With the rapid development of cities, the scale of urbanization continues to expand, and the energy consumption of construction enterprises continues to increase. The people's demand for the environmental level is increasing, and the environmental problems make the people put forward higher demand for the living environment. Due to the rapid development of contemporary economy and the continuous improvement of living standards as well as the increasing number of "empty nest" elderly, the limitations of home-based care have begun to emerge. The limitations of home-based care are emerging, and the traditional model has been impacted. All industries are accelerating the pace of digital transformation, especially in the medical industry and interior design industry. It has become a trend to use artificial intelligence technology to enable and achieve life-cycle health management. This paper took the life cycle of the indoor living environment of the "empty nest" elderly as the research object. Based on big data algorithm, artificial intelligence and multimedia technology were used to establish the comfort evaluation model of residential environment elements to achieve the optimal design of indoor residential environment. The simulation results showed that by testing the comfort evaluation model of the residential environment elements, it could not only improve the performance level of the indoor residential environment by 12%, but also realize the sustainable development of indoor living and harmonious coexistence between human and nature.

Zongyi Dong. Optimization Design of Indoor Living Environment Based on Big Data Algorithms from the Perspective of the Entire Life Cycle. The Frontiers of Society, Science and Technology (2024), Vol. 6, Issue 7: 89-101. https://doi.org/10.25236/FSST.2024.060714.

[1] Moya Tatiana Armijos. A review of green systems within the indoor environment. Indoor and built environment, 2019, 28(3): 298-309.

[2] Feng Zhuangbo, Chuck Wah, and Shijie Cao. Fast prediction for indoor environment: models assessment. Indoor and Built Environment, 2019, 28(6): 727-730.

[3] Li Jianqiang. PSOTrack: A RFID-based system for random moving objects tracking in unconstrained indoor environment. IEEE Internet of Things Journal, 2018, 5(6): 4632-4641.

[4] Chhikara Prateek. DCNN-GA: A deep neural net architecture for navigation of UAV in indoor environment. IEEE Internet of Things Journal, 2020, 8(6): 4448-4460.

[5] Al Horr Yousef. Occupant productivity and indoor environment quality: A case of GSAS. International Journal of Sustainable Built Environment, 2017, 6(2): 476-490.

[6] Xie Ting, Ruihua Liu, and Zhengyuan Wei. Improvement of the Fast Clustering Algorithm Improved by-Means in the Big Data. Applied Mathematics and Nonlinear Sciences, 2020, 5(1): 1-10.

[7] Li Weiwei. Defending malicious check-in using big data analysis of indoor positioning system: An access point selection approach. IEEE Transactions on Network Science and Engineering, 2020, 7(4): 2642-2655.

[8] Yang Jun. Botanical internet of things: Toward smart indoor farming by connecting people, plant, data and clouds. Mobile Networks and Applications, 2018, 23(2): 188-202.

[9] Hamdy Yomna. Application of Artificial Intelligence in the Development of Interior Design Operations Management. Journal of Design Sciences and Applied Arts, 2022, 3(2): 239-245.

[10] Hou Rui. Unstructured big data analysis algorithm and simulation of Internet of Things based on machine learning. Neural Computing and Applications, 2020, 32(10): 5399-5407.

[11] Ali Muhammad Ubaid. Pollution characteristics, mechanism of toxicity and health effects of the ultrafine particles in the indoor environment: Current status and future perspectives. Critical Reviews in Environmental Science and Technology, 2022, 52(3): 436-473.

[12] Borrego Sofía, and Alian Molina. Fungal assessment on storerooms indoor environment in the National Museum of Fine Arts, Cuba. Air quality, atmosphere & health, 2019, 12(11): 1373-1385.

[13] Rohde Lasse. Framing holistic indoor environment: Definitions of comfort, health and well-being. Indoor and Built Environment, 2020, 29(8): 1118-1136. 

[14] Finell E. The associations of indoor environment and psychosocial factors on the subjective evaluation of indoor air quality among lower secondary school students: a multilevel analysis. Indoor air, 2017, 27(2): 329-337.

[15] Ageel Hassan Khalid, Stuart Harrad, and Mohamed Abou-Elwafa Abdallah. Occurrence, human exposure, and risk of microplastics in the indoor environment. Environmental Science: Processes & Impacts, 2022, 24(1): 17-31.

[16] Chen Haofu. Application of polyhedral meshing strategy in indoor environment simulation: Model accuracy and computing time. Indoor and Built Environment, 2022, 31(3): 719-731.

[17] Sando Ole Johan. The physical indoor environment in ECEC settings: Children’s well-being and physical activity. European Early Childhood Education Research Journal, 2019, 27(4): 506-519.

[18] Bai Jinqiang. Smart guiding glasses for visually impaired people in indoor environment. IEEE Transactions on Consumer Electronics, 2017, 63(3): 258-266.

[19] Salthammer Tunga. Assessing human exposure to organic pollutants in the indoor environment. Angewandte Chemie International Edition, 2018, 57(38): 12228-12263.

[20] Zhou Lipu, Daniel Koppel, and Michael Kaess. LiDAR SLAM with plane adjustment for indoor environment. IEEE Robotics and Automation Letters, 2021, 6(4): 7073-7080.