CN109659007A - Dining room dining method of servicing, device, computer equipment and storage medium - Google Patents
Dining room dining method of servicing, device, computer equipment and storage medium Download PDFInfo
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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Abstract
The embodiment of the invention discloses a kind of dining room dining method of servicing, device, computer equipment and storage mediums, include the following steps: the body image for obtaining target person；The body image is input to training to convergence state in advance to be used to carry out human body image in the neural network model of body classification, judges the posture information of the target person；It is that the target person recommends dining vegetable according to the posture information and preset recommendation rules.When vegetable is valuated, method for distinguishing is known using vegetable, it is automatic to calculate dining expense, and the facial information auto deduction by scanning the personnel that have dinner, the efficiency of expense calculating is improved, valuation mistake is reduced.By this method, the eating habit for having dinner and settling accounts rate, the personnel of having dinner being helped to form health is effectively improved.
Description
Technical field
The present invention relates to technical field of information processing, more particularly to a kind of dining room dining method of servicing, device, computer
Equipment and storage medium.
Background technique
Currently, dining room can satisfy the dining and quick clearing demand of user, in places such as school, companies, dining room becomes
The dining of many people is selectively.In order to reduce cash flow, accelerate settlement efficiency, IC has all been used in current most dining room instead
Card or mobile APP payment, effectively facilitate the payment operation for the personnel of having dinner.But the valuation of vegetable is all by the people that works
Member is artificial to be calculated, and many times be will appear staff and is forgotten or misremember vegetable price, or calculates that omission, to miscalculate vegetable total
Valence, cause dining calculation of price it is slow, be easy to appear mistake.It is paid using IC card or mobile APP, it may appear that have dinner people
The case where member loses or leaves behind IC card or mobile phone, can not pay the bill, there is also the risks of stolen brush for the IC card of loss when causing to have dinner.
On the other hand, it goes to the mess, is substantially left what dish eats what dish, nutrition of being far from being is had dinner.Have dinner personnel
Most of without the reasonable eating habit of health, much the young and the middle aged personages are put energy in career, are careless about very much, are put down to nutrition
When or regardless of food, have enough just, perhaps pause the chicken and duck flesh of fish or dinner party constantly, it is high-fat, high protein is a large amount of
Intake, so that beer belly, fatty liver, hypertension, hyperglycemia etc.；In addition also some Mies fearness is fat, keeps a diet unilaterally.
Hardly realize, these unreasonable, unsound eating habits, eventually causes physical fitness downslide, or even be eaten up with illness.
Summary of the invention
The embodiment of the present invention be capable of providing it is a kind of raising have dinner settle accounts rate, help the personnel of having dinner form health diet practise
Used dining room dining method of servicing, device, computer equipment and storage medium.
In order to solve the above technical problems, the technical solution that the embodiment of the invention uses is: providing a kind of food
Hall dining method of servicing, comprising the following steps:
Obtain the body image of target person；
The posture information of the target person is judged according to the body image；
It is that the target person recommends dining vegetable according to the posture information and preset recommendation rules.
Optionally, the step of posture information that the target person is judged according to the body image, including it is following
Step:
The body image is input in preset Shape analysis model, wherein the Shape analysis model is preparatory
Training to convergence state is used to carry out human body image the neural network model of body classification；
Obtain the classification results of the Shape analysis model output；
The body data for defining the classification results characterization are the posture information of the target person.
Optionally, the step of body image for obtaining target person, comprising the following steps:
Obtain real-time pictures；
The timing extraction frame image from the real-time pictures, and judge in the frame image with the presence or absence of body profile；
When there are when body profile, confirm that the frame image is the body image of the target person in the frame image.
Optionally, the training method of the neural network model includes:
Training sample set is obtained, the training sample set includes an at least body image for same target；
An at least body image is sequentially inputted in preset disaggregated model, is obtained respectively at least one described
The posture information classification value of body image；
It is ranked up by posture information classification value of the qualifications at least one body image of numerical value；
Confirm that posture information classification value in an intermediate position in the ranking results is an at least body image
Expectation classification value.
Optionally, the posture information according to the target person is that the target person pushes away with preset recommendation rules
The step of recommending dining vegetable, includes the following steps:
Obtain the cuisine information of current restocking vegetable, wherein include every portion vegetable institute in the cuisine information
The heat for including；
Target vegetable is determined in restocking vegetable according to the posture information of the target person and preset matching rule；
The target vegetable is showed into the target person by display terminal, to guide the target person selection mesh
Mark vegetable is eaten.
Optionally, described to recommend dining vegetable according to the posture information and preset recommendation rules for the target person
Later, include the following steps:
Obtain the face image of target person；
The identity information of target person is confirmed according to the face image, wherein include the button bound in identity information
Money channel；
Corresponding dining expense is deducted by the channel of withholing.
Optionally, it is described by paying dining expense in the mobile payment application program the step of before, including it is following
Step:
Obtain the service plate image of target person；
The service plate image is input in preset vegetable identification model, wherein the vegetable identification model is preparatory
Training to convergence state is used to carry out vegetable the neural network model of price category；
Obtain the price category result of each vegetable in the service plate image；
Determine that the sum of price of the vegetable is the dining expense.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of dining room dining service unit, comprising:
Module is obtained, for obtaining the body image of target person；
Processing module, for judging the posture information of the target person according to the body image；
Execution module, for being that the target person recommends dining dish according to the posture information and preset recommendation rules
Product.
Optionally, the dining room dining service unit, further includes:
First input submodule, for the body image to be input in preset Shape analysis model, wherein described
Shape analysis model is the neural network model that training in advance to convergence state is used to carry out human body image body classification；
First acquisition submodule, for obtaining the classification results of the Shape analysis model output；
First implementation sub-module, the body data for defining the classification results characterization are the posture of the target person
Information.
Optionally, the dining room dining service unit, further includes:
Second acquisition submodule, for obtaining real-time pictures；
First processing submodule, for the timing extraction frame image from the real-time pictures, and judges in the frame image
With the presence or absence of body profile；
Second implementation sub-module, for when there are when body profile, confirm that the frame image is described in the frame image
The body image of target person.
Optionally, the dining room dining service unit, further includes:
Third acquisition submodule, for obtaining training sample set, the training sample set includes at least the one of same target
Open body image；
Second input submodule, for an at least body image to be sequentially inputted in preset disaggregated model,
The posture information classification value of at least one body image is obtained respectively；
Second processing submodule, for being qualifications to the posture information point of at least one body image using numerical value
Class value is ranked up；
Third implementation sub-module, for confirming posture information classification value in an intermediate position in the ranking results for institute
State the expectation classification value of at least one body image.
Optionally, the dining room dining service unit, further includes:
4th acquisition submodule, for obtaining the cuisine information of current restocking vegetable, wherein wrapped in the cuisine information
Include the heat that every portion vegetable is included；
Third handles submodule, for the posture information and preset matching rule according to the target person in restocking
Target vegetable is determined in vegetable；
4th implementation sub-module, for the target vegetable to be showed the target person by display terminal, to draw
The target person selection target vegetable is led to eat.
Optionally, the dining room dining service unit, further includes:
5th acquisition submodule, for obtaining the face image of target person；
Fourth process submodule, for confirming the identity information of target person according to the face image, wherein identity letter
It include the channel of withholing bound in breath；
5th implementation sub-module, for deducting corresponding dining expense by the channel of withholing.
Optionally, the dining room dining service unit, further includes:
6th acquisition submodule, for obtaining the service plate image of target person；
Third input submodule, for the service plate image to be input in preset vegetable identification model, wherein described
Vegetable identification model is the neural network model that training in advance to convergence state is used to carry out vegetable price category；
7th acquisition submodule, for obtaining the price category result of each vegetable in the service plate image；
6th implementation sub-module, the sum of price for determining the vegetable are the dining expense.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of computer equipment, including memory and processing
Device is stored with computer-readable instruction in the memory, when the computer-readable instruction is executed by the processor, so that
The processor executes the step of dining method of servicing in dining room described above.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of storage Jie for being stored with computer-readable instruction
Matter, when the computer-readable instruction is executed by one or more processors, so that one or more processors execute above-mentioned institute
The step of stating dining room dining method of servicing.
The beneficial effect of the embodiment of the present invention is: by obtaining the body image for the personnel of having dinner in real time, and the personnel that will have dinner
Body image be input to and trained into convergent neural network model, just according to the judgement of the output category result of neural network
The posture information of meal personnel, and be that the personnel that have dinner recommend to be suitble to certainly according to the posture information for the personnel of having dinner and preset recommendation rules
The cuisine of body guides the more healthy cuisine of the choice of members of having dinner, and reduces personnel's habituation of having dinner and selects unreasonable cuisine collocation
The case where, it helps the personnel of having dinner to form good eating habit, reduces because having dinner caused by unreasonable, unsound eating habit
The probability of personnel's disease improves physical fitness.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those skilled in the art, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is the basic procedure schematic diagram of dining room of embodiment of the present invention dining method of servicing；
Fig. 2 is the flow diagram that the embodiment of the present invention judges target person posture information；
Fig. 3 is the flow diagram that the embodiment of the present invention obtains body image；
Fig. 4 is the flow diagram of neural network training method of the embodiment of the present invention；
Fig. 5 is the flow diagram that the embodiment of the present invention recommends vegetable；
Fig. 6 is the flow diagram that the embodiment of the present invention pays dining expense；
Fig. 7 is the flow diagram that the embodiment of the present invention calculates dining expense；
Fig. 8 is the basic structure block diagram of dining room of embodiment of the present invention dining service unit；
Fig. 9 is computer equipment of embodiment of the present invention basic structure block diagram.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described.
In some processes of the description in description and claims of this specification and above-mentioned attached drawing, contain according to
Multiple operations that particular order occurs, but it should be clearly understood that these operations can not be what appears in this article suitable according to its
Sequence is executed or is executed parallel, and serial number of operation such as 101,102 etc. is only used for distinguishing each different operation, serial number
It itself does not represent and any executes sequence.In addition, these processes may include more or fewer operations, and these operations can
To execute or execute parallel in order.It should be noted that the description such as " first " herein, " second ", is for distinguishing not
Same message, equipment, module etc., does not represent sequencing, does not also limit " first " and " second " and be different type.
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those skilled in the art's every other implementation obtained without creative efforts
Example, shall fall within the protection scope of the present invention.
Those skilled in the art of the present technique are appreciated that " terminal " used herein above, " terminal device " both include wireless communication
The equipment of number receiver, only has the equipment of the wireless signal receiver of non-emissive ability, and including receiving and emitting hardware
Equipment, have on bidirectional communication link, can execute two-way communication reception and emit hardware equipment.This equipment
It may include: honeycomb or other communication equipments, shown with single line display or multi-line display or without multi-line
The honeycomb of device or other communication equipments；PCS (PersonalCommunicationsService, PCS Personal Communications System), can be with
Combine voice, data processing, fax and/or communication ability；PDA (PersonalDigitalAssistant, individual digital
Assistant), may include radio frequency receiver, pager, the Internet/intranet access, web browser, notepad, calendar and/
Or GPS (GlobalPositioningSystem, global positioning system) receiver；Conventional laptop and/or palmtop computer
Or other equipment, have and/or the conventional laptop including radio frequency receiver and/or palmtop computer or other equipment.
" terminal " used herein above, " terminal device " can be it is portable, can transport, be mounted on the vehicles (aviation, sea-freight and/
Or land) in, or be suitable for and/or be configured in local runtime, and/or with distribution form, operate in the earth and/or sky
Between any other position operation." terminal " used herein above, " terminal device " can also be communication terminal, access terminals,
Music/video playback terminal, for example, can be PDA, MID (MobileInternetDevice, mobile internet device) and/or
Mobile phone with music/video playing function is also possible to the equipment such as smart television, set-top box.
Specifically referring to Fig. 1, Fig. 1 is the basic procedure schematic diagram of the present embodiment dining room dining method of servicing.
The method of servicing as shown in Figure 1, a kind of dining room is eaten, comprising the following steps:
S1100, the body image for obtaining target person；
It is had dinner the real-time pictures of personnel by picture pick-up device captured in real-time, the timing extraction frame figure from the real-time pictures
Picture, and judge in the frame image with the presence or absence of body profile, when, there are when body profile, confirming the frame in the frame image
Image is the body image of the target person.
S1200, the posture information that the target person is judged according to the body image；
The body image is input in preset Shape analysis model, wherein the Shape analysis model is preparatory
Training to convergence state is used to carry out human body image the neural network model of body classification, in some embodiments, nerve
It is CNN convolutional neural networks model or VGG convolutional neural networks model that network model, which can be,.Obtain the Shape analysis mould
The classification results of type output, the body data for defining the classification results characterization are the posture information of the target person.
It S1300, is that the target person recommends dining vegetable according to the posture information and preset recommendation rules；
Obtain the cuisine information of current restocking vegetable, wherein include every portion vegetable institute in the cuisine information
The heat for including determines target dish according to the posture information of the target person and preset matching rule in restocking vegetable
Product, for example, to be judged as that the partially fat personnel that have dinner recommend more light vegetable.The target vegetable is passed through into display terminal exhibition
Show to the target person, to guide the target person selection target vegetable to eat.
As shown in Fig. 2, step S1200 specifically includes the following steps:
S1210, the body image is input in preset Shape analysis model, wherein the Shape analysis model
To train to convergence state the neural network model for being used to carry out human body image body classification in advance；
The body image that will acquire, which has been input to, trains in advance to convergent neural network model, in present embodiment
Neural network model can be CNN convolutional neural networks model or VGG convolutional neural networks model.The neural network mould
Type is in training, by an at least body image for the same target of acquisition as training sample set, when the body image is more
When one, multiple body images are sequentially inputted in preset disaggregated model, obtain the posture of multiple body images respectively
Information classification value is ranked up by posture information classification value of the qualifications to multiple body images of numerical value, confirms the row
Posture information classification value in an intermediate position is the expectation classification value of at least one body image in sequence result；When described
When body image only has one, body image is input in preset disaggregated model, obtains the posture information point of body image
Expectation classification value of the class value as body image.
S1220, the classification results for obtaining the Shape analysis model output；
In some embodiments, the Shape analysis model is classified equipped with multiple bodies, and the classification of each body is right respectively
A body classification standard value is answered, therefore, the classification data of Shape analysis model output is that body image belongs to each body classification
Probability value, obtain each body and classify corresponding probability value, and drop power sequence is carried out to each probability value according to the size of numerical value.
Maximum classification value in multiple classification values is obtained according to ranking results, i.e., is arranged in primary point in ranking results
Class value, the corresponding body classification of the classification value.Illustrate that the classification results of Shape analysis model show that body image belongs to such
Other maximum probability, i.e. classification results show that the body classification of body image belongs to the corresponding body point of the maximum number of classification value
Class.
S1230, the posture information that the body data for defining the classification results characterization are the target person；
The corresponding body data of the classification results are obtained after confirmation classification results, in some embodiments, by the shape of people
Volume data is divided into different postures, such as " obesity ", " partially fat ", " common " and " partially thin ".The division of posture is not limited to this,
According to the difference of concrete application scene, posture classification can be more detailed, can also be more rough.It is described for defining the body data
The posture information of target person.By this method, it can faster and more accurately judge the body information of target person.
As shown in figure 3, step S1100 specifically includes the following steps:
S1110, real-time pictures are obtained；
In some embodiments, real-time pictures can be the real-time video data that photographic device shooting uploads.For example,
The have dinner corner of troop is provided with photographic device, and shooting is had dinner the dynamic menu of personnel, and the content of shooting should at least wrap
The whole image of the have dinner personnel nearest from photographic device is included, and picture is uploaded to server.
S1120, the timing extraction frame image from the real-time pictures, and judge in the frame image with the presence or absence of body wheel
It is wide；
By Video processing software (such as OpenCV, but not limited to this) real-time pictures are handled, by real-time pictures
It is split as several frame pictures.(such as mode of every 0.5s one picture of extraction) by way of timing extraction, at several
Multiple frame pictures are successively extracted in frame picture, then frame picture are input in preset body outline identification model, judgment frame
It whether there is body image in picture.In some embodiments, body outline identification model can be trains in the prior art
For judge body image whether there is or not CNN convolutional neural networks model or VGG convolutional neural networks model.
S1130, when in the frame image there are when body profile, confirm the frame image be the target person body
Image；
When there are when body profile, confirm that the frame image is the body image of target person in judgment frame image.By the shape
Body image is input in neural network model, carries out the judgement of posture information to the personnel that have dinner of captured in real-time.By this method
The body image that the personnel of having dinner can effectively be got is avoided because there is no body or body are endless in the image that gets
It judges incorrectly caused by whole.
As shown in figure 4, further comprising the steps of:
S1201, training sample set is obtained, the training sample set includes an at least body image for same target；
In some embodiments, when being trained to neural network model, using several training sample sets (such as 10
Ten thousand), wherein each training sample set includes at least body image of a people.To at least one of the same person
Before body image is trained, need to prejudge the posture information desired value of each training sample, anticipation can use
Trained to convergent posture judgment models are judged in the prior art.For example, CNN convolutional neural networks model or
VGG convolutional neural networks model.
In present embodiment, the posture information desired value for multiple body images that same training sample is concentrated is identical.
S1202, an at least body image is sequentially inputted in preset disaggregated model, acquisition is described extremely respectively
The posture information classification value of a few body image；
Disaggregated model is that already existing all kinds of posture judgment models are classified in some embodiments in the prior art
Model can be CNN convolutional neural networks model or VGG convolutional neural networks model.
Posture judge is carried out at least photo that the same person shoots in different environments first, then by described in extremely
A few body image is sequentially inputted in disaggregated model, obtains the posture score of each body image.
S1203, it is ranked up by posture classification value of the qualifications at least one body image of numerical value；
After getting the posture classification results of at least one body image of the same person, with the big of posture information classification value
Small relationship carries out drop power sequence to the posture information classification value of at least one body image.
Posture information classification value in an intermediate position is an at least shape in S1204, the confirmation ranking results
The expectation classification value of body image；
Posture information classification value centrally located in sequencing table is taken, as the same person's at least body image
Common posture desired value.When the body image of a people is odd number, the median in sequencing table is obtained as posture information
Desired value；When the body image of a people is even number, the average value of two medians in sequencing table is obtained as posture
Information desired value.
By above method training to convergent neural network model, due under same person's varying environment in training
Posture information desired value is identical, and therefore, the posture information of the same person of neural network model output under various circumstances has
The dispersion of higher convergence, data is low.
As shown in figure 5, step S1300 specifically include the following steps:
S1310, the cuisine information for obtaining current restocking vegetable, wherein include described in every portion in the cuisine information
The heat that vegetable is included；
Each cuisine information is uploaded into server by canteen management personnel, wherein cuisine information includes menu name and every
The heat etc. that a vegetable is included, but not limited to this.Such as: stuffed bean curd, 895 kilocalories；Mashed garlic eggplant, 137 kilocalories；Preserved vegetable
Braised pork, 1898 kilocalories；Tian jin cabbage pickled in sweet and sour, 28 kilocalories.Canteen management personnel update the vegetable of restocking daily, activation restocking vegetable
State obtains the cuisine information for the vegetable being active when carrying out dining recommendation.
S1320, mesh is determined in restocking vegetable according to the posture information and preset matching rule of the target person
Mark vegetable；
In some embodiments, the preset matching rule can be the corresponding recommendation vegetable of each posture information point
Thermal range, for example, " obesity ": [0,500]；" partially fat ": [501,800]；" common ": [801,1200]；" partially thin ": [1201,
2000].The posture information of combining target personnel searches the vegetable in the thermal range from restocking vegetable, arranges in pairs or groups
Recommend.For example, the calorie value of " partially fat " type corresponding [501,800], searches calorie value 501-800's from restocking vegetable
Vegetable, such as wax gourd rib soup, 530 kilocalories；Goulash, 750 kilocalories.Determine that the vegetable that the heat found meets is mesh
Mark vegetable.
S1330, the target vegetable is showed into the target person by display terminal, to guide the target person
Selection target vegetable is eaten；
Target vegetable is shown by display terminal, and with related guidance and excitation text, in some embodiments, is shown
Show terminal can for display or display screen etc., but not limited to this.For example, being " wax gourd rib soup " and " soil when recommending vegetable
It when beans roasted beef ", shows over the display and " recommends you that wax gourd rib soup and goulash is selected to eat, reasonably select
Dining cuisine, forms good eating habit, can effectively improve physical fitness." to guide personnel selection target dish of having dinner
Product are eaten.
By this vegetable way of recommendation, the vegetable for meeting own situation can be provided according to the different personnel that have dinner, led to
Prompting is crossed, randomness and irrationality that the personnel of having dinner select vegetable can be effectively reduced, helps the personnel of having dinner to form good
Good eating habit.
As shown in fig. 6, further including following step after step S1300:
S1410, the face image for obtaining target person；
By the face image of picture pick-up device photographic subjects personnel, and face image described in face is input to identity and judges mould
In type.Specifically, one photographic device is set in disbursement and sattlement position, the photographic device can take the face of target person
Portion, in some embodiments, photographic device are equipped with induction module, and when sensing someone in front of photographic device, shooting is current
Face image of the image as the target person.
S1420, the identity information that target person is confirmed according to the face image, wherein include having tied up in identity information
Fixed channel of withholing；
The face image that will acquire is input in identity judgment models, wherein dining room of typing in identity judgment models
Multiple face feature images of all dining personnel.In some embodiments, it can be in typing face feature image method
Target person carries out specific headwork before target camera, and (such as left and right puts, comes back, bows and blinks, but is not limited to
This), video is enrolled by target camera and therefrom extracts the face feature image for the personnel that have dinner under different angle and light, it is defeated
Enter into face feature image library.And the correspondingly identity information of the typing personnel in typing face feature image,
In, biological information includes name, gender, department, position and the channel of withholing bound etc., but not limited to this.
The corresponding one group of identity information of each personnel and various faces characteristic image in the present embodiment.
When identity judgment models get face image, the image in face image and face feature image library is carried out
Similarity comparison, and comparing result is ranked up according to similarity, it defines corresponding to the highest face feature image of similarity
Personnel be target person, and call the identity information of the personnel；If the similarity of face image and all face feature images
Below preset threshold (such as 50%) then confirms in the face image there is no the personnel of typing information, re-shoots face
Portion's image.
S1430, corresponding dining expense is deducted by the channel of withholing.
In some embodiments, the channel of withholing can be the mobile payments class APP such as wechat payment or Alipay,
It is also possible to the channels such as Internetbank quick payment.It is had dinner personnel by the confirmation that loses face, and deducts and correspond in corresponding channel of withholing
Dining expense method, the personnel of having dinner in traditional IC card method of payment that avoid forget to lead to not with IC card the feelings of payment
Condition, and can effectively improve payment efficiency when having dinner, reduce the waiting time of other dining personnel.
As shown in fig. 7, further including following step before step S1410:
S1401, the service plate image for obtaining target person；
Payment position is provided with picture pick-up device, the service plate image for photographic subjects personnel.In some embodiments,
Position is paid the bill equipped with a payment position agreed with service plate shape, and payment position is provided with sensing device, when induction fills
Set recognize the payment position there are when article, then start picture pick-up device shooting service plate image.Specifically, service plate is arranged with more
For a grid for holding vegetable, picture pick-up device has one or more, when service plate is in payment position, each picture pick-up device point
The different grid in service plate are not corresponded to, and shoot the image of corresponding grid as service plate image.
S1402, the service plate image is input in preset vegetable identification model, wherein the vegetable identification model
To train to convergence state the neural network model for being used to carry out vegetable price category in advance；
The service plate image that will acquire, which is input to, trains in advance to convergent neural network model, in some embodiments
In, neural network model can be CNN convolutional neural networks model or VGG convolutional neural networks model.The neural network
Model, will an at least cuisine by an at least vegetable image for the same vegetable of acquisition as training sample set in training
Image is sequentially inputted in preset disaggregated model, by successive ignition until model is restrained.
S1403, the price category result for obtaining each vegetable in the service plate image；
In some embodiments, the vegetable identification model is equipped with multiple price categories, and each price category is right respectively
A price category standard value is answered, therefore, the classification data of vegetable identification model output is that each cuisine belongs to respectively in service plate image
The probability value of price category obtains the corresponding probability value of each price category, and is carried out according to the size of numerical value to each probability value
Power sequence is dropped.
Maximum classification value in multiple classification values is obtained according to ranking results, i.e., is arranged in primary point in ranking results
Class value, the corresponding price category of the classification value.Illustrate that the classification results of vegetable identification model show some in service plate image
Vegetable belongs to the maximum probability of the category, i.e. classification results show that the price category of some vegetable in service plate image belongs to classification
It is worth the corresponding price category of maximum number.Get the price category knot of each vegetable in service plate image respectively by this method
Fruit.
The sum of S1404, the price for determining the vegetable are the dining expense.
Each price category result respectively corresponds the price of a vegetable, and the price for getting each vegetable defines dish later
The sum of price of product is the expense of this dining.Dining expense can be effectively improved by the calculation method of above-mentioned vegetable price
Computational efficiency, and reduce and vegetable or use dinner cost caused by calculating deviation because forgetting vegetable price when artificial calculate, admitting one's mistake
With mistake.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of dining room dining service unit.Referring specifically to
Fig. 8, Fig. 8 are the basic structure block diagram of this implementation dining room dining service unit.
The service unit as shown in figure 8, dining room is eaten, comprising: obtain module 2100, processing module 2200 and execution module
2300.Wherein, the body image that module is used to obtain target person is obtained；Processing module is used to be judged according to the body image
The posture information of the target person；Execution module is used to according to the posture information and preset recommendation rules be the target
Personnel recommend dining vegetable.
By obtaining the body image for the personnel of having dinner in real time, and the body image for the personnel of having dinner is input to and has been trained to receipts
In the neural network model held back, had dinner the posture information of personnel according to the judgement of the output category result of neural network, and according to just
The posture information and preset recommendation rules of meal personnel is that the personnel that have dinner recommend the cuisine for being suitble to itself, guides the choice of members of having dinner
More healthy cuisine reduces the case where personnel's habituation of having dinner selects the collocation of unreasonable cuisine, helps the personnel of having dinner to form good
Good eating habit reduces the probability because of personnel's disease of having dinner caused by unreasonable, unsound eating habit, improves body
Voxel matter.
In some embodiments, dining room dining service unit further include: the first input submodule, first obtain submodule
Block, the first implementation sub-module.Wherein the first input submodule is used to the body image being input to preset Shape analysis mould
In type, wherein the Shape analysis model is the mind that training in advance to convergence state is used to carry out human body image body classification
Through network model；First acquisition submodule is used to obtain the classification results of the Shape analysis model output；First executes submodule
The body data that block is used to define the classification results characterization are the posture information of the target person.
In some embodiments, dining room dining service unit further include: the second acquisition submodule, the first processing submodule
Block, the second implementation sub-module.Wherein, the second acquisition submodule is for obtaining real-time pictures；First processing submodule is used for from institute
Timing extraction frame image in real-time pictures is stated, and is judged in the frame image with the presence or absence of body profile；Second implementation sub-module
For when there are when body profile, confirm that the frame image is the body image of the target person in the frame image.
In some embodiments, dining room dining service unit further include: third acquisition submodule, the second input submodule
Block, second processing submodule, third implementation sub-module.Wherein, third acquisition submodule is for obtaining training sample set, the instruction
Practice at least body image that sample set includes same target；Second input submodule is used for will an at least body figure
As being sequentially inputted in preset disaggregated model, the posture information classification value of at least one body image is obtained respectively；The
Two processing submodules by posture information classification value of the qualifications at least one body image of numerical value for arranging
Sequence；Third implementation sub-module is for confirming posture information classification value in an intermediate position in the ranking results for described at least
The expectation classification value of one body image.
In some embodiments, dining room dining service unit further include: the 4th acquisition submodule, third handle submodule
Block, the 4th implementation sub-module.Wherein, the 4th acquisition submodule is used to obtain the cuisine information of current restocking vegetable, wherein institute
Stating includes heat that every portion vegetable is included in cuisine information；Third handles submodule and is used for according to the target person
Posture information and preset matching rule in restocking vegetable determine target vegetable；4th implementation sub-module is used for will be described
Target vegetable shows the target person by display terminal, to guide the target person selection target vegetable to be used
Meal.
In some embodiments, dining room dining service unit further include: the 5th acquisition submodule, fourth process submodule
Block, the 5th implementation sub-module.Wherein, the 5th acquisition submodule is used to obtain the face image of target person；Fourth process submodule
Block is used to confirm according to the face image identity information of target person, wherein withholds in identity information including what is bound
Channel；5th implementation sub-module is used to deduct corresponding dining expense by the channel of withholing.
In some embodiments, dining room dining service unit further include: the 6th acquisition submodule, third input submodule
Block, the 7th acquisition submodule.Wherein, the 6th acquisition submodule is used to obtain the service plate image of target person；Third inputs submodule
Block is for the service plate image to be input in preset vegetable identification model, wherein the vegetable identification model is to instruct in advance
Practice to convergence state and is used to carry out vegetable the neural network model of price category；7th acquisition submodule is for obtaining the meal
The price category result of each vegetable in disk image；6th implementation sub-module is for determining that the sum of price of the vegetable is the use
Dinner cost is used.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of computer equipment.Referring specifically to Fig. 9, Fig. 9
For the present embodiment computer equipment basic structure block diagram.
As shown in figure 9, the schematic diagram of internal structure of computer equipment.As shown in figure 9, the computer equipment includes passing through to be
Processor, non-volatile memory medium, memory and the network interface of bus of uniting connection.Wherein, the computer equipment is non-easy
The property lost storage medium is stored with operating system, database and computer-readable instruction, can be stored with control information sequence in database
Column when the computer-readable instruction is executed by processor, may make processor to realize a kind of dining room dining method of servicing.The calculating
The processor of machine equipment supports the operation of entire computer equipment for providing calculating and control ability.The computer equipment
It can be stored with computer-readable instruction in memory, when which is executed by processor, processor may make to hold
A kind of dining room dining method of servicing of row.The network interface of the computer equipment is used for and terminal connection communication.Those skilled in the art
Member is it is appreciated that structure shown in figure, and only the block diagram of part-structure relevant to application scheme, is not constituted to this
The restriction for the computer equipment that application scheme is applied thereon, specific computer equipment may include more than as shown in the figure
Or less component, perhaps combine certain components or with different component layouts.
Processor obtains module 2100, processing module 2200 and execution module for executing in present embodiment in Fig. 8
2300 concrete function, program code and Various types of data needed for memory is stored with the above-mentioned module of execution.Network interface is used for
To the data transmission between user terminal or server.Memory in present embodiment is stored in the dining service unit of dining room
Program code needed for executing all submodules and data, server is capable of the program code of invoking server and data execute institute
There is the function of submodule.
The present invention also provides a kind of storage mediums for being stored with computer-readable instruction, and the computer-readable instruction is by one
When a or multiple processors execute, so that one or more processors execute the dining service side of dining room described in any of the above-described embodiment
The step of method.
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, which can be stored in a computer-readable storage and be situated between
In matter, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, storage medium above-mentioned can be
The non-volatile memory mediums such as magnetic disk, CD, read-only memory (Read-OnlyMemory, ROM) or random storage note
Recall body (RandomAccessMemory, RAM) etc..
It should be understood that although each step in the flow chart of attached drawing is 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, can execute in the other order.Moreover, at least one in the flow chart of attached drawing
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, execution sequence, which is also not necessarily, successively to be carried out, but can be with other
At least part of the sub-step or stage of step or other steps executes in turn or alternately.
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
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (10)
- The method of servicing 1. a kind of dining room is eaten, which comprises the following steps:Obtain the body image of target person；The posture information of the target person is judged according to the body image；It is that the target person recommends dining vegetable according to the posture information and preset recommendation rules.
- The method of servicing 2. dining room as described in claim 1 is eaten, which is characterized in that described that institute is judged according to the body image The step of stating the posture information of target person, comprising the following steps:The body image is input in preset Shape analysis model, wherein the Shape analysis model is training in advance It is used to carry out human body image the neural network model of body classification to convergence state；Obtain the classification results of the Shape analysis model output；The body data for defining the classification results characterization are the posture information of the target person.
- The method of servicing 3. dining room as described in claim 1 is eaten, which is characterized in that the body image for obtaining target person The step of, comprising the following steps:Obtain real-time pictures；The timing extraction frame image from the real-time pictures, and judge in the frame image with the presence or absence of body profile；When there are when body profile, confirm that the frame image is the body image of the target person in the frame image.
- The method of servicing 4. dining room as claimed in claim 2 is eaten, which is characterized in that the training method of the neural network model Include:Training sample set is obtained, the training sample set includes an at least body image for same target；An at least body image is sequentially inputted in preset disaggregated model, obtains an at least body respectively The posture information classification value of image；It is ranked up by posture information classification value of the qualifications at least one body image of numerical value；Confirm that posture information classification value in an intermediate position in the ranking results is the phase of at least one body image Hope classification value.
- The method of servicing 5. dining room as described in claim 1 is eaten, which is characterized in that the posture according to the target person Information and preset recommendation rules are the step of target person recommends dining vegetable, are included the following steps:Obtain the cuisine information of current restocking vegetable, wherein include that every portion vegetable is included in the cuisine information Heat；Target vegetable is determined in restocking vegetable according to the posture information of the target person and preset matching rule；The target vegetable is showed into the target person by display terminal, to guide the target person selection target dish Product are eaten.
- The method of servicing 6. dining room as described in claim 1-5 any one is eaten, which is characterized in that described according to the posture Information and preset recommendation rules are after the target person recommends dining vegetable, to include the following steps:Obtain the face image of target person；According to the face image confirm target person identity information, wherein in identity information include bound withhold it is logical Road；Corresponding dining expense is deducted by the channel of withholing.
- The method of servicing 7. dining room as claimed in claim 6 is eaten, which is characterized in that described to pass through the mobile payment application journey Before the step of paying dining expense in sequence, include the following steps:Obtain the service plate image of target person；The service plate image is input in preset vegetable identification model, wherein the vegetable identification model is training in advance It is used to carry out vegetable the neural network model of price category to convergence state；Obtain the price category result of each vegetable in the service plate image；Determine that the sum of price of the vegetable is the dining expense.
- The service unit 8. a kind of dining room is eaten characterized by comprisingModule is obtained, for obtaining the body image of target person；Processing module, for judging the posture information of the target person according to the body image；Execution module, for being that the target person recommends dining vegetable according to the posture information and preset recommendation rules.
- 9. a kind of computer equipment characterized by comprisingProcessor；Memory for storage processor executable instruction；Wherein, the processor is configured to executing the dining method of servicing of dining room described in the claims 1-7 any one.
- 10. a kind of non-transitorycomputer readable storage medium, when the instruction in the storage medium is by the processing of mobile terminal When device executes, so that mobile terminal is able to carry out a kind of dining room dining method of servicing, the method includes the claims 1-7 The dining method of servicing of dining room described in any one.
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