Recommendation system of fresh cut flower knowledge graph based on intelligent algorithm

doi:10.14088/j.cnki.issn0439-8114.2022.20.036

Abstract

Abstract: In order to further advance smart agriculture to promote the in-depth development of the fresh cut flower industry in Yunnan Province, provide accurate information on best-selling and unsalable varieties for growers, planting companies and the entire fresh-cut flower industry to avoid the undesirable phenomenon of large-scale cultivation of unsalable varieties, and ensure that the benefits of the fresh-cut flower industry were maximized, taking Yunnan fresh-cut flowers as the research object, the problems existed in a series of processes such as seed selection, planting, sales were analyzed. Then the associations and differences of different fresh cut flowers were found, and the artificial neural network algorithm to construct the intelligent recommendation model for fresh cut flowers recommends different varieties of fresh cut flowers to different populations. At the same time, the introduction of the cloud platform provided a reference basis for flower farmers, retail investors, planting companies, and scientific researchers, which could more accurately determine the types of customers, conduct targeted sales and research, and promote the healthy and orderly development of the fresh cut flower industry in Yunnan.

Key words: intelligence, model, fresh-cut flower, knowledge graph, recommendation

CLC Number:

TP399

QIAN Ye, SUN Ji-hong. Recommendation system of fresh cut flower knowledge graph based on intelligent algorithm[J]. HUBEI AGRICULTURAL SCIENCES, 2022, 61(20): 188-194.

References

[1] 郭小辉. 云南花卉产业发展分析及对策研究[D].昆明:昆明理工大学,2010.
[2] 任伟宏. 农产品市场价格预测方法探析[J].中国农学通报,2011,27(26):209-212.
[3] 孙吉红,张丽莲,武尔维,等.基于智能算法的价格预测模型探究[J].计算机技术与发展,2014,24(11):107-110.
[4] 钱晔,孙吉红,彭琳,等.基于智能算法的月季鲜切花病虫害预测模型的探究[J].计算机技术与发展,2017, 27(12):156-160.
[5] 钱晔,孙吉红,孙媛媛,等.农业大数据环境下的鲜切花行情监测系统设计[J].安徽农业科学,2018,15(57):185-187.
[6] 钱晔,孙吉红,彭琳,等.基于智能模型群的鲜切花产业平台的构建[J].北方园艺,2019(4),162-166.
[7] LIU C J,WECHSLER H.Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J].IEEE transactions on image processing,2002,11(4):467-476.
[8] QIAO L S,CHEN S C,TAN X Y.Sparsity preserving projections with applications to face recognition[J].Pattern recognition,2010,43(1): 331-341.
[9] ZHANG F, YUAN N J, LIAN D, et al.Collaborative knowledge base embedding for recommender systems [A].Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining[C]. 2016.353-362.
[10] 何峰. 基于文本分类的人才自动推荐系统[D]. 江苏苏州: 苏州大学, 2006.
[11] 路小瑞. 基于Hadoop 平台的职位推荐系统的设计与实现 [D]. 上海: 上海交通大学, 2014.
[12] 徐孟奇,熊熙,李斌勇,等.基于知识图谱的人岗推荐系统构建[J].计算机应用研究,2021,39(1):194-198.
[13] 王昊奋, 漆桂林, 陈华钧. 知识图谱方法、实践与应用[M].北京:电子工业出版社, 2019.
[14] 张栋豪, 刘振宇, 郏维强, 等. 知识图谱在智能制造领域的研究现状及其应用前景综述[J]. 机械工程学报,2021, 57(5): 90-113.
[15] 罗玲, 孙学, 唐德波. 知识图谱在战术云服务平台中的应用[J]. 电讯技术, 2020, 60(9): 1035-1042.