A method and device for dynamic simulation optimization analysis of bridge crane

Abstract

The present invention provides a method and device for dynamic simulation optimization analysis of a bridge crane, which relates to the technical field of mechanical simulation optimization. The present invention establishes a geometric model by collecting the physical parameters of the bridge crane, and uses finite element software to perform initial simulation. Then, the grid is adjusted according to the error between the simulation result and the actual data, and the simulation parameters such as lifting height, running speed, load weight and motor efficiency are filtered and optimized using a Kalman filter, and the stress size, deformation of structural components and motor power consumption are used as simulation outputs. Finally, according to the simulation output, safety maximization and energy consumption minimization are set as optimization goals, and the simulation parameters are optimized using a genetic algorithm to achieve dynamic optimization of the simulation parameters of the bridge crane.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical simulation optimization, and in particular to a method and device for dynamic simulation optimization analysis of a bridge crane. Background Art

[0002] Bridge cranes undertake key material handling tasks in industrial production, so they have extremely high requirements for performance and safety. In order to avoid abnormal situations that may occur during the actual handling process, it is essential to simulate the bridge crane to optimize the working parameters before actual work. Traditional simulation optimization design often relies on static analysis and simplified physical models, which makes it difficult to fully consider the complex dynamic behavior in actual operation, and thus cannot accurately predict and optimize the actual handling process. On the one hand, it may waste energy, and on the other hand, it may even lead to insufficient safety in the working process. Therefore, there is an urgent need for a method that can accurately simulate and optimize the dynamic performance of bridge cranes, so as to accurately optimize the operating parameters of bridge cranes and improve operating efficiency and safety.

[0003] In the prior art, publication number CN115455583A discloses a method for dynamic simulation optimization analysis of a bridge crane. Based on the optimization analysis of QUEST, the bridge crane is modeled, including a geometric model and a logical model. The bridge crane is divided into a bridge, a trolley, a small car and an electrical control part to reflect the shape, position and processing relationship of the physical entity in a static mode. Then, degrees of freedom, a self-coordinate system, a processing coordinate system and a motion script are added to the static geometric model to reflect the relative movement between various important equipment and components of the bridge crane. Logical modeling is performed with an abstract description of the decision-making activities of various manufacturing resources in the bridge crane to control the selection and scheduling of the production model; simulation parameters are determined, and the correlation between the mechanism models of the bridge crane is determined before simulation.

[0004] The main problems with the above scheme are: relying on static models to define the geometry and logical relationships of the bridge crane limits its accuracy and responsiveness under dynamic conditions; static models usually cannot repeatedly capture dynamic changes such as vibrations and external disturbances that may occur during actual operation, which affects the accuracy of simulating dynamic motion and interaction; there are insufficient adjustment measures for optimization parameters, and the simulation parameters are not dynamically adjusted and optimized, making it impossible to respond or adjust quickly to adapt to dynamic changes and emergencies during the operation of the bridge crane.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method and device for dynamic simulation optimization analysis of a bridge crane to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for dynamic simulation optimization analysis of a bridge crane, the specific steps comprising:

[0009] Step 1: Collect the physical parameters of the bridge crane, establish a geometric model, set the initial grid size for different parts of the bridge crane, divide the geometric model according to the initial grid size through finite element software, generate a simulation model of the bridge crane, and perform initial simulation on the simulation model. The physical parameters include load, component stiffness, length, diameter, mass, elastic modulus, motor rated power and efficiency;

[0010] Step 2: Calculate the error between the initial simulation results and the actual working data of the bridge crane, and adjust the grid size and density according to the size of the error;

[0011] Step 3: The lifting height, running speed, actual load weight and motor efficiency of the bridge crane are used as simulation parameters. The set simulation parameters are filtered through the Kalman filter, and the filtered parameters are input into the simulation model to output the stress size, structural component deformation and motor power consumption of the bridge crane;

[0012] Step 4: Generate a fitness function that reflects the safety and energy consumption of the bridge crane based on the stress magnitude, deformation of structural components, and motor power consumption. Use a genetic algorithm to optimize the values ​​of simulation parameters based on maximizing safety and minimizing energy consumption of the fitness function.

[0013] Furthermore, the principle for setting the initial grid size is:

[0014] Main beam: The initial mesh size is the main beam height. ;

[0015] End beam: The initial mesh size is the end beam height. ;

[0016] Car: The initial grid size is the height of the car ;

[0017] Support and rotation joints: The initial mesh size is the joint size .

[0018] Furthermore, the principle for calculating the error between the initial simulation results and the actual working data of the bridge crane is:

[0019] ;

[0020] in, Indicates the error, represents the number of nodes in the model mesh, represents the index of the grid node, Indicates The simulation results of the nodes are Indicates The actual value of each node;

[0021] The error threshold is determined by experts in the field. When the error exceeds the error threshold, the size and density of the grid are adjusted for the node area where the difference between the simulation result and the actual value is the largest, and the error is recalculated until the error is within the error threshold.

[0022] Furthermore, the formula for filtering the set simulation parameters is:

[0023] ;

[0024] in, Indicates The state estimation of the system after filtering is: Indicates The state estimation of the system after filtering is: represents the Kalman gain, Indicates The simulation parameters used for the secondary filtering are: represents the observation matrix.

[0025] Furthermore, the principles for the output of the bridge crane’s stress magnitude, structural component deformation, and motor power consumption are as follows:

[0026] The formula for calculating stress magnitude is:

[0027] ;

[0028] in, represents the magnitude of stress, Indicates the load weight of the bridge crane. represents the acceleration due to gravity, Represents the cross-sectional area of ​​the load-bearing structure;

[0029] The formula for calculating the deformation of structural components is:

[0030] ;

[0031] in, represents the deformation of the structural component, Indicates the lifting height. represents the horizontal displacement distance, It represents the elastic modulus of the structural component;

[0032] The formula for calculating the motor power consumption is:

[0033] ;

[0034] ;

[0035] ;

[0036] in, Indicates torque, represents the pulley radius, represents the angular velocity, Indicates the running speed, Indicates the power consumed by the motor. Indicates motor efficiency.

[0037] Furthermore, the principle for optimizing the values ​​of simulation parameters by genetic algorithm is as follows:

[0038] Each set of simulation parameter values ​​is regarded as an individual, and the formula for generating the fitness function of the individual is:

[0039] ;

[0040] ;

[0041] in, Indicates safety, represents the magnitude of stress, represents the deformation of the structural component, represents the influence weight of stress magnitude, Indicates the influence weight of the deformation of the structural component, ,and , represents the fitness function, Represents the impact weight of security on the fitness function, Represents the weight of the influence of motor power consumption on the fitness function, ,and ;

[0042] Individuals are selected through the roulette algorithm, and the selected individuals are arranged from high to low according to fitness. A pair of adjacent individuals form parents, and genes are exchanged between the parents to generate offspring individuals. Genes are randomly selected from the offspring individuals for change, and the fitness function of the individuals is recalculated. The genetic operation is repeated until the preset number of iterations is reached, and the layout scheme with the highest fitness is selected as the value of the simulation parameter;

[0043] The genes represent simulation parameters that need to be optimized.

[0044] The present invention also provides a bridge crane dynamic simulation optimization analysis device, which is used to implement the above bridge crane dynamic simulation optimization analysis method, and specifically includes:

[0045] A data acquisition module is used to collect physical parameters of the bridge crane, establish a geometric model, set initial grid sizes for different parts of the bridge crane, divide the geometric model according to the initial grid size through finite element software, generate a simulation model of the bridge crane, and perform initial simulation on the simulation model. The physical parameters include load, stiffness, length, diameter, mass, elastic modulus, motor rated power and efficiency of components;

[0046] Model adjustment module, used to calculate the error between the initial simulation result and the actual working data of the bridge crane, and adjust the grid size and density according to the error;

[0047] The simulation output module is used to use the lifting height, running speed, actual load weight and motor efficiency of the bridge crane as simulation parameters, filter the set simulation parameters through the Kalman filter, input the filtered parameters into the simulation model, and output the stress size, structural component deformation and motor power consumption of the bridge crane;

[0048] The simulation optimization module is used to generate a fitness function that reflects the safety and energy consumption of the bridge crane based on the stress magnitude, deformation of the structural components and the power consumption of the motor. The genetic algorithm is used to optimize the values ​​of the simulation parameters based on the maximization of safety and minimization of energy consumption of the fitness function.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention establishes a geometric model according to the physical parameters of the bridge crane, and establishes a mesh model through finite element division based on the geometric model, which can capture the detailed features of different parts of the bridge crane, provide an accurate model basis, and perform initial simulation on the model. Through the error between the initial simulation results and the actual working data, the inaccuracy of the model can be effectively identified, and targeted adjustments can be made to make the simulation results closer to the actual performance, thereby achieving continuous optimization and improvement of the model and ensuring the reliability of design and analysis.

[0051] The present invention also obtains simulation results indicating stress magnitude, deformation of structural components and power consumption of motors by filtering preset simulation parameters and then inputting them into a simulation model. Safety is reflected by stress magnitude and deformation of structural components, and a fitness function is generated. The simulation results are used as optimization targets, and the simulation parameters are optimized by a genetic algorithm. The present invention can continuously improve in multiple iterations, thereby improving the rationality of the simulation parameter values ​​and the overall performance of the solution. The safety and stability of the bridge crane in various working conditions are ensured through optimization, and energy consumption is reduced, thereby improving economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a schematic diagram of a method flow of an embodiment of the present invention;

[0053] Figure 2 Schematic diagram of a device module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0055] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0056] Example:

[0057] See also Figure 1 , the present invention provides a technical solution:

[0058] A method for dynamic simulation optimization analysis of a bridge crane, the specific steps comprising:

[0059] Step 1: Collect the physical parameters of the bridge crane, establish a geometric model, set the initial grid size for different parts of the bridge crane, divide the geometric model according to the initial grid size through finite element software, generate a simulation model of the bridge crane, and perform initial simulation on the simulation model. The physical parameters include load, component stiffness, length, diameter, mass, elastic modulus, motor rated power and efficiency;

[0060] In this embodiment, the principle for setting the initial grid size is:

[0061] Main beam: The initial mesh size is the main beam height. ;

[0062] End beam: The initial mesh size is the end beam height. ;

[0063] Car: The initial grid size is the height of the car ;

[0064] Support and rotation joints: The initial mesh size is the joint size .

[0065] Import the geometric model into ANSYS, define the material properties and unit size ratio, set the global grid according to the set initial grid size, generate a preliminary grid simulation model, and refine the grid by comparing the error conditions until the computational efficiency and accuracy of the grid meet the requirements.

[0066] Step 2: Calculate the error between the initial simulation results and the actual working data of the bridge crane, and adjust the grid size and density according to the size of the error;

[0067] In this embodiment, the principle for calculating the error between the initial simulation result and the actual working data of the bridge crane is:

[0068] ;

[0069] in, Indicates the error, represents the number of nodes in the model mesh, represents the index of the grid node, Indicates The simulation results of the nodes are Indicates The actual value of each node;

[0070] The error threshold is determined by experts in the field. When the error exceeds the error threshold, the size and density of the grid are adjusted for the node area where the difference between the simulation result and the actual value is the largest, and the error is recalculated until the error is within the error threshold.

[0071] The error reflects the deviation between the simulation results of the entire simulation model and the actual value. The smaller the error, the closer the simulation results are to the actual value and the more accurate the model is. When the error exceeds the error threshold, the grid size is adjusted from the node with the largest error, and the error is recalculated after each adjustment. When the error of the entire model is within the error threshold, it is considered that the grid has achieved independence and the model meets the requirements. After that, the boundary conditions of the model are determined, the contact points between the bridge crane and the ground or other supporting structures are selected, and these contact points are fixed; displacement in all directions is prohibited; the moving parts of the crane, including the hook and pulley, are analyzed, the moving direction and trajectory of the moving parts are determined, displacement or rotation in the moving direction is allowed, and displacement or rotation in other directions is prohibited.

[0072] Step 3: The lifting height, running speed, actual load weight and motor efficiency of the bridge crane are used as simulation parameters. The set simulation parameters are filtered through the Kalman filter, and the filtered parameters are input into the simulation model to output the stress size, structural component deformation and motor power consumption of the bridge crane;

[0073] In this embodiment, the formula for filtering by setting the simulation parameters is:

[0074] ;

[0075] ;

[0076] ;

[0077] The first The second filter is used as the filter to be predicted, where represents the forecast error covariance, represents the state transfer matrix, represents the error covariance of the previous prediction error covariance, represents the transposed matrix of the state transition matrix, represents the process noise covariance, represents the Kalman gain, represents the observation matrix, represents the transposed matrix of the observation matrix, represents the measurement noise covariance;

[0078] in, Indicates The state estimation of the system after filtering is: Indicates The state estimation of the system after filtering is: Indicates The simulation parameters used for the secondary filtering.

[0079] By filtering the simulation parameters through the Kalman filter and then inputting them, the actual operating conditions can be better simulated, the accuracy of dynamic simulation can be improved, and the system state can be recursively estimated by combining the dynamic model of the system with uncertain measurement data; using the two steps of prediction and update, the system state estimation can be optimized while considering noise. By adjusting the Kalman gain, the predicted value is combined with the measured value to achieve an accurate estimation of the real state and achieve a filtering effect.

[0080] The principles for the output of the bridge crane's stress, deformation of structural components and motor power consumption are as follows:

[0081] The formula for calculating stress magnitude is:

[0082] ;

[0083] in, represents the magnitude of stress, Indicates the load weight of the bridge crane. represents the acceleration due to gravity, Represents the cross-sectional area of ​​the load-bearing structure;

[0084] Stress reflects the strength distribution inside the bridge crane due to external forces, that is, the force per unit area. In the bridge crane model, the main force on the bridge crane comes from the weight of the hoisted object, that is, the load weight. The stress is inversely proportional to the force borne by the load structure and directly proportional to the cross-sectional area of ​​the load structure.

[0085] The formula for calculating the deformation of structural components is:

[0086] ;

[0087] in, represents the deformation of the structural component, Indicates the lifting height. represents the horizontal displacement distance, It represents the elastic modulus of the structural component;

[0088] The deformation of structural components reflects the degree of deformation of the structure under the action of external force, that is, the degree of extension or compression of structural components in the direction of force. In bridge cranes, the main force comes from the load weight. The direction of force includes the vertical pulling of the load object and the horizontal translation of the load object. Therefore, the displacement in the direction of force comprehensively considers the horizontal and vertical directions. The deformation of structural components is proportional to the stress, proportional to the displacement in the direction of force, and inversely proportional to the elastic modulus of the structural component material.

[0089] The formula for calculating the motor power consumption is:

[0090] ;

[0091] ;

[0092] ;

[0093] in, Indicates torque, represents the pulley radius, represents the angular velocity, Indicates the running speed, Indicates the power consumed by the motor. Indicates motor efficiency.

[0094] The power consumption of the motor reflects the heat required by the motor when performing its work tasks. It is the power required for the motor to overcome resistance such as load and friction. The above formula mainly considers the amount of power consumed by the motor to provide power to the rotating joint. The power consumption of the motor is proportional to the torque. The torque represents the moment when the motor rotates, which is proportional to the rotation speed of the rotating joint and inversely proportional to the working efficiency of the motor.

[0095] Step 4: Generate a fitness function that reflects the safety and energy consumption of the bridge crane based on the stress magnitude, deformation of structural components, and motor power consumption. Use a genetic algorithm to optimize the values ​​of simulation parameters based on maximizing safety and minimizing energy consumption of the fitness function.

[0096] In this embodiment, the principle for optimizing the values ​​of simulation parameters by genetic algorithm is as follows:

[0097] Each set of simulation parameter values ​​is regarded as an individual, and the formula for generating the fitness function of the individual is:

[0098] ;

[0099] ;

[0100] in, Indicates safety, represents the magnitude of stress, represents the deformation of the structural component, represents the influence weight of stress magnitude, Indicates the influence weight of the deformation of the structural component, represents the fitness function, Represents the impact weight of security on the fitness function, Represents the weight of the influence of motor power consumption on the fitness function;

[0101] The safety of the system is jointly determined by the stress of the system and the deformation of the structural components. The greater the stress of the system and the deformation of the structural components, the lower the safety of the system. The two jointly determine the safety of the system and have the same influence weight. The fitness function is constructed based on safety and motor power consumption. For the bridge crane system, the optimization goal is to ensure high safety while reducing the motor power consumption. Therefore, safety is proportional to the value of the fitness function, and the motor power consumption is inversely proportional to the value of the fitness function. In the bridge crane system, safety is the most important, so the influence weight values ​​are , ;

[0102] Individuals are selected through the roulette algorithm, and the selected individuals are arranged from high to low according to fitness. A pair of adjacent individuals form parents, and genes are exchanged between the parent individuals to generate offspring individuals. Genes are randomly selected from the offspring individuals for change, and the fitness function of the individuals is recalculated. The genetic operation is repeated until the preset number of iterations is reached, and the individual with the highest fitness is selected as the value of the simulation parameter;

[0103] The steps to select individuals with high fitness through the roulette algorithm are:

[0104] Calculate the sum of the fitness of all individuals. For each individual, divide its fitness value by the sum of the fitness of all individuals to generate the probability of selection of the corresponding individual. Build a cumulative probability table based on the selection probability of each individual, and generate a random number between 0 and 1. Select the corresponding individual in the cumulative probability table based on the random number. This can maintain the diversity of the population while selecting individuals with higher fitness.

[0105] The genes represent the simulation parameters that need to be optimized, namely, lifting height, operating speed, actual load weight and motor efficiency.

[0106] See also Figure 2 The present invention also provides a bridge crane dynamic simulation optimization analysis device, which is used to implement the above bridge crane dynamic simulation optimization analysis method, specifically comprising:

[0107] A data acquisition module is used to collect physical parameters of the bridge crane, establish a geometric model, set initial grid sizes for different parts of the bridge crane, divide the geometric model according to the initial grid size through finite element software, generate a simulation model of the bridge crane, and perform initial simulation on the simulation model. The physical parameters include load, stiffness, length, diameter, mass, elastic modulus, motor rated power and efficiency of components;

[0108] Model adjustment module, used to calculate the error between the initial simulation result and the actual working data of the bridge crane, and adjust the grid size and density according to the error;

[0109] The simulation output module is used to use the lifting height, running speed, actual load weight and motor efficiency of the bridge crane as simulation parameters, filter the set simulation parameters through the Kalman filter, input the filtered parameters into the simulation model, and output the stress size, structural component deformation and motor power consumption of the bridge crane;

[0110] The simulation optimization module is used to generate a fitness function that reflects the safety and energy consumption of the bridge crane based on the stress magnitude, deformation of the structural components and the power consumption of the motor. The genetic algorithm is used to optimize the values ​​of the simulation parameters based on the maximization of safety and minimization of energy consumption of the fitness function.

[0111] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0112] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0113] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A dynamic simulation optimization analysis method for a bridge crane, characterized in that: The specific steps include: Step 1: Collect the physical parameters of the bridge crane, establish a geometric model, set the initial grid size for different parts of the bridge crane, divide the geometric model according to the initial grid size through finite element software, generate a simulation model of the bridge crane, and perform initial simulation on the simulation model. The physical parameters include load, component stiffness, length, diameter, mass, elastic modulus, motor rated power and efficiency; Step 2: Calculate the error between the initial simulation results and the actual working data of the bridge crane, and adjust the grid size and density according to the size of the error; Step 3: The lifting height, running speed, actual load weight and motor efficiency of the bridge crane are used as simulation parameters. The set simulation parameters are filtered through the Kalman filter, and the filtered parameters are input into the simulation model to output the stress size, structural component deformation and motor power consumption of the bridge crane; Step 4: Generate a fitness function that reflects the safety and energy consumption of the bridge crane based on the stress magnitude, deformation of the structural components, and motor power consumption. Use a genetic algorithm to optimize the values ​​of the simulation parameters based on the maximization of safety and minimization of energy consumption in the fitness function. The principles for the output of the bridge crane's stress, deformation of structural components and motor power consumption are as follows: The formula for calculating stress magnitude is: Among them, σ represents the stress magnitude, W represents the load weight of the bridge crane, g represents the acceleration of gravity, and A represents the cross-sectional area of ​​the load structure; The formula for calculating the deformation of structural components is: Among them, δ represents the deformation of the structural component, L represents the lifting height, D represents the horizontal displacement distance, and E represents the elastic modulus of the structural component; The formula for calculating the motor power consumption is: T=W·g·r Among them, T represents torque, r represents pulley radius, ω represents angular velocity, v represents running speed, P represents motor power consumption, and η represents motor efficiency; Each set of simulation parameter values ​​is regarded as an individual, and the formula for generating the fitness function of the individual is: Wherein, S represents safety, σ represents stress, L represents deformation of structural components, a1 represents the influence weight of stress, a2 represents the influence weight of deformation of structural components, a1+a2=1, and a1=a2, M represents fitness function, w1 represents the influence weight of safety on fitness function, w2 represents the influence weight of motor power consumption on fitness function, w1+w2=1, and w1>w2; Individuals are selected through the roulette algorithm, and the selected individuals are arranged from high to low according to fitness. A pair of adjacent individuals form parents, and genes are exchanged between the parents to generate offspring individuals. Genes are randomly selected from the offspring individuals for change, and the fitness function of the individuals is recalculated. The genetic operation is repeated until the preset number of iterations is reached, and the layout scheme with the highest fitness is selected as the value of the simulation parameter; The genes represent simulation parameters that need to be optimized.

2. The method for dynamic simulation optimization analysis of a bridge crane according to claim 1, characterized in that: The principle for setting the initial grid size in step 1 is: Main beam: The initial mesh size is the main beam height. End beam: The initial mesh size is the end beam height. Car: The initial grid size is the height of the car Support and rotation joints: The initial mesh size is the joint size 3. The method for dynamic simulation optimization analysis of a bridge crane according to claim 1, characterized in that: The principle for calculating the error between the initial simulation result and the actual working data of the bridge crane in step 2 is: Where E represents the error, N represents the number of nodes in the model grid, i represents the index of the grid node, and u i represents the simulation result of the i-th node, u′ i Represents the actual value of the i-th node; The error threshold is determined by experts in the field. When the error exceeds the error threshold, the size and density of the grid are adjusted for the node area where the difference between the simulation result and the actual value is the largest, and the error is recalculated until the error is within the error threshold.

4. A bridge crane dynamic simulation optimization analysis device, characterized in that: The device is used to implement the bridge crane dynamic simulation optimization analysis method according to any one of claims 1 to 3, and specifically comprises: A data acquisition module is used to collect physical parameters of the bridge crane, establish a geometric model, set initial grid sizes for different parts of the bridge crane, divide the geometric model according to the initial grid size through finite element software, generate a simulation model of the bridge crane, and perform initial simulation on the simulation model. The physical parameters include load, stiffness, length, diameter, mass, elastic modulus, motor rated power and efficiency of components; Model adjustment module, used to calculate the error between the initial simulation result and the actual working data of the bridge crane, and adjust the grid size and density according to the error; The simulation output module is used to use the lifting height, running speed, actual load weight and motor efficiency of the bridge crane as simulation parameters, filter the set simulation parameters through the Kalman filter, input the filtered parameters into the simulation model, and output the stress size, structural component deformation and motor power consumption of the bridge crane; The simulation optimization module is used to generate a fitness function that reflects the safety and energy consumption of the bridge crane based on the stress magnitude, deformation of the structural components and the power consumption of the motor. The genetic algorithm is used to optimize the values ​​of the simulation parameters based on the maximization of safety and minimization of energy consumption of the fitness function.

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