Control method and device of intelligent temperature control hair straightening comb

Abstract

The invention provides a control method and device for an intelligent temperature control hair straightening comb, and the method comprises the steps: determining a plurality of temperature control instructions of a plurality of different regions of the intelligent temperature control hair straightening comb, and enabling one region to correspond to one temperature control instruction; the temperatures of a plurality of detection points on the areas are obtained respectively, a progressive incremental algorithm is adopted to calculate a control increment based on the target temperature of each area, and a corresponding heating signal is generated so as to adjust the temperature control instruction, and each area has the corresponding target temperature; based on the obtained temperature sequence of each area, the heating power and the environment temperature, a model based on a Koopman operator is adopted, and linear representation of the intelligent temperature control hair straightening comb is determined; according to the linear representation of the intelligent temperature control hair straightening comb, constructing a quadratic regulator controller, and adopting the quadratic regulator controller to regulate the control parameters of the intelligent temperature control hair straightening comb.

Description

Technical Field

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

[0002] With the development of hairdressing equipment technology, smart temperature-controlled straight hair combs have been widely used. However, the smart temperature-controlled straight hair combs currently on the market have the following technical problems: First, traditional temperature-controlled hair straightening combs mostly use a simple switch control solution with a fixed temperature threshold. The temperature of the heating plate is detected by a temperature sensor. When the set temperature is reached, the power is turned off, and the temperature is lowered before reheating. This solution has large temperature fluctuations, low control accuracy, and cannot achieve coordinated control of multiple temperature zones.

[0003] Second, some temperature-controlled straightening combs use PID control solutions. Although they can maintain a relatively stable temperature in a steady state, the control effect is not ideal during the dynamic process, especially when the heating power and heat dissipation conditions change, and problems such as large temperature fluctuations and slow response speed may easily occur.

[0004] Third, existing temperature-controlled straightening combs lack intelligent data-driven control solutions and are unable to adaptively adjust control strategies according to usage environments and user habits, resulting in low temperature control accuracy, unsatisfactory straightening effects, and easy damage to hair.

[0005] Fourth, the existing technology cannot achieve coordinated and precise control of multiple temperature zones, and lacks a complete safety protection mechanism, which affects the safety and reliability of use. Summary of the invention

[0006] In view of this, the present application provides a control method and device for an intelligent temperature-controlled straight hair comb, which solves the problems of low temperature control accuracy, inability to achieve multi-temperature zone coordinated control, and lack of intelligent data-driven control of the temperature-controlled straight hair comb in the prior art.

[0007] The present application embodiment provides a control method for an intelligent temperature-controlled hair straightening comb, comprising: 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 quadratic regulator controller is used to adjust the control parameters of the intelligent temperature-controlled hair straightening comb.

[0008] 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 quadratic regulator controller is used to adjust the control parameters of the intelligent temperature-controlled hair straightening comb.

[0009] Optionally, 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.

[0010] Optionally, one or more of the following are met: Thermodynamic models for the second region include: dT2 / dt = k1P2(t) / m2c2 - k2(T2-T1) / R12- (T2-T3) / R23 - k4( -Ta) / Ra in, is the temperature zone of the second area, is the heating power, is the mass of the heating component in the second zone, is the specific heat capacity of the second zone heating component, 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 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.

[0011] Optionally, the temperatures of multiple detection points on the area are respectively obtained, and based on the target temperature of each area, a progressive incremental algorithm is used to calculate the control increment to generate a corresponding heating signal to adjust the temperature control instruction, wherein each area 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.

[0012] Optionally, one or more of the following are met: 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.

[0013] Optionally, it also includes: A temperature sensor is placed on the power supply device to obtain the surface temperature distribution of the power supply device; and the heat dissipation power is determined based on the established heat accumulation model and the surface temperature distribution of the power supply device.

[0014] Optionally, 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 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.

[0015] Optionally, 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 control parameters of the intelligent temperature-controlled hair straightening comb includes: 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.

[0016] Optionally, one or more of the following are met: 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.

[0017] The present application also provides a control device for an intelligent temperature-controlled hair straightening comb, the device comprising: 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 optimizing control unit are interconnected via a data bus to form a complete temperature closed-loop control system.

[0018] The present application also provides a computer device, the computer device comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed 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 straight hair comb.

[0019] An embodiment of the present application also provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the control method of the above-mentioned intelligent temperature-controlled straight hair comb.

[0020] The embodiment of the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the control method of the above-mentioned intelligent temperature-controlled straight hair comb.

[0021] This application has the following technical effects: By adopting the online convex optimization method of nonlinear system constraint control and combining it with the progressive incremental control algorithm, the coordinated precise control of multiple temperature zones is achieved, which significantly improves the temperature control accuracy. For the first time, the Koopman operator was used for system modeling and control in the field of hairdressing appliances. The control parameters were continuously optimized in a data-driven way, which improved the adaptability and robustness of the system. Optimal control is achieved based on a linear quadratic regulator, which ensures control accuracy while taking energy efficiency into consideration, thus improving the practicality of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following is a brief introduction to the drawings required for use in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate 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 ordinary technicians in this field, other relevant drawings can also be obtained based on these drawings without creative work.

[0023] Figure 1 It is a schematic flow chart of a control method of an intelligent temperature-controlled hair straightening comb provided in an embodiment of the present application; Figure 2 This is a schematic diagram of the control area division provided in an embodiment of the present application; Figure 3 is a schematic diagram of the structure of a control device provided in an embodiment of the present application; Figure 4 This is a physical picture of the hair straightening comb provided in the embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0025] In the description of this application, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood 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 ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0026] The specific implementation of the present application will be further described below in conjunction with the accompanying drawings.

[0027] like Figure 1 , the embodiment of the present application provides a control method for an intelligent temperature-controlled hair straightening comb, the method comprising: Step S1: 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; Further, the plurality of different regions include a first region, a second region, and a third region.

[0028] On this basis, the method 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: First, a nonlinear optimization model including the first region, the second region, and the third region is established, and 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 maximum power limit, temperature change rate limit, and safety temperature upper limit.

[0029] Second, obtain the current temperatures of the first area, the second area, and the third area, and the ambient temperature within a preset sampling period; bring the current temperatures of the first area, the second area, and the third area, and the ambient temperature into the thermodynamic model to obtain the heating power of the first area, the heating power of the second area, and the heating power of the third area that satisfy the objective function and the constraints.

[0030] Third, 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 conditions, determine the first PWM signal of the first area, the second PWM signal of the second area and the third PWM signal of the third area, wherein the first PWM signal, the second PWM signal and the third PWM signal serve as the temperature control instructions.

[0031] Furthermore, one or more of the following are met: Thermodynamic models for the second region include: dT2 / dt = k1P2(t) / m2c2 - k2(T2-T1) / R12- (T2-T3) / R23 - k4( -Ta) / Ra in, is the temperature zone of the second area, is the heating power, is the mass of the heating component in the second zone, is the specific heat capacity of the second zone heating component, 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 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.

[0032] Specifically, if Figure 2 The intelligent temperature-controlled hair straightening comb of the present application includes three different temperature control areas, namely the first area, the second area and the third area. The first area is the cold tooth area, and the working temperature is set to about 40°C; the second area is the medium temperature area, and the working temperature is set to about 160°C; the third area is the high temperature area, and the working temperature is set to about 180°C. This multi-temperature zone design can meet the temperature requirements of different hair qualities and styling needs.

[0033] When determining the temperature control instructions, the following aspects need to be considered: First, according to the target temperature requirements of different temperature zones, set the basic temperature control instructions for each zone. Second, based on the heat conduction characteristics between the zones, adjust the temperature control instructions to achieve uniform temperature distribution. Third, considering the safety requirements of the equipment, set the maximum temperature limit for each temperature zone. Finally, dynamically adjust the temperature control instructions according to the actual usage scenario.

[0034] For each temperature zone, the temperature control instructions mainly include parameters such as target temperature setting value, heating power limit, temperature change rate limit, etc. These parameters are transmitted to the heating control circuit in the form of PWM signals to achieve precise control of the temperature of each zone. For example, when the user selects the standard mode, the system will automatically set the temperature of the first zone to 40°C, the second zone to 160°C, and the third zone to 180°C, and generate corresponding PWM control signals.

[0035] It should be noted that the temperature control instructions for each area are independent, which can more flexibly respond to 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 based on the monitoring results to ensure the control effect.

[0036] Step S2: 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, generating a corresponding heating signal to adjust the temperature control instruction, wherein each region has its own corresponding target temperature; Furthermore, S2 specifically includes: First, a temperature deviation sequence is generated based on the temperatures of each detection point in the same area and the target temperature of the area.

[0037] Second, based on the temperature deviation sequence, a progressive incremental algorithm is used to calculate the control increment, and the calculation of the control increment includes a proportional term, an integral term, and a differential term.

[0038] Third, the heating power is updated according to the control increment, and the heating power is converted into a PWM control signal, the PWM control signal.

[0039] Further, one or more of the following conditions are satisfied: the multiple different areas include a first area, a second area, and a third area, and the progressive incremental algorithm of the second area 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.

[0040] In the embodiment of the present application, the temperature control of the second area (medium temperature area) is taken as an example to specifically illustrate the generation of the temperature deviation sequence and the implementation process of the progressive incremental algorithm.

[0041] First, multiple temperature detection points are set up in the second area, and temperature data is collected in real time through high-precision NTC temperature sensors.

[0042] For example, three detection points T1, T2 and T3 are set in the area, and the sampling period is 100ms. Assume that the temperature data collected at a certain moment are: T1=158.5℃, T2=157.8℃, T3=159.2℃, and the target temperature of the area is set to 160℃.

[0043] 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℃. At the same time, the average temperature data of the previous two sampling periods is saved, for example, T(k-1)=156.2℃, T(k-2)=154.8℃. By comparing these temperature values ​​with the target temperature of 160℃, a temperature deviation sequence is generated: e(k)=160-158.5=1.5℃ e(k-1)=160-156.2=3.8℃ e(k-2)=160-154.8=5.2℃ Then, the system uses a progressive incremental algorithm to calculate the control increment. The algorithm contains three key items: the proportional term reflects the change of the current deviation, the integral term eliminates the steady-state error, and the differential term improves the dynamic response characteristics. The specific calculation formula is: ΔU(k)=Kp[e(k)-e(k-1)]+Ki·e(k)+Kd[e(k)-2e(k-1)+e(k-2)] Among them, the initial values ​​of the control parameters are set as: Kp=0.8, Ki=0.1, Kd=0.05.

[0044] Finally, the calculated control increment is superimposed on the current heating power.

[0045] For example, if the current heating power is 32 W, the calculated control increment ΔU(k) ​​= -1.79 W, and the updated heating power is 30.21 W. The system converts the power value into a PWM signal, and outputs a control signal with a duty cycle of 75.5% within a 10ms PWM cycle, that is, the high level duration is 7.55ms and the low level duration is 2.45ms.

[0046] It should be noted that the system will dynamically adjust the control parameters according to the control effect.

[0047] For example, when the temperature rise rate is detected to be too slow (less than 1°C / s), the proportional coefficient Kp will be appropriately increased; when the steady-state error is found to be large (greater than 2°C), 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.

[0048] In addition, in order to prevent sudden changes in the control amount from causing impact on the system, a control increment limit of ±5W is set to ensure the stability of each power adjustment. At the same time, the system also sets an upper limit of the heating power of 40W as a safety protection. These measures jointly ensure the safety and reliability of temperature control.

[0049] Furthermore, the control method also includes: providing a temperature sensor on the power supply device to obtain the surface temperature distribution of the power supply device; and determining the heat dissipation power based on the established heat accumulation model and the surface temperature distribution of the power supply device.

[0050] In the specific implementation process of this application, a 3000mAh lithium battery is taken as an example of a power supply device to illustrate how to achieve temperature monitoring and heat dissipation control of the power supply device.

[0051] First, four NTC temperature sensors with an accuracy of ±0.1°C are arranged on the surface of the lithium battery to monitor the temperature distribution on the battery surface in real time. Sensor 1 is arranged at the positive end of the battery, sensor 2 is located in the middle of the battery close to the heating zone, sensor 3 is arranged in the middle of the battery away from the heating zone, and sensor 4 is arranged at the negative end of the battery. This layout can comprehensively monitor the temperature changes of various parts of the battery.

[0052] Secondly, a battery heat accumulation model is established, which takes into account the heat generated during the charging process, the heat generated during the discharging process and the environmental heat dissipation.

[0053] Specifically, under the ambient temperature of 25℃, the heat generated by the charging process is calculated by the formula Qc=I²R+IT(dE / dT), where the internal resistance R=0.05Ω and the temperature coefficient dE / dT=-0.003V / ℃ under the charging current of 2A; the heat generated by the discharging process is calculated by the formula Qd=I²R-IT(dE / dT), and the maximum discharge current is 3A; the ambient 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.

[0054] For example, at a certain moment, the temperatures detected by the four temperature sensors are: T1=38.2℃, T2=41.5℃ (exceeding the warning temperature of 40℃), T3=37.8℃, and T4=36.5℃. 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 needs to increase the heat dissipation power. Calculated by the heat accumulation model, the required heat dissipation power is 0.8W at this time, corresponding to a fan speed of 2000RPM.

[0055] The specific heat dissipation control adopts a graded adjustment method: when the temperature is lower than 35℃, the cooling fan stops working; when the temperature is between 35℃ and 38℃, the fan speed is set to 1500RPM; when the temperature is between 38℃ and 42℃, the fan speed is set to 2000RPM; when the temperature exceeds 42℃, the fan speed is increased to 2500RPM, and the system will automatically reduce the charging current to prevent the battery from overheating.

[0056] It should be noted that the system will dynamically adjust the cooling strategy based on actual usage conditions.

[0057] For example, in fast charging mode, due to the large charging current, the system will start the cooling fan in advance to dissipate heat in advance; when the ambient temperature is high, the system will lower the cooling temperature threshold accordingly and start cooling in advance; during the natural cooling process when the device is powered off, the system will dynamically adjust the fan speed according to the temperature drop rate to balance the cooling effect and energy consumption.

[0058] In addition, the system has 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 cooling; when the temperature sensor fails, the system will automatically enter the safety mode, limit the charging current and start the maximum cooling power to ensure the safety of the device.

[0059] Step S3: 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.

[0060] In step S3, the system uses the theory based on the Koopman operator to achieve a linear representation of the dynamic system of the intelligent temperature-controlled straight hair comb.

[0061] The specific implementation process of this step is described in detail below.

[0062] First, the system needs to collect the operating data of each temperature zone. Taking multi-temperature zone coordinated control as an example, the system uses a sampling period of 100ms to collect 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 the zone, the current heating power, the ambient temperature, and the temperature difference with the adjacent temperature zone.

[0063] Specifically, for the second area (medium temperature area), the data collected are 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 32.5W, 31.8W and 31.2W; the ambient temperature is basically stable at around 25.2°C; the temperature difference with the high temperature area (third area) is 17.2°C, 16.7°C and 16.2°C respectively. Similar data sets are also collected for the first and third areas.

[0064] Secondly, for each temperature zone, the system constructed a feature space.

[0065] Taking the second area as an example, six basis functions were selected: the original temperature value, the temperature difference with the environment, the square term of the temperature, the exponential decay term of the temperature, the heating power, and the temperature difference with the adjacent temperature zone. The selection of these basis functions takes into account the physical meaning and includes both basic linear relationships and important nonlinear characteristics.

[0066] 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, the data matrix is ​​first constructed to arrange the state vectors obtained by continuous sampling in chronological order; then the matrix representation of the Koopman operator is obtained by solving the least squares problem. This matrix can accurately describe the evolution law of the system in the feature space.

[0067] It is worth noting that the system uses an adaptive mechanism to update the Koopman operator. The re-identification process is triggered when the following conditions are detected: the ambient temperature changes by more than 5°C, the control performance is significantly reduced (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 changes in working conditions.

[0068] 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 last 20 seconds of running data to update the model, which not only ensures computational efficiency but also reflects the latest dynamic characteristics of the system. When solving the least squares problem, the QR decomposition method is used to improve numerical stability.

[0069] Finally, the system obtained a linear state space model: z(k+1)=Kz(k)+Bu(k). The state vector z contains the characteristic quantities of all temperature zones, and the control input u corresponds to the heating power adjustment quantity of each temperature zone. This linear model accurately describes the coupling relationship between multiple temperature zones and provides a theoretical basis for subsequent temperature coordinated control.

[0070] 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 adaptive update capabilities to cope with changes in system parameters; the amount of calculation is moderate and suitable for embedded system implementation. These characteristics make this method particularly suitable for application scenarios such as smart temperature-controlled hair straighteners that require precise temperature control.

[0071] In one embodiment, S3 specifically includes: A data matrix is ​​constructed according to the temperature sequence, heating power, ambient temperature of each area, and adjacent temperatures between other areas; a basis function is selected as the feature space of the Koopman operator according to the data matrix; a linear system representation including an extended state vector and a control input is constructed based on the basis function; and a Koopman operator is determined as the linear representation of the intelligent temperature-controlled straight hair comb using an extended dynamic mode decomposition algorithm.

[0072] The embodiment of the present application converts the nonlinear temperature control system into a linear representation through the Koopman operator theory, and the temperature control in the medium temperature zone (the second zone) is used as a specific example for explanation below.

[0073] First, the system samples once every 100ms, and the collected state variables include: temperature sequence T(k) (unit ℃), heating power P(k) (unit W), ambient temperature Ta(k) (unit ℃) and adjacent temperature zone temperature T_adj(k) (unit ℃). For example, the data fragment obtained from a continuous sampling is: Time k-2: T(k)=158.3℃, P(k)=32.5W, Ta(k)=25.2℃, T_adj(k)=175.5℃ Time k-1: T(k)=159.1℃, P(k)=31.8W, Ta(k)=25.3℃, T_adj(k)=175.8℃ Time k: T(k)=159.8℃, P(k)=31.2W, Ta(k)=25.2℃, T_adj(k)=176.0℃ Secondly, based on the collected data, the system selected 6 basis functions to construct the Koopman feature space: : Indicates the original temperature value; : Indicates the temperature difference with the environment; : represents the nonlinear term of temperature; : represents the exponential decay characteristic of temperature; : Indicates heating power; : Indicates the temperature difference with the adjacent temperature zone; Then, the Koopman operator is estimated using the extended dynamic mode decomposition (EDMD) algorithm.

[0074] The specific steps are as follows: First, construct data matrices X and Y, where , ; Then solve the least squares problem K=YX + (Where X + is the pseudo-inverse), and the Koopman matrix K is obtained. Taking the above data as an example, the calculated Koopman matrix example value is: 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; 0.02 0.04 0.97 -0.03 0.15 0.08; -0.01 0.06 0.04 0.95 0.12 0.03; 0.03 0.02 0.08 0.05 0.96 0.04; 0.04 -0.02 0.06 0.07 0.08 0.99] 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 nonlinear temperature control system is transformed into a linear system representation.

[0075] It should be noted that the system will continuously update the data matrix and recalculate the Koopman operator during operation to achieve online adaptation of the model. For example, when the ambient temperature changes significantly (more than ±5°C) or when the control effect is detected to be significantly deteriorated (steady-state error exceeds 2°C), the system will trigger the re-identification process and update the parameters of the Koopman matrix.

[0076] In addition, in order to improve the calculation efficiency, the system uses a sliding time window to update the data matrix, 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 the numerical calculation. These optimization measures ensure that the system can complete the model update in real time and guarantee the control performance.

[0077] 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 the subsequent controller design, laying the foundation for achieving precise temperature control. At the same time, because the model has the ability to adaptively update, it can well cope with problems such as changes in the use environment and system parameter drift.

[0078] Step S4: 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.

[0079] In step S4, the system designs a linear quadratic regulator (LQR) controller based on the linear system representation obtained by the aforementioned Koopman operator to achieve precise temperature control. The specific implementation process of this step is described in detail below.

[0080] Firstly, the system designs the state weight matrix Q and control weight matrix R of the LQR controller according to the linear state space expression z(k+1)=Kz(k)+Bu(k).

[0081] Taking the second area (medium temperature zone) as an example, the state weight matrix Q adopts a diagonal matrix form: Q=diag([10, 5,2, 2, 3, 4]), in which the original temperature value and the temperature difference term are given a larger weight, reflecting the importance of temperature control accuracy; the control weight takes the scalar R=0.1, which indicates a moderate constraint on the control energy consumption.

[0082] 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 cycle: u(k)=-Kz(k).

[0083] Then, the system uses online learning methods to continuously optimize the control parameters. The specific optimization strategy is as follows: When the temperature rises too slowly (less than 1°C / s), the temperature-related weight in the Q matrix is ​​increased by 20%; When the power fluctuation is too large (greater than 5W / s), the control weight R is increased by 30%; When the steady-state error is too large (greater than 2°C), the weight associated with the integral action is increased by 25%.

[0084] For example, during a certain control process, the system detected the following performance indicators: The temperature rise rate is 0.8℃ / s (lower than the target value) Power fluctuation is 3.2W / s (within the allowable range) The steady-state error is 1.5°C (within the allowable range) Based on these indicators, the system adjusts the control parameters as follows: Update the Q matrix to Q'=diag([12, 6, 2, 2, 3, 4]) Keep the R value unchanged, R'=0.1 Get the new feedback gain: K'=[-2.58, 1.02, -0.35, 0.42, -1.25, 0.65] Taking a specific control cycle as an example, assuming that the current extended state vector is: z(k)=[159.8, 134.6, 25547, 0.202, 31.2, 16.2

[0085] The control increment is calculated by the new feedback gain: Δu=-K'z(k)=-1.85W

[0086] The final output heating power is: P(k+1)=P(k)+Δu=31.2-1.85=29.35W

[0087] It should be noted that the system also implements an adaptive learning rate adjustment mechanism. When the control performance does not improve significantly after multiple consecutive adjustments, the learning rate will be automatically reduced; 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.

[0088] In addition, the system sets safety limits for parameter adjustments: The diagonal elements of the state weight Q matrix are adjusted within a range of 0.5-2 times the initial value. The adjustment range of the control weight R is limited to 0.2-5 times the initial value The change rate of each element of feedback gain K shall not exceed 30% each time 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 ​​at the next startup to achieve continuous improvement of controller performance.

[0089] Finally, the entire control process forms a closed-loop optimization mechanism of "obtaining state quantity → calculating control quantity → executing control → evaluating effect → 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, so that the system can always maintain the best control performance.

[0090] Furthermore, S4 specifically includes: designing a linear quadratic regulator according to the linear representation of the intelligent temperature-controlled straight hair comb, the linear quadratic regulator being used to characterize and define a state weight matrix and a control weight matrix; obtaining a 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 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; and adjusting the control parameters of the intelligent temperature-controlled straight hair comb based on the controller parameters to adjust the luminous power.

[0091] The following is a specific example to illustrate the implementation process of S4: First, when designing the linear quadratic regulator, the state weight matrix Q and the control weight matrix R are defined according to the control requirements of each temperature zone of the intelligent temperature-controlled hair straightening comb. Taking the second zone (medium temperature zone) as an example, the state weight matrix Q is initially set to a diagonal matrix: Q=diag([10, 5, 2, 2, 3, 4]), where larger weights are assigned to temperature-related state quantities; the control weight matrix takes the scalar R=0.1.

[0092] 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. In order to evaluate the control performance, the following performance evaluation function is constructed: J = α·e² + β·(du / dt)² + γ·ts Wherein, e is the temperature control error, du / dt is the control amount change rate, ts is the adjustment time, and the weight coefficients are initially set to α=0.6, β=0.3, and γ=0.1.

[0093] Then, the control parameters are optimized via an online learning method.

[0094] Assume that the performance index of a control process is: Temperature control error e=1.8℃ Control quantity change rate du / dt=4.2W / s Adjustment time ts=2.3s The calculated performance index is J=2.47.

[0095] When the gradient descent method is used to update the parameters, the learning rate is initially set to η = 0.01. Taking the first diagonal element q1 of the Q matrix as an example, the update formula is: q1_new = q1_old - η· J / q1 After one iteration: The Q matrix is ​​updated to Q'=diag([11.2, 5.3, 2.1, 2.0, 3.1, 4.2]) R is updated to R'=0.12 The feedback gain is updated to K'=[-2.28, 0.92, -0.37, 0.43, -1.28, 0.68] After using the new parameters, measure the performance indicators again: Temperature control error reduced to e=1.5℃ The control quantity change rate is reduced to du / dt=3.8W / s The adjustment time is reduced to ts=2.1s The new performance index J'=1.96, and the performance improvement rate is (2.47-1.96) / 2.47≈20.6%.

[0096] Due to the obvious 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 less than 5%, the learning rate will be reduced to 0.8 times the current value.

[0097] In order to ensure the stability of control, the limits of parameter changes are set: The diagonal elements of the Q matrix are limited to within ±50% of the initial value. The variation range of R is limited to ±80% of the initial value. The single change of K matrix elements does not exceed ±30% Finally, the system calculates the control quantity based on the optimized controller parameters. For example, the extended state vector at a certain moment is: z(k)=[159.8, 134.6, 25547, 0.202, 31.2, 16.2

[0098] The control increment is calculated using the optimized feedback gain K': Δu=-K'z(k)=-1.65W Update heating power: P(k+1)=31.2W-1.65W=29.55W It should be noted that the system performs parameter optimization every 100 control cycles (i.e. 10 seconds) and saves the optimized parameters in 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 control amount change rate does not exceed 5W / s, and the adjustment time does not exceed 3s. When the actual performance indicators are close to these ideal values, the system will reduce the frequency of parameter optimization to reduce the computational burden.

[0099] Furthermore, one or more of the following is satisfied: obtaining 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 located in a dark environment, performing a disinfection process, wherein the disinfection parameters of the disinfection process are controllable; obtaining a plurality of operating parameters of the intelligent temperature-controlled hair straightening comb, and outputting an indication signal based on a threshold value adapted to the type of the operating parameter, wherein the indication signal is used to indicate the actual value of the operating parameter and the relationship between the value and the threshold value.

[0100] The embodiment of the present application also provides disinfection and safety protection functions, and the specific implementation process is described in detail below.

[0101] First, the system uses multiple sensors to monitor the working status of the smart temperature-controlled hair straightening comb in real time. Specifically, the current detection circuit monitors whether there is heating current to determine whether the device is in the heating working state; the light intensity of the environment is detected by the photosensitive sensor, and the light intensity threshold is set to 10lux. When it is lower than this value, it is determined to be a dark environment; the acceleration sensor detects whether the device is in a stationary state. 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 standby state.

[0102] Secondly, the disinfection function is activated when the system meets the following conditions at the same time: the device is in a non-heating state, the ambient light intensity is less than 10 lux, and the device has been stationary for more than 5 minutes. The setting of these conditions ensures that the disinfection function is automatically activated when the device is not in use and in an environment suitable for disinfection.

[0103] Specific disinfection parameter controls include: Light output of UV LED: adjustable within 2-5W range Single disinfection time: can be set to 5-15 minutes Disinfection interval: can be set to 4-12 hours For example, in standard disinfection mode, the UV LED power is set to 3W, the single disinfection time is 10 minutes, and the disinfection interval is 8 hours.

[0104] In addition, the system monitors several key operating parameters in real time, including: 1. Temperature parameters: The actual temperature of each temperature zone, the warning threshold is: 45℃ in cold zone, 165℃ in medium temperature zone, 185℃ in high temperature zone Battery surface temperature, warning threshold is 42°C Circuit board temperature, warning threshold is 75℃ 2. Electrical parameters: Input voltage, normal range 5V±5% or 9V±5% Charging current, maximum limit 3A Discharge current, maximum limit 3.5A Battery power, low power warning value 20% 3. Power parameters: The maximum limits of heating power in each temperature zone are: cold zone 10W, medium temperature zone 35W, high temperature zone 40W Total power consumption, maximum limit 50W When any parameter exceeds the warning threshold, the system will display the corresponding warning message through the LED indicator: Steady green: All parameters are normal Flashing yellow: The parameter is close to the warning threshold Flashing red: The parameter exceeds the warning threshold For example, when it is detected that the temperature in the medium temperature zone reaches 163°C, the LED indicator will flash yellow at a frequency of 1Hz; when the temperature exceeds 165°C, the LED will flash red at a frequency of 2Hz, and the system will automatically reduce the heating power.

[0105] When a serious abnormality occurs, the system will take corresponding protective measures: When the temperature exceeds the safety threshold: immediately disconnect the heating circuit and start cooling When the current is abnormal: cut off the power supply immediately and display the fault code When the battery temperature is too high: stop charging and reduce discharge power It should be noted that the system will record all abnormal events, including the time of occurrence, 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 resolved and continues to be normal for more than a preset time (such as 5 minutes), it can automatically resume normal working mode.

[0106] The present application also provides a control device for an intelligent temperature-controlled hair straightening comb. Figure 3 As shown, the device is arranged in Figure 4 In the straight hair brush shown, or by wireless communication control such as Figure 4 A hair straightening comb, the device comprises: a providing unit, used to determine 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; a control unit, used to respectively obtain the temperatures of a plurality of detection points on the areas, and based on the target temperature of each area, use a progressive incremental algorithm to calculate the control increment, generate a corresponding heating signal, so as to adjust the temperature control instruction, wherein each area has its own corresponding target temperature; and based on the obtained temperature sequence of each area, the heating power, and the ambient temperature, use a model based on the Koopman operator to determine the linear representation of the intelligent temperature-controlled hair straightening comb; an optimization control unit, used to construct a quadratic regulator controller according to the linear representation of the intelligent temperature-controlled hair straightening comb, and use the quadratic regulator controller to adjust the control parameters of the intelligent temperature-controlled hair straightening comb; 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 quadratic regulator controller is used to adjust the control parameters 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) / R12- (T2-T3) / R23 - k4( -Ta) / Ra in, is the temperature zone of the second area, is the heating power, is the mass of the heating component in the second zone, is the specific heat capacity of the second zone heating component, 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 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 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 a 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.

8. 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.

9. 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.

10. 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 optimizing control unit are interconnected via a data bus to form a complete temperature closed-loop control system.

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