Anti-falling device integration method and system based on light guide rail coupling and deformation control

Abstract

The invention relates to an anti-falling device integration method and system based on light guide rail coupling and deformation control, and the method comprises the following steps: carrying out the stress distribution scanning of a light guide rail through a laser interference measurement system, and obtaining a guide rail stress distribution map; based on the guide rail stress distribution map, carrying out dynamic rigidity analysis on the light guide rail to obtain a guide rail rigidity characteristic curve; carrying out coupling calculation on the light guide rail based on the guide rail rigidity characteristic curve to obtain guide rail coupling parameters; based on the guide rail coupling parameters, structural design and layout optimization are conducted on the anti-falling device, and an anti-falling device integration scheme is obtained; and performing virtual assembly and performance simulation on the integration scheme of the anti-falling device to obtain a performance evaluation report of the anti-falling system, thereby solving the technical problem that the integrated design of the anti-falling system generally lacks in-depth analysis on the mechanical characteristics of the guide rail body, so that potential safety hazards are difficult to find and correct in time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lightweight guide rail, in particular to a kind of based on the coupling and deformation control of lightweight guide rail Anti-falling device integration method and system. BACKGROUND

[0002] In the field of high-altitude operation, large equipment maintenance and rail transit, anti-falling safety has become a key link to protect personnel and equipment safety. Traditional anti-falling system relies on rigid guide rail and mechanical anti-falling device, but such system often has problems such as heavy weight, complex installation and poor adaptability, which is difficult to meet the demand of modern engineering for lightweight, high efficiency and intelligence. With the development of new materials and precision manufacturing technology, lightweight guide rail has gradually become a research hotspot, but its mechanical properties and deformation behavior under dynamic load are relatively complex, which makes it difficult for existing anti-falling device to match its performance, thereby affecting the safety and reliability of the overall system.

[0003] Currently, the integration design of anti-falling system generally lacks in-depth analysis of the mechanical properties of guide rail body, most schemes directly use empirical design or parameter setting under static conditions, ignoring the stress distribution and stiffness change of guide rail in actual working conditions. This design method is easy to cause the coupling between anti-falling device and guide rail to be out of coordination, and then cause problems such as response lag, insufficient braking force or false triggering. In addition, due to the lack of effective measurement and simulation means, the traditional performance evaluation method is difficult to fully reflect the running state of the system in complex environment, so that safety hazards are difficult to find and correct in time.

[0004] Therefore, a design method combining high-precision measurement, dynamic stiffness analysis and structure optimization is needed to realize efficient coupling and collaborative control between lightweight guide rail and anti-falling device. By introducing advanced technologies such as laser interferometry, numerical simulation and virtual assembly, the working characteristics of the guide rail can be more accurately mastered, and the customized design and layout optimization of the anti-falling device can be carried out accordingly, ultimately improving the safety, stability and adaptability of the entire anti-falling system. This not only helps to solve the problem of poor matching between guide rail and anti-falling device in existing technology, but also provides a new technical path for the development of future intelligent anti-falling system. SUMMARY

[0005] The main purpose of the present application is to provide an anti-falling device integration method based on the coupling and deformation control of lightweight guide rail, which solves the technical problem that the integration design of anti-falling system generally lacks in-depth analysis of the mechanical properties of guide rail body, so that safety hazards are difficult to find and correct in time.

[0006] To achieve the above purpose, the present application provides an anti-falling device integration method based on the coupling and deformation control of lightweight guide rail, applied to an anti-falling system, the anti-falling system comprising a lightweight guide rail and an anti-falling device, comprising the following steps: scanning the light guide rail by a laser interferometry system to obtain a guide rail stress distribution atlas; performing dynamic stiffness analysis on the light guide rail based on the guide rail stress distribution atlas to obtain a guide rail stiffness characteristic curve; performing coupling calculation on the light guide rail based on the guide rail stiffness characteristic curve to obtain guide rail coupling parameters; performing structural design and layout optimization on the fall arrest device based on the guide rail coupling parameters to obtain a fall arrest device integration scheme; performing virtual assembly and performance simulation on the fall arrest device integration scheme to obtain a fall arrest system performance evaluation report.

[0007] Further, the scanning the light guide rail by a laser interferometry system to obtain a guide rail stress distribution atlas comprises: performing multi-angle strain field acquisition on the surface of the light guide rail by a laser interferometry system to obtain a guide rail surface strain data set, and performing reconstruction calculation on the internal stress state of the guide rail based on the strain data set using an elastic mechanics inversion algorithm to obtain a three-dimensional stress tensor field; performing principal stress direction decomposition on the three-dimensional stress tensor field to obtain anisotropic stress components, and performing comparative analysis on the local yield strength threshold of the light guide rail based on the anisotropic stress components to obtain a stress concentration area identification layer; performing local grid encryption modeling on the light guide rail based on the stress concentration area identification layer to obtain an encrypted finite element model, and performing nonlinear iterative solution on the encrypted finite element model by applying dynamic load boundary conditions to obtain a stress evolution path atlas of the light guide rail under typical working conditions.

[0008] Further, the performing dynamic stiffness analysis on the light guide rail based on the guide rail stress distribution atlas to obtain a guide rail stiffness characteristic curve comprises the following steps: extracting modal parameters of the light guide rail based on the guide rail stress distribution atlas to obtain a modal frequency set and a modal shape matrix, and calculating the mode shape energy distribution of the light guide rail based on the modal shape matrix to obtain a mode shape energy density field; simulating the dynamic load working condition of the light guide rail based on the mode shape energy density field to obtain a dynamic load time sequence, and performing transient dynamics response solution on the light guide rail based on the dynamic load time sequence to obtain displacement response time history data; performing frequency response function calculation on the light guide rail based on the displacement response time history data to obtain a frequency response function matrix, and performing stiffness parameter inversion on the light guide rail based on the frequency response function matrix to obtain a stiffness matrix element set; The light guide rail is coupled stiffness corrected based on the set of stiffness matrix elements, a coupling stiffness correction coefficient is obtained, and a guide rail stiffness characteristic curve is drawn based on the coupling stiffness correction coefficient, to obtain a guide rail stiffness characteristic curve.

[0009] Further, the light guide rail is coupled calculated based on the guide rail stiffness characteristic curve, to obtain a guide rail coupling parameter, including: The guide rail damping boundary condition is obtained by calculating the critical damping ratio of the light guide rail based on the guide rail stiffness characteristic curve, and the guide rail damping boundary condition is processed by frequency domain convolution to obtain a coupling energy transmission efficiency spectrum; The guide rail multi-degree-of-freedom coupling response matrix is obtained by modal superposition analysis on the coupling energy transmission efficiency spectrum, and the optimal coupling point position distribution map is obtained by eigenvalue reconstruction processing based on the coupling response matrix; The optimal coupling point position distribution map is optimized by gradient descent, and the optimized optimal coupling point position distribution map is processed by contour extraction to obtain a coupling strength threshold partition; The dynamic frequency of the light guide rail is tested based on the coupling strength threshold partition to obtain a coupling frequency response curve, and the guide rail coupling parameter is obtained by harmonic component decomposition processing based on the coupling frequency response curve.

[0010] Further, the guide rail coupling parameter is used to design the structure and optimize the layout of the anti-falling device to obtain an anti-falling device integration scheme, including: The anti-falling device is topologically constrained mapped based on the guide rail coupling parameter to obtain a coupling constraint boundary condition, and the anti-falling mechanism geometric topology map is obtained by multi-objective optimization solution of the coupling constraint boundary condition; The anti-falling device is parameterized modeled through the anti-falling mechanism geometric topology map to obtain a three-dimensional entity geometric model, and the geometric size of the three-dimensional entity geometric model is iteratively optimized to obtain an anti-falling mechanism geometric size distribution matrix, wherein the anti-falling mechanism geometric size distribution matrix includes the local stiffness contribution degree, mass density distribution function and installation interface matching degree of each component; The material properties of the anti-falling device are mapped based on the guide rail coupling parameter to obtain a material selection candidate set, and the material selection candidate set is analyzed by multi-attribute decision to obtain an anti-falling device material property matching matrix; The anti-falling device material property matching matrix is hierarchically clustered to obtain a material selection priority sequence, and the installation position of the anti-falling device is planned based on the material selection priority sequence to obtain an anti-falling device integration scheme.

[0011] Further, the virtual assembly and performance simulation are performed on the anti-falling device integration scheme to obtain an anti-falling system performance evaluation report, including: The anti-falling device and the light guide rail are virtually assembled based on the anti-falling device integration scheme to obtain a virtual assembly model, and the virtual assembly model is subjected to finite element mesh division to obtain a finite element analysis model, wherein the virtual assembly model includes the relative position relationship and the connection state of the anti-falling device and the light guide rail. The preset falling load and boundary conditions are applied to the finite element analysis model to obtain system dynamic response data, and the energy absorption characteristics of the anti-falling system are analyzed based on the system dynamic response data to obtain an energy absorption curve. The light guide rail is subjected to deformation analysis based on the energy absorption curve to obtain a guide rail deformation curve, and the guide rail stability index is determined based on the guide rail deformation curve, wherein the guide rail stability index includes a buckling critical load and a safety factor. The overall reliability of the anti-falling system is evaluated based on the system dynamic response data and the guide rail stability index to obtain an anti-falling system performance evaluation report.

[0012] Further, the light guide rail is subjected to deformation analysis based on the energy absorption curve to obtain a guide rail deformation curve, including: The energy absorption curve is subjected to time domain integral processing to obtain a dynamic load distribution function of the light guide rail, and the light guide rail is subjected to transient stress analysis based on the dynamic load distribution function to obtain a guide rail stress wave propagation spectrum. The guide rail stress wave propagation spectrum is subjected to numerical simulation by a nonlinear finite element method to obtain a dynamic constitutive relationship curve of the light guide rail, and a plastic deformation region distribution map is constructed based on the dynamic constitutive relationship curve. The light guide rail is subjected to geometric nonlinear large deformation analysis based on the plastic deformation region distribution map to obtain a guide rail deformation evolution sequence, and the guide rail deformation evolution sequence is subjected to stability evaluation by an elastic-plastic bifurcation theory to obtain a critical deformation threshold curve. The critical deformation threshold curve is subjected to piecewise linearization processing to obtain a guide rail deformation feature point sequence, and the guide rail deformation feature point sequence is subjected to spline interpolation fitting to obtain a guide rail deformation curve.

[0013] The application further provides an anti-falling device integration system based on light guide rail coupling and deformation control, which is applied to an anti-falling system including a light guide rail and an anti-falling device, and includes: A scanning device is configured to scan the stress distribution of the light guide rail by a laser interference measurement system to obtain a guide rail stress distribution spectrum. An analysis device is configured to perform dynamic stiffness analysis on the light guide rail based on the guide rail stress distribution map, and obtain a guide rail stiffness characteristic curve; A calculation device is configured to perform coupling calculation on the light guide rail based on the guide rail stiffness characteristic curve, and obtain guide rail coupling parameters; An optimization device is configured to perform structural design and layout optimization on the anti-falling device based on the guide rail coupling parameters, and obtain an anti-falling device integration scheme; A simulation device is configured to perform virtual assembly and performance simulation on the anti-falling device integration scheme, and obtain an anti-falling system performance evaluation report.

[0014] The application further provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of the preceding embodiments when executing the computer program.

[0015] The application further provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the steps of the method according to any one of the preceding embodiments.

[0016] The application provides an anti-falling device integration method based on light guide rail coupling and deformation control, including the following steps: performing stress distribution scanning on a light guide rail by a laser interference measurement system to obtain a guide rail stress distribution map; performing dynamic stiffness analysis on the light guide rail based on the guide rail stress distribution map to obtain a guide rail stiffness characteristic curve; performing coupling calculation on the light guide rail based on the guide rail stiffness characteristic curve to obtain guide rail coupling parameters; performing structural design and layout optimization on an anti-falling device based on the guide rail coupling parameters to obtain an anti-falling device integration scheme; and performing virtual assembly and performance simulation on the anti-falling device integration scheme to obtain an anti-falling system performance evaluation report. The method solves the technical problem that the integration design of an anti-falling system generally lacks in-depth analysis of the mechanical properties of a guide rail body, and the safety hazards are difficult to be found and corrected in time, and achieves the technical effects that the entire anti-falling system can be comprehensively evaluated before the implementation of a scheme by using virtual assembly and performance simulation technology, potential defects or risk points are found in advance, the design scheme is optimized, and the safety of system operation and the reliability of long-term use are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a step schematic diagram of the anti-falling device integration method based on light guide rail coupling and deformation control in an embodiment of the application; Figure 2 is a structural block diagram of an anti-falling device integration system based on light guide rail coupling and deformation control in an embodiment of the application; Figure 3 is a structural schematic block diagram of a computer device in an embodiment of the application.

[0018] The purposes, functional features and advantages of the present application will be further illustrated in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0020] As shown in Figure 1 , Figure 1 is an integration method of a fall protection device based on lightweight guide rail coupling and deformation control in an embodiment of the present application, applied to a fall protection system, the fall protection system including a lightweight guide rail and a fall protection device, comprising the following steps: Step S1, scanning the stress distribution of the lightweight guide rail by a laser interference measurement system to obtain a guide rail stress distribution map.

[0021] Specifically, scanning the stress distribution of the lightweight guide rail by the laser interference measurement system to obtain a guide rail stress distribution map is a key technical step in the integration method of the fall protection device, aiming to realize high-precision, non-contact real-time measurement of the internal stress distribution of the lightweight guide rail under stress state. In the specific implementation process, first, the lightweight guide rail is installed on a test platform simulating the actual working condition, and a plurality of reference mark points are arranged on the surface of the lightweight guide rail, then the laser interference measurement system is started, and the high-sensitivity optical sensor of the system is used to scan the small deformation of the guide rail under the loading state, the system analyzes the change of the light wave interference fringes to inverse the strain distribution of each region on the surface of the guide rail, and a complete guide rail stress distribution map is constructed. For example, in the application scenario of the aerial work platform, when the worker moves along the lightweight guide rail, the guide rail will produce uneven stress distribution due to local load change, at this time, the high stress area and weak position of the guide rail can be accurately identified through this step, providing reliable data support for subsequent dynamic stiffness analysis, ensuring that the subsequent coupling calculation and fall protection device design can be optimized based on the real mechanical response, thereby improving the adaptability and safety of the entire fall protection system.

[0022] Step S2, based on the guide rail stress distribution map, performing dynamic stiffness analysis on the lightweight guide rail to obtain a guide rail stiffness characteristic curve.

[0023] Specifically, based on the rail stress distribution map, the dynamic stiffness analysis of the lightweight rail is carried out to obtain the rail stiffness characteristic curve, which is an important link for realizing the quantitative evaluation of the structure performance in the integrated method. In the specific implementation process, first, the rail stress distribution map obtained in the previous step is taken as the input data and imported into the finite element analysis software or the special mechanical simulation platform, and a high-precision three-dimensional mechanical model is established combined with the actual material properties and geometric structure of the lightweight rail; then, the dynamic load changes of the rail under the actual working conditions are simulated in the simulation environment, such as the periodic excitation caused by personnel walking, wind disturbance and other factors during the movement of the aerial work platform, and the deformation response of the rail under different frequencies and loads is calculated through modal analysis and harmonic response analysis method; finally, the system outputs the relationship curve of the stiffness of the rail at each key position with the load change, i.e. the rail stiffness characteristic curve, which is used to represent the mechanical stability of the rail under dynamic conditions. For example, during the movement of the aerial work platform along the lightweight rail, the external force on the rail at different points is different, causing the local stiffness of the rail to change, and through this step, the change trend can be accurately captured to provide a basis for the extraction of subsequent coupling parameters, so as to ensure that the fall arrest device can be accurately matched and dynamically designed according to the actual stiffness characteristics of the rail.

[0024] Step S3, based on the rail stiffness characteristic curve, coupling calculation is carried out on the lightweight rail to obtain the rail coupling parameters.

[0025] Specifically, based on the rail stiffness characteristic curve, coupling calculation is carried out on the lightweight rail to obtain the rail coupling parameters, which is a key technical link for realizing the mechanical matching of the fall arrest device and the rail system in the integrated method. In the specific implementation process, first, the rail stiffness characteristic curve obtained in the previous step is taken as the input data, combined with the preliminary design model of the fall arrest device, a mechanical coupling model between the rail and the fall arrest device is established in the multi-body dynamics simulation environment, and the typical working conditions during the operation of the aerial work platform are simulated through boundary condition setting; then, the dynamic coupling analysis of the contact force, constraint reaction force and motion response between the two is carried out by using numerical calculation method, so as to extract the coupling parameters which can represent the interaction relationship between the rail and the fall arrest device, including but not limited to coupling stiffness coefficient, damping ratio and dynamic response delay time, etc.; these rail coupling parameters not only reflect the stress characteristics of the fall arrest device under different rail stiffness regions, but also provide quantitative basis for subsequent structure optimization. For example, during the movement of the aerial work personnel along the lightweight rail, the stiffness distribution of the rail changes due to local deformation, which further affects the clamping force and response speed of the fall arrest device, and through this step, the coupling effect caused by the change can be accurately identified, and the action threshold and installation position of the fall arrest device are adjusted accordingly to ensure that the system can still maintain stable and reliable fall arrest performance under complex working conditions.

[0026] Step S4, based on the guide rail coupling parameters, the structure of the anti-falling device is designed and layout is optimized to obtain an anti-falling device integration scheme.

[0027] Specifically, based on the guide rail coupling parameters, the structure of the anti-falling device is designed and layout is optimized to obtain an anti-falling device integration scheme, which is the core link of realizing system-level performance matching and functional collaboration in the method. In the specific implementation process, first, the guide rail coupling parameters obtained in the previous step are taken as the key input conditions for structural design, combined with the functional requirements and use environment of the anti-falling device, an initial structural model of the anti-falling device is constructed in a three-dimensional modeling platform, and the strength, stiffness and dynamic response characteristics of key components such as clamping mechanisms, sensing elements and buffer assemblies are designed through finite element analysis and multi-objective optimization algorithm; at the same time, according to the stress distribution characteristics and dynamic response regions reflected by the guide rail coupling parameters, the installation positions of multiple anti-falling devices on the guide rail are optimized in space layout to ensure that they can effectively respond under different load conditions and maintain the overall stability of the system; finally, an anti-falling device integration scheme is formed, which takes into account lightweight, high adaptability and high reliability. For example, in the application scenario of the aerial work platform moving along the lightweight guide rail, since the local stiffness change of the guide rail may affect the triggering time and clamping effect of the anti-falling device, through this step, the geometric structure and arrangement of the device can be precisely adjusted, so that it can enhance the response sensitivity in the weak area of the guide rail and optimize the stress distribution in the high stiffness area, thereby comprehensively improving the safety and adaptability of the anti-falling system.

[0028] Step S5, virtual assembly and performance simulation are performed on the anti-falling device integration scheme to obtain an anti-falling system performance evaluation report.

[0029] Specifically, the virtual assembly and performance simulation of the fall protection device integration scheme are performed to obtain a fall protection system performance evaluation report, which is a key closed-loop link for realizing design verification and system optimization in the method. In the specific implementation process, first, the completed fall protection device integration scheme with structure design and layout optimization is imported into the virtual assembly platform, and a complete digital prototype is constructed based on the actual geometric model and material properties of the lightweight guide rail, ensuring that the assembly relationship, motion constraint, and actual installation state between components are consistent; then, in the multi-body dynamics simulation environment, typical working condition parameters such as dynamic load during the movement of the high-altitude worker, sudden falling impact force, and environmental vibration interference are set, and the response characteristics, clamping force distribution, motion synchronization, and energy absorption capacity of the entire fall protection system are simulated and analyzed throughout the process; finally, the fall protection system performance evaluation report covering key performance indicators is automatically generated, including trigger time, maximum impact acceleration, structural stress peak value, and fatigue life prediction. For example, in the application scenario of the high-altitude work platform running along the lightweight guide rail, the response lag or clamping failure risk caused by local stiffness changes can be identified in advance through this step, and the design scheme is iteratively optimized accordingly, thereby ensuring that the fall protection system has fast, stable, and reliable protection capability in real applications.

[0030] In specific embodiments, the stress distribution scanning of the lightweight guide rail by the laser interference measurement system obtains a guide rail stress distribution map, including: The surface of the lightweight guide rail is collected by the laser interference measurement system at multiple angles to obtain a guide rail surface strain data set, and the stress state inside the guide rail is reconstructed and calculated based on the strain data set using an elastic mechanics inversion algorithm to obtain a three-dimensional stress tensor field; The three-dimensional stress tensor field is decomposed in the principal stress direction to obtain anisotropic stress components, and the local yield strength threshold of the lightweight guide rail is compared and analyzed based on the anisotropic stress components to obtain a stress concentration area identification layer; Based on the stress concentration area identification layer, the lightweight guide rail is locally grid-encrypted modeled to obtain an encrypted finite element model, and the encrypted finite element model is subjected to non-linear iterative solving by applying dynamic load boundary conditions to obtain a stress evolution path map of the lightweight guide rail under typical working conditions.

[0031] Specifically, the stress distribution scanning of the light guide rail by the laser interferometry system to obtain the guide rail stress distribution map is a key step in the integrated method of the fall protection device to realize accurate perception and modeling analysis of structural mechanics characteristics. The purpose is to obtain the full-field strain response of the guide rail under complex load without damaging the guide rail body, and further deduce the internal three-dimensional stress state to provide high-precision data support for subsequent dynamic stiffness analysis and coupling design. In the specific implementation process, first, the laser interferometry system is used to collect the strain field of the surface of the light guide rail at multiple angles. By controlling the optical probe of the measurement system to scan the surface of the guide rail in different directions non-contact, the micro-deformation information of each region of the guide rail under load is obtained, and the guide rail surface strain data set is formed. The strain data set not only includes the displacement vector of each point on the surface of the guide rail, but also covers the strain components in different directions, providing complete basic data support for subsequent elastic mechanics inversion. On this basis, an inversion algorithm based on elastic mechanics theory, such as finite element assisted inversion method or Green function method, is used to map the surface strain data to the interior of the guide rail, and then reconstruct the three-dimensional stress tensor field in the interior of the guide rail. This process fully considers the anisotropic material properties of the light guide rail and its geometric boundary conditions, ensuring that the obtained stress field has high spatial resolution and physical accuracy. Further, in order to identify potential weak spots in the guide rail structure, the three-dimensional stress tensor field needs to be decomposed in the principal stress direction to extract the anisotropic stress components, i.e. the maximum and minimum principal stress distribution along the material principal direction (such as fiber direction, lamination direction, etc.). By comparing and analyzing these stress components with the local yield strength threshold of the light guide rail material, the stress concentration areas that may occur under the current load condition of the guide rail can be effectively identified, and a stress concentration area identification layer is generated. This layer visually marks the key positions in the guide rail where plastic deformation or fatigue damage may occur, laying the foundation for subsequent refined modeling and dynamic performance evaluation. For example, in the application scenario of the aerial work platform moving along the light guide rail, when the personnel carrying tools quickly move or suddenly stop, the local area of the guide rail will bear periodic impact load, causing significant stress concentration in some cross sections. At this time, through this step, the high-risk areas can be accurately identified to avoid overall system failure caused by local failure.Finally, based on the identified stress concentration areas, the lightweight guide rail is locally grid-encrypted modeled based on the stress concentration area identification layer, and a higher spatial resolution encrypted finite element model is constructed; the model uses finer unit division in the key area to ensure the convergence and accuracy of the simulation results, while retaining the coarse grid in the non-key area to improve the calculation efficiency; then, by applying dynamic load boundary conditions such as periodic moving load, impact load or wind vibration excitation to the encrypted finite element model, typical working conditions under high-altitude operation environment are simulated, and nonlinear iterative solution is carried out to track the stress evolution path of the guide rail under stress process; finally, the stress evolution path atlas of the lightweight guide rail under typical working conditions is output, which clearly shows the stress change trend of each area of the guide rail at different time steps, helps to deeply understand its response mechanism under dynamic load, and provides dynamic behavior basis for subsequent stiffness analysis and fall arrest device design. For example, during the continuous operation of the aerial work platform, the guide rail is repeatedly subjected to moving load, and through this step, the residual stress gradually accumulated over time in a certain section of the guide rail can be captured, so as to predict its long-term service performance and optimize the arrangement strategy of the fall arrest device, thereby improving the reliability and safety of the entire fall arrest system.

[0032] In specific embodiments, the dynamic stiffness analysis of the lightweight guide rail based on the guide rail stress distribution atlas to obtain the guide rail stiffness characteristic curve includes the following steps: Based on the guide rail stress distribution atlas, modal parameter extraction is performed on the lightweight guide rail to obtain a modal frequency set and a modal shape matrix, and based on the modal shape matrix, the mode shape energy distribution of the lightweight guide rail is calculated to obtain a mode shape energy density field; Based on the mode shape energy density field, the dynamic load working condition of the lightweight guide rail is simulated to obtain a dynamic load time sequence, and based on the dynamic load time sequence, transient dynamics response solving is performed on the lightweight guide rail to obtain displacement response time history data; Based on the displacement response time history data, frequency response function calculation is performed on the lightweight guide rail to obtain a frequency response function matrix, and based on the frequency response function matrix, stiffness parameter inversion is performed on the lightweight guide rail to obtain a stiffness matrix element set; Based on the stiffness matrix element set, coupling stiffness correction is performed on the lightweight guide rail to obtain a coupling stiffness correction coefficient, and based on the coupling stiffness correction coefficient, stiffness characteristic curve drawing is performed on the lightweight guide rail to obtain the guide rail stiffness characteristic curve.

[0033] Specifically, the dynamic stiffness analysis of the light guide rail based on the guide rail stress distribution map obtains a guide rail stiffness characteristic curve, which is a core step in the integrated method of the fall protection device to realize the transition from static mechanical response to dynamic performance modeling. The purpose is to extract the stiffness evolution law of the light guide rail under complex load through high-precision structural dynamics analysis means, so as to provide dynamic parameter support for subsequent coupling calculation and fall protection device design. In the specific implementation process, first, based on the key stress area and stress concentration characteristics revealed by the guide rail stress distribution map, a finite element model with high fidelity is constructed, and on this basis, modal parameter extraction is carried out to obtain the modal frequency set and modal shape matrix of the guide rail in the free vibration state; among them, the modal frequency set reflects the distribution of the natural frequencies of the guide rail, and the modal shape matrix describes the relative vibration form of each node of the guide rail at different frequencies; then, the modal shape matrix is used to further calculate the mode shape energy distribution of the light guide rail, and the mode shape energy density field is generated, which can directly reflect the spatial distribution of the energy concentration degree of the guide rail under different modes, and provide key input basis for subsequent dynamic load simulation. Then, based on the known mode shape energy density field, combined with the typical working conditions that may be encountered in the actual operation process of the aerial work platform (such as personnel movement, equipment vibration, wind load disturbance, etc.), the dynamic load working condition of the light guide rail is simulated, and the corresponding dynamic load time sequence is generated accordingly; this time sequence not only contains the change trend of the load amplitude with time, but also considers the spatial distribution characteristics of the load application position, ensuring that the simulation conditions are highly consistent with the real application scene; then, based on the dynamic load time sequence, the transient dynamics response of the light guide rail is solved, and the explicit or implicit integration algorithm is used to track the displacement response of the guide rail under impact or periodic load throughout the process, and the displacement response time data is output; this data records the motion trajectory and deformation amplitude of each part of the guide rail at different time nodes, and is an important basis for evaluating the stability of its dynamic behavior. For example, during the movement of the aerial work personnel along the guide rail, due to the foot impact and platform shaking, the guide rail will experience a complex vibration process, and at this time, the displacement response characteristics of the key parts of the guide rail can be accurately captured through this step, laying a foundation for subsequent frequency response analysis. Further, after obtaining the displacement response time data, it is processed by frequency domain transformation, and a frequency response function matrix is established based on the relationship between the system input and output, which describes the response amplitude and phase information of each measuring point of the guide rail under different frequency excitations; through the analysis of the frequency response function matrix, the stiffness weak links in the guide rail structure and their frequency-dependent characteristics can be identified; then, based on the frequency response function matrix, the stiffness parameter inversion is carried out, and the least squares method, regularization inversion and other methods are used to estimate the stiffness matrix element set of each element in the guide rail; these stiffness matrix elements not only reflect the elastic modulus distribution of the guide rail material itself, but also contain the influence factors of structural geometric nonlinearity and boundary constraints, and have high engineering application value.On this basis, considering the interaction force between the fall protection device and the guide rail, the light guide rail also needs to be coupled stiffness corrected based on the set of stiffness matrix elements, and a coupling stiffness correction coefficient is introduced to reflect the change of contact stiffness between the two, and finally the guide rail stiffness characteristic curve suitable for integrated design is formed; The curve is presented in the form of load-displacement relationship, which clearly expresses the stiffness evolution trend of the guide rail in different loading stages, and provides accurate mechanical basis for the subsequent response threshold setting and layout optimization of the fall protection device. For example, in the application scenario of frequent starting and braking of aerial work platforms, the local stiffness of the guide rail may cause unstable triggering time of the fall protection device, and this step can identify and correct such stiffness difference in advance, thereby improving the dynamic matching and protection reliability of the entire fall protection system.

[0034] In specific embodiments, the coupling calculation of the light guide rail based on the guide rail stiffness characteristic curve obtains guide rail coupling parameters, including: Based on the guide rail stiffness characteristic curve, the critical damping ratio calculation of the light guide rail obtains guide rail damping boundary conditions, and the guide rail damping boundary conditions are subjected to frequency domain convolution processing to obtain a coupled energy transmission efficiency spectrum; The modal superposition analysis is performed on the coupled energy transmission efficiency spectrum to obtain a guide rail multi-degree-of-freedom coupling response matrix, and the eigenvalue reconstruction processing is performed based on the coupling response matrix to obtain an optimal coupling point position distribution map; The gradient descent optimization is performed on the optimal coupling point position distribution map, and the optimized optimal coupling point position distribution map is subjected to contour extraction processing to obtain a coupling strength threshold partition; The dynamic frequency of the light guide rail is tested based on the coupling strength threshold partition to obtain a coupled frequency response curve, and the harmonic component decomposition processing is performed based on the coupled frequency response curve to obtain guide rail coupling parameters.

[0035] Specifically, the coupling calculation of the light guide rail based on the guide rail stiffness characteristic curve obtains a guide rail coupling parameter, which is a key step for realizing system-level dynamic response matching and function coordination in the fall protection device integration method. The purpose is to extract key coupling characteristic parameters between the guide rail and the fall protection device through high-precision mechanical modeling and numerical analysis means, thereby providing quantitative basis for subsequent structure design and layout optimization. In the specific implementation process, first, based on the load-displacement relationship represented by the guide rail stiffness characteristic curve, combined with material damping characteristics and boundary constraint conditions, the critical damping ratio of the light guide rail is calculated, so as to determine the maximum energy dissipation capacity that the guide rail can withstand under typical working conditions. On this basis, the guide rail damping boundary condition is further obtained, that is, the energy attenuation rate and its spatial distribution characteristics of the guide rail under different frequency excitations; then, the guide rail damping boundary condition is subjected to frequency domain convolution processing, that is, the damping response of the guide rail under different frequencies is subjected to mathematical convolution operation with the external excitation function, so as to simulate the influence of high-frequency vibration or sudden impact on the guide rail in the actual environment, and finally generate a coupling energy transmission efficiency spectrum; this spectrum clearly reflects the whole process from external load input to guide rail internal energy response, and can identify which areas have higher energy transmission efficiency, thereby providing a physical basis for subsequent multi-degree-of-freedom coupling modeling. Next, on the basis of obtaining the coupling energy transmission efficiency spectrum, modal superposition analysis is performed, that is, the modal shape matrix and modal frequency set extracted in the foregoing are used, the guide rail as a whole is regarded as a plurality of interrelated vibration subsystems, and the dynamic response behavior of the whole system is reconstructed by linear combination; this process can identify the multi-degree-of-freedom coupling response matrix of the guide rail under the participation of different modes, which contains the movement degrees of freedom of each node in multiple directions and their interaction relationship; then, eigenvalue reconstruction processing is performed based on the coupling response matrix, that is, principal component analysis or singular value decomposition method is adopted to extract the key feature vectors of the dominant coupling behavior, so as to identify the most representative optimal coupling point position distribution diagram in the guide rail; these optimal coupling points are usually located at the stress concentration areas or energy transmission path intersections of the guide rail, and have high mechanical sensitivity and control value. For example, in the application scenario of the aerial work platform moving along the light guide rail, when the personnel carrying heavy objects suddenly stop, the local guide rail will experience transient impact, at this time, through this step, the several points (such as the 3rd, 5th and 7th guide rail connection parts) with the densest energy transmission in the guide rail can be accurately identified, and they are used as the key areas for subsequent installation of the fall protection device.Further, after obtaining the optimal coupling point position distribution map, a gradient descent optimization algorithm is applied to minimize the overall system response error as the objective function, and the position distribution and number configuration of the coupling points are continuously adjusted to enable the fall arrest device to achieve optimal matching response in different stiffness regions of the guide rail. After optimization, the optimized optimal coupling point position distribution map is subjected to contour extraction processing, that is, according to the spatial distribution law of coupling strength, several contour lines are drawn to form a coupling strength threshold partition; the partition map marks the area where the coupling strength in the guide rail is greater than the set threshold with color or gray scale (such as red for strong coupling area and blue for weak coupling area), providing a clear measurement range for subsequent frequency response testing; then, based on the coupling strength threshold partition, dynamic frequency testing is performed on the lightweight guide rail, that is, a controllable sinusoidal sweep excitation is applied to the guide rail in the experimental environment, and its resonance response at different frequencies is recorded to obtain the coupling frequency response curve; the curve reveals the amplitude amplification effect of the guide rail under different frequency excitations, which can be used to evaluate its dynamic stability and resonance risk. Finally, based on the obtained coupling frequency response curve, harmonic component decomposition processing is performed, that is, the complex response signal in the curve is decomposed into multiple single-frequency harmonic components by using the fast Fourier transform (FFT) technique, and then the key frequency components of the dominant coupling behavior and their corresponding amplitude information are extracted; through statistical analysis of these harmonic components, the main vibration frequency range of the guide rail in different coupling strength regions (such as 12Hz~28Hz as the main response interval) can be identified, and thus the guide rail coupling parameters are finally obtained; these coupling parameters include but are not limited to coupling stiffness coefficient, coupling damping ratio, dynamic response delay time and energy transfer efficiency, etc., which constitute the core parameter set of the mechanical interaction between the fall arrest device and the guide rail. For example, under the frequent start-stop operating state of the aerial work platform, the guide rail causes inconsistent response of the fall arrest device clamping force due to coupling effect, and through this step, the coupling strength variation law between the guide rail and the fall arrest device at different frequencies can be accurately identified, and accordingly the action sensitivity and installation density of the fall arrest device are adjusted, so that the system can still maintain stable and reliable protection performance when encountering sudden impact (such as wind speed suddenly increasing to 15m / s), thereby comprehensively improving the dynamic adaptability and safety redundancy of the entire fall arrest system.

[0036] In specific embodiments, based on the guide rail coupling parameters, the structure of the fall arrest device is designed and the layout is optimized to obtain a fall arrest device integration scheme, including: Based on the guide rail coupling parameters, the fall arrest device is subjected to topological constraint mapping to obtain coupling constraint boundary conditions, and the coupling constraint boundary conditions are subjected to multi-objective optimization solving to obtain a fall arrest mechanism geometric topology map; The anti-falling mechanism geometry topological map is used to parameterize modeling of the anti-falling device, to obtain a three-dimensional entity geometry model, and to iteratively optimize the geometry size of the three-dimensional entity geometry model, to obtain an anti-falling mechanism geometry size distribution matrix, wherein the anti-falling mechanism geometry size distribution matrix includes local stiffness contribution of each component, mass density distribution function, and installation interface matching degree; Based on the guide rail coupling parameters, material properties of the anti-falling device are mapped to obtain a material selection candidate set, and multi-attribute decision analysis is performed on the material selection candidate set to obtain an anti-falling device material property matching matrix; The anti-falling device material property matching matrix is subjected to hierarchical clustering processing to obtain a material selection priority sequence, and based on the material selection priority sequence, an installation position of the anti-falling device is planned to obtain an anti-falling device integration scheme.

[0037] Specifically, based on the guide rail coupling parameters, the anti-falling device is subjected to structure design and layout optimization to obtain an anti-falling device integration scheme, which is the core step in the method to realize the transformation from system-level mechanical properties to specific engineering implementation, and the purpose is to convert the dynamic coupling relationship between the guide rail and the anti-falling device into operable geometric, material, and installation parameters, to ensure that the designed anti-falling system has comprehensive performance of fast response, stable clamping, and high reliability under complex working conditions. In the specific implementation process, first, based on the key mechanical characteristics represented by the guide rail coupling parameters (such as the coupling stiffness coefficient is 1.2×10 5N / m, damping ratio is 0.15, etc.), topological constraint mapping is performed on the fall arrest device, that is, the requirements of the fall arrest device on support stiffness, motion freedom, and contact force transmission path in different regions are mapped into the structure topological space, so that coupled constraint boundary conditions are generated; these boundary conditions clearly define the force limitation and motion constraint of each component of the fall arrest device in space, for example: the clamping mechanism needs to provide higher initial clamping force (such as not less than 3000N) in the weakly coupled region of the guide rail to compensate for the local stiffness deficiency. On this basis, multi-objective optimization is further solved for the coupled constraint boundary conditions, and intelligent optimization methods such as genetic algorithm or particle swarm optimization are used to find the optimal structure topology under the premise of meeting multiple objectives such as strength, lightweight, response speed, and finally obtain the fall arrest mechanism geometric topology map; this map not only defines the overall configuration of the fall arrest device (such as double-arm linkage or single-point clamping), but also calibrates the spatial distribution and connection mode of the key functional areas, providing guidance for subsequent three-dimensional modeling. Subsequently, the fall arrest mechanism geometric topology map is used to parameterize the fall arrest device, and a three-dimensional solid geometric model with adjustable parameter set (such as arm length, joint angle, clamping surface width, etc.) is constructed; on this basis, iterative optimization is performed, the geometric size is constantly adjusted and its influence on the overall performance is evaluated, and finally a fall arrest mechanism geometric size distribution matrix is formed; this matrix records the quantitative indicators of each component in terms of local stiffness contribution, mass density distribution function, and installation interface matching degree, for example, the local stiffness contribution of a certain clamping arm reaches 45%, the mass density is controlled within 2.7g / cm³, and the interface matching degree with the guide rail connector is as high as 92 points, indicating that it has good structure efficiency and adaptability. At the same time, in order to ensure that the fall arrest device has excellent durability and environmental adaptability in actual service process, the material properties of the fall arrest device need to be further mapped based on the guide rail coupling parameters, that is, according to the temperature variation range (such as -10℃~+60℃), load frequency (such as 12Hz~28Hz), impact acceleration peak (such as maximum 3g) and other factors of the guide rail under typical working conditions, a candidate set of materials suitable for the fall arrest device is selected; this candidate set contains various high-performance alloys, composite materials and engineering plastics, such as aluminum alloy 7075-T6, carbon fiber reinforced resin matrix composite (CFRP), and polyamide (PA66), etc., which have the characteristics of high strength, low density or good wear resistance.Then, the candidate set of materials is subjected to multi-attribute decision analysis, and the analytic hierarchy process (AHP) or fuzzy comprehensive evaluation method is used to score and rank various materials from multiple dimensions such as tensile strength, fatigue life, processing cost, maintenance cycle, etc., and finally form a material attribute matching matrix of the fall arrest device; for example, a certain type of carbon fiber material scores 95 in tensile strength, 90 in fatigue life, but only 65 in processing cost, while aluminum alloy scores higher in processing cost (85), but slightly lower in fatigue life (80) than composite materials (80), thus determining which material should be preferred in different application scenarios. Further, the material attribute matching matrix of the fall arrest device is subjected to hierarchical clustering processing, and materials with similar performance characteristics are classified into one category to form a material selection priority sequence; for example, the first echelon is high-performance composite materials, suitable for high-stress concentration areas (such as the clamping head part); the second echelon is high-strength aluminum alloy, used for the main structural frame; the third echelon is engineering plastic, suitable for low-stress areas and auxiliary connecting parts; this process helps to reasonably allocate material resources and avoid the problems of "overdesign" or "insufficient performance". Finally, based on the material selection priority sequence, the installation position of the fall arrest device is planned, and combined with the optimal coupling point position distribution map obtained in the previous step (such as the connection parts of the 3rd, 5th, and 7th guide rails are high-coupling areas), the specific arrangement strategy of the fall arrest device on the guide rail is determined, including installation spacing (such as one device every 4 meters), installation direction (such as a 15° angle with the guide rail axis to improve clamping efficiency), and installation sequence (high-frequency coupling areas first, then low-frequency areas), etc. details, so as to finally obtain the fall arrest device integration scheme. For example, in the application scenario of a high-altitude work platform moving along a lightweight guide rail, when the worker carries heavy objects and frequently starts and stops, the guide rail will experience periodic impact and vibration in some areas (such as lateral disturbance caused by sudden wind speed increase to 15 m / s), at this time, through this step, several key points (such as the central part of the 5th guide rail) with dense energy transfer and low stiffness in the guide rail can be accurately identified, and the fall arrest device made of high-performance composite materials is preferentially deployed at these positions, with a clamping force of 3500N and a response time controlled within 0.1 seconds, while aluminum alloy structure fall arrest devices are used on adjacent 4th and 6th guide rails as supplements, thus building an integrated protection system that has both high response capability and economic efficiency, significantly improving the safety redundancy and engineering applicability of the entire fall arrest system in complex dynamic environments.

[0038] In specific embodiments, the fall arrest device integration scheme is subjected to virtual assembly and performance simulation to obtain a fall arrest system performance evaluation report, including: The falling protection device and the light guide rail are virtually assembled based on the falling protection device integration scheme to obtain a virtual assembly model, and the virtual assembly model is subjected to finite element meshing to obtain a finite element analysis model; wherein the virtual assembly model comprises relative position relationship and connection state of the falling protection device and the light guide rail; A preset falling load and boundary condition are applied to the finite element analysis model to obtain system dynamic response data, and energy absorption characteristics of the falling protection system are analyzed based on the system dynamic response data to obtain an energy absorption curve; Deformation analysis is performed on the light guide rail based on the energy absorption curve to obtain a guide rail deformation curve, and a guide rail stability index is determined based on the guide rail deformation curve, wherein the guide rail stability index comprises a buckling critical load and a safety factor; Overall reliability of the falling protection system is evaluated based on the system dynamic response data and the guide rail stability index to obtain a falling protection system performance evaluation report.

[0039] Specifically, the virtual assembly and performance simulation of the fall arrest device integration scheme to obtain a fall arrest system performance evaluation report is the core link of realizing design closed-loop verification and system-level performance prediction in the method, and the purpose is to comprehensively evaluate the mechanical matching, dynamic response characteristics and overall safety margin between the fall arrest device and the lightweight guide rail through high-precision digital modeling and simulation analysis means before physical manufacturing, and to ensure that the formed fall arrest system has the ability of rapid triggering, stable clamping and high energy absorption under complex working conditions. In the specific implementation process, first, based on the three-dimensional entity geometric model, material attribute matching matrix and installation position planning result in the fall arrest device integration scheme, the fall arrest device and the lightweight guide rail are virtually assembled to construct a virtual assembly model containing the relative position relationship and connection state of the two. This model not only accurately restores the spatial layout between the fall arrest device and the guide rail (for example, three fall arrest devices are installed on the 3rd, 5th and 7th guide rails respectively, and the clamping angle is set to 15°), but also simulates the contact interface between components, the pre-tightening force application method and the motion constraint conditions, thereby providing real and reliable initial conditions for subsequent finite element analysis. Subsequently, the virtual assembly model is subjected to finite element meshing to generate a finite element analysis model with local encryption areas; among them, fine mesh with a mesh size of 0.5mm or less is used at the contact part between the fall arrest device clamping head and the guide rail to improve the calculation accuracy of the contact pressure, while relatively coarse mesh is used in the structure part away from the key stress area to improve the calculation efficiency; the whole model plans to divide about 280,000 unit nodes, covering all components such as the fall arrest device main body, the guide rail body and its connecting parts. On this basis, the finite element analysis model is subjected to pre-set falling load and boundary conditions, such as simulating the impact load (up to 4000N) generated when a high-altitude worker accidentally falls from 6 meters above and acting on the guide rail, and setting the guide rail ends as fixed supports and allowing small lateral displacement in the middle, thereby obtaining the dynamic response data of the system during the falling process; these data include but are not limited to the variation curve of the fall arrest device clamping force with time, the local stress distribution evolution of the guide rail, the system overall acceleration response peak value (such as a maximum of 2.8g), and other key parameters, which provide rich mechanical basis for subsequent performance evaluation. Further, based on the system dynamic response data, the energy absorption characteristics of the fall arrest system are analyzed, that is, the total amount of energy consumed by the fall arrest device during clamping and its distribution over time are counted, and finally the energy absorption curve is obtained; the curve shows that within 0.3 seconds after the falling event occurs, the fall arrest device has absorbed more than 85% of the impact energy, of which the first stage (0~0.1s) absorbs 52%, the second stage (0.1~0.2s) absorbs 28%, and the third stage (0.2~0.3s) absorbs 5%; this indicates that the system has good energy dissipation capacity and response consistency.Meanwhile, deformation analysis is performed on the lightweight guide rail based on the energy absorption curve to extract the longitudinal and transverse displacement variation trends of the guide rail under the falling impact, and a guide rail deformation curve is generated. The results show that under the action of the maximum impact load, the maximum transverse displacement of the central section of the guide rail is 2.4 mm, and the longitudinal compression amount is 0.9 mm, both of which are controlled within the design allowable range (transverse ≤ 3 mm, longitudinal ≤ 1.2 mm), indicating that the guide rail structure has sufficient bearing capacity and anti-deformation ability. On this basis, the guide rail stability index is further determined based on the guide rail deformation curve, mainly including two parameters of buckling critical load and safety factor; through linear buckling analysis, it is found that the lightweight guide rail can still maintain structural stability when bearing 1.5 times the design load (i.e. 6000N), and no instability phenomenon occurs, the buckling critical load is 6800N, and the safety factor reaches 1.7, meeting the safety redundancy requirement of not less than 1.5 in engineering application; in addition, combined with nonlinear large deformation analysis, it is found that under extreme conditions (such as sudden increase of wind speed to 20 m / s and superimposed personnel falling impact), the maximum strain value of the guide rail is 0.0032 (i.e. 0.32%), which is far lower than the material yield limit (0.6%), indicating that it still has good structural integrity and service safety under dynamic load. Finally, the overall reliability of the anti-falling system is evaluated based on the system dynamic response data and guide rail stability index, and a complete anti-falling system performance evaluation report is formed; this report not only summarizes the key performance parameters of the system (such as response time 0.12 seconds, maximum clamping force 3800N, energy absorption efficiency 87%, maximum stress of guide rail 245MPa, etc.), but also classifies and evaluates the performance of the system under different working conditions; for example, under normal operating environment (wind speed ≤ 10 m / s, personnel moving frequency ≤ 1 time / minute), the system runs stably and the clamping action accuracy is as high as 98%; while under harsh environmental conditions (wind speed ≥ 15 m / s, personnel frequent start-stop), the system still maintains the response delay within 0.2 seconds and the clamping force fluctuation range within ±5%, showing strong adaptability and robustness. For example, in the application scenario of a high-altitude maintenance platform moving along the lightweight guide rail, when the working personnel falls due to sudden slipping, the anti-falling system triggers the clamping action within 0.1 seconds to stabilize the personnel in the air, without causing secondary impact or guide rail fracture risk, fully verifying the effectiveness and safety of the integrated scheme in actual engineering. Through this step, not only the physical prototype test cost is greatly reduced, but also the research and development efficiency and engineering feasibility of the anti-falling system are significantly improved, providing solid technical support for subsequent mass production and field deployment.

[0040] In specific embodiments, the deformation analysis of the lightweight guide rail based on the energy absorption curve to obtain a guide rail deformation curve comprises: The energy absorption curve is subjected to time domain integral processing to obtain a dynamic load distribution function of the lightweight guide rail, and a transient stress analysis is performed on the lightweight guide rail based on the dynamic load distribution function to obtain a guide rail stress wave propagation map; A numerical simulation is performed on the guide rail stress wave propagation map by a nonlinear finite element method to obtain a dynamic constitutive relationship curve of the lightweight guide rail, and a plastic deformation region distribution map is constructed based on the dynamic constitutive relationship curve; Based on the plastic deformation region distribution map, a geometric nonlinear large deformation analysis is performed on the lightweight guide rail to obtain a guide rail deformation evolution sequence, and a stability evaluation is performed on the guide rail deformation evolution sequence by an elastoplastic bifurcation theory to obtain a critical deformation threshold curve; The critical deformation threshold curve is subjected to piecewise linearization processing to obtain a guide rail deformation feature point sequence, and a spline interpolation fitting is performed based on the guide rail deformation feature point sequence to obtain a guide rail deformation curve.

[0041] Specifically, the deformation analysis of the light guide rail based on the energy absorption curve to obtain the guide rail deformation curve is an important link in the performance evaluation process of the anti-falling system, which realizes the conversion from energy dissipation behavior to structural response characteristics. The purpose is to reveal the dynamic deformation evolution law of the light guide rail during the clamping process of the anti-falling device through high-precision numerical simulation and mechanical modeling means, and finally form a guide rail deformation curve that can be used for engineering design optimization and safety evaluation. In the specific implementation process, first, the energy absorption curve is subjected to time domain integral processing, that is, the energy absorbed by the anti-falling device per unit time is cumulatively calculated, thereby obtaining the dynamic load distribution function of the light guide rail during the entire impact process; this function reflects the effective load change of the guide rail at different time points, for example, within 0.1 seconds after the falling impact occurs, the load on the central region of the guide rail rapidly rises to 3800N and slowly decays to 1500N within the subsequent 0.2 seconds, and this load change trend provides accurate input conditions for subsequent transient stress analysis. On this basis, the transient stress analysis of the light guide rail is carried out based on the dynamic load distribution function, the stress propagation behavior of the guide rail under dynamic load is simulated by using an explicit dynamics solver (such as LS-DYNA or ABAQUS / Explicit), and finally a guide rail stress wave propagation map is generated; this map clearly shows the propagation path, reflection and superposition effect of the stress wave inside the guide rail, for example, about 0.03 seconds after the falling event starts, the maximum stress peak appears in the middle of the 5th guide rail (about 265MPa), and gradually spreads to both sides within the subsequent 0.07 seconds, finally forming a secondary peak value area (220MPa and 210MPa respectively) at the 4th and 6th guide rails; this dynamic stress distribution characteristic is of great significance for judging the local weak area of the guide rail. Further, the guide rail stress wave propagation map is numerically simulated by a nonlinear finite element method, considering the influence of material nonlinearity and geometric nonlinearity, and a constitutive relationship model of the light guide rail under dynamic load is established; this model can reflect the response behavior of the guide rail material under high strain rate, for example, when the strain rate of the aluminum alloy guide rail reaches 0.1 / s, its yield strength increases by about 12%, showing obvious strain rate strengthening effect; finally, the dynamic constitutive relationship curve of the light guide rail is output, which describes the true stress state of the guide rail material under different strain levels, providing theoretical support for subsequent plastic deformation analysis. Then, the plastic deformation region distribution map is constructed based on the dynamic constitutive relationship curve, and the region and range of the guide rail that has undergone plastic deformation are identified, for example, at the central part of the 5th guide rail, when the strain reaches 0.35%, the material enters the plastic stage, forming a plastic deformation zone with a diameter of about 40mm, and the maximum equivalent plastic strain of this region is 0.52%, indicating that it has appeared a certain degree of permanent deformation.Subsequently, a geometric nonlinear large deformation analysis is performed on the lightweight guide rail based on the plastic deformation region distribution map, that is, an updated Lagrangian format is used to iteratively calculate the displacement field of the guide rail under the action of the sustained load, track the change trend of the overall shape of the guide rail, and thus obtain a guide rail deformation evolution sequence; the sequence records the displacement change trajectory of the guide rail in the whole process of the falling impact, for example, within 0.25 seconds after the falling occurs, the maximum transverse displacement of the central region of the guide rail rapidly increases from the initial 0.2 mm to 2.6 mm, and then tends to be stable within the subsequent 0.15 seconds; at the same time, the guide rail deformation evolution sequence is evaluated for stability in combination with the elastic-plastic bifurcation theory, a possible instability critical point in the deformation process of the guide rail is identified, and a critical deformation threshold curve is drawn; the curve shows that when the transverse displacement of the guide rail exceeds 3.0 mm, the structure will enter an unstable deformation stage and there is a risk of local buckling, so this value needs to be taken as the control upper limit of the safety design. Finally, the critical deformation threshold curve is subjected to piecewise linearization processing, and is divided into a plurality of representative deformation intervals, each interval corresponding to a different structural response mode, for example, within the range of 0~1.5 mm, the guide rail is in an elastic deformation stage; between 1.5~2.8 mm, part of the region begins to enter a plastic deformation stage; and between 2.8~3.2 mm, it belongs to a transition stage close to instability; on this basis, key deformation feature points in each deformation interval are extracted to form a guide rail deformation feature point sequence, and a spline interpolation fitting is performed based on the sequence to finally generate a continuous and smooth guide rail deformation curve; the curve not only accurately describes the deformation trend of the guide rail in different load stages, but also provides direct data support for the subsequent triggering logic setting, installation position optimization and structural strengthening measures of the anti-falling device. For example, in the process of moving the high-altitude work platform along the lightweight guide rail, if a sudden falling event causes the displacement of a certain segment of the guide rail to reach 2.9 mm, close to the instability threshold, the deformation curve can be used to judge whether the action parameters of the anti-falling device (such as early triggering or enhanced clamping force) need to be adjusted, so as to effectively prevent structural failure and ensure the safe and reliable operation of the whole anti-falling system.

[0042] The above describes the anti-falling device integration method based on lightweight guide rail coupling and deformation control in the embodiment of the application. The following describes the anti-falling device integration system based on lightweight guide rail coupling and deformation control in the embodiment of the application. Please refer to Figure 2 The anti-falling device integration system based on lightweight guide rail coupling and deformation control in the embodiment of the application includes one embodiment: The scanning device 21 is used for scanning the stress distribution of the lightweight guide rail through a laser interference measurement system to obtain a guide rail stress distribution map; The analysis device 22 is used for performing dynamic stiffness analysis on the lightweight guide rail based on the guide rail stress distribution map to obtain a guide rail stiffness characteristic curve; A computing device 23 is configured to perform coupling calculation on the light guide rail based on the guide rail stiffness characteristic curve, to obtain guide rail coupling parameters. An optimization device 24 is configured to perform structural design and layout optimization on the fall arrest device based on the guide rail coupling parameters, to obtain a fall arrest device integration scheme. A simulation device 25 is configured to perform virtual assembly and performance simulation on the fall arrest device integration scheme, to obtain a fall arrest system performance evaluation report.

[0043] In the embodiment, the specific implementation of each unit in the system embodiment is described above in the method embodiment, and will not be described here.

[0044] Referring to Figure 3 , the embodiment of the present application also provides a computer device, and the internal structure of the computer device can be as shown in Figure 3 . The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store the corresponding data in the embodiment. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the above method.

[0045] Those skilled in the art can understand Figure 3 that the structure shown in the embodiment is only a block diagram of part of the structure related to the present application scheme, and does not constitute a limitation on the computer device to which the present application scheme is applied.

[0046] The embodiment of the present application also provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by the processor to implement the above method. It can be understood that the computer readable storage medium in the embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0047] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium provided by the present application and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0048] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, device, article or method that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, device, article or method. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, device, article or method that includes the element.

[0049] The above description is only the preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, based on the content of the present application specification and drawings, are also included in the patent protection scope of the present application.

Claims

1. A method for integrating a fall prevention device based on lightweight guide rail coupling and deformation control, characterized in that: Applied to a fall prevention system, the fall prevention system includes a lightweight guide rail and an anti-fall device, including the following steps: The stress distribution of the lightweight guide rail is scanned by a laser interferometry system to obtain a stress distribution map of the guide rail; Based on the guide rail stress distribution map, dynamic stiffness analysis is performed on the lightweight guide rail to obtain a guide rail stiffness characteristic curve; performing coupling calculation on the lightweight guide rail based on the guide rail stiffness characteristic curve to obtain guide rail coupling parameters; Based on the guide rail coupling parameters, the anti-fall device is structurally designed and layout optimized to obtain an anti-fall device integration solution; The anti-fall device integration solution is virtually assembled and simulated to obtain an anti-fall system performance evaluation report.

2. The anti-fall device integration method based on lightweight guide rail coupling and deformation control according to claim 1 is characterized in that: The stress distribution of the lightweight guide rail is scanned by a laser interferometer measurement system to obtain a stress distribution map of the guide rail, including: The surface of the lightweight guide rail is subjected to multi-angle strain field acquisition by a laser interferometry system to obtain a guide rail surface strain data set, and the internal stress state of the guide rail is reconstructed and calculated using an elastic mechanics inversion algorithm based on the strain data set to obtain a three-dimensional stress tensor field; Decomposing the three-dimensional stress tensor field in terms of principal stress directions to obtain anisotropic stress components, and comparing and analyzing the local yield strength thresholds of the lightweight guide rail based on the anisotropic stress components to obtain a stress concentration area identification layer; Based on the stress concentration area identification layer, the lightweight guide rail is locally mesh encrypted and modeled to obtain an encrypted finite element model. The encrypted finite element model is then subjected to nonlinear iterative solution by applying dynamic load boundary conditions to obtain a stress evolution path map of the lightweight guide rail under typical working conditions.

3. The anti-fall device integration method based on lightweight guide rail coupling and deformation control according to claim 1 is characterized in that: The method of performing a dynamic stiffness analysis on the lightweight guide rail based on the guide rail stress distribution map to obtain a guide rail stiffness characteristic curve includes the following steps: Extracting modal parameters of the lightweight guide rail based on the guide rail stress distribution map to obtain a modal frequency set and a modal vibration shape matrix, and calculating the vibration shape energy distribution of the lightweight guide rail based on the modal vibration shape matrix to obtain a vibration shape energy density field; Simulating the dynamic load condition of the lightweight guide rail based on the mode energy density field to obtain a dynamic load time sequence, and solving the transient dynamic response of the lightweight guide rail based on the dynamic load time sequence to obtain displacement response time history data; Performing a frequency response function calculation on the lightweight guide rail based on the displacement response time history data to obtain a frequency response function matrix, and performing a stiffness parameter inversion on the lightweight guide rail based on the frequency response function matrix to obtain a stiffness matrix element set; The coupling stiffness of the lightweight guide rail is corrected based on the stiffness matrix element set to obtain a coupling stiffness correction coefficient, and the stiffness characteristic curve of the lightweight guide rail is drawn based on the coupling stiffness correction coefficient to obtain a guide rail stiffness characteristic curve.

4. The anti-fall device integration method based on lightweight guide rail coupling and deformation control according to claim 1 is characterized in that: The performing coupling calculation on the lightweight guide rail based on the guide rail stiffness characteristic curve to obtain guide rail coupling parameters includes: Calculating the critical damping ratio of the lightweight guide rail based on the guide rail stiffness characteristic curve to obtain the guide rail damping boundary condition, and performing frequency domain convolution processing on the guide rail damping boundary condition to obtain a coupling energy transfer efficiency spectrum; Performing modal superposition analysis on the coupling energy transfer efficiency spectrum to obtain a multi-degree-of-freedom coupling response matrix of the guide rail, and performing eigenvalue reconstruction processing based on the coupling response matrix to obtain an optimal coupling point position distribution map; Performing gradient descent optimization on the optimal coupling point position distribution map, and performing contour extraction processing on the optimized optimal coupling point position distribution map to obtain coupling strength threshold partitions; The dynamic frequency of the lightweight guide rail is tested based on the coupling strength threshold partition to obtain a coupling frequency response curve, and harmonic component decomposition processing is performed based on the coupling frequency response curve to obtain guide rail coupling parameters.

5. The anti-fall device integration method based on lightweight guide rail coupling and deformation control according to claim 1 is characterized in that: Based on the guide rail coupling parameters, the anti-fall device is structurally designed and the layout is optimized to obtain an anti-fall device integration solution, including: Performing topological constraint mapping on the anti-fall device based on the guide rail coupling parameters to obtain coupling constraint boundary conditions, and performing multi-objective optimization on the coupling constraint boundary conditions to obtain a geometric topological map of the anti-fall mechanism; The anti-fall device is parametrically modeled using the anti-fall mechanism geometric topology map to obtain a three-dimensional solid geometric model, and the geometric dimensions of the three-dimensional solid geometric model are iteratively optimized to obtain a geometric dimension distribution matrix of the anti-fall mechanism, wherein the geometric dimension distribution matrix of the anti-fall mechanism includes the local stiffness contribution, mass density distribution function, and installation interface matching degree of each component; Mapping the material properties of the anti-fall device based on the guide rail coupling parameters to obtain a material selection candidate set, and performing multi-attribute decision analysis on the material selection candidate set to obtain a material property matching matrix for the anti-fall device; A hierarchical clustering process is performed on the material property matching matrix of the anti-fall device to obtain a material selection priority sequence, and the installation position of the anti-fall device is planned based on the material selection priority sequence to obtain an anti-fall device integration solution.

6. The anti-fall device integration method based on lightweight guide rail coupling and deformation control according to claim 1 is characterized in that: The anti-fall device integration solution is subjected to virtual assembly and performance simulation to obtain an anti-fall system performance evaluation report, including: Based on the anti-fall device integration solution, the anti-fall device and the lightweight guide rail are virtually assembled to obtain a virtual assembly model, and the virtual assembly model is meshed by finite element to obtain a finite element analysis model; wherein the virtual assembly model includes the relative position relationship and connection status of the anti-fall device and the lightweight guide rail; Applying a preset fall load and boundary conditions to the finite element analysis model to obtain system dynamic response data, and analyzing the energy absorption characteristics of the fall prevention system based on the system dynamic response data to obtain an energy absorption curve; Performing deformation analysis on the lightweight guide rail based on the energy absorption curve to obtain a guide rail deformation curve, and determining a guide rail stability index based on the guide rail deformation curve, wherein the guide rail stability index includes a buckling critical load and a safety factor; The overall reliability of the anti-fall system is evaluated based on the system dynamic response data and the guide rail stability index to obtain an anti-fall system performance evaluation report.

7. The anti-fall device integration method based on lightweight guide rail coupling and deformation control according to claim 6 is characterized in that: The deformation analysis of the lightweight guide rail based on the energy absorption curve to obtain the guide rail deformation curve includes: Performing time-domain integration processing on the energy absorption curve to obtain a dynamic load distribution function of the lightweight guide rail, and performing transient stress analysis on the lightweight guide rail based on the dynamic load distribution function to obtain a guide rail stress wave propagation map; Numerical simulation of the guide rail stress wave propagation spectrum is performed using a nonlinear finite element method to obtain a dynamic constitutive relationship curve of the lightweight guide rail, and a plastic deformation area distribution map is constructed based on the dynamic constitutive relationship curve; Based on the plastic deformation area distribution map, a geometrically nonlinear large deformation analysis is performed on the lightweight guide rail to obtain a guide rail deformation evolution sequence, and the stability of the guide rail deformation evolution sequence is evaluated using elastic-plastic bifurcation theory to obtain a critical deformation threshold curve; The critical deformation threshold curve is subjected to piecewise linearization processing to obtain a guide rail deformation feature point sequence, and spline interpolation fitting is performed based on the guide rail deformation feature point sequence to obtain a guide rail deformation curve.

8. An integrated system of anti-falling devices based on lightweight guide rail coupling and deformation control, characterized in that: Applicable to a fall prevention system, the fall prevention system includes a lightweight guide rail and a fall prevention device, including: A scanning device, used to scan the stress distribution of the lightweight guide rail through a laser interferometry system to obtain a stress distribution map of the guide rail; An analysis device, configured to perform a dynamic stiffness analysis on the lightweight guide rail based on the guide rail stress distribution map to obtain a guide rail stiffness characteristic curve; a calculation device for performing coupling calculation on the lightweight guide rail based on the guide rail stiffness characteristic curve to obtain guide rail coupling parameters; An optimization device, configured to perform structural design and layout optimization on the anti-fall device based on the guide rail coupling parameters, and obtain an integrated solution for the anti-fall device; The simulation device is used to perform virtual assembly and performance simulation on the anti-fall device integration solution to obtain an anti-fall system performance evaluation report.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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