A three-dimensional simulation design method and system based on elevator structure

Abstract

The present invention relates to the field of mechanical engineering technology, and in particular to a three-dimensional simulation design method and system based on an elevator structure. The method comprises the following steps: obtaining an elevator structure drawing; constructing a three-dimensional elevator assembly according to the elevator structure drawing; simulating a car static load according to the three-dimensional elevator assembly to obtain car static load data; detecting the car structure strength based on the car static load data; evaluating welding defects based on the car structure strength to generate welding defect data; identifying weld weak areas according to the welding defect data; determining the amount of car structure wear based on the weld weak areas; predicting the risk of car floor fracture according to the wear of the car structure; performing welding optimization design based on the risk of car floor fracture to obtain welding optimization design data; and performing traction simulation according to the three-dimensional elevator assembly to obtain traction data. The present invention improves the safety and stability of the elevator structure based on mechanical engineering technology, and significantly improves the design accuracy and efficiency.

Description

Technical Field

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

[0002] The structural features of an elevator primarily consist of a mechanical drive system, traction system, car and counterweight balancing system, guide rails, buffers, and control system. These components work together to ensure stable operation and safety. These components, including the mechanical drive system, cable traction, car and counterweight balancing system, and control system, ensure the elevator's vertical transportation function. Elevator structural design considers the rational layout and optimized design of key components such as the car, traction system, guide rails, and buffers to ensure stability and safety during operation. Traditional 3D elevator structural simulation design methods suffer from low computational efficiency and often rely on complex computational models and extensive numerical simulations, resulting in a slow calculation process. This consumes significant time and computing resources, especially for large-scale simulations. Accuracy is also a significant issue. Traditional 3D simulation methods can lack accuracy in stress, fatigue, and fracture risk predictions, impacting the reliability of design results and failing to ensure elevator structural safety. Traditional methods lack real-time optimization, often requiring multiple adjustments to the model and parameters. The optimization process is not intelligent or timely, reducing efficiency and leading to a disconnect between the design and actual operating conditions, failing to fully account for dynamic changes under varying operating conditions. Traditional design methods usually only focus on a single optimization objective, such as strength or noise, while modern elevator design needs to comprehensively consider multiple objectives, such as structural strength, durability, comfort and noise control. It cannot meet the needs of these multiple objectives and lacks 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 elevator structure to solve at least one of the above technical problems.

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

[0005] Step S1: Obtain an elevator structural drawing; construct a three-dimensional elevator component according to the elevator structural drawing; perform a car static load simulation based on the three-dimensional elevator component to obtain car static load data; and detect the car structural strength based on the car static load data.

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

[0007] Step S3: performing traction simulation based on the three-dimensional elevator assembly to obtain traction data; identifying noise sources based on the traction data; performing vibration isolation optimization design based on the noise sources to obtain vibration isolation optimization design data;

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

[0009] The present invention significantly improves the accuracy and efficiency of elevator design through a three-dimensional simulation design method based on the elevator structure. After obtaining the elevator structure drawings and constructing the three-dimensional elevator components, the static load simulation of the car is performed, providing accurate data support for the strength testing 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 static load data identifies structural weaknesses, especially the welded parts, and provides a basis for welding optimization design to ensure structural stability. By identifying welding defects and weak areas, the risk of wear and fracture of the car floor is predicted, the design is optimized in advance, equipment failures are reduced, and the life of the elevator is extended. Traction and traction simulation optimizes noise source identification and vibration isolation design, reduces operating noise, and improves passenger comfort. Based on extreme working condition test data, the elevator buffer structure is optimized to improve impact resistance and durability, ensuring stable performance under extreme working conditions. The overall optimized design improves the safety, stability and comfort of the elevator, balances multi-objective requirements, and solves the problems of low accuracy, slow efficiency, and incomplete optimization in traditional design methods.

[0010] Preferably, this specification also provides a three-dimensional simulation design system based on an elevator structure, which is used to execute the three-dimensional simulation design method based on an elevator structure as described above. The three-dimensional simulation design system based on an elevator structure includes:

[0011] A 3D elevator component construction module is used to obtain elevator structural drawings; construct 3D elevator components based on the elevator structural drawings; simulate the static load of the car based on the 3D elevator components to obtain the static load data of the car; and detect the structural strength of the car based on the static load data of the car;

[0012] The welding optimization design module is used to evaluate welding defects based on the strength of the car structure and generate welding defect data; identify weak weld areas based on the welding defect data; determine the amount of wear on the car structure based on the weak weld areas; predict the risk of car floor fracture based on the wear of the car structure; and perform 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 three-dimensional elevator components to obtain traction data; identify noise sources based on the traction data; and 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 based on welding optimization design data and vibration isolation optimization design data, and conduct extreme working condition tests to obtain extreme working condition test data; based on the extreme working condition test data, the elevator buffer structure is optimized to obtain elevator buffer optimization structure data.

[0015] The three-dimensional simulation design system based on elevator structure of the present invention can implement any one of the three-dimensional simulation design methods based on elevator structure of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the three-dimensional simulation design method based on elevator structure. The internal modules of the system cooperate with each other, thereby 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] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0017] Figure 1 This is a schematic diagram of the steps of a three-dimensional simulation design method based on an elevator structure according to the present invention;

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0020] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

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

[0022] Step S1: Obtain an elevator structural drawing; construct a three-dimensional elevator component according to the elevator structural drawing; perform a car static load simulation based on the three-dimensional elevator component to obtain car static load data; and detect the car structural strength based on the car static load data.

[0023] In this embodiment, detailed drawings of the elevator structure are obtained from the elevator manufacturer or designer. The drawings include the dimensions and material specifications of the elevator car, traction system, guide rail system, counterweight and door system. Then, based on the dimensional 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 must include each component of the car and the details of the mechanical connections. Next, a finite element analysis (FEA) software (such as ANSYS or Abaqus) is used to perform a static load simulation on the car, assuming that the elevator is fully loaded and at rest. The key parameters for the static load simulation of the car include the load weight (for example, the maximum load is set to 1000kg), the car's own weight, the acceleration of gravity (taken as 9.81m / s 2 ) etc. 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 load-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 250MPa) and the maximum allowable stress threshold (for example, set to 300MPa) will affect the calculation results. If the strength of any part exceeds the set threshold, it will be marked as a possible safety hazard.

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

[0025] In this embodiment, ultrasonic testing, combined with electromagnetic inspection technology, identifies defects in car welds and determines the area and type of weld defects. Based on the welding process manual, welding defect standards are set (for example, the threshold for unqualified weld depth is 0.5 mm), generating weld defect data. Based on this welding defect data, further analysis is performed to identify weak weld areas. Ultrasonic or X-ray testing is then applied to these weak areas to determine the potential damage risk of the welds. Based on the inspection results of these weak weld areas, the wear of the car structure is calculated. A wear analysis model is used to predict annual wear based on specific usage scenarios (such as elevator usage frequency and environmental conditions), for example, setting the annual wear to 0.2 mm. By calculating the relationship between wear and weld location, the fracture risk of the car floor is further predicted. If the wear exceeds the set fracture risk threshold (e.g., 2 mm), the system issues a warning and performs structural optimization. Finally, welding optimization is performed based on the fracture risk data, adjusting the weld material, welding process, and weld layout to generate optimized welding design data. This optimized design data is used for subsequent structural improvements.

[0026] Step S3: performing traction simulation based on the three-dimensional elevator assembly to obtain traction data; identifying noise sources based on the traction data; performing vibration isolation optimization design based on the noise sources to obtain vibration isolation optimization design data;

[0027] In this embodiment, a traction analysis is performed using a traction simulation tool (such as an elevator dynamics simulation tool in Matlab or Simulink) based on a three-dimensional elevator component model. This simulation uses input parameters such as the traction force, wire rope tension, and elevator acceleration during elevator operation. In this step, the traction motor power (e.g., 5 kW) and the traction sheave diameter (e.g., 0.5 m), as well as the load condition (full load of 1000 kg), are set. The traction force output and traction speed of the traction system are calculated. The simulation generates traction data covering parameters such as traction force, wire rope tension, and elevator speed under different operating conditions. Next, the simulation data is analyzed to identify noise sources during elevator operation. Noise sources are primarily caused by mechanical friction in the traction system, contact between the wire rope and the pulley, and aerodynamic effects during elevator operation. Spectral analysis is used to determine the frequency range and sound pressure level of the noise (e.g., setting the noise source frequency range to 50-200 Hz and the sound pressure level to 80 dB). Based on the noise source data, a vibration isolation system is designed and optimized for vibration isolation. The key to vibration isolation design is selecting appropriate isolation materials (such as high-density rubber pads and spring isolation devices) and installing appropriate isolation devices between the elevator base and the ground to reduce noise. During the design, a vibration isolation frequency response function is established to ensure that the resonant frequency of the isolation system does not coincide with the elevator's operating frequency. After optimizing the design, optimized vibration isolation design data is obtained and used in subsequent vibration isolation system implementation.

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

[0029] In this embodiment, a complete three-dimensional elevator simulation model is constructed based on welding optimization design data and vibration isolation optimization design data. This simulation model includes components such as the car, traction system, guide rails, counterweight, and other components, as well as design parameters for welding optimization and vibration isolation optimization. When building the model, all optimized design data is embedded in simulation software (such as ANSYS or Simulink) in conjunction with existing elevator component design drawings. Next, an elevator extreme operating condition test is conducted to simulate elevator operation under conditions such as overload, ultra-high speed, and extreme temperatures. During the test, extreme operating condition parameters are set, such as a load overload of 1.5 times and temperatures ranging from -20°C to 50°C. Through extreme operating condition testing, response data of the elevator under special operating conditions is collected, including vibration data, temperature changes, and acceleration response. Based on the extreme operating condition test data, the elevator buffer structure is optimized, with a focus on optimizing the elevator's shock absorption system and buffer parameters to ensure safety and comfort under extreme operating conditions. During buffer structure optimization, the buffer's operating range and preload are set (for example, a buffer's operating range of 5-10 cm and a preload of 200 N). The spring stiffness and damping characteristics are adjusted to reduce impact and vibration during operation. Ultimately, the optimized buffer structure design data is obtained, ensuring elevator stability and comfort under all operating conditions.

[0030] It is particularly important that step S4 includes the following steps:

[0031] Step S41: constructing a three-dimensional elevator simulation model based on the welding optimization design data and the vibration isolation optimization design data, and performing an extreme working condition test to obtain extreme working condition test data;

[0032] In this embodiment, a three-dimensional simulation model of the elevator is constructed using three-dimensional modeling software (such as SolidWorks or ANSYS) based on the welding structure optimization design data and vibration isolation optimization design data of the elevator. The model needs to include the elevator car, guide rail system, buffer, door system and power drive system. Next, an extreme working 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 working conditions are obtained. These data include but are not limited to physical quantities such as stress, strain, and displacement. These working 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 working conditions in order to evaluate the performance of the elevator under the most severe conditions.

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

[0034] In this embodiment, based on data obtained from extreme operating condition tests, the dynamic response of the elevator system under various operating conditions is extracted, specifically the impact loads generated during elevator start-up, stopping, and movement. Dynamic calculation methods are used to calculate the impact loads experienced by the elevator system using the elevator's mass, acceleration, and velocity data, combined with measured load responses (such as acceleration, force, and displacement). These calculations can be performed using 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 elevator component, the shock absorption effect, and the impact of vibration frequency on the impact load must be considered. The resulting impact load data will provide the necessary load basis for subsequent buffer design.

[0035] Step S43: Identifying 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 source of elevator vibration. First, the impact load data is spectrally analyzed by Fourier transform (FFT) to extract the main vibration frequency of the system under various working conditions. Next, the sensor data is used to distinguish the different components of the elevator vibration and determine the source of the vibration. The vibration sources of the elevator include but are not limited to the starting and stopping of the elevator drive system, the friction between the car and the guide rails, and the dynamic loads caused by passengers. In this process, it is also necessary to consider the impact of the different stiffness and damping of the elevator structure on the vibration source. Finite element analysis is used to further identify the dynamic response of different components in the elevator structure and determine the specific location and characteristics of the vibration source.

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

[0038] In this embodiment, a dynamic modeling method is used to calculate the optimal size of the buffer based on the identified elevator vibration structural source and combined with the impact load data. First, the preliminary design parameters of the buffer are set, including the expected maximum load-bearing capacity, operating frequency range and 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 and required stiffness are calculated. At this time, it is necessary to determine parameters such as the length, width, height and material thickness of the buffer so that it can withstand the vibration impact generated by the elevator system under the most extreme working conditions. Finally, the operating frequency, damping characteristics and required shock absorption effect of the buffer are comprehensively considered to optimize the size of the buffer to ensure its reliability and effectiveness under different load conditions.

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

[0040] In this embodiment, based on the elevator vibration structure source and impact load data, a structural optimization algorithm (such as topology optimization or shape optimization) is used to further determine the optimal shape of the buffer. During this process, the vibration frequency and impact load range that require the most buffering are determined by analyzing the elevator vibration source. Next, finite element analysis is used to simulate different buffer shapes and calculate the vibration transmission characteristics, elastic deformation capacity, and energy absorption capacity of the buffer in different forms. By adjusting the buffer's shape, such as round, square, or other special shapes, its stiffness distribution, energy absorption effect, and damping characteristics are optimized, thereby more effectively reducing vibration transmission. The shape optimization process may require multiple iterations to ensure that the buffer can provide the best shock absorption effect in actual operation.

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

[0042] In this embodiment, after separately optimizing the buffer's size and shape, these two data sets are integrated to construct a complete optimized elevator buffer structure. First, the optimized size and shape parameters are input into a three-dimensional elevator simulation model for comprehensive analysis. By applying the optimized size and shape to the elevator system, dynamic simulation is performed again to simulate the elevator's response under various operating conditions and evaluate the buffer's performance. Based on the simulation results, structural parameters are further adjusted to ensure the buffer effectively absorbs shock under extreme operating conditions, avoiding excessive vibration or system failure. Ultimately, the optimized structural data for the elevator buffer is obtained, including its optimal size, shape, material, and installation location, providing detailed design data for actual elevator manufacturing and installation.

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

[0044] Step S11: Obtaining 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 obtained drawing format must 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 drawing is a paper document, it must be converted into a digital file using a high-precision scanner (resolution of not less than 600dpi), and OCR software (such as ABBYY FineReader) must be used to identify the annotation information on the drawing to ensure accurate extraction of each dimensional parameter. For digitized design files, modeling software (such as SolidWorks or AutoCAD) can be used directly for parsing.

[0046] Step S12: identifying the car structure based on the elevator structural drawing, and constructing the car assembly according to the car structure;

[0047] In this embodiment, the main structure of the car in the drawings is analyzed, including the car frame, car bottom, car top, side walls, guide rail connection parts and safety clamp installation parts. Computer-aided design software (such as SolidWorks or CATIA) is used to extract the three-dimensional data of the car structure, and the car components are constructed according to the dimensions marked in the drawings. The car frame is modeled in detail, and the components are constructed using structural parameters such as frame cross-sectional dimensions (such as 50mm×50mm square tube), material properties (such as Q235B steel), and connection methods (such as welding or bolt connection). For welding parts, the welding size and shape are defined using weld standards (such as ISO 5817), and material non-uniformity parameters are applied to the welding connection parts. The car bottom plate needs to set the plate thickness parameter (such as 3mm), and the reinforcement distribution is calculated according to the load-bearing capacity of the car. Finite element analysis software (such as ANSYS) is used to perform structural verification on the car components to ensure that the constructed car components meet the design requirements.

[0048] Step S13: identifying the traction structure based on the elevator structural drawing, and constructing the traction assembly according to the traction structure;

[0049] In this embodiment, the components involved in the traction system in the drawings are analyzed, including the traction machine, traction wheel, traction rope, drive motor and traction wheel groove type. The CAD tool is used to extract the traction machine size data, such as the traction wheel diameter (such as 450mm), wheel groove angle (such as 45°) and rope diameter (such as 10mm). CATIA or SolidWorks is used to perform three-dimensional modeling of the traction machine components, and the traction wheel geometric model is constructed according to the specifications provided by the manufacturer, and its material properties are set (such as HT250 cast iron). For the drive motor, the power parameters (such as 11kW), rated speed (such as 960r / min) and output torque (such as 110N·m) are extracted according to the drawings. During the three-dimensional modeling process, the connection relationship between the motor and the traction wheel is ensured, including the coupling structure and installation accuracy (such as H7 / g6 fit). The flexible rope modeling method is used for the traction rope, and the rope elastic modulus (such as 1.1×10 5 MPa) and preload (e.g. 2000N) to ensure that the three-dimensional components of the traction system meet the design requirements.

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

[0051] In this embodiment, the car assembly and the traction assembly are aligned, and the connection relationship between the car and the traction system is configured according to the drawing requirements. The car guide rail spacing is set (such as 700mm), and the suspension method of the traction rope is ensured to meet the design requirements. For example, when using the "2:1" suspension method, the rope winding angle of the traction wheel is set (such as 180°). Use 3D modeling software (such as SolidWorks or UG NX) to create constraint relationships, define the sliding freedom of the car on the guide rails, and ensure that the force direction of the traction rope is consistent with the direction of elevator operation. Perform assembly constraint inspection on the 3D elevator assembly to ensure that the interference between the components is less than 0.1mm, and export the complete 3D model file (such as STEP format) for subsequent simulation analysis.

[0052] Step S15: performing a car static load simulation based on the three-dimensional elevator components to obtain car static load data;

[0053] In this embodiment, the load conditions of the car are set, including full load condition (such as 1000kg) and rated load condition (such as 500kg). A uniform load (such as 9.81kN / m 2 ), use finite element analysis software (such as ANSYS) to perform static analysis. Set the constraints of the car frame, including fixed supports at the four corners of the bottom, to simulate the constraint relationship of the car on the guide rail. When dividing the grid, use tetrahedral elements, set the element size to 10mm, and perform grid independence verification. During the solution process, calculate the maximum stress value (such as no more than 250MPa) and the maximum deformation (such as no more than 2mm) of the car floor. Extract the calculation results and generate a static load data file to record the displacement, stress and load distribution of key nodes for subsequent strength testing.

[0054] Step S16: Detecting the car structure strength based on the car static load data.

[0055] In this embodiment, static load data, including maximum stress, minimum stress and deformation, are extracted, and strength verification is performed using structural strength standards (such as GB / T3811-2008). For the car floor, a yield strength threshold is set (such as 235MPa), and the safety factor of the car floor is calculated (such as ≥1.5). The von Mises stress criterion is used to evaluate the safety of the car frame. If the maximum stress exceeds the yield strength of the material, the overloaded area is reinforced, such as adding reinforcement ribs or adjusting the material thickness. For welding parts, the weld strength is evaluated according to the ISO 5817 standard. If the weld stress exceeds the allowable weld stress (such as 180MPa), the welding process is adjusted, such as using multi-pass welding or increasing the weld cross-section. Finite element analysis software is used to generate a car structure strength test report, and a stress cloud map and deformation distribution map are output to provide a basis for subsequent optimization design.

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

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

[0058] In this embodiment, static load data of key parts of the car are collected. The static load data of the car floor is derived from the distribution of gravity on the car floor. The load values ​​per unit area of ​​different areas are obtained according to the mass distribution. For example, the load density of the center area of ​​the floor is set to 9.81 kN / m 2 , and the concentrated load in the four corner support areas is 2.5kN. The static load data of the side walls needs to be obtained in combination with the support force distribution of the car guide rail connection points. According to the load transfer path when the car moves, the 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 deadweight and the elevator traction force distribution. The top area is gridded, and the unit load is calculated at the center point of each grid. The grid size is set to 10mm×10mm to ensure the refinement of the load distribution. All static load data are meshed by finite element analysis pre-processing tools (such as HyperMesh), stored in matrix form, and a static load distribution data file is generated. The storage format is CSV. The data fields include node coordinates, load direction, load value, etc., to ensure that subsequent stress calculations can be directly called.

[0059] Step S162: Calculating stress based on the static load distribution data to obtain stress data;

[0060] In this embodiment, the static load distribution data file is read and imported into 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 is made of Q235B steel, its elastic modulus is set to 206000MPa, and its Poisson's ratio is set to 0.3. The base plate is made of aluminum alloy material, its elastic modulus is set to 70000MPa, and its Poisson's ratio is set to 0.33. According to the material type and load action mode, the corresponding boundary conditions are defined, such as the bottom support point is fixed and the guide rail connection point is set to a sliding constraint. The static analysis module is used to calculate the force of the entire car and extract the stress values ​​of each key part. The stress data is stored in a database, and each data item includes the node number, stress direction, stress value and the structural area where it is located. For areas with greater stress, such as the joints between the car base plate and the side wall, additional fine mesh division is performed to extract more accurate stress data, and a visual stress distribution diagram is exported to facilitate subsequent assessment of the degree of strain.

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

[0062] In this embodiment, stress data is read and the stress direction of each node is matched. Based on the elastic modulus of the car material, the deformation of the bottom plate, side wall, frame and other components is calculated respectively. Due to the concentrated force on the car bottom plate, the deformation in the edge area is relatively large. The edge point data is taken for detailed calculation to ensure the accuracy of the strain distribution. Since the car side wall is supported by the guide rail, the strain value is extracted near the guide rail connection point to analyze the local stress situation. The strain data is stored in an EXCEL table, and each row records the coordinates, strain value and location of a node. For the welding part, local refinement analysis is performed and high-precision grids are divided to ensure the accuracy of the strain gradient. After the data storage is completed, Matplotlib is used to generate the strain distribution cloud map, and a comparative analysis is performed with the static load distribution and stress distribution data to determine the high strain area and provide data support for subsequent yield analysis.

[0063] Step S164: determining the yield degree of the material 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 car floor material Q235B is set to 0.0011, and the yield strain of the aluminum alloy side wall is set to 0.0025. For the strain value of each node, it is determined whether it exceeds the yield limit of the corresponding material. If it exceeds the yield limit, the node enters the plastic deformation stage. If it does not exceed the yield limit, 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 welding parts, especially the weld area where the base plate and the side wall are connected, a local yield analysis is performed, and the maximum force position of the weld is calculated to ensure that the weld strength will not fail due to yield. All yield analysis data are stored as a database table, and each data record the coordinates, strain value and yield state of a node. Generate a yield distribution diagram and mark the range of the yield area for subsequent safety factor calculation.

[0065] Step S165: Calculating the safety factor based on the yield strength of the material;

[0066] In this embodiment, based on the degree of material yield, the stress value of the maximum yield area is extracted. According to industry standards, the safety factor of the car floor is set to no less than 1.5, the safety factor of the side wall structure is set to no less than 1.3, and the safety factor of the weld area is set to no less than 1.2. For the yield condition of each node, its safety factor is calculated and compared with the set standard. If the safety factor is lower than the threshold, it is marked as a safety hazard area and the specific location is recorded. For the bottom plate support part, local grid encryption analysis is used to ensure the accuracy of the safety factor calculation. For the side wall stress area, a comprehensive analysis is performed in combination with the guide rail support force to ensure the rationality of the safety factor calculation. All safety factor data are stored in the database, and a safety factor distribution curve is generated for subsequent car structure strength assessment.

[0067] Step S166: Evaluate the car structure strength according to the safety factor.

[0068] In this embodiment, based on the safety factor, the car floor, side walls, frame and welding parts are analyzed in different 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 hazards, listing the node coordinates, corresponding safety factors, material types and structural parts that exceed the allowable stress range. When analyzing the low safety factor area, the static load distribution data, stress data and strain data are combined to identify the main factors that may lead to insufficient structural strength. For the case where the safety factor of the car floor is lower than 1.5, the force mode of the floor is first analyzed, including the weight of the car, passenger load, impact load transmitted by the buffer device, etc., to determine whether there is a problem of local stress concentration or local weakness of the material. If the floor's load-bearing capacity is insufficient, the floor's structure can be optimized by adding localized reinforcements. Specific measures include adding reinforcements along the longitudinal or transverse directions of the car in stress-concentrated areas, setting the rib thickness to 3mm to 5mm and maintaining a spacing between ribs between 100mm and 200mm. Finite element analysis can be used to verify the reinforcement's effect on the floor's structural strength. If the analysis indicates stress concentration at welds, the weld layout can be adjusted or the welding process optimized, such as using double-sided welding to improve weld strength and adjusting the welding sequence to reduce residual weld stress. For sidewall structural safety factors below 1.3, the sidewall stress conditions are first analyzed, including the vertical load distribution of the traction system, the guide rail support force, and the rigidity characteristics of the sidewalls to determine if there are localized weak areas. If stress concentration in the sidewalls is caused by the guide rail support method, the guide rail support layout can be adjusted to evenly distribute the support points to reduce excessive stress at any single point, or localized reinforcements can be added to the sidewalls to improve their deformation resistance. For weld safety factors below 1.2, the stress concentration in the weld area is first analyzed to determine whether the maximum stress point of the weld exceeds the yield limit of the weld material. If the maximum stress on the weld exceeds its fatigue limit, higher-strength welding materials are used, such as replacing ordinary carbon steel electrodes with high-strength, low-hydrogen electrodes, and re-evaluating the weld strength. At the same time, welding parameters are optimized, such as controlling the welding current within the range of 120A to 150A and ensuring that the weld penetration reaches more than 3mm to improve the load-bearing capacity of the weld. After completing all optimization measures, static load simulation is re-performed and the safety factor data of the car structure is updated to ensure that the safety factors of all structural parts meet the set standards. The optimized safety factor data is compared with the original data to generate a car structure strength assessment report and save it in PDF format. The report includes a safety factor distribution diagram, an optimization plan description, a comparison table of data before and after optimization, and the finite element analysis verification results after optimization to ensure the effectiveness of the optimization plan.

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

[0070] Identifying low-strength car structure areas of a three-dimensional elevator assembly based on car structure strength;

[0071] In this embodiment, stress distribution data of key parts of the car are obtained from finite element simulation, strain gauge measurement and dynamic load test. Finite element simulation is based on the three-dimensional geometric model of the car. Simulation analysis software is used to calculate the static and dynamic loads of the car structure. The grid unit size is set to 2mm, and the elastic modulus of the high-strength steel Q345B used in the car is input as 2.0×10 5 MPa, a Poisson's ratio of 0.3, and a yield strength of 345 MPa. Boundary conditions were set as fixed supports at the four corners of the car. The stress state of the car under different load conditions was simulated, and the maximum principal stress distribution, maximum shear stress distribution, and equivalent stress distribution were calculated. Strain gauge measurements were performed by attaching strain gauges to key locations such as the car floor, side walls, and top support structure. Six measurement points were deployed in each area, and the data acquisition frequency was set to 1000 Hz. The strain data of the car structure during operation was recorded and converted into stress data. This data was then compared with the finite element simulation results to calibrate the calculation accuracy. The safety factor for the strength assessment of the car floor was set to no less than 1.5, meaning that the equivalent stress must not exceed 1 / 1.5 times the material's yield strength. The safety factor for the side wall area was set to no less than 1.3, and the safety factor for the top support structure was set to no less than 1.2. Based on the car 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 force direction and connection method to form a low-strength car structure area data table, and the actual area of ​​the low-strength car structure is obtained based on this.

[0072] Perform surface cleaning on the low-strength car structure area to obtain a clean car structure area;

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

[0074] Apply crack penetrant to the clean car structure area to obtain penetrant coating data;

[0075] In this embodiment, when applying crack penetrant to the clean car structure area, a fluorescent penetrant detection agent (compliant with GB / T 18851.2-2005 standard) is selected. The penetrant type is a water-washable fluorescent penetrant with a viscosity of 5-10 cSt to ensure that it can penetrate into fine cracks. The coating method adopts low-pressure spraying, and the spraying equipment uses a spray gun (nozzle diameter 0.5mm, spraying pressure 0.2MPa). The spraying angle is set to 45° and the spraying distance is 150mm-200mm to ensure that the penetrant can evenly cover the entire clean area. The penetrant film thickness is controlled between 0.05mm-0.1mm. After coating, let it stand for 10 minutes, during which the ambient temperature is controlled at 25℃±2℃ and the humidity is controlled at 50%-60% to ensure that the penetrant can fully penetrate into cracks or micropores. After the penetration time is over, use deionized water (spraying pressure 0.1MPa, spraying time 30s) to remove excess penetrant on the surface to avoid affecting the subsequent imaging effect. The penetration data record contains information such as spraying parameters, penetrant thickness, penetration time, etc., forming penetrant coating data.

[0076] Applying a defect developer based on the penetrant coating data to generate defect imaging data;

[0077] In this embodiment, a dry developer is prepared, and the particle size of the developer powder is set to no more than 50nm to ensure that the developer can be evenly distributed and cover the cracks or micropores where the penetrant remains. The developer is sprayed by electrostatic spraying. The spraying equipment uses a spray gun equipped with a high-voltage electrostatic generator. The spraying voltage is set to 30kV to ensure that the developer powder can be adsorbed on the detection surface to avoid uneven distribution of the developer due to gravity or wind flow. The spraying distance is controlled at 200mm-250mm, and the movement speed of the spray gun during spraying is set to 200mm / s to ensure that the developer is evenly attached to the surface. The developer film thickness is controlled within the range of 0.02mm-0.05mm. After the developer is applied, wait for 5 minutes to allow it to fully absorb and stabilize. At the same time, ensure that the ambient temperature is maintained at 25℃±2℃ and the humidity is maintained at 50%-60% to avoid excessive air humidity causing the developer to become damp and ineffective. Subsequently, ultraviolet light is used to irradiate the coated area. The ultraviolet light wavelength is set to 365nm, the light source power is set to 5W, and the irradiation distance is controlled at 150mm-200mm. The ultraviolet light irradiation time is set to 15s, and the irradiation angle is adjusted to 90° to ensure that the light source evenly irradiates the entire test surface, causing the penetrant to produce a fluorescent reaction at the cracks or micropores. The fluorescence intensity is collected by a high-resolution CCD camera with a camera resolution set to 1920×1080, an exposure time set to 50ms, and automatic gain adjustment to ensure that the fluorescent image is clearly visible. The collected imaging image data is stored, and the background noise is removed by an image preprocessing algorithm to form defect imaging data.

[0078] Identify crack morphology based on defect imaging data to obtain crack defect data;

[0079] In this embodiment, the defect imaging data is image processed, and the Gaussian filtering algorithm is used to reduce the noise of the imaging image. The filter window size 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 Canny operator gradient threshold is set to (50,150) to ensure that the crack edge is clearly visible. In order to further enhance the characteristics of the crack area, the crack area is optimized using morphological expansion and corrosion operations. The expansion kernel size is set to 3×3 pixels, and the corrosion kernel size is set to 3×3 pixels to maintain the integrity of the crack morphology. After the crack identification is completed, the connected domain analysis technology is used to mark the crack area, and the starting point, end point, maximum width, maximum length of the crack and whether the crack has branches are analyzed. The minimum crack width detection accuracy is set to 0.01mm, and the crack length resolution is set to 0.1mm. The crack trunk information is extracted by calculating the crack skeleton structure, and the crack growth direction is recorded. Finally, the identified crack defect data is stored in coordinate format, including key information such as the crack's starting point, end point, width, length, and branching, ensuring that the data can be used for subsequent crack propagation analysis.

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

[0081] In this embodiment, the defect imaging data is segmented, and the pore area is segmented using an adaptive threshold method so that the brightness value of the pore area contrasts with the background area. Then, the morphological opening operation is used to remove isolated noise points, and the opening operation kernel size is set to 5×5 pixels to ensure that small artifacts do not affect the pore identification results. After the pore area segmentation is completed, the connected domain analysis method is used to mark all independent pore areas, and the area, diameter, and position coordinates of the pores are extracted. The minimum detection resolution of the pore diameter is set to 0.05mm. To ensure measurement accuracy, the pore depth is measured using X-ray detection equipment. The X-ray source power is set to 100kV, the detection accuracy is set to 0.1mm, and the scanning interval is set to 0.2mm to obtain the depth information of the pores. All 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] Perform crack propagation evaluation based on 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 crack stress intensity factor range (ΔK) is used to infer the crack propagation speed. The stress intensity factor range ΔK is calculated based on the geometry of the crack, the cyclic load stress of the material, and the current length of the crack. In this evaluation, C and m are crack propagation material constants, set to 2.2×10^-12 and 3.0 respectively, Y is a geometric factor, whose value is selected according to the crack morphology, usually between 0.7-1.2, the cyclic load stress σ is set to 150MPa, and the current length a of the crack is measured in millimeters. Next, the crack propagation calculation is performed with a step size of 0.05mm, and the number of calculation steps is set to 100 steps to simulate the crack propagation behavior in future cycles. In addition, the crack propagation analysis is performed using the finite element simulation method, the simulation load condition is 3 million cyclic loads, and the meshing accuracy is set to 0.2mm to ensure the accuracy of the calculation results and the simulation accuracy of the crack propagation path. Through simulation, the crack expansion range under the set load is obtained, thereby generating crack growth data, and ultimately providing a prediction of the crack growth trend, providing a basis for structural safety assessment and prevention of crack failure.

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

[0085] In this embodiment, the pore data undergoes a three-dimensional coordinate transformation to ensure that the pore data from different inspection surfaces is unified into the same coordinate system. The pore data is then clustered using the DBSCAN clustering algorithm to calculate dense pore areas. The DBSCAN algorithm's neighborhood radius is set to 2 mm, and the minimum number of points is set to 5 to ensure that high-density pore clusters can be identified. Subsequently, the average pore spacing is calculated. If the pore spacing within a certain area is less than 2 mm, the area is marked as a high-risk area, and the 3D coordinate information of the area is recorded to form the pore defect location data.

[0086] The crack growth data and the location of the porosity defect are integrated to obtain the welding defect data.

[0087] In this embodiment, the crack propagation data and the pore defect location data are cross-analyzed to determine whether the crack propagation path overlaps with the high-density pore area. By comparing the calculation of the crack propagation path with the pore location data, if it is found that the crack propagation path passes through a high-density pore area, the area is marked as a high-risk welding defect area. In this process, the crack propagation data includes the crack propagation direction, propagation rate and expected propagation end point. This information will be combined with the pore distribution. The pore distribution records the spatial distribution characteristics of the pores and the high-density aggregation areas of the pores. Then, the high-risk areas are marked using a spatial coordinate system to mark the welding defect areas that may cause structural failure. Ultimately, the welding defect data includes the crack defect propagation trend, pore distribution and high-risk area coordinates. These data provide a basis for optimizing the welding process and help adjust the welding process, thereby improving the welding quality and safety of the car structure.

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

[0089] Calibrate welding defect locations based on welding defect data;

[0090] In this embodiment, the spatial coordinates and crack propagation paths of each weld defect are obtained through integrated analysis of weld defect data. CAD software is used to create a three-dimensional model of the elevator car structure. The weld defect data is then matched with the weld joint locations in the structural model to determine the precise location of each weld defect. By analyzing information such as crack direction, length, and depth from the weld defect data and combining it with the actual geometry of the weld joints, the location data of all weld defects is ultimately extracted, and a weld defect location map is generated. This extraction of weld defect locations provides the foundation for subsequent welding process analysis.

[0091] Extract the welded elevator joint information based on the elevator structure drawings, and build a welded joint model based on the welded elevator joint information;

[0092] In this example, the welded joints in the elevator structural drawings were digitized and extracted. Image processing techniques were used to perform edge detection, morphological processing, and feature extraction on the drawings to identify the geometric shape and dimensions of the welded joints. The accuracy of the extracted joint information was ensured by comparing it with engineering standards. Based on this, a welded joint model was created using 3D modeling software, taking into account material properties and connection methods to generate a complete welded joint model. This model served as the basis for subsequent analysis.

[0093] Identify the weld joint geometry contour of the weld joint model;

[0094] In this example, the weld joint model was refined, and geometric modeling software was used to extract the weld joint's contours, determine the joint's boundaries, and identify the weld area. The geometric characteristics of the weld joint were determined by accurately measuring the geometric dimensions of each component in the model, such as the radius, length, and thickness of the joint. This ensured that the weld joint's geometric data met design specifications, and all relevant dimensional parameters were recorded to form the weld joint's geometric profile data.

[0095] Determine the weld area of ​​the weld joint geometric contour according to the welding defect position to obtain the weld area;

[0096] In this embodiment, weld defect location data is mapped to the geometric contours of the weld joint, ensuring a one-to-one correspondence between weld defects and weld areas. The weld boundary and distribution are determined by combining the shape characteristics of the weld joint model, generating weld area data. The weld area is primarily determined based on the weld joint geometry and welding process requirements. All weld defects within the weld area require further mechanical analysis.

[0097] Calculate tensile stress based on weld area;

[0098] In this example, stress simulation of the weld region was performed using finite element analysis. Based on the weld geometry and material properties, boundary and loading conditions were set to calculate the tensile stress within the weld region. The loading condition was obtained from experimental data and set to 150 MPa to simulate the stress field that may be generated during welding. The calculation results provide the stress distribution at different points within the weld region and provide basic data for subsequent stress analysis.

[0099] 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;

[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. The areas corresponding to different stress values ​​are marked with different colors by color coding to generate a weld stress distribution diagram. The color changes in the figure will reflect the high and low distribution of stress, and the stress concentration area is clearly marked. This figure provides an intuitive basis for subsequent identification of weld stress concentration areas. Set the stress threshold. According to the yield strength of Q345B material 345MPa and the fatigue limit 120MPa, the critical stress threshold for stress concentration area identification is set to 0.8 times the yield strength, i.e. 276MPa, as the standard for judging excessive stress areas. Using the stress concentration algorithm, by calculating the stress gradient change rate of each grid node, the stress concentration is defined as K = σ_max / σ_nom, where σ_max is the local maximum stress value, σ_nom is the nominal stress value of the weld area, and σ_nom in the weld area is obtained by averaging. Set the area with K>1.5 as the stress concentration area, and further screen the grid points with stress greater than 276MPa. The identified stress concentration areas are refined and analyzed to reduce the area to less than 0.5mm 2 High stress points are eliminated to avoid misjudgment of local stress peaks due to finite element meshing errors. Finally, the areas that meet the conditions are marked as weld stress concentration areas. The spatial coordinates, maximum stress value, and weld area number of each stress concentration point are recorded to form a stress concentration area data table, which provides a basis for subsequent welding optimization and crack growth analysis.

[0101] Plastic deformation simulation is performed based on the weld area, where 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 example, key welding parameters were set, such as a welding speed of 1-10 mm and a welding electrode diameter of 1.6-5.0 mm. Simulation software was used to simulate the temperature, stress, and plastic deformation of the weld region during welding. Using a reasonable heat source model and stress field distribution, the simulation model calculated the plastic deformation of the weld region at each moment during the welding process. After the welding process simulation was complete, the generated plastic deformation data was used to describe the deformation characteristics of the weld region.

[0103] Mapping the plastic deformation data to the weld area and generating a plastic deformation distribution map; identifying high plastic deformation areas based on the plastic deformation distribution map;

[0104] In this embodiment, the plastic deformation obtained by simulation is mapped to the three-dimensional model of the weld area, the degree of plastic deformation of each point is calculated, and different deformation amounts are marked with different colors by color coding. The generated plastic deformation distribution diagram shows the deformation experienced by the weld area during the welding process, providing data support for subsequent deformation control. The plastic deformation threshold is set. According to the fracture elongation of 24% and the yield strain of 0.002 of Q345B material, the identification threshold of the high plastic deformation area is set to the area where the total strain exceeds 0.012, which is 6 times the yield strain, to ensure that the area where fatigue failure may occur is 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. The finite element analysis method is used to interpolate the plastic strain distribution of the weld area to ensure that the identified high plastic deformation area has spatial continuity and eliminates areas with an area less than 1mm. 2 The system isolates points of high plastic deformation to avoid misidentification due to meshing errors. By calculating the plastic deformation gradient and performing a detailed analysis of the areas of sudden plastic deformation, it screens for regions with plastic deformation gradients greater than 0.005 / mm. This, combined with regions with plastic strains greater than 0.012, ultimately identifies regions of high plastic deformation. The spatial coordinates, maximum plastic deformation value, and distribution range of each region of high plastic deformation are recorded, and a data table of these regions is output, providing data support for subsequent welding process adjustments and fatigue life prediction.

[0105] The weld weak area is generated by performing regional intersection calculation based on the weld stress concentration area and the high plastic deformation 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 of the stress concentration area and the high plastic deformation area. These areas are usually the weak areas of the weld and are the most likely locations for crack extension or welding failure. Through this analysis, the weak areas of the weld can be accurately calibrated, providing a basis for subsequent welding process optimization. The weld stress distribution map and the plastic deformation distribution map are aligned 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 overlapped and analyzed to extract the intersection area of ​​the two. The data of the weld stress concentration area consists of areas with stress exceeding 180MPa, and the data of the high plastic deformation area consists of areas with plastic strain exceeding 0.012. The result of the intersection operation is the area that meets both conditions at the same time. Using three-dimensional grid mapping technology, the grid of the intersection area is refined to ensure that the spatial accuracy of the intersection area reaches 0.2mm to eliminate the recognition error caused by grid division differences. Further, morphological dilation and erosion operations were used to perform connectivity analysis on the intersection areas, and isolated areas with an area less than 2 mm were removed.2 The system then combines discrete points of the weld seam and merges adjacent intersections with a distance of less than 1 mm to ensure the consistency and accuracy of the weld weak area. It ultimately outputs the 3D coordinates, area, maximum stress value, and maximum plastic deformation value of the weld weak area, and visually displays the weld weak area in the form of a 3D heat map, providing an accurate reference for subsequent welding parameter optimization, structural reinforcement, and fatigue life prediction.

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

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

[0109] In this embodiment, the mesh discretization is performed on the weak weld area to ensure that the calculation accuracy reaches a spatial resolution of 0.2 mm, and the contact stress of the weak weld area is calculated using the finite element analysis method. The material parameters are set, where the weld area material is Q345B and the elastic modulus is 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 status. The car's own weight is 1500kg. Considering the rated load of 1000kg, the total weight is calculated to be 2500kg, and the applied vertical load is 25000N. The contact interface is calculated using Hertz contact theory, and the average contact pressure in 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 nonlinear contact algorithm is used, and the maximum contact pressure threshold is set to 120MPa. The iterative calculation method is used to solve the local contact pressure point by point, and finally the contact pressure distribution in the weak area of ​​the weld is output.

[0110] Identify contact normal force based on contact pressure;

[0111] In this embodiment, the calculated contact pressure data is integrated to obtain the total contact normal force in the weld area. First, the local normal force F = P × A is calculated on each weld mesh element, where P is the contact pressure of the element and A is the area of ​​the element. The area calculation is based on triangular or quadrilateral element division, and the element area resolution is set to 0.04 mm. 2 During the contact normal force calculation process, to prevent local outliers from affecting the overall calculation, an outlier rejection rule is set. If the contact pressure of a unit exceeds 150 MPa or is less than 1 MPa, this data is excluded from the total normal force calculation. Ultimately, the local normal forces of all valid units are accumulated to obtain the total contact normal force in the weak area of ​​the weld. The calculated results are stored in a 3D coordinate point set for subsequent analysis.

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

[0113] In this embodiment, the normal force is calculated by region in combination with the car support structure. The car support distribution adopts a four-point support method, and each support point bears a different normal load. According to the contact normal force data calculated in the previous step, the total contact normal 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 normal force on each grid unit in the weld area. The force distribution ratio of the support points is set, and the front support point bears 40% of the total load and the rear support point bears 60% of the total load. In the specific calculation process, the positive load of the front support point is calculated as F1 = 0.4 × F_total, and the positive load of the rear support point is calculated as F2 = 0.6 × F_total. Finally, the total positive load of the weld area is obtained, and a load distribution diagram is established to display the load distribution in a visual way.

[0114] Calculate friction based on the total forward load; identify the car's trajectory based on the car's static load data; determine relative slip based on friction and the car's trajectory to obtain relative slip data;

[0115] In this embodiment, when calculating friction based on the total forward load, Coulomb's friction law is used to calculate the friction force magnitude. 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 weld surface roughness and material properties. Based on experimental data, the friction coefficient between the Q345B weld surface and the car guide rail is set to 0.15. When weld defects exist in the weld area, the local friction coefficient may increase. Therefore, during the calculation process, a regional friction coefficient setting method is used, and the friction coefficient of the defective area is increased to 0.18. Finally, the local friction force of each weld unit is calculated and accumulated to obtain the total friction force of the weld area. A friction force distribution map is also generated to clarify the friction conditions at different locations of the weld. When identifying the car's operating trajectory based on the car's static load data, displacement data at different time points during the car's operation is first collected. The data is derived from the car's displacement sensor, and the sampling frequency is set to 100 Hz. The entire trajectory of the car from rest to its maximum operating speed of 1.5 m / s is recorded. Based on the displacement data, the car motion trajectory is fitted with a curve, and the cubic spline interpolation method is used to calculate the displacement curves at different time points, and the starting position, end position and key displacement points of the car operation on the entire operation path are extracted. The car operation trajectory data is stored as a time-displacement two-dimensional array, and the car acceleration and speed change trend are calculated in combination with the time information to provide support for subsequent friction and slip analysis. When making relative slip judgments based on friction and car operation trajectory, the slip displacement of the weld area is first calculated. The slip displacement Δs = F_friction / (k×A), where F_friction is the friction force of the weld area calculated in the previous step, and k is the elastic stiffness coefficient of the weld material (valued at 2.5×10 5 N / mm 2 ), where A is the weld stress area. After calculating the slip displacement of different weld regions, a slip threshold is set. When the slip displacement of a region exceeds 0.02 mm, that region is marked as experiencing relative slip. The final output is relative slip data, including the slip displacement magnitude, slip direction, and the coordinates of the weld point where slip occurred, and is stored in a slip dataset.

[0116] Detect weld wear based on relative slip data to obtain weld wear data;

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

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

[0119] In this embodiment, the weld wear depth is calculated using the formula D = W / A, where D is the local weld wear depth, W is the weld wear volume on a certain unit, and A is the contact area of ​​the weld surface of that unit. In the actual calculation process, the weld surface is discretized into uniform grid units. The area A of each grid unit is determined by the weld geometry. The finite element meshing method is usually used, and the unit area resolution is set to 0.04mm. 2 To ensure the accuracy of the calculation. First, the wear volume W of all weld mesh elements is extracted. This data comes from the calculation results of the Archard wear equation in the previous step. Then, all mesh elements are traversed and their wear depth D = W / A is calculated. The units of the calculation process are unified, that is, the unit of W is mm. 3 , the unit of A is mm 2, to obtain the wear depth D in mm. To ensure the rationality of the calculation results, an outlier filtering mechanism is implemented. When the calculated wear depth D of a cell exceeds the recognized limit wear depth of the material (e.g., 0.2 mm) or falls below the measurement error threshold (e.g., 0.005 mm), the data is discarded and the mean of the data of the adjacent grid cells is used for compensation to eliminate the influence of local outliers. After the calculation is completed, the wear depth data of all weld cells is stored in a three-dimensional spatial data format, and a weld wear depth distribution map is generated for visualization. To further analyze high-wear areas, a wear depth threshold is set. When the wear depth D of a grid cell exceeds 0.05 mm, it is marked as a high-wear area. The coordinate information of these high-wear areas is extracted for subsequent analysis. Finally, the wear depth data for each weld location is output and stored in a three-dimensional wear depth dataset, ensuring that the data can be used for subsequent weld life assessment and weld optimization design. Based on the weld wear depth data from the previous step, a connected region analysis algorithm is used to identify all continuous wear areas. In this process, the connected component labeling algorithm (CCL) in image processing is first used to classify the mesh units on the weld surface, and the adjacent units with wear depth exceeding the set threshold (0.05mm) are classified as the same connected area. The 8-connected region method is used, that is, if a unit is connected to its left, right, top, bottom, upper left, lower left, upper right, and lower right units and all belong to high wear areas, they are classified as the same wear area. Subsequently, for each connected area, its boundary coordinates are calculated, and the contour point set of the wear area is obtained through the boundary tracing algorithm (such as Moore-Neighbor Tracing), and the area A_region of the area is calculated based on the boundary point set. The area calculation uses the Gaussian area formula to ensure the calculation accuracy. In order to eliminate non-significant wear areas, the wear range threshold A_threshold is set to 5mm. 2 , that is, if the area of ​​a certain wear region A_region is less than 5mm 2 , this area is ignored to avoid misidentifying small wear areas as effective wear areas. For effective wear areas, their center coordinates, boundary coordinates, and area size are extracted and stored in the wear range dataset. The final output is weld wear range data, including the three-dimensional spatial distribution of the wear area, area size, and boundary coordinates. A visual weld wear range map is also generated for subsequent weld life analysis and welding optimization design.

[0120] Determine the amount of car structure wear based on the wear depth and wear range.

[0121] In this embodiment, when determining the car structure wear 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 weld location and dA is the weld grid unit area. The total weld wear volume is calculated by integrating the entire weld area. Combined with the overall car structure data, the weld wear volume is converted into the overall car wear volume. Ultimately, the total car structure wear data is output, and a three-dimensional wear distribution visualization is generated to provide a basis for car welding maintenance.

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

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

[0124] In this embodiment, complete car structure data is extracted from the elevator's three-dimensional structural data. This data includes the car's roof, sidewalls, floor, reinforcements, and guide rail connections. To accurately identify the car floor structure, spatial coordinate projection is used to project the overall car structure data horizontally. The three-dimensional contour of the car floor is then extracted based on the geometric features of the car's bottom boundary. Using three-dimensional point cloud filtering technology, non-floor data from the car structure is removed, retaining only the point cloud data related to the floor. The RANSAC (random sampling consensus) plane fitting algorithm is used to fit the floor surface, removing irregular boundary points. A boundary detection algorithm is then used to obtain the precise outer contour of the floor. Based on this, the floor surface is meshed using quadrilateral elements to ensure a mesh size within the 2mm×2mm range to meet subsequent computational requirements. Finally, the complete three-dimensional data of the car floor is stored, and the floor's geometric topology is generated for subsequent analysis.

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

[0126] In this embodiment, when mapping the car structure wear to the car floor structure, the car structure wear data calculated in the previous step is first called, including the wear depth, wear range, and spatial distribution information. The coordinate transformation method is used to convert the wear data from the overall car coordinate system to the local car floor coordinate system to ensure data alignment. The nearest neighbor interpolation method is used to interpolate the car structure wear data onto the car floor grid, and the wear data is smoothed to eliminate the error caused by data discreteness. During the interpolation process, for each grid cell on the floor, the corresponding wear depth is calculated and stored in the floor wear matrix. The wear threshold T_w is set to 0.1mm, that is, when the wear depth of a unit is less than the threshold, it is regarded as a normal area and is not marked; when the wear depth of a unit is greater than T_w, it is marked as a wear area and the wear depth value is recorded. Finally, the wear mapping of the car floor is completed, and the three-dimensional data of the worn car floor structure is output for subsequent analysis.

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

[0128] In this embodiment, when identifying concentrated wear areas of the worn car floor structure, a connected area analysis is performed based on the wear data. The wear depth matrix of the floor is binarized and the wear depth threshold T_c is set to 0.3mm. That is, when the wear depth of a certain area is greater than T_c, it is defined as a wear area. Then, a connected area detection algorithm is used to identify all continuous wear areas and calculate the area of ​​each area. The concentrated wear area threshold A_c is set to 10mm. 2 , that is, when the area of ​​a certain wear area is greater than 10mm 2 When the wear zone is detected, it is marked as a concentrated wear area. A contour extraction algorithm (such as the MarchingSquares algorithm) is used to extract the boundary coordinates of the concentrated wear area and calculate its geometric characteristics, including area, shape factor, and aspect ratio. Finally, the 3D coordinate information of the concentrated wear area is stored, and a visual wear area distribution map is generated to facilitate fatigue damage analysis.

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

[0130] In this embodiment, when calculating fatigue damage based on concentrated wear areas, the 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 Miner's linear cumulative damage theory, and the formula D = ∑(n_i / N_i) is used, where n_i is the number of cycles at stress level i, and N_i is the fatigue life at the corresponding stress level. Using the weld stress distribution data and the car operating condition data, the number of stress cycles in the wear area is counted, and the fatigue damage is calculated in combination with the SN curve (stress-life curve). The fatigue damage threshold D_c is set to 0.7, that is, when the D of a certain area exceeds 0.7, it is marked as a high fatigue damage area. Finally, 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 based on fatigue damage data;

[0132] In this embodiment, when evaluating the fracture toughness of the car floor based on 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 the area is calculated using the formula K = Y·σ√πa, 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 the K of a certain area is greater than T_f, it is marked as a low-toughness area. Combined with 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 risk of car floor fracture 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, the crack propagation analysis method is used to calculate the crack propagation rate and predict the final crack propagation path. The crack propagation rate is calculated using the Paris-Erdogan formula da / dN=C(ΔK)^m, where da / dN is the crack propagation rate, C and m are material constants, and ΔK is the stress intensity factor range. Combined with the fatigue damage data of the car floor, the crack starting 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, the area is judged to have a fracture risk and 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 to facilitate 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 risk of car floor fracture;

[0137] In this embodiment, the car floor fracture risk prediction data is called, including the fracture toughness distribution, crack propagation path and stress intensity factor (K value) distribution. The high fracture risk threshold T_r is set to 0.9K_IC, where K_IC is the critical value of the fracture toughness of the car floor material. When the stress intensity factor K of a certain area is greater than T_r, the area is marked as a high fracture risk area. The crack propagation simulation method is used to calculate the final extension length of the crack, and the crack growth rate of the high fracture risk area is statistically calculated in combination with the Paris-Erdogan crack propagation equation. Using the regional connectivity analysis method, all high fracture risk areas are subjected to connectivity detection, and the area, boundary coordinates and crack length of each area are calculated. Finally, the spatial distribution data of the high fracture risk area is output and stored in the high fracture risk area database for weld optimization design.

[0138] Detect deformation based on high fracture risk areas and optimize weld shape based on deformation;

[0139] In this embodiment, a three-dimensional model of the elevator structure is used, with particular attention paid to the weld joint area, and a detailed simulation of this area is performed using finite element analysis software such as ANSYS or ABAQUS. By applying the elevator's working load, vibration load, temperature change and other working conditions, the deformation data of each weld joint area is obtained. For areas with high fracture risk, special attention is paid to weld geometric features such as weld angle, width and thickness, and the deformation of the area is obtained through simulation analysis. Next, based on the deformation data, the weld shape is optimized. 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 is reduced.

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

[0141] In this example, a thermodynamic analysis tool was used to simulate the temperature field and thermal stresses in the weld area, utilizing a model of the heat source generated during welding and combining it with the welding process and material properties of the elevator structure. This temperature field simulation takes into account the heat input distribution during welding and the thermal expansion characteristics of the weld metal, calculating the thermal stresses in the weld area. Stress analysis identifies areas of concentrated thermal stress and demarcates regions of high stress concentration. Next, based on these areas of concentrated thermal stress, the welding sequence was optimized. This sequence was adjusted according to the thermal stress distribution, ensuring a gradual and even release of thermal stress during welding, thus avoiding excessive stress concentration.

[0142] Detect welding material strength based on high fracture risk areas and optimize welding materials based on welding material strength;

[0143] In this embodiment, the mechanical properties of the welding materials, including tensile strength, yield strength, and elongation, are tested using materials testing equipment such as a universal testing machine. The strength parameters of the welding materials are derived from the experimental data. In areas with high fracture risk, appropriate welding materials are selected, taking into account the relationship between the strength of these materials and the forces and stress concentrations in the elevator structure. For example, welding materials with higher tensile strength and yield strength are selected. The composition or processing of the welding materials is adjusted based on the strength requirements to improve the overall strength of the welded joint.

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

[0145] In this embodiment, weld shape optimization data, welding sequence optimization data, and welding material optimization data are called. A data fusion method is used to match the three types of optimization data to ensure the compatibility of weld shape, welding sequence, and welding materials. A multi-objective optimization method is used to calculate the comprehensive performance indicators of the welding optimization design scheme, including key parameters such as weld strength, fracture toughness, residual stress, and crack growth rate. The welding optimization design goal is set to 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 growth rate is reduced by more than 50%. The welding optimization design data is generated and stored 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: performing traction simulation based on the three-dimensional elevator assembly, wherein the traction power range is: 0.5-10kW, and the wire rope tension range is: 500-5000N, to obtain traction data;

[0148] In this embodiment, when constructing a traction simulation environment, the operating state of the traction system is determined based on the structural parameters and dynamic characteristics of the elevator's three-dimensional components. This involves loading the geometric structural data of the traction machine, traction sheave, wire rope, car, counterweight, and guide rails, and calculating the mass, moment of inertia, and friction coefficient of each component based on material properties. The traction machine's traction power is set between 0.5 and 10 kW to ensure coverage of various operating conditions, and the wire rope tension is set between 500 and 5000 N to match the mechanical characteristics under different load conditions. During the simulation, numerical calculation methods are used to iteratively update the car's acceleration, velocity, and displacement, taking into account the effects of motor torque, friction, and traction on the traction system. First, the car's forces are calculated based on the input wire rope tension, and then the car's motion trajectory is solved through numerical integration. To ensure accuracy, the simulation time step is set to 0.001 seconds. Within each step, the car's real-time position information is calculated, and key traction system parameters such as wire rope tension, traction, car acceleration, and motor power are output. All data are stored as time series for subsequent processing.

[0149] Step S32: converting 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 status of the traction system. In order to extract its vibration characteristics, the data needs to be converted into a frequency domain signal. First, the traction force, wire rope tension, motor speed and other data are extracted from the stored time series and discretely sampled. The sampling frequency is set to 10kHz, that is, 10,000 data points are recorded per second to ensure that high-frequency vibration components are covered. Before signal processing, the data is processed by a window function to reduce the impact of spectrum leakage on the analysis. Then, the fast Fourier transform (FFT) is used to calculate the spectral characteristics of the signal, and the main vibration frequency and the corresponding amplitude information are extracted. After the conversion is completed, the frequency domain signal is stored as a frequency-amplitude comparison table, with phase information attached for subsequent noise analysis.

[0151] Step S33: performing 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, some of which may come from noise sources. In order to distinguish normal vibration from noise interference, discrete wavelet transform (DWT) is used to decompose the signal. First, the Daubechies4 (db4) wavelet basis function is selected to ensure the accuracy of the time-frequency analysis of the signal. Then, the traction frequency domain signal is decomposed into multiple frequency bands, each of which 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 component, and the signal below the threshold is reset to zero to remove unnecessary high-frequency interference. Subsequently, the signal is reconstructed by inverse wavelet transform (IDWT), retaining only the noise component, 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, the noise signal is first preprocessed, 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 instantaneous energy, and the average value is taken over the entire sampling range, and the root mean square value (RMS) of the signal is then obtained by square root extraction to characterize the overall intensity of the noise signal. After the calculation is completed, the noise intensity value obtained is compared with a set noise intensity threshold. The setting of the threshold is based on noise standards or experimental data. For example, the environmental noise allowable value of conventional industrial equipment is set below 60dB. If the noise intensity exceeds the set threshold, it is determined to be abnormal noise, providing a basis for subsequent analysis. When calculating the noise frequency of the noise signal, a short-time Fourier transform (STFT) is used to analyze the time-frequency distribution of the signal. First, an appropriate window function is selected, such as a Hanning window, to reduce spectral leakage, and a sliding window of fixed length is set. The window length is usually set to 1024 points, and the window step size is set to 512 points to ensure the balance of time-frequency resolution. Then, the window is moved step by step on the time axis, and the signal in each window is Fourier transformed to obtain the spectral characteristics of the corresponding time period, and the energy distribution of each frequency component is calculated. Next, the energy distribution 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 energy size, and the first three frequency components with the highest energy are selected for storage to fully characterize the frequency characteristics of the noise signal. Finally, the noise intensity and noise frequency data are stored as a set of feature data. The data format includes time label, RMS value, main frequency and its corresponding energy value to ensure that the data can be used for subsequent noise source identification and analysis.

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

[0156] In this embodiment, when integrating noise intensity and noise frequency, the noise signal characteristic data obtained previously by calculation is first extracted, including information such as amplitude, main frequency, energy distribution, time-frequency relationship, etc., and the data is formatted and stored to ensure standardization for subsequent analysis. The storage format of the noise characteristic data adopts a time series data structure. The noise characteristics at each time point include noise intensity (expressed as root mean square value RMS), noise main frequency (main energy frequency point), noise frequency band distribution (distribution of energy at different frequencies) and noise signal amplitude change trend, so as to facilitate subsequent comparative analysis. 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 source is determined. The bearing noise database contains noise characteristics under various bearing operating conditions, such as rolling element failure, poor lubrication, bearing eccentricity, inner ring or outer ring wear, etc. The vibration spectrum characteristics corresponding to each state have been experimentally calibrated. For example, rolling element faults typically generate harmonic components between 100Hz and 300Hz, while high-frequency noise from poor lubrication conditions is typically distributed above 500Hz. Bearing eccentricity can lead to enhanced harmonics at 1 to 3 times the rotational frequency. To improve the accuracy of noise source identification, a dynamic time warping (DTW) algorithm is used to calculate the similarity between the current noise signature and the noise signatures of various bearings in a database. The DTW algorithm uses dynamic programming to calculate the optimal matching path between two time series, thereby measuring the morphological similarity between different time series. During the calculation process, the time-frequency characteristic data of the current noise signal is first converted into a two-dimensional matrix. This data is then compared point by point with the noise signature matrices of various bearings in the database, and a similarity score is calculated. When the similarity exceeds 90%, the bearing is identified as the primary noise source, and the fault type is further analyzed. The faulty bearing's location information is recorded, and detailed data such as the fault type, noise frequency, and energy distribution are stored to provide data support for subsequent vibration isolation optimization design.

[0157] Step S36: Perform vibration isolation optimization design based on the noise source to obtain vibration isolation optimization design data.

[0158] In this embodiment, the vibration isolation optimization design is based on the stiffness, damping ratio, and installation position of the vibration isolator to reduce the impact of vibration generated by the noise source on the elevator structure. First, the stiffness of the vibration isolator is adjusted, and the stiffness range is set between 100-1000N / mm. The stiffness value is selected based on the main vibration frequency of the noise source. For example, if the noise frequency is concentrated below 200Hz, a lower stiffness vibration isolator is used to absorb low-frequency vibrations; if the noise frequency is above 500Hz, a higher stiffness vibration isolator is used to prevent high-frequency resonance. In actual implementation, the available vibration isolator models are first screened according to the noise frequency range, and the damping characteristics of the vibration isolator at different vibration frequencies are tested through loading experiments to ensure optimal vibration isolation performance within the target noise frequency band. The final set value of the stiffness needs to be calculated based on the vibration transmissibility. By calculating the ratio of the natural frequency of the vibration isolation system to the target noise frequency, it is ensured 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, the damping ratio is optimized. 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 70dB, the damping ratio is set to 0.4-0.5 to accelerate vibration attenuation; when the noise intensity is lower than 50dB, the damping ratio is set to 0.05-0.2 to reduce the change in system rigidity caused by excessive damping. The optimization calculation of the damping ratio is based on the relationship curve between the damping ratio and the vibration transmissibility. First, the vibration amplitude change of the elevator structure at different frequencies is measured, and the transmissibility under different damping ratio conditions is calculated 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 propagation of vibration energy. When the target noise is mainly composed of medium and low-frequency components, the selection of dampers tends to be low-damping vibration isolators based on rubber or composite materials to avoid excessive damping causing the natural frequency of vibration to shift upward, thereby reducing the low-frequency vibration isolation effect. After the damping ratio is adjusted, the vibration isolation system's attenuation effect within the target noise frequency band is verified through impact response experiments to ensure that the actual vibration energy attenuation rate reaches more than 60%. The installation location of the vibration isolator is determined, and the modal parameters of the elevator structure are calculated through finite element analysis (FEA) to identify the area with the highest vibration energy. For example, in the 1st to 5th order modal analysis, if the vibration energy in the bearing seat area accounts for more than 70%, a vibration isolator is installed in this area to reduce the propagation of vibration to the elevator guide rails and car. The modal analysis calculation process is based on the finite element model of the elevator structure. After inputting material parameters, boundary conditions, and external forces, the vibration mode distribution under different modes is calculated, and the vibration displacement and stress energy of each structural unit are extracted. The area with concentrated vibration energy is selected as the installation point of the vibration isolator. During the actual installation process, the vibration acceleration at different locations is monitored by a vibration tester, and the changes in vibration amplitude before and after installation are compared 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 isolator stiffness, damping ratio, installation position, and vibration data before and after optimization. This data serves as the basis for elevator system optimization and is used for subsequent vibration isolation design adjustments and verification.

[0159] It is particularly important that step S36 includes the following steps:

[0160] Step S361: Counting noise vibration frequency based on 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 to above 10kHz to ensure that the main vibration components in the 0-5kHz frequency band can be captured. Then, the fast Fourier transform (FFT) is used to perform spectral analysis on the vibration signal to obtain the frequency distribution of the vibration signal. By calculating the spectral energy distribution, the main frequency components with an energy share of more than 80% are extracted and used as noise vibration frequency data. If there are multiple peaks in the noise frequency, the first three frequencies with the highest amplitude are selected as characteristic frequencies, and their amplitudes and relative energy ratios are recorded.

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

[0163] In this embodiment, the noise vibration frequency generated by different components during elevator operation is measured, and the required vibration isolator stiffness value is determined based on the measured frequency range. The vibration isolator stiffness is set based on the mass of the components in the elevator structure that are directly connected to the noise source and is adjusted in combination with the resonance characteristics. When the noise frequency is low (below 200Hz), the vibration isolator stiffness is controlled between 100-500N / mm to reduce the transmission effect of low-frequency vibration. When the noise frequency is in the medium range (200Hz-500Hz), the stiffness is set between 500-2000N / mm to ensure the vibration isolation effect within this frequency range. When the noise frequency is high (above 500Hz), the stiffness is set above 2000N / mm to prevent high-frequency vibration from propagating through the structure. Subsequently, under the rated load state of the elevator, the deformation of the vibration isolator is calculated to ensure its adaptability under actual operating conditions. Static and dynamic stiffness testing methods are further used to verify the compatibility of the set stiffness value, and ultimately determine the vibration isolator stiffness parameters that meet the elevator operation requirements.

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

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

[0166] Step S364: performing installation position analysis based on the stiffness and 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 extract the 1st to 5th order modal vibration modes. Then, the vibration energy distribution under each vibration mode is calculated, and the areas with the highest vibration energy are identified, and vibration isolators are preferentially arranged in these areas. For example, in the 1st order mode, if the vibration energy in the elevator guide rail base area accounts for more than 70%, vibration isolators are preferentially installed in this area; in the 3rd order mode, if the vibration energy peak of the traction machine base reaches 85dB, the vibration isolator installation position is adjusted to that location to reduce the impact of the vibration transmission path. Finally, the optimal installation position of the vibration isolator is comprehensively determined based on the stress conditions of the elevator operation.

[0168] Step S365: performing 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 a simulation environment, including the connections between noise sources, structural nodes, vibration isolators, and major elevator components. Then, based on the isolator stiffness and damping ratio parameters, a dynamic equation is constructed, and the vibration response under different operating conditions is calculated through numerical integration. For static conditions, the static deformation of the isolator under rated load is calculated; for dynamic conditions, the vibration attenuation curve of the isolator during elevator operation is calculated, and the attenuation ratio at the main vibration frequencies is extracted. The simulation results include data such as vibration energy distribution diagrams, displacement-time curves, and frequency response curves, which provide reference for subsequent structural optimization.

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

[0171] In this embodiment, the goal of the vibration isolator structure optimization 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, the attenuation ratio in different frequency ranges is analyzed, and it is identified whether the attenuation capacity of the vibration isolator at a specific frequency meets the design requirements. For example, if the attenuation ratio in the 100Hz-200Hz frequency band is lower than 30%, the thickness of the internal damping layer of the vibration isolator is optimized to improve the energy absorption capacity; if the attenuation ratio above 500Hz is too high, resulting in an increase in the stiffness of the system, the elastic layer material is optimized to reduce the impact of high-frequency resonance. Optimize the shape of the vibration isolator. Finite element simulation (FEA) is used 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, the cylindrical cross-section is optimized to an elliptical cross-section to improve the lateral deformation capacity; in a compression-type vibration isolator, the support structure is optimized to improve the force uniformity. In addition, the installation method of the vibration isolator is optimized, and the preload force and installation angle of the connection point are adjusted. For example, a flexible support structure is used in the guide rail connection area to reduce vertical impact vibration, and a bolted connection method is used in the traction machine base area to enhance lateral stability. Ultimately, the optimized vibration isolator parameters are stored as a standardized data set, forming vibration isolation optimization design data for practical application and adjustment of elevator systems.

[0172] Preferably, this specification also provides a three-dimensional simulation design system based on an elevator structure, which is used to execute the three-dimensional simulation design method based on an elevator structure as described above. The three-dimensional simulation design system based on an elevator structure includes:

[0173] A 3D elevator component construction module is used to obtain elevator structural drawings; construct 3D elevator components based on the elevator structural drawings; simulate the static load of the car based on the 3D elevator components to obtain the static load data of the car; and detect the structural strength of the car based on the static load data of the car;

[0174] The welding optimization design module is used to evaluate welding defects based on the strength of the car structure and generate welding defect data; identify weak weld areas based on the welding defect data; determine the amount of wear on the car structure based on the weak weld areas; predict the risk of car floor fracture based on the wear of the car structure; and perform welding optimization design based on the risk of car floor fracture to obtain welding optimization design data.

[0175] The vibration isolation optimization design module is used to perform traction simulation based on three-dimensional elevator components to obtain traction data; identify noise sources based on the traction data; and perform vibration isolation optimization design based on the noise sources 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 based on welding optimization design data and vibration isolation optimization design data, and conduct extreme working condition tests to obtain extreme working condition test data; based on the extreme working condition test data, the elevator buffer structure is optimized to obtain elevator buffer optimization structure data.

Claims

1. A three-dimensional simulation design method based on elevator structure, characterized in that: The following steps are involved: Step S1: Obtain an elevator structural drawing; construct a three-dimensional elevator component according to the elevator structural drawing; Car static load simulation is performed based on the three-dimensional elevator components to obtain car static load data; car structural strength is detected based on the car static load data; Step S2: Evaluate welding defects based on the car structure strength and generate welding defect data; Identify weak weld areas based on welding defect data; determine the amount of car structure wear based on the weak weld areas; and predict the risk of car floor fracture based on the amount of car structure wear. Based on the risk of car floor fracture, welding optimization design is performed to obtain welding optimization design data. The identification of weak weld areas in step S2 includes: Calibrate welding defect locations based on welding defect data; Extract the welded elevator joint information based on the elevator structure drawings, and build a welded joint model based on the welded elevator joint information; Identify the weld joint geometry contour of the weld joint model; Determine the weld area of ​​the weld joint geometric contour according to the welding defect position to obtain the weld area; Calculate tensile stress based on weld area; Mapping the tensile stress to the weld area and generating a weld stress distribution map; identifying weld stress concentration areas based on the weld stress distribution map; Plastic deformation simulation is performed based on the weld area, where 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; Mapping the plastic deformation data to the weld area and generating a plastic deformation distribution map; identifying high plastic deformation areas based on the plastic deformation distribution map; Perform regional intersection calculation based on the weld stress concentration area and high plastic deformation area to generate the weld weak area; Step S3: performing traction simulation based on the three-dimensional elevator assembly to obtain traction data; identifying noise sources based on the traction data; performing vibration isolation optimization design based on the noise sources to obtain vibration isolation optimization design data; Step S4: constructing a three-dimensional elevator simulation model based on the welding optimization design data and the vibration isolation optimization design data, and performing extreme working condition tests to obtain extreme working condition test data; optimizing the elevator buffer structure based on the extreme working condition test data to obtain elevator buffer optimization structure data.

2. The three-dimensional simulation design method based on elevator structure according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Obtaining elevator structure drawings; Step S12: identifying the car structure based on the elevator structural drawing, and constructing the car assembly according to the car structure; Step S13: identifying the traction structure based on the elevator structural drawing, and constructing the traction assembly according to the traction structure; Step S14: Integrate the car assembly and the traction assembly to obtain a three-dimensional elevator assembly; Step S15: performing a car static load simulation based on the three-dimensional elevator components to obtain car static load data; Step S16: Detecting the car structure strength based on the car static load data.

3. The three-dimensional simulation design method based on elevator structure according to claim 2, characterized in that: Step S16 is specifically as follows: Step S161: identifying static load distribution based on the car static load data to obtain static load distribution data; Step S162: Calculating stress based on the static load distribution data to obtain stress data; Step S163: Evaluating the degree of strain based on the stress data; Step S164: determining the yield degree of the material according to the strain degree; Step S165: Calculating the safety factor based on the yield strength of the material; Step S166: Evaluate the car structure strength according to the safety factor.

4. The three-dimensional simulation design method based on elevator structure according to claim 1, characterized in that: The evaluation of welding defects in step S2 includes: Identifying low-strength car structure areas of a three-dimensional elevator assembly based on car structure strength; Perform surface cleaning on the low-strength car structure area to obtain a clean car structure area; Apply crack penetrant to the clean car structure area to obtain penetrant coating data; Applying a defect developer based on the penetrant coating data to generate defect imaging data; Identify crack morphology based on defect imaging data to obtain crack defect data; Identify pore morphology based on defect imaging data to obtain pore defect data; Perform crack propagation evaluation based on crack defect data to obtain crack propagation data; Identify the location of the pore defect based on the pore defect data; The crack growth data and the location of the porosity defect are integrated to obtain the welding defect data.

5. The three-dimensional simulation design method based on elevator structure according to claim 1, characterized in that: Determining the amount of car structure wear in step S2 includes: Calculate contact pressure based on the weak areas of the weld; Identify contact normal force based on contact pressure; Calculate the total normal load based on the contact normal force; Calculate friction based on the total forward load; identify the car's trajectory based on the car's static load data; determine relative slip based on friction and the car's trajectory to obtain relative slip data; Detect weld wear based on relative slip data to obtain weld wear data; Calculate the wear depth of weld wear data; calculate the wear range of weld wear data; Determine the amount of car structure wear based on the wear depth and wear range.

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

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

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

9. A three-dimensional simulation design system based on elevator structure, characterized in that: For executing the three-dimensional simulation design method based on elevator structure according to claim 1, the three-dimensional simulation design system based on elevator structure comprises: A 3D elevator component construction module is used to obtain elevator structural drawings; construct 3D elevator components based on the elevator structural drawings; simulate the static load of the car based on the 3D elevator components to obtain the static load data of the car; and detect the structural strength of the car based on the static load data of the car; The welding optimization design module is used to evaluate welding defects based on the strength of the car structure and generate welding defect data; identify weak weld areas based on the welding defect data; determine the amount of wear on the car structure based on the weak weld areas; predict the risk of car floor fracture based on the amount of wear on the car structure; perform welding optimization design based on the risk of car floor fracture and obtain welding optimization design data, wherein identifying weak weld areas includes: Calibrate welding defect locations based on welding defect data; Extract the welded elevator joint information based on the elevator structure drawings, and build a welded joint model based on the welded elevator joint information; Identify the weld joint geometry contour of the weld joint model; Determine the weld area of ​​the weld joint geometric contour according to the welding defect position to obtain the weld area; Calculate tensile stress based on 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; Plastic deformation simulation is performed based on the weld area, where 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; Mapping the plastic deformation data to the weld area and generating a plastic deformation distribution map; identifying high plastic deformation areas based on the plastic deformation distribution map; Perform regional intersection calculation based on the weld stress concentration area and high plastic deformation area to generate the weld weak area; The vibration isolation optimization design module is used to perform traction simulation based on three-dimensional elevator components to obtain traction data; identify noise sources based on the traction data; and perform vibration isolation optimization design based on the noise sources to obtain vibration isolation optimization design data; The elevator three-dimensional simulation model construction module is used to construct an elevator three-dimensional simulation model based on welding optimization design data and vibration isolation optimization design data, and conduct extreme working condition tests to obtain extreme working condition test data; based on the extreme working condition test data, the elevator buffer structure is optimized to obtain elevator buffer optimization structure data.

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