Hanging chair position detection method for skiing hanging chair cableway roundabout station

Abstract

The invention discloses a method for detecting the position of a chairlift of a roundabout station of a skiing chairlift, and relates to the field of roundabout safe operation control, and the method comprises the steps: carrying out the nonlinear chairlift dynamics modeling of the chairlift, obtaining a correction envelope, carrying out the topological feature analysis of the chairlift based on the correction envelope, and obtaining a topological detection result. The method comprises the following steps: acquiring a geometric relationship of a hanging chair, laser installation parameters and a laser detection signal to carry out tensor risk fusion decision making to obtain a safety control instruction, carrying out fractional order optimal braking analysis based on the safety control instruction to obtain a structured data frame, acquiring hanging chair displacement, hanging chair motion manifold and laser signal parameters of the hanging chair, and carrying out conformal geometric maintenance to obtain the safety control instruction. Maintenance operation guidance is obtained, wind vibration dynamics, laser detection and safety control parameter space-time coupling modeling and detection precision guarantee under wind and snow interference can be achieved, the offside risk can be perceived in the early stage, the equipment state is judged based on a dynamic threshold value, the trend of dynamic degradation is considered, and maintenance decision response is timely.

Description

Technical Field

[0001] The present invention relates to the field of cableway safe operation control, and in particular to a method for detecting the position of a ski chairlift at a detour station of a ski chairlift. Background Art

[0002] Passenger aerial ropeways are used to transport people, and the safety and reliability of the entire chairlift system are required to be high. However, the existing method for detecting the chair position at the detour station of a ski chairlift has the following shortcomings: 1. Existing methods analyze wind-induced vibration dynamics, laser detection, and safety control parameters independently, lacking spatiotemporal coupling modeling. For example, they use only displacement sensors to monitor position, without integrating wind-induced swing angle response and chaos index to construct safety boundaries. Laser signals are decoupled from mechanical motion, resulting in a significant decrease in detection accuracy under wind and snow interference and the inability to integrate multi-source data. 2. Relying on threshold-based braking triggers, such as a single indicator like wheel-center distance, fails to detect offside risks early and fails to utilize the topological characteristics of the space-time curvature field. False alarms often occur, leading to delayed risk identification. 3. The braking control accuracy is rough. The existing method uses a step-by-step braking method, which causes the chair to shake violently. It does not consider the constraints of fractional-order kinematics, resulting in an excessively long braking distance during emergency braking. 4. Health assessment is static, judging equipment status based on fixed thresholds without considering dynamic degradation trends. Maintenance decisions rely on manual experience, resulting in delayed responses.

[0003] In order to solve the above-mentioned defects, a technical solution is now provided. Summary of the Invention

[0004] In order to solve the technical problems raised by the above background technology, the present invention proposes a method for detecting the position of a ski chairlift at a detour station of a ski chairlift.

[0005] The object of the present invention can be achieved by the following technical solution: A method for detecting the position of a chairlift at a detour station of a ski chairlift comprises the following steps: Step S100: obtaining a modified envelope by modeling the nonlinear dynamics of the hanging chair; Step S200: performing topological feature analysis on the hanging chair based on the modified envelope to obtain a topological detection result; Step S300: Obtaining the geometric relationship of the chairlift, laser installation parameters and laser detection signals to perform tensor risk fusion decision-making and obtain safety control instructions; Step S400: performing fractional-order optimal braking analysis based on the safety control instruction to obtain a structured data frame; Step S500: Obtain the chair displacement, chair motion manifold, and laser signal parameters of the chair, perform conformal geometry maintenance, and obtain maintenance operation guidance.

[0006] Furthermore, the modified envelope analysis steps are as follows: Obtaining the chair body geometric parameters and cableway system installation parameters of the chairlift and constructing the original envelope equation through spatial coordinate system transformation, wherein the chair body geometric parameters include the chairlift rotation radius and the rotation center coordinates, and the cableway system installation parameters include the horizontal offset and the vertical reference plane; A real-time safety boundary model is constructed by introducing the wind-induced dynamic displacement of the chair, the dynamic response of the chair swing angle and the maximum chaos index as dynamic correction terms into the original envelope equation to obtain the modified envelope.

[0007] Furthermore, the steps for analyzing the maximum chaotic index of the chairlift displacement divergence are as follows: A second-order damping system for the chair under horizontal wind excitation is established to solve the wind-induced dynamic displacement of the chair. The torsional dynamic equation of the chair is established to solve the dynamic response of the chair's swing angle. Based on the wind-induced dynamic displacement of the hanging chair, the instantaneous velocity is calculated by the central difference method. The displacement value and the instantaneous velocity value at the same moment are combined into a two-dimensional state point, which is connected in time sequence to form a continuous phase trajectory to obtain the displacement velocity phase space. The time average value of the trajectory phase space divergence rate is calculated based on the integration of the displacement velocity phase space along the time series, and the maximum chaos index of the hanging chair displacement divergence is obtained by the limit definition.

[0008] Furthermore, the topology detection result analysis steps are as follows: The modified envelope is expanded into an embedded surface with a time dimension, and the first basic form coefficients and the second basic form coefficients of the embedded surface with a time dimension are calculated. Based on the first basic form coefficients and the second basic form coefficients, the spacetime curvature field of the hanging chair motion is generated using the Gaussian curvature formula. The critical point set whose Gaussian curvature is greater than the curvature threshold of the hanging chair edge is extracted from the spacetime curvature field of the hanging chair motion and marked as the hanging chair motion curvature manifold; The Euler characteristic of the chairlift motion manifold is obtained by performing directed surface integral on the chairlift motion curvature manifold. The Euler characteristic of the chairlift motion manifold is analyzed and judged with the reference value to obtain the topology detection result.

[0009] Furthermore, the security control instruction analysis steps are as follows: Perform high-order singular value decomposition on the fourth-order risk tensor, calculate the covariance matrix along the four modes and solve its eigenvalue decomposition to obtain the factor matrix of each mode. Then, use multilinear projection to map the original tensor to the characteristic subspace to generate a dimensionally compressed risk core tensor. The singular values ​​of the four modes and their corresponding main eigenvectors are extracted from the Tucker decomposition of the dimensionally compressed risk core tensor. The eigenvector modulus of each mode is calculated and multiplied by the corresponding singular value. The dynamic risk value is output based on the time-domain attenuation effect of the maximum chaotic index of chairlift displacement divergence on risk. The dynamic risk value is compared with the set threshold, and the corresponding risk quantification value is mapped to a safety control instruction.

[0010] Furthermore, the fourth-order risk tensor analysis steps are as follows: Calculate the pattern probability based on the number of similar vector pairs and output the entropy complexity through the Shannon entropy formula; The wind-induced dynamic displacement and the dynamic response of the chair's swing angle are used to form the chair's dynamic parameters. The phase modulation signal, edge feature map, Euler characteristic of the chair's motion manifold and entropy complexity are used to form the optical detection parameters. The wheel center distance, linear velocity and acceleration are used to form the safety control parameters. The chair's dynamic parameters, optical detection parameters and safety control parameters are organized into a chair's dynamic optical safety parameter matrix. A real-time risk feature is constructed based on the phase modulation signal, the Euler characteristic of the chair's motion manifold and the wheel center distance. A time risk vector containing a timestamp and a real-time risk feature is constructed. The chair's dynamic optical safety parameter matrix and the time risk vector are fully coupled through a tensor product operation to generate a fourth-order risk tensor.

[0011] Furthermore, the steps of analyzing the number of similar vector pairs are as follows: The optical path difference is calculated based on the geometric relationship of the hanging chair through the dynamic displacement and swing angle caused by wind, and the phase modulation signal is obtained by substituting the optical path difference into the interference equation. Based on the laser installation parameters, the spatial carrier term of the window function is superimposed on the Gaussian attenuation term to obtain the spatial window function. The phase modulated signal is reconstructed in time and space by the spatial window function. The virtual light intensity field is constructed by the spatial window function. The gradient modulus of the virtual light intensity field is calculated and a fractional differential operator is applied. The signal mutation characteristics are enhanced by convolution operation to obtain the edge feature map. The time domain sequence of laser detection signals is subjected to multi-scale coarse-graining processing. The original sequence is divided into non-overlapping windows according to the scale factor τ=5. An m-dimensional vector is constructed in the phase space based on the coarse-grained sequence, and the number of similar vector pairs that meet the distance threshold r is counted.

[0012] Furthermore, the structured data frame analysis steps are as follows: Based on the safety deviation between the chair's real-time position and the center of the driving wheel, a Caputo-type semi-order derivative is used to define the chair's fractional-order error term. The deceleration and jerk are used as control input constraints. The weighting coefficients are set according to the cableway safety standard, and the safety area constraint functional is generated through time integration. Based on the safety region constraint functional, fractional-order kinematic constraints are used as coupling conditions for the Lagrange multiplier terms. An extended safety region constraint functional is constructed, and variational operations are performed on the extended safety region constraint functional. The governing equations and adjoint equations are derived through the Euler-Lagrange equations. Combined with the boundary conditions and the definition of fractional-order derivatives, a predictive-correction algorithm is used to iteratively solve the differential-algebraic equations to obtain the brake-loss-optimized deceleration curve. During the control cycle, discrete integral operation is performed based on the brake consumption adaptive deceleration curve to update the speed instruction in real time. At the same time, when the dynamic risk value is equal to or greater than the set threshold, the human-machine interface sound and light alarm is triggered, and the encapsulated updated speed instruction and alarm flag are synthesized into a structured data frame through the PROFINET protocol.

[0013] Furthermore, the maintenance operation guidance analysis steps are as follows: Based on the mapping function and feature space weight of the healthy manifold, the local metric tensor is calculated. The healthy reference feature vector is converted to the healthy manifold reference vector through the mapping function. The current healthy manifold vector is obtained based on the healthy manifold. The geodesic equation on the three-dimensional manifold is established based on the local metric tensor. The boundary conditions are that the starting point of the path is the healthy manifold reference vector and the end point of the path is the current healthy manifold vector. The optimal parameterized path is obtained through numerical optimization. The manifold distance is calculated based on the optimal parameterized path. Based on the manifold distance, it is mapped into maintenance action guidance through preset decision rules.

[0014] Furthermore, the steps of health manifold analysis are as follows: The maximum chaotic index of the chairlift displacement divergence, the Euler characteristic of the chairlift motion manifold, the entropy complexity and the signal-to-noise ratio of the laser signal are Z-score normalized to obtain the standardized four-dimensional eigenvector. Through the Riemannian manifold embedding algorithm, the four-dimensional eigenvector is mapped to a three-dimensional hypersurface with the geodesic distance as the intrinsic metric constraint, ensuring that the Euclidean distance between any two points on the manifold maintains the geodesic distance relationship in its original eigenspace, thus generating a healthy manifold in the phase space.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains a modified envelope by modeling the nonlinear dynamics of the hanging chair, performs a topological feature analysis on the hanging chair based on the modified envelope, obtains a topological detection result, obtains the geometric relationship of the hanging chair, the laser installation parameters and the laser detection signal to perform a tensor risk fusion decision, obtains a safety control instruction, performs a fractional-order optimal braking analysis based on the safety control instruction, obtains a structured data frame, and can model the spatiotemporal coupling of wind vibration dynamics, laser detection and safety control parameters, ensures detection accuracy under wind and snow interference, can perceive offside risks at an early stage, and greatly reduces false alarms by utilizing the topological characteristics of the spatiotemporal curvature field. 2. The present invention performs conformal geometric maintenance by obtaining the chair's displacement, chair motion manifold, and laser signal parameters to obtain maintenance operation guidance. It uses fractional-order kinematics to constrain motion, thereby reducing the violent shaking of the chair and significantly reducing the emergency braking distance. It judges the equipment status based on dynamic thresholds, considers the trend of dynamic degradation, and ensures timely maintenance decision responses. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present invention.

[0017] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a structural schematic diagram of the present invention; Figure 3 This is a flow chart of the method of step S400 of the present invention.

[0018] In the figure: 1. Hanging chair 1; 2. Hanging chair 2; 3. Receiver; 4. Transmitter; 5. Wheel body; 6. Laser beam detection switch; 7. Hanging chair passing area; 8. Outer edge of hanging chair; 9. Inner edge of hanging chair. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts also fall within the scope of protection of the present invention.

[0020] like Figure 1 and Figure 2As shown, the ski lift position detection device for a detour station in this embodiment includes lift 1, lift 2, a receiver 3, a transmitter 4, a wheel 5, a laser photoelectric detection switch 6, a lift passage area 7, a lift outer edge 8, and a lift inner edge 9. Transmitter 4 is mounted 1.5 meters from the lift centerline and 1.5 meters from receiver 3. The core operating principle of laser photoelectric detection switch 6 is as follows: laser emission → beam transmission and occlusion detection → photoelectric signal conversion → signal processing and output. When a descending passenger in the lift reaches the disembarkation zone and forcibly crosses the passenger off-track detection area, the laser photoelectric detection switch 6 blocks the signal from transmitter 4 to receiver 3, and the signal from receiver 3 to the electronic control system PLC is lost, the ski lift stops, the HIM interface displays a passenger off-track fault message, and an audible and visual alarm is issued. The laser beam photoelectric detection switch 6 has a low safe operating voltage (DC12V~24V), a simple structure, a model number E3F-20C1, and a detection distance of 0~20 meters. A method for detecting the position of a chairlift at a detour station of a ski chairlift is provided for the device, including the following steps: Step S100: obtaining a modified envelope by modeling the nonlinear dynamics of the hanging chair; Step S200: performing topological feature analysis on the hanging chair based on the modified envelope to obtain a topological detection result; Step S300: Obtaining the geometric relationship of the chairlift, laser installation parameters and laser detection signals to perform tensor risk fusion decision-making and obtain safety control instructions; Step S400: performing fractional-order optimal braking analysis based on the safety control instruction to obtain a structured data frame; Step S500: obtaining the chair displacement, chair motion manifold, and laser signal parameters of the chair, performing conformal geometry maintenance, and obtaining maintenance operation guidance; Specifically, the analysis steps of step S100 are as follows: A second-order damping system under horizontal wind excitation of the hanging chair is established, and the wind-induced dynamic displacement of the hanging chair is obtained by solving it. The torsional dynamic equation of the hanging chair is established, and the dynamic response of the hanging chair's swing angle is obtained by solving it. The formulas involved are as follows:

[0021] Where m represents the mass of the chair, quantifying the inertial characteristics of the chair, I represents the moment of inertia of the chair, represents the horizontal damping coefficient, represents the horizontal stiffness coefficient, represents the torsional damping coefficient and the torsional stiffness of the suspension system. represents the torsional stiffness, the torsional stiffness of the suspension system, and controls the rotation of the chairlift. ρ represents the air density. represents the drag coefficient, A represents the frontal area, Indicates wind speed, the actual value measured by the meteorological sensor, φ indicates the wind direction angle, the angle between the wind direction and the cableway axis, represents the horizontal acceleration, the second derivative of the displacement level x, represents the horizontal velocity, x represents the horizontal displacement, and the output quantity to be solved is, represents the angular acceleration, represents the angular velocity, θ represents the swing angle, and the output quantity to be solved is, represents the wind amplitude, , d represents the length of the lever arm, the distance from the wind action point to the suspension point. The wind-induced dynamic displacement x(t) of the hanging chair and the dynamic response of the hanging chair's swing angle θ(t) are obtained. By coupling translational and rotational dynamics, the impact of wind vibration on the hanging chair's trajectory is accurately quantified, providing basic motion parameters for subsequent chaos analysis and laser detection.

[0022] Based on the wind-induced dynamic displacement of the chairlift, the instantaneous velocity is calculated by the central difference method (the forward / backward difference is used for the first and last points). The displacement value and the instantaneous velocity value at the same moment are combined into a two-dimensional state point, which is connected in time sequence to form a continuous phase trajectory to obtain the displacement velocity phase space. In this phase space, the horizontal axis x represents the horizontal displacement of the chairlift, and the vertical axis Represents the instantaneous horizontal velocity of the chairlift. The time average of the trajectory phase space divergence rate is calculated based on the integral of the displacement velocity phase space along the time series. The maximum chaos index of the chairlift displacement divergence is obtained by the limit definition. , where T represents the integration time, represents the phase space divergence rate and the sensitive dependence of the chairlift quantization trajectory; The chair lift's geometric parameters and cableway system installation parameters are obtained and the original envelope equation is constructed through spatial coordinate system transformation. The chair lift's geometric parameters include the chair lift's rotation radius (the maximum envelope radius of the chair lift's mechanical structure is obtained through 3D laser scanning) and the rotation center coordinates (the geometric relationship between the chair lift's suspension point and the load-bearing cable is measured using a total station to establish the origin of the rotation coordinate system). The cableway system installation parameters include the horizontal offset (the cableway centerline coordinates are calibrated using a GNSS positioning system, and the horizontal projection distance between the chair lift's rotation center and the cableway centerline is measured using a laser rangefinder) and the vertical reference plane (the elevation of the laser transmitter installation plane relative to the geoid is measured using a digital level). The original envelope equation includes: In the ski ropeway position detection system, a geometric envelope boundary model of the chairlift under ideal static conditions is constructed. The parameterized equation consists of rotation terms and translation terms. With the chair suspension point as the center, the radius is generated by polar coordinate transformation as the chair radius The circular trajectory point set completely characterizes the mechanical contour of the chair body, the translation term The installation reference coordinate system of the cableway system is defined, where the horizontal component Determine the lateral offset of the chairlift's rotation center relative to the cableway centerline, the vertical component The installation height of the laser detection plane relative to the ground plane is calibrated, and the spatial mapping relationship between the laser detection system and the kinematics of the hanging chair is established to form a static benchmark model for the position detection algorithm.

[0023] The real-time safety boundary model is constructed by introducing the wind-induced dynamic displacement of the chair, the dynamic response of the chair swing angle and the maximum chaos index as dynamic correction terms into the original envelope equation, and the modified envelope is obtained. ,in is the sensitivity coefficient, in units of s, which converts the chaos intensity (1 / s) into a dimensionless gain.

[0024] Specifically, the analysis steps of step S200 are as follows: Expand the modified envelope to embed the surface with time dimension ,in , represents the real-time position of the cableway centerline direction, , represents the real-time height in the vertical direction of the ground, calculates the first basic form coefficient and the second basic form coefficient of the time dimension embedded surface, and uses the Gaussian curvature formula based on the first basic form coefficient and the second basic form coefficient Generate space-time curvature field of chairlift motion , in the spacetime curvature field of the hanging chair motion, we extract the critical point set whose Gaussian curvature is greater than the curvature threshold κ0 of the hanging chair edge, and mark it as the hanging chair motion curvature manifold, where the first basic form coefficient is , where E represents the square of the length of the tangent vector in the θ direction, representing the scale expansion rate of the parametric curve θ, F represents the dot product of θ and the tangent vector in the t direction, representing the cosine of the angle between the two parametric curves (orthogonality), G represents the square of the length of the tangent vector in the t direction, representing the measure of the time dimension motion speed, and the second basic form coefficient is , where L represents the normal curvature in the θ direction, which characterizes the curvature of the surface along the θ direction (such as the curvature of the chairlift profile), M represents the torsion in the θ and t directions, which characterizes the torsion caused by space-time coupling (such as the swing distortion caused by wind vibration), and N represents the normal curvature in the t direction, which characterizes the normal component of the time axis acceleration (such as the swing acceleration of the chairlift). represents the surface unit normal vector, and its direction is determined by the right-hand rule; Performing directed surface integral on the curvature manifold of the hanging chair motion, we obtain the Euler characteristic of the hanging chair motion manifold. , where the area element , determined by the first basic form (E, F, G come from surface parameterization), M is the chairlift motion curvature manifold, and the deviation of the Euler characteristic of the chairlift motion manifold from the reference value Q0 exceeds the threshold δ0, which is marked as "1", indicating that the current signal feature deviates from the normal (or preset) topology mode and is judged as offside. The passenger offside fault information is displayed on the HIM interface, and an audible and visual alarm prompt is issued; if the deviation does not exceed the limit, it is marked as "0", indicating that the signal topology feature is within the normal range, and the topology detection result is obtained.

[0025] Specifically, the analysis steps of step S300 are as follows: The optical path difference is calculated based on the geometric relationship of the hanging chair through the dynamic response of wind-induced dynamic displacement and swing angle , where x(t) represents the wind-induced dynamic displacement, θ(t) represents the dynamic response of the swing angle, represents the radius of the chair, and the optical path difference is substituted into the interference equation to obtain the phase modulation signal , where λ represents the laser wavelength. The phase signal is updated every 0.01 seconds and is used for anti-interference light field analysis. Its rate of change directly reflects the offside risk intensity of the hanging chair. The mechanical motion is converted into an optical signal through the optical path difference ΔL, realizing non-contact precision detection. The spatial carrier term of the window function designed based on the laser installation parameters is superimposed on the Gaussian attenuation term to obtain the spatial window function. , where x and y represent the horizontal and vertical coordinates in two-dimensional space, respectively. Indicates the spatial frequency in the x direction , β is the laser incident angle, λ is the wavelength, the laser installation parameters are the laser incident angle and wavelength, Indicates the spatial frequency in the y direction , σ represents the standard deviation of the Gaussian window, which is equal to the diameter of the hanging chair. The phase modulated signal is reconstructed in time and space, and a virtual light intensity field is constructed through the spatial window function. , calculate the virtual light intensity field gradient modulus and apply the fractional differential operator, enhance the signal mutation characteristics through convolution operation, and obtain the edge feature map ,in represents the fractional differential order, which optimizes the detection accuracy of mutation features, Γ represents the gamma function, represents the normalization factor of fractional differential, τ is the integration time variable, the time parameter of convolution operation, represents the light intensity field gradient modulus, t is the time variable; The time domain sequence of the laser detection signal is processed by multi-scale coarse-graining, and the original sequence is divided into non-overlapping windows according to the scale factor τ=5. , k = 1, 2, ..., N / τ, where The kth element of the coarse-grained sequence generates a coarse-grained sequence, which reflects the coarse-grained characteristics of the original signal at this scale. N is the total length of the original time domain sequence s(t), and N / τ is the length of the coarse-grained sequence. The number of elements (i.e., the number of windows) is used to construct an m-dimensional vector in the phase space based on the coarse-grained sequence , m represents the dimension of the vector constructed during phase space reconstruction, which is used to reconstruct the coarse-grained sequence y into the phase space, generate an m-dimensional vector, and mine the nonlinear characteristics of the signal. m=3, and counts the number of similar vector pairs that meet the distance threshold r. and , the threshold r = 0.15 × std (y), where std (y) is the standard deviation of the coarse-grained sequence y, r is used to count the number of similar vector pairs that meet the distance conditions in the phase space, and is the pre-judgment basis for calculating the pattern probability. and It is the number of similar vector pairs that meet the distance threshold r obtained by statistics based on m-dimensional and m+1-dimensional vectors in phase space; Calculating pattern probabilities , which reflects the probability characteristics of the vector pattern in the phase space, through the Shannon entropy formula The output entropy complexity is used to quantify the chaotic characteristics of the signal under wind and snow disturbances. Different working conditions (normal, occlusion, etc.) correspond to different MSE ranges, which assist in judging the signal status. Specifically, the time domain series of the laser detection signal is obtained through laser sensor hardware acquisition. Simply put, in detection scenarios such as ski chairlift detour stations, laser detection equipment sensors are arranged to continuously collect chairlift-related physical quantities (such as changes in optical path caused by position, etc.), convert the optical signal into an electrical signal, and then use analog-to-digital conversion (ADC) to convert the analog signal into a digital sequence. After arranging them in chronological order, the time domain sequence s(t) that changes with time is obtained, and then multi-scale coarse-graining and other processing and analysis are performed based on it.

[0026] The dynamic displacement caused by the wind and the dynamic response of the chair's swing angle are combined into the dynamic parameters of the chair. The phase modulation signal, edge feature map, Euler characteristic of the chair's motion manifold and entropy complexity are combined into optical detection parameters. The wheel center distance, linear velocity and acceleration are combined into safety control parameters. The dynamic parameters, optical detection parameters and safety control parameters of the chair are organized into a 3×3 parameter matrix of chair power optical safety. The real-time risk feature quantity is constructed based on the phase modulation signal, Euler characteristic of the chair's motion manifold and wheel center distance. ,in 、 and are weight factors, where , tanh is the hyperbolic tangent function, 、 is the maximum and minimum value of the Euler characteristic of the chairlift motion manifold, d represents the wheel center distance, and a time risk vector containing a timestamp and a real-time risk characteristic is constructed. The chairlift dynamic optical safety parameter matrix is ​​fully coupled with the time risk vector through a tensor product operation to generate a fourth-order risk tensor with dimensions of 3×3×2×1. ,in It represents the distance between the chairlift and the cableway wheel center, v represents the linear speed of the chairlift running on the cableway, a represents the speed of the chairlift during operation, and each element The calculation follows the Kronecker product rule ,in is the parameter matrix element, is the element of the temporal risk vector, which completely encapsulates the joint distribution characteristics of the chair state in the spatiotemporal risk manifold; Perform a high-order singular value decomposition on the fourth-order risk tensor and calculate the covariance matrix along the four modes separately And solve its eigenvalue decomposition , obtain the modal factor matrix , the original tensor is mapped to the feature subspace through multilinear projection to generate the dimensionally compressed risk core tensor ,in represents the expansion matrix of the fourth-order risk tensor T along the nth mode (the tensor is "pulled" into a matrix according to a specific mode to facilitate the calculation of covariance), and the superscript T represents the matrix transpose. The diagonal matrix of eigenvalues, It is a multilinear projection operation, which means that the original tensor T is projected along the nth mode with the corresponding factor matrix Perform projection transformation, gradually mapping the original tensor to the characteristic subspace spanned by each modal factor matrix, and finally obtain the core tensor G to achieve dimensionality compression and redundancy removal; Extract the singular values ​​of the four modes from the Tucker decomposition of the dimensionally compressed risk core tensor (covariance matrix The square root of the eigenvalue of ), calculate the eigenvector modulus for each mode And multiply it by the corresponding singular value, combine the time domain attenuation effect of the maximum chaotic index of chair lift displacement divergence on risk, and output the dynamic risk value ; The dynamic risk value is compared with the set thresholds η1 and η2. If the dynamic risk value is equal to or greater than the set threshold η1, the emergency braking protocol is activated and step four of fractional-order optimal braking is started. If the dynamic risk value is greater than η2 and less than η1, the early warning protocol is activated and the yellow warning icon on the human-machine interface is controlled to continuously flash. If the dynamic risk value is less than or equal to η2, the normal operating state is maintained. This logic maps the risk quantification value to specific execution instructions through the PLC's real-time decision engine, achieving precise control of safety response.

[0027] like Figure 3 Specifically, the analysis steps of step S400 are as follows: Safety deviation based on the real-time position of the chairlift and the center of the driving wheel , where d is the distance between the center of mass of the chairlift and the center of the wheel, represents the driving wheel diameter, and the Caputo-type semi-order derivative is used to define the chairlift fractional error term. , τ is the integral time variable, t is the time variable, deceleration and jerk are used as control input constraints, weighting coefficients are set according to the cableway safety standard, and the safety area constraint functional is generated by time integration ,in Indicates deceleration, the rate of speed reduction, a positive value indicates braking intensity, the inverter outputs braking intensity, j indicates jerk, the deceleration change rate j=da / dt, q indicates the error term weight, r indicates the deceleration weight, s indicates the jerk weight, Indicates the total braking time, , where v0 represents the initial braking velocity, the real-time linear velocity of the cableway (read by PLC), Indicates the maximum allowable deceleration, 2.0m / s 2 , (chaos compensation); Fractional-order kinematic constraints based on safe region constraint functionals (α=0.5, is the deceleration) as the coupling condition of the Lagrange multiplier term λ(t), where v represents the linear velocity, the real-time speed of the cableway, and the value read by the PLC, and the extended safety area constraint functional is constructed. , performing variational operations on the extended safe region constraint functional ,in It means taking the variation (derivative) of the extended functional and setting it to zero, thereby obtaining the extreme value condition and deriving the control equation through the Euler-Lagrange equation and the adjoint equation , combined with the boundary conditions , (Initial speed setting, terminal speed is zero (stop), and terminal position is constrained (the chairlift needs to be accurately parked at the wheel center)) and fractional derivative definition , the prediction and correction algorithm is used to iteratively solve the differential algebraic equations to obtain the braking consumption optimal deceleration curve ; During the control cycle, discrete integral operation is performed based on the braking consumption adaptive deceleration curve to update the speed command in real time. ,in Indicates the speed of the previous cycle and the current speed fed back by the cableway encoder. Indicates the control cycle, the PLC program cycle execution interval (fixed 0.1s), The optimal deceleration curve for braking consumption is represented. Simultaneously, when the dynamic risk value is equal to or greater than the set threshold η1, an audible and visual alarm on the human-machine interface is triggered. The encapsulated update speed command and alarm flag (human-machine interface audible and visual alarm) are synthesized into a structured data frame ⟨Speed, Alarm> and sent to the ABB inverter via the PROFINET protocol, driving it to execute the S-shaped braking curve. Simultaneously, the real-time speed curve and risk status are displayed on the HMI, forming a closed loop of "control-feedback-display".

[0028] Specifically, the analysis steps of step S500 are as follows: The maximum chaotic index of chair displacement divergence, the Euler characteristic of chair motion manifold, entropy complexity and laser signal noise ratio (corresponding to chair displacement, chair motion manifold and laser signal parameters) are normalized by Z-score to obtain the normalized four-dimensional feature vector ,in It represents the maximum chaotic index of chair lift displacement divergence after normalization, the Euler characteristic of chair lift motion manifold, entropy complexity and laser signal noise ratio. All characteristic means after normalization are in the same order of magnitude. T represents the transpose of the matrix. Through the Riemann manifold embedding algorithm, the geodesic distance is used. is an intrinsic metric constraint, where 、 Represents the components of the normalized eigenvector, subscripts i1 and j1 represent different data points, and k represents the index of the feature dimension (k1=1, 2, 3, 4). Represents the feature space weight, which is used to adjust the importance of different features in distance calculation. =[0.4, 0.3, 0.2, 0.1], and Represents the normalized four-dimensional feature vector, representing two different data points, mapping the four-dimensional feature vector to the three-dimensional hypersurface, ensuring that the Euclidean distance between any two points on the manifold maintains the geodesic distance relationship in its original feature space, and generating a healthy manifold in the phase space , where the health manifold is a parameterized three-dimensional hypersurface used to characterize the overall health status of the system. Represents a point on the health manifold, which is a three-dimensional vector that represents the position after transformation from the feature space by the mapping function f. represents the mapping function, namely the Riemannian manifold embedding algorithm, which maps the four-dimensional feature vector to the three-dimensional manifold point. Represents the gradient of the mapping function, the rate of change of the mapping function in each direction, Represents a positive threshold, which is used to constrain the gradient norm and ensure the local stability and differentiability of the mapping; It should be noted that the laser signal-to-noise ratio (SNR) refers to the ratio of the effective signal power to the noise power in the laser detection signal, taking the base-10 logarithm and multiplying it by 10. The effective signal power represents the signal power that carries useful information, such as the chair's position and operating status, during the laser detection process. For example, when using a laser interferometer modulation model to detect changes in optical path length caused by chair swing, the power corresponding to the portion of the optical signal generated by the chair's position change that reflects information such as displacement and swing angle is the effective signal power. The noise power refers to the power of interfering signals present during the laser detection process. Interference can arise from environmental factors (such as external light interference and laser scattering caused by weather conditions such as wind and snow) or from the detection equipment's own electronic noise. This noise can interfere with the detection and analysis of the effective signal, reducing detection accuracy.

[0029] Based on the mapping function of the health manifold and the feature space weight, the local metric tensor is calculated ,in Jacobian matrix representing the mapping function The transpose of diag(w) represents the diagonal matrix of feature space weights. The healthy baseline feature vector is transformed into the healthy manifold baseline vector through the mapping function. The current healthy manifold vector is obtained based on the healthy manifold. , based on the local metric tensor to establish the geodesic equation on the three-dimensional manifold , the boundary condition is that the starting point of the path is the healthy manifold reference vector ( ), the end point of the path is the current healthy manifold vector ( ),in represents a parameterized path on the manifold, represents the αth component of the path in the manifold coordinate system, represents the Christoffel symbol, derived from the local metric tensor, , β is an index representing the direction of the local coordinate system of the manifold, and together with α and γ, it constitutes the coordinate index of the three-dimensional manifold, α, β, γ∈{1, 2, 3} (corresponding to the three coordinate directions of the three-dimensional space), represents the inverse matrix of the local metric tensor, 、 and represents the components of the local metric tensor, 、 and Represents the coordinates of the local coordinate system of the manifold, μ represents the Einstein summation index, and the optimal parameterized path is obtained through numerical optimization. , calculated based on the optimal parameterized path, the manifold distance is obtained , the length of the tangent vector along the optimal parameterized path integral; Specifically, the health benchmark feature vector is the mean of the historical health data vector, and then the health manifold benchmark vector is obtained by the calculation method of the health manifold. ,in represents the mean of the historical health data vector; Based on the manifold distance, it is mapped to a maintenance action guide through preset decision rules. Specifically, if the manifold distance is less than a1, the "normal" status is output (the HMI displays a green safety zone). If the manifold distance is greater than or equal to a1 and less than or equal to a2, the "early warning" protocol is triggered (the HMI flashes yellow and prompts "next shutdown for maintenance"). If the manifold distance is greater than a2, the "immediate maintenance" instruction is activated (the HMI red alarm).

[0030] Specifically, a wind-induced dynamics model of the chairlift is established to solve the wind-induced dynamic displacement and swing angle response, and the original envelope equation is constructed in combination with the chairlift's geometric parameters; a real-time safety boundary model is generated using the chaos index, optical path difference phase signal and swing angle response, which is expanded into a time-space embedded surface and the Gaussian curvature field is calculated, and the curvature manifold is extracted for directional integration to obtain the Euler characteristic; at the same time, the entropy complexity is calculated based on the multi-scale coarse-graining of the laser signal, and the fourth-order risk tensor is constructed by combining the dynamic parameters, optical parameters and safety control parameters. The dynamic risk value is output through high-order singular value decomposition to achieve graded early warning; finally, the displacement chaos index, Euler characteristic, entropy complexity and laser signal-to-noise ratio are standardized, and then mapped to the healthy manifold through the Riemann manifold embedding algorithm, and the degree to which the equipment status deviates from the healthy benchmark is quantified based on the geodesic distance, and maintenance instructions are output. This method achieves non-contact and precise monitoring of the chairlift position by integrating dynamic response, optical detection and topological analysis, and utilizing wind vibration-chaos-laser multi-source data coupling modeling. 1) Offside risks are identified based on the space-time curvature field, and the sensitivity of topological integral detection is improved; 2) The fractional-order optimal braking algorithm shortens the emergency braking distance; 3) The healthy manifold geodesic distance quantifies the equipment degradation state, greatly improving the maintenance response speed; 4) Laser anti-interference light field analysis technology ensures improved detection accuracy in windy and snowy environments.

[0031] The above is an illustration of the present invention and should not be considered as limiting thereof. Although several exemplary embodiments of the present invention have been described, it will be readily understood by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined by the claims. It should be understood that the above is an illustration of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. A method for detecting the position of a chairlift at a detour station of a ski lift, characterized in that: The following steps are involved: Step S100: obtaining a modified envelope by modeling the nonlinear dynamics of the hanging chair; Step S200: performing topological feature analysis on the hanging chair based on the modified envelope to obtain a topological detection result; Step S300: Obtaining the geometric relationship of the chairlift, laser installation parameters and laser detection signals to perform tensor risk fusion decision-making and obtain safety control instructions; Step S400: performing fractional-order optimal braking analysis based on the safety control instruction to obtain a structured data frame; Step S500: Obtain the chair displacement, chair motion manifold, and laser signal parameters of the chair, perform conformal geometry maintenance, and obtain maintenance operation guidance.

2. A method for detecting the position of a chairlift at a detour station of a ski lift according to claim 1, characterized in that: The steps of the modified envelope analysis are as follows: Obtaining the chair body geometric parameters and cableway system installation parameters of the chairlift and constructing the original envelope equation through spatial coordinate system transformation, wherein the chair body geometric parameters include the chairlift rotation radius and the rotation center coordinates, and the cableway system installation parameters include the horizontal offset and the vertical reference plane; A real-time safety boundary model is constructed by introducing the wind-induced dynamic displacement of the chair, the dynamic response of the chair swing angle and the maximum chaos index as dynamic correction terms into the original envelope equation to obtain the modified envelope.

3. A method for detecting the position of a chairlift at a detour station of a ski lift according to claim 2, characterized in that: The steps for analyzing the maximum chaotic index of chairlift displacement divergence are as follows: A second-order damping system for the chair under horizontal wind excitation is established to solve the wind-induced dynamic displacement of the chair. The torsional dynamic equation of the chair is established to solve the dynamic response of the chair's swing angle. Based on the wind-induced dynamic displacement of the hanging chair, the instantaneous velocity is calculated by the central difference method. The displacement value and the instantaneous velocity value at the same moment are combined into a two-dimensional state point, which is connected in time sequence to form a continuous phase trajectory to obtain the displacement velocity phase space. The time average value of the trajectory phase space divergence rate is calculated based on the integration of the displacement velocity phase space along the time series, and the maximum chaos index of the hanging chair displacement divergence is obtained by the limit definition.

4. A method for detecting the position of a chairlift at a detour station of a ski lift according to claim 1, characterized in that: The topology detection result analysis steps are as follows: The modified envelope is expanded into an embedded surface with a time dimension, and the first basic form coefficients and the second basic form coefficients of the embedded surface with a time dimension are calculated. Based on the first basic form coefficients and the second basic form coefficients, the spacetime curvature field of the hanging chair motion is generated using the Gaussian curvature formula. The critical point set whose Gaussian curvature is greater than the curvature threshold of the hanging chair edge is extracted from the spacetime curvature field of the hanging chair motion and marked as the hanging chair motion curvature manifold; The Euler characteristic of the chairlift motion manifold is obtained by performing directed surface integral on the chairlift motion curvature manifold. The Euler characteristic of the chairlift motion manifold is analyzed and judged with the reference value to obtain the topology detection result.

5. The method for detecting the position of a chairlift at a detour station of a ski lift according to claim 1, wherein: The steps for analyzing the safety control instructions are as follows: Perform high-order singular value decomposition on the fourth-order risk tensor, calculate the covariance matrix along the four modes and solve its eigenvalue decomposition to obtain the factor matrix of each mode. Then, use multilinear projection to map the original tensor to the characteristic subspace to generate a dimensionally compressed risk core tensor. The singular values ​​of the four modes and their corresponding main eigenvectors are extracted from the Tucker decomposition of the dimensionally compressed risk core tensor. The eigenvector modulus of each mode is calculated and multiplied by the corresponding singular value. The dynamic risk value is output based on the time-domain attenuation effect of the maximum chaotic index of chairlift displacement divergence on risk. The dynamic risk value is compared with the set threshold, and the corresponding risk quantification value is mapped to a safety control instruction.

6. A method for detecting the position of a chairlift at a detour station of a ski lift according to claim 5, characterized in that: The steps of the fourth-order risk tensor analysis are as follows: Calculate the pattern probability based on the number of similar vector pairs and output the entropy complexity through the Shannon entropy formula; The wind-induced dynamic displacement and the dynamic response of the chair's swing angle are used to form the chair's dynamic parameters. The phase modulation signal, edge feature map, Euler characteristic of the chair's motion manifold and entropy complexity are used to form the optical detection parameters. The wheel center distance, linear velocity and acceleration are used to form the safety control parameters. The chair's dynamic parameters, optical detection parameters and safety control parameters are organized into a chair's dynamic optical safety parameter matrix. A real-time risk feature is constructed based on the phase modulation signal, the Euler characteristic of the chair's motion manifold and the wheel center distance. A time risk vector containing a timestamp and a real-time risk feature is constructed. The chair's dynamic optical safety parameter matrix and the time risk vector are fully coupled through a tensor product operation to generate a fourth-order risk tensor.

7. A method for detecting the position of a chairlift at a detour station of a ski lift according to claim 6, characterized in that: The steps for analyzing the number of similar vector pairs are as follows: The optical path difference is calculated based on the geometric relationship of the hanging chair through the dynamic displacement and swing angle caused by wind, and the phase modulation signal is obtained by substituting the optical path difference into the interference equation. Based on the laser installation parameters, the spatial carrier term of the window function is superimposed on the Gaussian attenuation term to obtain the spatial window function. The phase modulated signal is reconstructed in time and space by the spatial window function. The virtual light intensity field is constructed by the spatial window function. The gradient modulus of the virtual light intensity field is calculated and a fractional differential operator is applied. The signal mutation characteristics are enhanced by convolution operation to obtain the edge feature map. The time domain sequence of laser detection signals is subjected to multi-scale coarse-graining processing. The original sequence is divided into non-overlapping windows according to the scale factor τ=5. An m-dimensional vector is constructed in the phase space based on the coarse-grained sequence, and the number of similar vector pairs that meet the distance threshold r is counted.

8. The method for detecting the position of a chairlift at a detour station of a ski lift according to claim 1, wherein: The steps of analyzing the structured data frame are as follows: Based on the safety deviation between the chair's real-time position and the center of the driving wheel, a Caputo-type semi-order derivative is used to define the chair's fractional-order error term. The deceleration and jerk are used as control input constraints. The weighting coefficients are set according to the cableway safety standard, and the safety area constraint functional is generated through time integration. Based on the safety region constraint functional, fractional-order kinematic constraints are used as coupling conditions for the Lagrange multiplier terms. An extended safety region constraint functional is constructed, and variational operations are performed on the extended safety region constraint functional. The governing equations and adjoint equations are derived through the Euler-Lagrange equations. Combined with the boundary conditions and the definition of fractional-order derivatives, a predictive-correction algorithm is used to iteratively solve the differential-algebraic equations to obtain the brake-loss-optimized deceleration curve. During the control cycle, discrete integral operation is performed based on the brake consumption adaptive deceleration curve to update the speed instruction in real time. At the same time, when the dynamic risk value is equal to or greater than the set threshold, the human-machine interface sound and light alarm is triggered, and the encapsulated updated speed instruction and alarm flag are synthesized into a structured data frame through the PROFINET protocol.

9. The method for detecting the position of a chairlift at a detour station of a ski lift according to claim 1, wherein: The maintenance operation-oriented analysis steps are as follows: Based on the mapping function and feature space weight of the healthy manifold, the local metric tensor is calculated. The healthy reference feature vector is converted to the healthy manifold reference vector through the mapping function. The current healthy manifold vector is obtained based on the healthy manifold. The geodesic equation on the three-dimensional manifold is established based on the local metric tensor. The boundary conditions are that the starting point of the path is the healthy manifold reference vector and the end point of the path is the current healthy manifold vector. The optimal parameterized path is obtained through numerical optimization. The manifold distance is calculated based on the optimal parameterized path. Based on the manifold distance, it is mapped into maintenance action guidance through preset decision rules.

10. A method for detecting the position of a chairlift at a detour station of a ski lift according to claim 9, characterized in that: The steps of health manifold analysis are as follows: The maximum chaotic index of the chairlift displacement divergence, the Euler characteristic of the chairlift motion manifold, the entropy complexity and the signal-to-noise ratio of the laser signal are Z-score normalized to obtain the standardized four-dimensional eigenvector. Through the Riemannian manifold embedding algorithm, the four-dimensional eigenvector is mapped to a three-dimensional hypersurface with the geodesic distance as the intrinsic metric constraint, ensuring that the Euclidean distance between any two points on the manifold maintains the geodesic distance relationship in its original eigenspace, thus generating a healthy manifold in the phase space.

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