Suspension type traction hanging seat three-dimensional attitude monitoring method based on multiple intelligent sensors

Abstract

The invention relates to the technical field of intelligent sensor technology and attitude monitoring, and provides a suspension type traction hanging seat three-dimensional attitude monitoring method based on multiple intelligent sensors. Comprising the following steps: acquiring and processing original sensor data to obtain sensor data; calculating attitude data by using the sensor data; generating an attitude data predicted value by constructing and training a multivariate autoregressive integral moving average model; calculating dynamic attitude data by using sensor data; fusing the attitude data predicted value and the data in the dynamic attitude data to obtain a comprehensive pitch angle, a comprehensive roll angle and a comprehensive yaw angle; further, obtaining a comprehensive attitude value; and based on the comprehensive attitude value, judging whether an early warning mechanism needs to be triggered, and starting an automatic adjustment operation or an emergency shutdown program. The problems that an existing attitude prediction method is low in prediction precision in a complex environment, potential risks are difficult to recognize in advance, and an enough accurate basis cannot be provided for safety monitoring and risk early warning are solved.

Description

Technical Field

[0001] The present application relates to the fields of intelligent sensor technology and attitude monitoring, and particularly relates to a three-dimensional attitude monitoring method for a suspended traction gondola based on multiple intelligent sensors. Background Art

[0002] With the rapid development of intelligent sensor technology, attitude monitoring systems have gradually evolved from traditional mechanical equipment and basic sensors to high-precision, intelligent, and diversified sensor networks. In particular, the performance improvement of inertial sensors such as accelerometers, gyroscopes, and magnetometers has enabled significant breakthroughs in the real-time performance and accuracy of three-dimensional attitude estimation. At the same time, attitude prediction methods have also been continuously optimized, and can significantly improve the prediction accuracy and robustness by deeply analyzing a large amount of historical data. In addition, sensor fusion technology has become one of the cores of modern attitude monitoring systems. By comprehensively processing multi-sensor data, it not only greatly improves the accuracy of attitude estimation, but also enhances the reliability of the attitude monitoring system. With the improvement of computing power and the continuous evolution of algorithms, dynamic attitude prediction and environmental adaptability algorithms have gradually been applied to more complex scenarios, promoting the wide application and continuous development of intelligent attitude monitoring systems in multiple fields.

[0003] However, there are still some key problems to be solved in the existing technology. Traditional attitude monitoring methods often rely on the calculation of static models or single data sources and are difficult to fully cope with the dynamic changes during the operation of the gondola. Therefore, there is an urgent need to provide a three-dimensional attitude monitoring method based on multiple intelligent sensors to improve the above problems. Summary of the Invention

[0004] The present invention provides a three-dimensional attitude monitoring method for a suspended traction gondola based on multiple intelligent sensors to solve the problems that most of the existing attitude prediction methods only rely on the simple weighting of historical data and fail to effectively combine the changing factors in the dynamic environment, so the prediction accuracy is low in complex environments and it is difficult to identify potential dangers in advance; the existing attitude calculation methods often ignore the noise and errors in sensor data, resulting in inaccurate estimation of instantaneous attitude changes, especially in the case of large external interference, the real-time performance and accuracy are poor; and the data fusion method in the existing attitude prediction fails to fully consider the synergistic effect between dynamic data and prediction data, there is a risk of error accumulation, resulting in a large estimation error of the overall attitude and unable to provide a sufficiently accurate basis for safety monitoring and risk warning.

[0005] The three-dimensional attitude monitoring method for a suspended traction gondola based on multiple intelligent sensors includes the following steps:

[0006] S1: Obtain the original sensor data, perform noise filtering and baseline adjustment on the original sensor data to obtain sensor data, including acceleration, angular velocity, and magnetic field intensity data; calculate the pitch angle, roll angle, and yaw angle using the sensor data, and use the pitch angle, roll angle, and yaw angle as attitude data; generate predicted attitude data values, including predicted pitch angle values, predicted roll angle values, and predicted yaw angle values, by constructing and training a multiple autoregressive integrated moving average model.

[0007] S2: Calculate the instantaneous pitch angle, enhanced roll angle, and dynamic yaw angle using the sensor data, and use the instantaneous pitch angle, enhanced roll angle, and dynamic yaw angle as dynamic attitude data.

[0008] S3: Use the dynamic weighted attitude fusion method to fuse the data in the predicted attitude data values and the dynamic attitude data respectively to obtain the comprehensive pitch angle, comprehensive roll angle, and comprehensive yaw angle; fuse the comprehensive pitch angle, comprehensive roll angle, and comprehensive yaw angle to obtain the comprehensive attitude value; based on the comprehensive attitude value, determine whether it is necessary to trigger the warning mechanism, start the automatic adjustment operation, or start the emergency shutdown procedure.

[0009] Preferably, the S1 specifically includes:

[0010] Train a multiple autoregressive integrated moving average model using historical attitude data, and use the trained multiple autoregressive integrated moving average model to calculate the attitude data at future moments to obtain the predicted attitude data values.

[0011] Preferably, the S1 specifically includes:

[0012] The calculation formula for the predicted attitude data values is:

[0013]

[0014] Where is the predicted attitude data value at time t + 1; t is the time index variable; p is the order of the autoregressive part, representing the number of historical attitude data; i is the time index variable, representing the time of the historical attitude data; C i represents the contribution degree of the i-th historical attitude data to the future prediction; is the historical attitude data, representing the historical attitude data at time t - i; q is the order of the moving average part; j is the time index variable used to index the historical prediction errors; B j represents the influence degree of the past j-th historical prediction error on the current predicted attitude data value; ∈(t - j) is the historical prediction error at time t - j; ∈(t - j - 1) is the historical prediction error at time t - j - 1; α is the scaling factor of the error difference term; and They are the weight decay factor of historical attitude data and the weight decay factor of historical prediction error respectively.

[0015] Preferably, the S2 specifically includes:

[0016] By taking the first and second derivatives of the acceleration data in the sensor data, the instantaneous pitch angle is calculated, and the formula is as follows:

[0017]

[0018] where θ pitch (t) represents the instantaneous pitch angle of the gondola at time t; arctan is the arctangent function; A x 、A y 、A z represent the acceleration components in the X-axis, Y-axis, and Z-axis directions at time t respectively; are the first derivatives of the accelerations in the X-axis, Y-axis, and Z-axis directions at time t respectively; are the second derivatives of the accelerations in the X-axis, Y-axis, and Z-axis directions at time t respectively.

[0019] Preferably, the S2 specifically includes:

[0020] Based on the angular velocity data in the sensor data, the enhanced roll angle is calculated using the enhanced roll angle calculation formula; the calculation formula is:

[0021]

[0022] where θ roll (t) is the enhanced roll angle at time t; N is the sampling number of historical angular velocity data, representing the length of the historical time window used to calculate the roll angle; is the time index variable; is the angular velocity data at time; Δt is the time interval; ξ is the calibration factor; τ is the time index variable representing the iteration in the integration process; f wind (τ) is the wind speed influence factor at time τ; f inertia (τ) is the inertial effect factor at time τ.

[0023] Preferably, the S2 specifically includes:

[0024] Introduce the magnetic field correction factor and the change amount of magnetic field strength data, and calculate the dynamic yaw angle by combining the magnetic field strength data in the sensor data.

[0025] Preferably, the S3 specifically includes:

[0026] The specific implementation formula of the dynamic weighted attitude fusion method is:

[0027]

[0028] Among them, θ fusion (t) represents the fused data at time t, specifically the combined pitch angle, combined roll angle, and combined yaw angle; ρ is the weighting coefficient used to control the fusion weight between the dynamic attitude data and the predicted value of the attitude data; θ(t) represents the dynamic attitude data at time t; represents the predicted value of the attitude data at time t + 1; β is the change coefficient; Δθ(t) is the angle change at time t, Δθ(t) = θ(t) - θ(t - 1); θ(t - 1) represents the dynamic attitude data at time t - 1.

[0029] Preferably, the step S3 specifically includes:

[0030] Set the safety range of the combined attitude value. When the combined attitude value exceeds the set safety range, trigger the warning mechanism and start the automatic adjustment operation to restore the combined attitude value to the safety range, or start the emergency shutdown procedure to prevent danger from occurring.

[0031] The beneficial effects of the technical solution of the present invention are:

[0032] 1. The multiple autoregressive integrated moving average model is adopted to predict the attitude data at future moments by combining historical attitude data. By using the expert experience method to select appropriate historical attitude data and utilizing autoregression and moving average, the dynamic change law of the gondola attitude is effectively captured to provide more accurate attitude estimation. Especially during the gondola operation process, it can predict the change trend of the attitude in advance, thereby improving the early warning ability for potential dangers. Compared with the traditional static model, it not only improves the prediction accuracy but also can cope with more complex dynamic environmental changes.

[0033] 2. By finely processing the sensor data, the calculation methods of the instantaneous pitch angle, enhanced roll angle, and dynamic yaw angle are introduced, significantly improving the response speed and accuracy of attitude monitoring. Especially in complex environments, it can flexibly adjust the attitude change dynamically to ensure the real-time performance and accuracy of attitude estimation under different operating conditions.

[0034] 3. The dynamic weighted attitude fusion method is adopted. By dynamically adjusting the weighting coefficient between the predicted value of the attitude data and the dynamic attitude data, the predicted value of the attitude data and the dynamic attitude data are weighted and fused, reasonably utilizing the complementarity between the predicted value of the attitude data and the dynamic attitude data; the fused combined attitude value can reflect the overall attitude of the gondola, effectively avoiding the error accumulation problem caused by a single data source, and providing a more accurate data basis for safety monitoring and risk warning. Description of the Drawings

[0035] Figure 1 Flowchart of the three-dimensional attitude monitoring method for a suspended traction chairlift based on multiple intelligent sensors according to the present invention. Detailed implementation manners

[0036] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0038] The following specifically describes the specific solution of the three-dimensional attitude monitoring method for a suspended traction chairlift based on multiple intelligent sensors provided by the present invention in conjunction with the accompanying drawings.

[0039] Refer to the attached Figure 1 , which shows a flowchart of the three-dimensional attitude monitoring method for a suspended traction chairlift based on multiple intelligent sensors provided by an embodiment of the present invention. The method includes the following steps:

[0040] S1: Obtain the original sensor data, perform noise filtering and baseline adjustment on the original sensor data to obtain the sensor data, including acceleration, angular velocity, and magnetic field strength data; calculate the pitch angle, roll angle, and yaw angle using the sensor data, and use the pitch angle, roll angle, and yaw angle as the attitude data; generate the predicted attitude data values, including the predicted pitch angle value, predicted roll angle value, and predicted yaw angle value, by constructing and training a multivariate autoregressive integrated moving average model.

[0041] First, arrange intelligent sensors on the chairlift, such as accelerometers, gyroscopes, magnetometers, and wind speed sensors. The accelerometers, gyroscopes, and magnetometers are used to collect the original acceleration, original angular velocity, and original magnetic field strength data of the chairlift in the horizontal front-back direction, horizontal left-right direction, and vertical up-down direction, that is, the X, Y, and Z directions; use the collected original acceleration, original angular velocity, and original magnetic field strength data in the X, Y, and Z directions as the original sensor data to reflect the dynamic motion characteristics of the chairlift.

[0042] Since there is certain noise and error in the original sensor data, noise filtering and baseline adjustment are required. Use a low-pass filter and static offset calibration for processing to obtain the sensor data, including acceleration, angular velocity, and magnetic field strength data, and calculate the pitch angle, roll angle, and yaw angle of the chairlift using the sensor data.

[0043] Taking the pitch angle, roll angle, and yaw angle as attitude data, a multiple autoregressive integrated moving average model is established. The multiple autoregressive integrated moving average model is trained using historical attitude data, which is the pitch angle, roll angle, and yaw angle in a past period selected according to the expert experience method. During the training process, the parameters of the multiple autoregressive integrated moving average model are optimized by analyzing the historical attitude data and historical prediction errors. Based on the known historical attitude data, the trained multiple autoregressive integrated moving average model is used to calculate the attitude data at future times, that is, the predicted values of attitude data, including the predicted values of pitch angle, roll angle, and yaw angle.

[0044] The prediction formula is:

[0045]

[0046] Where, is the predicted value of attitude data at time t + 1; t is the time index variable; p is the order of the autoregressive part, representing the number of historical attitude data, which is set by the expert experience method; i is the time index variable, which is used to represent the time of historical attitude data in the prediction formula. Specifically, it represents the i-th historical attitude data considered, that is, the historical attitude data at time t - i; C i represents the contribution degree of the i-th historical attitude data to future prediction, reflecting the relationship between historical attitude data and future prediction. It is a parameter obtained by training the multiple autoregressive integrated moving average model using historical attitude data. The specific training process is well-known to those skilled in the art and will not be elaborated here; is the historical attitude data, representing the historical attitude data at time t - i; q is the order of the moving average part, representing the influence of the multiple autoregressive integrated moving average model considering the past q historical prediction errors, which is set according to the expert experience method; j is the time index variable, which is used to index historical prediction errors; B j represents the influence degree of the past j-th historical prediction error on the current predicted value of attitude data. It is a parameter obtained by training the multiple autoregressive integrated moving average model using historical attitude data; ∈(i - j) is the historical prediction error at time t - j, reflecting the difference between the predicted value of historical attitude data and historical attitude data; ∈(t - j - 1) is the historical prediction error at time t - j - 1; is the error difference term, which is used to describe the change trend of the error; α is the scaling factor of the error difference term, which is used to adjust the amplitude of the error difference term and is set according to the specific implementation scenario; and are the weight decay factors of historical attitude data and historical prediction errors respectively.

[0047] S2: Calculate the instantaneous pitch angle, enhanced roll angle, and dynamic yaw angle using the sensor data, and use the instantaneous pitch angle, enhanced roll angle, and dynamic yaw angle as dynamic attitude data;

[0048] Take the first and second derivatives of the acceleration data in the sensor data to capture the information of the instantaneous acceleration change and reflect the dynamic behavior of the suspended traction gondola equipment. Specifically, based on the first derivative and second derivative of the acceleration data, the acceleration change rate and the change of the acceleration change rate can be obtained, which helps to accurately reflect the instantaneous motion change of the suspended traction gondola equipment under dynamic conditions when calculating the pitch angle.

[0049] The formula for calculating the instantaneous pitch angle is as follows:

[0050]

[0051] where, θ pitch (t) represents the instantaneous pitch angle of the gondola at time t; arctan is the arctangent function used to convert the ratio of the acceleration vector into an angle; A x 、A y 、A z represent the acceleration components in the X-axis, Y-axis, and Z-axis directions at time t, respectively; represent the first derivatives of the accelerations in the X-axis, Y-axis, and Z-axis directions at time t, respectively, that is, the acceleration change rate; represent the second derivatives of the accelerations in the X-axis, Y-axis, and Z-axis directions at time t, respectively, that is, the change of the acceleration change rate.

[0052] Based on the angular velocity data in the sensor data, calculate the enhanced roll angle using the enhanced roll angle calculation formula. By collecting and accumulating historical angular velocity data, and introducing the influence of external factors at the same time. External factors such as wind speed and inertial effect are corrected by the wind speed influence factor and inertial effect factor respectively, and the calibration factor is used to adjust the influence of external factors on the roll angle to ensure that the calculation of the roll angle is more accurate and reliable in a dynamic environment.

[0053] The formula for calculating the enhanced roll angle:

[0054]

[0055] where, θ roll (t) is the enhanced roll angle at time t; t is the time index variable; N is the sampling number of historical angular velocity data, representing the length of the historical time window used to calculate the roll angle, which is set according to the specific implementation scenario; is the time index variable, representing the offset of the historical angular velocity data considered in the enhanced roll angle calculation formula; is The angular velocity data at a moment; Δt is the time interval, which is set according to the specific implementation scenario; ξ is the calibration factor, representing the influence degree of wind speed and inertial effect on the roll angle calculation, which is set according to the expert experience method; τ is the time index variable representing the iteration in the integration process; f wind (τ) is the wind speed influence factor at the moment of τ, representing the influence of wind speed on the roll angle, which is derived from the data collected by the wind speed sensor; f inertia (τ) is the inertial effect factor at the moment of τ, representing the influence on the roll angle due to the inertia or acceleration of the object. According to the dynamic data of the accelerometer and gyroscope in the specific implementation scenario, combined with the mass distribution and motion state of the gondola, the existing technologies such as physical modeling method or numerical integration method are used for estimation.

[0056] Introduce the magnetic field correction factor and the change amount of the magnetic field intensity data, and calculate the dynamic yaw angle by combining the magnetic field intensity data in the sensor data.

[0057] The calculation formula of the dynamic yaw angle is as follows:

[0058]

[0059] Among them, θ yaw (t) is the dynamic yaw angle at the moment of t; M x (t), M y (t), M z (t) are the magnetic field intensity data, respectively representing the magnetic field intensity components of the X-axis, Y-axis and Z-axis measured by the magnetometer at the moment of t; C mag (y) is the magnetic field correction factor, which is set and adjusted by using the expert experience method based on the influence of surrounding power facilities, metal structures, etc. on the magnetic field, as well as the changes of environmental factors such as the distribution of electromagnetic interference sources, temperature and humidity; ΔM x (t) and ΔM y (t) represent the change amounts of the magnetic field intensity components of the X-axis and Y-axis at the moment of t relative to the magnetic field intensity components of the X-axis and Y-axis at the previous moment.

[0060] Take the instantaneous pitch angle, enhanced roll angle, and dynamic yaw angle as the dynamic attitude data for subsequent calculations.

[0061] S3: Use the dynamic weighted attitude fusion method to fuse the data in the attitude data prediction value and the dynamic attitude data respectively to obtain the comprehensive pitch angle, comprehensive roll angle and comprehensive yaw angle; fuse the comprehensive pitch angle, comprehensive roll angle and comprehensive yaw angle to obtain the comprehensive attitude value; based on the comprehensive attitude value, judge whether it is necessary to trigger the warning mechanism, start the automatic adjustment operation or start the emergency shutdown procedure.

[0062] The predicted attitude data includes the predicted pitch angle, the predicted roll angle, and the predicted yaw angle; the dynamic attitude data includes the instantaneous pitch angle, the enhanced roll angle, and the dynamic yaw angle. The dynamic weighted attitude fusion method is used to fuse the data in the predicted attitude data and the dynamic attitude data respectively to obtain the comprehensive pitch angle, the comprehensive roll angle, and the comprehensive yaw angle.

[0063] The dynamic weighted attitude fusion method optimizes the fusion process by dynamically adjusting the weighting coefficient between the predicted attitude data and the dynamic attitude data and combining the angle change amount, thereby improving the accuracy and stability of attitude estimation in a complex dynamic environment.

[0064] The specific formula is as follows:

[0065]

[0066] Among them, θ fusion (t) represents the fused data at time t, specifically the comprehensive pitch angle, the comprehensive roll angle, and the comprehensive yaw angle; ρ is the weighting coefficient, which is used to control the fusion weight between the dynamic attitude data and the predicted attitude data, and is set according to the expert experience method; θ(t) represents the dynamic attitude data at time t, specifically the instantaneous pitch angle, the enhanced roll angle, and the dynamic yaw angle; represents the predicted attitude data at time t+1, specifically the predicted pitch angle, the predicted roll angle, and the predicted yaw angle; β is the change amount coefficient, which is used to adjust the influence degree of the angle change amount on the fusion result, and is set according to the expert experience method; Δθ(t) is the angle change amount at time t, Δθ(t) = θ(t) - θ(t-1); θ(t-1) represents the dynamic attitude data at time t-1.

[0067] According to the specific scenario, the weights of the comprehensive pitch angle, the comprehensive roll angle, and the comprehensive yaw angle are set, and the existing weighted average method is used for fusion to obtain the comprehensive attitude value, which is used to reflect the overall attitude of the gondola chair.

[0068] Finally, based on the specific implementation scenario, such as the safety operation specifications, design parameters, and actual use environment of the gondola chair, the safety range of the comprehensive attitude value is set. When the comprehensive attitude value exceeds the set safety range, the warning mechanism is triggered, such as issuing a warning to the operator or passenger through visual or sound alarms, reminding them of potential dangers, and starting the automatic adjustment operation. By adjusting the power system or other mechanisms of the gondola chair, the comprehensive attitude value is restored to the safety range, or the emergency shutdown procedure is started to prevent further dangers.

[0069] In summary, the three-dimensional attitude monitoring method of the suspended traction gondola chair based on multiple intelligent sensors is completed.

[0070] The sequence of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0072] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A three-dimensional posture monitoring method for a suspended traction chair based on multiple intelligent sensors, characterized in that: The following steps are involved: S1: Obtain raw sensor data, perform noise filtering and baseline adjustment on the raw sensor data to obtain sensor data, including acceleration, angular velocity and magnetic field strength data; use the sensor data to calculate the pitch angle, roll angle and yaw angle, and use the pitch angle, roll angle and yaw angle as attitude data; generate attitude data prediction values, including pitch angle prediction value, roll angle prediction value and yaw angle prediction value, by constructing and training a multivariate autoregressive integrated moving average model; S2: Calculate the instantaneous pitch angle, enhanced roll angle, and dynamic yaw angle using sensor data, and use the instantaneous pitch angle, enhanced roll angle, and dynamic yaw angle as dynamic attitude data; S3: Use the dynamic weighted attitude fusion method to fuse the attitude data prediction value and the data in the dynamic attitude data respectively to obtain the comprehensive pitch angle, comprehensive roll angle and comprehensive yaw angle; fuse the comprehensive pitch angle, comprehensive roll angle and comprehensive yaw angle to obtain the comprehensive attitude value; based on the comprehensive attitude value, determine whether it is necessary to trigger the early warning mechanism, start the automatic adjustment operation or start the emergency shutdown procedure.

2. The method for monitoring the three-dimensional posture of a suspended traction chair based on multiple intelligent sensors according to claim 1 is characterized in that: The S1 specifically includes: The historical posture data is used to train the multivariate autoregressive integrated moving average model. The posture data at future moments are calculated using the trained multivariate autoregressive integrated moving average model to obtain the predicted value of the posture data.

3. The method for monitoring the three-dimensional posture of a suspended traction chair based on multiple intelligent sensors according to claim 2 is characterized in that: The S1 specifically includes: the calculation formula of the posture data prediction value is: in, is the predicted value of the posture data at time t+1; t is the time index variable; p is the order of the autoregressive part, indicating the number of historical posture data; i is the time index variable, indicating the time of the historical posture data; C i Indicates the contribution of the i-th historical posture data to future predictions; is the historical posture data, which represents the historical posture data at time ti; q is the order of the sliding average part; j is the time index variable, which is used to index the historical prediction error; B j Indicates the degree of influence of the jth historical prediction error in the past on the predicted value of the current posture data; ∈(tj) is the historical prediction error at time tj; ∈(tj-1) is the historical prediction error at time tj-1; α is the scaling factor of the error difference term; and They are the weight decay factor of historical posture data and the weight decay factor of historical prediction error respectively.

4. The method for monitoring the three-dimensional posture of a suspended traction chair based on multiple intelligent sensors according to claim 1 is characterized in that: The S2 specifically includes: By taking the first and second order derivatives of the acceleration data in the sensor data, the instantaneous pitch angle is calculated as follows: Among them, θ pitch (t) represents the instantaneous pitch angle of the chairlift at time t; arctan is the inverse tangent function; A x , A y , A z Respectively represent the acceleration components in the X-axis, Y-axis and Z-axis directions at time t; are the first-order derivatives of acceleration in the X-axis, Y-axis and Z-axis directions at time t; They are the second-order derivatives of the acceleration in the X-axis, Y-axis, and Z-axis directions at time t.

5. The method for monitoring the three-dimensional posture of a suspended traction chair based on multiple intelligent sensors according to claim 1 is characterized in that: The S2 specifically includes: Based on the angular velocity data in the sensor data, the enhanced roll angle is calculated using the enhanced roll angle calculation formula; the calculation formula is: Among them, θ roll (t) is the enhanced roll angle at time t; N is the number of samples of historical angular velocity data, indicating the length of the historical time window used to calculate the roll angle; is the moment index variable; yes Angular velocity data at the moment; Δt is the time interval; ξ is the calibration factor; τ is the time index variable representing the iteration in the integration process; f wind (τ) is the wind speed factor at time τ; f inertia (τ) is the inertial effect factor at time τ.

6. The method for monitoring the three-dimensional posture of a suspended traction chair based on multiple intelligent sensors according to claim 1 is characterized in that: The S2 specifically includes: The magnetic field correction factor and the change in magnetic field strength data are introduced, and the dynamic yaw angle is calculated by combining the magnetic field strength data in the sensor data.

7. The method for monitoring the three-dimensional posture of a suspended traction chair based on multiple intelligent sensors according to claim 1 is characterized in that: The S3 specifically includes: The specific implementation formula of the dynamic weighted posture fusion method is: Among them, θ fusion (t) represents the fused data at time t, specifically the integrated pitch angle, integrated roll angle and integrated yaw angle; ρ is the weighting coefficient, which is used to control the fusion weight between the dynamic attitude data and the attitude data prediction value; θ(t) represents the dynamic attitude data at time t; represents the predicted value of the posture data at time t+1; β is the coefficient of change; Δθ(t) is the angle change at time t, Δθ(t) = θ(t) - θ(t-1); θ(t-1) represents the dynamic posture data at time t-1.

8. The method for monitoring the three-dimensional posture of a suspended traction chair based on multiple intelligent sensors according to claim 1, characterized in that: The S3 specifically includes: Set the safety range of the comprehensive attitude value. When the comprehensive attitude value exceeds the set safety range, the early warning mechanism is triggered and the automatic adjustment operation is started to restore the comprehensive attitude value to the safety range, or the emergency shutdown procedure is started to prevent danger.

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