Intelligent blower control method and system based on temperature feedback adjustment

Abstract

The invention discloses an intelligent hair dryer control method and system based on temperature feedback adjustment, and relates to the field of hair dryer control. Comprising the following steps: acquiring core data required for controlling the blower, and preprocessing and analyzing the core data to obtain a standard distance, a common air speed range and an air outlet temperature range; acquiring and analyzing the feature data, judging whether a mutation event exists or not, and resetting the core data after the mutation event; an analysis model is built based on the core data, the standard distance, the common wind speed range and the air outlet temperature range, and the wind speed range and the temperature range used by the blower at this time are analyzed through the analysis model; the technical key points are as follows: an intelligent hair dryer control system for data acquisition, intelligent analysis and dynamic adjustment is constructed, the defect that an existing gear adjustment hair dryer depends on a fixed gear and cannot adapt to individual differences of users and environmental changes is overcome, and a technical foundation is laid for the hair dryer from passive gear selection to active intelligent adaptation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hair dryer control, in particular to an intelligent hair dryer control method and system based on temperature feedback adjustment. BACKGROUND

[0002] A hair dryer is a small household appliance that uses a fan blade driven by an electric motor to generate airflow, and a built-in heating element to heat the airflow, achieving fast hair drying and styling through controllable hot or cold airflow. It is widely used in daily hair care scenarios. Currently, the mainstream hair dryers on the market use gear adjustment to adjust the temperature and wind speed. Essentially, it is a function division achieved by presetting fixed parameters: the wind power gear corresponds to the fixed speed of the fan motor, determining the air flow per unit time; the temperature gear corresponds to the fixed power of the heating element, determining the temperature of the airflow. After the user selects the gear, the wind power and temperature enter the fixed output mode. However, there is a clear upper limit to the tolerance of hair and scalp to temperature during the use of a hair dryer: the appropriate contact temperature of a healthy scalp is usually not more than 45℃, and more than 50℃ can cause damage to the scalp barrier, and long-term high temperature can also damage the hair follicle; the hair scales will expand and open at a temperature above 60℃, causing water loss and dry and split hair. In the existing fixed temperature gears, the hot air gear is usually set at 60-80℃ to pursue the speed of drying hair. When the user holds the hair dryer too close to the scalp or stays in the same area for a longer time, the fixed high temperature will continuously accumulate heat, exceeding the tolerance threshold of the scalp and hair, and eventually causing burns or hidden damage. Moreover, the temperature is fixed, and users need different hot air temperatures in different seasons, which cannot meet the user's requirements.

[0003] Therefore, the existing hair dryer control method has certain drawbacks. To improve the intelligence and use effect of the hair dryer, an intelligent hair dryer control method and system based on temperature feedback adjustment are provided. SUMMARY

[0004] To achieve the above purpose, the present application is implemented by the following technical solutions: An intelligent hair dryer control method based on temperature feedback adjustment, comprising the following steps: S1, obtaining the core data required to control the hair dryer, and preprocessing and analyzing to obtain the standard distance, the commonly used wind speed range and the outlet temperature range; S2, acquiring and analyzing the feature data, determining whether there is a mutation event, and resetting the core data after the mutation event; S3, constructing an analysis model based on the core data, the standard distance, the commonly used wind speed range and the outlet temperature range, and using the analysis model to analyze the wind speed range and temperature range of the hair dryer in this use. S4. Acquire and analyze the current environmental data to obtain the environmental impact value. Adjust the wind speed range and temperature range based on the environmental impact value to obtain the actual temperature range and actual wind speed range. S5. Develop a dynamic parameter adjustment scheme for the hair dryer based on the actual temperature range and actual wind speed range, and set the hair dryer parameters; S6. When using the hair dryer dynamic parameter adjustment scheme, collect the distance value between the hair dryer outlet and the user's hair, and adjust and apply the hair dryer parameters based on the distance value.

[0005] Preferably, the core data includes hair feature data, usage behavior data, and environment and distance data; among which, hair feature data is obtained by extracting hair region features from user head images using a ResNet-50 network, and hair feature data includes hair coverage, hair length, and hair type; usage behavior data is data generated during the use of the hair dryer; and environment and distance data includes collected environmental data and distance data between the air outlet and the hair. The DBSCAN clustering algorithm was used to perform cluster analysis on the preprocessed distance data between the air outlet and the hair to identify the distance cluster with the highest density. The arithmetic mean of the distance cluster with the highest density was calculated as the standard distance. The sliding window-exponential decay composite algorithm was used to process the usage behavior data to obtain the common wind speed range and the air outlet temperature range.

[0006] Preferably, the feature data includes the hair humidity decay curve recorded by the hair dryer's built-in infrared humidity sensor, the hair drying time, the current hair length, and the haircut events manually marked by the user. To determine whether a mutation event exists, the core data is first analyzed to obtain the hair drying time and hair length for each instance, and to calculate the average hair drying time and average hair length. The current hair drying time is compared with the average hair drying time to calculate the first difference ratio, and the current hair length is compared with the average hair length to calculate the second difference ratio. If any of the following exists: the first difference ratio > A1, the second difference ratio > A2, or any of the user-marked haircut events, a mutation event is determined to exist.

[0007] Preferably, the analysis model includes an input layer, a feature enhancement layer, a decision tree layer, a hair quality classification layer, and an output fusion layer. The input layer takes the original feature vector as input, integrates scene and environmental data, and outputs a 9-dimensional feature vector. The feature enhancement layer expands the 9-dimensional feature vector to 20-dimensional feature interaction, strengthens the association between hair quality and scene, and outputs 20-dimensional enhanced features. The decision tree layer uses the 20-dimensional enhanced features to provide the decision tree with learning patterns and outputs the base learner results. The hair quality classification layer identifies the 20-dimensional enhanced features and outputs 4-dimensional classification probabilities. The output fusion layer fuses the base learner results and the 4-dimensional classification probabilities to obtain the wind speed range and temperature range.

[0008] Preferably, after obtaining the wind speed range and temperature range, the wind speed range and temperature range are corrected based on the hair type. During the correction, the maximum and minimum values ​​in the wind speed range are multiplied by the wind speed correction coefficient corresponding to the hair type; the maximum and minimum values ​​in the temperature range are multiplied by the temperature correction coefficient corresponding to the hair type; thus obtaining the corrected wind speed range and temperature range.

[0009] Preferably, the current environmental data includes ambient temperature, ambient humidity, and current air pressure. When calculating the environmental impact value, a piecewise function is set based on physical logic and domain experience to convert ambient temperature, ambient humidity, and air pressure into standardized impact coefficients, resulting in humidity impact coefficient, temperature impact coefficient, and air pressure impact coefficient. The humidity impact coefficient, temperature impact coefficient, and air pressure impact coefficient are processed by weighted summation to obtain the environmental impact value.

[0010] Preferably, when adjusting the wind speed range and temperature range based on the environmental impact value, the environmental impact value is multiplied by the maximum and minimum values ​​of the corrected wind speed range to obtain the adjusted wind speed range, and the environmental impact value is multiplied by the maximum and minimum values ​​of the corrected temperature range to obtain the adjusted temperature range. The adjustable wind speed range and adjustable temperature range are verified by comparing the adjustable wind speed range with the commonly used wind speed range and taking the intersection of the two ranges as the actual wind speed range. Similarly, the adjustable temperature range is compared with the outlet air temperature range and taken as the actual temperature range.

[0011] Preferably, the steps for developing a dynamic parameter adjustment scheme for a hair dryer based on the actual temperature range and the actual wind speed range are as follows: Obtain the linear relationship between motor speed and wind speed, and convert the actual wind speed range into the actual speed range; Obtain the relationship between heating power and outlet air temperature, and convert the actual temperature range into the actual power range; The single-use duration of hair dryers is extracted from the core data. Outliers are removed using the 3σ criterion. The standard single-use duration of hair dryers is calculated by applying exponential decay weighted average. A linear decay algorithm is used to design the motor speed decay and heating power decay. The motor speed decreases from the maximum value to the minimum value of the actual speed range as the standard hair dryer is used for a single session. The heating power decreases from the maximum value to the minimum value of the actual power range as the standard hair dryer is used for a single session.

[0012] Preferably, the steps for adjusting the hair dryer parameters based on the distance value are as follows: The distance between the hair dryer's air outlet and the hair is collected, processed by a Kalman filter, and then the distance value is output. Determine the distance range in which the distance value falls. The distance range includes the near distance range, the standard range, and the far distance range. The adjustment strategy based on the distance range adjusts the set speed and power, and then executes the adjusted speed and power.

[0013] Preferably, an intelligent hair dryer control system based on temperature feedback adjustment includes a data acquisition module, a first analysis module, an event determination module, a second analysis module, a correction processing module, a scheme setting module, and a parameter adjustment module. Data acquisition module: Obtains the core data required to control the hair dryer and performs preprocessing; The first analysis module analyzes the core preprocessed data to obtain the standard distance, common wind speed range, and outlet air temperature range. Event determination module: Acquires and analyzes feature data, determines whether a mutation event exists, and resets core data after a mutation event occurs; The second analysis module: Based on core data, standard distance, common wind speed range and outlet temperature range, an analysis model is built, and the analysis model is used to analyze the wind speed range and temperature range used by the hair dryer in this case. Correction processing module: Acquires and analyzes current environmental data to obtain environmental impact values, adjusts wind speed range and temperature range based on environmental impact values, and obtains actual temperature range and actual wind speed range; Solution setting module: Based on the actual temperature range and actual wind speed range, formulate a dynamic parameter adjustment scheme for the hair dryer and set the hair dryer parameters; Parameter adjustment module: When using the hair dryer dynamic parameter adjustment scheme, the distance value between the hair dryer outlet and the user's hair is collected, and the hair dryer parameters are adjusted and applied based on the distance value.

[0014] This invention provides a solution with the following beneficial effects: 1. This invention provides a smart hair dryer control method and system based on temperature feedback adjustment. It constructs a smart hair dryer control system with data acquisition, intelligent analysis, and dynamic adjustment, which solves the defects of existing hair dryers with adjustable speed that rely on fixed speeds and cannot adapt to individual user differences and environmental changes. It lays the technical foundation for hair dryers to move from passive speed selection to active intelligent adaptation and realizes the upgrade from single mechanical adjustment to multi-dimensional dynamic perception.

[0015] 2. This invention provides a smart hair dryer control method and system based on temperature feedback adjustment. It uses a sliding window-exponential decay composite algorithm to process user behavior data, combines the weighted quantile method to calculate the commonly used wind speed and temperature range, and verifies the rationality of the parameters by allowing the user to manually correct them. This effectively solves the defect of existing hair dryers with fixed settings that cannot capture changes in user habits, resulting in a mismatch between wind speed and temperature and actual needs. It realizes the transformation of wind speed and temperature range from fixed values ​​to dynamic updates, improving the comfort and accuracy of adaptation.

[0016] 3. This invention provides a smart hair dryer control method and system based on temperature feedback adjustment. It adopts a seasonal feature vector correction mechanism and a modular gradient boosting decision tree model to generate the usage range. By integrating multi-dimensional features such as hair volume, length, and hair quality through the model, it accurately generates a detailed range that meets seasonal needs. This effectively solves the problem that the existing technology does not consider the impact of seasons on hair drying needs, and improves the comfort and safety of use in different seasons.

[0017] 4. This invention provides an intelligent hair dryer control method and system based on temperature feedback adjustment. It uses piecewise functions to quantify environmental parameters and calculate environmental impact values. Based on the impact values, it adjusts the temperature and air speed range, and then converts it into motor speed and heating power. This effectively solves the problem that existing technologies ignore the impact of environmental temperature, humidity and air pressure on users' use of hair dryers, and balances drying efficiency and hair protection.

[0018] 5. This invention provides an intelligent hair dryer control method and system based on temperature feedback adjustment. It collects distance data and dynamically adjusts the speed and power by dividing the range. When the distance is too close, a low power alarm is triggered. When the distance is far, the parameters are increased to the upper limit according to the difference. The standard range remains stable. It effectively solves the defects of existing technology, such as lack of distance perception, easy high temperature burns at close range, hair damage from high speed, and low drying efficiency at long distance. It eliminates distance-related safety risks and changes the hair dryer parameters from fixed output to distance adaptive dynamic adjustment. It has good performance and good application prospects. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the overall method of the present invention; Figure 2 This is a system framework diagram of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention addresses the problem that existing hair dryers typically use fixed airflow and power, resulting in more heat being blown out as usage time increases, leading to a poor user experience. Example 1:

[0022] like Figure 1 As shown, the present invention provides a smart hair dryer control method based on temperature feedback adjustment, comprising the following steps: S1. Obtain the core data required to control the hair dryer and perform preprocessing; S11, Core Data Acquisition; By establishing a two-way encrypted connection between the hair dryer's built-in Bluetooth module and the user's terminal device, and using the AES-256 encryption protocol for data transmission, a local edge computing node is established to achieve localized storage of sensitive data. Only anonymized feature parameters are uploaded to the cloud, eliminating user privacy concerns and laying a foundation of trust for subsequent data analysis.

[0023] The core data includes hair feature data, usage behavior data, and environmental and distance data. The core data consists of the most recent 15-20 data collections and is automatically updated; the oldest core data is discarded as each new core data point is collected. Hair feature data is obtained by uploading images taken by the user's terminal device to the hairdryer's accompanying app, which analyzes the data. Hair feature data includes hair coverage, hair length, and hair texture. Specifically, images of the head (front / side view) are captured using the phone's camera, and a ResNet-50 network is used to extract hair region features, calculating hair coverage, hair length, and hair texture. The data is obtained through phone camera... After the images of the front / side of the head are captured and uploaded to the APP, the resolution is unified and normalized, and the pixel values ​​are scaled to the range of [0, 1]. The hair area is selected and the image after the hair area is selected is uploaded to the ResNet-50 network. A 2048-dimensional feature vector is generated through the global average pooling layer to capture global features such as overall hair texture and color. The global feature vector is then input into the fully connected layer, which outputs a 3-dimensional vector corresponding to the sparse, medium or dense hair coverage. The Softmax activation function is used to calculate the probability, for example, sparse <30%, medium 30%-70% and dense >70%.

[0024] The loss function used is cross-entropy loss, and the optimizer chosen is Adam.

[0025] The global features are input into the regression layer, which outputs a continuous numerical value predicting hair length, using mean squared error as the loss function.

[0026] The predicted values ​​are mapped to discrete intervals, and classification is achieved by setting thresholds, such as short hair <15cm, medium hair 15-30cm, and long hair >30cm. The reflectivity is calculated through the HSV channel to obtain the image reflectivity features. The image reflectivity features are combined with threshold comparisons to determine the hair type, such as dry, oily, normal, and damaged. User behavior data is collected through the hair dryer's built-in sensors. The hair dryer's built-in Hall sensor records the number of times the fan speed is adjusted and the duration, the NTC temperature sensor collects the real-time air outlet temperature curve, and the infrared timer records the total duration of a single use. Environmental and distance data are collected through the hair dryer's built-in sensors. The SHT30 temperature and humidity sensor deployed at the hair dryer's air inlet collects environmental parameters, and the TOF laser rangefinder integrated at the air outlet records the distance between the air outlet and the hair during the hair drying process. Data is collected at a set sampling frequency, preferably once every 1-2 seconds.

[0027] S12, Data preprocessing; The original distance data is smoothed using a sliding window filtering algorithm to remove instantaneous jumps caused by hand tremors, i.e., single changes > 5cm; Kalman filtering is used to eliminate environmental noise in temperature and humidity data; outliers in usage behavior data are identified using the Z-score method, and the processed data is stored to establish a user-specific time-series database.

[0028] S13, Standard Distance Judgment; The number of data in the time series database is counted. If the set data volume is reached, the standard calculation process is executed. The DBSCAN clustering algorithm is used to perform cluster analysis on the preprocessed distance data to identify the distance cluster with the highest density. The arithmetic mean of the distance cluster with the highest density is calculated as the standard distance, and the usage time corresponding to this distance is required to account for ≥60%. Every time 10-15 new valid usage data are added, the clustering calculation is re-executed. When the deviation between the newly calculated standard distance and the standard distance is >2cm, the standard distance is updated.

[0029] S14. Data verification; A usage report is generated on the mobile app, displaying standard distance, common wind speed range, outlet air temperature range, and core data. Key parameters are manually corrected and confirmed by the user to ensure their reasonableness. The analysis of the common wind speed range and outlet air temperature range employs a sliding window-exponential decay composite algorithm to process usage behavior data. The sliding window size is set to 15 days, and only usage data within the window is included. The exponential decay function W(t) = Wj × e is used. -k×（t0-t）W(t) represents the actual weight of one historical data point at the current time, Wj represents the initial weight of that historical data point (usually set to 1, i.e., the baseline weight when the data was first collected), e is the natural constant, k is the decay coefficient (generally taken as 0.05; the larger the value of k, the faster the weight decreases over time), t represents the collection time of that historical data point, and (t0-t) represents the number of days since that historical data point was collected. The larger the difference, the older the data. For example, historical data from 3 days ago (t0-t=3) has a weight of Wj×e. -0.05×3 ≈0.85, meaning 86% weight is retained; recently used data receives higher weight, while the influence of outdated data gradually decreases, ensuring that parameter updates always meet the latest user needs.

[0030] After assigning weights to all data based on timestamps, normalization is performed.

[0031] Common wind speed range: The wind speed data is sorted by weight, and the 90% confidence interval is calculated using the weighted quantile method to obtain the common wind speed range; The outlet air temperature range: The outlet air temperature is sorted by weight, and the 90% confidence interval is calculated using the weighted quantile method to obtain the outlet air temperature range; The middle 90% of the commonly used ranges for wind speed and temperature calculated by the above weighted methods are taken as the commonly used values. For example, the commonly used wind speed range is usually 3-7 m / s, and the outlet air temperature range is usually 45-60℃.

[0032] S15, Seasonal Dynamic Correction; After establishing the commonly used wind speed range and outlet air temperature range, user requirements differ in different seasons. Therefore, a corresponding seasonal correction mechanism is established, introducing a seasonal feature vector Jj. For example, the seasonal feature vector Jj can be set to Spring=3, Summer=2, Autumn=3, and Winter=4. The seasonal feature vector Jj is set in conjunction with TF1 and TF2 to achieve correction. Alternatively, it can be set to Spring=2, Summer=1, Autumn=2, and Winter=3. In this case, the value ranges of TF1 and TF2 need to be adjusted. To facilitate recording and viewing the value ranges of TF1 and TF2, the preferred scheme is Spring=3, Summer=2, Autumn=3, and Winter=4. The commonly used wind speed range and outlet air temperature range are corrected by modifying the minimum and maximum values ​​of the commonly used wind speed range and outlet air temperature range. When correcting for wind speed, multiply the maximum and minimum values ​​in the commonly used wind speed range by (1-Jj×TF1). When correcting for temperature, the maximum and minimum values ​​in the outlet air temperature range are multiplied by [1 + (Jj-3) × TF2]; TF1 and TF2 are both adjustment coefficients, with 0.01≤TF1≤0.04 and 0.04≤TF2≤0.06. Both TF1 and TF2 are set relatively small to avoid discomfort caused by excessive adjustment at once. TF2 is normally set to be greater than TF1 because the range of change has been reduced after adjustment by Jj-3. The setting of Jj-3 ensures that the temperature is lowered in summer and raised in winter, making the hair dryer more comfortable to use.

[0033] Adjustments are made based on the season to make the temperature and wind speed more suitable for the season, thus improving the comfort and safety of the hair dryer. When the core data is reset and the number of core data items reaches 15 and the number of dynamically updated core data items reaches 15, the commonly used wind speed range and air outlet temperature range are recalculated.

[0034] S2. Acquire and analyze feature data, determine whether a mutation event exists, and reset the core data after a mutation event. S21. Feature data acquisition; The feature data includes the hair humidity decay curve recorded by the hair dryer's built-in infrared humidity sensor, the hair drying time, the current hair length, and the haircut events manually marked by the user. Based on the core data analysis, the hair drying time for each instance is calculated, the average hair drying time is calculated, and the average hair length is calculated. The current hair drying time is compared with the average hair drying time, and a first difference ratio is calculated. If the first difference ratio is greater than A1, where A1 ranges from 10% to 15%, a sudden change event is determined. The first difference ratio is (average hair drying time - current hair drying time) / average hair drying time. The current hair length is compared with the average hair length, and a second difference ratio is calculated. If the second difference ratio is greater than A2, where A2 ranges from 8% to 15%, a sudden change event is determined. The second difference ratio is (average hair length - current hair length) / average hair length. A haircut event manually marked by the user is considered a mutation event.

[0035] S22, Reset core data; When resetting core data, all original core data is removed, and core data is collected again. If no core data exists, the database is checked for historical data with an environmental parameter similarity of 95% or higher to the current hair feature data. If such data exists, the hair dryer parameters from the historical data are retrieved and set as the initial parameters. If not, the initial parameters are set based on the previous hair dryer parameters. When setting the initial parameters, the initial airflow speed is reduced by 5%, and the initial airflow temperature is reduced by 3-6℃. User data on hair dryer usage is obtained, adjusted by the user, and included as the first core data entry. The initial airflow speed and initial airflow temperature are compared with the commonly used airflow speed range and the commonly used airflow temperature range. If the initial airflow speed is greater than or equal to the lower limit of the commonly used airflow speed range, no adjustment is made; otherwise, the initial airflow speed is adjusted to the commonly used airflow speed range. If the initial outlet air temperature is greater than or equal to the lower limit of the outlet air temperature range, no adjustment is made; otherwise, the initial airflow speed of the blower is adjusted to the lower limit of the outlet air temperature range. If core data exists but does not meet the quantity requirement (i.e., fewer than 15-20 core data entries), the usage parameters are set based on the parameters of the previous core data entry. If the user adjusts the parameters, the airflow speed range and temperature range of the previous core data entry are directly adopted. If the user does not adjust the parameters, the airflow speed in the airflow speed range of the previous core data entry is increased by 1%, and the outlet air temperature in the temperature range is increased by 1-2℃. The adjusted airflow speed range is compared with the commonly used airflow speed range, and the adjusted temperature range is compared with the outlet air temperature range. The intersection of the adjusted airflow speed range and the commonly used airflow speed range is taken as the new airflow speed range, and the intersection of the adjusted temperature range and the outlet air temperature range is taken as the new temperature range.

[0036] S23, Dynamic Update; The system employs a dual triggering mode of real-time time updates and quantity analysis to automatically update core data. A fixed update cycle is set to 30 days, but this can also be customized, typically ranging from 20 to 40 days. The timestamps of the core data are checked to determine if the core data is more than 30 days old. If it is, the core data is removed. For quantity analysis, when the number of core data entries within a 30-day period exceeds 15-20, newly added core data will replace the core data with the oldest timestamp.

[0037] This invention provides a smart hair dryer control method and system based on temperature feedback adjustment. It uses a sliding window-exponential decay composite algorithm to process user behavior data, combines it with the weighted quantile method to calculate commonly used wind speed and temperature ranges, and verifies the rationality of parameters by allowing users to manually adjust them. This effectively solves the defect of existing hair dryers with fixed settings that cannot capture changes in user habits, resulting in a mismatch between wind speed and temperature and actual needs. It realizes the transformation of wind speed and temperature range from fixed values ​​to dynamic updates, improving user comfort and accuracy of adaptation. It has good performance and promising application prospects.

[0038] S3. Based on core data, standard distance, common wind speed range and outlet temperature range, construct an analysis model, and use the analysis model to analyze the wind speed range and temperature range used by the hair dryer in this case. S31, Feature definition; A scene classification system is constructed by multi-dimensional quantification, which provides a structured input foundation for the model. This includes the hair volume dimension obtained from hair coverage analysis, the length dimension obtained from hair strand length analysis, the hair quality dimension obtained from hair type analysis, and the humidity dimension obtained from humidity data collected by the built-in infrared humidity sensor of the hair dryer. These dimensions are then encoded and transformed into computable scene labels.

[0039] S32, Model Input; Construct a complete processing flow from image to raw feature vector to ensure hair texture feature extraction. A. Processing historical user images, the specific process is as follows: The gray-world algorithm is used to eliminate the influence of ambient light color temperature, making the RGB mean values ​​of the neutral gray area in the image equal, thus achieving white balance correction; histogram equalization is performed to expand the dynamic range of gray values ​​to 0-255, improving the distinction between hair and background, thereby enhancing contrast; the OTSU adaptive thresholding algorithm is used to generate a binary mask to accurately segment the hair area, thus achieving background separation.

[0040] B. Use the brightness formula to convert the RGB values ​​of the background-separated image into grayscale values, calculate the average reflectance and the standard deviation of reflectance. The average reflectance and the standard deviation of reflectance constitute the reflectance characteristics, reflecting the uniformity of reflection. C. Concatenate the nine features—hair volume coefficient, length coefficient, hair humidity value, reflectivity characteristics, common wind speed range, air outlet temperature range, ambient temperature, and ambient humidity—into an original feature vector. The hair volume coefficient consists of three coefficients corresponding to sparse, medium, and dense hair, and the length coefficient consists of three coefficients corresponding to short, medium, and long hair. The preset coefficient is used to determine the type of hair volume and length. See S11, Core Data Collection section, for the types of hair volume and length.

[0041] S33, Model Architecture; The analysis model employs a modular gradient boosting decision tree model, comprising an input layer, a feature enhancement layer, a decision tree layer, a hair quality classification layer, and an output fusion layer. The input layer concatenates nine features into an original feature vector, integrating multi-source scene information, which is then used as input to the feature enhancement layer. The feature enhancement layer performs feature interaction calculations to strengthen the correlation between hair quality and the basic scene, resulting in 20-dimensional enhanced features, which are then input into the decision tree layer. The decision tree layer consists of an ensemble model composed of 120 CART decision trees, each with a depth limit of 6 layers and ≤25 leaf nodes. The Gini coefficient is used as the gradation factor. The model employs a splitting criterion, parallel computation of feature importance, and a decision tree layer that extracts the nonlinear mapping between the scene and parameters through ensemble learning, resulting in the outputs of 120 base learners. The hair quality classification layer trains four 5-layer CART trees separately to process reflectivity features and output classification probabilities for four hair quality categories. The output fusion layer receives the outputs of the 120 base learners and the classification probabilities for the four hair quality categories, generating appropriate wind speed and temperature ranges. The model's loss function is a comprehensive analysis function, calculated by weighting the mean absolute error of the wind speed range, the mean absolute error of the temperature range, and the cross-entropy loss of the hair quality classification.

[0042] S34. Training and Validation; Dataset Construction: Over 10,000 real user records were collected, each including image data, sensor data, and manually labeled hair quality tags. The training and validation sets were randomly divided in a 7:3 ratio to ensure consistent distribution of different scenarios and hair types across the two sets. The Adam adaptive optimizer was used to train the model on the training set, with an initial learning rate of 0.008, an exponential decay rate β1=0.9 for the first moment estimate, an exponential decay rate β2=0.95 for the second moment estimate, and a weight decay coefficient of 1e-4, where e is the exponent in scientific notation. The total number of iterations was 160. The validation set loss was calculated every 20 iterations. Training was terminated when the validation set cross-entropy loss did not decrease for 8 consecutive iterations, and the optimal model parameters were saved.

[0043] The verification indicators used were the average absolute error of wind speed range and the accuracy of hair quality classification.

[0044] S35, Range Correction; Nine features—hair volume coefficient, length coefficient, hair humidity value, reflectivity characteristics, common wind speed range, outlet air temperature range, ambient temperature, and ambient humidity—are concatenated into an original feature vector and input into the analysis model to obtain the wind speed range and temperature range. Different hair types have different requirements for wind speed and temperature, therefore, the obtained wind speed and temperature ranges need to be corrected. For wind speed correction, the maximum and minimum values ​​in the wind speed range are multiplied by the wind speed correction coefficient corresponding to the hair type. For temperature correction, the maximum and minimum values ​​in the temperature range are multiplied by the temperature correction coefficient corresponding to the hair type. For example, the wind speed correction coefficient for dry hair is 0.95, for normal hair it is 0.9, for oily hair it is 1, for damaged hair it is 1.05, and for damaged hair it is 0.9.

[0045] The main reason for the design is that dry hair is prone to frizz, so reducing the air speed reduces friction and lowering the temperature prevents excessive moisture loss. Oily hair needs to accelerate moisture evaporation, so increasing the air speed enhances convection and moderately raising the temperature promotes oil evaporation. Damaged hair has a fragile cuticle, so significantly lowering the temperature avoids protein denaturation and slowing down the air speed reduces damage.

[0046] This invention provides a smart hair dryer control method and system based on temperature feedback adjustment. It adopts a seasonal feature vector correction mechanism and a modular gradient boosting decision tree model to generate the usage range. By integrating multi-dimensional features such as hair volume, length, and hair quality, the model accurately generates a detailed range that meets seasonal needs. This effectively solves the problem that the existing technology does not consider the impact of seasons on hair drying needs, improves the comfort and safety of use in different seasons, and has good application prospects.

[0047] S4. Acquire and analyze current environmental data to obtain environmental impact values, and adjust the wind speed range and temperature range based on the environmental impact values; S41, Data Quantification; Environmental data includes ambient temperature and humidity, and can also be further obtained by obtaining the local air pressure.

[0048] Based on physical logic and domain experience, a piecewise function is set to map continuous parameter values ​​to standardized single-factor influence coefficients. Ambient temperature, ambient humidity, and air pressure are transformed into standardized influence coefficients to obtain humidity influence coefficient, temperature influence coefficient, and air pressure influence coefficient. The humidity influence coefficient, temperature influence coefficient, and air pressure influence coefficient are processed by weighted summation to obtain the environmental influence value. If there are more than one data point after the core data is reset and the quantity requirement is not met, the following steps are also performed.

[0049] S42, Range Adjustment; The wind speed and temperature ranges are adjusted based on environmental impact values. For wind speed adjustment, the maximum and minimum values ​​in the corrected wind speed range are multiplied by the environmental impact value to obtain the adjusted wind speed range. For temperature adjustment, the maximum and minimum values ​​in the corrected temperature range are multiplied by the environmental impact value to obtain the adjusted temperature range. After adjustment, verification is required. Verification involves comparing the adjusted wind speed range with the commonly used wind speed range to determine if the adjusted wind speed range falls within the commonly used wind speed range. If it does, no processing is required; if any data falls outside the commonly used wind speed range, the data outside the commonly used wind speed range is removed. Similarly, the adjusted temperature range is compared with the outlet air temperature range to determine if the adjusted temperature range falls within the outlet air temperature range. If it does, no processing is required; if any data falls outside the outlet air temperature range, the data outside the outlet air temperature range is removed. Verification involves taking the intersection of the wind speed range and the commonly used wind speed range, and the intersection of the temperature range and the outlet air temperature range, to form the actual temperature range and the actual wind speed range.

[0050] S5. Develop a dynamic parameter adjustment scheme for the hair dryer based on the actual temperature range and actual wind speed range, and set the hair dryer parameters; S51, parameter conversion; Obtain the linear relationship between motor speed and wind speed, and convert the actual wind speed range into the actual speed range; The relationship between heating power and outlet air temperature is obtained, and the heating power corresponding to the minimum and maximum values ​​of the actual temperature range is compared to convert the actual temperature range into the actual power range. The actual speed range and actual power range are calculated to facilitate the direct adjustment of the hair dryer parameters.

[0051] S52, Duration Analysis; Obtain core data, extract the single-use duration of the hair dryer from it, and use the 3σ criterion to remove outliers; The standard hair dryer's single-use duration (Tdb) is calculated using an exponential decay weighted average. The specific calculation formula is as follows: In the formula , For the current date, The date marked by the timestamp of the i-th data item. Let n be the single usage duration corresponding to the i-th data point, and n be the number of remaining single usage durations of the hair dryer after outlier removal using the 3σ criterion. Let be the weight of the i-th data point, reflecting its importance in the calculation. The time decay formula divides the data into weights based on the data collection time, with more recent data having a higher weight. Through exponential decay weighted calculation, the weights can be amplified for recent data, making the obtained standard hair dryer single-use duration more accurate.

[0052] S53, Plan Formulation; When the environmental impact value is <1.5, i.e., under normal conditions, a linear attenuation algorithm is used to design the motor speed attenuation. The speed decreases from the maximum value to the minimum value of the actual speed range as the standard hair dryer is used for a single session. The specific formula for the change of speed VS is VS=Vmax-(Vmax-Vmin)×Ts / Tdb, where Vmax is the maximum value of the actual speed range, Vmin is the minimum value of the actual speed range, and Ts is the hair dryer usage time. A linear attenuation algorithm is also used to design the heating power attenuation. The heating power decreases from the maximum value to the minimum value of the actual power range as the standard hair dryer is used for a single session. The specific formula for the change of speed PS is PS=Pmax-(Pmax-Pmin)×Ts / Tdb, where Pmax is the maximum value of the actual power range, Pmin is the minimum value of the actual power range, and Ts is the hair dryer usage time. When the environmental impact value is greater than 1.5, it is considered extreme weather, such as high humidity and low temperature. In this case, segmented attenuation optimization is adopted. The initial parameters are maintained for the first 50% of the time, and the attenuation is completed for the last 50% of the time to ensure drying efficiency.

[0053] This invention provides a smart hair dryer control method and system based on temperature feedback adjustment. It uses piecewise functions to quantify environmental parameters and calculate environmental impact values. Based on the impact values, it adjusts the temperature and air speed range, which is then converted into motor speed and heating power. This effectively solves the problem of existing technologies ignoring the influence of environmental temperature, humidity, and air pressure on users' hair dryer use. It balances drying efficiency and hair protection, resulting in good performance and promising application prospects.

[0054] S6. When using the hair dryer dynamic parameter adjustment scheme, collect the distance value between the hair dryer outlet and the user's hair, and adjust and apply the hair dryer parameters based on the distance value; S61. Collect distance data; An infrared sensor installed at the air outlet of a hair dryer measures the distance between the hair dryer outlet and the hair, taking a sample every 3-5 seconds, and outputting the distance value after Kalman filtering.

[0055] S62, Parameter Adjustment; Set distance ranges, including the near distance range (5, standard distance - M), the standard range [standard distance - M, standard distance + M], and the far distance range (standard distance + M, 100cm), where M is a preset fluctuation value, 4 < M < 8. 100cm can be freely set, but is generally not lower than 80cm. Different adjustment strategies are set for different distance ranges, as follows: When the distance value is in the close range, the difference between the standard distance - M and the distance value is calculated. The speed and power are adjusted based on the difference. Specifically, for every 1cm increase in the difference, the speed decreases by 2% and the power decreases by 3%. The adjustment is based on the speed and power at the corresponding time set in the program. When the distance between the hair dryer and the hair is less than 5cm, the protection mode is forcibly triggered. The power limit is locked at low power, the speed is reduced to the base value, and the buzzer sounds an alarm. When the distance value is in the standard range, no adjustment is made. When the distance value is in the far range, the difference between the distance value and the standard distance + M is calculated. The speed and power are adjusted based on the difference. Specifically, for every 1cm increase in the difference, the speed increases by 1% and the power increases by 1.5%, until the hair dryer's set speed and power limits are reached. When the hair dryer reaches its set speed and power limits, an alarm is sounded. The above methods can eliminate distance-related safety risks, such as close-range high-temperature burns and high-speed airflow damage to hair.

[0056] S63, Scheme Revision; After each use of the solution, user feedback is collected, and evaluations of wind speed and temperature adjustments are extracted. If feedback indicates that the temperature is too high at close range, the power change ratio during the close-range adjustment is increased by 10%. For example, if the distance value is in the close-range range, the power was originally reduced by 3% for every 1cm increase in the difference, but after modification, the power is reduced by 3.3% for every 1cm increase in the difference. If feedback indicates that the wind speed is too high at close range, the power change ratio during the close-range adjustment is increased by 10%. For example, if the distance value is in the close-range range, the speed was originally reduced by 2% for every 1cm increase in the difference, but after modification, the speed is reduced by 2.2% for every 1cm increase in the difference. During the use of the hair dryer, the outlet air temperature is monitored in real time. When the sensor detects that the outlet air temperature exceeds the set error range, which is usually ±3°C of the current set temperature, the power is adjusted to correct the temperature.

[0057] For example, at a certain time, the hair dryer is in the standard rotation range, and the temperature corresponding to the set heating power is 50℃. However, due to the long continuous use, the air temperature is 55℃, which does not meet the error range (0, 3). At this time, the power is reduced so that the air temperature is between 50-53℃. The hair dryer has an infrared beam sensor built into the air inlet channel to detect the state of hair or foreign objects blocking the air. When the blocking area is greater than 30%, an alarm is triggered and the blowing and heating are stopped.

[0058] This invention provides an intelligent hair dryer control method and system based on temperature feedback regulation. It collects distance data and dynamically adjusts the speed and power by dividing the range. When the distance is too close, a low power alarm is triggered. When the distance is far, the parameters are increased to the upper limit according to the difference. The standard range remains stable. It effectively solves the defects of existing technologies, such as lack of distance perception, easy high temperature burns at close range, hair damage from high speed, and low drying efficiency at long distance. It eliminates distance-related safety risks and changes the hair dryer parameters from fixed output to distance adaptive dynamic adjustment. It has good performance and good application prospects. Example 2:

[0059] Based on Example 1, such as Figure 2 As shown, an intelligent hair dryer control system based on temperature feedback adjustment includes a data acquisition module, a first analysis module, an event determination module, a second analysis module, a correction processing module, a scheme setting module, and a parameter adjustment module. Data acquisition module: Obtains the core data required to control the hair dryer and performs preprocessing; The first analysis module analyzes the core preprocessed data to obtain the standard distance, common wind speed range, and outlet air temperature range. Event determination module: Acquires and analyzes feature data, determines whether a mutation event exists, and resets core data after a mutation event occurs; The second analysis module: Based on core data, standard distance, common wind speed range and outlet temperature range, an analysis model is built, and the analysis model is used to analyze the wind speed range and temperature range used by the hair dryer in this case. Correction processing module: Acquires and analyzes current environmental data to obtain environmental impact values, adjusts wind speed range and temperature range based on environmental impact values, and obtains actual temperature range and actual wind speed range; Solution setting module: Based on the actual temperature range and actual wind speed range, formulate a dynamic parameter adjustment scheme for the hair dryer and set the hair dryer parameters; Parameter adjustment module: When using the hair dryer dynamic parameter adjustment scheme, the distance value between the hair dryer outlet and the user's hair is collected, and the hair dryer parameters are adjusted and applied based on the distance value.

[0060] This invention provides a smart hair dryer control method and system based on temperature feedback adjustment. It constructs a smart hair dryer control system that integrates data acquisition, intelligent analysis, and dynamic adjustment. This solves the shortcomings of existing hair dryers that rely on fixed gears and cannot adapt to individual user differences and environmental changes. It lays the technical foundation for hair dryers to move from passive gear selection to active intelligent adaptation, and realizes the upgrade from single mechanical adjustment to multi-dimensional dynamic perception. It has good performance and promising application prospects.

[0061] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0062] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0063] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A smart hair dryer control method based on temperature feedback adjustment, characterized in that, Includes the following steps: S1. Obtain the core data required to control the hair dryer, and after preprocessing and analysis, obtain the standard distance, common wind speed range and outlet air temperature range; S2. Acquire and analyze feature data, determine whether a mutation event exists, and reset the core data after a mutation event. S3. Based on core data, standard distance, common wind speed range and outlet temperature range, construct an analysis model, and use the analysis model to analyze the wind speed range and temperature range used by the hair dryer in this case. S4. Acquire and analyze the current environmental data to obtain the environmental impact value. Adjust the wind speed range and temperature range based on the environmental impact value to obtain the actual temperature range and actual wind speed range. S5. Develop a dynamic parameter adjustment scheme for the hair dryer based on the actual temperature range and actual wind speed range, and set the hair dryer parameters; S6. When using the hair dryer dynamic parameter adjustment scheme, collect the distance value between the hair dryer outlet and the user's hair, and adjust and apply the hair dryer parameters based on the distance value.

2. The intelligent hair dryer control method based on temperature feedback adjustment according to claim 1, characterized in that, The core data includes hair feature data, usage behavior data, and environmental and distance data. Among them, hair feature data is obtained by extracting hair region features from user head images using a ResNet-50 network. Hair feature data includes hair coverage, hair length, and hair type. Usage behavior data is data generated during the use of the hair dryer. Environmental and distance data includes collected environmental data and distance data between the air outlet and the hair. The DBSCAN clustering algorithm was used to perform cluster analysis on the preprocessed distance data between the air outlet and the hair to identify the distance cluster with the highest density. The arithmetic mean of the distance cluster with the highest density was calculated as the standard distance. The sliding window-exponential decay composite algorithm was used to process the usage behavior data to obtain the common wind speed range and the air outlet temperature range.

3. The intelligent hair dryer control method based on temperature feedback adjustment according to claim 2, characterized in that, The feature data includes the hair humidity decay curve recorded by the hair dryer's built-in infrared humidity sensor, the hair drying time, the current hair length, and the haircut events manually marked by the user. When determining whether a mutation event exists, the core data is first analyzed to obtain the hair drying time and hair length for each time, and the average hair drying time and average hair length are calculated. Compare the current hair drying time with the average hair drying time to calculate the first difference ratio. Compare the current hair length with the average hair length to calculate the second difference ratio. If any of the following exists: the first difference ratio > A1, the second difference ratio > A2, or any of the haircut events manually marked by the user, a sudden event is determined to exist.

4. The intelligent hair dryer control method based on temperature feedback adjustment according to claim 1, characterized in that, The analysis model includes an input layer, a feature enhancement layer, a decision tree layer, a hair quality classification layer, and an output fusion layer. The input layer takes the original feature vector as input, integrates scene and environmental data, and outputs a 9-dimensional feature vector. The feature enhancement layer expands the 9-dimensional feature vector to 20-dimensional features, strengthens the association between hair quality and scene, and outputs 20-dimensional enhanced features. The decision tree layer uses the 20-dimensional enhanced features to learn the rules of the decision tree and outputs the base learner results. The hair quality classification layer identifies the 20-dimensional enhanced features and outputs 4-dimensional classification probabilities. The output fusion layer fuses the base learner results and the 4-dimensional classification probabilities to obtain the wind speed range and temperature range.

5. The intelligent hair dryer control method based on temperature feedback adjustment according to claim 4, characterized in that, After obtaining the wind speed range and temperature range, the wind speed range and temperature range are corrected based on the hair type. During the correction, the maximum and minimum values ​​in the wind speed range are multiplied by the wind speed correction coefficient corresponding to the hair type; the maximum and minimum values ​​in the temperature range are multiplied by the temperature correction coefficient corresponding to the hair type; thus obtaining the corrected wind speed range and temperature range.

6. The intelligent hair dryer control method based on temperature feedback adjustment according to claim 5, characterized in that, Current environmental data includes ambient temperature, ambient humidity, and current air pressure. When calculating the environmental impact value, a piecewise function is set based on physical logic and domain experience to convert ambient temperature, ambient humidity, and air pressure into standardized impact coefficients, resulting in humidity impact coefficient, temperature impact coefficient, and air pressure impact coefficient. The humidity impact coefficient, temperature impact coefficient, and air pressure impact coefficient are processed by weighted summation to obtain the environmental impact value.

7. The intelligent hair dryer control method based on temperature feedback adjustment according to claim 6, characterized in that, When adjusting the wind speed and temperature ranges based on environmental impact values, the environmental impact value is multiplied by the maximum and minimum values ​​of the corrected wind speed range to obtain the adjusted wind speed range, and the environmental impact value is multiplied by the maximum and minimum values ​​of the corrected temperature range to obtain the adjusted temperature range. The adjustable wind speed range and adjustable temperature range are verified by comparing the adjustable wind speed range with the commonly used wind speed range and taking the intersection of the two ranges as the actual wind speed range. Similarly, the adjustable temperature range is compared with the outlet air temperature range and taken as the actual temperature range.

8. The intelligent hair dryer control method based on temperature feedback adjustment according to claim 7, characterized in that, The steps for developing a dynamic parameter adjustment scheme for a hair dryer based on the actual temperature range and actual wind speed range are as follows: Obtain the linear relationship between motor speed and wind speed, and convert the actual wind speed range into the actual speed range; Obtain the relationship between heating power and outlet air temperature, and convert the actual temperature range into the actual power range; The single-use duration of hair dryers is extracted from the core data. Outliers are removed using the 3σ criterion. The standard single-use duration of hair dryers is calculated by applying exponential decay weighted average. A linear decay algorithm is used to design the motor speed decay and heating power decay. The motor speed decreases from the maximum value to the minimum value of the actual speed range as the standard hair dryer is used for a single session. The heating power decreases from the maximum value to the minimum value of the actual power range as the standard hair dryer is used for a single session.

9. The intelligent hair dryer control method based on temperature feedback adjustment according to claim 8, characterized in that, The steps to adjust the hair dryer parameters based on distance values ​​are as follows: The distance between the hair dryer's air outlet and the hair is collected, processed by a Kalman filter, and then the distance value is output. Determine the distance range in which the distance value falls. The distance range includes the near distance range, the standard range, and the far distance range. The adjustment strategy based on the distance range adjusts the set speed and power, and then executes the adjusted speed and power.

10. A smart hair dryer control system based on temperature feedback regulation, characterized in that, include: Data acquisition module: Obtains the core data required to control the hair dryer and performs preprocessing; The first analysis module analyzes the core preprocessed data to obtain the standard distance, common wind speed range, and outlet air temperature range. Event determination module: Acquires and analyzes feature data, determines whether a mutation event exists, and resets core data after a mutation event occurs; The second analysis module: Based on core data, standard distance, common wind speed range and outlet temperature range, an analysis model is built, and the analysis model is used to analyze the wind speed range and temperature range used by the hair dryer in this case. Correction processing module: Acquires and analyzes current environmental data to obtain environmental impact values, adjusts wind speed range and temperature range based on environmental impact values, and obtains actual temperature range and actual wind speed range; Solution setting module: Based on the actual temperature range and actual wind speed range, formulate a dynamic parameter adjustment scheme for the hair dryer and set the hair dryer parameters; Parameter adjustment module: When using the hair dryer dynamic parameter adjustment scheme, the distance value between the hair dryer outlet and the user's hair is collected, and the hair dryer parameters are adjusted and applied based on the distance value.

Images