the three-dimensional attitude of traveling cables, including lateral sway, longitudinal sway, and torsion. Therefore, a traveling cable monitoring system capable of stable operation and three-dimensional attitude reconstruction in high-speed, long-stroke elevator environments is needed. Summary of the Invention

[0006] The technical problem to be solved by this invention is: how to achieve comprehensive monitoring of the three-dimensional posture of the accompanying cable, including swaying, twisting, and other three-dimensional postures.

[0007] To solve the above-mentioned technical problems, the technical solution of the present invention is to provide an ultra-high speed elevator traveling cable attitude detection system, characterized in that it includes a plurality of first MEMS nine-axis sensors located on the traveling cable and arranged along the length direction of the traveling cable, and a second MEMS nine-axis sensor located at the bottom of the car, wherein the first MEMS nine-axis sensor and the second MEMS nine-axis sensor are respectively connected to a host computer.

[0008] Preferably, the second MEMS nine-axis sensor is installed in the same direction as the first MEMS nine-axis sensor.

[0009] Preferably, the first MEMS nine-axis sensor is at least evenly distributed on the section of the traveling cable that bends due to the car's movement.

[0010] Preferably, the first MEMS nine-axis sensor and the second MEMS nine-axis sensor respectively use built-in clocks and adopt a unified time synchronization protocol to perform periodic clock calibration, so that all sensor data have a unified time reference.

[0011] Preferably, it also includes a camera located at the bottom of the car, which is connected to the host computer.

[0012] A method for detecting the attitude of a traveling cable in an ultra-high-speed elevator, characterized by using an ultra-high-speed elevator traveling cable attitude detection system, comprising the following steps: Step 1: Initial state, the car is at the bottom, and the data from the first MEMS nine-axis sensor and the second MEMS nine-axis sensor are reset to zero; Step 2: As the car moves, the attitude of the first MEMS nine-axis sensor changes, and corresponding data is collected, including acceleration, attitude angle, and direction. Step 3: Remove gravity from the collected data, process the elevator operation data, and perform corresponding filtering and noise reduction. Step 4: Perform quadratic frequency domain integration on the data in each direction to calculate the velocity and swaying displacement; Step 5: Fit and reconstruct the three-dimensional running trajectory based on the swaying displacement data in each direction. If the swaying amount is too large, an early warning will be issued.

[0013] Preferably, the first MEMS nine-axis sensor undergoes spatial attitude changes as the accompanying cable swings, and the installation direction of the first MEMS nine-axis sensor rotates relative to the initial direction. The attitude angles of each first MEMS nine-axis sensor are collected in real time, and the attitude matrix is ​​used to perform coordinate system transformation and orientation compensation on the measurement data. When the attitude of the first MEMS nine-axis sensor changes, rotating by a certain angle, the relative positional relationship between the inertial coordinate system and the coordinate system of the accompanying cable is represented by the attitude matrix M. The inertial coordinate system describes the spatial position coordinates of the accompanying cable, while the coordinate system of the accompanying cable describes the motion angle and attitude angle of the accompanying cable. The three components of the matrix are represented by three rotations from the coordinate system of the accompanying cable to the inertial coordinate system, which are: ; The first MEMS nine-axis sensor measures the acceleration it senses. When stationary, it does not accelerate. Since the acceleration measured by the first MEMS nine-axis sensor includes a gravitational component, the acceleration in the sensor coordinate system is transformed to the world coordinate system using the attitude matrix obtained by fusing the gyroscope and magnetometer, and then separated from the gravity vector to obtain the linear acceleration of each node. ; Among them, a obs This is the acceleration vector observed in the world coordinate system, and M is the current rotation matrix used to rotate the gravity vector [0,0,g]. T Rotate to the current orientation of the first MEMS nine-axis sensor, a lin That is, the linear acceleration vector after compensating for the gravitational component. By using the lifting acceleration measured by the second MEMS nine-axis sensor at the bottom of the car as the reference signal and removing it from the linear acceleration of each node, the net oscillating acceleration of the traveling cable after eliminating the influence of the elevator's lifting motion is obtained, thus decoupling the oscillating motion of the traveling cable from the elevator's running motion. a last =a lin -a yun ; a lin That is, the linear acceleration vector after compensating for the gravitational component, a yun It is the acceleration of car 3 during operation, a last This is the net oscillation acceleration of the traveling cable.

[0014] Preferably, the net oscillation acceleration is filtered and integrated, and the displacement is calculated using frequency domain integration or zero-drift correction methods. When integrating the net oscillation acceleration signal to obtain the velocity and displacement signals, a Fourier transform is first performed, transforming the time-domain integration operation into a frequency-domain trigger operation, and then an inverse Fourier transform is performed to obtain the time-domain velocity and displacement signals. The basic principle is as follows: Integral properties of the Fourier transform:

[0015] For acceleration signal a n Perform a Fourier transform:

[0016] The formula for calculating a first integral is:

[0017] The formula for calculating a second integral is:

[0018] in,

[0019] In the formula, F represents the Fourier transform, ∆t is the frequency resolution, d and t are the lower and upper cutoff frequencies, respectively, t is the frequency domain corresponding to the Fourier component, and N is the number of data points.

[0020] Preferably, after calculating the sway displacement data in each direction, the data is fitted to reconstruct the sway situation at each point, and the displacement and attitude information of each node are fused to reconstruct the three-dimensional dynamic trajectory of the traveling cable along its entire length; and relevant thresholds are set, and when the calculated sway of the traveling cable exceeds the safety value or when the attitude changes, twists, or knots, an abnormal alarm is triggered.

[0021] Preferably, an external measurement input is used through a camera at the bottom of the car to correct the inertial integral drift; when the calculated sway is greater than the set safety value, the camera is called to acquire a real-time image of the traveling cable, and the presence of an abnormal state is confirmed by image recognition, which is used to assist in verifying the sensor judgment results.

[0022] This invention provides a monitoring system that can accurately detect multi-directional swaying and attitude changes of elevator traveling cables, thereby solving the problems of limited detection directions, inability to identify torsional attitudes, and poor adaptability to high-speed and long-stroke conditions in the prior art, thus realizing real-time health monitoring of traveling cables during elevator operation.

[0023] Compared with the prior art, the present invention has the following significant advantages: (1) By integrating a multi-node MEMS (micro-mechanical system) nine-axis sensor with a vision system, the three-dimensional swing and torsional posture of the accompanying cable can be detected in all directions. (2) By adopting gravity compensation and car acceleration decoupling technology, it can maintain high-precision recognition under complex working conditions such as high-speed operation and elevator acceleration / deceleration; (3) The three-dimensional running trajectory of the traveling cable is reconstructed by fitting distributed sensor data, which significantly improves the detection accuracy and stability.

[0024] This invention integrates a MEMS nine-axis sensor with a camera, collects sensor information on cable swaying and combines it with the car's running acceleration as a reference, and utilizes real-time images of the accompanying cable captured by a camera at the bottom of the car. By fusing multi-sensor data and visual information, the swaying trajectory of the accompanying cable is reconstructed and its attitude is evaluated.

[0025] The system of this invention can realize multi-directional sway monitoring under high-speed elevator operation, and has filtering and noise reduction, data fusion and image-assisted correction mechanisms to realize real-time assessment of the swaying posture of the traveling cable and abnormal alarm, thereby effectively improving safety performance and detection reliability. Attached Figure Description

[0026] Figure 1 A schematic diagram of the overall structure of a high-speed elevator traveling cable attitude detection system; Figure 2 This is a schematic diagram of the attitude change of the first MEMS nine-axis sensor; Figure 3 This is a graph showing the attitude coordinate changes of the first MEMS nine-axis sensor at different positions on the accompanying cable. Figure 4 A schematic diagram showing the elevator (car) in different positions. Figure 5 Calculate and fit a 3D trajectory diagram of a point in the data; Figure 6 This is an application flowchart of an ultra-high-speed elevator traveling cable attitude detection system.

[0027] Numbering on the map: 1. Traveling cable; 2. First MEMS nine-axis sensor; 3. Car; 4. Second MEMS nine-axis sensor; 5. Camera. Detailed Implementation

[0028] To make the present invention more apparent and understandable, preferred embodiments are described in detail below with reference to the accompanying drawings.

[0029] This invention provides a system for detecting the attitude of a traveling cable in an ultra-high-speed elevator, such as... Figure 1As shown, it includes a row (or multiple) of first MEMS nine-axis sensors 2 mounted and fixed on the traveling cable 1, a second MEMS nine-axis sensor 4 mounted on the bottom of the elevator car 3, and a camera 5 mounted on the bottom of the car 3 for auxiliary detection. One end of the traveling cable 1 is fixed to the junction box at the bottom of the car 3, and the other end is fixed to a bracket on the hoistway wall or control cabinet side (generally located at the highest point accessible to the car 3 within the hoistway).

[0030] The MEMS nine-axis sensor communicates wirelessly with the host computer. Each wireless MEMS nine-axis sensor uses a built-in clock and a unified time synchronization protocol to periodically calibrate its clock, ensuring that all sensor data has a unified time reference. In case of data loss, the system uses a timestamp-based interpolation compensation mechanism to recover the data, ensuring the consistency of distributed sensor data points on the time axis and thus meeting the synchronization requirements for trajectory reconstruction. Camera 5 communicates with the host computer via a network cable.

[0031] In this embodiment, the first MEMS nine-axis sensor 2 is at least evenly distributed on the section of the traveling cable 1 that bends due to the lifting and lowering of the car 3; the host computer is a computer.

[0032] The first MEMS nine-axis sensor 2 is fixedly connected to the accompanying cable 1 by a special strap to ensure that it can maintain a reliable fit and not loosen or slip under high-speed operation and large shaking conditions.

[0033] The second MEMS nine-axis sensor 4 is installed in the same direction as the first MEMS nine-axis sensor 2 initially. It is used to collect data generated by the elevator itself during operation, and to exclude data from the up-and-down movement when calculating the sway amount later.

[0034] Camera 5 is used for auxiliary detection. When the calculated amount of shaking is greater than the set safety value, the system of the present invention calls the camera device installed at the bottom of the car 3 to obtain a real-time image of the traveling cable 1. The image recognition is used to confirm whether there are abnormal conditions such as cable knots or hooks, which is used to assist in the verification of the sensor judgment results.

[0035] like Figure 6 As shown, the traveling cable 1 of the ultra-high speed elevator is tested. The following is the specific application process.

[0036] 1) Equipment installation and data initialization In the initial state, multiple first MEMS nine-axis sensors 2 are installed at equal intervals in the middle section of the traveling cable 1 (i.e., the section of the traveling cable 1 that bends due to the rise and fall of the car 3). When the elevator (car 3) runs at a height of 200m and the suspended length of the traveling cable 1 is about 100m, multiple first MEMS nine-axis sensors 2 can be arranged at equal intervals of about 10m in the suspended section. When the elevator is at the bottom floor, the first MEMS nine-axis sensors 2 are all located on the side close to the shaft wall, with the Z-axis of the first MEMS nine-axis sensor 2 facing upwards and the X and Y axes parallel to the direction of the shaft, which is convenient for analysis and calculation.

[0037] 2) Elevator operation and data collection Initially, the elevator is at the bottom floor, and the data from the first MEMS nine-axis sensor 2 and the second MEMS nine-axis sensor 4 are both zero. Ideally, the first MEMS nine-axis sensor 2 is on the right, with the Z-axis pointing upwards, and the X and Y axes parallel to the direction of the shaft.

[0038] like Figure 4 As shown, during elevator operation, and after reaching a certain height, some of the first MEMS nine-axis sensors 2 may undergo spatial attitude changes due to the swinging motion of the following cable 1, and their installation orientation may rotate relative to the initial orientation. For example... Figure 3 As shown, the Z-axis changes direction from top to bottom as it moves from right to left. The system of this invention acquires the attitude angles of each of the first MEMS nine-axis sensors 2 in real time, and uses the attitude matrix to perform coordinate system transformation and orientation compensation on the measurement data.

[0039] 3) Data processing like Figure 2 As shown, when the attitude of the first MEMS nine-axis sensor 2 changes and rotates to a certain angle, the relative positional relationship between the inertial coordinate (O-XYZ) and the coordinate (o-xyz) system of the accompanying cable 1 is represented by the attitude matrix M. Wherein, Figure 2 In this context, α is the roll angle; β is the pitch angle; and γ is the yaw angle. α1 is the roll angle after the elevator has been running for a certain period of time; β1 is the pitch angle after the elevator has been running for a certain period of time; and γ1 is the yaw angle after the elevator has been running for a certain period of time.

[0040] The inertial coordinate system is used to describe the spatial position coordinates of the traveling cable 1, while the coordinate system of the traveling cable 1 is used to describe the motion angles and attitude angles of the traveling cable 1. The three components of the matrix are represented by three rotations from the coordinate system of the traveling cable 1 to the inertial coordinate system, which are:

[0041] The first MEMS nine-axis sensor 2 measures the acceleration it senses. When stationary, it does not accelerate. Since the acceleration measured by the first MEMS nine-axis sensor 2 includes a gravitational component, the acceleration in the sensor coordinate system needs to be transformed to the world coordinate system using the attitude matrix obtained by fusing the gyroscope and magnetometer, and separated from the gravity vector, so as to obtain the linear acceleration of each node.

[0042]

[0043] Where a obs This is the acceleration vector observed in the world coordinate system, and M is the current rotation matrix used to rotate the gravity vector [0,0,g]. T Rotate to the current orientation of the first MEMS nine-axis sensor 2. lin This is the linear acceleration vector after compensating for the gravitational component. By using the lifting acceleration measured by the second MEMS nine-axis sensor 4 at the bottom of the car 3 as the reference signal and removing it from the linear acceleration of each node, the "net oscillation acceleration" of the traveling cable 1 after eliminating the influence of the elevator's lifting motion can be obtained, thus decoupling the oscillation motion of the traveling cable 1 from the elevator's running motion.

[0044] a last =a lin -a yun a lin That is, the linear acceleration vector after compensating for the gravitational component, a yun It is the acceleration of car 3 during operation. last This is the "net oscillation acceleration" of the traveling cable 1.

[0045] The dynamic displacement is obtained by performing a second integration on the calculated and denoised acceleration data in each direction.

[0046] 4) Calculate sway displacement from acceleration data The net oscillation acceleration is filtered and integrated, and the displacement is calculated using frequency domain integration or zero drift correction methods. At the same time, the absolute position observations provided by the camera are used as external measurement inputs to correct the inertial integral drift. The displacement and attitude information of each node are fused to reconstruct the three-dimensional dynamic trajectory of the traveling cable along its entire length.

[0047] Specifically, frequency domain integration offers higher accuracy, faster computation speed, and better stability compared to direct integration. When integrating the net oscillation acceleration signal to obtain the velocity and displacement signals, a Fourier transform is first performed, converting the time-domain integration operation into a frequency-domain operation. Then, an inverse Fourier transform is performed to obtain the velocity and displacement signals in the time domain. The basic principle is as follows: Integral properties of the Fourier transform:

[0048] For acceleration signal a n Perform a Fourier transform:

[0049] The formula for calculating a first integral is:

[0050] The formula for calculating a second integral is:

[0051] in,

[0052] In the formula, F represents the Fourier transform, ∆t is the frequency resolution, d and t are the lower and upper cutoff frequencies, respectively, t is the frequency domain corresponding to the Fourier component, and N is the number of data points.

[0053] 5) Reconstruction of three-dimensional running trajectory The real-time calculated swing amplitude, torsion angle and other features are compared with safety thresholds. When the limits are exceeded, an early warning is triggered, and the type of abnormality is further confirmed through camera footage.

[0054] Specifically, such as Figure 5 As shown, after calculating the sway displacement data in each direction, the data is fitted to restore the sway situation of each point. Relevant thresholds are set, and when the calculated sway of the traveling cable 1 exceeds the safety value or when the attitude changes, twists, or knots, an abnormal alarm is triggered.

Claims

1. A posture detection system for a high-speed elevator traveling cable, characterized in that, It includes multiple first MEMS nine-axis sensors (2) located on the traveling cable (1) and arranged along the length of the traveling cable (1) and a second MEMS nine-axis sensor (4) located at the bottom of the car (3). The first MEMS nine-axis sensor (2) and the second MEMS nine-axis sensor (4) are respectively connected to the host computer.

2. The ultra-high-speed elevator traveling cable attitude detection system as described in claim 1, characterized in that, The second MEMS nine-axis sensor (4) is installed in the same direction as the first MEMS nine-axis sensor (2) initially.

3. The ultra-high-speed elevator traveling cable attitude detection system as described in claim 1, characterized in that, The first MEMS nine-axis sensor (2) is at least evenly distributed on the section of the traveling cable (1) that bends due to the lifting and lowering of the car (3).

4. The ultra-high-speed elevator traveling cable attitude detection system as described in claim 1, characterized in that, The first MEMS nine-axis sensor (2) and the second MEMS nine-axis sensor (4) respectively use built-in clocks and adopt a unified time synchronization protocol to perform periodic calibration of the clocks, so that all sensor data have a unified time reference.

5. The ultra-high-speed elevator traveling cable attitude detection system as described in claim 1, characterized in that, It also includes a camera (5) located at the bottom of the car (3), which is connected to the host computer.

6. A method for detecting the attitude of a traveling cable in an ultra-high-speed elevator, characterized in that, The ultra-high-speed elevator traveling cable attitude detection system as described in any one of claims 1-5 includes the following steps: Step 1, Initial state: The car (3) is at the bottom, and the data of the first MEMS nine-axis sensor (2) and the second MEMS nine-axis sensor (4) are zeroed. Step 2: The car (3) is running. The attitude of the first MEMS nine-axis sensor (2) changes and the corresponding data is collected. The data includes acceleration, attitude angle and direction. Step 3: Remove gravity from the collected data, process the elevator operation data, and perform corresponding filtering and noise reduction. Step 4: Perform quadratic frequency domain integration on the data in each direction to calculate the velocity and swaying displacement; Step 5: Fit and reconstruct the three-dimensional running trajectory based on the swaying displacement data in each direction. If the swaying amount is too large, an early warning will be issued.

7. The method for detecting the attitude of a traveling cable in an ultra-high-speed elevator as described in claim 6, characterized in that, The first MEMS nine-axis sensor (2) changes its spatial attitude as it swings with the following cable (1). The installation direction of the first MEMS nine-axis sensor (2) rotates relative to the initial direction. The attitude angle of each first MEMS nine-axis sensor (2) is collected in real time, and the attitude matrix is ​​used to perform coordinate system transformation and orientation compensation on the measurement data. When the attitude of the first MEMS nine-axis sensor (2) changes and rotates to a certain position, the relative positional relationship between the inertial coordinates (O-XYZ) and the coordinates (o-xyz) of the accompanying cable (1) is represented by the attitude matrix M; the inertial coordinate system is used to describe the spatial position coordinates of the accompanying cable (1), while the coordinate system of the accompanying cable (1) is used to describe the motion angle and attitude angle of the accompanying cable (1); the three components of the matrix are represented by three rotations from the coordinate system of the accompanying cable (1) to the inertial coordinate system, which are: ； The first MEMS nine-axis sensor (2) measures the acceleration it senses. When stationary, it does not accelerate. Since the acceleration measured by the first MEMS nine-axis sensor (2) includes a gravitational component, the acceleration in the sensor coordinate system is transformed to the world coordinate system using the attitude matrix obtained by fusing the gyroscope and magnetometer, and separated from the gravity vector, thereby obtaining the linear acceleration of each node: ； Among them, a obs This is the acceleration vector observed in the world coordinate system, and M is the current rotation matrix used to rotate the gravity vector [0,0,g]. T Rotate to the current attitude direction of the first MEMS nine-axis sensor (2), a lin That is, the linear acceleration vector after the gravity component is compensated. By taking the lifting acceleration measured by the second MEMS nine-axis sensor (4) at the bottom of the car (3) as the reference signal and removing it from the linear acceleration of each node, the net swing acceleration of the traveling cable (1) after eliminating the influence of the elevator lifting motion is obtained, thus realizing the decoupling of the swing motion of the traveling cable (1) from the elevator running motion. a last =a lin -a yun ; a lin That is, the linear acceleration vector after compensating for the gravitational component, a yun It is the acceleration of car 3 during operation, a last This is the net oscillation acceleration of the traveling cable (1).

8. The method for detecting the attitude of a traveling cable in an ultra-high-speed elevator as described in claim 7, characterized in that, The net oscillation acceleration is filtered and integrated, and the displacement is calculated using frequency domain integration or zero drift correction method. When integrating the net oscillation acceleration signal to obtain the velocity and displacement signals, a Fourier transform is first performed to transform the time-domain integration operation into a frequency-domain trigger operation, and then an inverse Fourier transform is performed to obtain the velocity and displacement signals in the time domain. Its basic principle is as follows: Integral properties of the Fourier transform: For acceleration signal a n Perform a Fourier transform: The formula for calculating a first integral is: The formula for calculating a second integral is: in, In the formula, F represents the Fourier transform, ∆t is the frequency resolution, d and t are the lower and upper cutoff frequencies, respectively, t is the frequency domain corresponding to the Fourier component, and N is the number of data points.

9. The method for detecting the attitude of a traveling cable in an ultra-high-speed elevator as described in claim 8, characterized in that, After calculating the sway displacement data in each direction, the data is fitted to restore the sway situation of each point. The displacement and attitude information of each node are fused to reconstruct the three-dimensional dynamic trajectory of the traveling cable along the entire length. Relevant thresholds are set, and when the sway of the traveling cable (1) is greater than the safety value and the attitude changes, twists, or knots, an abnormal alarm is triggered.

10. The method for detecting the attitude of a traveling cable in an ultra-high-speed elevator as described in claim 9, characterized in that, The inertial integral drift is corrected by using a camera (5) at the bottom of the car (3) as an external measurement input; when the calculated sway is greater than the set safety value, the camera (5) is called to obtain a real-time image of the traveling cable (1), and the presence of an abnormal state is confirmed by image recognition, which is used to assist in verifying the sensor judgment results.

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