Big Data Platform Analysis System Based on Dining Service Robot and Its Control Method

Abstract

The present invention relates to a big data platform analysis system based on a dining service robot and a control method thereof, including: a catering background module connected to a merchant processing module, an ordering data module, a dialogue data module, a filtering module, a screening module, an extraction module, a comprehensive analysis module, a dish adjustment module, and a speech art adjustment module connected to the catering background module; The advantages of the present invention are: Ordering data is generated through ordering by an intelligent ordering robot, and a large amount of communication information will also be generated when customers interact with the intelligent ordering robot through voice. Through the analysis of this information, it will have a certain effect on the operation of the restaurant in reverse.

Description

Technical Field

[0001] The present invention relates to the technical field of catering robots, and particularly relates to a big data platform analysis system for a dining service robot based on a restaurant table and a control method thereof. Background Art

[0002] With the progress and development of technology, robots are used more and more widely. The existing robot technology is already very developed and highly intelligent. Various robots with advanced technologies play important roles in our life and work.

[0003] Intelligent ordering robots are one of many types of robots. In recent years, intelligent ordering robots have gradually come into everyone's view. Many restaurants introduce intelligent ordering robots, which can reflect the intelligent characteristics of the restaurant, save manpower and facilitate management. In the prior art, the ordering robots placed on the table only have the function of ordering dishes, and the functions are relatively single. Based on the above problems, our company developed a robot for dining service that can be completed on the restaurant table. The Chinese patent publication number is 114997857A, which can realize intelligent ordering recommendations on the table. However, the above intelligent ordering recommendation method cannot perform comprehensive analysis on multiple dimensions on the big data platform, and there is an urgent need for a big data analysis system that can realize intelligent ordering. Summary of the Invention

[0004] In view of the above problems, the purpose of the present invention is to provide a big data platform analysis system for a dining service robot and a control method thereof, which are used to help restaurants timely adjust the dishes provided by them to meet the needs of customers, so as to overcome the deficiencies of the above prior art.

[0005] A control method for a big data platform analysis system for a dining service robot provided by the present invention specifically includes the following steps:

[0006] Step S1: Use the catering background module connected to the merchant processing module to comprehensively analyze the data of customer orders for dishes, ingredients, flavors, and cooking methods, so as to understand the customers' understanding of the flavors, ingredients, and cooking methods of the dishes provided by the restaurant. Among them, the specific data is the dish information of customer orders and the flavors and cooking methods described in the remarks.

[0007] Step S2: Use the order data module connected to the catering background module to extract the data of customer orders and merchant dish information in the catering background module. The order data includes the dish information of customer orders and the flavors and cooking methods described in the remarks, as well as the default flavors and cooking methods of merchant dish information.

[0008] Step S3: Use the dialogue data module in conjunction with the filtering module, screening module, and extraction module to filter the voice interaction data of the customer in the catering background module, extract the question dialogue information related to the business scope, features, and dishes of the restaurant, and conduct a comprehensive analysis. Among them, moderate adjustment suggestions are put forward for different dishes in combination with the season, weather changes, and the business scope and features of the restaurant, and the attention of customers to the special dishes and recommended dishes of the restaurant, as well as the taste and cooking methods of the overall dishes of the restaurant, are timely understood;

[0009] Step S4: Use the results of the comprehensive analysis of the dish type, ingredients, taste, and cooking method to adjust the taste, ingredients, and cooking method of the dishes in the dish adjustment module to meet the needs of customers;

[0010] Step S5: Use the dialogue script adjustment module to analyze the interactive dialogue data according to the results of the comprehensive analysis module, and then adjust the answer dialogue script and display pictures of the features and recommended questions that the restaurant cares about for customers, so that customers can understand the features and recommended information of the restaurant faster and better;

[0011] Step S6: Transmit the adjustment results of the dialogue script adjustment module to the merchant processing module and the kitchen processing module for synchronous adjustment.

[0012] As a preference of the present invention, it further includes Step S7: Dining scene selection;

[0013] Step S71: Obtain the diner information through the head camera and perform analysis. Among them, the analysis data is one or more of the number of people, gender, and age group information;

[0014] Step S72: Send the analysis data of Step S71 to the server. The server combines the business features of the store with the specially recommended dishes reserved by the store to form a preliminary ordering menu. Among them, the business features are cuisine type, hot pot, and barbecue; the recommended dishes include seasonal recommendations.

[0015] Step S73: According to the preliminary recommended menu formed in Step S72, guide the customer to order and record the customer's preferences and tastes for the dishes ordered by the customer. Among them, the ordering is the recommended specialties of the store, today's recommendations, and seasonal recommendations, and the tastes are slightly spicy and slightly light;

[0016] Step S74: According to the customer's ordered dish categories and quantities, and in combination with the diner information obtained in Step S71, put forward suggestions for the dishes ordered by the customer. Among them, if there are more meat dishes, it is recommended to assist with green vegetables, and if there are more ladies and children, desserts are recommended.

[0017] Another object of the present invention is to provide a big data platform analysis system based on a dining service robot, including: a catering background module connected to a merchant processing module, an order-taking data module, a dialogue data module, a filtering module, a screening module, an extraction module, a comprehensive analysis module, a dish adjustment module, and a speech adjustment module connected to the catering background module;

[0018] The catering background module is used to extract data information stored in the client module, the merchant processing module, the robot processing module, and the kitchen processing module;

[0019] The order-taking data module is used to store customer order-taking data and merchant dish information, where the order-taking data includes the dish information ordered by the customer and the taste and cooking method described in the remarks, as well as the default taste and cooking method of the merchant dish information;

[0020] The dialogue data module is used to collect data on the voice interaction between the customer and the robot processing module, and the collection stage is divided into the order-taking time period, the dining time period, and the post-meal time period;

[0021] The filtering module is used to filter out unnecessary information;

[0022] The screening module is used to refine keywords prepared by the merchant, where the keywords are problem dialogue information related to the business scope, features, and dishes of the restaurant;

[0023] The extraction module is used to extract necessary information, and the extracted information is classified;

[0024] The comprehensive analysis module is used to comprehensively analyze the data of the order-taking data module and the extraction module. Among them, moderate adjustment suggestions are put forward for different dishes in combination with the season, weather changes, and the business scope and features of the restaurant;

[0025] The dish adjustment module is used to adjust the taste, ingredients, and cooking method of the dish according to the adjustment suggestions to meet the needs of customers;

[0026] The speech adjustment module is used to analyze the interactive dialogue data according to the results analyzed by the comprehensive analysis module, and then display the features, recommended answers to questions that customers care about, and display pictures of the restaurant, so that customers can understand the features and recommended information of the restaurant faster and better.

[0027] The advantages and positive effects of the present invention are:

[0028] 1. The intelligent order-taking recommendation big data platform of the present invention generates order-taking data through intelligent order-taking robots. When customers interact with intelligent order-taking robots by voice, a large amount of communication information will also be generated. By analyzing this information, it will have a certain impact on the operation of the restaurant in reverse.

[0029] 2. The intelligent ordering recommendation big data platform of the present invention comprehensively analyzes the data of customer orders (including the dish information of customer orders and the taste and cooking methods described in the remarks) from multiple dimensions such as dish types, ingredients, taste, and cooking methods to understand the customer's understanding and recognition of the taste, ingredients, and cooking methods of the dishes provided by the restaurant.

[0030] 3. The intelligent ordering recommendation big data platform of the present invention filters the data of the voice interaction between the customer and the intelligent ordering robot, extracts the problem dialogue information related to the business scope, characteristics, and dishes of the restaurant, and conducts comprehensive analysis to timely understand the customer's attention to the special dishes and recommended dishes of the restaurant, as well as the attention to the taste and cooking methods of the overall dishes of the restaurant.

[0031] 4. Through comprehensive analysis of the data, the present invention combines the season, weather changes, and the business scope and characteristics of the restaurant to put forward appropriate adjustment suggestions (such as taste, ingredients, cooking methods) for different dishes, so as to help the restaurant timely adjust the dishes it provides to meet the customer's needs, increase the customer's order volume, and thus increase the revenue.

[0032] 5. Through the analysis of the interactive dialogue data, the present invention helps the restaurant adjust the answering words and display pictures of the characteristics, recommendations, etc. that customers care about, so that customers can understand the characteristics and recommendation information of the restaurant faster and better, and enhance the customer's ordering experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] By referring to the following description in conjunction with the drawings, and with a more comprehensive understanding of the present invention, other objects and results of the present invention will become more apparent and easier to understand. In the drawings:

[0034] Figure 1 The flowchart in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] In the following description, for the purpose of illustration, in order to provide a comprehensive understanding of one or more embodiments, many specific details are set forth. However, it is obvious that these embodiments can also be implemented without these specific details. In other examples, well-known structures and devices are shown in block diagram form for the convenience of describing one or more embodiments.

[0036] Embodiment 1

[0037] Figure 1 Shows the overall structural schematic diagram according to the embodiment of the present invention.

[0038] As Figure 1As shown in the figure, a control method for a big data platform analysis system based on a dining service robot provided by an embodiment of the present invention specifically includes the following steps:

[0039] Step S1: Use the catering background module connected to the merchant processing module to comprehensively analyze the data of customer orders for dishes, ingredients, flavors, and cooking methods, so as to understand the customers' understanding of the flavors, ingredients, and cooking methods of the dishes provided by the restaurant. Among them, the specific data is the dish information of customer orders and the flavors and cooking methods described in the remarks.

[0040] Step S2: Use the order data module connected to the catering background module to extract the data on customer orders and merchant dish information in the catering background module. Among them, the order data includes the dish information of customer orders and the flavors and cooking methods described in the remarks, as well as the default flavors and cooking methods of merchant dish information.

[0041] Step S3: Use the dialogue data module in cooperation with the filtering module, screening module, and extraction module to filter the voice interaction data of customers in the catering background module, extract the question dialogue information related to the business scope, characteristics, and dishes of the restaurant among them, and conduct comprehensive analysis. Among them, combined with the season, weather changes, and the business scope and characteristics of the restaurant, appropriate adjustment suggestions are put forward for different dishes, and the attention of customers to the special dishes and recommended dishes of the restaurant, as well as the attention to the flavors and cooking methods of the overall dishes of the restaurant, are timely understood.

[0042] Step S4: Use the results of the comprehensive analysis of the dishes, ingredients, flavors, and cooking methods to adjust the flavors, ingredients, and cooking methods of the dishes in the dish adjustment module to meet the needs of customers.

[0043] Step S5: Use the conversation adjustment module to analyze the interactive dialogue data according to the results of the comprehensive analysis module, and then adjust the answering conversation and display pictures of the characteristics and recommended questions that the restaurant cares about for customers, so that customers can understand the characteristics and recommended information of the restaurant faster and better.

[0044] Step S6: Transmit the adjustment results of the conversation adjustment module to the merchant processing module and the kitchen processing module for synchronous adjustment.

[0045] Embodiment 2

[0046] The selection of the dining scenario in this embodiment includes the following steps;

[0047] Step S71: Obtain the information of the diners through the head camera and perform analysis. Among them, the analysis data is one or more of the number of people, gender, and age group information.

[0048] Step S72: Send the analysis data of Step S71 to the server. The server combines the business characteristics of the store and the featured recommended dishes reserved by the store to form a preliminary order menu. Among them, the business characteristics are cuisine type, hot pot, and barbecue; the recommended dishes include seasonal recommendations.

[0049] Step S73: According to the preliminary recommended menu formed in Step S72, guide the customer to order food and record the customer's preferences and tastes for the dishes ordered by the customer. Among them, the order options are recommended store specialties, today's recommendations, and seasonal recommendations; the tastes are slightly spicy and slightly light.

[0050] Step S74: According to the customer's ordered food categories and quantities, and combined with the diner information obtained in Step S71, give suggestions on the dishes ordered by the customer. Among them, if there are more meat dishes, it is recommended to supplement with green vegetables; if there are more ladies and children, desserts are recommended.

[0051] Embodiment 3

[0052] This embodiment provides a big data platform analysis system based on a dining service robot, including: a catering background module connected to the merchant processing module, an order data module, a dialogue data module, a filtering module, a screening module, an extraction module, a comprehensive analysis module, a dish adjustment module, and a speech adjustment module connected to the catering background module;

[0053] The catering background module is used to extract the data information stored in the client module, the merchant processing module, the robot processing module, and the kitchen processing module;

[0054] The order data module is used to store the customer's order data and the merchant's dish information. Among them, the order data includes the dish information ordered by the customer and the noted tastes and cooking methods, as well as the default tastes and cooking methods of the merchant's dish information;

[0055] The dialogue data module is used to collect the data of the voice interaction between the customer and the robot processing module. Among them, the collection stage is divided into the order period, the dining period, and the post-dining period;

[0056] The filtering module is used to filter out unnecessary information;

[0057] The screening module is used to refine the keywords prepared by the merchant. Among them, the keywords are the problem dialogue information related to the business scope, characteristics, and dishes of the restaurant;

[0058] The extraction module is used to extract the necessary information. Among them, the extracted information is classified;

[0059] The comprehensive analysis module is used to comprehensively analyze the data of the order data module and the extraction module. Among them, combined with the season, weather changes, and the business scope and characteristics of the restaurant, appropriate adjustment suggestions are put forward for different dishes;

[0060] The dish adjustment module is used to adjust the taste, ingredients, and cooking method of the dish according to the adjustment suggestions to meet the needs of customers;

[0061] The conversation adjustment module is used to analyze the interactive conversation data based on the results analyzed by the comprehensive analysis module, and then adjust the featured information that customers care about in the restaurant, the answers to recommended questions, and the displayed pictures, so that customers can understand the features and recommended information of the restaurant faster and better.

[0062] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A control method for a big data platform analysis system based on a dining service robot, characterized in that, It includes the following steps: Step S1: Use the catering background module connected to the merchant processing module to comprehensively analyze the data of customer orders for dishes, ingredients, flavors, and cooking methods, so as to understand the customers' understanding of the flavors, ingredients, and cooking methods of the dishes provided by the restaurant. Among them, the specific data is the dish information of customer orders and the flavors and cooking methods described in the remarks; Step S2: Use the order data module connected to the catering background module to extract the data on customer orders and merchant dish information in the catering background module. Among them, the order data includes the dish information of customer orders and the flavors and cooking methods described in the remarks, as well as the default flavors and cooking methods of merchant dish information; Step S3: Use the dialogue data module in cooperation with the filtering module, screening module, and extraction module to filter the voice interaction data about customers in the catering background module, extract the question dialogue information related to the business scope, features, and dishes of the restaurant, and conduct comprehensive analysis. Among them, the analysis method is to put forward appropriate adjustment suggestions for different dishes in combination with the season, weather changes, and the business scope and features of the restaurant, and timely understand the customers' attention to the special dishes and recommended dishes of the restaurant, as well as the attention to the flavors and cooking methods of the overall dishes of the restaurant; Step S4: Use the results of the comprehensive analysis of dishes, ingredients, flavors, and cooking methods to adjust the flavors, ingredients, and cooking methods of the dishes in the dish adjustment module to meet the needs of customers; Step S5: Use the conversation adjustment module to analyze the interactive dialogue data according to the results of the comprehensive analysis module, and then adjust the answering words and display pictures of the features and recommended questions that the restaurant cares about for customers, so that customers can understand the features and recommended information of the restaurant faster and better; Step S6: Transmit the adjustment results of the conversation adjustment module to the merchant processing module and the kitchen processing module for synchronous adjustment.

2. The control method for a big data platform analysis system based on a dining service robot according to claim 1, characterized in that, It also includes step S7: Dining scene selection; Step S71: Obtain the information of the diners through the front camera and perform analysis. Among them, the analysis data is one or more of the number of people, gender, and age group information; Step S72: Send the analysis data of step S71 to the server, and the server forms a preliminary order menu by combining the business features of the store with the specially recommended dishes reserved for the store. Among them, the business features are cuisine, hot pot, and barbecue; the recommended dishes include seasonal recommendations; Step S73: According to the preliminary recommended menu formed in step S72, guide the customer to order and record the customer's preferences and flavors for the dishes ordered. Among them, the order is to recommend the store's specialties, today's recommendations, and seasonal recommendations, and the flavors are slightly spicy and slightly light; Step S74: According to the types and quantities of the customer's orders, combined with the information of the diners obtained in step S71, give suggestions on the dishes ordered by the customer. Among them, if there are more meat dishes, it is recommended to supplement with green vegetables, and if there are more ladies and children, desserts are recommended.

3. A big data platform analysis system based on a dining service robot, characterized in that, It includes: The catering background module connected to the merchant processing module, the order data module connected to the catering background module, the dialogue data module, the filtering module, the screening module, the extraction module, the comprehensive analysis module, the dish adjustment module, and the conversation adjustment module; The catering back-end module is used to extract the data information stored in the client module, the merchant processing module, the robot processing module, and the kitchen processing module; The order-taking data module is used to store the data of customer orders and merchant dish information. The order-taking data includes the dish information ordered by the customer, as well as the taste and cooking method described in the remarks, and the default taste and cooking method of the merchant dish information; The dialogue data module is used to collect the data of the voice interaction between the customer and the robot processing module. The collection stage is divided into the order-taking time period, the dining time period, and the post-meal time period; The filtering module is used to filter out unnecessary information; The screening module is used to extract the keywords prepared by the merchant. Among them, the keywords are the problem dialogue information related to the business scope, characteristics, and dishes of the restaurant; The extraction module is used to extract the necessary information. Among them, the extracted information is classified; The comprehensive analysis module is used to comprehensively analyze the data of the order-taking data module and the extraction module. Among them, appropriate adjustment suggestions are put forward for different dishes in combination with the season, weather changes, and the business scope and characteristics of the restaurant; The dish adjustment module is used to adjust the taste, ingredients, and cooking method of the dish according to the adjustment suggestions to meet the needs of customers; The conversation adjustment module is used to analyze the interactive dialogue data according to the results analyzed by the comprehensive analysis module, and then display the characteristics of the restaurant that the customer cares about, the answers to the recommended questions, and the display pictures, so that the customer can understand the characteristics and recommended information of the restaurant faster and better.

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