Big data-based personalized waist support cushion design method and waist support cushion

Abstract

The invention relates to a personalized waist support cushion design method based on big data. The method comprises the steps that user feature information is collected; querying in a user database according to the user feature information to obtain cushion purchase information; querying in a cushion database according to the cushion purchase information to obtain cushion design parameter information; extracting personalized feature information of the user; seat cushion feedback information is collected; performing first correction on the cushion design parameters according to the user personalized feature information in combination with the cushion design parameter information, and obtaining a first correction result; performing a second correction result on the cushion design parameters according to the first correction result and the cushion feedback information; and performing information updating on the cushion historical database according to the second correction result. According to the method, the big data technology is utilized, the collected identity information data are cleaned and de-duplicated, the personalized feature information of the users is extracted, the data are converted into accurate personalized design parameters, and it is ensured that each user can obtain the customized waist support cushion.

Description

Technical Field

[0001] The invention belongs to the technical field of waist support cushion testing, and in particular relates to a personalized waist support cushion design method based on big data and a waist support cushion. Background Art

[0002] With the changes in people's work and lifestyles, sitting for a long time has become the norm. However, sitting for a long time will cause great pressure on the lumbar spine, leading to frequent lumbar diseases. The lumbar support cushion products on the market are designed to reduce the burden on the lumbar spine by improving the sitting posture; however, most of the existing lumbar support cushions are selected from existing finished cushions, and cannot be manufactured and selected according to each person's different physical conditions and personalized needs.

[0003] Specifically, each person's height, weight, pelvic shape, lumbar curvature and other physical characteristics are different. These factors will affect the sitting posture and lumbar stress. Therefore, a cushion that suits one person may not be suitable for another person; similarly, everyone's sitting habits and health conditions are different. Some people are used to sitting forward, while others like to lean back. Some people may already have problems with their lumbar spine and need more detailed support and protection. In other words, different people's sitting habits and health conditions will also affect the demand for cushions. These personalized needs cannot be met by existing finished cushions.

[0004] In summary, existing lumbar cushions have many limitations in design and manufacturing, and cannot be manufactured and selected according to each person's different physical conditions and personalized needs. Therefore, developing a personalized lumbar cushion design method based on big data, by collecting and analyzing each person's physical characteristics, sitting habits, health status and other data, to customize the lumbar cushion that best suits their needs, has important practical significance and application value. Summary of the invention

[0005] The purpose of the present invention is to provide a personalized lumbar support cushion design method and lumbar support cushion based on big data, so as to solve the problems raised in the above-mentioned background technology.

[0006] The first aspect of the present invention provides a method for designing a personalized waist support cushion based on big data, comprising the following steps:

[0007] Collect user characteristic information;

[0008] According to the user characteristic information, query the user database to obtain the cushion purchase information;

[0009] According to the cushion purchase information, query the cushion database to obtain cushion design parameter information;

[0010] Extract user personalized feature information and collect cushion feedback information;

[0011] Performing a first correction on the seat cushion design parameters according to the user personalized feature information combined with the seat cushion design parameter information, and obtaining a first correction result;

[0012] Performing a second correction on the seat cushion design parameters according to the first correction result and the seat cushion feedback information;

[0013] The seat cushion history database is updated according to the second correction result.

[0014] This plan also includes:

[0015] The seat cushion design parameter information is obtained by querying the seat cushion database according to the seat cushion purchase information, including:

[0016] Extracting user identity information according to the user characteristic information;

[0017] Matching information of the cushion purchased by the user with the user's identity information through the user database;

[0018] Obtaining seat cushion design parameter information by querying the seat cushion database according to the matching information of the seat cushion;

[0019] The seat cushion design parameter information is obtained, including material parameter information, softness and hardness parameter information, elasticity parameter information and angle parameter information of the seat cushion.

[0020] This plan also includes:

[0021] Extracting user personalized feature information includes:

[0022] Performing data processing according to the user characteristic information to obtain user personalized characteristic information;

[0023] Extracting the user's gender information and physical sign information according to the user's personalized feature information;

[0024] The physical sign information includes height information, weight information, curve information, body temperature information and body stability information;

[0025] Processing the user personalized feature information through a preset cushion design adaptation model to obtain a first cushion adaptation parameter;

[0026] The seat cushion design parameters are first corrected according to the first adaptation parameters of the seat cushion.

[0027] This plan also includes:

[0028] Collect cushion feedback information including:

[0029] Extracting environmental information and user seating information according to the cushion feedback information;

[0030] The environmental information includes temperature and humidity information, geographical information and time information;

[0031] The user's sitting information includes sitting posture information, human body pressure information and sitting time information;

[0032] Obtaining personalized adaptation parameters of the cushion according to the cushion feedback information by using preset user personalized adaptation parameters;

[0033] A second correction is performed on the seat cushion design parameters according to the seat cushion personalized adaptation parameters.

[0034] The present solution also includes a storage device and a processor. The storage device includes a personalized cushion design method program based on big data. When the personalized cushion design method program based on big data is executed by the processor, the following steps are implemented:

[0035] Collect user characteristic information;

[0036] According to the user characteristic information, query the user database to obtain the cushion purchase information;

[0037] According to the cushion purchase information, query the cushion database to obtain cushion design parameter information;

[0038] Extract user personalized feature information and collect cushion feedback information;

[0039] Performing a first correction on the seat cushion design parameters according to the user personalized feature information combined with the seat cushion design parameter information, and obtaining a first correction result;

[0040] Performing a second correction on the seat cushion design parameters according to the first correction result and the seat cushion feedback information;

[0041] The seat cushion history database is updated according to the second correction result.

[0042] The second aspect of the present invention provides a waist cushion, which is designed by a personalized waist cushion design method based on big data, including:

[0043] The main body is divided into a cushion part and a backrest part, and the backrest part is rotatably connected to the cushion part at the rear;

[0044] Soft cushions, 2 in number, installed on the backrest and seat cushions;

[0045] The support part is connected between the backrest part and the seat cushion part, and together with the main part forms a space that fits the curve of the human body;

[0046] A sensor, disposed between the support portion and the main body portion, for detecting a user's body posture and pressure changes;

[0047] The controller is respectively arranged on the backrest part and the support part, and is used to adjust and control the backrest part and the support part to fit the curve of the human body;

[0048] A calculator, arranged in the controller, for automatically calculating and outputting the most suitable positions to which the backrest portion and the support portion should be adjusted according to the detection results of the sensor;

[0049] The actuator is connected to the backrest and the support part, and is used to adjust the position and angle of the backrest and the support part according to the adjustment instructions output by the computer.

[0050] This plan also includes:

[0051] The rear side of the seat portion is connected with three hinged rods through three first hinged seats, the tops of the hinged rods are hinged with second hinged seats, and the second hinged seats are connected with the rear side of the backrest portion.

[0052] This plan also includes:

[0053] An inflatable lumbar pillow is connected between the front sides of the hinged rods.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. The present invention uses big data technology to clean and deduplicate the collected identity information data, extract user personalized feature information, and then convert these data into accurate personalized design parameters to ensure that each user can obtain a tailor-made waist cushion;

[0056] 2. During the use of the cushion of the present invention, the design method of the model and the cushion will be continuously updated and improved according to the user's feedback and actual use, so that it can be iteratively optimized, and finally better meet the user's needs and market changes, and be closer to the user's actual needs;

[0057] 3. Personalized design based on big data can optimize the shape, material and hardness distribution of the seat cushion according to the physical characteristics and sitting habits of different users, so as to guide users to form a correct sitting posture. Long-term use can help prevent lumbar diseases such as lumbar disc herniation and lumbar muscle strain.

[0058] 4. Combined with the Internet of Things technology, users can remotely adjust the seat support angle, lumbar support strength and other parameters through the mobile phone app according to their real-time needs and physical condition, thereby improving the flexibility of the seat cushion when using it. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1It is a flow chart of the personalized waist support cushion design method based on big data of the present invention;

[0060] Figure 2 This is a body shape reference diagram for male BM I;

[0061] Figure 3 This is a body shape reference diagram for female BM I;

[0062] Figure 4 It is a schematic diagram of the three-dimensional structure of the waist protection cushion of the present invention;

[0063] Figure 5 Another three-dimensional structural schematic diagram of the waist protection cushion of the present invention;

[0064] Figure 6 This is an enlarged view of point a.

[0065] Numbers in the figure: 1-main body, 2-seat cushion, 3-backrest, 4-support, 5-cushion, 6-inflatable lumbar pillow, 7-first hinge seat, 8-hinged rod, 9-second hinge seat;

[0066] 10- storage, 11- processor, 12- sensor, 13- controller, 14- calculator, 15- actuator. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Example 1

[0069] like Figure 1 A personalized waist cushion design method based on big data is shown, comprising the following steps:

[0070] S10. Collect user feature information;

[0071] S20. Query the user database to obtain cushion purchase information based on user feature information;

[0072] S30. Query the cushion database to obtain cushion design parameter information based on cushion purchase information;

[0073] S40. Extracting user personalized feature information;

[0074] S50. Collect cushion feedback information;

[0075] S60. Performing a first correction on the cushion design parameters according to the user personalized feature information combined with the cushion design parameter information, and obtaining a first correction result;

[0076] S70. Perform a second correction on the cushion design parameters based on the first correction result combined with the cushion feedback information;

[0077] S80. Update the seat cushion history database according to the second correction result.

[0078] It should be noted that, based on big data information, each cushion is posted with a QR code storing relevant information when it leaves the factory. First, the user scans the QR code on the cushion with a mobile phone, and a related app and online questionnaire pop up to obtain the user's basic information, such as identity information, gender, height, weight, age, usage habits, etc. The user's identity information is extracted and combined with the cushion code to enter the user database, and the design parameters of the cushion are queried to obtain the purchased cushion, and the cushion design parameters are bound to the user's identity information; secondly, the user's personalized feature information is extracted first, and the user's personalized feature information includes height information, weight information, curve information, body temperature information, etc. The user's personalized feature information and body stability information are processed through a preset cushion design adaptation model to obtain the first cushion adaptation parameters, and then the cushion design parameters are first corrected according to the first cushion adaptation parameters. The cushion design parameters are corrected for the second time according to the first correction result combined with the cushion feedback information, where the cushion feedback information includes temperature and humidity information, geographical information and time information, sitting posture information, human body pressure information and sitting time information. Finally, the cushion history database is updated according to the second correction result to form personalized cushion design parameters for the user, which are closer to the user's actual needs. During this period, the model and cushion design methods are continuously updated and improved according to user feedback and actual usage, so that they are iteratively optimized and ultimately more in line with user needs and market changes.

[0079] Example 2

[0080] Furthermore, if Figure 1 As shown, the embodiment of the present invention also includes:

[0081] S301. Extracting user identity information based on user feature information;

[0082] S302. Matching information of cushions purchased by the user with the user's identity information through the user database;

[0083] S303. Query the cushion database to obtain cushion design parameter information according to the cushion matching information;

[0084] S304. Acquire the seat cushion design parameter information, including the seat cushion material parameter information, appearance parameter information, angle parameter information and load-bearing capacity parameter information.

[0085] It should be noted that a common seat is divided into two parts, a cushion part 2 in contact with the user's legs, and a backrest part 3 in contact with the user's back. After the user sits down, a triangular space is formed between the cushion part 2 and the two parts, that is, the user's waist will be suspended for a long time. At the same time, when a person is in a sitting state, the force borne by the lumbar spine is twice that of standing. For example, a person weighing 70 kg has to bear a weight of about 140 kg when sitting. After sitting for a long time, the lumbar spine will be subjected to excessive pressure, which will cause lumbar injury or pain. Referring to the above background, the cushion supported by this embodiment should be improved in two aspects:

[0086] A support portion 4 is provided between the seat cushion portion 2 and the backrest portion 3 to contact with the user's waist and support the waist;

[0087] Among the three parts of the seat cushion, a soft pad 5 is provided at the position in contact with the user, so as to relieve the pressure on the buttocks in the sitting state and reduce the discomfort caused by sitting for a long time.

[0088] Based on big data information, the user first scans the QR code on the cushion with a mobile phone, and a related app and online questionnaire pop up to obtain the user's basic information, such as identity information, gender, height, weight, age, usage habits, etc. The user's identity information is extracted and combined with the cushion code to enter the user database, and the design parameters of the cushion are queried and obtained, and the cushion design parameters are bound to the user's identity information; each cushion is posted with a QR code storing its relevant information when it leaves the factory. After scanning, a 3D model of the cushion will appear, in which the specifications of each part of the cushion are marked, including but not limited to:

[0089] 1. Material information, including seat cushion material information, such as polyurethane (PU) and polypropylene, which are materials with strong load-bearing capacity, and cushion 5 material information, such as memory foam, TPE (thermoplastic elastomer), latex, high-density foam, and other soft and highly resilient materials;

[0090] 2. Appearance parameter information, i.e. the overall appearance of the seat cushion, setting curves of various parts, etc.;

[0091] 3. Angle parameter information, i.e. the factory set angle of the seat cushion and the adjustable angle display, which is divided into one-piece seat cushion and support part 4, and non-one-piece seat cushion with adjustable backrest;

[0092] 4. Load-bearing capacity parameter information, specifically the load-bearing capacity of seat cushions made of different materials, the load-bearing capacity after installing different soft cushions 5, the load-bearing capacity of one-piece seat cushions, and the load-bearing capacity of non-one-piece seat cushions after adjusting different angles.

[0093] It should be noted that the design parameters of each cushion have a specified cushion design adaptation model, that is, after the user binds the identity information to the cushion, the computer 14 cleans and removes the duplicates of the identity information data, extracts the user's personalized feature information, and processes the user's personalized feature information through a preset cushion design adaptation model to obtain the first cushion adaptation parameters, and then performs a first correction on the cushion design parameters according to the first cushion adaptation parameters, and the correction content includes but is not limited to:

[0094] 1. Corrected the material of cushion 5;

[0095] 2. Correct the angle between the cushion part 2 and the support part 4;

[0096] 3. Correct the angle between the support part 4 and the backrest part 3;

[0097] 4. Correct the wrapping width of the support part 4.

[0098] The cushion design parameters are revised for the second time based on the first revision result and the cushion feedback information, where the cushion feedback information includes temperature and humidity information, regional information and time information, sitting posture information, human body pressure information and sitting time information. Finally, the cushion history database is updated based on the second revision result to form personalized cushion design parameters for the user, which is closer to the user's actual needs. During this period, the model and cushion design methods are continuously updated and improved based on user feedback and actual usage, so that they are iteratively optimized and ultimately more in line with user needs and market changes.

[0099] Example 3

[0100] Furthermore, if Figures 1 to 3 As shown, the embodiment of the present invention also includes:

[0101] S401. Perform data processing based on user characteristic information to obtain user personalized characteristic information;

[0102] S402. Extracting the user's gender information and physical information based on the user's personalized feature information;

[0103] S403. Physical information includes height information, weight information, curve information, body temperature information and body stability information;

[0104] S404. Processing the user's personalized feature information through a preset cushion design adaptation model to obtain a first cushion adaptation parameter;

[0105] S405. Perform a first correction on the seat cushion design parameters according to the first adaptation parameters of the seat cushion.

[0106] It should be noted that, based on big data information, each seat cushion is posted with a QR code storing relevant information when it leaves the factory. First, the user scans the QR code on the seat cushion with a mobile phone, and a related app and online questionnaire pop up to obtain the user's basic information, such as identity information, gender, height, weight, age, usage habits, etc., and then processes the user's characteristic information to obtain the user's personalized characteristic information, first extracting the user's age and gender information, and then extracting the user's physical information, including but not limited to:

[0107] Height information: taller individuals have a larger heat dissipation area, which helps to dissipate heat more effectively under the same conditions, thereby lowering body temperature. That is, taller people tend to have lower body temperatures.

[0108] Weight information: After extracting weight information, the body mass index (BMI) can be calculated by the ratio of height to weight based on the specified gender and height information. This can help you get and judge the user's actual physical information. The heavier the person, the thicker their fat layer and the higher their body temperature.

[0109] Curve information, after obtaining the specified gender, height and weight information, refer to the (BMI) body shape comparison table to roughly obtain the user's body curve;

[0110] Information on body stability is obtained through answers to online questionnaires, that is, the user's self-judgment, including whether the person has a physique that sweats easily, whether he or she has any old illness, etc.

[0111] Next, the user's personalized feature information is processed through a preset cushion design adaptation model to obtain first adaptation parameters of the cushion, and then the cushion design parameters are first corrected according to the first adaptation parameters of the cushion; the cushion adaptation model is a model obtained by training cushion-related information and adaptation parameters of a large number of historical samples, and the corresponding adaptation parameters can be obtained by inputting relevant information for processing, and the cushion design parameters are first corrected through the first adaptation parameters, so that the initial design parameters of the cushion are closer to the actual needs of the user after being corrected by the design parameter information of the cushion and the personalized feature information of the user.

[0112] Example 4

[0113] Furthermore, if Figure 1 As shown, the embodiment of the present invention also includes:

[0114] S501. Extracting environmental information and user seat information based on cushion feedback information;

[0115] S502. Environmental information includes temperature and humidity information, location information and time information;

[0116] S503. User seat information includes sitting posture information, human body pressure information and sitting time information;

[0117] S504. Obtain personalized adaptation parameters of the cushion according to the cushion feedback information through the preset user personalized adaptation parameters;

[0118] S505. Perform a second correction on the seat cushion design parameters according to the seat cushion personalized adaptation parameters.

[0119] It should be noted that the cushion feedback information is specifically the state presented after the cushion design parameters are first corrected. Within half an hour after the user sits down, the cushion will continue to highly sense the user's usage and feed back the sensing data to the model of the storage 10. The cushion feedback information mainly includes environmental information and user sitting information.

[0120] Environmental information includes the following:

[0121] Temperature and humidity information: The temperature and humidity of the air in the use environment will directly affect the user's feeling when sitting. In actual use, if the material of the cushion 5 is cloth cotton, a temperature that is too high will cause the user to feel stuffy and uncomfortable for a short time after sitting. A humidity that is too high will also cause the moisture in the air to be absorbed by the cloth cotton, and the user will easily feel damp after sitting. Sitting for a long time may even cause serious consequences such as eczema and other skin infections;

[0122] Location information, i.e. the user’s usage environment, which can be the workplace or home location. The user’s mental state in different locations is different, which requires different trend adjustments;

[0123] Time information: When the user uses the cushion for work, the user's working status can be detected, and the calculation starts when the user starts working to obtain the user's sitting time information, etc.

[0124] User seat information:

[0125] Sitting posture information: When the user uses the cushion, the upper body, lumbar spine and buttocks are all leaning on the cushion. The sensor 12 on the cushion detects the user's body posture and determines whether the user's lumbar spine is tilted, and makes appropriate adjustments. The above data is also recorded in the storage 10 to achieve subtle correction of the user's wrong sitting posture;

[0126] Human body pressure information: When working, the sensor detects the human body's pressure information and determines whether the user is in a high-pressure state. Then, the app sends a reminder to the user, suggesting whether the user needs to adjust the seat cushion or take a rest.

[0127] The sitting time information is connected to the seat cushion to calculate the user's usage time. After sitting for a long time, the user will be reminded through the app to suggest whether the seat cushion needs to be adjusted or to take a rest.

[0128] Therefore, when extracting the personalized characteristic information of the user, the personalized characteristic information of the user includes height information, weight information, curve information, body temperature information and body stability information. The personalized characteristic information of the user is processed through the preset cushion design adaptation model to obtain the first cushion adaptation parameters, and the cushion design parameters are first corrected according to the first cushion adaptation parameters; then the cushion design parameters are corrected for the second time according to the first correction result combined with the cushion feedback information, wherein the cushion feedback information includes temperature and humidity information, geographical information and time information, sitting posture information, human body pressure information and sitting time information. Finally, according to the second correction result, the cushion history database is updated to form personalized cushion design parameters for the user, which are closer to the user's actual needs. During this period, according to user feedback and actual usage, the model and cushion design method are continuously updated and improved, so that it is iteratively optimized and ultimately more in line with user needs and market changes.

[0129] Example 5

[0130] like Figures 3 to 6 As shown, on the other hand, the present embodiment provides a waist protection cushion, including a main part 1 of the cushion, the main part 1 is divided into a cushion part 2 in contact with the user's legs, a backrest part 3 in contact with the user's back, and a support part 4 in contact with the user's waist and supporting the waist, wherein the cushion part 2 and the backrest part 3 are integrally formed by mold injection, and the side surfaces present a rounded arc that expands outward, and adopt a design that fits the human body curve to ensure that the user's back, buttocks, legs and other key parts are in the correct position, so as to help the user correct the sitting posture and reduce the problems such as back pain caused by improper sitting posture for a long time; wherein the front side of the cushion part 2 will form an "ω "Type", it can be understood that both the left and right ends of the cushion portion 2 extend upward and show a tendency to warp inward. When a human body sits on the cushion, the left and right sides of the cushion portion 2 can exert an inward squeezing force to regulate the user's sitting posture; at the same time, the middle part of the front edge of the cushion portion 2 bulges upward to form a bulge, and the left and right sides of the bulge cooperate with the outer edge of the cushion portion 2 to form two recesses for supporting the thigh part of the human body. When the human body is on the cushion, the thighs will be located in the recesses respectively, and the middle bulge can form a certain distance between the thighs, effectively avoiding the human body from sitting in irregular postures such as crossing legs and legs, and playing the role of retracting the pelvis and preventing pelvic displacement.

[0131] The cushion part 2 and the backrest part 3 may be made of one of the common materials such as polypropylene, glass fiber and calcium carbonate. Polypropylene has strong wear resistance, which can offset the friction caused by the frequent contact between the cushion and the human body. Polypropylene also has the characteristics of being waterproof and oil-proof, which can effectively prevent the hygienic factors caused by the splashing of stains on the cushion, and is also easy to clean. Furthermore, adding calcium carbonate during production can also improve the hardness and rigidity of the cushion, enhance the support and durability of the cushion, and ensure its service life. In summary, designers can choose the materials they need during production, or mix them to effectively improve the performance and cost-effectiveness of the cushion, and meet the needs of different products and the use experience of different users.

[0132] In order to improve the comfort, soft and elastic cushions 5 made of sponge or the like are installed on the surfaces of the backrest portion 3 and the seat cushion portion 2 to reduce the discomfort caused by sitting for a long time. That is, the cushions 5 are detachable and can be made of sponge pads, gel pads, memory foam, etc. During the design process, the above-mentioned cushions 5 can be provided for users' reference and selection when purchasing, and are replacement packs. When the user gets the seat cushion and starts to use it, the material of the cushion 5 can be obtained by sensing or manually filling in.

[0133] Example 6

[0134] like Figures 3 to 6 As shown, this embodiment provides a waist cushion, which, in addition to the cushion portion 2 in contact with the user's legs and the backrest portion 3 in contact with the user's back, also includes a support portion 4 in contact with the user's waist and supports the waist. The support portion 4 is connected between the backrest portion 3 and the cushion portion 2, and together with the cushion main body portion 1 forms a space that fits the human body curve, so that the user can use this product in a relaxed posture. An inflatable lumbar pillow 6 is installed on the front side of the support portion 4 (i.e., the side in contact with the human body), which can be simply understood as follows: the inflatable lumbar pillow 6 is inflated through an inflation nozzle until the gas fills the inflatable lumbar pillow 6, and when the user sits down, the inflatable lumbar pillow 6 can support the waist and lumbar spine. Due to the plasticity of the inflatable lumbar pillow 6, it can be easily adapted to users of different body shapes. Based on big data, the advantage of designing the inflatable lumbar pillow 6 to be inflated and deflated through an app is that the user can adjust the use height of the inflatable lumbar pillow 6 by reducing the inflation amount of the inflatable lumbar pillow 6, so that the waist fits more closely against the inflatable lumbar pillow 6 and gets support.

[0135] Furthermore, the support part 4 and the backrest are set to be adjustable and rotatable. With the support of big data, corresponding adjustments are made according to users with different characteristics to adapt to users with different body shapes. The rear side of the cushion part 2 is connected to three hinged rods 8 through three first hinged seats 7, that is, the hinged rods 8 can rotate on the first hinged seats 7; at the same time, three second hinged seats 9 are set on the rear side of the backrest part 3, and the other end of the hinged rod 8 is hinged to the second hinged seat 9, that is, the hinged rod 8 is hinged between the backrest part 3 and the cushion part 2, and the inflatable lumbar pillow 6 is installed on the front side of the hinged rod 8. In actual application, the seat cushion part 2 is fixed at one end, and the user's waist will be located in front of the hinged rod 8, that is, the waist is close to the inflatable waist pad, and at this time the first hinged seat 7 is fixed, and the hinged rod 8 rotates on the first hinged seat 7, which means that the rotation of the hinged rod 8 can indirectly achieve the angle adjustment between the waist and the legs; at the same time, the second hinged seat 9 can be rotated relative to the hinged rod 8, which is understood as: when the user adjusts the relative angle of the waist, the backrest part 3 can also be driven to rotate by rotating the second hinged seat 9 to achieve the angle adjustment between the back and the waist.

[0136] In addition, sensors 12 are provided at each fitting position for detecting the user's body posture and pressure changes, and providing numerical values ​​for a personalized seat cushion design model; a calculator 14 is provided in the controller 13 for automatically calculating and outputting the most suitable positions to which the backrest and the support portion 4 should be adjusted according to the detection results of the sensor 12; actuators 15 are provided between the two articulated seats and the articulated rod 8 for adjusting and controlling the backrest and the support portion 4 to a specified angle according to the adjustment instructions output by the calculator 14, so that they fit the curve of the human body.

[0137] And a memory and a processor 11, the memory stores a program of a personalized seat cushion design method based on big data, and the processor 11 executes the program to implement the above-mentioned design method.

[0138] Example 7

[0139] like Figures 1 to 6 A lumbar support cushion is obtained by a personalized lumbar support cushion design method based on big data. This embodiment will verify the applicability and effect of the design method in different user groups through experiments.

[0140] Experimental subjects: including people of different ages, genders, weights and those with or without back injuries.

[0141] Specific groups: young group (18-30 years old), middle-aged group (31-50 years old), elderly group (over 51 years old), male group, female group, normal weight group (BMI 18.5-24.9), overweight group (BMI 25-29.9), obese group (BMI above 30), group without lumbar injury and group with lumbar injury.

[0142] Experimental process:

[0143] Data collection → Seat cushion design parameter query → Personalized feature extraction → Seat cushion adaptation model processing → Seat cushion design correction → Seat cushion feedback information collection → Database update.

[0144] Experiment Feedback Form:

[0145]

[0146]

[0147] Note: "-" in the table means that the group is not applicable or data is not available.

[0148] From the above table we can conclude that:

[0149] Among the different age groups, the younger group prefers softer seat cushion materials and a larger angle of movement to adapt to a more active lifestyle; the middle-aged group needs more lumbar support and a seat cushion of moderate hardness to reduce lumbar pressure; the elderly group prefers a harder seat cushion to provide better support, and needs more soft cushion materials to increase comfort.

[0150] Among the different gender groups, the average weight of the male group is heavier, requiring stronger load-bearing capacity and a larger contact area; the female group pays more attention to the curve design and softness of the seat cushion to adapt to different body curves.

[0151] Among the different weight groups, the normal weight group required more balanced cushion design parameters, focusing on the balance between comfort and support; the overweight group required stronger load-bearing capacity and larger contact area to reduce the pressure per unit area; the obese group preferred softer materials and larger contact areas to reduce pressure when sitting.

[0152] In the group with lumbar injury, the group without lumbar injury paid more attention to the comfort of the seat cushion and the support for daily use; the group with lumbar injury needed special support to reduce the pressure on the lumbar spine, and the angle and hardness of the seat cushion were adjusted according to the specific situation.

[0153] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0154] The above description is only used to illustrate the technical solution of the present invention rather than to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A personalized waist cushion design method based on big data, characterized in that: The following steps are involved: Collect user characteristic information; According to the user characteristic information, query the user database to obtain the cushion purchase information; According to the cushion purchase information, query the cushion database to obtain cushion design parameter information; Extract user personalized feature information and collect cushion feedback information; Performing a first correction on the seat cushion design parameters according to the user personalized feature information combined with the seat cushion design parameter information, and obtaining a first correction result; Performing a second correction on the seat cushion design parameters according to the first correction result and the seat cushion feedback information; The seat cushion history database is updated according to the second correction result.

2. The method for designing a personalized waist cushion based on big data according to claim 1, characterized in that: The seat cushion design parameter information is obtained by querying the seat cushion database according to the seat cushion purchase information, including: Extracting user identity information according to the user characteristic information; Matching information of the cushion purchased by the user with the user's identity information through the user database; Obtaining seat cushion design parameter information by querying the seat cushion database according to the matching information of the seat cushion; The seat cushion design parameter information is obtained, including material parameter information, softness and hardness parameter information, elasticity parameter information and angle parameter information of the seat cushion.

3. The method for designing a personalized waist cushion based on big data according to claim 1, characterized in that: Extracting user personalized feature information includes: Performing data processing according to the user characteristic information to obtain user personalized characteristic information; Extracting the user's gender information and physical sign information according to the user's personalized feature information; The physical sign information includes height information, weight information, curve information, body temperature information and body stability information; Processing the user personalized feature information through a preset cushion design adaptation model to obtain a first cushion adaptation parameter; The seat cushion design parameters are first corrected according to the first adaptation parameters of the seat cushion.

4. According to claim 3, a personalized waist cushion design method based on big data is characterized in that: Collect cushion feedback information including: Extracting environmental information and user seating information according to the cushion feedback information; The environmental information includes temperature and humidity information, geographical information and time information; The user's sitting information includes sitting posture information, human body pressure information and sitting time information; Obtaining personalized adaptation parameters of the cushion according to the cushion feedback information by using preset user personalized adaptation parameters; A second correction is performed on the seat cushion design parameters according to the seat cushion personalized adaptation parameters.

5. The method for designing a personalized lumbar cushion based on big data according to claims 1 to 4, further comprising a storage device and a processor, wherein the storage device comprises a program for designing a personalized cushion based on big data, and when the program for designing a personalized cushion based on big data is executed by the processor, the following steps are implemented: Collect user characteristic information; According to the user characteristic information, query the user database to obtain the cushion purchase information; According to the cushion purchase information, query the cushion database to obtain cushion design parameter information; Extract user personalized feature information and collect cushion feedback information; Performing a first correction on the seat cushion design parameters according to the user personalized feature information combined with the seat cushion design parameter information, and obtaining a first correction result; Performing a second correction on the seat cushion design parameters according to the first correction result and the seat cushion feedback information; The seat cushion history database is updated according to the second correction result.

6. A waist cushion, which is designed by using the personalized waist cushion design method based on big data described in claims 1-5, characterized in that: Included are: The main body is divided into a cushion part and a backrest part, and the backrest part is rotatably connected to the cushion part at the rear; Soft cushions, 2 in number, installed on the backrest and seat cushions; The support part is connected between the backrest part and the seat cushion part, and together with the main part forms a space that fits the curve of the human body; A sensor, disposed between the support portion and the main body portion, for detecting a user's body posture and pressure changes; The controller is respectively arranged on the backrest part and the support part, and is used to adjust and control the backrest part and the support part to fit the curve of the human body; A calculator, arranged in the controller, for automatically calculating and outputting the most suitable positions to which the backrest portion and the support portion should be adjusted according to the detection results of the sensor; The actuator is connected to the backrest portion and the support portion, and is used to adjust the position and angle of the backrest portion and the support portion according to the adjustment instruction output by the calculator.

7. According to a lumbar support cushion according to claim 6, the rear side of the seat portion is connected to three hinged rods through three first hinged seats, the tops of the hinged rods are hinged to second hinged seats, and the second hinged seats are connected to the rear side of the backrest portion.

8. A lumbar support cushion according to claim 7, wherein an inflatable lumbar pillow is connected between the front sides of the hinged rods.

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