2021 Vol.4 Jun.No.2

• Title: Research on fast prediction method of whole life cycle project cost based on Internet of things technology
• Name: Zheng Liu
• Company: China Electronics Technology Group Corporation No.15 Research Institute
• Abstract:

  Limited by the accuracy of the initial data of the project cost, the traditional project cost prediction method has the problem of large prediction error and time-consuming. Therefore, the Internet of Things technology is applied to the prediction method to realize the rapid prediction method of the whole life cycle project cost Optimize the design. Build an Internet of Things environment and use the Internet of Things technology to collect historical engineering data, divide the entire life cycle of the project, and calculate the project cost based on the division results. By analyzing the influencing factors of the project cost, the prediction index is determined, and the collected initial data is substituted into it, and the final fast prediction result of the whole life cycle project cost is obtained. Compared with the traditional prediction methods, it can be found that the design prediction method has more advantages in prediction accuracy and prediction speed.

   

• Keyword: Internet of Things technology; Full life cycle; Project cost; Rapid prediction;
• DOI: 10.12250/jpciams2021090220
• Citation form: Zheng Liu.Research on fast prediction method of whole life cycle project cost based on Internet of things technology[J]. Computer Informatization and Mechanical System,2021,Vol.4,pp.1-6.

References

[1] Wang L , Xu N , Xu N , et al. Research on Investment Decision of Substation Project Based on Life Cycle Cost[J]. IOP Conference Series Earth and Environmental ence, 2019, 242(2):022016.

[2]Yi X , Chuanyu X , Wu W , et al. Life Cycle Cost Prediction of Substation Based on Advanced PSO and Least Squares Support Vector Machine[J]. IOP Conference Series Earth and Environmental ence, 2020, 461:012083.

[3] Ed-Daoudy A , Maalmi K . A new Internet of Things architecture for real-time prediction of various diseases using machine learning on big data environment[J]. Journal of Big Data, 2019, 6(1):1-25.

[4] Gao H , Xu Y , Yin Y , et al. Context-Aware QoS Prediction With Neural Collaborative Filtering for Internet-of-Things Services[J]. IEEE Internet of Things Journal, 2020, 7(5):4532-4542.

[5] Righi R D R , Goldschmidt G , Kunst R , et al. Towards combining data prediction and internet of things to manage milk production on dairy cows[J]. Computers and Electronics in Agriculture, 2020, 169:105156.

[6]Uélison Jean L. dos Santos, Pessin G , Cristiano André da Costa, et al. AgriPrediction: A proactive internet of things model to anticipate problems and improve production in agricultural crops[J]. Computers and Electronics in Agriculture, 2019, 161:202-213.

[7]Li Z . Engineering Cost Information Management in Big Data Era[J]. Journal of Physics Conference Series, 2020, 1533:042072.

[8] Shi Xiaobo. Analysis on the Case Teaching Method of Engineering Cost and Claim Management[J]. Creative Education Studies, 2019, 07(6):724-729.

[9]Wang B , Dai J . Discussion on the prediction of engineering cost based on improved BP neural network algorithm[J]. Journal of Intelligent and Fuzzy Systems, 2019(7):1-8.

[10] Liu Shuai, Lu Mengye, Li Hanshuang, Zuo Yongchun. Prediction of Gene Expression Patterns with Generalized Linear Regression Model. Frontiers in Genetics, 2019, 10: 120.