CN110766494A

CN110766494A - List generation method, device and system

Info

Publication number: CN110766494A
Application number: CN201810839793.4A
Authority: CN
Inventors: 武天路; 雷东勇; 朱玲; 李旭远; 葛波; 白雪美
Original assignee: BSH Electrical Appliances Jiangsu Co Ltd; BSH Home Appliances Co Ltd
Current assignee: BSH Electrical Appliances Jiangsu Co Ltd; BSH Home Appliances Co Ltd; BSH Hausgeraete GmbH
Priority date: 2018-07-27
Filing date: 2018-07-27
Publication date: 2020-02-07

Abstract

A list generation method, a device and a system are provided, wherein the list generation method is suitable for generating a shopping list and comprises the following steps: at least one recommendation phase; the recommendation stage comprises at least one question and answer recommendation process, and the question and answer recommendation process corresponds to a preset recommendation factor; the process of recommending question and answer comprises the following steps: acquiring background data corresponding to the recommendation factor, wherein the background data comprises at least one dish; generating a guide question according to the background data; when the guiding question answers input by the user are obtained, the recommended dishes are obtained based on the guiding question answers and by combining with the background data; and obtaining a recipe of the recommended dishes according to the recipe database and the recommended dishes to generate a shopping list. By utilizing big data and home interconnection technology, the shopping list generation method helps a user make a purchasing decision to generate a shopping list in interaction, can effectively reduce the complexity of shopping list generation, improves the interestingness of shopping list generation, and is beneficial to improvement of user experience.

Description

List generation method, device and system

Technical Field

The invention relates to the technical field of household appliances, in particular to a method, a device and a system for generating a list.

Background

With the widespread use of display screens in household appliances, the function of displaying stock food information on display screens has been popularized. In particular, a display screen of a food storage device such as a refrigerator can often display more food material information.

However, in the prior art, the input of the required food materials and the purchased food materials often requires the user to make a decision and input the food materials one by one, so that the use complexity of the user can be increased, and the improvement of the function utilization rate is not facilitated.

Disclosure of Invention

The invention aims to provide a list generation method, a device and a system, which are used for assisting a customer to make a purchase decision in interaction through big data and a home interconnection technology, so that the workload of user data input is reduced, and the purpose of improving user experience is achieved.

In order to solve the above problem, the present invention provides a method for generating a list, wherein the method for generating a list is suitable for generating a shopping list, and the method for generating a list comprises: at least one recommendation phase; the recommendation stage comprises at least one recommended question and answer process, and the recommended question and answer process corresponds to a preset recommendation factor; the recommended question-answering process comprises the following steps: acquiring background data corresponding to the recommendation factor, wherein the background data at least comprises one food material; generating a guidance question based on the context data; when a guiding question answer input by a user is obtained, obtaining a recommended dish based on the guiding question answer and in combination with the background data; and obtaining a recipe of the recommended dish according to a recipe database and the recommended dish to generate the shopping list.

According to the list generation method, through a recommendation question-answering process in a recommendation phase, based on background data, big data and a home interconnection technology are utilized, and in interaction, a user is assisted in making a purchase decision to generate a shopping list, so that the complexity of shopping list generation can be effectively reduced, the interestingness of shopping list generation is improved, and the user experience is improved.

Optionally, the step of acquiring the background data includes: obtaining inventory food material data, the inventory food material data including one or more inventory food materials; the step of generating the guidance question comprises: generating the guide question according to a recipe database and the inventory food materials, the guide question comprising: a plurality of inventory alternative dishes, a recipe of the inventory alternative dishes comprising at least one of the inventory food materials; and when the guiding question answer input by the user is obtained, obtaining the recommended dish based on the guiding question answer and combined with the stock alternative dish.

Based on the stock food material data, the guidance problem is generated to obtain recommended dishes, so that the utilization rate of stock food materials can be effectively improved, food waste is reduced, and user experience can be effectively improved.

Optionally, the step of obtaining the background data further includes: acquiring popular diet data; the step of generating the guidance question comprises: and generating the guide question according to a recipe database and the stock food material data and by combining mass diet data.

Optionally, the public dietary data includes: mass dishes stored by all users in a preset mass counting time period before the current date and the eating times corresponding to the mass dishes are counted; the step of generating the guidance question comprises: generating an inventory usage question based on the inventory food material data, the inventory usage question including at least one of the inventory food materials; obtaining a plurality of inventory backup dishes based on the inventory use question answers in combination with a recipe database and the inventory food material data, the recipe of the inventory backup dishes comprising at least one of the inventory food materials; sorting the plurality of stock backup dishes according to the eating times of the popular diet data; obtaining inventory backup dishes according to the ordered plurality of inventory backup dishes; generating an inventory selection question based on the inventory alternatives; the step of obtaining the recommended dish comprises: selecting answers to the questions based on inventory input by the user and obtaining the recommended dish in combination with the inventory alternatives.

In the process of generating the guide problem, public diet data are introduced, the types of recommended dishes can be enriched according to popular diet popularity, and diet diversification can be effectively improved.

Optionally, the recommending stage includes: a plurality of recommended question-answering processes are sequentially carried out; the recommendation factor comprises an inventory factor; the recommended question-answering process corresponding to the inventory factor is the last recommended question-answering process.

Optionally, the process of recommending question and answer corresponding to the inventory factor further includes: comparing a shopping list with the inventory food material data after obtaining the inventory food material data and before generating the guide question; and generating the guide question based on the comparison result of the shopping list and the stock food material data and in combination with a recipe database.

The method comprises the steps of setting a recommended question-answering process corresponding to an inventory factor as a last recommended question-answering process, eliminating food materials in a shopping list in the recommended question-answering process corresponding to the inventory factor, generating a guide problem based on residual food material data, effectively avoiding repeated guide problems and effectively improving question-answering efficiency.

Optionally, the step of obtaining the inventory food material data includes: and acquiring the data of the food materials in stock through a home terminal.

Optionally, the home terminal includes a refrigerator.

The method has the advantages that the stocked food material data are directly obtained from a home terminal, particularly from a refrigerator, the types and the number of the household residual food materials can be timely and accurately obtained, and the data do not need to be acquired manually, so that the real-time performance and the accuracy of the obtained stocked food material data can be effectively guaranteed on the premise of not increasing the use cost of a user.

Optionally, the method includes: a plurality of recommendation stages performed in sequence; the process of recommending question and answer further comprises: generating a supplementary guidance question after obtaining the recommended dish; starting another recommended question-answering process or starting the next recommendation phase based on the supplementary guidance question answer.

By means of the generation of the supplementary guide questions, the quantity of the recommended question answering processes can be effectively controlled, and the shopping list generation efficiency can be effectively improved.

Optionally, the recommending stage includes: a main menu recommendation stage and a supplementary recommendation stage; the recommendation factors comprise main menu recommendation factors in a main menu recommendation stage and supplementary recommendation factors in a supplementary recommendation stage.

By carrying out different question and answer recommending processes in different recommending stages, the main dish and the auxiliary dish can be recommended, so that the complete catering can be recommended, and the completeness of the shopping list coverage is facilitated.

Optionally, the main menu recommendation factor includes: one or more of a date factor, a history factor, a prevalence factor, and an inventory factor; the supplemental recommendation factor includes: one or more of a season factor, a prevalence factor, a low-price factor, and an inventory factor.

Optionally, the main menu recommendation stage includes: a plurality of recommended question-answering processes performed in order of a date factor, a history factor, a popularity factor, and an inventory factor; the supplementary recommendation phase comprises: a plurality of recommended question-answering processes performed in order of the season factor, the low-price factor, the popularity factor, and the inventory factor.

Through the setting of different main menu recommendation factors and supplementary recommendation factors, the efficiency of recommending dishes by interactive question answering can be guaranteed, the generation of guide problems can be guaranteed to be adaptive to factors such as dates, seasons and personal preferences, the personal interests of dish recommending users can be matched, and the user experience can be improved.

Optionally, after obtaining the recipe of the recommended dish to generate the shopping list, the method further includes: and sending the shopping list to a home terminal.

The obtained shopping list is sent to the home terminal, the user does not need to manually input the names of the dishes or the food materials again, repeated input can be effectively reduced, and the complexity of user operation is reduced.

Optionally, when the recommendation factor is a date factor, the background data includes: the festival data, the step of obtaining the background data includes: acquiring festival data, wherein the festival data comprises festivals in a festival reminding time period preset after the current date and festival dishes of the festivals; the step of generating the guidance question comprises: generating the guide question according to the festival data, wherein the guide question comprises festival dishes; the step of obtaining the recommended dish includes: obtaining the recommended dishes according to the holiday dishes on the basis of answers of guide questions input by a user; when the recommendation factor is a history factor, the background data comprises individual diet data, and the step of obtaining the background data comprises the following steps: obtaining personal diet data, wherein the personal diet data comprise historical dishes stored by a user in a personal statistical time period preset before the current date and the eating date of each historical dish; the step of generating the guidance question comprises: obtaining historical alternative dishes according to the difference value between the eating date and the current date; generating the guide question according to the historical alternative dishes, wherein the guide question comprises the historical alternative dishes; the step of obtaining the recommended dish includes: obtaining the recommended dishes according to the historical alternative dishes based on answers of guide questions input by a user; when the recommendation factor is a popularity factor, the background data comprises popular diet data, and the step of obtaining the background data comprises: obtaining mass dietary data, the mass dietary data comprising: mass dishes stored by all users in a preset mass counting time period before the current date and the eating times of each mass dish; the step of generating the guidance question comprises: obtaining popular alternative dishes according to the eating times; generating the guide question according to the popular alternative dish, wherein the guide question comprises the popular alternative dish; the step of obtaining the recommended dish includes: obtaining the recommended dish according to the popular alternative dish based on a guiding question answer input by a user; when the recommendation factor is a current time factor, the background data includes current time data, and the step of obtaining the background data includes: obtaining season data, the season data comprising: the method comprises the steps that a seasonal cuisine food material and a preset harvesting time period of the seasonal cuisine food material are obtained, and the current date is located in the harvesting time period; the step of generating the guidance question comprises: generating the guide question according to the recipe database and the season data, wherein the guide question comprises: a plurality of seasonal cuisine, wherein the recipe of the seasonal cuisine comprises at least one seasonal cuisine food material; the step of obtaining the recommended dish comprises: when a guiding question answer input by a user is obtained, the recommended dishes are obtained based on the guiding question answer and in combination with the current dishes; when the recommendation factor is a low price factor, the background data comprises: the method comprises the following steps of obtaining a target area and price data in the target area, wherein the step of obtaining the background data comprises the following steps: obtaining a target area based on the position data and a preset comparison radius; according to the target area, obtaining price data in the target area, wherein the price data comprise food materials, the average selling price of the food materials in the target area on the current date and the average selling price of the food materials in the target area in the valuation time period before the current date; the step of generating the guidance question comprises: obtaining low-price food materials according to the price data; generating the guide question based on the low-price food materials, wherein the guide question comprises a plurality of low-price alternative dishes, and the recipe of the low-price alternative dishes comprises the low-price food materials; the step of obtaining the recommended dish comprises: and when a guiding question answer input by the user is obtained, obtaining the recommended dishes based on the guiding question answer and in combination with the low-price alternative dishes.

Optionally, when the number of the holiday dishes is 1, in the step of obtaining the recommended dishes according to the holiday dishes, the holiday dishes are taken as the recommended dishes; when the number of the festival dishes is multiple, the step of obtaining the recommended dishes according to the festival dishes comprises the following steps: generating a holiday selection question according to the plurality of holiday dishes, wherein the holiday selection question comprises the plurality of holiday dishes; and obtaining the recommended dishes based on the answers of the festival selection questions input by the user.

Optionally, the step of obtaining historical alternative dishes includes: and taking the historical dish corresponding to the maximum difference value between the eating date and the current date as the historical alternative dish.

Optionally, the step of obtaining popular alternative dishes comprises: and taking the popular dish with the largest eating frequency as the popular alternative dish.

Optionally, the step of obtaining low-price food materials comprises: obtaining food materials and the difference value of the average selling price of the food materials on the current date and the average selling price of the food materials in the valuation time period based on the price data; and taking the food material with the largest difference value as the low-price food material.

Accordingly, the present invention also provides a list generating device, adapted to generate a shopping list, the list generating device comprising: at least one recommendation module; the recommendation module comprises at least one recommendation unit, the recommendation unit is suitable for recommending questions and answers, and the recommendation unit corresponds to a preset recommendation factor; the recommendation unit includes: the collector obtains background data corresponding to the recommendation factor, and the background data at least comprises one food material; a director generating a directing question according to the background data; the recommender obtains a guide question answer input by a user and obtains recommended dishes based on the guide question and the guide question answer; and the generator obtains the recipe of the recommended dish according to the recipe database and the recommended dish to generate the shopping list.

Optionally, the recommendation module comprises an inventory recommendation unit; the inventory recommendation unit includes: an inventory collector that obtains inventory food material data, the inventory food material data including one or more inventory food materials; an inventory director that generates the lead question from a recipe database and the inventory food material data, the lead question comprising: a plurality of inventory alternative dishes, a recipe of the inventory alternative dishes comprising at least one of the inventory food materials; an inventory recommender to obtain a guided question answer input by a user, to obtain the recommended dish based on the guided question answer in combination with the inventory of alternative dishes.

Optionally, the inventory collector further acquires public diet data; the inventory guider is used for generating the guiding problem according to the recipe database and the inventory food material data and by combining with the public diet data.

Optionally, the public dietary data includes: mass dishes stored by all users in a preset mass counting time period before the current date and the eating times corresponding to the mass dishes are counted; the stock guide includes: an inventory use element that generates inventory use questions based on the inventory food material data, the inventory use questions comprising at least one of the inventory food materials; the inventory use element obtaining user-entered inventory use question answers and, based on the inventory use questions and the inventory use question answers, in conjunction with a recipe database, obtaining a plurality of inventory backup recipes, a recipe of the inventory backup recipes including at least one of the inventory food materials; an inventory alternatives component that orders the plurality of inventory back-up dishes according to the number of servings of the popular diet data; obtaining inventory backup dishes according to the ordered plurality of inventory backup dishes; the inventory alternatives component also generates an inventory selection question based on the inventory alternatives.

Optionally, the recommendation module comprises an inventory recommendation unit; the recommendation module further comprises at least one recommendation unit, the inventory recommendation unit being configured to last run.

Optionally, the inventory recommendation unit further comprises: an inventory comparator that compares a shopping list and the inventory food material data; the stock guide generates the guide question according to the comparison result of the stock comparator and in combination with a recipe database.

Optionally, the stock collector acquires the stock food material data through a home terminal.

Optionally, the home terminal includes a refrigerator.

Optionally, the list generating device has a plurality of recommending modules that operate in sequence, and the recommending modules include a plurality of recommending units that operate in sequence; the recommendation unit further includes: a replenishment connector that generates a replenishment guidance question after the recommender of the recommending unit obtains the recommended dish; the supplementary connector also obtains supplementary guide question answers input by a user, and starts a next running recommending unit when the supplementary guide question answers are affirmative; and starting a next running recommending module when the answer of the supplementary guide question input by the user is negative.

Optionally, the method includes: the system comprises a main menu recommendation module and a supplementary recommendation module; the recommendation factors comprise main menu recommendation factors and supplementary recommendation factors.

Optionally, the main menu recommending module includes: one or more of a date recommendation unit, a history recommendation unit, a popular recommendation unit, and an inventory recommendation unit; the supplemental recommendation module comprises: one or more of a season recommendation unit, a popular recommendation unit, a low-priced recommendation unit, and an inventory recommendation unit.

Optionally, the main menu recommending module includes: a date recommending unit, a history recommending unit, a popular recommending unit and an inventory recommending unit which are operated in sequence; the supplementary recommendation unit includes: a season recommendation unit, a low price recommendation unit, a popular recommendation unit, and an inventory recommendation unit that operate in sequence.

Optionally, the date recommending unit includes: the date collector obtains festival data, and the festival data comprises festivals in a festival reminding time period preset after the current date and festival dishes of the festivals; a date director, generating the guide question according to the festival data, the guide question comprising festival dishes; the date recommender obtains the recommended dishes according to the holiday dishes on the basis of answers of guide questions input by a user; the history recommending unit comprises: the history collector obtains personal diet data, and the personal diet data comprise historical dishes stored by a user in a preset personal statistic time period before the current date and the eating date of each historical dish; the history guider is used for obtaining history alternative dishes according to the difference value between the eating date and the current date; the history guider is also used for generating the guiding question according to the history alternative dishes, and the guiding question comprises the history alternative dishes; the history recommender obtains the recommended dishes according to the history alternative dishes based on answers of guide questions input by a user; the popular recommendation unit comprises: a popularity collector that obtains popular diet data, the popular diet data including: mass dishes stored by all users in a preset mass counting time period before the current date and the eating times of each mass dish; the popularity guider is used for obtaining popular alternative dishes according to the eating times; the popularity director further generates the guide question according to the popular alternative dish, the guide question including the popular alternative dish; a popular recommender, which obtains the recommended dish from the popular alternative dish based on a guide question answer input by a user; the season recommending unit comprises: the system comprises a season collector, wherein the season collector obtains season data, and the season data comprise: the method comprises the steps that a seasonal cuisine food material and a preset harvesting time period of the seasonal cuisine food material are obtained, and the current date is located in the harvesting time period; the guide question is generated by the season guide according to the recipe database and the season data, and the guide question comprises: a plurality of seasonal cuisine, wherein the recipe of the seasonal cuisine comprises at least one seasonal cuisine food material; the system comprises a season recommender, a server and a server, wherein when the season recommender obtains a guide question answer input by a user, the season recommender obtains the recommended dishes based on the guide question answer and in combination with the season dishes; the low-price recommending unit comprises: the low price collector obtains a target area based on the position data and a preset comparison radius; the low price collector obtains price data in the target area according to the target area, wherein the price data comprise food materials, the average selling price of the food materials in the target area on the current date and the average selling price of the food materials in the target area in the valuation time period before the current date; the low-price guider is used for obtaining low-price food materials according to the price data; generating the guide question based on the low-price food materials, wherein the guide question comprises a plurality of low-price alternative dishes, and the recipe of the low-price alternative dishes comprises the low-price food materials; and the low-price recommender, when obtaining the guide question answer input by the user, obtains the recommended dish based on the guide question answer and in combination with the low-price alternative dish.

Optionally, when the number of the festival dishes is 1, the date recommender takes the festival dish as the recommended dish; when the number of the festival dishes is multiple, the date recommender generates a festival selection problem according to the festival dishes, wherein the festival selection problem comprises the festival dishes; the date recommender also receives a festival selection question answer input by the user, and obtains the recommended dishes based on the festival selection question and the festival selection question answer.

Optionally, the history director uses the history dish corresponding to the maximum difference between the eating date and the current date as the history alternative dish.

Optionally, the popularity director uses the popular dish with the largest eating frequency as the popular alternative dish.

Optionally, the low price collector further obtains a food material and a difference between an average selling price of the food material on a current date and an average selling price of the food material in the valuation time period based on the price data; and the low-price collector takes the food material with the largest difference value as the low-price food material.

Optionally, the method further includes: and the transmission module is suitable for sending the shopping list to a home terminal.

In addition, the present invention also provides a list generating system, including: a list generating device, which is the list generating device of the present invention; and the interactive end is connected with the list generating device.

The interaction with the user is realized by displaying the guide question and acquiring the answer of the guide question through the interaction end connected with the list generating device, so that the convenience of shopping list generation can be effectively improved.

Compared with the prior art, the technical scheme of the invention has the following advantages:

Drawings

FIG. 1 is a schematic flow chart diagram illustrating a method for generating a manifest according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart diagram illustrating a process of recommending question answering in the embodiment of the manifest generation method shown in FIG. 1;

FIG. 3 is a schematic flow chart illustrating a process of recommending question answering corresponding to a date factor in the embodiment of the list generating method shown in FIG. 1;

FIG. 4 is a flow chart illustrating a process of recommending question answering corresponding to a history factor in the embodiment of the list generating method shown in FIG. 1;

FIG. 5 is a flow chart illustrating a process of recommending question answering corresponding to popularity factors in the embodiment of the manifest generation method shown in FIG. 1;

FIG. 6 is a schematic flow diagram of a process for recommending question answering corresponding to an inventory factor in the embodiment of the inventory generation method shown in FIG. 1;

FIG. 7 is a schematic flow chart diagram illustrating a supplemental question-answering process in the embodiment of the manifest generation method shown in FIG. 1;

FIG. 8 is a flow chart illustrating a process of recommending question answering corresponding to the season factor in the embodiment of the list generating method shown in FIG. 1;

FIG. 9 is a flow chart illustrating a process of recommending question answering corresponding to a low price factor in the embodiment of the list generating method shown in FIG. 1;

FIG. 10 is a functional block diagram of one embodiment of a manifest generation apparatus of the present invention;

FIG. 11 is a functional block diagram of a recommendation unit in the embodiment of the manifest generation apparatus shown in FIG. 11;

fig. 12 is a detailed functional block diagram of a main menu recommending module (3000) in the embodiment of the list generating apparatus shown in fig. 10;

fig. 13 is a functional block diagram of the supplementary recommendation module (4000) in the embodiment of the manifest generation apparatus shown in fig. 10.

Detailed Description

As known from the background art, the problems of repeated input and high complexity degree often exist in the prior art for acquiring and displaying household side food material information.

In order to solve the above technical problem, the present invention provides a method for generating a list, wherein the method for generating a list is suitable for generating a shopping list, and the method for generating a list comprises: at least one recommendation phase; the recommendation stage comprises at least one recommended question and answer process, and the recommended question and answer process corresponds to a preset recommendation factor; the recommended question-answering process comprises the following steps: acquiring background data corresponding to the recommendation factor, wherein the background data comprises at least one dish; generating a guide question according to the background data; when a guiding question answer input by a user is obtained, obtaining a recommended dish based on the guiding question answer and in combination with the background data; and obtaining a recipe of the recommended dish according to a recipe database and the recommended dish to generate the shopping list.

By utilizing big data and home interconnection technology, the shopping list generation method helps a user make a purchasing decision to generate a shopping list in interaction, can effectively reduce the complexity of shopping list generation, improves the interestingness of shopping list generation, and is beneficial to improvement of user experience.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.

Referring to fig. 1, a flowchart of an embodiment of a manifest generation method of the present invention is shown.

The list generation method is suitable for generating a shopping list, and comprises the following steps: at least one recommendation phase; the recommendation phase comprises at least one question and answer recommendation process, and the question and answer recommendation process corresponds to a preset recommendation factor.

It should be noted that, in this embodiment, the list generating method displays the question through the interactive end and obtains the user input, so as to implement the interactive question answering with the user. The interaction terminal may be an app set on the mobile phone, or an application program or a web page set on the computer.

Whether the meal is Chinese meal or western meal, one meal comprises a plurality of dishes, and the comprehensive nutrient intake of the user during the meal can be ensured. While different dishes in a meal play different roles and roles than in a meal. Generally, a meal includes a primary dish and a secondary dish that serves as a supplemental aid. The recommending stage is suitable for obtaining dishes with different effects and different statuses.

In this embodiment, the list generation method includes two recommendation stages, namely a main menu recommendation stage (1000) and a supplementary recommendation stage (2000). The main menu recommendation stage (1000) is suitable for obtaining recommended dishes in the meal as main menus; the supplemental recommendation phase (2000) is adapted to obtain recommended dishes as side dishes in the meal.

For example, when making a shopping list for a western meal, the main menu is typically the one with the most weight, richest, most variety, and most satiety; the auxiliary dish is auxiliary food such as a front dish, salad and soup; when a shopping list is generated for Chinese food, the main dish is usually meat dish; the accessory vegetables are usually vegetables.

Specifically, the recommendation stages include a main menu recommendation stage and a supplementary recommendation stage, and thus the recommendation factors include a main menu recommendation factor of the main menu recommendation stage (1000) and a supplementary recommendation factor of the supplementary recommendation stage (2000), that is, a recommended question and answer process of the main menu recommendation stage corresponds to the main menu recommendation factor, and a recommended question and answer process of the supplementary recommendation stage corresponds to the supplementary recommendation factor.

It should be noted that, in this embodiment, the method for generating a list further includes: kan phase (not shown in the figure) adapted to randomly question and answer with the user. Therefore, before entering the recommendation phase, the list generation method further comprises: generating a daily greeting conversation; and when the daily greeting feedback input by the user is obtained, entering the recommendation stage or entering the cancy stage according to the daily greeting feedback.

Specifically, the daily greeting dialog may be set to select a question, such as "do it tonight? Or just want to play me? "and the like. The user entered daily greeting feedback is therefore an option for the daily greeting dialog, and when the daily greeting feedback is positive or meal related, the recommendation phase is entered, for example entering: "meal-related" or other diet-related; when the daily greeting feedback is negative or not diet dependent, enter the canon phase, for example entering: "not eat today" or other kan related.

It should be noted that, in this embodiment, the list generating method further includes: before generating a daily greeting dialog, a purchase objective is obtained, and one of the at least one recommendation phases is initiated in accordance with the purchase objective.

Specifically, the list generation method comprises a main menu recommendation stage (1000) and a supplementary recommendation stage (2000) which are sequentially performed, and when the purchase target is empty, the main menu recommendation stage (1000) is started; -starting the complementary recommendation phase (2000) when the purchase target only comprises food material for making a main menu; and when the purchase target comprises the food materials for making the main dish and the accessory dish, starting to generate a shopping list.

As shown in fig. 1, in this embodiment, the recommending step includes: a plurality of recommended question-answering processes are sequentially carried out; the recommendation factors include an inventory factor, and the question and answer recommendation process (1400), (2400) corresponding to the inventory factor is the last question and answer recommendation process.

Since the question-answering direction of the recommended question-answering process corresponding to the inventory factor is related to the inventory food materials, the recommended question-answering process corresponding to the inventory factor is set (1400) and (2400) as the last recommended question-answering process, so that the food materials in the shopping list can be excluded in the recommended question-answering processes (1400) and (2400) corresponding to the inventory factor, repeated guide problems can be effectively avoided, and the question-answering efficiency can be effectively improved.

In this embodiment, the main menu recommendation factors include: one or more of a date factor, a history factor, a popularity factor, and an inventory factor, i.e., the main menu recommendation phase (1000), includes a recommended question-and-answer process (1100) corresponding to the date factor, a recommended question-and-answer process (1200) corresponding to the history factor, a recommended question-and-answer process (1300) corresponding to the popularity factor, and a recommended question-and-answer process (1400) corresponding to the inventory factor.

The supplemental recommendation factor includes: the replenishment recommendation phase (2000) includes one or more of a season factor, a popularity factor, a low price factor, and an inventory factor: a recommended question-and-answer process (2100) corresponding to the season factor, a recommended question-and-answer process (2200) corresponding to the low-price factor, a recommended question-and-answer process (2300) corresponding to the popularity factor, and a recommended question-and-answer process (2400) corresponding to the inventory factor.

Specifically, the main menu recommendation stage comprises: a plurality of recommended question-answering processes performed in order of a date factor, a history factor, a popularity factor, and an inventory factor; the supplementary recommendation phase comprises: a plurality of recommended question-answering processes performed in order of the season factor, the low-price factor, the popularity factor, and the inventory factor.

The main menu and the side dish of each meal may include one or more dishes, so that each recommended question-answering process in the recommendation phase is used for obtaining one recommended dish serving as the main menu or the side dish, and the recommendation factor corresponding to the recommended question-answering process is suitable for representing the question-answering direction of the recommended question-answering process.

Referring to fig. 2 in combination, a flow diagram of a process of recommending a question and answer in the embodiment of the list generating method shown in fig. 1 is shown.

The recommended question-answering process comprises the following steps: executing step S10, obtaining background data corresponding to the recommendation factor, wherein the background data at least comprises one food material; executing step S20, generating a guide question according to the background data; when the answer of the guide question input by the user is obtained, executing step S30, obtaining recommended dishes based on the answer of the guide question and in combination with the background data; thereafter, step S40 is executed to obtain a recipe of the recommended dish according to the recipe database and the recommended dish to generate the shopping list.

The background data is the data basis for the manifest generation method and also the basis for the guidance question.

Because the question-answering directions of the question-answering recommending processes corresponding to different recommending factors are different, the background data of the question-answering recommending processes corresponding to different recommending factors are different, and the obtained background data are different for each question-answering recommending process corresponding to different recommending factors.

In this embodiment, the number of the question and answer recommending processes in the recommending stage is multiple, and in each question and answer recommending process, the step of acquiring the background data is performed in the previous question and answer recommending process, so that the operating efficiency can be effectively improved, and the waiting time of a user can be shortened. However, in other embodiments of the present invention, before all the question and answer recommending processes start, the background data of all the question and answer recommending processes may be obtained to improve the smoothness of operation.

The guidance questions are suitable for assisting the user in making decisions on the dishes used in the meal.

The guidance question may include one or both of a non-question which is a question asking the user to answer positively or negatively, or a selection question which is a question asking the user to select from two or more cases.

The method of setting the guide question as the closed question can effectively reduce the difficulty and complexity of the decision of the user, thereby being beneficial to assisting the user to obtain recommended dishes and reducing the complexity of the use of the user.

And after the guide question is generated, waiting for the user to input a guide question answer, and when the guide question answer input by the user is obtained, obtaining the recommended dishes based on the guide question answer and by combining the background data.

The recommended dishes are dishes obtained based on the answers of the guide questions and are feedback of the user to the guide questions, so that the recommended dishes can meet the requirements of the user and meanwhile the operation of the user is effectively simplified.

The recipe database comprises dishes and recipes of the dishes. Therefore, according to the recommended dish, all food materials required for making the recommended dish can be obtained by combining the recipe database, and a shopping list can be generated according to the food materials required for making the recommended dish.

In this embodiment, the recipe database is pre-stored to simplify the database and ensure the accuracy of the recipe database. In other embodiments of the present invention, the recipe database may also be obtained by online searching.

In addition, in this embodiment, the question is displayed and the answer to the question is obtained through the interactive terminal, that is, the list generating method utilizes the interactive terminal to realize information exchange with the user. Therefore, after obtaining the recipe of the recommended dish, the list generating method further includes: and sending the recipe to the interactive end, and storing the recipe to a shopping list through the interactive end, so that the generation of the shopping list is realized. In other embodiments of the present invention, the recipe may be directly saved to a shopping list, so as to generate the shopping list.

It should be noted that, as shown in fig. 2, in step S40 in this embodiment, after obtaining the recipe of the recommended dish to generate the shopping list, the list generating method further includes: and step S50 is executed, and the shopping list is sent to the home terminal. The obtained shopping list is sent to the home terminal, the user does not need to manually input the names of the dishes or the food materials again, repeated input can be effectively reduced, and the complexity of user operation is reduced.

Since in this embodiment, the shopping list is generated at the interactive end, step S40 is executed, and after the interactive end generates the shopping list, in this embodiment, the method for generating a list further includes: generating a synchronization prompt; and when the synchronous prompt feedback input by the user is positive, receiving the shopping list and sending the shopping list to the home terminal.

It should be further noted that, as shown in fig. 1, in this embodiment, the list generating method includes: a plurality of recommendation stages performed in sequence; therefore, as shown in fig. 2, the process of recommending question and answer further includes: step S30, after obtaining the recommended dish, executing step S60 to generate a supplementary guidance question; starting another recommended question-answering process or starting the next recommendation phase based on the supplementary guidance question answer. By means of the generation of the supplementary guide problems, on the premise that the quantity of recommended dishes is met, the quantity of the recommended questions and answers can be effectively controlled, and the shopping list generation efficiency can be effectively improved.

Specifically, the supplementary guidance question is a non-question, for example, a question such as "it is not to prepare other staple food/side food", and the answer to the supplementary guidance question is affirmative, for example, when the answer to the supplementary guidance question is "yes", step S70 is executed to start another question and answer recommending process; if the answer to the supplementary guidance question is negative, for example, if the supplementary guidance question is "no", step S80 is executed to start the next recommendation phase.

Specifically, in this embodiment, the list generating method includes: the main menu recommendation phase (1000) and the supplementary recommendation phase (2000), wherein the main menu recommendation phase (1000) comprises a recommended question and answer process (1100) corresponding to a date factor, a recommended question and answer process (1200) corresponding to a history factor, a recommended question and answer process (1300) corresponding to a popularity factor and a recommended question and answer process (1400) corresponding to an inventory factor which are sequentially carried out.

A recommended question and answer process (1100) corresponding to the date factor in the main menu recommendation stage (1000) is explained as an example: generating a supplementary guidance question after obtaining the recommended dish, the supplementary guidance question being a non-question, such as a question of "whether to prepare other staple food" or not; when the obtained supplementary guidance question is answered in the affirmative, starting a recommended question-answering process corresponding to the history factor (1200); when the obtained answer to the supplementary guidance question is negative, the supplementary recommendation phase (2000) is started.

The following describes in detail a technical solution of a recommended question-answering process corresponding to different recommendation factors in the embodiment of the list generation method shown in fig. 1 with reference to the accompanying drawings.

Referring to fig. 1, fig. 2 and fig. 3, fig. 3 is a flow chart illustrating a process of recommending question answering corresponding to a date factor in the embodiment of the list generating method shown in fig. 1.

And the question-answer direction of the recommended question-answer process corresponding to the date factor is related to the current date. The festival is an important day with commemorative significance in life, and different nationalities and regions have respective festivals. Holidays often originate from traditional customs, religions, or commemoration of someone or an event, etc. Some festivals have traditional festivals, so the festivals in the preset time period before and after the current date are obtained according to the current date to obtain the recommended question-answering process of the recommended dishes, the influence of folk custom can be considered in the dining arrangement, the life of a user can be enriched, and the purpose of improving the user experience is achieved.

Specifically, as shown in fig. 2 and 3, when the recommendation factor is a date factor, the background data includes: festival data, and therefore step S10, the step of acquiring the background data includes: step S1110 is executed, festival data are obtained, and the festival data comprise festivals in a festival reminding time period preset after the current date and festival dishes of the festivals; step S20, the step of generating the guidance question includes: executing step S1120, generating the guidance question according to the festival data, wherein the guidance question includes festival dishes; in step S30, the step of obtaining recommended dishes includes: and obtaining the recommended dishes according to the holiday dishes on the basis of the answers of the guide questions input by the user.

In this embodiment, the holiday data is holiday information in one week, that is, the time span of the preset holiday reminding time period is 1 week, and thus the holiday data includes holidays within 1 week after the current date and holiday dishes of the holiday.

It should be noted that, in order to ensure that the festival information is obtained and the life of the user is enriched, in this embodiment, a festival database is stored in advance, where the festival database includes festival data of various regions and nationalities of the world and corresponding festival data, so as to ensure that there is a festival every seven days, that is, an interval between two adjacent festival days is less than seven days.

And after the festival data is obtained, generating the guide question according to the festival data, wherein the guide question is a festival guide question. Specifically, the festival guiding problem is suitable for judging whether the user needs to prepare for a festival. The holiday guidance question may be a non-question sentence, such as "XXX section is arriving, do not have to be ready? "and the like, wherein XXX is the name of the festival in the festival information.

And after the festival guiding question is generated, waiting for a user to answer the festival guiding question. Since the holiday guidance question is a non-question sentence, the step of obtaining the recommended dish includes: if the answer to the festival guiding question input by the user is affirmative, for example, if the answer to the festival guiding question is a sentence such as "prepare festival", it is determined that the user has prepared a festival, that is, if the answer to the festival guiding question is affirmative, step S1130 is executed to obtain the recommended dishes according to the festival dishes.

Specifically, different festivals have different festival dishes, and the number of the festival dishes corresponding to the different festivals is different. Therefore, when the number of the holiday dishes is 1, in step S1130, in the step of obtaining the recommended dish according to the holiday dish, the holiday dish is taken as the recommended dish; when there are a plurality of holiday dishes, in step S1130, the step of obtaining the recommended dish according to the holiday dish includes: generating a holiday selection question according to the plurality of holiday dishes, wherein the holiday selection question comprises the plurality of holiday dishes; and obtaining the recommended dishes based on the answers of the festival selection questions input by the user.

The festival selection questions are selection question sentences, and options of the festival selection questions are the plurality of festival dishes respectively. Therefore, in the step of obtaining the recommended dishes, the holiday dishes corresponding to the answers to the holiday selection questions are used as the recommended dishes.

As shown in fig. 3, in step S1130, after obtaining the recommended dish, the process of recommending a question and answer corresponding to the date factor further includes: executing step S1160 to generate a supplementary guidance question; starting another recommended question-answering process or starting the next recommendation phase based on the supplementary guidance question answer.

Specifically, the supplementary guidance question is answered by a non-question, for example, a question such as "whether or not to prepare other staple food". Therefore, when the answer of the supplementary guide question is positive, for example, the answer of the supplementary guide question is 'yes', a recommendation question-answering process corresponding to the history factor is started (1200); and entering a supplementary recommendation phase (2000) when the answer of the supplementary guide question is negative, for example, the answer of the supplementary guide question is 'no'.

It should be further noted that, as shown in fig. 3, when the obtained answer to the holiday guidance question is negative, for example, the answer to the holiday guidance question is "no prepared holiday", it is determined that the user is not prepared for holiday, and then the question and answer recommending process (1200) corresponding to the history factor is started.

With continuing reference to fig. 1, with combined reference to fig. 2 and fig. 4, fig. 4 is a flow chart illustrating a process of recommending question answering corresponding to a history factor in the embodiment of the list generating method shown in fig. 1.

And recommending a question and answer process corresponding to the history factor, wherein the question and answer direction of the question and answer process is related to the individual eating habits of the user. The eating habits are different from person to person, so that the recommended dishes can be obtained according to the personal eating habits of the user, and the user experience can be effectively improved.

Specifically, as shown in fig. 2 and 4, when the recommendation factor is a history factor, the background data includes personal diet data, and therefore, in step S10, the step of acquiring the background data includes: executing step S1210 to obtain personal diet data, wherein the personal diet data comprises historical dishes stored by a user in a preset personal statistic time period before the current date and the eating date of each historical dish; in step S20, the step of generating the guidance question includes: step S1221 is executed, and historical alternative dishes are obtained according to the difference value between the eating date and the current date; then, step S1222 is executed to generate the guiding question according to the historical alternative dishes, where the guiding question includes the historical alternative dishes; in step S30, the step of obtaining recommended dishes includes: and obtaining the recommended dishes according to the historical alternative dishes based on the answers of the guide questions input by the user.

The individual eating habits are not invariable, so in the embodiment, the individual eating data is the eating data of the user within 3 weeks, namely the length of the individual statistical time period is 3 weeks, and the individual eating data comprises historical dishes stored by the user within 3 weeks before the current date and the eating date of each historical dish.

After the individual diet data is obtained, historical alternative dishes are obtained according to the individual diet data, and the guide question is generated based on the historical alternative dishes, wherein the guide question is a historical guide question. Specifically, the history guide problem is suitable for judging whether the user eats the history dishes.

In this embodiment, the historical alternative dish is a dish with the longest time interval in the personal diet data. Specifically, in step S1221, the step of obtaining the historical alternative dishes includes: and taking the historical dish corresponding to the maximum difference value between the eating date and the current date as the historical alternative dish.

And after the historical alternative dishes are obtained, generating the historical guide question. In this embodiment, the history guide question is a question of nothing, such as "do you have been eating YYY for XX days, not consider? "the question of waiting, wherein YYY is the name of the dish of the historical alternative dish, and XX is the interval duration between the eating date and the current date of the historical alternative dish.

And after the historical guide question is generated, waiting for a user to answer the historical guide question. Since the history guide question is a non-question, the step of obtaining the recommended dish includes: if the answer to the history guide question input by the user is positive, for example, if the obtained answer to the history guide question is "good", it is determined that the user has eaten the history dish, and if the history alternative dish is obtained as the recommended dish, that is, if the answer to the history guide question is positive, step S1230 is executed, and the history alternative dish is used as the recommended dish.

It should be noted that, similar to the aforementioned question and answer recommending process corresponding to the date factor, as shown in fig. 4, in step S1230, after the dish recommendation is obtained, the question and answer recommending process corresponding to the history factor further includes: executing step S1260 to generate a supplementary guidance question; starting a recommended question-answering process corresponding to the popularity factor (1300) or entering a supplemental recommendation phase (2000) based on the supplemental guidance question answers.

In addition, when the obtained answer to the history guide question is negative, for example, the answer to the history guide question is 'unused', the user is judged not to eat the history dishes, and the recommending question answering process corresponding to the popularity factor is directly started (1300).

With continuing reference to fig. 1, in conjunction with fig. 2 and fig. 5, fig. 5 is a flow chart illustrating a process of recommending question answering corresponding to popularity factors in the embodiment of the list generating method shown in fig. 1.

And the question-answer direction of the recommended question-answer process corresponding to the popularity factor is related to the recent diet popularity of the public. With the evolution of the times, the dietary habits of the public also change, so that the recommended dishes are more fashionable by combining the recent dietary popularity of the public, thereby being beneficial to breaking through the invariable life of the user and enriching the dietary contents of the user.

Specifically, as shown in fig. 2 and 5, when the recommendation factor is a popularity factor, the background data includes mass diet data, so in step S10, the step of acquiring the background data includes: executing step S1310 to obtain mass diet data, where the mass diet data includes: mass dishes stored by all users in a preset mass counting time period before the current date and the eating times of each mass dish; in step S20, the step of generating the guidance question includes: executing step S1321, and obtaining popular alternative dishes according to the eating times; then, step S1322 is executed, the guiding question is generated according to the popular alternative dish, and the guiding question includes the popular alternative dish; in step S30, the step of obtaining recommended dishes includes: and obtaining the recommended dish according to the popular alternative dish based on the guiding question answer input by the user.

In order to control the data collection amount and improve the operation efficiency, in this embodiment, the public diet data is public diet data within 1 day, that is, the length of the public statistical time period is 1 day, and the public diet data includes public dishes stored by all users within 1 day before the current date and the number of eating times of each public dish.

After the popular diet data is obtained, popular alternative dishes are obtained according to the popular diet data, and the guide problem is generated based on the popular alternative dishes, wherein the guide problem is a popular guide problem. Specifically, the popularity guide question is suitable for judging whether the user eats popular dishes.

In this embodiment, the popular alternative dish is a dish with the highest frequency of occurrence in the popular diet data. Specifically, in step S1321, the step of obtaining popular alternative dishes includes: and taking the popular dish with the largest eating frequency as the popular alternative dish.

And after the popular alternative dishes are obtained, generating the popular guide problem. In this embodiment, the popular guide question is whether a file, for example, "most recent YYY is popular, do not consider? "isoquiz, wherein YYY is the name of the popular alternative dish.

After the popular guide question is generated, waiting for a user to answer the popular guide question. Since the popularity guide question is a non-question, the step of obtaining the recommended dish includes: if the answer to the popularity guide question input by the user is positive, for example, if the answer to the popularity guide question is "good", it is determined that the user consumes popular dishes, and if the answer to the popularity guide question is positive, the dish is obtained as the recommended dish according to the popular alternative dishes, that is, if the answer to the popularity guide question is positive, step S1330 is executed, and the popular alternative dishes are used as the recommended dish.

Similar to the aforementioned question and answer recommending process corresponding to the date factor and the history factor, in step S1330, after obtaining the recommended dish, the question and answer recommending process corresponding to the popularity factor further includes: executing step S1360 to generate a supplementary guidance question; based on the replenishment guidance question answer, a recommendation question and answer process corresponding to the inventory factor is started (1400), or a replenishment recommendation phase is entered (2000).

Further, when the obtained result of the popular guiding question is negative, for example, the answer of the popular guiding question is "unused", it is judged that the user does not eat popular dishes, and the recommended question-answering process corresponding to the stock factor is directly started (1400).

With continued reference to FIG. 1, in conjunction with FIGS. 2 and 6, FIG. 6 shows a flow diagram of a process for recommending question and answer corresponding to an inventory factor in the embodiment of the inventory generation method shown in FIG. 1.

And in the recommended question-answering process corresponding to the inventory factor, the question-answering direction is related to the residual food materials at the home end of the user. The consumption of the remaining dishes can be accelerated by combining the formation of the remaining food materials at the home terminal, so that the phenomenon that the remaining food materials are not fresh due to overlong storage time can be avoided, the phenomenon that the user forgets the remaining food materials at the home terminal to cause the overdue food materials can be avoided, the freshness of the food materials used by the user can be guaranteed, and the food waste can be reduced.

Specifically, as shown in fig. 2 and 6, when the recommendation factor is an inventory factor, the background data includes inventory food material data, so in step S10, the step of acquiring the background data includes: performing step S1411, obtaining inventory food material data, the inventory food material data including one or more inventory food materials; in step S20, the step of generating the guidance question includes: generating the guide question according to a recipe database and the stock food material data, wherein the guide question comprises: a plurality of inventory alternative dishes, a recipe of the inventory alternative dishes comprising at least one of the inventory food materials; step S30, the step of obtaining the recommended dish includes: when the answer to the guide question input by the user is obtained, step S1430 is executed to obtain the recommended dish based on the answer to the guide question in combination with the inventory of alternative dishes.

The recipe database comprises dishes and recipes of the dishes, so that the recipes of the dishes can be obtained according to the recipe database, and the dishes containing the food materials in the recipes can be obtained according to the food materials. Therefore, the guidance problem is generated to obtain the recommended dishes based on the stock food material data, so that the utilization rate of the stock food materials can be effectively improved, the food waste is reduced, and the user experience can be effectively improved.

Specifically, the stock food material data refers to leftovers information of the user's home. Specifically, in step S1410, the step of obtaining the inventory food material data includes: and acquiring the data of the food materials in stock through a home terminal. Wherein, the house end includes the refrigerator. The method has the advantages that the stocked food material data are directly obtained from a home terminal, particularly from a refrigerator, the types and the number of the residual food materials can be timely and accurately obtained, and the data do not need to be acquired manually, so that the real-time performance and the accuracy of the obtained stocked food material data can be effectively guaranteed on the premise of not increasing the use cost of a user.

In this embodiment, the inventory food material data further includes: an expiration time corresponding to the inventory food material. And the home terminal obtains the expiration time corresponding to the stocked food materials based on a preset food material storage database in the process of recording the stocked food materials.

It should be further noted that, in this embodiment, the recommended question-answering process corresponding to the inventory factor is the last recommended question-answering process of the recommendation phase, as shown in fig. 1, the recommended question-answering process (1400) corresponding to the inventory factor is the last recommended question-answering process of the main menu recommendation phase (1000), and the recommended question-answering process (2400) corresponding to the inventory factor is the last recommended question-answering process of the supplementary recommendation phase (2000).

Therefore, the recommended question-answering process corresponding to the inventory factor further includes: step S1411, comparing a shopping list with the stock food material data after obtaining the stock food material data and before generating the guidance question; and then generating the guide question based on the comparison result of the shopping list and the stock food material data and in combination with a recipe database.

And comparing the shopping list with the stock food material data, namely removing the used food materials on the shopping list from the stock food material data, thereby effectively avoiding repeated guide problems and effectively improving the question and answer efficiency.

In addition, in the process of recommending question and answer corresponding to the inventory factor, the background data further includes mass diet data, that is, step S1410, the step of acquiring the background data further includes: executing the step S1413, and acquiring mass diet data; the step of generating the guidance question comprises: and generating the guide question according to a recipe database and the stock food material data and by combining mass diet data. In the process of generating the guide problem, public diet data are introduced, the types of recommended dishes can be enriched according to popular diet popularity, and diet diversification can be effectively improved.

In this embodiment, the public diet data includes: the method comprises the steps that mass dishes stored by all users in a mass statistical time period preset before the current date and the eating times corresponding to the mass dishes are obtained, mass diet data in background data in a recommended question and answer process corresponding to inventory factors are the same as mass diet data in background data in a recommended question and answer process corresponding to popularity factors, and therefore mass diet can be stored when the recommended question and answer process corresponding to the popularity factors is carried out, so that data flow is reduced, and operation efficiency is improved. The mass dietary data includes recipe information for all users during the day.

In other embodiments of the present invention, the question-answering recommending process corresponding to the inventory factor may also use data different from the mass diet data in the background data of the question-answering recommending process corresponding to the popularity factor.

It should be noted that, in the recipe database, there are a large number of dishes containing inventory food materials in the recipe, and in order to further reduce the difficulty of user decision making, in the present embodiment, in the recommendation question and answer process corresponding to the inventory factor, the guidance question includes an inventory use question and an inventory selection question.

In the present embodiment, in the recommendation question-answering process corresponding to the inventory factor, the guidance question includes an inventory use question and an inventory selection question. Specifically, as shown in fig. 6, the step of generating the guidance question includes: step S1421 is executed to generate an inventory use problem according to the inventory food material data, where the inventory use problem includes at least one of the inventory food materials, and in this embodiment, the inventory use problem is generated as a result of comparing the shopping list with the inventory food material data.

The inventory use problem is suitable for judging whether the user uses the inventory food materials. Specifically, the inventory use question may be a non-question, such as "there is XXX in the home, there is YY due, has eaten or not? "wait for question," where XXX is the inventory material, and YY is the expiration time corresponding to the inventory material.

After generating the inventory use question, the user is awaited to answer the inventory use question. Upon obtaining the user-entered inventory use question answers, obtaining a plurality of inventory backup dishes based on the inventory use question answers in conjunction with a recipe database and the inventory food material data, a recipe of the inventory backup dishes including at least one of the inventory food materials.

In this embodiment, since the inventory use question is a non-question, the answer to the inventory use question is affirmative, and for example, when the answer to the inventory use question is a sentence such as "eat up", step S1422 is performed to obtain the plurality of inventory backup dishes according to the recipe database and the inventory food material data.

It should be noted that, as shown in fig. 1, the main menu recommendation phase (1000) further includes the supplementary question-and-answer process (1500) to add the recommended dishes used as the main menu, the supplementary question-and-answer process (1500) is the next recommended question-and-answer process performed after the recommended question-and-answer process (1400) corresponding to the inventory factor is completed; so, as shown in fig. 6, when the answer to the stock use question is negative, for example, when the answer to the stock use question is "not used", the replenishment question-answering process is started (1500).

It should be noted that, in other embodiments of the present invention, the inventory use question may also be a question for selecting the inventory food material, for example, "do the following inventory in the home, eat up? "etc., wherein the options of the inventory use question respectively include a plurality of the inventory food materials and an omnino instruction; when the inventory use question answer is an option corresponding to inventory food materials, obtaining a plurality of inventory backup dishes in combination with a recipe database, wherein the recipe of the inventory backup dishes comprises the inventory food materials corresponding to the inventory use question answer; starting the replenishment question-answering process when the inventory use question answer is a no-all instruction.

The recipe database includes dishes and recipes of the dishes, so that the dishes of the recipes including the stock food material can be obtained according to the recipe database, and the dishes of the recipes including the stock food material are used as the stock backup dishes.

After the stock backup dishes are obtained, step S1423 is executed to sort the stock backup dishes according to the number of eating times of the popular diet data; executing step S1424, obtaining inventory spare dishes according to the ordered plurality of inventory spare dishes; generating an inventory selection question based on the inventory alternatives; thus, step 30, the step of obtaining said recommended dish comprises: selecting answers to the questions based on inventory input by the user and obtaining the recommended dish in combination with the inventory alternatives.

Specifically, in step S1423, the step of sorting the inventory alternatives includes: according to the order from many to few of number of times of eating, it is right a plurality of stock reserve dishes are sequenced, obtain the first reserve dish that the number of times of eating is the most, the second reserve dish that the number of times of eating is the most, the third reserve dish that the number of times of eating is the most, the fourth reserve dish that the number of times of eating is the most, the number of times of eating is the fourth reserve dish that the number of times of eating is the most and one or more random reserve dishes that the number of times of eating is less than fourth reserve dish and, random reserve dish with first reserve dish the second reserve dish the third reserve dish with fourth reserve dish is all inequality.

In step S1424, the stock alternative dishes include: the step of obtaining the stock of the first batch of alternative dishes and the second batch of alternative dishes further comprises: obtaining a first batch of alternative dishes, wherein the first batch of alternative dishes at least comprise a first backup dish and a second backup dish; obtaining a second batch of alternative dishes, the second batch of alternative dishes comprising at least the third backup dish and the fourth backup dish.

In this embodiment, the number of the first batch of alternative dishes is 5, and therefore, the first batch of alternative dishes further includes 3 random backup dishes; the number of the second batch of alternative dishes is 5, so that the second batch of alternative dishes also comprises 3 random backup dishes.

So as shown in FIG. 6, the step of generating an inventory selection problem includes: executing step S1425a, generating a first inventory selection question based on the first lot of alternative dishes; step S1425b is executed to generate a second inventory selection question based on the second batch of alternative dishes.

It should be noted that, in this embodiment, in order to avoid repeated questioning, after the first inventory selection question answer is obtained, the second inventory selection question is generated based on the first inventory selection question answer input by the user.

Specifically, the first inventory selection question is a selection question, for example, "are the five dishes available? "wait for question, the options of the first inventory selection question are the first batch of alternative dishes respectively; further, the options for the first inventory selection issue further include: and judging whether the first batch of alternative dishes contains the dishes favored by the user or not.

The second inventory selection question is a selection question, e.g., "is there a suitable one of the five dishes? "wait for question, the options of the second inventory selection question being the second batch of alternative dishes, respectively; further, the options for the second inventory selection issue further include: and judging whether the second batch of alternative dishes have the dishes desired by the user or not by the total-negative instruction.

Therefore, when the answer to the first inventory selection question input by the user is the option corresponding to the first batch of alternative dishes, step S1430 is executed to obtain the recommended dish according to the inventory alternative dishes, that is, the first batch of alternative dishes corresponding to the answer to the first inventory selection question are used as the recommended dish; when the answer to the first inventory selection question input by the user is the total no instruction, and it is determined that there is no dish intended by the user in the first batch of alternative dishes, step S1425b is executed, and the second inventory selection question is generated based on the second batch of alternative dishes.

Then, when the answer to the second inventory selection question input by the user is the option corresponding to the second batch of alternative dishes, executing step S1430, and obtaining the recommended dish according to the inventory alternative dishes, that is, taking the second batch of alternative dishes corresponding to the answer to the second inventory selection question as the recommended dish; and when the answer of the second inventory selection question input by the user is the total-no instruction, judging that no dish intended by the user exists in the second batch of alternative dishes, and starting the question-answering supplementing process (1500).

Similar to the aforementioned recommended question-answering process (1100) corresponding to the date factor, the recommended question-answering process (1200) corresponding to the history factor, and the recommended question-answering process (1300) corresponding to the popularity factor, step S1430, after obtaining the recommended dishes, the recommended question-answering process (1400) corresponding to the inventory factor further includes: step S1460 is executed to generate a supplementary guidance question; starting the supplementary question-answering process (1500) or entering a supplementary recommendation phase (2000) based on the supplementary guidance question answers.

With combined reference to fig. 1 and fig. 7, fig. 7 is a schematic flow chart illustrating a supplementary question and answer process in the embodiment of the manifest generation method shown in fig. 1.

The supplementary question answering process comprises the following steps: step S1501, obtaining a plurality of supplementary alternative dishes according to the food spectrum database, wherein the supplementary alternative dishes are unrepeated dishes randomly generated in the food spectrum database; executing step S1502, generating a supplementary recommendation question according to the plurality of supplementary alternative dishes, where the supplementary recommendation question includes the plurality of supplementary alternative dishes; and obtaining the recommended dishes based on the answers of the supplementary recommended questions input by the user.

Specifically, the supplementary recommendation problem is a selection question, and the options of the supplementary recommendation problem are the plurality of supplementary alternative dishes respectively; in addition, the option of supplementing the recommendation question further includes: and a total negative instruction, wherein the total negative instruction is suitable for judging whether the plurality of supplementary alternative dishes have the dishes which are favorite by the user.

When the supplementary recommended question answer input by the user is the supplementary alternative dish, executing a step S1503, and obtaining the recommended dish according to the supplementary alternative dish, namely taking the supplementary alternative dish corresponding to the supplementary recommended question answer as the recommended dish; and when the supplement recommended question answer input by the user is a total-negative instruction, judging that no dish intended by the user exists in the supplement candidate dishes, restarting the supplement question-answering process (1500), namely executing the step S1501 again, and obtaining a plurality of supplement candidate dishes according to the recipe database.

In addition, in step S1503, after obtaining the recommended dish, the supplementing question and answer stage further includes: step S1560 is executed to generate a supplementary guidance question; based on the supplementary guidance question answers input by the user, the supplementary question answering process (1500) is started again or the next recommendation phase is started.

The supplementary guidance question is answered with a non-question, such as a question of "whether or not to prepare other staple food". Therefore, when the answer of the supplementary guidance question is affirmative, for example, the answer of the supplementary guidance question is yes, the supplementary question answering process (1500) is started again, that is, step S1501 is executed again, and a plurality of supplementary alternative dishes are obtained according to the recipe database; and entering a supplementary recommendation phase (2000) when the answer of the supplementary guide question is negative, for example, the answer of the supplementary guide question is 'no'.

With continuing reference to fig. 1, after the supplementing recommendation unit (3500) completes, in this embodiment, the method for generating a list further includes: the complementary recommending phase (2000) to obtain a recommended dish for use as a side dish. In particular, the recommended meal obtained in the supplementary recommendation phase (2000) is typically a supplementary meal such as a meal for a western-style meal, salad or soup, or a vegetable meal for a Chinese meal.

With combined reference to fig. 1, fig. 2 and fig. 8, fig. 8 is a schematic flow chart illustrating a process of recommending a question and answer corresponding to a season factor in the embodiment of the list generating method shown in fig. 1.

In the recommended question-answer process corresponding to the season factor, the question-answer direction is related to the season corresponding to the current date, so that the recommended dishes can better conform to the season climate, the diet of the user is enriched, the diet health is facilitated, and the eating of the anti-season food is reduced as much as possible.

Specifically, as shown in fig. 2 and 8, when the recommendation factor is a current time factor, the background data includes current time data, and therefore in step S10, the step of acquiring the background data includes: step S2110 is executed to obtain the hour data, where the hour data includes: the method comprises the steps that a seasonal cuisine food material and a preset harvesting time period of the seasonal cuisine food material are obtained, and the current date is located in the harvesting time period; in step S20, the step of generating the guidance question includes: generating the guide question according to the recipe database and the season data, wherein the guide question comprises: a plurality of seasonal cuisine, wherein the recipe of the seasonal cuisine comprises at least one seasonal cuisine food material; step S30, the step of obtaining the recommended dish includes: and when a guiding question answer input by a user is obtained, obtaining the recommended dish based on the guiding question answer and in combination with the current dish.

In this embodiment, the season data refers to food material data collected in the season. It should be noted that, in order to ensure the accuracy and richness of the time data, in this embodiment, a time database is stored in advance, and the time database includes various food materials and harvesting time periods thereof in various regions of the world.

After the season data is obtained, a guide question is generated to obtain recommended dishes as shown in fig. 8. Since there is often more than one kind of the seasonal food material, in this embodiment, in the process of recommending questions and answers corresponding to the seasonal factor, the step of generating the guidance question includes: and S2121, generating a time service problem according to the time data, wherein the time service problem comprises at least one time food material.

The season use problem is suitable for judging whether the user uses the season food material. Specifically, the issue of using the current season may be a non-question sentence, such as "whether current season vegetable has XXX, is purchased? "wait for question sentence, wherein XXX is season food material.

After the seasonal usage question is generated, the user is waited to reply to the seasonal usage question. After obtaining the inventory use question answers input by the user, obtaining a plurality of season back-up dishes based on the season use question answers and by combining a recipe database and the season data, wherein the recipe of the season back-up dishes comprises at least one season food material.

In this embodiment, since the issue of using the current season is a non-question, the answer to the issue of using the current season is affirmative, for example, when the answer to the issue of using the current season is a sentence such as "purchase", step S2122 is executed to obtain the plurality of current season backup dishes according to the recipe database and the current season data.

Further, as shown in fig. 1, in the supplementary recommendation phase (2000), the recommended question-and-answer process (2200) corresponding to the low-price factor is the next recommended question-and-answer process performed after the recommended question-and-answer process corresponding to the season factor is completed; therefore, as shown in fig. 8, when the answer to the season use question is negative, for example, when the answer to the season use question is a sentence such as "do not buy", the recommended question answering process corresponding to the low-priced factor is started (2200).

In other embodiments of the present invention, the issue of using the current season may be a question of selecting the season food material, for example, "whether to purchase current season vegetables as follows? "wait for question sentence, wherein, the options of the said time using question include a plurality of said time food material and no order respectively; when the current-order use question answers are options corresponding to current-order food materials, obtaining a plurality of current-order backup dishes by combining a recipe database, wherein the recipes of the current-order backup dishes comprise the current-order food materials corresponding to the current-order use question answers; when the answer to the season use question is a no command, a recommended question-answering process corresponding to a low-price factor is started (2200).

After the reserve vegetable of the current stature is obtained, executing step S2123, and generating a current stature selection problem according to the reserve vegetable of the current stature, wherein the current stature selection problem comprises a current stature vegetable, and the current stature vegetable comprises at least part of the reserve vegetable of the current stature; and then, selecting a question answer based on a time command input by the user, and combining the time command with the backup dishes to obtain the recommended dishes.

In this embodiment, the season dish is a non-repetitive dish randomly generated in the reserve season dish; the season selection question is a selection question, for example, "are the five dishes appropriate? The choices of the season selection problem are the season dish and a total negative instruction respectively, wherein the total negative instruction is used for judging whether the season dish contains a dish intended by a user.

The current dishes are randomly generated and non-repetitive dishes; in other embodiments of the present invention, the season dish may also be obtained by sorting the reserve season dish according to a certain rule. The invention is not limited in this regard.

When the answer to the current-time selection question input by the user is the current-time dish, executing the step S2130, and obtaining the recommended dish according to the current-time dish, namely, taking the current-time dish corresponding to the answer to the current-time selection question as the recommended dish; and when the answer of the current-time selection question input by the user is a total-negative instruction, judging that no dishes intended by the user exist in the current-time dishes, and starting a recommended question-answering process corresponding to the low-price factor (2200).

In addition, similar to each question and answer recommending process of the main menu recommending phase (1000), the question and answer recommending process corresponding to the season factor of the supplementary recommending phase (2000) further comprises the following steps: in step S2130, after the recommended dish is obtained, step S2160 is executed to generate a supplementary guidance question. Specifically, the steps of generating the supplementary guidance questions and the subsequent procedures in the question and answer recommending process corresponding to the current command factor are similar to those in the question and answer recommending process in the main menu recommending stage (1000), and the present invention is not repeated again.

With combined reference to fig. 1, fig. 2 and fig. 9, fig. 9 is a flow chart illustrating a process of recommending question answering corresponding to a low price factor in the embodiment of the list generating method shown in fig. 1.

In the recommended question-answering process corresponding to the low-price factor, the question-answering direction is related to the selling price of the food materials, particularly to the selling price of the food materials within a certain range, so that the control of the user on the daily cost can be effectively improved on the premise of not excessively increasing the time cost, and the diet cost of the user can be saved.

Specifically, as shown in fig. 2 and 9, when the recommendation factor is a low-price factor, the background data includes: target area and price data within said target area, so step S10, the step of obtaining said context data comprises: executing step S2211, obtaining a target area based on the position data and a preset comparison radius; then, step S2212 is executed, and according to the target area, price data in the target area is obtained, where the price data includes average selling prices of food materials and the food materials in the target area at the current date and average selling prices of the food materials in an assessment time period before the current date; then, in step S20, the step of generating the guidance question includes: executing step S2221, and obtaining low-price food materials according to the price data; generating the guide question based on the low-price food materials, wherein the guide question comprises a plurality of low-price alternative dishes, and the recipe of the low-price alternative dishes comprises the low-price food materials; step S30, the step of obtaining the recommended dish includes: and when a guiding question answer input by the user is obtained, obtaining the recommended dishes based on the guiding question answer and in combination with the low-price alternative dishes.

In this embodiment, the low-price food material is a food material with the largest price reduction range in a target area within 1 week; therefore, the length of the evaluation time period is 1 week, so in step S2221, the step of obtaining low-price food materials includes: obtaining food materials and the difference value of the average selling price of the food materials on the current date and the average selling price of the food materials in the valuation time period based on the price data; and taking the food material with the largest difference value as the low-price food material.

After obtaining the low-priced food material, as shown in fig. 9, the guidance question is generated based on the low-priced food material to obtain a recommended dish. Specifically, the step of generating the guidance question includes: executing step S2222, and generating a low-price use problem based on the low-price food materials, wherein the low-price use problem comprises the low-price food materials.

The low-price use problem is suitable for judging whether the user uses the low-price food material. Specifically, the low-price use question may be a non-question sentence, such as "XXX, relatively cheap, and purchased in the area? "isoquiz, wherein XXX is the low-price food material.

And after the low-price use question is generated, waiting for the user to answer the low-price use question. After obtaining answers to the low-price use questions input by the user, obtaining a plurality of low-price backup dishes based on the answers to the low-price use questions in combination with a recipe database and the low-price food materials, wherein the recipe of the low-price backup dishes comprises the low-price food materials.

In this embodiment, since the low-price question is a non-question, if the answer to the low-price question is affirmative, for example, if the answer to the low-price question is a sentence such as "purchase", step S2223 is executed to obtain the plurality of low-price backup dishes according to the recipe database and the low-price food materials.

Further, as shown in fig. 1, in the supplementary recommendation phase (2000), the recommended question-answering process (2300) corresponding to the popularity factor is the next recommended question-answering process performed after the recommended question-answering process (2200) corresponding to the low-price factor is completed; therefore, as shown in fig. 9, when the answer to the low-priced use question is negative, for example, when the answer to the low-priced use question is a sentence of "do not purchase", the process of recommending a question and answer corresponding to the popularity factor is started (2300).

After the low-price backup dishes are obtained, executing step S2224, and generating a low-price selection problem according to the low-price backup dishes, wherein the current ordering selection problem comprises low-price alternative dishes, and the low-price alternative dishes comprise at least part of the low-price backup dishes; and then, selecting a question answer based on the low price input by the user, and combining the low-price backup dishes to obtain the recommended dishes.

In this embodiment, the low-price alternative dishes are non-repetitive dishes randomly generated in the low-price backup dishes; the low price selection question is a selection question, e.g., "is there a suitable one among the five dishes? The options of the low-price selection question are the low-price alternative dishes and a total negative instruction respectively, wherein the total negative instruction is used for judging whether the low-price alternative dishes contain dishes intended by the user.

The low-price alternative dishes are randomly generated and are not repeated; in other embodiments of the present invention, the low-price alternative dishes are obtained by sorting the low-price backup dishes according to a certain rule. The invention is not limited in this regard.

When the answer to the low-price selection question input by the user is obtained as the low-price alternative dish, executing step S2230, and obtaining the recommended dish according to the low-price alternative dish, that is, taking the low-price alternative dish corresponding to the answer to the low-price selection question as the recommended dish; and when the answer of the low-price selection question input by the user is a total-negative instruction, judging that no dish favorite by the user exists in the low-price alternative dishes, and starting a recommending question-answering process corresponding to the popularity factor (2300).

Further, similar to each of the recommended question-answering processes of the main menu recommendation phase (1000), the recommended question-answering process of the supplementary recommendation phase (2000) corresponding to the low price factor further includes: after the recommended dishes are obtained in step S2130, step S2260 is executed to generate a supplementary guidance question. Specifically, the steps of generating the supplementary guidance questions and the subsequent procedures in the question and answer recommending process corresponding to the low-price factor are similar to those in the question and answer recommending process in the main menu recommending stage (1000), and the present invention is not repeated again.

With continued reference to fig. 1, in the present embodiment, after the completion of the question-answering recommendation process (2200) corresponding to the low-price factor, the supplemental recommendation phase (2000) further includes: a recommended question-and-answer process (2300) corresponding to a popularity factor, a recommended question-and-answer process (2400) corresponding to the inventory factor, and a supplemental question-and-answer process (2500). Specifically, the technical solutions of the recommended question-answering process (2300) corresponding to the popularity factor, the recommended question-answering process (2400) corresponding to the inventory factor, and the supplementary question-answering process (2500) refer to the recommended question-answering process (1300) corresponding to the popularity factor, the recommended question-answering process (1400) corresponding to the inventory factor, and the supplementary question-answering process (1500) in the main menu recommendation stage (1000), and the present invention is not described herein again.

In addition, it should be noted that the list generation method further includes: in any recommending stage and in any recommending question and answer process, acquiring a specified dish input by a user; and obtaining a recipe of the specified dish to generate the shopping list by combining the recipe database based on the specified dish.

It should be further noted that, in this embodiment, the recipe database further includes: one or more feature tags corresponding to the dishes; the list generation method further comprises: a feature search question-and-answer process, the feature search question-and-answer process comprising: in any recommending stage and in any recommending question and answer process, acquiring dish characteristics input by a user; obtaining a plurality of characteristic backup dishes based on the dish characteristics by combining the recipe database, wherein the characteristic table labels of the characteristic backup dishes comprise the dish characteristics; obtaining a plurality of characteristic alternative dishes based on the plurality of characteristic alternative dishes; generating the guide question based on the characteristic alternative dishes; and obtaining the recommended dishes based on the answers of the guide questions input by the user.

Specifically, the characteristic label is suitable for characterizing the characteristics of dishes, such as vegetables, Sichuan dishes, sweet foods and the like; and the characteristic backup dishes are all dishes with the characteristic of the dishes as characteristic labels in the recipe database.

It should be noted that, referring to the aforementioned recommendation question and answer process related to the inventory factor, in this embodiment, after the characteristic backup dish is obtained, before the plurality of characteristic alternative dishes are obtained, the list generation method can also sort the plurality of characteristic backup dishes according to the eating times in the popular diet data, so as to obtain a first characteristic backup dish with the largest number of eating times, a second characteristic backup dish with the second largest number of eating times, a third characteristic backup dish with the third largest number of eating times, a fourth characteristic backup dish with the fourth largest number of eating times and one or more random characteristic backup dishes with the eating times less than that of the fourth characteristic backup dish, the random characteristic backup dish is different from the first characteristic backup dish, the second characteristic backup dish, the third characteristic backup dish and the fourth characteristic backup dish.

Thus, the characteristic alternative dishes include: the method for obtaining the characteristic alternative dishes comprises the following steps of: obtaining a first batch of characteristic alternative dishes, wherein the first batch of characteristic alternative dishes at least comprise a first characteristic backup dish and a second characteristic backup dish; and obtaining a second batch of characteristic alternative dishes, wherein the second batch of characteristic alternative dishes at least comprise the third characteristic backup dish and the fourth characteristic backup dish.

In this embodiment, the number of the first batch of feature alternative dishes is 5, and therefore, the first batch of feature alternative dishes further include 3 random feature backup dishes; the number of the second batch of characteristic alternative dishes is 5, so that the second batch of characteristic alternative dishes further comprise 3 random characteristic backup dishes.

The step of generating the guidance question comprises: generating a first characteristic guide problem based on the first batch characteristic alternative dishes; and generating a second characteristic guide question based on the second batch of characteristic alternative dishes.

In this embodiment, in order to avoid repeated questioning, after obtaining the first feature guide question answer input by the user, the second feature guide question is generated based on the first feature guide question answer.

Specifically, the first feature guide question is a selection question, for example, "do the five dishes fit? "wait for question, the options of the said first characteristic guide question are the said first batch characteristic alternative dishes separately; further, the options for the first feature guide question further include: and judging whether the dishes with the user favorite in the first batch of characteristic alternative dishes exist according to a total-negative instruction.

The second feature guide question is a selection question, e.g., "is there a suitable one among the five dishes? "wait for question, the options of the second characteristic guide question are the second batch of characteristic alternative dishes respectively; further, the options for the second feature guide question further include: and judging whether the dishes in the second batch of characteristic alternative dishes have the favorite dishes of the user.

Therefore, when the answer to the first feature guide question input by the user is the option corresponding to the first batch of feature alternative dishes, the recommended dishes are obtained according to the feature alternative dishes, namely, the first batch of feature alternative dishes corresponding to the answer to the first feature guide question are used as the recommended dishes; and when the answer to the first characteristic guide question input by the user is the total-negative instruction, judging that no dish intended by the user exists in the first batch of characteristic alternative dishes, and generating the second characteristic guide question based on the second batch of characteristic alternative dishes.

Then, when a second characteristic guide question answer input by the user is a choice corresponding to the second batch of characteristic alternative dishes, obtaining the recommended dish according to the characteristic alternative dishes, namely, taking the second batch of characteristic alternative dishes corresponding to the second characteristic guide question answer as the recommended dish; and when the answer of the second characteristic guide question input by the user is the total-no instruction, judging that no dish intended by the user exists in the second batch of characteristic alternative dishes, and continuing the last question and answer recommending process.

For example, in the recommended question-answering process corresponding to the stock factor, the dish characteristics input by the user are obtained, and then the characteristic searching question-answering process is started; the recommended question-answering process corresponding to the inventory factor continues when the second feature directs the answer to the question to be an all-no order.

Further, similar to each recommended question-answering process, after obtaining the recommended dishes, the feature search question-answering process further includes: generating a supplemental guidance question; starting another recommended question-answering process or starting the next recommendation phase based on the supplementary guidance question answer.

When the answer of the supplementary guidance question is positive, for example, the answer of the supplementary guidance question is 'yes', continuing the last question-answering recommending process, for example, obtaining the dish characteristics input by the user in the question-answering recommending process corresponding to the history factor, and then starting the characteristic searching question-answering process; and when the supplementary guide question is obtained to be answered affirmatively, continuing the recommended question-answering process corresponding to the historical factor.

Correspondingly, the invention also provides a list generating device. Referring to fig. 10, a functional block diagram of an embodiment of the manifest generation apparatus is shown.

Specifically, the list generating device is adapted to generate a shopping list, and the list generating device includes: at least one recommendation module; the recommendation module comprises at least one recommendation unit, the recommendation unit is suitable for recommending questions and answers, and the recommendation unit corresponds to a preset recommendation factor.

It should be noted that, in this embodiment, the list generating apparatus displays the question through the interactive end and obtains the user input, so as to implement the interactive question answering with the user. The interaction terminal may be an app set on the mobile phone, or an application program or a web page set on the computer.

As shown in fig. 10, in the present embodiment, the list generating apparatus includes a main menu recommending module (3000) and a supplementary recommending module (4000). The main menu recommending module (3000) is suitable for obtaining recommended dishes serving as main menus in meals; the supplementary recommending module (4000) is suitable for obtaining recommended dishes serving as side dishes in the meal.

Specifically, the list generating device comprises a main menu recommending module (3000) and a supplementary recommending module (4000), so that the recommending factors comprise main menu recommending factors of the main menu recommending module (3000) and supplementary recommending factors of the supplementary recommending module (4000), that is, recommending units in the main menu recommending module (3000) correspond to the main menu recommending factors, and recommending units in the supplementary recommending module (4000) correspond to the supplementary recommending factors.

It should be noted that, in this embodiment, the list generating apparatus further includes: a canon module (not shown in the figure) adapted to randomly question and answer with the user. Therefore, the list generating apparatus further includes: a daily greeting module (not shown) adapted to generate a daily greeting dialog and to activate the recommendation module or the canon module according to the daily greeting feedback when the daily greeting feedback entered by the user is obtained.

Specifically, when the obtained daily greeting feedback is positive or related to diet, the recommendation module is started; activate the canon module when the obtained daily greeting feedback is negative or not diet dependent.

As shown in fig. 10, in the present embodiment, the recommendation module includes an inventory recommendation unit (3400)/(4400), and the inventory recommendation unit (3400)/(4400) corresponds to a preset inventory factor; in addition, the recommendation module further comprises: at least one recommending unit; the inventory recommendation unit (3400)/(4400) is set to the last run.

In this embodiment, the main menu recommendation factors include: one or more of a date factor, a history factor, a popularity factor, and an inventory factor, i.e., main menu recommendation module (3000) includes one or more of a date recommendation unit (3100) corresponding to the date factor, a history recommendation unit (3200) corresponding to the history factor, a popularity recommendation unit (3300) corresponding to the popularity factor, and the inventory recommendation unit (3400).

The supplemental recommendation factor includes: one or more of a season factor, a popularity factor, a low-price factor, and an inventory factor, namely, a replenishment recommendation module (4000) comprising: one or more of an season recommender (4100) corresponding to an season factor, a low-priced recommender (4200) corresponding to a low-priced factor, a popular recommender (4300) corresponding to a popular factor, and the inventory recommender (4400).

Specifically, in the main menu recommendation module (3000), the plurality of recommendation units are sequentially operated according to the order of a date factor, a history factor, a popularity factor and a stock factor, that is, the main menu recommendation module (3000) includes: a date recommendation unit (3100), a history recommendation unit (3200), a popular recommendation unit (3300), and an inventory recommendation unit (3400) that are sequentially operated; in the replenishment recommendation module (4000), the plurality of recommendation units are sequentially arranged according to the sequence of the season factor, the low price factor, the popularity factor and the stock factor, that is, the replenishment recommendation unit 4000 comprises: a season recommending unit (4100), a low-price recommending unit (4200), a popular recommending unit (4300), and an inventory recommending unit (4400) that are sequentially operated.

Referring collectively to FIG. 11, a functional block diagram of one recommendation unit in the embodiment of the manifest generation apparatus shown in FIG. 11 is shown.

The recommendation unit includes: the collector (10) acquires background data corresponding to the recommendation factor, wherein the background data at least comprises one food material; a guide (20), wherein the guide (20) generates a guide question and obtains a guide question answer input by a user according to the background data; a recommender (30), said recommender (30) obtaining a recommended dish based on said answers to said guide questions in combination with said context data; a generator (40), the generator (40) obtaining a recipe of the recommended dish to generate the shopping list according to a recipe database and the recommended dish.

The collector (10) is adapted to obtain the context data to provide a data basis for the generation of the guiding question and for the generation of the shopping list.

Because different recommending units correspond to different preset recommending factors and the directions of the questions and answers recommended by the different recommending units are different, the background data obtained by the collectors (10) of the different recommending units are different.

The guide (20) is adapted to generate a guide question to assist the user in making decisions about the dishes to be used in the meal. The guider (20) is connected with the collector (10) and obtains the background data from the collector (10); the navigator (20) generates the guidance issue from the background data.

Specifically, the guidance device (20) generates a non-question or a selective question, that is, the guidance question may include one or both of the non-question and the selective question, wherein the non-question refers to a question that asks the user to answer positively or negatively, and the selective question refers to a question that asks two or more conditions to allow the user to select from.

The non-question and the selection question belong to closed problems, namely the guider (20) is suitable for generating closed problems, and can effectively reduce the difficulty and complexity of decision making of a user, thereby being beneficial to assisting the user to obtain recommended dishes and reducing the complexity of use of the user.

The recommender (30) is adapted to obtain recommended dishes.

Specifically, the recommender (30) is connected to the guide (20) and obtains the guide question from the guide (20); the recommender (30) also obtains answers to the guiding questions input by the user; the recommender (30) obtains recommended dishes based on the guide question and the guide question answer.

Because the recommended dishes are obtained by the recommender (30) based on the answers to the guide questions and combined with the background data, and the answers to the guide questions are input by the user, the recommended dishes can reflect the requirements of the user in a feedback manner, and the operation of the user can be effectively simplified while the requirements of the user are met.

The generator (40) is adapted to obtain a recipe of the recommended dish to generate a shopping list.

The generator (40) is connected with the recommender (30) and obtains the recommended dish from the recommender (30); a recipe database is stored in the generator (40) in advance, and comprises dishes and recipes of the dishes; and the generator (40) queries the recipe database according to the recommended dishes to obtain the recipes of the recommended dishes so as to generate the shopping list.

It should be noted that, in this embodiment, the recipe database is pre-stored in the generator (40), and in other embodiments of the present invention, the generator may also query an online recipe database in a networked manner.

In addition, in this embodiment, the list generating apparatus is connected to an interactive end, and information exchange with a user is achieved through the interactive end. Therefore, as shown in fig. 10, the list generating apparatus further includes: a transmission module (5000), wherein the transmission module (5000) sends the recipe of the recommended dish to the interactive terminal to generate the shopping list.

The transmission module (5000) is connected with the plurality of recommendation modules, and the transmission module (5000) obtains recipes of recommended dishes from the plurality of recommendation modules; the transmission module (5000) is further connected with an interaction end, and the transmission module (5000) sends the recipes of the recommended dishes to the interaction end to generate the shopping list.

In other embodiments of the present invention, the list producing apparatus may further include: the list module is connected with the plurality of recommendation modules, acquires recipes of recommended dishes from the plurality of recommendation modules, and generates a shopping list according to the recipes of the recommended dishes; the transmission module is connected with the list module, and the transmission module obtains the shopping list from the list module; and the transmission module sends the shopping list to the interactive terminal.

It should be noted that, in this embodiment, the transmission module (5000) is further adapted to send the shopping list to a home terminal. Specifically, the transmission module (5000) is connected with an interactive terminal, and the transmission module (5000) obtains the shopping list from the interactive terminal; the transmission module (5000) is connected with the home terminal, and the transmission module (5000) sends the shopping list to the home terminal.

It should be further noted that, as shown in fig. 10, in this embodiment, the list generating apparatus has a plurality of recommending modules that operate sequentially, and each recommending module includes a plurality of recommending units that operate sequentially;

the recommendation unit further includes: a complementary connector (60), the complementary connector (60) generating a complementary guidance question after the recommender (30) of the recommending unit obtains the recommended dish; the supplementary connector (60) also obtains a supplementary guide question answer input by a user, and starts a next running recommending unit when the supplementary guide question answer is affirmative; and when the answer of the supplementary guide question input by the user is negative, starting a next running recommending module.

Specifically, when the recommender (30) obtains the recommended dish, the replenishment connector (60) receives a replenishment start instruction. In this embodiment, the recommender (30) is further adapted to generate a supplemental start instruction when the recommended dish is obtained; the complementary connector (60) is connected with the recommender (30), and the complementary connector (60) generates a complementary guide question when receiving the complementary start instruction.

The supplementary connector (60) also obtains a supplementary guide question answer input by a user; the supplement connector (60) is connected with a recommending unit of the next operation, and when the answer of the supplement guide question is affirmative, a starting instruction is sent to the recommending unit of the next operation; the supplementary connector (60) is connected with the next running recommending module, and when the answer of the supplementary guide question input by the user is negative, the starting instruction is sent to the next running recommending module.

Specifically, the complementary connector (60) is adapted to generate a non-question, and thus the complementary guide question is a non-question, such as a question of "is not to prepare other staple/non-staple food"; when the obtained supplementary guidance question is answered in the affirmative, for example, the supplementary guidance question is answered in the affirmative, another recommended question-answering process is started; and if the answer of the supplementary guide question is negative, for example, if the supplementary guide question is 'no', starting a next recommending module.

With reference to fig. 10 and fig. 11 in combination, in the present embodiment, the list generating apparatus has the main menu recommending module (3000) and the replenishment recommending module (4000), wherein the main menu recommending module (3000) includes a date recommending unit (3100), a history recommending unit (3200), a popular recommending unit (3300), and an inventory recommending unit (3400) which are operated in sequence.

Taking a date recommending unit (3100) in the main menu recommending module (3000) as an example for explanation: after the generator obtains the recommended dish, a supplement connector (60) of the date recommendation unit (3100) generates a supplement guide question which is a non-question, for example, a question such as "whether to prepare other staple food"; when the obtained supplementary guide question is answered in the affirmative, the supplementary connector (60) transmits an activation instruction to the history recommendation unit (3200) to activate the history recommendation unit (3200); when the obtained answer to the supplementary guidance question is negative, the supplementary connector (60) sends an activation instruction to the supplementary recommendation module (2000) to activate the supplementary recommendation module (2000).

The following describes specific functional blocks of the main menu recommendation module (3000) and the supplementary recommendation module (4000) in the embodiment of the list generation apparatus shown in fig. 10 in detail with reference to the accompanying drawings.

Referring to fig. 12, a detailed functional block diagram of the main menu recommending module (3000) in the embodiment of the list generating apparatus shown in fig. 10 is shown.

The date recommendation unit (3100) includes:

the date collector (3110) is used for obtaining festival data, and the festival data comprises festivals in a festival reminding time period preset after the current date and festival dishes of the festivals; a date guide (3120), the date guide (3120) generating the guide question based on the holiday data, the guide question including holiday dishes; a date recommender (3130), the date recommender (3130) obtaining the recommended meal from the holiday meal based on a leading question answer input by a user.

The date collector (3110) is internally pre-stored with a festival database, which comprises festival data of different regions of the world, different nationalities and corresponding festival data, so as to ensure that each seven days has a festival, namely, the interval between two adjacent festival is less than seven days.

The date guider (3120) is connected with the date collector (3110), and obtains the festival data from the date collector (3110); the date guider (3120) also generates the guidance question according to the holiday data, the guidance question being a holiday guidance question.

The date guide (3120) generates a non-question, i.e., the holiday guide question is a non-question, e.g., "XXX section is up, do not have to be prepared? "and the like, wherein XXX is the name of the festival in the festival information.

A date recommender (3130) connected to the date guide (3120) for obtaining the holiday guide question from the date guide (3120); the date recommender (3130) is further adapted to obtain answers to the holiday guide questions input by the user, and obtain the recommended dishes based on the holiday guide questions and the answers to the holiday guide questions.

Since the holiday guide question is a non-question, when a positive answer to the holiday guide question is obtained, for example, a sentence such as "prepare holiday" is given as the answer to the holiday guide question, the date recommender (3130) judges that the user has prepared a holiday, that is, when the answer to the holiday guide question is positive, the date recommender (3130) obtains the recommended dish from the holiday guide question and the answer to the holiday guide question.

When the number of the festival dishes is 1, the date recommender (3130) takes the festival dish as the recommended dish; when the number of the holiday dishes is multiple, the date recommender (3130) generates a holiday selection question according to the holiday dishes, wherein the holiday selection question comprises the holiday dishes; the date recommender (3130) also receives a festival selection question answer input by a user and obtains the recommended dishes based on the festival selection question and the festival selection question answer.

Specifically, the date recommender (3130) generates a selection question, and therefore the holiday selection question is a selection question, and the date recommender (3130) takes a holiday dish corresponding to the answer to the holiday selection question as the recommended dish.

As shown in fig. 12, the date recommendation unit (3100) further includes: a date replenishment connector (3160), the date replenishment connector (3160) being connected to the date recommender (3130), the date replenishment connector (3160) generating a replenishment guidance question after the date recommender (3130) obtains the recommended dish; the date replenishment connector (3160) also receives a replenishment guidance question answer, and activates the history recommendation unit (3200) or the replenishment recommendation module (2000) based on the replenishment guidance question answer.

Specifically, the date replenishment connector (3160) generates a non-question sentence, and therefore, the replenishment guidance question is answered with the non-question sentence, such as a question sentence of "whether or not to prepare another staple food". -activating said history recommendation unit (3200) when said date replenishment connector (3160) receives a positive replenishment guidance question answer, for example a yes replenishment guidance question answer; the supplementary recommendation module (2000) is activated when a negative answer to the supplementary guidance question is obtained, e.g. the obtained answer to the supplementary guidance question is "no".

Further, the date recommendation unit (3100) further includes: a date generator (3140), the date generator (3140) obtaining a recipe of the recommended dish from the recommended dish and the recipe database to generate the shopping list.

The date generator (3140) is connected to the date recommender (3130) and obtains the recommended dish from the date recommender (3130); the date generator (3140) queries the recipe database based on the recommended dish, obtains a recipe of the recommended dish, and generates the shopping list.

It should be noted that the date recommender (3130) is further connected to the history recommendation unit (3200), and when a negative answer to the holiday guidance question is obtained, for example, the answer to the holiday guidance question is "no holiday preparation", the date recommender (3130) determines that the user is not ready to use holidays, and activates the history recommendation unit (3200).

With continued reference to fig. 12, the history recommendation unit (3200) includes:

the history collector (3210) obtains personal diet data, which includes historical dishes stored by the user within a preset personal statistic time period before a current date and an eating date of each historical dish; a history guider (3220), wherein the history guider (3220) obtains history alternative dishes according to the difference value between the eating date and the current date; the history director (3220) further generates the directing question from the history alternative dish, the directing question including the history alternative dish; a history recommender (3230), the history recommender (3230) obtaining the recommended dish from the history alternative dishes based on a guiding question answer input by a user.

The personal diet data collected by the history collector (3210) is diet data of the user in 3 weeks, namely the length of the personal statistic time period is 3 weeks, and the personal diet data comprises historical dishes stored by the user in 3 weeks before the current date and the eating date of each historical dish.

The history guider (3220) is connected with the history collector (3210), and the personal diet data is obtained from the history collector (3210); the history director (3220) further obtains history alternative dishes according to the personal diet data; the history conductor (3220) generates the guiding question based on the history alternative dish, the guiding question being a history guiding question including the history alternative dish. Specifically, the history guide problem is suitable for judging whether the user eats the history alternative dishes.

In this embodiment, the alternative dish is a dish with the longest time interval in the personal diet data. Specifically, the history director (3220) takes the history dish corresponding to the largest difference between the eating date and the current date as the history alternative dish.

The history guide element generates a non-question, so the history guide question is a non-question, e.g. "has not eaten YYY for XX days, do not take one's consideration? "the question of waiting, wherein YYY is the name of the dish of the historical alternative dish, and XX is the interval duration between the eating date and the current date of the historical alternative dish.

The history recommender (3230) is connected with the history director (3220) and obtains the history guiding question from the history director (3220); the history recommender (3230) is further adapted to obtain historical lead question answers input by the user; the history recommender (3230) obtains the recommended dishes according to the history guide question and the history guide question answer.

Since the history guide question is a non-question sentence, a positive history guide question answer is obtained, for example, when the history guide question answer is a good sentence, the history recommender (3230) judges that the user consumes a history dish, the history recommender (3230) obtains the dish to be recommended according to the history alternative dish, namely when the history guide question answer is positive, the history recommender (3230) takes the history alternative dish to be recommended.

It should be noted that, the history recommender (3230) is further connected to the popularity recommendation unit (3300), and when a negative history guide question answer is obtained, for example, the history guide question answer is "no good", the history recommender (3230) determines that the user does not eat the history dishes, and then starts the popularity recommendation unit (3300).

The history recommendation unit (3200) further comprises: a history generator (3240), the history generator (3240) obtaining recipes for the recommended dishes from the recommended dishes and the recipe database to generate the shopping list.

The history generator (3240) is connected with the history recommender (3230) and obtains the recommended dishes from the history recommender (3230); the history generator (3240) queries the recipe database based on the recommended dishes, obtains recipes for the recommended dishes to generate the shopping list.

As shown in fig. 12, the history recommendation unit (3200) further includes: a history replenishment connector (3260), the history replenishment connector (3260) generating a replenishment guidance question after the history recommender (3230) obtains the recommended dish; the historical replenishment connector (3260) also receives a replenishment guidance question answer and activates the popular recommendation unit (3300) or activates the replenishment recommendation module (2000) based on the replenishment guidance question answer.

With continued reference to fig. 12, the popularity recommendation unit (3300) includes:

a popularity collector (3310), the popularity collector (3310) obtaining popular diet data, the popular diet data comprising: mass dishes stored by all users in a preset mass counting time period before the current date and the eating times of each mass dish; a popularity director (3320), said popularity director (3320) obtaining popular alternatives based on said number of meals; the popularity director (3320) further generates the lead question from the popular alternative dish, the lead question including the popular alternative dish; a popularity recommender (3330), the popularity recommender (3330) obtaining the recommended dishes from the popular alternative dishes based on answers to guide questions input by a user.

The popular collector (3310) collects popular diet data within 1 day, namely the popular diet data is popular diet data within 1 day, the length of the popular statistical time period is 1 day, and the popular diet data comprises popular dishes stored by all users within 1 day before the current date and the eating times of each popular dish.

The popularity director (3320) is coupled to the popularity gatherer (3310) from which the popular diet data is obtained; the popularity guider is also used for obtaining the popular alternative dishes according to the popular diet data; the popularity director generates the guide question based on the popular alternative dish, the guide question being a popularity guide question that includes the historical alternative dish. Specifically, the popularity guide question is suitable for judging whether the user eats popular dishes.

In this embodiment, the popular alternative dish is a dish with the highest frequency of occurrence in the popular diet data. Specifically, the popular guider (3320) takes the popular dish with the largest eating frequency as the popular alternative dish.

The popularity director (3320) generates a non-question, so the popularity directing question is whether a file, such as "recent YYY is popular, do not consider? "isoquer, where YYY is the name of the alternative dish.

The popularity recommender (3330) is connected to the popularity director (3320) to obtain the popularity directing problem from the popularity director (3320); the popularity recommender (3330) is further adapted to obtain a popularity guide question answer input by the user; the popularity recommender (3330) obtains the recommended dishes according to the popularity guide question and the popularity guide question answer.

Since the popularity guide question is a non-question, when a positive answer to the popularity guide question is obtained, for example, when the obtained answer to the popularity guide question is "good", the popularity recommender (3330) judges that the user consumes popular dishes, the popularity recommender (3330) obtains the popular dish candidates as the recommended dish, that is, when the answer to the popularity guide question is positive, the popularity recommender (3330) uses the popular dish candidates as the recommended dish.

It should be noted that the popularity recommender (3330) is further connected to the stock recommendation unit (3400), and when a negative answer to the popularity guide question is obtained, for example, the answer to the holiday guide question is "no preparation holiday", the popularity recommender (3330) determines that the user does not eat popular dishes, and activates the stock recommendation unit (3400).

The popular recommendation unit (3300) further includes: a popularity generator (3340), the popularity generator (3340) obtaining a recipe for the recommended dish from the recommended dish and the recipe database to generate the shopping list.

The popularity generator (3340) is connected with the popularity recommender (3330) and obtains the recommended dishes from the popularity recommender (3330); the popularity generator (3340) queries the recipe database based on the recommended dishes, obtains recipes for the recommended dishes to generate the shopping list.

As shown in fig. 12, the popularity recommendation unit (3300) further includes: a popular replenishment connector (3360), the popular replenishment connector (3360) generating a replenishment guidance question after the popular recommender (3330) obtains the recommended dish; the popular replenishment connector (3360) also receives a replenishment guidance question answer and activates the inventory recommendation unit (3400) or activates the replenishment recommendation module (2000) based on the replenishment guidance question answer.

With continued reference to FIG. 12, the inventory recommendation unit (3400) includes:

an inventory collector (3410), the inventory collector (3410) obtaining inventory food material data, the inventory food material data including one or more inventory food materials; an inventory director (3420), said inventory director (3420) generating said guidance question from a recipe database and said inventory food material data, said guidance question comprising: a plurality of inventory alternative dishes, a recipe of the inventory alternative dishes comprising at least one of the inventory food materials; an inventory recommender (3430), said inventory recommender (3430) obtaining a guided question answer input by a user, obtaining said recommended meal based on said guided question answer and in conjunction with said inventory alternative meal.

The stock food material data refers to leftovers information of the user home. Specifically, the stock collector (3410) acquires the stock food material data through a home terminal. Wherein, the house end includes the refrigerator. The method has the advantages that the stocked food material data are directly obtained from a home terminal, particularly from a refrigerator, the types and the number of the residual food materials can be timely and accurately obtained, and the data do not need to be acquired manually, so that the real-time performance and the accuracy of the obtained stocked food material data can be effectively guaranteed on the premise of not increasing the use cost of a user.

The inventory collector (3410) also obtains mass diet data, the mass diet data including: and counting the popular dishes stored by all users in a preset popular counting time period before the current date and the eating times corresponding to the popular dishes. In this embodiment, the inventory collector (3410) is connected to the popular recommendation unit (3300), and the popular diet data is obtained from the popular recommendation unit (3300).

In this embodiment, the inventory recommendation unit (3400) further includes: an inventory comparator (3411), the inventory comparator (3411) comparing a shopping list with the inventory food material data.

The inventory comparator (3411) is connected to the inventory collector (3410) and obtains the inventory material data from the inventory collector (3410); said inventory comparator (3411) further connected to an interactive end from which shopping lists are obtained; the inventory comparator (3411) compares the shopping list with the inventory food material data.

Said inventory director (3420) is coupled to said inventory collector (3410) to obtain said background data from said inventory collector (3410); the inventory conductor (3420) also generates a lead question based on the context data and the recipe database, the lead question including: a plurality of inventory alternative dishes, a recipe of the inventory alternative dishes comprising at least one of the inventory food materials;

in the present embodiment, the stock guide (3420) is connected to the stock collector (3410) through the stock comparator (3411), and therefore the stock guide (3420) obtains the guide problem based on the comparison result of the stock comparator (3411).

Specifically, the stock guide (3420) includes: an inventory use element (3421), said inventory use element (3421) generating inventory use questions based on said inventory food material data, said inventory use questions comprising at least one of said inventory food materials. In the present embodiment, the stock use element (3421) generates a stock use problem based on the comparison result of the stock comparator (3411).

The inventory use problem is suitable for judging whether the user uses the inventory food materials. Specifically, the stock use element (3421) generates a non-question, and therefore the stock use problem is a non-question, for example, "do you have XXX in home, but YY has expired, has you eat? "wait for question," where XXX is the inventory material, and YY is the expiration time corresponding to the inventory material.

The inventory use element (3421) is further adapted to obtain user-entered answers to inventory use questions and, based on the inventory use questions and the inventory use question answers, in combination with a recipe database and the inventory food material data, obtain a plurality of inventory backup recipes, a recipe of the inventory backup recipes including at least one of the inventory food materials.

In the present embodiment, since the inventory use question is a non-question, when a positive answer to the inventory use question is obtained, for example, a sentence such as "eat up" is given as the answer to the inventory use question, the inventory use element (3421) obtains the plurality of inventory back-up dishes based on the recipe database and the inventory material data.

In this embodiment, with reference to fig. 10 and 12 in combination, the main menu recommending module (3000) further includes the replenishment recommending unit (3500) to add the recommended dishes used as the main menu, and the replenishment recommending unit (3500) operates after the stock recommending unit (3400), so that the stock using element (3421) is further connected to the replenishment recommending unit (3500) to obtain a negative answer to the stock using question, for example, when the answer to the stock using question is "no use", the replenishment recommending unit (3500) is started.

It should be noted that, in other embodiments of the present invention, the inventory using component (3421) may also generate a selection question, that is, the inventory using question may also be a selection question including the inventory material, such as "do the following inventories in the home, eat up? "etc., wherein the options of the inventory use question respectively include a plurality of the inventory food materials and an omnino instruction; when the inventory use question answer is an option corresponding to an inventory food material, the inventory use element obtains the plurality of backup dishes in combination with a recipe database, the recipe of the backup dishes including the inventory food material corresponding to the inventory use question answer; activating the replenishment recommendation unit when the inventory use question answer is an all-no instruction.

The stock guide (3420) further comprises: an inventory backup element (3422) that ranks the plurality of inventory backups according to the number of servings of the popular diet data; obtaining inventory backup dishes according to the ordered plurality of inventory backup dishes; generating an inventory selection question based on the inventory alternatives.

Said stock alternative element (3422) connected to said stock use element (3421) to obtain said plurality of stock back-up items from said stock use element (3421); said inventory alternative element (3422) further connected to said inventory collector (3410) to obtain said popular diet data from said inventory collector (3410); the inventory replacement element (3422) ranks the plurality of backup dishes according to the number of times of consumption of the mass diet data, and obtains an inventory replacement dish according to the ranked plurality of inventory backup dishes; the inventory alternatives component (3422) also generates an inventory selection question based on the inventory alternatives.

Specifically, the stock replacement component (3422) sorts the stock backup dishes according to the order of the number of times of consumption from the top to the bottom, and obtains a first backup dish with the largest number of times of consumption, a second backup dish with the second largest number of times of consumption, a third backup dish with the third largest number of times of consumption, a fourth backup dish with the fourth largest number of times of consumption, and one or more random backup dishes with the number of times of consumption less than that of the fourth backup dish, wherein the random backup dish is different from the first backup dish, the second backup dish, the third backup dish, and the fourth backup dish.

Wherein the inventory of alternative dishes comprises: the step of obtaining the stock of the first batch of alternative dishes and the second batch of alternative dishes further comprises: obtaining a first batch of alternative dishes, wherein the first batch of alternative dishes at least comprise a first backup dish and a second backup dish; obtaining a second batch of alternative dishes, the second batch of alternative dishes comprising at least the third backup dish and the fourth backup dish.

Therefore, the inventory alternatives component (3422) generates a first inventory selection question based on the first lot of alternative dishes; the inventory alternatives component (3422) also generates a second inventory selection question based on the second batch of alternative dishes.

It should be noted that, in the present embodiment, in order to avoid repeated questioning, after the first inventory selection question answer is obtained, the inventory alternative element (3422) generates a second inventory selection question based on the first inventory selection question answer.

Specifically, the inventory candidate element (3422) generates a selection question, so the first inventory selection question is a selection question, e.g., "are there any more than five dishes? "etc., the options of the first inventory selection question are the first batch of alternative dishes respectively, and the options of the first inventory selection question further comprise: a total negative instruction is suitable for judging whether the first batch of alternative dishes have the dishes favored by the user; the second inventory selection question is a selection question, e.g., "is there a suitable one of the five dishes? "wait for question, the options of the second inventory selection question being the second batch of alternative dishes, respectively; further, the options for the second inventory selection issue further include: and judging whether the second batch of alternative dishes have the dishes desired by the user or not by the total-negative instruction.

The inventory recommender (3430) selects answers to the questions based on inventory input by the user and obtains the recommended dish in conjunction with the inventory alternatives.

Specifically, the inventory recommender (3430) is connected to the inventory guide (3420) to obtain the inventory selection question answer from the inventory guide (3420); the inventory recommender (3430) also obtains the inventory selection question answer; the inventory recommender (3430) obtains the recommended dish based on the inventory selection question answer and the inventory selection question.

In this embodiment, the inventory recommender (3430) is adapted to obtain a first inventory selection question answer entered by the user and a second inventory selection question answer entered by the user.

When the answer to the first inventory selection question is a choice corresponding to the first batch of alternative dishes, the inventory recommender (3430) obtains the recommended dish according to the inventory alternative dishes, namely, the inventory recommender (3430) takes the first batch of alternative dishes corresponding to the answer to the first inventory selection question as the recommended dish; when the answer to the first inventory selection question is the no-all instruction, the inventory recommender (3430) determines that there is no user intended dish in the first lot of alternative dishes, the inventory recommender (3430) triggers an inventory alternative element (3422) to generate the second inventory selection question.

When the answer to the second inventory selection question is the option corresponding to the second batch of alternative dishes, the inventory recommender (3430) obtains the recommended dish according to the inventory alternative dishes, namely, the inventory recommender (3430) takes the second batch of alternative dishes corresponding to the answer to the second inventory selection question as the recommended dish; when the answer to the second inventory selection question is the full-no instruction, the inventory recommender (3430) judges that there is no dish intended by the user in the second batch of alternative dishes, and then starts a replenishment recommending unit (3500).

Further, the inventory recommendation unit (3400) further includes: an inventory generator 3440 connected to said inventory recommender (3430) for obtaining said recommended dish from said inventory recommender (3430); the inventory generator 3440 obtains recipes for the recommended dishes from the recipe database based on the recommended dishes to generate the shopping list.

With reference to fig. 10 and 12 in combination, the inventory recommendation unit (3400) also includes: an inventory replenishment connector (3460) connected to said inventory recommender (3430), said inventory replenishment connector (3460) generating a replenishment guidance question after said inventory recommender (3430) obtains said recommended dish; the inventory replenishment connector (3460) also receives a replenishment guidance question answer, and activates a replenishment recommendation unit (3500) or activates a replenishment recommendation module (4000) based on the replenishment guidance question answer.

In this embodiment, the list generating apparatus further includes: a supplementary recommending unit (not shown in the figure), which obtains a plurality of supplementary alternative dishes according to the food spectrum database, wherein the supplementary alternative dishes are unrepeated dishes randomly generated in the food spectrum database; the supplementary recommending unit generates a supplementary recommending problem according to the supplementary alternative dishes, wherein the supplementary recommending problem comprises the supplementary alternative dishes; and the supplementary recommending unit obtains the recommended dishes based on supplementary recommending question answers input by the user.

The supplementary recommendation unit generates a selection question, so that the supplementary recommendation problem is the selection question, and the options of the supplementary recommendation problem are the supplementary alternative dishes; in addition, the option of supplementing the recommendation question further includes: and a total negative instruction, wherein the total negative instruction is suitable for judging whether the plurality of supplementary alternative dishes have the dishes which are favorite by the user.

When the supplementary alternative dish is obtained as the supplementary recommended question answer input by the user, the supplementary recommending unit obtains the recommended dish according to the supplementary alternative dish, namely the supplementary recommending unit takes the supplementary alternative dish corresponding to the supplementary recommended question answer as the recommended dish; and when the answer of the supplementary recommendation question input by the user is a total-negative instruction, the supplementary recommendation unit judges that no dish intended by the user exists in the supplementary alternative dishes, and then starts a supplementary recommendation module (4000).

In addition, after the recommended dish is obtained, the supplementary recommending unit also generates a supplementary guidance question; and starting the supplementary recommending unit again or starting the next recommending module based on the supplementary guide question answer input by the user.

The supplementary recommendation unit also generates a non-question sentence, and thus the supplementary guidance question is answered with a non-question sentence, such as a question sentence of "whether to prepare other staple food". Therefore, when a positive answer to the supplementary guidance question is obtained, for example, when the answer to the supplementary guidance question is yes, the supplementary recommending unit obtains a plurality of supplementary alternative dishes again; and if the answer of the supplementary guide question is negative, for example, the answer of the supplementary guide question is 'no', starting the supplementary recommendation module (4000).

With continuing reference to fig. 10, in this embodiment, the list generating apparatus further includes: the supplementary recommending module (4000) is used for obtaining a recommended dish serving as a side dish. Specifically, the recommended dishes obtained by the supplementary recommending module (4000) are auxiliary meals such as a front dish in a western-style meal, salad or soup, or vegetable dishes in a Chinese meal.

Referring to fig. 13, a functional block diagram of the supplemental recommendation module (4000) in the embodiment of the manifest generation apparatus shown in fig. 10 is shown.

The replenishment recommendation module (4000) includes a season recommendation unit (4100), a low-price recommendation unit (4200), a popular recommendation unit (4300), an inventory recommendation unit (4400), and a replenishment recommendation unit (4500).

The season recommendation unit (4100) includes:

a season collector (4110), the season collector (4110) obtaining season data, the season data comprising: the method comprises the steps that a seasonal cuisine food material and a preset harvesting time period of the seasonal cuisine food material are obtained, and the current date is located in the harvesting time period; an epoch director (4120), said epoch director (4120) generating said guidance questions from a cookbook database and said epoch data, said guidance questions comprising: a plurality of seasonal cuisine, wherein the recipe of the seasonal cuisine comprises at least one seasonal cuisine food material; an occasion recommender (4130), wherein when the occasion recommender (4130) obtains the answer of the guide question input by the user, the recommended dishes are obtained based on the answer of the guide question and combined with the occasion dishes.

The season collector (4110) collects food material data in the season. It should be noted that, in order to ensure the accuracy and richness of the season data, in this embodiment, the season data base is stored in advance in the season collector (4110), and the season data base includes various food materials and harvesting time periods thereof in various regions of the world.

After obtaining the season data, the season director (4120) generates a guidance question to obtain a recommended dish. Since there is often more than one kind of the season food material, in the embodiment, the season director (4120) generates the season use problem according to the season data, and the season use problem includes at least one kind of the season food material.

The season use problem is suitable for judging whether the user uses the season food material. Specifically, the season pass director (4120) generates a non-question sentence. Thus, the issue of the season use is a non-question, e.g., "is current season vegetable XXX, is purchased? "wait for question sentence, wherein XXX is season food material.

The said etiquette guide (4120) also obtains the answers to the etiquette use questions input by the user; the season guider (4120) also obtains a plurality of season back-up dishes based on the season use question answers and by combining a recipe database and the season data, wherein the recipe of the season back-up dishes comprises at least one season food material.

Since the issue of the season use is a non-question, when a positive answer to the season use issue is obtained, for example, a statement such as "buy" is given as the answer to the season use issue, the season guider (4120) obtains the plurality of season back-up dishes based on the recipe database and the season data.

In addition, in the supplementary recommendation module (4000), the low-price recommendation unit (4200) is the next recommendation unit operated after the season recommendation unit (4100); the low priced recommendation unit (4200) is activated when a negative answer to the season use question is obtained, for example, a sentence that the answer to the season use question is "not to buy", etc.

In other embodiments of the present invention, the issue of using the current season may be a question of selecting the season food material, for example, "whether to purchase current season vegetables as follows? "wait for question sentence, wherein, the options of the said time using question include a plurality of said time food material and no order respectively; the season guider is combined with a recipe database to obtain a plurality of season backup dishes when the season use question answers are options corresponding to season food materials, and the recipes of the season backup dishes comprise the season food materials corresponding to the season use question answers; the season guider starts a recommended question-answering process (2200) corresponding to a low-price factor when the season use question answer is a no-all instruction.

The season recommender (4130) is connected with the season guider (4120) to obtain the season backup dish; the season recommender (4130) generates a season selection problem according to the season backup dish, wherein the season selection problem comprises a season dish, and the season dish comprises at least part of the season backup dish; the season recommender (4130) selects answers to the questions based on the season input by the user and combines the season backup dish to obtain the recommended dish.

The season recommender (4130) randomly generates nonrepeating dishes from the season backup dishes; the season recommender (4130) generates a selection question, so the season selection question is a selection question, e.g. "is there is a suitable of these five dishes? The choices of the season selection problem are the season dish and a total negative instruction respectively, wherein the total negative instruction is used for judging whether the season dish contains a dish intended by a user.

The season recommender (4130) generates non-repetitive dishes randomly from the season backup dishes; in other embodiments of the present invention, the season recommender (4130) may also obtain the season dish by sorting the season reserve dish according to a certain rule. The invention is not limited in this regard.

When the season recommender (4130) obtains the answer of the season selection question input by the user as the season dish, the season recommender (4130) obtains the recommended dish according to the season dish, namely, the season dish corresponding to the answer of the season selection question of the season recommender (4130) is used as the recommended dish; when the answer to the current-time selection question input by the user of the current-time recommender (4130) is a total-negative instruction, the current-time recommender (4130) judges that no dishes intended by the user exist in the current-time dishes, and the current-time recommender (4130) starts the low-price recommending unit (2200).

Similar to the recommending unit in the main menu recommending module 300, the season recommending unit (4100) also comprises: an hour supplementary connector (4160) connected to the hour recommender (4130) generates a supplementary guidance question to start the low priced recommender unit (4200) or to end the operation after the hour recommender (4130) obtains the recommended dish.

Further, the season recommendation unit (4100) further includes: an occasion generator 4140, said occasion generator 4140 connected to said occasion recommender (4130), obtaining said recommended dish from said occasion recommender (4130); the season generator 4140 obtains a recipe of the recommended dish from the recipe database based on the recommended dish to generate the shopping list.

With continued reference to fig. 13, the low price recommending unit (4200) includes:

a low price collector (4210), the low price collector (4210) obtaining a target area based on the position data and a preset comparison radius; the low price collector (4210) obtains price data in the target area according to the target area, wherein the price data comprise food materials and the average selling price of the food materials in the target area in the current date and the average selling price of the food materials in the valuation time period before the current date; a low price guide (4220), the low price guide (4220) obtaining low price food material according to the price data; generating the guide question based on the low-price food materials, wherein the guide question comprises a plurality of low-price alternative dishes, and the recipe of the low-price alternative dishes comprises the low-price food materials; and the low-price recommender (4230), when obtaining the guidance question answer input by the user, the low-price recommender (4230) obtains the recommended dish based on the guidance question answer and combined with the low-price alternative dish.

The low price collector (4210) obtains the food material with the largest price reduction range in a target area in 1 week; therefore, the length of the assessment time period is 1 week, the low price collector (4210) acquires position data, and a comparison radius is prestored in the low price collector (4210); the low price collector (4210) obtains the target area according to the position data and the comparison radius; in addition, the low price collector (4210) also obtains the price data according to the target area.

In addition, the low price collector (4210) obtains a food material and a difference value between the average selling price of the food material on the current date and the average selling price of the food material in the valuation time period based on the price data; the low price collector (4210) takes the food material with the largest difference value as the low price food material.

A low price director (4220) generates a low price use question based on the low price food material, the low price use question including the low price food material.

The low-price use problem is suitable for judging whether the user uses the low-price food material. Specifically, the low-price director (4220) generates a non-question sentence, and thus the low-price use question may be a non-question sentence, such as "XXX, relatively inexpensive, purchase? "isoquiz, wherein XXX is the low-price food material.

The low-price director (4220) also obtains a low-price use question answer input by a user; the low price director (4220) obtains a plurality of backup dishes based on the low price use question answer in combination with a recipe database and the low price food material after obtaining the low price use question answer, the recipe of the backup dishes including the low price food material.

Since the low-price use question is a non-question, when a positive low-price use question answer is obtained, for example, a sentence such as "buy" is obtained, the low-price guide (4220) obtains the plurality of low-price backup dishes based on the recipe database and the low-price food material.

Further, when a negative low-priced use question answer, for example, a sentence in which the low-priced use question answer is "not to buy", is obtained, the low-priced guider (4220) activates the popular recommendation unit (4300).

The low price guider (4220) generates a low price selection problem according to the low price backup dishes, the season selection problem comprises low price alternative dishes, and the low price alternative dishes comprise at least part of the low price backup dishes; and then, selecting a question answer based on the low price input by the user, and combining the low-price backup dishes to obtain the recommended dishes.

In this embodiment, the low price director (4220) takes a nonrepeating dish randomly generated for the low price backup dish as the low price alternative dish; the low price guide (4220) generates a selection question, and therefore the low price selection question is a selection question, for example, "are there suitable in these five dishes? The options of the low-price selection question are the low-price alternative dishes and a total negative instruction respectively, wherein the total negative instruction is used for judging whether the low-price alternative dishes contain dishes intended by the user.

The low price guide (4220) selects a non-repetitive dish randomly generated from among the low price backup dishes as the low price candidate dish; in other embodiments of the present invention, the low price director (4220) may also sort the low price backup dishes, and obtain the low price alternative dishes according to the sorted low price backup dishes.

The low price recommender (4230) is adapted to obtain answers to low price selection questions input by a user, and obtain the recommended dishes according to the low price selection questions and the answers to the low price selection questions.

Specifically, when the low price recommender (4230) obtains that the answer to the low price selection question is the low price alternative dish, the low price recommender (4230) obtains the recommended dish according to the low price alternative dish, that is, the low price recommender (4230) uses the low price alternative dish corresponding to the answer to the low price selection question as the recommended dish; when the low-price recommender (4230) obtains the answer of the low-price selection question as a full-negative instruction, the low-price recommender (4230) judges that no dish intended by the user exists in the low-price alternative dishes, and the low-price recommender (4230) starts the popular recommending unit (4300).

Further, the low-price recommending unit (4200) further includes: a low price generator (4240) connected to the low price recommender (4230) to obtain the recommended dish from the low price recommender (4230); the low price generator (4240) obtains a recipe of the recommended dish from the recipe database based on the recommended dish to generate the shopping list.

Similar to the aforementioned recommending unit, the low-priced recommending unit (4200) also includes: a low price supplement connector (4260) connected with the low price recommender (4230), after the low price recommender (4230) obtains the recommended dish, generates supplement guide material to start the popular recommendation unit (4300) or end the operation.

With continued reference to fig. 10, the supplemental recommendation module (4000) further comprises: a popular recommendation unit (4300), an inventory recommendation unit (4400), and a replenishment recommendation unit (4500). In this embodiment, for specific technical solutions of the popular recommendation unit (4300), the inventory recommendation unit (4400), and the replenishment recommendation unit (4500), reference is made to technical solutions of the popular recommendation unit (3300), the inventory recommendation unit (3400), and the replenishment recommendation unit (3500) in the main menu recommendation module (3000), and details of the present invention are not repeated herein.

In addition, in this embodiment, the list generating device further includes: a search module (not shown in the figure) adapted to obtain a specified dish input by a user, and based on the specified dish, obtain a recipe of the specified dish in combination with the recipe database to generate the shopping list.

It should be further noted that, in this embodiment, the recipe database further includes: one or more feature tags corresponding to the dishes; the manifest generation apparatus further includes: a fuzzy search module (not shown in the figure), adapted to obtain the dish features input by the user, and obtain a plurality of backup dishes based on the dish features and in combination with the recipe database, wherein the feature table labels of the alternative dishes include the dish features; the fuzzy search module is also used for obtaining a plurality of alternative dishes based on the plurality of backup dishes and generating the guide problem based on the alternative dishes; and the fuzzy search module obtains the recommended dishes based on the answers of the guide questions input by the user.

Specifically, the specific technical solution of the fuzzy search module refers to the embodiment of the list generation method, and the details of the present invention are not repeated herein.

In this embodiment, the list generating apparatus obtains the shopping list by using the list generating method provided by the present invention. However, the list generating method of the present invention does not provide additional limitations to the list generating apparatus.

Correspondingly, the invention also provides a list generating system, which comprises a list generating device, wherein the list generating device is the list generating device; and the interactive end is connected with the list generating device.

The list generating device is the list generating device of the present invention, and therefore, the specific technical solution of the list generating device refers to the foregoing list generating device embodiment, and the present invention is not described herein again.

The interactive terminal is connected with the list generating device and is suitable for displaying questions and obtaining user input, so that interactive question answering with the user is realized. The interaction terminal may be an app set on the mobile phone, or an application program or a web page set on the computer.

The interactive end is connected with the list generating device through a wired or wireless network so as to realize the transmission of data and instructions.

Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A method for generating a shopping list, the method comprising: at least one recommendation phase;

the recommendation stage comprises at least one recommended question and answer process, and the recommended question and answer process corresponds to a preset recommendation factor;

the recommended question-answering process comprises the following steps:

acquiring background data corresponding to the recommendation factor, wherein the background data at least comprises one food material;

generating a guidance question based on the context data;

when a guiding question answer input by a user is obtained, obtaining a recommended dish based on the guiding question answer and in combination with the background data;

and obtaining a recipe of the recommended dish according to a recipe database and the recommended dish to generate the shopping list.

2. The manifest generation method as recited in claim 1, wherein said step of obtaining said context data comprises: obtaining inventory food material data, the inventory food material data including one or more inventory food materials;

the step of generating the guidance question comprises: generating the guide question according to a recipe database and the inventory food materials, the guide question comprising: a plurality of inventory alternative dishes, a recipe of the inventory alternative dishes comprising at least one of the inventory food materials;

and when the guiding question answer input by the user is obtained, obtaining the recommended dish based on the guiding question answer and combined with the stock alternative dish.

3. The manifest generation method of claim 2, wherein the step of obtaining the context data further comprises: acquiring popular diet data;

the step of generating the guidance question comprises: and generating the guide question according to a recipe database and the stock food material data and by combining mass diet data.

4. The manifest generation method of claim 3, wherein the mass diet data comprises: mass dishes stored by all users in a preset mass counting time period before the current date and the eating times corresponding to the mass dishes are counted;

the step of generating the guidance question comprises:

generating an inventory usage question based on the inventory food material data, the inventory usage question including at least one of the inventory food materials;

obtaining a plurality of inventory backup dishes based on the inventory use question answers in combination with a recipe database and the inventory food material data, the recipe of the inventory backup dishes comprising at least one of the inventory food materials;

sorting the plurality of stock backup dishes according to the eating times of the popular diet data;

obtaining inventory backup dishes according to the ordered plurality of inventory backup dishes;

generating an inventory selection question based on the inventory alternatives;

the step of obtaining the recommended dish comprises: selecting answers to the questions based on inventory input by the user and obtaining the recommended dish in combination with the inventory alternatives.

5. The manifest generation method of claim 1, wherein said recommendation phase comprises: a plurality of recommended question-answering processes are sequentially carried out; the recommendation factor comprises an inventory factor;

the recommended question-answering process corresponding to the inventory factor is the last recommended question-answering process.

6. The inventory generation method of claim 5, wherein the recommended question-answering process corresponding to the inventory factor further comprises:

comparing a shopping list with the inventory food material data after obtaining the inventory food material data and before generating the guide question;

and generating the guide question based on the comparison result of the shopping list and the stock food material data and in combination with a recipe database.

7. The list generation method of any one of claims 2 to 6, wherein the step of obtaining the inventory food material data comprises: and acquiring the data of the food materials in stock through a home terminal.

8. The manifest generation method as recited in claim 1, comprising: a plurality of recommendation stages performed in sequence;

the process of recommending question and answer further comprises:

generating a supplementary guidance question after obtaining the recommended dish;

starting another recommended question-answering process or starting the next recommendation phase based on the supplementary guidance question answer.

9. The manifest generation method of claim 1, wherein said recommendation phase comprises: a main menu recommendation stage and a supplementary recommendation stage;

the recommendation factors comprise main menu recommendation factors in a main menu recommendation stage and supplementary recommendation factors in a supplementary recommendation stage.

10. The list generation method of claim 9, wherein the main menu recommendation factor comprises: one or more of a date factor, a history factor, a prevalence factor, and an inventory factor;

the supplemental recommendation factor includes: one or more of a season factor, a prevalence factor, a low-price factor, and an inventory factor.

11. A list generating device adapted to generate a shopping list, the list generating device comprising: at least one recommendation module;

the recommendation module comprises at least one recommendation unit, the recommendation unit is suitable for recommending questions and answers, and the recommendation unit corresponds to a preset recommendation factor;

the recommendation unit includes:

the collector (10) acquires background data corresponding to the recommendation factor, wherein the background data at least comprises one food material;

a director (20), said director (20) generating a directing question in accordance with said background data;

a recommender (30), the recommender (30) obtaining a guide question answer input by a user and obtaining a recommended dish based on the guide question and the guide question answer;

a generator (40), the generator (40) obtaining a recipe of the recommended dish to generate the shopping list according to a recipe database and the recommended dish.

12. The manifest generation apparatus of claim 11, wherein said recommendation module comprises an inventory recommendation unit;

the inventory recommendation unit (3400) comprising:

an inventory collector (3410), the inventory collector (3410) obtaining inventory food material data, the inventory food material data including one or more inventory food materials;

an inventory director (3420), said inventory director (3420) generating said guidance question from a recipe database and said inventory food material data, said guidance question comprising: a plurality of inventory alternative dishes, a recipe of the inventory alternative dishes comprising at least one of the inventory food materials;

an inventory recommender (3430), said inventory recommender (3430) obtaining a guided question answer input by a user, obtaining said recommended meal based on said guided question answer and in conjunction with said inventory alternative meal.

13. The list generating apparatus of claim 11, wherein the inventory collector (3410) further obtains mass diet data;

the inventory guide (3420) generates the guide question based on a dietary database and the inventory food material data in combination with popular diet data.

14. The list generating apparatus of claim 13, wherein the mass diet data comprises: mass dishes stored by all users in a preset mass counting time period before the current date and the eating times corresponding to the mass dishes are counted;

the stock guide (3420) includes:

an inventory use element (3421), said inventory use element (3421) generating inventory use questions from said inventory food material data, said inventory use questions comprising at least one of said inventory food materials; said inventory use element (3421) obtaining user-entered answers to inventory use questions and, based on said inventory use questions and said inventory use question answers, in conjunction with a recipe database, obtaining a plurality of inventory back-up recipes whose recipes include at least one of said inventory food materials;

an inventory sparing element (3422), the inventory sparing element (3422) ordering the plurality of inventory spares according to the number of servings of the popular diet data; obtaining inventory backup dishes according to the ordered plurality of inventory backup dishes; the inventory alternatives component (3422) also generates an inventory selection question based on the inventory alternatives.

15. The manifest generation apparatus of claim 11, wherein said recommendation module comprises an inventory recommendation unit; the recommendation module further comprises at least one recommendation unit, the inventory recommendation unit being configured to last run.

16. The list generating apparatus of claim 11, wherein the inventory recommendation unit further comprises:

an inventory comparator (3411), the inventory comparator (3411) comparing a shopping list and the inventory food material data;

the stock guide (3420) generates the guide question based on the comparison result of the stock comparator (3411) in conjunction with a recipe database.

17. The list generating apparatus as claimed in any one of claims 11 to 16, wherein the stock collector (3410) acquires the stock material data through a home terminal.

18. The list generating apparatus according to claim 11, wherein the list generating apparatus has a plurality of recommending modules that operate in sequence, the recommending modules including a plurality of recommending units that operate in sequence;

the recommendation unit further includes: a complementary connector (60), the complementary connector (60) generating a complementary guidance question after the recommender (30) of the recommending unit obtains the recommended dish; the supplementary connector (60) also obtains a supplementary guide question answer input by a user, and starts a next running recommending unit when the supplementary guide question answer is affirmative; and starting a next running recommending module when the answer of the supplementary guide question input by the user is negative.

19. The manifest generation apparatus of claim 11, comprising: a main menu recommending module (3000) and a supplementary recommending module (4000); the recommendation factors comprise main menu recommendation factors and supplementary recommendation factors.

20. The list generating apparatus of claim 19, wherein the main menu recommending module (3000) comprises: one or more of a date recommendation unit (3100), a history recommendation unit (3200), a popular recommendation unit (3300), and an inventory recommendation unit (3400);

the supplemental recommendation module (4000) comprises: one or more of a season recommendation unit (4100), a popular recommendation unit (4300), a low-price recommendation unit (4200), and an inventory recommendation unit (4400).

21. A manifest generation system, comprising:

a list generating means as claimed in any one of claims 11 to 20;

and the interactive end is connected with the list generating device.