doi: 10.18178/ijimt.2023.14.2.938
A Practical R&D Expenditure Statistic and Management Method Based on Spatial-Temporal Representation of Multi-factors and Data Twin Technology
Haitao Liu1,6,*
, , , , , , ,
- 1Guangxi Artificial Intelligence and Big Data Applying Institute, Nanning, 530201, China.
- 2Hangzhou ChinaOly Technology Co., Ltd, Hangzhou, 310015, China.
- 3Guangxi Academy of Social Sciences, Nanning, 530022, China.
- 4State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, China.
- 5RunJian Co., Ltd, Nanning, 530022, China.
- 6Guangxi CAIH Smart Communication Techonology Co., Ltd., Nanning, 530000, China.
- 7Mifpay Network Technology Co., Ltd, Nanning, 530000, China.
Abstract
Nowadays, R&D Expenditure plays an importantrole in more and more creative activities of enterprises andother entities, especially in research activities and programs ofsociety. However there still have a big problem that is how tocollect and classify R&D Expenditure accurately. In this paper,after analyzing the restrictive collection factors on R&DExpenditure statistically, a practical scheme was provided thatincluding R&D Expenditure Feature Vector and “ObjectWood” concept were defined firstly, intelligent receiptrecognizing model (IRPM), intelligent receipt persona model(IRRM) based on spatial-temporal representation ofmulti-factors and R&D expenditure data Twin(REDT) basedon data multi relationship were developed creatively. Besides,intelligence carrier-class R&D expenditure management system(REMS) was developed based on above novel technologies anddeployed on cloud with SaaS mode. For calling advantageouslyand updating conveniently, API standard interface and FullStack Security Mechanism were also improved and used inREMS. Meanwhile, it was proved that REMS had betterperformance on assisting enterprise in collecting and using theirR&D Expenditure after REMS employed by 50 industrialenterprises at first batch in practical over a period of time.There also have better economic benefits and social benefitsafter REMS was used by 211 enterprises in practically. Next,REMS would be utilized and tested in more scope of importantentities so that the correlation technologies could be tested,iterated and optimized forward in the future. Actually, REMSis becoming R&D Expenditure industrial promoted by investorand market. Eventually, REMS would become one of the bestR&D Expenditure collecting and using tools, it would not onlypromote R&D Expenditure increase but also become aindustrial correlating with R&D Expenditure.
Keywords
- R&D expenditure
- statistic
- spatial-temporalrepresentation
- data twin
How to Cite
Haitao Liu, Haibo Gong, Shenjun Zheng, Yujuan Cao, Yong Hong, Yongle Hu, Zuo Liu, and Hao Dai, "A Practical R&D Expenditure Statistic and Management Method Based on Spatial-Temporal Representation of Multi-factors and Data Twin Technology," International Journal of Innovation, Management and Technology, vol. 14, no. 2, pp. 59-63, 2023. https://doi.org/10.18178/ijimt.2023.14.2.938
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).