Abstract

To improve the ability of balanced evaluation, dynamic allocation, level of output, and profitability of human resources, a balanced evaluation model of human resources allocation is proposed based on big data driven and the Internet of Things. An evaluation architecture model of human resource allocation balance in view of big data driven and the Internet of Things is established; a multithread big data driven configuration model and the Internet of Things are used to construct a table model of enterprise human resource optimal allocation in the form of reports; statistical regression analysis method is used to detect the risks and process parameters in the process of enterprise human resource optimal allocation; and employment elasticity theory analysis is taken to establish a resource factor analysis model of human resource balanced allocation under the constraint of economic growth mode. According to the changing trend of the scale and structure of labor resources, statistical regression analysis is adopted to make big data driven analysis in the process of balanced evaluation of human resources allocation, and balanced game control is adopted to analyze the relevant factors affecting human resources allocation to realize balanced scheduling of population flow and human resources allocation and automatic post adjustment. The empirical analysis results show that the system has a good balance in human resource allocation, the output efficiency among the various elements of human resource allocation is improved, and there is a high level of efficiency in human resource output.

References

1.
Roztocki
N.
,
Soja
P.
, and
Weistroffer
H. R.
, “
Enterprise Systems in Transition Economies: Research Landscape and Framework for Socioeconomic Development
,”
Information Technology for Development
26
, no. 
1
(
2020
):
1
37
, https://doi.org/10.1080/02681102.2017.1377148
2.
Mirjah
Z. G.
,
Al-Kaabi
H. S.
, and
Taher
A. K. M.
, “
The Capabilities of the Human Resources of Al-Rafidain College in Light of the Exercise of Its Leadership Entrepreneurial
,”
International Journal of Research in Social Sciences and Humanities
11
, no. 
2
(April–June
2021
):
17
35
, https://doi.org/10.37648/ijrssh.v11i02.002
3.
Song
X.
,
Liu
J.
,
Cao
Y.
, and
Long
H.
, “
Optimization of Human Resource Allocation Problem Considering Costs Reduction and Balance in Chinese State-Owned Enterprise
,” in
ICMSS 2020: Proceedings of the 2020 Fourth International Conference on Management Engineering, Software Engineering and Service Sciences
(New York:
Association for Computing Machinery
,
2020
), 211–215, https://doi.org/10.1145/3380625.3380658
4.
Chen
S.-H.
, “
Integrating Service Quality Evaluation Model to Improve Employees’ Satisfaction for High‐Tech Iindustry
,”
Human Factors and Ergonomics in Manufacturing & Service Industries
22
, no. 
6
(November/December
2012
):
517
527
, https://doi.org/10.1002/hfm.20294
5.
Yue
W.
,
Gao
J.
, and
Suo
W.
, “
Efficiency Evaluation of S&T Resource Allocation Using an Accurate Quantification of the Time-Lag Effect and Relation Effect: A Case Study of Chinese Research Institutes
,”
Research Evaluation
29
, no. 
1
(January
2020
):
77
86
, https://doi.org/10.1093/reseval/rvz027
6.
Sultana
S.
,
Akter
S.
,
Kyriazis
E.
, and
Wamba
S. F.
, “
Architecting and Developing Big Data-Driven Innovation (DDI) in the Digital Economy
,”
Journal of Global Information Management
29
, no. 
3
(
2021
):
165
187
, https://doi.org/10.4018/JGIM.2021050107
7.
Serniak
I.
,
Serniak
O.
,
Mykhailyshyn
L.
,
Skrynkovskyy
R.
, and
Kasian
S.
, “
Evaluation of the Level of the Usage of Social Instruments for Human Resource Management: Example of Agro-processing Enterprises of Ukraine
,”
Agricultural and Resource Economics: International Scientific E-Journal
7
, no. 
4
(December
2021
):
82
99
, https://doi.org/10.22004/ag.econ.316822
8.
Zhang
Y.
,
Xu
S.
,
Zhang
L.
, and
Yang
M.
, “
Big Data and Human Resource Management Research: An Integrative Review and New Directions for Future Research
,”
Journal of Business Research
133
(September
2021
):
34
50
, https://doi.org/10.1016/j.jbusres.2021.04.019
9.
Xu
Z.
and
Lei
X.
, “
Analysis on Equity of Health Human Resource Allocation in Hubei Province Based on the Gini Coefficient and a HRDI Model
,”
Journal of Physics: Conference Series
1941
, no. 
1
(
2021
): 012063, https://doi.org/10.1088/1742-6596/1941/1/012063
10.
Wen
H.
,
Zeng
Y.
, and
Tang
Z.
, “
Sustainability and Resource Equilibrium Evaluation of a Tourism Traffic Network Based on a Tourism Traffic Matching Curve
,”
Sustainability
11
, no. 
20
(October
2019
): 5769, https://doi.org/10.3390/su11205769
11.
Ojonta
O. I.
and
Ogbuabor
J. E.
, “
Access to Credit and Physical Capital Stock: A Study of Non-farm Household Enterprises in Nigeria
,”
Bulletin of Monetary Economics and Banking
24
, no. 
4
(December
2021
):
631
640
, https://doi.org/10.21098/bemp.v24i4.1515
12.
Raza
S. A.
and
Khan
K. A.
, “
Impact of Green Human Resource Practices on Hotel Environmental Performance: the Moderating Effect of Environmental Knowledge and Individual Green Values
,”
International Journal of Contemporary Hospitality Management
34
, no. 
6
(June
2022
):
2154
2175
, https://doi.org/10.1108/IJCHM-05-2021-0553
13.
Nikulsheeva
V. F.
,
Khokhlova
G. I.
,
Kretova
N. V.
, and
Borisova
A. S.
, “
Human Capital as a Factor of Development of Innovative Activity of Construction Industry Enterprises
,”
IOP Conference Series: Earth and Environmental Science
751
, no. 
1
(
2021
): 012163, https://doi.org/10.1088/1755-1315/751/1/012163
14.
Zhang
X. L.
,
Wang
C. Z.
, and
He
L. Z.
, “
Interaction Mechanism and Empirical Analysis of Population Growth and Economic Development–Granger Causal Analysis Method and Cointegration Technology Based on Horizontal VAR
” (in Chinese),
South China Population
21
, no. 
1
(
2006
):
59
64
, https://doi.org/10.3969/j.issn.1004-1613.2006.01.009
15.
O’Sullivan
M.
,
Cross
C.
, and
Lavelle
J.
, “
The Forgotten Labour Force: Characteristics and Trends for Older Female Part-Time Workers in Ireland
,”
The Irish Journal of Management
39
, no. 
1
(August
2020
):
47
60
, https://doi.org/10.2478/ijm-2010-0006
16.
Basrowi
R. W.
,
Koe
L. C.
, and
Sundjaya
T.
, “
Investing in Adult Nutrition to Reduce Mobility Problems in Ageing Population
,”
World Nutrition Journal
4
, no. 
2
(August
2021
):
10
17
, https://doi.org/10.25220/WNJ.V04.i2.0003
17.
Mayombe
C.
, “
Needs Assessment for Vocational Skills Training for Unemployed Youth in eThekwini Municipality, South Africa
,”
Higher Education, Skills and Work-Based Learning
11
, no. 
1
(August
2021
):
18
33
, https://doi.org/10.1108/HESWBL-09-2019-0126
18.
Wu
Y. N.
,
Dong
S. W.
,
Xiao
C.
,
Li
X. C.
,
Pan
Y. C.
, and
Niu
C.
, “
Spatial Stratification Mode and Differentiation Evaluation for Accuracy Assessment of Remote Sensing Classification
” (in Chinese),
Transactions of the Chinese Society for Agricultural Machinery
52
, no. 
8
(
2021
):
147
153
, https://doi.org/10.6041/j.issn.1000-1298.2021.08.014
19.
Solihah
B.
,
Azhari
A.
, and
Musdholifah
A.
, “
The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction
,”
JUITA
9
, no. 
1
(May
2021
):
131
138
, https://doi.org/10.30595/juita.v9i1.9969
20.
Ge
B.
,
Ishaku
M. M.
, and
Lewu
H. I.
, “
Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana
,”
Journal of Computer and Communications
9
, no. 
10
(October
2021
):
1
14
, https://doi.org/10.4236/jcc.2021.910001
21.
Widiarti
,
Oktafiani
D. D.
,
Usman
M.
, and
Kurniasari
D.
, “
Application of the Spatial Empirical Best Linear Unbiased Prediction Method for Estimating per Capita Expenditure in Lampung Province
,”
Journal of Physics: Conference Series
1567
, no. 
2
(July
2020
): 022082, https://doi.org/10.1088/1742-6596/1567/2/022082
22.
Wang
J.
,
Chen
Q.
, and
Gong
H.
, “
STMAG: A Spatial-Temporal Mixed Attention Graph-Based Convolution Model for Multi-data Flow Safety Prediction
,”
Information Sciences
525
(July
2020
):
16
36
, https://doi.org/10.1016/j.ins.2020.03.040
23.
Qian
C.
,
Sun
X.
,
Zhang
S.
,
Xing
D.
,
Li
H.
,
Zheng
X.
,
Pan
G.
, and
Wang
Y.
, “
Nonlinear Modeling of Neural Interaction for Spike Prediction Using the Staged Point-Process Model
,”
Neural Computation
30
, no. 
12
(December
2018
):
3189
3226
, https://doi.org/10.1162/neco_a_01137
24.
Zhang
Z.
,
Chen
Y.
,
Liu
X.
, and
Wang
W.
, “
Two-Stage Robust Security-Constrained Unit Commitment Model Considering Time Autocorrelation of Wind/Load Prediction Error and Outage Contingency Probability of Units
,”
IEEE Access
7
(
2019
):
25398
25408
, https://doi.org/10.1109/ACCESS.2019.2900254
25.
Wang
J.
and
Lin
X.
, “
A Bayesian Approach for Semiparametric Regression Analysis of Panel Count Data
,”
Lifetime Data Analysis
26
, no. 
2
(April
2020
):
402
420
, https://doi.org/10.1007/s10985-019-09471-3
26.
He
K.
, “
Online Screening of Complex Network Input Nodes Driven by Big Data
” (in Chinese),
Computer Simulation
36
, no. 
4
(April
2019
):
252
255
, 264, https://doi.org/10.3969/j.issn.1006-9348.2019.04.052
This content is only available via PDF.
You do not currently have access to this content.