Intelligent frequency conversion temperature control method and system for high-rotating-speed double-frequency hair drier

Abstract

The invention relates to the technical field of household appliance control, and discloses an intelligent frequency conversion temperature control method and system of a high-rotating-speed double-frequency hair drier. The method comprises the following steps: collecting multi-modal operation data; constructing a current harmonic model related to the rotating speed of the motor; self-adaptive signal separation is conducted on original temperature data through the model, and the real air outlet temperature is extracted; pre-judging a temperature change trend in a frequency conversion process by combining a thermal dynamic prediction model; calculating a heating power compensation amount; and a heating and frequency conversion control instruction is generated through an intelligent temperature control decision algorithm. The system comprises a multi-modal data acquisition module, a harmonic analysis module, a signal separation module, a thermal prediction module, a power compensation module, a temperature control decision module and an execution control module. According to the technical scheme, the temperature control precision, the response speed and the use safety are improved.

Description

Technical Field

[0001] This invention belongs to the field of household appliance control technology, specifically relating to an intelligent variable frequency temperature control method and system for a high-speed dual-frequency hair dryer. Background Technology

[0002] With the accelerating development of intelligent and high-performance home appliances, high-speed hair dryers occupy an important position in the consumer market due to their advantages such as fast drying and flexible styling. Modern hair dryers generally use brushless DC motors and introduce dual-frequency or multi-frequency operating modes to balance airflow and energy efficiency. At high-frequency settings, the motor speed increases, and the harmonic components of the current are enhanced accordingly; while during the switching of low-frequency settings, the transient harmonic spectrum generated by the motor drive circuit is prone to overlap with the characteristic frequency band of the temperature sensor output signal.

[0003] Since temperature sampling is the core basis for overheat protection and constant temperature control, such frequency band overlap causes the temperature signal collected by the analog-to-digital converter (ADC) front end to be seriously mixed with harmonic interference, which in turn causes the temperature control logic to misjudge, resulting in unexpected power adjustment or safety shutdown, seriously affecting user experience and equipment reliability.

[0004] Intelligent variable frequency temperature control technology aims to dynamically adjust heating power and motor speed based on ambient temperature, air outlet speed, and user settings to achieve precise temperature control and energy-saving operation. This technology relies on a high-precision, low-latency temperature feedback mechanism, requiring stable acquisition of accurate temperature information even under high-speed motor operation and frequency switching conditions. However, existing solutions often use fixed-parameter RC low-pass filters to preprocess the temperature signal, and their cutoff frequency, once set, cannot adapt to changes in the noise spectrum under different operating modes. When the current harmonic frequency caused by dual-frequency switching falls within the temperature signal bandwidth, the RC filter, lacking frequency selectivity, cannot suppress co-frequency interference, resulting in a persistently high false trigger rate for temperature sampling.

[0005] While existing technologies have attempted to reduce the probability of misjudgment through software post-processing or increasing sampling redundancy, these methods are difficult to implement in embedded control systems with stringent real-time requirements and cannot fundamentally eliminate noise pollution from the ADC front-end. More importantly, traditional filtering strategies do not establish a dynamic correlation model between the motor's operating state and the interference spectrum, leading to a dilemma of both temperature control failure and safety risks in high-frequency switching scenarios.

[0006] Therefore, there is an urgent need for an intelligent temperature control method that can sense the harmonic characteristics of the motor in real time and actively cancel interference in specific frequency bands, so as to break through the performance bottleneck of the existing static filtering architecture under dynamic working conditions. Summary of the Invention

[0007] To address the technical challenges of high false trigger rates in temperature sampling due to the overlap of current harmonics and temperature signal frequency bands during dual-frequency switching, and the inability of existing RC filters to separate co-frequency interference, this invention provides an intelligent variable frequency temperature control method and system for a high-speed dual-frequency hair dryer. This system utilizes a high-precision multi-modal sensor to collect the hair dryer's operating parameters in real time. Combined with advanced signal processing and machine learning algorithms, it achieves accurate identification and adaptive suppression of current harmonic interference. Simultaneously, it performs high-precision extraction and prediction of the actual temperature signal, dynamically adjusting the heating power and motor speed to maintain a stable outlet air temperature during dual-frequency switching, thereby improving the accuracy, response speed, and user experience of temperature control.

[0008] This invention provides an intelligent variable frequency temperature control method for a high-speed dual-frequency hair dryer, which includes the following steps: The multimodal real-time operating data of the high-speed dual-frequency blower is acquired. The multimodal real-time operating data includes raw temperature data collected by the temperature sensor, raw current data collected by the current sensor, motor speed data, and target air outlet temperature data set by the user. Time-domain and frequency-domain multi-scale harmonic characteristic analysis is performed on the raw current data to construct current harmonic models under different motor speeds. The current harmonic models include harmonic frequency, harmonic amplitude and harmonic phase parameters. The original temperature data is subjected to adaptive signal separation processing using the current harmonic model to accurately extract the true air outlet temperature data of the high-speed dual-frequency blower. The adaptive signal separation processing cancels the co-frequency interference of current harmonics on temperature sampling. Based on the motor speed data and the user-set target air outlet temperature data, combined with historical operating data, the trend of air outlet temperature change during frequency conversion switching is predicted by a thermal dynamic prediction model. Based on the predicted trend of air outlet temperature change and the actual air outlet temperature data, calculate the heating power compensation required for the high-speed dual-frequency blower. Based on the actual outlet air temperature data, the target outlet air temperature data set by the user, and the heating power compensation amount, a real-time heating power control command and a motor frequency conversion control command are generated for the heating element through an intelligent temperature control decision algorithm. The output power of the heating element is adjusted according to the real-time heating power control command, and the speed of the high-speed dual-frequency blower motor is adjusted according to the motor frequency conversion control command, so as to achieve stable temperature control during the frequency conversion switching process.

[0009] As one embodiment of the present invention, the acquisition of the multimodal real-time operating data of the high-speed dual-frequency hair dryer specifically includes: Raw temperature data near the air outlet of the hair dryer is collected in real time using at least one NTC thermistor or platinum resistance temperature sensor at a preset sampling frequency and accuracy. The raw current data supplied to the motor and heating element is collected in real time using a Hall effect current sensor at a preset sampling frequency and accuracy. The motor speed feedback signal is obtained through the motor controller, and the speed feedback signal indicates the current actual operating speed of the motor. The user-defined target air outlet temperature data is obtained through a user interface interface, which is connected to the intelligent temperature control decision module.

[0010] As one embodiment of the present invention, the step of performing time-domain-frequency-domain multi-scale harmonic characteristic analysis on the original current data to construct a current harmonic model under different motor speeds specifically includes: During the start-up phase, frequency conversion switching phase, and stable operation phase of the high-speed dual-frequency blower, the raw current data is periodically sampled. Perform a Discrete Fourier Transform or a Fast Fourier Transform on the sampled raw current data to generate a current spectrum. The harmonic components related to the motor speed frequency and its integer multiples are identified and extracted from the current spectrum, and the frequency, amplitude and phase of the harmonic components are determined. The identified harmonic features are associated with the corresponding motor speeds to establish a multidimensional lookup table or a harmonic model based on regression analysis. The model can provide the corresponding harmonic frequency, amplitude, and phase parameters according to the current motor speed.

[0011] As one embodiment of the present invention, the step of using the current harmonic model to perform adaptive signal separation processing on the original temperature data to accurately extract the true air outlet temperature data of the high-speed dual-frequency blower specifically includes: Based on the harmonic parameters provided by the current harmonic model at the current motor speed, establish a digital filter parameter set; The raw temperature data is input into an adaptive Kalman filter or an adaptive minimum mean square filter. The adaptive Kalman filter or adaptive minimum mean square filter uses the digital filter parameter set to estimate and cancel the interference components in the original temperature data that coincide with the current harmonic frequency in real time. The adaptive Kalman filter or adaptive minimum mean square filter dynamically adjusts its filtering parameters to adapt to the changes in harmonic characteristics caused by changes in motor speed, thereby outputting true outlet air temperature data free of harmonic interference.

[0012] As one embodiment of the present invention, the step of predicting the trend of air outlet temperature change during frequency conversion switching by using a thermal dynamic prediction model based on the motor speed data, the user-set target air outlet temperature data, and historical operating data specifically includes: Before the frequency conversion switching command is issued, extract the temperature response curves under similar operating conditions (e.g., switching from the first speed to the second speed, or switching from the second speed to the first speed) from historical operating data; Based on the historical temperature response curve, combined with the current motor speed, ambient temperature and target air outlet temperature, the air outlet temperature change path is predicted within a preset time window (e.g., 1 to 3 seconds in the future) using a prediction model or lookup table model based on a time-series neural network. The prediction model models the effects of changes in motor inertia, heating element thermal inertia, and airflow resistance on temperature, and predicts the transient temperature response at the moment of frequency conversion switching.

[0013] As one embodiment of the present invention, the step of calculating the heating power compensation required for the high-speed dual-frequency blower based on the predicted air outlet temperature change trend and the actual air outlet temperature data specifically includes: When the predicted temperature shows a trend of deviating from the target outlet air temperature, the deviation between the predicted temperature and the target outlet air temperature is calculated; The feedforward heating power compensation amount is calculated based on the deviation value and the preset compensation strategy, such as proportional-integral-derivative compensation strategy or fuzzy logic compensation strategy. The compensation strategy takes into account the direction (frequency increase or frequency decrease) and magnitude of frequency switching. For example, when increasing the frequency, the heating power is appropriately reduced to avoid overshoot, and when decreasing the frequency, the heating power is appropriately increased to compensate for the decrease.

[0014] As one embodiment of the present invention, the step of generating real-time heating power control commands and motor frequency conversion control commands for the heating element through an intelligent temperature control decision algorithm based on the actual outlet air temperature data, the target outlet air temperature data set by the user, and the heating power compensation amount specifically includes: The actual outlet air temperature data, the target outlet air temperature data, and the heating power compensation amount are used as inputs and passed to an adaptive proportional-integral-derivative controller or a controller based on reinforcement learning. The adaptive proportional-integral-derivative controller dynamically adjusts the proportional, integral, and derivative parameters according to the input to generate refined heating power control commands. The reinforcement learning-based controller, through a pre-trained model, directly outputs the optimal heating power control command and motor speed switching timing command under different operating states and switching scenarios. The model takes minimizing temperature fluctuations and reaching the target temperature as the optimization objective.

[0015] This invention also provides an intelligent variable frequency temperature control system for a high-speed dual-frequency hair dryer, comprising: The multimodal data acquisition module is used to acquire the multimodal real-time operating data of the high-speed dual-frequency blower. The multimodal real-time operating data includes raw temperature data acquired by the temperature sensor, raw current data acquired by the current sensor, motor speed data, and user-set target air outlet temperature data. The harmonic feature analysis module is used to perform time-domain-frequency-domain multi-scale harmonic feature analysis on the raw current data and construct a current harmonic model under different motor speeds. The current harmonic model includes harmonic frequency, harmonic amplitude and harmonic phase parameters. An adaptive signal separation module is used to perform adaptive signal separation processing on the original temperature data using the current harmonic model, and accurately extract the true air outlet temperature data of the high-speed dual-frequency blower. The adaptive signal separation processing cancels the co-frequency interference of current harmonics on temperature sampling. The thermal dynamic prediction module is used to predict the trend of air outlet temperature change during frequency conversion switching based on the motor speed data, the target air outlet temperature data set by the user, and historical operating data, using a thermal dynamic prediction model. The power compensation calculation module is used to calculate the amount of heating power compensation required by the high-speed dual-frequency blower based on the predicted air outlet temperature change trend and the actual air outlet temperature data. The intelligent temperature control decision module is used to generate real-time heating power control commands and motor frequency conversion control commands for the heating element based on the actual outlet air temperature data, the target outlet air temperature data set by the user, and the heating power compensation amount, through an intelligent temperature control decision algorithm. The execution control module is used to adjust the output power of the heating element according to the real-time heating power control command, and to adjust the speed of the high-speed dual-frequency blower motor according to the motor frequency conversion control command, so as to achieve stable temperature control during the frequency conversion switching process.

[0016] In one embodiment of the present invention, the multimodal data acquisition module specifically includes: A temperature sensor is used to collect raw temperature data near the air outlet of a hair dryer in real time at a preset sampling frequency and accuracy. The temperature sensor is at least one NTC thermistor or platinum resistance temperature sensor. A current sensor is used to collect raw current data supplied to the motor and heating element in real time at a preset sampling frequency and accuracy. The current sensor is a Hall effect current sensor. A speed acquisition unit is used to acquire a motor speed feedback signal from a motor controller, the speed feedback signal indicating the current actual operating speed of the motor; The user interface is used to obtain the target air outlet temperature data set by the user, and the user interface is connected to the intelligent temperature control decision module.

[0017] As one embodiment of the present invention, the harmonic characteristic analysis module specifically includes: A current sampling unit is used to periodically sample the raw current data during the start-up phase, frequency conversion switching phase, and stable operation phase of the high-speed dual-frequency blower. The spectrum analysis unit is used to perform discrete Fourier transform or fast Fourier transform on the sampled raw current data to generate a current spectrum. The harmonic parameter extraction unit is used to identify and extract harmonic components related to the motor speed frequency and its integer multiples from the current spectrum diagram, and to determine the frequency, amplitude and phase of the harmonic components; The harmonic model construction unit is used to associate the identified harmonic features with the corresponding motor speed and establish a multidimensional lookup table or a harmonic model based on regression analysis. The model can provide the corresponding harmonic frequency, amplitude and phase parameters according to the current motor speed.

[0018] In one embodiment of the present invention, the adaptive signal separation module specifically includes: The filter parameter generation unit is used to establish a digital filter parameter set based on the harmonic parameters at the current motor speed provided by the current harmonic model. An adaptive filtering unit is used to input the original temperature data and, using the digital filter parameter set, to estimate and cancel the interference components in the original temperature data that coincide with the current harmonic frequency in real time. The adaptive filtering unit is an adaptive Kalman filter or an adaptive minimum mean square filter. The adaptive filtering unit dynamically adjusts its filtering parameters to adapt to the changes in harmonic characteristics caused by changes in motor speed, thereby outputting true outlet air temperature data free of harmonic interference.

[0019] As one embodiment of the present invention, the thermal dynamic prediction module specifically includes: The historical data extraction unit is used to extract temperature response curves under similar operating conditions from historical operating data before the frequency conversion switching command is issued. The temperature prediction model is used to predict the path of air outlet temperature change within a preset time window in the future, based on the historical temperature response curve, combined with the current motor speed, ambient temperature and target air outlet temperature, through a prediction model or lookup table model based on a time-series neural network. The temperature prediction model models the effects of changes in motor inertia, heating element thermal inertia, and airflow resistance on temperature, and predicts the transient temperature response at the moment of frequency conversion switching.

[0020] In one embodiment of the present invention, the power compensation calculation module specifically includes: The deviation calculation unit is used to calculate the deviation between the predicted temperature and the target outlet air temperature when the predicted temperature shows a trend of deviating from the target outlet air temperature. The compensation amount generation unit is used to calculate the feedforward heating power compensation amount based on the deviation value and a preset compensation strategy, such as a proportional-integral-derivative compensation strategy or a fuzzy logic compensation strategy. The compensation strategy takes into account the direction and magnitude of frequency switching. For example, when increasing the frequency, the heating power is appropriately reduced to avoid overshoot, and when decreasing the frequency, the heating power is appropriately increased to compensate for the decrease.

[0021] As one embodiment of the present invention, the intelligent temperature control decision module specifically includes: The parameter input interface is used to receive the actual outlet air temperature data, the target outlet air temperature data set by the user, and the heating power compensation amount. The control command generation unit is used to generate refined heating power control commands and motor speed switching timing commands based on the input parameters, using an adaptive proportional-integral-derivative controller or a reinforcement learning-based controller. The adaptive proportional-integral-derivative controller dynamically adjusts the proportional, integral, and derivative parameters according to the input; The reinforcement learning-based controller, through a pre-trained model, directly outputs the optimal heating power control command and motor speed switching timing command under different operating states and switching scenarios. The model takes minimizing temperature fluctuations and reaching the target temperature as the optimization objective.

[0022] In one embodiment of the present invention, the execution control module specifically includes: A heating element driving unit is used to adjust the output power of the heating element according to the real-time heating power control command. The heating element driving unit drives a solid-state relay or an insulated gate bipolar transistor module to control the on-off cycle of the heating wire through pulse width modulation technology. The motor frequency conversion drive unit is used to adjust the speed of the high-speed dual-frequency hair dryer motor according to the motor frequency conversion control command. The motor frequency conversion drive unit drives the motor through an inverter circuit.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention solves the technical problem that in the process of switching between two frequencies in a high-speed dual-frequency hair dryer, the frequency bands of current harmonics and temperature signals overlap, resulting in a high false trigger rate of temperature sampling, and that existing RC filters cannot separate interference at the same frequency.

[0024] 2. By introducing a harmonic characteristic analysis module and an adaptive signal separation module, this invention can accurately identify and cancel the interference of current harmonics on temperature sampling in real time, thereby improving the acquisition accuracy of real air outlet temperature data and avoiding overheating or underheating problems caused by misjudgment of temperature.

[0025] 3. This invention uses a thermal dynamic prediction module and a power compensation calculation module to predict and compensate for temperature changes during frequency conversion switching, reducing temperature overshoot and hysteresis, and ensuring that the outlet temperature can maintain a smooth transition when the speed changes, thus improving the comfort and safety of using the hair dryer.

[0026] 4. This invention adopts an intelligent temperature control decision algorithm, which combines the actual temperature, target temperature and predicted compensation amount to dynamically generate the optimal heating power and frequency conversion control command, realizing intelligent and precise temperature control of high-speed dual-frequency hair dryer under multiple working conditions, and improving the overall performance of the product and user experience.

[0027] 5. This invention improves the accuracy and response speed of the hair dryer's temperature control, helps extend the service life of the heating element and motor, reduces energy consumption, and ensures the stability and reliability of the equipment operation. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the overall technical architecture of the intelligent variable frequency temperature control method and system for a high-speed dual-frequency hair dryer proposed in this invention. Figure 2 This is a schematic diagram of the core principle framework of adaptive signal separation processing driven by the current harmonic model in this invention; Figure 3 This is a logical flowchart of the multimodal real-time running data acquisition and preprocessing in this invention; Figure 4 This is a flowchart of the variable frequency switching temperature trend prediction logic based on the thermal dynamic prediction model in this invention. Figure 5 This is a flowchart illustrating the logical process of calculating heating power compensation and generating intelligent temperature control decisions in this invention. Figure 6 This is a schematic diagram of the multi-level interaction relationship and data flow between the terminal execution control module and various functional modules in this invention. Detailed Implementation

[0029] Please refer to Figures 1 to 6This invention provides an intelligent variable frequency temperature control method and system for a high-speed dual-frequency hair dryer, aiming to solve the technical problem of high false trigger rate of temperature sampling caused by the overlap of current harmonics and temperature signal frequency bands during dual-frequency switching. Existing RC filters cannot distinguish between co-frequency interference and the actual temperature signal, leading to frequent misjudgments in the control system, resulting in inaccurate heating power adjustment, drastic fluctuations in outlet air temperature, and even equipment overheat protection shutdown.

[0030] This embodiment achieves high-precision extraction of the actual outlet air temperature and stable temperature control during the frequency conversion process by constructing a multi-modal data acquisition system, establishing a current harmonic model related to motor speed, implementing adaptive signal separation processing, introducing a thermal dynamic prediction mechanism, and executing a feedforward-feedback collaborative control strategy.

[0031] The high-speed dual-frequency hair dryer has two stable operating speed modes, namely the first speed and the second speed. There is a difference between the two. Generally, the first speed corresponds to the low air volume mode, and the second speed corresponds to the high air volume mode.

[0032] When the user switches the fan speed, the motor drive circuit performs frequency conversion operation, which results in abundant harmonic components in the power supply current.

[0033] These harmonic frequencies often overlap with the baseband frequency band of the temperature sensor output signal. Especially in architectures that use pulse width modulation to drive heating elements and brushless DC motors, high-frequency switching noise and motor commutation harmonics are coupled together to the temperature sensing circuit, forming common-mode or differential-mode interference that is difficult to eliminate by traditional analog filtering methods.

[0034] As one embodiment of the present invention, the acquisition of multimodal real-time operating data of a high-speed dual-frequency hair dryer specifically includes: Using at least one negative temperature coefficient thermistor or platinum resistance temperature sensor, with a sampling frequency of more than 100 times per second and a measurement accuracy of more than 0.5 degrees Celsius, the raw temperature data of the airflow area near the air outlet of the blower is collected in real time. The temperature sensor is installed on the inner wall of the air outlet duct, with its sensing element exposed in the main airflow path, and is connected to the signal conditioning circuit via a shielded twisted pair cable to suppress electromagnetic interference. The Hall effect current sensor collects the total raw current data supplied to the motor windings and heating wires in real time with a sampling frequency of more than 1,000 times per second and a measurement accuracy of more than 0.5%. The current sensor is installed around the main power supply wire, and its output signal is sent to the analog-to-digital converter after isolation and amplification. The motor speed feedback signal is obtained through the encoder or back EMF detection module inside the motor controller. This signal represents the actual operating speed of the motor in the form of digital pulses, with the unit being revolutions per minute. The system receives the target air outlet temperature data set by the user through a user interface, which can be a physical button, knob, or touch screen. The set value ranges from 30 degrees Celsius to 220 degrees Celsius, with a resolution greater than 1 degree. The target temperature value is then transmitted to the central control unit via a serial communication bus.

[0035] After acquiring the above multimodal real-time operating data, proceed to step S101: Time-domain and frequency-domain multi-scale harmonic characteristic analysis was performed on the original current data to construct current harmonic models under different motor speeds.

[0036] The specific implementation process for this step is as follows: During the hair dryer startup phase, each frequency conversion switching phase, and the continuous operation phase after reaching any stable speed, the system periodically extracts raw current data segments of 0.5 seconds in length. Perform a fast Fourier transform on each data segment to generate the corresponding current spectrum, with a spectral resolution of two hertz. Identify all frequency components whose amplitude is greater than 5% of the fundamental frequency from the spectrum and determine whether they are integer multiples of the current motor speed frequency. For each component identified as a harmonic, record its frequency, amplitude, and phase angle relative to the fundamental wave; The above harmonic parameters are bound and stored with the motor speed value that triggered the sampling, forming a harmonic feature record; The system continuously accumulates such records and eventually constructs a current harmonic model in the form of a multidimensional lookup table. This model takes the motor speed as the input index and outputs a set of harmonic frequencies, amplitudes and phase parameters at the corresponding speed. The model supports online updates. When the deviation of the harmonic characteristics of three consecutive harmonics at the same speed is detected to be greater than the preset threshold, the old records are automatically replaced to adapt to harmonic drift caused by motor aging or load changes.

[0037] Then proceed to step S102: The original temperature data is subjected to adaptive signal separation processing using the current harmonic model to accurately extract the actual air outlet temperature data of the high-speed dual-frequency blower.

[0038] The core of this step lies in counteracting the co-frequency interference of current harmonics on temperature sampling. In practice, the system first queries the current harmonic model based on the current motor speed to obtain a set of harmonic frequency parameters; based on this parameter set, the filter parameter generation unit calculates the state transition matrix and observation matrix of the adaptive Kalman filter. The original temperature data sequence is input point by point into the adaptive Kalman filter; a joint state vector containing the real temperature state and several harmonic interference states is established inside the filter; At each sampling moment, the filter predicts the current temperature reading based on the observation equation and compares it with the actual input value to calculate the residual. The residual is used to update the state estimate, where the harmonic interference state is used to characterize the instantaneous amplitude and phase of the interference signal; through state feedback, the filter dynamically adjusts the suppression weights of each harmonic component. Ultimately, the portion of the state vector output by the filter that corresponds to the actual temperature is the actual outlet air temperature data after removing harmonic interference. The covariance matrix of the filter is adaptively scaled according to the rate of change of motor speed. When the speed is detected to be in a rapid switching phase, the process noise covariance is increased to improve the filter's ability to track sudden interference. During the stable operation phase, the covariance is reduced, enhancing the smoothing effect on steady-state noise.

[0039] After obtaining high-precision real outlet air temperature data, proceed to step S103: Based on the motor speed data and the user-set target air outlet temperature data, combined with historical operating data, the thermal dynamic prediction model predicts the trend of air outlet temperature change during frequency conversion switching.

[0040] This step is implemented on the premise that the system has stored a large amount of historical operating data, and each record contains complete operating condition labels and corresponding temperature response curves.

[0041] When the user issues a frequency conversion command, the historical data extraction unit immediately searches the database and filters out the ten historical records that best match the current operating conditions. The matching degree is calculated by weighted Euclidean distance, with the following weights: switching direction weight 0.4, absolute value of speed difference weight 0.3, ambient temperature difference weight 0.2, and target temperature difference weight 0.1. The selected 10 temperature response curves were time-aligned and amplitude-normalized. The normalized curves were then input into a lightweight temporal neural network, which contains 3 layers of gated recurrent units, with 32 hidden units in each layer. The network output is the temperature prediction value every 0.1 seconds for the next 3 seconds. The predictive model has learned the airflow lag effect caused by motor inertia, the temperature rise delay caused by the thermal inertia of the heating element, and the effect of airflow resistance increasing with the square of the rotational speed on heat exchange efficiency during the offline training phase. Therefore, its prediction results can accurately reflect the sudden drop or rise in temperature caused by the instantaneous change in air volume during frequency conversion.

[0042] Next, proceed to step S104: Based on the predicted trend of air outlet temperature change and the actual air outlet temperature data, the required heating power compensation for the high-speed dual-frequency blower is calculated.

[0043] The calculation process employs a feedforward compensation mechanism to offset the predicted temperature deviation in advance.

[0044] Specifically, the deviation calculation unit continuously monitors the predicted temperature sequence. If it finds that the absolute deviation between the predicted temperature and the target outlet air temperature at any future time is greater than two degrees Celsius, it determines that there is a deviation trend; the compensation generation unit then starts the calculation. The compensation strategy employs a piecewise proportional-integral-differential rule: when the predicted temperature is lower than the target value and the frequency is decreasing, the compensation amount is positive, and its magnitude is equal to the deviation value multiplied by the gain coefficient. , The value is 0.8 watts per degree Celsius; When the predicted temperature is greater than the target value and the frequency is increasing, the compensation is negative, and its magnitude is equal to the deviation value multiplied by the gain coefficient. , The value is 1.2 watts per degree Celsius; If the deviation remains greater than 0.5 seconds, the integral term is added, and the integral gain is increased. The value is 0.1 watts per degree Celsius per second; The differential term is used to suppress abrupt changes in the compensation amount; differential gain. The value is 0.05 watt-seconds per degree Celsius; The final output heating power compensation is the sum of the above three items, in watts, and its value range is limited to -50 watts to +50 watts to prevent overcompensation from causing new instability.

[0045] Then proceed to step S105: Based on the actual outlet air temperature data, the target outlet air temperature data set by the user, and the heating power compensation amount, a real-time heating power control command for the heating element and a motor frequency conversion control command are generated through an intelligent temperature control decision algorithm.

[0046] This step is completed by the intelligent temperature control decision module, the core of which is an adaptive proportional-integral-derivative controller. The controller receives three inputs: Actual air outlet temperature Target air outlet temperature Heating power compensation First, calculate the feedback error: ; Then As a feedforward term, it is directly superimposed on the controller output; the controller's proportional gain Dynamically adjust based on the current work mode: In low airflow mode The value is 2.0, in high airflow mode. The value is 1.5, during the frequency converter switching transition period. Linear interpolation; Integral time constant The timeframe is adaptively shortened or lengthened based on the historical cumulative temperature error. When the error persists in the same direction for more than 5 seconds... Shorten the time to 10 seconds to accelerate convergence, otherwise maintain it at 30 seconds; Differential time constant Fixed at 0.5 seconds; Controller output: ; After being limited, the output value is converted into a duty cycle signal, which serves as a heating power control command. Meanwhile, the motor frequency conversion control command is generated by the frequency converter scheduler. The scheduler receives the user's air volume setting command and, in conjunction with the current temperature control requirements, decides whether to delay the frequency conversion action or switch the speed in stages. For example, when a significant temperature drop is detected, the frequency increase operation can be temporarily suspended until the heating power is increased to the required level.

[0047] Finally, execute step S106: The output power of the heating element is adjusted according to the real-time heating power control command, and the speed of the high-speed dual-frequency blower motor is adjusted according to the motor frequency conversion control command, so as to achieve stable temperature control during the frequency conversion switching process.

[0048] The execution control module contains two independent drive units.

[0049] The heating element drive unit receives the duty cycle command and drives the insulated gate bipolar transistor module after optocoupler isolation. This module controls the on-off cycle of the nickel-chromium alloy heating wire at a switching frequency of 20 kHz, thereby precisely adjusting the average heating power. The motor frequency conversion drive unit receives the speed command, converts it into a pulse width modulation signal of the three-phase inverter, and drives the brushless DC motor to smoothly switch between the first speed and the second speed. The two drive units share the same power bus, but each has an independent current feedback loop to ensure the stability of power distribution; Throughout the frequency conversion process, the system continuously monitors the actual outlet air temperature. If the temperature fluctuation is detected to be greater than the preset safety threshold, the protection mechanism is immediately triggered to reduce the heating power and lock the current speed until the temperature returns to stability.

[0050] The above-described methods and steps constitute the core control logic of this invention.

[0051] To support the implementation of this method, the present invention also provides an intelligent variable frequency temperature control system for a high-speed dual-frequency hair dryer.

[0052] The system includes a multimodal data acquisition module, a harmonic characteristic analysis module, an adaptive signal separation module, a thermal dynamic prediction module, a power compensation calculation module, an intelligent temperature control decision module, and an execution control module.

[0053] The multimodal data acquisition module integrates a high-precision temperature sensor, a Hall current sensor, a speed feedback interface, and a user setting interface, and is responsible for the synchronous acquisition and preliminary conditioning of raw data.

[0054] The harmonic feature analysis module has a built-in fast Fourier transform engine and harmonic parameter extraction algorithm, which periodically updates the current harmonic model.

[0055] The adaptive signal separation module deploys an adaptive Kalman filter, which relies on a fixed-point arithmetic library and can run efficiently on resource-constrained embedded processors.

[0056] The thermal dynamics prediction module includes a historical database and a time-series neural network inference engine, supporting online prediction.

[0057] The power compensation calculation module implements feedforward compensation logic, and its parameters are configurable.

[0058] The intelligent temperature control decision module integrates an adaptive proportional-integral-derivative controller, which has parameter self-tuning function.

[0059] The execution control module includes an insulated gate bipolar transistor drive circuit and a three-phase inverter, and has overcurrent and overheat protection capabilities.

[0060] All modules are interconnected via a high-speed internal bus, with a data exchange delay of less than 1 millisecond, ensuring the real-time performance of the control closed loop.

[0061] This embodiment, through the collaborative work of the above method and system, separates current harmonic interference during dual-frequency switching, extracts high-fidelity real temperature signals, and realizes forward-looking adjustment of heating power based on prediction and compensation mechanisms. Ultimately, it achieves a control accuracy of less than ±2 degrees Celsius for outlet air temperature fluctuation, which is better than the ±8 degrees Celsius level of traditional solutions. This solves the long-standing problem of co-frequency interference and improves product safety and user experience.

[0062] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0063] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent variable frequency temperature control of a high-speed dual-frequency hair dryer, characterized in that, include: The multimodal real-time operating data of the high-speed dual-frequency blower is acquired. The multimodal real-time operating data includes raw temperature data collected by the temperature sensor, raw current data collected by the current sensor, motor speed data, and target air outlet temperature data set by the user. Time-domain and frequency-domain multi-scale harmonic characteristic analysis is performed on the raw current data to construct current harmonic models under different motor speeds. The current harmonic models include harmonic frequency, harmonic amplitude and harmonic phase parameters. The original temperature data is subjected to adaptive signal separation processing using the current harmonic model to accurately extract the true air outlet temperature data of the high-speed dual-frequency blower. The adaptive signal separation processing cancels the co-frequency interference of current harmonics on temperature sampling. Based on the motor speed data and the user-set target air outlet temperature data, combined with historical operating data, the trend of air outlet temperature change during frequency conversion switching is predicted by a thermal dynamic prediction model. Based on the predicted trend of air outlet temperature change and the actual air outlet temperature data, calculate the heating power compensation required for the high-speed dual-frequency blower. Based on the actual outlet air temperature data, the target outlet air temperature data set by the user, and the heating power compensation amount, a real-time heating power control command and a motor frequency conversion control command are generated for the heating element through an intelligent temperature control decision algorithm. The output power of the heating element is adjusted according to the real-time heating power control command, and the speed of the high-speed dual-frequency blower motor is adjusted according to the motor frequency conversion control command, so as to achieve stable temperature control during the frequency conversion switching process.

2. The intelligent variable frequency temperature control method for a high-speed dual-frequency hair dryer according to claim 1, characterized in that, Obtaining the multimodal real-time operating data of the high-speed dual-frequency hair dryer specifically includes: Raw temperature data near the air outlet of the hair dryer is collected in real time using at least one NTC thermistor or platinum resistance temperature sensor at a preset sampling frequency and accuracy. The raw current data supplied to the motor and heating element is collected in real time using a Hall effect current sensor at a preset sampling frequency and accuracy. The motor speed feedback signal is obtained through the motor controller, and the speed feedback signal indicates the current actual operating speed of the motor. The user-defined target air outlet temperature data is obtained through a user interface interface, which is connected to the intelligent temperature control decision module.

3. The intelligent variable frequency temperature control method for a high-speed dual-frequency hair dryer according to claim 1, characterized in that, The time-domain and frequency-domain multi-scale harmonic characteristic analysis of the raw current data is performed to construct current harmonic models at different motor speeds, specifically including: During the start-up phase, frequency conversion switching phase, and stable operation phase of the high-speed dual-frequency blower, the raw current data is periodically sampled. Perform a Discrete Fourier Transform or a Fast Fourier Transform on the sampled raw current data to generate a current spectrum. The harmonic components related to the motor speed frequency and its integer multiples are identified and extracted from the current spectrum, and the frequency, amplitude and phase of the harmonic components are determined. The identified harmonic features are associated with the corresponding motor speeds to establish a multidimensional lookup table or a harmonic model based on regression analysis. The model can provide the corresponding harmonic frequency, amplitude, and phase parameters according to the current motor speed.

4. The intelligent variable frequency temperature control method for a high-speed dual-frequency hair dryer according to claim 1, characterized in that, The adaptive signal separation processing of the original temperature data using the current harmonic model to accurately extract the true air outlet temperature data of the high-speed dual-frequency blower specifically includes: Based on the harmonic parameters provided by the current harmonic model at the current motor speed, establish a digital filter parameter set; The raw temperature data is input into an adaptive Kalman filter or an adaptive minimum mean square filter. The adaptive Kalman filter or adaptive minimum mean square filter uses the digital filter parameter set to estimate and cancel the interference components in the original temperature data that coincide with the current harmonic frequency in real time. The adaptive Kalman filter or adaptive minimum mean square filter dynamically adjusts its filtering parameters to adapt to the changes in harmonic characteristics caused by changes in motor speed, thereby outputting true outlet air temperature data free of harmonic interference.

5. The intelligent variable frequency temperature control method for a high-speed dual-frequency hair dryer according to claim 1, characterized in that, Based on the motor speed data and the user-defined target outlet air temperature data, combined with historical operating data, the thermal dynamic prediction model predicts the outlet air temperature change trend during frequency conversion switching, specifically including: Before the frequency conversion switching command is issued, temperature response curves under similar operating conditions are extracted from historical operating data; Based on the historical temperature response curve, combined with the current motor speed, ambient temperature and target air outlet temperature, the air outlet temperature change path within a preset time window is predicted using a prediction model or lookup table model based on a time-series neural network. The prediction model models the effects of changes in motor inertia, heating element thermal inertia, and airflow resistance on temperature, and predicts the transient temperature response at the moment of frequency conversion switching.

6. The intelligent variable frequency temperature control method for a high-speed dual-frequency hair dryer according to claim 1, characterized in that, Based on the predicted air outlet temperature change trend and the actual air outlet temperature data, the calculation of the heating power compensation required for the high-speed dual-frequency hair dryer specifically includes: When the predicted temperature shows a trend of deviating from the target outlet air temperature, the deviation between the predicted temperature and the target outlet air temperature is calculated; Based on the deviation value and the preset compensation strategy, calculate the feedforward heating power compensation amount; The compensation strategy takes into account the direction and amplitude of frequency switching. When increasing the frequency, the heating power is appropriately reduced to avoid overshoot, and when decreasing the frequency, the heating power is appropriately increased to compensate for the decrease.

7. The intelligent variable frequency temperature control method for a high-speed dual-frequency hair dryer according to claim 6, characterized in that, The preset compensation strategy is either a proportional-integral-differential compensation strategy or a fuzzy logic compensation strategy.

8. The intelligent variable frequency temperature control method for a high-speed dual-frequency hair dryer according to claim 1, characterized in that, Based on the actual outlet air temperature data, the user-set target outlet air temperature data, and the heating power compensation amount, the intelligent temperature control decision algorithm generates real-time heating power control commands and motor frequency conversion control commands for the heating element, specifically including: The actual outlet air temperature data, the target outlet air temperature data, and the heating power compensation amount are used as inputs and passed to an adaptive proportional-integral-derivative controller or a controller based on reinforcement learning. The adaptive proportional-integral-derivative controller dynamically adjusts the proportional, integral, and derivative parameters according to the input to generate refined heating power control commands. The reinforcement learning-based controller, through a pre-trained model, directly outputs the optimal heating power control command and motor speed switching timing command under different operating states and switching scenarios. The model takes minimizing temperature fluctuations and reaching the target temperature as the optimization objective.

9. An intelligent variable frequency temperature control system for a high-speed dual-frequency hair dryer, characterized in that, The intelligent variable frequency temperature control is achieved using the intelligent variable frequency temperature control method for the high-speed dual-frequency hair dryer according to any one of claims 1 to 8. The system includes: The multimodal data acquisition module is used to acquire the multimodal real-time operating data of the high-speed dual-frequency blower. The multimodal real-time operating data includes raw temperature data acquired by the temperature sensor, raw current data acquired by the current sensor, motor speed data, and user-set target air outlet temperature data. The harmonic feature analysis module is used to perform time-domain-frequency-domain multi-scale harmonic feature analysis on the raw current data and construct a current harmonic model under different motor speeds. The current harmonic model includes harmonic frequency, harmonic amplitude and harmonic phase parameters. An adaptive signal separation module is used to perform adaptive signal separation processing on the original temperature data using the current harmonic model, and accurately extract the true air outlet temperature data of the high-speed dual-frequency blower. The adaptive signal separation processing cancels the co-frequency interference of current harmonics on temperature sampling. The thermal dynamic prediction module is used to predict the trend of air outlet temperature change during frequency conversion switching based on the motor speed data, the target air outlet temperature data set by the user, and historical operating data, using a thermal dynamic prediction model. The power compensation calculation module is used to calculate the amount of heating power compensation required by the high-speed dual-frequency blower based on the predicted air outlet temperature change trend and the actual air outlet temperature data. The intelligent temperature control decision module is used to generate real-time heating power control commands and motor frequency conversion control commands for the heating element based on the actual outlet air temperature data, the target outlet air temperature data set by the user, and the heating power compensation amount, through an intelligent temperature control decision algorithm. The execution control module is used to adjust the output power of the heating element according to the real-time heating power control command, and to adjust the speed of the high-speed dual-frequency blower motor according to the motor frequency conversion control command, so as to achieve stable temperature control during the frequency conversion switching process.

10. The intelligent variable frequency temperature control system for the high-speed dual-frequency hair dryer according to claim 9, characterized in that, The multimodal data acquisition module specifically includes: A temperature sensor is used to collect raw temperature data near the air outlet of a hair dryer in real time at a preset sampling frequency and accuracy. The temperature sensor is at least one NTC thermistor or platinum resistance temperature sensor. A current sensor is used to collect raw current data supplied to the motor and heating element in real time at a preset sampling frequency and accuracy. The current sensor is a Hall effect current sensor. A speed acquisition unit is used to acquire a motor speed feedback signal from a motor controller, the speed feedback signal indicating the current actual operating speed of the motor; The user interface is used to obtain the target air outlet temperature data set by the user, and the user interface is connected to the intelligent temperature control decision module.

Images