CN116628040B - Big data-based cooking menu acquisition and updating method - Google Patents
Big data-based cooking menu acquisition and updating method Download PDFInfo
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- 238000010411 cooking Methods 0.000 title claims abstract description 197
- 238000000034 method Methods 0.000 title claims abstract description 49
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- 235000009508 confectionery Nutrition 0.000 description 2
- 239000000796 flavoring agent Substances 0.000 description 2
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- 235000000346 sugar Nutrition 0.000 description 2
- 239000000052 vinegar Substances 0.000 description 2
- 235000021419 vinegar Nutrition 0.000 description 2
- 235000002566 Capsicum Nutrition 0.000 description 1
- 239000006002 Pepper Substances 0.000 description 1
- 235000016761 Piper aduncum Nutrition 0.000 description 1
- 235000017804 Piper guineense Nutrition 0.000 description 1
- 244000203593 Piper nigrum Species 0.000 description 1
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- 235000005686 eating Nutrition 0.000 description 1
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- 238000010025 steaming Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
- G06F16/2308—Concurrency control
- G06F16/2315—Optimistic concurrency control
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2425—Iterative querying; Query formulation based on the results of a preceding query
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract
The invention provides a cooking menu acquisition and update method based on big data, which comprises the following steps: step 1: searching based on information to be queried input by a user to obtain a target menu; step 2: the target menu is sent to display equipment associated with the electric cooker for display; step 3: and acquiring and analyzing the confirmation information of the user on the target menu, judging whether the target menu is a new menu, if so, storing the target menu into a cooking menu library, and updating a menu catalog of the cooking menu library. The invention realizes the acquisition and updating of the cooking menu of the electric cooker, reduces the limitation of the menu of the electric cooker, ensures that the production menu of the electric cooker is not only limited to the factory preset menu, but also can be acquired and updated according to the actual cooking requirement of a user by combining big data, enriches the cooking menu of the electric cooker, realizes the diversification of dishes and improves the user experience.
Description
Technical Field
The invention relates to the technical field of big data application, in particular to a cooking menu acquisition and update method based on big data.
Background
Cooking is a complex and regular processing process for converting food materials into food, and is a processing mode and method for processing the food materials to make the food more delicious, better looking and better smelling. A delicious food is good in color, smell, taste, meaning and shape, and can be eaten by people, and the nutrition of food can be absorbed by human body more easily. With the rapid development of society, people are busy day by day, no much time is needed for preparing three meals a day, particularly under the condition of hot weather, open fire is generally needed in the cooking process, so that the kitchen is more stuffy, in addition, the actual cooking has certain requirements on mastering the fire, and some people cannot well master the skill and cannot well complete the normal cooking process, so that devices such as an automatic cooker, a cookable electric cooker and the like which can assist users in completing the cooking work can be produced, but the cooking process of the devices such as the conventional automatic cooker, the cookable electric cooker and the like which can assist users in completing the cooking work can only be produced according to preset menus, and the preset menus of the devices are basically set by default in factory, cannot update the menus according to the user requirements, and cannot meet the abundant and diverse cooking requirements of people.
Disclosure of Invention
The invention provides a cooking menu obtaining and updating method based on big data, which realizes the obtaining and updating of the cooking menu of an electric cooker and reduces the limitation of the menu of the electric cooker, so that the production menu of the electric cooker is not only limited to a factory preset menu, but also can be obtained and updated according to the actual cooking requirement of a user by combining the big data, enriches the cooking menu of the electric cooker, realizes the diversity of dishes and improves the user experience.
The invention provides a cooking menu acquisition and update method based on big data, which comprises the following steps:
step 1: searching based on information to be queried input by a user to obtain a target menu;
step 2: the target menu is sent to display equipment associated with the electric cooker for display;
step 3: and acquiring and analyzing the confirmation information of the user on the target menu, judging whether the target menu is a new menu, if so, storing the target menu into a cooking menu library, and updating a menu catalog of the cooking menu library.
Preferably, in a cooking menu obtaining and updating method based on big data, step 1 includes:
acquiring information to be queried input by a user, and judging the information type of the information to be queried;
when the information to be queried input by the user is a dish name, judging the information to be queried as first information;
Otherwise, judging the information to be queried as second information.
Preferably, in a cooking menu obtaining and updating method based on big data, when information to be queried is first information, the method includes:
Searching on a menu catalog based on the first information, and judging whether a menu meeting the user requirement exists in the cooking menu library or not;
If yes, acquiring a menu meeting the user requirement as a target menu;
if the target menu does not exist, searching the menu based on the Internet, and acquiring the target menu according to the taste preference of the user.
Preferably, in a cooking menu obtaining and updating method based on big data, when the information to be queried is the second information, the method includes:
Classifying the second information according to requirements, and determining the names of food materials to be manufactured and the number of dishes to be manufactured of a user;
Searching in menu details of a cooking menu library based on the names of the food materials to be manufactured, and obtaining a first menu to be selected containing the names of the food materials to be manufactured;
screening the first menu to be selected based on the number of dishes to be made and by combining the eating habits of the user to obtain a target menu;
Checking main food materials corresponding to the target menu according to the names of the food materials to be manufactured, judging whether the food materials to be manufactured are used completely, if not, acquiring the names of the unused food materials, taking the names of the unused food materials as the main food materials, and searching based on the Internet to acquire a second menu to be selected;
Based on the first menu to be selected and the second menu to be selected, determining a target menu which accords with the number of dishes to be made by the user by combining the eating habits and taste preference of the user.
Preferably, in the cooking menu obtaining and updating method based on big data, the method further comprises: step 0: determining eating habits and taste preferences of the user based on the dish query record or the cooking record of the user, comprising:
Acquiring dish inquiry records and cooking records of a user, determining seasonings and seasoning consumption of dishes corresponding to the inquiry records and the cooking records, and generating a temporary dish batching table;
Determining a primary flavoring of the influence of the dish taste based on the dish taste influencing characteristics;
determining that different main seasonings are highlighted by using corresponding tastes according to the analysis results of the big data of the taste deviation of different dishes, and generating a taste reference table;
According to the temporary dish batching table, the consumption of main seasonings corresponding to each dish and the proportion corresponding to the consumption of the main seasonings are obtained;
determining the outstanding use seasoning of each dish according to the proportion, determining the dish taste corresponding to each dish based on the taste reference table, and determining the user taste preference.
Preferably, in a cooking menu obtaining and updating method based on big data, step 0 further includes:
classifying cooking dishes based on the recording time corresponding to the cooking records, and taking continuous cooking dishes in the same time period as the same-meal cooking dishes to generate a same-meal set;
The dishes in each same meal set are marked with the types and the tastes of the dishes, and the cooking characteristics corresponding to each same meal set are obtained according to the marking result;
classifying the same meal sets according to the number of cooking dishes corresponding to each same meal set to obtain a plurality of cooking clusters;
and comprehensively comparing the cooking characteristics corresponding to the same meal sets in each cooking cluster to obtain the combination characteristics of different numbers of dishes cooked by the same meal, and determining the eating habits of the user.
Preferably, in a method for acquiring and updating a cooking menu based on big data, the cooking characteristics corresponding to each same meal set in each cooking cluster are comprehensively compared to obtain different numbers of dish combination characteristics cooked by the same meal, and the method further comprises the steps of:
and determining the cooking quantity of the same meal habit of the user and the corresponding dish combination characteristics according to the quantity proportion of different cooking clusters, and adding a priority label.
Preferably, in a cooking menu obtaining and updating method based on big data, step 3 includes:
Acquiring confirmation information of a user on a target menu, analyzing the confirmation, and confirming the target menu finally confirmed by the user and a target menu name corresponding to the target menu;
Comparing the target menu name with the existing menu directory of the cooking menu library, and judging the target menu as an old menu when the existing menu directory contains the target menu name;
Otherwise, judging the target menu as a new menu;
and storing the new menu into a cooking menu library, adding the target menu name corresponding to the new menu into a menu catalog of the cooking menu library, and updating the cooking menu library.
Preferably, in the method for acquiring and updating a cooking menu based on big data, the method further comprises:
Acquiring calling time corresponding to all stored menus in a cooking menu library;
screening all stored menus according to the calling time, determining the menu which is not commonly used by the user, and generating a menu list which is not commonly used;
And deleting the stored menu in the cooking menu library based on the unusual menu list, and synchronously updating the menu catalog of the cooking menu library.
Preferably, in the cooking menu obtaining and updating method based on big data, the method further comprises: step 4: based on big data, regularly acquire the online red recipe on the internet, carry out automatic update to the online red recipe of the online red menu module of culinary art menu storehouse, include:
Based on big data analysis, periodically acquiring a latest network red dish list on the Internet and corresponding manufacturing processes of each network red dish;
according to the manufacturing flow, the manufacturing characteristics corresponding to the net red dishes are obtained, the cooking characteristics of the electric cooker are referred, and whether the net red recipes meet the manufacturing requirements of the electric cooker is judged;
if yes, adding a green mark to the network red dishes in the latest network red dishes list;
If the net red dishes are not matched with the net red dishes, adding gray marks to the net red dishes in a latest net red dish list;
acquiring network red dishes carrying green marks in the latest network red dish list as dishes to be updated, determining network favorites of each dish to be updated based on big data analysis, and sequencing the dishes to be updated according to the network favorites to obtain a dish sequence;
Based on the preset net red dish storage amount, acquiring a target updated dish in a dish sequence, acquiring a net red recipe corresponding to the target updated dish, transmitting the net red recipe to a net red menu module of a cooking menu library to replace the original net red recipe, and synchronously updating the net red recipe catalog corresponding to the net red menu module.
Compared with the prior art, the invention at least comprises the following beneficial effects:
Searching based on information to be queried input by a user to obtain a target menu; step 2: the target menu is sent to display equipment associated with the electric cooker for display; step 3: the method comprises the steps of acquiring and analyzing confirmation information of a user on a target menu, judging whether the target menu is a new menu, if so, storing the target menu into a cooking menu library, updating a menu catalog of the cooking menu library, acquiring and updating the cooking menu of the electric cooker, reducing the limitation of the electric cooker menu, limiting the manufacturing menu of the electric cooker to a factory preset menu, acquiring and updating the menu according to the actual cooking requirement of the user by combining big data, enriching the cooking menu of the electric cooker, realizing the diversification of dishes and improving the user experience.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a big data based cooking menu acquisition and update method of the present invention;
FIG. 2 is a flow chart for judging the information type of the information to be queried, which is input by a user;
Fig. 3 is a flowchart of step 3 of the big data based cooking menu acquisition and update method of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the invention provides a cooking menu acquisition and updating method based on big data, as shown in figure 1, comprising the following steps:
step 1: searching based on information to be queried input by a user to obtain a target menu;
step 2: the target menu is sent to display equipment associated with the electric cooker for display;
step 3: and acquiring and analyzing the confirmation information of the user on the target menu, judging whether the target menu is a new menu, if so, storing the target menu into a cooking menu library, and updating a menu catalog of the cooking menu library.
In this embodiment, the information to be queried refers to information input when the user performs menu query.
The beneficial effects of the embodiment are that: searching based on information to be queried input by a user, acquiring a target menu, and sending the target menu to display equipment associated with an electric cooker for display; step 3: the method comprises the steps of acquiring and analyzing confirmation information of a user on a target menu, judging whether the target menu is a new menu, if so, storing the target menu into a cooking menu library, updating a menu catalog of the cooking menu library, acquiring and updating the cooking menu of the electric cooker, reducing the limitation of the electric cooker menu, limiting the manufacturing menu of the electric cooker to a factory preset menu, acquiring and updating the menu according to the actual cooking requirement of the user by combining big data, enriching the cooking menu of the electric cooker, realizing the diversification of dishes and improving the user experience.
Example 2:
on the basis of embodiment 1, as shown in fig. 2, step 1 includes:
acquiring information to be queried input by a user, and judging the information type of the information to be queried;
when the information to be queried input by the user is a dish name, judging the information to be queried as first information;
Otherwise, judging the information to be queried as second information;
In this embodiment, the first information refers to the information to be queried input by the user as the name of the dish.
In this embodiment, the second information refers to the information to be queried input by the user, which is not a direct dish name, but a number of food names with production and the number of dishes waiting to be produced, when the information of the number of dishes waiting to be produced is not contained in the second information, the production dish data is determined based on the type of food, when the type of food is greater than the preset number, the determination is performed based on the eating habit of the user, otherwise, only one dish is defaulted.
The beneficial effects of the embodiment are that: the invention judges the information type of the information to be queried, determines the type of the information to be queried, judges the direct dish information or the indirect dish information when the user inputs the information, provides a basis for determining the search target of the subsequent target menu, improves the search efficiency of the target menu, and is convenient and more accurate to obtain the target menu meeting the actual requirement of the user.
Example 3:
on the basis of embodiment 2, when the information to be queried is the first information, the method includes:
Searching on a menu catalog based on the first information, and judging whether a menu meeting the user requirement exists in the cooking menu library or not;
If yes, acquiring a menu meeting the user requirement as a target menu;
if the target menu does not exist, searching the menu based on the Internet, and acquiring the target menu according to the taste preference of the user.
In this embodiment, the menu meeting the user's requirement refers to a menu with the same names as the menu input by the user.
In this embodiment, the taste preference refers to taste preference of the user, for example, the user likes a sweet dish.
The beneficial effects of the embodiment are that: when the information to be queried input by the user is direct information, the method and the device directly search in the cooking menu library according to the names of the dishes input by the user, which is beneficial to quickly determining the dishes which accord with the taste preference of the user, reduce the time cost on choosing the dishes, search the dishes based on the Internet if the dishes are not in the cooking menu library, acquire the target dishes according to the taste preference of the user, shorten the acquisition time of the target dishes as much as possible, fully consider the taste preference of the user when searching the target dishes, quickly screen out the target dishes which accord with the taste of the user in a plurality of the same-name dishes on the Internet, promote the diversity of the provided dishes of the electric cooker, meet the rich and diverse cooking demands of the user as much as possible, and improve the user experience.
Example 4:
on the basis of embodiment 2, when the information to be queried is the second information, it includes:
Classifying the second information according to requirements, and determining the names of food materials to be manufactured and the number of dishes to be manufactured of a user;
Searching in menu details of a cooking menu library based on the names of the food materials to be manufactured, and obtaining a first menu to be selected containing the names of the food materials to be manufactured;
screening the first menu to be selected based on the number of dishes to be made and by combining the eating habits of the user to obtain a target menu;
Checking main food materials corresponding to the target menu according to the names of the food materials to be manufactured, judging whether the food materials to be manufactured are used completely, if not, acquiring the names of the unused food materials, taking the names of the unused food materials as the main food materials, and searching based on the Internet to acquire a second menu to be selected;
Based on the first menu to be selected and the second menu to be selected, determining a target menu which accords with the number of dishes to be made by the user by combining the eating habits and taste preference of the user.
In this embodiment, the requirement classification refers to classifying the information to be queried differently when the information to be queried is the second information, and determining the names of the food materials to be made and the number of dishes to be made.
In this embodiment, the menu details refer to the parts of the menu that specifically introduce the food materials needed by the menu.
In this embodiment, the first menu to be selected refers to a menu containing food materials to be made in the cooking menu library.
In this embodiment, the unused food material refers to food material that is not used in the target menu obtained after the first menu is selected based on the eating habit of the user.
In this embodiment, the second menu to be selected refers to all menus on unused food materials obtained by searching on the internet using names of unused food materials as main food materials.
In this embodiment, the eating habit of the user refers to the recipe collocation (for example, one dish and one soup, one meat and two vegetables) and the number of dishes of the user for daily eating, and when the user inputs the number of dishes to be made in the current search, the eating habit does not consider the number of dishes.
The beneficial effects of the embodiment are that: when the information to be queried is determined to be the second information, the method and the device quickly classify the second information, determine the names of food materials to be made and the quantity of dishes to be made of a user, search in menu details in a cooking menu library according to the names of the food materials to be made to obtain a first menu to be selected, match and screen according to the eating habits of the user to obtain a target menu, realize intelligent matching of dishes while obtaining the target menu, solve the trouble of matching the food materials of the user, and check the main food materials corresponding to the target menu according to the names of the food materials to be made after the first matching is completed, judge whether the food materials input by the user are fully utilized, if not, obtain the names of the unused food materials, search based on the Internet to obtain a second menu to be selected; based on the first menu to be selected and the second menu to be selected, the target menu which accords with the quantity of dishes to be made by the user is determined by combining the dietary habits and the taste preferences of the user, so that the user can input food materials as much as possible, the trouble and solution of using the food materials by the user are solved, the intelligent screening and the intelligent collocation of the menu are realized, and the menu which accords with the dietary habits and the taste preferences of the user is provided for the user as much as possible.
Example 5:
On the basis of embodiment 4, further comprising: step 0: determining eating habits and taste preferences of the user based on the dish query record or the cooking record of the user, comprising:
Acquiring dish inquiry records and cooking records of a user, determining seasonings and seasoning consumption of dishes corresponding to the inquiry records and the cooking records, and generating a temporary dish batching table;
Determining a primary flavoring of the influence of the dish taste based on the dish taste influencing characteristics;
determining that different main seasonings are highlighted by using corresponding tastes according to the analysis results of the big data of the taste deviation of different dishes, and generating a taste reference table;
According to the temporary dish batching table, the consumption of main seasonings corresponding to each dish and the proportion corresponding to the consumption of the main seasonings are obtained;
determining the outstanding use seasoning of each dish according to the proportion, determining the dish taste corresponding to each dish based on the taste reference table, and determining the user taste preference.
In this embodiment, the query record refers to a record of a menu query performed by a user on the electric cooker.
In this embodiment, the cooking record refers to a record of cooking performed by a user using the electric cooker.
In this embodiment, the temporary dish ingredients table refers to a table that includes all the seasonings and the usage amounts of the seasonings corresponding to the query record and the cooking record, which are automatically deleted after confirming the user's taste preference.
In this embodiment, the dish taste influencing feature refers to the seasoning proportion feature of the recipes with different tastes.
In this embodiment, the main seasoning is seasoning affecting the taste of the recipe, such as sugar, pepper, vinegar, etc.
In this embodiment, the main flavoring is used with different tastes due to the ratio of one or more flavoring materials.
In this embodiment, the more used seasoning in the recipes with different flavors is highlighted, for example, the highlighted seasoning in the recipes with sweet and sour flavors is sugar and vinegar.
The beneficial effects of the embodiment are that: the method comprises the steps of obtaining dish inquiry records and cooking records of a user, determining seasonings and seasoning consumption of dishes corresponding to the inquiry records and the cooking records, and generating a temporary dish batching table; determining a primary flavoring of the influence of the dish taste based on the dish taste influencing characteristics; determining that different main seasonings are highlighted by using corresponding tastes according to the analysis results of the big data of the taste deviation of different dishes, and generating a taste reference table; according to the temporary dish batching table, the consumption of main seasonings corresponding to each dish and the proportion corresponding to the consumption of the main seasonings are obtained; determining the outstanding use seasoning of each dish according to the proportion, determining the dish taste corresponding to each dish based on the taste reference table, determining the user taste preference according to the seasoning proportion of the dish inquiry record or the cooking record corresponding to the menu of the user, and providing a reference basis for intelligent screening of the target menu.
Example 6:
On the basis of embodiment 5, step 0 further includes:
classifying cooking dishes based on the recording time corresponding to the cooking records, and taking continuous cooking dishes in the same time period as the same-meal cooking dishes to generate a same-meal set;
The dishes in each same meal set are marked with the types and the tastes of the dishes, and the cooking characteristics corresponding to each same meal set are obtained according to the marking result;
classifying the same meal sets according to the number of cooking dishes corresponding to each same meal set to obtain a plurality of cooking clusters;
and comprehensively comparing the cooking characteristics corresponding to the same meal sets in each cooking cluster to obtain the combination characteristics of different numbers of dishes cooked by the same meal, and determining the eating habits of the user.
In this embodiment, the same meal set refers to a set of all dishes cooked by the same meal by the user, and one set corresponds to one meal.
In this embodiment, the cooking characteristic refers to the number of dishes corresponding to the set of meals and the matching of dishes.
In this embodiment, the cooking clusters refer to clusters constructed by the same set of meals with the same number of cooking dishes.
In this embodiment, the comprehensive comparison refers to comparison between the cooking characteristics corresponding to the same meal sets in the cooking clusters, including, but not limited to, comparison of dish matching and dish taste.
In this embodiment, the dish combination feature refers to the dish and taste matching feature of the user, wherein one meal corresponds to different cooking dish numbers.
The beneficial effects of the embodiment are that: the method classifies cooking dishes based on the recording time corresponding to the cooking records, takes continuous cooking dishes in the same time period as the same-meal cooking dishes, and generates a same-meal set; the dishes in each same meal set are marked with the types and the tastes of the dishes, and the cooking characteristics corresponding to each same meal set are obtained according to the marking result; classifying the same meal sets according to the cooking dishes corresponding to the same meal sets to obtain a plurality of cooking clusters; and comprehensively comparing the cooking characteristics corresponding to the same meal sets in each cooking cluster to obtain the dish combination characteristics of different numbers of same meal cooking, determining the eating habits of the user, and providing a reference for intelligent screening of target menus and intelligent collocation of food materials.
Example 7:
Based on embodiment 6, the method comprehensively compares the cooking characteristics corresponding to the same meal sets in each cooking cluster to obtain different numbers of dish combination characteristics cooked by the same meal, determines the eating habits of the user, and further comprises:
and determining the cooking quantity of the same meal habit of the user and the corresponding dish combination characteristics according to the quantity proportion of different cooking clusters, and adding a priority label.
In this embodiment, the number of dishes in the same meal habit refers to the number of dishes in a meal corresponding to the cooking cluster with the highest number of the same meal set.
In this embodiment, the number ratio refers to a ratio of the number of the same-meal sets contained in a certain cooking cluster to the number of the same-meal sets contained in all the cooking clusters.
In this embodiment, the priority tag refers to a tag carried by the cooking dish amount that is preferentially considered when the user inputs more food materials but does not limit the number of dishes to be made.
The beneficial effects of the embodiment are that: according to the method, the cooking characteristics corresponding to the same meal sets in the cooking clusters are comprehensively compared to obtain the dish combination characteristics of different numbers of same meal cooking, the eating habits of the user are determined, the cooking quantity of the same meal habits of the user and the dish combination characteristics corresponding to the same meal habits of the user are determined according to the number proportion of the different cooking clusters, and the priority labels are added, so that effective basis is provided for intelligent collocation and screening of target dishes when the user does not have the limitation of the number of the dishes, and the time for shortening the dishes of the user is shortened.
Example 8:
on the basis of embodiment 1, as shown in fig. 3, step 3 includes:
Step 301: acquiring confirmation information of a user on a target menu, analyzing the confirmation information, and confirming the target menu finally confirmed by the user and a target menu name corresponding to the target menu;
step 302: comparing the target menu name with the existing menu directory of the cooking menu library, and judging the target menu as an old menu when the existing menu directory contains the target menu name;
Otherwise, judging the target menu as a new menu;
Step 303: and storing the new menu into a cooking menu library, adding the target menu name corresponding to the new menu into a menu catalog of the cooking menu library, and updating the cooking menu library.
In this embodiment, the old menu refers to the existing menu in the cooking menu library.
In this embodiment, the new menu refers to a menu that is not in the cooking menu library.
The beneficial effects of the embodiment are that: according to the invention, after the user checks and confirms the target menu, the target menu name of the target menu is obtained, the target menu name is compared with the existing menu catalogue of the cooking menu library, the new menu is stored in the cooking menu library after the target menu is confirmed to be a new menu, the target menu name corresponding to the new menu is added into the menu catalogue of the cooking menu library, the cooking menu library is updated according to the target menu after the target menu is confirmed to be the required menu by the user, the menu of the cooking menu library is fully ensured to be the menu conforming to the eating habit and taste preference of the user, the use probability of the user on the cooking menu library is improved, the target menu obtaining time is reduced, meanwhile, the new menu is stored in the cooking menu library, the target menu name corresponding to the new menu is added into the menu catalogue of the cooking menu library, the cooking menu library is updated, the stored menu and the displayed menu catalogue is ensured, and the user can conveniently check the existing menu of the electric cooker.
Example 9:
On the basis of embodiment 8, in updating the cooking menu library, further comprising:
Acquiring calling time corresponding to all stored menus in a cooking menu library;
screening all stored menus according to the calling time, determining the menu which is not commonly used by the user, and generating a menu list which is not commonly used;
And deleting the stored menu in the cooking menu library based on the unusual menu list, and synchronously updating the menu catalog of the cooking menu library.
In this embodiment, the calling time refers to the time when the stored recipe was last outputted as the search result. Wherein the stored menu refers to a menu stored in a cooking menu library.
In this embodiment, the unusual menu refers to a stored menu in which the time difference between the calling time and the current time is greater than a preset time (for example, three months).
In this embodiment, the list of the unusual recipes refers to a list generated according to names of all unusual recipes.
The beneficial effects of the embodiment are that: the method comprises the steps of obtaining calling time corresponding to all stored menus in a cooking menu library; screening all stored menus according to the calling time, determining the menu which is not commonly used by the user, and generating a menu list which is not commonly used; based on the unusual menu list, deleting the stored menu in the cooking menu library, synchronously updating the menu list of the cooking menu library, deleting the unusual menu of the user from the cooking menu library, reducing the data storage pressure of the cooking menu library, and effectively ensuring the response speed of the menu inquiry of the user.
Example 10:
based on embodiment 1, a cooking menu obtaining and updating method based on big data further includes: step 4: based on big data, regularly acquire the online red recipe on the internet, carry out automatic update to the online red recipe of the online red menu module of culinary art menu storehouse, include:
Based on big data analysis, periodically acquiring a latest network red dish list on the Internet and corresponding manufacturing processes of each network red dish;
according to the manufacturing flow, the manufacturing characteristics corresponding to the net red dishes are obtained, the cooking characteristics of the electric cooker are referred, and whether the net red recipes meet the manufacturing requirements of the electric cooker is judged;
if yes, adding a green mark to the network red dishes in the latest network red dishes list;
If the net red dishes are not matched with the net red dishes, adding gray marks to the net red dishes in a latest net red dish list;
acquiring network red dishes carrying green marks in the latest network red dish list as dishes to be updated, determining network favorites of each dish to be updated based on big data analysis, and sequencing the dishes to be updated according to the network favorites to obtain a dish sequence;
Based on the preset net red dish storage amount, acquiring a target updated dish in a dish sequence, acquiring a net red recipe corresponding to the target updated dish, transmitting the net red recipe to a net red menu module of a cooking menu library to replace the original net red recipe, and synchronously updating the net red recipe catalog corresponding to the net red menu module.
In this embodiment, the web-red menu refers to a popular food preparation recipe on the internet.
In this embodiment, the making feature refers to the making mode of the net red dishes, such as frying.
In this embodiment, the cooking characteristics of the electric cooker refer to the cooking manners of dishes that can be cooked by the electric cooker, including stir-frying, boiling, steaming, stewing, etc.
In this embodiment, the green mark refers to the color mark of the red dishes that can be made by the electric cooker, and the gray mark refers to the color mark of the red dishes that cannot be made by the electric cooker.
In this embodiment, the dishes to be updated are net red dishes that can be made by the electric cooker.
In this embodiment, the dish sequence refers to ranking all the dishes to be updated according to the network preference degree of each dish to be updated to obtain a ranking sequence.
In this embodiment, the target updated dishes refer to dishes to be updated of the storage amount of the pre-set net red dishes in the dishes sequence. The preset net red dish storage amount refers to the maximum number of net red dishes which can be stored in the cooking menu library.
The beneficial effects of the embodiment are that: according to the invention, based on big data, the network red recipe on the Internet is obtained regularly, the network red recipe of the network red menu module of the cooking menu library is automatically updated, the network red recipe is matched with the network wind direction, and dishes which can be produced by the hottest electric cooker on the network are obtained and updated regularly for the user, so that the user experience is improved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (6)
1. A big data based cooking menu acquisition and update method, comprising:
step 1: searching based on information to be queried input by a user to obtain a target menu;
step 2: the target menu is sent to display equipment associated with the electric cooker for display;
step 3: acquiring and analyzing the confirmation information of the user on the target menu, judging whether the target menu is a new menu, if so, storing the target menu into a cooking menu library, and updating a menu directory of the cooking menu library;
Wherein, step 1 includes:
acquiring information to be queried input by a user, and judging the information type of the information to be queried;
when the information to be queried input by the user is a dish name, judging the information to be queried as first information;
Otherwise, judging the information to be queried as second information;
wherein, when the cooking menu library is updated, the method further comprises:
Acquiring calling time corresponding to all stored menus in a cooking menu library;
screening all stored menus according to the calling time, determining the menu which is not commonly used by the user, and generating a menu list which is not commonly used;
Deleting a stored menu in the cooking menu library based on the unusual menu list, and synchronously updating a menu directory of the cooking menu library;
further comprises: step 4: based on big data, regularly acquire the online red recipe on the internet, carry out automatic update to the online red recipe of the online red menu module of culinary art menu storehouse, include:
Based on big data analysis, periodically acquiring a latest network red dish list on the Internet and corresponding manufacturing processes of each network red dish;
according to the manufacturing flow, the manufacturing characteristics corresponding to the net red dishes are obtained, the cooking characteristics of the electric cooker are referred, and whether the net red recipes meet the manufacturing requirements of the electric cooker is judged;
if yes, adding a green mark to the network red dishes in the latest network red dishes list;
If the net red dishes are not matched with the net red dishes, adding gray marks to the net red dishes in a latest net red dish list;
acquiring network red dishes carrying green marks in the latest network red dish list as dishes to be updated, determining network favorites of each dish to be updated based on big data analysis, and sequencing the dishes to be updated according to the network favorites to obtain a dish sequence;
Acquiring a target updated dish in a dish sequence based on a preset net red dish storage amount, acquiring a net red recipe corresponding to the target updated dish, transmitting the net red recipe to a net red menu module of a cooking menu library to replace an original net red recipe, and synchronously updating a net red recipe catalog corresponding to the net red menu module;
when the information to be queried is the second information, the method comprises the following steps:
Classifying the second information according to requirements, and determining the names of food materials to be manufactured and the number of dishes to be manufactured of a user;
Searching in menu details of a cooking menu library based on the names of the food materials to be manufactured, and obtaining a first menu to be selected containing the names of the food materials to be manufactured;
screening the first menu to be selected based on the number of dishes to be made and by combining the eating habits of the user to obtain a target menu;
Checking main food materials corresponding to the target menu according to the names of the food materials to be manufactured, judging whether the food materials to be manufactured are used completely, if not, acquiring the names of the unused food materials, taking the names of the unused food materials as the main food materials, and searching based on the Internet to acquire a second menu to be selected;
Based on the first menu to be selected and the second menu to be selected, determining a target menu which accords with the number of dishes to be made by the user by combining the eating habits and taste preference of the user.
2. The method for acquiring and updating a cooking menu based on big data according to claim 1, wherein when the information to be queried is the first information, comprising:
Searching on a menu catalog based on the first information, and judging whether a menu meeting the user requirement exists in the cooking menu library or not;
If yes, acquiring a menu meeting the user requirement as a target menu;
if the target menu does not exist, searching the menu based on the Internet, and acquiring the target menu according to the taste preference of the user.
3. The big data based cooking menu acquisition and update method of claim 1, further comprising: step 0: determining eating habits and taste preferences of the user based on the dish query record or the cooking record of the user, comprising:
Acquiring dish inquiry records and cooking records of a user, determining seasonings and seasoning consumption of dishes corresponding to the inquiry records and the cooking records, and generating a temporary dish batching table;
Determining a primary flavoring of the influence of the dish taste based on the dish taste influencing characteristics;
determining that different main seasonings are highlighted by using corresponding tastes according to the analysis results of the big data of the taste deviation of different dishes, and generating a taste reference table;
According to the temporary dish batching table, the consumption of main seasonings corresponding to each dish and the proportion corresponding to the consumption of the main seasonings are obtained;
determining the outstanding use seasoning of each dish according to the proportion, determining the dish taste corresponding to each dish based on the taste reference table, and determining the user taste preference.
4. The cooking menu obtaining and updating method based on big data as claimed in claim 3, wherein the step 0 further comprises:
classifying cooking dishes based on the recording time corresponding to the cooking records, and taking continuous cooking dishes in the same time period as the same-meal cooking dishes to generate a same-meal set;
The dishes in each same meal set are marked with the types and the tastes of the dishes, and the cooking characteristics corresponding to each same meal set are obtained according to the marking result;
classifying the same meal sets according to the number of cooking dishes corresponding to each same meal set to obtain a plurality of cooking clusters;
and comprehensively comparing the cooking characteristics corresponding to the same meal sets in each cooking cluster to obtain the combination characteristics of different numbers of dishes cooked by the same meal, and determining the eating habits of the user.
5. The method for acquiring and updating a cooking menu based on big data according to claim 4, wherein the method for comprehensively comparing the cooking characteristics corresponding to each set of the same meal in each cooking cluster to obtain the combination characteristics of different numbers of dishes cooked with the same meal, and determining the eating habits of the user, further comprises:
and determining the cooking quantity of the same meal habit of the user and the corresponding dish combination characteristics according to the quantity proportion of different cooking clusters, and adding a priority label.
6. The cooking menu obtaining and updating method based on big data as claimed in claim 1, wherein step 3 comprises:
Acquiring confirmation information of a user on a target menu, analyzing the confirmation, and confirming the target menu finally confirmed by the user and a target menu name corresponding to the target menu;
Comparing the target menu name with the existing menu directory of the cooking menu library, and judging the target menu as an old menu when the existing menu directory contains the target menu name;
Otherwise, judging the target menu as a new menu;
and storing the new menu into a cooking menu library, adding the target menu name corresponding to the new menu into a menu catalog of the cooking menu library, and updating the cooking menu library.
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