The article applies mathematical statistics to the dynamic analysis of the demand for talents in large companies. Prediction of the overall demand for talents through the use of deep learning models. We use the theory of diminishing marginal utility to solve the degree of enterprise human resource investment under each index. Based on the analysis of traditional human resource management methods, this paper uses the three-dimensional fuzzy model to construct a new three-dimensional fuzzy model of human resource management model. The research found that the algorithm proposed in this paper can improve the quality of data training and improve the matching of people and posts.

#### Keywords

- Mathematical statistics equation
- Human resource management model
- Talent demand
- grey model

#### MSC 2010

- 46N30

The competition of the market is the competition of talents. Talent forecasting is an important prerequisite for the implementation of the talent resource development strategy. The use of modern scientific methods to do a good job of talent forecasting highlights its importance. One of our group’s talent needs in 2020 has made a macro forecast [1]. This article only introduces the quick process of using mathematical models to make macro forecasts. At the same time, the article focuses on giving statistical analysis methods and satisfactory prediction results.

The talent management department provides the historical data we collect. The planned value of the original value of fixed assets is plotted on a scatter plot based on twenty years of statistical data [2]. The estimated value is calculated using time-series simulation extrapolation and random time series forecasting based on the changing trend presented. Finally, we solicit the opinions of relevant experts from the group company and make appropriate amendments (Table 1).

Statistics of main indicators of group companies from 2000 to 2020

Years | Conversion turnover | Total number of professional and technical personnel (ten thousand) | Total number of employees (ten thousand) | |
---|---|---|---|---|

Transportation production department | Non-transportation production sector | |||

2000 | 44303.2 | 0.7826 | 10.108 | 2.1199 |

2001 | 46466.6 | 0.8602 | 10.2225 | 2.2537 |

2002 | 50624.2 | 1.089 | 10.0275 | 2.9387 |

2003 | 56962.1 | 1.1465 | IO.5152 | 3.0979 |

2004 | 68242.9 | 1.2613 | 11.5404 | 2.382 |

2005 | 75376.5 | 1.3288 | 11.8932 | 2.4988 |

2006 | 84037.1 | 1.4543 | 12.139 | 2.8373 |

2007 | 91678.6 | 1.8422 | 13.1091 | 2.806 |

2008 | 96612 | 1.9312 | 12.8916 | 2.9591 |

2009 | 98949 | 1. 9061 | 13. 1643 | 2.8623 |

2010 | 108965.8 | 2.0025 | 13.3669 | 2.8981 |

2011 | 120063.3 | 2.2908 | 13.362 | 2.8719 |

2012 | 131010.8 | 2.4121 | 12.9326 | 3.3311 |

2013 | 139288.4 | 2.4982 | 16.2635 | 2.9982 |

2014 | 140999.5 | 2.5156 | 13.2385 | 3.0245 |

2015 | 133743.6 | 2.6669 | 13.039 | 3.5383 |

2016 | 135593.8 | 2.7206 | 12. 3611 | 4.3116 |

2017 | 132645.1 | 2.6023 | 10.4685 | 5.6665 |

2018 | 130739.8 | 2.7532 | 11.521 | 4.6589 |

2019 | 150521.7 | 2.7604 | 11.1572 | 4.9899 |

2020 | 223010 |

The choice of methods when making talent predictions should be multifaceted. One method can only reveal the development trend of talents from a certain side. In the forecast of the total demand for talents in 2020, we use various methods to combine and select reasonable mathematical models to calculate to improve the prediction accuracy.

It is difficult to determine the various factors that affect the demand for talents. Instead of considering multiple factors, it is better to consider only the relationship with a single time. We take the time factor as a combination of various influencing factors [3]. The time-series extrapolation model predicts the total demand for talents and focuses on analyzing the development trend of talents themselves.

There are many types of time series extrapolation models. After screening, we use the following model to predict the total number of talents:

Make a scatter plot based on the historical data of the total number of employees of the group company. According to its changing trend, it is considered that it conforms to the characteristics of the Logistic growth curve, so we use this model to predict the total number of employees. The Logistic prediction model is:

Where

The historical data of talent density conforms to the characteristics of the growth curve, so this model is used to predict talent density. The growth curve prediction model is:

Suppose

The production function is a mathematical model describing the relationship between input and output. The transportation industry uses conversion turnover as output and the original value of fixed assets as a capital investment [4]. The labor volume uses the annual average number of employees. We establish a production function model of the total labor force in the transportation production sector. The production function model is:

_{1} historical data and calculated using multiple linear regression procedures.

Grey system theory is an original theory created by Professor Deng Julong of Huazhong University of Science and Technology in 1982. The starting point of the theory is to study the system from its internal characteristics. It can make full use of the known information in the system [5]. As a marginal subject, this method has been widely used in many fields. The talent system is a half-dark gray system. Among them, there are many uncertain and invisible factors. Therefore, we regard the gray forecasting model as one of the forecasting models for the total talent demand.

Model _{i}^{(0)}(1), _{i}^{(0)}(2),∧, _{i}^{(0)}(_{i}_{i}^{(0)}(1), _{i}^{(0)}(2),∧, _{i}^{(0)}(

We use the above formula to predict the future value (_{1}^{(1)} (_{1}, _{2} is the parameter determined by historical data _{1}^{(0)}(

The solution of this differential equation

The above formula represents the predicted value of the number generated in the _{1}^{(1)} (_{1} (

The following forecasting models are established using the relevant procedures in the “Economic Forecasting Methods, Procedures and Examples.” Data processed by the computer. Prediction

The correlation coefficient is 0.9847, and the standard deviation is 0.1175. The result proved that the inspection passed.

The predicted value (

The predictive model of talent density is

The correlation coefficient is 0.9894, and the standard deviation is 0.053. The result proved that the inspection passed [7]. The predicted value of talent density in 2020 is 0.2376.

Prediction 1: Time series model of the total number of employees:

Among them:

The correlation coefficient is 0.9847, and the standard deviation is 0.2809. This result indicates that various tests passed. The predicted value of the total number of employees in 2020 (

Forecast 2: Sub-sector forecasting method

We divide the various departments of the group company into two major categories: transportation production department and non-transportation production department. This way, we can choose different forecasting methods.

Production Letter Model of Total Labor Force in Transportation Production Department

Let us suppose:

Then we have a linear model of the deformed production function:

We calculate

1) The value of several statistics

The mean value of the explained variable is −0.3191. The multiple correlation coefficient is 0.9885. The remaining sum of squares is 0.0440. The statistic F value is 341.8480. Regression Sum of squares: 1.8819D. The W check value is 2.4054. The remaining standard deviation is 0.0525.

2) F test and goodness of fit test

For a given significance level _{0.05}(2.17) = 3.59. Because the overall F-test value is

3) D-W inspection

At the significance level

The predicted value of the total labor force in the transportation and production sector in 020 is 91,481 people

The forecast of the total labor force in the non-transportation production sector is based on the relevant regulations of the organization and personnel department. They use the fixed post and fixed number estimation method [8]. The forecast value of the total labor force in the non-transportation production sector in 2020 is 65,899 million. Comprehensively (1) and (2) can be obtained: the forecast value of the total number of employees in 2020 (2) is 1573.8 million. We take the average of the two forecast results of the total number of employees, and the forecast value of the total number of employees in 2020 is 163,230. According to the relationship (*), the forecast value of the total demand for talents in 2020 can be obtained. The forecast value (

Prediction 3: The gray prediction model

_{1} (_{2}(

If

Forecast results of total talent demand unit: 10,000

Method of prediction | Forecast I | Forecast II | Forecast III | Average predicted |
---|---|---|---|---|

Predictive value | 3.7215 | 3.8734 | 3.8056 | 3.8002 |

The above three methods are used to forecast the total talent demand of the group company. Although the workload increases, it can be calculated from different angles, and the results can be evaluated [9]. This is conducive to improving prediction accuracy. The final result is as follows: In 2020, the company's total talent demand is 38,002 million.

The total number of talents in the group company in 2019 is 27,604. The total demand for talents in 2020 is 38,002. This is 1.37 times of 2019. The average annual talent demand from 2021 to 2020 is 1040.

In 2020, the converted turnover of the group company was 150521.7 million ton-kilometers. It is planned that the converted turnover volume in 2020 will be 223,010 million ton-kilometers. The average annual growth rate is 4.01%. According to the forecast results of talent demand in 2020, the demand for talents of the group company will increase by 3.25% every year on average, and the growth rate of employees will be 0.096%. Therefore, the annual average increase rate of output value>the average annual increase rate of talents>the average annual increase rate of the total number of employees, the forecast result is reasonable in terms of economic benefits. This also reflects the needs of the group company to accelerate the training and improvement of the quality of the care team.

In 2020, the number of employees of the group company per million converted ton-kilometers was 0.1834, and the number of employees per million converted ton-kilometers was 1.027. It is predicted that in 2021, the number of talents per million converted ton-kilometers will be 0.17, and the number of employees per million converted ton-kilometers will be 0.73. While the content of talents has been reduced, the content of employees has been greatly reduced. This improves economic efficiency and is also a requirement for streamlining and streamlining the ranks of cadres.

In 2020, the talents of the group company accounted for 23.31%, which is a larger increase than the current ratio of 17.10%. This indicates that the task of personnel training in the future will be very arduous. The group company must adopt strong policies and measures to encourage existing cadres to improve their academic qualifications and technical titles and speed up personnel training. At the same time, the talent introduction policy should be tilted towards high-level talents to meet the group company's development needs.

#### Statistics of main indicators of group companies from 2000 to 2020

Years | Conversion turnover | Total number of professional and technical personnel (ten thousand) | Total number of employees (ten thousand) | |
---|---|---|---|---|

Transportation production department | Non-transportation production sector | |||

2000 | 44303.2 | 0.7826 | 10.108 | 2.1199 |

2001 | 46466.6 | 0.8602 | 10.2225 | 2.2537 |

2002 | 50624.2 | 1.089 | 10.0275 | 2.9387 |

2003 | 56962.1 | 1.1465 | IO.5152 | 3.0979 |

2004 | 68242.9 | 1.2613 | 11.5404 | 2.382 |

2005 | 75376.5 | 1.3288 | 11.8932 | 2.4988 |

2006 | 84037.1 | 1.4543 | 12.139 | 2.8373 |

2007 | 91678.6 | 1.8422 | 13.1091 | 2.806 |

2008 | 96612 | 1.9312 | 12.8916 | 2.9591 |

2009 | 98949 | 1. 9061 | 13. 1643 | 2.8623 |

2010 | 108965.8 | 2.0025 | 13.3669 | 2.8981 |

2011 | 120063.3 | 2.2908 | 13.362 | 2.8719 |

2012 | 131010.8 | 2.4121 | 12.9326 | 3.3311 |

2013 | 139288.4 | 2.4982 | 16.2635 | 2.9982 |

2014 | 140999.5 | 2.5156 | 13.2385 | 3.0245 |

2015 | 133743.6 | 2.6669 | 13.039 | 3.5383 |

2016 | 135593.8 | 2.7206 | 12. 3611 | 4.3116 |

2017 | 132645.1 | 2.6023 | 10.4685 | 5.6665 |

2018 | 130739.8 | 2.7532 | 11.521 | 4.6589 |

2019 | 150521.7 | 2.7604 | 11.1572 | 4.9899 |

2020 | 223010 |

#### Forecast results of total talent demand unit: 10,000

Method of prediction | Forecast I | Forecast II | Forecast III | Average predicted |
---|---|---|---|---|

Predictive value | 3.7215 | 3.8734 | 3.8056 | 3.8002 |

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