CN114513628A - Kitchen appliance system - Google Patents

Abstract

The invention discloses a kitchen appliance system. The kitchen appliance system comprises a housing and a camera. A cavity is formed in the shell. The camera is installed at the shell. The camera can collect food material images in the cavity and environment images outside the cavity. In the kitchen appliance system provided by the embodiment of the invention, the camera of the kitchen appliance system can acquire the image of the environment in the kitchen, so that the picture of the kitchen environment outside the cavity is captured and monitored, and the warning information is sent to the user for the abnormal condition in the kitchen, so that the user can know the safety condition of the kitchen in time.

Description

Kitchen appliance system

Technical Field

The invention relates to the technical field of kitchen electrical appliance systems, in particular to a kitchen electrical appliance system.

Background

With the continuous improvement of the quality of life, the requirements of people on the safety of the kitchen are also continuously increased. Generally, accidents such as gas leakage, fire hazard, water leakage and the like easily occur in a kitchen, and if the accidents cannot be found and processed in time, serious personal and property safety loss can be caused. For example, when a fire breaks out in a kitchen, sometimes the fire source cannot be found and extinguished in time, so that the whole house, even the whole building, is burned out, and huge property loss is caused. In addition, in daily life, news that children are dangerous in kitchens is frequently reported due to the characteristics of various appliances in kitchens, such as gas cookers, knives and forks, pots, and the like. Therefore, there is a need for a way to monitor the kitchen environment for kitchen safety issues.

Disclosure of Invention

The embodiment of the invention provides a kitchen appliance system.

The kitchen appliance system of the embodiment of the invention comprises:

the device comprises a shell, a first fixing piece and a second fixing piece, wherein a cavity is formed in the shell;

a camera mounted on the housing;

the camera can collect food material images in the cavity and environment images outside the cavity.

In the kitchen appliance system provided by the embodiment of the invention, the camera of the kitchen appliance system can acquire the image of the environment in the kitchen, so that the picture of the kitchen environment outside the cavity is captured and monitored, and the warning information is sent to the user for the abnormal condition in the kitchen, so that the user can know the safety condition of the kitchen in time.

In some embodiments, the kitchen appliance system further comprises:

the image acquisition device is used for acquiring the environment image;

the environment recognition device is used for processing the environment image to obtain an environment recognition result and recognizing an environment abnormal condition according to the environment recognition result;

and the control device is used for sending the environment image and the environment abnormal condition to the electronic terminal so as to enable the electronic terminal to send out warning information.

In some embodiments, the environment recognition device is configured to process the environment image using a convolutional neural network to obtain the environment recognition result.

In some embodiments, the kitchen appliance system further comprises a lighting device for emitting light into the cavity, and the image acquisition device is used for controlling the lighting device to be turned on to assist in placing food materials and acquiring images.

In some embodiments, the kitchen appliance system further comprises:

food material recognition means for processing the food material image to obtain a food material recognition result;

and the control device is used for controlling the kitchen electric appliance system to heat according to the food material identification result.

In some embodiments, the food material identification device is configured to process the food material image by using a convolutional neural network to obtain the food material identification result, wherein the food material identification result comprises the type and the weight of the food material.

In some embodiments, the camera is rotatably mounted in the cavity, and faces into the cavity to capture the food material image when the kitchen appliance system is heated;

when the kitchen electrical appliance system stops heating, the camera faces the outside of the cavity body to collect the environment image.

In some embodiments, the kitchen appliance system includes a plurality of the cameras, a portion of the plurality of cameras facing into the cavity and another portion of the plurality of cameras facing out of the cavity.

In some embodiments, the housing comprises:

the door body, the camera is installed at the door body.

In certain embodiments, the door body comprises:

a glass window comprising an interlayer, the camera rotatably mounted in the interlayer.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

FIG. 1 is a block schematic diagram of a kitchen appliance system in accordance with an embodiment of the present invention;

FIG. 2 is another block schematic diagram of a kitchen appliance system in accordance with an embodiment of the present invention;

fig. 3 is a schematic block diagram of a kitchen appliance system in accordance with an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.

In the description of the present invention, it should be noted that the terms "mounted," "connected," and "connected" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected unless otherwise explicitly stated or limited. Either mechanically or electrically. Either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.

The kitchen appliance system 100 according to the embodiment of the present invention is described in detail below with reference to fig. 1 to 3, and the kitchen appliance system 100 includes a housing 10 and a camera 20, and a cavity is formed in the housing 10. The camera 20 is mounted on the housing 10. Wherein, the camera 20 can collect food material images in the cavity and environment images outside the cavity.

In the kitchen appliance system 100 according to the embodiment of the present invention, the camera 20 of the kitchen appliance system 100 can acquire an image of an environment inside a kitchen, thereby capturing and monitoring an image of the environment outside the cavity in the kitchen, and sending warning information to a user about an abnormal situation occurring in the kitchen, so that the user can know the safety situation of the kitchen in time.

Specifically, the kitchen appliance system 100 may include, but is not limited to, a microwave oven, a steamer, a micro-steamer, a dishwasher, a sterilizer, and other cooking and washing appliances. Camera 20 can install in the shell 10 outside, perhaps camera 20 also can install the cavity in shell 10, in relevant kitchen electrical system product scheme, is equipped with the window on the shell, and the condition of food in the cavity can be monitored to the window to the user knows current culinary art state. A camera 20 is mounted to the housing and faces the housing window to monitor the environment outside the chamber.

It should be noted that, in one embodiment, the kitchen appliance system may include the kitchen appliance itself and various software and hardware installed on the kitchen appliance. In another embodiment, the kitchen appliance system may include the kitchen appliance itself and a server, a camera of the kitchen appliance captures an image, the kitchen appliance system uploads the image captured by the camera to the server, and the server performs image recognition on the image to obtain a corresponding recognition result, and the like. In the following embodiments, the kitchen appliance system of the former embodiment will be described in detail. It is to be understood that reference is made to the detailed description of the latter embodiment.

In some embodiments, the kitchen appliance system 100 further includes an image capture device 30, an environment recognition device 60, and a control device 50. The image capturing device 30 is used to capture an environmental image. The environment recognition device 60 is configured to process the environment image to obtain an environment recognition result, and recognize an environment abnormal condition according to the environment recognition result. The control device 50 is used for sending the environment image and the environment abnormal condition to the electronic terminal so as to enable the electronic terminal to send out warning information.

So, kitchen electrical apparatus system 100's camera 20 can gather the interior environmental image of kitchen to carry out the picture to the outside kitchen environment of cavity and absorb and monitor, send warning information to the user to the abnormal conditions that takes place in the kitchen, make the user can in time know the safe condition in kitchen. Specifically, the camera 20 may be installed on the housing 10, specifically, may be installed in the cavity, or may be installed outside the cavity, the camera 20 may include a single camera, or may be a combination of multiple cameras, and the camera 20 is configured to collect an image of an environment outside the kitchen. Of course, the camera 20 may be replaced with any image capturing device having a photographing function. The environment recognition device 60 may recognize the environment image through a neural network technology, thereby obtaining an environment recognition result. Environmental anomalies can include, but are not limited to, kitchen fires, smoke, falls by the elderly in the kitchen, children entering the kitchen, objects falling, etc.

In addition, the kitchen appliance system 100 can also be equipped with a voice component, and after receiving the warning information that the dangerous condition appears, the user can use the electronic terminal to remotely talk with the voice component kitchen, so as to know the condition in time.

In some embodiments, the environment recognition device 60 is configured to process the environment image using a convolutional neural network to obtain an environment recognition result.

Therefore, the accuracy of the environment recognition result is improved in a self-learning mode. Specifically, the environment identification result includes an environmental normal condition and an environmental abnormal condition, and it should be noted that the environmental abnormal condition includes a kitchen fire, smoke, a fall of an old person in the kitchen, a child entering the kitchen, an object falling, and the like.

The convolutional neural network is used for identifying the environment image, belongs to a supervised learning algorithm, and has the advantages of less weight, high training speed and the like compared with other deep artificial neural networks. The convolutional neural network is mainly composed of three parts, namely an input layer, a hidden layer and an output layer. The input layer of the convolutional neural network is an environment image, the hidden layer in the middle performs feature extraction on the environment image by using convolution operation, and finally a specific kitchen environment type is output through the output layer.

Specifically, training the convolutional neural network in advance includes: acquiring a plurality of images under normal environment conditions and images under abnormal environment conditions, putting the images into a convolutional neural network for feature learning, and optimizing parameters by minimizing a loss function. In one example, an image of a normal environment, an image of a kitchen fire, an image of smoke, an image of a fall of an old person in a kitchen, an image of a child entering the kitchen, and an image of an object fall are put into a convolutional neural network in advance to perform feature learning, wherein the features may include colors, textures, gray scales, heights and shapes of persons, and the like, so that in an actual operation process, an environment recognition result is determined through the acquired environment image.

In the above embodiment, the convolutional neural network model at least includes: the system comprises an input layer, a convolutional layer, a pooling layer and a full-link layer, wherein the convolutional neural network acquires a sub-picture to be learned or subjected to feature extraction through the input layer, then performs feature learning and information extraction by utilizing the convolutional layer and the pooling layer, finally connects all features through the full-link layer, and inputs the features into a classifier to obtain a classification result. The convolution layer and the pooling layer can be combined in various different ways, the fully-connected layer can also be provided with multiple layers, and the specific layers and the network depth can be selected according to actual needs. The more the number of layers, the more accurate the recognition result and the more complex the network.

The environmental abnormal condition identified by the environment identifying device 60 may include: fire, smoke, falling down of the old in the kitchen, and children entering the kitchen. After the environmental abnormal condition is identified, the environmental abnormal condition can be sent to the electronic terminal so that the electronic terminal sends out warning information. Specifically, the abnormal environment condition may be sent to the server, and the server pushes the abnormal environment condition to the electronic terminal and sends out the warning information, or the kitchen appliance system directly sends the abnormal environment condition to the electronic terminal. In addition, the environment image in the kitchen can be updated and sent to the electronic terminal or the server in real time so as to display the current situation in the kitchen for the monitoring of the user. Electronic terminals include, but are not limited to, cell phones, tablets, personal computers, wearable smart devices, in-vehicle terminals, and the like.

In addition, taking kitchen fire identification as an example, the acquired kitchen environment image is put into a convolutional neural network for flame feature extraction and identification, so that whether the kitchen fire is generated or the flame of a gas stove and other equipment in the normal use process is determined, and if the kitchen fire is determined, the electronic terminal sends fire warning information; if the flame of the equipment such as the gas stove in the normal use process is determined, no warning is given.

In the above embodiment, the convolutional neural network is used to identify the kitchen fire, and is required to be trained before the kitchen fire, and the convolutional neural network comprises: acquiring a plurality of images belonging to dangerous situations of fire in a kitchen and images of flames in the kitchen but normal situations, putting the images into a convolutional neural network for feature learning, and optimizing parameters by minimizing a loss function. Identifying a kitchen fire using a convolutional neural network, comprising: and acquiring an environment image in the kitchen, inputting the environment image into a convolutional neural network to perform feature extraction, and judging as a dangerous situation if the environment image meets the features of the dangerous situation of fire.

In some embodiments, the kitchen appliance system 100 further includes a lighting device for emitting light into the cavity, and the image capturing device 30 is used for controlling the lighting device to be turned on to assist in placing food and capturing images.

Therefore, food materials can be conveniently placed and images can be conveniently acquired under the condition that the lighting device is turned on. In particular, in some embodiments, the lighting device comprises a light control component for assisting the user in placing food material and for assisting in image capture. In one example, when the kitchen appliance system 100 is in operation, the light control component controls the light to be turned on, so as to facilitate the observation of the food material condition from the outside, and the camera 20 is used for shooting the image inside the cavity so as to upload the image to the electronic terminal or the server. Briefly, the electronic terminal of the user is bound to the kitchen appliance system 100; the shooting range of the camera 20 is adjusted to be in the cavity, the user puts food into the electric appliance, the user starts the kitchen electric appliance system 100 to heat, the camera 20 shoots food images in the cavity and identifies the food in the cavity to acquire a food identification result, and the kitchen electric appliance system 100 starts to heat. After the kitchen electrical appliance system 100 is used, the kitchen electrical appliance system 100 adjusts the shooting range of the camera 20 to be outside the cavity, namely inside the kitchen, processes the obtained environment image to obtain an environment recognition result, and transmits the collected scene picture in the kitchen and the recognized abnormal result to the cloud server for storage after the recognized abnormal condition; and pushing the kitchen picture and the abnormal condition to the electronic terminal by the cloud server, and sending out warning information.

In some embodiments, the kitchen appliance system 100 further comprises a food material recognition device 40 and a control device 50. The food material recognition device 40 is used for processing the food material image to obtain a food material recognition result. The control device 50 is used for controlling the kitchen appliance system 100 to heat according to the food material identification result.

In this way, the kitchen appliance system 100 can identify food materials and automatically heat the food materials, so that a user does not need to manually select a heating mode, and user experience is improved. Specifically, the food material image in the cavity can be acquired through the camera 20 installed on the housing 10 of the kitchen appliance system 100. The food material image can be identified through a convolutional neural network or other models to determine the food material category so as to finally determine the cooking mode, the cooking time and the like.

In one example, the food material image may include: dough, hard foamed egg-flour mixed liquid, chicken wings, pig feet, beef skewers, mutton skewers, Chinese chives, eggplants, potatoes, sweet potatoes and other food materials. Under the condition that the food material is identified as chicken wings, the kitchen electrical appliance system 100 can be controlled to heat at 150-165 ℃ with medium fire for 10 minutes, and a less-oil mode can be selected. In the case of recognizing that the food material is the mixed egg-flour liquid which is hard foamed, the kitchen appliance system 100 may be controlled to heat with a strong fire at 200 ℃, and the heating time is 20 minutes to 25 minutes.

Wherein, can obtain the culinary art data of prediction according to the type of food, the culinary art data of prediction includes at least: cooking temperature, pressure inside the cooking appliance, cooking time and venting time for different cooking stages. For example, the correspondence between the food type and the cooking data may be stored in advance in a smart chip or a cloud server of the local device. The cooking data may include a food soaking time, an opening degree of an exhaust valve, and the like, in addition to cooking temperatures, pressures in the cooking appliance, cooking times, and exhaust times of different cooking stages.

For example, the kitchen appliance system 100 is an electric cooker, the built-in camera 20 of the electric cooker collects food material images in the cooker, the type of food is determined to be rice according to the convolutional neural network, then cooking data of 10 minutes of rice soaking, 21 minutes of cooking and 5 minutes of exhaust time are obtained based on the corresponding relation between the type of the food and the cooking data which are stored in an intelligent chip in the cooker in advance, and a heating resistor and a timing module of the electric cooker are controlled to cook the food material.

In some embodiments, the food material identifying device 40 is configured to process the food material image by using a convolutional neural network to obtain a food material identification result, wherein the food material identification result comprises the type and weight of the food material.

Therefore, accuracy of the food material identification result is improved in a self-learning mode. Specifically, the kitchen appliance system 100 automatically selects a cooking mode according to the food material recognition result output by the convolutional neural network model. Taking daily steamed rice in a kitchen as an example, according to the type of rice output by the convolutional neural network, such as Thailand jasmine rice, northeast pearl rice, long-grain rice, etc., and the weight of the rice, such as 300 g, 500 g, 700 g, etc., the current temperature of the rice, such as 25 ℃, 36 ℃, 50 ℃, etc., is identified by the temperature sensor, so as to set the water injection amount, the rice soaking time, the cooking time, the exhaust time of the exhaust valve, the opening degree of the exhaust valve, the heat preservation time, etc., of the kitchen electrical appliance system 100, so as to obtain the optimal cooking mode, ensure the taste of food, and simultaneously ensure that the nutrition is not lost.

In some embodiments, the camera 20 is rotatably mounted in the cavity, and when the kitchen appliance system 100 is heated, the camera 20 faces the cavity to collect the food material image; when the kitchen appliance system 100 stops heating, the camera 20 faces the outside of the cavity to acquire an environmental image.

Thus, by providing a rotatable camera 20, picture taking and monitoring of the interior and exterior kitchen environments is performed. In particular, the rotation range of the camera 20 may be between 0-180 °, or greater than 180 °, in order to be able to acquire images of the inside of the cavity and of the environment of the kitchen outside the cavity, respectively.

In some embodiments, the kitchen appliance system 100 includes a plurality of cameras 20, with a portion of the plurality of cameras 20 facing into the cavity and another portion of the plurality of cameras 20 facing out of the cavity.

So, the convenience is gathered kitchen internal environment and edible material image to intelligent control culinary art mode and real time monitoring kitchen environment promote modern kitchen's travelling comfort and security energetically. Specifically, the camera 20 may be set to a camera 20 for shooting the cavity internal environment and a camera 20 for shooting the cavity external environment, or the camera 20 may be set to a camera 20 for shooting the cavity internal environment and two cameras 20 for shooting the cavity external environment, or the camera 20 may be set to two cameras 20 for shooting the cavity internal environment and two cameras 20 for shooting the cavity external environment. A plurality of cameras 20 can also construct a camera suit, gather kitchen internal environment and edible material image simultaneously to intelligent control culinary art mode and real time monitoring kitchen environment promote modern kitchen's travelling comfort and security vigorously.

In one example, when the kitchen appliance system 100 is in a heating state, the camera 20 facing the inside of the cavity is controlled to be turned on to acquire an image of the food material inside the cavity, the camera 20 facing the outside of the cavity is controlled to be turned on to acquire an image of the environment outside the cavity, and the camera 20 facing the outside of the cavity is also controlled to be turned off; when the kitchen appliance system 100 is in an unheated state, the camera 20 facing the inside of the cavity is controlled to be turned off, and the camera 20 facing the outside of the cavity can be controlled to be turned on so as to acquire an environment image outside the cavity.

In certain embodiments, the enclosure 10 comprises a door body. The camera 20 is mounted on the door body.

Therefore, the environment and food material images in the kitchen can be conveniently collected, and the kitchen cabinet is convenient to disassemble, maintain or replace. Specifically, the outer shell 10 further includes a shell, the door body is rotatably connected to the shell, and when the door body is in a closed state, the door body and the shell form a closed cavity.

In certain embodiments, the door body includes a glass window. The glass window comprises a sandwich in which the camera 20 is rotatably mounted.

Thus, the camera 20 can effectively shoot the internal images of the cavity and the environment images of the kitchen outside the cavity. It can be understood that, for the kitchen appliance system 100 such as an electric oven, a microwave oven, a steaming cube, etc., a glass window is usually disposed on a door body thereof, and the glass window usually includes a double-layer or triple-layer structure, an interlayer is formed in the middle of the double-layer or triple-layer structure, the camera 20 may be disposed in the interlayer of the glass window, and the camera 20 may rotate by 180 ° or more in the interlayer of the glass window.

In addition, the pictures shot by the image capturing device 30, the corresponding food material identification and the dangerous scene identification result in the kitchen can also be directly transmitted to the electronic terminal bound by the user by the kitchen electrical appliance system 100 through the communication module 80. The communication module 80 may communicate with the electronic terminal using WIFI, bluetooth, a mobile communication network, etc.

Specifically, in conjunction with fig. 3, the embodiment of the invention provides a kitchen appliance system 100 capable of monitoring and identifying dangerous situations in a kitchen, and applies the kitchen appliance system 100 to monitoring and identifying sudden dangerous situations in the kitchen. The kitchen appliance system 100 may include a housing 10, a cooking assembly 70, an image collecting device 30, a food material recognizing device 40, an environment recognizing device 60, a communication module 80 and a control device 50, wherein the housing 10 has a baking chamber, a door body, an operation panel, etc., wherein the operation panel may be a liquid crystal touch display screen, and a user may directly input a selection request; the cooking assembly 70 is used for cooking functions such as heating of the kitchen appliance system 100, for example, the cooking assembly 70 may include a microwave source, a heating tube, and the like. The image acquisition device 30 comprises a rotatable camera 20, the kitchen appliance system 100 is used for acquiring images of food materials in a cavity of the kitchen appliance system 100 when in use, and when the kitchen appliance system 100 is in a non-working state, the orientation of the camera 20 can be adjusted to acquire the scene pictures in an external kitchen in an all-round manner; the food material identification device 40 is used for identifying food in the cavity; the environment recognition device 60 is used for recognizing dangerous situations of the acquired images in the kitchen, wherein the dangerous situations include but are not limited to the situations that the kitchen generates heavy smoke, a fire disaster occurs, objects fall, children enter the kitchen, and the old falls down in the kitchen; the communication module 80 is used for establishing binding and connection between the kitchen appliance system 100 and the cloud server and the user electronic terminal; the control device is used for transmitting a heating instruction to the cooking assembly 70 according to the recognized food material category, receiving the picture in the kitchen and the abnormal result recognized by the kitchen scene recognition device, which are shot by the image acquisition device 30, and uploading the abnormal result to the cloud server for storage through comprehensive processing; and the cloud server receives and processes the image, transmits the image to the client in real time and pushes warning information.

The kitchen appliance system 100 is further provided with a voice component, and after the user receives the warning information of the dangerous situation, the user can use the electronic terminal to remotely communicate with the kitchen through the voice component so as to know the situation in time.

In one embodiment, the food material identification device 40 and the environment identification device 60 can be configured in a kitchen appliance system, in another embodiment, the food material identification device 40 and the environment identification device 60 can also be configured in a server, and the image acquisition device 30 transmits the acquired images to the server for image identification through the control device 50 to obtain corresponding identification results.

The various functional unit modules of the kitchen appliance system 100 described above may be integrated into one module in various combinations or exist separately.

Above-mentioned kitchen electrical apparatus system 100, camera 20 can gather and eat material image and environmental image to carry out the picture to in the cavity and outside kitchen environment and absorb and monitor, send warning information to the user to the abnormal conditions that takes place in the kitchen, make the user can in time know the safe condition in kitchen.

The disclosure herein provides many different embodiments or examples for implementing different features of the invention. In order to simplify the disclosure of the present invention, the components and arrangements of specific examples are described herein. Of course, they are merely examples and are not intended to limit the present invention. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples, such repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. In addition, the present invention provides examples of various specific processes and materials, but one of ordinary skill in the art may recognize applications of other processes and/or uses of other materials.

In the description of the present specification, reference to the description of the terms "one embodiment", "some embodiments", "an illustrative embodiment", "an example", "a specific example", or "some examples", etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (10)

1. A kitchen appliance system, characterized in that the kitchen appliance system comprises:

the device comprises a shell, a first fixing piece and a second fixing piece, wherein a cavity is formed in the shell;

a camera mounted on the housing;

the camera can collect food material images in the cavity and environment images outside the cavity.

2. The kitchen appliance system of claim 1, further comprising:

the image acquisition device is used for acquiring the environment image;

the environment recognition device is used for processing the environment image to obtain an environment recognition result and recognizing an environment abnormal condition according to the environment recognition result;

and the control device is used for sending the environment image and the environment abnormal condition to the electronic terminal so as to enable the electronic terminal to send out warning information.

3. The kitchen appliance system of claim 2, wherein the environment recognition device is configured to process the environment image using a convolutional neural network to obtain the environment recognition result.

4. The kitchen appliance system of claim 2, further comprising a lighting device emitting light into the cavity, wherein the image capturing device is configured to control the lighting device to turn on to assist in placing food material and capturing images.

5. The kitchen appliance system of claim 1, further comprising:

food material recognition means for processing the food material image to obtain a food material recognition result;

and the control device is used for controlling the kitchen electric appliance system to heat according to the food material identification result.

6. The kitchen appliance system of claim 5, wherein the food material identification device is configured to process the food material image by using a convolutional neural network to obtain the food material identification result, and the food material identification result comprises the type and weight of the food material.

7. The kitchen appliance system of claim 1, wherein the camera is rotatably mounted in the cavity, the camera facing into the cavity to capture the food material image when the kitchen appliance system is heating;

when the kitchen electrical appliance system stops heating, the camera faces the outside of the cavity body to collect the environment image.

8. The kitchen appliance system of claim 1, comprising a plurality of the cameras, a portion of the plurality of cameras facing into the cavity and another portion of the plurality of cameras facing out of the cavity.

9. The kitchen appliance system of claim 1, wherein the housing comprises:

the door body, the camera is installed at the door body.

10. The kitchen appliance system of claim 9, wherein the door body comprises:

a glass window comprising an interlayer, the camera rotatably mounted in the interlayer.

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