Chef machine intelligent cooking mode switching control method

Abstract

The invention relates to the technical field of kitchen appliances, and discloses a chef machine intelligent cooking mode switching control method, which specifically comprises the following steps that an image acquisition and analysis module acquires image data in a working vessel, and transmits food material parameter data to a cooking mode selection module; according to the intelligent cooking method and system, in the using process, intelligent cooking is completed in an intelligent mode, in this way, in the using process of the cook machine, the cooking mode of the food materials in the working vessel of the current cook machine is completed, and finally intelligent verification is conducted on the cooked food materials, so that in the using process of the cook machine, the cooking efficiency of the cook machine is improved. According to the technical scheme, the processing mode can be intelligently selected directly according to the food materials needing to be cooked, and by adopting the mode, the convenience in the using process of the chef machine can be effectively improved, so that the mode switching step is reduced, and the practicability and the convenience can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent kitchen appliances, and in particular to an intelligent cooking mode switching control method for a chef machine. Background Art

[0002] The chef machine is a multifunctional kitchen appliance that has brought great convenience to modern kitchens. It uses intelligent equipment to complete the stir-frying of dishes to reduce manual labor, which can not only standardize the production of dishes, but also improve cooking efficiency. It is usually equipped with a stirring paddle or a rotating pot body, driven by a motor, and stir-frying the ingredients in the pot according to the preset program and speed. The design of the stirring paddle can ensure that the ingredients are evenly heated during the stir-frying process to avoid local overheating or overcooking. The intelligent temperature control system is used to monitor the temperature in the pot in real time through the temperature sensor. When the temperature is lower than the preset value, the power of the heating device increases and the temperature is quickly raised; when the temperature reaches or exceeds the preset value, the power of the heating device decreases or stops heating, so as to accurately simulate the heat required for different cooking stages, such as stir-frying with high heat, slow stewing with low heat, etc.

[0003] In the prior art, the mode of the food processor often needs to be switched manually according to the ingredients to be processed. If this manual method is used, there is a problem of low flexibility, which results in the need to switch modes back and forth during the cooking process, causing the food processor to have cumbersome and time-consuming steps and interruptions that affect the cooking effect. At the same time, since there are many ways to process ingredients, this makes it difficult to fully control the operation of the food processor, which in turn leads to limitations in its use.

[0004] After searching, it is found that a Chinese patent document discloses an intelligent chef machine, an automatic cooking method, and an application (publication number: CN112716265A). The present invention is applicable to the technical field of kitchen appliances, and provides an intelligent chef machine, an automatic cooking method, and an application. The intelligent chef machine includes a chassis and a rotating pot for making dishes. The intelligent chef machine also includes: a material discharge component, which is arranged at one end of the chassis away from the base; a heating component, which is arranged on one side of the rotating pot; and a control component, which is arranged in the chassis; by transferring the materials to be made into the chassis, controlling the material discharge component to transfer the materials into the rotating pot through the control component and controlling the material transfer sequence and time interval, controlling the heating component to heat the rotating pot through the control component, and controlling the heating time and heating temperature of the heating component, so that the rotating pot mixes, heats, and cooks the materials inside it; the device mechanizes and replaces manual cooking operations, and completes the cooking of ingredients in a standardized manner in a timed and quantitative manner, thereby realizing fully automatic intelligent cooking, thereby reducing the cost of making dishes, but there are still the following defects:

[0005] Although the above-mentioned smart food processor, automatic cooking method and application have achieved fully automatic intelligent cooking, thereby reducing the cost of food preparation, they still have the problem of low flexibility, which requires switching modes back and forth during the cooking process, resulting in the food processor having cumbersome and time-consuming steps and interruptions that affect the cooking effect. At the same time, because there are many ways to process ingredients, it is difficult to fully control the operation of the food processor, which leads to limitations in its use. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent cooking mode switching control method for a chef machine, which solves the problem of low flexibility in the background technology, which results in the need to switch modes back and forth during the cooking process, causing the chef machine to have cumbersome and time-consuming steps and interruptions that affect the cooking effect. At the same time, since there are many ways to process ingredients, this makes it difficult to fully control the operation of the chef machine, which in turn leads to limitations in its use.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] The intelligent cooking mode switching control method of a chef machine comprises the following steps:

[0009] S1: First, when using the chef machine to cook, place the ingredients to be cooked by the chef machine into the working container of the chef machine;

[0010] S2: The image data in the working vessel is collected by the image collection and analysis module in the intelligent control system of the food processor;

[0011] S3: extracting food parameter data through the image acquisition and analysis module, and transmitting the food parameter data to the cooking method selection module;

[0012] S4: completing the cooking method of the ingredients in the working vessel of the current chef machine through the cooking method selection module;

[0013] S5: The chef machine performs intelligent cooking according to the cooking method selected by the decision maker;

[0014] S6: After the food processor completes intelligent cooking, it performs intelligent verification on the cooked ingredients.

[0015] Preferably, the image acquisition and analysis module is electrically connected to the image acquisition probe, and acquires image data in the working vessel through the image acquisition probe. The function of the image acquisition and analysis module is to receive at least one image data, analyze the image data, and acquire at least one food parameter data.

[0016] Preferably, in S3, the specific steps of obtaining food parameter data are:

[0017] S3.1: First, obtain at least one food parameter data in the image data;

[0018] S3.2: construct a self-training model based on the acquired food parameter data;

[0019] S3.3: Compare at least one food parameter data by self-training the model;

[0020] S3.4: Determine the status of the at least one food parameter data obtained.

[0021] Preferably, in S3.4, the specific method for determining the state of the at least one food parameter data obtained is:

[0022] Step 1: Obtain classified image data of all food parameter data;

[0023] Step 2: Obtain color parameter data of classified image data;

[0024] Step 3: Compare the color parameter data with the food doneness parameter data;

[0025] Step 4: Obtain the doneness value of each food parameter data;

[0026] Step 5: Add a floating threshold to the doneness value of each ingredient parameter data to obtain the status value of each ingredient parameter data.

[0027] Preferably, the function of the cooking method selection module is to obtain at least one cooking method according to the set of acquired ingredient parameter data, and after the cooking method is determined, update the cooking progress according to the status value of each ingredient parameter data.

[0028] Preferably, the cooking progress specific updating method steps are:

[0029] (1) Obtaining the status value of each food parameter data;

[0030] (2) Obtaining cooking progress according to the status value of each ingredient parameter data;

[0031] (3) to obtain the cooking progress value;

[0032] (4) The standard cooking steps are updated according to the obtained cooking progress value to obtain the optimal cooking task.

[0033] Preferably, the cooking method selection module is electrically connected to the interaction module, and the role of the interaction module is to interact various information to the display terminal, and the interaction mode of the interaction module includes voice interaction mode and keyboard input interaction mode;

[0034] The specific method of the voice interaction method is as follows: the decision maker communicates with the system by speaking, and the voice interaction method specifically includes that the decision maker issues a voice command based on the interaction information, and the device performs the corresponding operation and gives feedback, and then the interaction ends;

[0035] The specific method of the disk input interaction mode is that on a computer, mobile phone or other device, the user interacts with the system by inputting text through the keyboard.

[0036] Preferably, in S6, when the chef machine completes intelligent cooking, the cooked ingredients are intelligently verified using the intelligent verification module in the intelligent control system, and a number of picture parameter data are obtained from the cooked ingredients through an image acquisition probe, and the cooked ingredients are judged based on the number of picture parameter data to determine whether the dishes meet the serving requirements.

[0037] Preferably, the judging standard is to judge the surface color of the ingredients cooked by the chef machine. Susong judges the color of the ingredients by obtaining the vividness parameter data of the ingredients in the picture parameter data, and uses the vividness parameter data to complete the color judgment. At the same time, it is also necessary to judge the integrity of the ingredients cooked by the chef machine and the clarity of the surface texture of the ingredients cooked by the chef machine.

[0038] Preferably, the intelligent control system includes an image acquisition and analysis module, a cooking method selection module, an interaction module and an energy verification module.

[0039] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0040] 1. During use, the present invention adopts an intelligent method to complete intelligent cooking, so that the chef machine can directly select the processing mode intelligently according to the ingredients to be cooked during use. This method can effectively improve the convenience of using the chef machine, thus reducing the steps of mode switching, and thus effectively improving practicality and convenience.

[0041] 2. During use, the present invention can effectively obtain data on semi-processed ingredients, and intelligently modify subsequent cooking tasks based on the progress of processed ingredients. This makes the chef machine have no limitations in the cooking process of various ingredients, and can effectively improve the efficiency of the chef machine in the cooking process, thereby improving practicality and convenience.

[0042] 3. After the chef machine finishes cooking, the present invention can make a preliminary judgment on whether the ingredients cooked by the chef machine meet the dish serving standards. The acquired image parameter data can be used to judge the surface color of the ingredients cooked by the chef machine, the integrity of the ingredients cooked by the chef machine, and the clarity of the surface texture of the ingredients cooked by the chef machine. Image processing is performed to quickly determine whether the current dish is qualified. This is not available in traditional chef machines, thereby improving the practicality and convenience of the chef machine during use. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of specific steps of the intelligent cooking mode switching control method of the chef machine in the present invention;

[0044] Figure 2 This is a schematic diagram of the specific steps of obtaining food parameter data in the present invention;

[0045] Figure 3 A schematic diagram of the specific steps of a method for determining the state of at least one food parameter data to be obtained in the present invention;

[0046] Figure 4 It is a schematic diagram of the specific steps of the cooking progress updating method in the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1

[0049] See also Figure 1-4 The intelligent cooking mode switching control method of the chef machine includes the following steps:

[0050] S1: First, when using the chef machine to cook, place the ingredients to be cooked by the chef machine into the working container of the chef machine;

[0051] S2: The image data in the working vessel is collected by the image collection and analysis module in the intelligent control system of the food processor;

[0052] S3: extracting food parameter data through the image acquisition and analysis module, and transmitting the food parameter data to the cooking method selection module;

[0053] S4: completing the cooking method of the ingredients in the working vessel of the current chef machine through the cooking method selection module;

[0054] S5: The chef machine performs intelligent cooking according to the cooking method selected by the decision maker;

[0055] S6: After the food processor completes intelligent cooking, it performs intelligent verification on the cooked ingredients.

[0056] The image acquisition and analysis module is electrically connected to the image acquisition probe, and acquires image data in the working vessel through the image acquisition probe. The image acquisition and analysis module is used to receive at least one image data, analyze the image data, and acquire at least one food parameter data.

[0057] The function of the cooking method selection module is to obtain at least one cooking method according to the set of acquired food parameter data. After the cooking method is determined, the cooking progress is updated according to the status value of each food parameter data.

[0058] When several cooking methods are searched, all cooking methods are interacted with the decision maker through an interactive method, so that the decision maker can complete the interaction through the interactive module to determine the cooking method.

[0059] The cooking method selection module is electrically connected to the interaction module, and the role of the interaction module is to interact various information to the display terminal, and the interaction mode of the interaction module includes voice interaction mode and keyboard input interaction mode;

[0060] The specific method of voice interaction is: the decision maker communicates with the system by speaking. The specific method of voice interaction includes that the decision maker issues voice commands based on the interaction information, and the device performs the corresponding operation and gives feedback, and then the interaction ends;

[0061] The specific method of the disk input interaction method is that on computers, mobile phones and other devices, users interact with the system by inputting text through the keyboard.

[0062] The interactive module performs voice enhancement processing in the voice interaction mode to improve the accuracy and effect of voice recognition. It removes silence at the beginning and end through voice activity detection to reduce interference. At the same time, it uses echo cancellation to prevent the microphone from picking up the sound played by the speaker and generating echoes that affect recognition. It also uses beamforming to enhance the original voice signal and suppress bypass signals, thereby playing a role in noise reduction and reverberation.

[0063] In S6, when the food processor completes intelligent cooking, the intelligent verification module in the intelligent control system is used to intelligently verify the cooked ingredients. The image acquisition probe is used to obtain several picture parameter data of the cooked ingredients, and the several picture parameter data are used to judge whether the dishes meet the serving requirements.

[0064] The judging criteria are to determine the surface color of the ingredients cooked by the food processor. Susong determines the color of the ingredients by obtaining the vividness parameter data of the ingredients from the picture parameter data, and uses the vividness parameter data to complete the color judgment. At the same time, it is also necessary to determine the integrity of the ingredients cooked by the food processor and the clarity of the surface texture of the ingredients cooked by the food processor.

[0065] Vividness is achieved by reading food images using Python's OpenCV library and converting the read images from the common RGB color space to HSV. The HSV color space performs better in separating the different attributes of color, among which the V (brightness) channel is closely related to glossiness.

[0066] The food texture clarity operation uses image processing technology to convert color images into grayscale images to simplify subsequent processing. This is because texture clarity is mainly related to brightness changes, and grayscale images are sufficient to capture this information. Image gradients can also highlight detail information such as texture edges. Texture clarity is measured by calculating the gradient amplitude, and statistics such as the mean or standard deviation of the gradient amplitude are calculated as indicators of texture clarity. The larger the mean, the richer the texture details in the image, that is, the higher the texture clarity.

[0067] The intelligent control system includes an image acquisition and analysis module, a cooking method selection module, an interaction module and an energy verification module. The intelligent control system is also electrically connected to the intelligent temperature control system. The function of the intelligent temperature control system is to automatically adjust the temperature to meet the temperature requirements of specific environments or equipment.

[0068] The intelligent temperature control system includes at least a temperature sensor, a controller and an actuator.

[0069] The function of the temperature sensor is to monitor the temperature of the environment or an object in real time, and the temperature sensor is a thermocouple, thermal resistor, thermistor and infrared temperature sensor structure.

[0070] The controller receives the signal from the temperature sensor and compares it with the preset temperature value. Based on the comparison result and a specific control algorithm, it calculates the parameters that need to be adjusted and then issues a control instruction.

[0071] The function of the actuator is to execute the instructions issued by the controller and adjust the temperature. Common actuators include heating devices.

[0072] Example 2

[0073] See also Figure 2 , the intelligent cooking mode switching control method of the chef machine, in S3, the specific steps of obtaining the food parameter data are:

[0074] S3.1: First, obtain at least one food parameter data in the image data;

[0075] S3.2: construct a self-training model based on the acquired food parameter data;

[0076] S3.3: Compare at least one food parameter data by self-training the model;

[0077] S3.4: Determine the status of the at least one food parameter data obtained.

[0078] In S3.2, the self-training model contains a large number of image datasets containing target objects, and the target objects all have standard object names.

[0079] In S3.3, the self-trained model completes the comparison of at least one ingredient parameter data and uses a deep learning framework to build a convolutional neural network. After a long period of training and optimization, it can accurately identify the objects in the picture and output the name.

[0080] Example 3

[0081] See also Figure 3 , the intelligent cooking mode switching control method of the chef machine, in S3.4, the specific method for determining the state of at least one ingredient parameter data obtained is:

[0082] Step 1: Obtain classified image data of all food parameter data;

[0083] Step 2: Obtain color parameter data of classified image data;

[0084] Step 3: Compare the color parameter data with the food doneness parameter data;

[0085] Step 4: Obtain the doneness value of each food parameter data;

[0086] Step 5: Add a floating threshold to the doneness value of each ingredient parameter data to obtain the status value of each ingredient parameter data.

[0087] In step 2, if the color parameter data extraction of the classified image data is unreliable, and the cooking progress indicator is obtained according to the change state of the surface texture of the food, in the process of surface texture acquisition, the gray level co-occurrence matrix texture feature extraction method is used to reflect the texture information according to the spatial distribution relationship of the gray level pairs in the image.

[0088] In step three, the color parameter data is compared with the food doneness parameter data using a comparison model and training the comparison model. The comparison model uses different cooking progresses of 0%, 20%, 40%, 60%, 80%, 100% and so on for each food.

[0089] Example 4

[0090] See also Figure 4 , the chef machine intelligent cooking mode switching control method, the cooking progress specific update method steps are as follows:

[0091] (1) Obtaining the status value of each food parameter data;

[0092] (2) Obtaining cooking progress according to the status value of each ingredient parameter data;

[0093] (3) to obtain the cooking progress value;

[0094] (4) The standard cooking steps are updated according to the obtained cooking progress value to obtain the optimal cooking task.

[0095] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit thereof, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the switching of intelligent cooking modes of a chef machine, characterized in that: The following steps are involved: S1: First, when using the chef machine to cook, place the ingredients to be cooked by the chef machine into the working container of the chef machine; S2: The image data in the working vessel is collected by the image collection and analysis module in the intelligent control system of the food processor; S3: extracting food parameter data through the image acquisition and analysis module, and transmitting the food parameter data to the cooking method selection module; S4: completing the cooking method of the ingredients in the working vessel of the current chef machine through the cooking method selection module; S5: The chef machine performs intelligent cooking according to the cooking method selected by the decision maker; S6: After the food processor completes intelligent cooking, it performs intelligent verification on the cooked ingredients.

2. The method for controlling the intelligent cooking mode switching of a chef machine according to claim 1, characterized in that: The image acquisition and analysis module is electrically connected to the image acquisition probe, and acquires image data in the working vessel through the image acquisition probe. The image acquisition and analysis module is used to receive at least one image data, analyze the image data, and acquire at least one food parameter data.

3. The method for controlling the intelligent cooking mode switching of a chef machine according to claim 1, characterized in that: In S3, the specific steps of obtaining food parameter data are as follows: S3.1: First, obtain at least one food parameter data in the image data; S3.2: construct a self-training model based on the acquired food parameter data; S3.3: Compare at least one food parameter data by self-training the model; S3.4: Determine the status of the at least one food parameter data obtained.

4. The method for controlling the intelligent cooking mode switching of a chef machine according to claim 1, characterized in that: In S3.4, the specific method for determining the state of the at least one food parameter data obtained is: Step 1: Obtain classified image data of all food parameter data; Step 2: Obtain color parameter data of classified image data; Step 3: Compare the color parameter data with the food doneness parameter data; Step 4: Obtain the doneness value of each food parameter data; Step 5: Add a floating threshold to the doneness value of each ingredient parameter data to obtain the status value of each ingredient parameter data.

5. The method for controlling the intelligent cooking mode switching of a chef machine according to claim 1, characterized in that: The function of the cooking method selection module is to obtain at least one cooking method according to the set of acquired food parameter data, and after the cooking method is determined, update the cooking progress according to the state value of each food parameter data.

6. The method for controlling the intelligent cooking mode switching of a chef machine according to claim 5, characterized in that: The specific cooking progress updating method steps are as follows: (1) Obtaining the status value of each food parameter data; (2) Obtaining cooking progress according to the status value of each ingredient parameter data; (3) to obtain the cooking progress value; (4) The standard cooking steps are updated according to the obtained cooking progress value to obtain the optimal cooking task.

7. The method for controlling the intelligent cooking mode switching of a chef machine according to claim 5, characterized in that: The cooking method selection module is electrically connected to the interaction module, and the role of the interaction module is to interact various information to the display terminal, and the interaction mode of the interaction module includes voice interaction mode and keyboard input interaction mode; The specific method of the voice interaction method is as follows. The decision maker communicates with the system by speaking. The voice interaction method includes that the decision maker issues a voice command based on the interaction information, and the device performs the corresponding operation and gives feedback, and then the interaction ends. The specific method of the disk input interaction mode is that on a computer, mobile phone or other device, the user interacts with the system by inputting text through the keyboard.

8. The method for controlling the intelligent cooking mode switching of a chef machine according to claim 1, characterized in that: In S6, when the chef machine completes intelligent cooking, the cooked ingredients are intelligently verified using the intelligent verification module in the intelligent control system. The cooked ingredients are evaluated by obtaining a number of picture parameter data using an image acquisition probe to determine whether the dishes meet the serving requirements.

9. The method for controlling the intelligent cooking mode switching of a chef machine according to claim 1, characterized in that: The judging standard is to determine the surface color of the ingredients cooked by the food processor. Susong determines the color of the ingredients by obtaining the vividness parameter data of the ingredients from the picture parameter data, and uses the vividness parameter data to complete the color judgment. At the same time, it is also necessary to determine the integrity of the ingredients cooked by the food processor and the clarity of the surface texture of the ingredients cooked by the food processor.

10. The method for controlling the intelligent cooking mode switching of a chef machine according to claim 1, characterized in that: The intelligent control system includes an image acquisition and analysis module, a cooking method selection module, an interaction module and an energy verification module.

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