Food preparation service system device and method of intelligent cooking equipment

Abstract

The invention provides an intelligent cooking equipment household food preparation service system device and method. The intelligent cooking equipment household food preparation service system device comprises an intelligent cooking equipment terminal 100, a cloud service system 200 and a supply chain management system 300. Three technical main lines of food material digitization, cooking process programming and kitchen space intelligentization are adopted, and the problem that the dynamic sensing ability of existing intelligent cooking equipment on the food material state and environment is lack of deep integration with a prefabricated food material supply chain is solved; the intelligent level of equipment is insufficient, the cross-equipment cooperative ability is weak, and the equipment cannot completely adapt to different food materials and user requirements (only fixed recipes can be executed, and the characteristics of the food materials cannot be dynamically adapted); according to the method, the problems that full-process automatic cooking decisions are not achieved, so that the sticky mechanism of a user is weak (the rate of charge is low due to pure hardware sales) are solved, and good life of food safety, environmental protection, convenience, health management and the like is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of food preparation of intelligent cooking devices, and particularly relates to a service system and method integrating intelligent cooking devices, prefabricated food distribution, cloud recipe management and user interaction. Background Art

[0002] The food preparation service of intelligent cooking devices refers to intelligent cooking devices with multimedia functions, such as fully automatic stir-fry machines, low-temperature slow cookers, multi-functional cooking machines, intelligent electric steamers, etc. Starting from personalized service, through the platform of traditional cooking stoves, kitchen appliances are integrated by using infrared temperature measurement technology, sensing technology, automatic control technology, security prevention technology, audio and video technology, and combined with the distribution of raw materials in a newly emerging industry, "home food preparation service", through technologies such as network communication, Internet of Things, cloud computing and artificial intelligence, to form a business model called "intelligent kitchen ecosystem" in the industry.

[0003] In essence, this model moves the kitchen part of the restaurant forward to the consumer's home, only distributes pre-processed ingredients and seasoning packs, and combines with the deep integration of the prefabricated food supply chain. Users can quickly complete the final cooking process through intelligent cooking devices, realizing true "cook and eat immediately", and forming a convenient, comfortable and simple cooking environment.

[0004] Currently, the development report of the food delivery industry points out that the growth rate of the food delivery industry has dropped from nearly 200% in 2015 to 12.3% in 2024, and the growth curve is gradually flattening. All signs indicate that the traditional food delivery model is hitting the ceiling. Traditional food delivery faces problems such as rising costs, poor user experience and environmental protection issues, while a new industry is emerging.

[0005] And intelligent cooking devices are in a critical period of transitioning from "tool replacement" to "experience reconstruction". With the development of technology, certain automation functions have been achieved through the integration and innovation of automation and Internet of Things technologies. For example, current mainstream intelligent cooking devices (such as intelligent rice cookers, air fryers) have achieved basic automation, controlling temperature and time through preset programs; or through the integration of the Internet of Things (IoT), some devices have started to be connected to the home IoT, supporting remote control and data synchronization (such as Midea and Siemens brands); or integrating the initial application of AI, a few high-end products introduce AI recipe recommendations (such as Haier's AI dietitian), relying on users to manually input preferences, etc.

[0006] Looking ahead, technological innovation pursues a better life in aspects such as efficiency improvement, food safety, environmental protection, convenience, and health management, forming a super ecosystem centered around the home kitchen and connecting agriculture, logistics, and the health industry. In this ideal process, the three major technological mainlines of digitizing ingredients, programmable cooking processes, and intelligent kitchen spaces will become the key fulcrums for reshaping human dietary civilization.

[0007] In summary, the existing defects in the "intelligent kitchen ecosystem" technology are as follows: 1. The existing intelligent cooking equipment lacks the deep integration of the dynamic perception ability of ingredient status and environment with the prefabricated ingredient supply chain; 2. The level of equipment intelligence is insufficient, and the cross-device collaboration ability is weak, unable to fully adapt to different ingredients and user needs (only able to execute fixed recipes and unable to dynamically adapt to ingredient characteristics); 3. The full-process automated cooking decision-making has not been realized, resulting in a weak user stickiness mechanism (low renewal rate due to simple hardware sales). Summary of the Invention

[0008] Aiming at the defects of the existing technology, the purpose of the present invention is to provide an intelligent cooking equipment home food preparation service system device and method, which integrates the three major technological mainlines of digitizing ingredients, programmable cooking processes, and intelligent kitchen spaces. The intelligent kitchen ecosystem solves these problems through preprocessing ingredients and intelligent devices to achieve a "smart kitchen ecosystem" better life in aspects such as food safety, environmental protection, convenience, and health management.

[0009] The purpose of the present invention is achieved through the following technical solutions: An intelligent cooking equipment food preparation service system device and method, comprising an intelligent cooking equipment terminal, a cloud service system, and a supply chain management system architecture; characterized in that: the intelligent cooking equipment terminal refers to at least one full-automatic stir-fry machine, low-temperature slow cooker, multi-functional blender, intelligent electric steamer, etc. These devices support functions in aspects such as audio, video, and data, and also include: equipped with a multi-modal sensing system, an adaptive cooking unit, and a human-computer interaction interface, and connected to the cloud service system through network communication and the Internet of Things. The supply chain management system can dynamically adjust cooking parameters according to the physical characteristics of ingredients.

[0010] The multi-modal sensing system also includes integrated temperature and humidity sensors, infrared rangefinders, and gas detection modules. Through the sensor collaborative working mechanism, the elastic modulus of the ingredient is detected by the pressure sensor as the main texture (range 0-50 kPa, accuracy ±0.5%), the surface deformation displacement is verified by the infrared rangefinder as an auxiliary (resolution 0.01 mm), and the chemical signal of texture change is supplemented by gas detection, such as the concentration of volatile organic compounds (detection limit 1 ppm), to dynamically monitor the texture change of the ingredient and dynamically adjust the texture change of the ingredient; Principle basis: Mechanical response model (Math mathematics / Delta change amount P = k * / frac{ / delta h}{h0} + b), where: ΔP: Pressure change amount (kPa), k: Stiffness coefficient of the food ingredient (N / m³), δh: Deformation amount measured by the infrared rangefinder (mm), h0: Initial thickness (mm), b: Environmental compensation coefficient; The working process of the multi-modal sensing system (1001): S1 Sensor group ->> Control unit: Real-time pressure data (sampling rate 10Hz); S2 Sensor group ->> Control unit: Synchronized infrared deformation data; S3 Control unit ->> Control unit: Calculate texture parameters (elastic modulus / viscosity coefficient); S4 Control unit ->> Actuator: Adjust parameters (heating power ±15% / stirring frequency 2 - 15Hz); S5 Actuator ->> Sensor group: Form a closed-loop feedback, fuse multi-sensor data, use the D-S evidence theory to weight the confidence of pressure, infrared, and gas data, and trigger parameter adjustment when the confidence > 90%, and dynamically adjust the real-time monitoring of the texture change of the food ingredient; The implementation steps of the multi-modal sensing system for real-time monitoring of the texture change of the food ingredient and dynamic adjustment: Step 1. Initial stage: Detect a pressure value of 18.3 kPa (corresponding to medium rare); Step 2. After cooking for 5 minutes: The pressure drops to 15.2 kPa (the k value drops by 12%); Step 3. System response: Reduce the electromagnetic heating power by 8%; Extend the standing time by 30 seconds; Trigger the steam replenishment instruction (humidity +10% RH).

[0011] The adaptive cooking unit further includes: An electromagnetic heating module with a PID temperature control algorithm + a variable-frequency stirring device; The electromagnetic heating module with the PID temperature control algorithm measures the error between the actual temperature and the set temperature continuously during temperature control, and adjusts the output of the heating or cooling device according to the size, change trend, and historical error of the error, which can ensure the heating power output (±15% fluctuation compensation), flipping frequency (adjustable from 2 to 15 times per second), and feeding timing (error ≤ 3 seconds), thereby improving quality and efficiency; The variable-frequency stirring device also includes an intelligent speed-adjusting stirring head and dynamic frequency control, which can automatically increase or decrease the rotation speed of high-elastic food ingredients and low-elastic food ingredients (the maximum rotation speed of high-elastic food ingredients does not exceed 15 revolutions per second, and the rotation speed of low-elastic food ingredients automatically slows down to 2 revolutions per second); The calculation of the control quantity of the PID temperature control electromagnetic heating module follows the formula: The output of the PID controller $u(t)=K_p e(t)+K_i\int_{0}^{t} e(\tau)d\tau+K_d \frac{de(t)}{dt}$, where $K_p$, $K_i$, and $K_d$ are the proportional, integral, and derivative coefficients respectively.

[0012] Among them, $K_p$, $K_i$, and $K_d$ correspond to the proportional, integral, and derivative coefficients respectively. Parameter tuning needs to be combined with the system characteristics. For example, the critical proportion method gradually increases $K_p$ until the system reaches the oscillation critical point, and then calculates the initial parameters according to the Ziegler-Nichols rule; the empirical rule relies on the observer's observation of the system response to gradually optimize the parameter combination. The parameter settings are as follows: the proportional coefficient $K_p = 8.5$ (response speed), the integral time $T_i = 12s$ (eliminating steady-state error), and the derivative time $T_d = 0.8s$ (suppressing overshoot). The control theory of the PID temperature control electromagnetic heating module 1021 is a classical control method based on error feedback. By adjusting the parameters of the proportional, integral, and derivative links, the system stability and optimization are achieved. Its core lies in dynamically adjusting the control quantity to quickly eliminate errors. The proportional control ($K_p$) directly responds to the current error and quickly reduces the deviation; the integral control ($T_i$) eliminates the steady-state error caused by the accumulation of historical errors; the derivative control ($T_d$) predicts the change trend of errors and suppresses overshoot and oscillation. The three work together to improve the response speed and ensure the system stability. The implementation steps of the adaptive cooking unit control are as follows: Step 1. Initialization stage: Preset the reference temperature according to the type of ingredients (such as 180°C for meat). Step 2. Real-time adjustment: Collect temperature data every 200ms (PT1000 sensor); calculate the PID output value → PWM modulation (duty cycle 10 - 100%). Step 3. Safety protection: Trigger an emergency power-off when the temperature exceeds the set value by ±20°C. The human-machine interface (1003) refers to at least one hardware part including a processor, a display unit, an input unit, a communication interface, etc., and a software part including system software and screen configuration software, which is used to implement the interaction logic and visual presentation between the user and the system; it also includes support for multi-channel control such as touch / voice / mobile APP.

[0013] The cloud service system refers to at least one cloud server and a central processing unit, and further includes: a prefabricated recipe database, a dynamic scheduling engine, an AI recommendation system, which is connected to the cooking equipment terminal through a network, and a supply chain management system, which can dynamically adapt to the characteristics of ingredients; the prefabricated recipe database: further includes storing more than 5000 standardized recipes (including ingredient ratios / cooking curves / nutritional data); the dynamic scheduling engine: further includes an LBS-based optimized algorithm for the ingredient delivery route; the AI recommendation system: further includes generating personalized menus by analyzing users' eating habits through machine learning. The dynamic scheduling engine: refers to at least being configured with data collection: real-time collection of data related to scheduling, such as equipment status, task progress, resource availability, etc.; data analysis: analyzing the collected data to identify the current status and potential problems; decision-making: formulating adjustment plans according to the analysis results, such as reallocating resources or adjusting task priorities; execution and feedback: implementing the adjustment plan and continuously monitoring the execution effect to form a closed-loop feedback; it also includes an LBS-based optimized algorithm for the ingredient delivery route. The dynamic scheduling engine is a management method for resource allocation and task adjustment in a scheduling environment and tasks with unpredictable disturbances. The dynamic scheduling engine can make immediate adjustments according to real-time feedback data (such as equipment status, task progress, market demand, etc.), so as to optimize the overall process, improve resource utilization rate and response speed. The dynamic scheduling engine is used to optimize the delivery route and vehicle scheduling, reduce delivery time and fuel consumption, and is used to allocate computing resources to ensure that high-priority tasks are completed first. The AI recommendation system 2003: further includes generating personalized menus by analyzing users' eating habits through machine learning.

[0014] The supply chain management system includes: a central kitchen pretreatment center, an intelligent warehousing network, and a cold chain distribution system to ensure the health and hygiene of the whole life cycle of ingredients; the central kitchen pretreatment center: further includes: a food processing production line certified by HACCP; the intelligent warehousing network: further includes: tracking the whole life cycle of ingredients using RFID technology; the cold chain distribution system: further includes: distributed storage nodes maintaining a constant temperature of 4±1°C. The central kitchen pretreatment center: further includes: a food processing production line certified by HACCP. The intelligent warehousing network: further includes: tracking the whole life cycle of ingredients using RFID technology. The cold chain distribution system: further includes: distributed storage nodes maintaining a constant temperature of 4±1°C. The execution process of the central kitchen pretreatment center includes: S1: The central kitchen performs ingredient pretreatment (cutting accuracy ≤ 2mm). S2: Vacuum low-temperature slow cooking (60 - 70 °C / 30 minutes); S3: Quick freezing (-40 °C / 90 minutes); S4: Modified atmosphere packaging (CO2:N2 = 3:7); The execution process of the supply chain management system includes: S1: The user reserves dinner through the APP, and the system automatically matches the prefabricated food package with the fastest delivery time. S2: Initialization stage: Preset the reference temperature according to the type of ingredients (such as 180 °C for meat). S3: After receiving the instruction, the device starts the preheating program and completes the cooking preparation 15 minutes in advance. S4: Real-time adjustment: Collect temperature data every 200 ms (PT1000 sensor), calculate the PID output value → PWM modulation (duty cycle 10 - 100%, safety protection: trigger emergency power-off when the temperature exceeds the set value by ±20 °C). S5: Feeding timing control: The cloud issues timestamps (NTP synchronization error ≤ 0.1 s), the local FPGA hardware timing triggers, and the pneumatic valve is controlled (opening and closing time 80 ms). S6: After cooking is completed, it automatically enters the heat preservation mode and maintains a temperature of 65 °C ± 2 °C for 2 hours.

[0015] Beneficial effects: An intelligent cooking device food preparation service system device and method integrate three major technical threads: digitizing ingredients, programming the cooking process, and intelligentizing the kitchen space, to solve the problems of the lack of dynamic perception ability of existing intelligent cooking devices for the state of ingredients and the environment and the lack of deep integration with the prefabricated food supply chain; the insufficient intelligent level of the device, the weak cross-device collaboration ability, and the inability to fully adapt to different ingredients and user needs (only able to execute fixed recipes and unable to dynamically adapt to the characteristics of ingredients); the lack of full-process automated cooking decision-making, resulting in a weak user stickiness mechanism (low renewal rate due to pure hardware sales), etc., and realizes a better life in terms of food safety, environmental protection, convenience, and health management. Brief Description of the Drawings

[0016] To more clearly illustrate the technical solutions of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0017] Figure 1 Shows the schematic diagram of the architecture of the intelligent cooking device food preparation service system device and method; Figure 2 Shows the implementation step diagram of the working process of the multimodal sensing system; Figure 3Shows the implementation steps diagram of adaptive cooking unit control; Figure 4 Shows the execution flow chart of ingredient pre - treatment in the central kitchen; Figure 5 Shows the execution process of intelligent cooking equipment supply chain management; In the figure, the markings are: intelligent cooking equipment terminal 100, multi - modal sensing system 1001, adaptive cooking unit 1002, human - machine interaction interface 1003, Internet of Things connected cloud service system 200, pre - made recipe database 2001, dynamic scheduling engine 2002, AI recommendation system 2003, supply chain management system 300, central kitchen pre - treatment center 3001, intelligent warehousing network 3002, cold - chain distribution system 3003. Detailed implementation manners

[0018] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0019] As shown in the attached Figure 1 A food preparation service system and method for an intelligent cooking device, including an intelligent cooking device terminal 100, a cloud service system 200, and a supply chain management system 300 architecture.

[0020] As shown in the attached Figure 1 、 2 The multi - modal sensing system 1001 also includes a device - integrated temperature and humidity sensor, an infrared rangefinder, and a gas detection module. Through the sensor collaborative working mechanism, with the pressure sensor as the main component, the elastic modulus of the ingredient is detected (range 0 - 50 kPa, accuracy ±0.5%), the infrared rangefinder is used to assist in verifying the surface deformation displacement (resolution 0.01 mm), and the gas detection supplements the chemical signal of texture change, such as the concentration of volatile organic compounds (detection limit 1 ppm), to dynamically adjust by real - time monitoring of the texture change of the ingredient.

[0021] The principle of dynamic adjustment of the ingredient texture change is based on: Mechanical response model (Math mathematics / Delta change amount P = k * / frac{ / delta h}{h0}+b), where: ΔP: pressure change amount (kPa), k: ingredient stiffness coefficient (N / m³), δh: deformation amount measured by the infrared rangefinder (mm), h0: initial thickness (mm), b: environmental compensation coefficient; Its working process: S1 Sensor group -> Control unit: Real-time pressure data (sampling rate 10Hz); S2 Sensor group -> Control unit: Synchronized infrared deformation data; S3 Control unit -> Control unit: Calculate texture parameters (elastic modulus / viscosity coefficient); S4 Control unit -> Actuator: Adjust parameters (heating power ±15% / stirring frequency 2 - 15Hz); S5 Actuator -> Sensor group: Form a closed-loop feedback, fuse multi-sensor data, use D-S evidence theory to weight the confidence of pressure, infrared, and gas data, and trigger parameter adjustment when the confidence > 90%, and dynamically adjust by real-time monitoring of food texture changes.

[0022] The implementation steps for the multi-modal sensing system to dynamically adjust by real-time monitoring of food texture changes: Step 1. Initial stage: Detect a pressure value of 18.3 kPa (corresponding to medium rare); Step 2. After cooking for 5 minutes: The pressure drops to 15.2 kPa (the k value drops by 12%); Step 3. System response: Reduce the electromagnetic heating power by 8%; Extend the standing time by 30 seconds; Trigger the steam replenishment instruction (humidity +10%RH).

[0023] As shown in the appendix Figure 1 、 3 The adaptive cooking unit 1002, further includes: An electromagnetic heating module with a PID temperature control algorithm + a variable-frequency stirring device. In temperature control, the PID algorithm continuously measures the error between the actual temperature and the set temperature, and adjusts the output of the heating or cooling device according to the error size, change trend, and historical error, which can ensure the heating power output (±15% fluctuation compensation), flipping frequency (adjustable from 2 to 15 times per second), and feeding timing (error ≤ 3 seconds), thereby improving quality and efficiency. The calculation of the control quantity of the PID temperature control electromagnetic heating module follows the formula: SS u(t)=K_p e(t)+K_iNint_0^t e(\tau)d\tau+K_d \frac{de(t){dt}SS.

[0024] Where Kp, Ki, and Ka correspond to the proportional, integral, and differential coefficients respectively.

[0025] Parameter tuning needs to be combined with the system characteristics. For example, the critical ratio method gradually increases Kp until the system reaches the oscillation critical point, and then calculates the initial parameters according to the Ziegler-Nichols rule; the empirical rule relies on the debugger's observation of the system response to gradually optimize the parameter combination.

[0026] Its parameter settings are as follows: proportional coefficient Kp = 8.5 (response speed), integral time Ti = 12 s (to eliminate steady-state error), and derivative time Td = 0.8 s (to suppress overshoot). The control theory of the PID temperature control electromagnetic heating module 1021 is a classic control method based on error feedback. It realizes system stability and optimization by adjusting the parameters of the proportional, integral, and derivative links. Its core lies in dynamically adjusting the control quantity to quickly eliminate errors. Proportional control (Kp) directly responds to the current error and quickly reduces the deviation; integral control (Ti) eliminates the steady-state error caused by the accumulation of historical errors; derivative control (Td) predicts the change trend of errors and suppresses overshoot and oscillation. The three work together to improve the response speed and ensure system stability.

[0027] The implementation steps of the adaptive cooking unit control are as follows: Step 1. Initialization stage: Preset the reference temperature according to the type of ingredients (e.g., 180 °C for meat). Step 2. Real-time adjustment: Collect temperature data every 200 ms (PT1000 sensor); calculate the PID output value → PWM modulation (duty cycle 10 - 100%). Step 3. Safety protection: Trigger an emergency power-off when the temperature exceeds the set value by ±20 °C.

[0028] The human-machine interface 1003 refers to at least one hardware part including a processor, a display unit, an input unit, a communication interface, etc., and a software part including system software and graphic configuration software, which is used to implement the interaction logic and visual presentation between the user and the system; it also includes support for multi-channel control such as touch / voice / mobile APP.

[0029] As shown in the appendix Figure 1 、 5 The cloud service system 200 refers to at least one cloud server and a central processing unit, and also includes: a prefabricated recipe database 2001, a dynamic scheduling engine 2002, and an AI recommendation system 2003, which are connected to the cooking equipment terminal 100 through the network, and a supply chain management system 300, which can dynamically adapt to the characteristics of ingredients.

[0030] The prefabricated recipe database 2001: It also includes storing more than 5000 standardized recipes (including ingredient ratios / cooking curves / nutritional data). The dynamic scheduling engine 2002: It is at least configured with data collection: real-time collection of scheduling-related data, such as equipment status, task progress, resource availability, etc.; data analysis: analysis of the collected data to identify the current status and potential problems; decision-making: formulation of adjustment plans based on the analysis results, such as reallocating resources or adjusting task priorities; execution and feedback: implementation of the adjustment plan and continuous monitoring of the execution effect to form a closed-loop feedback; it also includes an LBS-based optimized algorithm for the food delivery route. The dynamic scheduling engine 2002 is a management method for resource allocation and task adjustment in the presence of unpredictable disturbances in the scheduling environment and tasks. The dynamic scheduling engine can make immediate adjustments based on real-time feedback data (such as equipment status, task progress, market demand, etc.), thereby optimizing the overall process, improving resource utilization rate and response speed. The dynamic scheduling engine is used to optimize the delivery route and vehicle scheduling, reduce delivery time and fuel consumption, and to allocate computing resources to ensure that high-priority tasks are completed first.

[0031] The AI recommendation system 2003: It also includes generating personalized menus by analyzing users' eating habits through machine learning.

[0032] As shown in Appendix Figure 1 、 4 、Figure 5, the supply chain management system 300 includes: a central kitchen pretreatment center 3001, an intelligent warehousing network 3002, and a cold chain distribution system 3003 to ensure the health and hygiene of the entire life cycle of food ingredients.

[0033] The central kitchen pretreatment center 3001: It also includes: a food processing production line certified by HACCP. The intelligent warehousing network 3002: It also includes: tracking the entire life cycle of food ingredients using RFID technology. The cold chain distribution system 3003: It also includes: distributed storage nodes maintaining a constant temperature of 4 ± 1°C.

[0034] The execution process of the central kitchen pretreatment center includes: S1: The central kitchen preprocesses food ingredients (cutting accuracy ≤ 2 mm) S2: Vacuum low-temperature slow cooking (60 - 70°C / 30 minutes) S3: Quick freezing (-40°C / 90 minutes) S4: Modified atmosphere packaging (CO2:N2 = 3:7) The execution process of the supply chain management system includes: S1: The user reserves dinner through the APP, and the system automatically matches the pre-prepared food package with the fastest delivery time. S2: Initialization stage: Preset a reference temperature according to the type of food ingredients (e.g., 180°C for meat); S3: After receiving the instruction, the device starts the preheating program and completes the cooking preparation 15 minutes in advance; S4: Real-time adjustment: Collect temperature data (PT1000 sensor) every 200 ms, calculate the PID output value → PWM modulation (duty cycle 10 - 100%), safety protection: Trigger emergency power-off when the temperature exceeds the set value by ±20°C; S5: Feeding timing control: The cloud issues a timestamp (NTP synchronization error ≤ 0.1 s), triggered by the local FPGA hardware timing, pneumatic valve control (opening and closing time 80 ms); S6: After cooking is completed, it automatically enters the heat preservation mode and maintains a temperature of 65°C ± 2°C for 2 hours.

[0035] The selection and description of the specific implementation manners of the above embodiments are for better explaining the principle and practical applications, so that those skilled in the art can better use the said implementation manners and various different deformed implementation manners suitable for specific uses. The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting it; Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A smart cooking equipment food preparation service system device and method, comprising a smart cooking equipment terminal (100), a cloud service system (200), and a supply chain management system (300) architecture; characterized in that: The intelligent cooking equipment terminal (100) refers to at least one fully automatic cooking machine, a low-temperature slow cooker, a multi-function food processor, an intelligent electric steamer, etc. These devices support audio, video, data and other functions, and also include: the device has a multimodal sensing system (1001), an adaptive cooking unit (1002), a human-computer interaction interface (1003), and is connected to a cloud service system (200) through network communication and the Internet of Things. The supply chain management system (300) can dynamically adjust cooking parameters according to the physical characteristics of the food.

2. The intelligent cooking equipment food preparation service system device and method according to claim 1, characterized in that: The multimodal sensing system (1001) also includes a device integrating a temperature and humidity sensor, an infrared rangefinder, and a gas detection module. Through the sensor collaborative working mechanism, the pressure sensor is used as the main texture to detect the elastic modulus of the food (range 050kPa, accuracy ±0.5%), the infrared rangefinder assists in verifying the surface deformation displacement (resolution 0.01mm), and the gas detection supplements the chemical signal of the texture change, such as the concentration of volatile organic compounds (detection limit 1ppm), to monitor the texture change of the food in real time and dynamically adjust the texture change of the food; Principle: Mechanical response model (Math / Delta change P = k / cdot / frac{ / delta h}{h0} + b), where: ΔP: pressure change (kPa), k: food stiffness coefficient (N / m³), δh: deformation measured by infrared rangefinder (mm), h0: initial thickness (mm), b: environmental compensation coefficient; The multimodal sensing system (1001) has the following working process: S1 sensor group ->> control unit: real-time pressure data (sampling rate 10Hz); S2 sensor group->>control unit: synchronize infrared deformation data; S3 control unit->>control unit: calculate texture parameters (elastic modulus / viscosity coefficient); S4 control unit->>actuator: adjust parameters (fire power ±15% / stirring frequency 2-15Hz); S5 actuator ->> sensor group: form a closed-loop feedback, integrate multi-sensor data, use DS evidence theory to weight the confidence of pressure, infrared, and gas data, trigger parameter adjustment when the confidence > 90%, and monitor the texture changes of food in real time for dynamic adjustment; The multimodal sensing system monitors the texture changes of food materials in real time and dynamically adjusts the implementation steps: Step 1. Initial stage: The pressure value detected was 18.3 kPa (corresponding to medium rare); Step 2. After 5 minutes of cooking: the pressure drops to 15.2 kPa (k value drops by 12%); Step 3. System response: reduce electromagnetic heating power by 8%; extend standstill time by 30 seconds; trigger steam replenishment command (humidity +10%RH).

3. The intelligent cooking equipment food preparation service system device and method according to claim 1, characterized in that: The adaptive cooking unit (1002) further comprises: an electromagnetic heating module equipped with a PID temperature control algorithm + a variable frequency stirring device; the electromagnetic heating module with the PID temperature control algorithm continuously measures the error between the actual temperature and the set temperature during temperature control, and adjusts the output of the heating or cooling device according to the error size, change trend and historical error, thereby ensuring the fire output (±15% fluctuation compensation), the flipping frequency (adjustable 2-15 times per second), and the timing of adding materials (error ≤ 3 seconds), thereby improving the quality and efficiency; the variable frequency stirring device further comprises an intelligent speed regulating stirring head and a dynamic frequency control, which can automatically speed up or slow down the rotation speed of high-elasticity food and low-elasticity food (the rotation speed of high-elasticity food does not exceed 15 revolutions per second, and the rotation speed of low-elasticity food is automatically slowed down to 2 revolutions per second); The control quantity calculation of the PID temperature control electromagnetic heating module follows the formula: SS u(t)=K_p e(t)+K_iNint_0^te(\tau)d\tau+K_d \frac{de(t){dt}SS.

4. Kp, Ki, and Ka correspond to the proportional, integral, and differential coefficients respectively; Parameter tuning needs to be combined with system characteristics. For example, the critical proportion method gradually increases Kp to the critical point of system oscillation, and then calculates the initial parameters according to the Ziegler-Nichols rule. The empirical rule relies on the debugger's observation of the system response and gradually optimizes the parameter combination. Its parameter settings: proportional coefficient Kp=8.5 (response speed), integral time Ti=12s (eliminating steady-state error), differential time Td=0.8s (suppressing overshoot); The PID temperature control electromagnetic heating module 1021 control theory is a classic control method based on error feedback, which achieves system stability and optimization by adjusting the parameters of the three links of proportion, integration and differentiation; its core lies in dynamically adjusting the control amount to quickly eliminate the error, the proportional control (Kp) directly responds to the current error and quickly reduces the deviation; the integral control (Ti) eliminates the steady-state error caused by the accumulation of historical errors; the differential control (Td) predicts the error change trend and suppresses overshoot and oscillation; the three work together to improve the response speed and ensure the stability of the system; The adaptive cooking unit control implementation steps: Step 1. Initialization phase: preset the base temperature according to the type of food (e.g. 180°C for meat); Step 2. Real-time adjustment: collect temperature data every 200ms (PT1000 sensor); calculate PID output value → PWM modulation (duty cycle 10-100%); Step 3. Safety protection: trigger emergency power off when the temperature exceeds the set value by ±20℃; The human-computer interaction interface (1003) refers to at least one hardware part including a processor, a display unit, an input unit, a communication interface, etc., and the software part includes system software and screen configuration software, which are used to implement the interaction logic and visual presentation between the user and the system; it also includes support for touch / voice / mobile phone APP multi-channel control.

5. The intelligent cooking equipment food preparation service system device and method according to claim 1, characterized in that: The cloud service system (200) refers to at least one cloud server and a central processor, and also includes: a pre-made recipe database (2001), a dynamic scheduling engine (2002), an AI recommendation system (2003), which is connected to the cooking device terminal (100) and the supply chain management system (300) through the network and can dynamically adapt to the characteristics of the ingredients; the pre-made recipe database (2001): also includes the storage of 5000+ standardized recipes (including ingredient ratios / cooking curves / nutritional data); the dynamic scheduling engine (2002): also includes an LBS-based ingredient delivery path optimization algorithm; the AI ​​recommendation system (2003): also includes the generation of personalized menus by analyzing user eating habits through machine learning; The dynamic scheduling engine 2002 is configured with at least data collection: real-time collection of scheduling-related data, such as equipment status, task progress, resource availability, etc.; data analysis: analysis of the collected data to identify the current status and potential problems; decision-making: formulation of adjustment plans based on the analysis results, such as reallocation of resources or adjustment of task priorities; execution and feedback: implementation of the adjustment plan and continuous monitoring of the execution effect to form a closed-loop feedback loop; and also includes an LBS-based food delivery route optimization algorithm; The dynamic scheduling engine 2002 is a management method for resource allocation and task adjustment in the presence of unpredictable disturbances in the scheduling environment and tasks. The dynamic scheduling engine can make instant adjustments based on real-time feedback data (such as equipment status, task progress, market demand, etc.), thereby optimizing the overall process, improving resource utilization and response speed. The dynamic scheduling engine is used to optimize delivery routes and vehicle scheduling, reduce delivery time and fuel consumption, and allocate computing resources to ensure that high-priority tasks are completed first; The AI ​​recommendation system 2003 also includes generating personalized menus by analyzing user eating habits through machine learning.

6. The intelligent cooking equipment food preparation service system device and method according to claim 1 or 4, characterized in that: The supply chain management system (300) includes: a central kitchen pre-processing center (3001), an intelligent storage network (3002), and a cold chain distribution system (3003), which ensures the hygiene and health of food throughout its life cycle; the central kitchen pre-processing center (3001) also includes: a food processing line using HACCP certification; the intelligent storage network (3002) also includes: food life cycle tracking using RFID technology; the cold chain distribution system (3003) also includes: distributed storage nodes that maintain a constant temperature of 4±1°C; The central kitchen pre-processing center (3001): also includes: a food processing line using HACCP certification; The intelligent storage network (3002): also includes: food material full life cycle tracking using RFID technology; The cold chain distribution system (3003): also includes: distributed storage nodes that maintain a constant temperature of 4±1°C; The execution process of the central kitchen pre-processing center includes: S1: The central kitchen pre-processes ingredients (cutting accuracy ≤ 2mm); S2: Sous-vide slow cooking (60-70°C / 30 minutes); S3: rapid freezing (-40°C / 90 minutes); S4: Modified atmosphere packaging (CO2:N2=3:7); The supply chain management system execution process includes: S1: Users make dinner reservations through the APP, and the system automatically matches the fastest pre-made meal packages; S2: Initialization stage: preset the base temperature according to the type of food (e.g. 180°C for meat); S3: After receiving the instruction, the device starts the preheating process and completes the cooking preparation 15 minutes in advance; S4: Real-time adjustment: collect temperature data (PT1000 sensor) every 200ms, calculate PID output value → PWM modulation (duty cycle 10-100%, safety protection: trigger emergency power off when exceeding the set value ±20℃; S5: Feeding timing control: cloud sends timestamp (NTP synchronization error ≤ 0.1s), local FPGA hardware timing trigger, pneumatic valve control (opening and closing time 80ms); S6: After cooking is completed, it automatically enters the keep warm mode and maintains 65℃±2℃ for 2 hours.

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