16 results about "Smart kitchen" patented technology

This application discloses a method and device for controlling the airflow of a smart kitchen appliance. The method includes: collecting pressure difference data and air velocity data within a target area corresponding to each air intake; determining the target local oil fume concentration and target local airflow direction for each air intake based on the pressure difference data and air velocity data; judging the overall oil fume source and distribution based on the target local oil fume concentration and target local airflow direction of all air intakes to obtain overall oil fume source and distribution information; and controlling several target air intakes based on the overall oil fume source location information and overall oil fume distribution information. The airflow control method for smart kitchen appliances provided by this application accurately judges the oil fume distribution and controls the air intakes by using pressure difference data and air velocity data from multiple air intakes, significantly reducing energy consumption and noise, and achieving highly efficient and energy-saving intelligent oil fume management.

The application discloses a target recipe determination method and device, electronic equipment and an intelligent kitchen system. The method comprises the following steps: in response to a cooking instruction of a target object, obtaining an initial recipe set corresponding to the target object, target health information of the target object and an initial trunk part; each initial recipe is traversed in turn, and for the current initial recipe traversed, current object health information, a current trunk part and a current recipe load level of the current initial recipe are obtained; the target health information and the current object health information are subjected to health information matching processing to obtain a current health matching degree of the current initial recipe; the current load adaptation degree of the current initial recipe is determined according to the initial trunk part, a current trunk part corresponding to the current initial recipe and the current recipe load level; and the target recipe is determined according to the current health matching degree and the current load adaptation degree of the current initial recipe. The application reduces the fatigue degree of the target object in the cooking process.

This invention discloses a method and system for recognizing gas usage behavior based on temperature time-series data and an LSTM network, belonging to the field of intelligent detection and pattern recognition technology for combustible gases. The invention constructs an LSTM network model comprising a multi-scale feature encoding layer, a first LSTM layer, a second LSTM layer, an adaptive gated attention layer, and a feature fusion layer. Multi-scale feature encoding is used to simultaneously capture details and macroscopic trends of temperature changes. A gating mechanism is used to dynamically suppress noise and focus on key time steps in the hidden state vector output by the LSTM layer. The purified deep features are then fused with temperature time-series engineering features for gas usage behavior classification. This invention achieves high-precision real-time recognition of various gas usage behaviors, such as turning on / off the stove, adding food, stir-frying, adding water, and simmering, using only a single temperature sensor and fully protecting privacy. It can be applied to the linkage control of intelligent kitchen equipment.

This application discloses a method, device, and smart kitchen appliance for controlling the heat of a smart kitchen appliance. The method includes: acquiring current temperature data, current smoke data, and current image data; determining the current cooking mode and its corresponding current rating weight; determining a food status score based on the current temperature data, current smoke data, current image data, and current rating weight; generating heat control parameters based on the food status score; and adjusting the heat of the smart kitchen appliance based on the heat control parameters. In this application embodiment, by collecting three types of data—temperature, smoke, and image—and combining them with the rating weight corresponding to the current cooking mode, a real-time food status score is calculated. Then, heat control parameters are generated based on this score, achieving precise and intelligent heat control. This breaks through the reliance on traditional experience, quantifies the food status through multi-dimensional data, avoids the limitations of single-sensor monitoring, comprehensively captures key cooking information, and reduces problems such as burnt exterior and undercooked interior, and stale texture.

This invention discloses a smart kitchen middleware management system, relating to the technical field of edge computing. It includes a data acquisition module for acquiring operational data and constructing a final directed acyclic graph (DAG) based on this data; an indicator acquisition module for acquiring static and dynamic indicators of each node and calculating a comprehensive indicator based on these indicators; an indicator judgment module for determining that the current edge computing device cannot complete the task if the comprehensive indicator exceeds a threshold; and a solution generation module for determining optimization objectives based on the DAG, generating an optimal scheduling scheme based on the optimization objectives, and executing the optimal scheduling scheme. By constructing the DAG and evaluating nodes, it achieves collaborative optimization of latency and energy consumption in a dynamic environment. Based on a dynamic threshold early warning mechanism, it can proactively avoid node overload risks, improve resource utilization, and reduce overall system energy consumption.

This application discloses a method, apparatus, device, and storage medium for offline activation of smart kitchen appliances. The method includes: sending a device address and a random number to a mobile terminal when the smart kitchen appliance fails to connect to a cloud server; the smart kitchen appliance has a pre-installed authentication certificate and is connected to a router; the mobile terminal sends the device address and random number to the cloud server; the cloud server encrypts the data based on the device address and random number and sends it to the mobile terminal; receiving the encrypted data sent by the mobile terminal; verifying the encrypted data based on the authentication certificate, device address, and random number to obtain a verification result; if the verification result indicates that the encrypted data has been successfully verified, sending device information to the mobile terminal; and receiving activation information sent by the mobile terminal. By using the mobile terminal as an intermediary, the authentication and activation binding of the smart kitchen appliance can be completed without internet access.

This application discloses a cooktop control system, relating to the field of smart kitchen appliances. The cooktop control system includes a temperature sensing device and a control device. The control device is configured to: perform spatial mapping processing on infrared image information of temperature distribution to obtain a three-dimensional temperature field model corresponding to the target cookware; extract data from the target temperature field plane of the three-dimensional temperature field model to obtain surface temperature distribution data of the target temperature field; determine a target sub-region from the target temperature field whose temperature difference meets a preset difference condition based on the temperature difference distribution between the surface temperature distribution data and the reference surface distribution data; and adjust the flame operation parameters of the target flame-spraying component based on the temperature difference data corresponding to the target sub-region in the temperature difference distribution until the temperature data corresponding to the target sub-region matches the reference surface distribution data. By extracting the temperature field plane from the temperature field model, the surface temperature of multiple heating areas can be sensed and the flame-spraying component can be adjusted, improving heating uniformity and cooking accuracy.

The application relates to the technical field of information processing, and particularly discloses an order sorting control system for an unmanned intelligent kitchen, which comprises an order automatic receiving module, an order analysis module and an order sorting module.The order automatic receiving module is used for acquiring order information of different channels and integrating all the order information to obtain an integrated order list.The order analysis module is used for acquiring a plurality of dish cooking information according to the integrated order list, merging the same dish names in the dish cooking information to obtain dish cooking merged information, and acquiring dish arrangement cooking information according to the dish cooking merged information.The order sorting module is used for sorting the dish arrangement cooking information according to the dish arrangement cooking information.The order automatic receiving module integrates multi-channel orders, avoids information dispersion and confusion, lays a foundation for subsequent processes, and improves the convenience and efficiency of order management.

The invention relates to an intelligent kitchen appliance control method and system based on ultrasonic liquid level detection and a medium, and relates to the technical field of intelligent kitchens. Ultrasonic waves are transmitted to a cookware area on a cooker, and echo signals are received; extracting peak voltage and rise time in the echo signal; the rise time is the time required for transmitting the ultrasonic wave until the echo signal reaches the target intensity; based on the peak voltage and the rise time, whether the cooking state in the pot is a dry burning state or not is evaluated; and the working state of the range hood or the cooker is adjusted based on the cooking state, the problems that safety response lags behind, and autonomous judgment and linkage control capacity for the cooking process is lacked are solved, real-time accurate recognition of the state of the cooker and active prevention and control of risks are achieved, and therefore kitchen safety and use experience are comprehensively improved.

This application relates to the field of smart appliance technology, and in particular to a smart kitchen appliance control method and smart kitchen appliance based on sound pressure energy. The method includes: real-time acquisition of sound pressure signals from a cookware, and extraction of the characteristics of the sound pressure signals; based on the characteristics of the sound pressure signals, obtaining the boiling energy corresponding to the liquid boiling in the cookware, the dry-burning energy corresponding to the cookware being dry-burned, and the energy ratio of the dry-burning energy; based on the boiling energy, the dry-burning energy, and the energy ratio, determining whether the cookware is in a dry-burning state, triggering a dry-burning alarm when it is in a dry-burning state, and controlling the range hood fan to operate at the corresponding setting. This application utilizes sound pressure signals to identify the dry-burning state of the cookware, triggering an early warning before the moisture completely evaporates, solving the technical problem of lagging dry-burning detection in existing technologies, and proactively controlling the operation of the range hood fan.

This invention discloses an intelligent cake baking device, belonging to the field of intelligent kitchen equipment technology. It includes a main body, a feeding mechanism, a whipping mechanism, a baking mechanism, a decorating mechanism, an intelligent control system, a safety protection system, and a cleaning mechanism. The internal modules of the main body are integrated according to their respective processes. The feeding mechanism ensures precise ingredient delivery; the whipping mechanism efficiently whips egg yolk batter and meringue through a double-bowl grading system and variable frequency speed control; the baking mechanism ensures cake shaping; the decorating mechanism prints cream and adds fruit; the intelligent control system supports touch, voice, and remote control via an app; the cleaning mechanism uses a self-cleaning system and a superhydrophobic coating to ensure hygiene; and the safety protection system provides multiple safety safeguards. This invention solves the problems of inaccurate ingredient delivery, unstable whipping, low integration, inconvenient cleaning, and insufficient safety in existing cake machines through full-process automation, high-precision control, and intelligent interaction, enabling one-click production of high-quality cakes, suitable for everyday home baking scenarios.

This application discloses an obstacle avoidance method, device, storage medium, and smart appliance for a lift-up range hood. The obstacle avoidance method for the lift-up range hood can be applied to the field of smart kitchen appliance technology. The method includes: acquiring obstacle distance detection data during the lifting and lowering process of the range hood; reconstructing the obstacle distance detection data based on a pre-trained data reconstruction model to obtain obstacle distance target data; the training data of the pre-trained data reconstruction model includes obstacle distance occlusion data as input features and obstacle distance sample data as pseudo-labels; the obstacle distance occlusion data is obtained by randomly occluding the obstacle distance sample data; the obstacle distance sample data is collected under scenarios with different interference factors; and the lifting and lowering action of the range hood is controlled based on the obstacle distance target data. This application effectively avoids the problem of existing sensors failing to detect or misdetecting obstacles due to material and other factors, improving the accuracy and reliability of obstacle avoidance.

The present disclosure relates to a cooking parameter determination method and device based on a smart kitchen knife and the smart kitchen knife. The method comprises obtaining material information collected when the smart kitchen knife cuts a target material, the material information being used to represent data measured when the smart kitchen knife contacts an internal region of the target material; determining a material type and a material freshness corresponding to the target material based on the material information and a preset multi-modal fusion model; obtaining a cooking method corresponding to the target material; and determining a cooking parameter corresponding to the target material based on a material cooking knowledge base, the cooking method, the material type, and the material freshness. The present disclosure can detect the state of the internal region of the material, comprehensively consider various influencing factors, accurately determine the freshness of the material, and determine the appropriate cooking parameter based on the freshness, thereby improving the convenience and success rate of cooking and enhancing the user experience.

This application discloses a signal debouncing method, device, electronic device, and storage medium for smart kitchen appliances. The method includes: acquiring an initial voltage signal and a first current voltage signal in an idle state; if the initial voltage signal and the first current voltage signal indicate a voltage jump, the state machine switches from the idle state to a detection state; in the detection state, a current time window is determined; the current waiting time is acquired; if the current waiting time is greater than the current time window, the state machine switches from the detection state to a confirmation state; in the confirmation state, a second current voltage signal is acquired; if the second current voltage signal and the first current voltage signal confirm a voltage jump, the state machine switches from the confirmation state to the idle state and sends an execution command. This application ensures the reliability of signal triggering by setting a three-level state machine, and at the same time, it has a faster response speed by debouncing through a dynamically calculated current time window.