Intelligent dish selecting and cooking method based on big data

Abstract

An intelligent dish selecting and preparing method based on big data comprises the following steps: S100, data collection: based on address positioning, introducing take-out platform data and dish delivery platform data; s200, a menu is generated, the menu is generated according to the collected dish data and the raw material category, and the menu is ranked according to the sales volume of the raw materials and the sales volume of the take-out platform; s300, interest collection: displaying features of dishes in an operation interface for a user to select, recording interest points of the user, and storing the interest points as key points of information pushing; s400, determining a recommendation mode; s500, follow-up pushing: after the user selects the dishes, the system inputs dish ordering data of the user, regularly summarizes diet records of the time period, and pushes the diet records to the user according to the time period; s600, dish distribution: after the user selects the dish, the dish is distributed according to the address of the user; and S700, cooking is executed, the user puts the dishes in the dish box into a cooking machine, and the cooking machine processes the dishes.

Description

Technical Field

[0001] The invention relates to a cooking machine food distribution technology, and in particular to an intelligent cooking method based on big data. Background Art

[0002] The use of fresh boxed vegetables in conjunction with cooking machines is the future development direction of the kitchen. Fresh boxed vegetables reduce the user's workload of preparing meals, and cooking machines reduce the user's workload of cooking. How to make the user more convenient in the food selection process is also an important step that needs to be followed up and improved.

[0003] The simplest way to choose dishes is to select according to the menu, choose the right dishes and arrange for delivery. However, this preset menu makes the user's selection method rather rigid and does not help users reduce their thinking. Users choose what to eat from the same list every day, and they cannot place an order for dishes that are only available in autumn if they order them in spring. This way of choosing dishes that lacks recommendation effects cannot provide users with a good experience.

[0004] To address the above issues, a dish selection method with timeliness and recommendation effects is needed to provide users with a better user experience. Summary of the invention

[0005] In view of the above-mentioned defects, the purpose of the present invention is to propose an intelligent cooking method based on big data. The method obtains the current menu through data and cooperates with the recommendation method to assist users in selecting dishes, and then combines with a cooking machine to realize intelligent cooking.

[0006] To achieve this object, the present invention adopts the following technical solutions:

[0007] A method for intelligently selecting and cooking dishes based on big data comprises the following steps:

[0008] S100, data collection, based on address positioning, introduces food delivery platform data and food delivery platform data;

[0009] S200, generating a menu, generating a menu based on the collected dish data and raw material categories, and ranking the menu based on the sales volume of the raw materials and the sales volume of the food delivery platform;

[0010] S300, interest collection, displaying dishes for the user to choose in the operation interface, and then recording the user's interest points, and storing the interest points as the key points of the push information;

[0011] S400, determining a recommendation method, if the user has entered a record of interests and preferences, according to the dishes in the generated menu, the dishes are pushed in priority according to interests and preferences; if the user has no record of interests and preferences, the dishes are pushed in the order of the ranking list in the generated menu;

[0012] S500, subsequent push: after the user selects a dish, the system will record the user's order data, regularly summarize the dietary records of the time period, and push them to the user according to the time period;

[0013] S600, food delivery: after the user selects a dish, the dish is delivered according to the user's address;

[0014] S700, cooking is executed. The user puts the dishes in the dish box into the cooking machine, and the cooking machine processes the dishes.

[0015] Preferably, the preferences include spicy, non-spicy and sweet.

[0016] Preferably, in step S100, data of the food delivery platform and the food delivery platform are obtained through a Python crawler.

[0017] Preferably, the dishes are delivered to the user's designated address using a takeaway delivery method.

[0018] Furthermore, a sealed film vegetable box is used as a container for the vegetables.

[0019] Furthermore, the color box is sprayed with a QR code for the cooking machine to identify, and the QR code is preset control information of the cooking machine.

[0020] Furthermore, the cooking machine is provided with a signal communication module, and the cooking machine exchanges information with the user's smart terminal through the signal communication module.

[0021] One of the above technical solutions includes the following beneficial effects: This solution combines big data with recommendation methods to recommend dishes to the user based on the user's historical ordering records and the ordering history records of other users with the same preferences as the user, making it more convenient for the user to choose dishes when using a cooking machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present invention. DETAILED DESCRIPTION

[0023] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0024] like Figure 1 As shown, a method for intelligently selecting and cooking dishes based on big data comprises the following steps:

[0025] S100, data collection, based on address positioning, introduces food delivery platform data and food delivery platform data;

[0026] S200, generating a menu, generating a menu based on the collected dish data and raw material categories, and ranking the menu based on the sales volume of the raw materials and the sales volume of the food delivery platform;

[0027] S300, interest collection, displaying dishes for the user to choose in the operation interface, and then recording the user's interest points, and storing the interest points as the key points of the push information;

[0028] S400, determining a recommendation method, if the user has entered a record of interests and preferences, according to the dishes in the generated menu, the dishes are pushed in priority according to interests and preferences; if the user has no record of interests and preferences, the dishes are pushed in the order of the ranking list in the generated menu;

[0029] S500, subsequent push: after the user selects a dish, the system will record the user's order data, regularly summarize the dietary records of the time period, and push them to the user according to the time period;

[0030] S600, food delivery: after the user selects a dish, the dish is delivered according to the user's address;

[0031] S700, cooking is executed. The user puts the dishes in the dish box into the cooking machine, and the cooking machine processes the dishes.

[0032] This solution combines big data with recommendation methods to recommend dishes to the user based on the user's historical ordering records and the ordering history records of other users with the same preferences as the user, making it more convenient for the user to choose dishes when using the cooking machine.

[0033] Among them, interests and preferences include spicy, not spicy and sweet.

[0034] Among them, in step S100, the data of the food delivery platform and the food delivery platform are obtained through the Python crawler.

[0035] Among them, the dishes are delivered to the user's designated address by takeaway delivery.

[0036] Among them, a sealed film vegetable box is used as a container for the dishes.

[0037] In addition, the color box is sprayed with a QR code for the cooking machine to identify, and the QR code is the preset control information of the cooking machine.

[0038] In addition, the cooking machine is provided with a signal communication module, through which the cooking machine exchanges information with the user's smart terminal.

[0039] The technical principle of the present invention is described above in conjunction with specific embodiments. These descriptions are only for explaining the principle of the present invention and cannot be interpreted as limiting the scope of protection of the present invention in any way. Based on the explanations herein, those skilled in the art can associate other specific implementations of the present invention without paying creative labor, and these methods will fall within the scope of protection of the present invention.

Claims

1. An intelligent cooking method based on big data, characterized in that: The following steps are involved: S100, data collection, based on address positioning, introduces food delivery platform data and food delivery platform data; S200, generating a menu, generating a menu based on the collected dish data and raw material categories, and ranking the menu based on the sales volume of the raw materials and the sales volume of the food delivery platform; S300, interest collection, displaying dishes for the user to choose in the operation interface, and then recording the user's interest points, and storing the interest points as the key points of the push information; S400, determining a recommendation method, if the user has entered a record of interests and preferences, according to the dishes in the generated menu, the dishes are pushed preferentially according to the interests and preferences; If the user has no interest in the admission record, it will be pushed in the order of the ranking list in the generated menu; S500, subsequent push: after the user selects a dish, the system will record the user's order data, regularly summarize the dietary records of the time period, and push them to the user according to the time period; S600, food delivery: after the user selects a dish, the dish is delivered according to the user's address; S700, cooking is executed. The user puts the dishes in the dish box into the cooking machine, and the cooking machine processes the dishes.

2. The intelligent cooking method based on big data according to claim 1 is characterized in that: Preferences include spicy, non-spicy and sweet.

3. The intelligent cooking method based on big data according to claim 1 is characterized in that: In step S100, data of the food delivery platform and the food delivery platform are obtained through a Python crawler.

4. The intelligent cooking method based on big data according to claim 1 is characterized in that: The dishes are delivered to the user's designated address using takeout delivery.

5. The intelligent cooking method based on big data according to claim 1 is characterized in that: Use sealed film vegetable boxes as containers for dishes.

6. The intelligent cooking method based on big data according to claim 1 is characterized in that: The color box is sprayed with a QR code for the cooking machine to identify, and the QR code is the preset control information of the cooking machine.

7. The intelligent cooking method based on big data according to claim 1 is characterized in that: The cooking machine is provided with a signal communication module, and the cooking machine exchanges information with the user's smart terminal through the signal communication module.

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