Catering industry application method and system based on intelligent ingredient statistics and recommendation system

Abstract

The invention discloses a catering industry application method and system based on an intelligent ingredient statistics and recommendation system. The system comprises an intelligent weighing module, a data acquisition module, a user database, a data analysis module, an ingredient naming module, an ingredient sharing module, an ingredient matching module and a recommendation engine module. According to the system, through intelligent weighing, data analysis and a recommendation algorithm, ingredient selection of customers is recorded, personalized recommendation is provided, and naming and sharing of ingredient combinations by the customers are supported. The problem that a traditional self-service batching system lacks personalized recommendation and cannot record and reuse batching combinations is solved, and the system has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent catering, and particularly relates to an application method and system for the catering industry based on an intelligent ingredient statistics and recommendation system, which is used to record the ingredient selections of customers in the self-service ingredient area of a restaurant, provide personalized recommendations, and support customers in naming and sharing ingredient combinations. Background Art

[0002] In the catering industry, many restaurants provide a self-service ingredient area where customers can choose ingredients according to their personal tastes. However, the existing self-service ingredient systems have the following problems:

[0003] Lack of personalized recommendations: It is difficult for customers to quickly find suitable ingredient combinations according to their personal preferences.

[0004] Inability to record and reuse: Customers cannot save their favorite ingredient combinations, resulting in the need to re-match each time.

[0005] Lack of interactivity: Customers cannot name or share ingredient combinations, reducing the fun of the dining experience.

[0006] In the prior art, there has not been a system that can combine intelligent weighing, data analysis, and recommendation algorithms to provide personalized ingredient recommendations for customers and support the naming and sharing of ingredient combinations. Therefore, there is an urgent need for an intelligent ingredient statistics and recommendation system that can solve the above problems. Summary of the Invention

[0007] The purpose of the present invention is to provide an application method and system for the catering industry based on an intelligent ingredient statistics and recommendation system, which records customers' ingredient selections, provides personalized recommendations, and supports customers in naming and sharing ingredient combinations through intelligent weighing, data analysis, and recommendation algorithms.

[0008] The application method and system for the catering industry based on the intelligent ingredient statistics and recommendation system of the present invention include the following modules:

[0009] Intelligent weighing module: Used to accurately weigh the weight of each ingredient used by customers in the self-service ingredient area and transmit the data to the data acquisition module.

[0010] Data acquisition module: Records information such as the types, weights, and times of ingredients used by customers each time and stores it in the user database.

[0011] User database: Stores data such as customers' ingredient selection histories, taste preferences, and naming records.

[0012] Data analysis module: Analyzes customer data, identifies the proportion and combination of ingredients they prefer, and generates personalized recommendations.

[0013] Ingredient Naming Module: Allows customers to name their ingredient combinations and saves them to the user database.

[0014] Ingredient Sharing Module: Supports customers to make their named ingredient combinations public for other customers to view and use.

[0015] Ingredient Matching Module: When a customer combines ingredients that already exist in the system, the system matches them through an algorithm and prompts the name of the ingredient.

[0016] Recommendation Engine Module: Based on the customer's historical records, taste preferences, and current ingredients, recommends suitable ingredient combinations.

[0017] Optimal Solution

[0018] The intelligent weighing module is integrated into the containers or devices in the self-service ingredient area to ensure the accuracy of data collection.

[0019] The system supports connection to mobile terminals (such as mobile phones, tablets). Customers can view the recommendation results, name ingredient combinations, and manage historical records through the application.

[0020] The system supports voice interaction functions. Customers can query recommendations or name ingredient combinations through voice commands.

[0021] The system supports data sharing among chain restaurants. When customers use the same ingredient combinations in different branches, the system will prompt the naming information. Description of the Drawings

[0022] Figure 1 : System architecture diagram. Shows the various modules of the system and their interrelationships, including the intelligent weighing module, data collection module, user database, data analysis module, ingredient naming module, ingredient sharing module, ingredient matching module, and recommendation engine module.

[0023] Figure 2 : Workflow diagram. Shows the workflow from when a customer selects ingredients to system recommendations, including steps such as data collection, data analysis, recommendation generation, ingredient naming, and sharing. Detailed Implementation Manner

[0024] Intelligent Weighing Module: When a customer selects ingredients in the self-service ingredient area, the intelligent weighing module records the weight of each ingredient in real-time and transmits the data to the data collection module through communication technologies (such as Wi-Fi, Bluetooth, or data cable).

[0025] Data Collection Module: The data collection module receives the data from the intelligent weighing module, records the customer's ingredient selections (type, weight, time, etc.), and stores the data in the user database.

[0026] User Database: The user database adopts distributed storage technology to ensure data security and scalability. The data of each customer includes the history of ingredient selection, taste preferences, naming records, etc.

[0027] Data Analysis Module: The data analysis module uses machine learning algorithms (such as collaborative filtering and clustering analysis) to analyze the customer's historical data and identify the preferred ingredient ratios and combinations.

[0028] Ingredient Naming Module: Customers can name their ingredient combinations through the mobile terminal or voice interaction function. The named combinations will be saved in the user database and support public sharing.

[0029] Ingredient Sharing Module: Customers can make their named ingredient combinations public, and other customers can view and use these combinations through the system. The sharing function supports social interactions such as liking and commenting.

[0030] Ingredient Matching Module: When a customer combines ingredients that already exist in the system, the ingredient matching module quickly identifies and prompts the name of the ingredient through data matching technology.

[0031] Recommendation Engine Module: The recommendation engine module generates personalized recommendations based on the customer's historical data, taste preferences, and current ingredients. The recommendation results are presented to the customer through the mobile terminal or voice interaction function.

[0032] Examples

[0033] Example 1: Personalized Recommendation

[0034] Customer A selects tomatoes, cucumbers, and onions in the self-service ingredient area. The intelligent weighing module records the weight of each ingredient, and the data acquisition module stores the data in the user database. The data analysis module identifies that Customer A prefers vegetable ingredients, and the recommendation engine module recommends the "Classic Salad" combination.

[0035] Example 2: Ingredient Naming and Sharing

[0036] Customer B selects chicken, lettuce, and sesame paste in the self-service ingredient area and names it "Chicken and Sesame" through the ingredient naming module. This combination is publicly shared, and other customers can view and use it through the ingredient sharing module.

[0037] Example 3: Data Sharing among Chain Restaurants

[0038] Customer C uses the "Chicken and Sesame" combination in Branch A. When Customer C selects the same ingredients in Branch B, the ingredient matching module prompts the naming information of this combination.

Claims

1. A catering industry application method and system based on intelligent ingredient statistics and recommendation system, characterized in that: include: Intelligent weighing module, data acquisition module, user database, data analysis module, ingredient naming module, ingredient sharing module, ingredient matching module and recommendation engine module.

2. The system according to claim 1, characterized in that The intelligent weighing module is integrated into the container or equipment in the self-service batching area.

3. The system according to claim 1, characterized in that The system supports connection with mobile terminals, and customers can view recommendation results, name ingredient combinations and manage history through the application.

4. The system according to claim 1, characterized in that The system supports voice interaction, and customers can query recommendations or name ingredient combinations through voice commands.

5. The system according to claim 1, characterized in that The system supports data sharing among chain restaurants. When customers use the same ingredient combination in different branches, the system will prompt the naming information.

6. The system according to claim 1, characterized in that The data analysis module generates personalized recommendations based on user historical data and preferences.

7. The system according to claim 1, characterized in that The ingredient matching module identifies existing ingredient combinations through data matching technology.

8. A catering industry application method based on intelligent ingredient statistics and recommendation system, characterized in that: The following steps are involved: The weight of each ingredient used by customers in the self-service ingredient area is recorded through the intelligent weighing module; The ingredient selection data is stored in the user database through the data acquisition module; Analyze customers’ ingredient selection history through the data analysis module to generate personalized recommendations; The ingredient naming module allows customers to name and save ingredient combinations; Support customers to make named ingredient combinations public through the ingredient sharing module; Identify existing ingredient combinations through the ingredient matching module and prompt naming information; Recommend suitable ingredient combinations to customers through the recommendation engine module.

Images