Three-dimensional simulation design method and system based on elevator structure

Abstract

The invention relates to the technical field of mechanical engineering, in particular to a three-dimensional simulation design method and system based on an elevator structure. The method comprises the following steps that an elevator structure drawing is obtained; a three-dimensional elevator assembly is constructed according to the elevator structure drawing; lift car static load simulation is conducted according to the three-dimensional elevator assembly, and lift car static load data are obtained; detecting the structural strength of the lift car based on the lift car static load data; evaluating welding defects based on the structural strength of the lift car, and generating welding defect data; identifying a weld weak area according to the welding defect data; determining the abrasion loss of the car structure based on the weld weak area; according to the lift car structure abrasion loss, the lift car bottom plate fracture risk is predicted; carrying out welding optimization design based on the fracture risk of the bottom plate of the lift car to obtain welding optimization design data; and traction and traction simulation is conducted according to the three-dimensional elevator assembly, and traction and traction data are obtained. Based on the mechanical engineering technology, the safety and stability of the elevator structure are improved, and the design precision and efficiency are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical engineering, and particularly relates to a three-dimensional simulation design method and system based on an elevator structure. Background Art

[0002] The structural characteristics of an elevator mainly consist of a mechanical drive system, a traction system, a car and counterweight balance system, guide rails, a buffer device, and a control system. Each component works together to ensure the stable operation and safety of the elevator. It includes a mechanical drive system, wire rope traction, a car and counterweight balance system, and a control system, etc. Through these technologies, the vertical transportation function of the elevator is ensured. The elevator structure design considers the reasonable layout and optimized design of key components such as the car, traction system, guide rails, and buffer device to ensure the stability and safety of the elevator during operation. Traditional three-dimensional elevator structure simulation design methods have low calculation efficiency and usually rely on complex calculation models and a large number of numerical simulations, resulting in a slow calculation process. Especially when performing large-scale simulations, it consumes a large amount of time and computing resources. The accuracy problem is also relatively prominent. Traditional three-dimensional simulation methods may have insufficient accuracy in predicting stress, fatigue, and fracture risks, affecting the reliability of the design results and unable to ensure the safety of the elevator structure. Traditional methods lack real-time optimization. Usually, the model and parameters need to be adjusted multiple times, and the optimization process is not intelligent and timely enough. This not only reduces efficiency but also leads to a disconnection between the design and the actual working conditions, and cannot fully consider the dynamic changes under different working conditions. Traditional design methods usually only focus on a single optimization goal, such as strength or noise, while modern elevator design requires comprehensive consideration of multiple goals, such as structural strength, durability, comfort, and noise control, etc., and cannot meet the needs of these multiple goals, lacking flexibility and diversity. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a three-dimensional simulation design method and system based on an elevator structure to solve at least one of the above technical problems.

[0004] To achieve the above object, a three-dimensional simulation design method based on an elevator structure includes the following steps:

[0005] Step S1: Obtain elevator structure drawings; construct three-dimensional elevator components according to the elevator structure drawings; perform car static load simulation based on the three-dimensional elevator components to obtain car static load data; detect the car structure strength based on the car static load data;

[0006] Step S2: Evaluate welding defects based on the car structure strength to generate welding defect data; identify weld weak areas according to the welding defect data; determine the car structure wear amount based on the weld weak areas; predict the car floor fracture risk based on the car structure wear amount; perform welding optimization design based on the car floor fracture risk to obtain welding optimization design data;

[0007] Step S3: Conduct a traction simulation based on the three-dimensional elevator components to obtain traction data; identify the noise sources based on the traction data; perform vibration isolation optimization design based on the noise sources to obtain vibration isolation optimization design data;

[0008] Step S4: Construct a three-dimensional elevator simulation model according to the welding optimization design data and the vibration isolation optimization design data, and conduct extreme condition tests to obtain extreme condition test data; optimize the elevator buffer structure based on the extreme condition test data to obtain elevator buffer optimization structure data.

[0009] Through the three-dimensional simulation design method based on the elevator structure, the present invention significantly improves the accuracy and efficiency of elevator design. After obtaining the elevator structure drawings and constructing the three-dimensional elevator components, a static load simulation of the car is carried out, providing accurate data support for the strength detection of the car structure. This helps to discover potential problems, ensure that the design meets the expected load, and avoid safety hazards. The strength assessment supported by the static load data identifies the structural weaknesses, especially the welded parts, providing a basis for the welding optimization design to ensure structural stability. By identifying welding defects and weak areas, predicting the risk of wear and fracture of the car floor, optimizing the design in advance, reducing equipment failures, and extending the elevator life. The traction simulation optimizes the noise source identification and vibration isolation design, reduces the operating noise, and improves the passenger comfort. Based on the extreme condition test data, the elevator buffer structure is optimized to improve the impact resistance and durability, ensuring stable performance under extreme conditions. The overall optimized design improves the safety, stability, and comfort of the elevator, balances the multi-objective requirements, and solves the problems of low precision, slow efficiency, and incomplete optimization in the traditional design method.

[0010] Preferably, this specification also provides a three-dimensional simulation design system based on the elevator structure for executing the three-dimensional simulation design method based on the elevator structure as described above. The three-dimensional simulation design system based on the elevator structure includes:

[0011] A three-dimensional elevator component construction module, configured to obtain elevator structure drawings; construct three-dimensional elevator components according to the elevator structure drawings; conduct a static load simulation of the car according to the three-dimensional elevator components to obtain car static load data; detect the strength of the car structure based on the car static load data;

[0012] A welding optimization design module, configured to evaluate welding defects based on the strength of the car structure to generate welding defect data; identify the weak areas of the welds according to the welding defect data; determine the wear amount of the car structure based on the weak areas of the welds; predict the risk of car floor fracture according to the wear amount of the car structure; conduct welding optimization design based on the risk of car floor fracture to obtain welding optimization design data;

[0013] The vibration isolation optimization design module is used to perform traction simulation based on the three-dimensional elevator components to obtain traction data; identify the noise sources based on the traction data; perform vibration isolation optimization design based on the noise sources to obtain vibration isolation optimization design data;

[0014] The elevator three-dimensional simulation model construction module is used to construct an elevator three-dimensional simulation model according to the welding optimization design data and the vibration isolation optimization design data, and perform extreme condition tests to obtain extreme condition test data; optimize the elevator buffer structure based on the extreme condition test data to obtain elevator buffer optimization structure data.

[0015] The three-dimensional simulation design system based on the elevator structure of the present invention can implement any three-dimensional simulation design method of the present invention, and is used as a medium for the operation and signal transmission between various modules to complete the three-dimensional simulation design method based on the elevator structure. The internal modules of the system cooperate with each other, improving the safety, stability, comfort and durability of the elevator structure, and significantly improving the design accuracy and efficiency. Brief Description of the Drawings

[0016] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects and advantages of the present invention will become more apparent:

[0017] Figure 1 It is a schematic flow chart of the steps of a three-dimensional simulation design method based on the elevator structure of the present invention;

[0018] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the drawings. Detailed Embodiments

[0019] The technical method of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0020] In addition, the drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0021] To achieve the above object, please refer to Figure 1, the present invention provides a three-dimensional simulation design method based on an elevator structure, and the method includes the following steps:

[0022] Step S1: Obtain elevator structure drawings; construct three-dimensional elevator components according to the elevator structure drawings; perform static load simulation on the car based on the three-dimensional elevator components to obtain car static load data; detect the structural strength of the car based on the car static load data;

[0023] In this embodiment, detailed drawings of the elevator structure are obtained from the elevator manufacturer or the designer. The drawing content includes the dimensions and material specifications of the elevator car, traction system, guide rail system, counterweight, door system, etc. Then, according to the size data of each component provided in the drawings, a three-dimensional model of the elevator is constructed using CAD (Computer-Aided Design) software (such as AutoCAD or SolidWorks). The three-dimensional elevator model needs to include each component of the car and the details of the mechanical connections. Next, finite element analysis (FEA) software (such as ANSYS or Abaqus) is used to perform static load simulation on the car, assuming that the elevator is fully loaded and stationary. The key parameters for the static load simulation of the car include the load weight (for example, the maximum load is set to 1000 kg), the self-weight of the car, and the acceleration due to gravity (taking 9.81 m / s 2 ). After the simulation is completed, the static load data of the car is obtained, including the load distribution of different parts. Then, the strength of the car structure is analyzed based on the static load data, focusing on the material strength and the bearing capacity of the connection parts. At this time, the input material parameters such as the yield strength of the steel (for example, the yield strength is 250 MPa) and the allowable maximum stress threshold (for example, set to 300 MPa) will affect the calculation results. If the strength of any part exceeds the set threshold, it is marked as possibly having a safety hazard.

[0024] Step S2: Evaluate welding defects based on the structural strength of the car to generate welding defect data; identify the weld weak areas according to the welding defect data; determine the car structure wear amount based on the weld weak areas; predict the risk of car floor fracture based on the car structure wear amount; perform welding optimization design based on the risk of car floor fracture to obtain welding optimization design data;

[0025] In this embodiment, an ultrasonic detection method is used in combination with electromagnetic detection technology to identify defects in the welding parts of the car, and the areas and types of welding defects are detected. In combination with the welding process manual, welding defect standards are set (for example, the threshold for unqualified weld depth is 0.5 mm), thereby generating welding defect data. Based on the welding defect data, the weak weld areas are further analyzed and identified. For the weak areas, ultrasonic or X-ray detection methods are applied to determine the potential failure risk of the welds. Based on the detection results of the weak weld areas, the wear amount of the car structure is calculated. Through the wear analysis model, combined with specific usage scenarios (such as elevator usage frequency, environmental conditions, etc.), the annual wear amount is predicted, for example, set to 0.2 mm per year. By calculating the relationship between the wear amount and the welding parts, the fracture risk of the car floor is further predicted. If the wear amount exceeds the set fracture risk threshold (such as 2 mm), the system will issue a warning and perform structural optimization design. Finally, welding optimization design is carried out according to the fracture risk data, and the material, welding process, and weld layout of the welding parts are readjusted to obtain welding optimization design data. These optimization design data will be used for subsequent structural improvement.

[0026] Step S3: Perform traction simulation based on the three-dimensional elevator components to obtain traction data; identify the noise sources based on the traction data; perform vibration isolation optimization design based on the noise sources to obtain vibration isolation optimization design data;

[0027] In this embodiment, according to the three-dimensional component model of the elevator, a traction simulation tool (such as the elevator dynamics simulation tool in Matlab or Simulink) is used to conduct traction analysis. The simulation takes the traction force, wire rope tension, and elevator acceleration during the elevator operation as input parameters. In this step, it is necessary to set the power of the traction machine (such as 5 kW) and the diameter of the traction wheel (for example, set to 0.5 m), as well as the load condition (fully loaded 1000 kg), and calculate the traction force output and traction speed of the traction system. Through the simulation, traction data is obtained, covering parameters such as the traction force, wire rope tension, and elevator speed under different operating conditions during the elevator operation. Then, the simulation data is analyzed to identify the noise sources during the elevator operation. The noise sources are mainly caused by the mechanical friction of the traction system, the contact between the wire rope and the pulley, and the aerodynamic effects during the elevator operation. Through spectrum analysis, the frequency range and sound pressure level of the noise are determined (for example, set the frequency range of the noise source to 50 - 200 Hz, and the sound pressure level to 80 dB). According to the noise source data, a vibration isolation system is designed for vibration isolation optimization design. The key point of the vibration isolation design is to select appropriate vibration isolation materials (such as high-density rubber pads, spring vibration isolation devices, etc.), and install appropriate vibration isolation devices between the elevator base and the ground to reduce noise. In the design, a vibration isolation frequency response function is set to ensure that the resonance frequency of the vibration isolation system does not coincide with the elevator operation frequency. After the optimization design, vibration isolation optimization design data is obtained for the subsequent implementation of the vibration isolation system.

[0028] Step S4: Construct a three-dimensional simulation model of the elevator based on the welding optimization design data and the vibration isolation optimization design data, and conduct extreme condition tests to obtain extreme condition test data; optimize the elevator buffer structure based on the extreme condition test data to obtain elevator buffer optimization structure data.

[0029] In this embodiment, a complete three-dimensional simulation model of the elevator is constructed based on the welding optimization design data and the vibration isolation optimization design data. The simulation model includes components such as the car, the traction system, the guide rails, and the counterweight, as well as the design parameters for welding optimization and vibration isolation optimization. When constructing the model, in combination with the existing design drawings of elevator components, all the optimization design data is embedded in the simulation software (such as ANSYS or Simulink). Then, the extreme condition test of the elevator is carried out to simulate the operating state of the elevator under conditions such as overload, ultra-high speed, and extreme temperature. During the test, the parameters of the extreme conditions are set, such as the load being overloaded by 1.5 times and the temperature reaching -20°C to 50°C. Through the extreme condition test, the response data of the elevator under special operating conditions is collected, including vibration data, temperature changes, acceleration response, etc. According to the extreme condition test data, the buffer structure of the elevator is optimized, with a focus on optimizing the shock absorption system and buffer parameters of the elevator to ensure the safety and comfort of the elevator under extreme conditions. In the optimization of the buffer structure, the working range and pre-pressure of the buffer are set (for example, the working range of the buffer is 5 - 10 cm and the pre-pressure is 200 N), and the stiffness and damping characteristics of the spring are adjusted to reduce the impact force and vibration during operation. Finally, the optimized buffer structure design data is obtained to ensure the stability and comfort of the elevator under all working conditions.

[0030] Particularly importantly, step S4 includes the following steps:

[0031] Step S41: Construct a three-dimensional simulation model of the elevator according to the welding optimization design data and the vibration isolation optimization design data, and conduct an extreme condition test to obtain extreme condition test data;

[0032] In this embodiment, according to the welding structure optimization design data and the vibration isolation optimization design data of the elevator, a three-dimensional simulation model of the elevator is constructed using three-dimensional modeling software (such as SolidWorks or ANSYS). The model needs to include the elevator car, the guide rail system, the buffer, the door system, and the power drive system. Then, an extreme condition test is carried out to simulate the operating scenarios of the elevator under full load, no load, and normal working conditions. By applying different external loads, vibration sources, and working environment conditions in the simulation model, the response data of the elevator system under extreme conditions is obtained. These data include but are not limited to physical quantities such as stress, strain, and displacement. These condition test data are the basis for subsequent optimization and design. Special attention should be paid to the dynamic response of the elevator system under different conditions in order to evaluate the performance of the elevator under the most stringent conditions.

[0033] Step S42: Calculate the impact load based on the extreme condition test data to obtain impact load data;

[0034] In this embodiment, based on the data obtained from the extreme condition tests, the dynamic responses of the elevator system in various operating states are extracted, especially the impact loads generated during elevator start-up, stop, and movement. Using dynamic calculation methods, with the mass, acceleration, and velocity data of the elevator, combined with the measured load responses (such as acceleration, force, and displacement, etc.), the impact loads experienced by the elevator system are calculated. These calculations can be carried out through mechanical formulas, such as F = ma (force equals mass multiplied by acceleration), or through finite element analysis (FEA). During this process, the inertia of each part of the elevator, the shock absorption effect, and the influence of the vibration frequency on the impact load must be considered. The obtained impact load data will provide the necessary load basis for the subsequent design of the buffer.

[0035] Step S43: Identify the elevator vibration structure source based on the impact load data;

[0036] In this embodiment, after obtaining the impact load data, frequency domain analysis and time domain analysis techniques are used to identify the main sources of elevator vibration. First, the spectral analysis of the impact load data is carried out through Fourier transform (FFT) to extract the main vibration frequencies of the system in various operating states. Then, the sensor data is used to distinguish different components of the elevator vibration to judge the source of the vibration. The vibration sources of the elevator include, but are not limited to, the start and stop of the elevator drive system, the friction between the car and the guide rail, and the dynamic load brought by passengers. During this process, the influence of the different stiffness and damping of the elevator structure on the vibration source also needs to be considered. Through finite element analysis, the dynamic responses of different components in the elevator structure are further identified, and the specific position and characteristics of the vibration source are determined.

[0037] Step S44: Calculate the optimized size of the buffer based on the elevator vibration structure source;

[0038] In this embodiment, according to the identified elevator vibration structure source, combined with the impact load data, dynamic modeling methods are used to calculate the optimized size of the buffer. First, the preliminary design parameters of the buffer are set, including the expected maximum bearing capacity, the working frequency range, and the required shock absorption effect. Then, through multi-point response analysis (MOR) or finite element analysis (FEA), the response of the buffer to the vibration source is calculated under different vibration modes, and the maximum impact load it can withstand and the required stiffness are calculated. At this time, parameters such as the length, width, height, and material thickness of the buffer need to be determined so that it can withstand the vibration impact generated by the elevator system under the most extreme working conditions. Finally, considering the working frequency, damping characteristics, and the required shock absorption effect of the buffer, the size of the buffer is optimized to ensure its reliability and effectiveness under different load conditions.

[0039] Step S45: Calculate the optimized shape of the buffer based on the elevator vibration structure source;

[0040] In this embodiment, based on the elevator vibration structure source and impact load data, the optimal shape of the buffer is further determined using a structural optimization algorithm (such as topology optimization or shape optimization). During this process, by analyzing the elevator vibration source, the vibration frequencies and impact load intervals that most require buffering are determined. Then, finite element analysis is used to simulate different shapes of the buffer, and the vibration transfer characteristics, elastic deformation ability, and energy absorption ability of the buffer in different forms are calculated. By adjusting the shape of the buffer, such as circular, square, or other special shapes, its stiffness distribution, energy absorption effect, and damping characteristics are optimized, so as to more effectively reduce the vibration transfer. The shape optimization process may require multiple iterations to ensure that the buffer can provide the best shock absorption effect during actual operation.

[0041] Step S46: Integrate the optimized size of the buffer and the optimized shape of the buffer to obtain the elevator buffer optimization structure data.

[0042] In this embodiment, after separately optimizing the size and shape of the buffer, these two pieces of data are integrated to construct a complete optimized structure of the elevator buffer. First, the optimized size and shape parameters are input into the three-dimensional simulation model of the elevator for comprehensive analysis. By applying the optimized size and shape to the elevator system, dynamic simulation is performed again to simulate the response of the elevator under various working conditions and evaluate the working effect of the buffer. According to the simulation results, the structural parameters are further adjusted to ensure that the buffer can effectively reduce vibration under extreme working conditions and avoid excessive vibration or system failure. Finally, the optimized structure data of the elevator buffer are obtained, including its optimal size, shape, material, and installation position, etc., providing detailed design data for the actual manufacturing and installation of the elevator.

[0043] Preferably, step S1 is specifically as follows:

[0044] Step S11: Obtain the elevator structure drawings;

[0045] In this embodiment, the elevator structure drawings are obtained from the elevator design unit or manufacturer. The drawings should include detailed design information such as the car structure, traction structure, guide rail layout, counterweight system, and buffer device. The format of the obtained drawings needs to support subsequent modeling processing, usually including CAD drawings in DWG format, three-dimensional design files in STEP format, or engineering blueprints in PDF format. If the drawings are paper documents, they need to be converted into digital files through a high-precision scanner (resolution not less than 600 dpi), and the annotation information on the drawings is recognized using OCR software (such as ABBYY FineReader) to ensure the accurate extraction of each dimension parameter. For the already digitized design files, modeling software (such as SolidWorks or AutoCAD) can be directly used for analysis.

[0046] Step S12: Identify the car structure based on the elevator structure drawings, and construct the car components according to the car structure;

[0047] In this embodiment, the main structures of the car in the drawings are analyzed, including the car frame, car bottom, car top, side walls, guide rail connection components, and safety gear installation parts, etc. Use computer-aided design software (such as SolidWorks or CATIA) to extract the three-dimensional data of the car structure, and construct the car components according to the dimensions marked on the drawings. Conduct detailed modeling on the car frame, and use structural parameters such as the frame cross-section size (such as a 50mm×50mm square tube), material properties (such as Q235B steel), and connection methods (such as welding or bolt connection) to construct the components. For the welded parts, use welding standards (such as ISO 5817) to define the welding dimensions and shapes, and apply material inhomogeneity parameters to the welded connection parts. The thickness parameter of the car floor (such as 3mm) needs to be set, and the distribution of the stiffeners is calculated according to the car load-bearing capacity. Use finite element analysis software (such as ANSYS) to conduct structural verification on the car components to ensure that the constructed car components meet the design requirements.

[0048] Step S13: Identify the traction structure based on the elevator structure drawings, and construct the traction components according to the traction structure;

[0049] In this embodiment, the components related to the traction system in the drawings are analyzed, including the traction machine, traction wheel, traction rope, drive motor, and traction wheel groove type, etc. Use CAD tools to extract the dimension data of the traction machine, such as the traction wheel diameter (such as 450mm), wheel groove angle (such as 45°), and rope diameter (such as 10mm). Use CATIA or SolidWorks to conduct three-dimensional modeling on the traction machine components, construct the geometric model of the traction wheel according to the specifications provided by the manufacturer, and set its material properties (such as HT250 cast iron). For the drive motor, extract the power parameter (such as 11kW), rated speed (such as 960r / min), and output torque (such as 110N·m) according to the drawings, and ensure the connection relationship between the motor and the traction wheel during the three-dimensional modeling process, including the coupling structure and installation accuracy (such as H7 / g6 fit). Adopt a flexible cable modeling method for the traction rope, set the rope elastic modulus (such as 1.1×10 5 MPa) and pre-tightening force (such as 2000N) to ensure that the three-dimensional components of the traction system meet the design requirements.

[0050] Step S14: Integrate the car components and the traction components to obtain a three-dimensional elevator component;

[0051] In this embodiment, the car assembly and the traction assembly are aligned in coordinates, and the connection relationship between the car and the traction system is configured according to the requirements of the drawings. Set the car guide rail spacing (such as 700 mm), and ensure that the suspension method of the traction rope meets the design requirements. For example, when the "2:1" suspension method is adopted, set the rope wrap angle of the traction sheave (such as 180°). Use 3D modeling software (such as SolidWorks or UG NX) to create constraint relationships, define the sliding degrees of freedom of the car on the guide rails, and ensure that the force direction of the traction rope is consistent with the elevator running direction. Conduct an assembly constraint inspection on the 3D elevator assembly to ensure that the interference amount between components is less than 0.1 mm, and export a complete 3D model file (such as STEP format) for subsequent simulation analysis.

[0052] Step S15: Perform a static load simulation on the car based on the 3D elevator assembly to obtain car static load data;

[0053] In this embodiment, set the load conditions of the car, including full load condition (such as 1000 kg) and rated load condition (such as 500 kg). Apply a uniform load (such as 9.81 kN / m 2 ) to the car floor, and perform a static analysis using finite element analysis software (such as ANSYS). Set the constraint conditions of the car frame, including fixed support at the four bottom corners, to simulate the constraint relationship of the car on the guide rails. When dividing the mesh, use tetrahedral elements, set the element size to 10 mm, and conduct a mesh independence verification. During the solution process, calculate the maximum stress value (such as not exceeding 250 MPa) and the maximum deformation amount (such as not exceeding 2 mm) of the car floor. Extract the calculation results and generate a static load data file, recording the displacements, stresses, and load distributions of key nodes for subsequent strength detection.

[0054] Step S16: Detect the structural strength of the car based on the car static load data.

[0055] In this embodiment, extract the static load data, including the maximum stress, minimum stress, and deformation amount, and perform strength verification using structural strength standards (such as GB / T3811-2008). For the car floor, set the yield strength threshold (such as 235 MPa), and calculate the safety factor of the car floor (such as ≥1.5). Adopt the von Mises stress criterion to evaluate the safety of the car frame. If the maximum stress exceeds the material yield strength, conduct a reinforcement design for the overloaded area, such as adding stiffeners or adjusting the material thickness. For the welded parts, evaluate the weld strength according to the ISO 5817 standard. If the weld stress exceeds the allowable weld stress (such as 180 MPa), adjust the welding process, such as using multi-pass welding or increasing the weld cross-section. Use the finite element analysis software to generate a car structural strength detection report, and output the stress nephogram and deformation distribution map to provide a basis for subsequent optimization design.

[0056] Preferably, step S16 is specifically as follows:

[0057] Step S161: Identify the static load distribution based on the car static load data to obtain static load distribution data;

[0058] In this embodiment, the static load data of the key parts of the car is collected. The static load data of the car floor comes from the distribution of the gravity acting on the car floor, and the unit area load values of different regions are obtained according to the mass distribution. For example, the load density in the central area of the floor is set to 9.81 kN / m 2 , and the concentrated load in the four-corner support area is 2.5 kN. The static load data of the side wall needs to be obtained by combining the support force distribution of the car guide rail connection points. According to the load transfer path during the movement of the car, local concentrated load data is extracted near the guide rail connection points. The static load data of the car top and frame are mainly determined by the self-weight and the elevator traction force distribution. Meshes are divided in the top area, and the unit load is calculated at the center point of each mesh. The mesh size is set to 10 mm × 10 mm to ensure the refinement of the load distribution. After all the static load data is meshed by a finite element analysis preprocessing tool (such as HyperMesh), it is stored in matrix form and a static load distribution data file is generated. The storage format is CSV, and the data fields include node coordinates, load directions, load values, etc., to ensure that subsequent stress calculations can be directly called.

[0059] Step S162: Calculate the stress according to the static load distribution data to obtain stress data;

[0060] In this embodiment, the static load distribution data file is read and imported into a finite element analysis software (such as ANSYS), and the physical parameters of the car structure material are selected, including elastic modulus, Poisson's ratio and density. For example, the car frame uses Q235B steel, and its elastic modulus is set to 206000 MPa and Poisson's ratio is set to 0.3. The floor uses aluminum alloy material, the elastic modulus is set to 70000 MPa, and Poisson's ratio is set to 0.33. According to the material type and load application method, corresponding boundary conditions are defined. For example, the bottom support points are fixed, and the guide rail connection points are set as sliding constraints. The static analysis module is used to calculate the forces on the entire car, and the stress values of each key part are extracted. The stress data is stored in a database manner, and each data item includes node number, stress direction, stress value and the structural area where it is located. For areas with large forces, such as the joint between the car floor and the side wall, additional fine meshing is performed to extract more accurate stress data, and a visual stress distribution diagram is exported for subsequent evaluation of the strain degree.

[0061] Step S163: Evaluate the strain degree based on the stress data;

[0062] In this embodiment, stress data is read and the stress directions of each node are matched. According to the elastic modulus of the car body material, the deformation amounts of components such as the bottom plate, side walls, and frame are calculated respectively. Due to the concentrated force on the car body bottom plate, the deformation amount in the edge area is relatively large, and the data of the edge points are taken for refined calculation to ensure the accuracy of the strain distribution. The side walls of the car are supported by the guide rails, and the strain values are extracted near the guide rail connection points to analyze the local stress conditions. The strain data is stored in an EXCEL table, and each row records the coordinates, strain values, and locations of a node. For the welded parts, local refined analysis is carried out, and high-precision grids are divided to ensure the accuracy of the strain gradient. After the data storage is completed, a strain distribution contour map is generated using Matplotlib and compared with the static load distribution and stress distribution data to determine the high-strain areas and provide data support for subsequent yield analysis.

[0063] Step S164: Determine the material yield degree according to the strain degree;

[0064] In this embodiment, the strain data is read and compared with the yield limit of the material. The yield strain of the Q235B material of the car body bottom plate is set to 0.0011, and the yield strain of the aluminum alloy side walls is set to 0.0025. For the strain value of each node, it is judged whether it exceeds the yield limit of the corresponding material. If it exceeds the yield limit, the node enters the plastic deformation stage; if not, it is still in the elastic deformation stage. For the plastic deformation area, its distribution is further analyzed to determine the boundary of the yield area. For the welded parts, especially the weld area where the bottom plate is connected to the side walls, local yield analysis is carried out, and the maximum stress position of the weld is calculated to ensure that the weld strength will not fail due to yield. All yield analysis data is stored in a database table, and each data record contains the coordinates, strain values, and yield status of a node. A yield distribution map is generated and the range of the yield area is marked for subsequent safety factor calculation.

[0065] Step S165: Calculate the safety factor based on the material yield degree;

[0066] In this embodiment, based on the material yield degree, the stress value of the largest yield area is extracted. According to industry standards, the safety factor of the car body bottom plate is set not to be lower than 1.5, the safety factor of the side wall structure is set not to be lower than 1.3, and the safety factor of the weld area is set not to be lower than 1.2. For the yield situation of each node, its safety factor is calculated and compared with the set standard. If the safety factor is lower than the threshold value, it is marked as a potential safety hazard area and the specific location is recorded. For the bottom plate support part, local grid encryption analysis is adopted to ensure the accuracy of the safety factor calculation. For the side wall stress area, comprehensive analysis is carried out in combination with the guide rail support force to ensure the rationality of the safety factor calculation. All safety factor data is stored in the database, and a safety factor distribution curve is generated for subsequent evaluation of the car body structure strength.

[0067] Step S166: Evaluate the car structure strength based on the safety factor.

[0068] In this embodiment, based on the safety factor, the car floor, side walls, frame, and welding parts are analyzed in regions. The minimum safety factor of the car floor is set to 1.5, the minimum safety factor of the side wall structure is set to 1.3, and the minimum safety factor of the weld area is set to 1.2. During the data analysis process, the safety factor values of all calculation nodes are traversed, and the areas below the set standard are screened out to generate a list of potential safety hazard locations, listing the node coordinates, corresponding safety factors, material types, and structural parts that exceed the allowable stress range. When analyzing the low safety factor regions, combined with the static load distribution data, stress data, and strain data, the main factors that may lead to insufficient structural strength are identified. For the case where the safety factor of the car floor is lower than 1.5, first analyze the force mode of the floor, including the car's own weight, passenger load, impact load transmitted by the buffer device, etc., to determine whether there are local stress concentrations or local material weaknesses. If the load-bearing capacity of the floor is insufficient, the floor structure is optimized by adding local stiffeners. Specific measures include adding stiffeners along the longitudinal or transverse direction of the car in the stress concentration area, setting the thickness of the stiffeners to be between 3 mm and 5 mm, and controlling the spacing of the stiffeners within the range of 100 mm to 200 mm. The finite element analysis is used to verify the improvement effect of the stiffeners on the floor structure strength. If the analysis results show that there is stress concentration in the welding part, the weld layout is adjusted or the welding process is optimized. For example, the double-sided welding method is used to improve the weld strength, and the welding sequence is adjusted to reduce the welding residual stress. For the case where the safety factor of the side wall structure is lower than 1.3, first analyze the force condition of the side wall, including the vertical load distribution of the traction system, the guide rail support force, and the rigidity characteristics of the side wall, to determine whether there are local weak areas. If the side wall has stress concentration due to the guide rail support method, the guide rail support arrangement is adjusted to make the support points evenly distributed and reduce the excessive single-point force, or the anti-deformation ability of the side wall is improved by adding local strengthening structures to the side wall. For the case where the safety factor of the welding part is lower than 1.2, first analyze the stress concentration in the welding area to determine whether the maximum stress point of the weld exceeds the yield limit of the weld material. If the maximum force of the weld exceeds its fatigue limit, a higher-strength welding material is used, such as replacing the ordinary carbon steel electrode with a high-strength low-hydrogen electrode, and the weld strength is re-evaluated. At the same time, the welding parameters are optimized. For example, the welding current is controlled within the range of 120 A to 150 A to ensure that the welding penetration reaches more than 3 mm to improve the load-bearing capacity of the weld. After all the optimization measures are completed, the static load simulation is carried out again, and the safety factor data of the car structure is updated to ensure that the safety factors of all structural parts meet the set standard. The optimized safety factor data is compared with the original data to generate a car structure strength assessment report, which is stored in PDF format. The report content includes the safety factor distribution map, description of the optimization plan, comparison table of data before and after optimization, and verification results of the finite element analysis after optimization to ensure the effectiveness of the optimization plan.

[0069] Preferably, the evaluation of welding defects in step S2 includes:

[0070] Identifying low-strength car body structure areas of three-dimensional elevator components based on the car body structure strength;

[0071] In this embodiment, stress distribution data of key parts of the car body is obtained, and the data sources include finite element simulation calculations, strain gauge measurements, and dynamic load tests. The finite element simulation calculation is based on the three-dimensional geometric model of the car body. A simulation analysis software is used to calculate the static and dynamic loads of the car body structure. The mesh division unit size is set to 2 mm, and the elastic modulus of high-strength steel Q345B used in the car body, 2.0×10 5 MPa, Poisson's ratio of 0.3, and yield strength of 345 MPa are input. The boundary conditions are set as fixed supports at the four corners of the car body to simulate the stress state of the car body under different load conditions, and the maximum principal stress distribution, maximum shear stress distribution, and equivalent stress distribution are calculated. Strain gauge measurements are carried out by pasting strain gauges at key parts such as the car body floor, side walls, and top support structures. 6 measuring points are arranged in each area, the data acquisition frequency is set to 1000 Hz, the strain data of the car body structure during operation is recorded, and converted into stress data, which is compared with the results of finite element simulation calculations to calibrate the calculation accuracy. The safety factor for the strength evaluation of the car body floor is set to not less than 1.5, that is, the equivalent stress shall not exceed 1 / 1.5 times the yield strength of the material. The safety factor for the side wall area is set to not less than 1.3, and the safety factor for the top support structure is set to not less than 1.2. Based on the car body structure strength data, areas that do not meet the above safety factor requirements are screened, and the low-strength areas are classified according to the coordinate range, area, main stress direction, and connection method to form a low-strength car body structure area data table, and the actual area of the low-strength car body structure is obtained accordingly.

[0072] Clean the surface of the low-strength car body structure area to obtain a clean car body structure area;

[0073] In this embodiment, a degreaser (such as a NaOH solution of an alkaline cleaning agent with a mass fraction of 5%) is used for degreasing treatment. The spraying method is low-pressure spraying (spraying pressure: 0.15 MPa), the nozzle diameter is 0.8 mm, and the spraying time is set to 10 s to ensure that the cleaning liquid can fully cover the surface of the low-strength area. After cleaning, it is left standing for 2 minutes to dissolve the oil stain, and then rinsed with deionized water (conductivity ≤ 5 μS / cm). The rinsing water flow rate is set to 5 L / min, and the water temperature is controlled at 40°C - 50°C to remove the residual cleaning agent on the surface. After the rinsing is completed, a non-woven fabric is dipped in anhydrous ethanol with a mass fraction of not less than 99% to wipe the cleaned low-strength car body structure area twice to remove the tiny residues on the surface and ensure the surface of the area is dry. Subsequently, it is dried with high-pressure air flow (pressure: 0.3 MPa, nozzle distance: 100 mm), and the drying time is set to 30 s to ensure that there is no residual liquid and grease in the cleaned area. After the cleaning is completed, a surface cleanliness tester (such as an FTIR spectrometer) is used for residual detection to ensure that the surface cleanliness reaches Ra ≤ 0.8 μm, and the cleaned car body structure area is obtained.

[0074] Apply a crack penetrant to the cleaned car body structure area to obtain penetrant coating data;

[0075] In this embodiment, when applying a crack penetrant to the cleaned car body structure area, a fluorescent penetrant (meeting the GB / T 18851.2-2005 standard) is selected. The type of penetrant is a water-washable fluorescent penetrant, and the viscosity is set to 5 - 10 cSt to ensure that it can penetrate into fine cracks. The coating method is low-pressure spraying, and a spray gun (nozzle diameter: 0.5 mm, spraying pressure: 0.2 MPa) is selected for the spraying equipment. The spraying angle is set to 45°, and the spraying distance is 150 mm - 200 mm to ensure that the penetrant can evenly cover the entire cleaned area, and the penetrant film thickness is controlled between 0.05 mm and 0.1 mm. After coating, it is left standing for 10 minutes. During this period, the ambient temperature is controlled at 25°C ± 2°C, and the humidity is controlled at 50% - 60% to ensure that the penetrant can fully enter the cracks or micropores. After the penetration time ends, use deionized water (spraying pressure: 0.1 MPa, spraying time: 30 s) to remove the excess penetrant on the surface to avoid affecting the subsequent imaging effect. The penetration data record includes information such as spraying parameters, penetrant thickness, and penetration time to form penetrant coating data.

[0076] Based on the penetrant coating data, apply a defect developer to generate defect imaging data;

[0077] In this embodiment, a dry developer is prepared, and the particle size of the imaging powder is set to not exceed 50 nm to ensure that the developer can be evenly distributed and cover the cracks or micropore areas where penetrant residues are present. The developer is sprayed by electrostatic spraying. A spray gun equipped with a high-voltage electrostatic generator is selected as the spraying device, and the spraying voltage is set to 30 kV to ensure that the imaging powder can be adsorbed on the detection surface and avoid uneven distribution of the developer caused by gravity or air flow. The spraying distance is controlled within 200 mm - 250 mm, and the moving speed of the spray gun during spraying is set to 200 mm / s to ensure that the developer adheres evenly to the surface, and the film thickness of the developer is controlled within the range of 0.02 mm - 0.05 mm. After the developer is coated, wait for 5 minutes to allow it to fully adsorb and stabilize. At the same time, ensure that the ambient temperature is maintained at 25°C ± 2°C and the humidity is maintained at 50% - 60% to avoid the developer being affected by high air humidity and becoming ineffective. Subsequently, the coated area is irradiated with ultraviolet light. The wavelength of the ultraviolet light is set to 365 nm, the power of the light source is set to 5 W, and the irradiation distance is controlled within 150 mm - 200 mm. The irradiation time of the ultraviolet light is set to 15 s, and the irradiation angle is adjusted to 90° to ensure that the light source evenly irradiates the entire detection surface and causes a fluorescence reaction of the penetrant at the cracks or micropores. The fluorescence intensity is collected by a high-resolution CCD camera. The resolution of the camera is set to 1920×1080, the exposure time is set to 50 ms, and automatic gain adjustment is performed to ensure that the fluorescence image is clearly visible. The collected imaging image data is stored, and background noise is removed through an image preprocessing algorithm to form defect imaging data.

[0078] Based on the defect imaging data, the crack morphology is identified to obtain crack defect data;

[0079] In this embodiment, image processing is performed on the defect imaging data. The Gaussian filtering algorithm is used to perform noise reduction processing on the imaging image, and the size of the filtering window is set to 5×5 pixels to remove high-frequency noise interference. Subsequently, the Canny edge detection algorithm is used to extract the crack boundary, and the gradient threshold of the Canny operator is set to (50, 150) to ensure that the crack edge is clearly visible. To further enhance the characteristics of the crack area, morphological dilation and erosion operations are used to optimize the crack area. The size of the dilation kernel is set to 3×3 pixels, and the size of the erosion kernel is set to 3×3 pixels to maintain the integrity of the crack morphology. After the crack identification is completed, the connected component analysis technique is used to mark the crack area, and the starting point, ending point, maximum width, maximum length of the crack, and whether there are branches in the crack are analyzed. The detection accuracy of the minimum width of the crack is set to 0.01 mm, and the resolution of the crack length is set to 0.1 mm. The backbone information of the crack is extracted by calculating the skeleton structure of the crack, and the crack growth direction is recorded. Finally, the identified crack defect data is stored in coordinate format, including key information such as the starting point, ending point, width, length, and branching situation of the crack, ensuring that the data can be used for subsequent crack propagation analysis.

[0080] Identify the pore morphology based on the defect imaging data to obtain pore defect data;

[0081] In this embodiment, image segmentation is performed on the defect imaging data, and the adaptive threshold method is used to segment the pore region, so that the brightness value of the pore region forms a contrast with the background region. Then, morphological opening operation is used to remove isolated noise points, and the size of the opening operation kernel is set to 5×5 pixels to ensure that smaller artifacts do not affect the pore recognition result. After the pore region segmentation is completed, the connected component analysis method is used to label all independent pore regions, and the area, diameter, and position coordinates of the pores are extracted. The minimum detection resolution of the pore diameter is set to 0.05 mm. To ensure the measurement accuracy, an X-ray detection device is used to measure the pore depth. The X-ray source power is set to 100 kV, the detection accuracy is set to 0.1 mm, and the scanning interval is set to 0.2 mm to obtain the depth information of the pores. All the identified pore data are stored in coordinate format, and the diameter, depth, and distribution of the pores are recorded to form pore defect data.

[0082] Conduct crack propagation assessment based on the crack defect data to obtain crack propagation data;

[0083] In this embodiment, the Paris crack propagation equation is used to calculate the crack propagation rate, and the stress intensity factor range (ΔK) of the crack is used to estimate the crack propagation speed. The stress intensity factor range ΔK is calculated based on the crack geometry, the cyclic load stress of the material, and the current length of the crack. In this assessment, C and m are crack propagation material constants, which are set to 2.2×10^-12 and 3.0 respectively, Y is the geometric factor, and its value is selected according to the crack morphology, usually between 0.7 - 1.2. The cyclic load stress σ is set to 150 MPa, and the current length a of the crack is measured in millimeters. Then, the crack propagation is calculated with a step size of 0.05 mm, and the number of calculation steps is set to 100 steps to simulate the crack propagation behavior in future cycles. In addition, the finite element simulation method is used to analyze the crack propagation. The simulation load condition is 3 million cycles of cyclic load, and the mesh division accuracy is set to 0.2 mm to ensure the accuracy of the calculation results and the simulation accuracy of the crack propagation path. The propagation range of the crack under the set load is obtained through simulation, thereby generating crack propagation data, and finally providing a prediction of the crack propagation trend, providing a basis for structural safety assessment and preventing crack failure.

[0084] Identify the pore defect location based on the pore defect data;

[0085] In this embodiment, three-dimensional coordinate transformation is performed on the pore data to ensure that the pore data on different detection surfaces are unified into the same coordinate system. Then, the DBSCAN clustering algorithm is used to perform clustering analysis on the pore data to calculate the dense area of the pores. The neighborhood radius of the DBSCAN algorithm is set to 2 mm, and the minimum number of points is set to 5 to ensure that high-density pore aggregation areas can be identified. Subsequently, the average pore spacing is calculated. If the pore spacing in a certain area is less than 2 mm, then this area is marked as a high-risk area, and the three-dimensional coordinate information of this area is recorded to form pore defect position data.

[0086] Integrate the crack propagation data and the pore defect positions to obtain the welding defect data.

[0087] In this embodiment, cross-analysis is performed on the crack propagation data and the pore defect position data to determine whether the crack propagation path overlaps with the high-density pore area. By calculating the crack propagation path and comparing it with the pore position data, if it is found that the crack propagation path passes through the high-density pore area, then this area is marked as a high-risk welding defect area. During this process, the crack propagation data includes the crack propagation direction, propagation rate, and predicted propagation end point. These information will be combined with the pore distribution situation, and the pore distribution situation records the spatial distribution characteristics of the pores and the high-density aggregation areas of the pores. Then, the high-risk areas are marked through the spatial coordinate system, and the welding defect areas that may cause structural failure are marked out. Finally, the welding defect data includes the crack defect propagation trend, pore distribution situation, and high-risk area coordinates. These data provide a basis for optimizing the welding process, helping to adjust the welding process, thereby improving the welding quality and safety of the car structure.

[0088] Preferably, the identification of the weld weak area in step S2 includes:

[0089] Calibrate the welding defect positions according to the welding defect data;

[0090] In this embodiment, through the integrated analysis of the welding defect data, the spatial coordinates and crack propagation paths of each welding defect are obtained. Use CAD software to perform three-dimensional modeling on the car structure, match the welding defect data with the welding joint positions in the structure model, and determine the precise positions of each welding defect. By analyzing information such as the crack direction, length, and depth in the welding defect data, combined with the actual geometric shape of the welding joint, the position data of all welding defects are finally extracted, and a welding defect position map is generated. The extraction of the welding defect positions provides a basis for subsequent welding process analysis.

[0091] Extract the welding elevator joint information based on the elevator structure drawings, and construct a welding joint model according to the welding elevator joint information;

[0092] In this embodiment, by digitally processing the elevator structure drawings, the welded joint parts in the drawings are extracted. Image processing technology is used to perform edge detection, morphological processing, and feature extraction on the drawings to identify the geometric shapes and dimensional data of the welded joints. By comparing with engineering standards, the accuracy of joint information extraction is ensured. On this basis, a welded joint model is established, and 3D modeling software is used to model the joint, considering material properties and connection methods, to generate a complete welded joint model. This model will serve as the basis for subsequent analysis.

[0093] Identify the geometric profile of the welded joint of the welded joint model;

[0094] In this embodiment, the welded joint model is refined. The contour line of the welded joint is extracted using geometric modeling software to determine the boundary of the joint and the weld area. By accurately measuring the geometric dimensions of each part in the model, such as the radius, length, thickness, etc. of the joint, the geometric features of the welded joint are obtained. Ensure that the geometric data of the welded joint conforms to the design specifications, and record all relevant dimensional parameters to form the geometric profile data of the welded joint.

[0095] Determine the weld area based on the position of the welding defect on the geometric profile of the welded joint to obtain the weld area;

[0096] In this embodiment, the welding defect position data is mapped onto the geometric profile of the welded joint to ensure a one-to-one correspondence between the welding defects and the welding area. Combining the shape characteristics of the welded joint model, the boundaries and distributions of the welds are determined to generate weld area data. The determination of the weld area is mainly based on the geometric shape of the welded joint and the welding process requirements. All welding defects within the weld area need to be further analyzed mechanically.

[0097] Calculate the tensile stress based on the weld area;

[0098] In this embodiment, stress simulation of the weld area is performed by the finite element analysis method. According to the geometric shape and material properties of the weld, boundary conditions and load conditions are set, and the tensile stress within the weld area is calculated. The load conditions are obtained from experimental data and set to 150 MPa to simulate the stress field that may occur during the welding process. The calculation results will give the stress distribution of different points within the weld area and provide basic data for subsequent stress analysis.

[0099] Map the tensile stress to the weld area and generate a weld stress distribution diagram; Identify the weld stress concentration area based on the weld stress distribution diagram;

[0100] In this embodiment, the calculated tensile stress value is applied to the three-dimensional model of the weld area and mapped to each grid node. By means of color coding, the areas corresponding to different stress values are marked with different colors to generate a weld stress distribution map. The color change in the figure will reflect the high and low distribution of stress and clearly mark the stress concentration areas. This figure provides an intuitive basis for subsequent identification of the weld stress concentration areas. A stress threshold is set. According to the yield strength of 345 MPa and the fatigue limit of 120 MPa of Q345B material, the critical stress threshold for identifying the stress concentration area is set to 0.8 times the yield strength, that is, 276 MPa, as the standard for judging the area with excessive stress. The stress concentration degree algorithm is adopted. By calculating the stress gradient change rate of each grid node, the stress concentration degree is defined as K = σ_max / σ_nom, where σ_max is the local maximum stress value and σ_nom is the nominal stress value of this weld area. σ_nom in the weld area is obtained by average calculation. The area where K>1.5 is set as the stress concentration area, and the grid points with stress greater than 276 MPa are further screened. The identified stress concentration areas are analyzed in detail, and the high-stress points with an area less than 0.5 mm 2 are removed to avoid misjudgment of local stress peaks caused by finite element mesh division errors. Finally, the areas meeting the conditions are marked as the weld stress concentration areas, and the spatial coordinates, maximum stress values and the weld area numbers where each stress concentration point is located are recorded to form a stress concentration area data table, providing a basis for subsequent welding optimization and crack propagation analysis.

[0101] Based on the weld area, plastic deformation simulation is carried out, in which the welding speed is set to 1 - 10 mm and the welding electrode diameter is set to 1.6 - 5.0 mm to generate plastic deformation data;

[0102] In this embodiment, the key parameters in the welding process are set. For example, the welding speed is set to 1 - 10 mm and the welding electrode diameter is set to 1.6 - 5.0 mm. The temperature field, stress field and plastic deformation process in the weld area during the welding process are simulated by simulation software. Through a reasonable heat source model and stress field distribution, the simulation model calculates the plastic deformation situation in the weld area at each moment during the welding process. After the welding process simulation is completed, the generated plastic deformation data is used to describe the deformation characteristics of the welding area.

[0103] The plastic deformation data is mapped to the weld area and a plastic deformation distribution map is generated; the high plastic deformation areas are identified based on the plastic deformation distribution map;

[0104] In this embodiment, the simulated plastic deformation amount is mapped into the three-dimensional model of the weld area, the plastic deformation degree of each point is calculated, and different deformation amounts are marked with different colors by means of color coding. The generated plastic deformation distribution map shows the deformation conditions experienced by the weld area during the welding process, providing data support for subsequent deformation control. A plastic deformation threshold is set. Based on the fracture elongation rate of 24% and the yield strain of 0.002 of Q345B material, the identification threshold for the high plastic deformation area is set as the area where the total strain exceeds 0.012, which is 6 times the yield strain, to ensure that the parts where fatigue failure may occur are covered. The Von Mises strain criterion is used to calculate the equivalent plastic strain of each grid point in the weld area and generate plastic deformation distribution data. Using the finite element analysis method, interpolation calculation is carried out on the plastic strain distribution of the weld area to ensure the spatial continuity of the identified high plastic deformation area and eliminate isolated high plastic deformation points with an area less than 1 mm 2 to avoid misidentification caused by mesh division errors. By calculating the plastic deformation gradient, the plastic deformation mutation area is refined and analyzed, the area where the plastic deformation gradient is greater than 0.005 / mm is screened, and combined with the area where the plastic strain is greater than 0.012, the high plastic deformation area is finally obtained. Record the spatial coordinates, maximum plastic deformation value and its distribution range of each high plastic deformation area, and output the high plastic deformation area data table to provide data support for subsequent welding process adjustment and fatigue life prediction.

[0105] According to the weld stress concentration area and the high plastic deformation area, area intersection operation is performed to generate the weld weak area.

[0106] In this embodiment, the data of the weld stress concentration area and the high plastic deformation area are overlapped and analyzed to find the intersection part of the stress concentration area and the high plastic deformation area. These areas are usually the weak areas of the weld and are the parts most likely to have crack propagation or welding failure. Through this analysis, the welding weak area can be accurately calibrated, providing a basis for subsequent welding process optimization. Align the coordinates of the weld stress distribution map and the plastic deformation distribution map to ensure that the two sets of data are calculated in the same three-dimensional coordinate system. Using the Boolean intersection operation method, the spatial data of the weld stress concentration area and the data of the high plastic deformation area are superimposed and analyzed to extract the intersection area of the two. The data of the weld stress concentration area consists of the area where the stress exceeds 180 MPa, and the data of the high plastic deformation area consists of the area where the plastic strain exceeds 0.012. The result of the intersection operation is the area that satisfies both of these two conditions. Using the three-dimensional grid mapping technology, the grid of the intersection area is refined to ensure that the spatial accuracy of the intersection area reaches 0.2 mm to eliminate the identification error caused by the grid division difference. Further, using morphological dilation and erosion operations, connectivity analysis is carried out on the intersection area to eliminate isolated areas with an area less than 2 mm2 Discrete points, and merge adjacent intersection regions with a distance less than 1 mm to ensure the coherence and accuracy of the welding weak areas. Finally, output the three-dimensional coordinates, area, maximum stress value, and maximum plastic deformation value of the welding weak areas, and visually display the welding weak areas in the form of a three-dimensional heat map, providing an accurate reference basis for subsequent welding parameter optimization, structural reinforcement, and fatigue life prediction.

[0107] Preferably, determining the wear amount of the car structure in step S2 includes:

[0108] Calculate the contact pressure based on the welding weak areas;

[0109] In this embodiment, the welding weak areas are discretized into grids to ensure that the calculation accuracy reaches a spatial resolution of 0.2 mm, and the finite element analysis method is used to calculate the contact stress of the welding weak areas. Set the material parameters, where the material of the welding area is Q345B, the elastic modulus is taken as 2.06×10 5 MPa, and the Poisson's ratio is set to 0.3. The loading conditions are set according to the car weight and operating conditions. The self-weight of the car is taken as 1500 kg, considering the rated load of 1000 kg, the total weight is calculated as 2500 kg, and the applied vertical load is 25000 N. The Hertz contact theory is used to calculate the contact interface, and the average contact pressure of the contact area is calculated by P = F / A, where F is the local normal force and A is the contact area. Since the contact area is not an ideal rigid interface, a non-linear contact algorithm is adopted, the maximum contact pressure threshold is set to 120 MPa, and the iterative calculation method is used to solve the local contact pressure point by point, and finally the contact pressure distribution of the welding weak areas is output.

[0110] Identify the contact normal force according to the contact pressure;

[0111] In this embodiment, integral calculation is performed on the calculated contact pressure data to obtain the total contact normal force within the welding area. First, calculate the local normal force F = P×A on each welding grid unit, where P is the contact pressure of the unit and A is the area of the unit. The area calculation is based on triangular or quadrilateral element division, and the unit area resolution is set to 0.04 mm 2 . During the calculation of the contact normal force, to avoid the influence of local abnormal values on the overall calculation, an abnormal rejection rule is set. When the contact pressure of a certain unit exceeds 150 MPa or is lower than 1 MPa, this data is rejected and not included in the calculation of the total normal force. Finally, the local normal forces of all valid units are accumulated to obtain the total contact normal force of the welding weak areas, and the calculation results are stored in a three-dimensional space coordinate point set for subsequent analysis.

[0112] Calculate the total normal load based on the contact normal force;

[0113] In this embodiment, the forward force is calculated in sub-regions in combination with the car support structure. The car support is distributed in a four-point support manner, and each support point bears a different forward load. According to the contact forward force data calculated in the previous step, the total contact forward force in the weld area is distributed to each support point according to the force distribution law. The calculation formula is F_total = ΣF_i, where F_i is the forward force on each grid unit in the weld area. Set the force distribution ratio of the support points. The front support points bear 40% of the total load, and the rear support points bear 60% of the total load. In the specific calculation process, the forward load of the front support points is calculated as F1 = 0.4 × F_total, and the forward load of the rear support points is calculated as F2 = 0.6 × F_total. Finally, the total forward load in the weld area is obtained, and a load distribution diagram is established to visually display the load distribution.

[0114] Calculate the frictional force according to the total amount of the forward load; identify the car running trajectory based on the car static load data; perform relative slip determination according to the frictional force and the car running trajectory to obtain relative slip data;

[0115] In this embodiment, when calculating the frictional force according to the total forward load, the Coulomb friction law is used to calculate the magnitude of the frictional force. The calculation formula is F_friction = μ × F_normal, where μ is the friction coefficient and F_normal is the forward load. The friction coefficient depends on the surface roughness of the weld and the material properties. According to the experimental data, the friction coefficient between the Q345B weld surface and the car guide rail is set to 0.15. When there are welding defects in the weld area, the local friction coefficient may increase. Therefore, in the calculation process, a method of setting the friction coefficient in sub-regions is adopted, and the friction coefficient in the defect area is increased to 0.18. Finally, the local frictional force of each weld unit is calculated and accumulated to obtain the total frictional force in the weld area. At the same time, a frictional force distribution map is generated to clarify the frictional force conditions at different parts of the weld. When identifying the car running trajectory based on the car static load data, first, the displacement data at different time points during the car running process are collected. The data is sourced from the car displacement sensor, and the sampling frequency is set to 100 Hz. The whole process trajectory of the car from rest to the maximum running speed of 1.5 m / s is recorded. Based on the displacement data, curve fitting is performed on the car movement trajectory. The cubic spline interpolation method is used to calculate the displacement curve at different time points, and the starting position, ending position of the car running, and the key displacement points on the entire running path are extracted. The car running trajectory data is stored as a two-dimensional time-displacement array, and the acceleration and speed change trends of the car are calculated in combination with the time information to provide support for subsequent friction slip analysis. When making a relative slip determination based on the frictional force and the car running trajectory, first, the slip displacement of the weld area is calculated. The slip displacement Δs = F_friction / (k × A), where F_friction is the frictional force in the weld area calculated in the previous step, k is the elastic stiffness coefficient of the weld material (taking a value of 2.5 × 10 5 N / mm 2 ), and A is the weld stress area. After calculating the slip displacements of different weld areas, a slip threshold is set. When the slip displacement of a certain area exceeds 0.02 mm, that area is marked as having relative slip. Finally, the relative slip data, including the magnitude of the slip displacement, the slip direction, and the weld coordinate points where slip occurs, are output and stored in the slip dataset.

[0116] Based on the relative slip data, weld wear is detected to obtain weld wear data;

[0117] In this embodiment, when detecting weld wear based on the relative slip data, the Archard wear equation W = K × (F_friction × s) / H is used to calculate the wear amount of the weld, where K is the material wear coefficient (taking a value of 1.2 × 10 -7 mm 3 / N·m), F_friction is the frictional force, s is the weld slip distance, and H is the hardness of the weld material (taking 250 HB). The point-by-point calculation method is used to calculate the wear amount at different positions of the weld and generate a weld wear distribution map. The area where the wear amount is greater than 0.02 mm 3 is marked as the key attention area.

[0118] Calculate the wear depth of the weld wear data; calculate the wear range of the weld wear data;

[0119] In this embodiment, the formula D = W / A is used to calculate the wear depth of the weld, where D is the wear depth of a local part of the weld, W is the wear volume of the weld on a certain unit, and A is the contact area of the surface of the weld of this unit. In the actual calculation process, the surface of the weld is discretized into uniform grid units. The area A of each grid unit is determined by the geometric characteristics of the weld. Usually, the finite element mesh division method is adopted, and the unit area resolution is set to 0.04 mm 2 , to ensure the calculation accuracy. First, the wear volume W of all weld grid units is extracted, and this data comes from the calculation result of the previous Archard wear equation. Subsequently, all grid units are traversed, and their wear depth D = W / A is calculated, ensuring that the units are unified during the calculation process, that is, the unit of W is mm 3 , and the unit of A is mm 2, so that the unit of the wear depth D is mm. To ensure the rationality of the calculation results, an outlier filtering mechanism is set. When the calculated result D of a certain unit exceeds the generally recognized limit wear depth of the material (for example, 0.2 mm) or is lower than the measurement error threshold (for example, 0.005 mm), the data is excluded and compensated with the mean value of the data of adjacent grid units to eliminate the influence of local abnormal data. After the calculation is completed, the wear depth data of all weld units is stored in a three-dimensional space data format, and a weld wear depth distribution map is visually generated. To further analyze the high wear areas, a wear depth threshold is set. When the wear depth D of a certain grid unit is greater than 0.05 mm, it is marked as a high wear area, and the coordinate information of these high wear areas is extracted for subsequent analysis. Finally, the wear depth data of each position of the weld is output, and the data is stored in a three-dimensional wear depth dataset to ensure that the data can be used for subsequent weld life assessment and weld optimization design. Based on the weld wear depth data of the previous step, a connected region analysis algorithm is used to identify all continuous wear areas. In this process, first, the connected component labeling algorithm (CCL) in image processing is used to classify the grid units on the weld surface, and the adjacent units with a wear depth exceeding the set threshold (0.05 mm) are classified into the same connected region. The 8-connected region method is adopted, that is, if a certain unit is connected to its left, right, up, down, upper left, lower left, upper right, and lower right units and all belong to the high wear area, it is classified into the same wear area. Subsequently, for each connected region, its boundary coordinates are calculated, and the contour point set of the wear area is obtained through a boundary tracing algorithm (such as Moore-Neighbor Tracing), and the area A_region of the region is calculated according to the boundary point set. The Gaussian area formula is used for area calculation to ensure the calculation accuracy. To eliminate non-significant wear areas, a wear range threshold A_threshold = 5 mm is set. 2 , that is, if the area A_region of a certain wear area is less than 5 mm 2 , then this area is ignored to avoid misjudging small-area wear areas as effective wear areas. For effective wear areas, their central coordinates, boundary coordinates, and area sizes are extracted and stored in the wear range dataset. Finally, the weld wear range data is output, including the three-dimensional space distribution, area size, and boundary coordinate information of the wear area, and a visual weld wear range map is generated for subsequent weld life analysis and welding optimization design.

[0120] Determine the wear amount of the car structure according to the wear depth and the wear range.

[0121] In this embodiment, when determining the wear amount of the car structure based on the wear depth and wear range, the volume integration method is used to calculate the weld wear volume V = ∫DdA, where D is the wear depth at each position of the weld and dA is the weld grid cell area. Integrating and calculating the entire weld area to obtain the total weld wear volume, and combining with the overall car structure data, converting the weld wear volume into the overall car wear amount, finally outputting the total wear data of the car structure, and generating a three-dimensional wear distribution visualization diagram to provide a basis for car welding maintenance.

[0122] Preferably, the prediction of the risk of the car floor fracture in step S2 includes:

[0123] Identifying the car floor structure based on the car structure;

[0124] In this embodiment, complete car structure data is extracted from the three-dimensional structure data of the elevator. The car structure data includes the top plate, side walls, floor, stiffeners, guide rail connection parts, etc. of the car. In order to accurately identify the car floor structure, the overall car structure data is projected onto the horizontal direction using the spatial coordinate projection method, and the three-dimensional contour of the car floor is extracted according to the geometric characteristics of the bottom boundary of the car. Using the three-dimensional point cloud filtering technology, the data of the non-floor part in the car structure is removed, and only the point cloud data related to the floor is retained. The RANSAC (Random Sample Consensus) plane fitting algorithm is used to fit the floor surface, removing irregular boundary points, and the precise outer contour of the floor is obtained through the boundary detection algorithm. On this basis, the floor surface is meshed, and the quadrilateral element division method is used to ensure that the mesh size is within the range of 2mm × 2mm to meet the subsequent calculation requirements. Finally, the complete three-dimensional data of the car floor is stored, and the geometric topology structure of the floor is generated for subsequent analysis and use.

[0125] Mapping the wear amount of the car structure to the car floor structure to generate a worn car floor structure;

[0126] In this embodiment, when mapping the wear amount of the car structure to the car floor structure, first call the car structure wear data calculated in the previous step, including wear depth, wear range, and spatial distribution information. Using the coordinate transformation method, convert the wear data from the overall car coordinate system to the local coordinate system of the car floor to ensure data alignment. Use the nearest neighbor interpolation method to interpolate the car structure wear data onto the car floor grid and smooth the wear data to eliminate the error caused by data discretization. During the interpolation process, for each grid cell on the floor, calculate its corresponding wear depth and store it in the floor wear matrix. Set the wear threshold \(T_w = 0.1\mathrm{mm}\), that is, when the wear depth of a certain cell is less than this threshold, it is regarded as a normal area and not marked; when the wear depth of a certain cell is greater than \(T_w\), it is marked as a worn area and the wear depth value is recorded. Finally, complete the wear mapping of the car floor and output the three-dimensional data of the worn car floor structure for subsequent analysis.

[0127] Identify the concentrated wear areas of the worn car floor structure;

[0128] In this embodiment, when identifying the concentrated wear areas of the worn car floor structure, perform connected region analysis based on the wear data. Binarize the wear depth matrix of the floor, and set the wear depth threshold \(T_c = 0.3\mathrm{mm}\), that is, when the wear depth of a certain area is greater than \(T_c\), it is defined as a worn area. Then, use the connected region detection algorithm to identify all continuous worn areas and calculate the area of each area. Set the concentrated wear area threshold \(A_c = 10\mathrm{mm}\) 2 , that is, when the area of a certain worn area is greater than \(10\mathrm{mm}\) 2 , it is marked as a concentrated wear area. Use the contour extraction algorithm (such as the Marching Squares algorithm) to extract the boundary coordinates of the concentrated wear area and calculate its geometric features, including area, shape factor, aspect ratio, etc. Finally, store the three-dimensional coordinate information of the concentrated wear area and generate a visualized wear area distribution map for fatigue damage analysis.

[0129] Calculate the fatigue damage based on the concentrated wear area to obtain the fatigue damage data;

[0130] In this embodiment, when calculating the fatigue damage based on the concentrated wear area, a mine fatigue damage model is used to calculate the fatigue damage amount of the car floor. The fatigue damage degree D of each wear area is calculated based on the Miner linear cumulative damage theory, and the formula D = ∑(n_i / N_i) is adopted, where n_i is the number of cycles at stress level i, and N_i is the fatigue life under the corresponding stress level. Using the weld stress distribution data and the car operation condition data, the stress cycle number of the wear area is counted, and the fatigue damage is calculated in combination with the S-N curve (stress-life curve). The fatigue damage threshold D_c = 0.7 is set, that is, when D of a certain area exceeds 0.7, it is marked as a high fatigue damage area. Finally, the fatigue damage data is generated and stored in the fatigue damage database for subsequent evaluation of the fracture toughness of the car floor.

[0131] Evaluate the fracture toughness of the car floor according to the fatigue damage data;

[0132] In this embodiment, when evaluating the fracture toughness of the car floor according to the fatigue damage data, the fracture toughness of the floor material is calculated based on the fracture mechanics method. Based on the stress data of the fatigue damage area, the stress intensity factor K of this area is calculated, and the formula K = Y·σ√πa is adopted, where Y is the geometric correction factor, σ is the stress, and a is the crack size. Using the material parameters of the car floor, the corresponding fracture toughness critical value K_IC is found and compared. The fracture toughness safety threshold T_f = 0.85K_IC is set, that is, when K of a certain area is greater than T_f, it is marked as a low toughness area. Combining the fatigue damage data, the fracture risk of the low toughness area is further evaluated, and the fracture toughness distribution data of the car floor is output to provide data support for subsequent fracture risk prediction.

[0133] Predict the fracture risk of the car floor based on the fracture toughness of the car floor.

[0134] In this embodiment, when predicting the fracture risk of the car floor based on the fracture toughness of the car floor, a crack propagation analysis method is used to calculate the crack propagation rate and predict the final crack propagation path. The Paris-Erdogan formula da / dN = C(ΔK)^m is used to calculate the crack propagation rate, where da / dN is the crack propagation rate, C and m are material constants, and ΔK is the stress intensity factor range. Combining the fatigue damage data of the car floor, the crack initiation point is counted, and the crack propagation length is calculated by numerical integration. The crack failure threshold L_f = 2mm is set, that is, when the crack propagation length of a certain area exceeds 2mm, it is determined that there is a fracture risk in this area and it is marked as a high fracture risk area. Finally, the fracture risk prediction data of the car floor is output, and a visual fracture risk distribution map is generated for the structural optimization design of the car floor.

[0135] Preferably, the welding optimization design in step S2 includes:

[0136] Identify high fracture risk areas based on the fracture risk of the car floor

[0137] In this embodiment, call the fracture risk prediction data of the car floor, including fracture toughness distribution, crack propagation path, and stress intensity factor (K value) distribution. Set the high fracture risk threshold Tr = 0.9KIC, where KIC is the critical fracture toughness value of the car floor material. When the stress intensity factor K of a certain area is greater than Tr, mark this area as a high fracture risk area. Use the crack propagation simulation method to calculate the final propagation length of the crack, and combine the Paris-Erdogan crack propagation equation to statistically analyze the crack growth rate in the high fracture risk area. Use the regional connectivity analysis method to perform connectivity detection on all high fracture risk areas, and calculate the area, boundary coordinates, and crack length of each area. Finally, output the spatial distribution data of the high fracture risk areas and store them in the high fracture risk area database for weld optimization design.

[0138] Detect the deformation amount based on the high fracture risk area and optimize the weld shape according to the deformation amount

[0139] In this embodiment, use the three-dimensional model of the elevator structure, pay special attention to the welded joint area, and use finite element analysis software such as ANSYS or ABAQUS to perform detailed simulations on this area. By applying working loads, vibration loads, temperature changes, etc. of the elevator, obtain the deformation amount data of each welded joint area. For high fracture risk areas, pay special attention to weld geometric features such as weld angle, width, and thickness, and obtain the deformation conditions of this area through simulation analysis. Next, optimize the weld shape according to the deformation amount data. By adjusting the weld angle range to 30° to 45°, increasing the weld transition fillet radius to more than 1.5 mm, and increasing the weld thickness to 1.2 times the original thickness, stress concentration can be reduced.

[0140] Calculate the welding thermal stress based on the high fracture risk area, and identify the thermal stress concentration area based on the welding thermal stress; optimize the welding sequence based on the thermal stress concentration area

[0141] In this embodiment, use the heat source model generated during the welding process, combine the welding process and material characteristics of the elevator structure, and use thermodynamic analysis tools to simulate the temperature field and thermal stress in the welding area. The temperature field simulation takes into account the heat input distribution during the welding process and the thermal expansion characteristics of the welding metal, and calculates the thermal stress in the welding area. Through stress analysis, identify the thermal stress concentration area and mark the high stress concentration area. Then, based on the thermal stress concentration area, optimize the welding sequence. The welding sequence is adjusted according to the thermal stress distribution to gradually and evenly release the thermal stress during the welding process and avoid excessive stress concentration.

[0142] Detect the strength of welding materials based on high fracture risk areas, and optimize the welding materials according to the strength of the welding materials;

[0143] In this embodiment, the mechanical properties of the welding materials are detected through tests, including tensile strength, yield strength, and elongation. A tensile test is carried out using material testing equipment such as a universal testing machine. Through the experimental data, the strength parameters of the welding materials are obtained. In high fracture risk areas, considering the relationship between the strength of these materials and the force and stress concentration in the elevator structure, corresponding welding materials are selected, such as welding materials with higher tensile strength and yield strength, and the composition or treatment process of the welding materials is adjusted according to the strength requirements to improve the overall strength of the welded joint.

[0144] Integrate the weld shape, welding sequence, and welding materials to obtain welding optimization design data.

[0145] In this embodiment, the optimized weld shape data, optimized welding sequence data, and optimized welding material data are called. Using a data fusion method, the three types of optimized data are matched to ensure the compatibility of the weld shape, welding sequence, and welding materials. Using a multi-objective optimization method, the comprehensive performance indicators of the welding optimization design scheme are calculated, including key parameters such as weld strength, fracture toughness, residual stress, and crack propagation rate. Set the welding optimization design goal, that is, ensure that the comprehensive performance indicators of the optimized weld meet the design requirements, so that the weld strength is increased by more than 10%, the fracture toughness is increased by more than 20%, the residual stress is reduced by more than 30%, and the crack propagation rate is reduced by more than 50%. Generate welding optimization design data and store it in the welding optimization database for use in welding process implementation and car floor structure optimization.

[0146] Preferably, step S3 is specifically as follows:

[0147] Step S31: Conduct a traction simulation according to the three-dimensional elevator components, where the traction power range is: 0.5 - 10 kW, and the wire rope tension range is: 500 - 5000 N, to obtain traction data;

[0148] In this embodiment, when constructing the traction simulation environment, the operating state of the traction system is determined based on the structural parameters and dynamic characteristics of the three-dimensional elevator components. The specific operations include loading the geometric structure data of the traction machine, traction wheel, wire rope, car, counterweight, and guide rail, and calculating the mass, moment of inertia, and friction coefficient of each component in combination with the material properties. The traction power of the traction machine is set within the range of 0.5 - 10 kW to ensure coverage of various operating conditions, and the tension range of the wire rope is set at 500 - 5000 N to match the mechanical characteristics under different load conditions. During the simulation process, numerical calculation methods are used to iteratively update the acceleration, speed, and displacement of the car, and the effects of motor torque, friction force, and traction force on the traction system are considered. First, the force on the car is calculated according to the input wire rope tension, and then the motion trajectory of the car is solved through numerical integration. To ensure calculation accuracy, the simulation time step is set to 0.001 seconds, the real-time position information of the car is calculated within each time step, and the key parameters of the traction system, such as wire rope tension, traction force, car acceleration, and motor power, are output. All data is stored as a time series for subsequent processing.

[0149] Step S32: Convert the traction data into a traction frequency-domain signal;

[0150] In this embodiment, the traction data is usually stored in the form of a time series, and each data point records the instantaneous operating state of the traction system. To extract its vibration characteristics, this data needs to be converted into a frequency-domain signal. First, data such as traction force, wire rope tension, and motor speed are extracted from the stored time series and discretely sampled. The sampling frequency is set to 10 kHz, that is, 10,000 data points are recorded per second to ensure coverage of high-frequency vibration components. Before signal processing, the data is processed with a window function to reduce the impact of spectral leakage on the analysis. Then, the fast Fourier transform (FFT) is used to calculate the spectral characteristics of the signal, and the main vibration frequencies and corresponding amplitude information are extracted. After the conversion is completed, the frequency-domain signal is stored as a frequency-amplitude look-up table and accompanied by phase information for subsequent noise analysis.

[0151] Step S33: Perform noise wavelet transform on the traction frequency-domain signal to obtain a noise signal;

[0152] In this embodiment, the traction frequency-domain signal contains multiple frequency components, and some of them may come from noise sources. To distinguish normal vibrations from noise interference, discrete wavelet transform (DWT) is used for signal decomposition. First, the Daubechies4 (db4) wavelet basis function is selected to ensure the accuracy of time-frequency analysis of the signal. Then, the traction frequency-domain signal is decomposed into multiple frequency bands, and each frequency band corresponds to vibration information in a different frequency range. During the decomposition process, the low-frequency components are smoothed to retain the main vibration characteristics, while the high-frequency components are subjected to noise suppression. The main characteristics of the noise signal are obvious in the high-frequency part. Therefore, a threshold is set for the high-frequency components, and the signals below this threshold are set to zero to remove unnecessary high-frequency interference. Subsequently, the signal is reconstructed through inverse wavelet transform (IDWT), and only the noise components are retained and stored as an independent data set for subsequent analysis.

[0153] Step S34: Calculate the noise intensity of the noise signal; calculate the noise frequency of the noise signal;

[0154] In this embodiment, when calculating the noise intensity of the noise signal, first, data preprocessing is performed on the noise signal, including removing the DC component to ensure the accuracy of the calculation. Then, the amplitude of each sampling point in the entire noise time series is squared to obtain the instantaneous energy, and the average value is taken within the entire sampling range, and then the square root is taken to obtain the root mean square value (RMS) of the signal to characterize the overall intensity of the noise signal. After the calculation is completed, the obtained noise intensity value is compared with the set noise intensity threshold. The threshold is set based on noise standards or experimental data. For example, the allowable environmental noise of conventional industrial equipment is set below 60 dB. If the noise intensity exceeds the set threshold, it is determined as abnormal noise, providing a basis for subsequent analysis. When calculating the noise frequency of the noise signal, short-time Fourier transform (STFT) is used to analyze the time-frequency distribution of the signal. First, an appropriate window function, such as the Hanning window, is selected to reduce spectral leakage, and a sliding window with a fixed length is set. The window length is usually set to 1024 points, and the window step is set to 512 points to ensure the balance of time-frequency resolution. Then, the window is gradually moved on the time axis, and the Fourier transform is performed on the signal within each window to obtain the spectral characteristics of the corresponding time period, and the energy distribution of each frequency component is calculated. Next, the energy distribution situation on the entire time-frequency plane is statistically analyzed, and the frequency component with the strongest energy is selected as the main noise frequency, and its amplitude and phase information are recorded. For multiple main noise frequencies, they are sorted according to the energy size, and the first three frequency components with the highest energy are selected for storage to comprehensively characterize the frequency characteristics of the noise signal. Finally, the noise intensity and noise frequency data are stored as a set of characteristic data, and the data format includes time tags, RMS values, main frequencies, and their corresponding energy values, ensuring that the data can be used for subsequent noise source identification analysis.

[0155] Step S35: Integrate the noise intensity and noise frequency to obtain noise characteristics; identify the noise source based on the preset bearing noise characteristics and the noise characteristics.

[0156] In this embodiment, when integrating the noise intensity and noise frequency, first extract the previously calculated noise signal characteristic data, including information such as amplitude, main frequency, energy distribution, time-frequency relationship, etc., and perform data formatting storage to ensure the standardization of subsequent analysis. The storage format of the noise characteristic data adopts a time series data structure, and the noise characteristics at each time point include the noise intensity (represented by the root mean square value RMS), the main noise frequency (the main energy frequency point), the noise frequency band distribution (the distribution of energy at different frequencies), and the variation trend of the noise signal amplitude, so as to facilitate subsequent comparison and analysis. The noise source identification is based on the bearing noise database. By comparing the current noise characteristic data with the bearing noise characteristics in the database, the possible source of the noise is judged. The bearing noise database contains the noise characteristics under various bearing operating states, such as rolling element faults, poor lubrication, bearing eccentricity, inner or outer ring wear, etc. The vibration spectrum characteristics corresponding to each state have been experimentally calibrated. For example, rolling element faults usually generate harmonic components between 100 Hz and 300 Hz, the high-frequency noise in the case of poor lubrication is usually distributed above 500 Hz, and bearing eccentricity faults will cause the harmonics of 1 to 3 times the rotation frequency to increase. In order to improve the accuracy of noise source identification, the dynamic time warping (DTW) algorithm is used to calculate the similarity between the current noise characteristics and various bearing noise characteristics in the database. The DTW algorithm calculates the optimal matching path between two time series based on the dynamic programming method, so as to measure the similarity in the morphology of different time series. In the calculation process, first convert the time-frequency characteristic data of the current noise signal into a two-dimensional matrix form, compare it point by point with the bearing noise characteristic matrices in the database, and calculate the similarity score. When the similarity is higher than 90%, then identify the bearing as the main noise source and further analyze the fault type. At the same time, record the position information of the faulty bearing, and store detailed data such as the fault type, noise frequency, energy distribution, etc., to provide data support for subsequent vibration isolation optimization design.

[0157] Step S36: Based on the noise source, perform vibration isolation optimization design to obtain vibration isolation optimization design data.

[0158] In this embodiment, the vibration isolation optimization design is adjusted around the stiffness, damping ratio and installation position of the vibration isolator to reduce the influence of the vibration generated by the noise source on the elevator structure. First, the stiffness of the vibration isolator is adjusted. The stiffness range is set between 100 - 1000 N / mm, and the stiffness value is selected based on the main vibration frequency of the noise source. For example, if the noise frequency is concentrated below 200 Hz, a vibration isolator with a lower stiffness is used to absorb low-frequency vibrations; if the noise frequency is higher than 500 Hz, a vibration isolator with a higher stiffness is used to prevent high-frequency resonance. During the actual implementation process, first screen the available vibration isolator models according to the noise frequency range, and test the damping characteristics of the vibration isolator at different vibration frequencies through a loading experiment to ensure the best vibration isolation performance within the target noise frequency band. The final set value of the stiffness needs to be calculated through the vibration transmissibility. By calculating the ratio of the natural frequency of the vibration isolation system to the target noise frequency, ensure that the system is in the effective vibration isolation range, that is, the natural frequency of the vibration isolator is at least 1.4 times lower than the noise frequency to achieve the best vibration attenuation effect. Secondly, optimize the damping ratio. The damping ratio range is set between 0.05 - 0.5 and adjusted according to the noise intensity. For example, when the noise intensity is higher than 70 dB, the damping ratio is set to 0.4 - 0.5 to accelerate the vibration attenuation; when the noise intensity is lower than 50 dB, the damping ratio is set to 0.05 - 0.2 to reduce the system rigidity change caused by over-damping. The optimization calculation of the damping ratio is based on the relationship curve between the damping ratio and the vibration transmissibility. First, measure the vibration amplitude change of the elevator structure at different frequencies, and calculate the transmissibility under different damping ratio conditions to ensure that high-damping materials, such as high-viscoelastic polymer layers or hydraulic dampers, are used in high-noise areas to effectively reduce the vibration energy propagation. When the target noise is mainly composed of medium and low-frequency components, the selection of the damper tends to be low-damping vibration isolators of rubber-based or composite materials to avoid the increase of the vibration natural frequency caused by excessive damping, thereby reducing the low-frequency vibration isolation effect. After the damping ratio adjustment is completed, verify the attenuation effect of the vibration isolation system within the target noise frequency band through an impact response experiment to ensure that the actual vibration energy attenuation rate reaches more than 60%. Determine the installation position of the vibration isolator. Calculate the modal parameters of the elevator structure through finite element analysis (FEA) and identify the parts with the highest vibration energy. For example, in the 1 - 5 order modal analysis, if the vibration energy ratio in the bearing seat area exceeds 70%, install the vibration isolator in this area to reduce the vibration transmission to the elevator guide rail and car. The calculation process of the modal analysis is based on the finite element model of the elevator structure. After inputting the material parameters, boundary conditions and external forces, calculate the vibration mode distribution under different modes, and extract the vibration displacement and stress energy of each structural unit, and select the area with concentrated vibration energy as the installation point of the vibration isolator. During the actual installation process, monitor the vibration acceleration at different positions through a vibration tester, and compare the vibration amplitude change before and after installation to ensure that the vibration isolator effectively reduces the vibration transmission in the target area.After the optimization design is completed, the vibration isolation optimization design data is stored as a standardized data set, including the stiffness of the vibration isolator, the damping ratio, the installation position, and the vibration data before and after optimization, so as to serve as the basis for the elevator system optimization and be used for subsequent adjustment and verification of the vibration isolation design.

[0159] Especially importantly, step S36 includes the following steps:

[0160] Step S361: Statistically analyze the noise vibration frequency based on the noise source data;

[0161] In this embodiment, a high-precision acceleration sensor is used to collect vibration signals, and the vibration time-domain waveform is recorded through a data acquisition system. The sampling frequency is set above 10 kHz to ensure that the main vibration components in the 0 - 5 kHz frequency band can be captured. Then, the fast Fourier transform (FFT) is used to perform spectral analysis on the vibration signals to obtain the frequency distribution of the vibration signals. By calculating the spectral energy distribution, the main frequency components with an energy ratio exceeding 80% are extracted and used as the noise vibration frequency data. If there are multiple peaks in the noise frequency, the top three frequencies with the highest amplitudes are selected as the characteristic frequencies, and their amplitudes and relative energy ratios are recorded.

[0162] Step S362: Determine the stiffness of the vibration isolator based on the noise vibration frequency;

[0163] In this embodiment, the noise vibration frequencies generated by different components during the elevator operation are measured, and the required vibration isolator stiffness values are determined according to the measured frequency ranges. The setting of the vibration isolator stiffness is based on the mass of the components directly connected to the noise source in the elevator structure and is adjusted in combination with the resonance characteristics. When the noise frequency is low (below 200 Hz), the stiffness of the vibration isolator is controlled at 100 - 500 N / mm to reduce the transmission effect of low-frequency vibrations. When the noise frequency is in the medium range (200 Hz - 500 Hz), the stiffness is set at 500 - 2000 N / mm to ensure the vibration isolation effect in this frequency range. When the noise frequency is high (above 500 Hz), the stiffness is set above 2000 N / mm to prevent high-frequency vibrations from propagating through the structure. Subsequently, under the rated load condition of the elevator, the deformation of the vibration isolator is calculated to ensure its adaptability under actual working conditions, and further, static and dynamic stiffness test methods are used to verify the matching of the set stiffness values, and finally, the vibration isolator stiffness parameters that meet the elevator operation requirements are determined.

[0164] Step S363: Determine the damping ratio of the vibration isolator based on the noise vibration frequency;

[0165] In this embodiment, based on the time-domain decay curve of the vibration signal, the logarithmic decay rate of the vibration signal is calculated, and the target damping ratio range is determined in combination with the vibration frequency. For the case where the noise vibration frequency is lower than 100 Hz, the damping ratio is set between 0.05 and 0.2 to reduce the low-frequency resonance effect; for the case where the noise vibration frequency is between 100 Hz and 500 Hz, the damping ratio is set between 0.2 and 0.4 to improve the energy decay ability; for the case where the noise vibration frequency is higher than 500 Hz, the damping ratio is set between 0.4 and 0.6 to reduce the high-frequency impact effect. Then, a damping material (such as rubber, viscoelastic material or hydraulic damper) is selected, the loss factor of the material is tested through dynamic mechanical analysis (DMA), and the actual damping ratio value is calculated, and finally the damping ratio parameter of the vibration isolator is determined.

[0166] Step S364: Analyze the installation position according to the stiffness of the vibration isolator and the damping ratio of the vibration isolator to obtain the installation position of the vibration isolator;

[0167] In this embodiment, finite element analysis (FEA) is used to perform modal calculations on the main structural components such as the elevator frame, guide rails, and traction machine, and the 1st to 5th order modal vibration modes are extracted. Then, the vibration energy distribution under each vibration mode is calculated, and the regions with the highest vibration energy are identified, and vibration isolators are preferentially arranged in these regions. For example, in the 1st order modal, if the vibration energy ratio in the elevator guide rail base region exceeds 70%, the vibration isolator is preferentially installed in this region; in the 3rd order modal, if the vibration energy peak value of the traction machine base reaches 85 dB, the installation position of the vibration isolator is adjusted to this place to reduce the influence of the vibration transmission path. Finally, in combination with the force condition of the elevator operation, the optimal installation position of the vibration isolator is comprehensively determined.

[0168] Step S365: Perform vibration attenuation simulation based on the installation position of the vibration isolator to obtain vibration attenuation simulation data;

[0169] In this embodiment, the vibration transmission path of the elevator structure is constructed in the simulation environment, including the connection relationship between the noise source, structural nodes, vibration isolators and the main components of the elevator. Then, based on the stiffness and damping ratio parameters of the vibration isolator, a dynamic equation is constructed, and the vibration response under different working conditions is calculated through numerical integration. For the static working condition, the static deformation of the vibration isolator under the rated load is calculated; for the dynamic working condition, the vibration decay curve of the vibration isolator during the elevator operation is calculated, and the decay ratio at the main vibration frequency is extracted. The simulation results include data such as vibration energy distribution diagrams, displacement-time curves, and frequency response curves for reference in subsequent structural optimization.

[0170] Step S366: Optimize the structure of the vibration isolator based on the vibration attenuation simulation data to obtain vibration isolation optimization design data.

[0171] In this embodiment, the goal of optimizing the vibration isolator structure is to adjust the shape, material, internal structure, and installation method of the vibration isolator to maximize the vibration attenuation effect. First, based on the vibration attenuation simulation data, analyze the attenuation ratio in different frequency ranges and identify whether the attenuation ability of the vibration isolator at a specific frequency meets the design requirements. For example, if the attenuation ratio in the frequency range of 100 Hz - 200 Hz is lower than 30%, then optimize the thickness of the internal damping layer of the vibration isolator to improve the energy absorption ability; if the attenuation ratio above 500 Hz is too high, resulting in an increase in system stiffness, then optimize the elastic layer material to reduce the influence of high-frequency resonance. Optimize the shape of the vibration isolator. Use finite element simulation (FEA) to calculate the stress distribution under different shapes and optimize the cross-sectional design of the vibration isolator. For example, in a shear-type vibration isolator, optimize the cylindrical cross-section to an elliptical cross-section to improve the lateral deformation ability; in a compression-type vibration isolator, optimize the support structure to improve the force uniformity. In addition, optimize the installation method of the vibration isolator and adjust the pre-tightening force and installation angle of the connection points. For example, in the guide rail connection area, use a flexible support structure to reduce vertical impact vibration; in the traction machine base area, use a bolt connection method to enhance lateral stability. Finally, store the optimized vibration isolator parameters as a standardized data set to form vibration isolation optimization design data for the actual application and adjustment of the elevator system.

[0172] Preferably, this specification also provides a three-dimensional simulation design system based on the elevator structure for performing the three-dimensional simulation design method based on the elevator structure as described above. This three-dimensional simulation design system based on the elevator structure includes:

[0173] A three-dimensional elevator component construction module for obtaining elevator structure drawings; constructing three-dimensional elevator components according to the elevator structure drawings; performing a static load simulation of the car based on the three-dimensional elevator components to obtain car static load data; detecting the structural strength of the car based on the car static load data;

[0174] A welding optimization design module for evaluating welding defects based on the car structural strength to generate welding defect data; identifying the weak areas of the weld based on the welding defect data; determining the wear amount of the car structure based on the weak areas of the weld; predicting the risk of car floor fracture based on the wear amount of the car structure; performing welding optimization design based on the risk of car floor fracture to obtain welding optimization design data;

[0175] A vibration isolation optimization design module for performing a traction simulation based on the three-dimensional elevator components to obtain traction data; identifying the noise source based on the traction data; performing vibration isolation optimization design based on the noise source to obtain vibration isolation optimization design data;

[0176] The elevator three-dimensional simulation model construction module is used to construct an elevator three-dimensional simulation model according to the welding optimization design data and the vibration isolation optimization design data, and conduct extreme condition tests to obtain extreme condition test data; optimize the elevator buffer structure based on the extreme condition test data to obtain elevator buffer optimization structure data.

Claims

1. A three-dimensional simulation design method based on an elevator structure, characterized in that, It includes the following steps: Step S1: Obtain the elevator structure drawings; construct a three-dimensional elevator component according to the elevator structure drawings; Conduct a car static load simulation based on the three-dimensional elevator component to obtain car static load data; detect the car structure strength based on the car static load data; Step S2: Evaluate welding defects based on the car structure strength to generate welding defect data; Identify the weld weak areas according to the welding defect data; determine the car structure wear amount based on the weld weak areas; predict the risk of car floor fracture according to the car structure wear amount; conduct welding optimization design based on the risk of car floor fracture to obtain welding optimization design data; Step S3: Conduct a traction simulation based on the three-dimensional elevator component to obtain traction data; Identify the noise sources based on the traction data; Conduct vibration isolation optimization design based on the noise sources to obtain vibration isolation optimization design data; Step S4: Construct an elevator three-dimensional simulation model according to the welding optimization design data and the vibration isolation optimization design data, and conduct extreme condition tests to obtain extreme condition test data; Optimize the elevator buffer structure based on the extreme condition test data to obtain elevator buffer optimization structure data.

2. The three-dimensional simulation design method based on the elevator structure according to claim 1, wherein Specifically, step S1 is as follows: Step S11: Obtain the elevator structure drawings; Step S12: Identify the car structure based on the elevator structure drawings, and construct a car component according to the car structure; Step S13: Identify the traction structure based on the elevator structure drawings, and construct a traction component according to the traction structure; Step S14: Integrate the car component and the traction component to obtain a three-dimensional elevator component; Step S15: Conduct a car static load simulation based on the three-dimensional elevator component to obtain car static load data; Step S16: Detect the car structure strength based on the car static load data.

3. The three-dimensional simulation design method based on the elevator structure according to claim 2, wherein Specifically, step S16 is as follows: Step S161: Identify the static load distribution based on the car static load data to obtain static load distribution data; Step S162: Calculate the stress according to the static load distribution data to obtain stress data; Step S163: Evaluate the strain degree based on the stress data; Step S164: Determine the material yield degree according to the strain degree; Step S165: Calculate the safety factor based on the material yield degree; Step S166: Evaluate the car structure strength according to the safety factor.

4. The three-dimensional simulation design method based on the elevator structure according to claim 1, characterized in that, The evaluation of welding defects in step S2 includes: Identify the low-strength car structure areas of the three-dimensional elevator component based on the car structure strength; Clean the surface of the low-strength car structure areas to obtain clean car structure areas; Apply a crack penetrant to the clean car structure areas to obtain penetrant coating data; Apply a defect developer based on the penetrant coating data to generate defect imaging data; Identify the crack morphology based on the defect imaging data to obtain crack defect data; Identify the pore morphology based on the defect imaging data to obtain pore defect data; Conduct a crack propagation evaluation according to the crack defect data to obtain crack propagation data; Identify the pore defect locations according to the pore defect data; Integrate the crack propagation data and the pore defect locations to obtain welding defect data.

5. The three-dimensional simulation design method based on the elevator structure according to claim 1, characterized in that The identification of the weld weak areas in step S2 includes: Calibrate the welding defect locations according to the welding defect data; Extract the welding elevator joint information based on the elevator structure drawings, and construct a welding joint model according to the welding elevator joint information; Identify the welding joint geometric profile of the welding joint model; Determine the weld area based on the welding defect location for the welding joint geometric profile to obtain the weld area; Calculate the tensile stress based on the weld area; Map the tensile stress to the weld area and generate a weld stress distribution map; Identify the weld stress concentration area based on the weld stress distribution map; Conduct plastic deformation simulation based on the weld area, where the welding speed is set to 1 - 10 mm and the welding electrode diameter is 1.6 - 5.0 mm to generate plastic deformation data; Map the plastic deformation data to the weld area and generate a plastic deformation distribution map; Identify the high plastic deformation area based on the plastic deformation distribution map; Perform an intersection operation on the weld stress concentration area and the high plastic deformation area to generate a weld weak area.

6. The three-dimensional simulation design method based on the elevator structure according to claim 1, characterized in that The determination of the car structure wear amount described in step S2 includes: Calculate the contact pressure based on the weld weak area; Identify the contact normal force according to the contact pressure; Calculate the total normal load based on the contact normal force; Calculate the frictional force according to the total normal load; Identify the car running trajectory based on the car static load data; Conduct relative slip determination based on the frictional force and the car running trajectory to obtain relative slip data; Detect weld wear based on the relative slip data to obtain weld wear data; Calculate the wear depth of the weld wear data; Calculate the wear range of the weld wear data; Determine the car structure wear amount according to the wear depth and the wear range.

7. The three-dimensional simulation design method based on the elevator structure according to claim 1, characterized in that, The prediction of the car floor fracture risk described in step S2 includes: Identify the car floor structure based on the car structure; Map the car structure wear amount to the car floor structure to generate a worn car floor structure; Identify the concentrated wear area of the worn car floor structure; Calculate the fatigue damage based on the concentrated wear area to obtain fatigue damage data; Evaluate the fracture toughness of the car floor according to the fatigue damage data; Predict the car floor fracture risk based on the fracture toughness of the car floor.

8. The three-dimensional simulation design method based on the elevator structure according to claim 1, characterized in that The welding optimization design described in step S2 includes: Identify the high fracture risk area based on the car floor fracture risk; Detect the deformation amount based on the high fracture risk area and optimize the weld shape according to the deformation amount; Calculate the welding thermal stress based on the high fracture risk area and identify the thermal stress concentration area based on the welding thermal stress; Optimize the welding sequence based on the thermal stress concentration area; Detect the strength of the welding material based on the high fracture risk area and optimize the welding material according to the welding material strength; Integrate the weld shape, welding sequence, and welding material to obtain welding optimization design data.

9. The three-dimensional simulation design method based on the elevator structure according to claim 1, characterized in that Step S3 is specifically: Step S31: Conduct traction simulation according to the three - dimensional elevator components, where the traction power range is: 0.5 - 10 kW, and the wire rope tension range is: 500 - 5000 N, to obtain traction simulation data; Step S32: Convert the traction simulation data into a traction frequency domain signal; Step S33: Perform noise wavelet transform on the traction frequency domain signal to obtain a noise signal; Step S34: Calculate the noise intensity of the noise signal; Calculate the noise frequency of the noise signal; Step S35: Integrate the noise intensity and the noise frequency to obtain noise characteristics; Identify the noise source based on the preset bearing noise characteristics and noise features; Step S36: Perform vibration isolation optimization design based on the noise source to obtain vibration isolation optimization design data.

10. A three-dimensional simulation design system based on an elevator structure, characterized in that, For implementing the three-dimensional simulation design method based on the elevator structure as claimed in claim 1, the three-dimensional simulation design system based on the elevator structure includes: A three-dimensional elevator component construction module, configured to obtain elevator structure drawings; construct three-dimensional elevator components according to the elevator structure drawings; perform static load simulation on the car based on the three-dimensional elevator components to obtain car static load data; detect the structural strength of the car based on the car static load data; A welding optimization design module, configured to evaluate welding defects based on the car structural strength to generate welding defect data; identify the weld weak areas according to the welding defect data; determine the car structure wear amount based on the weld weak areas; predict the risk of car floor fracture based on the car structure wear amount; perform welding optimization design based on the risk of car floor fracture to obtain welding optimization design data; A vibration isolation optimization design module, configured to perform traction simulation on the traction machine based on the three-dimensional elevator components to obtain traction data; identify the noise source based on the traction data; perform vibration isolation optimization design based on the noise source to obtain vibration isolation optimization design data; An elevator three-dimensional simulation model construction module, configured to construct an elevator three-dimensional simulation model according to the welding optimization design data and the vibration isolation optimization design data, and perform extreme condition tests to obtain extreme condition test data; optimize the elevator buffer structure based on the extreme condition test data to obtain elevator buffer optimization structure data.

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