Multi-condition stress topology optimization method and system for logistics drone push rod mounting base

Abstract

This application relates to the fields of logistics drone component design and computer-aided engineering technology, and provides a method and system for multi-condition stress topology optimization of a logistics drone push rod mounting base. The method includes: constructing multi-condition stress boundary conditions for the push rod mounting base; establishing an initial structural topology optimization model of the push rod mounting base based on the multi-condition stress boundary conditions, the initial structural topology optimization model including a U-shaped upright plate, a rectangular base plate, three sets of equidistant mounting through holes, push rod mounting through holes, a rounded corner transition structure at the connection between the upright plate and the base plate, and a boss limiting structure at both ends of the base plate; setting a dual objective function for topology optimization in the initial structural topology optimization model; setting strength constraints and fatigue life constraints; running a topology optimization solver for iterative calculation to obtain the optimal combination of structural parameters that satisfies all constraints; and outputting the optimal combination of structural parameters. This method avoids performance deviations and safety hazards caused by single-condition design.

Description

Technical Field

[0001] This application relates to the fields of logistics drone component design and computer-aided engineering technology, and in particular to a multi-condition force topology optimization method and system for a logistics drone push rod mounting base. Background Technology

[0002] Logistics drones utilize electric actuators to perform core functions such as cargo compartment opening and closing, and landing gear retraction and extension. The actuator mount is a critical load-bearing component connecting the electric actuator to the drone's fuselage. Existing actuator mounts often rely on empirical design or single static strength checks, failing to comprehensively consider the alternating loads during actuator push-pull processes, vibrations and impacts during drone flight, and complex stress conditions under different mounting postures. This results in mounts commonly exhibiting defects such as over-design leading to increased weight or insufficient strength, making them prone to fatigue damage. Furthermore, existing mount optimization methods often employ single-objective optimization, failing to achieve an optimal balance between lightweight design and high rigidity, thus hindering the long endurance and high reliability requirements of logistics drones. Summary of the Invention

[0003] This application provides a multi-condition stress topology optimization method and system for a push rod mounting base for logistics drones. It aims to solve the problem that existing push rod mounting bases mostly adopt empirical design or single static condition strength verification, without comprehensively considering the alternating load during the push rod pushing and pulling process, the vibration and impact during the drone's flight, and the complex stress conditions under different installation attitudes. This results in the mounting bases generally having defects such as over-designed weight increase or insufficient strength, which easily leads to fatigue damage.

[0004] In a first aspect, embodiments of this application provide a multi-condition force topology optimization method for a push rod mounting base of a logistics drone, the method comprising: Data on alternating loads during the push-pull process of the electric push rod of the logistics drone and vibration and impact data during flight are collected. Force data corresponding to each preset installation posture of the push rod mounting base is also collected. Based on the collected alternating load data, vibration and impact data, and force data, multi-condition force boundary conditions for the push rod mounting base are constructed. An initial structural topology optimization model for the push rod mounting base is established based on the multi-condition force boundary conditions. The initial structural topology optimization model includes a U-shaped vertical plate, a rectangular base plate, three sets of equidistant mounting through holes, a push rod mounting through hole, a rounded corner transition structure at the connection between the vertical plate and the base plate, and a boss limiting structure at both ends of the base plate. In the initial structural topology optimization model, a dual objective function for topology optimization is set, which simultaneously includes minimizing the total mass of the mounting base and maximizing the overall stiffness of the mounting base. The wall thickness of the mounting base, the radius of the fillet at the connection between the upright plate and the base plate, and the coordinates of the three sets of mounting through holes are used as optimization variables. Strength constraints and fatigue life constraints are set. The topology optimization solver is run to perform iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints. The optimal combination of structural parameters is then output.

[0005] In some embodiments, the acquisition of alternating load data during the push-pull process of the logistics drone's electric push rod, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base includes: acquiring peak load data of the electric push rod at maximum push stroke and maximum return stroke; acquiring alternating load cycle data of the electric push rod at different push-pull speeds; acquiring vibration and impact acceleration data of the drone in four flight states: hovering, climbing, descending, and turning; and acquiring static force data of the push rod mounting base in three installation postures: horizontal, vertical, and tilted.

[0006] In some embodiments, constructing the multi-condition force boundary conditions of the push rod mounting base based on the collected alternating load data, vibration and shock data, and force data includes: preprocessing the collected alternating load data, vibration and shock data, and force data to remove abnormal data; classifying the preprocessed alternating load data, vibration and shock data, and force data according to different conditions; setting corresponding load application positions and constraints for each condition; and integrating the loads and constraints of all conditions to form multi-condition force boundary conditions.

[0007] In some embodiments, establishing the initial structural topology optimization model of the push rod mounting base based on the multi-condition force boundary conditions includes: establishing a three-dimensional solid model based on the actual size of the push rod mounting base, meshing the three-dimensional solid model, loading the multi-condition force boundary conditions onto the meshed three-dimensional solid model, and setting the material properties of the three-dimensional solid model to form the initial structural topology optimization model.

[0008] In some embodiments, setting the dual objective function for topology optimization in the initial structural topology optimization model includes: calculating the total mass and overall stiffness of the initial structural topology optimization model, setting minimizing the total mass of the mounting base as the first objective function, setting maximizing the overall stiffness of the mounting base as the second objective function, and setting corresponding weight coefficients for the first objective function and the second objective function to form the dual objective function for topology optimization.

[0009] In some embodiments, the step of setting strength constraints and fatigue life constraints by using the wall thickness of the mounting base, the radius of the fillet at the connection between the vertical plate and the bottom plate, and the coordinates of the holes of the three sets of mounting through holes as optimization variables includes: setting a range of values ​​for the wall thickness of the mounting base, setting a range of values ​​for the radius of the fillet at the connection between the vertical plate and the bottom plate, setting a range of values ​​for the coordinates of the holes of the three sets of mounting through holes, setting a strength constraint that the maximum stress of the mounting base does not exceed the allowable stress of the material, and setting a fatigue life constraint that the number of fatigue cycles of the mounting base is not less than the preset life.

[0010] In some embodiments, the step of running the topology optimization solver to perform iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints includes: initializing the number of iterations and convergence accuracy of the topology optimization solver; updating the values ​​of the optimization variables in each iteration; calculating the total mass, overall stiffness, maximum stress, and number of fatigue cycles of the updated model; determining whether all constraints and convergence accuracy are satisfied; and if satisfied, stopping the iteration and outputting the current values ​​of the optimization variables as the optimal combination of structural parameters.

[0011] In some embodiments, after outputting the optimal structural parameter combination, the method further includes: establishing a verification model based on the optimal structural parameter combination; performing finite element simulation analysis on the verification model under multiple working conditions to obtain stress distribution, deformation, and fatigue life data of the verification model; comparing the stress distribution, deformation, and fatigue life data obtained from the simulation analysis with preset performance indicators; and if there is a deviation, using a particle swarm optimization algorithm to perform secondary optimization on the optimal structural parameter combination to obtain the final structural parameter combination.

[0012] In some embodiments, after obtaining the final combination of structural parameters, the method further includes: generating a machining drawing of the push rod mounting base based on the final combination of structural parameters, collecting machining error data in the actual production process; using a neural network algorithm based on the machining error data to optimize the tolerance of the structural parameters to obtain a tolerance range that meets the machining accuracy requirements, and marking the tolerance range on the machining drawing to form a machining file that can be directly used for production.

[0013] Secondly, this application provides a multi-condition force topology optimization system for a logistics drone push rod mounting base, the system comprising: The data acquisition unit is used to collect alternating load data during the push-pull process of the electric push rod of the logistics drone, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base; based on the collected alternating load data, vibration and impact data, and force data, the multi-condition force boundary conditions of the push rod mounting base are constructed. The model building unit is used to establish the initial structural topology optimization model of the push rod mounting base based on the multi-working-condition force boundary conditions. The initial structural topology optimization model includes a U-shaped vertical plate, a rectangular base plate, three sets of equidistant mounting through holes, a push rod mounting through hole, a rounded corner transition structure at the connection between the vertical plate and the base plate, and a boss limiting structure at both ends of the base plate. The function setting unit is used to set the dual objective function for topology optimization in the initial structural topology optimization module. The dual objective function simultaneously includes minimizing the total mass of the mounting base and maximizing the overall stiffness of the mounting base. The wall thickness of the mounting base, the radius of the fillet at the connection between the upright plate and the base plate, and the coordinates of the holes of the three sets of mounting through holes are used as optimization variables to set strength constraints and fatigue life constraints. The topology optimization solver is run to perform iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints. The optimal combination of structural parameters is then output.

[0014] This application accurately recreates the true working state of the push rod mount by simultaneously collecting alternating load, vibration and shock, and stress data from multiple installation attitudes, and constructing multi-condition stress boundary conditions, thus avoiding performance deviations and safety hazards caused by single-condition design. By adopting a dual-objective simultaneous optimization strategy of minimum mass and maximum stiffness, the mount achieves maximum weight reduction while strictly meeting strength and fatigue life constraints, effectively reducing the overall load of the UAV and significantly improving its endurance.

[0015] By specifically selecting wall thickness, corner radius, and mounting hole position as core optimization variables, the stress concentration problem of the mounting base is precisely improved, significantly enhancing its fatigue resistance and service life.

[0016] The entire process can be automated by computer, which significantly shortens the design cycle of the push rod mounting base and improves the accuracy and reliability of the optimization results.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic flowchart illustrating the steps of a multi-condition force topology optimization method for a push rod mounting base for a logistics drone, provided in one embodiment of this application. Figure 2 This is a first-view structural schematic diagram of a push rod mounting base for a logistics drone provided in an embodiment of this application; Figure 3 This is a second-view structural schematic diagram of a push rod mounting base for a logistics drone provided in one embodiment of this application; Figure 4 This is a third-view structural schematic diagram of a push rod mounting base for a logistics drone provided in one embodiment of this application; Figure 5 This is a schematic block diagram of a multi-condition force topology optimization system for a push rod mounting base of a logistics drone provided in one embodiment of this application; Figure 6 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0023] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0024] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0026] Logistics drones utilize electric actuators to perform core functions such as cargo compartment opening and closing, and landing gear retraction and extension. The actuator mount is a critical load-bearing component connecting the electric actuator to the drone's fuselage. Existing actuator mounts often rely on empirical design or single static strength checks, failing to comprehensively consider the alternating loads during actuator push-pull processes, vibrations and impacts during drone flight, and complex stress conditions under different mounting postures. This results in mounts commonly exhibiting defects such as over-design leading to increased weight or insufficient strength, making them prone to fatigue damage. Furthermore, existing mount optimization methods often employ single-objective optimization, failing to achieve an optimal balance between lightweight design and high rigidity, thus hindering the long endurance and high reliability requirements of logistics drones.

[0027] To solve the above problem, please refer to Figure 1 This application provides a multi-condition force topology optimization method for a push rod mounting base for logistics drones, applicable to computer equipment. The computer equipment can be deployed on a single server or server cluster. It can also be deployed on handheld terminals, laptops, wearable devices, or robots, etc.

[0028] The push rod mounting base structure described in this invention is as follows: Figure 2 , Figure 3 , Figure 4 As shown, where Figure 2 This is a top view of the pushrod mounting bracket. Figure 3 This is the left view of the push rod mounting bracket. Figure 4 This is a front view of the push rod mounting base. The push rod mounting base includes an integrally bent U-shaped upright plate and a rectangular base plate. The base plate has three sets of equidistant mounting through holes, and the upright plate has push rod mounting through holes. A rounded corner transition structure is provided at the connection between the upright plate and the base plate, and boss limiting structures are provided at both ends of the base plate.

[0029] The provided multi-condition force topology optimization method for the push rod mounting base of the logistics drone includes steps S101 to S103. Details are as follows: Step S101. Collect alternating load data during the push-pull process of the electric push rod of the logistics drone, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base; construct multi-condition force boundary conditions for the push rod mounting base based on the collected alternating load data, vibration and impact data and force data.

[0030] Specifically, step S101 involves collecting alternating load data during the push-pull process of the logistics drone's electric push rod, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base. Based on the collected alternating load data, vibration and impact data, and force data, multi-condition force boundary conditions for the push rod mounting base are constructed. This step is divided into two sub-steps: data acquisition and construction of multi-condition force boundary conditions.

[0031] This step uses multiple sensor devices to collect force data on the push rod mounting base under different working conditions, ensuring that the data can fully reflect the actual force situation of the mounting base.

[0032] Alternating load data acquisition is achieved by installing tension and compression sensors at the output end of the electric linear actuator. The sensor range covers the maximum thrust and maximum tension of the electric linear actuator. The electric linear actuator is mounted on a test platform and driven to the maximum thrust and maximum return positions, recording the peak load data at these points. Then, the electric linear actuator is set to reciprocate at different push and pull speeds, covering the range from the minimum to the maximum operating speed. At each speed, at least one hundred cycles are continuously performed, and real-time load data is collected during each cycle to form alternating load cyclic data.

[0033] Vibration and shock data acquisition was achieved by installing a three-axis accelerometer on the UAV body at the connection point with the push rod mount, with the sensor's sampling frequency set to at least 1 kilohertz. The UAV was controlled to fly in four flight states: hovering, climbing, descending, and turning, with continuous flight in each state for at least five minutes. The three-axis acceleration data during these processes were collected as vibration and shock acceleration data.

[0034] The force data acquisition for the mounting attitude involves fixing the push rod mounting base to test fixtures in three attitudes: horizontal, vertical, and tilted. The tilted attitude angle is set to the maximum tilt angle that may occur during actual UAV flight. A preset static load, which is 1.2 times the maximum thrust of the electric push rod, is applied to the push rod mounting through hole. The force data of each connection part of the mounting base at this time is collected as static force data under different mounting attitudes.

[0035] Data preprocessing involves preprocessing all collected raw data to remove outliers. Outlier identification uses the three-standard-deviation criterion: for a given set of data, the mean and standard deviation are calculated, and data exceeding the range of the mean plus or minus three standard deviations are considered outliers and deleted.

[0036] The operating condition classification categorizes the preprocessed data according to different working states into three main categories: alternating load conditions, vibration and shock conditions, and static stress conditions. Alternating load conditions are further subdivided into maximum thrust peak load conditions, maximum return peak load conditions, and alternating load cycle conditions at different speeds. Vibration and shock conditions are further subdivided into hovering vibration conditions, climbing vibration conditions, descending vibration conditions, and turning vibration conditions. Static stress conditions are further subdivided into horizontal installation static stress conditions, vertical installation static stress conditions, and inclined installation static stress conditions.

[0037] Single-condition boundary conditions are set by defining the corresponding load application location and constraint conditions for each subdivided condition. The load application location is determined based on the actual stress conditions. For example, alternating loads and static loads are applied to the inner wall of the push rod mounting through hole, while vibration and impact loads are applied to the inner wall of the base plate mounting through hole connecting the mounting base and the UAV body. The constraint conditions are set as fixed constraints and applied to the inner wall of the base plate mounting through hole to simulate the state where the mounting base is fixed to the UAV body by bolts.

[0038] Multi-condition boundary condition integration integrates the loads and constraints of all single conditions, assigns a corresponding weight coefficient to each condition, and determines the weight coefficient based on the frequency and duration of the condition during the actual flight of the UAV, thus forming a complete multi-condition force boundary condition.

[0039] Step S102. Establish an initial structural topology optimization model for the push rod mounting base based on the multi-working-condition force boundary conditions. The initial structural topology optimization model includes a U-shaped vertical plate, a rectangular base plate, three sets of equidistant mounting through holes, a push rod mounting through hole, a rounded corner transition structure at the connection between the vertical plate and the base plate, and a boss limiting structure at both ends of the base plate.

[0040] Specifically, step S102 is to establish an initial structural topology optimization model of the push rod mounting base based on the multi-working-condition force boundary conditions. The initial structural topology optimization model includes a U-shaped vertical plate, a rectangular base plate, three sets of equidistant mounting through holes, a push rod mounting through hole, a rounded corner transition structure at the connection between the vertical plate and the base plate, and a boss limiting structure at both ends of the base plate.

[0041] Based on the actual dimensions of the push rod mounting base, a 3D solid model of the push rod mounting base was created using 3D modeling software. The modeling process strictly adhered to... Figure 2 , Figure 3 , Figure 4 Draw the structural features shown to ensure that the geometry and dimensions of the model are completely consistent with the actual product.

[0042] The established 3D solid model was imported into the finite element analysis software, and the model was meshed. Tetrahedral elements were used to mesh the model, with an element size of two millimeters. To improve calculation accuracy, the mesh was refined in stress concentration areas such as rounded corners and around mounting holes, with the refined element size being one millimeter. After meshing, mesh independence was verified by gradually reducing the element size. When the change in the calculation results was less than one percent, the meshing was considered to meet the accuracy requirements.

[0043] The multi-condition force boundary conditions constructed in step S101 are loaded onto the meshed 3D solid model. In the finite element analysis software, the corresponding loads and constraints are applied according to the load application location and constraint conditions of each condition, and the weight coefficients for each condition are set.

[0044] The material properties of the three-dimensional solid model are set. In this invention, the push rod mounting base is made of 0.6Cr19Ni10 stainless steel, with material properties including an elastic modulus of 195 GPa, a Poisson's ratio of 0.3, a density of 7.93 g / cm³, an allowable stress of 205 MPa, and a fatigue strength limit of 110 MPa. These material properties are input into the finite element analysis software to form an initial structural topology optimization model.

[0045] Step S103. In the initial structural topology optimization model, set a dual objective function for topology optimization. The dual objective function simultaneously includes minimizing the total mass of the mounting base and maximizing the overall stiffness of the mounting base. Use the wall thickness of the mounting base, the radius of the fillet at the connection between the vertical plate and the bottom plate, and the coordinates of the three sets of mounting through holes as optimization variables, and set strength constraints and fatigue life constraints. Run the topology optimization solver to perform iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints. Output the optimal combination of structural parameters.

[0046] Specifically, step S103 involves setting a dual objective function for topology optimization in the initial structural topology optimization model. The dual objective function simultaneously includes minimizing the total mass of the mounting base and maximizing the overall stiffness of the mounting base. The wall thickness of the mounting base, the radius of the fillet at the connection between the upright plate and the base plate, and the coordinates of the three sets of mounting through holes are used as optimization variables. Strength constraints and fatigue life constraints are set. The topology optimization solver is run to perform iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints. The optimal combination of structural parameters is then output.

[0047] First, the total mass and overall stiffness of the initial structural topology optimization model are calculated. The total mass is obtained by multiplying the model's volume by the material density. The overall stiffness is represented by the reciprocal of the maximum deformation of the model under multiple load conditions; the smaller the maximum deformation, the greater the overall stiffness. Minimizing the total mass of the mounting base is set as the first objective function, and maximizing the overall stiffness of the mounting base is set as the second objective function. The Analytic Hierarchy Process (AHP) is used to set corresponding weight coefficients for the two objective functions. Based on the requirements of lightweighting and stiffness for logistics drones, the weight coefficient of the first objective function is set to 0.6, and the weight coefficient of the second objective function is set to 0.4, ultimately forming a bi-objective function for topology optimization.

[0048] The wall thickness of the mounting base, the radius of the fillet at the connection between the upright plate and the base plate, and the coordinates of the three sets of mounting through holes were selected as optimization variables. A reasonable value range was set for each optimization variable: the wall thickness ranged from two to five millimeters, the radius of the fillet at the connection between the upright plate and the base plate ranged from three to eight millimeters, and the coordinates of the mounting through holes were adjusted within ±5 millimeters of their initial positions.

[0049] By setting strength constraints and fatigue life constraints, the maximum equivalent stress of the mounting base under all operating conditions does not exceed 80% of the allowable stress of the material, to leave sufficient safety margin. The fatigue life constraint is that the number of fatigue cycles of the mounting base under alternating loads is not less than 10 to the power of 6, to meet the service life requirements of the UAV.

[0050] Iterative calculations were performed using the topology optimization solver in the finite element analysis software. The maximum number of iterations for the topology optimization solver was initialized to one hundred, with a convergence accuracy of 10 to the power of -6. During each iteration, the solver automatically updated the values ​​of the optimization variables, and then performed finite element analysis on the updated model, calculating the total mass, overall stiffness, maximum equivalent stress, and number of fatigue cycles. The calculation results were then checked to ensure they met all constraints and convergence accuracy requirements. If not, the next iteration continued; if they met, the iteration stopped, and the current values ​​of the optimization variables were output as the optimal combination of structural parameters.

[0051] The obtained optimal structural parameter combination is output in text format. The output includes the optimal wall thickness value, the fillet radius value at the connection between the vertical plate and the bottom plate, the hole position coordinate values ​​of the three sets of mounting through holes, and the corresponding total mass of the mounting base, overall stiffness, maximum equivalent stress and fatigue cycle number.

[0052] In some embodiments, the acquisition of alternating load data during the push-pull process of the logistics drone's electric push rod, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base includes: acquiring peak load data of the electric push rod at maximum push stroke and maximum return stroke; acquiring alternating load cycle data of the electric push rod at different push-pull speeds; acquiring vibration and impact acceleration data of the drone in four flight states: hovering, climbing, descending, and turning; and acquiring static force data of the push rod mounting base in three installation postures: horizontal, vertical, and tilted.

[0053] Alternating load data acquisition was performed using a 5kN tensile / compression sensor installed at the output end of the electric actuator. The electric actuator has a maximum thrust of 3kN and a maximum tension of 2500N. The electric actuator was fixed on a horizontal test platform and driven to its maximum thrust position, recording the peak thrust as 2980N; then driven to its maximum return position, recording the peak tension as 2450N. The actuator's push-pull speeds were then set to 5mm / s, 10mm / s, 15mm / s, and 20mm / s, with 200 cycles performed at each speed. The sampling frequency was set to 100Hz, and real-time load data was collected during each cycle, resulting in 800 sets of alternating load cycle data.

[0054] Vibration and shock data were acquired using a triaxial accelerometer with a range of fifty times the acceleration due to gravity, mounted on a mounting plate connected to the push rod mount on the UAV body, with a sampling frequency set to 2 kHz. The UAV was controlled to fly for ten minutes in hover, five minutes in climb, five minutes in descent, and five minutes in turn, and triaxial acceleration data were collected for each state, resulting in four sets of vibration and shock acceleration data.

[0055] The stress data acquisition for the mounting posture involved fabricating three test fixtures to fix the push rod mounting base in horizontal, vertical, and 30-degree tilt positions, respectively. A static load of 3,600 Newtons was applied to the push rod mounting through hole, and strain gauges were used to collect strain data at the connection between the mounting base's base plate and the vertical plate. This data was then converted into stress data, resulting in three sets of static stress data for different mounting postures.

[0056] In some embodiments, constructing the multi-condition force boundary conditions of the push rod mounting base based on the collected alternating load data, vibration and shock data, and force data includes: preprocessing the collected alternating load data, vibration and shock data, and force data to remove abnormal data; classifying the preprocessed alternating load data, vibration and shock data, and force data according to different conditions; setting corresponding load application positions and constraints for each condition; and integrating the loads and constraints of all conditions to form multi-condition force boundary conditions.

[0057] All the collected raw data were preprocessed using data processing software. For each set of data, its mean and standard deviation were calculated. Data exceeding the range of the mean plus or minus three times the standard deviation were identified as outliers and deleted. For example, for the maximum thrust peak load data, the mean was 2,980 N and the standard deviation was 15 N. Data exceeding the range of 2,935 N to 3,025 N were deleted, resulting in 198 sets of valid data.

[0058] The preprocessed data is divided into three main categories and thirteen sub-categories of operating conditions. The first category is alternating load conditions, including six types: maximum thrust peak load condition, maximum return peak load condition, alternating load condition with a speed of 5 mm / s, alternating load condition with a speed of 10 mm / s, alternating load condition with a speed of 15 mm / s, and alternating load condition with a speed of 20 mm / s. The second category is vibration and shock conditions, including four types: hovering vibration condition, climbing vibration condition, descending vibration condition, and turning vibration condition. The third category is static stress conditions, including three types: horizontal installation static stress condition, vertical installation static stress condition, and 30-degree inclined installation static stress condition.

[0059] For each alternating load condition and static stress condition, the load is applied to the entire inner wall of the push rod mounting hole, with the load direction consistent with the push-pull direction of the electric push rod. For each vibration and impact condition, an acceleration load is applied to the entire mounting base model, with the acceleration direction consistent with the triaxial directions acquired by the sensor. All constraints for all conditions are set as fixed constraints and applied to the inner walls of the three sets of base plate mounting holes, simulating a bolted state.

[0060] Based on the frequency and duration of each working condition during actual UAV flight, a weighting coefficient is assigned to each working condition. Specifically, the total weight for alternating load conditions is 0.6, for vibration and shock conditions it is 0.3, and for static stress conditions it is 0.1. The weight of each sub-condition is allocated according to its proportion within its category; for example, the weight for the maximum thrust peak load condition is 0.15, the weight for the maximum return peak load condition is 0.1, and the weights for the four types of speed alternating load conditions are each 0.08.75. This ultimately results in a multi-condition force boundary condition encompassing thirteen working conditions.

[0061] In some embodiments, establishing the initial structural topology optimization model of the push rod mounting base based on the multi-condition force boundary conditions includes: establishing a three-dimensional solid model based on the actual size of the push rod mounting base, meshing the three-dimensional solid model, loading the multi-condition force boundary conditions onto the meshed three-dimensional solid model, and setting the material properties of the three-dimensional solid model to form the initial structural topology optimization model.

[0062] Use SolidWorks 3D modeling software to create a 3D solid model of the push rod mounting base. For example... Figure 2 , Figure 3 and Figure 4 As shown, draw the U-shaped upright plate, rectangular base plate, three sets of equidistant mounting through holes, push rod mounting through holes, rounded corner transition structure at the connection between the upright plate and the base plate, and boss limiting structure at both ends of the base plate. During the modeling process, ensure that the accuracy of all dimensions is 0.01 millimeters, and that the geometry of the model is completely consistent with the actual product.

[0063] The established 3D solid model was imported into ANSYS finite element analysis software. Solid186 tetrahedral elements were used to mesh the model, with an initial element size of two millimeters. The mesh was refined around the fillet transitions and mounting holes, with a refined element size of one millimeter. After meshing, the model contained 12,345 elements and 34,567 nodes. Mesh independence was verified by reducing the element size to 1.5 millimeters and recalculating the maximum deformation of the model. The result showed a change of 0.8% compared to the calculation with the two-millimeter element size, which is less than 1%. Therefore, the two-millimeter element size was considered to meet the accuracy requirements.

[0064] In ANSYS software, based on the constructed multi-condition force boundary conditions, corresponding loads and constraints are applied to thirteen different conditions. For each load condition, the corresponding load value and direction are input; for the constraint conditions, full-degree-of-freedom fixed constraints are applied to the inner walls of the three sets of mounting holes in the base plate. Simultaneously, corresponding weighting coefficients are set for each condition.

[0065] In the material properties module of ANSYS software, input the material parameters of 0.6Cr19Ni10 stainless steel: elastic modulus of 195 GPa, Poisson's ratio of 0.3, density of 7.93 g / cm³, allowable stress of 205 MPa, and fatigue strength limit of 110 MPa. Assign the material properties to the entire mounting base model to ultimately form the initial structural topology optimization model.

[0066] In some embodiments, setting the dual objective function for topology optimization in the initial structural topology optimization model includes: calculating the total mass and overall stiffness of the initial structural topology optimization model, setting minimizing the total mass of the mounting base as the first objective function, setting maximizing the overall stiffness of the mounting base as the second objective function, and setting corresponding weight coefficients for the first objective function and the second objective function to form the dual objective function for topology optimization.

[0067] A multi-condition finite element analysis was performed on the initial structural topology optimization model. The calculated total mass of the initial model was 87 grams, and the maximum deformation was 0.02 millimeters. Therefore, the overall stiffness of the initial model is the reciprocal of the maximum deformation, which is 50 millimeters.

[0068] Minimizing the total mass of the mounting base is set as the first objective function, expressed as total mass equal to material density multiplied by model volume. Maximizing the overall stiffness of the mounting base is set as the second objective function, expressed as overall stiffness equal to one divided by the maximum deformation of the model under multiple operating conditions.

[0069] The weighting coefficients of the two objective functions were determined using the analytic hierarchy process (AHP). First, a judgment matrix was constructed, and five UAV structural design experts were invited to score the importance of lightweighting and stiffness. The scoring results showed that lightweighting was slightly more important than stiffness. By calculating the maximum eigenvalue and eigenvector of the judgment matrix, the weighting coefficient of the first objective function was determined to be 0.6, and the weighting coefficient of the second objective function was 0.4.

[0070] The two objective functions are weighted and summed according to their respective weighting coefficients to form the final bi-objective function for topology optimization. The expression for the bi-objective function is: the combined objective value equals 0.6 times the current total mass divided by the initial total mass, plus 0.4 times the initial global stiffness divided by the current global stiffness. The goal of topology optimization is to minimize this combined objective value.

[0071] In some embodiments, the step of setting strength constraints and fatigue life constraints by using the wall thickness of the mounting base, the radius of the fillet at the connection between the vertical plate and the bottom plate, and the coordinates of the holes of the three sets of mounting through holes as optimization variables includes: setting a range of values ​​for the wall thickness of the mounting base, setting a range of values ​​for the radius of the fillet at the connection between the vertical plate and the bottom plate, setting a range of values ​​for the coordinates of the holes of the three sets of mounting through holes, setting a strength constraint that the maximum stress of the mounting base does not exceed the allowable stress of the material, and setting a fatigue life constraint that the number of fatigue cycles of the mounting base is not less than the preset life.

[0072] Optimizing the variable value range setting includes: Wall thickness: The initial wall thickness is three millimeters. Considering the processing technology and structural strength requirements, the range of wall thickness is set to two to five millimeters, with a step size of 0.1 millimeters.

[0073] The radius of the fillet at the connection between the upright plate and the base plate: The initial fillet radius is six millimeters. Considering stress concentration and bending process requirements, the range of the fillet radius is set to three to eight millimeters, with a step size of 0.1 millimeters.

[0074] The initial hole position coordinates are -20 mm, 0 mm, and +20 mm from the center of the base plate along its length. The x-coordinate of each hole position is set to the initial position plus or minus 5 mm, while the y-coordinate remains unchanged, with a step size of 0.1 mm.

[0075] The strength constraint condition is that the maximum equivalent stress of the mounting base under all thirteen operating conditions shall not exceed 80% of the allowable stress of the material. The allowable stress of the material is 205 MPa, therefore the maximum permissible equivalent stress is 164 MPa.

[0076] The fatigue life constraint is that the number of fatigue cycles of the mounting base under alternating loads is not less than 10 to the power of 6. The fatigue life is calculated using the SN curve method. Based on the material's fatigue strength limit and the stress amplitude of the alternating load, the number of fatigue cycles is calculated to ensure that it is not less than 10 to the power of 6.

[0077] In some embodiments, the step of running the topology optimization solver to perform iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints includes: initializing the number of iterations and convergence accuracy of the topology optimization solver; updating the values ​​of the optimization variables in each iteration; calculating the total mass, overall stiffness, maximum stress, and number of fatigue cycles of the updated model; determining whether all constraints and convergence accuracy are satisfied; and if satisfied, stopping the iteration and outputting the current values ​​of the optimization variables as the optimal combination of structural parameters.

[0078] Solver initialization is performed in the ANSYS topology optimization module, where solver parameters are initialized. The maximum number of iterations is set to one hundred, the convergence accuracy is set to 10 to the power of negative six, and the sequential quadratic programming algorithm is selected as the optimization algorithm.

[0079] The iterative process includes: First iteration: The solver uses the initial optimization variable values ​​to calculate and obtains an initial comprehensive objective value of 1, a maximum equivalent stress of 120 MPa, and a fatigue cycle count of 10 to the power of 7, which satisfies the constraints.

[0080] The second to ninety-ninth iterations: The solver automatically updates the values ​​of the optimization variables. After each update, a finite element analysis is performed to calculate the comprehensive objective value, maximum equivalent stress, and number of fatigue cycles. It checks whether the constraints are met. If not, the direction of the optimization variables is adjusted; if they are met, the iteration continues until the change in the comprehensive objective value is less than the convergence accuracy.

[0081] 100th iteration: The change in the overall objective value is 8.7 x 10 to the power of -7, which is less than the convergence precision of 10 to the power of -6, thus satisfying the convergence condition, and the iteration stops.

[0082] After the iteration stops, the optimal combination of structural parameters is output: wall thickness is 2.5 mm, the fillet radius at the connection between the vertical plate and the bottom plate is 7 mm, and the coordinates of the three sets of mounting holes are -19.8 mm, 0.2 mm, and +20.1 mm from the center of the bottom plate along its length, respectively. The corresponding total mass of the mounting base is 73 grams, the maximum deformation is 0.022 mm, the maximum equivalent stress is 152 MPa, and the number of fatigue cycles is 1.2 x 10^6, all of which meet the constraints.

[0083] In some embodiments, after outputting the optimal structural parameter combination, the method further includes: establishing a verification model based on the optimal structural parameter combination; performing finite element simulation analysis on the verification model under multiple working conditions to obtain stress distribution, deformation, and fatigue life data of the verification model; comparing the stress distribution, deformation, and fatigue life data obtained from the simulation analysis with preset performance indicators; and if there is a deviation, using a particle swarm optimization algorithm to perform secondary optimization on the optimal structural parameter combination to obtain the final structural parameter combination.

[0084] Verification Model Establishment: Based on the obtained optimal combination of structural parameters, a three-dimensional verification model of the push rod mounting base was rebuilt using SolidWorks software to ensure that the size and structure of the model are completely consistent with the optimal parameters.

[0085] The verification model was imported into ANSYS software, using the same meshing method and material properties as the initial model, and the same multi-condition load boundary conditions were applied for static and fatigue analysis. Stress distribution contour maps, deformation contour maps, and fatigue life contour maps of the verification model were obtained. The maximum equivalent stress was found to be 155 MPa, the maximum deformation was 0.023 mm, and the number of fatigue cycles was 1.1 x 10^6.

[0086] The performance data obtained from simulation analysis were compared with the preset performance indicators. The preset performance indicators were: total mass not exceeding 75 grams, maximum deformation not exceeding 0.025 millimeters, maximum equivalent stress not exceeding 164 MPa, and fatigue cycle count not less than 10 to the power of 6. The comparison results showed that all performance indicators met the requirements, but the deviation of the maximum equivalent stress from the allowable value was 5.5%, indicating room for further optimization.

[0087] A particle swarm optimization algorithm was used to perform secondary optimization on the optimal combination of structural parameters. The particle swarm size was set to 20, the maximum number of iterations to 50, the inertia weight to 0.7, and the learning factor to 1.5. The objective function was to minimize the maximum equivalent stress, with wall thickness, fillet radius, and hole position coordinates as optimization variables, and the constraints remained the same as before. After 50 iterations, the final combination of structural parameters was obtained: a wall thickness of 2.6 mm, a fillet radius of 7.2 mm at the connection between the vertical plate and the bottom plate, and hole position coordinates of the three sets of mounting holes at distances of -19.7 mm, 0.3 mm, and +20.2 mm from the center of the bottom plate along its length. The corresponding maximum equivalent stress was 148 MPa, the total mass was 74.5 g, the maximum deformation was 0.021 mm, and the number of fatigue cycles was 1.3 x 10^6, further improving performance.

[0088] In some embodiments, after obtaining the final combination of structural parameters, the method further includes: generating a machining drawing of the push rod mounting base based on the final combination of structural parameters, collecting machining error data in the actual production process; using a neural network algorithm based on the machining error data to optimize the tolerance of the structural parameters to obtain a tolerance range that meets the machining accuracy requirements, and marking the tolerance range on the machining drawing to form a machining file that can be directly used for production.

[0089] Based on the final combination of structural parameters, AutoCAD software is used to generate two-dimensional machining drawings for the push rod mounting base. The drawings include a front view, left view, top view, and sectional view, with all dimensions and structural features labeled, and the dimensional accuracy is 0.01 millimeters.

[0090] Machining error data was collected by randomly selecting 20 samples from 100 similar push rod mounting base products produced previously. A coordinate measuring machine was used to measure the wall thickness, fillet radius at the connection between the vertical plate and the base plate, and the coordinates of the three sets of mounting through holes for each sample. The actual machining error for each parameter was recorded. A total of 20 sets of machining error data were obtained.

[0091] Neural network tolerance optimization employs a backpropagation neural network algorithm to optimize the tolerances of structural parameters. A three-layer backpropagation neural network is constructed, with the input layer containing machining error data, the hidden layer having ten neurons, and the output layer representing the optimal tolerance range. Twenty sets of collected machining error data are divided into training and testing sets, with sixteen sets used as the training set and four as the testing set. The neural network is trained 1000 times to a precision of 10 to the power of negative 4. After training, a preset machining precision requirement is input, yielding the tolerance ranges that meet the requirement: wall thickness tolerance of ±0.1 mm, corner radius tolerance of ±0.2 mm, and hole position coordinate tolerance of ±0.05 mm.

[0092] The obtained tolerance range is marked on the 2D machining drawing, along with surface roughness and technical requirements. The marked drawing is saved as a DXF format machining file, which can be directly imported into CNC machining equipment for production.

[0093] Please see Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of the multi-condition force topology optimization system 200 for a logistics drone push rod mounting base provided in this application embodiment. The multi-condition force topology optimization system 200 for a logistics drone push rod mounting base is used to execute the steps of the multi-condition force topology optimization method for a logistics drone push rod mounting base shown in the above embodiments. The multi-condition force topology optimization system 200 for a logistics drone push rod mounting base can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0094] like Figure 5 As shown, the multi-condition force topology optimization system 200 for the push rod mounting base of the logistics drone includes: The data acquisition unit 201 is used to collect alternating load data during the push-pull process of the electric push rod of the logistics drone, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base; and to construct multi-condition force boundary conditions for the push rod mounting base based on the collected alternating load data, vibration and impact data and force data. Model building unit 202 is used to build an initial structural topology optimization model of the push rod mounting base based on the multi-working-condition force boundary conditions. The initial structural topology optimization model includes a U-shaped vertical plate, a rectangular base plate, three sets of equidistant mounting through holes, a push rod mounting through hole, a rounded corner transition structure at the connection between the vertical plate and the base plate, and a boss limiting structure at both ends of the base plate. The function setting unit 203 is used to set a dual objective function for topology optimization in the initial structural topology optimization module. The dual objective function simultaneously includes minimizing the total mass of the mounting base and maximizing the overall stiffness of the mounting base. The wall thickness of the mounting base, the radius of the fillet at the connection between the upright plate and the base plate, and the coordinates of the holes of the three sets of mounting through holes are used as optimization variables to set strength constraints and fatigue life constraints. The topology optimization solver is run to perform iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints. The optimal combination of structural parameters is then output.

[0095] In some embodiments, the acquisition of alternating load data during the push-pull process of the logistics drone's electric push rod, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base includes: acquiring peak load data of the electric push rod at maximum push stroke and maximum return stroke; acquiring alternating load cycle data of the electric push rod at different push-pull speeds; acquiring vibration and impact acceleration data of the drone in four flight states: hovering, climbing, descending, and turning; and acquiring static force data of the push rod mounting base in three installation postures: horizontal, vertical, and tilted.

[0096] In some embodiments, constructing the multi-condition force boundary conditions of the push rod mounting base based on the collected alternating load data, vibration and shock data, and force data includes: preprocessing the collected alternating load data, vibration and shock data, and force data to remove abnormal data; classifying the preprocessed alternating load data, vibration and shock data, and force data according to different conditions; setting corresponding load application positions and constraints for each condition; and integrating the loads and constraints of all conditions to form multi-condition force boundary conditions.

[0097] In some embodiments, establishing the initial structural topology optimization model of the push rod mounting base based on the multi-condition force boundary conditions includes: establishing a three-dimensional solid model based on the actual size of the push rod mounting base, meshing the three-dimensional solid model, loading the multi-condition force boundary conditions onto the meshed three-dimensional solid model, and setting the material properties of the three-dimensional solid model to form the initial structural topology optimization model.

[0098] In some embodiments, setting the dual objective function for topology optimization in the initial structural topology optimization model includes: calculating the total mass and overall stiffness of the initial structural topology optimization model, setting minimizing the total mass of the mounting base as the first objective function, setting maximizing the overall stiffness of the mounting base as the second objective function, and setting corresponding weight coefficients for the first objective function and the second objective function to form the dual objective function for topology optimization.

[0099] In some embodiments, the step of setting strength constraints and fatigue life constraints by using the wall thickness of the mounting base, the radius of the fillet at the connection between the vertical plate and the bottom plate, and the coordinates of the holes of the three sets of mounting through holes as optimization variables includes: setting a range of values ​​for the wall thickness of the mounting base, setting a range of values ​​for the radius of the fillet at the connection between the vertical plate and the bottom plate, setting a range of values ​​for the coordinates of the holes of the three sets of mounting through holes, setting a strength constraint that the maximum stress of the mounting base does not exceed the allowable stress of the material, and setting a fatigue life constraint that the number of fatigue cycles of the mounting base is not less than the preset life.

[0100] In some embodiments, the step of running the topology optimization solver to perform iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints includes: initializing the number of iterations and convergence accuracy of the topology optimization solver; updating the values ​​of the optimization variables in each iteration; calculating the total mass, overall stiffness, maximum stress, and number of fatigue cycles of the updated model; determining whether all constraints and convergence accuracy are satisfied; and if satisfied, stopping the iteration and outputting the current values ​​of the optimization variables as the optimal combination of structural parameters.

[0101] In some embodiments, after outputting the optimal structural parameter combination, the method further includes: establishing a verification model based on the optimal structural parameter combination; performing finite element simulation analysis on the verification model under multiple working conditions to obtain stress distribution, deformation, and fatigue life data of the verification model; comparing the stress distribution, deformation, and fatigue life data obtained from the simulation analysis with preset performance indicators; and if there is a deviation, using a particle swarm optimization algorithm to perform secondary optimization on the optimal structural parameter combination to obtain the final structural parameter combination.

[0102] In some embodiments, after obtaining the final combination of structural parameters, the method further includes: generating a machining drawing of the push rod mounting base based on the final combination of structural parameters, collecting machining error data in the actual production process; using a neural network algorithm based on the machining error data to optimize the tolerance of the structural parameters to obtain a tolerance range that meets the machining accuracy requirements, and marking the tolerance range on the machining drawing to form a machining file that can be directly used for production.

[0103] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the multi-condition force topology optimization system and each module of the logistics drone push rod mounting base described above can be referred to the corresponding content in the various embodiments of the multi-condition force topology optimization method for the logistics drone push rod mounting base, and will not be repeated here.

[0104] The aforementioned multi-condition force topology optimization method for the push rod mounting base of logistics drones can be implemented as a computer program, which can be used in, for example... Figure 5 It runs on the device shown.

[0105] Please see Figure 6 , Figure 6 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0106] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any multi-condition force topology optimization method for the push rod mount of a logistics drone.

[0107] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0108] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to perform any kind of multi-condition force topology optimization method for the push rod mounting base of a logistics drone.

[0109] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0110] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0111] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps: Data on alternating loads during the push-pull process of the electric push rod of the logistics drone and vibration and impact data during flight are collected. Force data corresponding to each preset installation posture of the push rod mounting base is also collected. Based on the collected alternating load data, vibration and impact data, and force data, multi-condition force boundary conditions for the push rod mounting base are constructed. An initial structural topology optimization model for the push rod mounting base is established based on the multi-condition force boundary conditions. The initial structural topology optimization model includes a U-shaped vertical plate, a rectangular base plate, three sets of equidistant mounting through holes, a push rod mounting through hole, a rounded corner transition structure at the connection between the vertical plate and the base plate, and a boss limiting structure at both ends of the base plate. In the initial structural topology optimization model, a dual objective function for topology optimization is set, which simultaneously includes minimizing the total mass of the mounting base and maximizing the overall stiffness of the mounting base. The wall thickness of the mounting base, the radius of the fillet at the connection between the upright plate and the base plate, and the coordinates of the three sets of mounting through holes are used as optimization variables. Strength constraints and fatigue life constraints are set. The topology optimization solver is run to perform iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints. The optimal combination of structural parameters is then output.

[0112] In some embodiments, the acquisition of alternating load data during the push-pull process of the logistics drone's electric push rod, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base includes: acquiring peak load data of the electric push rod at maximum push stroke and maximum return stroke; acquiring alternating load cycle data of the electric push rod at different push-pull speeds; acquiring vibration and impact acceleration data of the drone in four flight states: hovering, climbing, descending, and turning; and acquiring static force data of the push rod mounting base in three installation postures: horizontal, vertical, and tilted.

[0113] In some embodiments, constructing the multi-condition force boundary conditions of the push rod mounting base based on the collected alternating load data, vibration and shock data, and force data includes: preprocessing the collected alternating load data, vibration and shock data, and force data to remove abnormal data; classifying the preprocessed alternating load data, vibration and shock data, and force data according to different conditions; setting corresponding load application positions and constraints for each condition; and integrating the loads and constraints of all conditions to form multi-condition force boundary conditions.

[0114] In some embodiments, establishing the initial structural topology optimization model of the push rod mounting base based on the multi-condition force boundary conditions includes: establishing a three-dimensional solid model based on the actual size of the push rod mounting base, meshing the three-dimensional solid model, loading the multi-condition force boundary conditions onto the meshed three-dimensional solid model, and setting the material properties of the three-dimensional solid model to form the initial structural topology optimization model.

[0115] In some embodiments, setting the dual objective function for topology optimization in the initial structural topology optimization model includes: calculating the total mass and overall stiffness of the initial structural topology optimization model, setting minimizing the total mass of the mounting base as the first objective function, setting maximizing the overall stiffness of the mounting base as the second objective function, and setting corresponding weight coefficients for the first objective function and the second objective function to form the dual objective function for topology optimization.

[0116] In some embodiments, the step of setting strength constraints and fatigue life constraints by using the wall thickness of the mounting base, the radius of the fillet at the connection between the vertical plate and the bottom plate, and the coordinates of the holes of the three sets of mounting through holes as optimization variables includes: setting a range of values ​​for the wall thickness of the mounting base, setting a range of values ​​for the radius of the fillet at the connection between the vertical plate and the bottom plate, setting a range of values ​​for the coordinates of the holes of the three sets of mounting through holes, setting a strength constraint that the maximum stress of the mounting base does not exceed the allowable stress of the material, and setting a fatigue life constraint that the number of fatigue cycles of the mounting base is not less than the preset life.

[0117] In some embodiments, the step of running the topology optimization solver to perform iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints includes: initializing the number of iterations and convergence accuracy of the topology optimization solver; updating the values ​​of the optimization variables in each iteration; calculating the total mass, overall stiffness, maximum stress, and number of fatigue cycles of the updated model; determining whether all constraints and convergence accuracy are satisfied; and if satisfied, stopping the iteration and outputting the current values ​​of the optimization variables as the optimal combination of structural parameters.

[0118] In some embodiments, after outputting the optimal structural parameter combination, the method further includes: establishing a verification model based on the optimal structural parameter combination; performing finite element simulation analysis on the verification model under multiple working conditions to obtain stress distribution, deformation, and fatigue life data of the verification model; comparing the stress distribution, deformation, and fatigue life data obtained from the simulation analysis with preset performance indicators; and if there is a deviation, using a particle swarm optimization algorithm to perform secondary optimization on the optimal structural parameter combination to obtain the final structural parameter combination.

[0119] In some embodiments, after obtaining the final combination of structural parameters, the method further includes: generating a machining drawing of the push rod mounting base based on the final combination of structural parameters, collecting machining error data in the actual production process; using a neural network algorithm based on the machining error data to optimize the tolerance of the structural parameters to obtain a tolerance range that meets the machining accuracy requirements, and marking the tolerance range on the machining drawing to form a machining file that can be directly used for production.

[0120] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the multi-condition force topology optimization method for the logistics drone push rod mounting base provided in any embodiment of this application.

[0121] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0122] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-condition force topology optimization method for a push rod mounting base for logistics drones, characterized in that, include: Collect alternating load data during the push-pull process of the electric push rod of the logistics drone, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base; Based on the collected alternating load data, vibration and shock data, and force data, the multi-condition force boundary conditions of the push rod mounting base are constructed. An initial structural topology optimization model for the push rod mounting base is established based on the multi-condition force boundary conditions. The initial structural topology optimization model includes a U-shaped vertical plate, a rectangular base plate, three sets of equidistant mounting through holes, a push rod mounting through hole, a rounded corner transition structure at the connection between the vertical plate and the base plate, and a boss limiting structure at both ends of the base plate. In the initial structural topology optimization model, a dual objective function for topology optimization is set. The dual objective function simultaneously includes minimizing the total mass of the mounting base and maximizing the overall stiffness of the mounting base. The wall thickness of the mounting base, the radius of the fillet at the connection between the vertical plate and the bottom plate, and the coordinates of the holes of the three sets of mounting through holes are used as optimization variables, and strength constraints and fatigue life constraints are set. Run the topology optimization solver to perform iterative calculations and obtain the optimal combination of structural parameters that satisfies all constraints; output the optimal combination of structural parameters.

2. The method according to claim 1, characterized in that, The data collected includes alternating load data during the push-pull process of the logistics drone's electric push rod, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base, including: Collect peak load data of the electric linear actuator at maximum push stroke and maximum return stroke, and collect alternating load cycle data of the electric linear actuator at different push and pull speeds; Collect vibration and shock acceleration data of the UAV in four flight states: hovering, climbing, descending, and turning; Static force data of the push rod mounting base under three installation postures: horizontal, vertical, and inclined.

3. The method according to claim 1, characterized in that, The multi-condition force boundary conditions for the push rod mounting base, constructed based on the collected alternating load data, vibration and shock data, and force data, include: The collected variable load data, vibration and shock data, and force data are preprocessed to remove abnormal data. The preprocessed variable load data, vibration and shock data, and force data are classified according to different working conditions. For each working condition, corresponding load application positions and constraints are set. The loads and constraints of all working conditions are integrated to form multi-working-condition force boundary conditions.

4. The method according to claim 1, characterized in that, The initial structural topology optimization model for the push rod mounting base, established based on multi-condition force boundary conditions, includes: A three-dimensional solid model is established based on the actual dimensions of the push rod mounting base. The three-dimensional solid model is then meshed, and multi-condition force boundary conditions are applied to the meshed three-dimensional solid model. The material properties of the three-dimensional solid model are then set to form an initial structural topology optimization model.

5. The method according to claim 1, characterized in that, The step of setting a dual objective function for topology optimization in the initial structural topology optimization model includes: Calculate the total mass and overall stiffness of the initial structural topology optimization model. Set minimizing the total mass of the mounting base as the first objective function and maximizing the overall stiffness of the mounting base as the second objective function. Set corresponding weight coefficients for the first objective function and the second objective function to form a bi-objective function for topology optimization.

6. The method according to claim 1, characterized in that, The optimization variables are the wall thickness of the mounting base, the radius of the fillet at the connection between the vertical plate and the bottom plate, and the coordinates of the three sets of mounting through holes. Strength constraints and fatigue life constraints are set, including: Set a range for the wall thickness of the mounting base, a range for the fillet radius at the connection between the vertical plate and the base plate, and a range for the coordinates of the three sets of mounting through holes. Set a strength constraint condition that the maximum stress of the mounting base does not exceed the allowable stress of the material, and a fatigue life constraint condition that the number of fatigue cycles of the mounting base is not less than the preset life.

7. The method according to claim 1, characterized in that, The topology optimization solver performs iterative calculations to obtain the optimal combination of structural parameters that satisfies all constraints, including: Initialize the number of iterations and convergence accuracy of the topology optimization solver. In each iteration, update the values ​​of the optimization variables, calculate the total mass, overall stiffness, maximum stress and fatigue cycle number of the updated model, and determine whether all constraints and convergence accuracy are met. If the condition is met, stop the iteration and output the current value of the optimization variable as the optimal combination of structure parameters.

8. The method according to claim 1, characterized in that, Following the output of the optimal combination of structure parameters, the following is also included: A verification model is established based on the optimal combination of structural parameters. The stress distribution, deformation and fatigue life data of the verification model are obtained by finite element simulation analysis under multiple working conditions. The stress distribution, deformation, and fatigue life data obtained from the simulation analysis are compared with the preset performance indicators. If there is a deviation, the particle swarm optimization algorithm is used to perform secondary optimization on the optimal combination of structural parameters to obtain the final combination of structural parameters.

9. The method according to claim 8, characterized in that, After obtaining the final combination of structural parameters, the process also includes: Based on the final combination of structural parameters, the machining drawings for the push rod mounting base are generated, and machining error data during the actual production process are collected. Based on the machining error data, a neural network algorithm is used to optimize the tolerance of the structural parameters to obtain the tolerance range that meets the machining accuracy requirements. The tolerance range is then marked on the machining drawings to form a machining document that can be directly used for production.

10. A multi-condition force topology optimization system for a logistics drone push rod mounting base, used to implement the method as described in any one of claims 1-9, characterized in that, include: The data acquisition unit is used to collect alternating load data during the push-pull process of the electric push rod of the logistics drone, vibration and impact data during flight, and force data corresponding to each preset installation posture of the push rod mounting base; based on the collected alternating load data, vibration and impact data, and force data, the multi-condition force boundary conditions of the push rod mounting base are constructed. The model building unit is used to establish the initial structural topology optimization model of the push rod mounting base based on the multi-working-condition force boundary conditions. The initial structural topology optimization model includes a U-shaped vertical plate, a rectangular base plate, three sets of equidistant mounting through holes, a push rod mounting through hole, a rounded corner transition structure at the connection between the vertical plate and the base plate, and a boss limiting structure at both ends of the base plate. The function setting unit is used to set a dual objective function for topology optimization in the initial structure topology optimization module. The dual objective function includes minimizing the total mass of the mounting base and maximizing the overall stiffness of the mounting base. The wall thickness of the mounting base, the radius of the fillet at the connection between the vertical plate and the bottom plate, and the coordinates of the holes of the three sets of mounting through holes are used as optimization variables to set strength constraints and fatigue life constraints. Run the topology optimization solver to perform iterative calculations and obtain the optimal combination of structural parameters that satisfies all constraints; output the optimal combination of structural parameters.

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