A method and system for simulating deformation of a spreader

Abstract

The application discloses a kind of simulation analysis method and system of sling deformation, and the application relates to hoisting reliability evaluation technical field, comprising the following steps: first, the geometric structure parameters of the sling to be monitored are obtained, a parameterized finite element reference digital model is constructed, and a sensor network is laid out in the component connection area to collect load, strain and vibration acceleration data, a plurality of hoisting instances are run in parallel using the ensemble Kalman filtering algorithm, the strain predicted by the finite element model is compared with the measured strain, the equivalent influence parameters of the connection area are dynamically updated, based on the updated parameters, the thermal-mechanical coupling reduced-order model processed by eigenvalue orthogonal decomposition is driven, the structural dynamics equation is solved to obtain the displacement field, the deformation is determined, the total damage index of the sling is calculated, the damage state is updated in real time, the remaining life is predicted, and the operation safety and reliability of the sling are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of hoisting reliability assessment technology, specifically to a simulation analysis method and system for hoisting deformation. Background Technology

[0002] Lifting equipment plays a crucial role in various industrial and engineering applications, especially in heavy object handling and hoisting operations. To ensure the safety and reliability of lifting equipment during use, its deformation and damage status must be monitored and assessed in real time. Existing technologies generally employ a combination of sensor placement and finite element analysis to acquire real-time data on the lifting equipment during operation and analyze its strain and load conditions. These methods typically rely on static or quasi-static analysis models, which, while providing some deformation prediction, still fall short in terms of accuracy and real-time performance under dynamic loads or complex environmental conditions.

[0003] Currently, existing technologies monitor structural health by establishing finite element models and collecting sensor data, but they often have significant limitations in dynamically updating models and providing real-time feedback. Traditional methods mainly rely on one-time data acquisition and subsequent analysis, which cannot adapt to the dynamic changes of the spreader during use and often fail to effectively integrate multiple dynamic influencing factors, resulting in slow model updates and inaccurate parameter corrections, which in turn affect the assessment of the overall performance of the spreader and damage prediction.

[0004] Therefore, there is an urgent need for a new method to overcome the shortcomings of existing technologies and improve the monitoring accuracy of spreader deformation and damage status by combining dynamically updated finite element reference models with real-time sensor data.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a simulation analysis method and system for the deformation of lifting devices, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A simulation analysis method for the deformation of a lifting device, comprising the following steps:

[0009] S1. Obtain the geometric structural parameters of the lifting device to be monitored, construct the parameterized finite element reference digital model of the lifting device to be monitored, and deploy a sensor network in the component connection area of ​​the lifting device to be monitored to form a sensor data stream for collecting the load, strain and vibration acceleration of the lifting device.

[0010] S2. Based on the sensor data stream, the ensemble Kalman filter algorithm is used to run multiple hoisting instances in parallel. By comparing the strain data predicted by the finite element reference digital model of the hoisting tool to be monitored with the measured strain data, the equivalent influence parameters of the connection area of ​​the hoisting tool component to be monitored in the finite element reference digital model are dynamically updated.

[0011] S3. Based on the updated equivalent influence parameters, drive the thermo-mechanical coupling reduced-order model after intrinsic orthogonal decomposition and solve the structural dynamic equation based on the temperature field distribution of different component connection areas to obtain the displacement field of the monitor, thereby determining the deformation of different component connection areas.

[0012] S4. Based on the deformation at different component connection locations, calculate the total damage index of the spreader to be monitored, update the damage status based on real-time data, predict the remaining life, and set up a multi-level early warning mechanism. When the total damage index exceeds the corresponding threshold, trigger the corresponding level of early warning signal.

[0013] Furthermore, the specific method for constructing the parametric finite element reference digital model of the lifting device to be monitored is as follows: Based on the design drawings of the lifting device to be monitored, a structured finite element model is established using parametric modeling finite element analysis software ANSYS or ABAQUS. Based on the structured finite element model, the initial material characteristic data of the lifting device to be monitored are input, specifically including the material's contact stiffness, elastic modulus, Poisson's ratio, yield strength, and coefficient of thermal expansion, to form a parametric finite element reference digital model. In the parametric finite element reference digital model, a Chaboche kinematic constitutive model is introduced to simulate the changes in material characteristic data under cyclic lifting loads.

[0014] The Chaboche kinematic stiffening constitutive model, specifically includes three parts in the yield evolution formula of the connection region of the lifting device component under monitoring: the initial value of the yield stress of the material in the connection region of the lifting device component under monitoring, the isotropic hardening function value of the material in the connection region of the lifting device component under monitoring, and the vector sum of the back stress components. The sum of the three parts is used as the predicted value of the yield stress of the material in the connection region of the lifting device under monitoring. The isotropic hardening function of the material in the connection region of the lifting device under monitoring uses the cumulative plastic strain of the material in the connection region of the lifting device under monitoring as the independent variable. The back stress components are used to describe the hardening behavior of the material in the connection region of the lifting device under monitoring under monitoring when subjected to cyclic loading.

[0015] The lifting motion of the lifting tool under different lifting loads is simulated by a parametric finite element reference digital model, and the predicted strain data of the connection area of ​​the lifting tool components under each lifting motion are output by the model.

[0016] Furthermore, the component connection areas of the lifting device to be monitored specifically include the connection areas between the pin and the lug, the pulley and the shaft, and the spring and the hook.

[0017] The sensor network specifically includes a triaxial accelerometer, a temperature and humidity sensor, and a stress and strain sensor; the equivalent influence parameters of the connection area of ​​the lifting device component to be monitored in the finite element reference digital model specifically include the material's contact stiffness, elastic modulus, Poisson's ratio, yield strength, coefficient of thermal expansion, and Rayleigh damping coefficient, as well as the initial yield stress, isotropic hardening saturation stress, and hardening rate in the Chaboche kinematic hardening constitutive model.

[0018] The logic behind obtaining the strain data predicted by the finite element reference digital model of the lifting device to be monitored and the measured strain data is as follows: For any connection area of ​​the lifting device component to be monitored, an initial equivalent influence parameter group is set, and based on each parameter in the initial equivalent influence parameter group, within a preset ratio range, a corresponding proportion of Gaussian random perturbation is added to each parameter in the initial equivalent influence parameter group. The operation of adding Gaussian random perturbation is performed multiple times to form several updated influence parameter groups.

[0019] For any component connection area of ​​the lifting device to be monitored, the sensor network collects the load and vibration acceleration data of the lifting device from the sensor data stream, uses it as external simulation data, and inputs each updated influence parameter group into the parameterized finite element reference digital model to perform running lifting simulation motion, and obtains the model predicted strain of each updated influence parameter group; at the same time, the sensor network collects the measured strain data of the corresponding component connection area of ​​the lifting device to be monitored.

[0020] Furthermore, the logic underlying the dynamic updating of the equivalent influence parameters of the connection area of ​​the monitored lifting device component in the finite element reference digital model is as follows:

[0021] Based on the model-predicted strain obtained from running different update influence parameter groups, a set of predicted strains for all update influence parameter groups is constructed, and the average predicted strain in the set is calculated. The covariance matrix of the set of predicted strains is calculated based on the average predicted strain. Based on the covariance matrix, combined with the observation operator matrix and the observation noise covariance matrix, the Kalman gain is calculated. The accuracy of the specific sensor network is set for the observation noise covariance matrix.

[0022] The method for updating the equivalent influence parameters based on the Kalman gain of the connection area of ​​the monitored lifting device component at the u-th location is as follows:

[0023] The difference between the measured strain data and the predicted average strain in the connection area of ​​each monitor component is obtained, and the product of this difference and the Kalman gain is used as the update guidance coefficient.

[0024] Based on the update guide coefficient, the equivalent influence parameters of each monitored lifting tool component connection area in the previous lifting simulation motion cycle are updated. Specifically, the sum of the equivalent influence parameters of the previous lifting simulation motion cycle and the update guide coefficient is used as the updated value of the equivalent influence parameters for the current cycle.

[0025] Furthermore, the thermo-mechanical coupling reduced-order model is specifically expressed as follows: The thermo-mechanical coupling reduced-order model is based on the dynamic equation and the heat conduction equation. In the dynamic part, the product of the second derivative of the generalized displacement coordinate of the center point of the connection area of ​​the monitored lifting device component with respect to the time variable and the reduced-order mass matrix is ​​used as the first coupling term. The product of the first derivative of the generalized displacement coordinate of the center point of the connection area of ​​the monitored lifting device component with respect to the time variable and the reduced-order damping matrix is ​​used as the second coupling term. The product of the reduced-order stiffness matrix dependent on the temperature generalized coordinate and the displacement generalized coordinate of the center point of the connection area of ​​the monitored lifting device component is used as the third coupling term.

[0026] The sum of the first, second, and third coupling terms equals the reduced-order hoisting load vector at the corresponding moment, thus constructing the reduced-order dynamic equation;

[0027] In the heat conduction section, the product of the reduced heat capacity matrix and the first derivative of the generalized temperature coordinate of the center point of the connection area of ​​the lifting device component to be monitored with respect to the time variable is taken as the first heat component; the product of the reduced heat conduction matrix and the generalized temperature coordinate of the center point of the connection area of ​​the lifting device component to be monitored is taken as the second heat component.

[0028] The sum of the first heat component and the second heat component is linked to the reduced-order heat source vector, thereby constructing a reduced-order heat conduction equation. The reduced-order heat source vector comprehensively considers the first derivative of the ambient temperature, the displacement generalized coordinate with respect to the time variable, and the displacement generalized coordinate.

[0029] The heat conduction reduction equation and the kinetic reduction equation are combined to form a thermo-mechanical coupled reduction model.

[0030] Furthermore, the reduced stiffness matrix and reduced heat conduction matrix are updated based on the updated equivalent influence parameters. Then, the backward Euler method is used to solve the thermal equation using the temperature field and environmental data from the previous round of hoisting simulation motion to obtain the generalized temperature coordinates and temperature distribution of the current round of hoisting simulation motion. The generalized temperature coordinates and temperature distribution of the current round of hoisting simulation motion are then imported into the reduced stiffness matrix that depends on the generalized temperature coordinates. Combined with the reduced hoisting load vector at the corresponding time, the generalized displacement coordinates are obtained. This gives the displacement field of the center point of the connection area of ​​the hoisting component to be monitored. Specifically, it refers to the displacement of the center point of the connection area of ​​the hoisting component to be monitored when the hoisting simulation motion returns to the hoisting starting position after completing the hoisting task.

[0031] The method for determining the deformation of different component connection areas based on the displacement of the center point of the connection area of ​​the lifting device component to be monitored is as follows: The displacement basis vector of the center point of the connection area of ​​the lifting device component to be monitored at the u-th location is determined. The product of the intrinsic orthogonal decomposition mode basis vector matrix of the displacement field and the generalized coordinate of the displacement of the center point of the connection area of ​​the lifting device component to be monitored at the u-th location is used as the deformation transformation matrix. The sum of the displacement basis vector of the center point of the connection area of ​​the lifting device component to be monitored at the u-th location and the deformation transformation matrix is ​​used as the basic displacement matrix. The basic displacement matrix is ​​then transformed into the deformation of the center point of the connection area of ​​the lifting device component to be monitored through the deformation-displacement matrix. Specifically, the intrinsic orthogonal decomposition mode basis vector matrix is ​​obtained by performing intrinsic orthogonal decomposition on the displacement of the center point of the connection area of ​​the lifting device component to be monitored during the lifting simulation motion.

[0032] Furthermore, the logic for calculating the total damage index of the spreader under monitoring is as follows: based on the connection gaps existing in the connection areas of each spreader component under monitoring, the deformation at the connection positions of different components is corrected to obtain the accurate deformation. The specific method is as follows: take the negative of the ratio of the contact gap to the reference gap in the connection area of ​​the spreader component under monitoring to obtain the correction coefficient. Use the correction coefficient as the independent variable of an exponential function with base e. Use the value of the exponential function as the deformation correction amount. Use the product of the deformation correction amount and the deformation of the component connection area as the accurate deformation amount at the center point of the connection area of ​​the spreader component under monitoring.

[0033] The total damage index of the monitored spreader is calculated based on the precise deformation of the center point of the connection area of ​​the spreader components. The specific method is as follows: the ratio of the difference between the preset deformation reference value and the precise deformation to the safety factor is used as the total damage index of the connection area of ​​the spreader components.

[0034] This invention also provides a simulation analysis system for the deformation of a lifting device, which is used to execute the above-described simulation analysis method for the deformation of a lifting device, including:

[0035] The simulation model building module is used to obtain the geometric parameters of the lifting device to be monitored, build a parameterized finite element reference digital model of the lifting device to be monitored, and deploy a sensor network in the component connection area of ​​the lifting device to be monitored to form a sensor data stream for collecting the load, strain and vibration acceleration of the lifting device.

[0036] The equivalent parameter update module is used to run multiple hoisting instances in parallel based on the sensor data stream using an ensemble Kalman filter algorithm. By comparing the strain data predicted by the finite element reference digital model of the hoisting tool to be monitored with the measured strain data, the equivalent influence parameters of the connection area of ​​the hoisting tool component to be monitored in the finite element reference digital model are dynamically updated.

[0037] The deformation characterization module is used to drive the thermo-mechanical coupling reduced-order model, which has undergone intrinsic orthogonal decomposition and order reduction processing, based on the updated equivalent influence parameters. It solves the structural dynamic equations based on the temperature field distribution of the connection areas of different components to obtain the displacement field of the lifting device to be monitored, thereby determining the deformation of the connection areas of different components.

[0038] The early warning generation module is used to calculate the total damage index of the spreader to be monitored based on the deformation of different component connection positions, update the damage status based on real-time data, predict the remaining life, and set up a multi-level early warning mechanism. When the total damage index exceeds the corresponding threshold, the corresponding level of early warning signal is triggered.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This scheme establishes a parametric finite element baseline digital model and combines it with a sensor network to collect data such as load, strain, and vibration acceleration of the lifting device in real time. It then uses an ensemble Kalman filter algorithm to dynamically update the equivalent influence parameters in the finite element model, overcoming the problem of mismatch between the finite element model and actual working conditions in traditional techniques. This method effectively solves the deficiency of existing technologies in accurately reflecting the deformation of the lifting device under dynamic working conditions, significantly improving the real-time performance and accuracy of simulation analysis.

[0041] Furthermore, by driving the thermo-mechanical coupling reduced-order model after intrinsic orthogonal decomposition with updated equivalent influence parameters, the influence of temperature field distribution on the deformation of the spreader structure is fully considered. This enables accurate calculation of the deformation in the connection areas of different components, filling the gaps in the thermo-mechanical coupling analysis of existing technologies. By calculating the total damage index of the spreader and updating the damage status in real time, potential hazards can be identified earlier, providing accurate remaining life prediction and a multi-level early warning mechanism, effectively improving the operational safety and reliability of the spreader. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0043] Figure 2 A curve fitting the deformation amount at the center point of the connection area of ​​different components versus the precise deformation amount at the center point.

[0044] Figure 3 Fitting curves of deformation amount at the center point of the connection area of ​​different components versus the reference value of deformation amount;

[0045] Figure 4 Fitting curves for the total damage index versus the reference value of deformation in the connection areas of different components;

[0046] Figure 5 This is a schematic diagram of the overall system structure of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0048] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0049] Example:

[0050] Please see Figures 1-4 The present invention provides a technical solution:

[0051] A simulation analysis method for the deformation of a lifting device, comprising the following steps:

[0052] S1. Obtain the geometric parameters of the lifting device to be monitored, construct a parameterized finite element reference digital model of the lifting device to be monitored, and deploy a sensor network in the component connection area of ​​the lifting device to form a sensor data stream for collecting the load, strain and vibration acceleration of the lifting device.

[0053] The specific method for constructing the parametric finite element baseline digital model of the lifting device to be monitored is as follows: Based on the design drawings of the lifting device to be monitored, a structured finite element model is established using parametric modeling finite element analysis software such as ANSYS or ABAQUS. The specific steps include: collecting the design drawings of the lifting device to be monitored, including its geometry, dimensions, material properties, and other relevant design parameters; selecting a suitable finite element analysis software, such as ANSYS or ABAQUS, according to the requirements; these software programs provide powerful parametric modeling functions, capable of handling complex structures and material properties; creating a new project in the software; importing geometric data from the design drawings; or manually drawing the geometry of the lifting device; applying parametric modeling techniques; and specifying the geometric parameters of each part, such as length, width, height, and radius, to ensure the flexibility and adjustability of the model.

[0054] Based on the structured finite element model, the initial material characteristic data of the lifting device to be monitored is input, specifically including the material's contact stiffness, elastic modulus, Poisson's ratio, yield strength, and coefficient of thermal expansion, to form a parametric finite element reference digital model. The specific steps include: selecting the material, such as steel or aluminum alloy, according to the design requirements of the lifting device, and defining its physical and mechanical properties, including contact stiffness, elastic modulus, Poisson's ratio, yield strength, coefficient of thermal expansion, and density, etc., and assigning corresponding material properties to each component in the model to reflect its true mechanical behavior;

[0055] Based on the working conditions of the lifting equipment, boundary conditions are defined in the model, such as fixed support points and sliding constraints. Corresponding operating loads are applied, including static loads, dynamic loads and stress sources under other working conditions, to reflect the stress state of the lifting equipment during actual use, thereby forming a parametric finite element reference digital model.

[0056] Furthermore, the Chaboche kinematic constitutive model is introduced into the parametric finite element baseline digital model to simulate the changes in material characteristic data under cyclic lifting loads.

[0057] The Chaboche kinematic stiffening constitutive model, in the yield evolution formula of the connection region of the lifting device component under monitoring, specifically includes three parts: the initial value of the yield stress of the material in the connection region of the lifting device component under monitoring, the isotropic hardening function value of the material in the connection region of the lifting device component under monitoring, and the vector sum of the back stress components. The sum of the three parts is used as the predicted value of the yield stress of the material in the connection region of the lifting device component under monitoring. The isotropic hardening function of the material in the connection region of the lifting device under monitoring uses the cumulative plastic strain of the material in the connection region of the lifting device under monitoring as the independent variable. The back stress components are used to describe the hardening behavior of the material in the connection region of the lifting device under monitoring under cyclic loading. The specific yield evolution formula in the connection region of the lifting device under monitoring is as follows:

[0058]

[0059] In the formula, The predicted yield stress of the material in the connection area of ​​the lifting device components to be monitored. The initial value of the yield stress of the material in the connection area of ​​the lifting device component to be monitored. Let be the isotropic hardening function of the material in the connection area of ​​the lifting device component to be monitored. The cumulative plastic strain of the material in the connection area of ​​the lifting device components to be monitored. Let i be the i-th back stress component, used to describe the hardening behavior of the material in the connection area of ​​the lifting device component under cyclic load, where i is the index of the back stress component.

[0060] It should be noted that during actual hoisting, the lifting equipment needs to withstand constantly changing loads, which are usually periodic. Cyclic loads can cause changes in the fatigue effect and yield properties of materials. Therefore, a constitutive model that can describe the behavior of materials under cyclic loading is required.

[0061] The Chaboche model captures the change in yield stress as a material undergoes loading history by introducing a yield evolution formula, in which the yield stress is... It depends not only on the initial yield stress Furthermore, the hardening characteristics that evolve over time are also taken into account. In this way, the model can dynamically reflect the strengthening behavior of materials after cyclic loading, which is the key to understanding and predicting the performance of lifting equipment under different working conditions.

[0062] The isotropic hardening function of the material in the connection area of ​​the lifting device components to be monitored. The specific formula used is as follows:

[0063]

[0064] In the formula, This is the saturation stress, i.e., the hardening limit. This is a dimensionless hardening rate parameter;

[0065] It should be noted that, As an isotropic hardening function, it represents the hardening effect of a material after undergoing plastic deformation. Cumulative plastic strain It is an important variable reflecting the historical loads experienced by the material, and it directly affects the yield stress of the material. When performing finite element analysis, the cumulative plastic strain can be calculated by simulating the response of the material under cyclic loading. The plastic strain is extracted from the analysis results, and the plastic strain in each cycle is accumulated to obtain the required cumulative plastic strain.

[0066] Obtaining saturation stress and hardening rate parameters The specific method used is as follows: uniaxial tensile testing is performed on the material in the connection area to measure the stress-strain curve and saturation stress. This represents the stress value near saturation in the plastic hardening stage of a material. The hardening behavior curve of the material is recorded by repeatedly applying cyclic loads; the experimental data are then fitted to... Curve, from which hardening rate parameters are extracted. and saturation stress .

[0067] The lifting motion of the lifting equipment under different lifting loads is simulated using a parametric finite element baseline digital model. The model outputs the predicted strain data of the connection area of ​​the lifting equipment components under each lifting motion. The specific steps include: selecting a suitable finite element solver, performing dynamic analysis to capture the material behavior under different loads, performing finite element analysis to obtain the stress and strain data of the structure under each lifting motion, and after the simulation is completed, extracting the strain data of each connection area using a post-processing tool. In the post-processing tool, there are usually options to select the type of strain to be extracted, such as principal strain, equivalent strain, etc. Equivalent strain is selected as the predicted strain data of the connection area of ​​the lifting equipment components under each lifting motion.

[0068] The connection areas of the components of the lifting device to be monitored specifically include the connection areas between the pin and the lug, the pulley and the shaft, and the spring and the hook; the sensor network specifically includes a triaxial accelerometer, a temperature and humidity sensor, and a stress and strain sensor; each sensor is connected to a central control unit, such as a data acquisition unit, via a wireless or wired network to form an integrated sensor network.

[0069] A triaxial accelerometer is used to measure acceleration changes in three directions, capturing the dynamic response of the spreader in the vertical, horizontal, and lateral directions; selecting an accelerometer with a high sampling rate and high sensitivity ensures the ability to capture rapidly changing vibration data;

[0070] Temperature and humidity sensors are used to monitor the temperature and humidity of the environment in real time, and to assess the impact of the environment on material properties, especially performance under extreme weather conditions; sensors with high accuracy and wide measurement range are selected to meet the monitoring needs under different environmental conditions.

[0071] Stress-strain sensors are used to directly measure the stress and strain state of connected areas, providing critical structural health monitoring data; strain gauges or fiber optic sensors are selected to ensure fast response and the ability to withstand high strain and stress environments.

[0072] S2. Based on the sensor data stream, an ensemble Kalman filter algorithm is used to run multiple hoisting instances in parallel. By comparing the strain data predicted by the finite element reference digital model of the hoisting tool to be monitored with the measured strain data, the equivalent influence parameters of the connection area of ​​the hoisting tool component to be monitored in the finite element reference digital model are dynamically updated.

[0073] The equivalent influence parameters of the connection area of ​​the lifting device component to be monitored in the finite element reference digital model specifically include the material's contact stiffness, elastic modulus, Poisson's ratio, yield strength, coefficient of thermal expansion and Rayleigh damping coefficient, as well as the initial yield stress, isotropic hardening saturation stress and hardening rate in the Chaboche kinematic hardening constitutive model.

[0074] The logic underlying the acquisition of strain data predicted by the finite element reference digital model of the lifting device under monitoring and the measured strain data is as follows: For any connection area of ​​the lifting device component under monitoring, an initial equivalent influence parameter group is set. Based on each parameter in the initial equivalent influence parameter group, within a preset proportion range (generally set to -15% to 15%), a corresponding proportion of Gaussian random perturbation is added to each parameter in the initial equivalent influence parameter group. This operation of adding Gaussian random perturbation is performed multiple times to form several updated influence parameter groups. By introducing Gaussian random perturbation, the model can better handle parameter uncertainty, reduce the impact caused by model assumptions or measurement errors, and thus improve the overall prediction reliability.

[0075] For any component connection area of ​​the lifting device to be monitored, the sensor network collects the load and vibration acceleration data of the lifting device from the sensor data stream, uses it as external simulation data, and inputs each updated influence parameter group into the parameterized finite element reference digital model to perform running lifting simulation motion, and obtains the model predicted strain of each updated influence parameter group; at the same time, the sensor network collects the measured strain data of the corresponding component connection area of ​​the lifting device to be monitored.

[0076] The logic underlying the dynamic updating of the equivalent influence parameters of the connection area of ​​the lifting device component to be monitored in the finite element reference digital model is as follows:

[0077] Based on the model-predicted strain obtained from running different updated influence parameter groups, a set of predicted strains for all updated influence parameter groups is constructed, and the average predicted strain in the set is calculated. The covariance matrix of the set of predicted strains is calculated based on the average predicted strain. Calculating the covariance matrix is ​​a conventional existing technique and will not be elaborated here.

[0078] The method for updating the equivalent influence parameters based on the Kalman gain of the connection area of ​​the monitored lifting device component at the u-th location is as follows:

[0079] The difference between the measured strain data and the predicted average strain in the connection area of ​​each monitor component is obtained, and the product of this difference and the Kalman gain is used as the update guidance coefficient.

[0080] Based on the update guide coefficient, the equivalent influence parameters of each monitored lifting tool component connection area in the previous lifting simulation motion cycle are updated. Specifically, the sum of the equivalent influence parameters of the previous lifting simulation motion cycle and the update guide coefficient is used as the updated value of the equivalent influence parameters for the current cycle.

[0081] The Kalman gain is calculated based on the covariance matrix and the observation operator matrix. The specific formula used to calculate the Kalman gain is as follows:

[0082]

[0083] In the formula, Let u be the Kalman gain of the connection area of ​​the lifting device component to be monitored. Let be the covariance matrix of the connection area of ​​the lifting device component to be monitored at the u-th location. The observation operator matrix, specifically the linearized Jacobian matrix, To observe the noise covariance matrix, the accuracy of the specific sensor network is set, where u is the index of the connection area of ​​the lifting device component to be monitored;

[0084] It should be noted that the Kalman gain The magnitude of this value determines the weight of the observations in the model update. If the uncertainty of the prediction is large, that is... If the observation noise is large, the Kalman gain will favor the observations, giving them greater weight; if the observation noise is large, i.e. If the value is large, the Kalman gain will be more inclined to favor the predicted value, giving the predicted value a greater weight;

[0085] The observation operator matrix (linearized Jacobian matrix) Its function is to map the predicted values ​​in the state space to the observation space, that is, to map the predicted values ​​to the measured values;

[0086] Observation noise covariance matrix This reflects the error and uncertainty of sensor measurements. The specific method for obtaining this information is as follows: calibrate the sensor before use, test the sensor output using known standard values ​​(i.e., true values), record the output error, perform multiple measurements on the same target, and calculate the mean and standard deviation of the measurement results. The square of the standard deviation is the variance of the observation noise. If historical measurement data is available, the statistical characteristics of the noise can be calculated based on this data. Calculate the mean and variance of the measurement data, and use the variance to construct the observation noise covariance matrix.

[0087] The formula used to update the equivalent influence parameters based on the Kalman gain of the connection area of ​​the monitored lifting device component at the u-th location is as follows:

[0088]

[0089] In the formula, This represents the updated value of the j-th equivalent influence parameter in the connection area of ​​the lifting device component to be monitored at location u, after the t-th round of simulated lifting motion. Let j be the updated value of the j-th equivalent influence parameter in the connection area of ​​the lifting device component to be monitored at the u-th location after the (t-1)-th round of simulated lifting motion. This represents the measured strain data of the connection area of ​​the lifting device component to be monitored at the u-th location. Let be the predicted average strain of the connection area of ​​the lifting device component to be monitored at the u-th location, j be the index of the equivalent influence parameter, and t be the index of the lifting simulation motion cycle. This refers to the updated guidance coefficient.

[0090] It should be noted that, It is the difference between measured strain data and predicted strain data, representing the deviation between the model prediction and the actual situation; Kalman gain. The Kalman gain determines the magnitude of the residual's impact on parameter updates. A larger Kalman gain indicates higher reliability of the current sensor data, and parameter updates will rely more on observed values. Conversely, a smaller Kalman gain indicates more accurate model predictions, and parameter updates will rely more on predicted values. After Kalman gain weighting correction, the parameters... The new round of simulations will be closer to reality;

[0091] Both the simulation model's predicted values ​​and the sensor's observed values ​​have uncertainties, which are quantified using the covariance matrix and the observation noise matrix. Kalman filtering reduces the uncertainty of the system state by optimizing the combined weights of the predicted and observed values. Kalman filtering is a recursive algorithm that can use the update results of the previous round as the basis for the prediction of the next round. Dynamically updating the equivalent influence parameters continuously reduces the gap between the model's predicted values ​​and the measured values, thereby making the model gradually more accurate.

[0092] S3. Based on the updated equivalent influence parameters, drive the thermo-mechanical coupling reduced-order model after intrinsic orthogonal decomposition and solve the structural dynamic equations based on the temperature field distribution of different component connection areas to obtain the displacement field of the monitor, thereby determining the deformation of different component connection areas.

[0093] The thermo-mechanical coupling reduced-order model is specifically expressed as follows: The thermo-mechanical coupling reduced-order model is based on the dynamic equation and the heat conduction equation. In the dynamic part, the product of the second derivative of the generalized displacement coordinate of the center point of the connection area of ​​the monitored lifting device component with respect to the time variable and the reduced-order mass matrix is ​​used as the first coupling term. The product of the first derivative of the generalized displacement coordinate of the center point of the connection area of ​​the monitored lifting device component with respect to the time variable and the reduced-order damping matrix is ​​used as the second coupling term. The product of the reduced-order stiffness matrix, which depends on the temperature generalized coordinate, and the displacement generalized coordinate of the center point of the connection area of ​​the monitored lifting device component is used as the third coupling term.

[0094] The sum of the first, second, and third coupling terms equals the reduced-order hoisting load vector at the corresponding moment, thus constructing the reduced-order dynamic equation;

[0095] In the heat conduction section, the product of the reduced heat capacity matrix and the first derivative of the generalized temperature coordinate of the center point of the connection area of ​​the lifting device component to be monitored with respect to the time variable is taken as the first heat component; the product of the reduced heat conduction matrix and the generalized temperature coordinate of the center point of the connection area of ​​the lifting device component to be monitored is taken as the second heat component.

[0096] The sum of the first heat component and the second heat component is linked to the reduced-order heat source vector, thereby constructing a reduced-order heat conduction equation. The reduced-order heat source vector comprehensively considers the first derivative of the ambient temperature, the displacement generalized coordinate with respect to the time variable, and the displacement generalized coordinate.

[0097] The heat conduction reduction equation and the kinetic reduction equation are combined to form a thermo-mechanical coupling reduction model; the thermo-mechanical coupling reduction model is specifically expressed as follows:

[0098]

[0099] In the formula, For the reduced-order quality matrix, The generalized coordinates are the displacements of the center point of the connection area of ​​the spreader component to be monitored. The reduced-order damping matrix, This represents a reduced-order stiffness matrix that depends on the temperature-generalized coordinates. The generalized coordinates for the temperature of the center point of the connection area of ​​the lifting device components to be monitored. for The reduced-order lifting load vector at time t, For reduced-order heat capacity matrix, For a reduced-order heat conduction matrix, Represents a reduced-order heat source vector. For ambient temperature, The first derivative of the displacement generalized coordinates with respect to the time variable. Let be the second derivative of the generalized displacement coordinates with respect to the time variable. The first derivative of the generalized temperature coordinate with respect to the time variable. This represents the time variable during the hoisting simulation process.

[0100] It should be noted that the purpose of the reduced-order model is to compress the dynamic behavior of a high-dimensional system into a low-dimensional representation, thereby reducing computational load and resource consumption, while preserving the main dynamic characteristics of the system as much as possible. High-dimensional systems are usually modeled using the finite element method (FEM). However, solving these high-dimensional models can be very time-consuming in dynamic analysis, especially in environments that require multiple simulations. Therefore, a thermo-mechanical coupling reduced-order model is designed to simplify the calculation and thus improve efficiency.

[0101] The coupling relationship between dynamics and heat conduction means that temperature changes affect the mechanical properties of materials, such as stiffness and strength, while the motion and deformation of the structure also affect its temperature distribution through internal and external heat sources and heat conduction effects. During hoisting, the dynamic loading of the structure not only causes mechanical stress but may also lead to changes in damping and thermal effects. Therefore, the two are coupled into a single model for analysis. By reducing the order of the system, the complexity of the solution can be significantly reduced while preserving the main characteristics of the system. Commonly used methods for reducing the order include intrinsic orthogonal decomposition and principal component analysis. These methods can extract the main dynamic characteristics of the system and represent them in a low-dimensional space. In this embodiment, intrinsic orthogonal decomposition is used for reducing the order.

[0102] The reduced stiffness and heat conduction matrices are updated based on the updated equivalent influence parameters. The specific steps are as follows: First, based on the material's properties, the relationship between the material's stiffness and temperature is clarified. Typically, the stiffness of a material decreases at high temperatures, so this relationship needs to be quantified using empirical formulas or experimental data. Second, the stiffness change caused by temperature variations is calculated and combined with the baseline stiffness to obtain the updated reduced stiffness matrix, reflecting the material's true stiffness characteristics during the current lifting process. Third, the heat conduction matrix is ​​updated based on the relationship between the material's thermal conductivity and temperature. Typically, thermal conductivity changes with temperature, especially in some composite materials. When updating the heat conduction matrix, the material's thermal conductivity under specific temperature conditions is considered, the temperature-related change is calculated, and this change is combined with the baseline heat conduction matrix to form a new reduced heat conduction matrix.

[0103] Based on the backward Euler method, the heat conduction equation is discretized using this method. This method can effectively handle stability problems, especially when the temperature changes rapidly. Time is divided into several small steps, each with a corresponding temperature state. The time step size is typically set between 0.05 and 0.2 seconds, and the relationship between the current temperature state and the previous temperature state is established. Using the current and previous temperature states, a linear equation is constructed to combine the temperature state with factors such as heat source terms, heat capacity, and the heat conduction matrix. Solving this equation yields the generalized temperature coordinates at the current moment, reflecting how the temperature field evolves over time during the hoisting process.

[0104] The generalized temperature coordinates and temperature distribution of this round of hoisting simulation are imported into a reduced-order stiffness matrix that depends on the generalized temperature coordinates. The structural dynamics equations are discretized, and based on the set time step, a solvable linear system of equations is formed by expressing the derivative terms in difference form. Numerical solution methods are then used, combined with... Reduced-order lifting load vector at time step Solving for the generalized displacement coordinates This reflects the deformation experienced by the lifting device structure during the lifting process, thereby obtaining the displacement field of the center point of the connection area of ​​the lifting device component to be monitored. Specifically, it refers to the displacement of the center point of the connection area of ​​the lifting device component to be monitored when it returns to the starting position after completing the lifting task in different lifting simulation movements. The specific method for obtaining the displacement field of the monitoring position is as follows: determine the center point of the connection area of ​​the lifting device component to be monitored and extract its corresponding displacement data; compare the displacement at the time of returning to the starting position after completing different lifting simulation movements, and evaluate the response and deformation of the lifting device component throughout the lifting process.

[0105] The method for determining the deformation of different component connection areas based on the displacement of the center point of the connection area of ​​the lifting device components under monitoring is as follows: Determine the displacement basis vector of the center point of the connection area of ​​the lifting device components under monitoring at the u-th location; multiply the intrinsic orthogonal decomposition mode basis vector matrix of the displacement field with the generalized coordinates of the displacement of the center point of the connection area of ​​the lifting device components under monitoring at the u-th location as the deformation transformation matrix; use the sum of the displacement basis vector of the center point of the connection area of ​​the lifting device components under monitoring at the u-th location and the deformation transformation matrix as the basic displacement matrix; and transform the basic displacement matrix into the deformation of the center point of the connection area of ​​the lifting device components under monitoring through the deformation-displacement matrix. The intrinsic orthogonal decomposition mode basis vector matrix is ​​specifically obtained by performing intrinsic orthogonal decomposition on the displacement of the center point of the connection area of ​​the lifting device components under monitoring during the lifting simulation motion. The formula for determining the deformation of different component connection areas is as follows:

[0106]

[0107] In the formula, Let be the deformation at the center point of the connection area of ​​the lifting device component to be monitored at the u-th location. Let be the eigenorthogonal decomposition mode basis vector matrix of the displacement field. Let be the generalized coordinate of the displacement of the center point of the connection area of ​​the lifting device component to be monitored at the u-th location. Let be the displacement basis vector of the center point of the connection area of ​​the lifting device component to be monitored. This is the deformation-displacement matrix;

[0108] It should be noted that the core idea of ​​this formula lies in representing a complex displacement field using a small number of modal bases, i.e., eigenvectors, through order reduction. In complex dynamic systems, the displacement at any point can be viewed as a superposition of the displacement offset from the reference position and a dynamic response. Therefore, by... The displacement matrix of the center point of the connection area of ​​the lifting device component to be monitored at the u-th location is represented by the form ;

[0109] Displacement basis vectors This refers to the displacement of the center point of the connection area of ​​the lifting device components under no external load or initial conditions. Numerical simulation methods such as finite element analysis are used to predict the displacement state of each connection area under initial conditions based on material properties and geometry; the deformation-displacement matrix is ​​also relevant. To convert the basic displacement matrix into deformation values ​​for each connection region, the method for obtaining this matrix involves: performing modal analysis to acquire the vibration characteristics of the monitored lifting device component under different modes. This can be achieved through modal testing or finite element analysis of the structure to obtain the displacement shape under each mode. By performing intrinsic orthogonal decomposition on the displacement data of the center points of the connection regions of the lifting device component, the contribution of each mode is determined. Finally, the modal basis vectors are combined with the actual displacement field to construct the deformation-displacement matrix. .

[0110] The eigenorthogonal decomposition modal basis vector matrix of the displacement field Specifically, the displacement of the center point of the connection area of ​​the lifting device component to be monitored is obtained by intrinsic orthogonal decomposition during the hoisting simulation. The specific steps include: before performing intrinsic orthogonal decomposition, sufficient displacement field data needs to be obtained. These data are obtained by recording the displacement of the center point of the connection area of ​​the lifting device components during the hoisting simulation. Displacement field information is recorded at multiple time steps, and the data can be organized into a discrete matrix. Each column represents the displacement field information at a specific moment, and the entire matrix represents the displacement field at all moments. The average displacement field value is subtracted from the displacement field values ​​at all moments to ensure a uniform distribution of the data around zero. The core of intrinsic orthogonal decomposition lies in uncovering the dominant features in the displacement field. It quantifies the correlation of displacement field data through the calculation of the covariance matrix. Matrix operations are performed on the displacement field data to obtain the covariance matrix. Eigenvalues ​​and eigenvectors are extracted from the covariance matrix. Eigenvalues ​​reflect the contribution of different modes in the covariance matrix; larger eigenvalues ​​mean that the corresponding modal basis vectors contribute more to the dynamic features of the displacement field. The eigenvectors are the intrinsic modal basis of the covariance matrix, forming the basis of intrinsic orthogonal decomposition. The eigenvectors of the covariance matrix are sorted according to the magnitude of their eigenvalues, and the eigenvector with the largest contribution is selected as the principal modal basis vector, constructing the intrinsic orthogonal decomposition modal basis vector matrix of the displacement field.

[0111] S4. Based on the deformation at different component connection locations, calculate the total damage index of the spreader to be monitored, update the damage status based on real-time data, predict the remaining life, and set up a multi-level early warning mechanism. When the total damage index exceeds the corresponding threshold, trigger the corresponding level of early warning signal.

[0112] The logic underlying the calculation of the total damage index of the lifting device under monitoring is as follows: Based on the connection gaps existing in the connection areas of each lifting device component under monitoring, the deformation at different component connection positions is corrected to obtain the accurate deformation. Specifically, the method involves taking the negative of the ratio of the contact gap to the reference gap in the connection area of ​​the lifting device component under monitoring to obtain a correction coefficient. This correction coefficient is used as the independent variable of an exponential function with base e. The value of this exponential function is used as the deformation correction amount. The product of the deformation correction amount and the deformation of the component connection area is taken as the accurate deformation amount at the center point of the connection area of ​​the lifting device component under monitoring. The specific formula for calculating the total damage index of the lifting device under monitoring is as follows: Based on the connection gaps existing in the connection areas of each lifting device component under monitoring, the deformation at different component connection positions is corrected to obtain the accurate deformation.

[0113]

[0114] In the formula, Let be the precise deformation at the center point of the connection area of ​​the u-th lifting device component to be monitored. and These represent the contact gap and reference gap of the connection area of ​​the lifting device component to be monitored at the u-th location, respectively. This is the correction factor;

[0115] It should be noted that in mechanical structures, connection gaps significantly affect the structure's stiffness and deformation characteristics. The presence of connection gaps implies potential relative movement between the connecting surfaces, leading to uneven distribution of stress and strain, thus affecting the overall deformation of the structure. Connection gaps reduce the effective stiffness of the connection area, resulting in a greater actual deformation of the connection region under external loads. Typically more than the original deformation Larger, therefore, considering the connection gap, the actual precise deformation amount The original deformation should be attenuated. The exponential decay factor, as obtained, reflects the degree of influence of the contact gap on the deformation; as the connection gap increases, the decay factor... The smaller the value, the more precise the deformation. Relative to the original deformation The decrease in the proportion reflects the inhibitory effect of the gap on deformation, reference gap. A standard is provided to allow for a relative comparison of the effects of different connection gaps on deformation, typically taken as 0.1 mm;

[0116] The total damage index of the monitored lifting device is calculated based on the precise deformation at the center point of the connection area of ​​the component to be monitored. Specifically, the method is as follows: the ratio of the difference between the preset deformation reference value and the precise deformation to the safety factor is used as the total damage index of the connection area of ​​the component to be monitored. The total damage index of the monitored lifting device is calculated based on the precise deformation at the center point of the connection area of ​​the u-th component to be monitored, using the following formula:

[0117]

[0118] In the formula, Let be the total damage index of the connection area of ​​the lifting device component to be monitored at the u-th location. For safety margin, it is generally taken between 1.5 and 3; The value is a preset reference value for deformation, which is set according to the mechanical structure and is generally between 1 and 5 cm; Table 1 shows some statistical data on the total damage index of different component connection areas.

[0119] Table 1: Partial Statistics of Total Damage Index in Component Connection Areas

[0120]

[0121] According to the data in Table 1, the total damage index The calculation results show that the damage index of most components is low, close to 0. However, the total damage index of individual components, such as component numbers 4 and 24, is close to 0, indicating that these components may have serious damage problems. It is recommended to repair or replace them as soon as possible. The overall condition of the spreader components is good, but individual components with high damage indices still need to be given special attention and maintenance to ensure the long-term safe and reliable operation of the spreader. Regular monitoring and data analysis will help to identify potential problems in advance and reduce the risk of accidents.

[0122] Reference gap Different reference values ​​can be selected for the connection area of ​​specific lifting device components. In the calculation process of this embodiment, a value of 0.1mm is used for all values, while the safety factor is also considered. Different reference values ​​can also be selected and different safety factor values ​​can be set according to the specific connection areas of the lifting equipment components, such as the safety factor for the connection area of ​​the first group of components. The value is taken as 2.0 in the calculation, while the safety factor for the connection area of ​​the second group of components is... In the calculation, it is taken as 1.8.

[0123] The specific logic for triggering the corresponding level of warning signal is as follows:

[0124] like At that time, a level-three warning signal will be issued;

[0125] like At that time, a level-two warning signal will be issued;

[0126] like At that time, a Level 1 warning signal will be issued;

[0127] In the formula, The preset damage threshold is set based on expert experience and historical experience, and the urgency of the warning decreases progressively with the warning signal level.

[0128] Please see Figure 5 The present invention also provides a simulation analysis system for the deformation of a lifting device, which is used to execute the above-described simulation analysis method for the deformation of a lifting device, including:

[0129] The simulation model building module is used to obtain the geometric parameters of the lifting device to be monitored, build a parameterized finite element reference digital model of the lifting device to be monitored, and deploy a sensor network in the component connection area of ​​the lifting device to be monitored to form a sensor data stream for collecting the load, strain and vibration acceleration of the lifting device.

[0130] The equivalent parameter update module is used to run multiple hoisting instances in parallel based on the sensor data stream using an ensemble Kalman filter algorithm. By comparing the strain data predicted by the finite element reference digital model of the hoisting tool to be monitored with the measured strain data, the equivalent influence parameters of the connection area of ​​the hoisting tool component to be monitored in the finite element reference digital model are dynamically updated.

[0131] The deformation characterization module is used to drive the thermo-mechanical coupling reduced-order model, which has undergone intrinsic orthogonal decomposition and order reduction processing, based on the updated equivalent influence parameters. It solves the structural dynamic equations based on the temperature field distribution of the connection areas of different components to obtain the displacement field of the lifting device to be monitored, thereby determining the deformation of the connection areas of different components.

[0132] The early warning generation module is used to calculate the total damage index of the spreader to be monitored based on the deformation of different component connection positions, update the damage status based on real-time data, predict the remaining life, and set up a multi-level early warning mechanism. When the total damage index exceeds the corresponding threshold, the corresponding level of early warning signal is triggered.

[0133] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0134] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0136] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for simulating deformation of a spreader, characterized by, The specific steps include: S1, acquiring the geometric structure parameters of the to-be-monitored spreader, constructing a parameterized finite element reference digital model of the to-be-monitored spreader, and arranging a sensor network at the component connection area of the to-be-monitored spreader to form a sensor data stream collecting the load, strain and vibration acceleration of the spreader; S2, based on the sensor data stream, adopting a set Kalman filtering algorithm, running multiple hoisting instances in parallel, comparing the strain data predicted by the finite element reference digital model of the to-be-monitored spreader with the measured strain data, and dynamically updating the equivalent influence parameters of the component connection area of the to-be-monitored spreader in the finite element reference digital model; S3, based on the updated equivalent influence parameters, driving a thermal-mechanical coupling reduced-order model subjected to eigenvalue orthogonal decomposition reduction, solving the structural dynamics equation based on the temperature field distribution of different component connection areas, obtaining the displacement field of the to-be-monitored spreader, and determining the deformation amount of different component connection areas; S4, based on the deformation amount of different component connection positions, calculating the total damage index of the to-be-monitored spreader, updating the damage state based on real-time data, predicting the remaining life, and setting a multi-level early warning mechanism, when the total damage index exceeds the corresponding threshold, triggering the corresponding level of early warning signal; The thermal-mechanical coupling reduced-order model is specifically represented as: the thermal-mechanical coupling reduced-order model is based on a dynamics equation and a heat conduction equation, wherein in the dynamics part, the second-order derivative of the displacement generalized coordinate of the center point of the component connection area of the to-be-monitored spreader with respect to the time variable multiplied by the reduced-order mass matrix is taken as the first coupling term, the first-order derivative of the displacement generalized coordinate of the center point of the component connection area of the to-be-monitored spreader with respect to the time variable multiplied by the reduced-order damping matrix is taken as the second coupling term, and the product of the displacement generalized coordinate of the center point of the component connection area of the to-be-monitored spreader and the reduced-order stiffness matrix dependent on the temperature generalized coordinate is taken as the third coupling term; The sum of the first coupling term, the second coupling term and the third coupling term is equal to the reduced-order hoisting load vector at the corresponding time, thereby constructing a dynamics reduced-order equation; In the heat conduction part, the product of the reduced-order heat capacity matrix and the first-order derivative of the temperature generalized coordinate of the center point of the component connection area of the to-be-monitored spreader with respect to the time variable is taken as the first heat component; the product of the reduced-order heat conduction matrix and the temperature generalized coordinate of the center point of the component connection area of the to-be-monitored spreader is taken as the second heat component; The sum of the first heat component and the second heat component is associated with the reduced-order heat source vector to thereby construct a heat conduction reduced-order equation, wherein the reduced-order heat source vector comprehensively considers the environmental temperature, the first-order derivative of the displacement generalized coordinate with respect to the time variable and the displacement generalized coordinate; The heat conduction reduced-order equation and the dynamics reduced-order equation are combined to form a thermal-mechanical coupling reduced-order model; the thermal-mechanical coupling reduced-order model is specifically represented as: wherein, is the reduced mass matrix, is the displacement generalized coordinate of the center point of the connection region of the hoist component to be monitored, is the reduced damping matrix, denotes the reduced stiffness matrix depending on the temperature generalized coordinate, is the temperature generalized coordinate of the center point of the connection region of the hoist component to be monitored, is the reduced hoisting load vector at the time instant, is the reduced hoisting load vector at the time instant, is the reduced heat capacity matrix, is the reduced heat conduction matrix, denotes the reduced heat source vector, is the ambient temperature, is the first order derivative of the displacement generalized coordinate with respect to the time variable, is the second order derivative of the displacement generalized coordinate with respect to the time variable, is the first order derivative of the temperature generalized coordinate with respect to the time variable, is the time variable in the course of the hoisting simulation; The logic for calculating the total damage index of the to-be-monitored sling is as follows: based on the connection gap existing in the connection area of each component of the to-be-monitored sling, the deformation amount of different component connection positions is corrected to obtain an accurate deformation amount, and the specific method is as follows: taking the inverse of the ratio of the contact gap and the reference gap of the connection area of the to-be-monitored sling component as the independent variable of the exponential function with e as the base, taking the value of the exponential function as the deformation correction amount, and taking the product of the deformation correction amount and the component connection area deformation amount as the accurate deformation amount of the center point of the to-be-monitored sling component connection area; The total damage index of the to-be-monitored sling is calculated based on the accurate deformation amount of the center point of the to-be-monitored sling component connection area, and the specific method is as follows: taking the ratio of the difference between the preset deformation reference value and the accurate deformation amount and the safety factor as the total damage index of the to-be-monitored sling component connection area.

2. The method of claim 1, wherein: The specific method for constructing the parametric finite element reference digital model of the to-be-monitored sling is as follows: based on the design drawings of the to-be-monitored sling, using the parametric modeling finite element analysis software ANSYS or ABAQUS, establishing a structured finite element model, inputting the initial material characteristic data of the to-be-monitored sling based on the structured finite element model, specifically including the contact stiffness, elastic modulus, Poisson's ratio, yield strength and thermal expansion coefficient of the material, to form a parametric finite element reference digital model, and introducing the Chaboche follow-up hardening constitutive model in the parametric finite element reference digital model to simulate the change of material characteristic data under cyclic lifting load; The Chaboche follow-up hardening constitutive model includes three parts in the yield evolution formula of the to-be-monitored sling component connection area: the initial value of the yield stress of the material of the to-be-monitored sling component connection area, the isotropic hardening function value of the material of the to-be-monitored sling component connection area, and the back stress component vector sum, and the sum of the three parts is taken as the predicted value of the yield stress of the material of the to-be-monitored sling component connection area, wherein the isotropic hardening function of the material of the to-be-monitored sling component connection area is specifically taken as the independent variable by the cumulative plastic strain of the material of the to-be-monitored sling component connection area, and the back stress component is used to describe the hardening behavior of the material of the to-be-monitored sling component connection area when it experiences cyclic load; The lifting movement of the to-be-monitored sling under different lifting loads is simulated by the parametric finite element reference digital model, and the predicted strain data of the to-be-monitored sling component connection area under each lifting movement is output by the model.

3. The method of claim 2, wherein: The component connection area of the to-be-monitored sling specifically includes the connection area between the pin shaft and the ear plate, the connection area between the pulley and the shaft, and the connection area between the spring and the hook cable; The sensor network specifically includes a three-axis accelerometer, a temperature and humidity sensor, and a stress and strain sensor; the equivalent influence parameters of the to-be-monitored sling component connection area in the finite element reference digital model specifically include the contact stiffness, elastic modulus, Poisson's ratio, yield strength, thermal expansion coefficient and Rayleigh damping coefficient of the material, and the initial yield stress, isotropic hardening saturation stress and hardening rate in the Chaboche follow-up hardening constitutive model; The logic for obtaining strain data predicted by the finite element reference digital model of the to-be-monitored spreader and the actually measured strain data is as follows: for any to-be-monitored spreader component connection area, an initial equivalent influence parameter group is set, and based on each parameter in the initial equivalent influence parameter group, a corresponding proportion of Gaussian random disturbance is added to each parameter in the initial equivalent influence parameter group within a preset proportion range, and the operation of adding Gaussian random disturbance is performed multiple times to form a plurality of updated influence parameter groups. For any to-be-monitored spreader component connection area sensor network, the spreader load and vibration acceleration data in the sensor data stream are collected as external simulation data, and each updated influence parameter group is input into the parameterized finite element reference digital model to perform a running hoisting simulation to obtain model predicted strain of each updated influence parameter group. At the same time, the actually measured strain data of the corresponding to-be-monitored spreader component connection area is collected by the sensor network.

4. The method of claim 3, wherein: The logic for dynamically updating the equivalent influence parameters of the to-be-monitored spreader component connection area in the finite element reference digital model is as follows: According to the model predicted strain obtained by running different updated influence parameter groups, a predicted strain set of all updated influence parameter groups is formed, and the average value of the predicted strain in the predicted strain set is calculated. The covariance matrix of the predicted strain set is calculated based on the average value of the predicted strain, and the Kalman gain is calculated according to the covariance matrix, combined with the observation operator matrix and the observation noise covariance matrix. The observation noise covariance matrix is specifically set according to the accuracy of the sensor network. The method for updating the equivalent influence parameters according to the Kalman gain of the u-th to-be-monitored spreader component connection area is as follows: The difference between the actually measured strain data and the average value of the predicted strain of each to-be-monitored spreader component connection area is obtained, and the product of the difference and the Kalman gain is taken as an update guide coefficient. According to the update guide coefficient, the equivalent influence parameters in each to-be-monitored spreader component connection area in the last hoisting simulation motion cycle are updated, specifically, the sum of the equivalent influence parameters in the last hoisting simulation motion cycle and the update guide coefficient is taken as the updated value of the equivalent influence parameters in the current cycle.

5. The method of claim 4, wherein: Based on the updated equivalent influence parameters, the reduced stiffness matrix and the reduced heat conduction matrix are updated, and the backward Euler method is used to solve the heat equation by using the temperature field and environmental data of the last hoisting simulation motion to obtain the temperature generalized coordinates and temperature distribution of the current hoisting simulation motion. The temperature generalized coordinates and temperature distribution of the current hoisting simulation motion are introduced into the reduced stiffness matrix which depends on the temperature generalized coordinates, and the displacement generalized coordinates are solved by combining the reduced hoisting load vector at the corresponding time to obtain the displacement field of the center point of the to-be-monitored spreader component connection area, specifically, the displacement of the center point of the to-be-monitored spreader component connection area when different hoisting simulation motions complete the hoisting task and return to the hoisting starting position. The method for determining the deformation of the different component connection regions based on the displacement of the center point of the component connection region of the to-be-monitored lifting appliance is as follows: a displacement base vector of the center point of the component connection region of the to-be-monitored lifting appliance is determined, a product of an eigenvalue orthogonal decomposition modal base vector matrix and a generalized coordinate of the displacement of the center point of the component connection region of the to-be-monitored lifting appliance is taken as a deformation conversion matrix, a sum of the displacement base vector of the center point of the component connection region of the to-be-monitored lifting appliance and the deformation conversion matrix is taken as a basic displacement matrix, and the basic displacement matrix is converted into the deformation of the center point of the component connection region of the to-be-monitored lifting appliance by a deformation-displacement matrix; the eigenvalue orthogonal decomposition modal base vector matrix is specifically obtained by performing eigenvalue orthogonal decomposition on the displacement of the center point of the component connection region of the to-be-monitored lifting appliance in the lifting simulation movement.

6. A system for simulating sling deformation, comprising: The lifting appliance deformation simulation analysis system is used to execute the lifting appliance deformation simulation analysis method in any one of claims 1-5, and comprises: a simulation model construction module, which is used to acquire geometric structure parameters of a to-be-monitored lifting appliance, construct a parameterized finite element reference digital model of the to-be-monitored lifting appliance, and arrange a sensor network in a component connection region of the to-be-monitored lifting appliance to form a sensor data stream for collecting lifting appliance load, strain and vibration acceleration; an equivalent parameter updating module, which is used to update equivalent influence parameters of the component connection region of the to-be-monitored lifting appliance in the finite element reference digital model of the to-be-monitored lifting appliance by comparing predicted strain data and measured strain data of the to-be-monitored lifting appliance by using a set Kalman filtering algorithm and running multiple lifting instances in parallel based on the sensor data stream; a deformation quantity representation module, which is used to drive a thermal-mechanical coupling reduced-order model subjected to eigenvalue orthogonal decomposition reduction processing based on the updated equivalent influence parameters, solve a structure dynamics equation based on a temperature field distribution of different component connection regions, obtain a displacement field of the to-be-monitored lifting appliance, and determine the deformation of the different component connection regions; and an early warning generation module, which is used to calculate a total damage index of the to-be-monitored lifting appliance based on the deformation of different component connection positions, update a damage state based on real-time data, predict a remaining life, and set a multi-level early warning mechanism, and trigger a corresponding level of early warning signal when the total damage index exceeds a corresponding threshold.

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