A smart kitchen hood central control gateway system

Abstract

The application relates to the technical field of smart home, and discloses a kind of intelligent kitchen fume exhaust fan hub control gateway system, comprising: fume exhaust fan device, built-in fume exhaust fan gateway and fume exhaust fan electric control board, fume exhaust fan electric control board communicates with fume exhaust fan gateway;Fume exhaust fan device connects kitchen equipment outside fume exhaust fan device, and kitchen equipment connects fume exhaust fan gateway through Wi-Fi or Bluetooth;Fume exhaust fan gateway communicates with cloud platform or intelligent terminal;Fume exhaust fan hub gateway mainly realizes the function of intelligent kitchen, can connect multiple sensors to monitor kitchen combustible gas leakage at the same time, and ensures user safety gas use.And, the priority of shutting down equipment can be controlled through algorithm.The fume exhaust fan device system of the application realizes the interconnection and intelligent linkage of various kitchen equipment, improves the safety and comfort of kitchen environment, simultaneously realizes diversified man-machine interaction mode, and greatly improves user experience.

Description

Technical Field

[0001] This invention relates to the field of smart home technology, and more specifically, to a smart kitchen range hood central control gateway system. Background Technology

[0002] In modern family kitchens, the number of intelligent kitchen appliances such as range hoods, gas stoves, and disinfection cabinets is constantly increasing, and their functions are becoming increasingly diverse. Users have higher and higher demands for intelligent and networked kitchen equipment, hoping to achieve remote monitoring and control of kitchen equipment, as well as the linkage between multiple devices, through mobile apps and other means.

[0003] Most existing smart kitchen devices use Wi-Fi communication technology to connect to the internet and then to a cloud platform for remote control. This presents the following problems:

[0004] Due to limitations in the bandwidth and stability of home networks, the real-time performance and reliability of device access to cloud platforms are difficult to guarantee.

[0005] Multiple devices are independently connected to the cloud platform, lacking the ability for local collaborative control, and thus unable to achieve automated linkage between devices;

[0006] Data and control commands are all processed in the cloud, and the system's response speed and privacy security need to be improved. Summary of the Invention

[0007] This invention provides a smart kitchen range hood central control gateway system, which solves the technical problem of needing a new smart kitchen system architecture to realize device interconnection, data processing and automated control locally in the home, thereby improving the system's real-time performance, reliability and security.

[0008] This invention provides an intelligent kitchen range hood central control gateway system, comprising:

[0009] The smoke hood equipment has a built-in smoke hood gateway and smoke hood electronic control board, and the smoke hood electronic control board communicates with the smoke hood gateway.

[0010] The range hood connects to other kitchen appliances, which in turn connect to the range hood gateway via Wi-Fi or Bluetooth.

[0011] The range hood gateway communicates with the cloud platform or smart terminal.

[0012] Furthermore, an app is installed on the smart terminal. The app includes a human-computer interaction interface, through which users can issue control commands. The app can remotely control the range hood and other kitchen appliances.

[0013] Furthermore, kitchen equipment other than range hoods includes:

[0014] Combustible gas sensors are used to monitor the concentration of combustible gases in the kitchen environment;

[0015] Smoke sensors are used to monitor the concentration of cooking fumes.

[0016] Temperature and humidity sensor used to monitor the temperature and humidity of the kitchen environment;

[0017] The gas stove wirelessly connects to the range hood and receives control commands.

[0018] The gas meter is wirelessly connected to the range hood equipment and reports gas usage data.

[0019] The electromagnetic shut-off valve cuts off the gas pipeline according to the control command.

[0020] Furthermore, the intelligent kitchen range hood central control gateway system can process the data received from the kitchen equipment, including the following steps:

[0021] Step 101, Data reception and verification;

[0022] The range hood gateway receives real-time data transmitted from kitchen equipment, including equipment operating status data and environmental monitoring data;

[0023] After receiving the data, a cyclic redundancy check method is used to verify the data and check whether any errors occurred during the transmission process.

[0024] If the data is incorrect, request the device to resend it until the correct data is received;

[0025] Step 102, edge computing module optimization processing;

[0026] The edge computing module in the range hood gateway processes the verified data;

[0027] First, the data is categorized and organized, and then optimized algorithms are used to analyze and process different types of data.

[0028] For the operating status data, the updated status judgment algorithm is used to analyze whether the device is operating normally or needs to adjust the operating mode.

[0029] For environmental monitoring data, analyze the updated environmental parameter thresholds to determine whether it is necessary to adjust the equipment operating parameters;

[0030] Step 103, instruction generation and execution optimization;

[0031] Based on the data processing results, corresponding control commands are generated. Before sending the commands to the kitchen equipment, the commands are verified again to ensure their accuracy. If the command is incorrect, it is regenerated and verified until the command is correct.

[0032] Furthermore, during data processing, the performance metrics of the edge computing module are monitored in real time, and the performance monitoring function is as follows: ,when At that time, the performance optimization mechanism is triggered, in which The performance threshold is set.

[0033] Furthermore, the kitchen equipment also includes visual sensors, auditory sensors, and tactile sensors.

[0034] Furthermore, the following steps are performed:

[0035] Step 201, Sensor state detection and preprocessing;

[0036] Step 202, Multimodal Information Acquisition;

[0037] Simultaneously, visual, auditory, and tactile sensors are activated to collect visual information from within the kitchen. Auditory information and tactile information :

[0038] Visual information This includes images of the user's hand gestures and images of the food's condition;

[0039] Auditory information This includes the noise from the range hood and the sound of food being put into the pan;

[0040] Tactile information This includes data on the surface temperature of the range hood and the vibration frequency of the dishwasher;

[0041] Step 203, Feature extraction and preprocessing;

[0042] For visual information Key features, including user hand gestures, are extracted using image recognition and computer vision technologies. Characteristics of food ingredients ;

[0043] The processed visual feature vector is Each component for arrive For real numbers between these two ranges, the formula is as follows:

[0044]

[0045] in Represents a function for processing visual information;

[0046] For auditory information Audio processing technology is used to extract noise frequency characteristics. Characteristics of cooking sound types After processing, auditory feature vectors are obtained. Each component for arrive The formula is: (The formula is incomplete and cannot be translated without further context.)

[0047]

[0048] It is an auditory information processing function;

[0049] Targeting tactile information Extraction equipment temperature characteristics Vibration frequency characteristics Generate tactile feature vectors Each component for arrive The formula for real numbers between 0 and 1 is:

[0050]

[0051] in This is a tactile information processing function;

[0052] Step 204, multimodal information fusion;

[0053] Extracted and preprocessed visual feature vectors Auditory feature vectors and tactile feature vectors By fusing the features, a comprehensive feature vector is obtained. ;

[0054] Step 205, Intelligent Decision Making;

[0055] The fused integrated feature vector Input into intelligent decision-making model In the process, the decision-making model can judge the current kitchen environment and user behavior based on the comprehensive feature vector, and output operation instructions for different kitchen equipment;

[0056] Step 206, instruction execution;

[0057] The executor performs the corresponding action based on the operation instructions output by the intelligent decision-making model.

[0058] Furthermore, the weights of the visual, auditory, and tactile feature vectors are respectively... , , And satisfy The fusion formula is:

[0059] .

[0060] Furthermore, this intelligent decision-making model is a neural network model or a decision tree model.

[0061] Furthermore, new data from the kitchen is collected periodically as new training data to supplement the intelligent decision-making model. The training focus is on retraining them.

[0062] The beneficial effects of this invention are as follows: the range hood central gateway mainly realizes the intelligent kitchen function, can connect to multiple sensors to simultaneously monitor combustible gas leaks in the kitchen, and ensure users' safe gas use. Furthermore, it can control the priority of shutting down devices through algorithms.

[0063] The range hood system of this invention realizes the interconnection and intelligent linkage of various kitchen equipment, improves the safety and comfort of the kitchen environment, and realizes diversified human-computer interaction methods, greatly enhancing the user experience.

[0064] Key control functions can be performed locally, unaffected by network conditions or cloud platform status.

[0065] The system's interconnectivity has been enhanced, allowing various kitchen equipment to be flexibly connected and enabling unified management and collaborative control.

[0066] Data security and privacy protection have been strengthened, with critical data storage and processing conducted locally, thus avoiding the risk of privacy data leakage.

[0067] It enables intelligent scene linkage, improving the system's automation level and user experience.

[0068] By leveraging a cloud-edge collaborative architecture, we can ensure the real-time nature of local control while utilizing cloud resources to achieve remote management and intelligent optimization. Attached Figure Description

[0069] Figure 1 This is an architecture diagram of the intelligent kitchen range hood central control gateway system of the present invention;

[0070] Figure 2 This is a schematic diagram of the device connection of the intelligent kitchen range hood central control gateway system of the present invention;

[0071] Figure 3 This is a flowchart of the present invention for processing data received from kitchen equipment;

[0072] Figure 4 This is a data processing flowchart of the detection visual sensor, auditory sensor and tactile sensor of the present invention. Detailed Implementation

[0073] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0074] At least one embodiment of the present invention discloses an intelligent kitchen range hood central control gateway system, such as Figure 1 As shown, it includes:

[0075] The smoke hood equipment has a built-in smoke hood gateway and smoke hood electronic control board, and the smoke hood electronic control board communicates with the smoke hood gateway.

[0076] The range hood connects to other kitchen appliances, which in turn connect to the range hood gateway via Wi-Fi or Bluetooth.

[0077] The range hood gateway communicates with the cloud platform or smart terminal.

[0078] An app is installed on the smart terminal. The app includes a human-computer interaction interface, through which users can issue control commands. The app can remotely control the range hood and other kitchen appliances.

[0079] Kitchen equipment other than range hoods includes:

[0080] Combustible gas sensors are used to monitor the concentration of combustible gases in the kitchen environment;

[0081] Smoke sensors are used to monitor the concentration of cooking fumes.

[0082] Temperature and humidity sensor used to monitor the temperature and humidity of the kitchen environment;

[0083] The gas stove wirelessly connects to the range hood and receives control commands.

[0084] The gas meter is wirelessly connected to the range hood equipment and reports gas usage data.

[0085] The electromagnetic shut-off valve cuts off the gas pipeline according to the control command.

[0086] The intelligent kitchen range hood central control gateway system can process data received from kitchen appliances, including the following steps:

[0087] Step 101: Data reception and verification;

[0088] The range hood gateway receives real-time data transmitted from kitchen appliances such as disinfection cabinets and range hoods, including equipment operating status data and environmental monitoring data.

[0089] After receiving the data, methods such as Cyclic Redundancy Check (CRC) are used to verify the data and check whether errors occurred during the transmission process.

[0090] If the data is incorrect, the device is requested to resend it until correct data is received. The range hood gateway receives real-time data transmitted from each kitchen appliance.

[0091] The time of receiving data is The received data set is ;

[0092] The verification function is ,when This indicates that the data is correct. This indicates that the data is incorrect.

[0093] Step 102: Edge computing module optimization processing;

[0094] The edge computing module in the range hood gateway processes the verified data;

[0095] First, the data is categorized and organized. For example, operational status data and environmental monitoring data are classified separately. The operational status data set is as follows: The environmental monitoring data set is Then, optimized algorithms are used to analyze and process different types of data:

[0096] The updated status judgment algorithm is used to analyze the operational status data to determine whether the device is operating normally or needs to adjust its operating mode.

[0097] The conditions for determining that the equipment is operating normally are: (The function value is 1 when normal operating conditions are met, and 0 otherwise.) The specific formula can be set according to the actual operating logic of the equipment. For example, for a range hood, if its motor speed is within the normal range and there is no fault alarm signal, it can be set to normal operation. If the motor speed is... The normal speed range is The fault alarm signal is (When there is no alarm) When an alarm is triggered ),but: ;

[0098] For environmental monitoring data, analyze the updated environmental parameter thresholds to determine whether it is necessary to adjust the equipment operating parameters, etc.

[0099] For example, regarding data on oil fume concentration, let the safe threshold for oil fume concentration be... When the concentration of cooking fumes exceeds a threshold, adjustments such as adjusting the suction power of the range hood are required. The function for determining if the cooking fume concentration exceeds the threshold is defined as follows: :

[0100] During data processing, the performance metrics of the edge computing module, such as computing speed and memory usage, are monitored in real time. Let the performance monitoring function be... ,when (in When the set performance threshold is reached, a performance optimization mechanism is triggered, such as dynamically adjusting algorithm parameters and releasing memory, to ensure the efficiency and accuracy of data processing.

[0101] Step 103: Instruction generation and execution optimization.

[0102] Based on the data processing results, corresponding control commands are generated, such as adjusting the suction power of the range hood and troubleshooting equipment malfunctions.

[0103] Before sending instructions to the kitchen equipment, the instructions are validated again to ensure accuracy. If an instruction is incorrect, it is regenerated and validated again until it is correct. Finally, the validated instructions are sent to the corresponding equipment for execution.

[0104] Through measures such as data verification, edge computing optimization, and instruction verification, the efficiency of data processing and instruction execution has been effectively improved, enabling the smart kitchen system to respond quickly and accurately to changes in the environment and equipment status, adjust equipment working modes and parameters in a timely manner, troubleshoot equipment faults, and ultimately improve the system's operating efficiency and user experience. Furthermore, it has stronger adaptability and stability in complex and ever-changing real-world environments.

[0105] See below Figure 1 and 2 Introducing an application example of a smart kitchen range hood central control gateway system:

[0106] Scenario 1: Wi-Fi products connect to the cloud platform via a Wi-Fi router and are remotely monitored using an app.

[0107] Scenario 2: Wi-Fi and Bluetooth products are connected to the range hood gateway for remote monitoring via an app or local area network control.

[0108] Wi-Fi devices:

[0109] Connection method: Connect to the Internet via a Wi-Fi router.

[0110] Examples of equipment: disinfection cabinets, wall-hung boilers, stoves, gas water heaters, water purifiers, dishwashers, etc.

[0111] Bluetooth devices:

[0112] Connection method: Connect via range hood gateway.

[0113] Equipment examples: flow meters, ultrasonic leak detectors, shut-off valves, stoves, etc.

[0114] The smoke hood equipment includes the smoke hood electronic control board and the smoke hood gateway;

[0115] The range hood gateway includes:

[0116] HLK-7628N Wi-Fi Gateway;

[0117] HM-BT2204L Bluetooth Gateway;

[0118] HM-BT4531B ​​Bluetooth module;

[0119] Supports both Bluetooth and Wi-Fi communication methods;

[0120] Bluetooth products connect to the range hood gateway via Bluetooth.

[0121] Wi-Fi products connect to the range hood gateway via Wi-Fi.

[0122] Range Hood Control Board:

[0123] Communicates with the smoke hood gateway via the UART interface;

[0124] Control system:

[0125] APP: Used for remote monitoring and control of equipment.

[0126] Cloud platform: Provides cloud service support.

[0127] The system enables smart kitchen appliances to be connected to the internet and remotely controlled via Wi-Fi and Bluetooth technologies, providing flexible connectivity options and a wide range of device types. This allows users to monitor and manage kitchen appliances anytime, anywhere via a mobile app.

[0128] Range Hood Gateway Configuration:

[0129] Press the network configuration button on the range hood, and the gateway will enter network configuration mode.

[0130] Use the app to connect the range hood gateway to the Wi-Fi router.

[0131] Range Hood Gateway Network Setup:

[0132] Press the network configuration button on the range hood, and the gateway will enter network mode.

[0133] At this time, the sub-device Bluetooth or Wi-Fi product enters the network configuration mode, and the gateway will automatically bind to the sub-device.

[0134] The gateway can only network one sub-device at a time, and multiple sub-devices can be networked repeatedly.

[0135] Remote monitoring:

[0136] The range hood gateway connects to the cloud platform.

[0137] You can use the app to monitor the status of all devices under the gateway.

[0138] Configure smart scene rules for devices under the gateway.

[0139] Local automated linkage:

[0140] Devices within the same local area network in a single household are centered around a range hood gateway.

[0141] Achieve stable and rapid local automated linkage.

[0142] Even when the external network is abnormal, pre-set automated scenarios can still be executed.

[0143] Rapid response and stable control ensure that the interconnection of smart devices is not restricted by external network anomalies.

[0144] Set device linkage conditions and timing rules on the APP.

[0145] Precautions:

[0146] For each new gateway device added, the gateway firmware does not need to be updated; instead, the gateway interface configuration in the app needs to be updated.

[0147] The range hood gateway enables the networking and remote control of smart kitchen equipment, providing flexible connection methods and a wide range of equipment types, ensuring that users can monitor and manage kitchen equipment anytime, anywhere via a mobile app.

[0148] In a smart kitchen environment, a range hood gateway is installed, connecting a smart disinfection cabinet and a smart range hood. The disinfection cabinet reports its own operating status data every 5 minutes, including parameters such as the current operating mode, temperature, and humidity; the range hood reports its operating status data every 1 minute, including parameters such as the current operating level, motor speed, and oil fume concentration, and also reports the temperature and humidity data of the kitchen environment.

[0149] At some point The range hood gateway received the following data:

[0150] Disinfection cabinet status data: ;

[0151] Range hood status data: ;

[0152] Kitchen environment data: ;

[0153] The gateway first performs a CRC check on the received data. After confirming that the data is correct, it categorizes it according to its type. Then, the edge computing module begins to analyze and process the data:

[0154] For the status data of the disinfection cabinet, determine whether its operating mode is correct and whether the temperature and humidity are within a reasonable range. In high-temperature mode, the temperature threshold is [70, 80] and the humidity threshold is [50, 70], then the disinfection cabinet is operating normally.

[0155] For the range hood's status data, first determine if the motor speed is normal. At the second fan speed, the normal motor speed range is [1000, 1500], so the motor speed is normal at this time. Next, determine if the oil fume concentration exceeds the standard. The oil fume concentration threshold is set to 10, so the oil fume concentration is not exceeded, and there is no need to adjust the suction power.

[0156] For the kitchen environment data, analyze whether the temperature and humidity are within the comfortable range. The comfortable temperature range is [22, 28], and the comfortable humidity range is [40, 60]. In this case, the kitchen temperature is too high, and the air conditioning settings need to be adjusted.

[0157] Based on the above analysis, the gateway generates the following control commands:

[0158] Keep the current operating mode and parameters of the disinfection cabinet unchanged.

[0159] Keep the current operating level and suction power of the range hood unchanged.

[0160] Lower the air conditioner temperature setting to 26 degrees Celsius.

[0161] Before issuing control commands, the gateway verifies the commands to ensure their accuracy. Then, the commands are sent to the disinfection cabinet, range hood, and air conditioner respectively, and the devices immediately perform the corresponding adjustments upon receiving the commands.

[0162] exist At that time, the gateway received the following data again:

[0163] Range hood status data:

[0164] Kitchen environment data:

[0165] At this point, the concentration of cooking fumes had exceeded the threshold. The gateway immediately generated a command to increase the range hood's setting to level 3 and lower the air conditioner temperature by 1 degree Celsius. After the command was issued, the concentration of cooking fumes quickly decreased, and the kitchen temperature returned to a comfortable range.

[0166] Based on the aforementioned embodiments, the kitchen equipment also includes a visual sensor, an auditory sensor, and a tactile sensor, and the range hood gateway further performs the following steps:

[0167] Step 201, Sensor state detection and preprocessing;

[0168] The equipped sensor fault detection and early warning device detects the operating status information of the vision sensor, hearing sensor, and touch sensor, respectively, and represents them as follows: , and .in:

[0169] : Visual sensor operating status information, with a value range of 0 to 1, indicating the working status of the visual sensor. 0 means completely failed, 1 means completely normal, and 0.95 means the visual sensor is working well.

[0170] : Hearing sensor operating status information, with a value range of 0 to 1, indicating the working status of the hearing sensor. 0.90 indicates that the hearing sensor is working well.

[0171] : Tactile sensor operating status information, with a value range of 0 to 1, indicating the working status of the tactile sensor. 0.98 indicates that the tactile sensor is working well.

[0172] If the vision sensor becomes contaminated with oil or other substances that affect image acquisition quality, activate the sensor fault repair device to clean it and ensure the quality of the acquired visual information. The clarity reaches The above. Among them:

[0173] Visual information acquired by visual sensors, including images and videos.

[0174] For auditory sensors, if their anti-interference capability index is detected... The display shows severe external noise interference, with noise levels exceeding [a certain decibel level]. Then adjust its anti-interference parameters to suppress the noise to... The following measures aim to improve the accuracy of target audio information acquisition and ensure the collection of auditory information. The signal-to-noise ratio reached .in:

[0175] Auditory information collected by the auditory sensor, including audio.

[0176] Decibel (dB): A unit of sound intensity.

[0177] When the sensitivity of the tactile sensor drops to The following or data transmission stability is less than At the same time, a sensor fault repair device is used to perform corresponding repair operations, so that the collected tactile information can be restored. The accuracy rate has been restored to The above. Among them:

[0178] : Tactile information collected by tactile sensors, including temperature, humidity, pressure, etc.

[0179] Step 202, Multimodal Information Acquisition

[0180] With all sensors functioning correctly, the visual, auditory, and tactile sensors are activated simultaneously to collect visual information from within the kitchen. Auditory information and tactile information :

[0181] Visual information This includes images of user hand gestures, food ingredients, etc., with an image resolution of [resolution value missing]. The frame rate is .in:

[0182] : Frames transmitted per second, representing the frame rate of a video or animation.

[0183] Auditory information This includes noise from the range hood and the sound of food being put into the pan, with an audio sampling rate of [missing information]. The number of sampling bits is .in:

[0184] : kilohertz, indicating the audio sampling frequency.

[0185] Bits represent the number of bits used in an audio sample.

[0186] Tactile information This includes data such as the surface temperature of the range hood and the vibration frequency data of the dishwasher. The temperature sensor accuracy is [insert accuracy here]. The frequency sensor sensitivity is .in:

[0187] Celsius is a unit of temperature.

[0188] Hertz is a unit of frequency.

[0189] Step 203, Feature Extraction and Preprocessing

[0190] For visual information Key features, such as user hand gestures, are extracted using image recognition and computer vision technologies. Characteristics of food ingredients Such features can be achieved through a series of complex image processing algorithms, resulting in a processed visual feature vector. Each component for arrive The formula is expressed as:

[0191]

[0192] in This represents a function for processing visual information.

[0193] For auditory information Audio processing techniques are used to extract features such as noise frequencies. Characteristics of cooking sound types After processing, auditory feature vectors are obtained. Each component for arrive The formula is: (The formula is incomplete and cannot be translated without further context.)

[0194]

[0195] here It is an auditory information processing function.

[0196] Targeting tactile information Extraction equipment temperature characteristics Vibration frequency characteristics Etc., generate tactile feature vectors Each component for arrive The formula is: (The formula is missing from the provided text.)

[0197]

[0198] in This is a function for processing tactile information.

[0199] Step 204, Multimodal Information Fusion

[0200] Extracted and preprocessed visual feature vectors Auditory feature vectors and tactile feature vectors By fusing the features, a comprehensive feature vector is obtained. Various fusion methods can be employed, such as weighted averaging and feature concatenation. Taking the weighted averaging method as an example, the weights of the visual, auditory, and tactile feature vectors are respectively... , , And satisfy The fusion formula is:

[0201]

[0202] Step 205, Intelligent Decision Making;

[0203] The fused integrated feature vector Input into intelligent decision-making model In this system, the intelligent decision-making model can be built based on machine learning (such as neural networks, decision trees, etc.) or other intelligent algorithms. The model judges the current kitchen environment and user behavior based on comprehensive feature vectors, and outputs operation instructions for different kitchen equipment.

[0204] Taking range hoods as an example, intelligent decision-making models The suction power setting for a range hood should be [not specified]. The formula is expressed as:

[0205]

[0206] in, This indicates the target suction power of the range hood. The unit of air volume is cubic meters per hour.

[0207] For a dishwasher, the model outputs its appropriate operating state. This indicates whether the dishwasher needs to be started:

[0208]

[0209] in, This indicates that the dishwasher needs to be started. This indicates that it does not need to be started.

[0210] For other related kitchen equipment, such as induction cookers, the model outputs the corresponding operating parameters. This indicates the power level and operating time of the induction cooker:

[0211]

[0212] in, Includes multiple parameters, such as This indicates that the induction cooker power is set to 800W and the timer is set to 15 minutes. Watt is a unit of power. The unit of time is minutes.

[0213] Step 206, Instruction Execution

[0214] Based on the operation instructions output by the intelligent decision-making model, the actuators (range hood, dishwasher, and other related kitchen equipment) perform the corresponding actions.

[0215] For range hoods, set their suction power to... This means adjusting the suction control device of the range hood to make its suction power reach the required level.

[0216] For dishwashers, set their operating status to... This means starting the dishwasher and beginning to wash the dishes.

[0217] For induction cookers and other related kitchen appliances, follow the output operating parameters. Make the necessary adjustments, setting the induction cooker power to 800W and the timer to 15 minutes.

[0218] Step 207, Model Update and Optimization

[0219] Regularly collect data on new scenarios and user behaviors in the kitchen, such as newly added... Multimodal sensor data is used as new training data to supplement the intelligent decision-making model. The training focus.

[0220] The intelligent decision-making model can be retrained and optimized using new training data, such as increasing the model accuracy from Upgraded to This enhances its adaptability and decision-making accuracy in the face of various complex situations within the kitchen. Through continuous model iteration and updates, the intelligent kitchen range hood central control gateway can continuously learn and evolve to better meet user needs.

[0221] During the model update and optimization process, machine learning techniques such as incremental learning and transfer learning can be introduced. For example, by using transfer learning, the knowledge of the model trained in other smart home appliance scenarios can be transferred to the smart kitchen scenario, further improving the model's learning efficiency and generalization ability, enabling it to quickly adapt to changes in the kitchen environment and the diversity of user behavior.

[0222] Through the above steps, this invention achieves multimodal fusion perception and intelligent decision-making in the central control gateway for intelligent kitchen range hoods. It can automatically adjust the suction power of the range hood, control the operating status of the dishwasher, and the operating parameters of other kitchen equipment based on the actual conditions in the kitchen and user behavior, thereby improving the intelligence level of kitchen equipment and the user experience. Simultaneously, through sensor status detection and fault repair, model update and optimization, the stability, reliability, and adaptability of the system are ensured, enabling it to operate efficiently and for a long time in complex and ever-changing kitchen environments.

[0223] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A smart kitchen range hood central control gateway system, characterized in that, include: The smoke hood equipment has a built-in smoke hood gateway and smoke hood electronic control board, and the smoke hood electronic control board communicates with the smoke hood gateway. The range hood connects to other kitchen appliances, which in turn connect to the range hood gateway via Wi-Fi or Bluetooth. The range hood gateway communicates with a cloud platform or smart terminal; An app is installed on the smart terminal. The app includes a human-computer interaction interface. Users can issue control commands through the human-computer interaction interface. The app can remotely control the range hood and other kitchen appliances. Kitchen equipment other than range hoods includes: Combustible gas sensors are used to monitor the concentration of combustible gases in the kitchen environment; Smoke sensors are used to monitor the concentration of cooking fumes. Temperature and humidity sensor used to monitor the temperature and humidity of the kitchen environment; The gas stove wirelessly connects to the range hood and receives control commands. The gas meter is wirelessly connected to the range hood equipment and reports gas usage data. The electromagnetic shut-off valve cuts off the gas pipeline according to control commands; Kitchen equipment also includes visual sensors, auditory sensors, and tactile sensors; Devices within the same local area network in a single household can achieve stable and rapid local automation linkage centered around a range hood gateway. Even if there is an external network failure, it can still execute pre-set automation scenarios, quickly respond and maintain stable control, so that the linkage of smart devices is not restricted by external network failures. The intelligent kitchen range hood central control gateway system can process data received from kitchen appliances, including the following steps: Step 101, Data reception and verification; The range hood gateway receives real-time data transmitted from kitchen equipment, including equipment operating status data and environmental monitoring data; The time of receiving data is The received data set is ; After receiving the data, a cyclic redundancy check method is used to verify the data and check whether any errors occurred during the transmission process. The verification function is ,when This indicates that the data is correct. This indicates that the data is incorrect; If the data is incorrect, request the device to resend it until the correct data is received; Step 102, edge computing module optimization processing; The edge computing module in the range hood gateway processes the verified data; First, the data is categorized and organized, and then optimized algorithms are used to analyze and process different types of data. Operational status data and environmental monitoring data are categorized separately, and the operational status data set is as follows: The environmental monitoring data set is ; For the operating status data, the updated status judgment algorithm is used to analyze whether the device is operating normally or needs to adjust the operating mode. The conditions for determining that the equipment is operating normally are: For range hoods, if the motor speed is within the normal range and there are no fault alarm signals, it can be set to normal operation. If the motor speed is... The normal speed range is If the fault alarm signal is A, then: ； For environmental monitoring data, analyze the updated environmental parameter thresholds to determine whether it is necessary to adjust the equipment operating parameters; For the data on oil fume concentration, let the safe threshold for oil fume concentration be... When the concentration of cooking fumes exceeds a threshold, the suction power of the range hood needs to be adjusted. The function for determining if the cooking fume concentration exceeds the threshold is defined as follows: : ； During data processing, the performance metrics of the edge computing module are monitored in real time. The performance monitoring function is as follows: ,when At that time, the performance optimization mechanism is triggered, in which The set performance threshold; Step 103, instruction generation and execution optimization; Based on the data processing results, corresponding control commands are generated. Before sending the commands to the kitchen equipment, the commands are verified again to ensure their accuracy. If the command is incorrect, it is regenerated and verified until the command is correct. Step 201, Sensor state detection and preprocessing; The equipped sensor fault detection and early warning device detects the operating status information of the vision sensor, hearing sensor, and touch sensor, respectively, and represents them as follows: , and ,in: : Visual sensor operating status information, with a value range of 0 to 1, indicating the working status of the visual sensor. 0 means completely failed, 1 means completely normal, and 0.95 means the visual sensor is working well. : Hearing sensor operating status information, with a value range of 0 to 1, indicating the working status of the hearing sensor, and 0.90 indicating that the hearing sensor is working well; : Tactile sensor operating status information, with a value range of 0 to 1, indicating the working status of the tactile sensor, and 0.98 indicating that the tactile sensor is working well; If the vision sensor becomes contaminated with oil, affecting the image acquisition quality, activate the sensor fault repair device to clean it and ensure the acquired visual information is preserved. The clarity reaches The above, of which Visual information acquired by vision sensors, including images and videos; For auditory sensors, if their anti-interference capability index is detected... If the display shows severe external noise interference, exceeding 60 dB, adjust its anti-interference parameters to suppress the noise to below 40 dB. This improves the accuracy of acquiring target audio information and ensures the quality of collected auditory information. The signal-to-noise ratio reached ,in Auditory information collected by the hearing sensor, including audio, dB is the unit of sound intensity; When the sensitivity of the tactile sensor drops below 80% or the data transmission stability is less than 90%, a sensor fault repair device is used to perform corresponding repair operations, ensuring that the collected tactile information is accurate and reliable. The accuracy rate has recovered to over 95%, of which The tactile information collected by the tactile sensor includes temperature, humidity, and pressure; Step 202, Multimodal Information Acquisition; With all sensors functioning correctly, the visual, auditory, and tactile sensors are activated simultaneously to collect visual information from within the kitchen. Auditory information and tactile information : Visual information This includes images of user hand gestures, food ingredients, etc., with an image resolution of [resolution value missing]. The frame rate is ,in Frames per second, representing the frame rate of a video or animation; Auditory information Including the noise of the range hood operating and the sound of food being put into the pot, the audio sampling rate is The number of sampling bits is ,in kilohertz indicates the audio sampling frequency. 1 digit represents the number of bits used for audio sampling; Tactile information This includes data on the surface temperature of the range hood and the vibration frequency of the dishwasher; the temperature sensor accuracy is... The frequency sensor sensitivity is ,in Celsius is the unit of temperature. Hertz is the unit of frequency. Step 203, Feature extraction and preprocessing; For visual information Key features, including user hand gestures, are extracted using image recognition and computer vision technologies. Characteristics of food ingredients ; The processed visual feature vector is Each component for arrive For real numbers between these two ranges, the formula is as follows: ； in Represents a function for processing visual information; For auditory information Audio processing techniques are used to extract features such as noise frequencies. Characteristics of cooking sound types After processing, auditory feature vectors are obtained. Each component for arrive For real numbers between these two ranges, the formula is: ； It is an auditory information processing function; Targeting tactile information Extraction equipment temperature characteristics Vibration frequency characteristics Generate tactile feature vectors Each component For real numbers between 0 and 100, the formula is: ； in This is a tactile information processing function; Step 204, multimodal information fusion; Extracted and preprocessed visual feature vectors Auditory feature vectors and tactile feature vectors By fusing the features, a comprehensive feature vector is obtained. ; The weights of the visual, auditory, and tactile feature vectors are respectively , , And satisfy The fusion formula is: ； Step 205, Intelligent Decision Making; The fused integrated feature vector Input into intelligent decision-making model In the process, the intelligent decision-making model judges the current kitchen environment and user behavior based on comprehensive feature vectors, and outputs operation instructions for different kitchen equipment; The intelligent decision-making model is either a neural network model or a decision tree model; For range hoods, intelligent decision-making models The suction power setting for a range hood should be [not specified]. The formula is expressed as: ； in, This indicates the target suction power of the range hood. The unit of air volume is cubic meters per hour. For a dishwasher, the intelligent decision-making model outputs its appropriate operating state. This indicates whether the dishwasher needs to be started: ； in, This indicates that the dishwasher needs to be started. This indicates that startup is not required; Corresponding to the induction cooker, the intelligent decision-making model outputs the corresponding operating parameters. This indicates the power level and operating time of the induction cooker: ； in, Includes multiple parameters, such as This indicates that the induction cooker power is set to 800W and the timer is set to 15 minutes. Watt is a unit of power. The unit of time is minutes; Step 206, instruction execution; The executor performs the corresponding action based on the operation instructions output by the intelligent decision-making model. Step 207, Model Update and Optimization; Regularly collect new data from the kitchen as new training data to supplement the training set of the intelligent decision-making model and retrain it; During the model update and optimization process, incremental learning and transfer learning are introduced. By using transfer learning, the model knowledge trained in other smart home appliance scenarios is transferred to the smart kitchen scenario, which improves the model's learning efficiency and generalization ability, enabling it to quickly adapt to changes in the kitchen environment and the diversity of user behaviors.

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