Automation equipment digital collaborative design and verification method and device and storage medium

Abstract

The application relates to an automation equipment digital collaborative design and verification method and device and a storage medium, wherein the method comprises the following steps: extracting effective creation instructions of a three-dimensional model creation process; creating a txt file and saving; extracting size key parameters, encapsulating and writing into a three-dimensional model creation class, and creating a callable three-dimensional model creation function; determining optimization index parameters; executing a model creation step, a finite element simulation step, a kinematics simulation step, and a parameter optimization step to realize optimization and update of the optimization index parameters; based on the updated optimization index parameters, re-executing the model creation step, the finite element simulation step, the kinematics simulation step and the parameter optimization step until an iteration termination condition is reached, and obtaining optimal optimization index parameters; and creating an optimized three-dimensional model based on the optimal optimization index parameters. Compared with the prior art, the application has the advantages of high automation degree, high design verification efficiency, better parameter design and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of collaborative design and verification, in particular to an automatic digital collaborative design and verification method and device for automation equipment and a storage medium. BACKGROUND

[0002] The current automation equipment design and integration mainly consists of three steps of mechanical structure design, finite element simulation and equipment motion simulation, and the design and simulation verification of the three steps are mainly completed by mechanical design engineers, simulation engineers and automation engineers. The current main defects are as follows:

[0003] 1) The design and simulation verification work is relatively fragmented, and a large amount of manpower is needed for work aggregation

[0004] The mechanical design modeling, finite element simulation and automation motion simulation verification are completed in independent software during the design, and there is less interface calling between the software, so it is difficult to realize automatic collaborative work. Especially after the design is modified, simulation and operation verification are needed, and the human cost of communication and exchange is huge.

[0005] 2) Parameter modification and verification depend on manual operation, and the efficiency is poor

[0006] The finite element simulation and motion simulation verify the operation process of the equipment, which needs the designer to wait and operate, occupies manpower, and is difficult to realize concurrency, so the efficiency is low. In addition, the designer needs to keep a large number of file versions, and needs to open the files and modify them separately for additional modifications in the design process, so the management process is complicated and the modification speed is slow.

[0007] 3) The number of design attempts is limited, and it is seriously dependent on experience and lacks scientificity

[0008] At present, the parameter modification and operation verification in the design process depend on manual operation. With the increase of the modification parameter variable dimension, the number of parameters modified by manual modification is relatively limited, and it is seriously dependent on the design experience of the designer, so it is difficult to generate a local or global optimal solution, and the scientificity of the design is weak.

[0009] Due to the above defects, the automation equipment design and integration efficiency is low, it is too dependent on manual experience, the automation degree is limited, and the parameter design is difficult to get a local or global optimal solution, so a lot of manpower and material resources are consumed. Therefore, the technical personnel in this field urgently need to find a technical scheme to solve the above technical problems. SUMMARY

[0010] The purpose of the present application is to provide an automatic digital collaborative design and verification method for automation equipment, which connects the design and simulation verification program, and only uses program adjustment parameters to realize automatic verification.

[0011] The object of the present application can be realized by the following technical solutions:

[0012] An automated equipment digital collaborative design and verification method, comprising the following steps:

[0013] Extracting effective creation instructions of a three-dimensional model creation process, the three-dimensional model being created based on FreeCAD visualization and created by Macro recording the creation process;

[0014] Creating a txt file based on the effective creation instructions and saving it;

[0015] Extracting size key parameters in the txt file and encapsulating them to write into a three-dimensional model creation class, creating a callable three-dimensional model creation function, the input of which being the size key parameters and the output being a three-dimensional model IGES file;

[0016] Determining optimization index parameters based on the size key parameters, the optimization index parameters being one or more of the size key parameters;

[0017] Performing a model creation step, which is based on the size key parameters, calling the three-dimensional model creation function, and obtaining a three-dimensional model IGES file;

[0018] Performing a finite element simulation step, which is based on pyansys to create a finite element simulation class, performing finite element simulation on the IGES file and load information to obtain finite element simulation results, and creating a finite element-based deformation model;

[0019] Performing a kinematics simulation step, which is based on an inverse algorithm to solve the motion step input of motion simulation, establishing communication with UE4 based on WebSocket, and performing motion simulation in UE4 based on the deformation model and the motion step input, real-time collecting the collision state of the motion state, and returning a collision signal;

[0020] Performing a parameter optimization step, which is based on an intelligent algorithm to create a parameter optimization algorithm to optimize the optimization index parameters, taking the finite element simulation results and the collision signal as dynamic optimization boundary conditions, and updating the optimization index parameters;

[0021] Re-performing the model creation step, the finite element simulation step, the kinematics simulation step and the parameter optimization step based on the updated optimization index parameters until an iteration termination condition is reached, obtaining the optimal optimization index parameters, wherein the iteration termination condition is determined based on the intelligent algorithm to be that a loss function of a judgment index function reaches a convergence state or the number of iterations reaches a pre-configured iteration threshold, and the judgment index function is determined based on design requirements and application requirements;

[0022] Based on the optimal optimization index parameter, a three-dimensional model creation function is called to obtain an optimized three-dimensional model.

[0023] The motion simulation based on the morphing model in UE4 is to simulate the deformation of the three-dimensional model in the finite element simulation step in the form of translation transformation and rotation transformation, obtain a simulation deformation model corresponding to the morphing model in UE4, and perform motion simulation based on the simulation deformation model.

[0024] The motion step input of the motion simulation based on the inverse solution algorithm includes the following steps:

[0025] An inverse solution operation is performed based on the initial position and the target position of the motion of the mechanical arm to obtain inverse solution values of six-axis rotation angles, and the mechanical arm is arranged on the morphing model;

[0026] It is judged whether the motion from the initial position to the target position is reachable, and if so, the inverse solution values of the six-axis rotation angles are divided into a motion list of a preconfigured number of steps as the motion step input of the motion simulation; if not, an inverse solution operation including a seventh axis is performed, wherein the inverse solution operation including the seventh axis includes the following steps: controlling the mechanical arm to move in the direction of the seventh axis with a preconfigured step length and performing an inverse solution operation, and obtaining a set of reachable solutions in a preconfigured movement range, the reachable solutions including the distance of the seventh axis movement and the six-axis rotation angles; calling an energy consumption function for each solution in the set of reachable solutions to obtain the energy consumption corresponding to the solution; based on the principle of minimum energy consumption, determining a solution in the set of reachable solutions as an optimal solution; and dividing the six-axis rotation angles in the optimal solution into a motion list of a preconfigured number of steps, together with the distance of the seventh axis movement, as the motion step input of the motion simulation.

[0027] The parameter optimization algorithm based on the intelligent algorithm includes a finite element parameter optimization algorithm and an obstacle avoidance algorithm, wherein the finite element parameter optimization algorithm optimizes the optimization index parameter, and the obstacle avoidance algorithm optimizes the motion step of the motion simulation when a motion planning collision occurs.

[0028] The collision signal includes a no collision signal, a motion planning collision signal, and a structure collision signal.

[0029] The collision signal includes a no collision signal, a motion planning collision signal, and a structure collision signal, if the collision signal is a structure collision signal, a finite element parameter optimization algorithm is called to optimize the optimization index parameter, and the current optimization index parameter is set as a discard value; if the collision signal is a motion planning collision signal, an obstacle avoidance algorithm is called to re-plan a motion path and perform motion simulation in UE4, the collision signal is updated, the type of the collision signal is re-judged, and subsequent steps are executed based on the type of the collision signal; if the collision signal is a no collision signal, a finite element parameter optimization algorithm is called to optimize the optimization index parameter.

[0030] The finite element parameter optimization algorithm includes an ant colony algorithm-based finite element parameter optimization algorithm, and when the finite element parameter optimization algorithm is the ant colony algorithm-based finite element parameter optimization algorithm, the iteration termination condition is that the number of iterations reaches a preconfigured iteration threshold.

[0031] The collision signal includes an unoccurred collision signal, a motion planning collision signal and a structure collision signal, if the collision signal is the structure collision signal, an ant colony algorithm-based finite element parameter optimization algorithm is called to optimize the optimization index parameter, and pheromone of an ant position corresponding to the collision signal is assigned a preconfigured maximum value.

[0032] The evaluation index function is determined based on model material and deformation degree.

[0033] A digital collaborative design and verification device for a dynamic device, comprising a memory, a processor and a program stored in the memory, and the processor implements the method described above when executing the program.

[0034] A storage medium having a program stored thereon, and the program implements the method described above when executed.

[0035] Compared with the prior art, the present application has the following beneficial effects:

[0036] (1) The present application realizes automatic collaborative work among design modeling, finite element simulation and kinematic simulation verification through program and interface calling, and uses translation and rotation transformation to simulate the finite element deformation model, so that kinematic simulation verification of the deformation model is realized without changing the input model, without manual operation of three software for file import and output, saving the human cost of communication and exchange, and having high automation degree.

[0037] (2) The present application automatically optimizes the model parameters based on the finite element simulation results and the kinematic simulation results, and obtains the optimal optimization index parameter, without saving a large number of files in the optimization design process, and without manually adjusting the parameters in each file, so that the work efficiency is high.

[0038] (3) The parameter optimization of the present application is based on intelligent algorithm, does not depend on artificial experience, has high reliability and strong scientificity, especially in the case of modifying parameters with more variable dimensions, the number and range of parameters modified by artificial are relatively limited, the present application is more easy to generate local or global optimal solution, and the design has strong scientificity. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The method flowchart of the present application is shown in the figure;

[0040] Figure 2 The three-dimensional model schematic diagram of the embodiment of the present application is shown in the figure;

[0041] Figure 3 A simulation diagram of the fork arm thickness in UE4 for an embodiment of the present application;

[0042] Figure 4 A simulation diagram of the fork arm rotation in UE4 for an embodiment of the present application;

[0043] Figure 5 A model diagram of the motion simulation in UE4 for an embodiment of the present application;

[0044] Figure 6 A flowchart of the parameter optimization step of the present application;

[0045] Figure 7 A flowchart of the finite element parameter optimization algorithm based on the ant colony algorithm for an embodiment of the present application. DETAILED DESCRIPTION

[0046] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present application, and detailed implementation and specific operation processes are given, but the protection scope of the present application is not limited to the following embodiments.

[0047] The present embodiment provides an automated equipment digital collaborative design and verification method, as shown in Figure 1 , taking a fork arm model as an example, including the following steps:

[0048] Step 1) Extract the effective creation instruction of the three-dimensional model creation process, which is based on FreeCAD visualization creation, as shown in Figure 2 , and created by Macro recording the creation process.

[0049] Step 2) Create a txt file based on the effective creation instruction and save it.

[0050] Step 3) Extract the size key parameters in the txt file and encapsulate them into a three-dimensional model creation class, and create a callable three-dimensional model creation function, which takes the size key parameters as input and outputs a three-dimensional model IGES file. In the present embodiment, the size key parameters are the diagonal brace length, the fork arm thickness, and the fork arm width.

[0051] Step 4) Determine the optimization index parameter based on the size key parameters.

[0052] The optimization index parameter is one or more of the size key parameters. In the present embodiment, according to the analysis of the influence degree of each size key parameter on the inflection point displacement, it is found that the fork arm thickness has the greatest influence on the inflection point displacement, so the optimization index parameter is determined as the fork arm thickness.

[0053] Step 5) Perform the model creation steps: Based on the key dimensional parameters, call the 3D model creation function to obtain the 3D model IGES file.

[0054] Step 6) Perform finite element simulation: Create a finite element simulation class based on pyansys, load the IGES file and load information, perform finite element simulation to obtain finite element simulation results, and create a deformation model based on finite elements.

[0055] In the finite element simulation step, it is necessary to define the element type, Young's modulus of the material, Poisson's ratio, and parameter variables. In this embodiment, the element type is Solid65, the material is Q235, and the parameter variables are dimensional critical parameters.

[0056] Step 7) Perform kinematic simulation: Solve the motion step input of the motion simulation based on the inverse kinematics algorithm, establish communication with UE4 based on WebSocket, and perform motion simulation in UE4 based on the deformation model and motion step input. Collect the collision status of the motion state in real time and return the collision signal.

[0057] Since it is difficult to modify the model after UE4 simulates a certain input model, it is necessary to simulate the deformation of the three-dimensional model in the finite element simulation step in the form of translation and rotation transformations to obtain the simulated deformation model in UE4 corresponding to the deformation model, and to perform motion simulation based on the simulated deformation model.

[0058] In this embodiment, the change in thickness is simulated by translating the fork arm downwards by a distance x, and the rotation of the fork arm is simulated by rotating it around point A. Figure 3 and Figure 4 As shown.

[0059] A schematic diagram of the motion simulation model in UE4 is shown below. Figure 5 As shown, the robotic arm is mounted on a fork arm and can move left and right, and forward and backward.

[0060] The process of solving the motion step input for motion simulation based on the inverse kinematics algorithm includes the following steps:

[0061] Inverse kinematics calculations are performed based on the initial and target positions to obtain the inverse kinematics values ​​for the six-axis rotation angles;

[0062] determines whether the mechanical arm is reachable from the initial position to the target position, if reachable, divides the six-axis rotation angle inverse solution value into a ten-step motion list as the motion step input of the motion simulation; if not reachable, proves that the initial position is too far from the target position, and then performs the inverse solution operation including the seventh axis. The inverse solution operation including the seventh axis includes the following steps: controlling the mechanical arm to move in the direction of the seventh axis with a pre-configured step size and performing the inverse solution operation, and obtaining a set of reachable solutions in the set movement range, the reachable solutions including the distance of the seventh axis movement and the six-axis rotation angle; calling the energy consumption function for each solution in the set of reachable solutions, that is, multiplying the distance of the seventh axis movement and the six-axis rotation angle by the energy consumption coefficient corresponding thereto to obtain the energy consumption of each axis, and then adding the energy consumption of each axis to obtain the energy consumption corresponding to the solution; determining an optimal solution from the reachable solutions based on the principle of minimum energy consumption; dividing the six-axis rotation angle in the optimal solution into a ten-step motion list, together with the distance of the seventh axis movement, as the motion step input of the motion simulation, and sending to the UE4 for motion simulation to realize the semi-linkage of the seventh axis and the six-axis. In the embodiment, the energy consumption coefficient corresponding to the distance of the seventh axis movement is 1000, and the energy consumption coefficients corresponding to the six-axis are 100, 90, 80, 70, 60, and 50 in turn, because the energy consumption of the seventh axis movement is large in practice.

[0063] When the UE4 completes the fork arm kinematics simulation task, the returned collision signal includes a no collision signal, a motion planning collision signal, and a structure collision signal.

[0064] Step 8) Perform parameter optimization step: create a parameter optimization algorithm based on intelligent algorithm to optimize the optimization index parameters, and use the finite element simulation results and the collision signal as the boundary condition of dynamic optimization to update the optimization index parameters.

[0065] The parameter optimization algorithm based on intelligent algorithm includes a finite element parameter optimization algorithm and an obstacle avoidance algorithm, wherein the finite element parameter optimization algorithm optimizes the optimization index parameters, and the obstacle avoidance algorithm optimizes the motion step of the motion simulation when the motion planning collision occurs. If the collision signal returned by the UE4 is the structure collision signal, the finite element parameter optimization algorithm is called to optimize the optimization index parameters, and the current optimization index parameters are set as the discard value; if the collision signal returned by the UE4 is the motion planning collision signal, the obstacle avoidance algorithm is called to re-plan the motion path and perform the motion simulation in the UE4, update the collision signal, re-determine the collision signal type, and perform the subsequent steps based on the collision signal type; if the collision signal returned by the UE4 is the no collision signal, the finite element parameter optimization algorithm is called to optimize the optimization index parameters. The parameter optimization step is as shown in Figure 6 .

[0066] The mechanical arm first moves the seventh axis, and then moves according to the six-axis rotation angle motion list when performing motion simulation in UE4. During the execution of each motion of the motion simulation, if a collision occurs, UE4 returns a collision signal. If the collision signal is a motion planning collision signal, the obstacle avoidance algorithm is called, the position before the collision in the motion list is returned, the six-axis rotation angle is recalculated based on the position and the target position, the ten-step motion list is obtained as the motion step input of the motion simulation, and the newly planned motion path is obtained.

[0067] In the embodiment, the finite element parameter optimization algorithm is realized based on an ant colony algorithm, and a logic flowchart thereof is as shown in Figure 7

[0068] (1) define the number of ants, dimensions, maximum number of iterations, pheromone evaporation coefficient and transition probability constant;

[0069] (2) determine the boundary (maximum value and minimum value) of the parameter variable;

[0070] (3) initialization: randomly set the initial position of the ant, and generate the initial pheromone according to the initial position, wherein the pheromone is the evaluation index function value, and the evaluation index function is determined based on the model material and the deformation degree. In the embodiment, the evaluation index function = (inflection point displacement + fork arm thickness) * fork arm volume. The evaluation index function considers both the fork arm volume and the inflection point displacement. Using it to evaluate the optimization result can achieve the purposes of using the least material and minimizing the deformation at the same time;

[0071] (4) search for the optimal pheromone;

[0072] (5) calculate the state transition probability of each ant, which shows the size of the gap from the current optimal pheromone;

[0073] (6) traverse each ant. When the transition probability is less than the transition probability constant, perform local search (at this time, the gap from the current optimal pheromone is small, and small-range movement is performed according to the step length); otherwise, perform global search (at this time, the gap from the current optimal pheromone is large, and large changes need to be made); at the same time, when generating the parameter value, it also needs to ensure that it is within the specified boundary;

[0074] (7) update the position, and judge whether the new position is better after the ant moves through the position. If the pheromone of the new position is less than the pheromone of the old position, it is judged that the new position is better;

[0075] ​(8) Update pheromone, in which, the collision signal needs to be acquired and judged, if the collision signal is a structure collision signal, the pheromone of this position is assigned a maximum value, so that this position will not be selected as the optimal solution, in the embodiment, the maximum value is 100; if the collision signal is a non-collision signal or a motion planning collision signal, the pheromone is calculated and updated normally using the formula;

[0076] (9) Search for the position of the ant with the minimum pheromone;

[0077] (10) Judge whether the iteration number reaches the maximum value, if not, re-execute (4)-(8), if yes, exit the loop to obtain the optimal optimization index parameter obtained by executing the finite element parameter optimization algorithm this time.

[0078] Step 9) Re-execute the model creation step, the finite element simulation step, the kinematics simulation step and the parameter optimization step based on the updated optimization index parameter until the maximum value of the iteration number is reached to obtain the optimal optimization index parameter.

[0079] In the embodiment, the condition for terminating iteration is that the maximum value of the iteration number is reached, because the finite element parameter optimization algorithm is based on the ant colony algorithm; if the finite element parameter optimization algorithm is based on a neural network or other machine learning algorithm, the iteration termination condition can be that the loss function of the evaluation index function reaches a convergence state.

[0080] Step 10) Based on the optimal optimization index parameter, call the three-dimensional model creation function to obtain the optimized three-dimensional model.

[0081] The step numbers above do not limit the execution order of the steps.

[0082] If the above functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0083] The preferred embodiments of the present application have been described above in detail. It should be understood that modifications and variations to the preferred embodiments could be made by those skilled in the art in light of the teachings above without departing from the spirit of the present application. It is therefore to be understood that what is desired to be protected by letters patent is defined by the scope of the claims below and that on the basis of the teachings of the present application, obvious modifications and equivalents can be adopted by those skilled in the art in their possession of the teachings of the present application without departing from the spirit and scope of the application.

Claims

1. A method for digital collaborative design and verification of automated equipment, characterized in that, Includes the following steps: Extract valid creation instructions from the 3D model creation process, where the 3D model is created based on FreeCAD visualization and the creation process is recorded by Macro; Create and save a txt file based on valid creation instructions; Extract key dimensional parameters from the txt file, encapsulate them and write them into a 3D model creation class, and create a callable 3D model creation function. The 3D model creation function takes the key dimensional parameters as input and outputs a 3D model IGES file. The optimization index parameters are determined based on the key size parameters, and the optimization index parameters are one or more of the key size parameters; The model creation steps are as follows: based on the key dimensional parameters, the 3D model creation function is called to obtain the 3D model IGES file; The finite element simulation steps are as follows: create a finite element simulation class based on pyansys, load the IGES file and load information, perform finite element simulation on it to obtain finite element simulation results, and create a deformation model based on finite elements. The kinematic simulation steps are as follows: solving the motion step input of the motion simulation based on the inverse kinematics algorithm, establishing communication with UE4 based on WebSocket, and performing motion simulation in UE4 based on the deformation model and motion step input, collecting the collision state of the motion state in real time, and returning the collision signal; The parameter optimization step is as follows: create a parameter optimization algorithm based on intelligent algorithm to optimize the optimization index parameters, and use finite element simulation results and collision signals as boundary conditions for dynamic optimization to update the optimization index parameters; Based on the updated optimized index parameters, the model creation step, finite element simulation step, kinematic simulation step, and parameter optimization step are re-executed until the iteration termination condition is reached to obtain the optimal optimized index parameters. The iteration termination condition is determined based on an intelligent algorithm as the loss function of the evaluation index function reaching a convergent state or the number of iterations reaching a pre-configured iteration threshold. The evaluation index function is determined based on design requirements and application requirements. Based on the optimal optimization index parameters, the 3D model creation function is called to obtain the optimized 3D model; The steps involved in solving the motion step input for motion simulation based on the inverse kinematics algorithm are as follows: Inverse kinematics calculations are performed based on the initial and target positions of the robotic arm to obtain the inverse kinematics values ​​of the six-axis rotation angles, wherein the robotic arm is mounted on a deformation model; Determine whether the movement from the initial position to the target position is reachable. If reachable, divide the inverse kinematics (IK) values ​​of the six-axis rotation angles into a pre-configured motion list, which serves as the motion step input for the motion simulation. If not reachable, perform an IK operation including the seventh axis. This IK operation includes the following steps: control the robotic arm to move along the seventh axis with a pre-configured step size and perform an IK operation to obtain a set of reachable solutions within the pre-configured movement range. The reachable solutions include the distance moved along the seventh axis and the rotation angle of the six-axis. For each solution in this set of reachable solutions, call the energy consumption function, which multiplies the distance moved along the seventh axis and the rotation angle of the six-axis by their corresponding energy consumption coefficients to obtain the energy consumption of each axis. Then, add the energy consumption of each axis to obtain the energy consumption corresponding to the solution. Based on the principle of minimizing energy consumption, determine a solution from the reachable solutions as the optimal solution. Divide the six-axis rotation angles in the optimal solution into a pre-configured motion list, which, together with the distance moved along the seventh axis, serves as the motion step input for the motion simulation.

2. The method for digital collaborative design and verification of automated equipment according to claim 1, characterized in that, The motion simulation based on the deformation model in UE4 is as follows: the deformation of the three-dimensional model in the finite element simulation step is simulated in the form of translation and rotation transformations to obtain the simulated deformation model in UE4 corresponding to the deformation model, and motion simulation is performed based on the simulated deformation model.

3. The method for digital collaborative design and verification of automated equipment according to claim 1, characterized in that, The parameter optimization algorithm based on intelligent algorithms includes a finite element parameter optimization algorithm and an obstacle avoidance algorithm. The finite element parameter optimization algorithm optimizes the optimization index parameters, and the obstacle avoidance algorithm optimizes the motion steps of the motion simulation when a motion planning collision occurs.

4. The method for digital collaborative design and verification of automated equipment according to claim 1, characterized in that, The collision signals include no collision signal, motion planning collision signal, and structural collision signal.

5. The method for digital collaborative design and verification of automated equipment according to claim 3, characterized in that, The collision signals include no collision signal, motion planning collision signal, and structural collision signal. If the collision signal is a structural collision signal, the finite element parameter optimization algorithm is called to optimize the optimization index parameters, and the current optimization index parameters are set to discard values. If the collision signal is a motion planning collision signal, the obstacle avoidance algorithm is called to replan the motion path and perform motion simulation in UE4. After updating the collision signal, the collision signal type is re-determined, and subsequent steps are executed based on the collision signal type. If the collision signal is no collision signal, the finite element parameter optimization algorithm is called to optimize the optimization index parameters.

6. The method for digital collaborative design and verification of automated equipment according to claim 3, characterized in that, The finite element parameter optimization algorithm includes a finite element parameter optimization algorithm based on ant colony algorithm. When the finite element parameter optimization algorithm is a finite element parameter optimization algorithm based on ant colony algorithm, the iteration termination condition is that the number of iterations reaches a pre-configured iteration threshold.

7. The method for digital collaborative design and verification of automated equipment according to claim 6, characterized in that, The collision signals include no collision signals, motion planning collision signals, and structural collision signals. If the collision signal is a structural collision signal, the finite element parameter optimization algorithm based on the ant colony algorithm is called to optimize the optimization index parameters, and the pheromone of the ant position corresponding to the collision signal is assigned a pre-configured maximum value.

8. A digital collaborative design and verification device for automation equipment, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-7.

9. A storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the method as described in any one of claims 1-7.

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