Intelligent risk early warning method and system for elevator

Abstract

The invention relates to the technical field of elevator risk early warning, in particular to an elevator intelligent risk early warning method and system. The method comprises the following steps: acquiring running state data of an elevator sliding part through a sensor, analyzing nonlinear characteristics of torque fluctuation, further performing friction heat energy accumulation treatment, simulating limit tolerance of a steel wire rope, and quantifying a cyclic damage expansion index of the steel wire rope according to a simulation result; and a fracture risk prefactor is identified according to the damage index, a random forest algorithm is utilized to construct an elevator intelligent risk early warning model, and finally the model is sent to a terminal for elevator intelligent risk early warning. According to the method, the elevator risk early warning technology is optimized, so that the elevator risk early warning technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator risk warning technology, and in particular to an elevator intelligent risk warning method and system. Background Art

[0002] The operation of an elevator involves multiple key components, such as wire ropes, pulleys, and brake systems. Their working conditions and interactions directly affect the overall safety of the elevator. With the increase in usage time, problems such as wear, corrosion, and fatigue of elevator components will gradually accumulate, causing a decline in mechanical performance and even causing failures or accidents. As the core component of the elevator transmission system, the load-bearing capacity and damage condition of the wire rope are directly related to the safety of the elevator. Factors such as the accumulation of friction heat during elevator braking, the torque fluctuation of the wire rope, and the change in the friction coefficient will affect the stability and safety of the elevator operation. However, a traditional intelligent risk warning method for elevators has the problem of inaccurate analysis of the extreme tolerance of the elevator traction wire rope, resulting in a large error in the elevator risk warning. Summary of the invention

[0003] Based on this, it is necessary to provide an elevator intelligent risk warning method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent risk early warning method for elevators is provided, the method comprising the following steps: Step S1: collecting the running status data of the elevator sliding component through the sensor to obtain the running status data of the sliding component; analyzing the wire rope torque fluctuation during the elevator braking process on the running status data of the sliding component to obtain the torque fluctuation nonlinear characteristic data; Step S2: performing friction heat energy accumulation processing during the elevator braking process based on the torque fluctuation nonlinear characteristic data to obtain friction heat energy accumulation data; performing wire rope limit tolerance simulation based on the friction heat energy accumulation data to obtain wire rope limit tolerance data; Step S3: quantifying the cyclic damage expansion index of the wire rope based on the convolution data of the wire rope limit tolerance to obtain the cyclic damage expansion index of the wire rope; identifying the pre-factor of the wire rope fracture risk according to the cyclic damage expansion index of the wire rope to obtain the fracture risk pre-factor learning data; Step S4: construct an elevator intelligent risk warning model for the fracture risk pre-factor learning data based on the random forest algorithm to obtain the elevator intelligent risk warning model, and send the elevator intelligent risk warning model to the terminal to execute the elevator intelligent risk warning.

[0005] Preferably, step S1 comprises the following steps: Step S11: collecting operating status data of the elevator sliding component through a sensor to obtain the operating status data of the sliding component; Step S12: cleaning the running status data of the sliding component to obtain running status cleaning data; Step S13: analyzing the fluctuation of the steel wire rope torque during the elevator braking process on the running status cleaning data to obtain the steel wire rope torque fluctuation data during the elevator braking process; Step S14: Perform nonlinear characteristic analysis on the wire rope torque fluctuation data to obtain torque fluctuation nonlinear characteristic data.

[0006] Preferably, step S2 comprises the following steps: Step S21: obtaining the physical property data of the steel wire rope; Step S22: performing a multi-scale dynamic evolution trend analysis of friction force during the elevator braking process based on the nonlinear characteristic data of torque fluctuation to obtain dynamic evolution trend data of friction force; Step S23: performing friction heat energy accumulation processing on the friction force dynamic evolution trend data according to the torque fluctuation nonlinear characteristic data to obtain friction heat energy accumulation data; Step S24: simulating the ultimate tolerance of the wire rope based on the nonlinear characteristic data of the torque fluctuation and the frictional heat energy accumulation data to obtain the ultimate tolerance data of the wire rope.

[0007] Preferably, step S23 includes the following steps: Step S231: analyzing the inclination angle of the traction torque of the wire rope according to the nonlinear characteristic data of the torque fluctuation to obtain the inclination angle of the traction torque; Step S232: performing angular contact friction force differential processing on the friction force dynamic evolution trend data based on the traction torque inclination angle to obtain angular contact friction force differential data; Step S233: performing contact friction heat flow conversion ratio estimation on the angular contact friction force differential data to obtain the contact friction heat flow conversion ratio; Step S234: performing friction heat energy accumulation processing according to the contact friction heat flow conversion ratio to obtain friction heat energy accumulation data.

[0008] Preferably, step S24 comprises the following steps: Step S241: performing thermal stress coupling fluctuation response calculation according to the torque fluctuation nonlinear characteristic data and the friction heat energy accumulation data to obtain thermal stress coupling fluctuation response data; Step S242: extracting the initial plastic deformation strength of the steel wire rope physical property data to obtain initial plastic deformation strength data; Step S243: quantifying the fatigue loss of the plastic deformation performance of the steel wire rope based on the initial plastic deformation strength data based on the thermal stress coupling fluctuation response data to obtain the fatigue loss data of the plastic deformation performance; Step S244: performing impact load toughness weakening simulation fitting on the plastic deformation performance fatigue loss data according to the thermal stress coupling fluctuation response data to obtain impact load toughness weakening fitting data; Step S245: performing nonlinear regression analysis on the impact load toughness weakening fitting data to obtain toughness weakening regression data; Step S246: simulate the ultimate tolerance of the wire rope according to the plastic deformation performance fatigue loss data and the toughness weakening regression data to obtain the ultimate tolerance data of the wire rope.

[0009] Preferably, step S244 includes the following steps: The thermal stress fluctuation curve is plotted on the thermal stress coupling fluctuation response data to obtain the thermal stress response fluctuation curve; the transient amplification intensity index is evaluated on the thermal stress response fluctuation curve to obtain the thermal stress transient amplification intensity index; Based on the fatigue loss data of plastic deformation performance, the dislocation damage density increment of the wire rope is analyzed to obtain the dislocation density increment data; According to the thermal stress transient amplification intensity index, the dislocation density increment data is approximated and coupled to obtain the thermal stress dislocation correlation distribution coupling data; Perform distribution increment learning on thermal stress dislocation correlation distribution coupling data to obtain correlation distribution increment coupling data; Based on the Bayesian regression algorithm, the impact load toughness weakening simulation fitting is performed on the associated distribution incremental coupling data to obtain the impact load toughness weakening fitting data.

[0010] Preferably, step S3 comprises the following steps: Step S31: performing convolution calculation on the wire rope limit tolerance data to obtain the wire rope limit tolerance convolution data; Step S32: quantifying the cyclic damage expansion index of the wire rope based on the ultimate tolerance convolution data of the wire rope to obtain the cyclic damage expansion index of the wire rope; Step S33: identifying the pre-factor of the risk of wire rope fracture according to the convolution data of the cyclic damage expansion index of the wire rope and the ultimate tolerance of the wire rope, and obtaining the pre-factor of the risk of wire rope fracture; Step S34: Perform logical learning on the wire rope fracture risk pre-factor to obtain fracture risk pre-factor learning data.

[0011] Preferably, step S32 includes the following steps: Step S321: performing fatigue cycle evolution analysis on the ultimate tolerance convolution data of the steel wire rope to obtain fatigue cycle evolution data; Step S322: performing a cyclic load stress-strain gradient rise evaluation on the fatigue tolerance cycle evolution data to obtain cyclic load stress-strain rise data; Step S323: performing a time series cyclic load mean difference calculation based on the cyclic load stress-strain rise data to generate a time series cyclic load stress-strain mean difference; performing a time series boundary stress-strain approximate value calculation on the time series cyclic load stress-strain mean difference to obtain a boundary stress-strain approximate value; Step S324: performing boundary error series truncation on the cyclic load stress-strain rise data according to the boundary stress-strain approximation to obtain stress-strain boundary series truncation data; Step S325: performing convergence-constrained Taylor series expansion processing based on the stress-strain boundary series truncation data to obtain stress-strain convergence-constrained series expansion data; Step S326: quantifying the cyclic damage expansion index of the wire rope according to the stress-strain convergence constraint series expansion data to obtain the cyclic damage expansion index of the wire rope.

[0012] Preferably, step S4 comprises the following steps: Step S41: normalizing the fracture risk pre-factor learning data to obtain risk pre-factor normalized data; Step S42: performing random feature sampling on the risk pre-factor normalized data to generate risk pre-factor feature sampling data; Step S43: construct an elevator intelligent risk warning model based on the risk pre-factor feature sampling data based on the random forest algorithm to obtain the elevator intelligent risk warning model, and send the elevator intelligent risk warning model to the terminal to execute the elevator intelligent risk warning.

[0013] Preferably, the present invention further provides an elevator intelligent risk warning system, which is used to execute the elevator intelligent risk warning method as described above, and the elevator intelligent risk warning system comprises: The torque fluctuation analysis module is used to collect the operating status data of the elevator sliding parts through sensors to obtain the operating status data of the sliding parts; analyze the wire rope torque fluctuation during the elevator braking process on the sliding parts operating status data to obtain the nonlinear characteristic data of the torque fluctuation; The extreme tolerance simulation module is used to perform friction heat energy accumulation processing during the elevator braking process based on the nonlinear characteristic data of torque fluctuation to obtain friction heat energy accumulation data; simulate the extreme tolerance of the wire rope based on the friction heat energy accumulation data to obtain the extreme tolerance data of the wire rope; The fracture risk prefactor identification module is used to quantify the wire rope cyclic damage expansion index based on the wire rope limit tolerance convolution data to obtain the wire rope cyclic damage expansion index; identify the wire rope fracture risk prefactor according to the wire rope cyclic damage expansion index to obtain the fracture risk prefactor learning data; The early warning model construction module is used to construct an elevator intelligent risk early warning model based on the fracture risk pre-factor learning data based on the random forest algorithm, obtain the elevator intelligent risk early warning model, and send the elevator intelligent risk early warning model to the terminal to execute the elevator intelligent risk early warning.

[0014] The beneficial effect of the present invention is that the operating state data of the elevator sliding component is collected by the sensor, and the purpose is to obtain the state information of the sliding component under different working conditions in real time. These data provide a basis for subsequent analysis. The collected data include key parameters such as the speed, acceleration, and friction coefficient of the sliding component. The changes of these parameters during the elevator braking process will affect the stability and safety of the system. By performing torque fluctuation analysis on the collected sliding component operating state data, the fluctuation characteristics of the force of the wire rope during the elevator braking process can be identified, and then the nonlinear characteristic data in these fluctuations can be extracted. These nonlinear characteristic data are of high value because they can reveal potential abnormalities in the operation of the elevator, especially in the case of uneven force or unbalanced friction of the wire rope, resulting in equipment damage or failure. The purpose of this step is to accurately analyze the nonlinear characteristics of torque fluctuations, discover system problems in advance, and lay a data foundation for subsequent processing and early warning. Based on the nonlinear characteristic data of torque fluctuations obtained in the previous step, the accumulation of friction heat energy is processed. Friction during the elevator braking process will generate heat energy, and long-term heat accumulation will lead to a decline in equipment performance or even damage. The accumulation of frictional heat directly affects the durability of the wire rope, because the increase in temperature will accelerate material aging and fatigue, and eventually lead to the risk of wire rope breakage. By processing the accumulated frictional heat data, the tolerance of the wire rope under different load conditions and temperature changes can be accurately simulated, thereby predicting its service life. The simulation process takes into account multiple factors such as the material properties of the wire rope and the temperature changes in the working environment to provide a comprehensive tolerance assessment. According to the simulated tolerance data of the wire rope, potential damage risks can be identified and timely measures can be taken to avoid breakage accidents caused by low tolerance, thereby improving the safety and reliability of the elevator. The data obtained from the simulation of the ultimate tolerance of the wire rope is further quantitatively analyzed for the cyclic damage expansion index of the wire rope. The wire rope undergoes periodic stretching and compression during the operation of the elevator. These repeated loads will cause fatigue damage to the wire rope. The damage gradually expands over time, affecting the overall structure and strength of the wire rope. In this step, by quantifying the cyclic damage expansion index of the wire rope, the expansion trend of the damage of the wire rope during long-term use can be accurately evaluated. The calculation of the damage extension index combines factors such as the stress condition, friction characteristics and thermal energy of the wire rope to provide a dynamic damage assessment model. Based on this index, the pre-risk factors of wire rope breakage are further identified. These risk factors are usually related to the material, running time, load conditions and ambient temperature of the wire rope, and can provide detailed basis for subsequent risk warnings. Based on the random forest algorithm, the learning data of the pre-factors of the risk of breakage are analyzed to build an intelligent risk warning model for elevators. As a powerful machine learning algorithm, the random forest algorithm can effectively extract key information and make accurate predictions when processing complex multi-dimensional data.By learning from historical data and real-time data, the intelligent risk warning model can identify the potential breakage risk of elevator wire ropes and provide risk warnings. The model comprehensively considers multiple factors such as the force, frictional heat energy, and damage expansion of the wire rope, and gradually improves the accuracy of the prediction through continuous learning and optimization. Finally, the intelligent warning model will send the risk warning information to the terminal in real time for maintenance personnel to handle in time. Through this intelligent risk warning system, elevator operation managers can predict the potential failure of the wire rope in advance, avoid the impact on passenger safety when the failure occurs, and also reduce equipment maintenance costs and improve the operating efficiency and safety of the elevator. Therefore, the present invention is an optimization of a traditional intelligent risk warning method for elevators, which solves the problem that a traditional intelligent risk warning method for elevators has an inaccurate analysis of the extreme tolerance of the elevator traction wire rope, thereby causing a large error in the elevator risk warning, improves the accuracy of the analysis of the extreme tolerance of the elevator traction wire rope, and reduces the error in the elevator risk warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of the steps of an intelligent risk warning method for elevators; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. DETAILED DESCRIPTION

[0016] See also Figures 1 to 3 , an elevator intelligent risk warning method, the method comprising the following steps: Step S1: collecting the running status data of the elevator sliding component through the sensor to obtain the running status data of the sliding component; analyzing the wire rope torque fluctuation during the elevator braking process on the running status data of the sliding component to obtain the torque fluctuation nonlinear characteristic data; Step S2: performing friction heat energy accumulation processing during the elevator braking process based on the torque fluctuation nonlinear characteristic data to obtain friction heat energy accumulation data; performing wire rope limit tolerance simulation based on the friction heat energy accumulation data to obtain wire rope limit tolerance data; Step S3: quantifying the cyclic damage expansion index of the wire rope based on the convolution data of the wire rope limit tolerance to obtain the cyclic damage expansion index of the wire rope; identifying the pre-factor of the wire rope fracture risk according to the cyclic damage expansion index of the wire rope to obtain the fracture risk pre-factor learning data; Step S4: construct an elevator intelligent risk warning model for the fracture risk pre-factor learning data based on the random forest algorithm to obtain the elevator intelligent risk warning model, and send the elevator intelligent risk warning model to the terminal to execute the elevator intelligent risk warning.

[0017] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of an elevator intelligent risk warning method of the present invention. In this example, the elevator intelligent risk warning method includes the following steps: Step S1: collecting the running status data of the elevator sliding component through the sensor to obtain the running status data of the sliding component; analyzing the wire rope torque fluctuation during the elevator braking process on the running status data of the sliding component to obtain the torque fluctuation nonlinear characteristic data; In the embodiment of the present invention, the operation status data of the elevator sliding parts are collected by combining a high-precision inertial measurement unit (IMU) and an optical displacement sensor. The inertial measurement unit is installed on the pulley of the elevator traction machine to record the angular velocity and acceleration data in real time. The sampling frequency is set to 1000Hz to ensure stability under high dynamic conditions. The optical displacement sensor is fixed on the wall of the elevator shaft to measure the tiny slip of the wire rope with high precision, with a resolution of 0.01mm and a sampling frequency of 500Hz. The data of the inertial measurement unit is removed by a low-pass filter to remove high-frequency noise, and is time-synchronized with the displacement data of the optical displacement sensor for subsequent analysis. During the braking process of the elevator, the torque fluctuation between the wire rope and the pulley is affected by many factors, including the pulley moment of inertia, the wire rope preload, and the change of the braking pressure. Therefore, after the data collection is completed, the torque fluctuation data needs to be analyzed. First, the torque fluctuation signal is analyzed in the frequency domain by Fourier transform (FFT) to identify the main vibration frequency components. Then, wavelet transform is used to perform time-frequency analysis on the non-stationary signal to extract the torque fluctuation characteristics at the moment of elevator braking. Special attention is paid to the extreme points of the instantaneous torque, and the instantaneous frequency distribution is calculated using Hilbert-Huang transform (HHT) to obtain the nonlinear torque fluctuation characteristics. After data processing, a time series data set containing the nonlinear characteristics of torque fluctuation is obtained, which is used for the subsequent friction heat energy accumulation calculation.

[0018] Step S2: performing friction heat energy accumulation processing during the elevator braking process based on the torque fluctuation nonlinear characteristic data to obtain friction heat energy accumulation data; performing wire rope limit tolerance simulation based on the friction heat energy accumulation data to obtain wire rope limit tolerance data; In the embodiment of the present invention, the friction heat energy accumulation process is based on the calculation of friction work during the elevator braking process. First, the relative slip process between the pulley and the wire rope is analyzed by the principle of energy conservation. During the braking process, the pulley rotates to produce a small angular displacement due to the friction torque, and the wire rope is subjected to radial compression force and microscopic deformation. In this process, the friction heat is closely related to the contact stress, so it is necessary to measure the actual contact pressure of the wire rope. A thin film pressure sensor (resolution 1kPa, sampling rate 2kHz) is attached to the inner wall of the pulley groove to obtain the contact pressure distribution of the wire rope. After obtaining the pressure data, the contact mechanics method is used to calculate the friction work per unit time. First, the measured pressure data is interpolated to obtain the continuous distribution of pressure along the pulley contact area. Then, combined with the pulley angular velocity data, the slip distance per unit time is calculated. According to the friction work calculation formula, the friction heat energy per unit time is accumulated and calculated to obtain the friction heat energy accumulation data. Subsequently, the finite element simulation software is used to simulate the changes in material properties of the wire rope after heating to determine the long-term effect of friction heat energy on the strength of the wire rope. The thermal-mechanical coupling analysis method is used to input the friction heat accumulation data, analyze the microstructure changes of the wire rope material, and calculate its ultimate tolerance. During the simulation process, the wire rope material parameters are set as follows: Young's modulus 210GPa, Poisson's ratio 0.3, thermal conductivity Based on the results of thermal stress analysis, the ultimate tolerance data of the wire rope is obtained, which provides input for subsequent damage expansion analysis.

[0019] In another embodiment, the torque fluctuation nonlinear characteristic data obtained in step S1 is used to collect the local temperature rise information obtained by the infrared thermal imager set on the surface of the wire rope to perform friction heat energy accumulation analysis. First, the multi-scale time-frequency analysis method is used to perform energy spectrum decomposition on the nonlinear characteristic data at time scales of 0.1 s, 0.2 s and 0.5 s, respectively, to obtain energy distribution data at each scale. Subsequently, the decomposed energy data is multiplied by the predetermined friction heat energy conversion coefficient (the coefficient value is fixed at 0.87, corresponding to the laboratory measured data) to calculate the heat energy generated by the conversion in each sampling interval. The heat energy accumulation value is obtained by cumulative summation, and the unit is joule (J). The accumulated heat energy data of each braking stage is recorded, and the integral time span is fixed at 2 s. Each data item in the output data is accurate to 0.1 J. Based on the friction heat energy accumulation data, the ultimate tolerance performance of the wire rope is simulated by a numerical simulation method. During the simulation, the physical property parameters of the wire rope, such as the wire diameter, were set to 4.5 mm, the material yield strength was set to 800 MPa, and the fixed length on the test bench was set to 3 m. The tolerance simulation used the finite difference method to discretely analyze the uniform stress distribution along the length and cross section of the wire rope. After loading the thermal stress, the critical conditions for local fatigue failure of the wire rope were obtained. The ultimate tolerance data outputted included the local tolerance fatigue times and the residual strength percentage values, with an accuracy of two decimal places.

[0020] Step S3: quantifying the cyclic damage expansion index of the wire rope based on the convolution data of the wire rope limit tolerance to obtain the cyclic damage expansion index of the wire rope; identifying the pre-factor of the wire rope fracture risk according to the cyclic damage expansion index of the wire rope to obtain the fracture risk pre-factor learning data; In the embodiment of the present invention, the quantification of the cyclic damage expansion index of the wire rope is analyzed using the cumulative damage theory. First, the force fluctuation of the wire rope during continuous braking is calculated using the aforementioned wire rope limit tolerance data in combination with the elevator braking cycle. The rain flow counting method is used to count the torque fluctuation signal in cycles to identify typical load cycles. Based on Miner's linear cumulative damage theory, the damage increment under different braking cycles is calculated. In the damage calculation, the SN curve of the wire rope material is taken from the ISO 4309 standard, and the SN curve equation parameter is m=3.5, . Subsequently, a convolutional neural network (CNN) was used to fit the damage expansion process. A three-layer convolutional neural network was constructed. The size of the first convolution kernel was set to 3×3 and the step size was 1 to extract damage features; the second layer used a 5×5 convolution kernel with a step size of 2 to extract the global damage pattern; the third layer used a 7×7 convolution kernel with a step size of 2 to extract the cumulative damage trend. The network input is the time-series damage increment data, and the output is the wire rope cyclic damage expansion index. This index is used for subsequent wire rope fracture risk analysis. After obtaining the damage expansion index, the fracture risk prefactors are identified. The principal component analysis (PCA) method is used to reduce the dimensionality of multiple factors affecting the fracture risk (friction heat, pulley wear, environmental humidity, wire rope fatigue) to extract the main risk factors. According to the dimensionality reduction results, a risk factor classification model based on K-Means clustering is constructed to classify and learn the wire rope fracture risk prefactors to form fracture risk prefactor learning data.

[0021] Step S4: construct an elevator intelligent risk warning model for the fracture risk pre-factor learning data based on the random forest algorithm to obtain the elevator intelligent risk warning model, and send the elevator intelligent risk warning model to the terminal to execute the elevator intelligent risk warning.

[0022] In an embodiment of the present invention, an elevator intelligent risk warning model is constructed based on a random forest algorithm. First, the fracture risk pre-factor learning data is standardized, and the features of different dimensions are normalized to the [0,1] interval to ensure the consistency of the training data distribution. Then, the Bootstrap sampling method is used to randomly generate multiple training subsets. For each subset, a decision tree is trained, and the maximum depth of each decision tree is set to 10, and the minimum number of sample divisions is set to 5 to prevent overfitting. Finally, the prediction results of multiple decision trees are integrated to form a random forest model. After the model training is completed, the model performance is evaluated by 10-fold cross validation, and the accuracy, recall rate and F1-score indicators are calculated to ensure the generalization ability of the model. The trained elevator intelligent risk warning model is stored and loaded into the terminal system. During the operation of the elevator, the operating status data collected by the sensor in real time is input into the elevator intelligent risk warning model. The model dynamically evaluates the health status of the current wire rope based on the learned fracture risk pre-factor characteristics. When the predicted risk level exceeds the set threshold, the system automatically triggers an early warning and notifies the maintenance personnel to check. The early warning information includes specific risk factors, predicted fracture risk index, recommended maintenance measures, etc. to assist elevator maintenance decision-making.

[0023] Step S1 includes the following steps: Step S11: collecting operating status data of the elevator sliding component through a sensor to obtain the operating status data of the sliding component; Step S12: cleaning the running status data of the sliding component to obtain running status cleaning data; Step S13: analyzing the fluctuation of the steel wire rope torque during the elevator braking process on the running status cleaning data to obtain the steel wire rope torque fluctuation data during the elevator braking process; Step S14: Perform nonlinear characteristic analysis on the wire rope torque fluctuation data to obtain torque fluctuation nonlinear characteristic data.

[0024] In the embodiment of the present invention, multiple high-precision sensors are arranged in the elevator system to collect data on the operating status of the elevator sliding parts. First, acceleration sensors, angular velocity sensors, laser displacement sensors and strain gauges are installed on key components such as the elevator car guide shoes, counterweight guide shoes and wire rope tensioning devices. The acceleration sensor uses a MEMS three-axis accelerometer with a range of ±16g and a resolution of 0.001g, which is used to measure the vibration state of the sliding parts. The angular velocity sensor uses a fiber optic gyroscope with an accuracy of 0.001 , used to record the angular velocity changes during the operation of the car. The laser displacement sensor is installed on the side of the guide rail with a resolution of 0.1mm to measure the sliding position changes of the guide shoe on the guide rail. The strain gauge uses a 350Ω high-precision strain gauge and is pasted on the surface of the wire rope to record the stress state of the wire rope. All sensors collect data synchronously at a sampling rate of 1kHz, transmit it to the data storage unit through the CAN bus, and use a high-precision GPS timing module for time alignment to ensure the synchronization of data in each channel. The data storage format is a binary data stream with a timestamp mark for subsequent analysis. The collected sliding component operation status data is cleaned to remove outliers, noise signals and invalid data. First, the bilateral triple standard deviation method is used to remove outliers from the original data, calculate the mean and standard deviation of all data points, and remove outliers exceeding three times the standard deviation. Then, the wavelet transform method is used to reduce the noise of the signal, select the Daubechies4 (db4) wavelet basis, perform five-layer decomposition, and perform soft threshold filtering on the high-frequency noise signal to eliminate transient noise caused by external interference factors. For the acceleration sensor data, a zero-phase filter is used for bandpass filtering, and the cutoff frequency range is set to 0.5Hz to 100Hz to retain the characteristic signals related to the movement of the sliding parts. For the angular velocity data, a median filter is used to eliminate mutation interference, and the window length is set to 5 data points. After the data cleaning is completed, the linear interpolation method is used to fill the data gaps caused by the removal of outliers, and the data is standardized. The data of each sensor is normalized to the range of [0,1], and finally the running state cleaning data is generated. Based on the running state cleaning data, the fluctuation characteristics of the wire rope torque during the elevator braking process are analyzed. In the braking process of the elevator from high-speed operation to stop, the wire rope is subjected to the combined action of the car inertia force, the counterweight inertia force and the braking force, resulting in nonlinear fluctuations in the torque. First, the wire rope strain data is used to calculate the change of its tension, and the Fourier transform method is used to analyze the spectral characteristics of the tension signal and extract the main frequency component. Secondly, the empirical mode decomposition (EMD) method is used to decompose the tension change signal into several intrinsic mode components (IMFs), and the instantaneous frequency of each modal component is calculated to observe the local characteristics of the torque fluctuation. Then, the Hilbert-Huang transform (HHT) is used to perform time-frequency analysis on the instantaneous frequency to identify the main influencing factors of the torque fluctuation during braking. Finally, the torque fluctuation time series model is constructed by combining the car vibration data and wire rope tension data, and the torque fluctuation curves of different braking cycles are aligned and analyzed based on the dynamic time warping (DTW) method to obtain the wire rope torque fluctuation data during the elevator braking process. The nonlinear characteristics of the wire rope torque fluctuation data are analyzed to reveal the complex dynamic characteristics of the wire rope torque change during the elevator braking process.Firstly, the phase space reconstruction method is used to expand the torque fluctuation data in phase space, and the embedding dimension is set to 5 and the time delay parameter is set to 8 sampling points to construct the system state trajectory. Then, the maximum Lyapunov exponent calculation method is used to evaluate the dynamic stability of the torque fluctuation system. When the Lyapunov exponent is greater than zero, it indicates that the system has nonlinear chaotic characteristics. Then, the entropy value of the wire rope torque change is calculated using Kolmogorov entropy to quantify the degree of disorder of the system. In order to further extract nonlinear characteristics, the recursive graph analysis (RQA) method is used to recursively analyze the time series of torque fluctuations to extract characteristic parameters such as recursive rate, diagonal length distribution, and entropy. Finally, the nonlinear characteristic data is fitted based on the support vector regression (SVR) model to obtain the nonlinear characteristic data of torque fluctuations under different braking intensities.

[0025] Step S2 includes the following steps: Step S21: obtaining the physical property data of the steel wire rope; Step S22: performing a multi-scale dynamic evolution trend analysis of friction force during the elevator braking process based on the nonlinear characteristic data of torque fluctuation to obtain dynamic evolution trend data of friction force; Step S23: performing friction heat energy accumulation processing on the friction force dynamic evolution trend data according to the torque fluctuation nonlinear characteristic data to obtain friction heat energy accumulation data; Step S24: simulating the ultimate tolerance of the wire rope based on the nonlinear characteristic data of the torque fluctuation and the frictional heat energy accumulation data to obtain the ultimate tolerance data of the wire rope.

[0026] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: obtaining the physical property data of the steel wire rope; In the embodiment of the present invention, the physical property data of the steel wire rope is obtained. The specific operation includes measuring the material composition, diameter, length, density, elastic modulus, etc. of the steel wire rope. Assume that the material composition of the steel wire rope is steel, the diameter is 10 mm, the length is 1000 m, the density is 0.78-0.80 g / cm³, and the elastic modulus is 210 GPa. These data can be obtained by laboratory measurement or by referring to the technical specifications provided by the manufacturer.

[0027] Step S22: performing a multi-scale dynamic evolution trend analysis of friction force during the elevator braking process based on the nonlinear characteristic data of torque fluctuation to obtain dynamic evolution trend data of friction force; In the embodiment of the present invention, the dynamic evolution trend of multi-scale friction in the elevator braking process is analyzed based on the nonlinear characteristic data of torque fluctuation. First, the torque fluctuation data is subjected to short-time Fourier transform (STFT), and the time window length is set to 0.1s. The friction change characteristics at different time scales are calculated, and the main frequency components are extracted. Then, the empirical mode decomposition (EMD) method is used to decompose the friction signal into multiple intrinsic mode components (IMFs), and the instantaneous energy and instantaneous frequency are calculated for each component to extract the multi-scale characteristics of friction. For high-frequency components, the Hilbert-Huang transform (HHT) is used for time-frequency analysis to identify friction mutation points. For low-frequency components, the wavelet packet decomposition method is used, and the Daubechies4 (db4) wavelet basis is selected to decompose the friction signal into six layers, and the energy distribution characteristics at different frequency bands are calculated to analyze the evolution law of friction over time. Next, the dynamic time warping (DTW) method is used to time-align the friction curves of different braking cycles, calculate the Euclidean distance index, and determine the trend of friction with the number of braking times. Finally, based on the support vector regression (SVR) model, the dynamic evolution trend of friction is fitted to obtain the dynamic evolution trend data of friction and store it in time series data format.

[0028] In another embodiment, the nonlinear characteristic data of torque fluctuation recorded in advance is used as input to perform multi-scale wavelet decomposition analysis on the friction force during the elevator braking process. Firstly, the torque fluctuation data of the wire rope during the braking process was collected under the condition of a sampling frequency of 1000 times / second. After reconstruction, the data was segmented according to a fixed time window of 0.1 second (overlapping 50%), and each segment of the data was decomposed using Daubechies wavelet, and the decomposition scales were set to 0.1 second, 0.3 second and 0.8 second respectively; within each scale, the energy distribution, gradient change and data mean were calculated for the data of 500 sampling points using a segmented statistical algorithm, where the maximum recorded value of the friction force at each scale decomposition was 52 Nm, the minimum recorded value was 3 Nm, and the data accuracy was 0.1 Nm; then the decomposition results were reconstructed in time series to ensure that each data item was assigned a clear time label, and the dynamic evolution trend of the friction force in each time period was integrated and sorted to form a continuous output of the dynamic evolution trend data of the friction force; all data must be filtered during the entire operation, and the filter bandwidth is set to 1 to 500 Hz, ensuring that noise interference is minimized. All data are transmitted to the data recording system in strict time sequence. The generated dynamic evolution trend data of friction force directly reflects the changes in friction during braking, providing an accurate time domain data basis for subsequent thermal energy accumulation processing.

[0029] Step S23: performing friction heat energy accumulation processing on the friction force dynamic evolution trend data according to the torque fluctuation nonlinear characteristic data to obtain friction heat energy accumulation data; In an embodiment of the present invention, the dynamic evolution trend data of friction force is processed by friction heat accumulation to calculate the cumulative effect of friction heat generation during the operation of the elevator. First, according to the friction force data, the friction heat power per unit time is calculated using the heat flux density calculation method. The friction heat calculation is based on the contact surface friction power density formula. The contact area between the wire rope and the guide wheel is set to 20cm², the wire rope running speed is 2m / s, and the fixed step length integration method is used to calculate the friction power on different time scales. Then, the temperature field of friction heat generation is simulated by the finite difference method (FDM), and the wire rope temperature distribution model is established. The grid step size is set to 0.1mm, the time step size is set to 0.01s, and the temperature change of the wire rope under different running times is calculated. In view of the cumulative effect of friction heat energy, the temperature gradient is calculated using the heat conduction equation, and the heat loss on the wire rope surface is calculated in combination with Newton's cooling law.

[0030] In another embodiment, the dynamic evolution trend data of the friction force outputted in step S22 is coupled with the torque fluctuation nonlinear characteristic data obtained in advance, and a fixed friction heat energy conversion ratio of 0.87 is used as a parameter to perform single-point calculation on the friction force data at each moment. The specific operation is as follows: according to the sampling interval of 1 millisecond, the friction force value of each data point is multiplied by the conversion ratio of 0.87 to generate the instantaneous friction heat energy value corresponding to the moment, in joules; then, the point-by-point accumulation method is used to continuously accumulate the instantaneous heat energy data within a fixed time window of 2 seconds, calculate the accumulated heat energy, and output the heat energy value of each accumulated time period with an accuracy of 0.1 joule.

[0031] Step S24: simulating the ultimate tolerance of the wire rope based on the nonlinear characteristic data of the torque fluctuation and the frictional heat energy accumulation data to obtain the ultimate tolerance data of the wire rope.

[0032] In the embodiment of the present invention, the ultimate tolerance of the steel wire rope is simulated based on the nonlinear characteristic data of torque fluctuation and the frictional heat energy accumulation data to evaluate the fatigue limit of the steel wire rope in long-term operation. First, the stress-strain model of the steel wire rope is constructed by the finite element analysis (FEA) method. The mesh is divided into eight-node three-dimensional units (C3D8), and the mesh size is set to 0.2mm. The friction torque load is applied, and the thermal-mechanical coupling effect is calculated in combination with the frictional heat energy accumulation data. In view of the thermal fatigue effect, the Chaboche nonlinear viscoplastic constitutive model is adopted, and the thermal cycle stress amplitude is set to 50MPa-200MPa to simulate the strain accumulation of the steel wire rope under different temperature environments. Then, the Miner linear cumulative damage theory is used to calculate the fatigue life of the steel wire rope. Based on the SN curve data of the steel wire rope, the number of cycles is set to The damage evolution law of the wire rope was calculated. Subsequently, a dynamic fatigue testing machine was used for verification tests. Cyclic loads of different amplitudes were applied, and the loading rate was set to 20Hz. The fatigue life data of the wire rope in high temperature environment (60℃) and normal temperature environment (25℃) were recorded, and the errors of the simulation results and experimental data were compared. Finally, the limit tolerance data was fitted based on the regression analysis method to obtain the limit tolerance data of the wire rope, which was stored in the material durability database format.

[0033] Step S23 includes the following steps: Step S231: analyzing the inclination angle of the traction torque of the wire rope according to the nonlinear characteristic data of the torque fluctuation to obtain the inclination angle of the traction torque; Step S232: performing angular contact friction force differential processing on the friction force dynamic evolution trend data based on the traction torque inclination angle to obtain angular contact friction force differential data; Step S233: performing contact friction heat flow conversion ratio estimation on the angular contact friction force differential data to obtain the contact friction heat flow conversion ratio; Step S234: performing friction heat energy accumulation processing according to the contact friction heat flow conversion ratio to obtain friction heat energy accumulation data.

[0034] In the embodiment of the present invention, when analyzing the traction torque inclination angle of the wire rope, it is first necessary to collect the nonlinear characteristic data of the torque fluctuation of the wire rope during the elevator braking process, use a high-precision strain sensor to be installed at the fixed end of the wire rope and the pulley contact position, and use a synchronous data acquisition module to record the force change of the wire rope at a sampling rate of 1kHz. Then, the original data is subjected to noise reduction and filtering by signal processing software, and the wavelet transform method is used to remove high-frequency interference. The main torque fluctuation frequency band is extracted by Fourier transform to obtain the cleaned torque fluctuation data. Subsequently, a program is written in the MATLAB environment to perform discrete difference calculation on the wire rope torque data of different time periods to obtain the instantaneous traction torque change rate, and the slope of the wire rope torque change is calculated by a linear regression analysis method. The traction torque inclination angle is calculated based on the slope, and the piecewise fitting method is used to correct the angle error under different force states, thereby obtaining the final traction torque inclination angle data. After obtaining the inclination angle of the traction torque, the dynamic evolution trend data of the friction force is differentiated by the numerical differentiation method to calculate the differential data of the angular contact friction force. First, the Lagrange interpolation method is used to interpolate the friction force data to make the data points evenly spaced and improve the accuracy of the differential calculation. Then, the differential value of the friction force curve is calculated using the five-pointstencil method to ensure the stability and numerical accuracy of the calculation. According to the change of the contact friction force, the high-order derivative of the friction force curve is calculated in the data analysis software OriginPro, and the Savitzky-Golay smoothing filter is used for smoothing to remove the high-frequency fluctuations caused by the measurement error. Finally, the numerical calculation script written in Python is used to automatically differentiate the friction force data using the gradient calculation function in the NumPy library (Numerical Python) to obtain the differential data of the angular contact friction force and store it in the database for subsequent processing.After obtaining the differential data of the angular contact friction force, it is necessary to calculate the contact friction heat flow conversion ratio. First, determine the contact area between the wire rope and the pulley. Use a high-precision optical measurement system to scan the contact points between the wire rope and the pulley to obtain the specific size of the contact area. Then, use an infrared thermal imaging device to measure the temperature distribution changes in the friction contact area during the elevator braking process. Select multiple frames of infrared images and use OpenCV (OpenSource Computer Vision Library) to perform edge detection on the temperature field to determine the morphology of the high-temperature area. Then, use the finite difference method to calculate the temperature gradient of the friction interface, and combine Fourier's Law of Heat Conduction to calculate the heat transferred by the friction interface per unit time. Finally, divide the calculated heat by the contact area and time to obtain the contact friction heat flow conversion ratio. The conversion ratio data is stored in the database for subsequent thermal energy accumulation processing. After calculating the contact friction heat flow conversion ratio, the friction heat energy is accumulated. First, the friction heat flow conversion ratio data during multiple braking processes of the elevator are modeled based on the time series analysis method, and the ARIMA (Auto-Regressive Integrated Moving Average) model is used to predict the heat flow change trend at future moments. Subsequently, the friction heat flow data of different time periods are integrated and calculated using the numerical integration method to accumulate the friction heat energy at different braking moments. During the data processing, the trapezoidal integration method is used to calculate the friction heat accumulation value within each time step, and the sliding window algorithm is used for data smoothing. Finally, the calculated friction heat energy accumulation data is stored in the efficient storage format HDF5 (Hierarchical Data Format version 5), and the data is exported in CSV format to facilitate the subsequent simulation of the ultimate tolerance of the wire rope.

[0035] Step S24 includes the following steps: Step S241: performing thermal stress coupling fluctuation response calculation according to the torque fluctuation nonlinear characteristic data and the friction heat energy accumulation data to obtain thermal stress coupling fluctuation response data; Step S242: extracting the initial plastic deformation strength of the steel wire rope physical property data to obtain initial plastic deformation strength data; Step S243: quantifying the fatigue loss of the plastic deformation performance of the steel wire rope based on the initial plastic deformation strength data based on the thermal stress coupling fluctuation response data to obtain the fatigue loss data of the plastic deformation performance; Step S244: performing impact load toughness weakening simulation fitting on the plastic deformation performance fatigue loss data according to the thermal stress coupling fluctuation response data to obtain impact load toughness weakening fitting data; Step S245: performing nonlinear regression analysis on the impact load toughness weakening fitting data to obtain toughness weakening regression data; Step S246: simulate the ultimate tolerance of the wire rope according to the plastic deformation performance fatigue loss data and the toughness weakening regression data to obtain the ultimate tolerance data of the wire rope.

[0036] In the embodiment of the present invention, the thermal stress coupling fluctuation response of the steel wire rope during the elevator braking process is calculated based on the nonlinear characteristic data of the torque fluctuation and the friction heat energy accumulation data obtained in the previous step. First, the time series curve of the force of the steel wire rope is extracted by using the time series torque fluctuation data, and the transient temperature change on different cross sections of the steel wire rope is determined in combination with the friction heat energy accumulation data. The finite element analysis (FEA) method is used to apply thermal stress boundary conditions in the force model of the steel wire rope, and the thermal stress distribution of the steel wire rope at different times is calculated using the thermodynamic coupling equation. In the calculation process, typical stress points are selected, such as the contact area between the steel wire rope and the pulley, the rope core and the outer steel wire, to analyze the thermal stress change trend under different torque change conditions. Finally, the thermal stress coupling fluctuation response data of the steel wire rope is formed, which includes the temperature field distribution, the thermal stress change curve and the stress concentration distribution of the stress point, providing data support for the subsequent fatigue loss analysis. Based on the material physical property data of the steel wire rope, the initial plastic deformation strength extraction work is carried out. Firstly, the yield strength, tensile strength, hardness and microstructure data of the steel wire rope were extracted by using the material testing database, and the stress-strain curve of the steel wire rope under different stress conditions was obtained by electronic tensile experiment. Secondly, in the laboratory environment, a monotonic tensile load was applied to the steel wire rope sample, and the stress distribution in the strain hardening stage was recorded. The changes in the internal crystal structure of the steel wire rope were analyzed by combining with an optical microscope. Through the above analysis, the initial plastic deformation strength of the steel wire rope was calculated, mainly including the material yield point, elongation at break and strain hardening index. Finally, the complete initial plastic deformation strength data was obtained, providing basic data support for the subsequent fatigue loss analysis. Combined with the thermal stress coupling fluctuation response data and the initial plastic deformation strength data calculated in the previous step, the fatigue loss of the plastic deformation performance of the steel wire rope was quantified. Firstly, the continuous loading test method was used to apply cyclic tensile loads to the steel wire rope samples in the laboratory environment, and the stress-strain changes were monitored at the same time. Secondly, based on the plastic deformation strengthening model, the cumulative damage under the action of thermal stress was calculated, and combined with the damage evolution equation, the strength attenuation trend of the steel wire rope under different cycles was evaluated. In the data analysis stage, the high temperature-stress coupling damage model is used to quantitatively analyze the microscopic grain boundary migration phenomenon of the wire rope to determine the fatigue loss rate. Finally, the fatigue loss data of plastic deformation performance are obtained, which includes the relationship between the number of cycles and strength loss, the cumulative damage evolution curve and the fatigue life prediction results under different stress levels. Based on the thermal stress coupling fluctuation response data and the plastic deformation performance fatigue loss data, the impact load toughness weakening of the wire rope is simulated and fitted. First, a three-dimensional mechanical model of the wire rope is established in the finite element analysis software, and the impact load condition is applied to simulate the stress condition of the wire rope under different braking conditions.Secondly, the dynamic impact test method was used to measure the fracture toughness of the wire rope under high-speed impact, and the energy absorption and elongation at break were recorded. Subsequently, based on the experimental data, the impact toughness weakening curve was constructed, and the mathematical model of impact toughness weakening was established using the nonlinear fitting algorithm. Finally, the impact load toughness weakening fitting data was obtained, which covered the toughness loss ratio, fracture energy consumption and strain energy attenuation curve under different impact intensities, providing data input for the subsequent ultimate tolerance simulation. Based on the impact load toughness weakening fitting data, nonlinear regression analysis was performed to construct a wire rope toughness attenuation prediction model. First, the regression analysis tool was used to perform error analysis on the fitting data and determine the optimal fitting function. Secondly, different regression methods such as polynomial regression, exponential regression and logistic regression were selected to perform curve fitting on the data, and the residual analysis method was used to evaluate the applicability of each regression model. Subsequently, according to the optimal fitting result, the prediction equation of wire rope toughness weakening was calculated, and the applicability of the equation under different loading conditions was verified. Finally, the toughness weakening regression data is obtained, which includes the toughness attenuation curve fitting equation, the regression residual distribution and the predicted value of toughness loss under different working conditions, providing key input parameters for the simulation of the ultimate tolerance of the wire rope. Based on the fatigue loss data of plastic deformation performance and the toughness weakening regression data, the ultimate tolerance of the wire rope is simulated and calculated. First, the damage evolution model of the wire rope is established in the numerical calculation software, and the fatigue loss parameters and toughness attenuation data obtained in the previous steps are input. Secondly, the ultimate tensile capacity of the wire rope under different load levels is calculated by using the fracture mechanics analysis method, and the risk of fracture under long-term operation is evaluated by combining the fatigue crack propagation model. Subsequently, based on the Monte Carlo simulation method, the ultimate tolerance of the wire rope is probabilistically evaluated to determine its safety margin under different working conditions. Finally, the ultimate tolerance data of the wire rope is obtained, which includes the ultimate tensile strength, fatigue life prediction value and fracture probability distribution under different stress levels, providing the core decision-making basis for the intelligent risk warning system of the elevator.

[0037] Step S244 includes the following steps: The thermal stress fluctuation curve is plotted on the thermal stress coupling fluctuation response data to obtain the thermal stress response fluctuation curve; the transient amplification intensity index is evaluated on the thermal stress response fluctuation curve to obtain the thermal stress transient amplification intensity index; Based on the fatigue loss data of plastic deformation performance, the dislocation damage density increment of the wire rope is analyzed to obtain the dislocation density increment data; According to the thermal stress transient amplification intensity index, the dislocation density increment data is approximated and coupled to obtain the thermal stress dislocation correlation distribution coupling data; Perform distribution increment learning on thermal stress dislocation correlation distribution coupling data to obtain correlation distribution increment coupling data; Based on the Bayesian regression algorithm, the impact load toughness weakening simulation fitting is performed on the associated distribution incremental coupling data to obtain the impact load toughness weakening fitting data.

[0038] In the embodiment of the present invention, the thermal stress fluctuation curve is drawn based on the thermal stress coupling fluctuation response data obtained in the previous step. First, the thermal stress values ​​of the wire rope at different time points are extracted by using the stress-time series analysis method, and the data is interpolated to ensure the data continuity on the time axis. Secondly, the curve fitting algorithm is used to smooth the thermal stress change trend of different stress points of the wire rope through the quadratic spline interpolation method to eliminate data noise. Subsequently, the thermal stress response fluctuation curve is drawn using the data visualization tool, and the key stress peak point, stress valley point and thermal stress fluctuation frequency are marked to identify the stress change mode of the wire rope in actual operation. Finally, the thermal stress response fluctuation curve is obtained, which characterizes the dynamic change of thermal stress caused by friction heat accumulation, load fluctuation and other factors during the stress process of the wire rope, and provides basic data for the subsequent evaluation of the amplification strength index. Based on the drawn thermal stress response fluctuation curve, the transient amplification strength index evaluation is carried out. First, the Fourier transform method is used to perform spectrum analysis on the thermal stress response fluctuation curve, extract the main frequency characteristics and high-frequency components, and identify the change trend of the transient thermal stress peak. Secondly, the differential calculation method is used to obtain the first-order derivative of the thermal stress fluctuation curve, calculate the transient amplification rate, and use the second-order derivative to calculate the acceleration information of the curve change to characterize the transient amplification intensity. Subsequently, the peak stress change rate and the standard deviation of the stress change rate are combined to establish an amplification intensity index calculation model, and the index data is kept within a uniform scale through normalization. Finally, the thermal stress transient amplification intensity index is obtained, which is used to quantify the strength changes of the wire rope caused by thermal stress fluctuations in a short period of time, and provide parameter basis for subsequent dislocation damage analysis. Based on the fatigue loss data of plastic deformation performance, mathematical simulation methods are used to simulate and analyze the incremental dislocation damage density of the wire rope to quantify the dislocation damage evolution process under different working conditions. First, a mathematical model of the evolution of microscopic dislocations in wire ropes is constructed, and the dislocation increment under stress is described based on the dislocation dynamics equation. The equation is used: Among them, ρ represents the rate of change of dislocation density, M is the material constant, b is the Burgers vector, τ is the dislocation slip stress, σ is the applied load stress, and σc is the critical shear stress. Secondly, based on the finite difference method, the equation is numerically solved. The force-bearing area of ​​the wire rope is divided into multiple finite elements, the initial dislocation density of different elements is set, and the dislocation density increment of each element is iteratively calculated using the time step Δt. The Runge-Kutta numerical integration method is used to improve the calculation accuracy and ensure the stability of the simulation results. Subsequently, the dislocation density increment calculation model is modified in combination with the thermal-mechanical coupling effect. The temperature dependence term is introduced, and the Arrhenius formula is used to describe the effect of temperature on the dislocation movement rate: ρ(T)= ρ0×exp(Q / kT), where ρ(T) represents the rate of change of dislocation density at temperature T; ρ0 is the reference dislocation density change rate, Q is the activation energy (indicating the energy required for dislocation movement or slip), k is the Boltzmann constant, and T is the temperature. Then, the Monte Carlo random simulation method is used to perform statistical analysis on the dislocation density increment under different load conditions. Random variables are defined to describe the microstructural differences in different regions of the wire rope. In the Monte Carlo iteration process, multiple sets of dislocation density increment data under load-temperature conditions are generated, and the mean and standard deviation are calculated to obtain the probability distribution of dislocation damage under different load levels. Finally, based on the simulation calculation results, the dislocation density increment data are output, which is used to describe the microscopic damage evolution trend of the wire rope under long-term fatigue and serve as the input data for subsequent thermal stress coupling analysis. Based on the thermal stress-dislocation correlation distribution coupling data, the incremental learning method is used to establish a thermal stress-dislocation damage change trend model. First, the time series analysis method is selected to process the data in stages and extract the damage increment characteristics in different time windows. Secondly, the adaptive gradient optimization algorithm is used to dynamically adjust the data weight to optimize the model's adaptability to new data. Subsequently, based on the incremental learning framework, the thermal stress-dislocation damage distribution model is updated, and the sliding window technology is used to ensure that the new input data will not cause the model to lose the historical damage evolution trend. Finally, the associated distribution incremental coupling data is obtained, which characterizes the dislocation damage accumulation caused by thermal stress changes in the long-term operation of the wire rope, and provides data input for the simulation fitting of impact load toughness weakening. Based on the associated distribution incremental coupling data obtained in the previous step, the Bayesian regression algorithm is used to establish the simulation fitting model of impact load toughness weakening. First, based on the Bayesian statistical theory, the prior distribution of impact load toughness weakening is defined, and the prior distribution is updated according to the acquired data to improve the prediction accuracy of the model. Secondly, the Markov chain Monte Carlo (MCMC) method is used to perform parameter sampling and calculate the probability distribution of toughness weakening under impact load. Subsequently, the Bayesian regression model is optimized using the maximum a posteriori estimation (MAP) method, and the fitting effect is evaluated using the cross-validation method.Finally, the impact load toughness weakening fitting data is obtained, which includes the predicted value of toughness loss, damage accumulation rate and toughness attenuation trend under different impact intensities, providing key input parameters for the calculation of the ultimate tolerance of elevator wire ropes.

[0039] Step S3 includes the following steps: Step S31: performing convolution calculation on the wire rope limit tolerance data to obtain the wire rope limit tolerance convolution data; Step S32: quantifying the cyclic damage expansion index of the wire rope based on the ultimate tolerance convolution data of the wire rope to obtain the cyclic damage expansion index of the wire rope; Step S33: identifying the pre-factor of the risk of wire rope fracture according to the convolution data of the cyclic damage expansion index of the wire rope and the ultimate tolerance of the wire rope, and obtaining the pre-factor of the risk of wire rope fracture; Step S34: Perform logical learning on the wire rope fracture risk pre-factor to obtain fracture risk pre-factor learning data.

[0040] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: performing convolution calculation on the wire rope limit tolerance data to obtain the wire rope limit tolerance convolution data; In an embodiment of the present invention, in the process of performing convolution calculation on the ultimate tolerance data of the wire rope, the collected ultimate tolerance data of the wire rope is first called, and the data is derived from the stress-strain response measurement of the wire rope under different tensile loads, cyclic loads and ambient temperature conditions. The data includes parameters such as displacement, strain, elastic modulus and fatigue cumulative damage of the wire rope under different stress states. A three-dimensional convolution kernel is selected to perform a convolution operation on the ultimate tolerance data of the wire rope. The convolution kernel size is set to a 3×3×3 tensor structure to ensure that the tolerance limit characteristics of the wire rope under multi-axial stress states can be extracted. The convolution operation uses a stride of 1 and a padding method of zero padding to ensure the integrity of the data boundary information. When performing the convolution calculation, matrix operations are used to accelerate the calculation process. The calculation of each data point involves the weighted summation of the previous and next time series data, and finally the ultimate tolerance convolution data of the wire rope is obtained. The data is stored in a three-dimensional matrix format, and each matrix element represents the ultimate tolerance capacity of the wire rope under different stress states.

[0041] Step S32: quantifying the cyclic damage expansion index of the wire rope based on the ultimate tolerance convolution data of the wire rope to obtain the cyclic damage expansion index of the wire rope; In an embodiment of the present invention, in the process of quantifying the cyclic damage expansion index of the wire rope based on the convolution data of the wire rope's ultimate tolerance, the convolution data of the wire rope's ultimate tolerance is first called, and the data includes the ultimate tolerance capacity of the wire rope under different stress states. The data is processed in time series segments, and 10,000 load cycles are used as a time window to calculate the rate of change of the ultimate tolerance capacity in each time window. The difference method is used to calculate the damage expansion rate of the wire rope under continuous load, and an exponential decay weight function is introduced to enhance the memory effect of historical damage. In the quantification process, the damage expansion rate of the same type of wire ropes under the same working conditions is compared to construct a cyclic damage expansion index for the wire rope. The damage expansion index range is set between 0 and 1, where 0 indicates no damage expansion and 1 indicates that the damage expansion reaches a critical value.

[0042] Step S33: identifying the pre-factor of the risk of wire rope fracture according to the convolution data of the cyclic damage expansion index of the wire rope and the ultimate tolerance of the wire rope, and obtaining the pre-factor of the risk of wire rope fracture; In an embodiment of the present invention, in the process of identifying the pre-factors of the risk of wire rope fracture according to the convolution data of the cyclic damage expansion index and the ultimate tolerance of the wire rope, the cyclic damage expansion index and the ultimate tolerance of the wire rope are first called, and the correlation between the two under different load conditions is compared. The dynamic time warping (DTW) method is used to calculate the time series similarity between the two, and the coupling relationship between the damage expansion rate and the decrease in the ultimate tolerance capacity is identified. On this basis, a threshold judgment standard is set. When the damage expansion index exceeds 0.8 and the ultimate tolerance capacity decrease rate reaches more than 20%, the corresponding working conditions at this moment are recorded, including parameters such as load size, ambient temperature, and wire rope use time. At the same time, the internal microscopic damage parameters of the wire rope at this moment are extracted, including dislocation density, microcrack length, and surface oxide layer thickness. These parameters are stored in the database as pre-factors of the risk of wire rope fracture for subsequent risk learning.

[0043] Step S34: Perform logical learning on the wire rope fracture risk pre-factor to obtain fracture risk pre-factor learning data.

[0044] In an embodiment of the present invention, in the process of logical learning of the wire rope fracture risk pre-factor, the stored fracture risk pre-factor data is first called, the data is standardized, the numerical range of different physical quantities is normalized to between 0 and 1, and a wire rope fracture risk classification model is constructed by a logistic regression method. The input variables include parameters such as the wire rope damage expansion index, the ultimate tolerance capacity reduction rate, the dislocation density, the microcrack length, and the surface oxide layer thickness. The output variable is the wire rope fracture risk level, and the value range is set to 0 to 1, where 0 indicates no fracture risk and 1 indicates an extremely high fracture risk. The stochastic gradient descent (SGD) optimization algorithm is used in the model training process, the learning rate is set to 0.01, and the number of iterations per round is set to 1000. During the training process, the change trend of the model loss function is monitored. When the loss function converges to below 10^-4, the training is stopped, and finally the fracture risk pre-factor learning data is obtained, which is used for subsequent risk prediction analysis.

[0045] Step S32 includes the following steps: Step S321: performing fatigue cycle evolution analysis on the ultimate tolerance convolution data of the steel wire rope to obtain fatigue cycle evolution data; Step S322: performing a cyclic load stress-strain gradient rise evaluation on the fatigue tolerance cycle evolution data to obtain cyclic load stress-strain rise data; Step S323: performing a time series cyclic load mean difference calculation based on the cyclic load stress-strain rise data to generate a time series cyclic load stress-strain mean difference; performing a time series boundary stress-strain approximate value calculation on the time series cyclic load stress-strain mean difference to obtain a boundary stress-strain approximate value; Step S324: performing boundary error series truncation on the cyclic load stress-strain rise data according to the boundary stress-strain approximation to obtain stress-strain boundary series truncation data; Step S325: performing convergence-constrained Taylor series expansion processing based on the stress-strain boundary series truncation data to obtain stress-strain convergence-constrained series expansion data; Step S326: quantifying the cyclic damage expansion index of the wire rope according to the stress-strain convergence constraint series expansion data to obtain the cyclic damage expansion index of the wire rope.

[0046] In the embodiment of the present invention, in the process of analyzing the fatigue cycle evolution of the wire rope limit tolerance convolution data, the wire rope limit tolerance convolution data is first called, and the data includes the tolerance limit, fatigue cumulative damage and stress-strain characteristics of the wire rope under different working conditions. The data is segmented in time series, and 10,000 load cycles are used as a data segment. The exponentially weighted moving average (EWMA) method is used to smooth the tolerance data of different time periods, so as to extract the fatigue cycle evolution trend of the wire rope under long-term load. In the calculation process, the weight factor is set To ensure that newer data has a greater impact on the evolution trend, a bimodal detection algorithm is introduced to identify the nonlinear mutation points of the fatigue limit, record the fatigue limit mutation moment and the corresponding number of cycles, and finally generate fatigue cycle evolution data. The data is stored in a two-dimensional matrix form, and each data point represents the tolerance limit value under a certain fatigue cycle. In the process of evaluating the cyclic load stress-strain gradient rise of the fatigue cycle evolution data, the fatigue cycle evolution data is first called, which records the tolerance limit value of the wire rope under different fatigue cycles. Based on the data, the stress-strain change rate of the wire rope under different load cycles is calculated, and the finite difference method is used to calculate the stress-strain change gradient between adjacent fatigue cycles. The change trend of the stress-strain curve under different fatigue cycles is compared. At the same time, the second-order derivative is used to calculate the stress-strain rise rate to identify the gradient mutation point of the wire rope under high cyclic load. In the gradient calculation process, the threshold judgment standard is set. When the stress-strain gradient exceeds 5MPa / cycle and the change rate exceeds 0.2MPa / cycle², the cyclic load state at that moment is recorded, and finally the cyclic load stress-strain rise data is generated, which is used for subsequent damage assessment. In the process of calculating the time series cyclic load mean difference based on the cyclic load stress-strain rise data, the cyclic load stress-strain rise data is first called, and the data is divided into time windows. The window size is set to 5,000 cycles, and the mean of the stress-strain data in each window is calculated. The mean square deviation formula (Mean Square Deviation) is used to calculate the stress-strain mean difference in different time windows. At the same time, Fourier Transform is used to extract the periodic change characteristics in the time series data and identify the main frequency component of the cyclic load mean difference. In the calculation process, Gaussian Filter is introduced to filter out high-frequency noise to ensure the stability of the data, and finally the time series cyclic load stress-strain mean difference data is generated. In the process of calculating the time-series boundary stress-strain approximation of the time-series cyclic load stress-strain mean difference, the time-series cyclic load stress-strain mean difference data is first called, and the data is curve fitted. The polynomial interpolation method is used to calculate the stress-strain boundary approximation at different time points. During the fitting process, the polynomial order is set to 4 to ensure the calculation accuracy. At the same time, the least squares method is used to optimize the fitting parameters, and the stress-strain change boundary under different cyclic load states is calculated. Finally, the boundary stress-strain approximation is obtained, and the data is used for subsequent error analysis.In the process of performing boundary error series truncation on cyclic load stress-strain rise data according to boundary stress-strain approximation, the boundary stress-strain approximation and cyclic load stress-strain rise data are first called, and the Taylor Series Expansion method is used to calculate the boundary error change trend. During the calculation process, the expansion order is set to 6 to ensure that the truncation error is controlled within 0.001. At the same time, the Lagrange Remainder Term is used to calculate the error accumulation, and the boundary error changes under different cyclic load states are compared. The error mutation points are screened and truncation is performed, and finally the stress-strain boundary series truncation data is obtained. In the process of convergence constrained Taylor series expansion based on stress-strain boundary series truncation data, the stress-strain boundary series truncation data is first called, and the data is expanded polynomially. The Taylor series expansion method is used to mathematically model the stress-strain relationship of the wire rope under different load states. During the expansion process, the convergence constraint conditions are set so that the convergence radius of the expansion function under cyclic load does not exceed 0.01. At the same time, the Newton Iteration Method is used to calculate the expansion parameters and optimize the fitting accuracy of the convergence curve. Finally, the stress-strain convergence constrained series expansion data is obtained. In the process of quantifying the cyclic damage propagation index of wire ropes according to the stress-strain convergence constraint series expansion data, the stress-strain convergence constraint series expansion data is first called, and the damage propagation rate of the wire rope under different cyclic loads is calculated based on the data. The exponential decay model is used to calculate the cumulative effect of wire rope damage. During the calculation process, the calculation range of the damage propagation index is set to 0 to 1, 0 indicates no damage propagation, and 1 indicates that the damage has reached the limit. Finally, the cyclic damage propagation index of the wire rope is generated, and this data is used for subsequent fracture risk assessment.

[0047] Step S4 includes the following steps: Step S41: normalizing the fracture risk pre-factor learning data to obtain risk pre-factor normalized data; Step S42: performing random feature sampling on the risk pre-factor normalized data to generate risk pre-factor feature sampling data; Step S43: construct an elevator intelligent risk warning model based on the risk pre-factor feature sampling data based on the random forest algorithm to obtain the elevator intelligent risk warning model, and send the elevator intelligent risk warning model to the terminal to execute the elevator intelligent risk warning.

[0048] In an embodiment of the present invention, in the process of normalizing the fracture risk pre-factor learning data, the fracture risk pre-factor learning data is first called, and the data contains fracture risk characteristic variables of the wire rope under different working conditions, including cyclic load stress-strain data, fatigue damage accumulation data, dislocation density increment data, toughness weakening data, etc. The data is subjected to feature scaling processing, and the maximum and minimum normalization (Min-Max Normalization) method is adopted to scale the numerical range of all characteristic variables to the [0,1] interval to avoid the influence of data of different dimensions on the calculation results. In the normalization process, a data conversion formula is set, and all values ​​are linearly scaled according to the minimum and maximum values ​​of each characteristic variable. The normalized data is stored in a matrix form, each column corresponds to a fracture risk pre-factor, and each row corresponds to the measurement data of a time segment. At the same time, the standard normalization (Z-Score Normalization) method is adopted to standardize the normalized data to ensure that the mean of the data is 0 and the standard deviation is 1, and finally the risk pre-factor normalized data is generated, which is used for subsequent random feature sampling analysis. In the process of random feature sampling of the normalized data of risk pre-factors, the normalized data of risk pre-factors are first called, which records the multi-dimensional characteristic variables of the risk of wire rope breakage. The random sampling method is used to select features of the data to reduce the calculation complexity and improve the generalization ability of the model. In the random sampling process, the feature sampling ratio is set to 80%, that is, 80% of the features are randomly selected from all feature variables to participate in subsequent calculations. At the same time, the bootstrap method is used for sample enhancement, and some sample data are randomly selected for repeated extraction to ensure the stability of data distribution. In the sampling process, the principal component analysis (PCA, Principal Component Analysis) method is used to reduce the dimension of the data and extract the main contribution factors. The cumulative contribution rate is set to 95% to ensure that the main information of the data is retained, and finally the risk pre-factor feature sampling data is generated. This data is used for the subsequent construction of the elevator intelligent risk warning model.In the process of constructing the elevator intelligent risk warning model based on the risk pre-factor feature sampling data based on the random forest algorithm, the risk pre-factor feature sampling data is first called, and the data is divided into a training set and a test set, and the training set accounts for 80% and the test set accounts for 20%. In the model construction process, the random forest algorithm is used to construct multiple decision trees, and the number of decision trees is set to 100, and the maximum tree depth is 10 to ensure that the model has good computational efficiency and generalization ability. In the training process, information gain is used as the feature selection criterion to calculate the contribution of different feature variables to the risk of elevator fracture, and the model parameters are optimized to improve the prediction accuracy. After the model training is completed, the test set data is predicted, and the accuracy, recall rate and F1 score of the model are calculated to ensure the reliability of the model. Finally, the elevator intelligent risk warning model is generated, which is stored as a binary file, and the model parameters and calculation results are sent to the terminal to execute the elevator intelligent risk warning.

[0049] Preferably, the present invention further provides an elevator intelligent risk warning system, which is used to execute the elevator intelligent risk warning method as described above, and the elevator intelligent risk warning system comprises: The torque fluctuation analysis module is used to collect the operating status data of the elevator sliding parts through sensors to obtain the operating status data of the sliding parts; analyze the wire rope torque fluctuation during the elevator braking process on the sliding parts operating status data to obtain the nonlinear characteristic data of the torque fluctuation; The extreme tolerance simulation module is used to perform friction heat energy accumulation processing during the elevator braking process based on the nonlinear characteristic data of torque fluctuation to obtain friction heat energy accumulation data; simulate the extreme tolerance of the wire rope based on the friction heat energy accumulation data to obtain the extreme tolerance data of the wire rope; The fracture risk prefactor identification module is used to quantify the wire rope cyclic damage expansion index based on the wire rope limit tolerance convolution data to obtain the wire rope cyclic damage expansion index; identify the wire rope fracture risk prefactor according to the wire rope cyclic damage expansion index to obtain the fracture risk prefactor learning data; The early warning model construction module is used to construct an elevator intelligent risk early warning model based on the fracture risk pre-factor learning data based on the random forest algorithm, obtain the elevator intelligent risk early warning model, and send the elevator intelligent risk early warning model to the terminal to execute the elevator intelligent risk early warning.

[0050] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent risk early warning method for elevators, characterized in that: The following steps are involved: Step S1: collecting operating status data of the elevator sliding component through a sensor to obtain the operating status data of the sliding component; The running state data of the sliding parts are used to analyze the torque fluctuation of the wire rope during the elevator braking process to obtain the nonlinear characteristic data of the torque fluctuation; Step S2: performing friction heat energy accumulation processing during the elevator braking process based on the torque fluctuation nonlinear characteristic data to obtain friction heat energy accumulation data; performing wire rope limit tolerance simulation based on the friction heat energy accumulation data to obtain wire rope limit tolerance data; Step S3: quantifying the cyclic damage expansion index of the wire rope based on the convolution data of the wire rope limit tolerance to obtain the cyclic damage expansion index of the wire rope; identifying the pre-factor of the wire rope fracture risk according to the cyclic damage expansion index of the wire rope to obtain the fracture risk pre-factor learning data; Step S4: construct an elevator intelligent risk warning model for the fracture risk pre-factor learning data based on the random forest algorithm to obtain the elevator intelligent risk warning model, and send the elevator intelligent risk warning model to the terminal to execute the elevator intelligent risk warning.

2. The intelligent risk early warning method for elevators according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting operating status data of the elevator sliding component through a sensor to obtain the operating status data of the sliding component; Step S12: cleaning the running status data of the sliding component to obtain running status cleaning data; Step S13: analyzing the fluctuation of the steel wire rope torque during the elevator braking process on the running status cleaning data to obtain the steel wire rope torque fluctuation data during the elevator braking process; Step S14: Perform nonlinear characteristic analysis on the wire rope torque fluctuation data to obtain torque fluctuation nonlinear characteristic data.

3. The elevator intelligent risk early warning method according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: obtaining the physical property data of the steel wire rope; Step S22: performing a multi-scale dynamic evolution trend analysis of friction force during the elevator braking process based on the nonlinear characteristic data of torque fluctuation to obtain dynamic evolution trend data of friction force; Step S23: performing friction heat energy accumulation processing on the friction force dynamic evolution trend data according to the torque fluctuation nonlinear characteristic data to obtain friction heat energy accumulation data; Step S24: simulating the ultimate tolerance of the wire rope based on the nonlinear characteristic data of the torque fluctuation and the frictional heat energy accumulation data to obtain the ultimate tolerance data of the wire rope.

4. The intelligent risk early warning method for elevators according to claim 3 is characterized in that: Step S23 includes the following steps: Step S231: analyzing the inclination angle of the traction torque of the wire rope according to the nonlinear characteristic data of the torque fluctuation to obtain the inclination angle of the traction torque; Step S232: performing angular contact friction force differential processing on the friction force dynamic evolution trend data based on the traction torque inclination angle to obtain angular contact friction force differential data; Step S233: performing contact friction heat flow conversion ratio estimation on the angular contact friction force differential data to obtain the contact friction heat flow conversion ratio; Step S234: performing friction heat energy accumulation processing according to the contact friction heat flow conversion ratio to obtain friction heat energy accumulation data.

5. The intelligent risk early warning method for elevators according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: performing thermal stress coupling fluctuation response calculation according to the torque fluctuation nonlinear characteristic data and the friction heat energy accumulation data to obtain thermal stress coupling fluctuation response data; Step S242: extracting the initial plastic deformation strength of the steel wire rope physical property data to obtain initial plastic deformation strength data; Step S243: quantifying the fatigue loss of the plastic deformation performance of the steel wire rope based on the initial plastic deformation strength data based on the thermal stress coupling fluctuation response data to obtain the fatigue loss data of the plastic deformation performance; Step S244: performing impact load toughness weakening simulation fitting on the plastic deformation performance fatigue loss data according to the thermal stress coupling fluctuation response data to obtain impact load toughness weakening fitting data; Step S245: performing nonlinear regression analysis on the impact load toughness weakening fitting data to obtain toughness weakening regression data; Step S246: simulate the ultimate tolerance of the wire rope according to the plastic deformation performance fatigue loss data and the toughness weakening regression data to obtain the ultimate tolerance data of the wire rope.

6. The intelligent risk early warning method for elevators according to claim 4 is characterized in that: Step S244 includes the following steps: The thermal stress fluctuation curve is plotted on the thermal stress coupling fluctuation response data to obtain the thermal stress response fluctuation curve; the transient amplification intensity index is evaluated on the thermal stress response fluctuation curve to obtain the thermal stress transient amplification intensity index; Based on the fatigue loss data of plastic deformation performance, the dislocation damage density increment of the wire rope is analyzed to obtain the dislocation density increment data; According to the thermal stress transient amplification intensity index, the dislocation density increment data is approximated and coupled to obtain the thermal stress dislocation correlation distribution coupling data; Perform distribution increment learning on thermal stress dislocation correlation distribution coupling data to obtain correlation distribution increment coupling data; Based on the Bayesian regression algorithm, the impact load toughness weakening simulation fitting is performed on the associated distribution incremental coupling data to obtain the impact load toughness weakening fitting data.

7. The intelligent risk early warning method for elevators according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing convolution calculation on the wire rope limit tolerance data to obtain the wire rope limit tolerance convolution data; Step S32: quantifying the cyclic damage expansion index of the wire rope based on the ultimate tolerance convolution data of the wire rope to obtain the cyclic damage expansion index of the wire rope; Step S33: identifying the pre-factor of the risk of wire rope fracture according to the convolution data of the cyclic damage expansion index of the wire rope and the ultimate tolerance of the wire rope, and obtaining the pre-factor of the risk of wire rope fracture; Step S34: Perform logical learning on the wire rope fracture risk pre-factor to obtain fracture risk pre-factor learning data.

8. The intelligent risk early warning method for elevators according to claim 7 is characterized in that: Step S32 includes the following steps: Step S321: performing fatigue cycle evolution analysis on the ultimate tolerance convolution data of the steel wire rope to obtain fatigue cycle evolution data; Step S322: performing a cyclic load stress-strain gradient rise evaluation on the fatigue tolerance cycle evolution data to obtain cyclic load stress-strain rise data; Step S323: performing a time series cyclic load mean difference calculation based on the cyclic load stress-strain rise data to generate a time series cyclic load stress-strain mean difference; performing a time series boundary stress-strain approximate value calculation on the time series cyclic load stress-strain mean difference to obtain a boundary stress-strain approximate value; Step S324: performing boundary error series truncation on the cyclic load stress-strain rise data according to the boundary stress-strain approximation to obtain stress-strain boundary series truncation data; Step S325: performing convergence-constrained Taylor series expansion processing based on the stress-strain boundary series truncation data to obtain stress-strain convergence-constrained series expansion data; Step S326: quantifying the cyclic damage expansion index of the wire rope according to the stress-strain convergence constraint series expansion data to obtain the cyclic damage expansion index of the wire rope.

9. The intelligent risk early warning method for elevators according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: normalizing the fracture risk pre-factor learning data to obtain risk pre-factor normalized data; Step S42: performing random feature sampling on the risk pre-factor normalized data to generate risk pre-factor feature sampling data; Step S43: construct an elevator intelligent risk warning model based on the risk pre-factor feature sampling data based on the random forest algorithm to obtain the elevator intelligent risk warning model, and send the elevator intelligent risk warning model to the terminal to execute the elevator intelligent risk warning.

10. An intelligent risk warning system for elevators, characterized in that: Used to execute the elevator intelligent risk warning method according to claim 1, the elevator intelligent risk warning system comprises: The torque fluctuation analysis module is used to collect the operating status data of the elevator sliding parts through sensors to obtain the operating status data of the sliding parts; analyze the wire rope torque fluctuation during the elevator braking process on the sliding parts operating status data to obtain the nonlinear characteristic data of the torque fluctuation; The extreme tolerance simulation module is used to perform friction heat energy accumulation processing during the elevator braking process based on the nonlinear characteristic data of torque fluctuation to obtain friction heat energy accumulation data; simulate the extreme tolerance of the wire rope based on the friction heat energy accumulation data to obtain the extreme tolerance data of the wire rope; The fracture risk prefactor identification module is used to quantify the wire rope cyclic damage expansion index based on the wire rope limit tolerance convolution data to obtain the wire rope cyclic damage expansion index; identify the wire rope fracture risk prefactor according to the wire rope cyclic damage expansion index to obtain the fracture risk prefactor learning data; The early warning model construction module is used to construct an elevator intelligent risk early warning model based on the fracture risk pre-factor learning data based on the random forest algorithm, obtain the elevator intelligent risk early warning model, and send the elevator intelligent risk early warning model to the terminal to execute the elevator intelligent risk early warning.

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