Personalized cushion customization method and system based on pressure sensing and 3D printing

Abstract

The invention discloses a personalized cushion customization method and system based on pressure sensing and 3D printing, and relates to the technical field of pressure sensing cushion customization, and the method comprises the steps: collecting a pressure value generated in a riding process, and preprocessing the pressure value to obtain a first parameter; a cushion stress area is divided according to the first parameter, and the riding habit is analyzed; obtaining lattice parameters according to the first parameters; the lattice units are printed through the 3D printing technology, and the three-dimensional model is dynamically adjusted according to the cushion stress area and the riding habit; through real-time pressure collection and data preprocessing, it is ensured that the system optimizes the personalized cushion based on high-quality data; through area division and riding habit analysis, accurate adaptation aiming at different user requirements is realized; and the lattice parameters are quantified to enable the structures of all the areas to be matched with stress characteristics, and the optimized lattice parameters are converted into an entity three-dimensional structure by adopting a 3D printing technology.

Description

Technical Field

[0001] The present invention belongs to the technical field of pressure-sensing seat cushion customization, and specifically relates to a method and system for personalized customized seat cushions based on pressure sensing and 3D printing. Background Art

[0002] With the continuous development of modern cycling sports and personalized seat design technology, people's demands for cycling comfort, ergonomic optimization, and intelligent manufacturing technology are increasing. Traditional cycling seat cushions usually use fixed-form foam fillers or gel materials, and their designs are mostly based on experience and general ergonomic parameters, making it difficult to meet the personalized needs of different users under different cycling conditions. Therefore, in recent years, new technologies such as intelligent sensing technology, data-driven optimization design, and 3D printing manufacturing have gradually been introduced into the field of cycling equipment to improve cycling comfort, reduce discomfort caused by long-term cycling, and optimize the cycling experience.

[0003] Although current high-end cycling seat cushions on the market have gradually introduced technologies such as pressure sensing, computer-aided optimization design, and 3D printing manufacturing, there are still several key technical difficulties and limitations: Most 3D printed cycling seat cushions use static optimization design, that is, after measuring the sitting posture pressure distribution of a user once, a fixed 3D printed seat cushion is generated based on this data. However, during actual cycling, the force-bearing situation is dynamically changing, and the postures and pressure distributions of cyclists under different road conditions and different cycling intensities will change significantly. Single optimization is difficult to meet the long-term comfort requirements. In addition, the material elasticity of existing 3D printed seat cushions is usually set uniformly, lacking the ability to intelligently adjust for different regions.

[0004] In view of the above technical deficiencies, the present invention uses intelligent algorithms to optimize pressure distribution analysis to ensure the accuracy of force-bearing area division. It intelligently analyzes cycling habits to achieve personalized optimization, combines actual measurement value matching algorithms, and intelligently adjusts the type, thickness, and density of the lattice to meet the personalized needs of different cyclists. Through 3D printing adaptive modeling, cycling optimization is achieved. Through real-time data-driven three-dimensional model optimization, the 3D printed seat cushion can adjust the support rigidity and buffer area according to the user's habits in terms of structure, providing a truly personalized dynamic optimization seat cushion. Summary of the Invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0006] Therefore, the technical problems solved by the present invention are: the existing seat cushion design based on a fixed structure cannot adapt to different cycling postures, has a low degree of personalization, cannot optimize for different force-bearing areas, and how to dynamically adjust the three-dimensional model based on pressure data.

[0007] To solve the above technical problems, the present invention provides the following technical solution, a personalized customized cushion method based on pressure sensing and 3D printing, including: collecting the pressure values generated during cycling, and preprocessing the pressure values; dividing the force-bearing area of the cushion according to the preprocessed pressure values, and analyzing cycling habits; obtaining lattice parameters according to the preprocessed pressure values; printing lattice units through 3D printing technology, and dynamically adjusting the three-dimensional model according to the force-bearing area of the cushion and cycling habits.

[0008] As a preferred solution of the personalized customized cushion method based on pressure sensing and 3D printing according to the present invention, wherein: collecting the pressure values generated during cycling includes collecting the pressure values generated on the cushion during cycling at multiple positions on the cushion by arranging flexible sensors within a preset time.

[0009] As a preferred solution of the personalized customized cushion method based on pressure sensing and 3D printing according to the present invention, wherein: obtaining the first parameter includes energizing the pressure sensor through a control board, recording the change in the output pressure when different pressures are applied to the cushion, matching the first parameter according to the output voltage generated when the cyclist applies pressure to the cushion during cycling, transmitting the first parameter to the first object through a Bluetooth module, transmitting the first parameter to a server through the first object, calculating the mean value of the first parameter at the measurement points of the cushion during the cycling time, and judging the force-bearing area of the cushion and analyzing cycling habits according to the mean value.

[0010] As a preferred solution of the personalized customized cushion method based on pressure sensing and 3D printing according to the present invention, wherein: dividing the force-bearing area of the cushion according to the preprocessed pressure values includes dividing the cushion into a core force-bearing area, an auxiliary support area, and a buffer area according to the mean value of the first parameter. The core force-bearing area is the area directly contacted by the human ischial tuberosity, the auxiliary support area is the area close to the root of the human thigh, and the buffer area is the front end and both side edge areas of the cushion. When the first parameter is greater than or equal to the first threshold, it is judged as the core force-bearing area. When the first parameter is greater than the second threshold and less than the first threshold, it is judged as the auxiliary support area. When the first parameter is less than or equal to the second threshold, it is judged as the buffer area.

[0011] As a preferred solution of the personalized customized cushion method based on pressure sensing and 3D printing according to the present invention, wherein: analyzing cycling habits includes judging the left side of the central axis of the core force-bearing area as the left area, and the right side of the central axis of the core force-bearing area as the right area. If the first parameter of the left area is greater than the first parameter of the right area, it is judged that the cycling habit is left-footed. If the first parameter of the left area is less than the first parameter of the right area, it is judged that the cycling habit is right-footed.

[0012] Divide the core stress area into the front half area and the rear half area according to the front and rear axes. If the first parameter of the front half area is greater than that of the rear half area, it is determined that the riding habit is forward-leaning riding. If the first parameter of the front half area is less than that of the rear half area, it is determined that the riding habit is backward-sitting riding.

[0013] As a preferred embodiment of the personalized customized seat cushion method based on pressure sensing and 3D printing according to the present invention, wherein: obtaining the lattice parameters according to the first parameter includes applying pressure to different types of lattices, monitoring the deformation depth of the lattices within the same square area, recording the first parameter applied when the lattice deformation depths are the same, obtaining lattice test data, matching the first parameter lattice test data applied to a single point of the seat cushion, obtaining the lattice type, lattice thickness, and lattice density corresponding to the first parameter, and controlling the seat cushion hardness through the lattice parameters.

[0014] As a preferred embodiment of the personalized customized seat cushion method based on pressure sensing and 3D printing according to the present invention, wherein: printing the lattice units through 3D printing technology includes designing a three-dimensional model according to the seat cushion hardness distribution and printing the three-dimensional model through 3D printing technology.

[0015] As a preferred embodiment of the personalized customized seat cushion method based on pressure sensing and 3D printing according to the present invention, wherein: dynamically adjusting the three-dimensional model includes dynamically adjusting the matched lattice parameters according to the seat cushion stress area and riding habit.

[0016] Calculate the pressure difference between the left area and the right area and express it as the first variable. Calculate the pressure difference between the front half area and the rear half area and express it as the second variable. Multiply the first variable and the second variable by a preset first adjustment coefficient and a second adjustment coefficient respectively, and sum the calculation results to obtain the riding habit correction factor.

[0017] The first adjustment coefficient is used to adjust the influence of the rider's left and right force exertion, and the second adjustment coefficient is used to adjust the influence of the rider's front and rear sitting postures.

[0018] Calculate the seat cushion stress area and the riding habit correction factor, and add the average value of the first parameter of the seat cushion stress area and the riding habit correction factor to obtain the lattice adjustment parameter.

[0019] Based on the minimum lattice thickness, multiply the difference between the maximum lattice thickness and the minimum lattice thickness by the lattice adjustment parameter, and accumulate the calculation result to the minimum lattice thickness to obtain the lattice thickness of the seat cushion stress area.

[0020] Based on the minimum lattice density, multiply the difference between the maximum lattice density and the minimum lattice density by the lattice adjustment parameter, and accumulate the calculation result to the minimum lattice density to obtain the lattice density of the seat cushion stress area.

[0021] Generate a 3D model based on the calculation results, and dynamically adjust the 3D model based on the lattice thickness and lattice density of the stress-bearing area of the seat cushion.

[0022] As a preferred solution of the personalized customized seat cushion system based on pressure sensing and 3D printing according to the present invention, it includes a preprocessing module, a region division module, a lattice parameter calculation module, and a 3D model dynamic adjustment module; the preprocessing module includes a data acquisition module and a data preprocessing module. The data acquisition module is used to collect riding data through a flexible sensor, and the data preprocessing module is used to convert the pressure value into a first parameter and calculate the average value of the first parameter; the region division module includes a seat cushion stress-bearing area division module and a riding habit analysis module. The seat cushion stress-bearing area division module is used to divide the seat cushion into a core stress-bearing area, an auxiliary support area, and a buffer area according to the average value of the first parameter. The riding habit analysis module is used to divide the core stress-bearing area into four areas, analyze the first parameter of each area, and obtain the riding habit; the lattice parameter calculation module is used to establish a lattice parameter test table and match the lattice parameters in the test table according to the first parameter; the 3D model dynamic adjustment module includes a 3D printing module and a dynamic adjustment module. The 3D printing module is used to design a 3D model according to the seat cushion hardness distribution and print the 3D model through 3D printing technology. The dynamic adjustment module is used to dynamically adjust the 3D model according to the region division and riding habit.

[0023] A computer device includes a memory and a processor. The memory stores a computer program. The processor is characterized in that when the processor executes the computer program, it implements the steps of the method described in any one of the methods of the personalized customized seat cushion method based on pressure sensing and 3D printing.

[0024] A computer-readable storage medium stores a computer program. The computer program is characterized in that when the computer program is executed by a processor, it implements the steps of the method described in any one of the methods of the personalized customized seat cushion method based on pressure sensing and 3D printing.

[0025] The beneficial effects of the present invention: By collecting real-time pressure and preprocessing the data, the accuracy and reliability of the subsequent stress-bearing area division are ensured, enabling the system to carry out personalized seat cushion optimization based on high-quality data. By dividing the area and analyzing the riding habit, a data-driven seat cushion optimization scheme is formed, making the seat cushion not only adaptable to ergonomic design but also meeting the personalized needs of different users. By quantifying the lattice parameters, different areas can accurately adapt to different stress requirements. Through dynamic lattice calculation, problems such as uneven rigidity, concentrated stress, and low comfort in traditional fixed-structure seat cushions are avoided, improving the overall performance of the seat cushion. Using 3D printing technology, the calculated personalized lattice parameters are converted into a physical 3D structure to ensure that the lattice units in different areas can adapt to the pressure distribution and habit characteristics of the rider. Brief Description of the Drawings

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0027] Figure 1 It is a schematic flowchart of a personalized customized cushion method based on pressure sensing and 3D printing provided for the first embodiment of the present invention.

[0028] Figure 2 It is a pressure sensing data graph of a personalized customized cushion method based on pressure sensing and 3D printing provided for the first embodiment of the present invention.

[0029] Figure 3 It is a three-dimensional modeling graph of a cushion of a personalized customized cushion method based on pressure sensing and 3D printing provided for the first embodiment of the present invention. Detailed Embodiments

[0030] To make the above objects, features, and advantages of the present invention more obvious and understandable, the detailed embodiments of the present invention will be described in detail below with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] Example 1, referring to Figures 1 - 3 , which is the first embodiment of the present invention. This embodiment provides a personalized customized cushion method based on pressure sensing and 3D printing, including:

[0032] S1: Collect the pressure values generated during the riding process, preprocess the pressure values, and obtain the first parameter.

[0033] It should be noted that collecting the pressure values generated during the riding process includes collecting the pressure values generated on the cushion during the riding process by arranging flexible sensors at multiple positions on the cushion within a preset time.

[0034] It should also be noted that flexible pressure sensors are arranged at multiple key stress points on the cushion (such as the ischial region, thigh support region, and buffer region) to ensure accurate measurement of the stress conditions in different regions during the riding process. An array sensor layout is adopted to enable the data collection to have spatial resolution ability, so as to distinguish the pressure distribution in different regions and improve the accuracy of stress analysis.

[0035] In the present invention, 5 to 10 minutes is preferably used as the preset time. The time window for pressure acquisition needs to be long enough to ensure coverage of different dynamic states during cycling (such as acceleration, constant speed, deceleration, standing cycling, etc.). 5 to 10 minutes can fully cover common short-distance cycling situations. If the preset time is less than 5 minutes, data instability may occur due to posture adjustment at the beginning of cycling, making it difficult to reflect the true cycling pressure distribution. If the preset time exceeds 10 minutes, although more data can be collected, the data change trend tends to be stable, and the additional time will not significantly improve the data quality, but will increase the storage and processing costs.

[0036] In this embodiment, the first parameter is preferably in kilograms. Obtaining the first parameter includes energizing the pressure sensor through the control board, recording the change in the output pressure when different pressures are applied to the seat cushion, matching the kilogram value according to the output voltage generated by the rider applying pressure to the seat cushion during cycling, transmitting the kilogram value to the first object through the Bluetooth module, transmitting the kilogram value to the server through the first object, calculating the average value of the kilogram values at the measurement points of the seat cushion during the cycling time, and judging the force-bearing area of the seat cushion and analyzing cycling habits based on the average value, such as Figure 2 shown.

[0037] In an alternative embodiment, obtaining the first parameter further includes that each pressure acquisition sensing chip needs to undergo precise numerical calculation before production. There are 6 acquisition points, and the calculated data needs to be output in tabular form, and the regional distribution method of the 6 calculation points on the sensing chip needs to be specified. Each pressure sensing chip needs to establish a one-to-one correspondence with its corresponding PCB board to ensure accurate binding at the hardware level. The acquisition area is divided into 6 independent regions, and each region is pressurized and acquired separately during testing. The calculation points in each region are marked with green circles and a unified naming rule is adopted. The mini-program background needs to add the functions of Bluetooth serial number and calculation form upload when a user device registers. The serial number is bound to the document one by one. When creating a merchant user, it is necessary to synchronously bind its exclusive Bluetooth device number (such as S1001) and its corresponding pressure calculation form. When the measured resistance value does not appear in the calculation form, the system automatically matches the closest resistance value and returns its corresponding kilogram value. Specifically, for the mini-program side: when developing, the functions of Bluetooth serial number and calculation document form need to be added when creating a user in the background. At the same time, the Bluetooth serial number and the document are bound one by one, and the Bluetooth serial number is also the same when displayed on the user front-end. For example: first, we need to give a specified device to user A, and the Bluetooth number of this device is named S1001. When creating user A in the background, it is necessary to bind the Bluetooth device S1001 given to user A, and at the same time bind the pressure sensing chip calculation form (S1001-1, S1001-2, 81001-3, S1001-4, S1001-5, S1001-6) corresponding to the Bluetooth device S1001. After binding, when the merchant is testing, it is necessary to bind the Bluetooth device. After connecting the Bluetooth device, the data on the front-end is transmitted to the background. The background retrieves the Bluetooth device number bound by the merchant and the calculation form bound to the Bluetooth device number. After retrieval, the kilogram value obtained by querying the calculation form for the resistance value transmitted by the pressure sensor to the mini-program will be displayed on the user front-end. When querying the resistance value in the form, fuzzy query is performed. For example: if the resistance value 2235.23 is not in the form and the closest value is 2236.32, and the corresponding kilogram value is 500g, then 0.5 will be displayed on the user front-end.

[0038] It should be noted that in this embodiment, the first object is preferably a mini-program. When the pressure sensor on the seat cushion is subjected to pressure, the resistance value of the pressure sensor will change. The control board is used to power on the sensor, and the voltage signal can be obtained by scanning the sensor matrix through the control of the conversion switch unit. The voltage signal is subjected to analog-to-digital conversion to obtain the voltage value after passing through the pressure sensor. In the present invention, by applying a kilogram value of 50g - 100kg to the seat cushion, the corresponding voltage output value is obtained, and the test data is stored in the database. During the cycling process, according to the output voltage value generated by the cyclist applying pressure to the seat cushion, the corresponding kilogram value is searched in the database. The MCU main control formats and packs the force data and sends it to the Bluetooth module. After the mobile phone is connected to the Bluetooth module, the force data from the sensor can be obtained in real time through the Bluetooth module and displayed on the interface of the mini-program. Calculate the average pressure of the measurement point, and accurately divide the seat cushion stress area according to the average pressure and change trend of different regions, identify the cyclist's power generation method, such as the left and right side power generation tendency, forward or backward sitting cycling habit, and provide data support for subsequent seat cushion structure optimization, lattice parameter calculation and 3D printing modeling, so as to realize a personalized and adaptive cycling seat cushion optimization scheme.

[0039] S2: Divide the seat cushion stress area according to the first parameter and analyze the cycling habit.

[0040] It should be noted that dividing the seat cushion stress area according to the first parameter includes dividing the seat cushion into a core stress area, an auxiliary support area, and a buffer area according to the average value of the first parameter. The core stress area is the area directly contacted by the human ischial tuberosity, the auxiliary support area is the area close to the root of the human thigh, and the buffer area is the front end and both side edge areas of the seat cushion. When the first parameter is greater than or equal to the first threshold, it is judged as the core stress area. When the first parameter is greater than the second threshold and less than the first threshold, it is judged as the auxiliary support area. When the first parameter is less than or equal to the second threshold, it is judged as the buffer area.

[0041] It should also be noted that the seat cushion is divided into a core stress area, an auxiliary support area, and a buffer area according to the average kilogram value. The core stress area corresponds to the area directly contacted by the human ischium, mainly bearing the weight pressure of the cyclist, usually the area with the highest support strength. The auxiliary support area is the area close to the root of the thigh, providing a certain amount of support, but requiring appropriate flexibility to maintain cycling comfort. The buffer area is located at the front end and both side edge areas of the seat cushion, and its main function is to shock absorb and buffer, reducing the impact force generated by road condition changes during cycling.

[0042] In this embodiment, the weight of the user is collected according to the mini-program, and the first threshold and the second threshold are dynamically set according to the weight, which is expressed as:

[0043] T1 = r1·W

[0044] T2 = r2·W

[0045] Among them, T1 is the first-level threshold, T2 is the second-level threshold, r1 is the first-level threshold adjustment factor, and r2 is the second-level threshold adjustment factor.

[0046] The adjustment factors r1 and r2 are respectively used to control the proportions of the first-level threshold and the second-level threshold in the overall body weight to adapt to different body types, riding habits, and comfort requirements. The setting of the adjustment factors includes, but is not limited to, factors such as the mean and standard deviation of the pressure data collected by the sensor, the difference in the user's left and right force exertion, and the trend of the front-back offset of the sitting posture. Through constructing a mapping function or a rule model, personalized adjustment is realized to ensure that the threshold setting has precise adaptability and dynamic response ability.

[0047] Furthermore, analyze the riding habit, including judging the left side of the central axis of the core stress area as the left area and the right side of the central axis of the core stress area as the right area. If the first parameter of the left area is greater than the first parameter of the right area, then judge the riding habit as left-foot force exertion. If the first parameter of the left area is less than the first parameter of the right area, then judge the riding habit as right-foot force exertion.

[0048] Divide the core stress area into the front half area and the back half area according to the front-back axis. If the first parameter of the front half area is greater than the first parameter of the back half area, then judge the riding habit as forward-leaning riding. If the first parameter of the front half area is less than the first parameter of the back half area, then judge the riding habit as sitting-back riding.

[0049] It should also be noted that the main stress of the rider is concentrated in the core stress area, which directly reflects the contact pressure distribution between the buttocks and the seat cushion. Therefore, in this embodiment, the riding habit is judged by dividing the core area. Riders do not always exert force evenly with both legs. For example, sprinters usually have one leg as the main force-exerting leg and the other as the auxiliary leg. If only the overall pressure mean is considered without analyzing the left-right distribution, misjudgment may occur. For example, some riders have stronger left-side force exertion, but due to the relatively high overall pressure value, if not subdivided into areas, it may be wrongly judged as even force exertion. Through the left-right division of the core area, it is possible to accurately identify which side has greater stress, thereby judging whether the rider is a left-side force-exerting type or a right-side force-exerting type. This information is of great significance for optimizing the support characteristics of the left and right sides of the seat cushion.

[0050] The pressure of forward-leaning riders is mainly concentrated in the front part of the core area because forward-leaning riders will actively shift the center of gravity forward to improve pedaling efficiency. The pressure of sitting-back riders is mainly concentrated in the back part of the core area because sitting-back riders will shift the center of gravity backward to obtain better comfort. By calculating the mean of the front and back pressures in the core stress area, it is possible to accurately judge whether the rider is more inclined to sprints (forward-leaning) or casual riding (sitting-back), thereby optimizing the front and back support characteristics of the seat cushion.

[0051] S3: Obtain the lattice parameters based on the first parameter.

[0052] It should be noted that obtaining the lattice parameters based on the first parameter includes applying pressure to different types of lattices, monitoring the deformation depth of the lattices within the same square area, recording the first parameter applied when the lattice deformation depths are the same to obtain lattice test data, matching the first parameter lattice test data applied to a single point of the seat cushion to obtain the lattice type, lattice thickness, and lattice density corresponding to the first parameter, and controlling the hardness of the seat cushion through the lattice parameters.

[0053] It should also be noted that by applying pressure to different types of lattices, measuring the deformation amount (deformation depth) of the lattices, and recording their response characteristics under the same pressure conditions, this database provides a benchmark for subsequent pressure value matching and optimizing lattice parameters.

[0054] During cycling, obtain the pressure values applied by the rider in different areas through pressure sensors. After converting the pressure values into kilogram values, match them with the lattice test database to find the matching lattice type, lattice thickness, and lattice density, ensure the rigid-flexible matching in different areas, calculate the adapted lattice parameters, and ensure that different areas of the seat cushion can provide optimal support and buffering effects.

[0055] S4: Print lattice units through 3D printing technology and dynamically adjust the three-dimensional model according to the force-bearing area of the seat cushion and riding habits.

[0056] It should be noted that printing lattice units through 3D printing technology includes designing a three-dimensional model according to the seat cushion hardness distribution and printing the three-dimensional model through 3D printing technology.

[0057] It should also be noted that according to the matched lattice type, lattice thickness, and lattice density parameters, construct a complete three-dimensional model of the seat cushion, and combine riding habits and force-bearing area division to locally optimize the model. For example, increase rigid support in high-force areas and add flexible padding in buffer areas. Secondly, use high-precision 3D printing technology to print layer by layer to ensure that the seat cushion structure has high precision, high strength, and long service life. In addition, multi-material composite printing can be used during 3D printing, using materials with different elasticity or strength in different areas of the seat cushion to further optimize comfort and support performance, and precisely achieve personalized customization of the seat cushion through 3D printing technology, enabling it to conform to the force-bearing characteristics and riding styles of different riders and improving the comfort and stability during long rides.

[0058] Furthermore, dynamically adjusting the three-dimensional model according to the force-bearing area of the seat cushion and riding habits includes dynamically adjusting the matched lattice parameters according to the force-bearing area of the seat cushion and riding habits.

[0059] Calculate the pressure difference between the left and right regions, which is expressed as the first variable. Calculate the pressure difference between the front and rear regions, which is expressed as the second variable. Multiply the first variable and the second variable by a preset first adjustment coefficient and a second adjustment coefficient respectively, and sum the calculation results to obtain a riding habit correction factor.

[0060] The first adjustment coefficient is used to adjust the influence of the rider's left and right force application, and the second adjustment coefficient is used to adjust the influence of the rider's front and rear sitting postures.

[0061] Calculate the seat cushion stress area and the riding habit correction factor, and add the average kilogram value of the seat cushion stress area and the riding habit correction factor to obtain a lattice adjustment parameter.

[0062] Based on the minimum lattice thickness, multiply the difference between the maximum lattice thickness and the minimum lattice thickness by the lattice adjustment parameter, and accumulate the calculation result to the minimum lattice thickness to obtain the lattice thickness of the seat cushion stress area.

[0063] Based on the minimum lattice density, multiply the difference between the maximum lattice density and the minimum lattice density by the lattice adjustment parameter, and accumulate the calculation result to the minimum lattice density to obtain the lattice density of the seat cushion stress area.

[0064] Generate a three-dimensional model according to the calculation result, as Figure 3 shown, and dynamically adjust the three-dimensional model based on the lattice thickness and lattice density of the seat cushion stress area.

[0065] It should also be noted that during the riding process, there are differences in the force application modes and riding postures of different riders. For example: some riders tend to apply force with their left or right foot, resulting in uneven stress on the seat cushion; some riders tend to ride in a forward or backward sitting position, affecting the pressure distribution in different areas of the seat cushion. In order to quantify the influence of these riding habits on the seat cushion, the present invention calculates the riding habit influence factor, and calculates the personalized habits of the rider through the kilogram difference, so as to adjust the parameters such as the support strength, flexibility, and stiffness of different areas during the subsequent optimization of the three-dimensional model.

[0066] In order to ensure that the seat cushion can accurately adapt to the stress situation of the rider, this embodiment introduces a lattice parameter adjustment factor to comprehensively consider the pressure level of this area and the influence of riding habits. If the kilogram value of a certain area is large and the influence of riding habits on this area is large, the lattice parameter adjustment factor is large, and the rigid support of this area needs to be increased; if the pressure of a certain area is small and the influence of riding habits on this area is small, the lattice parameter adjustment factor is small, and a softer buffer structure can be adopted for this area. By calculating the riding habit influence factor and adjusting the lattice thickness and density, this embodiment can dynamically optimize the three-dimensional structure of the seat cushion, improve comfort, support force, and personalized adaptation ability.

[0067] Embodiment 2 is an embodiment of the present invention, which provides a method for personalized customized cushion based on pressure sensing and 3D printing. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0068] The data acquisition process includes the user sitting on the collected cushion and cycling. When cycling, a certain pressure will be exerted on the cushion, and the resistance value of the pressure sensor will change. The control board is used to energize the sensor, and the voltage signal can be obtained by controlling the conversion switch unit to scan the sensor matrix. The voltage signal is subjected to analog-to-digital conversion to obtain the voltage value after passing through the pressure sensor. In the present invention, by applying a kilogram value of 50 g - 100 kg to the cushion, the corresponding voltage output value is obtained, and the test data is stored in the database. During cycling, according to the output voltage value generated by the rider's pressure on the cushion, the corresponding kilogram value is searched in the database.

[0069] For example, the resistance value of 1 KG pressing on a single point is 200. The control board is used to energize the sensor. Since different pressures applied to the pressure sensor result in different changes in resistance values and different output pressure values, by testing the kilogram values of 50 g - 100 kg, the corresponding output voltage values are obtained, and the corresponding kilogram values are matched according to the output voltage value of the rider during cycling.

[0070] The customization principle includes preparing different lattices and different thicknesses of the lattices in the present invention, performing different pressure tests on these lattices to form a pressure test table, and customizing the lattices in different regions according to the pressure test table. For example: The deformation of 1 KG of pressure pressing on different lattices and different thicknesses of lattices is different. When the collected value is 1 KG, the 1 KG pressure value causes different deformation situations on the surface of the cushion for different types of lattices, different densities of lattices, and different thicknesses of lattices. A pressure test table is generated through testing. When the pressure data generated by the rider on the cushion is obtained, the pressure value is converted into a kilogram value, and according to the kilogram value, the style and thickness of the corresponding lattice in the pressure test table are used to customize the lattice according to this kilogram value, so as to achieve the effect of relieving force, and thus different softness and hardness will be generated for different pressure values.

[0071] The saddle customization process includes a rider sitting on the saddle of a bicycle trainer to collect data, and at the same time, clicking "Start Test" on the mini-program side. The collected data is transmitted via Bluetooth through the pcb board to the data acquisition front end of the "Samassi Flexible Manufacturing Engine" mini-program. The way the front end presents the data can be the presentation of numerical values, the presentation of a color pressure map, or the presentation of a three-dimensional pressure map. After 5 - 10 minutes of riding, click "End Test" on the front end. The test results will be calculated, and the average value of a single point within the test time of the user will be calculated. At the same time, all the data will be transmitted to the background of the mini-program. The role of the average value is to be a basis for customizing the saddle. The softness and hardness of the saddle will be designed specifically according to the values collected by the rider to reduce the pressure on the buttocks brought by the whole body during riding and increase the comfort during riding.

[0072] Based on pressure sensing and 3D printing technology, the present invention realizes personalized customization of cycling saddles. By collecting the pressure data of riders in real time, combining intelligent algorithms to divide the force-bearing areas, and dynamically adjusting the saddle structure according to individual riding habits. Different from the existing saddle designs with fixed structures or static optimizations, the present invention uses a flexible sensor matrix for real-time data collection, and transmits the data to the intelligent system through analog-to-digital conversion and Bluetooth, accurately analyzing the force application patterns of riders, such as the tendency to apply force on the left or right side, the riding habits of leaning forward or sitting back, to ensure personalized adaptation. For different force-bearing areas, the present invention calculates the riding habit correction factor and matches the optimal lattice parameters, and uses high-precision 3D printing technology to achieve the stiffness and flexibility optimization of different areas of the saddle. Among them, the high-force-bearing area increases rigid support, and the buffer area uses flexible filling. At the same time, multi-material composite printing is used to ensure the structural stability and long-term comfort. Through the lattice parameter adjustment factor, the softness and hardness distribution are dynamically optimized, so that the saddle can adapt to different riding environments, reduce local pressure, and improve riding stability and comfort. Compared with traditional cycling saddles, the present invention breaks through the limitations of fixed structures and realizes intelligent and adaptive support optimization, providing more accurate personalized customization solutions for users of different body types and different riding styles, thereby improving the comfort of long-term riding and the overall riding experience.

[0073] Embodiment 3 is an embodiment of the present invention, which provides a personalized customization saddle system based on pressure sensing and 3D printing, including a preprocessing module 100, a region division module 200, a calculation lattice parameter module 300, and a three-dimensional model dynamic adjustment module 400.

[0074] Wherein S5: The preprocessing module 100 includes a data collection module 101 and a data preprocessing module 102. The data collection module 101 is used to collect riding data through a flexible sensor, and the data preprocessing module 102 is used to convert the pressure value into a kilogram value and calculate the first parameter average value.

[0075] It should also be noted that the data acquisition module 101 collects real-time pressure values and transmits them to the data processing module 102. The data processing module 102 converts the original pressure data into pressure values in kilograms, calculates and stores the pressure means of each region, and transmits the means to the region division module 200.

[0076] Among them, S6: The region division module 200 includes a seat cushion force-bearing region division module 201 and a riding habit analysis module 202. The seat cushion force-bearing region division module 201 is used to divide the seat cushion into a core force-bearing area, an auxiliary support area, and a buffer area according to the first parameter mean. The riding habit analysis module 202 is used to divide the core force-bearing area into four regions, analyze the first parameters of each region, and obtain the riding habit.

[0077] It should also be noted that the seat cushion force-bearing region division module 201 divides the seat cushion region into a core force-bearing area, an auxiliary support area, and a buffer area according to the mean value of kilogram-force. The riding habit analysis module 202 statistically analyzes the average pressure values of each region, analyzes the user's riding habit, and transmits the analysis result to the dynamic adjustment module 402.

[0078] Among them, S7: The lattice parameter calculation module 300 is used to establish a lattice parameter test table and match the lattice parameters in the test table according to the first parameter.

[0079] It should also be noted that a lattice parameter matching test table is established, the lattice size, material stiffness and other parameters are adjusted according to different first parameters, and the lattice parameters of different pressure regions are matched and transmitted to the 3D printing module 401.

[0080] Among them, S8: The three-dimensional model dynamic adjustment module 400 includes a 3D printing module 401 and a dynamic adjustment module 402. The 3D printing module 401 is used to design a three-dimensional model according to the seat cushion hardness distribution and print the three-dimensional model through 3D printing technology. The dynamic adjustment module 402 is used to dynamically adjust the three-dimensional model according to the region division and riding habit.

[0081] It should also be noted that the 3D printing module 401 manufactures the seat cushion using 3D printing technology based on the lattice structure data. The dynamic adjustment module 402 adjusts the three-dimensional model according to the riding habit and region division data to make it match the force-bearing characteristics of different users.

[0082] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which can store program codes of various kinds.

[0083] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0084] More specific examples (non-exhaustive list) of computer-readable media include the following: electronic connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROMs). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0085] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A personalized customized cushion method based on pressure sensing and 3D printing, characterized in that: Including: Collect the pressure values generated during cycling, preprocess the pressure values to obtain the first parameter; Divide the force-bearing area of the saddle according to the first parameter and analyze the cycling habits; Obtain the lattice parameters according to the first parameter; Print lattice units through 3D printing technology and dynamically adjust the 3D model according to the force-bearing area of the saddle and cycling habits.

2. The personalized customized cushion method based on pressure sensing and 3D printing according to claim 1, wherein: The collecting of the pressure values generated during cycling includes arranging flexible sensors at multiple points on the saddle and collecting the pressure values exerted on the saddle during cycling within a preset time.

3. The personalized customized cushion method based on pressure sensing and 3D printing according to claim 1 or 2, characterized in that: The obtaining of the first parameter includes energizing the pressure sensor through the control board, recording the changes in the output pressure when different pressures are applied to the saddle, matching the first parameter according to the output voltage generated by the cyclist applying pressure to the saddle during cycling, transmitting the first parameter to the first object through the Bluetooth module, transmitting the first parameter to the server through the first object, calculating the mean value of the first parameter at the measurement points of the saddle during the cycling time, and judging the force-bearing area of the saddle and analyzing the cycling habits according to the mean value.

4. The personalized customized cushion method based on pressure sensing and 3D printing according to claim 3, characterized in that: The dividing of the force-bearing area of the saddle according to the first parameter includes dividing the saddle into a core force-bearing area, an auxiliary support area, and a buffer area according to the mean value of the first parameter. The core force-bearing area is the area directly contacted by the ischial tuberosities of the human body. The auxiliary support area is the area close to the root of the human thigh. The buffer area is the front end and the side edge areas of the saddle. When the first parameter is greater than or equal to the first threshold, it is judged as the core force-bearing area. When the first parameter is greater than the second threshold and less than the first threshold, it is judged as the auxiliary support area. When the first parameter is less than or equal to the second threshold, it is judged as the buffer area.

5. The personalized customized cushion method based on pressure sensing and 3D printing according to claim 1 or 4, characterized in that: The analyzing of the cycling habits includes judging the left side of the central axis of the core force-bearing area as the left area and the right side of the central axis of the core force-bearing area as the right area. If the first parameter of the left area is greater than the first parameter of the right area, it is judged that the cycling habit is left-footed. If the first parameter of the left area is less than the first parameter of the right area, it is judged that the cycling habit is right-footed; Divide the core force-bearing area into the front half area and the rear half area according to the front-back axis. If the first parameter of the front half area is greater than the first parameter of the rear half area, it is judged that the cycling habit is forward-leaning cycling. If the first parameter of the front half area is less than the first parameter of the rear half area, it is judged that the cycling habit is sitting-back cycling.

6. The personalized customized cushion method based on pressure sensing and 3D printing according to claim 5, wherein: The obtaining of the lattice parameters according to the first parameter includes applying pressure to different types of lattices, monitoring the deformation depth of the lattices within the same square area, recording the first parameter applied when the lattice deformation depths are the same to obtain lattice test data, matching the first parameter lattice test data applied to a single point on the saddle, obtaining the lattice type, lattice thickness, and lattice density corresponding to the first parameter, and controlling the hardness of the saddle through the lattice parameters.

7. The personalized customized cushion method based on pressure sensing and 3D printing according to claim 1, 2, 4 or 6, characterized in that: The printing of the lattice units through 3D printing technology includes designing a 3D model according to the hardness distribution of the saddle and printing the 3D model through 3D printing technology.

8. The personalized customized cushion method based on pressure sensing and 3D printing according to claim 7, characterized in that: The dynamically adjusting of the 3D model includes dynamically adjusting the matched lattice parameters according to the force-bearing area of the saddle and cycling habits; Calculate the pressure difference between the left and right regions, which is represented as a first variable. Calculate the pressure difference between the front and rear regions, which is represented as a second variable. Multiply the first variable and the second variable by a preset first adjustment coefficient and a second adjustment coefficient respectively, and sum the calculation results to obtain a riding habit correction factor; The first adjustment coefficient is used to adjust the influence of the rider's left - right exertion, and the second adjustment coefficient is used to adjust the influence of the rider's front - rear sitting posture; Calculate the seating pressure area and the riding habit correction factor. Add the first parameter mean value of the seating pressure area and the riding habit correction factor to obtain a lattice adjustment parameter; Based on the minimum lattice thickness, multiply the difference between the maximum lattice thickness and the minimum lattice thickness by the lattice adjustment parameter, and accumulate the calculation result to the minimum lattice thickness to obtain the lattice thickness of the seating pressure area; Based on the minimum lattice density, multiply the difference between the maximum lattice density and the minimum lattice density by the lattice adjustment parameter, and accumulate the calculation result to the minimum lattice density to obtain the lattice density of the seating pressure area; Generate a 3D model according to the calculation results, and dynamically adjust the 3D model based on the lattice thickness and lattice density of the seating pressure area.

9. A personalized customized cushion system based on pressure sensing and 3D printing, characterized in that: It includes a pre - processing module (100), a region division module (200), a lattice parameter calculation module (300), and a 3D model dynamic adjustment module (400); The pre - processing module (100) includes a data acquisition module (101) and a data pre - processing module (102). The data acquisition module (101) is used to collect riding data through a flexible sensor, and the data pre - processing module (102) is used to convert the pressure value into a first parameter and calculate the first parameter mean value; The region division module (200) includes a seating pressure area division module (201) and a riding habit analysis module (202). The seating pressure area division module (201) is used to divide the seat into a core stress area, an auxiliary support area, and a buffer area according to the first parameter mean value. The riding habit analysis module (202) is used to divide the core stress area into four regions, analyze the first parameters of each region, and obtain the riding habit; The lattice parameter calculation module (300) is used to establish a lattice parameter test table and match the lattice parameters in the test table according to the first parameter; The 3D model dynamic adjustment module (400) includes a 3D printing module (401) and a dynamic adjustment module (402). The 3D printing module (401) is used to design a 3D model according to the seat hardness distribution and print the 3D model through 3D printing technology. The dynamic adjustment module (402) is used to dynamically adjust the 3D model according to the region division and riding habit.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method described in any one of claims 1 to 8.

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