Recipe recommendation method and system based on multi-dimensional scoring method, and electronic equipment

Abstract

The embodiment of the invention discloses a recipe recommendation method and system based on a multi-dimensional scoring method, and electronic equipment. The method comprises the following steps: S1,determining N evaluation dimensions corresponding to a to-be-evaluated recipe, wherein the types of the evaluation dimensions at least comprise total energy evaluation, energy supply ratio evaluation, health evaluation and risk evaluation; S2, acquiring user basic data, user target data and special condition data; S3, calculating an evaluation score corresponding to each determined evaluation dimension; and S4, based on each calculated evaluation score, obtaining a comprehensive evaluation score corresponding to the to-be-evaluated recipe, and giving a modification suggestion. According to the invention, comprehensive analysis and evaluation can be carried out in multiple dimensions to point out the problems of the recipe, so that the user can define the recipe improvement direction.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a recipe recommendation method, system and electronic device based on a multi-dimensional scoring method. Background technique [0002] At present, with the rapid development of the economy, people's living standards continue to improve, more and more attention is paid to dietary health issues, and diseases such as hypertension, diabetes, obesity and other diseases caused by improper diet are also reminding people all the time that they must be healthy Eat a reasonable diet. Whether three meals a day is scientific and reasonable and nutritionally balanced has become a topic of great concern to people at this stage. [0003] Most of the current dietary catering methods are based on a very general evaluation method to give nutritional advice. For example, based on the user's personal information, physical condition and nutritional value information, experts roughly analyze...

AI Technical Summary

Problems solved by technology

[0003] Most of the current diet and catering methods give nutritional advice in a very general way. For example, based on the user's personal information, physical condition, and nutritional value information, experts roughly analyze the user's nutritional indicators to give vague nutritional advice. , but the above method can only help users recommend the type and amount of raw ingredients, which is very inconvenient and lacks practicality and cannot provide analysis opinions for each user; at the same time, there are also some websites that can automatically provide users with recipes based on the information provided by users Or programs; but most of them match some recipes or recipe types based on user preferences and simple user nutritional requirements data, but this method has the following problems:

[0004] 1. The given recipes are single and incomplete. Because each user’s physical condition is different, and people of different ages and weights have different requirements for various nutrients, the recipes recommended to users by recipe websites may not be the same. It is not suitable for this user, and will bring harm to the user's health; at the same time, the biggest problem with the above-mentioned recipe websites is that there is no concept of "quantity", which does not match the user's supply and demand; there is no recommended consumption; only It is generally said that it is nutritious, but there is no specific data to support it, so it is difficult to distinguish between true and false; and for micronutrients, there is almost no data, and the lack of micronutrients is one of the most prominent public health problems at present

[0005] 2. Some existing nutrition calculation software only perform simple calculations on existing recipes. Although there are a lot of professional data piled up, these professional data are not only difficult for users to understand, but also have limited guiding functions, and there is no formed scoring system, which cannot match user personalization Requirements and did not consider multi-dimensional evaluation, unable to find the key problems of the recipe and the direction to solve the problem

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