污水处理与医疗大数据交互系统集成研究-国际计算机科学进展-CSCIED科技核心评价数据库-手机版

污水处理与医疗大数据交互系统集成研究

Research On The Integration Of The Interaction System Of Wastewater Treatment And Medical Big Data

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DOI	10.12208/j. aics.20220042
刊名	国际计算机科学进展 Advances in International Computer Science
年，卷(期)	2022, 2(3)
作者	曾泳丹*,王梓名,
作者单位	四川大学锦江学院四川成都 ; 四川大学华西第二医院四川成都 ;
摘要	医疗废水的主要特点是传染性、放射性、毒性和耐药性。近年来，医疗机构总数、床位数和医生数量均迅速增加，污水排放量急剧增加。当前阶段，各大医疗机构针对污水处理，在相关设施方面做了不断加强，特别是在污水污染控制方面发挥了一定的作用，但仍然存在处理设施拥有率低、相应处理水平低、管理不善、不完善等问题。本文充分考虑生态和环境因素安全本文以医疗废水为研究对象，以大数据信息为研究背景，利用计算机软件技术和环境科学技术完成了交互式系统的开发。采用K-means算法研究了医疗废水处理大数据交互系统的集成问题。分析医疗废水处理的超标程度，进行二次处理，提高废水处理率，通过实验仿真验证算法的有效性，完成交互系统的集成研究。
Abstract	Medical wastewater is characterized by infectivity, radioactivity, toxicity and drug resistance. In recent years, the total number of medical institutions, the number of beds, and the number of medical practitioners has increased rapidly, and the amount of wastewater discharged has increased dramatically. At present, the medical institution wastewater facilities have played a positive role in sewage pollution control, but there are many problems such as low ownership rate of treatment facilities, low treatment level, poor management and not fully considering ecological andenvironmental safety. This paper takes medical wastewater as the research object, takes big data information as the research background, and uses computer software technology and environmental science technology to complete the development of the interactive system. K-means algorithm isadopted to study the integration of big data interactive system of medical wastewater treatment. Analyze the exceeding degree of medical wastewater treatment to cluster, conduct secondarywastewater treatment, improve the treatment rate of wastewater, and verify the effectiveness of the algorithm through experimental simulation, thus completing the integration study of interactive system.
关键词	废水处理；医疗处理；环境；大数据；K-均值
KeyWord	Wastewater Treatment, Medical Treatment, The Environment, Big Data, K–means
基金项目
页码	34-38

参考文献
相关文献

[1] Sivarajah, U., Kamal, M.M., Irani, Z., Weerakkody, V. (2017) Critical analysis of Big Data challenges and analytical methods. Journal of Business Research. 70, 263-286.

[2] Akter, S., Wamba, S.F. (2017) Big data and disaster management: a systematic review and agenda for future research. Annals of Operations Research. 9, 1-21.

[3] Athey, S. (2017) Beyond prediction: using big data for policy problems. Science. 355, 483-485.

[4] Xu, W.C., Zhou, H.B., Cheng, N., Lyu, F., Shi, W.S.,Chen, J.Y., Shen, X.M. (2018) Internet of Vehicles inBig Data Era. IEEE/CAA Journal of Automatica Sinica. 5, 19-35.

[5] Bajari, P., Chernozhukov, V., Hortacsu, A., Hortaçsu, A., Suzuki, J. (2018) The Impact of Big Data on Firm Performance: An Empirical Investigation. Nber Working Papers, National Bureau of Economic Research, Inc.

[6] Yang, W.X., Li, L.G. (2018) Efficiency evaluation of

industrial waste gas control in China: A study based on data envelopment analysis (DEA) model. Journal ofCleaner Production. 179, 1-11.

[7] Song, Z.J., Liu, H.M., Meng, F.X., Yuan, X.Y., Feng,Q., Zhou, D.W., Romaní, J.R.V., Yan, H.B. (2019) Zircon U-Pb Ages and Hf Isotopes of Neoproterozoic Meta-Igneous Rocks in the Liansandao Area, NorthernSulu Orogen, Eastern China, and the Tectonic Implications. Journal of Earth Science. 30(6), 1230-1242.

[8] Choi, E.S., Min, K., Kim, G.J., Kwon, I., Kim, Y.H.(2017) Expression and characterization of Pantoea COdehydrogenase to utilize COcontaining industrial wastegas for expanding the versatility of CO dehydrogenase. Sci Rep. 7, 44323-44324.

[9] Guo, S.K., Chen, R., Li, H., Zhang, T.L., Liu, Y.Q. (2019) Identify Severity Bug Report with Distribution Imbalance by CR-SMOTE and ELM. International Journal of Software Engineering and Knowledge Engineering. 29, 139-175.

[10] https://blog.csdn.net/weixin_42137700/article/details/90230443.

引用本文

曾泳丹*,王梓名. 污水处理与医疗大数据交互系统集成研究 [J]. 国际计算机科学进展. 2022; 2; (3). 34 - 38.

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