An adaptive temperature control system and method for a smart high-speed electric hair dryer

Abstract

The application discloses a kind of self-adapting temperature control system and method of intelligent high-speed electric hair dryer barrel, the system includes PCB board, MCU host, environment perception module, execution module and interactive module;Environment perception module is configured two groups of NTC thermistor respectively to collect air inlet environment reference temperature, outlet real-time air temperature, and relies on current sampling circuit to collect brushless motor working current, multi-dimensional collection equipment operation and environmental data;Execution module receives MCU host instruction, drives brushless motor and heating module operation, while integrating multiple protection circuit, avoids overload, overtemperature, overcurrent and other fault risks.The application is based on multi-working condition measured data in advance to construct environment temperature-air speed-outlet air temperature three-dimensional dynamic temperature mapping table, carries fuzzy adaptive PID algorithm, with heating wire wave loss value as power regulation carrier, combined with real-time environmental parameters and air speed gear dynamic calibration, fine-tuning PID three core parameters, sets special temperature control compensation strategy for high-low air speed working condition.

Description

Technical Field

[0001] This invention relates to the field of intelligent electrical appliance technology, specifically to an adaptive temperature control system and method for an intelligent high-speed hair dryer. Background Technology

[0002] With the upgrading of national consumption, the personal care small appliance market is rapidly iterating. High-speed hair dryers, with their advantages of high wind speed, fast drying speed, and excellent hair care effect, are gradually replacing traditional AC motor hair dryers and becoming the mainstream product in the market. At present, the temperature control mode of mainstream high-speed hair dryers on the market is relatively simple. Most of them adopt a fixed wind speed and fixed heating power level control mode, which requires users to manually switch parameters. Only a few high-end products are equipped with basic constant temperature function, which only rely on a single NTC thermistor at the air outlet to achieve simple temperature limit protection, without combining ambient temperature and motor operating status to complete comprehensive closed-loop control.

[0003] Existing high-speed hair dryer temperature control technology has several industry pain points:

[0004] Firstly, the temperature control sensing dimension is singular and cannot adapt to different scenarios such as different ambient temperatures, different hair dryness and wetness, and different usage distances. In low-temperature environments, the air outlet temperature is insufficient and may cause users to catch a cold. In high-humidity environments, the drying efficiency is low, and close-range blow-drying may cause local high temperature burns to the scalp.

[0005] Secondly, the constant temperature control logic is rudimentary, and an integrated closed-loop control system for environment, equipment, and air outlet has not been established. After the wind speed is switched, the heat matching is lagging. Under high wind speed conditions, the hot air is easily carried away by the airflow, resulting in temperature overshoot and large fluctuations, which cannot balance the hair drying efficiency and hair care effect.

[0006] Third, the level of intelligence is low, and the fixed gear adjustment requires users to frequently switch manually, which is cumbersome and results in a poor user experience.

[0007] Fourth, the protection mechanism is inadequate. Only basic over-temperature power-off protection is set up, and no supporting protection strategies are designed for faults such as motor overload and circuit overcurrent. The safety of equipment operation needs to be improved.

[0008] To address the shortcomings of the existing technologies, there is an urgent need to develop a hair dryer temperature control system and method that features multi-parameter sensing, algorithmic adaptation, full-condition adaptability, and multiple safety protections, in order to solve problems such as lagging temperature control, large temperature fluctuations, and poor adaptability to operating conditions in traditional products. Summary of the Invention

[0009] The purpose of this invention is to solve the problems of existing high-speed hair dryers, such as strong temperature control lag, large temperature overshoot, poor adaptability to multiple wind speed conditions, sudden changes in hot and cold air, and limited safety protection. The invention provides an intelligent high-speed hair dryer adaptive temperature control system and method, which relies on multi-dimensional environmental perception technology and an optimized fuzzy adaptive PID algorithm to achieve high-precision and stable adaptive control of the outlet air temperature. At the same time, it integrates multiple fault protection circuits to improve the intelligence level and operational safety of the equipment.

[0010] To solve the above-mentioned technical problems, the present invention achieves this through the following solution: An adaptive temperature control system for an intelligent high-speed hair dryer, comprising a PCB board disposed within the hair dryer, the control system further comprising:

[0011] The MCU main controller is located on the PCB board;

[0012] The environmental sensing module includes a first temperature sensing module located at the air inlet of the blower, a second temperature sensing module located at the air outlet of the blower, and a sampling circuit for collecting motor current. The first temperature sensing module, the second temperature sensing module, and the sampling circuit are all electrically connected to the MCU main controller.

[0013] The execution module has its input terminal electrically connected to the MCU main controller and its output terminal electrically connected to the motor and heating module. The execution module is also equipped with a drive protection circuit.

[0014] Furthermore, the first temperature sensing module is a first NTC thermistor located at the air inlet, used to collect the ambient reference temperature during the device power-on and startup phases;

[0015] The second temperature sensing module is a second NTC thermistor located at the air outlet, used to collect the actual temperature of the air outlet in real time and build a temperature closed-loop control and overheat protection mechanism.

[0016] The motor current sampling circuit is used to collect the operating current of the brushless motor in real time and monitor the motor load status and actual speed.

[0017] Furthermore, the motor is a brushless motor, which is steplessly speed-regulated by the PWM signal output by the MCU main controller;

[0018] The heating element of the heating module is a heating wire, and the output power of the heating wire is adjusted by a silicon controlled rectifier or an IGBT device.

[0019] The drive protection circuit is electrically connected to the brushless motor and the heating module, and integrates a motor drive circuit, a heating drive circuit, and multiple protection circuits for overcurrent, overtemperature, and overload.

[0020] Furthermore, the MCU main controller has a dynamic temperature mapping table pre-stored inside, which is generated by modeling the actual temperature data measured near the air outlet under different ambient temperatures and different wind speed levels;

[0021] The MCU main controller has a built-in fuzzy adaptive PID control module, and the calculation formula of the fuzzy adaptive PID control module is:

[0022] P_out=KP·t_err+KI·t_integral+KD·t_der;

[0023] Where t_err = T_target − T_out is the temperature error, T_target is the NTC target temperature value, T_out is the actual NTC detected temperature, t_integral is the cumulative value of the periodic temperature error, t_der is the difference between the current periodic temperature error and the previous periodic temperature error, and P_out is the heating wire loss value.

[0024] Furthermore, the range of the heating wire drop wave value is [0, 49].

[0025] Furthermore, the MCU master controller is configured to: pre-calibrate the PID basic parameters KP, KI, and KD under different operating conditions based on measured data from multiple sets of temperature setpoints;

[0026] When the user changes the temperature setpoint, the incremental correction is generated based on the current operating condition PID basic parameters and combined with the real-time ambient temperature and wind speed setting to complete the smooth adaptive adjustment of PID parameters.

[0027] At the same time, temperature control compensation is performed for different wind speeds. Under the same ambient temperature, the higher wind speed setting matches a higher NTC target temperature, and the proportional coefficient KP is reduced and the integral coefficient KI is increased simultaneously. By adjusting the heating wire power, the air outlet temperature near the hair dryer outlet is stably controlled within the allowable deviation range of the target temperature.

[0028] Furthermore, the control system also includes an interaction module, which is electrically connected to the MCU main controller;

[0029] The interactive module includes function buttons, display indicator light components, and a mode storage unit, which is used to receive user commands for setting temperature and fan speed, and to display the device's operating status and store commonly used working mode parameters.

[0030] The present invention discloses an adaptive temperature control method for an intelligent high-speed hair dryer, which is applied to the aforementioned system. The control method includes the following steps:

[0031] Step 1: Complete dynamic temperature modeling in advance, collect measured data of the temperature near the air outlet of the temperature control device and the NTC detection temperature under different ambient temperatures and different fan speeds, and construct a temperature mapping table;

[0032] Step 2: Based on the temperature mapping table in Step 1, conduct multiple sets of temperature measurement experiments at multiple different temperature setpoints to match and calibrate the basic KP, KI, and KD parameters of the fuzzy adaptive PID algorithm under the corresponding working conditions.

[0033] Step 3: Real-time acquisition of the actual temperature T_out collected by the NTC temperature detection module, and calculation of the temperature error t_err based on the preset NTC target temperature value T_target, where t_err = T_target - T_out; Calculation of the temperature deviation differential term t_der based on the real-time temperature error t_err and the historical temperature deviation t_lasterr of the previous detection cycle, and accumulation of the temperature error in each detection cycle to obtain the integral term t_integral;

[0034] Step 4: The output value P_out of the heating module is calculated using the fuzzy adaptive PID algorithm. The calculation formula is: P_out=KP·t_err+KI·t_integral+KD·t_der; The output value P_out is limited to the value range of [0,49], and this output value is the loss value of the heating wire of the heating module.

[0035] Step 5: During the operation of the temperature control equipment, monitor the changes in the temperature setpoint in real time. When the temperature setpoint changes, use the basic PID parameters under the current operating conditions as a benchmark, and combine the real-time ambient temperature and the current fan speed to generate the incremental correction of the PID parameters. Dynamically fine-tune the KP, KI, and KD parameters to achieve real-time smooth mapping between the temperature operating point and the PID parameters.

[0036] Step 6: Perform temperature control response compensation for different wind speed conditions: Under the same ambient room temperature conditions, the NTC target temperature value is increased at high wind speed settings compared to low wind speed settings, while the proportional coefficient KP is decreased and the integral coefficient KI is increased; the temperature control equipment adjusts the working power of the heating element of the heating module according to the limited output value P_out, and stably controls the temperature near the air outlet within the allowable deviation range of the target temperature.

[0037] Furthermore, in step one, the dynamic temperature modeling and construction of the temperature mapping table include the following implementation steps:

[0038] Step 1, Divide the operating range: Set the ambient temperature collection range, set the gradient temperature value, and divide multiple ambient temperature nodes based on the gradient temperature value; at the same time, divide all the fan speed settings of the hair dryer, including three basic settings: low, medium and high, as well as stepless speed regulation settings.

[0039] Step 2, Data Acquisition: Under a single fixed ambient temperature and fixed fan speed, multiple sets of NTC target temperatures are set in a gradient manner. NTC temperature data under steady-state conditions and actual measured temperature data near the air outlet are collected respectively. Each set of conditions is collected repeatedly n times. After removing abnormal data, the average value is taken as the valid data.

[0040] Step 3: Establish a mapping relationship: Using ambient temperature, fan speed setting, and NTC detected temperature as independent variables, and the actual temperature near the air outlet as the dependent variable, establish a three-dimensional relational database and generate a standardized temperature mapping table; during subsequent equipment operation, the optimal NTC target temperature value corresponding to the target temperature of the air outlet is matched in reverse by looking up the table, so as to control the temperature error near the air outlet within a certain range.

[0041] Furthermore, in step two, the method for calibrating the basic parameters of the fuzzy adaptive PID algorithm includes:

[0042] Under each independent operating condition corresponding to the temperature mapping table, three types of temperature setpoints are set: low temperature, medium temperature, and high temperature.

[0043] The KP, KI, and KD parameters were iteratively adjusted using three indicators: temperature overshoot near the air outlet, steady-state error, and adjustment response time.

[0044] Set the basic PID parameters for the current operating conditions and simultaneously input them into the algorithm parameter database;

[0045] The temperature data acquisition and calculation rules in step three are as follows: the system sets a temperature detection cycle, and multiple sets of NTC temperature data are continuously collected in each detection cycle. After removing the maximum and minimum values, the average value of the remaining data is taken as the actual temperature T_out of the current cycle; the integral term t_integral is set with an integration limit.

[0046] The specific logic for dynamic fine-tuning of PID parameters in step five includes:

[0047] 1) When the temperature setpoint change is detected to be less than or equal to a certain value, only the proportional coefficient KP and integral coefficient KI are slightly increased and corrected, while the differential coefficient KD remains unchanged from the current basic parameters, thus avoiding algorithm oscillations caused by small temperature fluctuations.

[0048] 2) When the temperature setpoint change is detected to be greater than a certain value, the incremental correction values ​​of the three parameters KP, KI, and KD are output by combining the real-time ambient temperature and wind speed setting, and then superimposed on the basic PID parameters to complete the update.

[0049] 3) The parameter correction adopts a gradual output mode. The parameter correction amount within a single detection cycle does not exceed a certain percentage of the basic parameter, so as to achieve a smooth transition of temperature control parameters and prevent sudden rises and falls in the air outlet temperature.

[0050] In step six, the specific quantitative standard for temperature control response compensation under different wind speed conditions is as follows: Under the same ambient room temperature, when the wind speed is switched from low to high, the NTC target temperature value is increased; the proportional coefficient KP is decreased compared to the low wind speed setting, and the integral coefficient KI is increased compared to the low wind speed setting; the compensation value of the medium wind speed parameter is obtained by linear interpolation of the high and low wind speed parameters.

[0051] In step four, the heating wire dropout value is controlled as follows: the dropout value P_out corresponds to the number of energized waves on the heating wire within a single AC control cycle; when P_out=0, the heating wire completely stops heating.

[0052] When P_out=a, the heating wire continues to heat at its rated maximum power.

[0053] If P_out < 0 after the algorithm operation, force the value to be 0; if P_out > a, force the value to be a.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] 1. Multi-dimensional perception, adaptable to all scenarios: This invention innovatively adopts a perception architecture of dual NTC thermistors + current sampling circuit, which simultaneously collects ambient temperature, air outlet temperature and motor load parameters. Combined with a three-dimensional dynamic temperature mapping table, it breaks the limitation of traditional hair dryers' single temperature measurement and can adapt to the temperature control needs of different ambient temperatures, different wind speed levels and different usage scenarios.

[0056] 2. Algorithm optimization and high temperature control accuracy: Equipped with an optimized fuzzy adaptive PID algorithm, it uses the heating wire loss value as the power regulation carrier, combines the dynamic fine adjustment of PID parameters according to the working conditions, and makes special compensation for high and low wind speeds, effectively reducing temperature overshoot and steady-state error, solving pain points such as temperature control lag and sudden changes in hot and cold air, and greatly improving the stability of the outlet air temperature.

[0057] 3. High level of intelligence and convenient operation: It supports adaptive adjustment of temperature and wind speed parameters, eliminating the need for users to frequently switch gears manually. It is also equipped with a mode storage unit to save user-defined frequently used modes, simplifying the operation process and optimizing the user experience.

[0058] 4. Multiple safety protections for more reliable operation: The execution module integrates triple protection circuits for overcurrent, overtemperature, and overload, covering all core components such as the motor, heating module, and main control circuit, comprehensively avoiding equipment failure risks, extending equipment lifespan, and ensuring user safety. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the adaptive temperature control method of the present invention.

[0060] Figure 2-3 The connection of part A forms the flowchart of the adaptive temperature control method of the present invention.

[0061] Figure 4 This is a schematic diagram showing the installation positions of the two temperature sensing modules of the present invention.

[0062] Figure 5 This is a schematic diagram showing the installation positions of the heating module and the motor of the present invention.

[0063] Figure 6 This is the MCU main control circuit diagram of the present invention.

[0064] Figure 7 This is a circuit diagram of the execution module of the present invention.

[0065] Figure 8 This is a circuit diagram of the second temperature sensing module at the air outlet of the present invention.

[0066] Figure 9 This is a circuit diagram of the first temperature sensing module at the air inlet of the present invention.

[0067] Figure 10 This is a circuit diagram for the heating power control of the present invention.

[0068] Figure 11 This is a circuit diagram of the switch control circuit for the negative ion generator of the present invention.

[0069] The attached diagram is labeled: Heating module 1, Motor 2. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the scope of protection of the present invention. Obviously, the embodiments described in this invention are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0072] Example 1: The specific structure of the present invention is as follows:

[0073] Please refer to the appendix. Figure 4-5The present invention discloses an adaptive temperature control system for an intelligent high-speed hair dryer, comprising a hair dryer housing, a built-in PCB board, an MCU main controller, an environmental sensing module, an execution module, and an interaction module; the MCU main controller is soldered and fixed to the PCB board, serving as the core of the system control; the environmental sensing module, the execution module, and the interaction module are electrically connected to the MCU main controller to form a complete closed-loop control system.

[0074] like Figure 4 As shown, the environmental sensing module includes a first NTC thermistor, a second NTC thermistor, and a current sampling circuit. The first NTC thermistor is installed inside the air inlet of the blower and collects the ambient reference temperature at the moment the device is turned on, providing basic environmental data for PID parameter calibration and temperature control compensation. The second NTC thermistor is fixed to the inner wall of the air outlet and collects the real-time temperature of the air outlet, feeding it back to the MCU main control to form a closed-loop temperature control. The current sampling circuit is connected in series in the brushless motor power supply circuit and collects the motor operating current in real time. The MCU main control combines the current value to determine the motor load status and actual speed, and corrects the temperature control compensation parameters corresponding to the wind speed.

[0075] The execution module includes a drive circuit and a protection circuit, with its outputs connected to the brushless motor and the heating module, respectively. The brushless motor receives PWM speed control signals from the MCU main control to achieve stepless fan speed adjustment. The heating module uses an alloy heating wire as the heating element, and adjusts the energizing wave count of the heating wire using a thyristor device, achieving fine-tuned heating power adjustment through multi-level wave drop values. The drive and protection circuit integrates motor drive, heating drive, overcurrent protection, overtemperature protection, and overload protection units. When abnormal conditions such as circuit overcurrent, device overtemperature, or motor overload are detected, the power supply to the corresponding circuit is immediately cut off, achieving fault self-locking protection.

[0076] The MCU main controller has a dynamic temperature mapping table pre-stored inside. The temperature mapping table is generated by modeling and simulating the actual temperature data near the air outlet under different ambient temperatures and different wind speeds.

[0077] The MCU main controller has a built-in fuzzy adaptive PID control module, and the calculation formula of the fuzzy adaptive PID control module is:

[0078] P_out=KP·t_err+KI·t_integral+KD·t_der;

[0079] Where t_err=T_target−T_out is the temperature error, T_target is the NTC target temperature value, T_out is the NTC actual detected temperature, t_integral is the cumulative value of the periodic temperature error, t_der is the difference between the current periodic temperature error and the previous periodic temperature error; P_out is the heating wire loss value, and the heating wire loss value range is [0,49].

[0080] The MCU master controller is configured to pre-calibrate the PID basic parameters KP, KI, and KD under different operating conditions based on measured data from multiple sets of temperature setpoints.

[0081] When the user changes the temperature setpoint, the incremental correction is generated based on the current operating condition PID basic parameters and combined with the real-time ambient temperature and wind speed setting to complete the smooth adaptive adjustment of PID parameters.

[0082] At the same time, temperature control compensation is performed for different wind speeds. Under the same ambient temperature, the higher wind speed setting matches a higher NTC target temperature, and the proportional coefficient KP is reduced and the integral coefficient KI is increased simultaneously. By adjusting the heating wire power, the air outlet temperature near the hair dryer outlet is stably controlled within the allowable deviation range of the target temperature.

[0083] The control system also includes an interaction module, which is electrically connected to the MCU main controller.

[0084] The interactive module includes function buttons, display indicator light components, and a mode storage unit, which is used to receive user commands for setting temperature and fan speed, and to display the device's operating status and store commonly used working mode parameters.

[0085] Example 2:

[0086] Please refer to the appendix. Figure 1-3 The present invention discloses an adaptive temperature control method for an intelligent high-speed hair dryer, the control method comprising the following steps:

[0087] Step 1: Dynamic temperature modeling, constructing a three-dimensional temperature mapping table:

[0088] As shown in the table below:

[0089]

[0090] S1. Operating conditions: The ambient temperature collection range is set to 0℃~45℃, and 9 ambient temperature nodes are divided with a 5℃ gradient; the wind speed covers 3 fixed levels (low, medium, and high) and 0~100% stepless speed regulation level.

[0091] S2. Data Acquisition: Fix a single ambient temperature and wind speed setting, and set multiple NTC target temperatures from 10 to 80℃. After each set of parameters has been running steadily for 3 minutes, the NTC detection temperature and the actual measured temperature at the air outlet are collected simultaneously. Each set of operating conditions is collected 10 times, and the maximum and minimum values ​​are removed before taking the average value as valid data.

[0092] S3. Mapping Table Generation: Integrate all valid data to build a three-dimensional database of "ambient temperature - wind speed - NTC temperature - actual outlet air temperature", generate a standardized lookup table mapping table, and reverse match the optimal NTC target temperature when the equipment is running.

[0093] Step 2, PID basic parameter calibration: For each independent working condition in the mapping table, set three setpoints respectively: low temperature (35℃), medium temperature (55℃), and high temperature (75℃); with the evaluation criteria of temperature overshoot <3℃, steady state error ≤±1.5℃, and adjustment response time ≤2s, iteratively debug the KP, KI, and KD parameters, select the optimal basic parameters and enter them into the algorithm database.

[0094] Step 3, Real-time data acquisition and error calculation: The system sets 100ms as a temperature detection cycle. In each cycle, 5 sets of NTC temperature data are continuously collected. After removing extreme values, the average value is taken as the actual temperature T_out. The temperature error t_err is calculated in combination with the preset T_target. The cycle error is accumulated to obtain the integral term t_integral and the upper and lower limits of integration are set to avoid integration saturation. The differential term t_der is calculated in combination with the errors of adjacent cycles.

[0095] Step 4, Heating Power Calculation: Substitute the value of P_out into the fuzzy adaptive PID formula. If P_out < 0, force the value to be 0. If P_out > 49, force the value to be 49. 0 means the heating wire stops heating, and 49 means the heating wire continues to heat at full power.

[0096] Step 5, Dynamic fine-tuning of PID parameters: The preset temperature change threshold is 5℃; when the set value change is ≤5℃, only KP and KI are fine-tuned, while KD remains unchanged; when the set value change is >5℃, the increments of the three parameters are output through a fuzzy algorithm and superimposed on the basic parameters; the parameter correction amount within a single cycle shall not exceed 8% of the basic parameters, and the parameters are updated in a gradual mode.

[0097] Step 6, Wind speed condition compensation: Under normal temperature conditions of 25℃, the NTC target temperature for low wind speed setting is 50℃, matched with KP=2.5, KI=0.8, and KD=0.3; the NTC target temperature for high wind speed setting is increased to 58℃, matched with KP=1.8, KI=1.5, and KD=0.3; the medium wind speed parameters are calculated by linear interpolation of high and low speed parameters to complete the temperature control compensation for all settings.

[0098] Example 3:

[0099] like Figure 6 As shown, Figure 6 This is the main control circuit diagram of the MCU of this invention. The chip used for the main control of this MCU is LKS32MC038Y6P8B / TSSOP28.

[0100] Figure 7This is a circuit diagram of the execution module of the present invention. The core uses three LKS1M25004 gate driver chips, corresponding to the U, V, and W phase bridge arms respectively. In each phase drive circuit, the high-side drive signal (UH / VH / WH) and low-side drive signal (UL / VL / WL) from the controller are sent to the HIN and LIN pins of the LKS1M25004 via current-limiting resistors (R9 / R19 / R33 and R11 / R22 / R34), respectively. After processing by the chip's internal logic, drive signals are output from the P (high-side output) and VS (low-side output) pins to drive external power transistors, thereby controlling the three-phase windings of the motor.

[0101] The VB pin of each phase of the LKS1M25004 is connected to the VS pin via bootstrap capacitors (C3 / C11 / C16) to form the bootstrap power supply required for high-side drive, in conjunction with the VBUS power supply. The P pin is connected to the VS pin via capacitors (C9 / C17 / C19) to absorb spikes and suppress electromagnetic interference. In the current detection circuit of each phase, the sampling resistors (R16 / R30 / R42) connected in series at the lower end of the bridge arm convert the current signal into a voltage signal, which is then output to the input of the operational amplifier (OPA1_IP / OPA1_IN, OPA0_IP / OPA0_IN) via an RC filter network (R15 / R17, R28 / R31) for the controller to perform current sampling and overcurrent protection. The PSNS pin of the LKS1M25004 can also be used for overcurrent detection, and the FO pin is used for fault signal output. The MG terminal of the motor is grounded via capacitor C20 to absorb high-voltage spikes at the motor terminal and improve the system's anti-interference capability.

[0102] Figure 8The circuit diagram shows the second temperature sensing module at the air outlet of this invention. The live wire (HE-L) of the heating element is connected to the main electrode T1 of the bidirectional thyristor (BTA16 / BTA26), and is connected to pin 4 of the optocoupler (KLM3063) through resistors R21 and R25 in series. The trigger electrode (G) of the bidirectional thyristor (BTA16 / BTA26) is connected to pin 3 of the optocoupler (KLM3063). The other main electrode T2 of the bidirectional thyristor is connected to the AC power supply (AC-L), forming the AC power supply circuit for the heating element and the trigger circuit for the thyristor. The +5V power supply is connected to pin 1 of the optocoupler (KLM3063) via resistor R24. Pin 2 of the optocoupler is connected to the collector of NPN transistor Q1. The base of transistor Q1 is connected to ground via 2K resistor R3, and simultaneously receives a high-level active "HEATERCONTROL" control signal via resistor R29. The emitter is directly grounded, enabling the control signal to control the on / off state of the LED inside the optocoupler, thereby driving the SCR to turn on and off. The NTC thermistor is connected to the circuit via connector JP1. Pin 1 of JP1 is connected to one end of the NTC, and the other end is grounded. Pin 2 of JP1 is connected to a voltage divider circuit node, which consists of a pull-up resistor R2 connected to +5V and a pull-down resistor R44 grounded. An RC filter circuit is formed by resistor R4 and capacitor C2, ultimately outputting a "HEATER_NTC" temperature detection signal to acquire the temperature of the heating element.

[0103] Figure 9 This is a circuit diagram of the first temperature sensing module at the air inlet of the present invention. In the air inlet temperature sensing circuit, a negative temperature coefficient thermistor (NTC) is connected to the circuit through connector JP5. Pin 1 of JP5 is connected to one end of the NTC, and pin 2 of JP5 is connected to the other end of the NTC and connected to a voltage divider circuit consisting of a +5V power supply, resistor R8, and a 1% accuracy resistor R12. After passing through an RC filter circuit consisting of resistor R10 and capacitor C8, the voltage divider node outputs the AIR_NTC temperature detection signal to the subsequent circuit.

[0104] Figure 10 This is a circuit diagram of the heating power control circuit of the present invention. In this zero-crossing detection circuit, the AC neutral wire is directly connected to the input terminal of the optocoupler KL354. The AC live wire zero-crossing signal is connected to the other input terminal of the optocoupler KL354 after being limited by resistors R26 and R27 in sequence. The output collector of the optocoupler KL354 is connected to the +5V power supply through the pull-up resistor R20, and the emitter is grounded. The collector node (ZC) is simultaneously filtered by an RC filter circuit composed of resistor R23 and capacitor C13, and finally outputs the ZERO_SCR zero-crossing detection signal.

[0105] Figure 11This is a circuit diagram of the negative ion generator switch control circuit of the present invention. In the negative ion generator switch control circuit, the live wire terminal (FLZ-L) and neutral wire terminal (FLZ-N) of the negative ion generator (Irongenerator) are respectively connected to the IronSWITCH (iron switch). The AC live wire zero-crossing signal (ACLZERO) is also connected to the IronSWITCH. The output terminal of the IronSWITCH is connected to the bidirectional thyristor side of the optocoupler (KLM3053). Pin 1 of the LED side of the optocoupler (KLM3053) is connected to a +5V power supply through resistor R45, and pin 2 is connected to the collector of NPN transistor Q2. The base of transistor Q2 is grounded through resistor R7, and simultaneously receives the IRON_EN control signal through resistor R5. The emitter is grounded, thereby realizing the on / off control of the negative ion generator.

[0106] In summary, the adaptive temperature control method disclosed in this embodiment relies on a dual NTC temperature measurement structure to simultaneously collect ambient temperature and outlet air temperature. It combines this with a pre-calibrated three-dimensional temperature mapping table to establish a data correlation between the detected temperature and the actual outlet air temperature. Furthermore, it utilizes a fuzzy adaptive PID algorithm paired with a 0-49 level dropout temperature control mode to achieve precise stepless adjustment of heating power. Simultaneously, this method designs corresponding parameter fine-tuning mechanisms and temperature compensation strategies for temperature level switching, different ambient temperatures, and multi-gradient fan speed conditions. This dynamically offsets temperature fluctuations caused by changes in fan speed and ambient temperature differences, effectively suppressing outlet air temperature overshoot and steady-state temperature difference, thus fundamentally avoiding the problem of sudden changes in hot and cold air. The entire temperature control process has a closed-loop logic and strong temperature adaptability. It can supplement heating temperature in low-temperature environments and limit the upper limit of heating in high-temperature environments, adapting to the needs of multiple seasons. It can also maintain a stable outlet air temperature under different fan speeds, balancing hair drying efficiency and hair care effects. In addition, the underlying logic for temperature over-limit helps to achieve overheat protection of the device, significantly improving the overall performance and safety of the high-speed hair dryer.

[0107] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An adaptive temperature control system for an intelligent high-speed hair dryer, comprising a PCB board disposed within the hair dryer, characterized in that, The control system further includes: The MCU main controller is located on the PCB board; The environmental sensing module includes a first temperature sensing module located at the air inlet of the blower, a second temperature sensing module located at the air outlet of the blower, and a sampling circuit for collecting motor current. The first temperature sensing module, the second temperature sensing module, and the sampling circuit are all electrically connected to the MCU main controller. The execution module has its input terminal electrically connected to the MCU main controller and its output terminal electrically connected to the motor and heating module. The execution module is also equipped with a drive protection circuit.

2. The adaptive temperature control system for an intelligent high-speed hair dryer according to claim 1, characterized in that, The first temperature sensing module is a first NTC thermistor located at the air inlet, used to collect the ambient reference temperature during the device power-on and startup phases; The second temperature sensing module is a second NTC thermistor located at the air outlet, used to collect the actual temperature of the air outlet in real time and build a temperature closed-loop control and overheat protection mechanism. The motor current sampling circuit is used to collect the operating current of the brushless motor in real time and monitor the motor load status and actual speed.

3. The adaptive temperature control system for an intelligent high-speed hair dryer according to claim 1, characterized in that, The motor is a brushless motor, which is steplessly speed-regulated by the PWM signal output by the MCU main controller. The heating element of the heating module is a heating wire, and the output power of the heating wire is adjusted by a silicon controlled rectifier or an IGBT device. The drive protection circuit is electrically connected to the brushless motor and the heating module, and integrates a motor drive circuit, a heating drive circuit, and multiple protection circuits for overcurrent, overtemperature, and overload.

4. The adaptive temperature control system for an intelligent high-speed hair dryer according to claim 1, characterized in that, The MCU main controller has a dynamic temperature mapping table pre-stored inside. The temperature mapping table is generated by modeling and simulating the actual temperature data near the air outlet under different ambient temperatures and different wind speeds. The MCU main controller has a built-in fuzzy adaptive PID control module, and the calculation formula of the fuzzy adaptive PID control module is: P_out=KP·t_err+KI·t_integral+KD·t_der; Where t_err = T_target − T_out is the temperature error, T_target is the NTC target temperature value, T_out is the actual NTC detected temperature, t_integral is the cumulative value of the periodic temperature error, t_der is the difference between the current periodic temperature error and the previous periodic temperature error, and P_out is the heating wire loss value.

5. The adaptive temperature control system for an intelligent high-speed hair dryer according to claim 4, characterized in that, The range of the heating wire drop value is [0, 49].

6. The adaptive temperature control system for an intelligent high-speed hair dryer according to claim 4, characterized in that, The MCU master controller is configured to pre-calibrate the PID basic parameters KP, KI, and KD under different operating conditions based on measured data from multiple sets of temperature setpoints. When the user changes the temperature setpoint, the incremental correction is generated based on the current operating condition PID basic parameters and combined with the real-time ambient temperature and wind speed setting to complete the smooth adaptive adjustment of PID parameters. At the same time, temperature control compensation is performed for different wind speeds. Under the same ambient temperature, the higher wind speed setting matches a higher NTC target temperature, and the proportional coefficient KP is reduced and the integral coefficient KI is increased simultaneously. By adjusting the heating wire power, the air outlet temperature near the hair dryer outlet is stably controlled within the allowable deviation range of the target temperature.

7. The adaptive temperature control system for an intelligent high-speed hair dryer according to claim 1, characterized in that, The control system also includes an interaction module, which is electrically connected to the MCU main controller. The interactive module includes function buttons, display indicator light components, and a mode storage unit, which is used to receive user commands for setting temperature and fan speed, and to display the device's operating status and store commonly used working mode parameters.

8. An adaptive temperature control method for an intelligent high-speed hair dryer, wherein the temperature control method is applied to the system described in any one of claims 1-6, characterized in that, The control method includes the following steps: Step 1: Complete dynamic temperature modeling in advance, collect measured data of the temperature near the air outlet of the temperature control device and the NTC detection temperature under different ambient temperatures and different fan speeds, and construct a temperature mapping table; Step 2: Based on the temperature mapping table in Step 1, conduct multiple sets of temperature measurement experiments at multiple different temperature setpoints to match and calibrate the basic KP, KI, and KD parameters of the fuzzy adaptive PID algorithm under the corresponding working conditions. Step 3: Real-time acquisition of the actual temperature T_out collected by the NTC temperature detection module, and calculation of the temperature error t_err based on the preset NTC target temperature value T_target, where t_err = T_target - T_out; Calculation of the temperature deviation differential term t_der based on the real-time temperature error t_err and the historical temperature deviation t_lasterr of the previous detection cycle, and accumulation of the temperature error in each detection cycle to obtain the integral term t_integral; Step 4: The output value P_out of the heating module is calculated using the fuzzy adaptive PID algorithm. The calculation formula is: P_out=KP·t_err+KI·t_integral+KD·t_der; The output value P_out is limited to the value range of [0,49], and this output value is the loss value of the heating wire of the heating module. Step 5: During the operation of the temperature control equipment, monitor the changes in the temperature setpoint in real time. When the temperature setpoint changes, use the basic PID parameters under the current operating conditions as a benchmark, and combine the real-time ambient temperature and the current fan speed to generate the incremental correction of the PID parameters. Dynamically fine-tune the KP, KI, and KD parameters to achieve real-time smooth mapping between the temperature operating point and the PID parameters. Step 6: Perform temperature control response compensation for different wind speed conditions: Under the same ambient room temperature conditions, the NTC target temperature value is increased at high wind speed settings compared to low wind speed settings, while the proportional coefficient KP is decreased and the integral coefficient KI is increased; the temperature control equipment adjusts the working power of the heating element of the heating module according to the limited output value P_out, and stably controls the temperature near the air outlet within the allowable deviation range of the target temperature.

9. The control method according to claim 8, characterized in that, Step one, the dynamic temperature modeling and construction of the temperature mapping table, includes the following implementation steps: Step 1, Divide the operating range: Set the ambient temperature collection range, set the gradient temperature value, and divide multiple ambient temperature nodes based on the gradient temperature value; at the same time, divide all the fan speed settings of the hair dryer, including three basic settings: low, medium and high, as well as stepless speed regulation settings. Step 2, Data Acquisition: Under a single fixed ambient temperature and fixed fan speed, multiple sets of NTC target temperatures are set in a gradient manner. NTC temperature data under steady-state conditions and actual measured temperature data near the air outlet are collected respectively. Each set of conditions is collected repeatedly n times. After removing abnormal data, the average value is taken as the valid data. Step 3: Establish a mapping relationship: Using ambient temperature, fan speed setting, and NTC detected temperature as independent variables, and the actual temperature near the air outlet as the dependent variable, establish a three-dimensional relational database and generate a standardized temperature mapping table; during subsequent equipment operation, the optimal NTC target temperature value corresponding to the target temperature of the air outlet is matched in reverse by looking up the table, so as to control the temperature error near the air outlet within a certain range.

10. The control method according to claim 8, characterized in that, In step two, the method for calibrating the basic parameters of the fuzzy adaptive PID algorithm includes: Under each independent operating condition corresponding to the temperature mapping table, three types of temperature setpoints are set: low temperature, medium temperature, and high temperature. The KP, KI, and KD parameters were iteratively adjusted using three indicators: temperature overshoot near the air outlet, steady-state error, and adjustment response time. Set the basic PID parameters for the current operating conditions and simultaneously input them into the algorithm parameter database; The temperature data acquisition and calculation rules in step three are as follows: the system sets a temperature detection cycle, and multiple sets of NTC temperature data are continuously collected in each detection cycle. After removing the maximum and minimum values, the average value of the remaining data is taken as the actual temperature T_out of the current cycle; the integral term t_integral is set with an integration limit. The specific logic for dynamic fine-tuning of PID parameters in step five includes: 1) When the temperature setpoint change is detected to be less than or equal to a certain value, only the proportional coefficient KP and integral coefficient KI are slightly increased and corrected, while the differential coefficient KD remains unchanged from the current basic parameters, thus avoiding algorithm oscillations caused by small temperature fluctuations. 2) When the temperature setpoint change is detected to be greater than a certain value, the incremental correction values ​​of the three parameters KP, KI, and KD are output by combining the real-time ambient temperature and wind speed setting, and then superimposed on the basic PID parameters to complete the update. 3) The parameter correction adopts a gradual output mode. The parameter correction amount within a single detection cycle does not exceed a certain percentage of the basic parameter, so as to achieve a smooth transition of temperature control parameters and prevent sudden rises and falls in the air outlet temperature. In step six, the specific quantitative standard for temperature control response compensation under different wind speed conditions is as follows: Under the same ambient room temperature, when the wind speed is switched from low to high, the NTC target temperature value is increased; the proportional coefficient KP is decreased compared to the low wind speed setting, and the integral coefficient KI is increased compared to the low wind speed setting; the compensation value of the medium wind speed parameter is obtained by linear interpolation of the high and low wind speed parameters. In step four, the heating wire dropout value is controlled as follows: the dropout value P_out corresponds to the number of energized waves on the heating wire within a single AC control cycle; when P_out=0, the heating wire completely stops heating. When P_out=a, the heating wire continues to heat at its rated maximum power. If P_out < 0 after the algorithm operation, force the value to be 0; if P_out > a, force the value to be a.

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