Control Method and Device for an Intelligent Temperature-controlled Hair Straightener

Abstract

The present invention provides a control method and device for an intelligent temperature-controlled hair straightener. The method includes: determining a plurality of temperature control instructions for a plurality of different regions of the intelligent temperature-controlled hair straightener, where one region corresponds to one temperature control instruction; respectively obtaining the temperatures of a plurality of detection points on the region, and based on the target temperature of each region, using a progressive incremental algorithm to calculate a control increment and generate a corresponding heating signal to adjust the temperature control instruction, where each region has its own corresponding target temperature; based on the temperature sequences, heating powers, and ambient temperatures of the respective regions obtained, using a model based on the Koopman operator to determine a linear representation of the intelligent temperature-controlled hair straightener; according to the linear representation of the intelligent temperature-controlled hair straightener, constructing a quadratic regulator controller and using the quadratic regulator controller to adjust the control parameters of the intelligent temperature-controlled hair straightener.

Description

Technical Field

[0001] The present application relates to the technical field of hair styling appliance control, and particularly to a control method and device for an intelligent temperature-controlled hair straightener comb. Background Art

[0002] With the development of hair styling appliance technology, intelligent temperature-controlled hair straightener combs have been widely used. However, the current intelligent temperature-controlled hair straightener combs on the market mainly have the following technical problems:

[0003] First, traditional temperature-controlled hair straightener combs mostly adopt a simple on-off control scheme with a fixed temperature threshold. The temperature of the heating element is detected by a temperature sensor, and the power is cut off after reaching the set temperature and reheated after the temperature drops. This scheme has large temperature fluctuations and low control accuracy, and cannot achieve coordinated control of multiple temperature zones.

[0004] Second, some temperature-controlled hair straightener combs adopt a PID control scheme. Although the temperature can be maintained relatively stable at steady state, in the dynamic process, especially when the heating power and heat dissipation conditions change, the control effect is not ideal, and problems such as large temperature fluctuations and slow response speed are likely to occur.

[0005] Third, the existing temperature-controlled hair straightener combs lack an intelligent data-driven control scheme and cannot adaptively adjust the control strategy according to the use environment and user habits, resulting in low temperature control accuracy, unsatisfactory hair straightening effect and easy hair damage.

[0006] Fourth, the existing technology cannot achieve coordinated and precise control of multiple temperature zones and lacks a perfect safety protection mechanism, which affects the use safety and reliability. Summary of the Invention

[0007] In view of this, the present application provides a control method and device for an intelligent temperature-controlled hair straightener comb, which solve the problems of low temperature control accuracy, inability to achieve coordinated control of multiple temperature zones, and lack of intelligent data-driven control in the existing temperature-controlled hair straightener combs.

[0008] An embodiment of the present application provides a control method for an intelligent temperature-controlled hair straightener comb, including:

[0009] Determining a plurality of temperature control instructions for a plurality of different regions of the intelligent temperature-controlled hair straightener comb, where one region corresponds to one temperature control instruction;

[0010] Respectively obtaining the temperatures of a plurality of detection points on the regions, and calculating a control increment by using a progressive incremental algorithm based on the target temperature of each region, and generating a corresponding heating signal to adjust the temperature control instruction, where each region has its own corresponding target temperature;

[0011] Based on the temperature sequences, heating powers, and ambient temperatures of each region obtained, a model based on the Koopman operator is used to determine the linear representation of the intelligent temperature-controlled hair straightener;

[0012] According to the linear representation of the intelligent temperature-controlled hair straightener, a quadratic regulator controller is constructed, and the quadratic regulator controller is used to adjust the control parameters of the intelligent temperature-controlled hair straightener.

[0013] Determine multiple temperature control instructions for multiple different regions of the intelligent temperature-controlled hair straightener, where one region corresponds to one temperature control instruction;

[0014] Respectively obtain the temperatures of multiple detection points on the region, and based on the target temperature of each region, use a progressive incremental algorithm to calculate the control increment and generate a corresponding heating signal to adjust the temperature control instruction, where each region has its own corresponding target temperature;

[0015] Based on the temperature sequences, heating powers, and ambient temperatures of each region obtained, a model based on the Koopman operator is used to determine the linear representation of the intelligent temperature-controlled hair straightener;

[0016] According to the linear representation of the intelligent temperature-controlled hair straightener, a quadratic regulator controller is constructed, and the quadratic regulator controller is used to adjust the control parameters of the intelligent temperature-controlled hair straightener.

[0017] Optionally, the multiple different regions include a first region, a second region, and a third region;

[0018] The determining of multiple temperature control instructions for multiple different regions of the intelligent temperature-controlled hair straightener, where one region corresponds to one temperature control instruction, includes:

[0019] Establish a nonlinear optimization model including the first region, the second region, and the third region. The nonlinear optimization model includes: a thermodynamic model including the first region, the second region, and the third region, an objective function including temperature control accuracy, energy efficiency, and temperature uniformity, and constraint conditions including maximum power limit, temperature change rate limit, and safety temperature upper limit;

[0020] Obtain the current temperatures of the first region, the second region, and the third region, and the ambient temperature within a preset sampling period;

[0021] Substitute the current temperatures of the first region, the second region, and the third region, and the ambient temperature into the thermodynamic model to obtain the heating power of the first region, the heating power of the second region, and the heating power of the third region that satisfy the objective function and the constraint conditions;

[0022] Determine the first PWM signal for the first region, the second PWM signal for the second region, and the third PWM signal for the third region according to the heating power of the first region, the heating power of the second region, and the heating power of the third region, as well as the maximum power limit in the constraint conditions, where the first PWM signal, the second PWM signal, and the third PWM signal serve as the temperature control instructions.

[0023] Optionally, one or more of the following are satisfied:

[0024] The thermodynamic model of the second region includes:

[0025] dT2 / dt = k1P2(t) / m2c2 - k2(T2-T1) / R12- (T2-T3) / R23 - k4( -Ta) / Ra

[0026] Where is the temperature range of the second region, is the heating power, is the mass of the heating component in the second region, is the specific heat capacity of the heating component in the second region, is the thermal resistance between the first region and the second region, the thermal resistance between the third region and the second region, Ra is the thermal resistance to the environment, and Ta is the environmental temperature;

[0027] The objective function includes:

[0028] J = w1Σ(Ti-Tref,i)² + w2Σ(Pi / Pmax)² + w3Σ(Ti-Ti+1)²

[0029] Where w1, w2, and w3 are weighting coefficients; Ti is the actual temperature of the i-th region; Tref,i is the target temperature of the i-th region; Pi is the actual heating power of the i-th region; Pmax is the maximum heating power allowed by the system; Ti+1 is the temperature of the next region adjacent to the i-th region;

[0030] After the preset sampling period, compare the predicted temperature and the actual temperature to update the parameters of the thermodynamic model.

[0031] Optionally, respectively obtain the temperatures of multiple detection points on the regions, and based on the target temperature of each region, use the progressive incremental algorithm to calculate the control increment and generate the corresponding heating signal to adjust the temperature control instruction, where each region has its corresponding target temperature, including:

[0032] Generate a temperature deviation sequence based on the temperatures of each detection point in the same area and the target temperature of the area;

[0033] Based on the temperature deviation sequence, calculate a control increment using a progressive incremental algorithm, and the calculation of the control increment includes a proportional term, an integral term, and a differential term;

[0034] Update the heating power according to the control increment, and convert the heating power into a PWM control signal, the PWM control signal.

[0035] Optionally, satisfy one or more of the following:

[0036] The multiple different areas include a first area, a second area, and a third area, and the progressive incremental algorithm for the second area includes:

[0037] ΔU(k) = Kp[e(k) - e(k-1)] + Ki·e(k) + Kd[e(k) - 2e(k-1) + e(k-2)]

[0038] Where, ΔU(k) is the control increment at the current moment k, Kp is the proportional coefficient, Ki is the integral coefficient, Kd is the differential coefficient, e(k) is the temperature deviation at moment k, e(k-1) is the temperature deviation at moment k-1, and e(k-2) is the temperature deviation at moment k-2;

[0039] The calculation of the control increment by the progressive incremental algorithm includes at least one of: calculating temperature control performance indicators including rise time, overshoot, and adjustment time; dynamically adjusting the proportional coefficient, integral coefficient, and differential coefficient based on the performance indicators; applying the adjusted control parameters to the calculation of the control increment in the next control cycle.

[0040] Optionally, it further includes:

[0041] Install a temperature sensor on the power supply device to obtain the surface temperature distribution of the power supply device; determine the heat dissipation power based on the established heat accumulation model and the surface temperature distribution of the power supply device.

[0042] Optionally, the linear representation of the intelligent temperature-controlled hair straightener is determined by using a model based on the Koopman operator based on the temperature sequences, heating powers, and ambient temperatures of the obtained respective areas, including:

[0043] Construct a data matrix based on the temperature sequences, heating powers, ambient temperatures of the respective areas, and the adjacent temperatures between other areas;

[0044] Select a basis function as the eigen-space of the Koopman operator according to the data matrix;

[0045] Based on the basis functions, construct a linear system representation including an extended state vector and a control input;

[0046] Utilize the extended dynamic mode decomposition algorithm to determine the Koopman operator as the linear representation of the intelligent temperature-controlled hair straightener.

[0047] Optionally, based on the linear representation of the intelligent temperature-controlled hair straightener, construct a quadratic regulator controller, and use the quadratic regulator controller to adjust the control parameters of the intelligent temperature-controlled hair straightener, including:

[0048] Design a linear quadratic regulator according to the linear representation of the intelligent temperature-controlled hair straightener, where the linear quadratic regulator is used to characterize the defined state weight matrix and control weight matrix;

[0049] Solve the discrete Riccati equation to obtain the feedback gain matrix;

[0050] Optimize the element values in the state weight matrix, the control weight matrix, and the feedback gain matrix through an online learning method, including: constructing a performance evaluation function including control error, control quantity change rate, and dynamic response characteristics; using the gradient descent method to update the controller parameters; dynamically adjusting the learning rate based on the degree of performance improvement to optimize the controller parameters;

[0051] Based on the controller parameters, adjust the control parameters of the intelligent temperature-controlled hair straightener to adjust the luminous power.

[0052] Optionally, satisfy one or more of the following:

[0053] Obtain the working state and environmental light conditions of the intelligent temperature-controlled hair straightener; when it is determined that the intelligent temperature-controlled hair straightener is in the standby state and in a dark environment based on the working state and the environmental light conditions, perform a disinfection process, where the disinfection parameters of the disinfection process are controllable;

[0054] Obtain multiple operating parameters of the intelligent temperature-controlled hair straightener, and output an indication signal based on a threshold adapted to the type of the operating parameter, where the indication signal is used to represent the magnitude relationship between the actual value of the operating parameter and the threshold.

[0055] The embodiment of the present application also provides a control device for an intelligent temperature-controlled hair straightener, and the device includes:

[0056] A providing unit, configured to determine multiple temperature control instructions for multiple different regions of the intelligent temperature-controlled hair straightener, where one region corresponds to one temperature control instruction;

[0057] A control unit, configured to obtain the temperatures of a plurality of detection points on the area respectively, and based on the target temperatures of the respective areas, calculate a control increment by using a progressive incremental algorithm, generate a corresponding heating signal to adjust the temperature control instruction, wherein each area has its corresponding target temperature; and based on the obtained temperature sequences, heating powers, and ambient temperatures of the respective areas, determine a linear representation of the intelligent temperature-controlled hair straightener by using a model based on the Koopman operator;

[0058] An optimization control unit, configured to construct a quadratic regulator controller according to the linear representation of the intelligent temperature-controlled hair straightener, and use the quadratic regulator controller to adjust the control parameters of the intelligent temperature-controlled hair straightener;

[0059] The providing unit, the control unit, and the optimization control unit are interconnected through a data bus to form a complete temperature closed-loop control system.

[0060] An embodiment of the present application further provides a computer device, where the computer device includes:

[0061] At least one processor; and,

[0062] A memory communicatively connected to the at least one processor; wherein,

[0063] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the control method of the above-mentioned intelligent temperature-controlled hair straightener.

[0064] An embodiment of the present application further provides a computer-readable storage medium, which stores computer instructions for causing a computer to execute the control method of the above-mentioned intelligent temperature-controlled hair straightener.

[0065] An embodiment of the present application further provides a computer program product, including computer instructions, which implement the steps of the control method of the above-mentioned intelligent temperature-controlled hair straightener when executed by a processor.

[0066] The present application has the following technical effects:

[0067] By adopting an online convex optimization method for nonlinear system constrained control and combining a progressive incremental control algorithm, the collaborative precise control of multiple temperature zones is realized, and the temperature control accuracy is significantly improved;

[0068] The Koopman operator is applied to system modeling and control in the field of hair care appliances for the first time, and the control parameters are continuously optimized in a data-driven manner, improving the adaptability and robustness of the system;

[0069] Optimal control is achieved based on a linear quadratic regulator, taking into account energy efficiency while ensuring control accuracy, thereby improving the practicality of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. These drawings are incorporated into the specification and constitute a part of this specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0071] Figure 1 is a schematic flowchart of the control method for the intelligent temperature-controlled hair straightener provided by the embodiment of the present application;

[0072] Figure 2 is a schematic diagram of the control area division provided by the embodiment of the present application;

[0073] Figure 3 is a schematic structural diagram of the control device provided by the embodiment of the present application;

[0074] Figure 4 is a physical diagram of the hair straightener provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] To make the above objects, features, and advantages of the present application more apparent and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0076] In the description of the present application, it should be understood that terms such as "first", "second", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance. Unless otherwise clearly specified and limited, the terms "installation" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0077] The following further illustrates the specific embodiments of the present application with reference to the drawings in the specification.

[0078] As Figure 1 , the embodiment of the present application provides a control method for an intelligent temperature-controlled hair straightener, and the method includes:

[0079] Step S1: Determine multiple temperature control instructions for multiple different regions of the intelligent temperature-controlled hair straightener, where one region corresponds to one temperature control instruction;

[0080] Further, the multiple different regions include a first region, a second region, and a third region.

[0081] On this basis, the determining multiple temperature control instructions for multiple different regions of the intelligent temperature-controlled hair straightener, where one region corresponds to one temperature control instruction, includes:

[0082] First, establish a non-linear optimization model including the first region, the second region, and the third region. The non-linear optimization model includes: a thermodynamic model including the first region, the second region, and the third region, an objective function including temperature control accuracy, energy efficiency, and temperature uniformity, and constraint conditions including maximum power limit, temperature change rate limit, and upper safety temperature limit.

[0083] Second, obtain the current temperatures of the first region, the second region, and the third region, and the ambient temperature within a preset sampling period; substitute the current temperatures of the first region, the second region, and the third region, and the ambient temperature into the thermodynamic model to obtain the heating power of the first region, the heating power of the second region, and the heating power of the third region that satisfy the objective function and the constraint conditions.

[0084] Third, determine the first PWM signal of the first region, the second PWM signal of the second region, and the third PWM signal of the third region according to the heating power of the first region, the heating power of the second region, and the heating power of the third region, and the maximum power limit in the constraint conditions, where the first PWM signal, the second PWM signal, and the third PWM signal are used as the temperature control instructions.

[0085] Further, the following one or more are satisfied:

[0086] The thermodynamic model of the second region includes:

[0087] dT2 / dt = k1P2(t) / m2c2 - k2(T2-T1) / R12- (T2-T3) / R23 - k4( -Ta) / Ra

[0088] Where is the temperature range of the second region, is the heating power, is the mass of the heating component in the second region, is the specific heat capacity of the heating component in the second region, The thermal resistance between the first region and the second region, the thermal resistance between the third region and the second region, Ra is the thermal resistance to the environment, and Ta is the environmental temperature;

[0089] The objective function includes:

[0090] J = w1Σ(Ti - Tref,i)² + w2Σ(Pi / Pmax)² + w3Σ(Ti - Ti+1)²

[0091] where w1, w2, and w3 are weight coefficients; Ti is the actual temperature of the i-th region; Tref,i is the target temperature of the i-th region; Pi is the actual heating power of the i-th region; Pmax is the maximum heating power allowed by the system; Ti+1 is the temperature of the next region adjacent to the i-th region;

[0092] After a preset sampling period, the predicted temperature and the actual temperature are compared to update the parameters of the thermodynamic model.

[0093] Specifically, as Figure 2 , the intelligent temperature-controlled hair straightener of the present application includes three different temperature control regions, namely the first region, the second region, and the third region. Among them, the first region is the cold tooth region, and the operating temperature is set to about 40°C; the second region is the medium temperature region, and the operating temperature is set to about 160°C; the third region is the high temperature region, and the operating temperature is set to about 180°C. This multi-temperature zone design can meet the temperature requirements for different hair qualities and styling needs.

[0094] When determining the temperature control instruction, the following aspects need to be considered: First, based on the target temperature requirements of different temperature zones, set the basic temperature control instructions for each region. Second, based on the heat conduction characteristics between regions, adjust the temperature control instructions to achieve uniform temperature distribution. Third, considering the safety requirements of the device, set the maximum temperature limit for each temperature zone. Finally, dynamically adjust the temperature control instructions according to the actual usage scenario.

[0095] For each temperature zone, the temperature control instruction mainly includes parameters such as the target temperature set value, heating power limit, and temperature change rate limit. These parameters are transmitted to the heating control circuit in the form of PWM signals to achieve precise control of the temperature of each region. For example, when the user selects the standard mode, the system will automatically set the temperature of the first region to 40°C, the second region to 160°C, and the third region to 180°C, and generate corresponding PWM control signals.

[0096] It should be noted that the temperature control instructions for each area are independent, which can more flexibly meet different usage requirements. At the same time, the system will monitor the actual temperature of each area in real time and dynamically adjust the temperature control instructions according to the monitoring results to ensure the control effect.

[0097] Step S2: Obtain the temperatures of multiple detection points on the area respectively, and based on the target temperature of each area, calculate the control increment using the progressive incremental algorithm to generate the corresponding heating signal to adjust the temperature control instruction, where each area has its corresponding target temperature;

[0098] Further, S2 specifically includes:

[0099] First, generate a temperature deviation sequence based on the temperatures of the detection points on the same area and the target temperature of that area.

[0100] Second, calculate the control increment using the progressive incremental algorithm based on the temperature deviation sequence. The calculation of the control increment includes a proportional term, an integral term, and a differential term.

[0101] Third, update the heating power according to the control increment and convert the heating power into a PWM control signal, the PWM control signal.

[0102] Further, one or more of the following are satisfied: The multiple different areas include a first area, a second area, and a third area, and the progressive incremental algorithm for the second area includes:

[0103] ΔU(k) = Kp[e(k) - e(k-1)] + Ki·e(k) + Kd[e(k) - 2e(k-1) + e(k-2)]

[0104] where ΔU(k) is the control increment at the current moment k, Kp is the proportional coefficient, Ki is the integral coefficient, Kd is the differential coefficient, e(k) is the temperature deviation at moment k, e(k-1) is the temperature deviation at moment k-1, and e(k-2) is the temperature deviation at moment k-2;

[0105] The calculation of the control increment by the progressive incremental algorithm includes at least one of: calculating temperature control performance indicators including rise time, overshoot, and regulation time; dynamically adjusting the proportional coefficient, integral coefficient, and differential coefficient based on the performance indicators; applying the adjusted control parameters to the calculation of the control increment in the next control cycle.

[0106] In the embodiment of the present application, taking the temperature control of the second area (medium temperature area) as an example, the generation of the temperature deviation sequence and the implementation process of the progressive incremental algorithm are specifically described.

[0107] First, multiple temperature detection points are set in the second region, and temperature data is collected in real time through high-precision NTC temperature sensors.

[0108] For example, three detection points T1, T2, and T3 are set in this region, and their sampling period is 100 ms. Suppose the temperature data collected at a certain moment are: T1 = 158.5 °C, T2 = 157.8 °C, T3 = 159.2 °C, and the target temperature of this region is set to 160 °C.

[0109] Secondly, based on the collected temperature data, the system calculates the average temperature value at the current moment, that is, Tc = (T1 + T2 + T3) / 3 = 158.5 °C. At the same time, the average temperature data of the previous two sampling periods are saved. For example, T(k - 1) = 156.2 °C, T(k - 2) = 154.8 °C. By comparing these temperature values with the target temperature of 160 °C, a temperature deviation sequence is generated:

[0110] e(k) = 160 - 158.5 = 1.5 °C

[0111] e(k - 1) = 160 - 156.2 = 3.8 °C

[0112] e(k - 2) = 160 - 154.8 = 5.2 °C

[0113] Then, the system uses a progressive incremental algorithm to calculate the control increment. This algorithm includes three key terms: the proportional term reflects the change of the current deviation, the integral term eliminates the steady-state error, and the derivative term improves the dynamic response characteristics. The specific calculation formula is:

[0114] ΔU(k) = Kp[e(k) - e(k - 1)] + Ki·e(k) + Kd[e(k) - 2e(k - 1) + e(k - 2)]

[0115] Among them, the initial values of the control parameters are set as: Kp = 0.8, Ki = 0.1, Kd = 0.05.

[0116] Finally, the calculated control increment is superimposed on the current heating power.

[0117] For example, if the current heating power is 32 W and the calculated control increment ΔU(k) = -1.79 W, the updated heating power is 30.21 W. The system converts this power value into a PWM signal and outputs a control signal with a duty cycle of 75.5% within a PWM period of 10 ms, that is, the high-level duration is 7.55 ms and the low-level duration is 2.45 ms.

[0118] It should be noted that the system will dynamically adjust the control parameters according to the control effect.

[0119] For example, when it is detected that the temperature rising speed is too slow (less than 1℃ / s), the proportional coefficient Kp will be appropriately increased; when it is found that the steady-state error is large (greater than 2℃), the integral coefficient Ki will be increased; when the temperature fluctuates violently, the differential coefficient Kd will be increased. This adaptive adjustment mechanism can significantly improve the accuracy and stability of temperature control.

[0120] In addition, in order to prevent the impact on the system caused by the sudden change of the control quantity, a control increment limit value of ±5W is set to ensure the smoothness of each power adjustment. At the same time, the system also sets an upper limit value of 40W for the heating power as a safety protection. These measures together ensure the safety and reliability of temperature control.

[0121] Furthermore, the control method further includes: arranging temperature sensors on the power supply device to obtain the surface temperature distribution of the power supply device; determining the heat dissipation power based on the established heat accumulation model and the surface temperature distribution of the power supply device.

[0122] In the specific implementation process of this application, taking a 3000mAh lithium battery as the power supply device as an example, it is described how to achieve temperature monitoring and heat dissipation control of the power supply device.

[0123] First of all, 4 NTC temperature sensors with an accuracy of ±0.1℃ are arranged on the surface of the lithium battery to monitor the surface temperature distribution of the battery in real time. Among them, sensor 1 is arranged at the positive end of the battery, sensor 2 is located near the heating area in the middle of the battery, sensor 3 is arranged at a position far from the heating area in the middle of the battery, and sensor 4 is arranged at the negative end of the battery. This layout method can comprehensively monitor the temperature changes of each part of the battery.

[0124] Secondly, a battery heat accumulation model is established, which takes into account three parts: the heat generated during the charging process, the heat generated during the discharging process, and the environmental heat dissipation.

[0125] Specifically, under the condition of an ambient temperature of 25℃, the heat generated during the charging process is calculated by the formula Qc = I²R + IT(dE / dT), where under a charging current of 2A, the internal resistance R = 0.05Ω, and the temperature coefficient dE / dT = -0.003V / ℃; the heat generated during the discharging process is calculated by the formula Qd = I²R - IT(dE / dT), and the maximum discharging current is 3A; the environmental heat dissipation is calculated by the formula Qe = h·A(T - Ta), where the heat dissipation coefficient h = 10W / (m²·℃), and the heat dissipation area A = 0.002m².

[0126] For example, at a certain moment, the temperatures detected by four temperature sensors are: T1 = 38.2°C, T2 = 41.5°C (exceeding the warning temperature of 40°C), T3 = 37.8°C, and T4 = 36.5°C. Based on these temperature data, the system determines that the temperature in the middle of the battery near the heating area has exceeded the warning value, and the heat dissipation power needs to be increased. Through calculation using the heat accumulation model, the required heat dissipation power at this time is 0.8W, corresponding to a fan speed of 2000 RPM.

[0127] Specific heat dissipation control adopts a hierarchical adjustment method: when the temperature is below 35°C, the heat dissipation fan stops working; when the temperature is between 35°C and 38°C, the fan speed is set to 1500 RPM; when the temperature is between 38°C and 42°C, the fan speed is set to 2000 RPM; when the temperature exceeds 42°C, the fan speed is increased to 2500 RPM, and at the same time the system will automatically reduce the charging current to prevent the battery from overheating.

[0128] It should be noted that the system will dynamically adjust the heat dissipation strategy according to the actual usage conditions.

[0129] For example, in the fast charging mode, due to the large charging current, the system will pre-start the heat dissipation fan to dissipate heat in advance; in the case of a high ambient temperature, the system will correspondingly lower the heat dissipation temperature threshold and turn on the heat dissipation in advance; during the natural heat dissipation process when the device is in a power-off state, the system will dynamically adjust the fan speed according to the temperature drop rate to balance the heat dissipation effect and energy consumption.

[0130] In addition, the system also sets up multiple safety protection mechanisms. When any temperature measurement point exceeds 45°C, the system will immediately cut off the charging current and start the maximum power heat dissipation; when a temperature sensor fails, the system will automatically enter the safety mode, limit the charging current and turn on the maximum heat dissipation power to ensure the safety of the device.

[0131] Step S3: Based on the temperature sequences, heating powers, and ambient temperatures of each area obtained, use a model based on the Koopman operator to determine the linear representation of the intelligent temperature-controlled hair straightener.

[0132] In step S3, the system uses the theory based on the Koopman operator to achieve the linear representation of the dynamic system of the intelligent temperature-controlled hair straightener.

[0133] The following details the specific implementation process of this step.

[0134] First, the system needs to collect the operating data of each temperature zone. Taking the multi-temperature zone collaborative control as an example, the system adopts a sampling period of 100 ms and respectively collects the state variables of the first zone (cold tooth zone), the second zone (medium temperature zone), and the third zone (high temperature zone). For each temperature zone, the collected data includes: the temperature time series of this zone, the current heating power, the ambient temperature, and the temperature difference from the adjacent temperature zone.

[0135] Specifically, for the second zone (medium temperature zone), the collected data is exemplified as follows: the temperatures at three consecutive sampling moments are 158.3 °C, 159.1 °C, and 159.8 °C respectively; the corresponding heating powers are 32.5 W, 31.8 W, and 31.2 W; the ambient temperature is basically stable at about 25.2 °C; the temperature differences from the high temperature zone (third zone) are 17.2 °C, 16.7 °C, and 16.2 °C respectively. Similar data sets are also collected for the first zone and the third zone.

[0136] Secondly, for each temperature zone, the system constructs a feature space.

[0137] Taking the second zone as an example, 6 basis functions are selected: the original temperature value, the temperature difference from the environment, the square term of the temperature, the exponential decay term of the temperature, the heating power, and the temperature difference from the adjacent temperature zone. The selection of these basis functions takes into account the physical meaning, including both basic linear relationships and important non-linear characteristics.

[0138] Then, based on the constructed feature space, the system uses the Extended Dynamic Mode Decomposition (EDMD) algorithm to estimate the Koopman operator. In the specific implementation, first, a data matrix is constructed, and the state vectors obtained by continuous sampling are arranged in chronological order; then, by solving the least squares problem, the matrix representation of the Koopman operator is obtained. This matrix can accurately describe the evolution law of the system in the feature space.

[0139] It should be noted that the system adopts an adaptive mechanism to update the Koopman operator. When the following situations are detected, the re-identification process will be triggered: the ambient temperature changes by more than 5 °C, the control performance significantly deteriorates (such as the steady-state error exceeds 2 °C), or the running time exceeds the preset update period (such as 20 minutes). This dynamic update mechanism ensures that the model can adapt to the changes in working conditions.

[0140] In addition, to ensure real-time performance, the system uses a sliding time window of 200 sampling points to update the data matrix. This means that the system always uses the operating data of the most recent 20 seconds to update the model, which not only ensures the computational efficiency but also can reflect the latest dynamic characteristics of the system. When solving the least squares problem, the QR decomposition method is adopted to improve the numerical stability.

[0141] Finally, the system obtained a linear state-space model: z(k + 1) = Kz(k) + Bu(k). Here, the state vector z contains the characteristic quantities of all temperature zones, and the control input u corresponds to the heating power adjustment amount of each temperature zone. This linear model accurately describes the coupling relationship between multiple temperature zones and provides a theoretical basis for subsequent temperature collaborative control.

[0142] It should be emphasized that this modeling method based on the Koopman operator has the following advantages: it can accurately capture the nonlinear dynamic characteristics of the system; the obtained linear model is convenient for subsequent controller design; the model has the ability of adaptive update and can cope with system parameter changes; the computational complexity is moderate and suitable for implementation in embedded systems. These characteristics make this method particularly suitable for application scenarios such as intelligent temperature-controlled hair straighteners that require precise temperature control.

[0143] In one embodiment, S3 specifically includes:

[0144] Construct a data matrix according to the temperature sequences, heating powers, ambient temperatures of each region, and adjacent temperatures between other regions; select basis functions as the eigen-space of the Koopman operator according to the data matrix; construct a linear system representation including the extended state vector and the control input based on the basis functions; use the extended dynamic mode decomposition algorithm to determine the Koopman operator as the linear representation of the intelligent temperature-controlled hair straightener.

[0145] In the embodiment of the present application, the nonlinear temperature control system is transformed into a linear representation through the Koopman operator theory. The following takes the temperature control of the medium temperature zone (the second region) as a specific example for illustration.

[0146] First, the system samples once every 100 ms, and the collected state variables include: temperature sequence T(k) (unit: °C), heating power P(k) (unit: W), ambient temperature Ta(k) (unit: °C), and adjacent temperature zone temperature T_adj(k) (unit: °C). For example, a data segment obtained from a certain continuous sampling is as follows:

[0147] At time k - 2: T(k) = 158.3 °C, P(k) = 32.5 W, Ta(k) = 25.2 °C, T_adj(k) = 175.5 °C

[0148] At time k - 1: T(k) = 159.1 °C, P(k) = 31.8 W, Ta(k) = 25.3 °C, T_adj(k) = 175.8 °C

[0149] At time k: T(k) = 159.8 °C, P(k) = 31.2 W, Ta(k) = 25.2 °C, T_adj(k) = 176.0 °C

[0150] Secondly, based on the collected data, the system selected 6 basis functions to construct the Koopman feature space:

[0151] : represents the original temperature value;

[0152] : represents the temperature difference from the environment;

[0153] : represents the non - linear term of temperature;

[0154] : represents the exponential decay characteristic of temperature;

[0155] : represents the heating power;

[0156] : represents the temperature difference from the adjacent temperature zone;

[0157] Then, the Extended Dynamic Mode Decomposition (EDMD) algorithm was used to estimate the Koopman operator.

[0158] The specific steps are as follows: First, construct the data matrices X and Y, where , ; then solve the least - squares problem K = YX + (where X + is the pseudo - inverse) to obtain the Koopman matrix K. Taking the above data as an example, the example values of the calculated Koopman matrix are:

[0159] K = [1.02 -0.01 0.15 0.08 0.25 -0.05; 0.05 0.98 0.02 0.12 0.18 0.06;

[0161] 0.02 0.04 0.97 -0.03 0.15 0.08;

[0162] -0.01 0.06 0.04 0.95 0.12 0.03; 0.03 0.02 0.08 0.05 0.96 0.04;

[0164] 0.04 -0.02 0.06 0.07 0.08 0.99]

[0165] Finally, based on the obtained Koopman operator, the system established a linear state - space representation: z(k + 1)=Kz(k)+Bu(k), where z is the extended state vector and u is the control input. In this way, the originally complex non - linear temperature control system is transformed into a linear system representation.

[0166] It should be noted that the system will continuously update the data matrix during operation, recalculate the Koopman operator, and achieve online adaption of the model. For example, when the ambient temperature changes significantly (exceeding ±5°C), or when it is detected that the control effect deteriorates significantly (the steady-state error exceeds 2°C), the system will trigger a re-identification process to update the parameters of the Koopman matrix.

[0167] In addition, to improve the calculation efficiency, the system updates the data matrix using a sliding time window method, and the window length is set to 200 sampling points (i.e., 20 seconds of data). At the same time, the QR decomposition method is used to solve the least squares problem when calculating the Koopman operator, which improves the stability of numerical calculations. These optimization measures ensure that the system can complete model updates in real time and guarantee control performance.

[0168] This modeling method based on the Koopman operator can not only accurately capture the nonlinear dynamic characteristics of the system, but also the obtained linear model is convenient for subsequent controller design, laying a foundation for achieving precise temperature control. At the same time, due to the model's ability to adaptively update, it can well handle problems such as changes in the usage environment and system parameter drift.

[0169] Step S4: Construct a quadratic regulator controller based on the linear representation of the intelligent temperature-controlled hair straightener, and use the quadratic regulator controller to adjust the control parameters of the intelligent temperature-controlled hair straightener.

[0170] In step S4, the system designs a linear quadratic regulator (LQR) controller based on the linear system representation obtained from the aforementioned Koopman operator to achieve precise temperature control. The following details the specific implementation process of this step.

[0171] First, the system designs the state weight matrix Q and the control weight matrix R of the LQR controller according to the linear state space expression z(k + 1) = Kz(k) + Bu(k).

[0172] Taking the second region (medium temperature region) as an example, the state weight matrix Q adopts a diagonal matrix form: Q = diag([10, 5, 2, 2, 3, 4]), where larger weights are assigned to the original temperature value and the temperature difference term, reflecting the emphasis on temperature control accuracy; the control weight takes the scalar R = 0.1, indicating a moderate constraint on control energy consumption.

[0173] Secondly, the feedback gain matrix is obtained by solving the discrete Riccati equation. For example, for the second region, the calculated initial feedback gain is K = [-2.15, 0.85, -0.35, 0.42, -1.25, 0.65]. Based on this gain matrix, the system can calculate the optimal control input for each control period: u(k) = -Kz(k).

[0174] Then, the system continuously optimizes the control parameters using an online learning method. The specific optimization strategy is as follows:

[0175] When the temperature rising speed is too slow (less than 1 °C / s), increase the temperature-related weight in the Q matrix by 20%;

[0176] When the power fluctuation is too large (greater than 5 W / s), increase the control weight R by 30%;

[0177] When the steady-state error is too large (greater than 2 °C), increase the weight related to the integral action by 25%.

[0178] For example, during a certain control process, the system detects the following performance indicators:

[0179] The temperature rising speed is 0.8 °C / s (lower than the target value)

[0180] The power fluctuation is 3.2 W / s (within the allowable range)

[0181] The steady-state error is 1.5 °C (within the allowable range)

[0182] Based on these indicators, the system adjusts the control parameters as follows:

[0183] Update the Q matrix to Q' = diag([12, 6, 2, 2, 3, 4])

[0184] Keep the R value unchanged, R' = 0.1

[0185] Obtain the new feedback gain: K' = [-2.58, 1.02, -0.35, 0.42, -1.25, 0.65]

[0186] Taking a specific control period as an example, assume the current extended state vector is:

[0187] z(k) = [159.8, 134.6, 25547, 0.202, 31.2, 16.2

[0188] Calculate the control increment through the new feedback gain: Δu = -K'z(k) = -1.85 W

[0189] The final output heating power is: P(k + 1) = P(k) + Δu = 31.2 - 1.85 = 29.35W

[0190] It should be noted that the system also implements an adaptive learning rate adjustment mechanism. When the control performance shows no obvious improvement after consecutive adjustments, the learning rate will be automatically decreased; when the control performance improves rapidly, the learning rate will be increased accordingly. This mechanism can improve the optimization efficiency while ensuring the convergence of control parameters.

[0191] In addition, the system sets safety limits for parameter adjustment:

[0192] The adjustment range of the diagonal elements of the state weight Q matrix is restricted between 0.5 and 2 times the initial value

[0193] The adjustment range of the control weight R is restricted between 0.2 and 5 times the initial value

[0194] The change rate of each element of the feedback gain K shall not exceed 30% each time

[0195] These restrictions ensure the stability of the parameter optimization process and prevent drastic changes in the control quantity. At the same time, the system will periodically store the optimized parameters, which can be used as the initial values for the next startup to achieve continuous improvement of the controller performance.

[0196] Finally, the entire control process forms a closed-loop optimization mechanism of "obtaining state variables → calculating control variables → executing control → evaluating effects → optimizing parameters". This adaptive optimal control method based on LQR not only ensures the accuracy of temperature control but also realizes the adaptive optimization of control parameters, enabling the system to always maintain the best control performance.

[0197] Furthermore, S4 specifically includes: designing a linear quadratic regulator according to the linear representation of the intelligent temperature-controlled hair straightener, where the linear quadratic regulator is used to represent the defined state weight matrix and control weight matrix; obtaining the feedback gain matrix by solving the discrete Riccati equation; optimizing the element values in the state weight matrix, the control weight matrix, and the feedback gain matrix through an online learning method, including: constructing a performance evaluation function containing control error, control quantity change rate, and dynamic response characteristics; updating the controller parameters using the gradient descent method; dynamically adjusting the learning rate based on the degree of performance improvement to optimize the controller parameters; adjusting the control parameters of the intelligent temperature-controlled hair straightener based on the controller parameters to adjust the luminous power.

[0198] The implementation process of S4 is illustrated by the following specific examples:

[0199] First, when designing the linear quadratic regulator, according to the control requirements of each temperature zone of the intelligent temperature-controlled hair straightener, the state weight matrix Q and the control weight matrix R are defined. Taking the second zone (medium temperature zone) as an example, the state weight matrix Q is initially set as a diagonal matrix: Q = diag([10, 5, 2, 2, 3, 4]), where larger weights are assigned to the state variables related to temperature; the control weight matrix takes the scalar R = 0.1.

[0200] Secondly, by solving the discrete Riccati equation, the initial feedback gain matrix K = [-2.15, 0.85, -0.35, 0.42, -1.25, 0.65] is obtained. To evaluate the control performance, the following performance evaluation function is constructed:

[0201] J = α·e² + β·(du / dt)² + γ·ts

[0202] where e is the temperature control error, du / dt is the change rate of the control quantity, ts is the adjustment time, and the weight coefficients are initially set as α = 0.6, β = 0.3, γ = 0.1.

[0203] Then, the control parameters are optimized by an online learning method.

[0204] Suppose the performance index of a certain control process is:

[0205] The temperature control error e = 1.8 °C

[0206] The change rate of the control quantity du / dt = 4.2 W / s

[0207] The adjustment time ts = 2.3 s

[0208] The calculated performance index J = 2.47.

[0209] When using the gradient descent method to update the parameters, the learning rate is initially set as η = 0.01. Taking the first diagonal element q1 of the Q matrix as an example, its update formula is:

[0210] q1_new = q1_old - η· J / q1

[0211] After one iteration:

[0212] The Q matrix is updated to Q' = diag([11.2, 5.3, 2.1, 2.0, 3.1, 4.2])

[0213] R is updated to R' = 0.12

[0214] The feedback gain is updated to K' = [-2.28, 0.92, -0.37, 0.43, -1.28, 0.68]

[0215] After using the new parameters, the performance metrics are measured again:

[0216] The temperature control error is reduced to e = 1.5 °C

[0217] The rate of change of the control variable is reduced to du / dt = 3.8 W / s

[0218] The regulation time is reduced to ts = 2.1 s

[0219] The new performance metric is J' = 1.96, and the performance improvement rate is (2.47 - 1.96) / 2.47 ≈ 20.6%.

[0220] Due to the significant performance improvement, the system increases the learning rate to η' = 0.015 and continues the next round of parameter optimization. If the subsequent performance improvement rate is lower than 5%, the learning rate will be reduced to 0.8 times the current value.

[0221] To ensure the stability of the control, limits on parameter changes are set:

[0222] The range of change of the diagonal elements of the Q matrix is limited within ±50% of the initial value

[0223] The range of change of R is limited within ±80% of the initial value

[0224] The single change of the elements of the K matrix does not exceed ±30%

[0225] Finally, the system calculates the control variable according to the optimized controller parameters. For example, the extended state vector at a certain moment is:

[0226] z(k) = [159.8, 134.6, 25547, 0.202, 31.2, 16.2

[0227] The control increment is calculated using the optimized feedback gain K':

[0228] Δu = -K'z(k) = -1.65 W

[0229] The heating power is updated: P(k + 1) = 31.2 W - 1.65 W = 29.55 W

[0230] It should be noted that the system performs parameter optimization every 100 control cycles (i.e., 10 seconds), and saves the optimized parameters to the non-volatile memory as the initial parameters for the next startup. This mechanism ensures continuous improvement of control performance. At the same time, the system also sets the ideal range of performance indicators: the temperature control error does not exceed ±1°C, the change rate of the control quantity does not exceed 5W / s, and the adjustment time does not exceed 3s. When the actual performance indicators approach these ideal values, the system will reduce the frequency of parameter optimization to reduce the computational burden.

[0231] Furthermore, one or more of the following are satisfied: obtaining the working state and environmental light conditions of the intelligent temperature-controlled hair straightener; when it is determined that the intelligent temperature-controlled hair straightener is in the standby state and in a dark environment based on the working state and the environmental light conditions, performing a disinfection process, where the disinfection parameters of the disinfection process are controllable; obtaining multiple operating parameters of the intelligent temperature-controlled hair straightener, and outputting an indication signal based on a threshold value adapted to the type of the operating parameter, where the indication signal is used to represent the magnitude relationship between the actual value of the operating parameter and the threshold value.

[0232] The embodiments of the present application also provide a disinfection and safety protection function, and the specific implementation process thereof will be described in detail below.

[0233] First, the system monitors the working state of the intelligent temperature-controlled hair straightener in real time through multiple sensors. Specifically, it monitors whether there is a heating current through a current detection circuit to determine whether the device is in the heating working state; it detects the environmental light intensity through a photosensitive sensor, and the light intensity threshold is set to 10 lux, and it is determined to be a dark environment when the value is lower than this value; it detects whether the device is in a stationary state through an acceleration sensor, and when the detected acceleration value is less than 0.1g and lasts for more than 5 minutes, it is determined that the device is in the standby state.

[0234] Secondly, the disinfection function is started when the system simultaneously satisfies the following conditions: the device is in a non-heating state, the environmental light intensity is lower than 10 lux, and the stationary time of the device exceeds 5 minutes. The setting of these conditions ensures that the disinfection function is automatically started in an environment where the device is not in use and is suitable for disinfection.

[0235] Specific disinfection parameter control includes:

[0236] The luminous power of the ultraviolet LED: can be adjusted within the range of 2 - 5W

[0237] The single disinfection duration: can be set to 5 - 15 minutes

[0238] The disinfection interval time: can be set to 4 - 12 hours

[0239] For example, in the standard disinfection mode, the power of the ultraviolet LED is set to 3W, the single disinfection duration is 10 minutes, and the disinfection interval time is 8 hours.

[0240] In addition, the system monitors multiple key operating parameters in real time, including:

[0241] 1. Temperature parameters:

[0242] The actual temperature of each temperature zone, and the warning thresholds are: cold zone 45°C, medium temperature zone 165°C, high temperature zone 185°C

[0243] The surface temperature of the battery, and the warning threshold is 42°C

[0244] The temperature of the circuit board, and the warning threshold is 75°C

[0245] 2. Electrical parameters:

[0246] Input voltage, normal range 5V±5% or 9V±5%

[0247] Charging current, maximum limit 3A

[0248] Discharging current, maximum limit 3.5A

[0249] Battery power, low power warning value 20%

[0250] 3. Power parameters:

[0251] The heating power of each temperature zone, maximum limits are: cold zone 10W, medium temperature zone 35W, high temperature zone 40W

[0252] Total power consumption, maximum limit 50W

[0253] When any parameter exceeds the warning threshold, the system will display the corresponding warning message through the LED indicator:

[0254] Green always on: All parameters are normal

[0255] Yellow flashing: Parameter is close to the warning threshold

[0256] Red flashing: Parameter exceeds the warning threshold

[0257] For example, when it is detected that the temperature in the medium temperature zone reaches 163°C, the LED indicator will flash at a yellow frequency of 1Hz; when the temperature exceeds 165°C, the LED will flash at a red frequency of 2Hz, and at the same time the system will automatically reduce the heating power.

[0258] In case of serious abnormalities, the system will take corresponding protective measures:

[0259] When the temperature exceeds the safety threshold: Immediately disconnect the heating circuit and start cooling

[0260] When the current is abnormal: Immediately cut off the power supply and display the fault code

[0261] When the battery temperature is too high: Stop charging and reduce the discharge power

[0262] It should be noted that the system will record all abnormal events, including the occurrence time, specific parameter values, and measures taken. These records can be exported through a dedicated interface for subsequent analysis and optimization. At the same time, the system also has a self-recovery function. When the abnormal situation is lifted and the system has been operating normally for more than a preset time (such as 5 minutes), it can automatically resume the normal working mode.

[0263] The embodiment of the present application also provides a control device for an intelligent temperature-controlled hair straightener, as Figure 3 shown. This device is arranged inside the hair straightener as Figure 4 shown, or controls the hair straightener as Figure 4 shown through wireless communication. This device includes: a providing unit for determining a plurality of temperature control instructions for a plurality of different regions of the intelligent temperature-controlled hair straightener, where one region corresponds to one temperature control instruction; a control unit for respectively obtaining the temperatures of a plurality of detection points on the region, and based on the target temperature of each region, using a progressive incremental algorithm to calculate the control increment and generate a corresponding heating signal to adjust the temperature control instruction, where each region has its own corresponding target temperature; and determining a linear representation of the intelligent temperature-controlled hair straightener based on the obtained temperature sequence, heating power, and ambient temperature of each region using a model based on the Koopman operator; an optimization control unit for constructing a quadratic regulator controller according to the linear representation of the intelligent temperature-controlled hair straightener and using the quadratic regulator controller to adjust the control parameters of the intelligent temperature-controlled hair straightener; the providing unit, the control unit, and the optimization control unit are interconnected through a data bus to form a complete temperature closed-loop control system.

Claims

1. A control method for an intelligent temperature-controlled hair straightening comb, characterized in that: The method includes: Determining a plurality of temperature control instructions for a plurality of different areas of the intelligent temperature-controlled hair straightening comb, wherein one area corresponds to one temperature control instruction; Respectively obtaining the temperatures of a plurality of detection points on the region, and based on the target temperature of each region, using a progressive incremental algorithm to calculate a control increment, generating a corresponding heating signal to adjust the temperature control instruction, wherein each region has its own corresponding target temperature; Based on the acquired temperature sequence of each area, the heating power, and the ambient temperature, a model based on the Koopman operator is used to determine the linear representation of the intelligent temperature-controlled straight hair comb; According to the linear representation of the intelligent temperature-controlled hair straightening comb, a quadratic regulator controller is constructed, and the control parameters of the intelligent temperature-controlled hair straightening comb are adjusted by using the quadratic regulator controller; The method of determining the linear representation of the intelligent temperature-controlled hair straightening comb based on the acquired temperature sequence of each area, the heating power, and the ambient temperature thereof by using a model based on a Koopman operator includes: Construct a data matrix based on the temperature sequence, heating power, ambient temperature of each area, and the adjacent temperatures of other areas; According to the data matrix, a basis function is selected as a feature space of the Koopman operator; Based on the basis functions, construct a linear system representation including an extended state vector and a control input; The extended dynamic mode decomposition algorithm is used to determine the Koopman operator as the linear representation of the intelligent temperature-controlled hair straightening comb.

2. The control method according to claim 1, characterized in that: The plurality of different regions include a first region, a second region, and a third region; The step of determining a plurality of temperature control instructions for a plurality of different areas of the intelligent temperature-controlled hair straightening comb, wherein one area corresponds to one temperature control instruction, comprises: Establishing a nonlinear optimization model including the first region, the second region, and the third region, wherein the nonlinear optimization model includes: a thermodynamic model including the first region, the second region, and the third region, an objective function including temperature control accuracy, energy efficiency, and temperature uniformity, and constraints including a maximum power limit, a temperature change rate limit, and a safety temperature upper limit; Obtaining the current temperatures of the first area, the second area, and the third area within a preset sampling period, and the ambient temperature; Bringing the current temperatures of the first region, the second region, and the third region, and the ambient temperature into the thermodynamic model to obtain the heating power of the first region, the heating power of the second region, and the heating power of the third region that satisfy the objective function and the constraint conditions; According to the heating power of the first area, the heating power of the second area and the heating power of the third area, and the maximum power limit in the constraint condition, a first PWM signal of the first area, a second PWM signal of the second area and a third PWM signal of the third area are determined, wherein the first PWM signal, the second PWM signal and the third PWM signal are used as the temperature control instructions.

3. The control method according to claim 2, characterized in that: Meet one or more of the following: Thermodynamic models for the second region include: dT2 / dt=k1P2(t) / m2c2-k2(T2-T1) / R 12 -k3(T2-T3) / R 23 -k4(T2-Ta) / Ra Wherein, T2 is the temperature of the second region, P2 is the heating power, m2 is the mass of the heating component of the second region, c2 is the specific heat capacity of the heating component of the second region, R 12 is the thermal resistance between the first region and the second region, R 23 The thermal resistance between the third region and the second region, Ra is the thermal resistance to the environment, and Ta is the ambient temperature; The objective functions include: J=w1Σ(Ti-Tref,i) 2 +w2Σ(Pi / Pmax) 2 +w3Σ(Ti-Ti+1) 2 Among them, w1, w2, w3 are weight coefficients; Ti is the actual temperature of the i-th area; Tref,i is the target temperature of the i-th area; Pi is the actual heating power of the i-th area; Pmax is the maximum heating power allowed by the system; Ti+1 is the temperature of the next area adjacent to the i-th area; After a preset sampling period, the predicted temperature is compared with the actual temperature to update the parameters of the thermodynamic model.

4. The control method according to claim 1, characterized in that: The temperatures of the plurality of detection points on the region are respectively obtained, and based on the target temperature of each region, a progressive incremental algorithm is used to calculate the control increment, and a corresponding heating signal is generated to adjust the temperature control instruction, wherein each region has its own corresponding target temperature, including: Generate a temperature deviation sequence based on the temperature of each detection point in the same area and the target temperature of the area; Based on the temperature deviation sequence, a progressive incremental algorithm is used to calculate a control increment, wherein the calculation of the control increment includes a proportional term, an integral term and a differential term; According to the control increment, the heating power is updated, and the heating power is converted into a PWM control signal, the PWM control signal.

5. The control method according to claim 4, characterized in that: Meet one or more of the following: The plurality of different regions include a first region, a second region, and a third region, and the incremental algorithm for the second region includes: ΔU(k)=Kp[e(k)-e(k-1)]+Ki·e(k)+Kd[e(k)-2e(k-1)+e(k-2)] Wherein, ΔU(k) ​​is the control increment at the current moment k, Kp is the proportional coefficient, Ki is the integral coefficient, Kd is the differential coefficient, e(k) is the temperature deviation at moment k, e(k-1) is the temperature deviation at moment k-1, and e(k-2) is the temperature deviation at moment k-2; The incremental algorithm calculates the control increment including at least one of: calculating temperature control performance indicators including rise time, overshoot and adjustment time; dynamically adjusting the proportional coefficient, integral coefficient and differential coefficient based on the performance indicators; and applying the adjusted control parameters to the control increment calculation of the next control cycle.

6. The control method according to claim 4, characterized in that: Also includes: A temperature sensor is provided on the power supply device to obtain the surface temperature distribution of the power supply device; Based on the established heat accumulation model and the surface temperature distribution of the power supply device, the heat dissipation power is determined.

7. The control method according to claim 1, characterized in that: The method of constructing a quadratic regulator controller according to the linear representation of the intelligent temperature-controlled hair straightening comb and using the quadratic regulator controller to adjust the control parameters of the intelligent temperature-controlled hair straightening comb comprises: According to the linear representation of the intelligent temperature-controlled hair straightening comb, a linear quadratic regulator is designed, wherein the linear quadratic regulator is used to characterize and define a state weight matrix and a control weight matrix; By solving the discrete Riccati equation, the feedback gain matrix is ​​obtained; Optimizing the element values ​​in the state weight matrix, the control weight matrix and the feedback gain matrix by an online learning method, including: constructing a performance evaluation function including control error, control amount change rate and dynamic response characteristics; updating controller parameters by using a gradient descent method; dynamically adjusting the learning rate based on the degree of performance improvement to optimize the controller parameters; Based on the controller parameters, the control parameters of the intelligent temperature-controlled hair straightening comb are adjusted to adjust the light emitting power.

8. The control method according to claim 1, characterized in that: Meet one or more of the following: Acquiring the working state and ambient light conditions of the intelligent temperature-controlled hair straightening comb; when it is determined based on the working state and the ambient light conditions that the intelligent temperature-controlled hair straightening comb is in a standby state and in a dark environment, performing a disinfection process, wherein the disinfection parameters of the disinfection process are controllable; A plurality of operating parameters of the intelligent temperature-controlled hair straightening comb are obtained, and an indication signal is output based on a threshold value adapted to the type of the operating parameter, wherein the indication signal is used to indicate the magnitude relationship between the actual value of the operating parameter and the threshold value.

9. A control device for an intelligent temperature-controlled hair straightening comb, characterized in that: The device includes: providing a unit for determining a plurality of temperature control instructions for a plurality of different zones of the intelligent temperature-controlled hair straightening comb, wherein one zone corresponds to one temperature control instruction; A control unit, for respectively acquiring the temperatures of a plurality of detection points on the region, and based on the target temperature of each region, using a progressive incremental algorithm to calculate the control increment, and generating a corresponding heating signal to adjust the temperature control instruction, wherein each region has its own corresponding target temperature; and based on the acquired temperature sequence of each region, the heating power, and the ambient temperature, using a model based on a Koopman operator to determine the linear representation of the intelligent temperature-controlled straight hair comb; An optimization control unit, for constructing a quadratic regulator controller according to a linear representation of the intelligent temperature-controlled hair straightening comb, and using the quadratic regulator controller to adjust control parameters of the intelligent temperature-controlled hair straightening comb; The providing unit, the control unit and the optimization control unit are interconnected via a data bus to form a complete temperature closed-loop control system; The method of determining the linear representation of the intelligent temperature-controlled hair straightening comb based on the acquired temperature sequence of each area, the heating power, and the ambient temperature thereof by using a model based on a Koopman operator includes: Construct a data matrix based on the temperature sequence, heating power, ambient temperature of each area, and the adjacent temperatures of other areas; According to the data matrix, a basis function is selected as a feature space of the Koopman operator; Based on the basis functions, construct a linear system representation including an extended state vector and a control input; The extended dynamic mode decomposition algorithm is used to determine the Koopman operator as the linear representation of the intelligent temperature-controlled hair straightening comb.

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