Child engineering chair adjusting method and system based on growth rate prediction and chair

Abstract

The invention discloses a child engineering chair adjusting method and system based on growth rate prediction and a chair. The method comprises the following steps: receiving historical physiological data of a user in a plurality of unit periods; based on the historical physiological data, predicting physiological data in a plurality of unit cycles in the future by adopting a GM (1, 1) model, and recording the physiological data as predicted physiological data; the growth rate of the user is calculated according to the predicted physiological data, and one or more of the height, the waist support height and the chair back inclination angle of the child engineering chair are adjusted according to the growth rate so that the child engineering chair can be matched with a learning table. According to the embodiment of the invention, nonlinear growth characteristics of children are captured through the GM (1, 1) model, the growth rate grade and the growth rate grade dynamically matched with the adjustment period and amplitude are obtained, and compared with a fixed period adjustment scheme, the adaptation error is greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of seating technology, specifically to a method, system, and seat for adjusting a children's ergonomic chair based on growth rate prediction. Background Technology

[0002] As a core piece of furniture designed to support children's growth and development, the adjustable features of ergonomic chairs directly impact children's posture and comfort. Children's growth and development exhibit significant non-linear characteristics: growth rates vary considerably across seasons (monthly height increase can reach 1.5cm in spring, but only 0.5cm in winter), and individual growth rhythms differ (e.g., school-aged children grow 5-7cm annually, but monthly growth is not uniform). Ergonomic chairs designed to adapt to children's growth must precisely match this non-linear growth pattern to avoid discomfort due to insufficient adjustment or a decline in user experience due to over-adjustment.

[0003] The current adjustment schemes for children's ergonomic chairs are mainly divided into three categories: (1) Fixed-cycle adjustment type: a fixed adjustment cycle is preset (e.g., once a month / once every half month), and the adjustment range is a fixed value (e.g., 1cm each time); (2) Manual on-demand adjustment type: relying on parents' subjective judgment of the child's growth, manually adjusting parameters such as chair height and backrest, without data support, and the adjustment accuracy depends entirely on experience; (3) Single parameter monitoring type: measuring the current height with a height ruler, and matching fixed parameters according to the current height.

[0004] Its shortcomings are as follows: (1) Lack of growth pattern adaptation: It does not identify the non-linear characteristics of children's growth. Fixed cycle / amplitude adjustment cannot match the fluctuation of growth rate caused by seasonal and individual differences. It is easy to under-adjust in spring and over-adjust in winter; (2) Lack of prediction ability: It is only based on the current height / weight data and does not predict the future growth trend. The adjustment action lags behind the actual growth of children, and the adaptation error is generally >1cm; (3) Crude adjustment logic: There is no quantitative growth rate judgment standard. The adjustment cycle and amplitude are not supported by data. Over-adjustment is easy to cause problems such as children's sitting posture imbalance and insufficient lumbar support; (4) Operation depends on manual: Manual adjustment requires frequent measurement of children's height. Parents' operation is cumbersome and the ergonomic chair adaptability will continue to decline due to forgetting the adjustment; (5) No personalized adaptation: It does not establish a special adjustment model for the growth curve of different children. The adaptability is poor and it cannot meet the growth needs of children aged 3-12. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a method, system and seat for adjusting a children's ergonomic chair based on growth rate prediction, so as to achieve dynamic adaptation with the study desk and ensure children's healthy sitting posture and comfortable learning experience.

[0006] To address the aforementioned problems, the first aspect of this invention discloses a method for adjusting a child's ergonomic chair based on growth rate prediction, comprising the following steps: Receive historical physiological data from the user over multiple unit periods; Based on the historical physiological data, the GM(1,1) model is used to predict the physiological data in multiple future unit cycles, which are denoted as the predicted physiological data. The user's growth rate is calculated based on the predicted physiological data, and one or more of the child ergonomic chair's height, lumbar support height, and backrest angle are adjusted according to the growth rate to adapt to the study desk.

[0007] In a preferred embodiment, in a first aspect of the present invention, based on the historical physiological data, a GM(1,1) model is used to predict physiological data over multiple future unit cycles, denoted as predicted physiological data, including: The raw sequence data, composed of the historical physiological data, is sequentially accumulated to obtain the accumulated sequence data:

[0008] in, For cumulative sequence The k-th element represents the accumulated data for the k-th unit period. Original sequence The i-th element represents the original data of the k-th unit cycle, and 1≤k≤i≤n, where n is the total number of historical physiological data in the original sequence.

[0009]

[0010] Construct the grey differential equation:

[0011] in, Here, b is the development coefficient, and b is the gray action quantity. Generate a sequence for the nearest mean The j-th element in the array, 2≤j≤n, and:

[0012]

[0013] The development coefficient is solved using the least squares method. The amount of ash interaction b; Establish a predictive model:

[0014] in, This is the predicted value of the accumulated data for the k-th unit period; Based on the cumulative reduction and restoration of the prediction model, the predicted physiological data values ​​for m unit cycles are obtained:

[0015] in, For the first The predicted value of the original data for the current unit period, i.e., the predicted value of the future unit period. Predicted values ​​of physiological data per unit cycle, .

[0016] As a preferred embodiment, in a first aspect of the present invention, calculating the user's growth rate based on the predicted physiological data includes: The average value of the predicted physiological data over the m unit cycles is taken as the user's growth rate.

[0017] in, This represents the growth rate.

[0018] As a preferred embodiment, in a first aspect of the present invention, adjusting one or more of the child ergonomic chair height, lumbar support height, and backrest angle according to the growth rate to adapt it to a study desk includes: The user's growth rate level is determined based on the growth rate. Based on the growth level, the chair height, lumbar support height, and backrest angle of the children's ergonomic chair are automatically adjusted.

[0019] As a preferred embodiment, in a first aspect of the present invention, determining a user's growth rate level based on the growth rate includes: When the growth rate is greater than the first preset threshold, the corresponding growth rate level is the first level; When the growth rate is greater than or equal to the second preset threshold and less than or equal to the first preset threshold, the corresponding growth rate level is the second level. When the growth rate is less than the second preset threshold, the corresponding growth rate level is the third level.

[0020] As a preferred embodiment, in a first aspect of the present invention, automatically adjusting one or more of the child ergonomic chair's height, lumbar support height, and backrest angle based on the growth level includes: For the first level, the automatic adjustment cycle is set to the first preset time. The chair height is adjusted downward by the first percentage of the growth rate, the chair height is adjusted upward by the second percentage of the growth rate, and the chair back tilt is adjusted backward by the third percentage of the growth rate. The adjustment priority order is chair height, lumbar support height, and chair back tilt. For the second level, the second preset time is used as the automatic adjustment cycle. The chair height is adjusted downward by the fourth percentage of the growth rate, the chair height is adjusted upward by the fifth percentage of the growth rate, and the chair back tilt is adjusted backward by the sixth percentage of the growth rate. The adjustment priority order is chair height, lumbar support height, and chair back tilt. For the third level, the automatic adjustment cycle is set to the third preset time. The chair height is adjusted downward by the seventh percentage of the growth rate, the chair height is adjusted upward by the eighth percentage of the growth rate, and the chair back tilt is adjusted backward by the ninth percentage of the growth rate. The adjustment priority order is lumbar support height, chair height, and chair back tilt. Among them, the second preset time is greater than the first preset time and less than the third preset time, the fourth percentage is greater than the first percentage and less than the seventh percentage, the fifth percentage is greater than the second percentage and less than the eighth percentage, and the sixth percentage is greater than the third percentage and less than the ninth percentage.

[0021] A second aspect of this invention discloses a child ergonomic chair adjustment system based on growth rate prediction, comprising a physiological data sensor, an adjustment mechanism, and a controller, wherein the controller is used for: Receives historical physiological data over multiple unit cycles from user input or collected by physiological data sensors; Based on the historical physiological data, the GM(1,1) model is used to predict the physiological data in multiple future unit cycles, which are denoted as the predicted physiological data. The user's growth rate is calculated based on the predicted physiological data, and one or more of the child ergonomic chair's height, lumbar support height, and backrest tilt angle are adjusted according to the growth rate via an adjustment mechanism to adapt to the study desk.

[0022] As a preferred embodiment, in a second aspect of the present invention, the physiological data sensor includes an ultrasonic height sensor and / or a pressure-type weight sensor, wherein the ultrasonic height sensor is mounted on the backrest or armrest, and the pressure-type weight sensor is mounted on the seat surface. or / and, The adjustment mechanism includes one or more of a chair height adjustment motor, a lumbar support adjustment unit, and a chair back tilt adjustment motor, used to adjust the chair height, lumbar support height, and chair back tilt angle respectively.

[0023] A third aspect of the present invention discloses a chair that includes the growth rate prediction-based ergonomic chair adjustment system for children described in the second aspect of the present invention.

[0024] A fourth aspect of the present invention discloses an electronic device installed on a seat, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute a method for adjusting a child ergonomic chair based on growth rate prediction disclosed in the first aspect of the present invention.

[0025] The fifth aspect of this invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute a method for adjusting a child ergonomic chair based on growth rate prediction, as disclosed in the first aspect of this invention.

[0026] The sixth aspect of this invention discloses a computer program product that, when run on a computer, causes the computer to execute a method for adjusting a children's ergonomic chair based on growth rate prediction, as disclosed in the first aspect of this invention.

[0027] Compared with the prior art, the beneficial effects of the embodiments of the present invention are as follows: 1. By capturing the nonlinear growth characteristics of children through the GM(1,1) model, the growth rate level is obtained. The growth rate level is dynamically matched with the adjustment period and amplitude. Compared with the fixed period adjustment scheme, the adaptation error is greatly reduced. 2. Based on historical physiological data within multiple unit cycles, predict the growth rate for a future period of time, adjust actions in advance to match the growth trend, avoid adjustment lag, and ensure that the timeliness is adapted to the growth rate. 3. Establish a clear mapping relationship between growth rate judgment threshold and adjustment strategy, with data supporting the adjustment cycle / amplitude, to avoid discomfort caused by over-adjustment; 4. It automatically collects growth data, generates adjustment strategies, and executes adjustment actions, eliminating the need for frequent manual operation by parents and improving ease of use; 5. Create a personalized growth curve and adjustment model for each child, suitable for home learning, children's room furnishing, training institutions and other scenarios for children aged 3-12. It is especially suitable for school-age children (6-12 years old) whose growth rate fluctuates significantly, and can accurately match the growth rhythm of different seasons and individuals. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the children's ergonomic chair adjustment method based on growth rate prediction provided in an embodiment of the present invention. Figure 2This is a flowchart illustrating the physiological data prediction method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the children's ergonomic chair adjustment system based on growth rate prediction provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0029] This specific embodiment is merely an explanation of the embodiments of the present invention and is not intended to limit the embodiments of the present invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but as long as they are within the scope of the claims of the embodiments of the present invention, they are protected by patent law.

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the embodiments of the present invention.

[0031] The term "comprising" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product or device.

[0032] In embodiments of the present invention, the words "exemplarily" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0033] This invention captures the nonlinear growth characteristics of children through the GM(1,1) model to obtain the growth rate level. The adjustment period and amplitude are dynamically matched to the growth rate level. Compared with the fixed period adjustment scheme, the adaptation error is greatly reduced. The following is a detailed description with reference to the accompanying drawings.

[0034] Example 1

[0035] Please see Figure 1 As shown, Figure 1This is a flowchart illustrating the ergonomic chair adjustment method for children based on growth rate prediction disclosed in this invention. The execution entity of this invention includes software and hardware structures. The hardware structure mainly includes a controller, which can receive physiological data sent by sensors. The software mainly consists of control logic, such as calculating the growth rate, setting preset parameters, comparing the collected parameters with the preset parameters, determining whether to initiate ergonomic chair adjustment, and specifying the specific parameters for ergonomic chair adjustment. Please refer to... Figure 1 As shown, it may include the following steps: S110: Receives historical physiological data from the user over multiple unit cycles.

[0036] The users of this invention mainly refer to scenarios such as home learning for children aged 3-12, children's room furnishings, and training institutions, especially suitable for school-aged children (6-12 years old) whose growth rate fluctuates significantly. When a user uses the system for the first time, the user's initial physiological data needs to be entered. The system automatically creates a growth data profile. Afterwards, the system can collect the user's physiological data once or multiple times in each unit cycle through physiological data sensors, or the parents can manually supplement the data. When a preset number of unit cycles is obtained, such as one month and 12 months, 12 sets of physiological data (referred to as historical physiological data) can be obtained. Then, the GM(1,1) model can be started to predict the user's physiological data for a future period of time (e.g., the next 3 months), and some parameters of the children's ergonomic chair can be adjusted based on the predicted physiological data to achieve adaptation with the study desk.

[0037] Physiological data can be height data, weight data, or a combination of both. In a preferred embodiment of the invention, height data is preferred. In other embodiments, a multivariate grey model MGM(1,n) can be introduced to integrate multi-dimensional data such as height, weight, and bone age, which can significantly improve prediction accuracy.

[0038] S120. Based on the historical physiological data, the GM(1,1) model is used to predict the physiological data in multiple future unit cycles, which are denoted as the predicted physiological data.

[0039] Please refer to Figure 2 As shown, it may specifically include the following steps: S121. The raw sequence data composed of the historical physiological data is sequentially accumulated to obtain accumulated sequence data:

[0040] in, For cumulative sequence The k-th element represents the accumulated data for the k-th unit period. Original sequence The i-th element represents the original data (historical physiological data) of the k-th unit cycle, and 1≤k≤i≤n, where n is the total number of historical physiological data in the original sequence.

[0041]

[0042] S122. Construct the grey differential equation:

[0043] in, Here, b is the development coefficient, and b is the gray action quantity. Generate a sequence for the nearest mean The j-th element in the array, 2≤j≤n, and:

[0044]

[0045] S123. Solving for the development coefficient using the least squares method. Ash interaction amount b:

[0046] in, Let B be the parameter to be solved, B be the accumulation matrix, and Y be the constant term vector, and:

[0047]

[0048] S124. Establish a prediction model:

[0049] in, This is the predicted value of the accumulated data for the k-th unit period.

[0050] S125. Based on the prediction model, the predicted physiological data for m unit cycles are obtained through cumulative subtraction and restoration:

[0051] in, For the first The predicted value of the original data for the current unit period, i.e., the predicted value of the future unit period. Predicted values ​​of physiological data per unit cycle, For example, m can be 3, which means predicting physiological data for the next 3 months. The predicted physiological data can be used to form a growth rate curve so that users or their parents can have a preliminary understanding of their growth.

[0052] It is understandable that the accuracy of the G(1,1) model needs to be verified before executing step S125. In a preferred embodiment of the present invention, the posterior error test is used to verify the accuracy of the model: the posterior error ratio C≤0.35, the small error probability P≥0.95, to ensure that the prediction accuracy meets the requirements; if the accuracy does not meet the requirements, it is suggested to collect additional original physiological data.

[0053] S130. Calculate the user's growth rate based on the predicted physiological data, and adjust one or more of the child ergonomic chair's height, lumbar support height, and backrest tilt angle according to the growth rate to adapt to the study desk.

[0054] Predicted physiological data is translated into the user's growth rate, which is then used to adjust the ergonomic chair for children. The growth rate can be obtained by averaging the predicted physiological data.

[0055] in, This represents the growth rate.

[0056] The user's growth rate level can be determined based on the growth rate by setting thresholds. For example, if there are three growth rate levels, two preset thresholds need to be set, referred to as the first preset threshold and the second preset threshold.

[0057] When the growth rate is greater than a first preset threshold, the corresponding growth rate level is level one; when the growth rate is greater than or equal to a second preset threshold and less than or equal to the first preset threshold, the corresponding growth rate level is level two; when the growth rate is less than the second preset threshold, the corresponding growth rate level is level three. The first and second preset thresholds can be selected according to the user's actual situation, or they can be set to default values, such as 1cm and 0.8cm respectively.

[0058] Based on the child's growth level, the ergonomic chair automatically adjusts one or more of the following parameters: chair height, lumbar support height, and backrest angle. Key parameters include the automatic adjustment cycle, whether the adjustment is upward or downward, the adjustment range, and the adjustment priority. Specifically, these include: For the first level, an automatic adjustment cycle is set to a first preset time. The chair height is adjusted downwards by a first percentage of the growth rate, upwards by a second percentage of the growth rate, and backwards by a third percentage of the growth rate. The adjustment priority order is chair height, lumbar support height, and backrest angle, with chair height adjustment having a slightly higher priority than lumbar support height adjustment. This means that for the first level, both chair height and lumbar support height are generally adjusted simultaneously during each adjustment. The adjustment cycle for the backrest angle can be appropriately increased. The second and third percentages can be multiples of the first percentage, for example, the second percentage being 0.8 times or 0.1 times the first percentage. The percentages for the second and third levels are similar.

[0059] For the second level, the automatic adjustment cycle is set to the second preset time. The chair height is adjusted downward by the fourth percentage of the growth rate, upward by the fifth percentage of the growth rate, and backward by the sixth percentage of the growth rate. The adjustment priority order is chair height, lumbar support height, and backrest angle.

[0060] For the third level, the automatic adjustment cycle is set to the third preset time. The chair height is adjusted downward by the seventh percentage of the growth rate, the chair height is adjusted upward by the eighth percentage of the growth rate, and the chair back tilt is adjusted backward by the ninth percentage of the growth rate. The adjustment priority order is lumbar support height, chair height, and chair back tilt.

[0061] Among them, the second preset time is greater than the first preset time and less than the third preset time, the fourth percentage is greater than the first percentage and less than the seventh percentage, the fifth percentage is greater than the second percentage and less than the eighth percentage, and the sixth percentage is greater than the third percentage and less than the ninth percentage.

[0062] Assuming m=3, meaning we are predicting height data for the next 3 months, the sum of the predicted height data for the next 3 months can be used as the total predicted growth for the next 3 months. Therefore, the adjustment parameters for the children's ergonomic chair can be shown in Table 1. Table 1. Adjustment parameters of children's ergonomic chairs

[0063] In other embodiments, a sitting pressure sensor array can be added to combine growth data and sitting posture data to optimize the adjustment range of the lumbar support and chair back, further reducing the adaptation error to ≤0.3cm.

[0064] In other embodiments, data on children's study desk usage can be collected simultaneously to generate a linkage adjustment strategy between the ergonomic chair and the study desk, enabling coordinated adjustment of the height and tilt angle of the desk and chair to suit children's reading and writing postures.

[0065] The system updates growth data monthly, retrains the GM(1,1) model, corrects the prediction curve, and dynamically adjusts subsequent adjustment strategies. When the time until the next adjustment is ≤3 days, the status indicator light turns yellow to remind parents to pay attention. If no adjustment is made within the adjustment cycle, the indicator light turns red. Parents can manually adjust the adjustment cycle / amplitude through the touch screen. The system automatically saves personalized settings to adapt to the child's special growth stages (such as growth spurts).

[0066] Example 2

[0067] Please refer to Figure 3 As shown, a child ergonomic chair adjustment system based on growth rate prediction includes: a physiological data sensor 210, an adjustment mechanism 220, and a controller 230, wherein the controller is used for: Receives historical physiological data over multiple unit cycles from user input or collected by physiological data sensors; Based on the historical physiological data, the GM(1,1) model is used to predict the physiological data in multiple future unit cycles, which are denoted as the predicted physiological data. The user's growth rate is calculated based on the predicted physiological data, and one or more of the child ergonomic chair's height, lumbar support height, and backrest tilt angle are adjusted according to the growth rate via an adjustment mechanism to adapt to the study desk.

[0068] The adjustment mechanism 220 may include a chair height adjustment motor, a backrest tilt adjustment motor, and a lumbar support adjustment unit. The chair height adjustment motor is installed at the bottom of the seat and can be a DC geared motor with an adjustment accuracy of ±0.1cm and a response time of ≤500ms. It drives the seat to rise or fall after receiving adjustment commands from the controller. The backrest tilt adjustment motor is installed at the connection between the backrest and the seat and can be a stepper motor with a tilt adjustment accuracy of ±0.5° and a response time of ≤800ms, adapting to the child's back support angle. The lumbar support adjustment unit is installed at the waist of the backrest and can be an electric push rod structure with an adjustment accuracy of ±0.1cm and a response time of ≤600ms, matching the changes in the child's lumbar curve.

[0069] In other embodiments, the adjustment mechanism 220 can be a magnetic levitation adjustment motor, which reduces the adjustment noise to below 30dB and shortens the adjustment response time to ≤300ms, resulting in a better user experience.

[0070] The physiological data sensors include an ultrasonic height sensor (measurement range 80-170cm, accuracy ±0.1cm) and a pressure-type weight sensor (measurement range 10-80kg, accuracy ±0.1kg). The ultrasonic height sensor is installed on the back (side) or armrest of the chair, and the pressure-type weight sensor is installed on the seat surface. Both support manual triggering or timed automatic data acquisition.

[0071] The controller uses an STM32L476 low-power microcontroller, which integrates the GM (1,1) gray prediction algorithm and adjustment strategy generation unit. The data storage unit communicates with the controller through a data line. Together with the peripheral circuits, they form the control module. The data storage unit uses a Flash memory chip to store the child's height / weight data for 12 consecutive months. It supports data supplementation and historical data query, and has a storage capacity of ≥100 sets of data.

[0072] In addition, the system includes a power module and other components, such as an interaction module. These are housed within the cavity inside the ergonomic chair back and connected to the adjustment mechanism, interaction module, and power module via wires.

[0073] The power module has an input voltage of 5V, supports USB power supply and lithium battery life (battery life ≥30 days), includes overcurrent and overvoltage protection circuits, and has an output power ≥10W to meet the needs of motor adjustment and algorithm calculation. Of course, the power module also uses solar auxiliary power supply (e.g., flexible solar panel integrated into the chair back), extending the battery life to ≥60 days, which is energy-saving and environmentally friendly.

[0074] The interactive module may include a touch screen, status indicator lights, and a data interface. The touch screen is a 2.8-inch high-definition touchscreen, installed at the front of the armrest of the ergonomic chair, displaying the child's historical growth curve, predicted growth rate, next adjustment time, and current adaptation parameters. The status indicator lights indicate that the adaptation is normal in green, yellow in yellow, and red in red, indicating that immediate adjustment is needed. The data interface is a data upload / download interface that can use a USB Type-C interface, supporting data export to mobile phones / computers or import of external growth data.

[0075] The interaction module can also include a voice interaction module, which allows parents to query growth data / regulation status by voice. The interaction module can also connect to the parent's mobile APP via Bluetooth to remotely view regulation records and growth curves, further improving the convenience of interaction.

[0076] When the adjustment cycle is reached, the chair height adjustment motor raises and lowers the chair seat according to the calculated adjustment range, the lumbar support adjustment unit adjusts the lumbar support position to match the lumbar curve, and the chair back tilt adjustment motor finely adjusts the tilt angle to ensure a comfortable fit for the back. After the adjustment is completed, the touch screen displays the adjusted parameters, the status indicator light remains green, and the touch screen records the adjustment time and range.

[0077] This invention also provides a chair, which is a children's ergonomic chair. Its main body is made of food-grade PP material and a lightweight aluminum alloy frame, suitable for children aged 3-12. It includes a mechanical structure that can adjust the seat height (adjustment range 25-45cm), the backrest angle (adjustment range 90°-110°), and the lumbar support position (adjustment range 0-5cm). The overall load-bearing capacity is ≥50kg, and the adjustment accuracy is ±0.1cm. In addition to the main body, the chair also includes the above-mentioned children's ergonomic chair adjustment system based on growth rate prediction and other necessary structures. Other necessary structures can adopt existing conventional technologies, which will not be described in detail here.

[0078] Example 3

[0079] Please see Figure 4 , Figure 4 A schematic diagram of an electronic device that can be used to implement embodiments of the present invention is shown. For example... Figure 4 As shown, the electronic device includes at least one processor 310 and a memory, such as a ROM (Read-Only Memory) 320 or a RAM (Random Access Memory) 330, communicatively connected to the at least one processor 310. The memory stores computer programs executable by the at least one processor. The processor 310 can perform various appropriate actions and processes based on the computer program stored in the ROM 320 or loaded into the RAM 330 from storage unit 380. The RAM 330 can also store various programs and data required for the operation of the electronic device. The processor 310, ROM 320, and RAM 330 are interconnected via a bus 340. An I / O (Input / Output) interface 350 is also connected to the bus 340.

[0080] Multiple components in the electronic device are connected to the I / O interface 350, including: an input unit 360, such as a keyboard, mouse, etc.; an output unit 370, such as various types of displays, speakers, etc.; a storage unit 380, such as a disk, optical disk, etc.; and a communication unit 390, such as a network card, modem, wireless transceiver, etc. The communication unit 390 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0081] Processor 310 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 310 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 310 performs one or more steps of the growth rate prediction-based ergonomic chair adjustment method for children described in Embodiment 1 above.

[0082] In some embodiments, a method for adjusting a child ergonomic chair based on growth rate prediction can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 380. In some embodiments, part or all of the computer program can be loaded into or / and installed on an electronic device via ROM 320 and / or communication unit 390. When the computer program is loaded into RAM 330 and executed by processor 310, one or more steps of the method for adjusting a child ergonomic chair based on growth rate prediction described in Embodiment 1 above can be performed. Alternatively, in other embodiments, processor 310 can be configured to perform a method for adjusting a child ergonomic chair based on growth rate prediction by any other suitable means (e.g., by means of firmware).

[0083] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0084] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0085] In the context of embodiments of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0086] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0087] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0088] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0089] The above provides a detailed description of a child ergonomic chair adjustment method, system, and seat based on growth rate prediction disclosed in this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for adjusting a child's ergonomic chair based on growth rate prediction, characterized in that, It includes the following steps: Receive historical physiological data from the user over multiple unit periods; Based on the historical physiological data, the GM(1,1) model is used to predict the physiological data in multiple future unit cycles, which are denoted as the predicted physiological data. The user's growth rate is calculated based on the predicted physiological data, and one or more of the child ergonomic chair's height, lumbar support height, and backrest angle are adjusted according to the growth rate to adapt to the study desk.

2. The method for adjusting a child's ergonomic chair based on growth rate prediction according to claim 1, characterized in that, Based on the historical physiological data, the GM(1,1) model is used to predict physiological data over multiple future unit cycles, denoted as predicted physiological data, including: The raw sequence data, composed of the historical physiological data, is sequentially accumulated to obtain the accumulated sequence data: in, For cumulative sequence The k-th element represents the accumulated data for the k-th unit period. Original sequence The i-th element represents the original data of the k-th unit cycle, and 1≤k≤i≤n, where n is the total number of historical physiological data in the original sequence. Construct the grey differential equation: in, Here, b is the development coefficient, and b is the gray action quantity. Generate a sequence for the nearest mean The j-th element in the array, 2≤j≤n, and: The development coefficient is solved using the least squares method. The amount of ash interaction b; Establish a predictive model: in, This is the predicted value of the accumulated data for the k-th unit period; Based on the cumulative reduction and restoration of the prediction model, the predicted physiological data values ​​for m unit cycles are obtained: in, For the first The predicted value of the original data for the current unit period, i.e., the predicted value of the future unit period. Predicted values ​​of physiological data per unit cycle, .

3. The method for adjusting a child's ergonomic chair based on growth rate prediction according to claim 2, characterized in that, Calculating the user's growth rate based on the predicted physiological data includes: The average value of the predicted physiological data over the m unit cycles is taken as the user's growth rate. in, This represents the growth rate.

4. The method for adjusting a child ergonomic chair based on growth rate prediction according to any one of claims 1-3, characterized in that, Adjusting one or more of the child ergonomic chair's height, lumbar support height, and backrest angle according to the stated growth rate to adapt it to the study desk, including: The user's growth rate level is determined based on the growth rate. Based on the growth level, the chair height, lumbar support height, and backrest angle of the children's ergonomic chair are automatically adjusted.

5. The method for adjusting a child's ergonomic chair based on growth rate prediction according to claim 4, characterized in that, Determining a user's growth rate level based on the growth rate includes: When the growth rate is greater than the first preset threshold, the corresponding growth rate level is the first level; When the growth rate is greater than or equal to the second preset threshold and less than or equal to the first preset threshold, the corresponding growth rate level is the second level. When the growth rate is less than the second preset threshold, the corresponding growth rate level is the third level.

6. The method for adjusting a child ergonomic chair based on growth rate prediction according to claim 5, characterized in that, Based on the aforementioned growth level, the automatic adjustment of one or more of the following parameters of the ergonomic children's chair: chair height, lumbar support height, and backrest angle, including: For the first level, the automatic adjustment cycle is set to the first preset time. The chair height is adjusted downward by the first percentage of the growth rate, the chair height is adjusted upward by the second percentage of the growth rate, and the chair back tilt is adjusted backward by the third percentage of the growth rate. The adjustment priority order is chair height, lumbar support height, and chair back tilt. For the second level, the second preset time is used as the automatic adjustment cycle. The chair height is adjusted downward by the fourth percentage of the growth rate, the chair height is adjusted upward by the fifth percentage of the growth rate, and the chair back tilt is adjusted backward by the sixth percentage of the growth rate. The adjustment priority order is chair height, lumbar support height, and chair back tilt. For the third level, the automatic adjustment cycle is set to the third preset time. The chair height is adjusted downward by the seventh percentage of the growth rate, the chair height is adjusted upward by the eighth percentage of the growth rate, and the chair back tilt is adjusted backward by the ninth percentage of the growth rate. The adjustment priority order is lumbar support height, chair height, and chair back tilt. Among them, the second preset time is greater than the first preset time and less than the third preset time, the fourth percentage is greater than the first percentage and less than the seventh percentage, the fifth percentage is greater than the second percentage and less than the eighth percentage, and the sixth percentage is greater than the third percentage and less than the ninth percentage.

7. A child ergonomic chair adjustment system based on growth rate prediction, characterized in that, It includes: The physiological data sensor, the adjustment mechanism, and the controller, wherein the controller is used for: Receives historical physiological data over multiple unit cycles from user input or collected by physiological data sensors; Based on the historical physiological data, the GM(1,1) model is used to predict the physiological data in multiple future unit cycles, which are denoted as the predicted physiological data. The user's growth rate is calculated based on the predicted physiological data, and one or more of the child ergonomic chair's height, lumbar support height, and backrest tilt angle are adjusted according to the growth rate via an adjustment mechanism to adapt to the study desk.

8. The ergonomic chair adjustment system for children based on growth rate prediction according to claim 7, characterized in that, The physiological data sensor includes an ultrasonic height sensor and / or a pressure-type weight sensor, wherein the ultrasonic height sensor is mounted on the chair back or armrest, and the pressure-type weight sensor is mounted on the chair surface. or / and, The adjustment mechanism includes one or more of a chair height adjustment motor, a lumbar support adjustment unit, and a chair back tilt adjustment motor, used to adjust the chair height, lumbar support height, and chair back tilt angle respectively.

9. A type of seat, characterized in that, It includes the child ergonomic chair adjustment system based on growth rate prediction as described in claim 7 or 8.

10. An electronic device mounted on a seat, characterized in that, It includes: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the ergonomic chair adjustment method for children based on growth rate prediction as described in any one of claims 1-6.

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