Method for adjusting surface temperature of pillow

Abstract

The invention provides a method for adjusting the surface temperature of a pillow, and the method comprises the steps: determining the initial parameters of a temperature hot spot arrangement scheme according to the material and size of the pillow, including the hot spot spacing and the hot spot number, carrying out the simulation of the temperature hot spot arrangement scheme, and obtaining a temperature distribution cloud picture of the surface of the pillow; on the premise that the temperature distribution uniformity index is met, a hot spot arrangement scheme is optimized, and the number of hot spots is reduced while the temperature adjusting effect is guaranteed; according to the optimized hot spot arrangement scheme, temperature sensors and heating elements are arranged at the corresponding positions of the surface of the pillow, the temperature sensors are used for collecting temperature data of the hot spots in real time, and the heating elements conduct power adjustment according to the temperature data; establishing a mathematical model between the heating element power and the hot spot temperature, and calculating and obtaining a control method of each heating element according to different target temperatures; ergonomic simulation software is adopted to analyze the curved surface of the surface of the pillow, and the hot spot arrangement scheme is finally adjusted.

Description

Technical Field

[0001] The invention relates to the field of information technology, and in particular to a method for regulating the surface temperature of a pillow. Background Art

[0002] When designing a temperature-adjustable pillow, it is necessary to arrange multiple temperature hot spots on the surface of the pillow, and each hot spot must be equipped with a temperature sensor and a heating element. However, how to determine the spacing between the hot spots so that the temperature regulation effect is guaranteed while not affecting the wearing comfort due to the hot spots being too dense is an issue worthy of in-depth discussion. If the spacing between the hot spots is too large, there will be obvious temperature differences between adjacent hot spots, making the temperature distribution on the surface of the pillow uneven, affecting the user experience. If the spacing between the hot spots is too small, there will be too many hot spots, which will not only increase the cost, but also may affect the accuracy of temperature regulation due to mutual interference between the hot spots. At the same time, too dense hot spots may also make the pillow surface uneven, affecting the wearing comfort. Therefore, how to balance the contradiction between the hot spot density and the temperature regulation effect, and find an optimal hot spot arrangement scheme that takes into account both temperature regulation performance and comfort is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The present invention provides a method for adjusting the surface temperature of a pillow, which mainly comprises:

[0004] According to the pillow material and size, the initial parameters of the temperature hot spot arrangement scheme are determined, including the hot spot spacing and the number of hot spots. The temperature hot spot arrangement scheme is simulated to obtain the temperature distribution cloud map on the pillow surface.

[0005] Analyze the temperature distribution cloud map and extract the uniformity index of the temperature distribution. If the uniformity index does not reach the preset threshold, adjust the parameters of the hotspot arrangement scheme and repeat the simulation until the temperature distribution uniformity requirement is met;

[0006] On the premise of meeting the temperature distribution uniformity index, the hot spot arrangement plan is optimized to reduce the number of hot spots while ensuring the temperature regulation effect;

[0007] According to the optimized hotspot arrangement scheme, temperature sensors and heating elements are set at corresponding positions on the pillow surface. The temperature sensors are used to collect temperature data at the hotspots in real time, and the heating elements adjust the power according to the temperature data.

[0008] Establish a mathematical model between the power of the heating element and the hot spot temperature, and calculate and obtain the control method of each heating element according to different target temperatures;

[0009] Ergonomic simulation software was used to analyze the curved surface of the pillow and make final adjustments to the hotspot placement plan;

[0010] According to the final adjusted hotspot arrangement plan, a control circuit and program for a temperature-adjustable pillow are established at the bottom of the pillow to process the data collected by the temperature sensor and the control strategy information output by the heating element, control the on and off of each heating element, and provide the user with temperature setting and mode selection functions.

[0011] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0012] The present invention discloses a method for adjusting the surface temperature of a pillow. By analyzing the material and size of the pillow, the initial layout of the temperature hot spots is determined, and the temperature distribution is optimized by using simulation and image processing technology to achieve uniform distribution of the surface temperature. On this basis, the present invention uses ergonomic simulation to make final adjustments to the hot spot layout, and sets temperature sensors and heating elements at corresponding positions. By establishing a mathematical model, the system can automatically calculate the control strategy of the heating element according to the target temperature. The present invention also includes a bottom control circuit and a human-computer interaction interface, which realizes real-time monitoring, intelligent adjustment and user-defined settings of the temperature. This method can not only provide a comfortable and uniform temperature experience, but also can be accurately adjusted according to personal needs, and can also achieve efficient use of energy while ensuring comfort, thereby improving the user's sleep quality and experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a flow chart of a method for adjusting the surface temperature of a pillow.

[0014] Figure 2 The present invention is a schematic diagram of a method for adjusting the surface temperature of a pillow.

[0015] Figure 3 It is another schematic diagram of a method for adjusting the surface temperature of a pillow according to the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0017] like Figure 1-3 In this embodiment, a method for adjusting the surface temperature of a pillow may specifically include:

[0018] Step S101, according to the material and size of the pillow, determine the initial parameters of the temperature hot spot arrangement scheme, including the hot spot spacing and the number of hot spots, simulate the temperature hot spot arrangement scheme, and obtain a temperature distribution cloud map on the pillow surface.

[0019] Obtain pillow material parameters and size data, select pillow material from the preset material list, and obtain corresponding thermal conductivity and specific heat capacity parameters based on the selected material. Perform finite element analysis meshing on the three-dimensional digital model, set boundary conditions and initial temperature distribution. Use a genetic algorithm to optimize the preset hotspot layout scheme, with hotspot spacing and number as decision variables and temperature uniformity as the objective function. If the fitness function is the inverse of the pillow surface temperature variance, then add constraints on hotspot power and total energy consumption. Obtain the optimal hotspot layout parameters through multiple iterative calculations, including hotspot position coordinates and power values. Perform transient heat conduction simulation based on the optimal hotspot layout parameters, and use a two-dimensional heat conduction partial differential equation to describe the temperature field change. The two-dimensional heat conduction partial differential equation is:

[0020]

[0021] In the formula, u represents temperature, t represents time, x and y represent spatial coordinates, and α represents the thermal diffusion coefficient. Discretize the temperature field equation, construct the differential format, and obtain the change process of the temperature field over time. Record the temperature value of each grid node at different times to form a temperature and time data matrix. Perform bilinear interpolation on the simulation result data to generate a continuous temperature distribution function. Map the temperature value to the corresponding color space. Draw a temperature cloud map of the pillow surface, generate a temperature scale, and mark the temperature range corresponding to different colors.

[0022] Specifically, a three-dimensional digital model is constructed according to the pillow material parameters and size data, and the specific pillow material is selected from the preset material list, and the corresponding thermal conductivity and specific heat capacity parameters are called. Finite element analysis is used to mesh the model, set boundary conditions and initial temperature distribution. The hotspot arrangement scheme is optimized using a genetic algorithm, with the hotspot spacing and number as decision variables and temperature uniformity as the objective function. The fitness function is defined as the inverse of the pillow surface temperature variance, and the hotspot power and total energy consumption constraints are added. The optimal hotspot arrangement parameters, including the hotspot position coordinates and power values, are obtained through multiple iterative calculations. Based on the optimal hotspot arrangement parameters, transient heat conduction simulation is performed, and the temperature field change is described using a two-dimensional heat conduction partial differential equation. The equation is discretized using the finite difference method, a differential format is constructed, and the temperature field change process over time is obtained by time stepping. The temperature value of each grid node at different times is recorded to form a temperature-time data matrix. The simulation result data is bilinearly interpolated to generate a continuous temperature distribution function. The rainbow color spectrum is used as a color mapping scheme to map the temperature value to the corresponding color space. Draw a temperature cloud map on the pillow surface to enhance the visualization of temperature changes. Generate a temperature scale and mark the temperature range corresponding to different colors to facilitate intuitive understanding of the temperature distribution. When constructing a three-dimensional digital model, select memory foam from the preset material list, whose thermal conductivity is 0.04W / (m·K) and specific heat capacity is 1800J / (kg·K). The pillow size is set to 60cm×40cm×15cm, and tetrahedral meshing is used with a unit size of 0.5cm. The boundary condition is set to natural convection, and the initial temperature is 25℃. The genetic algorithm optimizes the hotspot layout scheme, with the population size set to 100 and the number of iterations set to 500. The decision variables include the x, y coordinates and power values ​​of 10 hotspots, with value ranges of [0,60], [0,40], and [0,60], respectively.

[0023] [0.5,2]W. The fitness function is defined as the inverse of the pillow surface temperature variance, and the constraint is that the total power does not exceed 15W. After iterative optimization, the optimal hotspot arrangement parameters are obtained as follows:

[0024] [(10,5,1.2),(20,15,1.5),(30,25,1.8),(40,35,1.3),(50,10,1.1),(15,30,1.4),(25,20,1.6),(35,5,1.2),(45,25,1.7),(55,35,1.3)], the coordinate unit is cm, and the power unit is W. The transient heat conduction simulation uses the two-dimensional heat conduction equation: Where α is the thermal diffusion coefficient, T(x,y,t) is the temperature field, which represents the temperature at any given time t and any position (x,y); α is the thermal diffusion coefficient of the material, defined as α=k / ρ*cp, where k is the thermal conductivity, ρ is the density, and cp is the specific heat capacity. The explicit finite difference method is used for solution, with a time step of 0.1s and a spatial step of 0.5cm. The total simulation time is 1800s, and the temperature field data is recorded every 10s. The simulation results are bilinearly interpolated, and the interpolation grid size is 0.1cm×0.1cm. The rainbow color spectrum is used for color mapping, with blue representing the lowest temperature of 25℃ and red representing the highest temperature of 40℃. Draw a temperature cloud map and generate a temperature scale, marking the correspondence between color and temperature, with an interval of 1℃.

[0025] Step S102, analyzing the temperature distribution cloud map, extracting the uniformity index of the temperature distribution, if the uniformity index does not reach the preset threshold, adjusting the parameters of the hotspot arrangement scheme, repeating the simulation until the temperature distribution uniformity requirement is met.

[0026] A temperature distribution cloud map is obtained, and a Gaussian filter with a preset kernel size and standard deviation is used to filter the temperature distribution cloud map to obtain a filtered temperature distribution image; according to the filtered temperature distribution image, the filtered temperature distribution image is divided into multiple temperature regions; for the multiple temperature regions, the area and average temperature value of each temperature region are calculated; according to the area and average temperature value of each temperature region, a temperature distribution uniformity index is calculated using a weighted standard deviation method; if the temperature distribution uniformity index is greater than a preset threshold, the hotspot arrangement scheme parameters are adjusted; according to the adjusted hotspot arrangement scheme parameters, the heat source boundary conditions are updated, and the heat conduction equation is solved to obtain a new temperature distribution cloud map; filtering, temperature region segmentation, uniformity index calculation, parameter adjustment and heat conduction equation solution are repeated until the temperature distribution uniformity index is less than or equal to the preset threshold; a hotspot arrangement scheme that meets the preset threshold condition is output, and the hotspot arrangement scheme includes heat source coordinates and corresponding power values.

[0027] Specifically, the temperature distribution cloud map is image preprocessed, and a Gaussian filter with a kernel size of 5x5 and a standard deviation of 1.0 is used to eliminate noise. The image is segmented into different temperature regions by the Otsu adaptive threshold segmentation algorithm, and the area and average temperature value of each region are extracted. The temperature distribution uniformity index is calculated, and the temperature standard deviation of all temperature regions is calculated by the weighted standard deviation method. The weight is the proportion of the area of ​​each region to the total area, and the weighted temperature standard deviation is obtained as the uniformity index. If the uniformity index is greater than the preset threshold, the gradient descent algorithm is used to adjust the parameters of the hotspot layout scheme, and the uniformity index is used as the objective function to optimize the hotspot position and power. The initial learning rate is set to 0.01, the maximum number of iterations is 1000, and convergence is determined when the objective function change rate is less than 0.001, and a new hotspot layout scheme is generated, including the updated heat source coordinates and power values. Based on the adjusted hotspot layout scheme, the simulation is re-performed, the heat source boundary conditions are updated, the new heat source position and power are set, the heat conduction equation is solved, and a new temperature distribution cloud map is generated. Repeat the process of image preprocessing, uniformity calculation, parameter adjustment and simulation until the uniformity index is less than or equal to the preset threshold, and output the final hotspot layout plan, including the heat source coordinates and the corresponding power value. Perform image preprocessing on the temperature distribution cloud map, and use Gaussian filtering with a kernel size of 5x5 and a standard deviation of 1.0 to eliminate noise and obtain the filtered image. Apply the Otsu adaptive threshold segmentation algorithm to segment the image into 10 different temperature regions, and the temperature range of each region is 3℃. Extract the area pixel number and average temperature value of each region, such as region 1 has an area of ​​12,500 pixels and an average temperature of 26.5℃; region 2 has an area of ​​18,000 pixels and an average temperature of 29.3℃. Calculate the temperature distribution uniformity index, using the weighted standard deviation method, and the weight is the proportion of the area of ​​each region to the total area. Assuming the total area is 100,000 pixels, the weight of region 1 is 0.125 and the weight of region 2 is 0.18. The weighted temperature standard deviation is calculated to be 2.8℃, which is used as the uniformity index. If the uniformity index is greater than the preset threshold of 2.5°C, the gradient descent algorithm is used to adjust the hotspot layout plan. Set the initial learning rate to 0.01 and the maximum number of iterations to 1000. Optimize the positions and powers of the 10 hotspots, such as adjusting hotspot 1 from (10cm, 15cm, 1.5W) to (12cm, 16cm, 1.3W). When the rate of change of the objective function is less than 0.001, convergence is determined and a new hotspot layout plan is generated. Re-simulate based on the adjusted plan, update the heat source boundary conditions, set the new heat source position and power, solve the heat conduction equation for 60 seconds, and generate a new temperature distribution cloud map. Repeat the above process until the uniformity index drops to 2.4°C, which is less than the preset threshold, and output the final hotspot layout plan, including the coordinates and power values ​​of the 10 hotspots.

[0028] Step S103, on the premise of satisfying the temperature distribution uniformity index, the hot spot arrangement scheme is optimized to reduce the number of hot spots while ensuring the temperature regulation effect.

[0029] According to the existing hotspot arrangement scheme, a two-dimensional Gaussian diffusion model is used to calculate the temperature distribution around the hotspot to obtain the effective coverage radius of the hotspot, wherein the effective coverage radius is defined as the distance at which the temperature drops to the initial value 1 / e. A clustering algorithm is used to group the hotspots, and the hotspot coordinates are used as features to merge hotspots with close distances and overlapping temperature influence ranges, wherein the number of clusters is gradually reduced from the current number of hotspots. The updated hotspot arrangement scheme is obtained by calculating the cluster center as the new hotspot position and taking the sum of all hotspot powers in the cluster as the new hotspot power. For the updated hotspot arrangement scheme, the finite element method is used to solve the heat conduction equation, set the grid size, and the boundary condition is natural convection. The time step is solved to obtain a new temperature distribution cloud map. If the temperature distribution uniformity index meets the preset threshold, the current hotspot arrangement scheme is saved, the hotspot reduction operation continues, and the clustering and update operations are repeated until the temperature distribution uniformity index does not meet the requirements or the number of hotspots reaches the preset minimum value.

[0030] Specifically, according to the existing hotspot layout plan, the temperature influence range of each hotspot is calculated, and the temperature distribution around the hotspot is calculated using a two-dimensional Gaussian diffusion model to obtain the effective coverage radius of each hotspot, which is defined as the distance where the temperature drops to 1 / e of the initial value. The hotspots are grouped using the K-means clustering algorithm, with the hotspot coordinates as the feature. The number of clusters is gradually reduced from the current number of hotspots, and hotspots with close distances and overlapping temperature influence ranges are merged. The new position and power of the merged hotspot are calculated, the cluster center is taken as the new hotspot position, the sum of the powers of all hotspots in the cluster is taken as the new hotspot power, and the hotspot layout plan is updated. The temperature distribution of the updated hotspot layout plan is simulated, and the finite element method is used to solve the heat conduction equation. The grid size is set to 1cm×1cm, the boundary condition is natural convection, and the solution time step is 0.1s. The new temperature distribution cloud map is obtained, and the temperature distribution uniformity index is calculated. If the temperature distribution uniformity index meets the preset threshold, the current hotspot arrangement plan is saved, and the hotspot reduction operation is continued. The clustering, updating and simulation steps are repeated until the temperature distribution uniformity index does not meet the requirements or the number of hotspots reaches the preset minimum value. 50% of the initial number of hotspots is taken as the lower limit, and the final optimized hotspot arrangement plan is output. For the 10 hotspots in the initial hotspot arrangement plan, the two-dimensional Gaussian diffusion model is used to calculate the temperature influence range of each hotspot. Taking hotspot 1 as an example, the initial temperature is 40℃ and the ambient temperature is 25℃. The effective coverage radius is calculated to be 8cm. The K-means clustering algorithm is used to group the hotspots. The initial number of clusters is set to 9, and clustering is performed based on the hotspot coordinates. The clustering results show that hotspots 2 and 5 can be merged. The new hotspot position is (15cm, 20cm), and the new hotspot power is 3.5W, which is the power of the original hotspot 2 of 1.8W plus the power of hotspot 5 of 1.7W. The temperature distribution of the 9 updated hotspot layouts was simulated, and the heat conduction equation was solved using the finite element method. The grid size was set to 1 cm × 1 cm, and the boundary condition was a natural convection coefficient of 5 W / (m 2 ·K), the solution time step is 0.1s, and the total simulation time is 600s. After obtaining the new temperature distribution cloud map, the temperature distribution uniformity index is calculated to be 2.3℃, which meets the preset threshold of 2.5℃. Save the current layout plan of 9 hotspots and continue to perform the hotspot reduction operation. Repeat the above process until the number of hotspots is reduced to 5, which is 50% of the initial number of hotspots 10 or the temperature uniformity index exceeds 2.5℃. Finally, the optimized hotspot layout plan is obtained, which includes 7 hotspots, with positions and powers of (12cm, 18cm, 3.2W), (25cm, 30cm, 2.8W), (38cm, 15cm, 3.5W), (50cm, 25cm, 3.0W), (15cm, 40cm, 2.5W), (35cm, 45cm, 2.7W), (55cm, 35cm, 2.3W), and the temperature distribution uniformity index is 2.4℃.

[0031] Step S104, according to the optimized hotspot arrangement scheme, temperature sensors and heating elements are set at corresponding positions on the pillow surface, the temperature sensors are used to collect temperature data at the hotspots in real time, and the heating elements adjust power according to the temperature data.

[0032] The hot spot distribution information on the pillow surface is obtained, and the arrangement positions of the temperature sensor and the heating element are determined according to the hot spot distribution information; wherein the temperature sensor adopts a thermocouple type, and the heating element adopts a flexible thin film electric heater. The temperature data of the temperature sensor is collected at a preset frequency; the temperature data is converted into a digital signal through an analog-to-digital converter and stored in a circular queue buffer. The proportional-integral-differential control method is adopted to calculate the output power of the heating element according to the deviation between the current temperature and the target temperature; wherein the controller output of the proportional-integral-differential control method consists of a proportional term, an integral term, and a differential term. The PID parameters are determined by the Ziegler-Nichols tuning method, and the proportional gain is gradually increased until the system oscillates; the critical gain and the oscillation period are recorded to calculate the PID parameters. The calculated output power is converted into a voltage signal; the power output of the heating element is controlled by a pulse width modulation method, and the modulation frequency and resolution of the pulse width modulation method are preset values.

[0033] Specifically, according to the optimized hotspot arrangement scheme, the same number of temperature sensors and heating elements as the hotspots are arranged at the corresponding positions on the pillow surface. The temperature sensor adopts the thermocouple type with a measurement range of 0-100℃ and an accuracy of ±0.1℃. The heating element adopts a flexible thin film heater with a power range of 0-5W. The temperature data of the temperature sensor is collected at a frequency of 1Hz, and the analog signal is converted into a digital signal through an analog-to-digital converter and stored in a circular queue buffer with a capacity of 60 data points. The proportional-integral-differential (PID) control algorithm is adopted to calculate the output power of the heating element according to the deviation between the current temperature and the target temperature. The PID parameters are determined by the Ziegler-Nichols tuning method. The specific process includes gradually increasing the proportional gain Kp until the system has equal amplitude oscillation, recording the critical gain Ku and the oscillation period Tu, and then calculating the PID parameters according to the empirical formula. The output u(t) of the PID controller consists of the proportional term Kpe(t), the integral term Ki∫e(t)dt and the differential term Kdde(t) / dt, where e(t) is the temperature deviation. The calculated output power is converted into a corresponding voltage signal, and the pulse width modulation (PWM) method with a frequency of 1kHz and a resolution of 8 bits is used to control the power output of the heating element to achieve precise temperature regulation. Seven pairs of temperature sensors and heating elements are arranged at the optimized seven hot spots, such as K-type thermocouples and 5W flexible film heaters at (12cm, 18cm). The temperature data is collected at a frequency of 1Hz, and the 12-bit analog-to-digital converter converts the analog signal into a digital signal with a resolution of 0.024℃. The circular queue buffer stores the temperature data of the last 60 seconds. In the PID control algorithm, the parameters are determined by the Ziegler-Nichols method, and Kp is increased by 0.1 each time from 0 until equal amplitude oscillation occurs near the target temperature of 35℃, at which time Ku=1.5 and Tu=10s. According to the Ziegler-Nichols empirical formula, Kp = Ku / 2 = 0.75, Ki = Ku / 1.5*Tu = 0.1, and Kd = Ku*0.125 = 0.1875. During the PID controller calculation process, if the current temperature is 33.5°C, the target temperature is 35°C, the temperature deviation e(t) = 1.5°C, the integral term ∫e(t)dt = 5°C·s, and the differential term de(t) / dt = -0.1°C / s, then the output u(t) = 1.35 + 0.9 - 0.1125 = 2.1375W. Convert 2.1375W to a 2.677V voltage signal, assuming that 5W corresponds to 6.25V. The PWM control adopts 1kHz frequency, 8-bit resolution (256 levels), and a duty cycle of (2.677 / 6.25)*255≈109, that is, a PWM waveform with a duty cycle of 109 / 256 is output to control the power of the heating element and achieve precise temperature adjustment to 35°C.

[0034] Step S105, establishing a mathematical model between the power of the heating element and the hot spot temperature, and calculating and obtaining the control method of each heating element according to different target temperatures.

[0035] Obtain historical data of heating element power and hot spot temperature, wherein the historical data includes the temperature change process under different power inputs; according to the historical data, use the least squares method to fit the temperature change curve to obtain a mathematical model between the heating element power and the hot spot temperature; based on the mathematical model, use a feedback controller, and the feedback controller uses a model predictive control algorithm to solve the optimal power sequence; input the difference between the target temperature and the current temperature into the feedback controller to calculate the power control sequence of each heating element; establish a hot spot coupling coefficient matrix, and the hot spot coupling coefficient matrix is ​​determined according to the distance between the hot spots and the thermal conductivity of the material; according to the hot spot coupling coefficient matrix, adjust the power control sequence.

[0036] Specifically, the historical data of heating element power and hotspot temperature are collected, and the temperature change process under different power inputs is recorded once per second for 30 minutes, including the initial temperature, target temperature, ambient temperature and heating time. The temperature change curve is fitted by the least square method, and a mathematical model between the heating element power and the hotspot temperature is established, taking into account the factors of heat conduction and heat loss. The error function is defined as the sum of the squares of the actual temperature and the predicted temperature. By solving the parameter value that minimizes the error function, the function expression of temperature change over time is obtained: T(t) = a(1-e^(-bt)) + c, where a, b, and c are pending parameters, and e is the base of the natural logarithm. Parameter a represents the maximum amplitude of temperature change, parameter b determines the speed of temperature change, that is, how fast the temperature changes over time, and parameter c is the offset of the temperature change curve, which determines the starting temperature level of the temperature change curve. According to the established mathematical model, a feedback controller is used, the model predictive control algorithm is adopted, a 5-minute prediction time domain is selected, and the objective function is set as the weighted sum of the square of the temperature error and the power change. The optimal power sequence is solved by quadratic programming. For different target temperatures, the temperature difference is input into the feedback controller to calculate the power control sequence of each heating element. Considering the thermal influence between adjacent hot spots, the hot spot coupling coefficient matrix is ​​established. The coupling coefficient is calculated according to the distance between hot spots and the thermal conductivity of the material, and the power allocation strategy is adjusted to achieve precise temperature control. For the seven hot spot positions, power and temperature data are collected once a second for 30 minutes. Taking hot spot 1 as an example, the initial temperature is 25℃, the target temperature is 35℃, and the ambient temperature is 22℃. The power starts from 0.5W and increases by 0.5W every 5 minutes until 3W. The temperature change curve is fitted by the least squares method, and the function T(t)=10(1-e^(-0.005t))+25 is obtained, where T is temperature and t is time. Based on this model, the model predictive controller is designed, and the 5-minute prediction time domain is selected. The objective function is Σ(T-Tset)^2+0.1Σ(ΔP)^2, where Tset is the set temperature and ΔP is the power change. If the target temperature is 33°C, the controller calculates the optimal power sequence for the next 5 minutes as [2.1W, 2.0W, 1.9W, 1.9W, 1.8W]. Considering the influence of adjacent hot spots, a 7×7 coupling coefficient matrix is ​​established. For example, the coupling coefficient between hot spots 1 and 2 is 0.2, which means that 20% of the heat from hot spot 1 affects hot spot 2. According to the coupling matrix, the power output of hot spot 1 is adjusted to 1.75W to compensate for the 0.35W heat influence from hot spot 2. This achieves precise temperature control and keeps the temperatures of the seven hot spots within the range of 33±0.5°C.

[0037] Step S106, using ergonomic simulation software to analyze the curved surface of the pillow and make final adjustments to the hotspot arrangement plan.

[0038] The three-dimensional model of the pillow was obtained and imported into the ANSYS ergonomic simulation software. The density, elastic modulus and thermal conductivity coefficient were set according to the pillow material properties to construct a virtual human head and neck model. The finite element analysis method was used to simulate the contact process between the human head and the pillow, and the pressure distribution and contact area were calculated to determine the key support points and high-pressure areas. The hotspot layout plan was superimposed on the pressure distribution map to determine the degree of matching between the hotspot position and the high-pressure area and key support points, and the contact area and influence range covered by the hotspot were calculated. If the pressure distribution and curvature analysis results met the preset conditions, the gradient descent method was used to adjust the hotspot position and power distribution to optimize the uniformity of heat distribution. The final hotspot layout plan was generated based on the optimization results to obtain the hotspot coordinates and power parameters.

[0039] Specifically, the three-dimensional model of the pillow was imported into the ANSYS ergonomic simulation software, and the pillow material properties, including density, elastic modulus and thermal conductivity, were set. A virtual human head and neck model was constructed, with a head weight of 4.5 kg and a neck length of 12 cm. Two sleeping postures, supine and side-lying, were defined. Static pressure analysis was performed, and the finite element analysis method was used to simulate the contact process between the human head and the pillow, calculate the pressure distribution and contact area, identify the key support points and high-pressure areas, and obtain the curvature distribution map of the pillow surface. The hotspot layout scheme was superimposed on the pressure distribution map, and the matching degree between the hotspot position and the high-pressure area and the key support points was analyzed. The contact area and influence range covered by each hotspot were calculated, and the matching degree was defined as the percentage of the overlapping area between the hotspot and the high-pressure area to the total area of ​​the hotspot. Based on the pressure distribution and curvature analysis results, the gradient descent method was used to adjust the hotspot position and power distribution, optimize the uniformity of heat distribution, and the objective function was the weighted sum of the hotspot matching degree and the temperature uniformity. The optimization was iteratively optimized until convergence or the maximum number of iterations was reached, and the final hotspot layout scheme was generated, and the hotspot coordinates and power parameters were output. Import the 3D model of a 60cm×40cm×15cm pillow into the ANSYS software and set the memory foam material properties: density 50kg / m 3, elastic modulus 4000Pa, thermal conductivity 0.04W / (m·K). A virtual human head and neck model was constructed: head weight 4.5kg, neck length 12cm, head inclination angle 15° in supine position, and neck bending angle 20° in side-lying position. Static pressure analysis was performed, using tetrahedral meshing, unit size 0.5cm, setting head gravity load, and calculating the maximum pressure of 1.2kPa, located 5cm behind the center of the pillow. Generate a curvature distribution map with a maximum curvature radius of 25cm. Superimpose the 7 hotspot layout schemes on the pressure distribution map, and calculate the overlapping area of ​​each hotspot with the high-pressure area. For example, the overlapping area of ​​hotspot 1 accounts for 75%. Use the gradient descent method to optimize the hotspot layout, set the learning rate to 0.01, the maximum number of iterations to 100, and the objective function to 0.7×matching degree + 0.3×temperature uniformity. After 63 iterations, the final hotspot arrangement solution was obtained: Hotspot 1 was moved to (28cm, 22cm), and the power was adjusted to 2.8W; Hotspot 2 was moved to (35cm, 18cm), and the power was adjusted to 2.5W. Under the final solution, the average matching degree of all hotspots was increased to 85%, and the temperature uniformity was improved by 12%.

[0040] Step S107, based on the final adjusted hotspot arrangement plan, establish a control circuit and program for the temperature-adjustable pillow at the bottom of the pillow to process the data collected by the temperature sensor and the control strategy information output by the heating element, control the on and off of each heating element, and provide the user with temperature setting and mode selection functions.

[0041] The input signal of the microcontroller is obtained, and the input signal is generated by collecting temperature sensor data through an analog-to-digital converter; a control program is executed according to the input signal, and the control program includes a filtering processing module, a PID control algorithm module and a PWM output control module; a weighted average data fusion algorithm is used to process the data of multiple temperature sensors, and the optimal control output is calculated in combination with the control strategy information output by the PID controller; user input information sent by a human-computer interaction interface is received, and the user input information includes a temperature setting value and an operation mode selection; the user input information is transmitted to the microcontroller, and the microcontroller updates the control parameters according to the received user input information.

[0042] Specifically, the control circuit is designed according to the hotspot arrangement scheme, and the STM32F103 series microcontroller is selected as the main controller, equipped with a 12-bit analog-to-digital converter, a power drive module with a rated current of 5A and a UART communication interface, and the temperature sensor and the heating element are connected to the corresponding circuit ports. The control program is written to realize temperature data acquisition, filtering processing, PID control algorithm and PWM output control, and a 1ms timer interrupt is set for periodic sampling and control update. The Ziegler-Nichols tuning method is used to set the PID parameters. The weighted average data fusion algorithm is designed to comprehensively process the data of multiple temperature sensors and the control strategy information output by the PID controller, calculate the optimal control output, and judge the on-off state of each heating element according to the preset threshold of 0.5℃. The human-computer interaction interface is developed, including a 3.5-inch touch screen display module and 4 function buttons, to realize temperature setting (adjustable from 18 to 32℃), mode selection (normal, energy saving, sleep) and status display functions. The interface layout includes the current temperature display area, the target temperature adjustment slider, the mode selection button and the setting menu. The user input is transmitted to the main controller through the UART serial port communication, and the refresh rate is 10Hz. Based on the arrangement of 7 hot spots, the control circuit is designed using an STM32F103RBT6 microcontroller with a clock frequency of 72MHz, 7 12-bit ADC channels to collect temperature data, and 7 5A rated current MOS tube drive circuits to control the heating element. In the control program, a 1ms timer interrupt triggers temperature sampling, and a 20-point moving average filter is used. The filtered data enters the PID controller. The PID parameters are adjusted by the Ziegler-Nichols method, and Kp=2.5, Ki=0.8, and Kd=0.4 are obtained. The PID output is processed by a weighted average data fusion algorithm, and the weight is assigned according to the importance of the hot spot position, such as a center hot spot weight of 0.3 and an edge hot spot weight of 0.1. The fused control output modulates the power of the heating element through PWM, with a PWM frequency of 20kHz and a resolution of 8 bits. The human-computer interaction interface uses a 3.5-inch 320x240 resolution touch screen, which displays the current temperature of 33.5℃, the target temperature adjustment range of 18-32℃, and the slider step of 0.5℃. The interface layout includes a temperature display area, which occupies 60% of the area, a temperature adjustment slider, which occupies 20% of the area, a mode selection button, which occupies 15% of the area, including three modes: normal, energy saving, and sleep, and a setting menu, which occupies 5% of the area. The UART communication baud rate is set to 115200bps, and the user input data is sent to the main controller every 100ms.

[0043] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adjusting the surface temperature of a pillow, characterized in that: The method comprises: According to the pillow material and size, the initial parameters of the temperature hot spot arrangement scheme are determined, including the hot spot spacing and the number of hot spots. The temperature hot spot arrangement scheme is simulated to obtain the temperature distribution cloud map on the pillow surface. Analyze the temperature distribution cloud map and extract the uniformity index of the temperature distribution. If the uniformity index does not reach the preset threshold, adjust the parameters of the hotspot arrangement scheme and repeat the simulation until the temperature distribution uniformity requirement is met; On the premise of meeting the temperature distribution uniformity index, the hot spot arrangement plan is optimized to reduce the number of hot spots while ensuring the temperature regulation effect; According to the optimized hotspot arrangement scheme, temperature sensors and heating elements are set at corresponding positions on the pillow surface. The temperature sensors are used to collect temperature data at the hotspots in real time, and the heating elements adjust the power according to the temperature data. Establish a mathematical model between the power of the heating element and the hot spot temperature, and calculate and obtain the control method of each heating element according to different target temperatures; Ergonomic simulation software was used to analyze the curved surface of the pillow and make final adjustments to the hotspot placement plan; According to the final adjusted hotspot arrangement plan, a control circuit and program for a temperature-adjustable pillow are established at the bottom of the pillow to process the data collected by the temperature sensor and the control strategy information output by the heating element, control the on and off of each heating element, and provide the user with temperature setting and mode selection functions.

2. The method according to claim 1, characterized in that The initial parameters of the temperature hotspot arrangement scheme are determined according to the pillow material and size, including the hotspot spacing and the number of hotspots, and the temperature hotspot arrangement scheme is simulated to obtain a temperature distribution cloud map on the pillow surface, including: Obtain pillow material parameters and size data and build a three-dimensional digital model, select pillow material from a preset material list, and obtain corresponding thermal conductivity and specific heat capacity parameters according to the selected material; Perform finite element analysis meshing on the three-dimensional digital model, set boundary conditions and initial temperature distribution; Genetic algorithm is used to optimize the preset hotspot arrangement scheme, with hotspot spacing and number as decision variables and temperature uniformity as the objective function; If the fitness function is the inverse of the variance of the pillow surface temperature, then constraints on hotspot power and total energy consumption are added; The optimal hotspot arrangement parameters, including hotspot position coordinates and power values, are obtained through multiple iterative calculations; According to the optimal hot spot arrangement parameters, transient heat conduction simulation is performed, and the temperature field change is described by a two-dimensional heat conduction partial differential equation. The two-dimensional heat conduction partial differential equation is: u represents temperature, t represents time, x and y represent spatial coordinates, and α represents the thermal diffusion coefficient; Discretize the temperature field equation, construct the difference format, and obtain the change process of the temperature field over time; Record the temperature value of each grid node at different times to form a temperature and time data matrix; Perform bilinear interpolation on the simulation result data to generate a continuous temperature distribution function; Map the temperature value to the corresponding color space; Draw a temperature cloud map of the pillow surface, generate a temperature scale, and mark the temperature ranges corresponding to different colors.

3. The method according to claim 1, characterized in that The temperature distribution cloud map is analyzed to extract the uniformity index of the temperature distribution. If the uniformity index does not reach the preset threshold, the parameters of the hot spot arrangement scheme are adjusted and the simulation is repeated until the temperature distribution uniformity requirement is met, including: Acquire a temperature distribution cloud map, and filter the temperature distribution cloud map using a Gaussian filter with a preset kernel size and standard deviation to obtain a filtered temperature distribution image; According to the filtered temperature distribution image, segmenting the filtered temperature distribution image into a plurality of temperature regions; For the multiple temperature regions, calculating the area and average temperature value of each temperature region; According to the area and average temperature value of each temperature zone, the temperature distribution uniformity index is calculated using the weighted standard deviation method; If the temperature distribution uniformity index is greater than a preset threshold, the hotspot arrangement scheme parameters are adjusted; According to the adjusted hotspot arrangement parameters, the heat source boundary conditions are updated, the heat conduction equation is solved, and a new temperature distribution cloud map is obtained; Repeat the filtering process, temperature region segmentation, uniformity index calculation, parameter adjustment and heat conduction equation solution until the temperature distribution uniformity index is less than or equal to a preset threshold; A hotspot arrangement scheme that meets the preset threshold condition is output, wherein the hotspot arrangement scheme includes heat source coordinates and corresponding power values.

4. The method according to claim 1, characterized in that The method of optimizing the hotspot arrangement scheme under the premise of satisfying the temperature distribution uniformity index, while ensuring the temperature regulation effect, reducing the number of hotspots, includes: According to the existing hotspot arrangement scheme, a two-dimensional Gaussian diffusion model is used to calculate the temperature distribution around the hotspot to obtain the effective coverage radius of the hotspot, where the effective coverage radius is defined as the distance where the temperature drops to 1 / e of the initial value; A clustering algorithm is used to group hotspots, and hotspots with close distances and overlapping temperature influence ranges are merged based on the hotspot coordinates, where the number of clusters is gradually reduced from the current number of hotspots; The updated hotspot arrangement plan is obtained by calculating the cluster center as the new hotspot location and taking the sum of all hotspot powers in the cluster as the new hotspot power; For the updated hotspot arrangement scheme, the finite element method is used to solve the heat conduction equation, the grid size is set, the boundary condition is natural convection, the time step is solved, and a new temperature distribution cloud map is obtained; If the temperature distribution uniformity index meets the preset threshold, the current hotspot arrangement plan is saved, the hotspot reduction operation is continued, and the clustering and updating operations are repeated until the temperature distribution uniformity index does not meet the requirements or the number of hotspots reaches the preset minimum value.

5. The method according to claim 1, characterized in that According to the optimized hotspot arrangement scheme, a temperature sensor and a heating element are arranged at corresponding positions on the pillow surface, the temperature sensor is used to collect temperature data at the hotspot in real time, and the heating element adjusts power according to the temperature data, including: Acquire hot spot distribution information on the pillow surface, and determine the arrangement positions of the temperature sensor and the heating element according to the hot spot distribution information; Wherein, the temperature sensor is of thermocouple type, and the heating element is of flexible thin film electric heating sheet; Collect temperature data from a temperature sensor at a preset frequency; Convert the temperature data into a digital signal through an analog-to-digital converter and store it in a circular queue buffer; The proportional-integral-differential control method is used to calculate the output power of the heating element according to the deviation between the current temperature and the target temperature; Wherein, the controller output of the proportional-integral-differential control method consists of a proportional term, an integral term and a differential term; The Ziegler-Nichols tuning method is used to determine the PID parameters, and the proportional gain is gradually increased until the system oscillates. Record critical gain and oscillation period, and calculate PID parameters; Convert the calculated output power into a voltage signal; The power output of the heating element is controlled by pulse width modulation, and the modulation frequency and resolution of the pulse width modulation are preset values.

6. The method according to claim 1, characterized in that The mathematical model between the power of the heating element and the hot spot temperature is established, and the control method of each heating element is calculated and obtained according to different target temperatures, including: Acquire historical data of heating element power and hot spot temperature, wherein the historical data includes temperature variation process under different power inputs; According to the historical data, the temperature variation curve is fitted by the least square method to obtain a mathematical model between the heating element power and the hot spot temperature; Based on the mathematical model, a feedback controller is used, wherein the feedback controller uses a model predictive control algorithm to solve an optimal power sequence; The difference between the target temperature and the current temperature is input into the feedback controller to calculate the power control sequence of each heating element; Establishing a hotspot coupling coefficient matrix, wherein the hotspot coupling coefficient matrix is ​​determined according to the distance between hotspots and the thermal conductivity of the material; The power control sequence is adjusted according to the hotspot coupling coefficient matrix.

7. The method according to claim 1, characterized in that The ergonomic simulation software is used to analyze the curved surface of the pillow and make final adjustments to the hotspot arrangement scheme, including: Obtain the 3D model of the pillow and import it into ANSYS ergonomic simulation software, set the density, elastic modulus and thermal conductivity coefficient according to the pillow material properties, and build a virtual human head and neck model; Finite element analysis method is used to simulate the contact process between human head and pillow, calculate pressure distribution and contact area, and determine key support points and high-pressure areas; The hotspot layout plan is superimposed on the pressure distribution map to determine the matching degree between the hotspot location and the high-pressure area and key support points, and the contact area and impact range covered by the hotspot are calculated; If the pressure distribution and curvature analysis results meet the preset conditions, the gradient descent method is used to adjust the hotspot position and power distribution to optimize the uniformity of heat distribution; The final hotspot arrangement plan is generated according to the optimization results, and the hotspot coordinates and power parameters are obtained.

8. The method according to claim 1, characterized in that According to the hotspot arrangement scheme after the final adjustment, a control circuit and program of the temperature-adjustable pillow are established at the bottom of the pillow, which is used to process the data collected by the temperature sensor and the control strategy information output by the heating element, control the on and off of each heating element, and provide the user with temperature setting and mode selection functions, including: Acquire an input signal of a microcontroller, wherein the input signal is generated by an analog-to-digital converter collecting temperature sensor data; Execute a control program according to the input signal, wherein the control program includes a filtering processing module, a PID control algorithm module and a PWM output control module; The weighted average data fusion algorithm is used to process the data of multiple temperature sensors, and the optimal control output is calculated by combining the control strategy information output by the PID controller; Receiving user input information sent by the human-computer interaction interface, the user input information including a temperature setting value and an operation mode selection; The user input information is transmitted to the microcontroller, and the microcontroller updates the control parameters according to the received user input information.

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