Range hood control method and device based on gesture recognition, range hood and electronic equipment

Abstract

The invention discloses a range hood control method and device based on gesture recognition, a range hood and electronic equipment, and belongs to the technical field of kitchen appliances. The method comprises the following steps: controlling a laser transmitter to transmit a laser signal to a target direction of the range hood, and receiving a reflected light signal formed by reflecting the laser signal through a hand of a user through a receiver; acquiring an air quality index value, and matching a detection threshold value corresponding to the air quality index value; determining a target reflected light signal of which the signal intensity is greater than a detection threshold value in the reflected light signals; generating a depth map including the hand of the user according to the time difference between the laser signal and the target reflected light signal; recognizing a user gesture according to the depth map, and generating a control instruction corresponding to the user gesture to control the running state of the range hood. According to the embodiment of the invention, the influence of environmental interference on the recognition result can be reduced, the accuracy of gesture recognition is improved, and the consistency of the control instruction and the user operation intention is improved.

Description

Technical Field

[0001] This application belongs to the field of kitchen appliance technology, and in particular relates to a range hood control method, device, range hood and electronic equipment based on gesture recognition. Background Technology

[0002] The core function of a range hood is to generate negative pressure through an internal fan system, capture oil fumes, gas exhaust gases, and suspended particulate matter produced during cooking, and then exhaust them outdoors through exhaust pipes, thereby improving kitchen air quality.

[0003] In related technologies, the mainstream control methods for range hoods, depending on the interaction method, can include mechanical button control, touch button control, and infrared sensor gesture control. However, in the actual working scenario of a range hood, the cooking fumes create a high-concentration fume environment around the machine. When the infrared light emitted by the infrared emitting module passes through the fume area, it is scattered, refracted, and absorbed by the fume particles, resulting in reduced accuracy of gesture recognition. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method, device, range hood, and electronic device for controlling a range hood based on gesture recognition, in order to improve the accuracy of gesture recognition in the range hood.

[0005] In a first aspect, this application provides a method for controlling a range hood based on gesture recognition. The range hood is equipped with a gesture detection module, which includes a laser transmitter and a receiver. The method includes: The laser emitter is controlled to emit a laser signal in the target direction of the smoke machine, and the reflected light signal formed by the laser signal being reflected by the user's hand is received by the receiver; Acquire air quality index values, match the detection thresholds corresponding to the air quality index values, and identify target reflected light signals with signal intensity greater than the detection thresholds in the reflected light signals. A depth map, including the user's hand, is generated based on the time difference between the laser signal and the target reflected light signal. The system identifies user gestures based on depth maps and generates corresponding control commands to control the operation of the range hood.

[0006] According to the gesture recognition-based range hood control method of this application, a laser transmitter is controlled to emit a laser signal in the target direction of the range hood, and a receiver receives the reflected light signal formed by the laser signal reflecting off the user's hand; an air quality index value is obtained and matched with the detection threshold corresponding to the air quality index value; and a target reflected light signal with a signal intensity greater than the detection threshold is identified in the reflected light signal; a depth map including the user's hand is generated based on the time difference between the laser signal and the target reflected light signal; the user's gesture is recognized based on the depth map, and a control command corresponding to the user's gesture is generated to control the operating state of the range hood. This application embodiment integrates a gesture detection module, including a laser emitter and a receiver, into the range hood. By dynamically matching the detection threshold with the air quality index value in the cooking scenario, the intensity of the reflected light signal is filtered, thereby reducing noise pulses caused by suspended particles such as oil fumes and steam while retaining the effective target reflected light signal. Then, a depth map is constructed based on the time difference between the target reflected light signal and the emitted laser signal, which improves the signal-to-noise ratio and edge integrity of the depth map in the oil fume environment. This allows the depth map to more accurately reflect the spatial position and shape characteristics of the user's hand, reduces the impact of environmental interference on the recognition results, improves the accuracy of gesture recognition, and thus improves the consistency between control commands and user operation intentions.

[0007] According to one embodiment of this application, the laser emitter is a laser emitter array, and the receiver is a multi-angle recognition receiver matrix; Controlling the laser emitter to emit laser signals toward the target direction of the smoke machine includes: The laser emitter array is controlled to emit laser signals at regular intervals or sequentially toward the target direction of the smoke machine.

[0008] In this embodiment, by setting up a laser emitter array and a multi-angle recognition receiver matrix, and further controlling the laser emitter array to emit laser signals in the target direction of the range hood at regular intervals or sequentially, a multi-point coverage and multi-angle sampling laser detection network can be formed in the cooking space, which expands the spatial range and angular dimension of gesture detection, improves the spatial sampling density of reflected light signals, and enhances the ability to capture subtle hand movements and complex gesture trajectories of users.

[0009] According to one embodiment of this application, the laser emitter and receiver are further provided with an anti-oil and anti-fouling nano-oleophobic coating lens. The method also includes: The laser transmitter is controlled to send a calibration signal at preset intervals. The transmitted signal of the calibration signal is received by the receiver, and the reflection attenuation coefficient is calculated based on the signal strength of the calibration signal and the signal strength of the transmitted signal. The target reflected light signals whose signal intensity is greater than the detection threshold are identified, including: The signal intensity of the reflected light signal is compensated based on the reflection attenuation coefficient to determine the target reflected light signal whose signal intensity after compensation is greater than the detection threshold.

[0010] In this embodiment, by setting an anti-oil and oleophobic nano-coated lens on the outside of the laser transmitter and receiver, the adhesion of oil droplets and water vapor to the optical window during cooking can be reduced, thereby reducing the impact of lens contamination on light flux from the source. Further, timed calibration is performed, and the reflection attenuation coefficient is calculated in real time by comparing the original intensity and the returned intensity of the calibration signal. Then, the intensity of the reflected light signal obtained in the subsequent gesture detection stage is dynamically compensated by the reflection attenuation coefficient, thereby quantifying the light attenuation caused by lens contamination into a correctable error. This allows the compensated reflected light signal to truly reflect the reflection intensity of the user's hand, improving the accuracy of gesture recognition in long-term operation and high-oil environments.

[0011] According to one embodiment of this application, the method further includes: Record the emission time when the laser emitter sends a laser signal toward the target direction of the smoke machine; The receiver records the reception time when it receives the reflected light signal formed by the laser signal being reflected by the user's hand; Calculate the time difference between the laser signal and the reflected light signal based on the transmission and reception times; The time difference between the laser signal and the target reflected light signal is obtained from the time difference data.

[0012] In this embodiment, by recording the emission time at the instant the laser transmitter emits a laser signal and recording the reception time at the instant the receiver captures the reflected light signal formed by the user's hand, the time difference data can be accurately calculated.

[0013] According to one embodiment of this application, the range hood also includes a temperature sensor; The time difference between the laser signal and the target reflected light signal is obtained from the time difference data, including: Acquire temperature data collected by the temperature sensor; Based on the temperature data, the corresponding target error data is matched from the preset mapping relationship; the mapping relationship represents the correspondence between error data of different temperature data and data of different time differences; The time difference data is corrected based on the target error data, and the time difference between the laser signal and the target reflected light signal is obtained from the corrected time difference data.

[0014] In this embodiment, by integrating a temperature sensor into the smoke machine and collecting ambient temperature data, the target error data corresponding to the current temperature is dynamically matched based on a preset mapping relationship, and the original time difference data is corrected by temperature compensation. This reduces the ranging deviation caused by time base drift, electrical delay and air refractive index fluctuation of optoelectronic devices due to temperature changes, and further improves the accuracy of gesture recognition under thermal disturbance environment.

[0015] According to one embodiment of this application, a depth map including the user's hand is generated based on the time difference between a laser signal and a target reflected light signal, including: The distance between the user's hand and the receiver is calculated based on the time difference between the laser signal and the target reflected light signal; Based on the receiver's location information and distance, point cloud data representing the user's hand position in three-dimensional space is generated, resulting in a depth map including the user's hand.

[0016] In this embodiment, the distance between each sampling position of the user's hand and the receiver is calculated point by point based on the time difference data, and combined with the known spatial pose of the receiver, the distance is calculated in three dimensions to generate dense point cloud data that can truly reflect the surface morphology of the hand, making the obtained depth more accurate.

[0017] According to one embodiment of this application, recognizing user gestures based on depth maps includes: Obtain depth maps at multiple consecutive time points; Identify the user's hand movement trajectory based on depth maps from multiple consecutive time points; The motion trajectory is subjected to velocity continuity detection. If the abrupt rate of displacement of the user's hand in the motion trajectory is lower than the threshold, the detection is passed. If the detection passes, the similarity between the motion trajectory and each gesture in the preset template library is calculated, and the gesture with the highest similarity is identified as the user's gesture.

[0018] In this embodiment, by continuously acquiring multiple frames of depth maps, the temporal motion trajectory of the user's hand is reconstructed in three-dimensional space, and velocity continuity is detected. The displacement abrupt change rate is used as the discrimination index, which can reduce recognition errors caused by abnormal jump points due to instantaneous occlusion of oil fume particles or specular reflection. The trajectory that has been continuously verified is matched with the preset gesture template library for similarity, and the highest similarity is used as the gesture judgment result. This reduces false trajectories caused by environmental interference and further improves the accuracy of gesture recognition.

[0019] Secondly, this application provides a gesture recognition-based control device for a range hood. The range hood is equipped with a gesture detection module, which includes a laser transmitter and a receiver. The device includes: The transmitting module is used to control the laser transmitter to emit laser signals in the target direction of the smoke machine, and to receive the reflected light signal formed by the laser signal after being reflected by the user's hand through the receiver; The acquisition module is used to acquire air quality index values, match the detection thresholds corresponding to the air quality index values, and identify target reflected light signals with signal intensity greater than the detection thresholds. The generation module is used to generate a depth map, including the user's hand, based on the time difference between the laser signal and the target reflected light signal; The control module is used to recognize user gestures based on the depth map and generate control commands corresponding to the user gestures to control the operating status of the range hood.

[0020] According to the gesture recognition-based range hood control device of this application, a laser transmitter is controlled to emit a laser signal in the target direction of the range hood, and a receiver receives the reflected light signal formed by the laser signal reflecting off the user's hand; an air quality index value is obtained and matched with the detection threshold corresponding to the air quality index value; and a target reflected light signal with a signal intensity greater than the detection threshold is identified in the reflected light signal; a depth map including the user's hand is generated based on the time difference between the laser signal and the target reflected light signal; the user's gesture is recognized based on the depth map, and a control command corresponding to the user's gesture is generated to control the operating status of the range hood. This application embodiment integrates a gesture detection module, including a laser emitter and a receiver, into the range hood. By dynamically matching the detection threshold with the air quality index value in the cooking scenario, the intensity of the reflected light signal is filtered, thereby reducing noise pulses caused by suspended particles such as oil fumes and steam while retaining the effective target reflected light signal. Then, a depth map is constructed based on the time difference between the target reflected light signal and the emitted laser signal, which improves the signal-to-noise ratio and edge integrity of the depth map in the oil fume environment. This allows the depth map to more accurately reflect the spatial position and shape characteristics of the user's hand, reduces the impact of environmental interference on the recognition results, improves the accuracy of gesture recognition, and thus improves the consistency between control commands and user operation intentions.

[0021] Thirdly, this application provides a smoke hood, including a gesture detection module and a controller; the gesture detection module includes a laser transmitter and a receiver; A controller for performing the gesture recognition-based range hood control method as described in the first aspect above.

[0022] Fourthly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the gesture recognition-based smoke hood control method as described in the first aspect above.

[0023] Fifthly, this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the gesture recognition-based smoke machine control method as described in the first aspect above.

[0024] The above-described one or more technical solutions in the embodiments of this application have at least one of the following technical effects: According to the gesture recognition-based range hood control method of this application, a laser transmitter is controlled to emit a laser signal in the target direction of the range hood, and a receiver receives the reflected light signal formed by the laser signal reflecting off the user's hand; an air quality index value is obtained and matched with the detection threshold corresponding to the air quality index value; and a target reflected light signal with a signal intensity greater than the detection threshold is identified in the reflected light signal; a depth map including the user's hand is generated based on the time difference between the laser signal and the target reflected light signal; the user's gesture is recognized based on the depth map, and a control command corresponding to the user's gesture is generated to control the operating state of the range hood. This application embodiment integrates a gesture detection module, including a laser emitter and a receiver, into the range hood. By dynamically matching the detection threshold with the air quality index value in the cooking scenario, the intensity of the reflected light signal is filtered, thereby reducing noise pulses caused by suspended particles such as oil fumes and steam while retaining the effective target reflected light signal. Then, a depth map is constructed based on the time difference between the target reflected light signal and the emitted laser signal, which improves the signal-to-noise ratio and edge integrity of the depth map in the oil fume environment. This allows the depth map to more accurately reflect the spatial position and shape characteristics of the user's hand, reduces the impact of environmental interference on the recognition results, improves the accuracy of gesture recognition, and thus improves the consistency between control commands and user operation intentions.

[0025] In some embodiments, by setting up a laser emitter array and a receiver matrix for multi-angle recognition, and further controlling the laser emitter array to emit laser signals at regular intervals or sequentially toward the target direction of the range hood, a laser detection network with multi-point coverage and multi-angle sampling can be formed in the cooking space, which expands the spatial range and angular dimension of gesture detection, improves the spatial sampling density of reflected light signals, and enhances the ability to capture subtle hand movements and complex gesture trajectories of users.

[0026] In some embodiments, by setting an anti-oil and anti-fouling nano-oleophobic coating lens on the outside of the laser transmitter and receiver, the adhesion of oil droplets and water vapor to the optical window during cooking can be reduced, thereby reducing the impact of lens contamination on light flux from the source. Further, timed calibration is performed, and the reflection attenuation coefficient is calculated in real time by comparing the original intensity and the returned intensity of the calibration signal. Then, the intensity of the reflected light signal obtained in the subsequent gesture detection stage is dynamically compensated by the reflection attenuation coefficient, thereby quantifying the light attenuation caused by lens contamination into a correctable error. This allows the compensated reflected light signal to truly reflect the reflection intensity of the user's hand, improving the accuracy of gesture recognition in long-term operation and high-oil environments.

[0027] In some embodiments, by recording the emission time at the instant the laser transmitter emits a laser signal and recording the reception time at the instant the receiver captures the reflected light signal formed by the user's hand, the time difference data can be accurately calculated.

[0028] In some embodiments, by integrating a temperature sensor into the smoke machine and collecting ambient temperature data, the target error data corresponding to the current temperature is dynamically matched based on a preset mapping relationship, and the original time difference data is corrected by temperature compensation. This reduces the ranging deviation caused by time base drift, electrical delay and air refractive index fluctuation of optoelectronic devices due to temperature changes, and further improves the accuracy of gesture recognition under thermal disturbance environment.

[0029] In some embodiments, the distance between each sampling position of the user's hand and the receiver is calculated point by point based on the time difference data, and the distance is solved in three-dimensional geometry by combining the known spatial pose of the receiver, thereby generating dense point cloud data that can truly reflect the surface morphology of the hand, making the obtained depth more accurate.

[0030] In some embodiments, by continuously acquiring multiple frames of depth maps, the temporal motion trajectory of the user's hand is reconstructed in three-dimensional space, and velocity continuity is detected. The displacement abrupt change rate is used as the discrimination index, which can reduce recognition errors caused by abnormal jump points due to instantaneous occlusion of oil fume particles or specular reflection. The trajectory that has been continuously verified is matched with the preset gesture template library for similarity, and the highest similarity is used as the gesture judgment result, which reduces false trajectories caused by environmental interference and further improves the accuracy of gesture recognition.

[0031] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0032] The above and / or additional aspects and advantages of this application will become apparent and readily understood in conjunction with the following description of the embodiments in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating the gesture recognition-based range hood control method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of the gesture recognition-based smoke hood control device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0034] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0035] The following description, in conjunction with the accompanying drawings, details the gesture recognition-based range hood control method, device, range hood, and electronic equipment provided in this application through specific embodiments and application scenarios.

[0036] The gesture recognition-based range hood control method provided in this application embodiment can be executed by an electronic device or a functional module or entity in an electronic device that can implement the gesture recognition-based range hood control method. For example, the electronic devices mentioned in this application embodiment include, but are not limited to, range hood controllers, servers, etc. The following uses an electronic device as the execution subject to illustrate the gesture recognition-based range hood control method provided in this application embodiment.

[0037] A range hood, also known as a kitchen exhaust hood, is an essential piece of equipment in the kitchen used to remove cooking fumes and exhaust gases, maintaining fresh air in the kitchen. A range hood can include components such as a fan system, exhaust duct, control panel, air inlet, and air outlet. The air inlet is the part of the range hood that absorbs cooking fumes and exhaust gases; it is usually located at the bottom or side of the range hood, close to the stove, to capture the fumes generated during cooking. The air outlet is located at the top or rear of the range hood, expelling the absorbed fumes and exhaust gases outdoors through the exhaust duct. The fan system, as the core power component of the range hood, consists of a motor and an impeller. When the motor drives the impeller to rotate at high speed, a negative pressure area is created inside the fan. This pressure difference draws the cooking fumes into the machine and then exhausts them outdoors through the air outlet and exhaust duct. The control panel allows users to interact with the range hood. Users can control the range hood's on / off function, fan speed adjustment, lighting, and other functions through physical buttons or a touch interface on the control panel. In this embodiment, users can also control the range hood's on / off function, fan speed adjustment, lighting, and other functions through gestures. like Figure 1 As shown, the gesture recognition-based range hood control method of this application includes: step 110, step 120, step 130, and step 140.

[0038] Step 110: Control the laser emitter to emit a laser signal in the target direction of the smoke machine, and receive the reflected light signal formed by the laser signal reflected by the user's hand through the receiver.

[0039] In this embodiment, the range hood is equipped with a gesture detection module. This module is a sensing unit for gesture recognition and control, primarily comprising a laser emitter and a receiver. The laser emitter is a device capable of emitting laser signals of a specific wavelength, such as a vertical-cavity surface-emitting laser (VCSEL). Under the modulation of the driving circuit, the laser emitter emits laser pulses at a pulse repetition frequency. It should be noted that the peak pulse power of the laser emitter has undergone safety assessment and meets human eye safety standards, ensuring no optical radiation hazard to users in everyday use scenarios.

[0040] The receiver works in conjunction with the laser emitter to receive the reflected light signal after it has been reflected by the target object. Receivers typically include an array of photosensitive elements capable of sensing the corresponding laser wavelength, such as CCD (Charge-Coupled Device), CMOS (Complementary Metal-Oxide-Semiconductor), or SPAD (Single Photon Avalanche Diode).

[0041] The gesture detection module can be integrated or installed on the main body of the range hood, such as in the control panel area, the middle or upper part of the front of the range hood, or the area around the air inlet of the range hood. This allows users to make gestures in a natural operating range, such as above the stove, below the range hood, or in front of the range hood, without having to change their body posture significantly during cooking, so that the gesture detection module can capture the gestures.

[0042] In some embodiments, the laser emitter and receiver in the gesture detection module can be arranged coplanarly or approximately coplanarly behind the same optical window on the inner side of the front panel of the smoke hood. The optical window can be closed by a narrow-band filter with high transmittance and low transmittance in the visible light band to suppress ambient light interference. Specifically, the laser emitter is located 10mm–20mm to the left of the horizontal center line of the optical window, and the receiver is located symmetrically to the right of the center line. Of course, the laser emitter and receiver can also be set in other positions, such as the lower edge of the smoke hood, facing directly in front of the smoke hood. This application embodiment does not limit the position of the laser emitter and receiver.

[0043] When the range hood enters a gesture recognition control mode, the controller triggers the laser emitter to emit a laser signal in the target direction. The target direction is a pre-set and calibrated effective area for user gesture operations. For example, the target direction might be directly in front of the range hood, roughly the area where the user's hands are likely to appear when cooking in front of the stove. This area could be centered on the stove and within a certain space below the range hood's smoke collection hood, facilitating the recognition of the user's gestures in the target direction.

[0044] When the laser signal reaches the user's hand, it is reflected, forming a reflected light signal. When the reflected light signal reaches the receiver, it forms a detectable electrical signal.

[0045] Step 120: Obtain air quality index values, match the detection thresholds corresponding to the air quality index values; and identify the target reflected light signals whose signal intensity is greater than the detection threshold in the reflected light signals. To reduce the interference of oil fume particles, water vapor, and suspended grease in the complex atmosphere of the kitchen on subsequent gesture recognition, this application embodiment uses air quality index values ​​as a quantitative representation of environmental turbidity, dynamically adjusts the detection threshold, and performs intensity screening on the reflected light signal, thereby reducing noise pulses generated by suspended particles such as oil fumes and steam while retaining effective target reflected light signals.

[0046] Specifically, air quality index values ​​are parameters that reflect the cleanliness of the air or the concentration of pollutants in the environment where a range hood is located. Air quality index values ​​can be the concentration of a single pollutant, such as the concentration of particulate matter (PM2.5, PM10) produced during cooking, or the concentration of harmful gases (such as carbon monoxide, carbon dioxide, formaldehyde, volatile organic compounds, VOCs, etc.); or they can be a comprehensive air quality index calculated by combining the concentrations of multiple pollutants or other relevant parameters (such as odor intensity, turbidity, etc.).

[0047] In this embodiment, environmental data can be collected using an air quality sensor built into or connected to the range hood to obtain air quality index values. For example, the air quality sensor can be a laser scattering or photoresist dust sensor for detecting particulate matter concentration, an electrochemical or semiconductor gas sensor for detecting specific gas concentrations, or a metal oxide sensor array for detecting overall air quality. The air quality sensor converts the sensed physical or chemical signals into electrical signals, which are then amplified, filtered, and converted from analog to digital before being transmitted to the range hood's controller. The controller converts the received sensor data into corresponding air quality index values ​​according to a preset algorithm or calibration curve.

[0048] In some embodiments, air quality index values ​​can also be obtained through external devices that communicate with the range hood, such as smart home systems, air purifiers, etc.

[0049] The detection threshold is a signal strength benchmark value used to determine whether the reflected light signal is valid. In this embodiment, one or more air quality index value ranges and corresponding detection thresholds can be pre-stored. The higher the air quality, the lower the detection threshold; conversely, the lower the air quality, the higher the detection threshold. This mapping relationship is derived from extensive experimental data and analysis of real-world application scenarios. When air quality is poor (e.g., high concentration of cooking fumes, pervasive smoke), particulate matter in the environment will scatter and absorb the laser signal, resulting in a weakened effective signal strength reflected back to the receiver from the user's hand, and potentially increased background noise. To accurately recognize gestures under these conditions and reduce missed detections due to weak signals, a relatively low detection threshold can be matched. When air quality is good (e.g., clean air, no obvious smoke), the laser signal attenuation is small, and the reflected signal strength is relatively high. A relatively high detection threshold can be matched to reduce weak ambient light interference or slight reflections from non-target objects, thereby reducing the false detection rate.

[0050] After determining the detection threshold that matches the air quality index value, the reflected light signal output by the receiver (such as a digitized signal intensity sequence or image data) can be analyzed point by point or frame by frame. The intensity value of each reflected light signal sample point is compared with the detection threshold. The reflected light signals with a signal intensity greater than or equal to the detection threshold are selected as target reflected light signals; the part with a signal intensity less than the detection threshold is determined as invalid signal or background noise.

[0051] Step 130: Generate a depth map including the user's hand based on the time difference between the laser signal and the target reflected light signal.

[0052] In this embodiment, when the controller of the smoke machine issues a command to control the laser emitter to emit a laser signal, a high-precision timer, such as a time-to-digital converter or a high-frequency counter, can be started simultaneously. The laser signal propagates towards the target direction. When the laser signal shines on the user's hand, part of the light signal is reflected back by the surface of the hand, forming a reflected light signal. When the receiver receives the reflected light signal, it immediately generates an electrical signal, which triggers the timer to stop. The time difference between the laser signal and the reflected light signal is the time interval between the start of the timer (corresponding to the laser signal emission time) and the stop of the timer (corresponding to the reflection light signal being received).

[0053] Based on the time difference between the laser signal and the reflected light signal, the one-way distance of the laser signal propagation, which is the distance from the user's hand to the receiver, can be calculated. The calculation process is as follows:

[0054] Where d represents the one-way distance of the laser signal propagation, and c represents the speed of light. This represents the time difference between the laser signal and the reflected light signal.

[0055] The time difference between the laser signal and the target reflected light signal can be obtained through the above method.

[0056] In the embodiments of this application, the positions of the receiver and the laser transmitter are usually fixed. Therefore, the positions of the receiver and the laser transmitter can be used as the origin to establish a coordinate system, and the one-way distance of the laser signal propagation can be mapped to the coordinate system as a pixel. The pixels in one frame period constitute a depth map including the user's hand.

[0057] Step 140: Recognize user gestures based on the depth map and generate control commands corresponding to the user gestures to control the operating status of the range hood.

[0058] In the embodiments of this application, user gestures are actions or postures made by the user with their hands within the sensing area of ​​the range hood, which have specific meanings and patterns, such as waving, hovering, clicking, swiping, drawing circles, etc.

[0059] Since kitchen walls and cabinets can also reflect light, the depth map will include pixels corresponding to the user's hand, as well as pixels corresponding to walls and cabinets. Therefore, it is necessary to segment the area containing the user's hand from the area containing walls and cabinets in the depth map, and analyze the area containing the user's hand to identify the user's gesture.

[0060] In some embodiments, a distance threshold range can be set to identify regions in the depth map that are close to the receiver and conform to hand size characteristics as the region where the user's hand is located. Alternatively, region growing or deep learning models (such as semantic segmentation models based on convolutional neural networks) can be used to extract the region where the user's hand is located from the depth map.

[0061] After extracting the area containing the user's hand, features representing the gesture type can be further extracted, such as the center coordinates, outline, area, aspect ratio, number and posture of fingers (extended, bent), and palm orientation. For example, a "hover" gesture is characterized by the center point of the hand area remaining essentially unchanged over a period of time, and the outline shape being stable. A "click" gesture is characterized by a rapid contraction and expansion of the hand outline within a short period of time.

[0062] The extracted features can be input into a classification model, which then determines which predefined user gesture the gesture belongs to. The classification model can employ classic machine learning algorithms such as Support Vector Machines, Decision Trees, and Random Forests, or it can use deep learning algorithms. Alternatively, the extracted features can be compared with the features corresponding to predefined user gestures to calculate similarity and thus determine the type of user gesture.

[0063] Control commands can include power on / off, gear adjustment, lighting switch, delayed power off, etc.

[0064] In this embodiment, the range hood controller can store a gesture command mapping table, which defines the correspondence between each user gesture and its corresponding control command. The mapping relationship can be one-to-one, one-to-many, or many-to-one. For example, a user gesture of "waving from left to right" can uniquely correspond to the command "increase fan speed by one level"; a user gesture of "hovering for two seconds" can correspond to either a "power on" or "power off" command, depending on the current operating state of the range hood. For instance, when the range hood is in standby mode, a user gesture of "hovering for two seconds" corresponds to the power on control command, while when the range hood is running, a user gesture of "hovering for two seconds" corresponds to the power off control command.

[0065] After determining the user's gesture, the corresponding control command can be matched through the gesture command mapping table, and the operating status of the range hood can be controlled according to the control command.

[0066] According to the gesture recognition-based range hood control method of this application, a laser transmitter is controlled to emit a laser signal in the target direction of the range hood, and a receiver receives the reflected light signal formed by the laser signal reflecting off the user's hand; an air quality index value is obtained and matched with the detection threshold corresponding to the air quality index value; and a target reflected light signal with a signal intensity greater than the detection threshold is identified in the reflected light signal; a depth map including the user's hand is generated based on the time difference between the laser signal and the target reflected light signal; the user's gesture is recognized based on the depth map, and a control command corresponding to the user's gesture is generated to control the operating state of the range hood. This application embodiment integrates a gesture detection module, including a laser emitter and a receiver, into the range hood. By dynamically matching the detection threshold with the air quality index value in the cooking scenario, the intensity of the reflected light signal is filtered, thereby reducing noise pulses caused by suspended particles such as oil fumes and steam while retaining the effective target reflected light signal. Then, a depth map is constructed based on the time difference between the target reflected light signal and the emitted laser signal, which improves the signal-to-noise ratio and edge integrity of the depth map in the oil fume environment. This allows the depth map to more accurately reflect the spatial position and shape characteristics of the user's hand, reduces the impact of environmental interference on the recognition results, improves the accuracy of gesture recognition, and thus improves the consistency between control commands and user operation intentions.

[0067] In some embodiments, the laser emitter is a laser emitter array and the receiver is a multi-angle recognition receiver matrix; Controlling the laser emitter to emit laser signals toward the target direction of the smoke machine includes: The laser emitter array is controlled to emit laser signals at regular intervals or sequentially toward the target direction of the smoke machine.

[0068] In this embodiment, to improve the coverage, spatial resolution and anti-interference capability of gesture recognition, the gesture detection module of the smoke machine adopts an array and matrix hardware architecture, that is, the laser emitter is a laser emitter array and the receiver is a receiver matrix with multi-angle recognition capability.

[0069] For example, a laser emitter array can be formed by regularly arranging N×M vertical cavity surface-emitting laser units, where N≥2 and M≥2. Each vertical cavity surface-emitting laser unit can be independently controlled by a driving circuit and can independently emit laser pulses under the modulation of the driving current.

[0070] The receiver matrix can include multiple photosensitive receiving units arranged in an array. These photosensitive receiving units can be single-photon avalanche diodes or high-sensitivity CMOS image sensors. The multi-angle recognition capability of the receiver matrix can be achieved in the following ways: the physical arrangement angles of the multiple photosensitive receiving units are gradient-distributed, so that each photosensitive receiving unit corresponds to a different spatial receiving angle, covering all directions of the target detection area; alternatively, signal delay control technology can be used to enable different photosensitive receiving units to receive reflected light signals synchronously or asynchronously, thereby achieving three-dimensional perception of the hand's position in space.

[0071] Taking a single-photon avalanche diode as the photosensitive receiving unit as an example, the receiver matrix can include Q×R independent pixels arranged in rows and columns, that is, Q rows and R columns of independent pixels arranged in a two-dimensional row and column configuration, where Q≥N and R≥M. Each pixel can include a single-photon avalanche diode sensing area for converting incident photons into electrical signals, and can also include a timing circuit. The timing circuit can include, but is not limited to, a high-precision time-to-digital converter, a counter driven by a high-frequency oscillator, etc. Through the timing circuit, each pixel in the receiver matrix can record the corresponding laser signal emission time and the reflected light signal reception time.

[0072] When the laser emitter transmits a laser signal towards the target direction of the cigarette machine according to the transmission command, the timing circuit corresponding to each pixel unit in the receiver matrix is ​​synchronously activated by a global synchronization signal or a trigger signal from the laser emitter, recording the transmission time t1 at this moment. When the laser signal propagates to the user's hand and is reflected, the resulting reflected light signal returns towards the cigarette machine. When photons in the reflected light signal reach the array of single-photon avalanche diodes and are absorbed by the sensing area of ​​one or more pixels of the single-photon avalanche diode, an electrical pulse signal is generated. When the timing circuit detects the electrical pulse signal, it triggers a timing stop, recording the stop time as the reception time. Then, the transmission time and the reception time are subtracted to obtain the time difference between the laser signal and the reflected light signal.

[0073] It should be noted that since the user's hand is a three-dimensional object, the distance from each point on the hand's surface to the receiver matrix varies. Therefore, the optical path length of the laser signal emitted from the laser emitter, after reflection at different locations on the hand, also varies when it returns to different pixels in the receiver matrix. This results in slight differences in the reception time recorded by different pixels, and these differences are used to calculate the depth information of each point on the hand. In this way, each pixel in the receiver matrix has a corresponding time difference, and the collection of all pixel time differences constitutes a time difference image.

[0074] The laser emitter array can emit in two ways: timed emission and sequential emission. Timed emission and sequential emission can be used alone or in combination to adapt to different gesture recognition scenarios.

[0075] In the timed emission method, all or part of the emission units in the laser emitter array are synchronously controlled by a clock signal to emit laser signals simultaneously within a preset time interval. The time interval can range from 10ms to 50ms.

[0076] The sequential emission method involves triggering each emission unit in the laser emitter array to emit laser signals individually according to a preset sequence, with no overlap in the emission times of each emission unit.

[0077] In practical applications, combining timed and sequential transmission can achieve complementary advantages. For example, in the initial stage of gesture detection, timed transmission can be used to quickly scan the target area and preliminarily determine whether a hand target exists. Once a hand target is detected, the system automatically switches to sequential transmission to perform high-frequency sequential scanning of the transmission units corresponding to the target area, thereby obtaining richer hand contour and motion information and further improving the accuracy of gesture recognition.

[0078] In this embodiment, by setting up a laser emitter array and a multi-angle recognition receiver matrix, and further controlling the laser emitter array to emit laser signals in the target direction of the range hood at regular intervals or sequentially, a multi-point coverage and multi-angle sampling laser detection network can be formed in the cooking space, which expands the spatial range and angular dimension of gesture detection, improves the spatial sampling density of reflected light signals, and enhances the ability to capture subtle hand movements and complex gesture trajectories of users.

[0079] In some embodiments, the laser transmitter and receiver are further provided with an anti-oil and anti-fouling nano-oleophobic coating lens. The method also includes: The laser transmitter is controlled to send a calibration signal at preset intervals. The transmitted signal of the calibration signal is received by the receiver, and the reflection attenuation coefficient is calculated based on the signal strength of the calibration signal and the signal strength of the transmitted signal. The target reflected light signals whose signal intensity is greater than the detection threshold are identified, including: The signal intensity of the reflected light signal is compensated based on the reflection attenuation coefficient to determine the target reflected light signal whose signal intensity after compensation is greater than the detection threshold.

[0080] In actual cooking applications of range hoods, the laser transmitter and receiver of the gesture detection module are exposed to an environment full of oil fumes for a long time. Oil particles in the fumes easily adhere to the surface of its optical components, forming an oily coating layer, which interferes with the transmission and reception of laser signals.

[0081] The anti-oil and oleophobic nano-coated lens uses high-transmittance quartz glass as a substrate, with a fluorine-containing nano-coating of 50nm to 100nm thickness deposited on its surface using vacuum evaporation technology. This coating has a water contact angle greater than 110° and an oil contact angle greater than 90°, exhibiting excellent oleophobic and hydrophobic properties, effectively reducing the adhesion of grease molecules in cooking fumes. The anti-oil and oleophobic nano-coated lens can be installed using a sealed structure, with the lens edge tightly fitted to the gesture detection module housing of the range hood via a high-temperature resistant silicone sealing ring.

[0082] Although the oleophobic nano-coating on the lens can reduce oil adhesion, trace amounts of oil may still accumulate or the lens surface may wear over long-term use, causing changes in the transmission attenuation of the laser signal. To compensate for the error caused by this attenuation, this embodiment provides a timed calibration mechanism, which controls the laser transmitter to send a calibration signal at preset intervals, calculates the reflection attenuation coefficient based on the calibration signal, and compensates for the signal strength according to the reflection attenuation coefficient.

[0083] Specifically, the laser transmitter can be controlled to send calibration signals at preset intervals. These preset intervals can be pre-set or dynamically adjusted based on the frequency of use of the range hood. For example, initial preset interval values ​​could be 30 minutes, 50 minutes, 2 hours, or 10 hours. The parameters of the calibration signal, such as emission wavelength, pulse frequency, peak power, and pulse width, are the same as those of the laser signal during normal operation. The receiver receives the calibration reflection signal through an anti-oil and oleophobic nano-coated lens.

[0084] The reflection attenuation coefficient can be the ratio of the intensity of the calibration signal to the intensity of the calibration reflected signal. Specifically, the signal intensity S0 of the calibration signal sent by the laser transmitter can be recorded. The signal intensity S0 is a fixed parameter of the laser transmitter under standard attenuation-free conditions and is pre-stored in the storage unit. The actual signal intensity S1 of the calibration reflected signal received by the receiver can be read. The reflection attenuation coefficient α is calculated using the formula α = S1 / S0, where the value of α ranges from 0 to 1. The closer α is to 1, the smaller the signal attenuation of the lens; the smaller the value of α, the more severe the accumulation of oil or wear on the lens surface, and the greater the signal attenuation.

[0085] After obtaining the reflection attenuation coefficient, the signal intensity of the reflected light signal can be compensated. For example, if the original signal intensity of the reflected light signal received by the receiver is S_raw, the true signal intensity S_true should be greater than S_raw due to attenuation caused by the oleophobic nano-coated lens during transmission. Based on the calculated reflection attenuation coefficient α, the compensated signal intensity S_comp can be calculated using the compensation formula S_comp = S_raw / α. The compensated signal intensity S_comp restores the true signal intensity unaffected by lens attenuation.

[0086] After the intensity compensation of the reflected light signal is completed, the compensated signal intensity S_comp is compared with the detection threshold. When the S_comp of the reflected light signal is greater than the detection threshold, the reflected light signal is determined as the target reflected light signal.

[0087] In this embodiment, by setting an anti-oil and oleophobic nano-coated lens on the outside of the laser transmitter and receiver, the adhesion of oil droplets and water vapor to the optical window during cooking can be reduced, thereby reducing the impact of lens contamination on light flux from the source. Further, timed calibration is performed, and the reflection attenuation coefficient is calculated in real time by comparing the original intensity and the returned intensity of the calibration signal. Then, the intensity of the reflected light signal obtained in the subsequent gesture detection stage is dynamically compensated by the reflection attenuation coefficient, thereby quantifying the light attenuation caused by lens contamination into a correctable error. This allows the compensated reflected light signal to truly reflect the reflection intensity of the user's hand, improving the accuracy of gesture recognition in long-term operation and high-oil environments.

[0088] In some embodiments, the method further includes: Record the emission time when the laser emitter sends a laser signal toward the target direction of the smoke machine; The receiver records the reception time when it receives the reflected light signal formed by the laser signal being reflected by the user's hand; Calculate the time difference between the laser signal and the reflected light signal based on the transmission and reception times; The time difference between the laser signal and the target reflected light signal is obtained from the time difference data.

[0089] In this embodiment, a high-performance clock module and a synchronization control unit can also be integrated into the smoke generator. The clock module can be a temperature-controlled crystal oscillator, and the synchronization control unit is responsible for coordinating the timing of the laser transmitter, receiver, and clock module.

[0090] Specifically, when the laser emitter's drive circuit receives the emission command and begins outputting drive current, it synchronously sends a emission trigger signal to the synchronization control unit. Upon receiving the emission trigger signal, the synchronization control unit immediately reads the current absolute time from the clock module and uses this absolute time as the emission time of the laser signal. If the laser emitter has an array structure, each emission unit will independently generate an emission trigger signal when emitting a laser signal, and the synchronization control unit will record the emission time corresponding to each emission unit.

[0091] When the laser signal is reflected by the user's hand, the reflected light signal reaches the receiver. The photosensitive element inside the receiver detects the sudden change in light intensity and converts the light signal into an electrical signal. After processing by the preamplifier and filter circuits inside the receiver, the electrical signal outputs a receive trigger signal. Upon receiving the receive trigger signal from the receiver, the synchronization control unit uses a clock module with the same time as the recorded transmission time as a reference to read the current absolute time as the reception time of the reflected light signal. If the receiver has a matrix structure, each receiving unit generates an independent receive trigger signal upon receiving the reflected light signal, and the synchronization control unit records the reception time corresponding to each receiving unit.

[0092] Subtracting the receiving time from the corresponding transmission time yields the time difference data. The time difference between the target reflected light signal and the corresponding laser signal can then be extracted from this data.

[0093] In this embodiment, by recording the emission time at the instant the laser transmitter emits a laser signal and recording the reception time at the instant the receiver captures the reflected light signal formed by the user's hand, the time difference data can be accurately calculated.

[0094] In some embodiments, the range hood also includes a temperature sensor; The time difference between the laser signal and the target reflected light signal is obtained from the time difference data, including: Acquire temperature data collected by the temperature sensor; Based on the temperature data, the corresponding target error data is matched from the preset mapping relationship; the mapping relationship represents the correspondence between error data of different temperature data and data of different time differences; The time difference data is corrected based on the target error data, and the time difference between the laser signal and the target reflected light signal is obtained from the corrected time difference data.

[0095] In the actual application scenarios of range hoods, the air temperature in the kitchen will change with the cooking process. Temperature changes will cause changes in air density, which will affect the propagation speed of laser signals. Furthermore, the circuit components used to record time will also experience phase drift with temperature. Therefore, a temperature compensation mechanism is introduced to reduce time errors.

[0096] In this embodiment, the range hood also integrates a temperature sensor, which can be installed near the gesture detection module to collect ambient temperature data around the gesture detection module.

[0097] The correspondence between error data for different temperatures and different time differences can be pre-determined through experimental testing and data fitting. For example, in a standard experimental environment, simulating different temperature scenarios in a kitchen, at each temperature point, the distance between the laser emitter and the simulated hand is fixed, the actual time difference between the laser signal and the reflected light signal is measured, and compared with the theoretical time difference calculated based on the standard speed of light. The difference between the two is the error data corresponding to that temperature point. For example, at 20℃, if the actual distance is 1m, the actual time difference is approximately 6.666μs, the theoretical time difference is 6.6667μs, and the corresponding error data is -0.0007μs; at 60℃, at the same distance, the actual time difference is approximately 6.557μs, the theoretical time difference remains 6.6667μs, and the error data is -0.1097μs.

[0098] After acquiring the temperature data, it can be used as an index to find the corresponding target error data in the mapping relationship. Then, the time difference data is corrected. For example, the time difference data can be added to the corresponding target error data to obtain the corrected time difference data. The time difference between the laser signal and the target reflected light signal can then be obtained from the corrected time difference data.

[0099] In this embodiment, by integrating a temperature sensor into the smoke machine and collecting ambient temperature data, the target error data corresponding to the current temperature is dynamically matched based on a preset mapping relationship, and the original time difference data is corrected by temperature compensation. This reduces the ranging deviation caused by time base drift, electrical delay and air refractive index fluctuation of optoelectronic devices due to temperature changes, and further improves the accuracy of gesture recognition under thermal disturbance environment.

[0100] In some embodiments, generating a depth map including the user's hand based on the time difference between the laser signal and the target reflected light signal includes: The distance between the user's hand and the receiver is calculated based on the time difference between the laser signal and the target reflected light signal; Based on the receiver's location information and distance, point cloud data representing the user's hand position in three-dimensional space is generated, resulting in a depth map including the user's hand.

[0101] In this embodiment, the distance d from each position of the user's hand to the receiver can be calculated based on the time difference between the laser signal and the reflected light signal. The positions of the receiver and the laser transmitter are usually fixed. Therefore, the one-way distance of the laser signal propagation can be mapped to points in the three-dimensional world coordinate system as point cloud data by combining the positions of the receiver and the laser transmitter.

[0102] Specifically, to construct the three-dimensional coordinates of point cloud data, taking a photonic avalanche diode receiver as an example, the inherent position information of each pixel can be combined. Specifically, each pixel has predefined two-dimensional coordinates (u, v), which typically have the top-left corner of the photonic avalanche diode array as the origin, with the u-axis pointing horizontally to the right and the v-axis pointing vertically downwards. These coordinates can be the pixel's row and column indices; for example, the pixel in the i-th row and j-th column can be represented as (i, j), or they can be calibrated physical coordinates, for example, in millimeters, representing the horizontal and vertical offset of the pixel center relative to the array origin.

[0103] Using the two-dimensional coordinates (u, v) of a pixel and the corresponding distance d, and combining the intrinsic parameters of the photon avalanche diode, the coordinates (X, Y, Z) of the reflection point corresponding to each pixel in the three-dimensional world coordinate system are calculated through geometric transformation. The intrinsic parameters may include, but are not limited to: the physical size of the pixel (such as pixel pitch), effective focal length, principal point coordinates (the intersection of the optical axis and the sensor plane), etc. The intrinsic parameters can be obtained in advance through a pre-calibration process.

[0104] In one example, point cloud data can be calculated based on the following simplified geometric transformation model: assuming that the origin of the 3D world coordinate system coincides with the optical center of the single-photon avalanche diode array, the Z-axis is perpendicular to the array plane and points in the user direction (i.e., the depth direction), the X-axis is horizontal to the right, and the Y-axis is vertically downward.

[0105] X = ((u - c_u) × s_x ) × d / f Y = ( (v - c_v) × s_y ) × d / f Z = d Where: (c_u, c_v) represent the principal point coordinates; s_x and s_y represent the physical dimensions of the pixel in the u-axis and v-axis directions, respectively; f represents the effective focal length; and d represents the radial distance from the point on the user's hand surface to the single-photon avalanche diode.

[0106] Through the above transformation, the pixels in the single-photon avalanche diode array can be converted into points (X, Y, Z) in three-dimensional space. The set of all three-dimensional points constitutes point cloud data that can characterize the position, contour and shape of the user's hand in three-dimensional space.

[0107] The 3D point cloud data can be orthogonally projected onto an X–Y reference plane parallel to the front panel of the range hood, and voxelized using a grid of preset size. The mean Z-axis value of each point within the grid is calculated to generate an 8-bit grayscale depth map. The grayscale values ​​in the depth map correspond to the depth range, and the area where the user's hand is located can be highlighted and raised in the image.

[0108] In this embodiment, the distance between each sampling position of the user's hand and the receiver is calculated point by point based on the time difference data, and combined with the known spatial pose of the receiver, the distance is calculated in three dimensions to generate dense point cloud data that can truly reflect the surface morphology of the hand, making the obtained depth more accurate.

[0109] In some embodiments, recognizing user gestures based on depth maps includes: Obtain depth maps at multiple consecutive time points; Identify the user's hand movement trajectory based on depth maps from multiple consecutive time points; The motion trajectory is subjected to velocity continuity detection. If the abrupt rate of displacement of the user's hand in the motion trajectory is lower than the threshold, the detection is passed. If the detection passes, the similarity between the motion trajectory and each gesture in the preset template library is calculated, and the gesture with the highest similarity is identified as the user's gesture.

[0110] In this embodiment, user gestures can include static gestures and dynamic gestures. Dynamic gestures are essentially the movement of the hand over a period of time. Therefore, it is difficult to capture complete gesture information using only a single frame depth map; it is necessary to obtain depth maps from multiple consecutive moments to obtain a depth map time series.

[0111] Specifically, the receiver can continuously acquire reflected light signals at a preset frame rate and obtain a depth map based on the reflected light signals. For example, the frame rate can be set to 30 frames per second, or other values. Each frame of acquired reflected light signal can correspond to a depth map, and the continuously acquired depth maps in chronological order constitute a depth image time series.

[0112] After obtaining the depth image time series, the positional changes of the user's hand can be extracted and tracked from the depth image time series, thereby identifying the motion trajectory.

[0113] Specifically, for each frame of the depth map in a depth image time series, the hand region can be located using a hand detection algorithm. For example, the depth map provides distance information, and the hand region appears as a connected region with a significant depth difference from the background. Analyzing the distance information in the depth map allows for the location of the hand region. After detecting the hand region, the coordinates of its center point can be extracted as the representative position of the hand in that frame's depth map. Tracking algorithms, such as Kalman filtering, particle filtering, or deep learning-based tracking models, can be used to associate the hand region detected in the current frame's depth map with the hand region tracked in the previous frame's depth map, thereby tracking the same hand in consecutive frame depth maps.

[0114] In addition to tracking the overall center point of the hand, feature points such as fingertips and knuckles can be extracted within the hand region. By matching these feature points across consecutive depth maps, the movement and posture changes of the hand can be described more accurately. By recording the coordinates of the hand's center point or other feature points in multiple consecutive depth maps and connecting these coordinates in chronological order, the user's hand's motion trajectory in space can be generated. This trajectory is a temporal set of coordinate points that reflects information such as the path, direction, and speed of the hand's movement.

[0115] When users control the range hood with gestures, their hand movements are usually smooth and continuous. Hand tremors, accidental obstruction, or signal interference can cause abnormal trajectory segments, which are manifested as sudden displacement changes in the movement trajectory. Speed ​​continuity detection can distinguish between normal gestures and abnormal interference.

[0116] Specifically, the displacement abrupt change rate of the user's hand in the motion trajectory can be calculated. The displacement abrupt change rate is the ratio of the displacement change of a hand feature point between two adjacent moments to the displacement of the previous moment. The ratio of the displacement change of a hand feature point between two adjacent moments in the motion trajectory to the displacement of the previous moment can be calculated to obtain the displacement abrupt change rate of the hand feature point at different moments. The maximum or average displacement abrupt change rate of the hand feature point at different moments is determined as the displacement abrupt change rate of the user's hand.

[0117] The abrupt change rate of the user's hand displacement can be compared with a threshold. If the abrupt change rate is greater than or equal to the threshold, it indicates abnormal interference in the motion trajectory, which may not correspond to a normal user gesture, and the detection fails; this motion trajectory is not used for subsequent gesture recognition. If the abrupt change rate is less than the threshold, it indicates that the motion trajectory has little or no abnormal interference and can be considered a normal user gesture, thus passing the detection. The threshold can be preset, for example, 0.5, 0.6, 0.8, etc. If the detection passes, the similarity between the motion trajectory and each gesture in the preset template library is calculated, and the gesture with the highest similarity is identified as the user's gesture.

[0118] Specifically, the template library stores standard gesture motion trajectory templates corresponding to all control functions of the range hood, such as "horizontal wave (for controlling the range hood to start / stop)," "vertical swipe up (for controlling the range hood to increase airflow)," "vertical swipe down (for controlling the range hood to decrease airflow)," "clockwise rotation (for controlling the range hood to turn on the lighting)," and "counterclockwise rotation (for controlling the range hood to turn off the lighting)," etc. Each standard gesture motion trajectory template is motion trajectory data collected from a large number of user samples and standardized.

[0119] Trajectory features can be extracted from the motion trajectory, such as the shape of the trajectory, the direction of motion, the distance of motion, the changes in speed and acceleration, and the positional relationship between the starting and ending points of the trajectory. Trajectory features corresponding to each gesture are extracted from a template library. The similarity between the extracted trajectory features and the trajectory features corresponding to each gesture is calculated, for example, using Euclidean distance or cosine similarity. The calculated similarities are then ranked, and the gesture with the highest similarity is identified as the user's gesture.

[0120] In this embodiment, by continuously acquiring multiple frames of depth maps, the temporal motion trajectory of the user's hand is reconstructed in three-dimensional space, and velocity continuity is detected. The displacement abrupt change rate is used as the discrimination index, which can reduce recognition errors caused by abnormal jump points due to instantaneous occlusion of oil fume particles or specular reflection. The trajectory that has been continuously verified is matched with the preset gesture template library for similarity, and the highest similarity is used as the gesture judgment result. This reduces false trajectories caused by environmental interference and further improves the accuracy of gesture recognition.

[0121] The gesture recognition-based range hood control method provided in this application can be executed by a gesture recognition-based range hood control device. This application uses the example of a gesture recognition-based range hood control device executing the gesture recognition-based range hood control method to illustrate the gesture recognition-based range hood control device provided in this application.

[0122] This application also provides a gesture recognition-based control device for a range hood, wherein the range hood is equipped with a gesture detection module, which includes a laser transmitter and a receiver.

[0123] like Figure 2 As shown, the gesture recognition-based range hood control device includes: The transmitting module 210 is used to control the laser transmitter to emit laser signals toward the target direction of the smoke machine, and to receive the reflected light signal formed by the laser signal after being reflected by the user's hand through the receiver; The acquisition module 220 is used to acquire air quality index values, match the detection thresholds corresponding to the air quality index values, and determine the target reflected light signals whose signal intensity is greater than the detection thresholds in the reflected light signals. The generation module 230 is used to generate a depth map including the user's hand based on the time difference between the laser signal and the target reflected light signal; The control module 240 is used to recognize user gestures based on the depth map and generate control commands corresponding to the user gestures to control the operating status of the smoke machine.

[0124] According to the gesture recognition-based range hood control device of this application, a laser transmitter is controlled to emit a laser signal in the target direction of the range hood, and a receiver receives the reflected light signal formed by the laser signal reflecting off the user's hand; an air quality index value is obtained and matched with the detection threshold corresponding to the air quality index value; and a target reflected light signal with a signal intensity greater than the detection threshold is identified in the reflected light signal; a depth map including the user's hand is generated based on the time difference between the laser signal and the target reflected light signal; the user's gesture is recognized based on the depth map, and a control command corresponding to the user's gesture is generated to control the operating status of the range hood. This application embodiment integrates a gesture detection module, including a laser emitter and a receiver, into the range hood. By dynamically matching the detection threshold with the air quality index value in the cooking scenario, the intensity of the reflected light signal is filtered, thereby reducing noise pulses caused by suspended particles such as oil fumes and steam while retaining the effective target reflected light signal. Then, a depth map is constructed based on the time difference between the target reflected light signal and the emitted laser signal, which improves the signal-to-noise ratio and edge integrity of the depth map in the oil fume environment. This allows the depth map to more accurately reflect the spatial position and shape characteristics of the user's hand, reduces the impact of environmental interference on the recognition results, improves the accuracy of gesture recognition, and thus improves the consistency between control commands and user operation intentions.

[0125] In some embodiments, the transmitting module 210 is further configured to: The laser emitter array is controlled to emit laser signals at regular intervals or sequentially toward the target direction of the smoke machine.

[0126] In some embodiments, the transmitting module 210 is further configured to: The laser transmitter is controlled to send a calibration signal at preset intervals. The transmitted signal of the calibration signal is received by the receiver, and the reflection attenuation coefficient is calculated based on the signal strength of the calibration signal and the signal strength of the transmitted signal. The target reflected light signals whose signal intensity is greater than the detection threshold are identified, including: The signal intensity of the reflected light signal is compensated based on the reflection attenuation coefficient to determine the target reflected light signal whose signal intensity after compensation is greater than the detection threshold.

[0127] In some embodiments, the generation module 230 is further configured to: Record the emission time when the laser emitter sends a laser signal toward the target direction of the smoke machine; The receiver records the reception time when it receives the reflected light signal formed by the laser signal being reflected by the user's hand; Calculate the time difference between the laser signal and the reflected light signal based on the transmission and reception times; The time difference between the laser signal and the target reflected light signal is obtained from the time difference data.

[0128] In some embodiments, the generation module 230 is further configured to: Acquire temperature data collected by the temperature sensor; Based on the temperature data, the corresponding target error data is matched from the preset mapping relationship; the mapping relationship represents the correspondence between error data of different temperature data and data of different time differences; The time difference data is corrected based on the target error data, and the time difference between the laser signal and the target reflected light signal is obtained from the corrected time difference data.

[0129] In some embodiments, the generation module 230 is further configured to: The distance between the user's hand and the receiver is calculated based on the time difference between the laser signal and the target reflected light signal; Based on the receiver's location information and distance, point cloud data representing the user's hand position in three-dimensional space is generated, resulting in a depth map including the user's hand.

[0130] In some embodiments, the control module 240 is further configured to: Obtain depth maps at multiple consecutive time points; Identify the user's hand movement trajectory based on depth maps from multiple consecutive time points; The motion trajectory is subjected to velocity continuity detection. If the abrupt rate of displacement of the user's hand in the motion trajectory is lower than the threshold, the detection is passed. If the detection passes, the similarity between the motion trajectory and each gesture in the preset template library is calculated, and the gesture with the highest similarity is identified as the user's gesture.

[0131] The gesture recognition-based range hood control device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., and can also be a server, a controller in the range hood, or a functional module, etc. This application embodiment does not specifically limit the specific implementation.

[0132] The gesture recognition-based range hood control device in this application embodiment can be a device with an operating system. This operating system can be a Microsoft (Windows) operating system, an Android operating system, an embedded system, or other possible operating systems; this application embodiment does not specifically limit it.

[0133] In some embodiments, this application provides a smoke hood, including a gesture detection module and a controller; the gesture detection module includes a laser transmitter and a receiver; The controller is used to execute the various processes of the above-described embodiment of the gesture recognition-based smoke hood control method, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0134] In some embodiments, such as Figure 3 As shown, this application embodiment also provides an electronic device 300, including a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the program is executed by the processor 301, it implements the various processes of the above-described embodiment of the gesture recognition-based smoke machine control method and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0135] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0136] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described gesture recognition-based smoke machine control method embodiment and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0137] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0138] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described gesture recognition-based smoke machine control method.

[0139] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0140] This application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described embodiments of the gesture recognition-based smoke machine control method, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0141] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0142] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0143] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0144] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0145] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0146] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for controlling a range hood based on gesture recognition, characterized in that, The smoke hood is equipped with a gesture detection module, which includes a laser emitter and a receiver; the method includes: The laser emitter is controlled to emit a laser signal toward the target direction of the smoke machine, and the receiver receives the reflected light signal formed by the laser signal after being reflected by the user's hand; Obtain air quality index values, match the detection threshold corresponding to the air quality index values, and determine the target reflected light signal whose signal intensity is greater than the detection threshold in the reflected light signal; A depth map including the user's hand is generated based on the time difference between the laser signal and the target reflected light signal; The user's gesture is identified based on the depth map, and control commands corresponding to the user's gesture are generated to control the operating status of the range hood.

2. The method according to claim 1, characterized in that, The laser emitter is a laser emitter array, and the receiver is a multi-angle recognition receiver matrix; The control of the laser emitter to emit a laser signal toward the target direction of the smoke machine includes: The laser emitter array is controlled to emit laser signals at regular intervals or sequentially toward the target direction of the smoke machine.

3. The method according to claim 1, characterized in that, The laser emitter and the receiver are also provided with an anti-oil and anti-fouling nano-oleophobic coating lens. The method further includes: The laser transmitter is controlled to send a calibration signal at preset intervals, and the transmitted signal of the calibration signal is received by the receiver. The reflection attenuation coefficient is calculated based on the signal strength of the calibration signal and the signal strength of the transmitted signal. The determination of the target reflected light signal whose signal intensity is greater than the detection threshold includes: The signal intensity of the reflected light signal is compensated based on the reflection attenuation coefficient to determine the target reflected light signal whose signal intensity after compensation is greater than the detection threshold.

4. The method according to claim 1, characterized in that, The method further includes: When the laser emitter sends a laser signal toward the target direction of the smoke machine, the emission time is recorded; When the receiver receives the reflected light signal formed by the laser signal being reflected by the user's hand, the receiving time is recorded; Calculate the time difference data between the laser signal and the reflected light signal based on the emission time and the reception time; The time difference between the laser signal and the target reflected light signal is obtained from the time difference data.

5. The method according to claim 4, characterized in that, The range hood also includes a temperature sensor; The step of obtaining the time difference between the laser signal and the target reflected light signal from the time difference data includes: Acquire the temperature data collected by the temperature sensor; Based on the temperature data, the corresponding target error data is matched from a preset mapping relationship; the mapping relationship represents the correspondence between error data of different temperature data and different time difference data; The time difference data is corrected based on the target error data, and the time difference between the laser signal and the target reflected light signal is obtained from the corrected time difference data.

6. The method according to claim 1, characterized in that, The step of generating a depth map including the user's hand based on the time difference between the laser signal and the target reflected light signal includes: The distance between the user's hand and the receiver is calculated based on the time difference between the laser signal and the target reflected light signal; Based on the location information of the receiver and the distance, point cloud data representing the three-dimensional spatial position of the user's hand is generated, resulting in a depth map including the user's hand.

7. The method according to claim 1, characterized in that, The step of recognizing user gestures based on the depth map includes: Obtain depth maps at multiple consecutive time points; Identify the movement trajectory of the user's hand based on depth maps at multiple consecutive time points; The motion trajectory is subjected to velocity continuity detection. If the displacement abrupt rate of the user's hand in the motion trajectory is lower than a threshold, the detection is passed. If the detection passes, the similarity between the motion trajectory and each gesture in the preset template library is calculated, and the gesture with the highest similarity is determined as the user's gesture.

8. A smoke machine control device based on gesture recognition, characterized in that, The smoke hood is equipped with a gesture detection module, which includes a laser emitter and a receiver; the device includes: The transmitting module is used to control the laser transmitter to emit a laser signal toward the target direction of the smoke machine, and to receive the reflected light signal formed by the laser signal after being reflected by the user's hand through the receiver; The acquisition module is used to acquire air quality index values, match the detection threshold corresponding to the air quality index values, and determine the target reflected light signal whose signal intensity is greater than the detection threshold in the reflected light signal; The generation module is used to generate a depth map including the user's hand based on the time difference between the laser signal and the target reflected light signal; The control module is used to recognize user gestures based on the depth map and generate control commands corresponding to the user gestures to control the operating status of the smoke machine.

9. A range hood, characterized in that, It includes a gesture detection module and a controller; the gesture detection module includes a laser emitter and a receiver; The controller is configured to perform the method as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-7.

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