CN110135646B - Restaurant pre-estimation quick serving method and device and storage medium - Google Patents
Restaurant pre-estimation quick serving method and device and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device and a storage medium for restaurant to pre-estimate quick serving, which are used for acquiring historical service data of a plurality of scenes and preprocessing the service data to respectively establish scene parameter libraries of all the scenes; constructing parameter libraries of different scenes, respectively carrying out interval clustering according to the final menu serving similarity of a customer based on the parameter libraries of the different scenes, and distributing the different scenes to different interval classes; clustering time intervals in different dining time periods, extracting serving menu data in the serving menu predicted dining time period, inputting the serving menu data into a BP (back propagation) neural network for nonlinear fitting to obtain a prediction model of the serving menu predicted dining time period in the interval classes, and finishing training of the BP neural network; setting different weights for the prediction results of different scenes, and performing prediction weighting on dishes of the serving menu to obtain a final menu prediction result; therefore, the serving speed is shortened through the coordination control of the ordering link.
Description
Technical Field
The invention relates to the technical field of cooking automation, in particular to a restaurant forecast quick serving method, device and storage medium.
Background
With the continuous development of information technology, the restaurant in China starts to transform from traditional manual work to informatization, online reservation ordering gradually enters the lives of people due to the unique convenience of the online reservation ordering, and the restaurant can acquire order information of clients from a server by establishing connection between a client sending a reservation order and the server receiving the reservation order. However, the existing online meal ordering method only can meet the basic conditions of a restaurant such as menu and price viewed by a user, so that the user outside the restaurant cannot know the real-time information of the restaurant, and the meal ordering experience of the user at the restaurant is not ideal.
In the catering industry, how to recommend dishes satisfying customers to the customers who are difficult to eat is always a goal pursued by the catering industry. Because the flavor characteristics of each restaurant are different, the preferences of diners, the number of diners, the dining standards and the like are different, it is difficult to find a generally applicable method for meeting the dining requirements of each restaurant and each customer.
In the prior art, dish recommendation systems or methods are usually only a set of artificially-made rules summarized by merchants according to experience of the merchants, and the rules are mixed with excessive subjective evaluation standards, so that the dish recommendation systems or methods are usually only suitable for restaurants or specific dinning people in a specific period and in a specific taste type. For some newly-opened restaurants, these experiences are often not applicable. In addition, because the existing restaurant cannot know the personalized needs of the customers when attracting the customers, the same dish recommending is possibly a blind purpose, and due to factors such as dish quantity, price and taste, the dish recommending method is not suitable for all people having meals, so that the energy of the restaurant is consumed, and good dining experience cannot be provided for the customers having meals.
With the development of internet technology and cloud computing technology, more and more traditional experience summarized by manpower can be replaced by data mining technology. Data mining (Data mining) is a method in database knowledge discovery, and generally refers to a process of automatically searching a large amount of Data for information hidden therein and having special relationships. Data mining is generally related to computer science and achieves this through many methods such as statistics, online analytical processing, intelligence retrieval, machine learning, expert systems (relying on past rules of thumb), and pattern recognition. The data mining can also be used as a decision support process, and the data mining can be used for analyzing the data of the enterprise in a highly automatic manner, making inductive reasoning, mining out potential patterns from the data, helping a decision maker to adjust market strategies, reducing risks and making correct decisions based on artificial intelligence, machine learning, pattern recognition, statistics, databases, visualization technologies and the like. Therefore, a dish recommendation system and method capable of utilizing a data mining technology are needed, which can effectively solve the problem of dish recommendation blindness in the prior art, so as to improve the dining experience of diners.
Disclosure of Invention
The present invention is directed to at least solving the problems of the prior art. Therefore, the invention discloses a method for a restaurant to pre-estimate and quickly serve dishes, which comprises the steps of obtaining historical service data of a plurality of scenes, and preprocessing the service data to respectively establish scene parameter libraries of all the scenes; the scene comprises the following steps: a restaurant entrance scene, a customer ordering scene and a kitchen preparation scene; constructing parameter libraries of different scenes, respectively carrying out interval clustering according to the final menu serving similarity of a customer based on the parameter libraries of the different scenes, and distributing the different scenes to different interval classes; dividing historical service data into a plurality of dining time periods, acquiring menu conditions of interval classes in different dining time periods in a historical period from records of a final serving menu of a customer, and clustering time intervals in different dining time periods so as to cluster a plurality of basic time periods to different order prediction reference time periods; extracting serving menu data in the serving menu predicted dining time period, inputting the serving menu data into a BP (back propagation) neural network for nonlinear fitting so as to obtain a prediction model of the serving menu predicted dining time period in the interval class, and finishing training of the BP neural network; setting different weights for the prediction results of different scenes, and performing prediction weighting on dishes of the serving menu to obtain a final menu prediction result; therefore, the serving speed is shortened through the coordination control of the ordering link.
Further, the customer enters a restaurant scene, obtains the number of diners, the sex, the age and the body type of the customer through an image capturing technology, and obtains a preliminary prediction menu through a BP neural network based on the parameters.
Further, as the kitchen preparation scenario further includes: the storage amount of food materials and the cooking time.
Furthermore, the customer ordering scene comprises dishes ordered by the customer and dishes recommended by service personnel.
Further, the scene parameters comprise the number of dinning people, the sex, the age, the body type, the dinning time and the personal taste requirement of dinning customers; the service data includes: the number of service personnel, the single service time, the type of dishes, the waiting time for serving, the service personnel's recommended menu and the final serving menu.
Still further, cosine similarity is used to define whether all dining customers have similar background attributes between them:
wherein, S' (u, v) ∈ (-1,1), Au,AvIndicating the attributes that customer u and customer v have, since the customer has a plurality of attributes, it is necessary to calculate the similarity of all the attributes,if the customer has m attributes, it can be expressed as:
wherein, au,i,av,iThe ith attribute of customer u and customer v is represented, because the cosine similarity S' (u, v) belongs to (-1,1), and needs to be normalized, then,
therefore, similar dish recommendation is performed on customers with high similarity.
Furthermore, the service personnel is preferentially informed of the prediction menu obtained when the customer enters the restaurant scene through the real-time communication device, and the recommendation menu is dynamically modified based on the prediction result of other scenes while the customer orders the meal.
Further, facial feature records are made for customers with specific individual taste requirements.
The invention further discloses an electronic device, which is characterized by comprising:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the executable instructions to perform the above-described method for restaurant pre-forecast quick serving via execution.
The invention further discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for pre-estimating quick serving for a restaurant as described above.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a flow chart of a method for a restaurant to pre-estimate quick serving according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. It should be noted that the detailed description set forth in connection with the appended drawings is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The apparatus embodiments and method embodiments described herein are described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, units, components, circuits, steps, processes, algorithms, etc. (collectively referred to as "elements"). These elements may be implemented using electronic hardware, computer software, or any combination thereof. Whether such elements are implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. The terms first, second, etc. in the description and claims of the present invention and in the drawings of the specification, if used in describing various aspects, are used for distinguishing between different objects and not for describing a particular order.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
As shown in fig. 1, a method for a restaurant to pre-estimate a quick serving, which obtains historical service data of a plurality of scenes, and preprocesses the service data to establish scene parameter libraries of all the scenes respectively; the scene comprises the following steps: a restaurant entrance scene, a customer ordering scene and a kitchen preparation scene; constructing parameter libraries of different scenes, respectively carrying out interval clustering according to the final menu serving similarity of a customer based on the parameter libraries of the different scenes, and distributing the different scenes to different interval classes; dividing historical service data into a plurality of dining time periods, acquiring menu conditions of interval classes in different dining time periods in a historical period from records of a final serving menu of a customer, and clustering time intervals in different dining time periods so as to cluster a plurality of basic time periods to different order prediction reference time periods; extracting serving menu data in the serving menu predicted dining time period, inputting the serving menu data into a BP (back propagation) neural network for nonlinear fitting so as to obtain a prediction model of the serving menu predicted dining time period in the interval class, and finishing training of the BP neural network; setting different weights for the prediction results of different scenes, and performing prediction weighting on dishes of the serving menu to obtain a final menu prediction result; therefore, the serving speed is shortened through the coordination control of the ordering link.
Further, the customer enters a restaurant scene, obtains the number of diners, the sex, the age and the body type of the customer through an image capturing technology, and obtains a preliminary prediction menu through a BP neural network based on the parameters.
Further, as the kitchen preparation scenario further includes: the storage amount of food materials and the cooking time.
Furthermore, the customer ordering scene comprises dishes ordered by the customer and dishes recommended by service personnel.
Further, the scene parameters comprise the number of dinning people, the sex, the age, the body type, the dinning time and the personal taste requirement of dinning customers; the service data includes: the number of service personnel, the single service time, the type of dishes, the waiting time for serving, the service personnel's recommended menu and the final serving menu.
Still further, cosine similarity is used to define whether all dining customers have similar background attributes between them:
wherein, S' (u, v) ∈ (-1,1), Au,AvThe attributes that customer u and customer v have may be expressed as:
wherein, au,i,av,iThe ith attribute of customer u and customer v is represented, because the cosine similarity S' (u, v) belongs to (-1,1), and needs to be normalized, then,
therefore, similar dish recommendation is performed on customers with high similarity.
Furthermore, the service personnel is preferentially informed of the prediction menu obtained when the customer enters the restaurant scene through the real-time communication device, and the recommendation menu is dynamically modified based on the prediction result of other scenes while the customer orders the meal.
Further, facial feature records are made for customers with specific individual taste requirements.
The invention further discloses an electronic device, which is characterized by comprising:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the executable instructions to perform the above-described method for restaurant pre-forecast quick serving via execution.
The invention further discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for pre-estimating quick serving for a restaurant as described above.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Although the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the invention. It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention. The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (5)
1. A restaurant forecast rapid serving method is characterized in that historical service data of a plurality of scenes are obtained and preprocessed to respectively establish scene parameter libraries of all the scenes; the scene comprises the following steps: a restaurant entrance scene, a customer ordering scene and a kitchen preparation scene; constructing parameter libraries of different scenes, respectively carrying out interval clustering according to the final menu serving similarity of a customer based on the parameter libraries of the different scenes, and distributing the different scenes to different interval classes; dividing historical service data into a plurality of dining time periods, acquiring menu conditions of interval classes in different dining time periods in a historical period from records of a final serving menu of a customer, and clustering time intervals in different dining time periods;
the method comprises the following steps that a customer enters a restaurant scene, and the number of diners, the sex, the age and the body type of the customer are obtained through an image capturing technology;
the cook preparation scenario further comprises: the storage amount of food materials and the cooking time are long;
the customer ordering scene comprises dishes ordered by the customer and dishes recommended by service personnel;
the scene parameters comprise the number of dinning people, the sex, the age, the body type and the dinning time of the dinning customers and the personal taste requirements of the dinning customers; the service data includes: the service staff number, the single service duration, the dish type, the serving waiting duration, the service staff recommendation menu and the final serving menu;
and the service personnel is preferentially informed of the prediction menu obtained when the customer enters the restaurant scene through the real-time communication device, and the recommendation menu is dynamically corrected based on the prediction results of other scenes while the customer orders the food.
2. The method of claim 1, wherein cosine similarity is used to define whether all dining customers have similar background attributes:
wherein, S' (u, v) ∈ (-1,1), Au,AvThe attributes that customer u and customer v have may be expressed as:
wherein, au,i,av,iThe ith attribute of customer u and customer v is represented, because the cosine similarity S' (u, v) belongs to (-1,1), and needs to be normalized, then,
therefore, similar dish recommendation is performed on customers with high similarity.
3. The method of claim 2, wherein the customer's facial characteristics are recorded for customers having specific personal taste requirements.
4. An electronic device, comprising:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of restaurant forecast quick serve of any of claims 1-3 via execution of the executable instructions.
5. A computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method for pre-estimated quick serving by a restaurant of any of claims 1-3.
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| CN111428944B (en) * | 2020-04-26 | 2021-12-10 | 乐清市泰博恒电子科技有限公司 | Catering industry management system and method based on big data |
| CN113344666A (en) * | 2021-06-02 | 2021-09-03 | 易食便当香港有限公司 | Method, device and system for generating menu |
| CN113592183B (en) * | 2021-08-05 | 2022-04-19 | 杭州企智互联科技有限公司 | Dining peak prediction method and device |
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| CN109102860A (en) * | 2018-08-02 | 2018-12-28 | 青岛大学 | A kind of Shandong cuisine nutrition formula method of ordering based on wechat platform |
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