Braking clearance prediction method and device, electronic device, and readable storage medium

Abstract

The present disclosure provides a brake clearance prediction method and device, electronic device, and readable storage medium, belonging to the field of data prediction technology. The method includes: determining a fatigue inverse model based on simulated fatigue parameter data and historical fatigue parameter data, the fatigue inverse model being used to construct a mapping relationship between the brake's operating state data and fatigue state data; determining a brake clearance-fatigue relationship model based on the fatigue state data and first brake clearance data; the brake clearance-fatigue relationship model being used to construct a mapping relationship between the brake clearance and fatigue state data; and predicting the brake clearance of a target brake based on the fatigue inverse model, the brake clearance-fatigue relationship model, and the target operating state data to obtain second brake clearance data. The brake clearance prediction method and device, electronic device, and readable storage medium provided by the present disclosure can improve the accuracy of brake clearance data prediction.

Description

Technical Field

[0001] The present disclosure belongs to the field of data prediction technology, and more specifically, relates to a brake clearance prediction method and device, an electronic device, and a readable storage medium. Background Art

[0002] With the acceleration of modern urbanization and the increasing number of high-rise buildings, the safety and reliability of elevators, as a means of vertical transportation, are directly related to the safety of people's lives and property. As a key safety component of elevators, the performance of the brake directly affects the safe operation of the elevator. Braking clearance, as a key indicator of brake performance, has a significant impact on the braking effect and operational stability of the elevator. Therefore, accurate prediction and real-time monitoring of the brake clearance of elevator brakes have become important technical means to ensure safe elevator operation.

[0003] However, monitoring the brake clearance of some elevator brakes still relies on regular manual inspections, a method that is not only inefficient but also difficult to ensure accurate and timely monitoring. Some monitoring technologies have adopted some automated monitoring techniques, but they still suffer from low data prediction accuracy. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a brake clearance prediction method and device, an electronic device, and a readable storage medium to improve the accuracy of brake clearance data prediction of a brake.

[0005] A first aspect of an embodiment of the present disclosure provides a brake clearance prediction method, comprising:

[0006] Determine a fatigue inverse model based on simulated fatigue parameter data and historical fatigue parameter data. The fatigue inverse model is used to establish a mapping relationship between the brake's operating status data and fatigue status data.

[0007] determining a brake clearance-fatigue relationship model based on fatigue state data and first brake clearance data; the first brake clearance data is historical brake clearance data; the brake clearance-fatigue relationship model is used to establish a mapping relationship between the brake clearance and the fatigue state data;

[0008] The brake clearance of the target brake is predicted based on the fatigue inverse model, the brake clearance-fatigue relationship model and the target operating state data to obtain second brake clearance data.

[0009] A second aspect of the embodiments of the present disclosure provides a brake clearance prediction device, comprising:

[0010] A fatigue inverse model construction module is used to determine a fatigue inverse model based on simulated fatigue parameter data and historical fatigue parameter data. The fatigue inverse model is used to construct a mapping relationship between the brake's operating status data and fatigue status data;

[0011] a brake clearance-fatigue relationship model construction module, configured to determine a brake clearance-fatigue relationship model based on fatigue state data and first brake clearance data; the first brake clearance data being historical brake clearance data; and the brake clearance-fatigue relationship model being configured to construct a mapping relationship between the brake clearance and the fatigue state data of the brake;

[0012] The brake clearance prediction module is used to predict the brake clearance of the target brake based on the fatigue inverse model, the brake clearance-fatigue relationship model and the target operating state data to obtain second brake clearance data.

[0013] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned brake clearance prediction method are implemented.

[0014] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned brake clearance prediction method are implemented.

[0015] The brake clearance prediction method and device, electronic device, and readable storage medium provided by the embodiments of the present disclosure have the following beneficial effects: By integrating simulation with historical fatigue parameter data, the embodiments of the present disclosure accurately construct a fatigue inverse model, achieving a precise mapping between the brake's operating state and fatigue state. The embodiments of the present disclosure also combine historical brake clearance data to construct a brake clearance-fatigue relationship model, which deeply reveals the inherent connection between brake clearance and fatigue state. On this basis, the embodiments of the present disclosure can analyze the changes in the brake clearance of a target brake under specific operating conditions in real time or predictively, providing accurate secondary brake clearance data (i.e., predicted brake clearance data). This prediction result not only helps optimize the maintenance strategy of the brake system but also effectively prevents potential risks caused by improper brake clearance, improving the safety and reliability of the equipment and reducing failure rates and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 A schematic flow chart of a brake clearance prediction method provided in one embodiment of the present disclosure;

[0018] Figure 2 A schematic flow chart of another brake clearance prediction method provided by an embodiment of the present disclosure;

[0019] Figure 3 A structural block diagram of a brake clearance prediction device provided in one embodiment of the present disclosure;

[0020] Figure 4 A schematic block diagram of an electronic device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0022] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0023] Please refer to Figure 1 , Figure 1 This is a flow chart of a brake clearance prediction method provided by an embodiment of the present disclosure. The method may include S101 to S103.

[0024] S101: Determine a fatigue inverse model based on simulated fatigue parameter data and historical fatigue parameter data, where the fatigue inverse model is used to construct a mapping relationship between the operating state data and fatigue state data of the brake.

[0025] In this embodiment, simulated fatigue parameter data refers to brake fatigue parameter data simulated in a simulation environment. Historical fatigue parameter data refers to actual fatigue parameter data recorded during the historical operation and use of the elevator brake. Both simulated fatigue parameter data and historical fatigue parameter data may include stress-related parameters (such as stress amplitude, stress mean, number of stress cycles, etc.), brake performance parameters (such as brake response time and brake current), and elevator operating condition parameters (such as number of operations, operating vibration frequency, and elevator operating speed). Brake operating status data may include elevator brake operating status data under complex operating conditions, specifically including operating vibration frequency, brake response time, brake current, number of operations, and elevator operating speed.

[0026] Exemplarily, fatigue state data may be obtained by inputting the operating state data into a fatigue inverse model.

[0027] In this embodiment, the brake clearance prediction method further includes:

[0028] Fatigue state data is determined based on the simulated fatigue parameter data, and a mapping relationship between the brake's operating state data and the fatigue state data is established.

[0029] In this embodiment, the fatigue status data is an evaluation value or grade calculated based on the simulated fatigue parameter data, which can indicate the degree of brake fatigue. For example, the fatigue status data calculated based on the simulated fatigue parameter data is 80 points. The fatigue status of the brake can be determined based on this evaluation value.

[0030] For example, the ANSYS (Analysis Systems) simulation platform was used to analyze the operating status data of elevator brakes under complex operating conditions. Five key indicators were introduced: operating vibration frequency, brake response time, brake current, number of operations, and elevator speed. These five key indicators were used as input parameters for simulation within the ANSYS simulation environment. Using models such as material mechanics and structural dynamics, fatigue data for the brake under different operating conditions, i.e., simulated fatigue parameter data, was calculated. Furthermore, the calculation of simulated fatigue parameter data can be achieved through stress analysis and fatigue life prediction models. Within the ANSYS simulation environment, fatigue data can be predicted using stress-life (SN) curves or fatigue damage accumulation theories (such as Miner's law).

[0031] S102: Determine a brake clearance-fatigue relationship model based on the fatigue state data and the first brake clearance data. The first brake clearance data is historical brake clearance data. The brake clearance-fatigue relationship model is used to establish a mapping relationship between the brake clearance of the brake and the fatigue state data.

[0032] In this embodiment, the first brake clearance data is actual brake clearance data historically tested and recorded for elevator brakes. Changes in brake clearance affect the operating state of the brake, and thus its fatigue state. By analyzing the correspondence between a large number of brake clearances and fatigue states, patterns and patterns can be identified to determine the inherent connection between the two. This allows the construction of a model that describes this mapping relationship: a brake clearance-fatigue relationship model.

[0033] For example, elevator brakes can be regularly inspected, with actual brake clearance data recorded as first brake clearance data. Simultaneously, fatigue status data of the brake at that moment can be obtained. Fatigue status data can include various information reflecting fatigue, such as wear level, crack conditions, and performance degradation indicators. Regression analysis can be used to establish a brake clearance-fatigue relationship model, with brake clearance as the independent variable and fatigue-related indicators as the dependent variable.

[0034] S103: Predicting the brake clearance of the target brake based on the fatigue inverse model, the brake clearance-fatigue relationship model and the target operating state data to obtain second brake clearance data.

[0035] In this embodiment, the second brake clearance data is the predicted brake clearance data, the target brake is the elevator brake to be tested, and the target operating state data is the operating state data of the elevator brake to be tested.

[0036] For example, the fatigue inference model is used to infer the brake fatigue state based on known brake operating state data (such as operating vibration frequency and brake response time). The brake clearance-fatigue relationship model establishes a relationship between fatigue state and brake clearance. When the target operating state data (a new set of operating-related data) of the target brake is obtained, the fatigue inference model is first used to obtain the corresponding fatigue state data. Then, based on the brake clearance-fatigue relationship model, the corresponding brake clearance is determined based on the fatigue state, resulting in the second brake clearance data (predicted brake clearance data).

[0037] For example, before performing scheduled elevator maintenance, maintenance personnel can first collect recent elevator operating status data using relevant sensors and monitoring equipment as target operating status data. They can then use the aforementioned method to predict the target brake clearance. If the predicted brake clearance exceeds a reasonable range, maintenance personnel can make targeted adjustments to the brake clearance or inspect relevant components during actual maintenance. For example, if the predicted brake clearance is greater than or equal to a preset first brake clearance threshold (i.e., the brake clearance is too large), the brake pads should be inspected for wear and replacement. If the predicted clearance is less than or equal to a second brake clearance threshold (i.e., the brake clearance is too small), the brake installation should be checked for problems or obstructions. This allows for proactive maintenance preparation, improving maintenance efficiency and elevator safety. The first brake clearance threshold is greater than the second brake clearance threshold.

[0038] As can be seen from the above, this embodiment improves the comprehensiveness, accuracy, and efficiency of elevator brake monitoring and prediction. On the one hand, this embodiment integrates simulation and historical data to construct a fatigue inference model, and combines it with a brake clearance-fatigue relationship model to achieve comprehensive analysis and prediction of brake operating status and brake clearance changes. This overcomes the limitations of a single monitoring method and ensures comprehensive and accurate monitoring.

[0039] On the other hand, this embodiment enhances data processing capabilities, can efficiently mine the patterns behind the data, effectively reduce prediction errors and uncertainties, and improve prediction accuracy.

[0040] On the other hand, this embodiment fully considers the actual operating environment and working conditions of the brake, enhances the model's ability to handle complex problems such as nonlinearity and time-varying, and ensures that the brake clearance prediction results are closer to the actual situation.

[0041] In summary, the brake clearance prediction method constructed in this embodiment reduces the maintenance difficulty of the elevator brake, improves the accuracy of brake clearance prediction, and further promotes the intelligent development of elevator brake monitoring and prediction technology.

[0042] In one embodiment of the present disclosure, the brake clearance prediction method further includes:

[0043] The operating status data includes brake performance data and brake operating condition data.

[0044] The brake performance data and brake operating condition data are input into the simulation environment for calculation to obtain simulation fatigue parameter data.

[0045] In this embodiment, the simulation environment can be understood as a virtual platform built based on computer software and related technologies. Within this simulation environment, various operating conditions and performance characteristics of elevator brakes in actual operation can be simulated. Within this simulation environment, relevant brake operating parameters can be input and pre-established mathematical models and algorithms can be used to simulate and analyze the brake's operating state, yielding results similar to those produced in actual operation, such as fatigue parameter data. This environment integrates theories and models from multiple disciplines, including material mechanics, structural dynamics, and thermodynamics, to accurately simulate the complex behavior of brakes.

[0046] For example, sensors and monitoring equipment can be used to collect elevator brake performance data (such as brake response time and brake current) and operating condition data (such as operating vibration frequency, number of runs, and elevator speed). After preprocessing, the collected brake performance and operating condition data is fed into a simulation environment as input. Within the simulation environment, a mathematical model for elevator brake operation has been established that reflects the brake's operating characteristics and fatigue mechanisms under different parameter combinations. Once the performance and operating condition data are input into the simulation environment, the mathematical model calculates simulated fatigue parameter data based on built-in algorithms and logic, taking into account factors such as friction, energy loss, and stress distribution. The calculated simulated fatigue parameter data is then output for subsequent analysis and application. This simulated fatigue parameter data can be used to assess the brake's current condition, predict its remaining lifespan, and analyze the impact of different operating conditions on fatigue, providing a basis for maintenance and management decisions.

[0047] In this embodiment, the brake performance data may include: brake response time and brake current, etc., and the brake operating condition data may include: operating vibration frequency, operating times and elevator operating speed, etc.

[0048] Input the brake performance data and brake operating condition data into the simulation environment for calculation, and obtain the simulated fatigue parameter data, which may include:

[0049] The running vibration frequency, braking response time, braking current, running times and elevator running speed are input into the simulation environment for calculation to obtain the simulated fatigue parameter data.

[0050] For example, Elevator Maintenance Company A is responsible for elevator maintenance in multiple residential communities. They regularly collect various data about elevator brakes and input it into a simulation environment. Analysis of the resulting simulated fatigue parameter data revealed that after a certain number of runs, the simulated fatigue parameters for a particular elevator's brakes showed a gradual increase in braking response time, with fatigue levels reaching a preset safety threshold. Maintenance personnel can schedule brake inspections and maintenance in advance, such as adjusting brake clearance and checking brake pad wear, to avoid brake failures during actual operation and ensure safe elevator operation. Simulation analysis under different operating conditions can also optimize the elevator's operating strategy, such as adjusting the elevator's operating speed and start / stop frequency, to reduce brake fatigue, extend its service life, reduce maintenance costs, and improve elevator reliability.

[0051] This example builds a simulation environment to simulate the actual operation of elevator brakes. Combining multi-dimensional performance and operating condition data, it effectively predicts brake fatigue and brake clearance changes. This example not only improves prediction accuracy but also enables early warning and maintenance optimization for elevator brakes, significantly improving elevator safety and operating efficiency, reducing maintenance costs, and providing technical support for elevator maintenance and management.

[0052] In one embodiment of the present disclosure, determining a fatigue inverse model based on simulated fatigue parameter data and historical fatigue parameter data includes:

[0053] The initial fatigue inverse model is determined based on the simulated fatigue parameter data and operating status data.

[0054] The initial fatigue inverse model is updated based on the loss function and historical fatigue parameter data to obtain the fatigue inverse model.

[0055] In this embodiment, the fatigue inverse model is a model used to construct a mapping relationship between the operating state data and fatigue state data of the brake.

[0056] Fatigue state data can be obtained based on the simulated fatigue parameter data.

[0057] For example, a fatigue inverse model can be determined based on data-driven technologies such as the Nonlinear Autoregressive Network (NARX) model and inverse iterative technologies such as the Algebraic Reconstruction Technique (ART) algorithm, combined with simulated fatigue parameter data and operating status data. The NARX model can be expressed as:

[0058] y(t)=F(y(t−1),…,y(t−n),x(t),…,x(t−m))

[0059] Where y(t) represents the fatigue state data at time t, x(t) represents the operating state data at time t, n represents the order of the NARX model, m represents the order of the NARX model, and F(•) represents the function.

[0060] In this embodiment, y(t-1), …, y(tn) represent brake fatigue state data at a series of time steps (t-1 to tn). The NARX model considers past fatigue states because fatigue processes have a memory. For example, the current brake fatigue state may be affected by fatigue state data from the previous time step (y(t-1)), two time steps prior (y(t-2)), and so on, up to n time steps prior (y(tn)). n represents the order in which the NARX model considers past fatigue state data, meaning it looks back at fatigue state data from the past n time steps to predict the current state.

[0061] x(t-1), …, x(tm) represent the operating status data for a series of time steps prior to time t (t to tm). Past operating status data affects the current fatigue state. m represents the order in which the NARX model considers past operating status data, meaning it looks back at the operating status over the past m time steps to predict the current fatigue state. This data reflects the cumulative impact of historical operating status changes on the fatigue state.

[0062] For example, if m = 2, then x(t-1) and x(t-2) represent the operating status data for the previous two time steps. Assuming the elevator ran at a high speed or braked frequently in the previous few times, this historical operating status data will have a cumulative impact on the current brake fatigue state. By incorporating this data, the model can better understand the fatigue progression and thus more accurately predict y(t).

[0063] F(•) is a functional relationship that describes how to calculate the fatigue state data y(t) at the current time t based on past fatigue state data (y(t-1), …, y(tn)) and current and past operating state data (x(t), …, x(tm)). F(•) can be a nonlinear function, reflecting the interactions between various factors in the fatigue process. When building a model based on data-driven techniques, F(•) can be trained using methods such as neural networks. Its specific form and parameters are determined by the training data, with the goal of establishing an accurate mapping between operating state data and fatigue state data.

[0064] The ART algorithm can be used to iteratively adjust the parameters of the NARX model, minimize the error between the predicted fatigue state data value and the actual fatigue state data, and establish a mapping relationship from the brake operating state data to the fatigue state data.

[0065] In this embodiment, the loss function is:

[0066]

[0067] Among them, L represents the loss value, N represents the number of samples, represents the jth actual fatigue state data, represents the j-th predicted fatigue state data.

[0068] This embodiment can accurately map the operating status data of the brake to its fatigue state by establishing a fatigue inverse model based on simulated fatigue parameters and historical data, thereby improving prediction accuracy.

[0069] In one embodiment of the present disclosure, determining an initial fatigue inverse model based on simulated fatigue parameter data and operating status data includes:

[0070] Data preprocessing and feature extraction are performed on the simulated fatigue parameter data to obtain a fatigue feature set, which includes stress feature data and external fatigue feature data.

[0071] An initial fatigue inverse model is determined based on stress characteristic data, external fatigue characteristic data and operating status data.

[0072] In this embodiment, stress signature data may include data related to various stresses experienced by the brake during operation. Examples include contact stress caused by friction during elevator braking and stress distribution within components due to deformation caused by stress. Specifically, this data includes the average and maximum stress values ​​at the contact surface between the brake pad and the brake disc, as well as how stress changes over time or with the number of cycles. Stress signature data reflects the mechanical performance of the brake and is a key factor in fatigue assessment.

[0073] External fatigue characteristic data can include characteristic data corresponding to external factors that can affect the brake fatigue state, in addition to those characteristics directly related to stress. For example, the influence of vibration characteristics reflected by operating vibration frequency on brake fatigue, the cumulative effect reflected by the number of operations, and the characteristics of external conditions such as ambient temperature and humidity on brake material performance and fatigue.

[0074] For example, statistical methods and filtering techniques, such as mean filtering and median filtering, are used to clean the simulated fatigue parameter data, remove outliers and noise, and perform other data preprocessing to ensure data accuracy and reliability. Mathematical analysis methods, such as principal component analysis (PCA) and wavelet transforms, are used to extract fatigue feature sets that reflect the nature of fatigue. For stress data, statistical features such as stress amplitude, stress frequency, and stress mean can be calculated, as well as local features such as stress concentration areas. For external fatigue features, relevant feature quantities are extracted from the operating status data, such as quantifying the number of operations and performing spectral analysis on the vibration frequency to obtain relevant features.

[0075] This embodiment constructs a fatigue feature set, including stress and external fatigue features, by preprocessing and extracting simulated fatigue parameter data. This not only improves the accuracy and reliability of the data but also scientifically extracts key fatigue features, laying a solid foundation for establishing an initial fatigue inverse model. This enhances the model's predictive capabilities and adaptability, enabling it to more accurately reflect the actual fatigue state of the brake.

[0076] In one embodiment of the present disclosure, determining a brake clearance-fatigue relationship model based on fatigue state data and first brake clearance data includes:

[0077] A brake clearance-fatigue relationship model is determined based on the fatigue state data, the first brake clearance data, and the first formula.

[0078] The first formula is:

[0079]

[0080]

[0081] in, Indicates the fatigue state data of the brake, Gap indicates the first brake clearance data, Stress indicates the structural stress, S N Curve represents the stress-cycle number curve, and a, b, c, and d represent preset parameters.

[0082] In this embodiment, fatigue state data can be obtained based on the fatigue inverse model. The fatigue state data is combined with the first brake clearance data, the American Society of Mechanical Engineers (ASME) structural stress (i.e., Stress) and the main SN curve (i.e., S N Curve) data, a model of the relationship between brake clearance and fatigue can be constructed.

[0083] For example, the brake clearance-fatigue relationship model can be trained using brake clearance data as input and fatigue status data obtained from a fatigue inverse model as output to establish a mathematical relationship between the brake clearance data and the fatigue status data. The preset parameter values ​​can be: a=0.5, b=2, c=1, and d=0.

[0084] This embodiment constructs a brake clearance-fatigue relationship model, combining fatigue state data, brake clearance data, structural stress, and SN curves to effectively quantify the complex relationship between brake clearance and fatigue. This embodiment improves prediction accuracy and enhances the model's applicability, providing a scientific basis for elevator brake maintenance and lifespan prediction, helping to reduce maintenance costs and improve equipment safety and reliability.

[0085] In one embodiment of the present disclosure, the brake clearance prediction method further includes:

[0086] The brake clearance-fatigue relationship model is updated based on the particle swarm optimization algorithm to obtain an updated brake clearance-fatigue relationship model.

[0087] In this embodiment, the Particle Swarm Optimization (PSO) algorithm is:

[0088] vi(t+1)=w*vi(t)+cl*r1*(pi−xi(t))+c2*r2*(pg−xi(t))

[0089] xi(t+1)=xi(t)+vi(t+1)

[0090] Among them, vi(t) represents the velocity of particle i at time t, xi(t) represents the position of particle i at time t, w represents the inertia weight, c1 and c2 represent learning factors, r1 and r2 represent random numbers, pi represents the historical optimal position of particle i, and pg represents the global optimal position of all particles.

[0091] In this embodiment, a PSO algorithm optimizes the brake clearance-fatigue relationship model to assess the accuracy and reliability of the predicted brake clearance. The PSO algorithm optimizes the solution by simulating the predatory behavior of a flock of birds. By iteratively updating the particle velocities and positions, the PSO algorithm minimizes the prediction error of the brake clearance-fatigue relationship model.

[0092] For example, particle i at time t has velocity vi(t) = 3, position xi(t) = 5, inertia weight w = 0.8, learning factor c1 = c2 = 2, random number r1 = r2 = 0.5, particle i's historical optimal position pi = 8, and the global optimal position of all particles pg = 10. Then vi(t+1) = 10.4, and xi(t+1) = 15.4.

[0093] This embodiment introduces a particle swarm optimization algorithm to update the brake clearance-fatigue relationship model, improving the accuracy and reliability of the model's predictions. By simulating a natural optimization process, the PSO algorithm effectively reduces prediction errors, making the predicted results of the brake clearance-fatigue relationship more realistic. This embodiment not only enhances the scientific nature of brake performance evaluation but also provides more accurate data support for the maintenance and management of equipment such as elevators, helping to proactively identify and resolve potential problems and ensure safe equipment operation.

[0094] In one embodiment of the present disclosure, the brake clearance prediction method further includes:

[0095] The fatigue life of the target brake is calculated based on the second brake clearance data.

[0096] A durability result of the target brake is determined based on the fatigue life of the target brake.

[0097] In this embodiment, the target brake may represent the elevator brake to be tested. The fatigue life may include the time during which the brake can be used and operated normally in the future, or the time until the next failure. The durability result of the target brake determined based on the fatigue life of the target brake may include:

[0098] The durability result of the target brake is determined based on the fatigue life and durability calculation formula of the target brake.

[0099] The durability calculation formula is:

[0100] Durability=H(Fatigue,Time)= *Fatigue+ *Time+

[0101] Among them, Durability represents the durability result of the brake, H represents the fatigue state data-fatigue life function, Time represents the fatigue life, Indicates fixed parameters.

[0102] In this embodiment, the fatigue life of the brake can be calculated based on the predicted second brake clearance data, combined with the fatigue inverse model and ASME structural stress data. By repeating the above operation, the fatigue life of the brake under different brake clearances can be obtained, and the prediction result of the brake durability can be obtained based on the fatigue life.

[0103] For example, the value of the fixed parameter may be: =0.5, =0.2, =10.

[0104] For example, Figure 2 As shown in the figure, the process of the brake clearance prediction method may include: first, performing fatigue data simulation calculation to obtain simulated fatigue data. Then, performing data preprocessing and feature extraction on the simulated fatigue data. Based on the data obtained in the first two steps, a fatigue inversion method is established. Simultaneously, based on the established fatigue inversion method, a model of the relationship between brake clearance and fatigue is constructed. Finally, based on the fatigue inversion method and the model of the relationship between brake clearance and fatigue, brake clearance is predicted and evaluated, and brake durability is predicted.

[0105] This embodiment combines the second brake clearance data, a fatigue inverse model, and ASME structural stress data to accurately calculate and predict the fatigue life of elevator brakes, thereby assessing their durability. This embodiment not only improves the accuracy and scientific nature of the predictions, but also enables proactive management of brake performance. By setting fixed parameters, the calculation process is simplified while ensuring the reliability of the prediction results.

[0106] Corresponding to the brake clearance prediction method of the above embodiment, Figure 3 This is a structural block diagram of a brake clearance prediction device provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 3 The brake clearance prediction device 20 includes: a fatigue inverse model construction module 21, a brake clearance-fatigue relationship model construction module 22 and a brake clearance prediction module 23.

[0107] The fatigue inverse model construction module 21 is used to determine a fatigue inverse model based on the simulated fatigue parameter data and the historical fatigue parameter data. The fatigue inverse model is used to construct a mapping relationship between the operating status data and the fatigue status data of the brake.

[0108] The brake clearance-fatigue relationship model building module 22 is used to determine a brake clearance-fatigue relationship model based on the fatigue state data and the first brake clearance data. The first brake clearance data is historical brake clearance data. The brake clearance-fatigue relationship model is used to build a mapping relationship between the brake clearance of the brake and the fatigue state data.

[0109] The brake clearance prediction module 23 is used to predict the brake clearance of the target brake based on the fatigue inverse model, the brake clearance-fatigue relationship model and the target operating state data to obtain second brake clearance data.

[0110] In one embodiment of the present disclosure, the brake clearance prediction device 20 further includes:

[0111] The first calculation module is used for operating status data including brake performance data and brake operating condition data.

[0112] The brake performance data and brake operating condition data are input into the simulation environment for calculation to obtain simulation fatigue parameter data.

[0113] In one embodiment of the present disclosure, the fatigue inverse model construction module 21 is specifically used to determine an initial fatigue inverse model based on the simulated fatigue parameter data and the operating status data.

[0114] The initial fatigue inverse model is updated based on the loss function and historical fatigue parameter data to obtain the fatigue inverse model.

[0115] In one embodiment of the present disclosure, the fatigue inverse model building module 21 is further configured to perform data preprocessing and feature extraction on the simulated fatigue parameter data to obtain a fatigue feature set, which includes stress feature data and external fatigue feature data.

[0116] An initial fatigue inverse model is determined based on stress characteristic data, external fatigue characteristic data and operating status data.

[0117] In one embodiment of the present disclosure, the brake clearance-fatigue relationship model building module 22 is specifically configured to determine the brake clearance-fatigue relationship model based on fatigue state data, first brake clearance data, and a first formula.

[0118] The first formula is:

[0119]

[0120]

[0121] in, Indicates the fatigue state data of the brake, Gap indicates the first brake clearance data, Stress indicates the structural stress, S N Curve represents the stress-cycle number curve, and a, b, c, and d represent preset parameters.

[0122] In one embodiment of the present disclosure, the brake clearance prediction device 20 further includes:

[0123] The model optimization module is used to update the brake clearance-fatigue relationship model based on a particle swarm optimization algorithm to obtain an updated brake clearance-fatigue relationship model.

[0124] In one embodiment of the present disclosure, the brake clearance prediction device 20 further includes:

[0125] The fatigue life of the target brake is calculated based on the second brake clearance data.

[0126] A durability result of the target brake is determined based on the fatigue life of the target brake.

[0127] See also Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 4 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 3 The functions of modules 21 to 23 are shown.

[0128] It should be understood that in the embodiments of the present disclosure, the processor 301 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0129] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0130] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0131] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the brake clearance prediction method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device 300 described in the embodiments of the present disclosure, which will not be repeated here.

[0132] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0133] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0134] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0135] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0137] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present disclosure.

[0138] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0139] The above are only specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in this disclosure, and such modifications or replacements should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A brake clearance prediction method, characterized in that: include: Determining a fatigue inverse model based on the simulated fatigue parameter data and the historical fatigue parameter data, wherein the fatigue inverse model is used to establish a mapping relationship between the brake's operating state data and the fatigue state data; determining a brake clearance-fatigue relationship model based on the fatigue state data and the first brake clearance data; The first brake clearance data is historical brake clearance data; the brake clearance-fatigue relationship model is used to construct a mapping relationship between the brake clearance and fatigue state data of the brake; The brake clearance of the target brake is predicted based on the fatigue inverse model, the brake clearance-fatigue relationship model and the target operating state data to obtain second brake clearance data.

2. The brake clearance prediction method according to claim 1, characterized in that: Also includes: The operating status data includes brake performance data and brake operating condition data; The brake performance data and the brake operating condition data are input into a simulation environment for calculation to obtain the simulated fatigue parameter data.

3. The brake clearance prediction method according to claim 2, characterized in that: The determining of the fatigue inverse model based on the simulated fatigue parameter data and the historical fatigue parameter data includes: Determining an initial fatigue inverse model based on the simulated fatigue parameter data and the operating status data; The initial fatigue inverse model is updated based on the loss function and the historical fatigue parameter data to obtain the fatigue inverse model.

4. The brake clearance prediction method according to claim 3, characterized in that: The determining of the initial fatigue inverse model based on the simulated fatigue parameter data and the operating status data includes: Performing data preprocessing and feature extraction on the simulated fatigue parameter data to obtain a fatigue feature set; the fatigue feature set includes stress feature data and external fatigue feature data; The initial fatigue inverse model is determined based on the stress characteristic data, the external fatigue characteristic data and the operating status data.

5. The brake clearance prediction method according to claim 1, wherein: The determining of the brake clearance-fatigue relationship model based on the fatigue state data and the first brake clearance data includes: determining a brake clearance-fatigue relationship model based on the fatigue state data, the first brake clearance data, and a first formula; The first formula is: in, Indicates the fatigue state data of the brake, Gap indicates the first brake clearance data, Stress indicates the structural stress, S N Curve represents the stress-cycle number curve, and a, b, c, and d represent preset parameters.

6. The brake clearance prediction method according to claim 5, characterized in that: Also includes: The brake clearance-fatigue relationship model is updated based on a particle swarm optimization algorithm to obtain an updated brake clearance-fatigue relationship model.

7. The brake clearance prediction method according to claim 1, wherein: Also includes: calculating a fatigue life of a target brake based on the second brake clearance data; A durability result of the target brake is determined based on the fatigue life of the target brake.

8. A brake clearance prediction device, characterized in that: include: A fatigue inverse model construction module is used to determine a fatigue inverse model based on the simulated fatigue parameter data and the historical fatigue parameter data, wherein the fatigue inverse model is used to construct a mapping relationship between the operating state data and the fatigue state data of the brake; a brake clearance-fatigue relationship model building module, configured to determine a brake clearance-fatigue relationship model based on the fatigue state data and the first brake clearance data; The first brake clearance data is historical brake clearance data; the brake clearance-fatigue relationship model is used to construct a mapping relationship between the brake clearance and fatigue state data of the brake; The brake clearance prediction module is used to predict the brake clearance of the target brake based on the fatigue inverse model, the brake clearance-fatigue relationship model and the target operating state data to obtain second brake clearance data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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