CN108335729A - A kind of wisdom dining room nutrient diet recommendation method based on heat optimization - Google Patents
A kind of wisdom dining room nutrient diet recommendation method based on heat optimization Download PDFInfo
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Abstract
The present invention relates generally to internet intelligent nutrient diet technology for intensive large-scale catering place is consumed, and in particular to the intelligent nutrition food preparation method based on heat optimization includes the following steps：Step 1, vegetable database C is established；Disease corresponds to vegetable taboo database J；Early, middle and late pantry database D；Based on gender, the age, height, weight total amount of heat E* T, morning E* M, middle E* A, evening E* ECorresponding thermal data library E*；Step 2, feasible vegetable data C'=C J are calculated according to consumer information；Step 3, database D is called, feasible pantry data F feasible in the morning, afternoon and evening is calculated according to vegetable data C'；Step 4, the heat E (p) for calculating each pantry p, according to thermal difference square (E* M‑E(p))2(E* A‑E(p))2Criterion from small to large recommends from the pantry provided in F in morning, lists first 5；Step 5, calculating dinner needs supplying heat Y=E* T‑E* M‑E* A, thermal difference square (Y E (p))2, recommend according to dinner is provided from small to large.The beneficial effects of the invention are as follows large-scale catering meal institute is solved, dense population quick effect nutrient pantry problem improves efficiency of having dinner.
Description
Technical field
The present invention relates generally to internet intelligent nutrient diet technology, specifically for intensive large-scale catering place is consumed
It is related to the intelligent nutrition food preparation method optimized based on heat, belongs to wisdom dining room technical field.
Background technology
Large-scale catering place assumes responsibility for the substantial responsibility of normal science canteen.On May 13rd, 2016, country defended planning commission's publication
《Chinese residents dietary guidelines (2016)》, and large-scale catering place is urged to provide canteen according to guide, which points out, closely
Ratio in the China Nian Lai resident's diet shared by paddy potato and vegetables group food is decreased obviously, vitamin and mineral intake
Deficiency, and meat and the intake of grease based food are excessive, and resident's fat heat in big city is caused to account for the ratio of diet total amount of heat very
To more than 30%！Some associated chronic diseases, such as cardiovascular and cerebrovascular disease and malignant tumour illness rate also gradually on
It rises.On the other hand, in the crowd of rural area especially Poor Mountainous Area, nutrient deficiency diseases caused by due to food species rareness,
Such as：Hypoferric anemia, rickets etc. still exist.In order to be preferably national health service, need by existing Information Center
Technology develops scientific and effective pantry service system, improves national eating habit so that canteen is scientific, standardizes.
Intelligent nutrition catering system is the inevitable outcome of " big data+" and " internet+" food and drink.Nutrition catering system is not
Fangle, because since ancient times, the people of other countries pay attention to nutrition arrangement, rational diet.So nutrient diet is for food
For product and nutrition science, there is a good scientific research basis, most distinctive is China Agricultural University, 2002 7
Month, set up Food Science and nutrition engineering college.Have in Food Science and Engineering, food nutrition and the scientific domains such as safety
The entire educational system of standby culture scholar, master, doctor.The school " Food Science and Engineering " is that level-one doctor authorizes subject, packet
Nutrition is included, nutrient diet is also focus on research direction.Also there are similar profession in domestic many universities, but comparatively,
Characteristic is not apparent.Since nutrient diet is related to personal characteristics data, health examination data, medical data, movement and exercise
Data etc., so in the epoch that computer technology and internet development relatively lag behind, nutrition catering system also just loses
Bright spot and meaning.But as all trades and professions begin setting up large data center so that collecting the object data of nutrient diet becomes
One thing within reach, thus intelligent nutrition catering system Internet-based it is inevitable set foot on journey.
Intake thermal imbalance is an important factor for influencing health.The main reason for excessive heat is obesity, still
Energy intake is very few to also bring along health problem.It is prolonged to cross low caloric diet, it can be because heat be inconsistent human physiological activity
Primary demand, and body is made to play a kind of natural mechanism, the thermal energy that physiological action must consume is turned down.Therefore with energy
Amount optimization or with science capacity volume variance it is ordinary it is minimum for the intelligent nutrition pantry of criterion be the most reasonable approach.
Invention content
To solve the above problems, the present invention proposes that a kind of wisdom dining room nutrient diet optimized based on heat recommends method,
Include the following steps：
Step 1, following data library is established：1) vegetable database C={ C1,C2,...,Cm}；2) disease corresponds to vegetable taboo
Database J={ J1,J2,...,Jn}；3) early, middle and late pantry database D；4) based on gender, the age, weight total amount of heat E* T、
Early E* M, middle E* A, evening E* ECorresponding thermal data library E*；
Step 2, feasible vegetable data C'=C-J=C' is calculated according to consumer informationM∪C'A∪C'E；
Step 3, database D is called, feasible pantry data F in the morning, afternoon and evening is calculated according to reasonable vegetable data C'；
Step 4, pantry heat in the morning, afternoon and evening is calculated first, then calculates pantry thermal difference square, completes breakfast and lunch
Recommend；
Step 5, calculating dinner first needs supplying heat, calculates pantry and supplying heat square differences, completes dinner and recommends.
Preferably, the method for vegetable database being established in the step 1 is：By extensive food and drink place input data, often
A vegetable data CiAttribute includes product composition, heat e (Ci), wherein m is vegetable type.
Preferably, the method for early, middle and late pantry database D being established in the step 1 is:It is generated by product database early
Vegetable, lunch vegetable and dinner vegetable, are denoted as C respectivelyM,CA,CE, according to paddy potato, meat egg, vegetables, bean curd, fruit, grease, heavily fortified point
Fruit is collocation standard, by vegetable combination producing pantry database D=DM∪DA∪DE, wherein
Early pantry database
Middle pantry database
Late pantry database
Preferably, the method for early, middle and late pantry database D being established in the step 1 is:By CM,CA,CEAll vegetables
It is tested, according to test data, provides pantry database morning pantry database DM, middle pantry database DA, late pantry data
Library DE, wherein
Early pantry database
Middle pantry database
Late pantry database
Preferably, the method for feasible pantry data F calculating is in the morning, afternoon and evening in the step 3：F=FM∪FA∪FE, wherein
Wherein, FMFor early feasible pantry data, FAFeasible pantry data, F inEFor evening feasible pantry data.
Preferably, breakfast is established in the step 4 and the recommendation method of dinner is as follows：
Calculate pantry heat in the morning, afternoon and evening：
Breakfast pantry heat：
Lunch pantry heat：
Dinner pantry heat：
Corresponding heat normal data is called according to consumer information：Breakfast heat standard：E* M；Chinese meal heat standard：E* A；
Dinner heat standard：E* E；Total amount of heat standard：Data E* T；
Calculate pantry thermal difference square：
Breakfast pantry thermal difference square：
Lunch pantry thermal difference square：
Dinner pantry thermal difference square：
Recommend with 5 before the meal according to thermal difference square minimization：
Remember that candidate pantry index set is respectively：
Im=m1, m2 ..., msm}；
Ia=a1, a2 ..., ata}；
Ie=e1, e2 ..., ele}；
Recommendation method is：
Breakfast：
Lunch：
Each time recommend after the completion of by pantry index from index set remove, be repeated 5 times can be obtained preceding 5 breakfast and
Lunch is recommended.
Preferably, dinner recommends method as follows in the step 5：
Calculating dinner first needs supplying heat i.e. Y=E* T-EM-EA, wherein EMAnd EABreakfast and dinner have been selected for consumer
Calorie value,
Secondly thermal difference square is calculated：
Providing dinner recommendation method is：
Pantry index is removed from index set after the completion of recommending each time, is repeated 5 times and can be obtained preceding 5 dinners
Recommend.
The beneficial effects of the invention are as follows：
1. with energy-optimised ordinary minimum for criterion with science capacity volume variance, pantry is carried out according to the different characteristic of human body,
Best suit user's nutrient health demand；
2. solving large-scale catering meal institute, dense population quick effect nutrient pantry problem improves efficiency of having dinner.
Specific implementation mode
The present invention is described further with reference to embodiment.
Step 1, the constitution and implementation mode of four databases：
1) vegetable database C={ C1,C2,...,CmEstablish process, it is desirable that dining room the predetermined time complete data record
Enter, intelligent typing may be used, initially set up a common vegetable database, point was used to the second day vegetable to be supplied
Mode input system is selected, is rapidly completed, non-typing vegetable, using the menu mode typing of new addition vegetable, vegetable data CiBelong to
Property include that product constitutes (menu-style, may be selected), heat e (Ci) (vernier form, mix color)；
2) disease corresponds to vegetable taboo database J={ J1,J2,...,JnEmbodiment：National standard is used first
Medicine avoids knowledge base, then asks a certain number of experts, everyone provides a taboo list, and it is the corresponding taboo of disease to take general character
Avoid vegetable, implementation is unfolded using electric questionnaire mode, and finally statistics obtains corresponding taboo vegetable；Pantry scientific library D's
Embodiment:It is collocation standard and requirement according to paddy potato, meat egg, vegetables, bean curd, fruit, grease, nut, according to early, middle and late
Diet it is scientific, select vegetable combination.When specific implementation, perfect early, middle and late breakfast, lunch and dinner dish is provided to the cook in dining room
Product collocation electric questionnaire, each cook answer a questionnaire one by one according to oneself Heuristics, according to collocation as a result, selection union is made
For science pantry database, D=DM∪DA∪DE, wherein
Early pantry database:
Middle pantry database:
Late pantry database:
Second is that the pantry electric questionnaire in the morning, afternoon and evening that consumer method oneself is liked, union is selected, and provide quantity statistics
Parameter, after asking pantry science professionals to be chosen, typing scientific library D provides pantry database DM, MA, DE；Data indicate and public affairs
Formula (1), (2), (3) are consistent.
3) based on gender, the age, weight correspondence thermal data library E*Embodiment:The embodiment party of this database
Formula mainly uses, and provides total amount of heat E according to gender, age, height, weight etc. first* TCalculation formula is as follows：
E* T=α0+α1W+α2H+α3A；
Wherein W is weight feature, and unit is kg；H is height, and unit is cm；A is the age, and unit is year； α0It is constant
；α1,α2,α3It is weight coefficient.The selection of these coefficients is obtained using technological learning method, is known according to expertise first
Know part calibration heat, coefficient is obtained using multiple regression technology；From the point of view of empirical data, general empirical parameter is as follows：
Then according to 3:4:3 rule provides the corresponding distribution of thermal data in the morning, afternoon and evening morning E* M, middle E* A, evening E* E。
Step 2, feasible vegetable data C'=C-J=C' is calculated according to consumer informationM∪C'A∪C'EConsumer information can
The embodiment of row vegetable data is directly calculated by the way of set difference, and disease, which is corresponded to vegetable taboo database, to be rejected, and is kept away
Influence of the style of cooking to human body is exempted from.
Step 3, the embodiment of feasible pantry data F is in the morning, afternoon and evening：Database D is called first, according to reasonable vegetable number
Directly the pantry for meeting all reasonable vegetables is all listed according to C'；In addition it can also be used to delete directly from database D and own
It is obtained containing the pantry data for avoiding data；F=FM∪FA∪FE, wherein
Wherein, FMFor early feasible pantry data, FAFeasible pantry data, F inEFor evening feasible pantry data.
Step 4, pantry heat in the morning, afternoon and evening is calculated：
Breakfast pantry heat：
Lunch pantry heat：
Dinner pantry heat：
Corresponding heat normal data is called according to consumer information：Breakfast heat standard：E* M；Chinese meal heat standard：E* A；
Dinner heat standard：E* E；Total amount of heat standard：Data E* T；
Calculate pantry thermal difference square：
Breakfast pantry thermal difference square：
Lunch pantry thermal difference square：
Dinner pantry thermal difference square：
Recommend with 5 before the meal according to thermal difference square minimization：
Remember that candidate pantry index set is respectively：
Im=m1, m2 ..., msm}；
Ia=a1, a2 ..., ata}；
Ie=e1, e2 ..., ele}；
Recommendation method is：
Breakfast：
Lunch：
Each time recommend after the completion of by pantry index from index set remove, be repeated 5 times can be obtained preceding 5 breakfast and
Lunch is recommended.
Step 5, the embodiment that dinner is recommended uses, and calculates the heat that ought be fed day by day first, is subtracted with total amount of heat
The mode of heat is calculated in morning through intake；Then recommend dinner according to the embodiment of step 4.Dinner is calculated first
Need supplying heat i.e. Y=E* T-EM-EA, wherein EMAnd EABreakfast and the calorie value of dinner have been selected for consumer,
Secondly thermal difference square is calculated：
RYE={ R (p'e1),R(p'e2),...,R(p'ele)},R(p'mi)=(Y-EE(p'ei))2, i=1,2 ..., le；
Providing dinner recommendation method is：
Pantry index is removed from index set after the completion of recommending each time, is repeated 5 times and can be obtained preceding 5 dinners
Recommend.
Specific embodiment described herein is only an example for the spirit of the invention.Technology belonging to the present invention is led
The technical staff in domain can make various modifications or additions to the described embodiments or replace by a similar method
In generation, however, it does not deviate from the spirit of the invention or beyond the scope of the appended claims.
Claims (7)
1. a kind of wisdom dining room nutrient diet based on heat optimization recommends method, which is characterized in that include the following steps：
Step 1, following data library is established：1) vegetable database C={ C1,C2,...,Cm}；2) disease corresponds to vegetable taboo data
Library J={ J1,J2,...,Jn}；3) early, middle and late pantry database D；4) based on gender, the age, weight total amount of heat E* T, morning E* M, middle E* A, evening E* ECorresponding thermal data library E*；
Step 2, feasible vegetable data C'=C-J=C' is calculated according to consumer informationM∪C'A∪C'E；
Step 3, database D is called, feasible pantry data F in the morning, afternoon and evening is calculated according to reasonable vegetable data C'；
Step 4, pantry heat in the morning, afternoon and evening is calculated first, then calculates pantry thermal difference square, completes breakfast and lunch is recommended
Recommend；
Step 5, calculating dinner first needs supplying heat, calculates pantry and supplying heat square differences, completes dinner and recommends.
2. the wisdom dining room nutrient diet according to claim 1 based on heat optimization recommends method, which is characterized in that institute
Stating the method that vegetable database is established in step 1 is：By extensive food and drink place input data, each vegetable data CiAttribute packet
Include product composition, heat e (Ci), wherein m is vegetable type.
3. the wisdom dining room nutrient diet according to claim 2 based on heat optimization recommends method, which is characterized in that institute
Stating the method that early, middle and late pantry database D is established in step 1 is:Early vegetable, lunch vegetable and evening are generated by product database
Meal vegetable, is denoted as C respectivelyM,CA,CE, it is collocation standard according to paddy potato, meat egg, vegetables, bean curd, fruit, grease, nut, by dish
Product combination producing pantry database D=DM∪DA∪DE, wherein
Early pantry database
Middle pantry database
Late pantry database
4. the wisdom dining room nutrient diet according to claim 2 based on heat optimization recommends method, which is characterized in that institute
Stating the method that early, middle and late pantry database D is established in step 1 is:By CM,CA,CEAll vegetables tested, according to test
Data provide pantry database morning pantry database DM, middle pantry database DA, late pantry database DE, wherein
Early pantry database
Middle pantry database
Late pantry database
5. the wisdom dining room nutrient diet according to claim 3 or 4 based on heat optimization recommends method, feature to exist
In the method that feasible pantry data F is calculated in the morning, afternoon and evening in the step 3 is：F=FM∪FA∪FE, wherein
Wherein, FMFor early feasible pantry data, FAFeasible pantry data, F inEFor evening feasible pantry data.
6. the wisdom dining room nutrient diet according to claim 5 based on heat optimization recommends method, which is characterized in that institute
State established in step 4 breakfast and dinner recommendation method it is as follows：
Calculate pantry heat in the morning, afternoon and evening：
Breakfast pantry heat：
Lunch pantry heat：
Dinner pantry heat：
Corresponding heat normal data is called according to consumer information：Breakfast heat standard：E* M；Chinese meal heat standard：E* A；Dinner heat
Amount standard：E* E；Total amount of heat standard：Data E* T；
Calculate pantry thermal difference square：
Breakfast pantry thermal difference square：
Lunch pantry thermal difference square：
Dinner pantry thermal difference square：
Recommend with 5 before the meal according to thermal difference square minimization：
Remember that candidate pantry index set is respectively：
Im=m1, m2 ..., msm}；
Ia=a1, a2 ..., ata}；
Ie=e1, e2 ..., ele}；
Recommendation method is：
Breakfast：
Lunch：
Pantry index is removed from index set after the completion of recommending each time, is repeated 5 times and can be obtained preceding 5 breakfast and lunch
Recommend.
7. the wisdom dining room nutrient diet according to claim 6 based on heat optimization recommends method, which is characterized in that institute
Stating dinner in step 5 recommends method as follows：
Calculating dinner first needs supplying heat i.e. Y=E* T-EM-EA, wherein EMAnd EABreakfast and the heat of dinner have been selected for consumer
Value,
Secondly thermal difference square is calculated：
Providing dinner recommendation method is：
Pantry index is removed from index set after the completion of recommending each time, is repeated 5 times and be can be obtained preceding 5 dinners and push away
It recommends.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110598119A (en) * | 2019-09-23 | 2019-12-20 | 广东小乘科技有限公司 | Method and system for providing fruit health package customized service |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105844357A (en) * | 2016-03-23 | 2016-08-10 | 广州聚数信息科技有限公司 | Dish combination recommending method and dish combination recommending system based on required heat |
| CN107153079A (en) * | 2017-05-18 | 2017-09-12 | 金华职业技术学院 | A kind of method for measuring film coefficient of heat transfer |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105844357A (en) * | 2016-03-23 | 2016-08-10 | 广州聚数信息科技有限公司 | Dish combination recommending method and dish combination recommending system based on required heat |
| CN107153079A (en) * | 2017-05-18 | 2017-09-12 | 金华职业技术学院 | A kind of method for measuring film coefficient of heat transfer |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110598119A (en) * | 2019-09-23 | 2019-12-20 | 广东小乘科技有限公司 | Method and system for providing fruit health package customized service |
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Application publication date: 20180727 |