Motion control system based on automatic cooker

Abstract

The invention relates to a motion control system based on an automatic cooker, and belongs to the technical field of automatic control. The system comprises an acquisition unit used for acquiring recipe data, acquiring spectrum data, real-time image data, food material weight and real-time temperature data; the calculation unit is used for acquiring a recipe number according to the recipe data, retrieving a database according to the recipe number to obtain cooking process data and cooking mode data, presetting an auxiliary material placement ratio, obtaining an auxiliary material placement weight according to the auxiliary material placement ratio and the food material weight, and calculating the food material maturity through a maturity model according to the spectral data; according to the real-time image data, obtaining an oil smoke concentration grade through an image processing model; the control unit is used for controlling the cooker assembly to operate through the intelligent terminal according to the real-time temperature data, the cooking process data, the cooking mode data, the auxiliary material placing weight, the food material maturity and the oil smoke concentration grade. The automatic control of the automatic cooker and the gear adjustment of the range hood are realized.

Description

Technical Field

[0001] The invention belongs to the technical field of automatic control, and in particular relates to a motion control system based on an automatic cooking machine. Background Art

[0002] As the pace of modern life accelerates, people's demand for convenient, efficient and healthy cooking methods is growing. Traditional cooking methods are not only time-consuming and labor-intensive, but also difficult to accurately control the degree of cooking of ingredients, resulting in uneven taste and nutritional value of food. As a new type of cooking equipment, automatic cooking machines have improved cooking efficiency to a certain extent, but most products on the market are still at the simple mechanized operation level and lack intelligent cooking control systems.

[0003] At present, the automatic cooking machines on the market have many deficiencies in food processing, cooking process control, and fume management. For example, the preparation of food and the addition of ingredients often rely on the user's manual operation, lacking precision and intelligence; during the cooking process, the maturity of the food is difficult to monitor and control in real time, resulting in unstable cooking results; in addition, the emission of fume also lacks an effective automatic adjustment mechanism, affecting the comfort and safety of the kitchen environment. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a motion control system based on an automatic cooking machine. The purpose of the present invention can be achieved through the following technical solutions: A motion control system based on an automatic cooking machine, comprising: a collection unit, a calculation unit, and a control unit; The acquisition unit is used to obtain recipe data through a human-computer interaction interface, preset an acquisition frequency, collect spectrum data through a spectrum detector according to the acquisition frequency, collect real-time image data through a built-in visual sensor of the cooking machine, collect food weight through a built-in gravity sensor of the storage bin, and collect real-time temperature data through a temperature sensor; The calculation unit is used to obtain a recipe number according to the recipe data, retrieve a database according to the recipe number to obtain cooking process data and cooking mode data, preset an auxiliary material placement ratio, calculate the auxiliary material placement weight according to the auxiliary material placement ratio and the weight of the food, calculate the maturity of the food according to the spectrum data through a maturity model, and calculate the fume concentration level according to the real-time image data through an image processing model; The control unit is used to control the operation of the cooking machine components through the intelligent terminal according to the real-time temperature data, the cooking process data, the cooking mode data, the weight of the auxiliary materials, the maturity of the ingredients, and the oil fume concentration level.

[0005] Specifically, the cooking modes include stir-fry mode, soup-making mode, frying mode, and stewing mode; the cooking machine components include an automatic main ingredient feeding device, an automatic ingredient feeding device, an automatic auxiliary ingredient feeding device, an electromagnetic heating device, a range hood, and a power device.

[0006] Specifically, the maturity model calculation step includes: Spectral preprocessing data is obtained by calculating the spectral preprocessing model according to the spectral data; Extracting features using a CNN convolutional neural network based on the spectral preprocessing data to obtain feature data; The maturity of the food is calculated by using the LDA model according to the characteristic data.

[0007] Specifically, the spectrum preprocessing model calculation step includes: Obtaining correction data through black and white correction according to the spectral data; Binarization is performed according to the correction data by using a Threshold algorithm to obtain binary data; Filling holes using a Floodfill algorithm according to the binary data to obtain filling data; The connected domain data is obtained by calculating the DBSCAN algorithm according to the filling data; Performing noise reduction processing through a Gaussian filter on the connected domain data to obtain denoised data; The spectral preprocessing data is obtained by dividing the connected domain contours through a watershed algorithm according to the denoised data.

[0008] Specifically, the image processing model calculation step includes: Obtaining image preprocessing data by image preprocessing according to the real-time image data; Acquire the previous frame image data, and calculate the target image data through the background difference model according to the previous frame image data and the image preprocessing data; The smoke area data is calculated according to the target image data by using the Freeman chain code vector method, an area threshold is preset, and the smoke concentration is divided according to the area threshold and the smoke area data to obtain the oil smoke concentration level.

[0009] Specifically, the image preprocessing includes denoising, filtering, and enhancement.

[0010] Specifically, the specific calculation steps of the background difference model are: S201: performing a difference operation on the image preprocessing data and the previous frame image to obtain differential image data, and performing block division according to the differential image data to obtain image blocks; S202: Calculate the pixel grayscale mean and standard deviation of each image block; S203: Preset a pixel grayscale threshold and a standard deviation threshold, and classify the pixel blocks according to the pixel grayscale threshold, the standard deviation threshold, the pixel grayscale mean, and the standard deviation to obtain foreground blocks; S204: Divide the foreground block into blocks to obtain a secondary image block, and calculate the target image data on the secondary image block according to steps S202-S203.

[0011] Specifically, controlling the operation of the cooking machine components through the intelligent terminal according to the real-time temperature data, the cooking process data, the cooking mode data, the weight of the auxiliary materials, the maturity of the ingredients, and the fume concentration level includes: The cooking mode data is retrieved from the database to obtain the pot operation mode data, and the intelligent terminal generates an adaptive signal to control the operation of the power device according to the pot operation data; A maturity threshold is preset, and the maturity of the food materials is divided according to the maturity threshold to obtain operation steps. The intelligent terminal generates an adaptive signal according to the operation steps to control the operation of the main material automatic feeding device, the ingredient automatic feeding device, and the auxiliary material automatic feeding device, and obtains the required minimum temperature and the required maximum temperature of the operation steps. If the real-time temperature data is greater than the required maximum temperature, the intelligent terminal generates an adaptive signal to control the electromagnetic heating device to perform a temperature reduction process; if the real-time temperature data is less than the required minimum temperature, the intelligent terminal generates an adaptive signal to control the electromagnetic heating device to perform a temperature increase process; if the real-time temperature data is less than or equal to the required maximum temperature and greater than or equal to the required minimum temperature, no processing is performed; The intelligent terminal generates an adaptive signal according to the oil fume concentration level to control the range hood to switch gears.

[0012] Specifically, the pot body operation modes include stirring operation mode, drum operation mode, inverted pot operation mode, and mixed operation mode.

[0013] The beneficial effects of the present invention are: (1) Intelligent food processing and ingredient addition: Recipe data is obtained through the human-computer interaction interface, and the weight of the ingredients collected by the built-in sensor in the storage bin is combined to accurately calculate the weight of the auxiliary ingredients. The cooking process data and cooking method data are retrieved from the database according to the recipe number. At the same time, by collecting the spectral data in the automatic cooking machine, the maturity of the ingredients is calculated in real time using the maturity model, so as to determine the current program of the cooking machine according to the maturity of the ingredients, and control the automatic feeding device, the automatic feeding device for ingredients, and the automatic feeding device for auxiliary ingredients to accurately add materials. This innovation greatly improves the accuracy and intelligence level of cooking, making the cooking process more convenient and efficient.

[0014] (2) Real-time monitoring and automatic control of oil fume concentration: By collecting real-time image data inside the pot, the oil fume concentration level is calculated using the image processing model, and the range hood gear is automatically adjusted according to the oil fume concentration level. This function not only effectively reduces kitchen oil fume emissions and improves the kitchen environment, but also improves the efficiency of the range hood and extends its service life.

[0015] (3) Adaptive motor rotation control: Based on the cooking mode data determined by the recipe, an adaptive signal is generated to control the motor rotation, so that different ingredients use different rotation methods at different cooking stages. This innovation not only ensures that the ingredients are heated evenly and improves the cooking quality, but also ensures the integrity of the ingredients and avoids damage or deformation of the ingredients due to excessive stirring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 The present invention is a schematic structural diagram of a motion control system based on an automatic cooking machine. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0019] See also Figure 1 ,A motion control system based on an automatic cooking machine includes a collection unit, a calculation unit, and a control unit; The acquisition unit is used to obtain recipe data through a human-computer interaction interface, preset an acquisition frequency, collect spectrum data through a spectrum detector according to the acquisition frequency, collect real-time image data through a built-in visual sensor of the cooking machine, obtain the weight of ingredients through a built-in gravity sensor of the storage bin, and collect real-time temperature data through a temperature sensor; The calculation unit is used to obtain a recipe number according to the recipe data, retrieve a database according to the recipe number to obtain cooking process data and cooking mode data, preset an auxiliary material placement ratio, calculate the auxiliary material placement weight according to the auxiliary material placement ratio and the weight of the food, calculate the maturity of the food according to the spectrum data through a maturity model, and calculate the fume concentration level according to the real-time image data through an image processing model; The control unit is used to control the operation of the cooking machine components through the intelligent terminal according to the real-time temperature data, the cooking process data, the cooking mode data, the weight of the auxiliary materials, the maturity of the ingredients, and the oil fume concentration level.

[0020] Specifically, the cooking modes include stir-fry mode, soup-making mode, frying mode, and stewing mode; the cooking machine components include an automatic main ingredient feeding device, an automatic ingredient feeding device, an automatic auxiliary ingredient feeding device, an electromagnetic heating device, a range hood, and a power device.

[0021] Specifically, the maturity model calculation step includes: Spectral preprocessing data is obtained by calculating the spectral preprocessing model according to the spectral data; Extracting features using a CNN convolutional neural network based on the spectral preprocessing data to obtain feature data; The maturity of the food is calculated by using the LDA model according to the characteristic data.

[0022] It should be noted that the LDA model is similar to the linear discriminant analysis model.

[0023] Specifically, the spectrum preprocessing model calculation step includes: Obtaining correction data through black and white correction according to the spectral data; Binarization is performed according to the correction data by using a Threshold algorithm to obtain binary data; Filling holes using a Floodfill algorithm according to the binary data to obtain filling data; The connected domain data is obtained by calculating the DBSCAN algorithm according to the filling data; It should be noted that the DBSCAN algorithm is a density-based clustering algorithm; Performing noise reduction processing through a Gaussian filter on the connected domain data to obtain denoised data; The spectral preprocessing data is obtained by dividing the connected domain contours through a watershed algorithm according to the denoised data.

[0024] Specifically, the image processing model calculation step includes: Obtaining image preprocessing data by image preprocessing according to the real-time image data; Acquire the previous frame image data, and calculate the target image data through the background difference model according to the previous frame image data and the image preprocessing data; The smoke area data is calculated according to the target image data by using the Freeman chain code vector method, an area threshold is preset, and the smoke concentration is divided according to the area threshold and the smoke area data to obtain the oil smoke concentration level.

[0025] Specifically, the image preprocessing includes denoising, filtering, and enhancement.

[0026] Specifically, the specific calculation steps of the background difference model are: S201: performing a difference operation on the image preprocessing data and the previous frame image to obtain differential image data, and performing block division according to the differential image data to obtain image blocks; The image block calculation formula is: , Wherein, Zk is the differential image data, Zxy is the image block, k represents the k-th frame image, X and Y are the size of the differential image data, x and y are the image block coordinates, and m and n are the size of the image block; S202: Calculate the pixel grayscale mean and standard deviation of each image block; S203: Preset a pixel grayscale threshold and a standard deviation threshold, and classify the pixel blocks according to the pixel grayscale threshold, the standard deviation threshold, the pixel grayscale mean, and the standard deviation to obtain foreground blocks; S204: Divide the foreground block into blocks to obtain a secondary image block, and calculate the target image data on the secondary image block according to steps S202-S203.

[0027] Specifically, controlling the operation of the cooking machine components through the intelligent terminal according to the real-time temperature data, the cooking process data, the cooking mode data, the weight of the auxiliary materials, the maturity of the ingredients, and the fume concentration level includes: The cooking mode data is retrieved from the database to obtain the pot operation mode data, and the intelligent terminal generates an adaptive signal to control the operation of the power device according to the pot operation data; A maturity threshold is preset, and the maturity of the food materials is divided according to the maturity threshold to obtain operation steps. The intelligent terminal generates an adaptive signal according to the operation steps to control the operation of the main material automatic feeding device, the ingredient automatic feeding device, and the auxiliary material automatic feeding device, and obtains the required minimum temperature and the required maximum temperature of the operation steps. If the real-time temperature data is greater than the required maximum temperature, the intelligent terminal generates an adaptive signal to control the electromagnetic heating device to perform a temperature reduction process; if the real-time temperature data is less than the required minimum temperature, the intelligent terminal generates an adaptive signal to control the electromagnetic heating device to perform a temperature increase process; if the real-time temperature data is less than or equal to the required maximum temperature and greater than or equal to the required minimum temperature, no processing is performed; The intelligent terminal generates an adaptive signal according to the oil fume concentration level to control the range hood to switch gears.

[0028] In this embodiment, when the smart terminal controls the range hood to switch gears, PWM speed control is used to control the range hood motor; when the smart terminal performs heating and cooling regulation on the electromagnetic heating device, PID control is used; the power device includes motor No. 1 and motor No. 2, motor No. 1 is connected to the agitator, and motor No. 2 is connected to the pot body.

[0029] Specifically, the pot body operation modes include stirring operation mode, drum operation mode, inverted pot operation mode, and mixed operation mode.

[0030] It should be noted that the mixing method is carried out simultaneously in the stirring method and the drum method.

[0031] In this embodiment, the stirring operation mode is to control the No. 1 motor to drive the agitator to keep rotating in phase through the PWM control method; the drum operation mode is to control the No. 2 motor to drive the pot body to keep rotating in phase through the PWM control method; the pot-tilting operation mode is to control the No. 2 motor to drive the pot body to switch between the same phase and reverse phase operation modes through the PWM control method, thereby realizing the pot-tilting operation mode; the mixed operation mode is to simultaneously control the No. 1 motor and the No. 2 motor to move in the same phase operation mode through the PWM control method.

[0032] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0033] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0034] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0035] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A motion control system based on an automatic cooking machine, characterized in that: include: Acquisition unit, calculation unit, control unit; The acquisition unit is used to obtain recipe data through a human-computer interaction interface, preset an acquisition frequency, collect spectrum data through a spectrum detector according to the acquisition frequency, collect real-time image data through a built-in visual sensor of the cooking machine, collect food weight through a built-in gravity sensor of the storage bin, and collect real-time temperature data through a temperature sensor; The calculation unit is used to obtain a recipe number according to the recipe data, retrieve a database according to the recipe number to obtain cooking process data and cooking mode data, preset an auxiliary material placement ratio, calculate the auxiliary material placement weight according to the auxiliary material placement ratio and the weight of the food, calculate the maturity of the food according to the spectrum data through a maturity model, and calculate the fume concentration level according to the real-time image data through an image processing model; The control unit is used to control the operation of the cooking machine components through the intelligent terminal according to the real-time temperature data, the cooking process data, the cooking mode data, the weight of the auxiliary materials, the maturity of the ingredients, and the oil fume concentration level.

2. The motion control system based on the automatic cooking machine according to claim 1 is characterized in that: The cooking modes include stir-fry mode, soup-making mode, frying mode, and stewing mode; the cooking machine components include an automatic main ingredient feeding device, an automatic ingredient feeding device, an automatic auxiliary ingredient feeding device, an electromagnetic heating device, a range hood, and a power device.

3. The motion control system based on the automatic cooking machine according to claim 1 is characterized in that: The maturity model calculation step includes: Spectral preprocessing data is obtained by calculating the spectral preprocessing model according to the spectral data; Extracting features using a CNN convolutional neural network based on the spectral preprocessing data to obtain feature data; The maturity of the food is calculated by using the LDA model according to the characteristic data.

4. The motion control system based on the automatic cooking machine according to claim 3 is characterized in that: The spectrum preprocessing model calculation step comprises: Obtaining correction data through black and white correction according to the spectral data; Binarization is performed according to the correction data by using a Threshold algorithm to obtain binary data; Filling holes using a Floodfill algorithm according to the binary data to obtain filling data; The connected domain data is obtained by calculating the DBSCAN algorithm according to the filling data; Performing noise reduction processing through a Gaussian filter on the connected domain data to obtain denoised data; The spectral preprocessing data is obtained by dividing the connected domain contours through a watershed algorithm according to the denoised data.

5. The motion control system based on the automatic cooking machine according to claim 1 is characterized in that: The image processing model calculation step comprises: Obtaining image preprocessing data by image preprocessing according to the real-time image data; Acquire the previous frame image data, and calculate the target image data through the background difference model according to the previous frame image data and the image preprocessing data; The smoke area data is calculated according to the target image data by using the Freeman chain code vector method, an area threshold is preset, and the smoke concentration is divided according to the area threshold and the smoke area data to obtain the oil smoke concentration level.

6. The motion control system based on the automatic cooking machine according to claim 5 is characterized in that: The image preprocessing includes denoising, filtering and enhancement.

7. The motion control system based on the automatic cooking machine according to claim 5 is characterized in that: The specific calculation steps of the background difference model are: S201: performing a difference operation on the image preprocessing data and the previous frame image to obtain differential image data, and performing block division according to the differential image data to obtain image blocks; S202: Calculate the pixel grayscale mean and standard deviation of each image block; S203: Preset a pixel grayscale threshold and a standard deviation threshold, and classify the pixel blocks according to the pixel grayscale threshold, the standard deviation threshold, the pixel grayscale mean, and the standard deviation to obtain foreground blocks; S204: Divide the foreground block into blocks to obtain a secondary image block, and calculate the target image data on the secondary image block according to steps S202-S203.

8. The motion control system based on the automatic cooking machine according to claim 1 is characterized in that: The controlling of the cooking machine components by the intelligent terminal according to the real-time temperature data, the cooking process data, the cooking mode data, the weight of the auxiliary materials, the maturity of the ingredients, and the fume concentration level comprises: The cooking mode data is retrieved from the database to obtain the pot operation mode data, and the intelligent terminal generates an adaptive signal to control the operation of the power device according to the pot operation data; A maturity threshold is preset, and the maturity of the food materials is divided according to the maturity threshold to obtain operation steps. The intelligent terminal generates an adaptive signal according to the operation steps to control the operation of the main material automatic feeding device, the ingredient automatic feeding device, and the auxiliary material automatic feeding device, and obtains the required minimum temperature and the required maximum temperature of the operation steps. If the real-time temperature data is greater than the required maximum temperature, the intelligent terminal generates an adaptive signal to control the electromagnetic heating device to perform a temperature reduction process; if the real-time temperature data is less than the required minimum temperature, the intelligent terminal generates an adaptive signal to control the electromagnetic heating device to perform a temperature increase process; if the real-time temperature data is less than or equal to the required maximum temperature and greater than or equal to the required minimum temperature, no processing is performed; The intelligent terminal generates an adaptive signal according to the oil fume concentration level to control the range hood to switch gears.

9. The motion control system based on the automatic cooking machine according to claim 8 is characterized in that: The pot body operation modes include stirring operation mode, drum operation mode, pot shaking operation mode and mixing operation mode.

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