CN109434844A - Food materials handling machine people control method, device, system, storage medium and equipment - Google Patents
Food materials handling machine people control method, device, system, storage medium and equipment Download PDFInfo
- Publication number
- CN109434844A CN109434844A CN201811081931.3A CN201811081931A CN109434844A CN 109434844 A CN109434844 A CN 109434844A CN 201811081931 A CN201811081931 A CN 201811081931A CN 109434844 A CN109434844 A CN 109434844A
- Authority
- CN
- China
- Prior art keywords
- food materials
- information
- processed
- image
- neural network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 235000013305 food Nutrition 0.000 title claims abstract description 675
- 238000000034 method Methods 0.000 title claims abstract description 138
- 238000005007 materials handling Methods 0.000 title claims abstract description 67
- 239000000463 material Substances 0.000 claims abstract description 477
- 238000003913 materials processing Methods 0.000 claims abstract description 135
- 230000008569 process Effects 0.000 claims abstract description 106
- 238000003062 neural network model Methods 0.000 claims abstract description 84
- 238000004590 computer program Methods 0.000 claims description 19
- 230000002123 temporal effect Effects 0.000 claims description 7
- 235000013399 edible fruits Nutrition 0.000 claims description 6
- 235000021067 refined food Nutrition 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 235000013601 eggs Nutrition 0.000 description 40
- 238000010586 diagram Methods 0.000 description 10
- 238000005520 cutting process Methods 0.000 description 8
- 238000009835 boiling Methods 0.000 description 7
- 238000003756 stirring Methods 0.000 description 7
- 102000002322 Egg Proteins Human genes 0.000 description 6
- 108010000912 Egg Proteins Proteins 0.000 description 6
- 210000004218 nerve net Anatomy 0.000 description 5
- 238000010411 cooking Methods 0.000 description 4
- QCVGEOXPDFCNHA-UHFFFAOYSA-N 5,5-dimethyl-2,4-dioxo-1,3-oxazolidine-3-carboxamide Chemical compound CC1(C)OC(=O)N(C(N)=O)C1=O QCVGEOXPDFCNHA-UHFFFAOYSA-N 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 3
- 210000003278 egg shell Anatomy 0.000 description 3
- 235000014103 egg white Nutrition 0.000 description 3
- 210000000969 egg white Anatomy 0.000 description 3
- 210000002969 egg yolk Anatomy 0.000 description 3
- 230000007306 turnover Effects 0.000 description 3
- 244000276331 Citrus maxima Species 0.000 description 2
- 240000004371 Panax ginseng Species 0.000 description 2
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 description 2
- 238000010009 beating Methods 0.000 description 2
- 238000007710 freezing Methods 0.000 description 2
- 230000008014 freezing Effects 0.000 description 2
- 235000008434 ginseng Nutrition 0.000 description 2
- 235000013372 meat Nutrition 0.000 description 2
- 238000010025 steaming Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 244000298715 Actinidia chinensis Species 0.000 description 1
- 235000009434 Actinidia chinensis Nutrition 0.000 description 1
- 235000009436 Actinidia deliciosa Nutrition 0.000 description 1
- 241000251468 Actinopterygii Species 0.000 description 1
- 241000272525 Anas platyrhynchos Species 0.000 description 1
- 240000007087 Apium graveolens Species 0.000 description 1
- 235000015849 Apium graveolens Dulce Group Nutrition 0.000 description 1
- 235000010591 Appio Nutrition 0.000 description 1
- 235000017060 Arachis glabrata Nutrition 0.000 description 1
- 235000010777 Arachis hypogaea Nutrition 0.000 description 1
- 244000105624 Arachis hypogaea Species 0.000 description 1
- 235000018262 Arachis monticola Nutrition 0.000 description 1
- 235000011274 Benincasa cerifera Nutrition 0.000 description 1
- 244000036905 Benincasa cerifera Species 0.000 description 1
- 235000010149 Brassica rapa subsp chinensis Nutrition 0.000 description 1
- 235000000536 Brassica rapa subsp pekinensis Nutrition 0.000 description 1
- 241000499436 Brassica rapa subsp. pekinensis Species 0.000 description 1
- 235000004936 Bromus mango Nutrition 0.000 description 1
- 244000241235 Citrullus lanatus Species 0.000 description 1
- 235000012828 Citrullus lanatus var citroides Nutrition 0.000 description 1
- 235000001759 Citrus maxima Nutrition 0.000 description 1
- 240000008067 Cucumis sativus Species 0.000 description 1
- 235000010799 Cucumis sativus var sativus Nutrition 0.000 description 1
- 235000016623 Fragaria vesca Nutrition 0.000 description 1
- 240000009088 Fragaria x ananassa Species 0.000 description 1
- 235000011363 Fragaria x ananassa Nutrition 0.000 description 1
- 241000287828 Gallus gallus Species 0.000 description 1
- 240000005979 Hordeum vulgare Species 0.000 description 1
- 235000007340 Hordeum vulgare Nutrition 0.000 description 1
- 235000009814 Luffa aegyptiaca Nutrition 0.000 description 1
- 244000302544 Luffa aegyptiaca Species 0.000 description 1
- 235000007688 Lycopersicon esculentum Nutrition 0.000 description 1
- 235000014826 Mangifera indica Nutrition 0.000 description 1
- 240000007228 Mangifera indica Species 0.000 description 1
- 235000009811 Momordica charantia Nutrition 0.000 description 1
- 235000009812 Momordica cochinchinensis Nutrition 0.000 description 1
- 240000001740 Momordica dioica Species 0.000 description 1
- 235000018365 Momordica dioica Nutrition 0.000 description 1
- 240000005561 Musa balbisiana Species 0.000 description 1
- 235000018290 Musa x paradisiaca Nutrition 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 241000220324 Pyrus Species 0.000 description 1
- 240000003768 Solanum lycopersicum Species 0.000 description 1
- 235000002597 Solanum melongena Nutrition 0.000 description 1
- 244000061458 Solanum melongena Species 0.000 description 1
- 235000002595 Solanum tuberosum Nutrition 0.000 description 1
- 244000061456 Solanum tuberosum Species 0.000 description 1
- 244000062793 Sorghum vulgare Species 0.000 description 1
- 235000009337 Spinacia oleracea Nutrition 0.000 description 1
- 244000300264 Spinacia oleracea Species 0.000 description 1
- 235000009184 Spondias indica Nutrition 0.000 description 1
- 235000021307 Triticum Nutrition 0.000 description 1
- 244000098338 Triticum aestivum Species 0.000 description 1
- 240000004922 Vigna radiata Species 0.000 description 1
- 235000010721 Vigna radiata var radiata Nutrition 0.000 description 1
- 235000011469 Vigna radiata var sublobata Nutrition 0.000 description 1
- 240000008042 Zea mays Species 0.000 description 1
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 1
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 1
- 235000021028 berry Nutrition 0.000 description 1
- 235000013605 boiled eggs Nutrition 0.000 description 1
- 235000013339 cereals Nutrition 0.000 description 1
- 235000009508 confectionery Nutrition 0.000 description 1
- 235000005822 corn Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 235000019713 millet Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 235000012149 noodles Nutrition 0.000 description 1
- 235000020232 peanut Nutrition 0.000 description 1
- 235000021017 pears Nutrition 0.000 description 1
- 235000013594 poultry meat Nutrition 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000010257 thawing Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 235000013311 vegetables Nutrition 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J11/00—Manipulators not otherwise provided for
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
- B25J9/1697—Vision controlled systems
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Manipulator (AREA)
- General Preparation And Processing Of Foods (AREA)
Abstract
This application involves a kind of food materials handling machine people control method, device, system, storage medium and equipment, obtain the image of food materials to be processed；According to the image of food materials to be processed, the food materials information of food materials to be processed is obtained by neural network model and corresponding food materials handle information, and corresponding food materials process instruction is exported, neural network model is to be obtained according to food materials processing image and/or video training.When handling food materials, food materials type is identified by neural network model, and obtains corresponding food materials processing information, robot handles information according to food materials and handles food materials, different treatment processes is executed for different food materials, so that food materials processing mode is more flexible；In addition, the food materials that the neural network model can handle image and/or video study to a variety of different food materials according to different food materials handle information, so that application range is wider, and food materials processing is more scientific and reasonable.
Description
Technical field
This application involves equipment control technology field, more particularly to a kind of food materials handling machine people control method, device,
System, storage medium and equipment.
Background technique
With the raising of scientific and technological level, entire society all develops towards intelligent, automation direction.In food materials processing side
There is more and more automation equipments (such as food materials handling machine people) in face, so that the processing to food materials is more and more convenient.
In traditional technology, when using automation equipment processing food materials, it is necessary first to fixed food materials be written in a device
Processing routine, then the equipment is handled food materials according to step corresponding to fixed program.However, due to different food materials
Processing method is different, and fixed food materials processing step can not be suitable for all food materials, leads to the processing result of part food materials not
It is ideal；In addition, when food materials type and more processing mode then needing that a large amount of food materials processing routine, work is written to equipment
Work amount is larger.
Summary of the invention
Based on this, it is necessary to which the problem of being directed to traditional technology provides that a kind of application range is wider and processing mode
More flexible food materials handling machine people control method, device, system, storage medium and equipment.
A kind of food materials handling machine people's control method characterized by comprising
Obtain the image of food materials to be processed；
According to the image of the food materials to be processed, the food materials type of the food materials to be processed is obtained by neural network model
Information and corresponding food materials handle information, and export corresponding food materials process instruction, and the neural network model is according to food
Material processing image and/or video training obtain, and the food materials process instruction is used to indicate the food materials handling machine people according to institute
The food materials processing information stated in food materials process instruction handles the food materials to be processed.
The training data of the neural network model further includes at least one in following three in one of the embodiments,
:
Dynamics information in food materials treatment process；
Temperature information in food materials treatment process；
Temporal information in food materials treatment process.
In one of the embodiments, after the image for obtaining food materials to be processed, further includes:
Obtain the current state information of the food materials to be processed, the current state information include in following three at least
One: current strength information, Current Temperatures information, current time information.
The image according to the food materials to be processed in one of the embodiments, is obtained by neural network model
The food materials information of the food materials to be processed and corresponding food materials handle information, comprising:
According to the image of the food materials to be processed, the food materials type of the food materials to be processed is obtained by neural network model
Information；
According to the food materials information of the food materials to be processed and the current state information of the food materials to be processed, lead to
It crosses neural network model and obtains the food materials processing information of the food materials to be processed.
The food materials processing information includes food materials processing mode and food materials processing parameter in one of the embodiments,.
The food materials processing parameter includes in time parameter, temperature parameter and dynamics parameter in one of the embodiments,
At least one of.
In one of the embodiments, after the step of output corresponding food materials process instruction, further includes:
The actual treatment information of the food materials to be processed is obtained, the actual treatment information includes actual treatment time, reality
At least one of in border strength disposal and actual treatment temp；
Information and the actual treatment information are handled according to the food materials, determines the food materials processing of the food materials to be processed
As a result.
A kind of food materials handling machine people's control device, comprising:
Image collection module, for obtaining the image of food materials to be processed；
Control module is obtained described to be processed for the image according to the food materials to be processed by neural network model
The food materials information of food materials and corresponding food materials handle information, and export corresponding food materials process instruction, the nerve net
Network model is to be obtained according to food materials processing image and/or video training, and the food materials process instruction is used to indicate at the food materials
Reason robot is handled the food materials to be processed according to the food materials processing information in the food materials process instruction.
The control module is also used in one of the embodiments:
Obtain the current state information of the food materials to be processed, the current state information include in following three at least
One: current strength information, Current Temperatures information, current time information.
The control module is also used in one of the embodiments:
According to the image of the food materials to be processed, the food materials type of the food materials to be processed is obtained by neural network model
Information；
According to the food materials information of the food materials to be processed and the current state information of the food materials to be processed, lead to
It crosses neural network model and obtains the food materials processing information of the food materials to be processed.
The control module is also used in one of the embodiments:
The actual treatment information of the food materials to be processed is obtained, the actual treatment information includes actual treatment time, reality
At least one of in border strength disposal and actual treatment temp；
Information and the actual treatment information are handled according to the food materials, determines the food materials processing of the food materials to be processed
As a result.
A kind of food materials processing system, comprising:
Control device, for obtaining the image of food materials to be processed；According to the image of the food materials to be processed, pass through nerve net
Network model obtains the food materials information and corresponding food materials processing information of the food materials to be processed, and exports corresponding food materials
Process instruction, the neural network model are to be obtained according to food materials processing image and/or video training；
Food materials handling machine people, for handling information to the food to be processed according to the food materials in the food materials process instruction
Material is handled.
In one of the embodiments, further include at least one in following three:
Timer, for obtaining the reality of the food materials to be processed after the control device exports food materials process instruction
Border handles the time, and is sent to the control device；
Force snesor, for obtaining the food materials to be processed before control device output food materials process instruction
Current strength information, and it is sent to the control device；And for when the control device output food materials process instruction it
Afterwards, the actual treatment dynamics of the food materials to be processed is obtained, and is sent to the control device；
Temperature sensor, for obtaining the food materials to be processed before control device output food materials process instruction
Current Temperatures information, and be sent to the control device；And for when the control device output food materials process instruction it
Afterwards, the actual treatment temp of the food materials to be processed is obtained, and is sent to the control device.
A kind of computer equipment, including memory and processor, the memory are stored with computer program, the processing
Device performs the steps of when executing the computer program
Obtain the image of food materials to be processed；
According to the image of the food materials to be processed, the food materials type of the food materials to be processed is obtained by neural network model
Information and corresponding food materials handle information, and export corresponding food materials process instruction, and the neural network model is according to food
Material processing image and/or video training obtain, and the food materials process instruction is used to indicate food materials handling machine people according to the food
Food materials processing information in material process instruction handles the food materials to be processed.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
It is performed the steps of when row
Obtain the image of food materials to be processed；
According to the image of the food materials to be processed, the food materials type of the food materials to be processed is obtained by neural network model
Information and corresponding food materials handle information, and export corresponding food materials process instruction, and the neural network model is according to food
Material processing image and/or video training obtain, and the food materials process instruction is used to indicate food materials handling machine people according to the food
Food materials processing information in material process instruction handles the food materials to be processed.
Above-mentioned food materials handling machine people control method, device, system, storage medium and equipment, obtain food materials to be processed
Image；According to the image of food materials to be processed, the food materials information of food materials to be processed and right is obtained by neural network model
The food materials processing information answered, and exports corresponding food materials process instruction, neural network model be handled according to food materials image and/or
Video training obtains, and food materials process instruction is used to indicate food materials handling machine people according to the food materials processing letter in food materials process instruction
Breath handles food materials to be processed.When handling food materials, food materials type is identified by neural network model,
And corresponding food materials processing information is obtained, robot handles information according to food materials and handles food materials, that is, is directed to different foods
Material executes different treatment processes, so that food materials processing mode is more flexible；In addition, the neural network model can be according to difference
Food materials processing image and/or the food materials of video study to a variety of different food materials handle information so that application range is wider,
And food materials processing is more scientific and reasonable.
Detailed description of the invention
Fig. 1 is the flow diagram of food materials handling machine people's control method in one embodiment；
Fig. 2 is the structure type schematic diagram of food materials handling machine people in one embodiment；
Fig. 3 is the structure type schematic diagram of food materials handling machine people in another embodiment；
Fig. 4 is the structure type schematic diagram of food materials handling machine people in another embodiment；
Fig. 5 is the structure type schematic diagram of food materials handling machine people in further embodiment；
Fig. 6 is the structure type schematic diagram of food materials handling machine people in further embodiment；
Fig. 7 is the schematic diagram that fried egg processing is carried out in one embodiment；
Fig. 8 is the flow diagram of food materials handling machine people's control method in another embodiment；
Fig. 9 is the structural schematic diagram of food materials handling machine people's control device in one embodiment；
Figure 10 is the structural schematic diagram of food materials processing system in one embodiment.
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which the objects, technical solutions and advantages of the application are more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, not
For limiting the application.
In one embodiment, as shown in Figure 1, providing a kind of food materials handling machine people's control method, the food materials processor
Device people control method the following steps are included:
Step S100 obtains the image of food materials to be processed.When there are food materials to be processed, pass through imaging sensor first
Etc. the image that device/equipment of available image obtains food materials to be processed.The image of acquisition can be single image, can be
Multiple images are also possible to the video being made of multiple image, wherein and single image can save image acquisition time, and
It can accelerate image processing speed；Multiple images or the accuracy that image procossing can be improved by the video that multiple image forms.
Step S200 obtains the food materials kind of food materials to be processed by neural network model according to the image of food materials to be processed
Category information and corresponding food materials handle information, and export corresponding food materials process instruction.
After the image for obtaining food materials to be processed, image recognition processing is carried out to the image by neural network model,
The food materials information for the food materials to be processed for including in the image is obtained, and obtains corresponding food materials processing information.Wherein, wait locate
Manage food materials include common food materials in daily life, type includes: grain and oil class, such as: millet, wheat, barley, corn, mung bean,
Peanut etc.；Greengrocery, such as: celery, spinach, Chinese cabbage, sponge gourd, cucumber, wax gourd, balsam pear, eggplant, tomato etc.；Meat, such as:
Various fish and various poultry meats etc.；Fruits, such as: pears, shaddock, mango, Kiwi berry, banana, orange, strawberry, watermelon
Deng；And other classifications.It is corresponding, food materials processing information include: cut and (such as cut vegetables, cut fruit), cut and (such as peel a fruit),
Peeling (rossing, such as scrape potato；Peeling, such as stripping pomelo peel), stirring (such as whipping egg liquid), packet (such as make dumplings, wrap
Son, steamed bun etc.), fry (such as cooking), decoct (such as fried egg), fried (such as chips), boil (such as cooking noodle, the rice dumpling), steam
(such as steamed sweet bun), roasting (such as roast chicken, roast duck) etc..
Specifically, neural network model used in the present embodiment includes but is not limited to convolutional neural networks (CNN) mould
Type, which may include various network structures, such as: LeNet, AlexNet, ZFNet, VGG,
GoogLeNet, Residual Net, DenseNet, R-CNN, SPP-NET, Fast-RCNN, Faster-RCNN, FCN, Mask-
RCNN, YOLO, SSD, YOLO2 and other currently known or exploitation in the future network architecture.Used in the present embodiment
Neural network model be also possible to other kinds of neural network model, the neural network model be according to food materials handle image
And/or video training obtains, the training method of the model includes that supervised learning (being trained according to sample), intensified learning are (fixed
Adopted reward function) and learning by imitation, food materials information can be identified using the neural network model after training, and
Information is handled to corresponding food materials.
Further, it when being trained according to food materials processing figure and/or video to neural network model, can be straight
It connects and handles figure and/or video as the input of neural network model using the food materials to complete the training to neural network model；
Alternatively, it is also possible to first extract in food materials processing figure and/or video food materials handling machine people in each image/time point
Posture information, then completed as the input of neural network model using the posture information of food materials handling machine people to neural network mould
The training of type.It is appreciated that when being trained according to food materials processing figure and/or video to neural network model in the present embodiment
Used method is not limited to above two method, is also possible to using other directly or indirectly according to food materials processing figure
The method that shape and/or video are trained neural network model.
After the food materials information for obtaining food materials to be processed and corresponding food materials processing information, according to food materials type
Information and food materials processing information generate corresponding food materials process instruction, and are sent to food materials handling machine people, food materials process instruction
Food materials handling machine people is used to indicate to handle food materials to be processed according to the food materials processing information in food materials process instruction.
Further, if neural network model can not identify food materials to be processed according to the image of the food materials to be processed of acquisition
Food materials information, then reacquire the image of food materials to be processed, and image knowledge is carried out according to the image of reacquisition again
Other places reason, until identifying the food materials information of food materials to be processed.
It should be noted that the present embodiment does not limit the type of food materials handling machine people, the class of food materials handling machine people
Type can be depending on the variation with food materials type to be processed, for example, as shown in Fig. 2, food materials handling machine mankind's type specifically can be
The structures such as manipulator, so as to which food materials to be processed are fixed；Food materials handling machine mankind's type is also possible to include mechanical
Hand and actuator, as shown in Figures 3 and 4, the actuator can be the tool for holding other utensils, are also possible to cutter and (such as cut
Cutter etc.), the structure cutting tool (such as paring knife) etc. and food materials to be processed can be handled as shown in Figures 5 and 6 can also
Be can work in coordination with other food materials processing units (such as bowl, pot) it is common complete food materials treatment process structure (such as with bowl
The blender worked in coordination, the slice worked in coordination with pot etc.)；Food materials handling machine mankind's type, which can also be, controls other food materials
Device/equipment of processing unit working condition etc., such as device/equipment of control oven cooking times, control frying pan cooking temperature
Device/equipment of degree etc..
Further, when food materials handling machine people includes at least manipulator and actuator, it can be and held by actuator
Other utensils (such as cutter) are held, are also possible to for other utensils being directly anchored on manipulator.
The present embodiment proposes that a kind of food materials handling machine people's control method passes through nerve net when handling food materials
Network model identifies food materials type, and obtains corresponding food materials processing information, and robot handles information to food according to food materials
Material is handled, i.e., different treatment processes is executed for different food materials, so that food materials processing mode is more flexible；In addition,
The food materials that the neural network model can handle image and/or video study to a variety of different food materials according to different food materials are handled
Information, so that application range is wider, and food materials processing is more scientific and reasonable.
In one embodiment, the training data of neural network model further includes at least one in following three: food materials
Dynamics information in treatment process；Temperature information in food materials treatment process；Temporal information in food materials treatment process.According to food
When material processing image and/or video are trained neural network model, the information which obtains compares limitation,
Such as operation trace can only be obtained, the information such as mode of operation, and in food materials treatment process, in order to complete the processing to food materials,
In addition to needing operation trace, outside the information such as mode of operation, it is also necessary to specific some operating parameters, such as: dynamics, temperature and when
Between etc..
Operating parameter includes food materials strength disposal information, food materials process temperature information and food materials processing time in the present embodiment
At least one of in information.Specifically, food materials strength disposal information refers to the dynamics information being applied on food materials to be processed, example
Such as: great power being used to beat eggs the purpose that can achieve so that the eggshell of egg ruptures；Food materials process temperature information refers to
The required temperature information when handling food materials, such as: it can achieve the purpose that egg is cooked using how high temperature；Food materials
Processing temporal information refers to the required temporal information when handling food materials, such as: how long boil, which can achieve, is boiled egg
Ripe purpose.
The present embodiment when being trained to neural network model, training data further include food materials strength disposal information,
At least one of in food materials process temperature information, food materials processing temporal information, since above-mentioned parameter is all in food materials treatment process
Middle very important parameter, to there is conclusive effect with whether food materials handle completion, therefore, by using above-mentioned parameter pair
Neural network model is trained, and the food materials processing information that the food materials process instruction that the model can be made to export is included is more
It is specific and comprehensive, it is handled so as to preferably control food materials handling machine people to food materials to be processed.
In one embodiment, after step S100, food materials handling machine people's control method further include: obtain to be processed
The current state information of food materials, the current state information of acquisition helps to obtain the food materials are handled required at food materials
Manage information.
Current state information includes at least one in following three: current strength information, Current Temperatures information, when current
Between information, wherein current strength information refers to the dynamics information currently having been applied on food materials to be processed, Current Temperatures information
Refer to the Current Temperatures information of food materials to be processed, current time information refers to the various relevant temporal informations of food materials to be processed.
Such as: Fig. 3 is referred to, for the food materials in Fig. 3, current strength information is the dynamics of the fixed food materials of manipulator, due to needing
Cutting process is carried out to food materials, therefore, it is necessary to whether can slide in cutting process according to current strength validation of information food materials
Phenomena such as de-, carries out the food materials to cut required cutting force degree in addition, the current strength information obtained facilitates to obtain again
Information.Another example is: when handling the food materials (such as meat) of freezing, since the food materials of freezing state are compared to room temperature shape
For the food materials of state, the process of a defrosting is needed, therefore, treatment process can be more more complicated than under normal temperature state, correspondingly, food
Material processing strategie can also change.
The current state information that the present embodiment passes through acquisition food materials to be processed, it can be determined that the current shape of food materials to be processed
State, so as to help to obtain better food materials processing strategie, so that the food materials process instruction of output is more scientific and reasonable.
In one embodiment, in step S200, according to the image of food materials to be processed, by neural network model obtain to
The step of handling the food materials information and corresponding food materials processing information of food materials, specifically includes:
According to the image of food materials to be processed, the food materials information of food materials to be processed is obtained by neural network model；Root
According to the food materials information of food materials to be processed and the current state information of food materials to be processed, obtained by neural network model
The food materials of food materials to be processed handle information.
After the information for obtaining food materials to be processed, in conjunction with the current state information of food materials to be processed, it can be determined that go out
The current state of food materials to be processed, so as to obtain better food materials processing strategie, the food materials process instruction more section of output
It is reasonable to learn.
In one embodiment, food materials processing information includes food materials processing mode and food materials processing parameter.Pass through nerve net
The food materials processing information that network model obtains includes food materials processing mode and food materials processing parameter, which kind of food materials processing mode uses
Mode handles food materials, and food materials processing parameter is condition/environmental parameter in food materials treatment process.
Specifically, for different types of food materials, food materials processing mode be may be the same or different；To a kind of food
For material, food materials processing mode there is also a variety of, such as: for egg, processing mode includes: to beat eggs, stir, decocting
Egg steams a variety of processing modes such as egg.Corresponding, how corresponding food materials processing parameter expression of beating eggs beats eggs and could make egg
Eggshell ruptures；Stir how corresponding food materials processing parameter expression stirs the egg white that could make egg and yolk mixing is equal
It is even；The corresponding food materials processing parameter of fried egg indicates how fried egg could make egg fry cooked；Steam the corresponding food materials processing parameter of egg
Indicate that how steaming egg could make egg cook.
In the present embodiment, it includes food materials processing mode and food materials processing parameter, food materials handling machine people that food materials, which handle information,
Food materials can be handled according to food materials processing mode and food materials processing parameter, so that food materials treatment process is more intelligent
Change.
In one embodiment, food materials processing parameter includes at least one in time parameter, temperature parameter and dynamics parameter
?.Wherein, time parameter indicates the time handled food materials to be processed, and temperature parameter indicates to food materials to be processed
The environment temperature of reason, dynamics parameter indicate to treat the dynamics that processing food materials are handled.
Specifically, according to different food materials processing modes, the food materials processing parameter used is not quite similar, and can be and only uses
A parameter in above three parameter can be two parameters used in above three parameter, be also possible to use above-mentioned
All parameters in three parameters.It is appreciated that above three parameter is more common parameter in general food materials treatment process,
Food materials processing parameter is also possible to include the parameter arrived used in other than above three parameter, food materials treatment process, such as
Food materials number of processes etc..
Such as: Fig. 2 is referred to, during beating eggs, the parameter needed includes dynamics parameter, i.e., consolidates using great power
Determine egg, and ruptures the eggshell of egg using great power.With reference to Fig. 5, during stirring, the ginseng that needs
Number includes dynamics parameter and time parameter, i.e., stirs egg white and yolk using great power, and stir how long so that
It obtains egg white and yolk is uniformly mixed.During steaming egg, the parameter needed includes temperature parameter and time parameter, i.e., using more
High temperature steams egg, and steams and how long egg can be cooked.As shown in fig. 7, during fried egg, the ginseng that needs
Number includes time parameter, temperature parameter and dynamics parameter, i.e., using how high temperature come fried egg, using more during fried egg
Big power comes so that egg turn-over, and how long decoct can be egg fry cooked.
It should be noted that when the parameter used in food materials treatment process is two or more, therein one
A parameter can change with another or two Parameters variations, can during being trained to neural network model
With the correspondence variation relation being added between different parameters.Such as: when carrying out boiling processing to a certain food materials, temperature parameter one
As be 100 degrees Celsius, time parameter be 30 minutes, however, the boiling point of water can reduce, this allows for this in high altitude localities
Food materials boiling processing temperature parameter be lower than 100 degrees Celsius, at this time, it may be necessary to time parameter can be greater than 30 minutes.
In the present embodiment, food materials processing parameter includes at least one in time parameter, temperature parameter and dynamics parameter, food
Material handling machine people can one or more of root above-mentioned parameter food materials are handled so that food materials treatment process is more
Add scientific and reasonable.
In one embodiment, as shown in figure 8, after the step of exporting corresponding food materials process instruction, at the food materials
Managing robot control method further includes step S300 and step S400.
Step S300, obtains the actual treatment information of food materials to be processed, and actual treatment information includes actual treatment time, reality
At least one of in border strength disposal and actual treatment temp.When obtaining actual treatment information, handled according to the food materials of output
In instruction, the actual treatment information for the food materials to be processed that the food materials processing parameter selection that food materials processing information includes obtains.Such as:
Food materials processing parameter only includes dynamics parameter, then only obtains the actual treatment dynamics of food materials to be processed；Food materials processing parameter is simultaneously
Including dynamics parameter and time parameter, then actual treatment dynamics and the actual treatment time of food materials to be processed are obtained simultaneously.
Step S400 handles information and actual treatment information according to food materials, determines the food materials processing knot of food materials to be processed
Fruit.After obtaining actual treatment information, compares actual treatment information and food materials handle information, judge whether food materials processing is completed.
Such as: in the food materials processing information of a certain food materials, food materials processing mode is boiling, and food materials processing parameter includes time parameter,
Specially 30 minutes and temperature parameter, specially 100 degrees Celsius.In the actual treatment information of acquisition, if actual treatment temperature
Degree is 100 degrees Celsius, and the actual treatment time is 20 minutes, then judges the untreated completion of food materials；If actual treatment temp is taken the photograph for 100
Family name's degree, actual treatment time are 30 minutes, then judge that food materials processing is completed, terminate the processing to food materials.
Further, if the actual treatment environment of food materials is unable to reach food materials processing parameter in food materials processing information and wants
It asks, then can reacquire corresponding food materials processing parameter by neural network model, and export food materials process instruction again.Example
Such as: in the food materials processing information of a certain food materials, food materials processing mode is boiling, and food materials processing parameter includes time parameter, tool
Body be 30 minutes and temperature parameter, specially 100 degrees Celsius.In the actual treatment information of acquisition, if actual treatment temp
For 90 degrees Celsius (because height above sea level factor causes the boiling point of water to reduce), then reacquired at corresponding food materials by neural network model
Parameter is managed, i.e., when temperature is 90 degrees Celsius, needs the time (such as 45 minutes) of boiling, and the processing of output food materials refers to again
It enables, in the instruction exported again, temperature parameter is 90 degrees Celsius, and time parameter is 45 minutes.
The present embodiment after exporting food materials process instruction, further include obtain the actual treatment information of food materials to be processed, and
Information and actual treatment information are handled according to food materials, the food materials processing result of food materials to be processed is determined, to guarantee at food materials
Reason result is met the requirements, so that food materials treatment process is more scientific and reasonable.
In one embodiment, with the artificial example of food materials handling machine for cutting the dish, which includes manipulator and holds
Row device, actuator gripping have cutter, and manipulator carries out food materials for controlling cutter for fixing food materials to be processed, actuator
It cuts the dish processing.When being trained to neural network model, trained data include the image/video cut to the food materials
And the dynamics information of the force snesor feedback on table trencher is set, it can be learnt by food materials cutting image/video to this
Food materials carry out the information such as the operation trace of cutting process, can learn to complete to the food by the dynamics information that force snesor is fed back
Specific dynamics information required for the cutting of material.
In one embodiment, the step of executing food materials handling machine people's control method provided by above-described embodiment
When, the number of the neural network model used is not limited, food materials in the process of processing, can be using single mind
The food materials treatment process is executed through network model, is also possible to multiple neural network models and cooperates jointly to execute food materials processing
Process.Such as: when executing the food materials beaten eggs processing, use the single Neural model realization treatment process.It is decocted when executing
When the food materials processing of egg, the identification of egg is realized using neural network model 1, is realized using neural network model 2 to egg
Turn-over processing, the time of fried egg is controlled using neural network model 3, and the temperature etc. of fried egg is controlled using neural network model 4.It is logical
The mutual cooperation for crossing different neural network models, can make food materials treatment process more accurate.
Although it should be understood that Fig. 1,8 flow chart in each step successively shown according to the instruction of arrow,
These steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, these steps can execute in other order.Moreover, Fig. 1, at least one in 8
Part steps may include that perhaps these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, the execution sequence in these sub-steps or stage is also not necessarily successively
It carries out, but can be at least part of the sub-step or stage of other steps or other steps in turn or alternately
It executes.
In one embodiment, as shown in figure 9, providing a kind of food materials handling machine people's control device, the food materials processor
Device people's control device includes image collection module 100 and control module 200.
Image collection module 100 is used to obtain the image of food materials to be processed.The image that image collection module 100 obtains can be with
It is single image, can be multiple images, be also possible to the video being made of multiple image, wherein single image can be saved
Image acquisition time, and image processing speed can be accelerated；Multiple images can be improved by the video that multiple image forms
The accuracy of image procossing.
Control module 200 is used for the image according to food materials to be processed, obtains food materials to be processed by neural network model
Food materials information and corresponding food materials handle information, and export corresponding food materials process instruction, and neural network model is root
According to food materials handle image and/or video training obtain, food materials process instruction be used to indicate food materials handling machine people according to food materials at
Food materials processing information in reason instruction handles food materials to be processed.
In addition, image collection module 100 is also used to carry out in food materials treatment process in food materials handling machine people, to be processed
Food materials carry out localization process etc..Such as: Fig. 7 is referred to, during fried egg, image collection module 100 is also used to obtain egg
Position, thus facilitate food materials robot to egg carry out turn-over processing.
The present embodiment proposes that a kind of food materials handling machine people's control device passes through nerve net when handling food materials
Network model identifies food materials type, and obtains corresponding food materials processing information, and food materials handling machine people is handled according to food materials
Information handles food materials, i.e., different treatment processes is executed for different food materials, so that food materials processing mode is cleverer
It is living；In addition, the neural network model can handle image and/or video study to a variety of different food materials according to different food materials
Food materials handle information, so that application range is wider, and food materials processing is more scientific and reasonable.
In one embodiment, control module 200 is also used to control module and is also used to: obtaining the current shape of food materials to be processed
State information, current state information include at least one in following three: current strength information, Current Temperatures information, when current
Between information.
In one embodiment, control module 200 is also used to control module and is also used to: according to the image of food materials to be processed,
The food materials information of food materials to be processed is obtained by neural network model；According to the food materials information of food materials to be processed, with
And the current state information of food materials to be processed, information is handled by the food materials that neural network model obtains food materials to be processed.
In one embodiment, control module 200 is also used to: obtain the actual treatment information of food materials to be processed, it is practical at
Reason information includes at least one in actual treatment time, actual treatment dynamics and actual treatment temp；It is handled and is believed according to food materials
Breath and actual treatment information, determine the food materials processing result of food materials to be processed.
In the present embodiment, control module 200 is also used to obtain the reality of food materials to be processed after exporting food materials process instruction
Border handles information, and handles information and actual treatment information according to food materials, determines the food materials processing result of food materials to be processed, from
And guarantee that food materials processing result is met the requirements, so that food materials treatment process is more scientific and reasonable.
Specific restriction about food materials handling machine people's control device may refer to above for food materials handling machine people
The restriction of control method, details are not described herein.Modules in above-mentioned food materials handling machine people's control device can whole or portion
Divide and is realized by software, hardware and combinations thereof.Above-mentioned each module can be embedded in the form of hardware or independently of computer equipment
In processor in, can also be stored in a software form in the memory in computer equipment, in order to processor calling hold
The corresponding operation of the above modules of row.
In one embodiment, as shown in Figure 10, a kind of food materials processing system, including control device 300 and food materials are provided
Handling machine people 400.
Control device 300 is used to obtain the image of food materials to be processed；According to the image of food materials to be processed, pass through neural network
Model obtains the food materials information and corresponding food materials processing information of food materials to be processed, and exports corresponding food materials processing and refer to
It enables, neural network model is to be obtained according to food materials processing image and/or video training；
Food materials handling machine people 400 is used to carry out food materials to be processed according to the food materials processing information in food materials process instruction
Processing.
The present embodiment proposes a kind of food materials processing system, and when handling food materials, control device passes through neural network
Model identifies food materials type, and obtains corresponding food materials processing information, and food materials handling machine people is handled according to food materials to be believed
Breath handles food materials, i.e., different treatment processes is executed for different food materials, so that food materials processing mode is more flexible；
In addition, the neural network model can handle image and/or video study to the food of a variety of different food materials according to different food materials
Material handles information, so that application range is wider, and food materials processing is more scientific and reasonable.
In one embodiment, which further includes at least one in following three:
Timer is used for after control device exports food materials process instruction, when obtaining the actual treatment of food materials to be processed
Between, and it is sent to control device；
Force snesor, for obtaining the current strength of food materials to be processed before control device output food materials process instruction
Information, and it is sent to control device；And for obtaining food materials to be processed after control device exports food materials process instruction
Actual treatment dynamics, and be sent to control device；
Temperature sensor, for obtaining the current temperature of food materials to be processed before control device output food materials process instruction
Information is spent, and is sent to control device；And for obtaining food to be processed after control device exports food materials process instruction
The actual treatment temp of material, and it is sent to control device.
In the present embodiment, before handling food materials, the current state information of food materials is first obtained, can contribute to
Information is handled to more scientific food materials；During carrying out actual treatment to food materials, pass through timer, force snesor, temperature
One or more in degree sensor obtains the actual treatment information of food materials to be processed, and handles information and reality according to food materials
Information is handled, the food materials processing result of food materials to be processed is determined, to guarantee that food materials processing result is met the requirements, so that at food materials
Reason process is more scientific and reasonable.
Specific about control device limits the restriction that may refer to above for food materials handling machine people's control method,
Details are not described herein.Modules in above-mentioned control device can be realized fully or partially through software, hardware and combinations thereof.
Above-mentioned each module can be embedded in the form of hardware or independently of in the processor in computer equipment, can also deposit in a software form
It is stored in the memory in computer equipment, executes the corresponding operation of the above modules in order to which processor calls.
In one embodiment, a kind of computer equipment, including memory and processor are provided, memory is stored with calculating
Machine program, processor perform the steps of the image for obtaining food materials to be processed when executing computer program；According to food materials to be processed
Image, by neural network model obtain food materials to be processed food materials information and corresponding food materials handle information, and
Corresponding food materials process instruction is exported, neural network model is to obtain according to food materials processing image and/or video training, at food materials
Reason instruction is used to indicate food materials handling machine people and is carried out according to the food materials processing information in food materials process instruction to food materials to be processed
Processing.
In one embodiment, it is also performed the steps of when processor executes computer program and obtains food materials to be processed
Current state information, current state information include in following three at least one of: current strength information, Current Temperatures information,
Current time information.
In one embodiment, it also performs the steps of when processor executes computer program according to food materials to be processed
Image obtains the food materials information of food materials to be processed by neural network model；Believed according to the food materials type of food materials to be processed
The current state information of breath and food materials to be processed handles information by the food materials that neural network model obtains food materials to be processed.
In one embodiment, it is also performed the steps of when processor executes computer program and obtains food materials to be processed
Actual treatment information, actual treatment information include in actual treatment time, actual treatment dynamics and actual treatment temp at least
One；Information and actual treatment information are handled according to food materials, determines the food materials processing result of food materials to be processed.
In one embodiment, a kind of computer readable storage medium is provided, computer program, computer are stored thereon with
The image for obtaining food materials to be processed is performed the steps of when program is executed by processor；According to the image of food materials to be processed, pass through
Neural network model obtains the food materials information and corresponding food materials processing information of food materials to be processed, and exports corresponding food
Material process instruction, neural network model are to be obtained according to food materials processing image and/or video training, and food materials process instruction is for referring to
Show that food materials handling machine people is handled food materials to be processed according to the food materials processing information in food materials process instruction.
In one embodiment, it is also performed the steps of when computer program is executed by processor and obtains food materials to be processed
Current state information, current state information include in following three at least one of: current strength information, Current Temperatures letter
Breath, current time information.
In one embodiment, it also performs the steps of when computer program is executed by processor according to food materials to be processed
Image, the food materials information of food materials to be processed is obtained by neural network model；According to the food materials type of food materials to be processed
The current state information of information and food materials to be processed obtains the food materials processing letter of food materials to be processed by neural network model
Breath.
In one embodiment, it is also performed the steps of when computer program is executed by processor
The actual treatment information of food materials to be processed is obtained, actual treatment information includes actual treatment time, actual treatment power
At least one of in degree and actual treatment temp；Information and actual treatment information are handled according to food materials, determines food materials to be processed
Food materials processing result.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality
It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention
Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (15)
1. a kind of food materials handling machine people's control method characterized by comprising
Obtain the image of food materials to be processed；
According to the image of the food materials to be processed, the food materials information of the food materials to be processed is obtained by neural network model
And corresponding food materials handle information, and export corresponding food materials process instruction, the neural network model be according to food materials at
Reason image and/or video training obtain, and the food materials process instruction is used to indicate the food materials handling machine people according to the food
Food materials processing information in material process instruction handles the food materials to be processed.
2. food materials handling machine people's control method according to claim 1, which is characterized in that the neural network model
Training data further includes at least one in following three:
Dynamics information in food materials treatment process；
Temperature information in food materials treatment process；
Temporal information in food materials treatment process.
3. food materials handling machine people's control method according to claim 1, which is characterized in that described to obtain food materials to be processed
Image after, further includes:
The current state information of the food materials to be processed is obtained, the current state information includes at least one in following three
: current strength information, Current Temperatures information, current time information.
4. food materials handling machine people's control method according to claim 3, which is characterized in that described according to described to be processed
The image of food materials obtains the food materials information of the food materials to be processed by neural network model and corresponding food materials is handled
Information, comprising:
According to the image of the food materials to be processed, believed by the food materials type that neural network model obtains the food materials to be processed
Breath；
According to the food materials information of the food materials to be processed and the current state information of the food materials to be processed, pass through mind
The food materials for obtaining the food materials to be processed through network model handle information.
5. food materials handling machine people's control method according to claim 2, which is characterized in that the food materials handle packet
Include food materials processing mode and food materials processing parameter.
6. food materials handling machine people's control method according to claim 5, which is characterized in that the food materials processing parameter packet
Include at least one in time parameter, temperature parameter and dynamics parameter.
7. food materials handling machine people's control method according to claim 6, which is characterized in that described to export corresponding food materials
After the step of process instruction, further includes:
The actual treatment information of the food materials to be processed is obtained, at the actual treatment information includes the actual treatment time, is practical
At least one of in reason dynamics and actual treatment temp；
Information and the actual treatment information are handled according to the food materials, determines the food materials processing knot of the food materials to be processed
Fruit.
8. a kind of food materials handling machine people's control device characterized by comprising
Image collection module, for obtaining the image of food materials to be processed；
Control module obtains the food materials to be processed by neural network model for the image according to the food materials to be processed
Food materials information and corresponding food materials handle information, and export corresponding food materials process instruction, the neural network mould
Type is to be obtained according to food materials processing image and/or video training, and the food materials process instruction is used to indicate the food materials processor
Device people is handled the food materials to be processed according to the food materials processing information in the food materials process instruction.
9. food materials handling machine people's control device according to claim 8, which is characterized in that the control module is also used
In:
The current state information of the food materials to be processed is obtained, the current state information includes at least one in following three
: current strength information, Current Temperatures information, current time information.
10. food materials handling machine people's control device according to claim 9, which is characterized in that the control module is also used
In:
According to the image of the food materials to be processed, believed by the food materials type that neural network model obtains the food materials to be processed
Breath；
According to the food materials information of the food materials to be processed and the current state information of the food materials to be processed, pass through mind
The food materials for obtaining the food materials to be processed through network model handle information.
11. food materials handling machine people's control device according to claim 8, which is characterized in that the control module is also used
In:
The actual treatment information of the food materials to be processed is obtained, at the actual treatment information includes the actual treatment time, is practical
At least one of in reason dynamics and actual treatment temp；
Information and the actual treatment information are handled according to the food materials, determines the food materials processing knot of the food materials to be processed
Fruit.
12. a kind of food materials processing system characterized by comprising
Control device, for obtaining the image of food materials to be processed；According to the image of the food materials to be processed, pass through neural network mould
Type obtains the food materials information and corresponding food materials processing information of the food materials to be processed, and exports corresponding food materials processing
Instruction, the neural network model are to be obtained according to food materials processing image and/or video training；
Food materials handling machine people, for according in the food materials process instruction food materials processing information to the food materials to be processed into
Row processing.
13. food materials processing system according to claim 12, which is characterized in that further include at least one in following three
:
Timer, for obtaining the practical place of the food materials to be processed after the control device exports food materials process instruction
The time is managed, and is sent to the control device；
Force snesor, for obtaining the current of the food materials to be processed before control device output food materials process instruction
Dynamics information, and it is sent to the control device；And for obtaining after the control device exports food materials process instruction
The actual treatment dynamics of the food materials to be processed is taken, and is sent to the control device；
Temperature sensor, for obtaining working as the food materials to be processed before control device output food materials process instruction
Preceding temperature information, and it is sent to the control device；And be used for after the control device exports food materials process instruction,
The actual treatment temp of the food materials to be processed is obtained, and is sent to the control device.
14. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists
In the processor performs the steps of when executing the computer program
Obtain the image of food materials to be processed；
According to the image of the food materials to be processed, the food materials information of the food materials to be processed is obtained by neural network model
And corresponding food materials handle information, and export corresponding food materials process instruction, the neural network model be according to food materials at
Reason image and/or video training obtains, the food materials process instruction be used to indicate food materials handling machine people according to the food materials at
Food materials processing information in reason instruction handles the food materials to be processed.
15. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
It is performed the steps of when being executed by processor
Obtain the image of food materials to be processed；
According to the image of the food materials to be processed, the food materials information of the food materials to be processed is obtained by neural network model
And corresponding food materials handle information, and export corresponding food materials process instruction, the neural network model be according to food materials at
Reason image and/or video training obtains, the food materials process instruction be used to indicate food materials handling machine people according to the food materials at
Food materials processing information in reason instruction handles the food materials to be processed.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811081931.3A CN109434844B (en) | 2018-09-17 | 2018-09-17 | Food material processing robot control method, device and system, storage medium and equipment |
| PCT/CN2019/105912 WO2020057455A1 (en) | 2018-09-17 | 2019-09-16 | Food material processing robot control method, apparatus, system, storage medium and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811081931.3A CN109434844B (en) | 2018-09-17 | 2018-09-17 | Food material processing robot control method, device and system, storage medium and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109434844A true CN109434844A (en) | 2019-03-08 |
| CN109434844B CN109434844B (en) | 2022-06-28 |
Family
ID=65532869
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811081931.3A Expired - Fee Related CN109434844B (en) | 2018-09-17 | 2018-09-17 | Food material processing robot control method, device and system, storage medium and equipment |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN109434844B (en) |
| WO (1) | WO2020057455A1 (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020057455A1 (en) * | 2018-09-17 | 2020-03-26 | 鲁班嫡系机器人（深圳）有限公司 | Food material processing robot control method, apparatus, system, storage medium and device |
| CN111151368A (en) * | 2020-01-09 | 2020-05-15 | 珠海格力电器股份有限公司 | Garbage treatment method, system, storage medium and garbage treatment equipment |
| WO2020207293A1 (en) * | 2019-04-11 | 2020-10-15 | 上海长膳智能科技有限公司 | Method and device for automatically cooking food |
| CN111814862A (en) * | 2020-06-30 | 2020-10-23 | 平安国际智慧城市科技股份有限公司 | Fruit and vegetable identification method and device |
| CN111914777A (en) * | 2020-08-07 | 2020-11-10 | 广东工业大学 | Method and system for identifying robot instruction in cross-mode manner |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1099195A (en) * | 1996-10-03 | 1998-04-21 | Matsushita Electric Ind Co Ltd | Cooker |
| CN105444222A (en) * | 2015-12-11 | 2016-03-30 | 美的集团股份有限公司 | Cooking control method and system of microwave oven, cloud server and microwave oven |
| CN105867257A (en) * | 2016-05-30 | 2016-08-17 | 深圳市泰瑞达科技有限公司 | Intelligent pot and control and compiling method thereof |
| CN106393102A (en) * | 2015-07-31 | 2017-02-15 | 发那科株式会社 | Machine learning device, robot system, and machine learning method |
| CN106774876A (en) * | 2016-12-12 | 2017-05-31 | 大连文森特软件科技有限公司 | Based on the culinary art accessory system that AR augmented realities and menu are generated |
| CN106897661A (en) * | 2017-01-05 | 2017-06-27 | 合肥华凌股份有限公司 | A kind of Weigh sensor method of food materials image, system and household electrical appliance |
| CN108197635A (en) * | 2017-11-29 | 2018-06-22 | 珠海格力电器股份有限公司 | Methods of exhibiting and device, the smoke exhaust ventilator of cooking method |
| CN108303920A (en) * | 2018-01-18 | 2018-07-20 | 周晔 | A kind of intelligent electric appliance integrated management approach and system based on food materials identification |
| CN108527388A (en) * | 2018-04-20 | 2018-09-14 | 成都昂联科技有限公司 | A kind of the kitchen intelligent robot and its operating method of Behavior-based control analysis |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6650779B2 (en) * | 1999-03-26 | 2003-11-18 | Georgia Tech Research Corp. | Method and apparatus for analyzing an image to detect and identify patterns |
| US6468069B2 (en) * | 1999-10-25 | 2002-10-22 | Jerome H. Lemelson | Automatically optimized combustion control |
| CN102294695A (en) * | 2010-06-25 | 2011-12-28 | 鸿富锦精密工业（深圳）有限公司 | Robot calibration method and calibration system |
| CN104914720B (en) * | 2015-04-16 | 2017-11-17 | 贵州省烟草公司遵义市公司 | Electronic nose intelligence baking control system and control method with autolearn feature |
| CN105512676A (en) * | 2015-11-30 | 2016-04-20 | 华南理工大学 | Food recognition method at intelligent terminal |
| CN106878697A (en) * | 2016-06-29 | 2017-06-20 | 鲁班嫡系机器人 | A kind of image pickup method and its imaging method, device and equipment |
| CN108098773A (en) * | 2017-12-20 | 2018-06-01 | 芜湖哈特机器人产业技术研究院有限公司 | The sorting control system and method for a kind of robot |
| CN109434844B (en) * | 2018-09-17 | 2022-06-28 | 鲁班嫡系机器人（深圳）有限公司 | Food material processing robot control method, device and system, storage medium and equipment |
-
2018
- 2018-09-17 CN CN201811081931.3A patent/CN109434844B/en not_active Expired - Fee Related
-
2019
- 2019-09-16 WO PCT/CN2019/105912 patent/WO2020057455A1/en active Application Filing
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1099195A (en) * | 1996-10-03 | 1998-04-21 | Matsushita Electric Ind Co Ltd | Cooker |
| CN106393102A (en) * | 2015-07-31 | 2017-02-15 | 发那科株式会社 | Machine learning device, robot system, and machine learning method |
| CN105444222A (en) * | 2015-12-11 | 2016-03-30 | 美的集团股份有限公司 | Cooking control method and system of microwave oven, cloud server and microwave oven |
| CN105867257A (en) * | 2016-05-30 | 2016-08-17 | 深圳市泰瑞达科技有限公司 | Intelligent pot and control and compiling method thereof |
| CN106774876A (en) * | 2016-12-12 | 2017-05-31 | 大连文森特软件科技有限公司 | Based on the culinary art accessory system that AR augmented realities and menu are generated |
| CN106897661A (en) * | 2017-01-05 | 2017-06-27 | 合肥华凌股份有限公司 | A kind of Weigh sensor method of food materials image, system and household electrical appliance |
| CN108197635A (en) * | 2017-11-29 | 2018-06-22 | 珠海格力电器股份有限公司 | Methods of exhibiting and device, the smoke exhaust ventilator of cooking method |
| CN108303920A (en) * | 2018-01-18 | 2018-07-20 | 周晔 | A kind of intelligent electric appliance integrated management approach and system based on food materials identification |
| CN108527388A (en) * | 2018-04-20 | 2018-09-14 | 成都昂联科技有限公司 | A kind of the kitchen intelligent robot and its operating method of Behavior-based control analysis |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020057455A1 (en) * | 2018-09-17 | 2020-03-26 | 鲁班嫡系机器人（深圳）有限公司 | Food material processing robot control method, apparatus, system, storage medium and device |
| WO2020207293A1 (en) * | 2019-04-11 | 2020-10-15 | 上海长膳智能科技有限公司 | Method and device for automatically cooking food |
| CN111151368A (en) * | 2020-01-09 | 2020-05-15 | 珠海格力电器股份有限公司 | Garbage treatment method, system, storage medium and garbage treatment equipment |
| CN111814862A (en) * | 2020-06-30 | 2020-10-23 | 平安国际智慧城市科技股份有限公司 | Fruit and vegetable identification method and device |
| CN111914777A (en) * | 2020-08-07 | 2020-11-10 | 广东工业大学 | Method and system for identifying robot instruction in cross-mode manner |
| CN111914777B (en) * | 2020-08-07 | 2021-07-06 | 广东工业大学 | Method and system for identifying robot instruction in cross-mode manner |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020057455A1 (en) | 2020-03-26 |
| CN109434844B (en) | 2022-06-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109434844A (en) | Food materials handling machine people control method, device, system, storage medium and equipment | |
| US11117253B2 (en) | Methods and systems for food preparation in a robotic cooking kitchen | |
| US10942932B2 (en) | System and method for grading and scoring food | |
| CN110289079A (en) | Wired home recipe supplying system, the method and device for pushing recipe | |
| EP3068271B1 (en) | Smart cooking apparatus and method | |
| CN101943891B (en) | Cooking expert system and cooling device applying same | |
| CN106773859A (en) | A kind of intelligent cooking control method | |
| CN110101292A (en) | Rice cooking control method, device, storage medium and electric cooker | |
| CA3093243A1 (en) | System and method for grading and scoring food | |
| CN111096656A (en) | Cooking assistance method, apparatus, computer device and storage medium | |
| CN107625401A (en) | A kind of intelligent machine for stir-frying dishes device people | |
| CN112107232A (en) | Cooking control method, device and equipment and readable storage medium | |
| CN114173581A (en) | Determining a cooking recipe | |
| CN111178035A (en) | Method and device for generating electronic menu set, storage medium and terminal | |
| CN110794718B (en) | Multi-food cooking method, terminal and storage medium | |
| CN115291782A (en) | Method, system, computer readable medium and electronic device for automatically generating menu | |
| KR102648408B1 (en) | Maesengi chicken hot pot manufacturing method | |
| Demeure et al. | What We Learn about Process Specification Languages, from Studying Recipes | |
| CN112269341A (en) | Food material preprocessing method, computer-readable storage medium, intelligent cooking equipment, server and intelligent cooking system | |
| WO2022107086A1 (en) | Cooking appliance, and method and device for controlling the same | |
| Webb | Process control parameters for skipjack tuna (Katsuwonas pelamis) precooking | |
| KR20240014911A (en) | Frying robot controlling system for actively adjusting cooking conditions | |
| CN116400607A (en) | Cooking control method, device, medium and equipment | |
| Rahman et al. | Food process design: Overview | |
| CN112051782A (en) | Intelligent canteen standardized production method and system and cooking terminal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20220628 |