CN111325047A - Cooking safety auxiliary method and system, kitchen appliance and combination thereof - Google Patents
Cooking safety auxiliary method and system, kitchen appliance and combination thereof Download PDFInfo
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- 238000010411 cooking Methods 0.000 title claims abstract description 67
- 238000000034 method Methods 0.000 title claims abstract description 24
- 230000002159 abnormal effect Effects 0.000 claims abstract description 85
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- 238000012549 training Methods 0.000 claims description 19
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- 238000012545 processing Methods 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 5
- 230000006698 induction Effects 0.000 claims description 5
- 239000000779 smoke Substances 0.000 description 8
- 239000007789 gas Substances 0.000 description 6
- 230000005856 abnormality Effects 0.000 description 5
- 238000009529 body temperature measurement Methods 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
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- 239000003595 mist Substances 0.000 description 2
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- 238000012360 testing method Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 235000019504 cigarettes Nutrition 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
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- 235000021056 liquid food Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24C—DOMESTIC STOVES OR RANGES ; DETAILS OF DOMESTIC STOVES OR RANGES, OF GENERAL APPLICATION
- F24C15/00—Details
- F24C15/20—Removing cooking fumes
- F24C15/2021—Arrangement or mounting of control or safety systems
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24C—DOMESTIC STOVES OR RANGES ; DETAILS OF DOMESTIC STOVES OR RANGES, OF GENERAL APPLICATION
- F24C3/00—Stoves or ranges for gaseous fuels
- F24C3/12—Arrangement or mounting of control or safety devices
- F24C3/126—Arrangement or mounting of control or safety devices on ranges
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24C—DOMESTIC STOVES OR RANGES ; DETAILS OF DOMESTIC STOVES OR RANGES, OF GENERAL APPLICATION
- F24C7/00—Stoves or ranges heated by electric energy
- F24C7/08—Arrangement or mounting of control or safety devices
- F24C7/082—Arrangement or mounting of control or safety devices on ranges, e.g. control panels, illumination
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/12—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions
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Abstract
A cooking safety auxiliary method comprises the steps of obtaining first image data in a preset range of a cooking area through an image acquisition device; judging whether the current cooking state belongs to a preset abnormal state or not based on an object recognition model and the first image data; and when the abnormal state is judged, sending a control command corresponding to the abnormal state. By adopting the scheme, the method can accurately identify the change of the image data, monitor the condition of the cooking area in real time and timely control the working state of the related equipment when an abnormal condition is found.
Description
Technical Field
The invention relates to the technical field of household appliances, in particular to a cooking safety auxiliary method, a cooking safety auxiliary system, a kitchen appliance and a combination of the kitchen appliance and the cooking safety auxiliary system.
Background
The problem of kitchen safety is always a relatively concerned topic, especially for Chinese families with more natural gas. For example, in daily life, people sometimes forget to turn off the stove due to interruption of cooking for various reasons, causing the pan bottom to be dried, or causing the liquid food to overflow, and even causing gas leakage and fire.
The main solution of preventing dry burning and overflow in the market is realized by a temperature sensor or an infrared thermal imaging technology, and judgment is carried out by temperature change. Although the technology is mature, the temperature change, especially by the infrared technology, is easily interfered by the external environment, so the technology is not accurate enough in the aspect of kitchen safety detection.
Disclosure of Invention
One of the objectives of the embodiments of the present invention is to provide a method and a system for implementing safe cooking assistance through a visual recognition technology.
It is an object of embodiments of the present invention to provide a kitchen appliance with a cooking safety assistance system and a combination thereof.
The embodiment of the invention provides a cooking safety auxiliary method, which comprises the steps of obtaining first image data in a preset range of a cooking area through an image acquisition device; judging whether the current cooking state belongs to a preset abnormal state or not based on an object recognition model and the first image data; and when the abnormal state is judged, sending a control command corresponding to the abnormal state. Compared with the prior art, the embodiment of the invention has the advantages that the method can accurately identify the change of the image data, monitor the condition of the cooking area in real time and control the working state of the related equipment in time when the abnormal condition is found.
In one possible embodiment, the method further comprises performing recognition feature extraction on the first image data. Clear image data with obvious abnormal characteristics can be obtained by processing the first image data.
In one possible embodiment, the judging whether the current cooking state belongs to a preset abnormal state includes predefining second image data belonging to the abnormal state; the first image data is compared with the second image data.
In one possible embodiment, the second image data recording the abnormal state is trained by a visual recognition training model to obtain an object recognition model. The image data recognition is realized by acquiring and defining abnormal state data and constructing an object recognition model through a learning training model. In addition, the object recognition model can improve the recognition capability through continuous learning and training.
In one possible embodiment, the method further comprises detecting temperature changes in a preset range of the cooking area through an infrared temperature probe; and when the temperature change is larger than the threshold value within the preset time, judging the state as an abnormal state. The method can detect whether the area is abnormal or not by combining the temperature change of the cooking area in a short time.
In one possible embodiment, the issuing of the control instruction includes: presetting a corresponding relation between an input instruction and a control instruction in an abnormal state; and sending corresponding control instructions according to different abnormal states. The method can realize the transmission of the corresponding control instruction according to the identified abnormal state.
In one possible embodiment, the control instructions include at least one of: controlling the work of the range hood; controlling the gas valve to close; controlling the induction cooker to be closed; controlling an indicator light; and controlling a buzzer to alarm.
The embodiment of the invention also discloses a cooking safety auxiliary system, which comprises an image acquisition device, a visual identification unit and a control unit, wherein the image acquisition device is used for acquiring first image data in a preset range of a cooking area; the visual identification unit is used for receiving the first image data and judging whether the current cooking state belongs to a preset abnormal state or not; the control unit is connected with the visual identification unit and is configured to receive the visual identification unit information and send out a control instruction corresponding to the abnormal state instruction when receiving the abnormal state instruction of the current cooking state.
In a possible embodiment, the system further comprises a data processing unit for performing identification feature extraction on the first image data.
In one possible embodiment, the visual recognition unit further comprises a storage module and a comparison module, wherein the storage module is used for storing second image data which are predefined to belong to an abnormal state; the comparison module is used for comparing the first image data with the second image data in the storage module.
In a possible embodiment, the storage module is further configured to train the second image data of the abnormal state through a visual recognition training model to obtain an object recognition model.
In one possible embodiment, the device further comprises an infrared temperature measuring probe for detecting the temperature change in the preset range of the cooking area; and when the temperature change is larger than the threshold value within the preset time, judging the state as an abnormal state.
In one possible embodiment, the control instructions include at least one of: controlling the work of the range hood; controlling the gas valve to close; controlling the induction cooker to be closed; controlling an indicator light; and controlling a buzzer to alarm.
The embodiment of the invention also discloses a kitchen appliance which comprises the cooking safety auxiliary system.
The embodiment of the invention also discloses a kitchen appliance combination which comprises a cooking safety auxiliary system, wherein the image acquisition device is arranged in the middle of the cooking appliance or the middle of the smoke collecting hood of the range hood.
Drawings
FIG. 1 is a schematic view of a kitchen appliance assembly in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a range hood including a cooking safety assistance system in accordance with an embodiment of the present invention;
FIG. 3 is a schematic view of a cooktop including a cooking safety assistance system in accordance with an embodiment of the present invention;
FIG. 4 is a block diagram of a cooking safety assistance system of an embodiment of the present invention;
fig. 5 is a flowchart of a cooking safety assisting method according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Referring to fig. 1, a kitchen appliance combination includes a machine 10, a cooking appliance 20, and a cooking safety assist system 30. The cooking safety assist system 30 may be located on the range hood 10 or the cooking appliance 20, or both. For example, disposed in the middle of the smoke collection hood of the cigarette maker 10, see figure 2; or in the middle or at a position behind the middle between two burners of a cooking bench, a gas stove or an induction cooker, as shown in fig. 3.
Referring to fig. 4, a cooking safety assistance system 30 for monitoring conditions within the range of a kitchen hob or a hob by means of visual identification technology and assisting a user in enhancing the safety of cooking. Specifically, the system 30 includes an image capturing device 300, a visual recognition unit 301, and a control unit 302.
The image capturing device 300 is configured to obtain image data within a preset range of the cooking area, that is, first image data. The image capturing device 300 may be a camera, for example, a camera that monitors the kitchen top or the kitchen range in real time, and captures monitored pictures or video data. In other embodiments, the camera may also be monitored periodically.
The visual recognition unit 301 is configured to receive the first image data acquired by the image acquisition device 300, and determine whether the current cooking state belongs to a preset abnormal state.
In one embodiment, the visual recognition unit 301 further includes a storage module 3011 and a comparison module 3012, wherein the storage module 3011 is configured to store second image data predefined to belong to an abnormal state; the comparing module 3012 is configured to compare the first image data with the second image data in the storing module 3011.
In one embodiment, the storage module 3011 includes a database for storing second image data of abnormal states obtained after training. For example, first, an abnormal state picture or video in different scenes is previously taken through a large number of experiments or the like. Based on this, an abnormal state can be set as follows:
abnormal state 1: when a large amount of fog or white smoke is emitted from the pan;
abnormal state 2: the soup and the foam overflow from the pot mouth, the pot body or the table top at the bottom of the pot;
abnormal state 3: open fire is generated in the pan, the cooking bench and the like;
abnormal state 4: some abnormal states can be customized, the image data are uploaded to a server, and the abnormal state data are formed through learning of the visual recognition training model.
In one embodiment, after image data recording an abnormal state is acquired, the image set to the abnormal state (i.e., the second image data) is trained by the visual recognition training model to acquire an object recognition model. For example, the collected abnormal pictures or videos are subjected to data annotation, especially annotation of abnormal points, including fog, smoke, spilled objects, fire and the like. And then training through an image recognition model, and finally training to form an object recognition model.
The object recognition model can also be learned by continuously receiving various collected pictures or videos, so that the recognition capability is optimized.
The visual recognition unit 301 recognizes the acquired first image data through the object recognition model, and outputs the recognized abnormal result to the control unit 302, that is, the control unit inputs an instruction. Different exception results correspond to different input instructions and output instructions.
The control unit 302 is configured to receive an input command and output a control command according to a preset corresponding relationship.
For example, the control unit 302 sends the output instruction to each execution unit in the control execution system, and controls the wireless switch and the electric control valve to be powered off and turned on in a WIFI or bluetooth manner, and controls the gas valve to be switched on and off through the electric control valve. The working condition of the LED lamp can be controlled according to the output state of the control unit: the green light is normal and the red light is abnormal. The control unit can also send corresponding instructions to control the rotating speed of the range hood to remove water mist and smoke or switch on and off a power supply of the range hood. The control unit can also send a corresponding instruction according to whether open fire is found or not, or control a buzzer to perform alarm action and the like.
The specific correspondence relationship between the input command and the output command of the control unit 302 is shown in table 1 below.
In one embodiment, the system further comprises an infrared temperature probe 303 for detecting temperature changes within a preset range of the cooking area; and when the temperature change is larger than the threshold value within the preset time, judging the state as an abnormal state.
For example, the infrared temperature probe 303 performs temperature measurement monitoring on a specified test point of a pot opening or a kitchen range top. And through photoelectric signal conversion, acquiring and processing the electric signal. If a large temperature difference occurs in a short time, the temperature difference is regarded as abnormal, an abnormal instruction is sent to the control unit 302, and the control unit 302 takes the instruction as a reference value for judging the abnormality. The control unit 302 sends out a corresponding control instruction according to the abnormal reference value and in combination with the abnormal state reference instruction output by the visual recognition unit 301.
In one embodiment, when the cooking safety assisting system is arranged on the range hood 10, pictures or videos are shot in real time through the camera and compared with the pictures or videos in the database when abnormality occurs, and corresponding control is performed by combining data change of infrared temperature measurement.
In one embodiment, when the cooking safety assistance system is installed on the cooking appliance 20, such as a kitchen range or a cooking stove, a picture or a video is taken in real time through the camera and compared with a picture or a video in the database when an abnormality occurs, and a corresponding control is performed when the abnormality occurs.
Based on the above embodiment, the embodiment of the present invention further discloses a cooking safety assisting method, as shown in fig. 5, including:
s500, acquiring first image data in a preset range of a cooking area through an image acquisition device.
In an embodiment of the invention, the kitchen range top or the kitchen range can be monitored in real time through an image acquisition device such as a camera, and the monitored picture or video data is acquired to serve as the first image data. In other embodiments, periodic monitoring may also be used.
In one embodiment, processing the first image data to extract the identifying feature, such as sharpness processing or accent feature extraction, is also included.
S502 determines whether the current cooking state belongs to a preset abnormal state based on the object recognition model and the first image data.
The object recognition model can be formed through visual training model training. In one embodiment, an image or video belonging to an abnormal state is defined in advance as the second image data.
The second image data may be stored in a database. The second image data is data of an abnormal state obtained after training. For example, first, an abnormal state picture or video in different scenes is taken in advance through a large number of experiments and the like, and based on this, an abnormal state can be set as follows:
abnormal state 1: when a large amount of fog or white smoke is emitted from the pan;
abnormal state 2: the soup and the foam overflow from the pot mouth, the pot body or the table top at the bottom of the pot;
abnormal state 3: open fire is generated in the pan, the cooking bench and the like;
abnormal state 4: some abnormal states can be customized, the image data are uploaded to a server, and the abnormal state data are formed through learning of the visual recognition training model.
In one embodiment, the image set to the abnormal state (i.e., the second image data) is trained by the visual recognition training model to obtain the object recognition model. For example, the collected abnormal pictures or videos are subjected to data annotation, especially annotation of abnormal points, including fog, smoke, spilled objects, fire and the like. And then training through an image recognition model, and finally training to form an object recognition model.
And then, identifying the acquired image or video data, namely the first image data, through the object identification model, and outputting an identified abnormal result to the control unit as an input instruction of the control unit.
In one embodiment, the specific identification method may be to compare the first image data with the second image data to determine whether the current cooking state belongs to an abnormal state.
The object recognition model can also learn by continuously receiving various collected pictures or videos, so that the recognition capability is optimized.
When the abnormal state is judged in S504, a control command is sent.
In an embodiment of the present invention, the control unit receives the input command and outputs the control command according to a preset corresponding relationship. Different exception results correspond to different input instructions and output instructions.
The control unit sends the output instructions to the various execution units in the control execution system. For example, the wireless switch and the electric control valve are controlled to be powered off and opened through WIFI or Bluetooth, and the gas valve is controlled to be opened and closed through the electric control valve. The working condition of the LED lamp can be controlled according to the output state of the control unit: the green light is normal and the red light is abnormal. The control unit can also send corresponding instructions to control the rotating speed of the range hood so as to remove water mist and smoke. The control unit can also send a corresponding instruction according to whether open fire is found or not, or control a buzzer to perform alarm action and the like.
The specific correspondence between the input command and the output command of the control unit is shown in table 1.
In one embodiment, the method further comprises detecting temperature changes within a preset range of the cooking area by an infrared temperature probe; and when the temperature change is larger than the threshold value within the preset time, judging the state as an abnormal state.
For example, the infrared temperature measurement probe is used for carrying out temperature measurement monitoring on a pot opening and a specified test point of a kitchen range top. And through photoelectric signal conversion, acquiring and processing the electric signal. If the infrared temperature measurement probe detects a large temperature difference in a short time, the temperature difference is regarded as abnormal, an abnormal instruction is sent to the control unit, and the control unit takes the instruction as a reference value for judging the abnormality. And the control unit sends out a corresponding control instruction according to the abnormal reference value and in combination with the abnormal state reference instruction output by the visual identification unit.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: ROM, RAM, magnetic or optical disks, and the like.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (15)
1. A cooking safety assistance method, comprising:
acquiring first image data in a preset range of a cooking area through an image acquisition device;
judging whether the current cooking state belongs to a preset abnormal state or not based on an object recognition model and the first image data;
and when the abnormal state is judged, sending a control command corresponding to the abnormal state.
2. The method according to claim 1, further comprising performing recognition feature extraction on the first image data after the first image data within a preset range of the cooking area is acquired through the image acquisition unit.
3. The method of claim 1, determining whether the current cooking state belongs to a preset abnormal state, comprising,
predefining second image data belonging to an abnormal state;
the first image data is compared with the second image data.
4. The method of claim 3, wherein predefining second image data belonging to abnormal conditions comprises training the second image data recording abnormal conditions by a visual recognition training model to obtain an object recognition model.
5. The method of claim 1, further comprising detecting a temperature change within a preset range of the cooking zone by an infrared temperature probe; and when the temperature change is larger than the threshold value within the preset time, judging the state as an abnormal state.
6. The method of claim 1, the issuing control instructions comprising:
presetting a corresponding relation between an input instruction and a control instruction in an abnormal state;
and sending corresponding control instructions according to different abnormal states.
7. The method of claim 1 or 6, the control instructions comprising at least one of: controlling the work of the range hood; controlling the gas valve to close; controlling the induction cooker to be closed; controlling an indicator light; and controlling a buzzer to alarm.
8. A cooking safety auxiliary system comprises an image acquisition device, a visual identification unit and a control unit, and is characterized in that the image acquisition device is used for acquiring first image data in a preset range of a cooking area;
the visual identification unit is used for receiving the first image data and judging whether the current cooking state belongs to a preset abnormal state or not;
the control unit is connected with the visual identification unit and is configured to receive the visual identification unit information and send a control instruction corresponding to the abnormal state instruction when receiving the abnormal state instruction of the current cooking state.
9. The system of claim 8, further comprising a data processing unit for performing recognition feature extraction on the first image data.
10. The system of claim 8, the visual recognition unit further comprising a storage module and a comparison module, wherein the storage module is configured to store second image data predefined to belong to an abnormal state; the comparison module is used for comparing the first image data with the second image data in the storage module.
11. The system of claim 10, the storage module further to train the second image data of the abnormal state through a visual recognition training model to obtain an object recognition model.
12. The system of claim 8, further comprising an infrared temperature probe for detecting temperature changes within a preset range of the cooking zone; and when the temperature change is larger than the threshold value within the preset time, judging the state as an abnormal state.
13. The system of claim 8, the control instructions comprising at least one of: controlling the work of the range hood; controlling the gas valve to close; controlling the induction cooker to be closed; controlling an indicator light; and controlling a buzzer to alarm.
14. A kitchen appliance comprising a cooking safety aid system according to any of claims 8-13.
15. A kitchen appliance combination comprising a cooking appliance and a range hood, characterized in that it comprises a cooking safety assistance system as claimed in any one of claims 8 to 12, wherein the image acquisition device is mounted in an intermediate position of said cooking appliance or of a hood of the range hood.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811522452.0A CN111325047A (en) | 2018-12-13 | 2018-12-13 | Cooking safety auxiliary method and system, kitchen appliance and combination thereof |
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| CN201811522452.0A CN111325047A (en) | 2018-12-13 | 2018-12-13 | Cooking safety auxiliary method and system, kitchen appliance and combination thereof |
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| CN113313035A (en) * | 2021-05-31 | 2021-08-27 | 杭州老板电器股份有限公司 | Overflow detection method and device for cookware |
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| CN115100583A (en) * | 2022-08-29 | 2022-09-23 | 君华高科集团有限公司 | Method and system for real-time supervision of safety of kitchen food |
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Application publication date: 20200623 |