Temperature and wind speed self-adaptive control method and system for intelligent hair drier

Abstract

The invention provides an intelligent blower temperature and wind speed adaptive control method and system, and relates to the field of intelligent control, and the method comprises the steps: collecting temperature, humidity and wind speed, and constructing a local feature map based on feature vectors; a dynamic evaporation thermodynamic model is established according to the local feature map and used for calculating the evaporation rate of each area, and then the energy requirement for achieving the needed drying effect of each area is calculated according to the evaporation rate; a competitive sensing type wind energy mapping model is established to map energy requirements into control signals used for controlling air outlet parameters, namely wind cavity control signals, and regional self-adaptive adjustment of wind temperature and wind speed in space is achieved; actual air flow output collected in real time is compared with an air cavity control signal, and a feedback difference mechanism is constructed to conduct iterative correction on the dynamic evaporation thermodynamic model and the energy requirement; the physical state change in the hair drying process can be perceived in real time, and dynamic balance can be realized by integrating the use habit and comfort perception of the user.

Description

Technical Field

[0001] This invention relates to the field of intelligent control, and more particularly to an intelligent hair dryer temperature and wind speed adaptive control method and system. Background Technology

[0002] Currently, the rapid development of the smart home appliance industry has driven the continuous intelligentization of personal care devices, especially in the hair care field, where smart hair dryers are gradually becoming the mainstream. Existing smart hair dryers are generally equipped with temperature and humidity sensors, combined with a basic feedback control system, to achieve simple adaptive adjustment of temperature or airflow. This type of control logic is mostly based on a fixed rule base or PID control algorithm, adjusting airflow speed and temperature by detecting changes in ambient temperature and humidity or hair surface temperature.

[0003] However, these traditional solutions have significant shortcomings in practical use. First, existing technologies generally employ a "global control" strategy, which cannot achieve dynamic adjustment of different areas of the hair (such as roots and ends) during the drying process. This forces users to frequently manually adjust the angle and position of the hair dryer to achieve a relatively even drying effect. Second, the evaporation rate during hair drying exhibits a non-linear change, with rapid evaporation in the early stages and slowing down later. However, existing hair dryers cannot dynamically predict this trend, often resulting in overheating or prolonged high-temperature baking during the later stages of drying, damaging the hair. Furthermore, because traditional control solutions only focus on physical signals (such as temperature and humidity) and ignore individual user differences, they lack real-time perception and adaptive mechanisms for user subjective comfort and skin heat tolerance. This leads to discomfort for some users during use, such as localized discomfort caused by excessive heat stimulation or sudden changes in airflow.

[0004] Therefore, how to achieve a more intelligent, regional, and dynamic temperature and airflow adaptive control mechanism during the hair drying process, so as to simultaneously improve drying efficiency and user comfort, has become a key technical problem that has not yet been solved in the industry. Summary of the Invention

[0005] This invention addresses the problems of "rough control", "inaccurate zoning" and "ignoring user subjective experience" in existing smart hair dryers. It proposes a brand-new adaptive temperature and wind speed control method and system that can dynamically sense the evaporation state and energy demand of different areas of the hair during the drying process. Combined with the systematic adjustment of the spatial distribution of airflow, it can achieve precise zoning control of wind temperature and wind speed.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for adaptive control of temperature and wind speed in an intelligent hair dryer, comprising the following steps:

[0007] S1. Collect temperature, humidity and real-time wind speed of the hair dryer in different areas through sensors to construct a local feature map based on feature vectors;

[0008] S2. Establish a dynamic evaporation thermodynamic model based on the local feature map to calculate the evaporation rate of each region, and then calculate the energy requirement for each region to achieve the desired drying effect based on the evaporation rate.

[0009] S3. Establish a competitive sensing wind energy mapping model and map the energy demand into a control signal for controlling the air outlet parameters, i.e., the air cavity control signal, to realize the regional adaptive adjustment of wind temperature and wind speed in space.

[0010] S4. Compare the real-time collected actual airflow output with the air cavity control signal, and construct a feedback difference mechanism to iteratively correct the dynamic evaporation thermodynamic model and energy demand.

[0011] Preferably, in step S1, a temperature sensor is used to collect and monitor the surface temperature of different areas of the hair, such as the roots and ends; a humidity sensor is used to collect the humidity of different areas of the hair; and a wind speed sensor is used to collect the real-time wind speed of the smart hair dryer.

[0012] More preferably, a local feature map based on feature vectors is constructed based on the data measured by the temperature sensor, the humidity sensor, and the wind speed sensor.

[0013] Preferably, in step S2, a dynamic evaporation thermodynamic model is established based on the local feature map. By comprehensively considering the temperature, humidity, and real-time airflow speed of different areas of the hair and the smart hair dryer, the evaporation rate is predicted. The specific equation is as follows:

[0014]

[0015] Among them, E i The evaporation rate of region i is represented by ; k represents the heat conduction and mass diffusion coefficients during the evaporation process; T i T represents the temperature of region i; env Indicates ambient temperature; H i Represents the humidity of region i; (V i ) represents the function of the effect of wind speed on the evaporation rate, where β is a constant used to adjust the effect of wind speed on the evaporation rate; α represents the interaction coefficient between wind speed and temperature; V i T represents the wind speed in region i; i express.

[0016] More preferably, the evaporation rate is converted into energy demand, i.e., the combination of wind temperature and wind speed required to achieve the desired drying effect in region i. Therefore, the following energy demand prediction formula is proposed:

[0017] Q i =c1·E i +c2·f(T i V i )·(1+β·H i ),

[0018] Among them, Q i c1 represents the energy demand of region i; c1 is a constant representing the evaporation rate E. i The influence coefficient on energy demand; c2 is a constant representing the influence coefficient of temperature and wind speed on energy demand; ·f(T i V i ) represents the combined effect function of wind temperature and wind speed on energy demand, where f(T) i V i ) = T i ·V i , which is the weighted combination of temperature and wind speed; β represents the influence coefficient of humidity on energy demand.

[0019] Preferably, the evaporation rate is converted into energy demand, i.e., the combination of wind temperature and wind speed required to achieve the desired drying effect in region i. Therefore, the following energy demand prediction formula is proposed:

[0020] Q i =c1·E i +c2·f(T i V i )·(1+β·H i ),

[0021] Among them, Q i c1 represents the energy demand of region i; c1 is a constant representing the evaporation rate E. i The influence coefficient on energy demand; c2 is a constant representing the influence coefficient of temperature and wind speed on energy demand; ·f(T i V i ) represents the combined effect function of wind temperature and wind speed on energy demand, where f(T) i V i ) = T i ·V i , which is the weighted combination of temperature and wind speed; β represents the influence coefficient of humidity on energy demand.

[0022] Preferably, in step S4, a dynamic vibration damping feedback mechanism with time filtering and disturbance robustness is established to generate a feedback correction signal; the dynamic vibration damping feedback mechanism is used as a correction factor for the evaporation rate in the thermodynamic model to adjust the predicted energy demand in step S2.

[0023] A second aspect of the invention also provides an intelligent hair dryer temperature and wind speed adaptive control system, comprising: sequentially connected:

[0024] The data acquisition and processing module is used to collect the temperature, humidity and real-time wind speed of different areas of the hair and the smart hair dryer through sensors, and to construct a local feature map based on feature vectors.

[0025] The evaporation rate calculation module is used to establish a dynamic evaporation thermodynamic model based on the local feature map and calculate the evaporation rate of each region.

[0026] The drying energy calculation module is used to calculate the energy required to achieve the desired drying effect for any area of ​​the hair based on the evaporation rate.

[0027] The adaptive adjustment module is used to establish a competitive sensing wind energy mapping model and map the energy demand into a control signal for controlling the air outlet parameters, namely the air cavity control signal, so as to realize the regional adaptive adjustment of wind temperature and wind speed in space.

[0028] The error adjustment module is used to compare the real-time collected actual airflow output with the air cavity control signal, and to build a feedback difference mechanism to iteratively correct the dynamic evaporation thermodynamic model and energy demand.

[0029] The beneficial effects of this invention are as follows: Addressing the problems of existing smart hair dryers, such as "coarse control," "inaccurate zoning," and "ignoring user subjective experience," this invention proposes a novel adaptive temperature and wind speed control method and system. This system can dynamically sense the evaporation state and energy demand of different areas of the hair during the drying process, and, combined with systematic adjustment of the spatial distribution of airflow, achieve precise zoning control of wind temperature and wind speed. By establishing a dynamic control model with multi-factor correlation, this invention can not only sense the changes in the physical state of the hair during the drying process in real time, but also integrate user habits and comfort perception to form an intelligent control strategy driven by zoning energy demand, ultimately achieving a dynamic balance between drying efficiency, hair health, and user comfort. The system of this invention effectively solves the problems of "uneven drying in certain areas," "overheating and hair damage in the later stages," and "sluggish response and harsh experience in adjusting wind speed and temperature" in traditional hair dryers, providing users with a more intelligent and user-friendly hair drying experience. Attached Figure Description

[0030] Figure 1 This is a flowchart of an intelligent hair dryer temperature and wind speed adaptive control method according to the present invention.

[0031] Figure 2 This is a block diagram of an intelligent hair dryer temperature and wind speed adaptive control system according to the present invention. Detailed Implementation

[0032] Please see Figure 1 As shown, in a first aspect of the present invention, a method for adaptive temperature and airflow control of an intelligent hair dryer is provided, comprising the following steps:

[0033] S1. Collect temperature, humidity and real-time wind speed of the hair dryer in different areas through sensors to construct a local feature map based on feature vectors;

[0034] Multiple sensor systems are used to precisely collect data on temperature, humidity, and wind speed in different areas of the hair, such as the roots and ends, forming local feature maps F. i These data will provide foundational support for subsequent evaporation modeling (S2) and airflow adjustment (S3). The innovation of step S1 lies in providing precise local data for subsequent adaptive control through regional data acquisition and mapping, thereby avoiding the "global adjustment" problem of traditional blowers.

[0035] The hair dryer has a built-in array of multiple sensors to measure the temperature, humidity and wind speed in different areas.

[0036] These sensors include:

[0037] Temperature sensors (such as infrared sensors) are used to monitor the surface temperature T of different areas of the hair (such as the roots and ends). i ;

[0038] A humidity sensor is used to measure the humidity (H) in different areas of the hair. i ;

[0039] Wind speed sensors (such as wind pressure differential sensors) are used to monitor the wind speed V of airflow. i .

[0040] These sensors are installed in multiple air ducts and air outlets of the hair dryer to ensure that status information in different areas can be collected.

[0041] Each region (hair root, hair tip, etc.) has a feature vector F composed of measurement data from multiple sensors. i F i ={T i H i V i}, i.e., local feature map. Data includes:

[0042] T i : Temperature of region i; H i : Humidity of region i; V i : Wind speed in region i.

[0043] Corresponding to each region of the hair, these feature maps provide fundamental information for subsequent energy prediction and airflow adjustment. They reflect the dryness state of different regions, thus helping the system to make personalized adjustments.

[0044] Traditional hair dryers mostly use a single global sensor to measure overall temperature or humidity, ignoring the differences between different areas of the hair. This invention, through a multi-sensor system, finely adjusts the air temperature and speed based on real-time data from each area. This regionalized data acquisition scheme allows the system to independently adjust according to the drying needs of each area, thereby optimizing the drying effect and avoiding the common problems of "overheated roots and insufficient moisture at the ends."

[0045] Furthermore, to further enhance the system's adaptability, this invention weights the temperature, humidity, and wind speed data for each region to obtain a comprehensive characteristic value. This value reflects the drying progress and energy required for that region. This design dynamically adjusts the wind speed and temperature in each region, not only improving the hair dryer's drying efficiency but also providing precise data support for subsequent airflow adjustment and adaptive control.

[0046] S1 uses multiple sensors to collect data and combines it with a weighted model to transform the dryness features of different regions of the hair into refined local feature maps F. i This provides precise, localized data for subsequent steps. This innovative design avoids the "global adjustment" problem of traditional hair dryers, allowing the system to dynamically adjust air temperature and speed according to the drying needs of each area. Ultimately, this improves drying efficiency, reduces localized overheating, and ensures user comfort. This step provides a precise sensing basis for the entire patented solution, laying the foundation for subsequent energy prediction and airflow adjustment.

[0047] S2. Establish a dynamic evaporation thermodynamic model based on the local feature map to calculate the evaporation rate of each region, and then calculate the energy requirement for each region to achieve the desired drying effect based on the evaporation rate.

[0048] Using the local feature map F provided by S1 i (i.e., the temperature T in different areas of the hair) i Humidity H i Wind speed V i (Data), establish a dynamic evaporation thermodynamic model M evap This model is used to predict the evaporation rate (drying progress) and energy demand (combination of wind temperature and wind speed) for each region.

[0049] The specific steps are as follows:

[0050] In S1, the temperature T of each region is obtained. i Humidity H i Wind speed Vi Data. By establishing a dynamic thermodynamic model. To calculate the evaporation rate E of each region i The drying rate is affected not only by temperature, humidity, and wind speed, but also by the physical properties of the hair (for example, the humidity changes faster in the root area and the temperature changes slower in the tip area).

[0051] Therefore, an improved evaporation equation is proposed, which predicts the evaporation rate by comprehensively considering these factors. The specific equation is as follows:

[0052]

[0053] Among them, E i The evaporation rate of region i is represented by ; k represents the heat conduction and mass diffusion coefficients during the evaporation process; T i T represents the temperature of region i; env Indicates ambient temperature; H i Represents the humidity of region i; (V i ) represents the function of the effect of wind speed on the evaporation rate, where β is a constant used to adjust the effect of wind speed on the evaporation rate; α represents the interaction coefficient between wind speed and temperature; V i T represents the wind speed in region i; i express.

[0054] Operating procedures:

[0055] Calculate the evaporation rate E i First, based on the temperature T in region i... i and ambient temperature T env Calculate the temperature difference (T) i -T env The greater the temperature difference, the higher the evaporation rate.

[0056] Considering humidity H i The inhibitory effect of humidity on the evaporation rate is directly multiplied by the humidity value; the higher the humidity, the lower the evaporation rate.

[0057] The effect of wind speed on evaporation: through the wind speed function f(V) i The higher the wind speed, the greater the evaporation rate.

[0058] The interaction between wind speed and temperature: through This section considers the accelerating effect of wind speed on temperature changes, especially when wind speeds are high, as temperature changes have a stronger promoting effect on evaporation.

[0059] This comprehensive equation not only considers basic physical factors, but also provides a more refined prediction of evaporation rate through the interaction term between wind speed and temperature.

[0060] Local feature maps F acquired through multiple sensors i ={T i H i V i Temperature, humidity, and wind speed data for each region have been obtained, providing a foundation for energy demand forecasting. The next step is to analyze the evaporation rate E... i Converted into energy demand Q i In other words, region i represents the required combination of wind temperature and wind speed to achieve the desired drying effect. The specific steps are as follows:

[0061] The evaporation rate is converted into energy demand, i.e., the combination of wind temperature and wind speed required to achieve the desired drying effect in region i. Therefore, the following energy demand prediction formula is proposed:

[0062] Q i =c1·E i +c2·f(T i V i )·(1+β·H i ),

[0063] Among them, Q i c1 represents the energy demand of region i; c1 is a constant representing the evaporation rate E. i The influence coefficient on energy demand; c2 is a constant representing the influence coefficient of temperature and wind speed on energy demand; ·f(T i V i ) represents the combined effect function of wind temperature and wind speed on energy demand, where f(T) i V i ) = T i ·V i , which is the weighted combination of temperature and wind speed; β represents the influence coefficient of humidity on energy demand.

[0064] Based on the evaporation rate E i The energy demand Q for each region is calculated by weighting the relationship between temperature and wind speed. i ;

[0065] The weighting effect of humidity: through the humidity term β·H i We can dynamically adjust the energy demand of each area to avoid excessive dryness or humidity.

[0066] The combined effect of temperature and wind speed: through f(T) i V i We combine the interaction between temperature and wind speed to provide more accurate predictions of wind speed and wind temperature combinations.

[0067] S2 predicted the evaporation rate E for each region using a dynamic evaporation thermodynamics model.i And further calculate the energy demand Q for each region. i This provides a precise control basis for subsequent airflow adjustment. Innovative designs, such as the nonlinear interaction term between wind speed and temperature and the dynamic weighting of humidity, introduced into the model, enable more accurate predictions of evaporation rate and energy demand, allowing for real-time adaptation to changes during the hair drying process, thus achieving true adaptive control.

[0068] S3. Establish a competitive sensing wind energy mapping model and map the energy demand into a control signal for controlling the air outlet parameters, i.e., the air cavity control signal, to realize the regional adaptive adjustment of wind temperature and wind speed in space.

[0069] S3 is the energy requirement Q for each hair region calculated in step 2. i This is mapped to a control signal S used to control the air cavity structure and air outlet parameters. geo,i This allows for regional adaptive adjustment of wind temperature and wind speed in space. Here, "control signal" includes wind speed v. i Wind temperature t i and wind direction θ i Furthermore, through the combined control of directional and multi-channel wind regulation structures, a closed-loop response between local hair drying state and wind energy input is achieved.

[0070] The core technology of step S3 is to construct a nonlinear mapping model with constraint adjustment capability and spatial competition perception capability, which will convert the energy demand Q output in the previous step into a variable. i The model is adapted to the adjustable physical parameters of a real wind cavity. To this end, a "competitive sensing wind energy mapping model" Φ is proposed. comp The specific steps are as follows:

[0071] The energy demand Q for each region is obtained by inputting S2. i Q i This represents the intensity of wind energy demand (a combination of wind speed and wind temperature) in region i under the current dry conditions. Each Q... i The adjustable control signal triple S to be mapped as a wind cavity structure geo,i ={v i ,t i ,θ i}

[0072] The following mapping function model is proposed:

[0073]

[0074] Where, λ v , λ t These are the gain parameters, representing wind speed and wind temperature mappings, respectively, reflecting the maximum adjustment capability of the air cavity structure; C iThis represents the competition for wind energy resources between region i and its surrounding regions, used to restrict the concentrated allocation of resources to a single region; ρ v This represents the competition weight coefficient, used to adjust C. i The degree of suppression of actual wind speed distribution; This represents the initial reference wind direction angle for region i (determined by the structural layout); This represents the dynamic wind direction correction term, used to limit angle jumps over consecutive moments and improve the mechanical stability of the air cavity; Q j This indicates the energy demand in other regions.

[0075] Among them: wind speed item design

[0076] Using square root enhancement for low Q i Regional wind speed response sensitivity, while avoiding high Q i The distribution of wind speeds in the region is excessively concentrated.

[0077] Introducing C i The suppression term is designed to address the problem of "centralized energy supply - sacrifice in other areas" caused by limited system ventilation resources when energy demand is high in multiple regions.

[0078] C i The calculation method is as follows:

[0079]

[0080] in It is the set of neighboring regions of region i, ω ij It is the resource competition weight of adjacent region j to i, which depends on spatial layout and wind corridor coupling degree.

[0081] Wind temperature term design log(1+Q) i ):

[0082] High Q is compressed using logarithmic form. i The temperature regulation range of the area is adjusted to prevent the system from damaging the hair due to overheating.

[0083] Logarithmic models naturally possess the control characteristic of "rapid rise - late saturation", making them suitable for fine-tuning needs in the late stages of hair dryness.

[0084] Wind direction correction

[0085] To improve the physical stability of the wind field output in consecutive frames, an inertial smoothing correction is applied to the wind direction angle:

[0086]

[0087] The first term represents the inertial hysteresis response of the angle at the previous time step;

[0088] The second term is the anti-disturbance regularization term, which prevents excessive oscillation of the air cavity mechanism. η1 is the controller's anti-jump factor.

[0089] This step is done via Φ comp Achieving the thermodynamic energy requirement Q i Adjustable parameter S of the air cavity geo,i One-to-one correspondence mapping; the introduced competing modulation term C i This innovative model addresses resource contention in different areas within a shared airflow structure for blower systems, resulting in fairer wind energy allocation, smoother system regulation, and prevention of overall drying quality degradation caused by "excessive local optima." It also includes a second-order perturbation regularization term in wind direction adjustment. This demonstrates a deep understanding of the physical dynamics of the air cavity actuator, preventing structural oscillations or positioning failures in the controller during rapid response.

[0090] In summary, step S3 proposes an innovative control mapping mechanism for dynamic resource regulation in multiple regions based on traditional wind farm control schemes. By introducing competitive regulation, nonlinear mapping and execution regularization, the adaptability and stability of the control system are effectively improved.

[0091] S4. Compare the real-time collected actual airflow output with the air cavity control signal, and construct a feedback difference mechanism to iteratively correct the dynamic evaporation thermodynamic model and energy demand.

[0092] Step S4 receives the air cavity control signal S generated in step S3. geo,i ={v i ,t i ,θ i}, through the execution mechanism of the physical air cavity structure Driven airflow to achieve spatial distribution of target wind speed, temperature, and direction; simultaneously, the actual airflow output W is acquired through a real-time sensing system. i and the expected output S geo,i Comparisons are made to construct feedback discrepancies, which are then used to refine the thermodynamic model. and energy demand estimation Q i Iterative corrections are made to achieve closed-loop optimization of system control performance.

[0093] The specific plan is as follows:

[0094] S generated from step S3 geo,i (i.e., the wind speed v in the desired area i) i Wind temperature t i Wind direction θ i Input to the air cavity hardware system Air cavity hardware system It includes three sub-modules:

[0095] An electrically operated wind deflector system is used to control the wind direction θ. i ;

[0096] Adjustable heating wire current control is used to regulate the air temperature t. i ;

[0097] Variable speed fan (centrifugal motor + PWM modulation) is used to control the wind speed v. i .

[0098] To establish a response model between the control signal and the actual air output, we will use the air cavity system Modeled as a response function with nonlinear hysteresis and hardware-constrained saturation characteristics:

[0099]

[0100] Among them, W i Indicates the actual airflow output of region i (trinity: );· i This represents the system error rate, taking into account factors such as execution lag and non-ideal response (usually related to motor inertia, filament delay, etc.); ψ i This represents random disturbance noise, reflecting external disturbances such as ambient temperature fluctuations, air disturbances, and user head movements.

[0101] Feedback error extraction and stability regularization mechanism

[0102] After the system finishes executing S geo,i Then, collect the actual airflow output W at the current moment. i , and the target control value S geo,i Perform a difference comparison and calculate the feedback error vector R. i :

[0103] R i =S geo,i -W i ,

[0104] To prevent the system from overreacting due to transient disturbances or measurement errors, a dynamic vibration damping feedback mechanism with time filtering and disturbance robustness is proposed. Used to generate feedback correction signals:

[0105]

[0106] Among them, R i (t) represents the feedback error at the current time; R i (t-1) represents the feedback error at the previous moment; λ represents the feedback dynamic following coefficient, which controls the response sensitivity; η represents the second derivative of the wind flow output, indicating the degree of wind field fluctuation; η2 represents the stability regularization coefficient, used to suppress drastic changes or oscillations in the wind field.

[0107] This mechanism embodies two core principles:

[0108] On the one hand, through [R] i (t)-R i [t-1] Detects the trend of error change to prevent small disturbances from causing excessive system adjustment;

[0109] On the other hand, through The second-order terms constrain the stability of the system output, forming the regularization term of "wind field vibration resistance", making the airflow output more stable and avoiding discomfort perceived by users.

[0110] Adaptive model correction mechanism

[0111] we will As a system for dynamic evaporation thermodynamics model Estimation of evaporation rate E i The correction factor is used to adjust the energy demand Q in step S2. i The prediction results enable the model to learn online. The update method is as follows:

[0112]

[0113] in, This indicates the updated evaporation rate; ξ represents the previously predicted evaporation rate; ξ represents the feedback learning rate, which controls the correction step size.

[0114] This update mechanism ensures that when there is a deviation in the airflow response, it can reverse the constraint on the upstream modeling results, enabling the entire control chain to have closed-loop self-learning capabilities and achieve true adaptive adjustment.

[0115] In summary, step S4 not only completes the conversion of control signals into physical execution, but also constructs a closed-loop structure from prediction to execution and then to self-learning through high-precision sensing, dynamic vibration damping feedback and online model correction mechanisms. It is the core execution and evolution unit of the entire intelligent hair dryer temperature and wind speed adaptive control system, ensuring that the system is both "accurately adjusted" and "quickly learned", ultimately achieving a user experience with high efficiency, low disturbance and strong adaptability.

[0116] like Figure 2 As shown, in a second aspect of the present invention, an intelligent hair dryer temperature and wind speed adaptive control system is also provided, comprising: connected in sequence:

[0117] The data acquisition and processing module is used to collect the temperature, humidity and real-time wind speed of different areas of the hair and the smart hair dryer through sensors, and to construct a local feature map based on feature vectors.

[0118] The evaporation rate calculation module is used to establish a dynamic evaporation thermodynamic model based on the local feature map and calculate the evaporation rate of each region.

[0119] The drying energy calculation module is used to calculate the energy required to achieve the desired drying effect for any area of ​​the hair based on the evaporation rate.

[0120] The adaptive adjustment module is used to establish a competitive sensing wind energy mapping model and map the energy demand into a control signal for controlling the air outlet parameters, namely the air cavity control signal, so as to realize the regional adaptive adjustment of wind temperature and wind speed in space.

[0121] The error adjustment module is used to compare the real-time collected actual airflow output with the air cavity control signal, and to build a feedback difference mechanism to iteratively correct the dynamic evaporation thermodynamic model and energy demand.

[0122] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for adaptive temperature and airflow control of an intelligent hair dryer, characterized in that, Includes the following steps: S1. Collect temperature, humidity and real-time wind speed of the hair dryer in different areas through sensors to construct a local feature map based on feature vectors; S2. Establish a dynamic evaporation thermodynamic model based on the local feature map to calculate the evaporation rate of each region, and then calculate the energy requirement for each region to achieve the desired drying effect based on the evaporation rate. S3. Establish a competitive sensing wind energy mapping model and map the energy demand into a control signal for controlling the air outlet parameters, i.e., the air cavity control signal, to realize the regional adaptive adjustment of wind temperature and wind speed in space. S4. Compare the real-time collected actual airflow output with the air cavity control signal, and construct a feedback difference mechanism to iteratively correct the dynamic evaporation thermodynamic model and energy demand.

2. The intelligent hair dryer temperature and wind speed adaptive control method according to claim 1, characterized in that, In step S1, a temperature sensor is used to collect and monitor the surface temperature of different areas of the hair, such as the hair roots and hair ends; a humidity sensor is used to collect the moisture of different areas of the hair. The real-time wind speed of the smart hair dryer is collected by a wind speed sensor.

3. The intelligent hair dryer temperature and wind speed adaptive control method according to claim 2, characterized in that, A local feature map based on feature vectors is constructed using data measured by the temperature sensor, humidity sensor, and wind speed sensor.

4. The intelligent hair dryer temperature and wind speed adaptive control method according to claim 1, characterized in that, In step S2, a dynamic evaporation thermodynamic model is established based on the local feature map. By comprehensively considering the temperature, humidity, and real-time airflow speed of different areas of the hair and the smart hair dryer, the evaporation rate is predicted. The specific equations are as follows: Among them, E i The evaporation rate of region i is represented by ; k represents the heat conduction and mass diffusion coefficients during the evaporation process; T i T represents the temperature of region i; env Indicates ambient temperature; H i Represents the humidity of region i; (V i ) represents the function of the effect of wind speed on the evaporation rate, where β is a constant used to adjust the effect of wind speed on the evaporation rate; α represents the interaction coefficient between wind speed and temperature; V i This represents the wind speed in region i.

5. The intelligent hair dryer temperature and wind speed adaptive control method according to claim 4, characterized in that, The evaporation rate is converted into energy demand, i.e., the combination of wind temperature and wind speed required to achieve the desired drying effect in region i. Therefore, the following energy demand prediction formula is proposed: Q i =c1·E i +c2·f(T i ,V i )·(1+β·H i ), Among them, Q i c1 represents the energy demand of region i; c1 is a constant representing the evaporation rate E. i The influence coefficient on energy demand; c2 is a constant representing the influence coefficient of temperature and wind speed on energy demand; ·f(T i V i ) represents the combined effect function of wind temperature and wind speed on energy demand, where f(T) i V i ) = T i ·V i , which is the weighted combination of temperature and wind speed; β represents the influence coefficient of humidity on energy demand.

6. The intelligent hair dryer temperature and wind speed adaptive control method according to claim 1, characterized in that, In step S3, a competitive sensing wind energy mapping model is established. The energy demand in step S2 is input into the competitive sensing wind energy mapping model, and the wind energy demand intensity of each region under the current dry state is mapped into an adjustable control signal triplet of the wind cavity structure.

7. The intelligent hair dryer temperature and wind speed adaptive control method according to claim 1, characterized in that, In S4, a dynamic vibration damping feedback mechanism with time filtering and disturbance robustness is established to generate a feedback correction signal; the dynamic vibration damping feedback mechanism is used as a correction factor for the evaporation rate in the thermodynamic model to adjust the predicted energy demand in S2.

8. A smart hair dryer temperature and wind speed adaptive control system, characterized in that, Including those connected sequentially: The data acquisition and processing module is used to collect the temperature, humidity and real-time wind speed of different areas of the hair and the smart hair dryer through sensors, and to construct a local feature map based on feature vectors. The evaporation rate calculation module is used to establish a dynamic evaporation thermodynamic model based on the local feature map and calculate the evaporation rate of each region. The drying energy calculation module is used to calculate the energy required to achieve the desired drying effect for any area of ​​the hair based on the evaporation rate. The adaptive adjustment module is used to establish a competitive sensing wind energy mapping model and map the energy demand into a control signal for controlling the air outlet parameters, namely the air cavity control signal, so as to realize the regional adaptive adjustment of wind temperature and wind speed in space. The error adjustment module is used to compare the real-time collected actual airflow output with the air cavity control signal, and to build a feedback difference mechanism to iteratively correct the dynamic evaporation thermodynamic model and energy demand.

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