Intelligent electric flame stove system

Abstract

The invention discloses an intelligent electric flame stove system. The system comprises a cloud server, an AI algorithm, a cloud server, a cloud server and a cloud server, the APP end is used for generating a cooking navigation packet containing a voice instruction, a firepower parameter and sequential logic; the electric flame stove terminal analyzes the instruction packet and synchronously executes voice broadcasting and firepower regulation and control; and the electric flame stove terminal shares data to the cloud server through stand-alone data acquisition, and updates the data of the cloud server through AI federal learning, so that an AI model is iterated. The intelligent electric flame stove system integrates the functions of intelligent navigation, temperature monitoring, self-adaptive learning and the like, fire power is accurately regulated and controlled, and cooking experience is continuously optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent kitchen equipment, and in particular to a cooking teaching and firepower intelligent control system that combines electric flame stove technology, a voice interaction system and an artificial intelligence algorithm. Background Art

[0002] Traditional electric flame stoves lack the function of guiding the cooking process. Users need to rely on external devices (mobile phones / recipes) to operate step by step, which can easily lead to operational errors; firepower adjustment relies on user experience and cannot be dynamically adjusted according to the characteristics of the ingredients or the cooking stage, making it difficult to achieve precise temperature control; existing smart stoves have a single interaction method, and voice broadcast and firepower control are not deeply coupled, making it impossible to form a closed-loop teaching.

[0003] The existing technology still has limitations: it only realizes basic voice control and does not involve cooking navigation and firepower linkage; the induction cooker control solution based on preset recipes is not adapted to the high temperature characteristics of electric flame stoves, and lacks user behavior feedback optimization. Summary of the invention

[0004] In order to overcome the above-mentioned shortcomings, the present invention aims to provide a technical solution that can solve the above-mentioned problems.

[0005] An intelligent electric flame stove system, comprising: a cloud server, an AI algorithm analyzing user data and optimizing instruction packages; On the APP side, a cooking navigation package including voice commands, fire parameters, and timing logic is generated; The electric flame stove terminal parses the instruction package and executes voice broadcast and fire control simultaneously; The electric flame stove terminal collects data from a single machine, shares the data with the cloud server, updates the cloud server data through AI federated learning, and iterates the AI ​​model; Preferably, the voice navigation instruction generation method comprises: decomposing the recipe text into time-sequence operation instructions by natural language processing technology; Dynamically adjust the detail and speed of voice broadcasts based on user historical data; Preferably, the method for generating the firepower control parameters comprises: establishing a database of mapping relationships between physical properties of food materials and plasma parameters of electric flame cookers; dynamically correcting the firepower output through a PID algorithm based on real-time sensor data; Preferably, the synchronous control of voice broadcast and firepower regulation includes: the error between the voice command triggering timestamp and the firepower parameter switching timestamp does not exceed 0.5 seconds; automatically switching to the explanation mode and recording the behavior data when the user manually intervenes; Preferably, the intelligent electric flame stove system also includes: updating the cloud model through a federated learning framework to protect user privacy while realizing cross-device knowledge sharing.

[0006] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0008] Figure 1 It is the overall structure diagram of the system.

[0009] Figure 2 It is a working step diagram of the present invention. DETAILED DESCRIPTION

[0010] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0011] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they cannot be understood as limitations on the present invention.

[0012] In addition, in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can also be the internal communication of two components, it can be a wireless connection, or it can be a wired connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0013] In addition, the technical features involved in the different embodiments of the present invention described later can be combined with each other as long as they do not conflict with each other.

[0014] See also Figure 1In an embodiment of the present invention, an intelligent electric flame stove system includes: Cloud server 100, AI algorithm analyzes user data and optimizes instruction packets; The APP terminal 200 generates a cooking navigation package including voice instructions, fire parameters, and timing logic; The electric flame stove terminal 300 parses the instruction packet and simultaneously executes the voice broadcast and the fire control; The electric flame stove terminal 300 collects data from a single machine and shares the data with the cloud server 100, and updates the data of the cloud server 100 through AI federated learning to iterate the AI ​​model. Example

[0015] See also Figure 2 , the working process of intelligent electric flame stove system: S1. The user selects a recipe through the APP and adds ingredients to the pot.

[0016] S2. The APP generates voice commands and firepower commands. For example, when the "braised pork" menu is selected on the APP, the voice command code is: { "step1": {"text":"Please place the pot in the center of the stove", "delay":0, "voice_speed":1.2}, "step2": {"text":"Heat the pot over high heat for 60 seconds to 200℃", "delay":60, "warning":"If the oil temperature exceeds 250℃, the firepower will be automatically reduced"}}; the firepower command code is: { "time_0-60s": {"power_level":5,"target_temp":200},"time_61-300s":{"power_level":3,"target_temp":160}} .

[0017] S3, adjusting the output power of the plasma generator. After receiving the instruction packet, the electric flame cooker plays voice prompts in sequence and automatically adjusts the output power of the plasma generator; S4. The infrared temperature sensor monitors the temperature of the pot bottom in real time. If it detects that the actual temperature deviates from the target value by ±15%, it triggers a voice prompt and firepower compensation. Example

[0018] Adaptive learning function: For example, when more than 30% of users manually lower the heat in a recipe, the cloud model automatically reduces the default heat value of that step by 10% and adds "It is recommended to use medium heat to prevent sticking" in the voice prompt.

[0019] In an embodiment of the present invention, the method for generating a voice instruction package includes: step decomposition, parsing the recipe text through NLP, and breaking it down into "operation instruction + time node + expected effect" triples (such as: "Step 1: Heat the pan for 30 seconds until slightly smoking"); speech synthesis, generating a personalized voice package based on user preferences (speaking speed / dialect), and embedding incorrect operation warning audio (such as: "Too much water is detected in the pot, please wipe it dry before continuing").

[0020] In an embodiment of the present invention, a method for generating an intelligent firepower control instruction package includes: dynamic parameter mapping, establishing a mapping relationship library between "food type-weight-target taste" and electric flame stove firepower parameters (such as "steak / 300g / medium rare" corresponds to "1800°C for the first 30 seconds, then adjusted to 1200°C"); AI real-time correction, feedback data from the stove's built-in sensors (temperature / image), and using a convolutional neural network (CNN) to dynamically adjust the subsequent firepower curve.

[0021] In an embodiment of the present invention, the voice command is synchronized with the firepower control command, with an error of ≤0.5 seconds; when the user manually adjusts the firepower, the voice automatically switches to the explanation mode (such as: "You have lowered the firepower, and the current stage is suitable for stewing").

[0022] In an embodiment of the present invention, the electric flame stove terminal 300 records the user operation deviation, completion rate, and manual intervention frequency; updates the cloud AI model through federated learning and optimizes the instruction packet generation rules.

[0023] The technical effects obtained by the present invention are: 1. Intelligent cooking navigation: The APP generates a cooking navigation package containing voice instructions, fire parameters and timing logic. The electric flame stove terminal 300 can parse and synchronously execute voice broadcast and fire control, providing users with convenient and intelligent cooking guidance, reducing the difficulty of cooking. Even users with insufficient cooking experience can complete the preparation of complex dishes according to the instructions; 2. Real-time temperature monitoring and compensation: The temperature of the pot bottom is monitored in real time through an infrared temperature sensor. When the actual temperature deviates from the target value by ±15%, it can trigger a voice prompt and fire compensation to ensure stable temperature during cooking, which helps to improve the quality of dishes and avoid problems such as overcooked or undercooked dishes due to temperature deviation; 3. Adaptive learning capability: The system has an adaptive learning function. When more than 30% of users manually reduce the heat in a certain step of a recipe, the cloud model automatically reduces the default heat value of that step by 10% and adds relevant suggestions in the voice prompt. It can continuously optimize cooking instructions based on the actual operating habits of the user group, making the cooking process more in line with the needs of the public; 4. Personalized voice command generation: The voice command package generation method generates a personalized voice package based on user preferences (speech speed / dialect) through step decomposition and voice synthesis, and embeds the wrong operation warning audio, which not only meets the personalized needs of different users, but also can promptly remind users to correct wrong operations and improve user experience; 5. Precise firepower intelligent control: The firepower intelligent control instruction package generation method uses dynamic parameter mapping to establish a mapping relationship library between ingredients and firepower parameters, combined with AI real-time correction, using the feedback data from the built-in sensors of the stove, and dynamically adjusting the subsequent firepower curve through the convolutional neural network to achieve precise firepower control for different ingredients and cooking needs, further ensuring the cooking effect; 6. Command synchronization and intelligent switching: Voice commands and fire control commands are highly synchronized, with an error of ≤0.5 seconds, which improves the smoothness and experience of user operations; when users manually adjust the fire power, the voice automatically switches to explanation mode to promptly inform users of the cooking stage corresponding to the current operation, enhancing users' sense of control over the cooking process; 7. Data-driven continuous optimization: The electric flame stove terminal 300 records data such as user operation deviation, completion rate, and manual intervention frequency. It updates the cloud AI model through federated learning and optimizes the command package generation rules, so that the system can continue to evolve with user use and provide better quality and smarter cooking services.

[0024] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

Claims

1. An intelligent electric flame stove system, characterized in that: include: In the cloud server, AI algorithms analyze user data and optimize instruction packages; On the APP side, a cooking navigation package including voice commands, fire parameters, and timing logic is generated; The electric flame stove terminal parses the instruction package and executes voice broadcast and fire control simultaneously; The electric flame stove terminal collects data through a single machine, shares the data with the cloud server, updates the cloud server data through AI federated learning, and iterates the AI ​​model.

2. The intelligent electric flame cooker system according to claim 1, characterized in that: The method for generating the voice navigation instructions is: breaking down the recipe text into time-series operation instructions by natural language processing technology, and dynamically adjusting the detail and speech speed of the voice broadcast according to user historical data.

3. The intelligent electric flame cooker system according to claim 1, characterized in that: The method for generating the firepower control parameters is: establishing a mapping relationship database between the physical properties of food materials and the plasma parameters of the electric flame stove, and dynamically correcting the firepower output through a PID algorithm based on real-time sensor data.

4. The intelligent electric flame cooker system according to claim 1, characterized in that: The voice broadcast and the firepower regulation can be controlled synchronously, and the error between the voice command triggering timestamp and the firepower parameter switching timestamp does not exceed 0.5 seconds; when the user manually intervenes, it automatically switches to the explanation mode and records the behavior data.

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