CN107301318A - The generation method and device of dining plan - Google Patents
The generation method and device of dining plan Download PDFInfo
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- CN107301318A CN107301318A CN201710484792.8A CN201710484792A CN107301318A CN 107301318 A CN107301318 A CN 107301318A CN 201710484792 A CN201710484792 A CN 201710484792A CN 107301318 A CN107301318 A CN 107301318A
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Abstract
The invention provides a kind of generation method and device of plan of having dinner, it is related to the technical field of data processing, this method includes：Obtain preset time period user user behavior data, wherein, user behavior data be user in the client performance objective behavior when data；User behavior data is analyzed and processed using the neutral net pre-established, to determine the eating habit of user, wherein, the neutral net pre-established is trained obtained neutral net for the user behavior data in advance using different time sections user, the input of neutral net is user behavior data, and neutral net is output as eating habit；It is the dining plan that user formulates at least one day based on eating habit, wherein, daily dining plan includes at least one of：Breakfast dining is planned, and lunch, which has dinner to plan to have dinner with dinner, to be planned, and is alleviated in the prior art without the tool technique problem that instead of user itself can be its tailor-made daily dining plan.
Description
Technical field
The present invention relates to the technical field of data processing, more particularly, to a kind of generation method and device of plan of having dinner.
Background technology
With the fast development of society, many people enter fast pace life, in fast pace life, when people do not have
Between consider oneself daily this what is eaten, but select quick food (for example, fast food) to be used as daily diet content.Fast food is removed
Have outside quickly and easily advantage, it is high with heat, and grease high shortcoming.Therefore, long-term consumption fast food is to the person
Body will have a huge impact.But, allegro life causes many people not have the time to go to think deeply daily dining plan
What is, irregular eating habit is formed in the course of time, while also bringing certain harm to body.
The content of the invention
It is an object of the invention to provide a kind of generation method and device of plan of having dinner, do not have in the prior art to alleviate
It can be the tool technique problem of its tailor-made daily dining plan instead of user itself.
According to an aspect of the invention, there is provided a kind of generation method for plan of having dinner, including：Obtain preset time period
The user behavior data of user, wherein, the user behavior data be the user in the client performance objective behavior when
Data；The user behavior data is analyzed and processed using the neutral net pre-established, to determine the drink of the user
Dietary habits, wherein, the neutral net pre-established is in advance using the user behavior data of user described in different time sections
Obtained neutral net is trained, the input of the neutral net is user behavior data, and the neutral net is output as
Eating habit；It is the dining plan that the user formulates at least one day based on the eating habit, wherein, daily dining plan
Including at least one of：Breakfast dining plan, plan that lunch dining is planned and dinner is had dinner.
Further, it is that the dining plan that the user formulates at least one day includes based on the eating habit：Obtain
The cuisines data delivered in the client；Target cuisines data are determined in the cuisines data based on the eating habit；
According to the dining plan that the target cuisines data are at least one day described in the user formulates.
Further, according to the dining plan bag that the target cuisines data are at least one day described in the user formulates
Include：The time attribute label of the target cuisines data is determined, wherein, the time attribute label is used to determine that the target is beautiful
The edible time of eclipse number evidence；The objective time interval belonging to the edible time is determined, wherein, the objective time interval includes：During breakfast
Section, lunch period and dinner period；The target cuisines data are classified based on the affiliated objective time interval, morning is obtained
Meal data group, lunch data group and dinner data group；By breakfast data group, the lunch data group and the dinner data
Target cuisines data in group carry out permutation and combination, obtain the combination of at least one data；Based on the combination of at least one described data
The dining plan of at least one day described in formulating.
Further, the dining plan of at least one day described in formulating is combined based at least one described data to be included：Obtain
Association probability in the target cuisines data between any two target cuisines data, wherein, the association probability is represented
One is had dinner in the works, when a target cuisines data occur, the probability that another target cuisines data occurs；Based on institute
State the total correlation probability that association probability determines each data combination at least one data combination；In at least one described number
According to determination target data combination in combination, wherein, the total correlation probability of the target data combination is more than default total correlation probability；
It is the dining plan of user daily by each target set of data cooperation.
Further, determine that target cuisines data include in the cuisines data based on the eating habit：Obtain institute
The subscriber data of user is stated, wherein, the subscriber data includes at least one of：Native place, occupation, sex and age；It is based on
The subscriber data and the eating habit determine the target cuisines data in the cuisines data.
According to another aspect of the present invention, a kind of generating means of dining plan are additionally provided, including：Acquiring unit,
User behavior data for obtaining preset time period user, wherein, the user behavior data is the user in client
Data during middle performance objective behavior；Analysis and processing unit, for using the neutral net pre-established to the user behavior
Data are analyzed and processed, to determine the eating habit of the user, wherein, the neutral net pre-established is to adopt in advance
Obtained neutral net is trained with the user behavior data of user described in different time sections, the input of the neutral net is
User behavior data, the neutral net is output as eating habit；Unit is formulated, for being described based on the eating habit
User formulates the dining plan of at least one day, wherein, daily dining plan includes at least one of：Breakfast dining plan,
The plan of lunch dining and dinner, which are had dinner, to be planned.
Further, the formulation unit includes：Subelement is obtained, for obtaining the cuisines delivered in the client
Data；Determination subelement, for determining target cuisines data in the cuisines data based on the eating habit；Formulate son single
Member, for according to the dining plan that the target cuisines data are at least one day described in the user formulates.
Further, the formulation subelement includes：First determining module, for determine the target cuisines data when
Between attribute tags, wherein, the time attribute label is used to determine the edible times of the target cuisines data；Second determines mould
Block, for determining the objective time interval belonging to the edible time, wherein, the objective time interval includes：Breakfast period, lunch period
With the dinner period；Sort module, for being classified based on the affiliated objective time interval to the target cuisines data, is obtained
Breakfast data group, lunch data group and dinner data group；Composite module, for by breakfast data group, the lunch data
Target cuisines data in group and the dinner data group carry out permutation and combination, obtain the combination of at least one data；Formulate module,
Dining plan for combining at least one day described in formulation based at least one described data.
Further, the formulation module is used for：Obtain any two target cuisines data in the target cuisines data
Between association probability, wherein, the association probability is represented in a dining in the works, when target cuisines data occur
When, the probability that another target cuisines data occurs；Determined based on the association probability at least one data combination
The total correlation probability of each data combination；Target data combination is determined in the combination of at least one described data, wherein, the mesh
The total correlation probability for marking data combination is more than default total correlation probability；It is that user is daily by each target set of data cooperation
Dining plan.
Further, the determination subelement is used for：The subscriber data of the user is obtained, wherein, the subscriber data
Including at least one of：Native place, occupation, sex and age；Based on the subscriber data and the eating habit in described U.S.
Eclipse number determines the target cuisines data in.
In embodiments of the present invention, the user behavior data of user in preset time period is obtained first, then, using advance
The neural network model of foundation is analyzed and processed to user behavior data, to determine the eating habit of user；Next, being based on
Eating habit is the dining plan that user formulates at least one day, in embodiments of the present invention, is entered by the eating habit to user
Row analysis, so as to be its formulation dining plan, more accurately can formulate dining plan, and then alleviate existing skill for user
Do not have in art can replace user itself and be its tailor-made daily dining plan tool technique problem, it is achieved thereby that
Formulate the technique effect of dining plan automatically for user.
Brief description of the drawings
, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical scheme of the prior art
The accompanying drawing used required in embodiment or description of the prior art is briefly described, it should be apparent that, in describing below
Accompanying drawing is some embodiments of the present invention, for those of ordinary skill in the art, before creative work is not paid
Put, other accompanying drawings can also be obtained according to these accompanying drawings.
Fig. 1 is a kind of flow chart of the generation method of dining plan according to embodiments of the present invention；
Fig. 2 is a kind of schematic diagram of the generating means of dining plan according to embodiments of the present invention；
Fig. 3 be a kind of dining plan according to embodiments of the present invention generating means in formulate unit schematic diagram；
Fig. 4 be a kind of dining plan according to embodiments of the present invention generating means in formulate module schematic diagram.
Embodiment
Technical scheme is clearly and completely described below in conjunction with accompanying drawing, it is clear that described implementation
Example is a part of embodiment of the invention, rather than whole embodiments.Based on the embodiment in the present invention, ordinary skill
The every other embodiment that personnel are obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.
In the description of the invention, it is necessary to explanation, term " " center ", " on ", " under ", "left", "right", " vertical ",
The orientation or position relationship of the instruction such as " level ", " interior ", " outer " be based on orientation shown in the drawings or position relationship, merely to
Be easy to the description present invention and simplify description, rather than indicate or imply signified device or element must have specific orientation,
With specific azimuth configuration and operation, therefore it is not considered as limiting the invention.In addition, term " first ", " second ",
" the 3rd " is only used for describing purpose, and it is not intended that indicating or implying relative importance.
In the description of the invention, it is necessary to illustrate, unless otherwise clearly defined and limited, term " installation ", " phase
Even ", " connection " should be interpreted broadly, for example, it may be being fixedly connected or being detachably connected, or be integrally connected；Can
To be mechanical connection or electrical connection；Can be joined directly together, can also be indirectly connected to by intermediary, Ke Yishi
The connection of two element internals.For the ordinary skill in the art, with concrete condition above-mentioned term can be understood at this
Concrete meaning in invention.
Embodiment one：
According to embodiments of the present invention there is provided a kind of embodiment of the generation method of dining plan, it is necessary to explanation,
The step of flow of accompanying drawing is illustrated can perform in the computer system of such as one group computer executable instructions, also,
, in some cases, can be shown to be performed different from order herein although showing logical order in flow charts
The step of going out or describe.
Fig. 1 is a kind of flow chart of the generation method of dining plan according to embodiments of the present invention, as shown in figure 1, the party
Method comprises the following steps：
Step S102, obtains the user behavior data of preset time period user, wherein, user behavior data is user in visitor
Data in the end of family during performance objective behavior；
Wherein, client can preferably beans net client, the client is that can be used in formulating dining plan for user
Client, in the client, user can browse through the manufacturing process of various cuisines；And in the client, additionally it is possible to use
Formulate at least one dining plan in family.The goal behavior can be collection behavior, click on behavior, navigation patterns etc..
It should be noted that in embodiments of the present invention, the user is the registered user in good beans net client.
Step S104, is analyzed and processed using the neutral net pre-established to user behavior data, to determine user
Eating habit, wherein, the neutral net pre-established in advance using different time sections user user behavior data carry out
Obtained neutral net is trained, the input of neutral net is user behavior data, and neutral net is output as eating habit；
Step S106, is the dining plan that user formulates at least one day based on eating habit, wherein, daily dining plan
Including at least one of：Breakfast dining plan, plan that lunch dining is planned and dinner is had dinner.
In embodiments of the present invention, the user behavior data of user in preset time period is obtained first, then, using advance
The neural network model of foundation is analyzed and processed to user behavior data, to determine the eating habit of user；Next, being based on
Eating habit is the dining plan that user formulates at least one day, in embodiments of the present invention, is entered by the eating habit to user
Row analysis, so as to be its formulation dining plan, more accurately can formulate dining plan, and then alleviate existing skill for user
Do not have in art can replace user itself and be its tailor-made daily dining plan tool technique problem, it is achieved thereby that
Formulate the technique effect of dining plan automatically for user.
In an optional embodiment of the embodiment of the present invention, above-mentioned steps S106, i.e. based on eating habit be user
The dining plan formulated at least one day comprises the following steps：
Step S1061, obtains the cuisines data delivered in the client；
Step S1062, target cuisines data are determined based on eating habit in cuisines data；
Step S1063, is the dining plan that user formulates at least one day according to target cuisines data.
Specifically, in embodiments of the present invention, in order to more accurately formulate user's plan for registered users, need
Obtain the eating habit of registered users.For example, eating habit is inclined vegetarian diet, it is still partially carnivorous；It is the mouth that has a sweet tooth, still
Like eating peppery etc..
After determining the eating habit of registered users in by above-mentioned steps S104, it is possible in the U.S. got
Eclipse number determines target cuisines data in, wherein, target cuisines data are the cuisines that registered users may like.Determining
After target cuisines data, it is possible to based on the dining plan that target cuisines data are registered users formulation at least one day.
Wherein, daily dining plan includes breakfast dining plan, the plan of lunch dining and dinner dining plan.
In embodiments of the present invention, it is to combine the registered users when formulating dining plan for the registered users
Eating habit determines the cuisines that the registered users may like in the cuisines data delivered, so that, it is based on the cuisines
The registered users formulate dining plan, are developed programs by this, more accurately can formulate dining plan for the user, think
The user formulates the dining plan for meeting its taste.
In another optional embodiment of the embodiment of the present invention, above-mentioned steps S1063 is according to target cuisines data
The dining plan that user formulates at least one day comprises the following steps：
Step S1, determines the time attribute label of target cuisines data, wherein, time attribute label is used to determine that target is beautiful
The edible time of eclipse number evidence；
Step S2, determines the objective time interval belonging to edible time, wherein, objective time interval includes：Breakfast period, lunch period
With the dinner period；
Target cuisines data are classified by step S3 based on affiliated objective time interval, obtain breakfast data group, lunch number
According to group and dinner data group；
Step S4, by breakfast data group, the target cuisines data in lunch data group and dinner data group carry out arrangement group
Close, obtain the combination of at least one data；
Step S5, the dining plan formulated at least one day is combined based at least one data.
Specifically, in embodiments of the present invention, time attribute label is provided with for each cuisines data in advance, time category
Property label be used to characterize the cuisines data institute edible time.Wherein, it is determined that the cuisines data institute edible time
When, it is to be determined by cuisines most preferably edible period in itself, i.e. user absorbs best, and be when which edible period
The burden that the stomach of user is brought is minimum.
When being that user formulates the dining plan of at least one day according to target cuisines data, target cuisines data are obtained first
Time attribute label；Then, it is determined that the objective time interval in time attribute label belonging to edible time, i.e. when determining that this is edible
Between belonging breakfast period, or dinner period, or lunch period.Determine the objective time interval belonging to edible time it
Afterwards, it is possible to target cuisines data are classified based on objective time interval, i.e. by the cuisines of same period belonging to the cuisines time
Data are classified as a class.For example, the cuisines data of breakfast period belonging to edible time are classified as into breakfast data group；And when will be edible
Between the cuisines data of belonging lunch period be classified as lunch data group；And, by the cuisines of dinner period belonging to edible time
Data are classified as dinner data group.
So far, the dining plan eaten three meals a day in fact, user can be by checking breakfast data group, lunch number
Corresponding cuisines are selected according to the cuisines data in group and dinner data group.But, it is contemplated that carry out rational cuisines for user
Collocation, can also be by breakfast data group to ensure the optimal eating effect of cuisines, the mesh in lunch data group and dinner data group
Mark cuisines data and carry out permutation and combination.
For example, at least a target cuisines data are selected in breakfast data group as breakfast, then, in lunch data group
At least a target cuisines data of middle selection are as lunch, next, selecting at least a target cuisines in dinner data group
Data are used as dinner.Now, it is possible to by above-mentioned breakfast, lunch and dinner are combined (that is, daylong as a data
Dining plan) it is pushed to user.
In embodiments of the present invention, by the method to set up, it more intuitively can recommend cuisines for user, eliminate use
The process of corresponding target cuisines data is searched at family in breakfast data group, lunch data group and dinner data group respectively, is improved
Consumer's Experience.
In another optional embodiment of the embodiment of the present invention, step S5, i.e. combined and made based at least one data
Fixed at least one day dining plan comprises the following steps：
Step S51, obtains the association probability between any two target cuisines data in target cuisines data, wherein, close
Connection probability represents that in a dining in the works, when a target cuisines data occur, another target cuisines data occurs
Probability；
Step S52, the total correlation probability of each data combination in the combination of at least one data is determined based on association probability；
Step S53, will really set the goal in the combination of at least one data data combination, wherein, target data combination it is total
Association probability is more than default total correlation probability；
Step S54, is the dining plan of user daily by each target set of data cooperation.
Specifically, in embodiments of the present invention, obtain first in target cuisines data between any two cuisines data
Association probability；Then, the total correlation probability of data combination is determined based on the association probability；It is default total by being more than in total correlation probability
The target set of data cooperation of association probability is the dining plan of user daily.
In embodiments of the present invention, when association probability represents that a target cuisines data occur, another target cuisines
The probability that data occur, the association probability is person skilled in advance according to the combined probability of any two target cuisines data
Calculate what is obtained.For example, when there is dumpling, the probability for garlic occur is an association probability, in another example, when there are noodles,
There is probability of vinegar etc..The association probability can characterize the collocation degree between any two target cuisines data, the collocation
Degree depends on nutrient content of any two target cuisines data when being arranged in pairs or groups.If for example, by two target cuisines
When data are arranged in pairs or groups, two target cuisines data can interact, so that, the material for being conducive to human body is produced, or,
Interacting between two target cuisines data can mutually promote absorption when, the collocation journey of two target cuisines data
Degree is higher, and association probability is also higher.
In embodiments of the present invention, by the method described by above-mentioned steps S51 to step S54, it can be formulated for user
While dining plan, additionally it is possible to the collocation of nutrition is carried out for user.
, can be first in embodiments of the present invention when determining target cuisines data in cuisines data based on eating habit
The subscriber data of user is obtained, wherein, subscriber data includes at least one of：Native place, occupation, sex and age；Then, base
Target cuisines data are determined in cuisines data in subscriber data and eating habit.It is beautiful that target is screened by combining subscriber data
Eclipse number evidence, can obtain more conforming to the target cuisines data of user's taste.
Embodiment two：
The embodiment of the present invention additionally provides a kind of generating means of dining plan, and the generating means of the dining plan are mainly used
In the generation method for performing the dining plan that the above of the embodiment of the present invention is provided, below to provided in an embodiment of the present invention
The generating means of dining plan do specific introduction.
Fig. 2 is a kind of schematic diagram of the generating means of dining plan according to embodiments of the present invention, as shown in Fig. 2 the use
The generating means of meal plan mainly include：Acquiring unit 21, analysis and processing unit 22 and formulation unit 23, wherein：
Acquiring unit 21, the user behavior data for obtaining preset time period user, wherein, user behavior data is use
Family in the client performance objective behavior when data；
Analysis and processing unit 22, for being analyzed and processed using the neutral net pre-established to user behavior data,
To determine the eating habit of user, wherein, the neutral net pre-established is in advance using user's row of different time sections user
Obtained neutral net is trained for data, the input of neutral net is user behavior data, and neutral net is output as drink
Dietary habits；
Unit 23 is formulated, the dining plan for formulating at least one day for user based on eating habit, wherein, daily use
Meal plan includes at least one of：Breakfast dining plan, plan that lunch dining is planned and dinner is had dinner.
In embodiments of the present invention, the user behavior data of user in preset time period is obtained first, then, using advance
The neural network model of foundation is analyzed and processed to user behavior data, to determine the eating habit of user；Next, being based on
Eating habit is the dining plan that user formulates at least one day, in embodiments of the present invention, is entered by the eating habit to user
Row analysis, so as to be its formulation dining plan, more accurately can formulate dining plan, and then alleviate existing skill for user
Do not have in art can replace user itself and be its tailor-made daily dining plan tool technique problem, it is achieved thereby that
Formulate the technique effect of dining plan automatically for user.
Alternatively, as shown in figure 3, formulating unit includes：Subelement 31 is obtained, for obtaining the U.S. delivered in the client
Eclipse number evidence；Determination subelement 32, for determining target cuisines data in cuisines data based on eating habit；Formulate subelement
33, for according to the dining plan that target cuisines data are user's formulation at least one day.
Alternatively, as shown in figure 4, formulating subelement includes：First determining module 41, for determining target cuisines data
Time attribute label, wherein, time attribute label is used for the edible time for determining target cuisines data；Second determining module 42,
For determining the objective time interval belonging to edible time, wherein, objective time interval includes：Breakfast period, lunch period and dinner period；
Sort module 43, for classifying based on affiliated objective time interval to target cuisines data, obtains breakfast data group, lunch number
According to group and dinner data group；Composite module 44, for by breakfast data group, the target in lunch data group and dinner data group to be beautiful
Eclipse number obtains the combination of at least one data according to permutation and combination is carried out；Module 45 is formulated, is made for being combined based at least one data
Fixed at least one day dining plan.
Alternatively, formulating module is used for：Obtain the association between any two target cuisines data in target cuisines data
Probability, wherein, association probability is represented in a dining in the works, when a target cuisines data occur, another target
The probability that cuisines data occur；Determine that the total correlation of each data combination in the combination of at least one data is general based on association probability
Rate；Target data combination is determined in the combination of at least one data, wherein, the total correlation probability of target data combination is more than default
Total correlation probability；It is the dining plan of user daily by each target set of data cooperation.
Optionally it is determined that subelement is used for：Obtain user subscriber data, wherein, subscriber data include it is following at least it
One：Native place, occupation, sex and age；Target cuisines data are determined in cuisines data based on subscriber data and eating habit.
Finally it should be noted that：Various embodiments above is merely illustrative of the technical solution of the present invention, rather than its limitations；To the greatest extent
The present invention is described in detail with reference to foregoing embodiments for pipe, it will be understood by those within the art that：Its according to
The technical scheme described in foregoing embodiments can so be modified, or which part or all technical characteristic are entered
Row equivalent substitution；And these modifications or replacement, the essence of appropriate technical solution is departed from various embodiments of the present invention technology
The scope of scheme.
Claims (10)
1. a kind of generation method for plan of having dinner, it is characterised in that including：
The user behavior data of preset time period user is obtained, wherein, the user behavior data is the user in client
Data during middle performance objective behavior；
The user behavior data is analyzed and processed using the neutral net pre-established, to determine the diet of the user
Custom, wherein, the neutral net pre-established is entered for the advance user behavior data using user described in different time sections
The neutral net that row training is obtained, the input of the neutral net is user behavior data, and the neutral net is output as drink
Dietary habits；
It is the dining plan that the user formulates at least one day based on the eating habit, wherein, daily dining plan includes
At least one of：Breakfast dining plan, plan that lunch dining is planned and dinner is had dinner.
2. according to the method described in claim 1, it is characterised in that formulate at least one based on the eating habit for the user
It dining plan includes：
Obtain the cuisines data delivered in the client；
Target cuisines data are determined in the cuisines data based on the eating habit；
According to the dining plan that the target cuisines data are at least one day described in the user formulates.
3. method according to claim 2, it is characterised in that formulate institute according to the target cuisines data for the user
Stating the dining plan of at least one day includes：
The time attribute label of the target cuisines data is determined, wherein, the time attribute label is used to determine the target
The edible time of cuisines data；
The objective time interval belonging to the edible time is determined, wherein, the objective time interval includes：Breakfast period, the lunch period and
The dinner period；
The target cuisines data are classified based on the affiliated objective time interval, breakfast data group, lunch data is obtained
Group and dinner data group；
By breakfast data group, the target cuisines data in the lunch data group and the dinner data group carry out arrangement group
Close, obtain the combination of at least one data；
Based on the dining plan of at least one day described in the combination formulation of at least one described data.
4. method according to claim 3, it is characterised in that combined based at least one described data described in formulating at least
The dining plan of one day includes：
The association probability between any two target cuisines data in the target cuisines data is obtained, wherein, the association is general
Rate represents that in a dining in the works, when a target cuisines data occur, it is general that another target cuisines data occurs
Rate；
The total correlation probability of each data combination at least one data combination is determined based on the association probability；
Target data combination is determined in the combination of at least one described data, wherein, the total correlation of the target data combination is general
Rate is more than default total correlation probability；
It is the dining plan of user daily by each target set of data cooperation.
5. method according to claim 2, it is characterised in that determined based on the eating habit in the cuisines data
Target cuisines data include：
The subscriber data of the user is obtained, wherein, the subscriber data includes at least one of：Native place, occupation, sex and
Age；
The target cuisines data are determined in the cuisines data based on the subscriber data and the eating habit.
6. a kind of generating means for plan of having dinner, it is characterised in that including：
Acquiring unit, the user behavior data for obtaining preset time period user, wherein, the user behavior data is described
User in the client performance objective behavior when data；
Analysis and processing unit, for being analyzed and processed using the neutral net pre-established to the user behavior data, with
The eating habit of the user is determined, wherein, the neutral net pre-established is used to use in advance described in different time sections
The user behavior data at family is trained obtained neutral net, and the input of the neutral net is user behavior data, described
Neutral net is output as eating habit；
Unit is formulated, the dining plan for formulating at least one day for the user based on the eating habit, wherein, daily
Dining plan includes at least one of：Breakfast dining plan, plan that lunch dining is planned and dinner is had dinner.
7. device according to claim 6, it is characterised in that the formulation unit includes：
Obtain subelement, the cuisines data delivered for obtaining in the client；
Determination subelement, for determining target cuisines data in the cuisines data based on the eating habit；
Subelement is formulated, for according to the dining plan that the target cuisines data are at least one day described in the user formulates.
8. device according to claim 7, it is characterised in that the formulation subelement includes：
First determining module, the time attribute label for determining the target cuisines data, wherein, the time attribute label
Edible time for determining the target cuisines data；
Second determining module, for determining the objective time interval belonging to the edible time, wherein, the objective time interval includes：It is early
Eat period, lunch period and dinner period；
Sort module, for classifying based on the affiliated objective time interval to the target cuisines data, obtains breakfast number
According to group, lunch data group and dinner data group；
Composite module, for by breakfast data group, the target cuisines in the lunch data group and the dinner data group
Data carry out permutation and combination, obtain the combination of at least one data；
Module is formulated, the dining plan for combining at least one day described in formulation based at least one described data.
9. device according to claim 8, it is characterised in that the formulation module is used for：
The association probability between any two target cuisines data in the target cuisines data is obtained, wherein, the association is general
Rate represents that in a dining in the works, when a target cuisines data occur, it is general that another target cuisines data occurs
Rate；
The total correlation probability of each data combination at least one data combination is determined based on the association probability；
Target data combination is determined in the combination of at least one described data, wherein, the total correlation of the target data combination is general
Rate is more than default total correlation probability；
It is the dining plan of user daily by each target set of data cooperation.
10. device according to claim 7, it is characterised in that the determination subelement is used for：
The subscriber data of the user is obtained, wherein, the subscriber data includes at least one of：Native place, occupation, sex and
Age；
The target cuisines data are determined in the cuisines data based on the subscriber data and the eating habit.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108991913A (en) * | 2018-06-27 | 2018-12-14 | 佛山市云米电器科技有限公司 | It is a kind of realize food materials management scorch device and method |
| CN111566686A (en) * | 2019-09-20 | 2020-08-21 | 钟山 | Digital asset value management and operation method, device, medium and computing equipment |
| CN111883232A (en) * | 2020-07-30 | 2020-11-03 | 中国科学院上海营养与健康研究所 | Diet information output method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003208475A (en) * | 2002-01-17 | 2003-07-25 | Dainippon Printing Co Ltd | Health care system |
| CN103177339A (en) * | 2013-03-25 | 2013-06-26 | 中山大学 | Food management and recommendation system based on intelligent refrigerator |
| CN104731846A (en) * | 2014-11-17 | 2015-06-24 | 陕西师范大学 | Individuation catering recommendation method and system based on multiple targets |
| CN105677852A (en) * | 2016-01-07 | 2016-06-15 | 陕西师范大学 | Personalized healthy diet recommendation service method |
| CN105930656A (en) * | 2016-04-20 | 2016-09-07 | 北京小米移动软件有限公司 | Dietary habit analysis method and apparatus |
| CN106021564A (en) * | 2016-05-30 | 2016-10-12 | 美的集团股份有限公司 | Method and device for recommending menus |
| CN106871567A (en) * | 2015-12-14 | 2017-06-20 | 北京奇虎科技有限公司 | Food recommendation process method, device and intelligent refrigerator based on intelligent refrigerator |
-
2017
- 2017-06-23 CN CN201710484792.8A patent/CN107301318A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003208475A (en) * | 2002-01-17 | 2003-07-25 | Dainippon Printing Co Ltd | Health care system |
| CN103177339A (en) * | 2013-03-25 | 2013-06-26 | 中山大学 | Food management and recommendation system based on intelligent refrigerator |
| CN104731846A (en) * | 2014-11-17 | 2015-06-24 | 陕西师范大学 | Individuation catering recommendation method and system based on multiple targets |
| CN106871567A (en) * | 2015-12-14 | 2017-06-20 | 北京奇虎科技有限公司 | Food recommendation process method, device and intelligent refrigerator based on intelligent refrigerator |
| CN105677852A (en) * | 2016-01-07 | 2016-06-15 | 陕西师范大学 | Personalized healthy diet recommendation service method |
| CN105930656A (en) * | 2016-04-20 | 2016-09-07 | 北京小米移动软件有限公司 | Dietary habit analysis method and apparatus |
| CN106021564A (en) * | 2016-05-30 | 2016-10-12 | 美的集团股份有限公司 | Method and device for recommending menus |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108991913A (en) * | 2018-06-27 | 2018-12-14 | 佛山市云米电器科技有限公司 | It is a kind of realize food materials management scorch device and method |
| CN111566686A (en) * | 2019-09-20 | 2020-08-21 | 钟山 | Digital asset value management and operation method, device, medium and computing equipment |
| CN111883232A (en) * | 2020-07-30 | 2020-11-03 | 中国科学院上海营养与健康研究所 | Diet information output method and system |
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Application publication date: 20171027 |