Welding control method for fitness equipment
By collecting and optimizing the usage data of dumbbell racks, combining the use and disposable feature information, differentiated welding parameters are optimized, which solves the problem that traditional welding control methods cannot cope with changes in usage patterns, improves welding quality and durability, and extends use life.
Patent Information
- Application Number
- CN202411336659.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-25
- Publication Date
- 2024-12-17
- Estimated Expiration
- 2044-09-25
AI Technical Summary
Traditional welding control methods cannot effectively deal with the problems of vulnerability and insufficient durability of welding points due to changes in actual usage patterns and load parameters during use of dumbbell racks.
By collecting usage data from different placement positions of dumbbell racks, optimizing actual placement distribution and welding parameters, combining usage feature information and disposable feature information, integrating optimal actual and theoretical welding parameters, and performing differentiated optimization welding processing control.
It improves the welding quality and overall performance of dumbbell racks, extends service life, enhances safety, and ensures that the welding parameters meet actual usage requirements.
Smart Images
Figure CN118848324B_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, specifically to the field of welding control technology, and in particular to a welding control method for fitness equipment. Background Art
[0002] With the rapid expansion of the fitness equipment market and the diversification of consumer demand, the production process of fitness equipment has increasingly higher requirements for welding control technology. The welding quality of fitness equipment, especially those involving high-strength and high-durability requirements, such as dumbbell racks, directly determines the service life and safety of the product. Dumbbell racks usually contain multiple placement structures for dumbbells. Each placement position may need to bear different loads depending on the weight of the dumbbells placed, so there are also differences in the quality requirements after welding. Traditional welding control methods often use uniform welding parameters for welding, without considering the different requirements for welding quality at different placement positions, resulting in local fatigue fractures, deformation and other problems in the dumbbell rack during use, affecting service life and safety. Summary of the invention
[0003] The present application provides a welding control method for fitness equipment, aiming to solve the technical problem of fragility and insufficient durability of welding points of a dumbbell rack caused by changes in actual usage modes and load parameters during use.
[0004] In view of the above problems, the present application provides a welding control method for fitness equipment.
[0005] The present application provides a welding control method for fitness equipment, the method comprising: collecting usage data records of multiple placement positions on the dumbbell rack during a use period of the dumbbell rack to obtain multiple actual use data sets, wherein the multiple placement positions include preset placement distributions of multiple weight dumbbells; optimizing the actual placement distributions of multiple weight dumbbells at multiple placement positions during the use period according to the multiple actual use data sets to obtain an optimal actual placement distribution, wherein the optimization is performed to improve the conformity of the actual placement distribution with the multiple actual use data sets and to reduce the deviation between the actual placement distribution and the preset placement distribution; analyzing the impact load parameters and the release parameters of the multiple placement positions during the use period according to the multiple actual use data sets; The method comprises the following steps: obtaining a plurality of usage characteristic information by combining the plurality of usage characteristic information and the actual dumbbell weights of the plurality of placement positions in the optimal actual placement distribution, optimizing the welding parameters of the plurality of placement positions in practice, and obtaining a plurality of optimal actual welding parameters; obtaining the placement load parameters of the plurality of placement positions in the out-of-use time period, and obtaining a plurality of out-of-use characteristic information; combining the plurality of out-of-use characteristic information and the preset dumbbell weights of the plurality of placement positions in the preset placement distribution, optimizing the welding parameters of the plurality of placement positions in theory, and obtaining a plurality of optimal theoretical welding parameters; combining the plurality of optimal actual welding parameters, obtaining a plurality of optimal welding parameters through fusion processing, and performing processing control on the welding of the plurality of placement positions in the dumbbell rack production process.
[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0007] The welding control method for fitness equipment described above, by collecting usage data of different placement positions, understands the actual placement and distribution of dumbbells at different positions, and forms multiple actual use data sets. Subsequently, based on these actual use data sets, the placement distribution of dumbbells is optimized. The goal of optimization is to make the actual placement distribution more consistent with the collected usage data, while minimizing the deviation from the preset placement distribution. Afterwards, by analyzing the impact load parameters and placement load parameters in the actual use data set, multiple usage feature information of the dumbbell rack during use is obtained. This information can help understand the actual load conditions borne by the dumbbell rack at different positions. Then, the welding parameters of multiple placement positions on the dumbbell rack are actually optimized by combining the usage feature information and the actual weight of the dumbbells in the optimized actual placement distribution. The purpose of optimization is to ensure that the welding quality can meet the actual load requirements of each position, thereby improving the overall performance of the dumbbell rack. In addition, during the dumbbell rack out of use period, the placement load parameters of the dumbbells are also analyzed to obtain the out-of-use feature information of the dumbbell rack. This information can help predict the load changes that may occur during the out-of-use period of the dumbbell rack, thereby further adjusting the welding parameters. Finally, the welding parameters are theoretically optimized by combining the deactivated feature information and the preset weight of the dumbbells in the preset placement distribution to obtain multiple optimal theoretical welding parameters. These theoretical optimization parameters are then fused with the actual optimization parameters to obtain the final multiple optimal welding parameters. These parameters will be used for welding process control during the production of the dumbbell rack to ensure that the quality and performance of the dumbbell rack are optimal. Through the above steps, the differentiated optimization of the dumbbell rack welding parameters can be achieved, the service life and safety of the dumbbell rack can be improved, and it can be more in line with actual use needs.
[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0010] Figure 1 is a flow chart of a welding control method for fitness equipment in one embodiment;
[0011] Figure 2The figure is a flow chart of obtaining a plurality of actual use data sets in a welding control method for fitness equipment in one embodiment. DETAILED DESCRIPTION
[0012] The embodiment of the present application solves the technical problem of fragility and insufficient durability of welding points of a dumbbell rack caused by changes in actual usage modes and load parameters during use by providing a welding control method for fitness equipment.
[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0014] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.
[0015] Examples, such as Figure 1 As shown, the present application provides a welding control method for fitness equipment, the method comprising:
[0016] During the use period of the dumbbell rack, use data records of multiple placement positions on the dumbbell rack are collected to obtain multiple actual use data sets, wherein the multiple placement positions include preset placement distributions of dumbbells of multiple weights.
[0017] In the modern fitness equipment industry, dumbbell racks are commonly used as training auxiliary equipment. Their stability and durability are crucial to the safety of users and the training effect. However, in actual use, dumbbell racks often face multiple challenges. Due to the usage habits, training intensity and weight of dumbbells of different users, the load parameters of each placement position of the dumbbell rack will also change significantly. This change will not only cause the welding points of the dumbbell rack to be vulnerable, but may also affect the stability of the entire structure, thereby shortening the service life of the dumbbell rack.
[0018] In the embodiment of the present application, during the use time period of the dumbbell rack, the system terminal performs data collection to obtain the actual use data of multiple placement positions on the dumbbell rack. These data records cover the number of placements and placement times of dumbbells of different weights at multiple placement positions, thereby forming multiple actual use data sets. Among them, the multiple placement positions include preset placement distributions of dumbbells of various weights, which are used to reasonably distribute the placement positions of the dumbbells according to the different weights of the dumbbells to ensure the stability and safety of the dumbbell rack. Exemplarily, the preset placement distribution divides the dumbbell rack into three main areas: the upper layer (lightweight area), the middle layer (medium weight area) and the lower layer (weight area). The upper layer mainly places lightweight dumbbells, such as 2.5kg, 5kg and 7.5kg dumbbells. These dumbbells are light in weight and are suitable for being placed in a higher position for easy access. The middle layer places medium-weight dumbbells, such as 10kg, 12.5kg and 15kg dumbbells. The weight of these dumbbells is moderate, and placing them in the middle layer can keep the balance of the dumbbell rack. The lower layer is used to place heavier dumbbells, such as 17.5kg, 20kg and 22.5kg dumbbells. These dumbbells are heavier and require more stable support, so they are placed at the bottom of the dumbbell rack. By collecting actual usage data, we can understand the number of times and the placement time of dumbbells of different weights in various placement positions when users use the dumbbell rack. These data can help our system terminal analyze the actual usage patterns of the dumbbell rack, understand the user's usage habits and preferences, and provide a basis for optimizing the design and manufacture of the dumbbell rack.
[0019] Further, if Figure 2 As shown, the present application provides a method for collecting usage data records of multiple placement positions on a dumbbell rack during a period of use of the dumbbell rack to obtain multiple actual usage data sets, including:
[0020] During the period of use of the dumbbell rack, the number of times dumbbells of different weights are placed at each placement position, and the placement time of dumbbells of different weights are recorded and collected to obtain multiple placement record data; based on multiple placement record data sets within multiple usage time periods, the number of times dumbbells of different weights are placed at each placement position, and the statistical placement time of dumbbells of different weights are counted to obtain multiple actual usage data sets.
[0021] Preferably, during the use of the dumbbell rack, the system terminal collects the placement of dumbbells of different weights at each placement position, records the number of placements of dumbbells of various weights at each position, and the placement time of these dumbbells, to form a plurality of placement record data sets. Subsequently, these record data sets are statistically analyzed. The total number of placements and the total placement time of dumbbells of different weights at each placement position are counted, and these data reflect the actual use of the dumbbell rack. Based on these statistical results, a plurality of actual use data sets are formed, and these data sets provide the system terminal with the use frequency and load conditions of the dumbbell rack at each position in different time periods, which is helpful to understand the use mode and performance requirements of the dumbbell rack.
[0022] Based on the multiple actual usage data sets, the actual placement distribution of multiple weight dumbbells at multiple placement positions during a usage time period is optimized to obtain the optimal actual placement distribution, wherein the optimization is performed to improve the consistency of the actual placement distribution with the multiple actual usage data sets and to reduce the deviation between the actual placement distribution and the preset placement distribution.
[0023] In one embodiment, the system terminal optimizes the actual placement distribution of dumbbells at multiple placement positions on the dumbbell rack based on the collected multiple actual use data sets. This optimization process follows two principles: one is to improve the conformity of the actual placement distribution with the actual use data set, and the other is to reduce the deviation of the actual placement distribution from the preset placement distribution. In actual use, due to the personal habits and preferences of the user during exercise, the dumbbells may be placed in an area that does not conform to their preset positions. For example, although the 10kg dumbbells were originally preset in the middle layer (medium area), the user may often place them in the light area where the 5kg dumbbells are located for convenience or other reasons. This deviation from the preset position of actual use is reflected by the record of the actual use data set. In order to optimize the placement distribution of the dumbbells, the system terminal first analyzes the actual use data set to obtain the weight of the dumbbells that are placed the most times at multiple placement positions. Based on these dumbbell weights, the system terminal optimizes the actual placement distribution of multiple weight dumbbells at multiple placement positions during the use period in combination with the pre-built placement distribution optimization function to obtain the optimal actual placement distribution. This optimal actual placement distribution can not only meet the user's use needs, but also maintain the rationality of the preset placement distribution to a certain extent. Through this optimization, the use efficiency and user experience of the dumbbell rack can be improved while ensuring the stability and safety of the dumbbell rack.
[0024] Furthermore, the present application provides optimizing the actual placement distribution of multiple weight dumbbells at multiple placement positions during a usage period according to the multiple actual usage data sets to obtain an optimal actual placement distribution, including:
[0025] According to the multiple actual use data sets, the dumbbell weights with the most placement times at the multiple placement positions are counted respectively to obtain multiple enriched dumbbell weights; the dumbbell weights placed at the multiple placement positions are randomly arranged to construct a placement distribution space.
[0026] Optionally, the system terminal performs statistical analysis on the use of dumbbells at various placement positions on the dumbbell rack based on multiple actual use data sets. Specifically, the system terminal counts the dumbbell weights that are placed the most times at each placement position, and defines the dumbbell weights counted as enriched dumbbell weights. The enriched dumbbell weights reflect the user's usage preferences and habits at specific locations. Subsequently, in order to explore a wider range of placement possibilities and optimize the placement distribution of dumbbells, the system terminal determines all the positions on the dumbbell rack where dumbbells can be placed, and assigns a unique identifier to each position. After that, an empty placement distribution space is initialized, which will be used to store all possible dumbbell placement combinations. For each placement position, one or more dumbbell weights are randomly selected for arrangement. Then, the dumbbell weight combination randomly arranged at each position is added to the placement distribution space. Repeat this process to generate multiple different dumbbell placement distribution schemes. These schemes will constitute a placement distribution space containing multiple possibilities in order to analyze and optimize the actual placement distribution of dumbbells.
[0027] In order to improve the consistency between the actual placement distribution and the multiple actually used data sets and reduce the deviation between the actual placement distribution and the preset placement distribution, a placement distribution optimization function is constructed as follows: ; Where DDF is the placement fitness, and is the weight, M is the number of placement locations, is the dumbbell weight of the ith placement position in the optimized actual placement distribution, is the weight of the enriched dumbbell at the ith placement, is the dumbbell weight of the i-th placement position in the preset placement distribution.
[0028] Optionally, in order to improve the consistency of the actual placement distribution of dumbbells with multiple actual usage data sets and reduce the deviation from the preset placement distribution, the system terminal constructs a placement distribution optimization function (DDF). This function considers three key factors: actual placement distribution, enriched dumbbell weight, and preset placement distribution. The placement distribution optimization function is as follows: ; Among them, DDF is the placement fitness, which is calculated by weighted merging of two different distance metrics and is used to evaluate how close the current placement distribution is to the ideal state. and is the weight used to adjust and , and M is the number of multiple placement positions, that is, the number of different positions where dumbbells can be placed on the dumbbell rack. is the dumbbell weight at the i-th placement position in the optimized actual placement distribution. is the weight of the enriched dumbbell at the ith placement, which is an important reference for measuring actual usage. is the weight of the dumbbell at the i-th placement position in the preset placement distribution. The preset placement distribution is based on historical experience. Through this function, the system terminal can evaluate the degree of conformity between the current placement distribution and the actual usage data and the preset distribution, and adjust the placement of the dumbbells accordingly for subsequent optimization of the dumbbell rack.
[0029] According to the placement distribution optimization function, the actual placement distribution of dumbbells of various weights at the multiple placement positions during the use time period is optimized in the placement distribution space to obtain the optimal actual placement distribution.
[0030] Optionally, the system terminal iteratively optimizes the actual placement distribution of multiple placement positions on the dumbbell rack in the constructed placement distribution space based on the constructed placement distribution optimization function. The goal of the optimization is to find an actual placement distribution that can best meet the user's placement habits for dumbbells of different weights during the actual use period, while maintaining a certain degree of consistency with the preset placement distribution to avoid excessive deviation. When the number of iterations reaches the convergence optimization number, the system terminal uses the actual placement distribution with the maximum current placement fitness as the optimal actual placement distribution.
[0031] Furthermore, the present application provides optimizing the actual placement distribution of dumbbells of various weights at the multiple placement positions in the use time period in the placement distribution space according to the placement distribution optimization function, including:
[0032] A first actual placement distribution is randomly generated in the placement distribution space; according to the placement distribution optimization function, according to the actual dumbbell masses of multiple placement positions in the first actual placement distribution, combined with the preset placement distribution, and based on the placement distribution optimization function, a first placement fitness is calculated.
[0033] Optionally, in the placement distribution space, the system terminal randomly generates a dumbbell placement distribution as the first actual placement distribution. Subsequently, the effect of the first actual placement distribution is evaluated according to the constructed placement distribution optimization function. The evaluation process is mainly based on the actual dumbbell mass of each placement position in the first actual placement distribution, and at the same time refers to the preset placement distribution, calculates the degree of conformity of the distribution with the ideal state, and generates the first placement fitness. This fitness value will be used as a reference for the subsequent optimization process to help the system terminal gradually find the optimal dumbbell placement distribution.
[0034] Based on the first actual placement distribution, a second actual placement distribution is obtained by random adjustment in the placement distribution space, and a second placement fitness is obtained by processing and calculation; iterative optimization is continued until the convergence optimization times are reached, and the actual placement distribution with the largest placement fitness is output to obtain the optimal actual placement distribution.
[0035] Optionally, in the process of optimizing the placement distribution of dumbbells, in order to find a more optimal placement distribution, the system terminal randomly adjusts the first actual placement distribution in the placement distribution space to obtain a new placement distribution, i.e., the second actual placement distribution. This adjustment process includes changing the dumbbell weight of one or some placement positions, or redistributing multiple positions as a whole. Afterwards, the placement distribution optimization function is used again to calculate the placement fitness of the second actual placement distribution, i.e., the second placement fitness. By comparing the first placement fitness and the second placement fitness, it is determined whether this adjustment makes the distribution better. If so, a relatively high placement fitness is stored in the temporary placement fitness, and the corresponding actual placement distribution is stored in the temporary actual placement distribution. Then, the above process is repeated, and the actual placement distribution is continuously randomly adjusted in the placement distribution space, the corresponding placement fitness is calculated, and the temporary placement fitness and the temporary actual placement distribution are updated until the preset number of convergence optimizations is met. When the iteration reaches the convergence optimization number, the system terminal outputs the actual placement distribution with the largest placement fitness, which is the optimal actual placement distribution after optimization. This optimal distribution not only conforms to the user's actual usage habits to the greatest extent, but also maintains a reasonable deviation from the preset placement distribution, thereby achieving the optimization of the dumbbell placement distribution.
[0036] According to the multiple actual use data sets, impact load parameters and placement load parameters of the multiple placement positions within the use time period are analyzed to obtain multiple use feature information.
[0037] In one embodiment, the system terminal collects statistics and analyzes the collected multiple actual usage data sets to obtain impact load parameters and placement load parameters, and obtains multiple usage feature information based on these two parameters. These feature information can reveal which locations are more popular.
[0038] Further, the present application provides analyzing the impact load parameters and placement load parameters of the multiple placement positions during the use period according to the multiple actual use data sets, including:
[0039] According to the multiple actual use data sets, the total placement times and total placement time of dumbbells of various weights at each placement position are counted; the total placement times and total placement time are used as impact load parameters and placement load parameters to obtain multiple use characteristic information.
[0040] Preferably, the system terminal counts the total number of placements and total placement time of dumbbells of different weights at each placement position based on multiple actual use data sets. These statistical data directly reflect the frequency and duration of use of dumbbells at specific positions, thereby providing the system terminal with detailed information on the placement and use of dumbbells. Subsequently, the total number of placements is regarded as an impact load parameter, which represents the frequency of placing dumbbells at this position. Frequent placement and movement of dumbbells may cause certain impacts or wear to the position. At the same time, the total placement time is regarded as a placement load parameter, which reflects the length of time that the dumbbells are continuously placed at this position. Continuous placement for a long time may cause certain pressure or load to the placement position. By counting and analyzing these total number of placements and total placement time, the system terminal obtains multiple usage feature information. These usage feature information can not only help the system terminal understand the use of dumbbells at different positions, but also provide important basis for optimizing the design and layout of dumbbell racks.
[0041] The welding parameters of the multiple placement positions are actually optimized by combining the multiple usage feature information and the actual dumbbell weights of the multiple placement positions in the optimal actual placement distribution to obtain multiple optimal actual welding parameters.
[0042] In one embodiment, in order to optimize the structural strength and durability of dumbbell rack, to meet the actual use requirements of dumbbells of different positions and different weights, the system terminal optimizes the welding parameters of dumbbell rack. This optimization process is based on multiple use characteristic information, which includes the total placement times and total placement time of multiple weight dumbbells on each placement position, which represent the impact load parameter and the placement load parameter respectively. In addition, the actual dumbbell weights of multiple placement positions in the optimal actual placement distribution are also analyzed. These actual dumbbell weight data provide the actual load conditions that the dumbbell rack bears at a specific position for the system terminal, which is an important basis for optimizing welding parameters. Based on these data and information, the system terminal actually optimizes the welding parameters of multiple placement positions of the dumbbell rack through the actual welding optimization function, and obtains multiple optimal actual welding parameters. The purpose of optimizing welding parameters is to improve the structural strength and durability of the dumbbell rack, while ensuring that it has enough stability and safety when bearing the actual use load.
[0043] Further, the present application provides a method for actually optimizing the welding parameters of the multiple placement positions by combining the multiple usage feature information and the actual dumbbell weights of the multiple placement positions within the optimal actual placement distribution, including:
[0044] Obtain a welding parameter space for welding the multiple placement positions; obtain a maximum placement load generated when the maximum dumbbell weight is placed, and set the placement position strength after welding greater than the maximum placement load as a welding optimization constraint.
[0045] Optionally, when optimizing the welding parameters of multiple placement positions of the dumbbell rack, the system terminal first determines a welding parameter space, which contains all possible welding parameter combinations and is obtained based on historical welding data. These parameters include welding current, welding speed, welding materials, etc., which jointly determine the strength and other physical properties of the dumbbell rack placement position after welding. Subsequently, in order to ensure the safety of the dumbbell rack in actual use, the system terminal obtains the maximum load generated by the dumbbell when it is placed. This maximum load is generated by the dumbbell of the maximum weight when it is placed. In order to ensure that the dumbbell rack can withstand such a load without damage or deformation, the system terminal sets a welding optimization constraint, which requires that the placement position strength after welding must be greater than this maximum placement load. The establishment of this optimization constraint is based on safety considerations. It ensures that the dumbbells placed on the dumbbell rack are safe and will not cause harm to users or equipment. On the basis of meeting this constraint, the system terminal can further optimize the welding parameters to improve the overall performance and service life of the dumbbell rack.
[0046] In order to improve welding quality and reduce welding costs, an actual welding optimization function is constructed as follows: ; Among them, DWF is welding fitness, is the quality weight, is the cost weight, is the welding quality obtained by combining the characteristic information and the actual dumbbell weight analysis for the welding parameters at the i-th position, is the welding cost of the welding parameters at the i-th position.
[0047] Optional. In order to improve the welding quality and reduce the welding cost of the dumbbell rack, the system terminal constructs an actual welding optimization function. This function comprehensively considers the welding quality and cost corresponding to the welding parameters of each position, welding fitness, quality weight, cost weight, and welding parameters at each position. The actual welding optimization function is as follows: ; Among them, DWF is the welding fitness, which is obtained by weighted combination of two different metrics. is the quality weight, which represents the importance of welding quality in the optimization process. is the cost weight, which reflects the relative importance of welding cost in the optimization process. The welding quality obtained by combining the characteristic information and the actual dumbbell weight analysis for the welding parameters at the i-th position. The higher the Qi value, the better the welding quality at that position. is the welding cost of the welding parameters at the i-th position. The lower the value of Yi, the lower the welding cost of the position. By constructing the actual welding optimization function, the welding quality can be guaranteed while minimizing the welding cost.
[0048] A plurality of first actual welding parameters for a plurality of placement positions that satisfy the welding optimization constraints are randomly generated in the welding parameter space; a plurality of actual dumbbell weights for a plurality of placement positions within the optimal actual placement distribution are obtained, and the welding quality of the plurality of placement positions is predicted by combining the plurality of first actual welding parameters and a plurality of usage feature information to obtain a plurality of first actual welding quality information, and a plurality of first welding cost information for the plurality of first actual welding parameters are obtained, and a first welding fitness is obtained by calculation; the welding parameters for the plurality of placement positions are continuously optimized until convergence, and a plurality of optimal actual welding parameters with the largest welding fitness are output.
[0049] Optionally, in the process of optimizing the welding parameters of the dumbbell rack, the system terminal randomly generates multiple initial welding parameter combinations that meet the welding optimization constraints in a welding parameter space, that is, multiple first actual welding parameters, and these parameter combinations correspond to welding settings at different placement positions. Subsequently, the actual dumbbell weights of each placement position in the optimal actual placement distribution are obtained, and these weight data will be used as the basis for evaluating the welding quality. After that, the welding quality of each placement position is predicted by the welding quality predictor in combination with the actual dumbbell weight, the first actual welding parameter, and multiple usage feature information to obtain multiple first actual welding quality information. At the same time, the welding cost of each first actual welding parameter is calculated, that is, multiple first welding cost information. This information will be used for subsequent optimization calculations. After obtaining the welding quality information and welding cost information, the system terminal can calculate the first welding fitness according to the set actual welding optimization function, combined with the quality weight and the cost weight. This fitness takes into account the welding quality and cost, and is used to evaluate the overall performance of the current parameter combination. Then, the welding parameters of multiple placement positions are optimized. This process includes searching for new parameter combinations in the welding parameter space, re-predicting the welding quality and calculating the welding cost, and calculating the new welding fitness. This process is iterated continuously until the convergence optimization times are met, and multiple optimal actual welding parameters with the largest welding fitness are output. These parameter combinations are the best welding parameter settings obtained through optimization calculation based on the actual use and cost requirements of the dumbbells under the premise of meeting the welding optimization constraints.
[0050] Further, the present application provides a method of obtaining a plurality of actual dumbbell weights at a plurality of placement positions within the optimal actual placement distribution, combining the plurality of first actual welding parameters and a plurality of usage feature information to predict the welding quality of the plurality of placement positions, and obtaining a plurality of first actual welding quality information, including:
[0051] According to the welding history data of the dumbbell rack, a sample welding parameter set, a sample dumbbell mass set and a sample usage characteristic information set are collected, and the sample welding quality information set is identified according to the failure time of the placement position under different sample welding parameters, sample dumbbell masses and sample usage characteristic information, wherein the failure time is positively correlated with the sample welding quality information; the sample welding parameter set, the sample dumbbell mass set and the sample usage characteristic information set are used as input, and the sample welding quality information set is used as output to train a welding quality predictor, and welding quality prediction is performed on the multiple actual dumbbell weights, multiple first actual welding parameters and multiple usage characteristic information to obtain multiple first actual welding quality information.
[0052] Preferably, in the process of optimizing the welding of the dumbbell rack, the system terminal traces back the welding history data of the dumbbell rack. These data include the welding parameters of different batches of dumbbell racks, the actual quality of the dumbbells, and the use characteristic information experienced by the dumbbells during use. In order to evaluate the welding quality, the system terminal records the failure time of each dumbbell under specific welding parameters and use characteristic information. The longer the failure time, the higher the welding quality, because the dumbbell can maintain stability for a longer time during use. Based on these failure time data, the system terminal identifies and sorts out a sample welding quality information set, which directly reflects the welding quality level under different welding parameters and use characteristics. Subsequently, the system terminal uses a deep learning model as a welding quality predictor and sets the number of neurons in each layer according to actual needs. Then the sample welding parameter set, the sample dumbbell quality set, the sample use characteristic information set, and the sample welding quality information set are divided into a training set and a test set. Afterwards, the parameters of the welding quality predictor are initialized, including weights and bias terms. The training set is then used to iteratively train the welding quality predictor. In each iteration, the output and loss value of the welding quality predictor are calculated by forward propagation, the gradient is calculated by the back propagation algorithm, and the parameters of the welding quality predictor are updated using Adam. When the training reaches the maximum number of iterations, the system terminal uses the test set to evaluate the trained welding quality predictor. The prediction accuracy, error rate, etc. of the welding quality predictor on the test set are calculated to evaluate the generalization ability of the welding quality predictor. If the model performs poorly on the test set, the system terminal readjusts the model structure, hyperparameters, etc., and trains again. Otherwise, the current welding quality predictor is output. Then, the system terminal inputs multiple actual dumbbell weights, multiple first actual welding parameters, and multiple usage feature information into the welding quality predictor, and the predictor processes these data according to the previously learned pattern and outputs the corresponding multiple first actual welding quality information. This information will serve as an important basis for the subsequent optimization of welding parameters, helping to find a welding parameter combination that meets quality requirements and is cost-effective.
[0053] The placement load parameters of the multiple placement locations during the outage time period are obtained to obtain multiple outage feature information.
[0054] In one embodiment, in the process of optimizing the design and welding of the dumbbell rack, the system terminal also analyzes the state of the dumbbells during the idle time period. The idle time period refers to the time period when the dumbbells are not used but placed in a certain location, such as a warehouse, a corner of a gym, etc. In order to more comprehensively understand the state of the dumbbells during these idle time periods, the system terminal obtains the placement load parameters of multiple placement locations during the idle time periods. These load parameters include the stacking method, stacking height, and contact pressure distribution with the ground of the dumbbells. These factors will affect the storage quality of the dumbbells. For example, an inappropriate stacking method may cause the dumbbells to deform or be damaged. By collecting and analyzing these load parameters, the system terminal can extract multiple idle feature information. These feature information reflects the various impacts that the dumbbells may suffer during the idle period, thereby helping to better understand the storage requirements and conditions of the dumbbells.
[0055] In combination with the multiple deactivation feature information and the preset dumbbell weights of the multiple placement positions in the preset placement distribution, the welding parameters of the multiple placement positions are theoretically optimized to obtain multiple optimal theoretical welding parameters. In combination with the multiple optimal actual welding parameters, multiple optimal welding parameters are obtained through fusion processing, and processing control is performed on the welding of multiple placement positions in the dumbbell rack production process.
[0056] In one embodiment, in the production process of dumbbell racks, in order to ensure welding quality and optimize product performance, the system terminal needs to consider not only the welding process itself, but also the characteristics of the dumbbells during deactivation and storage. This involves theoretically optimizing the welding parameters of multiple placement positions in combination with multiple deactivation feature information and preset dumbbell weights within a preset placement distribution. Multiple optimal theoretical welding parameters are obtained. This theoretical optimization process is similar to the aforementioned actual optimization, except that the usage feature information is replaced with deactivation feature information. Subsequently, the system terminal performs weighted processing on the optimal theoretical welding parameters and the optimal actual welding parameters to generate multiple optimal welding parameters. Afterwards, these optimal welding parameters are used for welding process control of multiple placement positions during the production process of dumbbell racks to ensure the stability and reliability of welding quality and product performance.
[0057] Further, the present application provides a fusion process to obtain multiple optimal welding parameters, including
[0058] The multiple optimal theoretical welding parameters and the multiple optimal actual welding parameters are weightedly fused to obtain multiple optimal welding parameters.
[0059] Preferably, the system terminal assigns weights to the multiple optimal theoretical welding parameters and the multiple optimal actual welding parameters based on historical experience and expert advice, and then uses the assigned weights to perform a weighted average on each pair of the multiple optimal theoretical welding parameters and the multiple optimal actual welding parameters to obtain multiple optimal welding parameters.
[0060] In summary, the embodiments of the present application have at least the following technical effects:
[0061] The embodiment of the present application optimizes the manufacturing and welding process of the dumbbell rack by collecting usage data of different placement positions during the use period of the dumbbell rack. It includes recording the number of placements and placement time of the dumbbells, obtaining an actual use data set, and optimizing the actual placement distribution of the dumbbells based on these data. By analyzing the impact load parameters and placement load parameters during the use period, the use characteristic information is obtained, and the welding parameters are further optimized based on this information to improve the welding quality and reduce the cost. In addition, by constructing a placement distribution optimization function and a welding fitness optimization function, the placement position and welding parameters of the dumbbell rack are theoretically and practically optimized, and the optimal welding parameters are finally obtained. These technical effects jointly solve the technical problems of the fragility and insufficient durability of the welding points of the dumbbell rack caused by the actual use mode and load parameter changes during use, and realize the effect of optimizing the welding parameters based on differentiation, improving the durability of the dumbbell rack welding points and the stability of the overall structure, and extending the service life of the dumbbell rack.
[0062] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0063] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.
[0064] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.
Claims
1. A welding control method for fitness equipment, characterized in that: The method comprises: During the use period of the dumbbell rack, use data records of multiple placement positions on the dumbbell rack are collected to obtain multiple actual use data sets, wherein the multiple placement positions include preset placement distributions of dumbbells of multiple weights; According to the multiple actual use data sets, optimizing the actual placement distribution of multiple weight dumbbells at multiple placement positions during a use time period to obtain an optimal actual placement distribution, wherein the optimization is performed by improving the conformity of the actual placement distribution with the multiple actual use data sets and reducing the deviation between the actual placement distribution and the preset placement distribution; Analyzing the impact load parameters and placement load parameters of the multiple placement positions during a usage period according to the multiple actual usage data sets to obtain multiple usage feature information; In combination with the plurality of usage characteristic information and the actual dumbbell weights of the plurality of placement positions in the optimal actual placement distribution, the welding parameters of the plurality of placement positions are actually optimized to obtain a plurality of optimal actual welding parameters; Acquire placement load parameters of the plurality of placement locations within a deactivation period to obtain a plurality of deactivation feature information; Combine the multiple deactivation feature information and the preset dumbbell weights of the multiple placement positions in the preset placement distribution, theoretically optimize the welding parameters of the multiple placement positions to obtain multiple optimal theoretical welding parameters, combine the multiple optimal actual welding parameters, and fuse them to obtain multiple optimal welding parameters, and perform processing control on the welding of multiple placement positions in the dumbbell rack production process; The usage data records of multiple placement positions on the dumbbell rack are collected to obtain multiple actual usage data sets, including: During the use period of the dumbbell rack, the number of times dumbbells of different weights are placed at each placement position and the placement time of dumbbells of different weights are recorded and collected to obtain a plurality of placement record data; Based on multiple placement record data sets in multiple usage time periods, the number of placements of dumbbells of different weights at each placement position and the placement time of dumbbells of different weights are counted to obtain multiple actual usage data sets; The actual placement distribution of dumbbells of various weights at multiple placement positions during a use period is optimized to obtain the optimal actual placement distribution, including: According to the multiple actually used data sets, respectively counting the dumbbell weights with the most placement times at the multiple placement positions to obtain multiple enriched dumbbell weights; Randomly arranging the dumbbell weights placed at the multiple placement positions to construct a placement distribution space; In order to improve the consistency between the actual placement distribution and the multiple actually used data sets and reduce the deviation between the actual placement distribution and the preset placement distribution, a placement distribution optimization function is constructed as follows: ； Among them, DDF is the placement fitness, and is the weight, M is the number of placement locations, is the dumbbell weight of the ith placement position in the optimized actual placement distribution, is the weight of the enriched dumbbell at the ith placement, is the dumbbell weight of the ith placement position in the preset placement distribution; According to the placement distribution optimization function, in the placement distribution space, the actual placement distribution of dumbbells of various weights at the multiple placement positions during the use time period is optimized to obtain an optimal actual placement distribution; The actual placement distribution of dumbbells of various weights at the multiple placement positions within the use time period is optimized in the placement distribution space, including: Randomly generating a first actual placement distribution in the placement distribution space; According to the placement distribution optimization function, according to the actual dumbbell masses of multiple placement positions in the first actual placement distribution, combined with the preset placement distribution, and based on the placement distribution optimization function, a first placement fitness is calculated; Based on the first actual placement distribution, a random adjustment is performed in the placement distribution space to obtain a second actual placement distribution, and a second placement fitness is obtained by processing and calculating; Continue iterative optimization until the convergence optimization times are reached, output the actual placement distribution with the maximum placement fitness, and obtain the optimal actual placement distribution; Wherein, analyzing the impact load parameters and placement load parameters of the plurality of placement positions within the use time period according to the plurality of actual use data sets includes: According to the plurality of actual use data sets, counting the total number of placements and the total placement time of dumbbells of various weights at each placement position; The total number of placements and the total placement time are used as impact load parameters and placement load parameters to obtain a plurality of usage characteristic information; The step of optimizing the welding parameters of the plurality of placement positions in combination with the plurality of usage feature information and the actual dumbbell weights of the plurality of placement positions in the optimal actual placement distribution includes: Acquire a welding parameter space for performing welding processing on the plurality of placement positions; Obtaining a maximum placement load generated when placing the maximum dumbbell weight, and setting a strength of the placement position after welding that is greater than the maximum placement load as a welding optimization constraint; In order to improve welding quality and reduce welding costs, an actual welding optimization function is constructed as follows: ； Among them, DWF is welding fitness, is the quality weight, is the cost weight, is the welding quality obtained by combining the characteristic information and the actual dumbbell weight analysis for the welding parameters at the i-th position, is the welding cost of the welding parameters at the i-th position; randomly generating a plurality of first actual welding parameters at a plurality of placement positions satisfying the welding optimization constraint in the welding parameter space; Acquire multiple actual dumbbell weights of multiple placement positions within the optimal actual placement distribution, perform welding quality predictions for the multiple placement positions in combination with the multiple first actual welding parameters and multiple usage feature information to obtain multiple first actual welding quality information, and acquire multiple first welding cost information of the multiple first actual welding parameters to calculate and obtain a first welding fitness; Continue to optimize the welding parameters of multiple placement positions until convergence, and output multiple optimal actual welding parameters with the greatest welding fitness; Wherein, obtaining a plurality of actual dumbbell weights at a plurality of placement positions within the optimal actual placement distribution, combining the plurality of first actual welding parameters and a plurality of usage feature information to predict the welding quality of the plurality of placement positions, and obtaining a plurality of first actual welding quality information, comprises: According to the welding history data of the dumbbell rack, a sample welding parameter set, a sample dumbbell mass set and a sample usage characteristic information set are collected, and according to the failure time of the placement position under different sample welding parameters, sample dumbbell mass and sample usage characteristic information, a sample welding quality information set is obtained by marking, wherein the failure time is positively correlated with the sample welding quality information; Using the sample welding parameter set, the sample dumbbell mass set and the sample usage characteristic information set as input, using the sample welding quality information set as output, training a welding quality predictor, performing welding quality prediction on the multiple actual dumbbell weights, the multiple first actual welding parameters and the multiple usage characteristic information, and obtaining multiple first actual welding quality information; Among them, the fusion process obtains multiple optimal welding parameters, including: The multiple optimal theoretical welding parameters and the multiple optimal actual welding parameters are weightedly fused to obtain multiple optimal welding parameters.