Gear parameter determination method and device, equipment, storage medium and program product

Abstract

The invention provides a gear parameter determination method and device, equipment, a storage medium and a program product, and relates to the technical field of electric drive speed reducers. The method comprises the steps that the design requirement of the electric drive speed reducer is obtained, and the design requirement comprises the transmission ratio, the minimum tooth number limitation and a plurality of engineering constraint conditions; generating a plurality of feasible tooth number combinations of the high-speed gear pair and the low-speed gear pair according to the transmission ratio and the minimum tooth number limit; based on the multiple feasible tooth number combinations and at least one part of engineering constraint conditions, a preliminary feasible scheme is determined, and the preliminary feasible scheme comprises the feasible tooth number combinations, the center distance range and the tooth width range; based on the preliminary feasible scheme, obtaining one or more groups of optimal solutions of macroscopic parameters of the high-speed gear pair and the low-speed gear pair through a multi-objective optimization model under the condition that all engineering constraint conditions are met; and determining a target gear parameter scheme according to the one or more groups of optimal solutions. The method can improve the gear parameter optimization efficiency of the electric drive speed reducer.

Description

Technical Field

[0001] This disclosure relates to the field of electric drive reducer technology, and in particular to a method, apparatus, equipment, storage medium and program product for determining gear parameters. Background Technology

[0002] In the drive systems of electric and hybrid vehicles, the electric drive reducer is a key transmission component, and the design of its gear pair parameters directly affects the vehicle's transmission efficiency, noise and vibration (NVH) performance, structural weight, and reliability. With increasing demands for high efficiency, low noise, and lightweight performance in electric drive systems, determining the optimal gear parameter scheme for the electric drive reducer has become an urgent problem to be solved.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this disclosure is to provide a method, apparatus, device, storage medium, and program product for determining gear parameters.

[0005] According to a first aspect of the present disclosure, a method for determining gear parameters is provided, comprising: obtaining design requirements for an electric drive reducer, the design requirements including a transmission ratio, a minimum number of teeth limit, and multiple engineering constraints characterizing gear performance and reliability; generating multiple feasible tooth number combinations for a high-speed gear pair and a low-speed gear pair based on the transmission ratio and the minimum number of teeth limit; determining a preliminary feasible solution based on the multiple feasible tooth number combinations and at least some of the engineering constraints, the preliminary feasible solution including feasible tooth number combinations, center distance range, and tooth width range for subsequent optimization processes; based on the preliminary feasible solution, and under all engineering constraints, obtaining one or more optimal solutions for the macroscopic parameters of the high-speed gear pair and the low-speed gear pair through a multi-objective optimization model, the macroscopic parameters including structural parameters characterizing gear geometry and dimensions; and determining a target gear parameter scheme based on one or more optimal solutions.

[0006] In some implementations, based on a preliminary feasible solution and under all engineering constraints, a multi-objective optimization model is used to obtain one or more optimal solutions for the macroscopic parameters of the high-speed and low-speed gear pairs. This includes: initializing an initial population based on the range of macroscopic parameters of the high-speed and low-speed gear pairs defined by the preliminary feasible solution; performing selection, crossover, and mutation operations on the initial population to generate corresponding offspring populations, and merging the initial population and offspring populations to form a joint population; performing non-dominated sorting on the joint population and calculating the crowding degree of individuals in each non-dominated layer; selecting individuals from the joint population according to the non-dominated layer and crowding degree to form a new generation of parent populations; and repeatedly performing offspring population generation, merging, non-dominated sorting, crowding degree calculation, and selection operations based on the new generation of parent populations until a preset termination condition is met, thereby obtaining one or more optimal solutions that satisfy all engineering constraints and achieve equilibrium across multiple optimization objectives.

[0007] In some implementations, non-dominated sorting of the joint population includes: for each individual in the joint population, determining whether the individual is dominated by other individuals in the joint population based on the values ​​of multiple optimization objectives; assigning all individuals in the joint population that are not dominated by any other individual to a first non-dominated layer; assigning individuals that are not dominated by other individuals to a second non-dominated layer among the remaining individuals after removing the first non-dominated layer; repeating the above operations of removing already stratified individuals and selecting new non-dominated layers until all individuals in the joint population are assigned to the corresponding non-dominated layers.

[0008] In some implementations, calculating the crowding degree of individuals in each non-dominated layer includes: for each non-dominated layer, sorting all individuals in the non-dominated layer in ascending order according to the value of each optimization objective; for each individual, calculating the difference between the individual and its neighboring individuals in each optimization objective dimension; and summing the difference between the values ​​of each individual in all optimization objective dimensions to obtain the crowding degree of the individual.

[0009] In some implementations, design requirements include reducer functional requirements, operating environment requirements, gear transmission design requirements, and engineering constraints. Reducer functional requirements include transmission ratio and minimum number of teeth limits. Reducer functional requirements also include: spatial envelope constraints, reducer output load spectrum, static strength check condition requirements, noise and order avoidance requirements, and / or transmission efficiency requirements.

[0010] In some implementations, a preliminary feasible solution is determined based on multiple feasible tooth number combinations and at least some engineering constraints, including: calculating the corresponding center distance according to each feasible tooth number combination and selecting combinations that meet the spatial envelope constraints; and determining the center distance range and tooth width range based on the selected combinations, combined with the reducer output load spectrum and static strength verification conditions.

[0011] In some implementations, engineering constraints include constraints related to one or more of the following parameters: contact fatigue strength safety factor, flexural fatigue strength safety factor, total overlap, end face overlap, slip ratio, or manufacturing process requirements.

[0012] In some implementations, macroscopic parameters include one or more of the following: center distance, tooth width, module, number of teeth, pressure angle, helix angle, displacement coefficient, addendum coefficient, or clearance coefficient.

[0013] According to a second aspect of the present disclosure, a gear parameter determination device is provided, including a design requirement acquisition module, a tooth number combination generation module, a preliminary scheme determination module, a multi-objective optimization module, and a target scheme determination module.

[0014] The design requirements acquisition module is used to acquire the design requirements of the electric drive reducer. The design requirements include the transmission ratio, minimum number of teeth limit, and multiple engineering constraints used to characterize gear performance and reliability. The tooth number combination generation module is used to generate multiple feasible tooth number combinations of high-speed gear pairs and low-speed gear pairs based on the transmission ratio and minimum tooth number limit. The preliminary scheme determination module is used to determine a preliminary feasible scheme based on multiple feasible tooth number combinations and at least some engineering constraints. The preliminary feasible scheme includes feasible tooth number combinations, center distance range and tooth width range for subsequent optimization. The multi-objective optimization module is used to obtain one or more optimal solutions for the macroscopic parameters of the high-speed gear pair and the low-speed gear pair based on the preliminary feasible scheme and under all engineering constraints. The macroscopic parameters include structural parameters that characterize the gear geometry and size. The target solution determination module is used to determine the target gear parameter scheme based on one or more sets of optimal solutions. The target gear parameter scheme includes the specific values ​​of macroscopic parameters.

[0015] In some implementations, the multi-objective optimization module includes: An initialization unit is used to initialize the initial population based on the macroscopic parameter range of the high-speed and low-speed gear pairs defined by the preliminary feasible scheme. The operation execution unit is used to perform selection, crossover and mutation operations on the initial population to generate the corresponding offspring population, and to merge the initial population and the offspring population to form a joint population; Population processing unit is used to perform non-dominated sorting of the joint population and calculate the crowding degree of individuals in each non-dominated layer. Parental population building units are used to select individuals from a joint population based on non-dominant hierarchy and crowding to form a new generation of parental populations. The iterative execution unit is used to repeatedly execute operations such as generating offspring populations, merging, non-dominated sorting, crowding calculation, and selection based on a new generation of parent populations, until a preset termination condition is met, thereby obtaining one or more optimal solutions that satisfy all engineering constraints and achieve equilibrium on multiple optimization objectives.

[0016] In some implementations, the population processing unit performs non-dominated sorting on the joint population, including: for each individual in the joint population, determining whether the individual is dominated by other individuals in the joint population based on the values ​​of multiple optimization objectives; assigning all individuals in the joint population that are not dominated by any other individual to a first non-dominated layer; assigning individuals that are not dominated by other individuals to a second non-dominated layer among the remaining individuals after removing the first non-dominated layer; repeating the above operation of removing individuals from the already stratified layers and selecting new non-dominated layers until all individuals in the joint population are assigned to the corresponding non-dominated layers.

[0017] In some implementations, the population processing unit calculates the crowding degree of individuals in each non-dominated layer, including: for each non-dominated layer, sorting all individuals in the non-dominated layer in ascending order according to the value of each optimization objective; for each individual, calculating the difference between the individual and its neighboring individuals in each optimization objective dimension; and summing the difference between the values ​​of each individual in all optimization objective dimensions to obtain the crowding degree of the individual.

[0018] In some implementations, design requirements include reducer functional requirements, operating environment requirements, gear transmission design requirements, and engineering constraints. Reducer functional requirements include transmission ratio and minimum number of teeth limits. Reducer functional requirements also include: spatial envelope constraints, reducer output load spectrum, static strength check condition requirements, noise and order avoidance requirements, and transmission efficiency requirements.

[0019] In some implementations, the preliminary scheme determination module includes: The filtering unit is used to calculate the corresponding center distance for each feasible tooth number combination and filter out the combinations that meet the spatial envelope constraints. The processing unit is used to determine the center distance range and tooth width range based on the filtered combination, combined with the load spectrum of the reducer output end and the static strength verification conditions.

[0020] In some implementations, engineering constraints include constraints related to one or more of the following parameters: contact fatigue strength safety factor, bending fatigue strength safety factor, total overlap, end face overlap, slip ratio, and manufacturing process requirements.

[0021] In some implementations, macroscopic parameters include one or more of the following: center distance, tooth width, module, number of teeth, pressure angle, helix angle, displacement coefficient, addendum coefficient, or clearance coefficient.

[0022] According to a third aspect of the present disclosure, an electronic device is provided, characterized in that it includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the gear parameter determination method described above.

[0023] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, which, when the instructions in the storage medium are executed by a processor of a mobile terminal, enables the mobile terminal to perform the gear parameter determination method described above.

[0024] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the gear parameter determination method described above.

[0025] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: This disclosure first generates multiple feasible tooth number combinations, and then determines a preliminary feasible scheme based on some engineering constraints. The preliminary feasible scheme limits the reasonable range of center distance and tooth width, so that subsequent optimization can be carried out within the subspace that is feasible in engineering, reducing the search space of subsequent multi-objective optimization, avoiding a large amount of invalid calculation, and improving optimization efficiency and feasibility. Based on the preliminary feasible scheme, a multi-objective optimization model is used to solve for the optimal solution of macroscopic parameters, and all engineering constraints must be met to ensure that the results can be directly used for engineering implementation without secondary verification. By simultaneously coordinating the mutually restrictive performance indicators such as transmission efficiency, noise level and structural weight through multi-objective optimization, a gear parameter scheme with better overall performance is obtained.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0028] Figure 1 This is a flowchart illustrating a method for determining gear parameters according to some embodiments of the present disclosure.

[0029] Figure 2 This is a flowchart illustrating a multi-objective optimization model processing according to some embodiments of the present disclosure.

[0030] Figure 3This is a flowchart illustrating a non-dominated sorting according to some embodiments of the present disclosure.

[0031] Figure 4 This is a flowchart illustrating a congestion calculation according to some embodiments of the present disclosure.

[0032] Figure 5 This is a flowchart illustrating a multi-objective non-dominated genetic algorithm according to some embodiments of the present disclosure.

[0033] Figure 6 This is a flowchart illustrating the determination of a preliminary feasible solution according to some embodiments of this disclosure.

[0034] Figure 7 This is a flowchart illustrating a method for determining gear parameters according to some embodiments of the present disclosure.

[0035] Figure 8 This is a block diagram illustrating a gear parameter determination device according to some embodiments of the present disclosure.

[0036] Figure 9 This is a block diagram illustrating an electronic device according to some embodiments of the present disclosure. Detailed Implementation

[0037] Exemplary embodiments of this disclosure will be described in detail herein, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a particular order. Furthermore, for clarity and brevity, descriptions of features known in the art may be omitted.

[0038] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all content and steps, nor does it necessarily have to be executed in the described order or in the order of the step numbers. For example, some steps can be broken down, while others can be combined or partially combined, and multiple steps can have their order interchanged or be executed simultaneously. Therefore, the actual execution order may change depending on the actual situation.

[0039] The embodiments described below, which are examples of some of the embodiments of this disclosure, do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0040] In related technologies, verification methods based on empirical formulas and standard specifications are often used, combined with trial-and-error or single-objective optimization strategies to determine macroscopic parameters such as gear module, number of teeth, and displacement coefficient. For multi-stage reducers, the number of teeth for each stage is generally allocated according to the overall transmission ratio, and the minimum number of teeth is verified to avoid undercut.

[0041] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0042] Figure 1 This is a flowchart illustrating a method for determining gear parameters according to some embodiments of the present disclosure, such as... Figure 1 As shown, the gear parameter determination method can be applied to electronic devices, including but not limited to desktop computers, laptops, and other terminal devices, as well as server-side devices such as local servers and cloud servers. The server-side device can be deployed in a computer cluster consisting of one or more computers. The gear parameter determination method may include steps S110-S150.

[0043] In step S110, the design requirements of the electric drive reducer are obtained. The design requirements include the transmission ratio, minimum number of teeth limit, and multiple engineering constraints used to characterize gear performance and reliability.

[0044] In some embodiments, design requirements can be input by the user or automatically retrieved from a product development database, serving as initial boundary conditions for gear parameter design. The aforementioned transmission ratio refers to the ratio of the input shaft speed to the output shaft speed, or the ratio of the number of teeth on the driven gear to the number of teeth on the driving gear. In gear transmission, the transmission ratio determines the changes in speed and torque. If the transmission ratio is greater than 1, it is a reduction transmission, meaning the output shaft speed is lower than the input shaft speed; if it is less than 1, it is a speed-increasing transmission. The minimum number of teeth limit is primarily to avoid undercutting, which occurs during gear machining when the tool cuts too deeply, resulting in excessive removal of the tooth root, thus weakening the gear's strength and durability. Furthermore, the minimum number of teeth also affects the overlap ratio of the gear pair, which in turn relates to the smoothness of the transmission and the noise level.

[0045] In an exemplary embodiment, the engineering constraints may be constraints used to ensure the reliability and meshing quality of the gear pair throughout its entire lifespan.

[0046] In step S120, based on the transmission ratio and minimum number of teeth constraint, multiple feasible combinations of the number of teeth for the high-speed gear pair and the low-speed gear pair are generated.

[0047] The high-speed gear pair is the gear pair closest to the power source (such as an electric motor), typically responsible for converting high-speed rotation into lower-speed but higher-torque motion. The low-speed gear pair is located further away from the power source, further reducing the rotational speed and increasing torque output to meet operational requirements. Together, these two gear pairs form a multi-stage reducer, increasing output torque by progressively decreasing the rotational speed to meet different application requirements. In some embodiments, feasible tooth number combinations must satisfy the mathematical relationships of an integer number of teeth, no undercutting, and the product of the transmission ratios of each stage equaling the total transmission ratio.

[0048] In an exemplary embodiment, by iterating through the number of teeth of the driving gear that meets the minimum number of teeth limit and calculating the corresponding number of teeth of the driven gear, all tooth combination combinations that meet the transmission ratio tolerance range (e.g., ±0.5%) are selected as candidate schemes, i.e., multiple feasible tooth combination combinations.

[0049] In step S130, a preliminary feasible solution is determined based on multiple feasible tooth number combinations and at least some engineering constraints. The preliminary feasible solution includes feasible tooth number combinations, center distance ranges, and tooth width ranges for subsequent optimization processes.

[0050] In some embodiments, the process of determining preliminary feasible solutions is used to map discrete combinations of tooth numbers into a continuous feasible domain of geometric parameters, providing a bounded search space for subsequent multi-objective optimization.

[0051] In an exemplary embodiment, for each feasible tooth number combination, the theoretical center distance is calculated according to the standard center distance formula, and the allowable adjustment range of the center distance is determined in combination with the spatial envelope constraint of the reducer housing; at the same time, based on the load spectrum and strength verification conditions, the lower limit and upper limit of the tooth width are set, thereby forming a preliminary feasible solution that includes tooth number combination, center distance range and tooth width range.

[0052] In step S140, based on the preliminary feasible scheme, under the condition of satisfying all engineering constraints, one or more sets of optimal solutions for the macroscopic parameters of the high-speed gear pair and the low-speed gear pair are obtained through a multi-objective optimization model. The macroscopic parameters include structural parameters used to characterize the gear geometry and size.

[0053] In some embodiments, the multi-objective optimization model may be, but is not limited to, the following: genetic algorithm, particle swarm optimization, differential evolution algorithm, or ant colony optimization.

[0054] In some embodiments, the multi-objective optimization model employs a population-based evolutionary algorithm to dynamically evaluate whether individuals satisfy all engineering constraints during the iteration process and retain feasible and non-dominated solutions.

[0055] In some embodiments, macroscopic parameters refer to the basic design parameters used to characterize the overall geometry and dimensions of the gear, which determine the meshing characteristics, load-bearing capacity, spatial layout, and manufacturing feasibility of the gear pair. Macroscopic parameters are a key bridge connecting functional requirements (such as transmission ratio, strength, and noise) with the specific structural implementation.

[0056] In step S150, the target gear parameter scheme is determined based on one or more sets of optimal solutions. The target gear parameter scheme includes the specific values ​​of macroscopic parameters.

[0057] In some embodiments, the target gear parameter scheme can be directly output as modeling parameters or manufacturing process input files for subsequent gear machining and assembly.

[0058] In an exemplary embodiment, when multiple optimal solutions exist, one set can be selected as the final solution based on project priorities (such as prioritizing noise reduction or prioritizing lightweighting), or a solution set can be provided for designers to make interactive decisions.

[0059] As can be seen from the above steps, the gear parameter determination method provided in this disclosure can efficiently integrate transmission ratio allocation, geometric parameter optimization and multi-dimensional engineering constraints in the design stage of electric drive reducers, realize the collaborative optimization from discrete gear matching to continuous parameters, obtain a gear parameter scheme that takes into account performance, reliability and manufacturability, and significantly improve the development efficiency and design quality of electric drive systems.

[0060] In some embodiments, based on a preliminary feasible solution and under all engineering constraints, one or more optimal solutions to the macroscopic parameters of the high-speed gear pair and the low-speed gear pair are obtained through a multi-objective optimization model, which may include... Figure 2 As shown in S201-S205, this multi-objective optimization process adopts a population-based evolutionary strategy to collaboratively optimize multiple conflicting design objectives while ensuring engineering feasibility.

[0061] In step S201, the initial population is initialized based on the macroscopic parameter ranges of the high-speed gear pair and the low-speed gear pair as defined by the preliminary feasible scheme.

[0062] In some embodiments, the initial population consists of multiple individuals, each encoding a complete set of macroscopic parameters whose values ​​are restricted to the upper and lower bounds of parameters such as center distance, tooth width, and module provided by the preliminary feasible solution, so as to ensure that the initial solution has basic engineering rationality.

[0063] In an exemplary embodiment, the initial population can be generated by Latin hypercube sampling or random uniform sampling, and the population size can be set to 50 to 200 individuals to balance computational efficiency and diversity.

[0064] In step S202, selection, crossover, and mutation operations are performed on the initial population to generate the corresponding offspring population, and the initial population and the offspring population are merged to form a joint population.

[0065] In some embodiments, the selection operation is used to select individuals with higher fitness or better distribution from the parent population to participate in reproduction; the crossover operation generates a new solution by exchanging some parameters of the parent individuals; and the mutation operation applies small perturbations to some parameters of the individuals to enhance the local search capability of the population.

[0066] In an exemplary embodiment, selection may employ a tournament selection or a crowding-based selection strategy, crossover may employ simulated binary crossover (SBX), mutation may employ polynomial mutation, the crossover probability is set to 0.9, and the mutation probability is set to 1 / variable dimension.

[0067] In step S203, the joint population is sorted by non-dominated order, and the crowding degree of individuals in each non-dominated layer is calculated.

[0068] In some embodiments, non-dominated sorting is used to stratify individuals in a joint population according to Pareto dominance, prioritizing the retention of non-dominated solutions; crowding is used to measure the sparse distribution of individuals within the same non-dominated layer in the target space, in order to maintain the diversity of the solution set.

[0069] In an exemplary embodiment, the non-dominated sorting uses a fast non-dominated sorting algorithm, which categorizes individuals that are not dominated by any other individual into the first layer, and so on. When calculating the crowding degree, each optimization objective is sorted separately, and the distance difference between adjacent individuals in each objective dimension is accumulated as the crowding degree value of that individual.

[0070] In step S204, individuals are selected from the joint population based on the non-dominant hierarchy and crowding level to form a new generation of parent population.

[0071] In some embodiments, individuals with lower non-dominated hierarchies are preferred; within the same non-dominated hierarchy, individuals with higher crowding are preferred to avoid excessive aggregation of the unset.

[0072] In an exemplary embodiment, the same number of individuals as the original population size are selected from the joint population to fill the first non-dominated layer. If the capacity is insufficient, the second layer is filled until the upper limit of the population size is reached. Within the same layer, individuals are selected in descending order of crowding.

[0073] In step S205, based on the new generation parent population, the processes of generating offspring populations, merging, non-dominated sorting, crowding calculation, and selection are repeatedly executed until the preset termination condition is met, thereby obtaining one or more optimal solutions that satisfy all engineering constraints and achieve equilibrium on multiple optimization objectives.

[0074] In some embodiments, the preset termination condition may include the maximum number of iterations, the Pareto front convergence threshold, or the change in the optimal solution over multiple consecutive generations being less than a set tolerance. Throughout the optimization process, each individual must be verified by engineering constraints; any individual that violates any constraint is considered an infeasible solution and may be assigned low priority or directly eliminated before sorting.

[0075] In an exemplary embodiment, the maximum number of iterations is set to 100 generations. If the change in the hypervolume index of the Pareto front is less than 1% for 10 consecutive generations, the process is terminated early. Each solution in the final output optimal solution set satisfies all engineering constraints such as contact fatigue strength, bending strength, overlap ratio, and slip ratio, and achieves a good trade-off between multiple objectives such as transmission efficiency, noise level, and structural weight.

[0076] As can be seen from the above steps, the multi-objective optimization process provided in this disclosure can efficiently search for a Pareto optimal solution set that is uniformly distributed, has good convergence, and has practical engineering value, under the premise of strictly meeting the complex engineering constraints of electric drive reducers, thus providing a reliable basis for subsequent gear parameter decisions.

[0077] In some embodiments, non-dominated ranking of a joint population may include... Figure 3 As shown in S301-S304, this non-dominated sorting process is used to stratify individuals in the population according to the Pareto dominance relationship in multi-objective optimization, providing a basis for subsequent hierarchical and diversity-based selection operations.

[0078] In step S301, for each individual in the joint population, based on the values ​​of multiple optimization objectives, it is determined whether the individual is dominated by other individuals in the joint population.

[0079] In some embodiments, the determination of a "dominance" relationship is based on the following: if there exists another individual that is not inferior to the current individual in all optimization objectives and is strictly superior to the current individual in at least one optimization objective, then the other individual is said to dominate the current individual.

[0080] In an exemplary embodiment, assuming that the optimization objectives include transmission efficiency (to be maximized), noise level (to be minimized), and structural weight (to be minimized), then for individuals A and B, if A's efficiency is not lower than B's, its noise level is not higher than B's, its weight is not greater than B's, and at least one of these indicators is better, then A dominates B; otherwise, the two do not dominate each other.

[0081] In step S302, all individuals in the joint population that are not dominated by any other individual are assigned to the first non-dominated layer.

[0082] In some embodiments, the first non-dominated layer is the Pareto front solution set in the current population, representing the set of solutions with the best performance in the current search state.

[0083] In an exemplary embodiment, all individuals in the joint population are traversed, and the existence of a dominant individual is checked one by one. If an individual is not dominated by any other individual, it is marked and added to the first non-dominated layer until the entire population scan is completed.

[0084] In step S303, among the individuals remaining after removing the first non-dominated layer, individuals not dominated by the other individuals are assigned to the second non-dominated layer.

[0085] In some embodiments, the second non-dominated layer is the set of suboptimal non-dominated solutions re-identified from the remaining individuals after the first layer is removed, and so on, to construct a multi-layered non-dominated structure.

[0086] In an exemplary embodiment, after the extraction of the first non-dominated layer is completed, the remaining individuals are formed into a new subset, and the dominance relationship judgment is repeatedly performed in the subset, and the individuals that are not dominated are assigned to the second non-dominated layer.

[0087] In step S304, the above-described operation of removing stratified individuals and screening new non-dominated layers is repeated until all individuals in the joint population are assigned to the corresponding non-dominated layers.

[0088] In some embodiments, the process continues until all individuals are assigned to a certain level, ultimately forming a hierarchical structure consisting of multiple non-dominated levels, where the lower the level, the better the overall performance of the individuals.

[0089] In an exemplary embodiment, if the joint population contains 200 individuals, after sorting, it may form 5 to 8 non-dominated layers, each containing several individuals; the stratification result will be directly used for subsequent selection operations, with priority given to retaining individuals at lower levels to ensure convergence.

[0090] As can be seen from the above steps, the non-dominated sorting method provided in this disclosure can efficiently and accurately stratify individuals in a joint population according to Pareto superiority, providing a clear hierarchical structure for multi-objective optimization algorithms. This ensures the convergence of the solution set while laying the foundation for subsequent crowding calculation and environment selection.

[0091] In some embodiments, multiple optimization objectives include one or more of transmission efficiency, noise level, and structural weight. By using at least one of transmission efficiency, noise level, and structural weight as optimization objectives, energy efficiency, NVH performance, and lightweight requirements can be synergistically balanced during the electric drive reducer design phase. This avoids performance bias caused by optimizing a single objective, thereby obtaining a gear parameter scheme with more balanced overall performance and stronger engineering applicability.

[0092] In some embodiments, calculating the crowding degree of individuals in each non-dominated layer may include... Figure 4 As shown in S401-S403, the crowding calculation process is used to quantify the sparseness of the distribution of each individual in the same non-dominated layer in the target space, so as to prioritize the retention of individuals with a sparser distribution (i.e., a higher crowding degree) in subsequent selection operations, in order to maintain the diversity of the Pareto optimal solution set.

[0093] In step S401, for each non-dominated layer, all individuals in the non-dominated layer are sorted in ascending order according to the value of each optimization objective.

[0094] In some embodiments, since the optimization objectives may include objectives to be maximized (such as transmission efficiency) and objectives to be minimized (such as noise level and structural weight), all objectives can be uniformly converted into a minimized form before sorting, or sorted directly according to the original values. The sorting direction does not affect the relative comparison of congestion.

[0095] In an exemplary embodiment, if a non-dominated layer contains 30 individuals and the optimization objectives are three (transmission efficiency, noise level, and structural weight), then the individuals in the layer are independently sorted three times according to transmission efficiency from smallest to largest, noise level from smallest to largest, and structural weight from smallest to largest, to obtain three ordered lists, each of which corresponds to one objective dimension.

[0096] In step S402, for each individual, the difference between the individual and its neighboring individuals in the optimization objective dimension is calculated.

[0097] In some embodiments, "adjacent individuals" refers to the individuals located one position before and one position after the current individual in the sorting list of the target dimension; for boundary individuals at both ends of the sorting list, if there are no adjacent individuals on one side, the difference on that side can be set to a preset large value (such as the upper limit of the target value range) to ensure that boundary solutions are preferentially retained.

[0098] In step S403, the differences in values ​​of each individual across all optimization target dimensions are summed to obtain the crowding degree of the individual.

[0099] In some embodiments, the crowding degree is the sum of the differences across the target dimensions. The larger the value, the sparser the position of the individual in the target space, and the stronger the representativeness of the solution.

[0100] As can be seen from the above steps, the crowding calculation method provided in this disclosure can further evaluate the distribution characteristics of individuals within the same level based on non-dominated sorting, effectively avoiding excessive clustering of the solution set during the optimization process, thereby obtaining a Pareto optimal solution set with wide coverage and uniform distribution, and improving the engineering practicality and decision-making flexibility of the multi-objective optimization results.

[0101] In some embodiments, a set or more optimal solutions for the macroscopic parameters of the high-speed gear pair and the low-speed gear pair are obtained through a multi-objective optimization model (i.e., a multi-objective non-dominated genetic algorithm), which can be used as follows: Figure 5 As shown, this process includes S501-S514. Figure 1 Based on the preliminary feasible plan determined in the study, the population was initialized within its defined macroscopic parameter range, and combined with... Figure 2 The aforementioned evolutionary operations (including selection, crossover, and mutation) generate a progeny population; subsequently, the parent and progeny generations are merged to form a joint population, and then... Figure 3 The non-dominated ranking method shown stratifies individuals and, at the same time, according to Figure 4 The crowding degree of individuals in each non-dominated layer is calculated in the above manner. Based on this, the non-dominated layer and crowding degree information are combined, and an elite retention strategy is used to select a new generation of parent populations. The above iterative process continues until the preset termination condition is met, and finally a set of Pareto optimal solutions that meet all engineering constraints and achieve a balance in multiple optimization objectives such as transmission efficiency, noise level and structural weight are output.

[0102] In some embodiments, design requirements include reducer functional requirements, operating environment requirements, gear transmission design requirements, and engineering constraints. Reducer functional requirements include transmission ratio and minimum number of teeth limits. Reducer functional requirements also include: spatial envelope constraints, reducer output load spectrum, static strength check condition requirements, noise and order avoidance requirements (NVH), and / or transmission efficiency requirements.

[0103] In some embodiments, the operating environment requirements may include the operating environment temperature (e.g., -40 to 120 degrees Celsius), the lubrication method and the grade (viscosity, FZG) of the lubricating oil, the support stiffness of the housing and bearings, and the position accuracy of the bearing seat bores.

[0104] In some embodiments, gear transmission design requirements may include fatigue strength requirements, static strength requirements, gear anti-galling requirements, gear micropitting verification requirements, transmission efficiency requirements, and NVH (Noise, Vibration, and Harshness) property requirements. The aforementioned fatigue strength and static strength requirements can be limited by fatigue strength safety factors and static strength safety factors. The aforementioned transmission efficiency requirements may include limitations related to two-stage transmission efficiency and CLTC (Clear and Tight) efficiency. The aforementioned NVH property requirements may include limitations related to order avoidance, PPTE (Proportional Peripheral Traceability), and misalignment.

[0105] In some embodiments, the above design requirements may also include overlap constraints and relative slip ratios.

[0106] In some embodiments, the above design requirements may also include component materials and heat treatment methods.

[0107] The multi-dimensional integration of the aforementioned design requirements enables the gear parameter determination process to comprehensively cover the engineering realities of electric drive reducers in terms of function, environment, transmission performance, and manufacturing process. By uniformly incorporating requirements such as transmission ratio, spatial envelope, load spectrum, strength safety factor (SF / SH), NVH characteristics, efficiency index, contact ratio, slip ratio, and material heat treatment into the design input, the generated gear parameter scheme is ensured to not only meet the basic kinematic and dynamic requirements but also achieve comprehensive optimization in terms of fatigue life, anti-galling capability, noise control, transmission efficiency, and assembly feasibility, significantly improving the engineering applicability and development success rate of the design scheme.

[0108] In some embodiments, determining a preliminary feasible solution based on multiple feasible tooth number combinations and at least some engineering constraints may include... Figure 6 As shown in S601-S602, this process aims to transform discrete combinations of tooth numbers into a continuous parameter feasible region with well-defined geometric boundaries and strength guarantees, providing an efficient and reliable search space for subsequent multi-objective optimization.

[0109] In step S601, the center distance corresponding to each feasible tooth number combination is calculated, and the combination that meets the spatial envelope constraint is selected.

[0110] In some embodiments, the center distance is calculated according to the standard gear meshing formula, that is, the center distance is equal to the product of the module and the sum of the number of teeth of the two gears divided by 2 (for spur gears) or an equivalent formula after taking into account the helix angle (for helical gears); the space envelope limit refers to the maximum allowable value of the center distance in the available installation space inside the reducer housing, and tooth number combinations exceeding this range will cause assembly interference.

[0111] In step S602, based on the selected combination, and in conjunction with the load spectrum at the reducer output end and the static strength verification conditions, the center distance range and tooth width range are determined.

[0112] In some embodiments, the load spectrum includes torque, speed and duration under different operating conditions to evaluate the load requirements of the gear under extreme or typical operating conditions; the static strength verification condition usually selects the peak torque condition to verify the relationship between the tooth root bending stress and the allowable stress, thereby inversely estimating the required minimum tooth width; at the same time, the center distance can be allowed a certain adjustment margin (such as ±2%) based on the theoretical value to accommodate displacement design or manufacturing tolerances.

[0113] As can be seen from the above steps, the preliminary feasible solution determination method provided in this disclosure can effectively integrate kinematic tooth matching, spatial layout constraints and strength verification requirements, filter out a large number of infeasible tooth number combinations in advance, and assign reasonable geometric parameter boundaries to each feasible combination, which significantly improves the efficiency and engineering feasibility of subsequent multi-objective optimization.

[0114] In some embodiments, engineering constraints include constraints related to one or more of the following parameters: contact fatigue strength safety factor, bending fatigue strength safety factor, total overlap, end face overlap, slip ratio, or manufacturing process requirements.

[0115] As an example, engineering constraints can be shown in Table 1 below, including the constraint objectives for each parameter.

[0116] Table 1

[0117] Table 1 shows typical engineering constraints and their target values ​​used in the gear parameter optimization process according to embodiments of this disclosure. The parameters listed in Table 1 include contact fatigue strength safety factor, bending fatigue strength safety factor, total overlap ratio, slip ratio, and machining performance indicators. The corresponding target values ​​are merely illustrative settings, used to illustrate the specific constraint types that can be included in the optimization model in the design of electric drive reducers and their reasonable value ranges. It should be understood that the selection of the above parameters and the setting of target values ​​can be adjusted according to actual application scenarios, design standards, material properties, operating conditions, or enterprise specifications, and are not the only limitations. For example, the threshold of the safety factor can vary depending on lifespan requirements, the overlap ratio target can be adjusted according to noise control levels, and the slip ratio limit can also be redefined according to lubrication conditions. Therefore, the content shown in Table 1 is for illustrative purposes only and does not constitute a limitation on the scope of protection of this invention.

[0118] The aforementioned engineering constraints quantify key requirements in gear design, such as strength, meshing performance, and manufacturability, into calculable and verifiable indicators. By introducing a contact / bending fatigue strength safety factor, lifespan reliability is ensured; total overlap and end-face overlap are used to improve transmission smoothness and suppress noise; slip ratio control reduces the risk of galling; and manufacturing process requirements are considered to ensure the solution is manufacturable. This multi-dimensional constraint mechanism allows the optimization process to proceed while meeting stringent engineering specifications, effectively avoiding infeasible solutions and significantly improving the reliability, NVH performance, and engineering feasibility of the resulting gear parameter solutions.

[0119] In some embodiments, the macroscopic parameters include one or more of the following parameters: center distance, tooth width, module, number of teeth, pressure angle, helix angle, displacement coefficient, addendum coefficient, or clearance coefficient.

[0120] As an example, the macroscopic parameter design optimization range for multi-objective optimization in this embodiment may include the adjustable range, accuracy level, and unit of each structural parameter of the high-speed and low-speed gear pairs during the optimization process, including center distance, tooth width, module, number of teeth, pressure angle, helix angle, displacement coefficient, addendum coefficient, and clearance coefficient. These parameter ranges are determined based on preliminary feasible solutions, ensuring both the flexibility of the optimization process and limiting it within the boundaries allowed by actual manufacturing and assembly, ensuring that the final solution has good engineering applicability and feasibility. The parameter ranges can be adjusted according to the specific reducer structure, materials, and operating conditions, and do not constitute a limitation on the scope of protection of this invention.

[0121] The embodiments disclosed herein incorporate macroscopic parameters such as center distance, tooth width, and module into an adjustable range defined by preliminary feasible solutions for multi-objective optimization. This approach preserves design freedom while ensuring that all solutions meet manufacturing, assembly, and performance constraints, significantly improving the engineering feasibility and practicality of the optimization results.

[0122] Figure 7 This is a flowchart illustrating a method for determining gear parameters according to some embodiments of the present disclosure, such as... Figure 7 As shown, the gear parameter determination method can be applied to electronic devices, including but not limited to desktop computers, laptops and other terminal devices. The gear parameter determination method may include steps S710-S760.

[0123] In step S710, the design requirements of the electric drive reducer are obtained.

[0124] Design requirements include spatial envelope, reducer output load spectrum, static strength check conditions, noise and order avoidance requirements, transmission efficiency requirements, and multiple engineering constraints to characterize gear performance and reliability.

[0125] In step S720, the tooth number allocation is performed.

[0126] Output a set of gear matching schemes based on the order of avoidance requirements, namely, multiple feasible combinations of the number of teeth of the high-speed gear pair and the low-speed gear pair mentioned above.

[0127] In step S730, a coarse search is initiated.

[0128] For all feasible tooth number combinations, a preliminary solution that meets the requirements is generated through coarse optimization, i.e., a preliminary feasible solution. At the same time, the range of center distance and tooth width is narrowed during fine optimization.

[0129] In step S740, a fine search is initiated.

[0130] The optimization targets and scope can include the adjustable range, accuracy level and unit of each structural parameter of the high-speed and low-speed gear pairs during the optimization process, including center distance, tooth width, module, number of teeth, pressure angle, helix angle, displacement coefficient, addendum coefficient and clearance coefficient, etc.

[0131] The constraints can be found in Table 1 above.

[0132] Understandably, fine optimization is based on the center distance and tooth width range of coarse optimization, as well as all feasible combinations of tooth numbers.

[0133] In step S750, for all schemes output in S740, a multi-objective genetic algorithm is used to iteratively calculate the fitness values ​​that satisfy the gear parameter constraints. When the preset number of iterations is reached, the calculated fitness values ​​of each generation are sorted in descending order, and the gear parameters with the highest priority are selected as the result parameters of this optimization, that is, one or more optimal solutions mentioned above.

[0134] In step S760, among one or more optimal solutions, the scheme that meets the project requirements is evaluated and determined as the target gear parameter scheme.

[0135] This disclosed embodiment decomposes the complex multi-parameter coupled optimization problem into ordered stages: first, feasible tooth number combinations are generated based on requirements such as order avoidance; then, preliminary solutions that meet constraints such as space and strength are quickly screened through coarse search, and the range of center distance and tooth width is narrowed; finally, multi-objective fine optimization is performed in a constrained high-dimensional parameter space. This hierarchical strategy significantly reduces computational complexity and improves convergence efficiency, while ensuring that the final solution takes into account NVH, efficiency, strength, and manufacturability, effectively realizing efficient, reliable, and engineering-oriented design of gear parameters for electric drive reducers.

[0136] It should be noted that the above figures are merely illustrative representations of the processes included in methods according to some embodiments of this disclosure, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0137] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0138] Figure 8 This is a block diagram illustrating a gear parameter determining device according to some embodiments of the present disclosure. (Refer to...) Figure 8 The device includes: a design requirements acquisition module 801, a tooth number combination generation module 802, a preliminary scheme determination module 803, a multi-objective optimization module 804, and an objective scheme determination module 805.

[0139] The design requirements acquisition module 801 is used to acquire the design requirements of the electric drive reducer. The design requirements include the transmission ratio, minimum number of teeth limit, and multiple engineering constraints used to characterize gear performance and reliability. The tooth number combination generation module 802 is used to generate multiple feasible tooth number combinations of high-speed gear pairs and low-speed gear pairs based on the transmission ratio and minimum tooth number limit. The preliminary scheme determination module 803 is used to determine a preliminary feasible scheme based on multiple feasible tooth number combinations and at least some engineering constraints. The preliminary feasible scheme includes feasible tooth number combinations, center distance range and tooth width range for subsequent optimization. The multi-objective optimization module 804 is used to obtain one or more optimal solutions of the macroscopic parameters of the high-speed gear pair and the low-speed gear pair through a multi-objective optimization model based on the preliminary feasible scheme and under the condition of satisfying all engineering constraints. The macroscopic parameters include structural parameters used to characterize the gear geometry and size. The target solution determination module 805 is used to determine the target gear parameter scheme based on one or more sets of optimal solutions. The target gear parameter scheme includes the specific values ​​of macroscopic parameters.

[0140] In some embodiments of this disclosure, the multi-objective optimization module 804 includes: An initialization unit is used to initialize the initial population based on the macroscopic parameter range of the high-speed and low-speed gear pairs defined by the preliminary feasible scheme. The operation execution unit is used to perform selection, crossover and mutation operations on the initial population to generate the corresponding offspring population, and to merge the initial population and the offspring population to form a joint population; Population processing unit is used to perform non-dominated sorting of the joint population and calculate the crowding degree of individuals in each non-dominated layer. Parental population building units are used to select individuals from a joint population based on non-dominant hierarchy and crowding to form a new generation of parental populations. The iterative execution unit is used to repeatedly execute operations such as generating offspring populations, merging, non-dominated sorting, crowding calculation, and selection based on a new generation of parent populations, until a preset termination condition is met, thereby obtaining one or more optimal solutions that satisfy all engineering constraints and achieve equilibrium on multiple optimization objectives.

[0141] In some embodiments of this disclosure, the population processing unit performs non-dominated sorting on the joint population, including: for each individual in the joint population, determining whether the individual is dominated by other individuals in the joint population based on the values ​​of multiple optimization objectives; assigning all individuals in the joint population that are not dominated by any other individual to a first non-dominated layer; assigning individuals that are not dominated by the remaining individuals after removing the first non-dominated layer to a second non-dominated layer; repeating the above operation of removing already stratified individuals and selecting new non-dominated layers until all individuals in the joint population are assigned to the corresponding non-dominated layers.

[0142] In some embodiments of this disclosure, the population processing unit calculates the crowding degree of individuals in each non-dominated layer, including: for each non-dominated layer, sorting all individuals in the non-dominated layer in ascending order according to the value of each optimization objective; for each individual, calculating the difference between the individual and its neighboring individuals in each optimization objective dimension; and summing the difference between the values ​​of each individual in all optimization objective dimensions to obtain the crowding degree of the individual.

[0143] In some embodiments of this disclosure, the design requirements include reducer functional requirements, working environment requirements, gear transmission design requirements, and engineering constraints. The reducer functional requirements include transmission ratio and minimum number of teeth limits. The reducer functional requirements also include: spatial envelope constraints, reducer output load spectrum, static strength check condition requirements, noise and order avoidance requirements, and transmission efficiency requirements.

[0144] In some embodiments of this disclosure, the preliminary scheme determination module 803 includes: The filtering unit is used to calculate the corresponding center distance for each feasible tooth number combination and filter out the combinations that meet the spatial envelope constraints. The processing unit is used to determine the center distance range and tooth width range based on the filtered combination, combined with the load spectrum of the reducer output end and the static strength verification conditions.

[0145] In some embodiments of this disclosure, engineering constraints include constraints related to one or more of the following parameters: contact fatigue strength safety factor, bending fatigue strength safety factor, total overlap, end face overlap, slip ratio, and manufacturing process requirements.

[0146] In some embodiments of this disclosure, macroscopic parameters include one or more of the following parameters: center distance, tooth width, module, number of teeth, pressure angle, helix angle, displacement coefficient, addendum coefficient, or clearance coefficient.

[0147] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0148] Figure 9 This is a block diagram illustrating an electronic device 900 according to some embodiments of the present disclosure. For example, the electronic device 900 may be a desktop computer, a laptop computer, a smartphone, etc.

[0149] Reference Figure 9 The electronic device 900 may include one or more of the following components: a processing component 902, a memory 904, a power component 906, a multimedia component 908, an audio component 910, an input / output (I / O) interface 912, a sensor component 914, and a communication component 916.

[0150] Processing component 902 typically controls the overall operation of electronic device 900, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 902 may include one or more processors 920 to execute instructions to complete all or part of the steps of the gear parameter determination method described above. Furthermore, processing component 902 may include one or more modules to facilitate interaction between processing component 902 and other components. For example, processing component 902 may include a multimedia module to facilitate interaction between multimedia component 908 and processing component 902.

[0151] Memory 904 is configured to store various types of data to support the operation of device 900. Examples of this data include instructions for any application or method operating on electronic device 900, contact data, phonebook data, messages, pictures, videos, etc. Memory 904 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0152] Power supply component 906 provides power to various components of electronic device 900. Power supply component 906 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 900.

[0153] Multimedia component 908 includes a screen that provides an output interface between the electronic device 900 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 908 includes a front-facing camera and / or a rear-facing camera. When the device 900 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0154] Audio component 910 is configured to output and / or input audio signals. For example, audio component 910 includes a microphone (MIC) configured to receive external audio signals when electronic device 900 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 904 or transmitted via communication component 916. In some embodiments, audio component 910 also includes a speaker for outputting audio signals.

[0155] I / O interface 912 provides an interface between processing component 902 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0156] Sensor assembly 914 includes one or more sensors for providing state assessments of various aspects of electronic device 900. For example, sensor assembly 914 can detect the on / off state of device 900, the relative positioning of components such as the display and keypad of electronic device 900, changes in position of electronic device 900 or a component of electronic device 900, the presence or absence of user contact with electronic device 900, orientation or acceleration / deceleration of electronic device 900, and temperature changes of electronic device 900. Sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 914 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 914 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0157] Communication component 916 is configured to facilitate wired or wireless communication between electronic device 900 and other devices. Electronic device 900 can access wireless networks based on communication standards, such as WiFi, 3G, 4G, 5G, other communication standards, or combinations thereof. In some embodiments of this disclosure, communication component 916 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In some embodiments of this disclosure, communication component 916 further includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0158] In some embodiments of this disclosure, the electronic device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0159] In some embodiments of this disclosure, a computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, which can be executed by a processor 920 of an electronic device 900 to perform the above-described method. For example, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0160] This disclosure also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the gear parameter determination method described in the above method embodiments.

[0161] In this disclosure, the computer-readable storage medium is one capable of sending, propagating, or transmitting computer instructions for use by or in connection with an instruction execution system, apparatus, or device. As an example, the computer-readable storage medium is a non-volatile storage medium.

[0162] In some embodiments, more specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, USB flash drives, portable hard drives, or any suitable combination of the foregoing.

[0163] In some examples, computational instructions contained on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0164] This disclosure also provides a computer program product storing instructions that, when executed by a computer, cause the computer to perform the gear parameter determination method described in the above-described method embodiments. These instructions may be program code. In specific implementations, the program code may be written using any combination of one or more programming languages. The program code may be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0165] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0166] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for determining gear parameters, characterized in that, include: Obtain the design requirements for the electric drive reducer, which include the transmission ratio, minimum number of teeth limit, and multiple engineering constraints to characterize gear performance and reliability; Based on the transmission ratio and the minimum number of teeth constraint, multiple feasible combinations of the number of teeth for the high-speed gear pair and the low-speed gear pair are generated. Based on the multiple feasible tooth number combinations and at least some of the engineering constraints, a preliminary feasible solution is determined, which includes feasible tooth number combinations, center distance ranges, and tooth width ranges for subsequent optimization processes. Based on the aforementioned preliminary feasible scheme, under the condition of satisfying all the aforementioned engineering constraints, one or more optimal solutions for the macroscopic parameters of the high-speed gear pair and the low-speed gear pair are obtained through a multi-objective optimization model. The macroscopic parameters include structural parameters used to characterize the gear geometry and size. Based on one or more sets of optimal solutions, determine the target gear parameter scheme.

2. The method according to claim 1, characterized in that, Based on the preliminary feasible solution, and under the condition of satisfying all the engineering constraints, one or more optimal solutions for the macroscopic parameters of the high-speed gear pair and the low-speed gear pair are obtained through a multi-objective optimization model, including: Based on the macroscopic parameter ranges of the high-speed and low-speed gear pairs defined by the aforementioned preliminary feasible scheme, the initial population is initialized. The initial population is subjected to selection, crossover, and mutation operations to generate a corresponding offspring population, and the initial population and the offspring population are merged to form a joint population; The joint population is subjected to non-dominated sorting, and the crowding degree of individuals in each non-dominated layer is calculated. Individuals are selected from the joint population based on the non-dominant hierarchy and the crowding degree to form a new generation of parent population; Based on the new generation parent population, the processes of generating offspring populations, merging, non-dominated sorting, crowding calculation, and selection are repeatedly executed until a preset termination condition is met, thereby obtaining one or more optimal solutions that satisfy all the engineering constraints and achieve equilibrium on multiple optimization objectives.

3. The method according to claim 2, characterized in that, The non-dominated sorting of the joint population includes: For each individual in the joint population, based on the values ​​of the multiple optimization objectives, it is determined whether the individual is dominated by other individuals in the joint population; All individuals in the joint population that are not dominated by any other individual are classified into the first non-dominated layer; Among the individuals remaining after removing the first non-dominated layer, those individuals not dominated by the other individuals are classified into the second non-dominated layer. Repeat the above operations of removing individuals from the stratified layers and selecting new non-dominated layers until all individuals in the joint population are assigned to the corresponding non-dominated layers.

4. The method according to claim 3, characterized in that, The calculation of crowding degree of individuals in each non-dominated layer includes: For each non-dominated layer, all individuals in the non-dominated layer are sorted in ascending order according to the value of each optimization objective. For each individual, in each optimization objective dimension, calculate the difference between the value of the individual and its neighboring individuals in the optimization objective; The crowding degree of an individual is obtained by summing the differences in the values ​​of each individual across all optimization objective dimensions.

5. The method according to claim 1, characterized in that, The design requirements include the reducer's functional requirements, working environment requirements, gear transmission design requirements, and engineering constraints. The reducer's functional requirements include the transmission ratio and the minimum number of teeth limit. The functional requirements for the reducer also include: spatial envelope limitation, reducer output load spectrum, static strength verification working condition requirements, noise and order avoidance requirements and / or transmission efficiency requirements.

6. The method according to claim 5, characterized in that, The determination of preliminary feasible solutions based on the multiple feasible tooth number combinations and at least some of the engineering constraints includes: Calculate the center distance for each feasible tooth number combination, and select the combinations that satisfy the spatial envelope constraints; Based on the selected combinations, and in conjunction with the load spectrum at the output end of the reducer and the static strength verification requirements, the center distance range and the tooth width range are determined.

7. The method according to any one of claims 1-6, characterized in that, The engineering constraints include constraints related to one or more of the following parameters: Safety factors for contact fatigue strength, bending fatigue strength, total overlap, end face overlap, slip ratio, or manufacturing process requirements.

8. The method according to any one of claims 1-6, characterized in that, The macroscopic parameters include one or more of the following parameters: Center distance, tooth width, module, number of teeth, pressure angle, helix angle, displacement coefficient, addendum coefficient or clearance coefficient.

9. A gear parameter determining device, characterized in that, include: The design requirements acquisition module is used to acquire the design requirements of the electric drive reducer. The design requirements include the transmission ratio, minimum number of teeth limit, and multiple engineering constraints used to characterize gear performance and reliability. A tooth number combination generation module is used to generate multiple feasible tooth number combinations of high-speed gear pairs and low-speed gear pairs based on the transmission ratio and the minimum tooth number limit. The preliminary scheme determination module is used to determine a preliminary feasible scheme based on the multiple feasible tooth number combinations and at least a portion of the engineering constraints. The preliminary feasible scheme includes feasible tooth number combinations, center distance ranges, and tooth width ranges for subsequent optimization. The multi-objective optimization module is used to obtain one or more optimal solutions of the macroscopic parameters of the high-speed gear pair and the low-speed gear pair through a multi-objective optimization model based on the preliminary feasible scheme and under the condition of satisfying all the engineering constraints. The macroscopic parameters include structural parameters used to characterize the gear geometry and size. The target solution determination module is used to determine the target gear parameter scheme based on one or more sets of optimal solutions.

10. The apparatus according to claim 9, characterized in that, The multi-objective optimization module includes: An initialization unit is used to initialize an initial population based on the macroscopic parameter ranges of the high-speed gear pair and the low-speed gear pair as defined by the preliminary feasible scheme. An operation execution unit is used to perform selection, crossover, and mutation operations on the initial population to generate a corresponding offspring population, and to merge the initial population and the offspring population to form a joint population; A population processing unit is used to perform non-dominated sorting on the joint population and calculate the crowding degree of individuals in each non-dominated layer. A parent population building unit is used to select individuals from the joint population based on the non-dominant hierarchy and the crowding degree to form a new generation of parent population. The iterative execution unit is used to repeatedly execute operations such as generating offspring population, merging, non-dominated sorting, crowding calculation and selection based on the new generation parent population, until a preset termination condition is met, and obtain one or more optimal solutions that satisfy all the engineering constraints and achieve equilibrium on multiple optimization objectives.

11. The apparatus according to claim 10, characterized in that, The population processing unit performs non-dominated sorting on the joint population, including: for each individual in the joint population, determining whether the individual is dominated by other individuals in the joint population based on the values ​​of the multiple optimization objectives; assigning all individuals in the joint population that are not dominated by any other individual to the first non-dominated layer; assigning individuals that are not dominated by other individuals to the second non-dominated layer among the remaining individuals after removing the first non-dominated layer; repeating the above operation of removing individuals from the stratified layers and selecting new non-dominated layers until all individuals in the joint population are assigned to the corresponding non-dominated layers.

12. The apparatus according to claim 11, characterized in that, The population processing unit calculates the crowding degree of individuals in each non-dominated layer, including: for each non-dominated layer, sorting all individuals in the non-dominated layer in ascending order according to the value of each optimization objective; for each individual, calculating the value difference between the individual and its neighboring individuals in each optimization objective dimension; and summing the value differences of each individual in all optimization objective dimensions to obtain the crowding degree of the individual.

13. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the steps of the method according to any one of claims 1-8.

14. A computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of a mobile terminal, enable the mobile terminal to perform the steps of the method according to any one of claims 1-8.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-8.

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