A control method and system of an aromatherapy machine and the aromatherapy machine

Abstract

The application discloses a kind of control method, system and aromatherapy machine of aromatherapy machine, the method includes obtaining user operation gesture data, aromatherapy machine operation instruction data and voice instruction operation data;According to the user operation gesture data, construct user gesture feature model, and compare with pre-stored gesture feature database, obtain the gesture operation instruction data of highest coincidence rate;According to the gesture operation instruction data, the aromatherapy machine operation instruction data and the voice instruction operation data, by preset gesture threshold condition, obtain first instruction;Obtain environmental parameter data, the environmental parameter data include one or more of the following data: aromatherapy concentration, air negative ion concentration data;According to the environmental parameter, by preset intelligent operation model, obtain second instruction;According to the first instruction and the second instruction, control aromatherapy machine to work.This method improves the use experience of user by increasing voice, gesture operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aromatherapy machines, in particular to a control method and system of an aromatherapy machine and the aromatherapy machine. BACKGROUND

[0002] With the continuous rise in the popularity of intelligent electronic products, the intelligent trend is becoming more and more significant. Various products based on Internet of Things technology are emerging, including smart lights, refrigerators, air conditioners, televisions, curtains, etc. Among them, the smart aromatherapy machine, as a new choice to improve the quality of life, is also gradually integrated into people's daily life. As a new wave of global informationization after computers, the Internet and mobile communications, the rapid growth in the number, variety and density of terminal devices has been driven by the technological progress of Internet of Things. In order to further optimize user experience and service quality, services for specific scenarios are gradually developing, and are changing from passive response to a more proactive service mode. In this context, aromatherapy machines that can add a sense of technology and provide intelligent and scenario-based services are increasingly popular with consumers.

[0003] As a device designed to improve the natural space environment, the aromatherapy machine allows users to choose the appropriate product style according to different interior decoration needs, and is suitable for various occasions such as offices, homes, shopping centers, living rooms, bedrooms and even toilets. Currently, through manual or intelligent switch methods, users can adjust various parameters of the aromatherapy machine, such as air anion content, aroma concentration, fragrance adjustment mode, light brightness and its change mode, etc.

[0004] However, the traditional intelligent control scheme of the aromatherapy machine is often cumbersome to operate, and the control interface is not intuitive and friendly, which brings inconvenience to the actual use of the user. SUMMARY

[0005] The present application provides a control method and system of an aromatherapy machine, an electronic device and a storage medium, to make the control of the aromatherapy machine more convenient and accurate.

[0006] In a first aspect, to solve the above technical problems, the present application provides a control method of an aromatherapy machine, comprising:

[0007] Obtaining user operation gesture data, aromatherapy machine operation instruction data and voice instruction operation data;

[0008] Constructing a user gesture feature model according to the user operation gesture data, and comparing it with a pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate;

[0009] According to the gesture operation instruction data, the aromatherapy machine operation instruction data and the voice instruction operation data, a first instruction is obtained through a pre-set gesture threshold condition;

[0010] acquire environment parameter data, the environment parameter data including one or more of the following data: aromatherapy concentration, air anion concentration data, humidity data, light intensity data, temperature data, aromatherapy machine switch state;

[0011] According to the environment parameters, a second instruction is obtained through a preset intelligent operation model.

[0012] According to the first instruction and the second instruction, the aromatherapy machine is controlled to work.

[0013] Preferably, the aromatherapy machine operation instruction data includes: power-on instruction, power-off instruction, aromatherapy concentration setting instruction or aromatherapy concentration time setting instruction.

[0014] The voice instruction operation data includes user voice tone data and voice content data.

[0015] In an optional embodiment, the user gesture feature model is constructed according to the user operation gesture data, and is compared with a pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate, including:

[0016] The current gesture feature data set is calculated from the user operation gesture data; wherein the user operation gesture data is stored in the form of an image.

[0017] The current gesture feature data set is compared with the historical user gesture feature library to construct a current user gesture feature model.

[0018] The current user gesture feature model is compared with a pre-stored standard gesture feature database to obtain gesture operation instruction data with the highest coincidence rate, wherein the standard gesture feature database includes standard gesture operation instruction data.

[0019] In an optional embodiment, the first instruction is obtained through a preset gesture threshold condition according to the gesture operation instruction data, the aromatherapy machine operation instruction data and the voice instruction operation data, including:

[0020] When the coincidence rate of the gesture operation instruction data is lower than a preset coincidence rate threshold, the first instruction is obtained according to the aromatherapy machine operation instruction or the user voice instruction.

[0021] If the coincidence rate of the gesture operation instruction data is greater than or equal to the preset coincidence rate threshold, the gesture operation instruction data is taken as the first instruction.

[0022] The first instruction includes power-on / off instruction, temperature and humidity control instruction, aromatherapy type control instruction or control time instruction.

[0023] In an optional embodiment, the second instruction is obtained by inputting the environmental parameter into the preset intelligent operation model.

[0024] When the environmental parameter comprises the fragrance concentration, the fragrance concentration is input into the intelligent operation model to obtain the second instruction comprising a control instruction of the fragrance concentration.

[0025] When the environmental parameter comprises the light brightness data, the light brightness data is input into the intelligent operation model to obtain the second instruction comprising a power-on / off instruction.

[0026] The intelligent operation model is obtained by training historical big data, and the intelligent operation model can predict the corresponding operation instruction according to the input environmental parameter.

[0027] In an optional embodiment, the working of the fragrance machine is controlled according to the first instruction and the second instruction, comprising:

[0028] When it is determined that the first instruction and the second instruction conflict, the first instruction is preferentially executed.

[0029] When it is determined that the first instruction and the second instruction comprise a main function instruction, the main function instruction is preferentially executed.

[0030] The main function instruction comprises a power-on / off instruction, a temperature and humidity control instruction, or a fragrance type control instruction.

[0031] In an optional embodiment, after the working of the fragrance machine is controlled according to the first instruction and the second instruction, the method further comprises:

[0032] When the first instruction is not updated for more than a preset time, a reminder signal is sent to the user, and the working of the fragrance machine is controlled according to the second instruction.

[0033] After the reminder signal is sent to the user, if the user does not reply for more than a preset alarm time, it is determined that an accident occurs, and the fragrance machine is controlled to automatically alarm.

[0034] In a second aspect, the application provides a control system of a fragrance machine, comprising:

[0035] An instruction acquisition module is configured to acquire user operation gesture data, fragrance machine operation instruction data, and voice instruction operation data.

[0036] A gesture analysis module is configured to construct a user gesture feature model according to the user operation gesture data, and compare the user gesture feature model with a pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate.

[0037] A user instruction module is configured to obtain a first instruction according to the gesture operation instruction data, the aromatherapy machine operation instruction data, and the voice instruction operation data, and through a preset gesture threshold condition.

[0038] An environment acquisition module is configured to acquire environment parameter data, which includes one or more of the following data: aromatherapy concentration, air negative ion concentration data, humidity data, light brightness data, temperature data, and aromatherapy machine switch state.

[0039] An intelligent instruction module is configured to obtain a second instruction according to the environment parameter and through a preset intelligent operation model.

[0040] An instruction issuing module is configured to control the aromatherapy machine to work according to the first instruction and the second instruction.

[0041] In a third aspect, the present application further provides an aromatherapy machine comprising the aromatherapy machine control system as described above.

[0042] In a fourth aspect, the present application further provides a computer readable storage medium comprising a stored computer program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the control method of the aromatherapy machine according to any one of the above aspects when the computer program runs.

[0043] Compared with the prior art, the present application has the following beneficial effects:

[0044] The present application discloses a control method of an aromatherapy machine, which comprises acquiring user operation gesture data, aromatherapy machine operation instruction data, and voice instruction operation data; constructing a user gesture feature model according to the user operation gesture data, and comparing the user gesture feature model with a pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate; obtaining a first instruction according to the gesture operation instruction data, the aromatherapy machine operation instruction data, and the voice instruction operation data, and through a preset gesture threshold condition; acquiring environment parameter data, which includes one or more of the following data: aromatherapy concentration, air negative ion concentration data, humidity data, light brightness data, temperature data, and aromatherapy machine switch state; obtaining a second instruction according to the environment parameter and through a preset intelligent operation model; and controlling the aromatherapy machine to work according to the first instruction and the second instruction. The present application solves the problems of complex use process, difficulty in realizing intelligent matching and autonomous regulation and control of the existing aromatherapy machine, provides comprehensive working state data acquisition, storage, and feedback functions, supports remote wireless control and dynamic scene transformation. Through the integration of voice and gesture control, the present application increases the convenience and diversity of user interaction and improves the user experience. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1is a control method flow diagram of a fragrance machine provided by the first embodiment of the present application.

[0046] Figure 2 is a control system structure diagram of a fragrance machine provided by the second embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0048] With the continuous rise in the popularity rate of intelligent electronic products, the intelligent trend is becoming more and more significant. Various products based on Internet of Things technology are emerging, including smart lights, refrigerators, air conditioners, televisions, curtains, etc. Among them, the smart fragrance machine, as a new choice to improve the quality of life, is also gradually integrated into people's daily life. As a new wave of global informationization after computers, the Internet and mobile communication, the rapid growth of the number, types and density of terminal devices is driven by the technological progress of Internet of Things. In order to further optimize user experience and service quality, services for specific scenarios are gradually developing, and are changing from passive response to a more active service mode. In this context, fragrance machines that can add a sense of technology and provide intelligent and scenario-based services are increasingly popular with consumers.

[0049] As a device aimed at improving the natural space environment, the fragrance machine allows users to choose suitable product styles according to different interior decoration needs, and is suitable for various occasions such as offices, homes, shopping centers, living rooms, bedrooms and even toilets. Currently, through manual or intelligent switch mode, users can adjust various parameters of the fragrance machine, such as air anion content, aroma concentration, fragrance adjustment mode, light brightness and its change mode, etc.

[0050] However, the traditional fragrance machine intelligent control scheme is often cumbersome to operate, and the control interface is not intuitive and friendly, which brings inconvenience to the actual use of users.

[0051] Reference Figure 1 The first embodiment of the present application provides a control method of a fragrance machine, comprising the following steps:

[0052] S11, obtaining user operation gesture data, fragrance machine operation instruction data and voice instruction operation data;

[0053] S12, constructing a user gesture feature model according to the user operation gesture data, and comparing the user gesture feature model with a pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate;

[0054] S13, obtaining a first instruction through a preset gesture threshold condition according to the gesture operation instruction data, the aromatherapy machine operation instruction data, and the voice instruction operation data;

[0055] S14, obtaining environment parameter data, the environment parameter data including one or more of the following data: aromatherapy concentration, air anion concentration data, humidity data, light intensity data, temperature data, and aromatherapy machine switch state;

[0056] S15, obtaining a second instruction through a preset intelligent operation model according to the environment parameter data;

[0057] S16, controlling the aromatherapy machine to work according to the first instruction and the second instruction.

[0058] In step S11, user operation gesture data, aromatherapy machine operation instruction data, and voice instruction operation data are obtained.

[0059] It is worth noting that the aromatherapy machine operation instruction data includes: power-on instruction, power-off instruction, aromatherapy concentration setting instruction, or aromatherapy concentration time setting instruction. The power-on and power-off instructions are received by a built-in wireless communication module from a control signal sent by an application program of a user mobile device, or are directly triggered by a physical button on the device, for controlling the power state of the aromatherapy machine, and realizing remote or manual start and stop of the device. The aromatherapy machine concentration setting instruction is selected by the user through an application program interface to select the required aroma intensity level, or to set a specific concentration percentage value. The system converts this information into a digital signal and transmits it to the aromatherapy machine. This instruction can adjust the amount of aroma released according to the personal preferences of the user, and create a comfortable environmental atmosphere. The aromatherapy concentration time setting instruction is input by the user in the application program, including the desired duration and the corresponding concentration change curve, and then converted by the application software into a data packet of the corresponding format and sent to the device. This function allows the user to customize the aroma diffusion process, and is suitable for different scene requirements, such as gradually weakening the aroma before sleep to help sleep.

[0060] It is worth mentioning that the voice instruction operation data includes user voice data and voice content data. When the user issues a voice command, the microphone captures the sound signal and, after preprocessing, analyzes and extracts the unique acoustic features of the user using voiceprint recognition technology to obtain the user's voice data, helping the system to distinguish different users and thus provide personalized services. At the same time, it enhances security and prevents unauthorized strangers from operating. After the received sound signal is denoised, the specific command text is parsed through a natural language processing algorithm, aiming to understand the user's actual intention and execute the corresponding function adjustment, such as starting / stopping the device, changing the aromatherapy mode, etc.

[0061] In step S12, a user gesture feature model is constructed according to the user operation gesture data, and compared with a pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate.

[0062] In an embodiment, a current gesture feature data set is calculated from the user operation gesture data; wherein the user operation gesture data is stored in the form of an image; the current gesture feature data set is compared with a historical user gesture feature library to construct a current user gesture feature model; the current user gesture feature model is compared with a pre-stored standard gesture feature database to obtain gesture operation instruction data with the highest coincidence rate, wherein the standard gesture feature database includes standard gesture operation instruction data.

[0063] In one embodiment, the user's gesture actions are captured by a camera and stored in the form of an image sequence. Then, using computer vision techniques, the gesture images are analyzed to extract a series of key features that describe the gesture, including finger positions, gesture contours, and motion trajectories. These features collectively form a data set that represents the user's current gesture. The computer vision technique used is edge point detection. The purpose of this process is to extract key features from the user's gestures so that the user's intentions can be recognized and understood later. By converting gestures into feature data sets, data can be effectively simplified and significant properties related to specific gestures can be highlighted. The system maintains a historical user gesture feature library that contains various gesture features collected from the user's past usage. When a new gesture feature data set is received, the system matches it with the historical data to find the most similar gesture pattern and adjusts or creates a new gesture feature model based on it. This model reflects the user's unique gesture habits, allowing the system to accurately recognize the user's gesture instructions. This step aims to establish a personalized gesture model based on the user's personal habits, thereby improving the accuracy of gesture recognition and user experience. By comparing the user's current gesture with past recorded gestures, the system can better understand the user's unique gesture style. The standard gesture feature database contains a series of pre-set gestures and their corresponding control instructions, such as "swipe left" corresponding to "reduce incense concentration" and "swipe up" corresponding to "increase light brightness." After completing the construction of the user's gesture feature model, the system compares the model with each entry in the standard gesture library to calculate the similarity or matching degree between them. The standard gesture with the highest matching degree is selected as the recognition result, and the corresponding operation instruction is triggered. This step is used to determine the operation the user actually wants to perform. By comparing the personalized gesture model with the predefined standard gesture library, the system can identify the closest gesture instruction and execute the corresponding function.

[0064] In step S13, according to the gesture operation instruction data, the incense machine operation instruction data, and the voice instruction operation data, a first instruction is obtained through a pre-set gesture threshold condition.

[0065] In one embodiment, when the compliance rate of the gesture operation instruction data is lower than a pre-set compliance rate threshold, a first instruction is obtained according to the incense machine operation instruction or the user voice instruction; if the compliance rate of the gesture operation instruction data is greater than or equal to the pre-set compliance rate threshold, the gesture operation instruction data is taken as the first instruction; the first instruction includes a power on / off instruction, a temperature and humidity control instruction, an incense type control instruction, or a control time instruction.

[0066] It is worth mentioning that when the system analyzes the gesture features of the user and compares them with the standard gesture database, if the calculated coincidence rate is lower than a preset threshold, it indicates that the current gesture is not similar enough to any known standard gesture, and the specific operation that the user wants to perform cannot be reliably determined. At this time, the system will turn to the user's voice instructions or the operation instructions of the aromatherapy machine sent by the user through the application program to make decisions. If the system compares the gesture features with the standard gesture database and finds that the coincidence rate reaches or exceeds the preset threshold, it means that the current gesture is accurately identified as a certain standard gesture. In this case, the system will directly use the instruction corresponding to the gesture as the final control instruction. For example, if the user makes a "swipe to the right" gesture, and the system judges that the gesture meets the standard gesture of "increasing the concentration of aromatherapy", the system will automatically perform the operation of increasing the concentration of aromatherapy. This step ensures that in the case of ambiguous or unreliable gesture recognition, the system can rely on direct operation instructions or voice commands to determine the user's intention, which can avoid misoperation.

[0067] In an implementation, for the acquisition of the threshold, a large amount of data of various standard gestures performed by users is needed to be collected. These data include image or video sequences of the same gesture performed by different users in different environmental conditions. At the same time, in order to make the model have good generalization ability, the collected data should cover different light conditions, backgrounds, user ages and genders, etc. Further, key features such as finger position, hand contour, motion trajectory, etc. are extracted from the collected gesture images and standardized. Further, the collected data is divided into training set and test set. The training set is used to train the gesture recognition model, and the test set is used to verify the performance of the model. Based on the performance of the model on the test set, an initial coincidence rate threshold is set. For example, if the average recognition accuracy of the model on the test set is 90%, the initial threshold can be set to 85%. At the same time, a user feedback mechanism is introduced to allow users to manually adjust the threshold to adapt to personal needs and preferences.

[0068] It is worth mentioning that the first instruction is a user instruction, which embodies the user's own intention. This design embodies the advantages of multi-modal input, that is, the system can comprehensively judge the user's true intention according to multiple input sources, including gestures, voice, and direct instructions, thereby improving the flexibility and reliability of the system. Over time, the system can optimize the gesture model by continuously accumulating user gesture data, further improving the accuracy of gesture recognition. The first instruction content includes: power-on / off instruction, temperature and humidity control instruction, fragrance type control instruction, and control time instruction. When the gesture coincidence degree is higher than the threshold, if the gesture instruction conflicts with the aroma machine instruction or the voice instruction, for example: the gesture instruction requires to increase the temperature, while the voice instruction requires to decrease the temperature, the gesture instruction is taken as the first instruction. That is, the first instruction is to increase the temperature. When the voice instruction, the aroma machine instruction or the gesture instruction do not mention the content, the default option is automatically controlled.

[0069] In step S14, environmental parameter data is obtained, which includes one or more of the following data: fragrance concentration, air anion concentration data, humidity data, light intensity data, temperature data, and aroma machine switch state.

[0070] In an embodiment, the concentration of fragrance components in the air is detected by a built-in volatile organic compound (VOC) sensor. The output of the aroma machine can be adjusted according to the current fragrance concentration to maintain the user's set fragrance intensity or automatically adjust to the optimal state, thereby providing a comfortable fragrance environment. The number of negative ions in the air is measured using a negative ion detector or a negative ion sensor integrated in the aroma machine to monitor and control the concentration of negative ions in the air to improve air quality and improve the user's respiratory comfort and health level. The relative humidity in the environment is measured in real time using a humidity sensor, and the working mode of the aroma machine is automatically adjusted according to the change of indoor humidity. For example: increase the humidity in a dry environment, and reduce the humidity in a humid environment to maintain a suitable environmental humidity. The intensity of the ambient light is detected by a photosensitive sensor, and the light intensity of the aroma machine is automatically adjusted according to the intensity of the ambient light, and different scene modes are set according to the time, including night mode, reading mode, etc. At the same time, the environmental parameter data is used to generate intelligent instructions subsequently.

[0071] In step S15, a second instruction is obtained according to the environmental parameters through a pre-set intelligent operation model.

[0072] In an embodiment, when the environmental parameter comprises a scent concentration, the scent concentration is input into the intelligent operation model to obtain a second instruction comprising a control scent concentration; when the environmental parameter comprises light brightness data, the light brightness data is input into the intelligent operation model to obtain a second instruction comprising a power-on / off instruction; wherein the intelligent operation model is obtained by training historical big data, and the intelligent operation model can predict a corresponding operation instruction according to an input environmental parameter.

[0073] It is worth noting that the intelligent operation model is an algorithm based on machine learning or deep learning, which is obtained by training historical big data. The model can predict the corresponding operation instruction according to the input environmental parameter. The model collects a large amount of environmental parameter data and corresponding user operation instructions from actual use scenarios; removes noise data and outliers; and extracts useful features from the original data, including: scent concentration, light brightness, humidity, and temperature. The preprocessed data is divided into a training set and a test set using a neural network, the training set is used to train the model, and the model parameters are adjusted to minimize the prediction error. Finally, the intelligent operation model is obtained. In actual use, these environmental parameters are input into the trained intelligent operation model, and the model outputs the corresponding operation instruction. For example: input the scent concentration data into the intelligent operation model, and the model predicts the corresponding scent concentration control instruction according to the pattern and rule in the historical data. When the environmental scent concentration is 5%, the intelligent operation model predicts that it is a low concentration state at this time, and the scent output is increased to increase the scent concentration of the environment. For example: when the environmental brightness is low and the time is late at night, the intelligent operation model predicts that the user is in a sleep state, and the temperature is adjusted to the historical temperature at this time to achieve the user's health purpose.

[0074] S16, according to the first instruction and the second instruction, controlling the scent machine to work.

[0075] In an embodiment, the first instruction has the highest priority as an instruction directly issued by the user. The user can send these instructions through manual operation, voice control, or application. The second instruction is an instruction generated by the intelligent operation model according to the environmental parameter, which is used to automatically adjust the working state of the scent machine when there is no user instruction. If there is a valid user instruction, the system will immediately execute the instruction and ignore the second instruction generated by the intelligent operation.

[0076] It is worth mentioning that, according to the first instruction and the second instruction, after controlling the aromatherapy machine to work, further comprising: when the first instruction is not updated for more than a preset time, sending a reminder signal to the user, and controlling the aromatherapy machine to work according to the second instruction; after sending the reminder signal to the user, if more than a preset alarm time is exceeded without receiving a user reply, determining that an accident has occurred, and controlling the aromatherapy machine to automatically alarm.

[0077] In an embodiment, the system continuously monitors the first instruction sent by the user. When the first instruction is not updated within 8 hours, the system considers that the current user operation has stopped or the user has temporarily left the use area. Further, the system sends a reminder signal to the user's mobile device through a wireless communication module. The reminder signal includes: push notification, SMS and prompt in the application. The user is reminded to check the status of the aromatherapy machine and asked whether to continue the current operation mode or make new settings. If the user does not respond to the reminder signal, the system will control the working state of the aromatherapy machine according to the second instruction generated by the intelligent operation model. Further, the system automatically adjusts the parameters of the aromatherapy machine, such as the aroma concentration and light brightness, according to the second instruction, to maintain a comfortable environment. If after sending the reminder signal, more than a preset alarm time, for example, 30 minutes, is exceeded without receiving a user reply, the system determines that there is an accident, for example, the user cannot respond or the device malfunctions. The system will trigger an automatic alarm mechanism to contact the pre-set emergency contact or alarm center.

[0078] In order to facilitate the understanding of the present application, some preferred embodiments of the present application will be further described below.

[0079] The working process of the present application will be described below with a more common scenario as an example.

[0080] Suppose the user uses the aromatherapy machine in the bedroom at home, and sets the following initial settings through the mobile phone application: first instruction: the user sets the aroma concentration to 50%, and the light brightness to 50%.

[0081] The system starts monitoring the user's operation instruction. Suppose the user does not send a new instruction within the next 8 hours. After 8 hours, the system detects that the user instruction has not been updated for more than a preset time, sends a reminder signal to the user's mobile phone: "Your aromatherapy machine has been running for 8 hours. Please confirm whether you need to continue the current settings." If the user does not reply within 5 minutes of receiving the reminder signal, the system will control the working state of the aromatherapy machine according to the second instruction generated by the intelligent operation model. The second instruction generated by the intelligent operation model suggests reducing the light brightness to 30% and adjusting the aroma concentration to 60% to adapt to a quieter environment.

[0082] If the user sends a new instruction through the mobile app after receiving the reminder signal: adjust the aromatherapy concentration to 70%, and keep the light brightness unchanged.

[0083] If the user still does not respond within 30 minutes after sending the reminder signal, the system determines that an accident may have occurred. The aromatherapy machine emits an alarm sound and sends an emergency alert message to the user through the mobile app: "Your aromatherapy machine has not received a response for more than 30 minutes. Please confirm whether it is safe. If necessary, we will contact the emergency contact person."

[0084] In summary, the control method for the aromatherapy machine disclosed in the present application trains a model or compares gesture feature training models based on user operation gesture feature data, obtains the operation instruction with the highest compliance rate, and realizes dynamic transformation of the aromatherapy scene according to the aromatherapy machine operation instruction, greatly shortening the steps of user operation of the aromatherapy machine, thereby further improving the user's use experience. This method not only simplifies the user's operation process, but also enhances the naturalness and intuitiveness of human-computer interaction. Specifically, when the user executes a gesture command, the system captures the gesture image through the camera and extracts key features such as finger position, gesture contour, and motion trajectory using computer vision technology. These features are used to build the current gesture feature dataset. Then, the system compares the current gesture feature dataset with the historical user gesture feature library to identify the user's personalized gesture habits. Then, the identified gesture feature model is matched with the pre-stored standard gesture feature database to find the most suitable gesture operation instruction. If the compliance rate is below the preset threshold, the system will rely on other input methods, such as direct operation instructions or voice commands, to make decisions; if the compliance rate is higher than or equal to the preset threshold, the gesture instruction is directly executed. This multi-modal input processing method ensures the flexibility and reliability of the system, even if the gesture recognition is not completely accurate, it can still provide seamless user experience. In addition, the sensor module can also collect real-time parameters of the working state of the aromatherapy machine, including the concentration of aromatherapy, the concentration of air negative ions, humidity, light brightness, temperature, and the on-off state of the aromatherapy machine. These parameters are transmitted to the user's smartphone or other mobile devices through a wireless interaction module (such as Wi-Fi or Bluetooth). Users can view the working state of the aromatherapy machine through a dedicated application and make remote adjustments based on this information. For example, users can adjust the aromatherapy concentration, change the type of aromatherapy, set the working time or turn off the machine. Such design not only facilitates user monitoring and management of the aromatherapy machine, but also enables users to fine-tune the control of the aromatherapy machine anytime, anywhere, improving the convenience and comfort of use. Through real-time monitoring and intelligent adjustment, the system can automatically adapt to environmental changes to ensure that the aromatherapy machine is always in the best working state.

[0085] For example, when the indoor light becomes dim, the system can automatically reduce the light brightness to create a more comfortable atmosphere; when it is detected that the incense concentration is too low, the system can automatically increase the incense release amount to maintain the user-set concentration. This intelligent adaptive function not only improves the user experience, but also reduces the user's frequent manual adjustment trouble, making the incense machine a truly intelligent home device. Further, by integrating advanced gesture recognition, voice control, and environmental parameter monitoring technologies, intelligent management and control of the incense machine are realized. Not only does it simplify the user's operation steps, enhance the naturalness and intuitiveness of human-computer interaction, but also provides a more comfortable and healthy environment, bringing an innovative solution to the smart home field.

[0086] Referring to Figure 2 The second embodiment of the present application provides a control system of an incense machine, comprising:

[0087] An instruction acquisition module is configured to acquire user operation gesture data, incense machine operation instruction data, and voice instruction operation data.

[0088] A gesture analysis module is configured to construct a user gesture feature model according to the user operation gesture data, and compare it with a pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate.

[0089] A user instruction module is configured to obtain a first instruction by a pre-set gesture threshold condition according to the gesture operation instruction data, the incense machine operation instruction data, and the voice instruction operation data.

[0090] An environment acquisition module is configured to acquire environmental parameter data, which includes one or more of the following data: incense concentration, air anion concentration data, humidity data, light brightness data, temperature data, and incense machine switch state.

[0091] An intelligent instruction module is configured to obtain a second instruction by a pre-set intelligent operation model according to the environmental parameters.

[0092] An instruction issuing module is configured to control the incense machine to work according to the first instruction and the second instruction.

[0093] Preferably, the instruction acquisition module is configured to:

[0094] acquire user operation gesture data, incense machine operation instruction data, and voice instruction operation data.

[0095] The incense machine operation instruction data includes power-on instruction, power-off instruction, incense concentration setting instruction, or incense concentration time setting instruction; and the voice instruction operation data includes user voice tone data and voice content data.

[0096] Preferably, the gesture analysis module is configured to:

[0097] The user gesture feature model is constructed according to the user operation gesture data, and compared with the pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate, including:

[0098] The current gesture feature data set is calculated from the user operation gesture data; the user operation gesture data is stored in the form of an image; the current gesture feature data set is compared with the historical user gesture feature library to construct a current user gesture feature model; the current user gesture feature model is compared with the pre-stored standard gesture feature database to obtain gesture operation instruction data with the highest coincidence rate, wherein the standard gesture feature database includes standard gesture operation instruction data.

[0099] Preferably, the user instruction module is configured to:

[0100] According to the gesture operation instruction data, the aromatherapy machine operation instruction data, and the voice instruction operation data, a first instruction is obtained through a preset gesture threshold condition, including:

[0101] When the coincidence rate of the gesture operation instruction data is lower than a preset coincidence rate threshold, a first instruction is obtained according to the aromatherapy machine operation instruction or the user voice instruction; if the coincidence rate of the gesture operation instruction data is greater than or equal to the preset coincidence rate threshold, the gesture operation instruction data is taken as the first instruction; the first instruction includes a power-on / off instruction, a temperature and humidity control instruction, an aromatherapy type control instruction, or a control time instruction.

[0102] Preferably, the environment acquisition module is configured to:

[0103] Obtaining environmental parameter data, the environmental parameter data including one or more of the following data: incense concentration, air anion concentration data, humidity data, light intensity data, temperature data, incense machine switch state; through the built-in volatile organic compound (VOC) sensor, the concentration of incense components in the air is detected, and the output of the incense machine can be adjusted according to the current incense concentration, so as to maintain the incense intensity set by the user or automatically adjust to the optimal state, thereby providing a comfortable aroma environment; the number of negative ions in the air is measured using a negative ion detector or a negative ion sensor integrated in the incense machine, and the concentration of negative ions in the air is monitored and controlled to improve air quality and improve the respiratory comfort and health level of the user; the relative humidity in the environment is measured in real time by using a humidity sensor, and the working mode of the incense machine is automatically adjusted according to the change of indoor humidity, for example: increasing humidity in a dry environment and reducing humidity in a humid environment, so as to maintain a suitable environmental humidity; the intensity of the ambient light is detected by a photosensitive sensor, and the light intensity of the incense machine is automatically adjusted according to the intensity of the ambient light, and different scene modes are set according to the time, including night mode, reading mode, etc. At the same time, the environmental parameter data is used to generate intelligent instructions subsequently.

[0104] Preferably, the intelligent instruction module is used for:

[0105] According to the environmental parameters, a second instruction is obtained through a preset intelligent operation model, including: when the environmental parameters include incense concentration, the incense concentration is input into the intelligent operation model to obtain a second instruction including control of the incense concentration; when the environmental parameters include light intensity data, the light intensity data is input into the intelligent operation model to obtain a second instruction including a power-on / off instruction; wherein the intelligent operation model is obtained by training historical big data, and the intelligent operation model can predict the corresponding operation instruction according to the input environmental parameters.

[0106] Preferably, the instruction issuing module is used for:

[0107] According to the first instruction and the second instruction, the incense machine is controlled to work.

[0108] It is worth noting that after the incense machine is controlled to work according to the first instruction and the second instruction, the method further includes: when the first instruction is not updated for more than a preset time, a reminder signal is sent to the user, and the incense machine is controlled to work according to the second instruction; after the reminder signal is sent to the user, if no reply is received from the user after more than a preset alarm time, it is determined that an accident has occurred, and the incense machine is controlled to automatically alarm.

[0109] It should be noted that the control system of the aromatherapy machine provided in the embodiments of the present application is used to execute all process steps of the control method of the aromatherapy machine in the above embodiments, and the working principles and beneficial effects of the two are one-to-one corresponding, thus not being described again.

[0110] The embodiments of the present application further provide an aromatherapy machine, which comprises the control system of the aromatherapy machine as described in the above embodiments.

[0111] The embodiments of the present application further provide an electronic device. The electronic device comprises a processor, a memory, and a computer program, such as a gesture recognition program, stored in the memory and executable on the processor. The processor executes the computer program to implement the steps in the above control method embodiments of the aromatherapy machine, such as the step S11 shown in the figure. Figure 1 Alternatively, the processor executes the computer program to implement the functions of the modules / units in the above system embodiments, such as the instruction acquisition module.

[0112] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0113] The electronic device can be a desktop computer, a notebook computer, a palm computer, a smart tablet and the like. The electronic device can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device, and do not constitute a limitation on the electronic device, and can include more or fewer components than the above, or combine certain components, or different components, for example, the electronic device can further include an input / output device, a network access device, a bus and the like.

[0114] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the electronic device, and connects various parts of the electronic device through various interfaces and lines.

[0115] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0116] The modules / units integrated in the electronic device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or system, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electric carrier signals and telecommunication signals.

[0117] It should be noted that the above-described system embodiments are only illustrative, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the system embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0118] The above-described specific embodiments further detail the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only examples of the present application and are not intended to limit the scope of protection of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A control method of an aromatherapy machine, characterized in that, The method comprises the following steps: acquiring user operation gesture data, aromatherapy machine operation instruction data and voice instruction operation data; constructing a user gesture feature model according to the user operation gesture data, and comparing the user gesture feature model with a pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate; obtaining a first instruction by a preset gesture threshold condition according to the gesture operation instruction data, the aromatherapy machine operation instruction data and the voice instruction operation data; acquiring environmental parameter data, which comprises one or more of the following data: aromatherapy concentration, air negative ion concentration data, humidity data, light intensity data, temperature data and aromatherapy machine switch state; obtaining a second instruction by a preset intelligent operation model according to the environmental parameter; controlling the aromatherapy machine to work according to the first instruction and the second instruction. The method of obtaining the first instruction by the preset gesture threshold condition according to the gesture operation instruction data, the aromatherapy machine operation instruction data and the voice instruction operation data comprises the following steps: when the coincidence rate of the gesture operation instruction data is lower than a preset coincidence rate threshold, obtaining the first instruction according to the aromatherapy machine operation instruction or the user voice instruction; if the coincidence rate of the gesture operation instruction data is greater than or equal to the preset coincidence rate threshold, taking the gesture operation instruction data as the first instruction. The first instruction comprises a power-on / off instruction, a temperature and humidity control instruction, an aromatherapy type control instruction or a control time instruction. The method of controlling the aromatherapy machine to work according to the first instruction and the second instruction comprises the following steps: when it is determined that the first instruction and the second instruction conflict, the first instruction is preferentially executed; when it is determined that the first instruction and the second instruction contain a main function instruction, the main function instruction is preferentially executed. The main function instruction comprises a power-on / off instruction, a temperature and humidity control instruction or an aromatherapy type control instruction.

2. The control method of the aromatherapy machine according to claim 1, characterized in that, The aromatherapy machine operation instruction data comprises a power-on instruction, a power-off instruction, an aromatherapy concentration setting instruction or an aromatherapy concentration time setting instruction. The voice instruction operation data comprises user voice data and voice content data.

3. The control method of the aromatherapy machine according to claim 1, characterized in that, The method of constructing a user gesture feature model according to the user operation gesture data, and comparing the user gesture feature model with a pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate comprises the following steps: calculating a current gesture feature data set from the user operation gesture data, wherein the user operation gesture data is stored in the form of an image; comparing the current gesture feature data set with a historical user gesture feature library to construct a current user gesture feature model; comparing the current user gesture feature model with a pre-stored standard gesture feature database to obtain gesture operation instruction data with the highest coincidence rate, wherein the standard gesture feature database comprises standard gesture operation instruction data.

4. The control method of the aromatherapy machine according to claim 1, characterized in that, The method of obtaining a second instruction by a preset intelligent operation model according to the environmental parameter comprises the following steps: when the environmental parameter comprises aromatherapy concentration, inputting the aromatherapy concentration into the intelligent operation model to obtain a second instruction comprising control of the aromatherapy concentration. When the environmental parameter comprises light brightness data, the light brightness data is input to the intelligent operation model to obtain a second instruction comprising a power-on / off instruction; The intelligent operation model is obtained by training historical big data, and the intelligent operation model can predict a corresponding operation instruction according to input environmental parameters.

5. The control method of the aromatherapy machine according to claim 1, characterized in that, After the working of the aromatherapy machine is controlled according to the first instruction and the second instruction, the method further comprises: When the first instruction is not updated for more than a preset time, a reminder signal is sent to the user, and the working of the aromatherapy machine is controlled according to the second instruction; After the reminder signal is sent to the user, if the user does not reply for more than a preset alarm time, it is determined that an accident occurs, and the aromatherapy machine is controlled to automatically alarm.

6. A control system for an aromatherapy machine, characterized in that, The method for controlling the aromatherapy machine comprises: An instruction acquisition module is configured to acquire user operation gesture data, aromatherapy machine operation instruction data, and voice instruction operation data; A gesture analysis module is configured to construct a user gesture feature model according to the user operation gesture data, and compare the user gesture feature model with a pre-stored gesture feature database to obtain gesture operation instruction data with the highest coincidence rate; A user instruction module is configured to obtain a first instruction by a preset gesture threshold condition according to the gesture operation instruction data, the aromatherapy machine operation instruction data, and the voice instruction operation data; An environmental acquisition module is configured to acquire environmental parameter data, wherein the environmental parameter data comprises one or more of the following data: aromatherapy concentration, air negative ion concentration data, humidity data, light brightness data, temperature data, and aromatherapy machine on / off state; An intelligent instruction module is configured to obtain a second instruction by a preset intelligent operation model according to the environmental parameter data; An instruction issuing module is configured to control the working of the aromatherapy machine according to the first instruction and the second instruction.

7. An aromatherapy machine characterised in that, The aromatherapy machine control system comprises the aromatherapy machine control system according to claim 6.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein when the computer program runs, the computer readable storage medium controls the device where the computer readable storage medium is located to execute the control method of the aromatherapy machine according to any one of claims 1 to 5.

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