Intelligent control system for automatic identification and cleaning adaptation of multifunctional accessories of chef machine

Abstract

The invention belongs to the technical field of intelligent control of household appliances, and particularly relates to a chef machine multifunctional accessory automatic identification and cleaning adaptation intelligent control system which comprises a central control unit, an accessory identification module, an operation parameter self-adaptive adjustment module and a cleaning adaptation module. Wherein the accessory identification module is used for automatically identifying accessory types and key parameters; the operation parameter self-adaptive adjustment module dynamically adjusts operation parameters according to accessory information, cooking tasks, food material characteristics and real-time working conditions; the cleaning adaptation module automatically generates and executes a customized cleaning scheme according to the accessory information and the residues. The use convenience, the processing accuracy, the operation safety and the cleaning efficiency of the chef machine can be remarkably improved, the service life of accessories is prolonged, and food sanitation and safety are guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for household appliances, specifically an intelligent control system for automatic identification and cleaning adaptation of multi-functional accessories for food processors. Background Technology

[0002] With the accelerating pace of life and the upgrading of consumption quality, household kitchen appliances are developing towards intelligence and multi-functionality. Stand mixers, due to their integrated functions such as mixing, kneading, and whipping, as well as their high efficiency and convenience, have become core equipment in both home and professional kitchens. To meet diverse cooking needs, existing stand mixers generally adopt a modular design, allowing for multi-tasking by replacing different accessories such as mixing paddles, egg beaters, and meat grinders.

[0003] The core design logic of existing food processor systems is "power output + manual control," meaning that user commands are received through control units such as microcontrollers, which drive the motor to operate according to preset parameters such as speed and time, thus achieving basic mechanical control of the components. This model solved the problem of limited functionality of single-device systems for a certain period, improving cooking efficiency by replacing manual labor with mechanical force, and achieving the initial application goal of multi-functional food processors. With the increasing variety of accessories and the growing demand for intelligent features, the limitations of existing technologies are becoming more and more apparent. The core issue lies in the lack of automatic accessory recognition capabilities—the inability to perceive the type, material, optimal operating parameters, and working condition of the installed accessories, leading to a series of problems: on the operation side, users need to manually match parameters, and incorrect selection can easily lead to processing failure, accessory damage, or even safety risks. Furthermore, it is impossible to optimize parameters precisely based on accessory characteristics and the state of the ingredients. On the cleaning side, because it cannot identify accessory types and residue states, it cannot provide suitable cleaning solutions, requiring users to make manual judgments, which increases the user's burden and may shorten the lifespan of accessories and pose food safety hazards due to improper cleaning. Therefore, this invention provides a multi-functional intelligent control system for automatic identification and cleaning adaptation of accessories for food processors. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0005] The technical solution adopted by this invention to solve its technical problem is as follows: The multi-functional automatic identification and cleaning adaptation intelligent control system for food processors of this invention includes a central control unit; an accessory identification module; an operating parameter adaptive adjustment module; a cleaning adaptation module; a power management module; a user interaction module; and a data storage module. The central control unit is responsible for coordinating and managing the functional execution and data interaction of all the above modules.

[0006] The accessory identification module is located at the accessory mounting interface of the food processor main unit. When an accessory is connected to the mounting interface, it automatically identifies key parameters such as the type, material, size, physical structure, maximum speed limit, maximum torque limit, optimal operating temperature range, and whether it has special cleaning requirements. The accessory identification module is implemented through at least one identification technology, including but not limited to: The first type is the Radio Frequency Identification (RFID) unit. This RFID unit includes an RFID reader installed inside the main unit's mounting interface, and several passive or active RFID tags pre-embedded in different types of kitchen appliance accessories. Each RFID tag pre-stores the accessory's unique identification code and its technical parameters. When the accessory is correctly installed, the RFID reader communicates with the RFID tag via electromagnetic induction or radio waves to obtain the accessory's identification code and technical parameters, and transmits the information to the central control unit. The RFID reader operates in the UHF 860-960MHz band, with a reading distance of 0 to 50 cm and a data transmission rate of up to 640 kilobits per second, ensuring fast and accurate identification. The RFID tags feature an anti-metal interference design and an IP68 protection rating to withstand the humid and hot environment of a kitchen.

[0007] The second type is an optical recognition unit. This unit includes a high-resolution image acquisition device, such as a 5-megapixel CMOS image sensor with global shutter functionality, connected to the image processing unit via a MIPICSI-2 interface. The image acquisition device is installed inside the main unit of the food processor, facing the accessory mounting area, and can capture a digital image of the accessory once it is in place. The optical recognition unit also includes an image processing unit, such as a low-power application processor integrating a neural network processing unit (NPU). The image processing unit runs a pre-trained convolutional neural network (CNN) model to analyze the captured images in real time to identify the accessory's geometry, color features, surface texture, and specific markings. The CNN model is based on a deep learning architecture and has been trained and validated with thousands of different accessory image data, achieving an accuracy rate of over 99.5%. The image processing unit sends the identified accessory type and related feature information to the central control unit.

[0008] The third type is the mechanical contact and electrical signal recognition unit. This unit includes a set of mechanical coded pins or an array of electrical contacts located at the mounting interface of the food processor's main unit, and corresponding coded slots or conductive contact pieces located on the bases of each accessory. The mechanical coded pins are arranged in a specific physical configuration and, when an accessory is installed, mechanically interlock with the coded slots on its base to transmit information about the accessory's physical dimensions, shape, or interface type. The electrical contact array consists of several spring-loaded pins with a corrosion-resistant gold plating layer. When an accessory is installed, the pins connect to the conductive contact pieces on the accessory. The conductive contact pieces are connected to the internal storage chip of the accessory via a resistor divider network or a digital encoding circuit to represent the accessory's unique ID and preset parameters through different resistance values ​​or digital signal sequences. The central control unit parses the accessory information by reading the analog voltage signals or digital signals provided by the electrical contact array. The combination of mechanical coded pins and the electrical contact array provides multiple redundant recognition mechanisms, enhancing the robustness and reliability of the recognition.

[0009] The central control unit integrates information from the various identification units mentioned above, employing data fusion algorithms such as Bayesian inference or Kalman filtering to verify and optimize the identification results, generating a high-confidence accessory identification report. This report includes the accessory's unique identifier, general category, specific model, material composition, maximum safe speed, maximum safe torque, recommended operating temperature, and detailed attributes such as whether it is suitable for dishwasher cleaning and whether it is resistant to acid and alkali corrosion. The identification report is then stored in the data storage module and serves as the basis for subsequent adaptive adjustment of operating parameters and cleaning adaptation.

[0010] The adaptive adjustment module for operating parameters dynamically and precisely adjusts the operating parameters of the food processor based on the accessory identification module's information, combined with cooking task requirements, ingredient characteristics, and real-time operating condition feedback. The adaptive adjustment module for operating parameters includes the following components: A multi-dimensional operating condition sensor array. This sensor array includes: Torque sensors, such as non-contact magnetic or optical rotary torque sensors integrated on the motor output shaft or accessory drive shaft, offer an accuracy of 0.5% of full scale and a sampling rate up to 1 kHz. These sensors are used to monitor the torque load experienced by the accessory in real time during operation to assess the mixing resistance of ingredients or the viscoelasticity of dough.

[0011] Temperature sensor arrays, such as those composed of NTC thermistors, offer an accuracy of ±0.5°C. These arrays are strategically positioned within the motor windings, gearbox, and bottom of the mixing bowl to monitor the real-time temperature of critical system components and ingredients. Vibration sensors, such as triaxial MEMS accelerometers with a measurement range of ±16g and 12-bit resolution, are installed inside the food processor to monitor the amplitude and frequency of vibrations during machine operation, detecting abnormal loads or improper component installation.

[0012] Current sensors, such as those based on the Hall effect, have a measurement range of 0-20A and an accuracy of ±1%. These sensors are used to monitor the real-time operating current of a drive motor to reflect its load conditions and energy consumption.

[0013] A vision-based food condition monitoring unit. This unit utilizes the image acquisition device of the optical recognition unit to periodically capture images of the food inside the mixing bowl during the cooking process. The image processing unit runs a specially trained deep learning model to analyze visual features such as the uniformity of food mixing, the degree of gluten development in the dough, and the whipped state of the egg whites.

[0014] The Adaptive Control Algorithm Unit integrates a Model Predictive Controller (MPC) and a Reinforcement Learning (RL) agent. The MPC constructs and predicts a dynamic model of the processing procedure within a certain time window based on identified accessory characteristics, user-inputted cooking goals, and real-time data from a multi-dimensional sensor array. The RL agent continuously learns and optimizes the MPC's control strategy, receiving feedback from the sensor array and food condition monitoring unit as reward signals to adjust the motor's speed, direction of rotation, running time, and intermittent mode to achieve optimal cooking results and efficiency. The RL agent employs a Deep Q-Network (DQN) or policy gradient algorithm, continuously exploring and utilizing these techniques during actual operation to adapt to the complexity of different ingredients and cooking tasks.

[0015] The motor drive unit includes a high-performance brushless DC (BLDC) motor with an integrated high-resolution encoder. It also includes a motor controller based on a field-oriented control (FOC) algorithm, enabling precise and smooth control of the BLDC motor's speed and torque. The FOC controller receives control commands from the adaptive control algorithm unit and drives the BLDC motor via a three-phase inverter, ensuring the components operate under optimal parameters.

[0016] The cleaning adaptation module automatically generates and executes a customized cleaning program based on the accessory identification module's information and the detection results of accessory residue after cooking. The cleaning adaptation module includes the following components: Residue Detection Unit. This unit utilizes the image acquisition device of the optical recognition unit to recapture images of the appliance surface and the interior of the mixing bowl after the cooking process is complete. The image processing unit runs a specially trained deep learning model to identify the types and distribution areas of food residue. Furthermore, the residue detection unit includes a miniature spectrometer or a multi-channel chemical sensor array, which can be used to detect specific chemical substances remaining on the appliance surface, such as sugar, protein, or fat content, to provide more detailed residue information. The spectrometer operates in the visible and near-infrared bands, identifying the composition of substances by analyzing reflectance or absorption spectra.

[0017] A multi-channel detergent dispensing system. The system comprises a pump assembly integrating multiple independent micro-peristaltic pumps, each with a flow rate ranging from 10 to 100 ml per minute and an accuracy of ±2%. The peristaltic pump assembly connects to multiple replaceable detergent reservoirs, each storing different types of detergents such as enzyme-based detergents, degreasers, disinfectants, and rinsing agents. The central control unit precisely controls the operating time and flow rate of each peristaltic pump based on the identified component material, residue type, and cleaning level requirements, delivering the appropriate amount of detergent to the cleaning area.

[0018] Multimodal physical cleaning systems. Physical cleaning systems include: The high-pressure nozzle array, composed of multiple micro-orifice nozzles, each with an orifice diameter of 0.3 mm and an operating pressure range of 5 to 10 bar, is strategically positioned within the cleaning chamber of the food processor. It sprays high-pressure water onto the surfaces of the attachments and the inner walls of the mixing bowl to remove stubborn residue. The spray direction and angle are precisely controlled by a micro-stepping motor, achieving complete coverage cleaning of complex-shaped attachments.

[0019] The ultrasonic cleaning unit consists of multiple piezoelectric ceramic transducers operating at a frequency of 40 kHz with a total power of 50 watts. The transducers are embedded in the bottom of the mixing bowl or within the mounting structure, generating high-frequency sound waves to create a cavitation effect in the cleaning fluid, thereby removing tiny particles and hard-to-reach dirt.

[0020] The rotating brush head assembly, driven by a miniature DC motor, is equipped with a variety of replaceable brush heads, such as silicone, nylon, or soft-bristled brush heads. During cleaning, the brush head assembly rotates and moves at a preset trajectory and speed according to the shape of the accessory and the characteristics of the residue, mechanically scrubbing the accessory surface. The selection of the brush head, rotation speed, and movement trajectory are determined by an algorithm based on accessory identification information and residue type by the central control unit.

[0021] Drying and Sterilization Unit. This unit includes a PTC heating element and a tangential fan. The PTC heating element has a rated power of 500 watts and features self-limiting temperature characteristics to ensure safety during the heating process. The tangential fan, driven by a 12V DC motor, operates at a flow rate of 10 cubic feet per minute, evenly distributing the hot air generated by the PTC heating element to the fittings and the interior of the mixing bowl to accelerate the drying process. Furthermore, the drying and sterilization unit integrates a UV-C LED array with a peak wavelength of 275 nanometers and a total radiant flux of 100 milliwatts. The UV-C LED array briefly irradiates the surface of the fittings after drying to achieve a broad-spectrum sterilization effect.

[0022] Based on information from the residue detection unit, accessory information from the accessory identification module, and a pre-set cleaning strategy database, the central control unit automatically generates and executes a complete cleaning procedure using a rule-based expert system or state machine logic. The cleaning procedure includes parameters such as detergent type and dosage, water pressure, ultrasonic cleaning time, brush head rotation mode, drying time, and UV-C irradiation duration. For example, for stainless steel kneading hooks with dough residue, the system will prioritize enzyme-based detergents, combined with high-pressure water jets and mechanical scrubbing, followed by hot air drying and UV-C sterilization. For juicing components with electronic parts, the system will select milder detergents and lower-pressure water jets, avoiding ultrasonic cleaning to protect sensitive parts.

[0023] The central control unit is the core of the entire system. It employs a high-performance 32-bit ARM Cortex-M7 microcontroller with a clock frequency of up to 400 MHz, integrating a floating-point unit (FPU), a digital signal processing (DSP) instruction set, and ample on-chip memory. The microcontroller runs a real-time operating system (RTOS) to efficiently manage the concurrent execution of multiple tasks. The central control unit communicates with and transmits control signals through various bus interfaces and accessory identification modules, adaptive adjustment modules for operating parameters, cleaning adaptation modules, power management modules, and user interaction modules.

[0024] The power management module is responsible for the power supply, distribution, and protection of the entire food processor system. This module includes an AC-DC switching power converter, which converts AC mains power into the low-voltage DC power required by the system. The power management module also contains multiple independent DC-DC buck and boost converters to provide stable voltage for modules with different voltage requirements. Furthermore, this module integrates overvoltage protection, undervoltage protection, overcurrent protection, and short-circuit protection circuits to ensure safe operation of the system under abnormal conditions. The power management module also features energy recovery functionality, such as feeding some kinetic energy back to the DC bus during motor deceleration, improving system energy efficiency.

[0025] The user interaction module includes a 7-inch high-resolution color LCD touchscreen display with a resolution of 1024x600 pixels. The touchscreen displays current accessory information, cooking progress, real-time operating parameters, cleaning status, and provides operation menus and user feedback. The module also includes a voice recognition and speech synthesis unit, allowing users to control the food processor via natural language commands and receive voice feedback. The voice recognition unit uses an offline speech recognition chip, featuring low latency and high accuracy. The module also integrates a Wi-Fi module (IEEE 802.11b / g / n) and a Bluetooth Low Energy (BLE) module for wireless connectivity with external smart devices (such as smartphones and tablets), enabling remote control, recipe synchronization, firmware updates, and system diagnostics. The Wi-Fi module supports the 2.4GHz band with a maximum data transfer rate of 150Mbps.

[0026] The data storage module consists of high-speed non-volatile memory. The data storage module is used to store the following information: Parts Parameter Database: Contains detailed technical parameters and attributes of all identified parts, used to support parts identification and operation parameter adjustment.

[0027] Food ingredient properties database: Contains physical and chemical properties data of common food ingredients, such as density, viscosity, water content, heat capacity, etc., as well as ideal parameter ranges under different processing conditions, to assist adaptive control algorithms in making decisions.

[0028] Cooking recipe database: Stores the processing steps, parameter sequences, and target states of preset recipes, which users can select and load.

[0029] Cleaning procedure database: Stores cleaning strategies, cleaning agent ratios, physical cleaning parameters, and drying and sterilization parameters corresponding to different accessories and different types of residues.

[0030] User preference settings: Stores users' personalized cooking habits, frequently used recipes, and cleaning preferences. Operation logs and fault diagnosis data: Records key parameters, event logs, and error messages during system operation for system maintenance and fault analysis.

[0031] This invention achieves intelligent, full-lifecycle management of multi-functional accessories for a food processor through the coordinated operation of the aforementioned modules. When a user installs an accessory, the accessory identification module instantly and accurately identifies the accessory type and all relevant attributes. The central control unit receives this information and, combined with the user's input cooking goals, retrieves the corresponding ingredient characteristics and cooking recipe data from the data storage module. Based on this information and real-time feedback from a multi-dimensional operating condition sensor array, the adaptive adjustment module dynamically adjusts the speed, rotation direction, and operating mode of the motor drive unit through its embedded adaptive control algorithm unit, ensuring that the food processor completes the cooking task with optimal performance. The user interaction module displays the cooking progress and system status in real time and allows the user to make necessary interventions.

[0032] After the cooking task is completed, the cleaning adaptation module is activated. The residue detection unit first accurately identifies and classifies residues on the surfaces of the accessories and inside the mixing bowl. Then, the central control unit, based on the accessory identification information and residue detection results, searches the cleaning program database or intelligently generates the most suitable cleaning plan for the current situation. The cleaning adaptation module's detergent dispensing system, multimodal physical cleaning mechanism, and drying and sterilization unit work collaboratively in a precisely controlled manner according to the generated cleaning plan, efficiently and thoroughly cleaning, drying, and sterilizing the accessories. This process requires no manual intervention from the user, greatly reducing the user's cleaning burden and ensuring the cleanliness and hygiene of the accessories. The data storage module records each identification, operation, and cleaning data, providing a basis for subsequent system optimization and user habit learning.

[0033] The beneficial effects of this invention are as follows: By automatically and accurately identifying the multi-functional accessories of the food processor, the tediousness and errors of manually selecting and setting programs by users are eliminated, and the ease of operation is improved. Through a multi-dimensional sensor array and adaptive control algorithm, the food processor can dynamically optimize operating parameters based on accessory type, ingredient characteristics, and real-time processing status, thereby significantly improving the accuracy, efficiency, and final result of cooking. By integrating innovative residue detection and multimodal physical cleaning mechanisms, it can intelligently customize cleaning solutions according to the characteristics of accessories and the types of residues, achieving fully automatic, efficient, and thorough cleaning, drying, and sterilization. This effectively solves traditional cleaning problems, ensures food hygiene and safety, and extends the service life of accessories. Through the seamless integration of the central control unit, data storage module, and user interaction module, a highly intelligent food processor ecosystem has been built, supporting functions such as remote control, recipe synchronization, and firmware updates, providing an excellent user experience. Attached Figure Description

[0034] The invention will now be further described with reference to the accompanying drawings.

[0035] Figure 1 This is a system block diagram of the intelligent control system for automatic identification and cleaning adaptation of multi-functional accessories of the food processor of the present invention; Figure 2 This is a structural framework diagram of the accessory identification module in the system of this invention.

[0036] In the diagram: 1. Central control unit; 2. Accessory identification module; 3. Adaptive adjustment module for operating parameters; 4. Cleaning adaptation module; 5. Power management module; 6. User interaction module; 7. Data storage module; 21. Radio frequency identification unit; 211. Radio frequency identification reader; 212. Radio frequency identification tag; 22. Optical identification unit; 221. Image acquisition device; 222. Image processing unit; 23. Mechanical contact and electrical signal identification unit; 231. Mechanical coding pin; 232. Electrical contact array; 233. Conductive contact piece; 234. Memory chip. Detailed Implementation

[0037] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0038] Reference Figure 1 As shown, the multi-functional automatic identification and cleaning adaptation intelligent control system for food processors of the present invention comprises seven interconnected and complementary modules in its core architecture: a central control unit 1, an accessory identification module 2, an adaptive adjustment module for operating parameters 3, a cleaning adaptation module 4, a power management module 5, a user interaction module 6, and a data storage module 7. The central control unit 1, as the intelligent core of the system, is responsible for instruction scheduling, data fusion, task management, and communication coordination between modules, ensuring the efficient operation of the entire system as an organic whole.

[0039] Specifically, the accessory identification module 2 is cleverly integrated into the accessory mounting interface of the stand mixer main unit. Its main function is to instantly and accurately identify the key attributes of any type of accessory when it is physically connected to this mounting interface. These attributes include, but are not limited to, the accessory's general type (e.g., mixing paddle, dough hook, whisk), specific model, main material used (e.g., stainless steel, food-grade polymer, silicone), structural dimensions, maximum speed limit within the safe operating range, maximum torque limit, recommended operating temperature range, and any potential special cleaning requirements (e.g., whether it is dishwasher safe, resistant to acid and alkali corrosion, or sensitive to specific cleaning agents). To achieve this multi-dimensional identification capability, the accessory identification module 2 integrates various advanced identification technologies to ensure robustness and accuracy under different environments and accessory types.

[0040] Reference Figure 2 As shown, in a specific embodiment, the accessory identification module 2 includes a radio frequency identification (RFID) unit 21. This RFID unit 21 consists of an RFID reader 211 and / or multiple RFID tags 212. The RFID reader 211 is tightly embedded in the inner wall of the food processor's main unit mounting interface, and its operating frequency is set in the UHF 860-960MHz band. The reader can communicate non-contactly with the RFID tags 212 via electromagnetic induction or radio waves. The RFID tags 212 are pre-embedded in miniaturized form into the base or structure of different food processor accessories. These tags employ a passive or semi-active design, requiring no power supply, and are pre-programmed with the accessory's unique identifier (UID) and detailed technical parameters, such as maximum speed, torque limit, material code, and cleaning instructions. When the accessory is correctly installed into the food processor interface by the user, the RFID reader 211 can quickly and accurately capture the information stored in the RFID tag 212 at a data transmission rate of up to 640 kilobits per second within an effective reading distance of 0 to 50 centimeters, and transmit this data to the central control unit 1 via the SPI (Serial Peripheral Interface) bus. To adapt to the common metal utensils and humid environment of the kitchen, the RFID tag 212 features an anti-metal interference design and IP68-level waterproof and dustproof protection, ensuring reliability under harsh operating conditions.

[0041] Furthermore, the accessory recognition module 2 also integrates an optical recognition unit 22. The core component of this optical recognition unit 22 is a high-resolution image acquisition device 221, which employs a 5-megapixel CMOS image sensor with global shutter functionality. This image sensor is connected to the dedicated image processing unit 222 via MIPICSI-2 (Mobile Industry Processor Interface - Camera Serial Interface 2). The image acquisition device 221 is strategically installed inside the main unit of the food processor, its field of view precisely covering the accessory installation area. After the accessory is installed, the image acquisition device 221 is triggered to capture a digital image of the accessory. The image processing unit 222 is equipped with a low-power application processor integrating a neural network processing unit (NPU) designed for efficient image analysis. This processor runs a convolutional neural network (CNN) model pre-trained and validated on thousands of different accessory image datasets. This CNN model can analyze the captured image in real time, accurately identifying the accessory's geometry, specific color features, surface texture, and preset identifiers. After rigorous testing, the CNN model achieves a recognition accuracy rate exceeding 99.5%, ensuring highly reliable accessory classification. The identified accessory type and related visual feature information are then transmitted to the central control unit 1 via the UART (Universal Asynchronous Receiver / Transmitter) interface as a supplement and cross-validation of the identification results.

[0042] As a supplementary identification method of the present invention, the accessory identification module 2 may also include a mechanical contact and electrical signal identification unit 23. This unit consists of two parts: a set of mechanical coded pins 231 or an array of electrical contacts 232 disposed at the mounting interface of the food processor main unit, and corresponding coded slots or conductive contact pieces 233 disposed on the base of each accessory. The mechanical coded pins 231 exist in a specific physical arrangement and geometric dimensions. When the accessory is installed, these coded pins will precisely mechanically interlock with the coded slots on the accessory base. This interlocking mechanism not only ensures the correct installation of the accessory, but also transmits information about the physical dimensions, shape, or interface type of the accessory to the central control unit 1 through a specific combination of coded pins. The electrical contact array 232 consists of several gold-plated spring contacts with high corrosion resistance, which ensure reliable electrical connection even in humid or oily environments. When the accessory is installed, the contacts connect with the corresponding conductive contact pieces 233 on the accessory base. The conductive contact pieces 233 are further connected to the memory chip 234 inside the accessory through an internal resistor divider network or digital encoding circuit. The memory chip 234 pre-stores the unique ID and preset parameters of the accessory. Using different combinations of resistance values ​​or digital signal sequences, the central control unit 1 can parse detailed information about the accessory by reading the analog voltage signal or digital signal provided by the electrical contact array 232. This combination of mechanical coding pins and the electrical contact array provides a multi-redundant identification mechanism, significantly enhancing the robustness, reliability, and anti-interference capability of the entire accessory identification system, ensuring accuracy even if a single identification method fails.

[0043] After receiving identification information from the RFID unit 21, optical identification unit 22, and mechanical contact and electrical signal identification unit 23, the central control unit 1 initiates an advanced data fusion algorithm. For example, it uses Bayesian inference or Kalman filtering algorithms to perform real-time verification and optimization of multi-source data. This multimodal data fusion mechanism can effectively eliminate errors or uncertainties that may exist in a single sensor, ultimately generating a highly confident accessory identification report. This report is extremely detailed, including not only the accessory's unique identifier, general category, and specific model, but also its material composition (e.g., food-grade 304 stainless steel, BPA-free polypropylene), maximum safe speed limit, maximum safe torque limit, recommended optimal operating temperature range, and detailed attributes such as whether it is suitable for dishwasher cleaning and whether it is sensitive to specific chemicals (e.g., bleach). This identification report is then securely stored in the accessory parameter database of the data storage module 7, serving as key foundational data for subsequent adaptive adjustment of operating parameters and customized cleaning adaptation.

[0044] After the accessories are successfully identified, the adaptive adjustment module 3 will dynamically and precisely adjust and optimize the operating parameters of the food processor based on the accurate accessory information provided by the accessory identification module 2, combined with the user-input cooking task requirements, the specific characteristics of the current ingredients, and real-time operating condition feedback. The core of this module lies in its ability to combine static accessory attributes with dynamic processing to achieve intelligent and precise control.

[0045] The adaptive adjustment module 3 for operating parameters contains a multi-dimensional sensor array. This array consists of various high-precision sensors used to monitor the physical state of the food processor and the ingredients in real time. A non-contact magnetic or optical rotary torque sensor, integrated on the motor output shaft or accessory drive shaft, offers an accuracy of up to 0.5% of full scale and a sampling rate of up to 1 kHz. This sensor can monitor the torque load experienced by the accessory during operation in real time and at high frequency, thereby accurately assessing the resistance generated by ingredients during mixing, kneading, or stirring, or changes in the viscoelasticity of dough.

[0046] The temperature sensor array, composed of multiple high-precision NTC (negative temperature coefficient) thermistors, has a measurement accuracy of ±0.5°C. These sensors are strategically placed in key hot-spot areas of the food processor, including the drive motor windings, the inside of the gearbox, and the bottom of the mixing bowl, to monitor the temperature of the system's core components and the food being processed in real time, preventing overheating or unsuitable temperatures from affecting the quality of the food.

[0047] A triaxial MEMS (Micro-Electro-Mechanical Systems) accelerometer with a measurement range of ±16g and a 12-bit resolution is securely mounted inside the food processor. This sensor monitors the amplitude, frequency, and direction of vibrations during machine operation in real time, enabling rapid detection of potential faults such as abnormal loads, improper component installation, motor imbalance, or bearing wear, thus improving operational safety.

[0048] This current sensor, based on the Hall effect principle, has a measurement range of 0-20A and an accuracy of ±1%. It is used to monitor the instantaneous operating current of the drive motor in real time, accurately reflecting the motor load and system energy consumption through the current change curve, providing a key input for adaptive control.

[0049] The vision-based food condition monitoring unit utilizes the image acquisition device shared by the optical recognition unit 221 in the accessory recognition module 2 to capture real-time digital images of the food inside the mixing bowl at preset intervals or triggered events during the cooking process. The image processing unit 222 runs a specially trained deep learning model to perform advanced visual analysis on these images. This model can accurately assess visual features such as the uniformity of food mixing (e.g., no dry powder lumps in the batter), the degree of gluten development in the dough (by observing changes in surface gloss, texture, and elasticity), and the whipped state of egg whites (through the fineness of the foam, volume expansion rate, and stability), providing intuitive and quantitative feedback for adaptive control.

[0050] The core controller of the adaptive adjustment module 3 is the adaptive control algorithm unit. This unit embeds a model predictive controller (MPC) and a reinforcement learning (RL) agent. Based on the accessory characteristics provided by the accessory recognition module 2, the user-input cooking goals (e.g., "kneading dough to the extended stage," "whipping egg whites to stiff peaks"), and real-time data provided by the multi-dimensional working condition sensor array, the MPC constructs and predicts a dynamic model of the processing within a certain time window. By solving an optimization problem, the MPC determines a series of optimal control variables to ensure that the system, while meeting constraints (e.g., maximum speed, maximum torque, safe temperature), approaches the preset cooking goals as closely as possible. The RL agent continuously learns and optimizes the MPC's control strategy. It receives feedback from the working condition sensor array and the ingredient status monitoring unit (e.g., drastic torque fluctuations, abnormal temperature increases, dough viscosity exceeding the range, or visually unsatisfactory egg white foaming) as reward or penalty signals. The RL agent employs Deep Q-Network (DQN) or Policy Gradient Algorithm to continuously explore and utilize the system during actual operation. Through trial and error and experience accumulation, it adjusts the speed, direction of rotation, running time, and intermittent mode of the motor drive unit to adapt to the complexity and uncertainty of different ingredients and cooking tasks, ultimately achieving optimal cooking results (e.g., ideal dough elasticity, perfect egg white foaming) and energy efficiency. This hybrid control strategy combining MPC and RL balances the model's accurate predictive capabilities with its real-time self-learning and adaptive capabilities.

[0051] The actuator of the adaptive adjustment module 3 is a high-performance motor drive unit. This unit includes an advanced brushless DC (BLDC) motor with an integrated high-resolution incremental encoder, such as 2048 pulses per revolution, providing extremely precise speed and position feedback, ensuring smooth motor operation and fine-grained control. The motor drive unit also includes a motor controller based on a field-oriented control (FOC) algorithm. The FOC controller can precisely decouple the motor's flux linkage and torque current, achieving independent, precise, and smooth control of the BLDC motor's speed and torque. The FOC controller receives real-time control commands (such as target speed and target torque) from the adaptive control algorithm unit and drives the BLDC motor through a high-efficiency three-phase inverter. This design ensures that the components operate with optimized parameters at all times and under any load, maximizing energy conversion efficiency and minimizing noise and vibration.

[0052] After the cooking task is completed, the cleaning adaptation module 4 is activated. Its responsibility is to automatically generate and execute a customized and efficient cleaning program based on the accessory information provided by the accessory identification module 2 and the detection results of the accessories and residues inside the mixing bowl.

[0053] The cleaning adaptation module 4 incorporates a residue detection unit. This unit utilizes the image acquisition device shared with the optical recognition unit 221 to recapture digital images of the appliance surface and the interior of the mixing bowl after the cooking task is completed. The image processing unit 222 runs a deep learning model specifically trained for residue recognition, capable of accurately identifying the type of food residue (e.g., high-protein dough residue, high-fat oil stains, easily adhered fruit and vegetable fibers, or sugar crystals) and its distribution area on the appliance surface. This allows for highly targeted cleaning strategies. Furthermore, the residue detection unit integrates a more sophisticated miniature spectrometer or multi-channel chemical sensor array. The spectrometer operates in the visible and near-infrared bands (400-1000nm), identifying the chemical composition of residues by analyzing the spectral characteristics reflected or absorbed from the appliance surface, such as accurately determining the content of sugar, protein, or fat. The multi-channel chemical sensor array can detect specific chemical markers. These multimodal detection methods provide more refined and quantitative residue information, ensuring precise matching of cleaning solutions.

[0054] To achieve customized cleaning, the cleaning adapter module 4 includes a multi-channel detergent dispensing system. This system consists of a pump assembly integrating multiple independent micro-peristaltic pumps. The flow rate of each peristaltic pump is precisely controlled between 10 and 100 ml per minute with an accuracy of ±2%. This precise flow control ensures the economy and effectiveness of detergent usage. The peristaltic pump assembly is connected to multiple replaceable detergent reservoirs, which store different types and functions of specialized detergents, such as enzyme-based detergents for proteins and starches, powerful degreasers, broad-spectrum disinfectants, and rinsing agents for final rinsing. The central control unit 1 precisely controls the operating time and flow rate of each peristaltic pump based on the identified accessory material, residue type, residue level, and user-defined cleaning level requirements, delivering the appropriate amount of detergent to the accessory surfaces and the inner wall of the mixing bowl within the cleaning chamber according to the programmed procedure.

[0055] Cleaning adapter module 4 further includes a multimodal physical cleaning mechanism, designed to efficiently remove various types of residues through multiple physical actions: The high-pressure nozzle array, composed of multiple micro-orifice nozzles, each with an orifice diameter designed to be 0.3 mm, can spray high-pressure water jets within a working pressure range of 5 to 10 bar. Strategically positioned within the cleaning chamber of the food processor, the nozzle array's spray direction and angle are precisely controlled by a micro-stepping motor. This precise control allows the high-pressure water jets to achieve complete coverage of complex-shaped component surfaces and the inner walls of the mixing bowl, effectively removing stubborn residues.

[0056] The ultrasonic cleaning unit consists of multiple high-performance piezoelectric ceramic transducers, operating at a frequency of 40 kHz with a total power of 50 watts. The transducers are cleverly embedded in the bottom of the mixing bowl or within the fixture's mounting structure. By generating high-frequency sound waves, these transducers create millions of tiny cavitation bubbles in the cleaning fluid. The powerful impact of these bubbles bursting instantaneously effectively removes tiny particles and hard-to-reach dirt adhering to the surface of the fixture, making it particularly suitable for precision or finely structured fixtures.

[0057] The rotating brush head assembly, driven by a miniature DC motor, is designed with a variety of replaceable brush heads, such as soft silicone, durable nylon, or fine soft bristles. During cleaning, under the precise algorithmic decisions of the central control unit 1, the brush head assembly rotates and moves at a preset trajectory and speed based on the geometry of the accessory and the characteristics of the residue, mechanically scrubbing the accessory surface and removing stubborn stains that are difficult to remove with water or ultrasound. The selection of the brush head, rotation speed, and movement trajectory are all optimized based on accessory identification information and residue detection results.

[0058] After cleaning, the cleaning adapter module 4 also includes a drying and sterilization unit. This unit integrates a 500-watt PTC (Positive Temperature Coefficient) heating element with self-limiting temperature characteristics, ensuring safety and energy efficiency during the heating process. A tangential fan driven by a 12V DC motor, with an airflow of 10 cubic feet per minute, evenly and efficiently blows the hot air generated by the PTC heating element into the accessories and the interior of the mixing bowl, accelerating moisture evaporation and achieving rapid drying. Furthermore, the drying and sterilization unit integrates a UV-C LED array with a peak wavelength of 275 nanometers and a total radiant flux of 100 milliwatts. After hot air drying, the UV-C LED array briefly irradiates the surface of the accessories to achieve a broad-spectrum sterilization effect, effectively eliminating bacteria, viruses, and other microorganisms, ensuring the ultimate hygiene of food contact surfaces.

[0059] As the core decision-maker in the cleaning adaptation process, the central control unit 1 automatically generates and executes a complete, highly customized cleaning procedure based on information provided by the residue detection unit, accessory information provided by the accessory identification module 2, and a pre-set cleaning strategy database, using a rule-based expert system or state machine logic. For example, for stainless steel kneading hooks with a large amount of high-protein dough residue, the system will prioritize the use of enzyme-based detergents rich in protease, combined with the physical impact of high-pressure water jets and the mechanical scrubbing of a rotating nylon brush head to efficiently decompose and remove stubborn stains. Subsequently, it will perform thorough rinsing, rapid drying with hot air, and finally UV-C sterilization to ensure thorough cleaning. For juicing components with integrated electronic components or precision bearings, the system will intelligently select milder, non-corrosive detergents, use low-pressure water jets for rinsing, and carefully avoid ultrasonic cleaning or high-speed scrubbing to protect sensitive components from damage and extend their service life. This intelligent cleaning strategy requires no manual intervention or selection from the user, greatly reducing the user's cleaning burden and fundamentally ensuring the cleanliness of accessories and food contact safety.

[0060] The Central Control Unit 1 is the central nervous system of the entire system, and its hardware core uses a high-performance 32-bit ARM Cortex-M7 microcontroller. This microcontroller has a clock frequency of up to 400 MHz and integrates a floating-point unit (FPU) and a digital signal processing (DSP) instruction set, enabling it to efficiently handle complex control algorithms and data fusion tasks. It also has ample on-chip memory, including 2MB of flash memory for storing firmware and data, and 1MB of static random access memory (SRAM) for runtime data and stack. The microcontroller runs a real-time operating system (RTOS), which can efficiently manage the execution of multiple concurrent tasks, ensuring the system's responsiveness and stability. The Central Control Unit 1 communicates with the accessory identification module 2, the adaptive adjustment module for operating parameters 3, the cleaning adapter module 4, the power management module 5, and the user interaction module 6 through a series of standardized bus interfaces (such as SPI, I2C, UART, and CAN bus) for high-speed and reliable data communication and control signal transmission, building a seamless intelligent ecosystem.

[0061] Power Management Module 5 is responsible for the power supply, precise distribution, and multiple protections of the entire food processor system. This module first includes a high-efficiency AC-DC switching power converter, whose task is to accurately convert the input mains power (e.g., 220VAC) into the low-voltage DC power required by the system (e.g., 24VDC). Power Management Module 5 further includes multiple independent DC-DC buck and boost converters, which provide stable, accurate, and isolated voltage supplies for modules with different voltage requirements (e.g., high-performance motor drivers require high current 24V, sensor arrays may require 5V, and LED arrays may require 12V), ensuring that each module operates at its optimal voltage. In addition, this module fully integrates overvoltage protection (OVP), undervoltage protection (UVP), overcurrent protection (OCP), and short-circuit protection (SCP) circuits, which can quickly cut off the power or limit the current in the event of grid fluctuations or abnormal operating conditions within the system (such as a sudden current surge caused by motor stall), thereby maximizing the protection of system hardware and user safety. As an advanced energy efficiency enhancement function, the power management module 5 also has an energy recovery function. For example, when the BLDC motor decelerates or brakes, it can feed some of the kinetic energy back to the DC bus through the inverter, thereby realizing the recycling of energy and further improving the overall energy efficiency of the system.

[0062] User interaction module 6 serves as the bridge between the user and the stand mixer system. Its core component is a high-resolution 7-inch color LCD touchscreen display with a resolution of 1024x600 pixels, providing clear and vivid visual feedback. The touchscreen not only intuitively displays currently identified accessory information, real-time progress of the cooking task, various operating parameters (such as speed, torque, and temperature), and cleaning status, but also provides an intuitive and user-friendly graphical user interface (GUI), allowing users to select menus, set parameters, and receive system feedback via touch. To provide a more convenient interaction method, user interaction module 6 also integrates a voice recognition and speech synthesis unit. The voice recognition unit uses a dedicated offline voice recognition chip, which features low latency and high accuracy, enabling users to control the stand mixer using natural language commands (such as "start kneading," "adjust speed to level 3," "start cleaning") and receive voice feedback (such as "kneading complete, dough has reached the extended stage"). In addition, the user interaction module 6 also includes a built-in Wi-Fi module (supporting IEEE 802.11b / g / n standards, operating in the 2.4GHz band, with a maximum data transfer rate of 150Mbps) and a Bluetooth Low Energy (BLE) module for wireless connectivity with external smart devices such as smartphones and tablets. This connectivity enables users to achieve advanced functions such as remote control, recipe synchronization, remote firmware updates, and remote system diagnostics, creating a highly interconnected smart kitchen experience.

[0063] The data storage module 7 consists of high-speed, high-reliability non-volatile memory, such as a 16GB eMMC flash memory chip. This module serves as the carrier of system memory and knowledge base, and is used for long-term storage of the following key information: Accessory Parameter Database: This comprehensive database contains detailed technical parameters and attributes of all identified accessories, such as physical dimensions, material type, maximum safe operating parameters, recommended cleaning methods, and special precautions for each accessory. This database forms the basis for accessory identification and operating parameter adjustment. Ingredient Property Database: This database stores physical and chemical property data of common ingredients, such as the protein content of different types of flour, the density of different eggs, the fat content of butter, and the viscosity, moisture content, and heat capacity of various ingredients. Furthermore, it includes ideal parameter ranges for these ingredients under different processing conditions (e.g., ideal moisture content for dough, ideal whipping density for egg whites), providing precise decision-making basis for adaptive control algorithms.

[0064] Cooking Recipe Database: Stores the processing step sequences for various complex recipes, the required operating parameters for each step (such as rotation speed, time, and temperature curves), and the expected target state. Users can easily select and load recipes, and the system will automatically guide them through the cooking process. Cleaning Program Database: Stores cleaning strategies corresponding to different accessories and different residue types, recommended detergent ratios, physical cleaning parameters (such as water pressure, ultrasonic time, and brush head movement modes), and drying and sterilization parameters.

[0065] User Preference Settings: Records users' personalized cooking habits, frequently used recipes, preferred cleaning modes, and other system settings to provide a more personalized user experience. Operation Logs and Fault Diagnosis Data: Records detailed changes in key parameters during system operation, important event logs, any detected error messages and fault codes. This data is crucial for later system maintenance, performance optimization, and fault analysis.

[0066] The automatic identification and cleaning adaptation intelligent control system for multi-functional accessories of the food processor of the present invention achieves a highly intelligent closed-loop management of the entire life cycle of the food processor through the seamless collaborative work of the above-mentioned modules.

[0067] When a user installs any multi-functional accessory onto the food processor main unit, the accessory recognition module 2 immediately activates, using its multimodal recognition technology to identify the accessory's type and all relevant attributes with extremely high speed and accuracy. After receiving this precise accessory information, the central control unit 1 combines it with the cooking goals or preset recipes entered by the user on the user interaction module 6, and retrieves the corresponding ingredient characteristic data and recipe steps from the data storage module 7.

[0068] Subsequently, the adaptive adjustment module 3 for operating parameters is activated. It deeply integrates these preset information with real-time feedback from a multi-dimensional sensor array (including precise torque load, temperature of key components, machine vibration amplitude, motor operating current, and real-time visual status of the ingredients). Through its embedded adaptive control algorithm unit (MPC and RL agent), the system can dynamically and precisely adjust the motor drive unit's speed, rotation direction, running time, and intermittent mode to ensure the food processor always completes the current cooking task with optimal performance. For example, it can precisely control the gluten development of the dough during kneading or achieve perfect fluffiness and stability when whipping egg whites. During this process, the user interaction module 6 displays the cooking progress, current operating parameters, and system status in real time, allowing users to intuitively intervene or adjust as needed.

[0069] After the cooking task is successfully completed, the cleaning adaptation module 4 is automatically activated. Its primary task is to accurately identify and classify residues on the surfaces of the accessories and inside the mixing bowl using the residue detection unit, down to the type and distribution area of ​​the residues. Subsequently, the central control unit 1 will intelligently search the cleaning program database or generate a customized cleaning plan best suited to the current situation based on the accessory identification information and residue detection results. This plan will precisely specify the type and amount of detergent, the pressure and spray pattern of the high-pressure water jet, the duration and intensity of ultrasonic cleaning, the rotation mode and movement trajectory of the brush head assembly, as well as the subsequent drying time and UV-C irradiation duration. The multi-channel detergent dispensing system, multimodal physical cleaning mechanism, and drying and sterilization unit of the cleaning adaptation module 4 will work together in a precisely controlled manner according to the generated cleaning plan to efficiently and thoroughly clean, dry, and sterilize the accessories. This fully automatic process requires no manual intervention from the user, greatly reducing the user's cleaning burden and ensuring the cleanliness and hygiene of the accessories from both physical and microbiological perspectives. Meanwhile, the data storage module 7 records in detail each accessory identification, operating parameters, cooking effect, and cleaning data, providing valuable data for subsequent system performance optimization, algorithm iteration, and deep learning of user habits.

[0070] The system of this invention, through its highly integrated intelligent control, greatly enhances the user experience and expands the functional boundaries of the food processor. To more specifically illustrate the technical advantages of this invention, embodiments and comparative examples are provided below.

[0071] Example: Using a stand mixer's multi-functional accessory automatic identification and cleaning adaptation intelligent control system for bread dough kneading and cleaning. In this embodiment, the user wishes to use a stand mixer to knead 500 grams of high-gluten flour bread dough.

[0072] Accessory Identification Stage: The user installs the kneading hook (accessory type: kneading hook, material: food-grade 304 stainless steel, maximum speed: 150 RPM, maximum torque: 8 Nm, no electronic components, dishwasher safe) into the food processor interface. The RFID unit 211 in accessory identification module 2 immediately reads the RFID tag 212 inside the kneading hook to obtain its UID and preset parameters. Simultaneously, the optical identification unit 221 captures an image of the kneading hook, and the CNN model of the image processing unit 222 identifies it as a kneading hook. The mechanical contact and electrical signal identification units 231 / 232 also provide physical interlocking and electrical coding signals. The central control unit 1 integrates all information, confirms the type, material, and safe operating parameters of the kneading hook with high confidence, and stores the information in the data storage module 7.

[0073] Adaptive adjustment phase of operating parameters: The user selects the "bread dough kneading" task through the user interaction module 6 and inputs the flour weight (500 grams). The central control unit 1 retrieves the characteristic parameters of high-gluten flour from the ingredient characteristic database of the data storage module 7 and loads the standard program for "bread dough kneading" from the cooking recipe database. The adaptive control algorithm unit of the adaptive adjustment module 3 initially sets the initial speed and kneading time of the BLDC motor drive unit based on the characteristics of the kneading hook, the amount of flour, and the target (dough reaching the expansion stage).

[0074] Once kneading begins, a multi-dimensional sensor array starts real-time monitoring: a torque sensor monitors the dough's viscosity and resistance in real time, with a lower torque initially, which gradually increases as the dough forms and develops gluten; a current sensor monitors the motor's load current; a temperature sensor monitors the dough temperature at the bottom of the mixing bowl; and a vibration sensor monitors the machine's stability.

[0075] The visual food condition monitoring unit periodically captures dough images, and the CNN model of the image processing unit analyzes the dough's mixing uniformity, surface smoothness, and elasticity to assess the degree of gluten development.

[0076] The adaptive control algorithm unit continuously receives this real-time data as feedback. For example, if the dough viscosity is too high, causing the torque to remain above a preset threshold, the RL agent will instruct the FOC controller to appropriately reduce the motor speed by 10% while extending the kneading time. If the dough temperature approaches a critical value (e.g., 26°C), the system will automatically enter intermittent mode or reduce the speed to prevent the dough from overheating. When the visual analysis model confirms that the dough has reached the expansion stage (e.g., it can be stretched into a thin film, but the breaking edges are irregular), and the torque and current curves tend to stabilize, the system determines that the kneading task is complete and stops the motor.

[0077] Cleaning adaptation phase: After the kneading task is completed, the cleaning adaptation module 4 is activated.

[0078] The residue detection unit used an image acquisition device to photograph the mixing bowl and kneading hook again. A deep learning model identified a small amount of dough residue adhering to the surface of the kneading hook, mainly composed of starch and protein. A miniature spectrometer further confirmed the residual amounts of protein and starch.

[0079] Based on the identified information about the kneading hook (stainless steel, without electronic components) and the residue detection results (dough residue), the central control unit 1 selects a customized cleaning program for stainless steel dough residue from the cleaning program database.

[0080] The detergent dispensing system precisely dispenses 30 ml of enzyme-based detergent (targeting starch and protein) and 50 ml of rinsing agent into the cleaning chamber via a peristaltic pump.

[0081] The multimodal physical cleaning mechanism is then activated: a high-pressure nozzle array sprays high-pressure water at 8 bar to initially rinse the dough hook and remove most of the loose residue; then, the ultrasonic cleaning unit operates at a frequency of 40 kHz for 5 minutes to remove fine residue through cavitation; finally, a rotatable nylon brush head mechanically scrubs the surface of the dough hook at a medium speed (500 RPM) along a preset path for 30 seconds to ensure that there is no residue.

[0082] After cleaning, the drying and sterilization unit is activated: the PTC heating element provides hot air, and a tangential fan blows hot air to dry the kneading hook for 10 minutes, ensuring no water residue remains. After drying, the UV-C LED array is activated and irradiated for 30 seconds to perform broad-spectrum sterilization on the kneading hook.

[0083] User interaction module 6 displays "Cleaning complete, accessories have been disinfected".

[0084] Comparative example: Traditional non-intelligent food processors performing the same task In this comparative example, a traditional stand mixer without automatic recognition, adaptive control, and intelligent cleaning functions was used to perform the same bread dough kneading and cleaning.

[0085] Accessory Identification and Parameter Setting: Users need to manually install the kneading hook. Since the system cannot recognize the accessory, users must manually select the appropriate kneading speed (e.g., speed 3) and kneading time (e.g., 10-15 minutes) based on experience or by referring to the instruction manual, or by trial and error. Without torque, temperature, or visual feedback, users find it difficult to accurately judge the dough's condition.

[0086] Operation process: The user starts the stand mixer and kneads the dough at a constant speed for a set time.

[0087] If the dough is too hard or too soft, the system cannot detect it, which may cause the motor to overload, increase vibration, or prevent the dough from reaching the ideal state.

[0088] Users need to manually stop the machine and repeatedly remove the dough to observe its gluten development, which is time-consuming and laborious, and may result in over- or under-kneading due to untimely observation.

[0089] Without temperature monitoring, the dough may overheat due to prolonged kneading, affecting fermentation and the final taste.

[0090] If the accessories are not installed properly or malfunction during the kneading process, the system will not be able to issue a warning, which may lead to safety hazards.

[0091] Cleaning process: After kneading, the user needs to manually remove the kneading hook from the machine.

[0092] Users need to determine the type of residue and cleaning method themselves.

[0093] The dough hook usually needs to be manually scrubbed with a brush, sponge, and detergent to remove dough residue. Once the dough dries, it becomes very hard and difficult to clean.

[0094] For stubborn stains inside the mixing bowl, a long soaking time may be necessary.

[0095] Using cleaning agents based solely on experience can lead to waste or incomplete cleaning.

[0096] It lacks drying and sterilization functions, requiring users to manually wipe or air dry it, which poses a risk of secondary contamination and cannot guarantee microbial hygiene.

[0097] Data Comparison and Analysis The table below compares the key performance indicators of the embodiments of the present invention with those of a traditional non-intelligent food processor in kneading 500g of high-gluten flour bread dough and subsequent cleaning tasks. All tests were conducted in a controlled laboratory environment and repeated 5 times, with the average value taken.

[0098] Dough consistency score: 10 points represents perfect gluten development, moderate elasticity, and uniform internal structure. Traditional stand mixers lack real-time feedback, resulting in unstable dough conditions and a tendency to under- or over-knead.

[0099] Surface residue particle count: Counted over a 1 square centimeter area using an optical microscope.

[0100] The data comparison above demonstrates that the multi-functional accessory automatic identification and cleaning adaptation intelligent control system of this invention exhibits significant advantages in both the kneading and cleaning stages. During kneading, the system, through precise identification and adaptive control, not only reduces kneading time by 28%, but more importantly, improves the dough consistency score from 7.5 points using traditional methods to 9.8 points, ensuring ideal dough quality every time. This also reduces motor load and machine vibration, extending equipment lifespan and improving safety. Precise temperature control during kneading also prevents damage to dough activity due to overheating. In the cleaning stage, the system achieves fully automatic, efficient, and thorough cleaning, saving 50% of total time and reducing detergent and water consumption by 33% and 48% respectively, significantly reducing resource consumption. Crucially, the number of residual particles and the total number of colonies on the accessory surfaces after cleaning by this system are significantly lower than with manual cleaning, fundamentally ensuring food hygiene and safety. Users do not need any manual intervention, greatly improving ease of use and freeing them from tedious repetitive labor.

[0101] In summary, the multi-functional automatic identification and cleaning adaptation intelligent control system for stand mixers disclosed in this invention, through its precise accessory identification capabilities, intelligent adaptive control algorithms, multi-modal residue detection, and customized fully automatic cleaning solutions, not only solves many pain points of existing stand mixers in terms of automation, precision, and hygiene cleaning, but also constructs a future kitchen solution that is efficient, safe, intelligent, and offers an excellent user experience. This system design is highly modular and scalable, flexibly adapting to the introduction of new accessories and the upgrading of new functions in the future, setting a new benchmark for the development of intelligent kitchen appliances.

[0102] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multifunctional accessory automatic identification and cleaning adaptive intelligent control system for a chef machine, characterized in that, The system comprises: a central control unit (1) for coordinating and managing the system's function execution and data interaction; a accessory identification module (2) for automatically identifying the type, material, size, physical structure, maximum speed limit, maximum torque limit, optimal working temperature range, and cleaning special requirements of the accessory when it is connected, and transmitting the identification results to the central control unit (1); an operating parameter adaptive adjustment module (3) for dynamically adjusting and optimizing the operating parameters based on the information provided by the accessory identification module (2), combined with the cooking task requirements, food material characteristics, and real-time working condition feedback; a cleaning adaptation module (4) for automatically generating and executing a customized cleaning program based on the information provided by the accessory identification module (2) and the detection results of the residual materials on the accessory after cooking is completed; a power management module (5) for providing power supply, distribution, and protection; a user interaction module (6) for displaying system information, providing operation menus, and user feedback interfaces; a data storage module (7) for storing accessory parameters, food material characteristics, cooking recipes, cleaning programs, user preference settings, and operation logs and fault diagnosis data.

2. The multifunctional accessory automatic identification and cleaning adaptive intelligent control system of the chef machine according to claim 1, characterized in that, The accessory identification module (2) is implemented through at least one identification technology, including a radio frequency identification unit (21), an optical identification unit (22), or a mechanical contact and electrical signal identification unit (23).

3. The multifunctional accessory automatic identification and cleaning adaptive intelligent control system of the chef machine according to claim 2, characterized in that, The radio frequency identification unit (21) comprises: a radio frequency identification reader (211) for reading accessory information; a radio frequency identification tag (212) pre-embedded in the accessory, storing the unique identification code and technical parameter information of the accessory; When the accessory is correctly installed, the radio frequency identification reader (211) communicates with the tag (212) to obtain the accessory information.

4. The automatic identification and cleaning adaptive intelligent control system for multi-functional accessories of a chef machine according to claim 2, characterized in that, The optical identification unit (22) comprises: an image acquisition device (221) for capturing digital images of the accessory; an image processing unit (222) running a pre-trained convolutional neural network model to analyze the captured images in real time to identify the geometric shape, color features, surface texture, and specific identification of the accessory.

5. The automatic identification and cleaning adaptive intelligent control system for multi-functional accessories of a chef machine according to claim 2, characterized in that, The mechanical contact and electrical signal identification unit (23) comprises: a set of mechanical coding pins (231) or electrical contact arrays (232) provided on the host; a set of coding grooves or conductive contact sheets (233) provided on the base of each accessory, the conductive contact sheets (233) being connected to the storage chip (234) inside the accessory to represent the unique ID and preset parameters of the accessory; The mechanical coding pins (231) and the coding grooves are mechanically interlocked to transmit the physical information of the accessory, and the electrical contact arrays (232) and the conductive contact sheets (233) are connected to provide electrical information of the accessory, and the central control unit (1) analyzes the accessory parameters by reading the above information.

6. The multi-functional accessory automatic identification and cleaning adaptive intelligent control system of the chef machine according to claim 4, characterized in that, The operating parameter adaptive adjustment module (3) comprises a multi-dimensional working condition sensor array, which comprises: a torque sensor for detecting torque; a temperature sensor array for detecting temperature; a vibration sensor for detecting vibration; a current sensor for detecting current; The vision-based food status monitoring unit uses the image acquisition device (221) to capture food images and uses the image processing unit (222) to run a deep learning model to analyze the food status.

7. The multifunctional accessory automatic identification and cleaning adaptive intelligent control system of the chef machine according to claim 6, characterized in that, The adaptive adjustment module (3) for operating parameters also includes an adaptive control algorithm unit; The adaptive control algorithm unit integrates a model predictive controller and a reinforcement learning agent; The model predictive controller constructs and predicts a dynamic model of the processing process based on the identified accessory characteristics, the user-input cooking goals, and the real-time data provided by the multi-dimensional working condition sensor array, and determines the control variables. The reinforcement learning agent continuously learns and optimizes the control strategy, adjusting the motor's operating parameters by receiving feedback as a reward signal.

8. The multi-functional accessory automatic identification and cleaning adaptive intelligent control system of the chef machine according to claim 1, characterized in that, The cleaning adapter module (4) includes: A residue detection unit is used to identify the types and distribution areas of food residues. A multi-channel detergent dispensing system, comprising multiple independent micro peristaltic pumps connected to multiple replaceable detergent reservoirs; The central control unit (1) controls the delivery of cleaning agents based on the identified accessory material, type of residue and cleaning level requirements.

9. The multifunctional accessory automatic identification and cleaning adaptive intelligent control system of the chef machine according to claim 8, characterized in that, The cleaning adaptation module (4) also includes a multimodal physical cleaning mechanism, which includes: High-pressure nozzle array for spraying cleaning fluid; An ultrasonic cleaning unit is used to generate ultrasonic waves for cleaning. The rotating brush head assembly comes with a variety of replaceable brush heads for cleaning based on the shape of the accessory and the characteristics of the residue.

10. The multifunctional accessory automatic identification and cleaning adaptive intelligent control system of the chef machine according to claim 9, characterized in that, The cleaning adapter module (4) also includes a drying and sterilization unit, which includes: Heating elements are used to generate heat; A fan is used to blow hot air onto the fittings and mixing bowl; A UV-CLED array is used to sterilize the surface of the parts by irradiation after drying.

Images