基于人工智能知識庫的營養(yǎng)膳食推薦系統(tǒng)研究

作者:

張悅琳，王創(chuàng)劍

1. 武漢科技大學(xué) 冶金裝備及其控制教育部重點實驗室,武漢 430081 2. 武漢科技大學(xué) 機械傳動與制造工程湖北省重點實驗室,武漢 430081
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Research on Nutritional Dietary Recommendation System Based on Artificial Intelligence Knowledge Base

Author:

ZHANG Yuelin,WANG Chuangjian

1. Key Laboratory of Metallurgical Equipment and Control Ministry of Education Wuhan University of Science and Technology Wuhan 430081 China 2. Hubei Provincial Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan 430081 China
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摘要:

目的 針對飲食不均衡和搭配失當而造成的飲食問題給人們身心健康帶來不良影響,尤其是飲食對慢性病 研究的發(fā)展等問題,為了設(shè)計和開發(fā)一個能夠根據(jù)用戶個人狀況、喜好、口味等因素,提出向用戶推薦符合其身體 需求的營養(yǎng)膳食的智能系統(tǒng)。 方法 搭建了一個基于人工智能知識庫的營養(yǎng)膳食推薦系統(tǒng),利用專家驗證的膳食的 明確數(shù)據(jù)集,結(jié)合基于推理決策支持系統(tǒng)( RDSS) 和營養(yǎng)計劃( NP) 運用 NAct 本體論提供高度準確的飲食計劃,跨 越 10 個用戶組,包括健康的受試者和有健康狀況的參與者。 結(jié)果 該系統(tǒng)的有效性通過廣泛的實驗進行評估,評估 涉及合成數(shù)據(jù),包括生成 3 000 個虛擬用戶檔案和他們的每周膳食計劃。 結(jié)果顯示,在大多數(shù)用戶類別中,推薦適 當成分的精確度和召回率都很高,而膳食計劃生成器對所有營養(yǎng)素的推薦達到了 94%的總推薦精確度。 結(jié)論 基 于人工智能知識庫的營養(yǎng)膳食推薦系統(tǒng)可以根據(jù)用戶的身體狀況、喜好、飲食禁忌等方面進行個性化推薦。 這樣 的個性化推薦能夠更好地滿足用戶的需求,從而提高推薦的準確度。 專家知識庫包含了廣泛的營養(yǎng)學(xué)和健康知 識,這些知識可以幫助系統(tǒng)識別出最適合用戶的膳食方案。 這些方案不僅可以提供充足的營養(yǎng),還可以避免與用 戶的健康狀況不兼容的食物或成分。

Abstract:

Addressing the adverse effects of dietary problems caused by imbalanced nutrition and poor food combinations on people?? s physical and mental well-being particularly concerning research on the relationship between diet and chronic diseases an intelligent system capable of recommending sound and nutritionally balanced meals tailored to users?? conditions preferences and tastes was designed and developed. Methods A nutrition meal recommendation system based on an artificial intelligence knowledge base was constructed. It utilized a definitive dataset of diets validated by experts combined with a reasoning decision support system RDSS and a nutritional plan NP employing NAct ontology to provide highly accurate dietary plans. It spanned 10 user groups including healthy subjects and participants with health problems. Results The effectiveness of the system was assessed through extensive experiments which involved synthetic data including the generation of 3 000 virtual user profiles and their weekly dietary plans. The results indicated high precision and recall rates for recommending appropriate ingredients across most user categories with the dietary plan generator achieving an overall recommendation accuracy of 94% for all nutrients. Conclusion The nutrition meal recommendation system based on an artificial intelligence knowledge base can personalize recommendations based on users?? physical conditions preferences dietary restrictions and other aspects. Such personalized recommendations can better meet users ?? needs thereby enhancing the accuracy of the recommendations. The expert knowledge base encompasses extensive nutrition and health knowledge which can assist the system in identifying the most suitable dietary plans for users. These plans not only provide ample nutrition but also avoid foods or ingredients incompatible with users?? health conditions.

張悅琳,王創(chuàng)劍.基于人工智能知識庫的營養(yǎng)膳食推薦系統(tǒng)研究[J].重慶工商大學(xué)學(xué)報（自然科學(xué)版）,2024,41(5):16-27
ZHANG Yuelin, WANG Chuangjian. Research on Nutritional Dietary Recommendation System Based on Artificial Intelligence Knowledge Base[J]. Journal of Chongqing Technology and Business University(Natural Science Edition）,2024,41(5):16-27

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