Big data-based regional talent supply and demand balance quantitative analysis method

Abstract

The present invention provides a regional talent supply and demand balance quantitative analysis method. According to the method, firstly, data related to the demand of regional talents are acquired from recruitment websites, tissue recruitment plates and the compile recruitment information by utilizing a crawler. Secondly, data related to the supply of talents within a designated region are acquired from the admission information of various schools by utilizing the crawler. Finally, the data related to the demand of talents and the data related to the supply of talents are processed, and then the supply and demand quantity of talents within the designated region can be effectively analyzed from multiple angles and multiple levels. Therefore, the condition of talents within the designated region can be comprehensively quantized and analyzed, and the composition of regional talents is made clear. The quantization of confidants is realized. Meanwhile, the influence conditions of different talent policies on the supply and the demand of regional talents during different stages can be reflected, and the advantages and the disadvantages of different talent policies are concluded. As a result, the method provides the data support for the better formulation of talent policies.

Description

technical field [0001] The present invention relates to a quantitative analysis method of talent supply and demand balance, more specifically, a quantitative analysis method of regional talent supply and demand balance based on big data. Background technique [0002] Talents are the first resource and basic resource for regional economic development, which to a large extent affect or determine the direction, speed, potential, market competitiveness and economic benefits of regional economic development. Therefore, administrative districts at all levels hope to have an intuitive and quantitative control of the talent situation in the region, so that according to the regional economic advantages and talent supply and demand gap, Formulate matching talent development plans and talent introduction policies, macro-control the optimal allocation of regional resources and talents, and further promote the sound and rapid economic development of the region. [0003] The current anal...

AI Technical Summary

Problems solved by technology

[0006] Two, time-consuming

[0007] After determining the analysis target, whether it is a questionnaire or a consulting company, it takes a long time to collect and organize relevant data, and most of the collected data comes from statistical databases such as yearbooks and provincial records. These databases are usually summarized at the end of the year. A certain lag, it is difficult to reflect the current supply and demand of talents in real time

With the increase of analysis indicators and the increase of time span, the difficulty of data collection will increase, and the reliability of information will also decrease.

In addition, the writing of denoising rules after data collection and the construction of multi-dimensional analysis models also require a lot of manpower and time to process.

Moreover, the effectiveness of the analysis model cannot be tested in advance before the report is finally presented. When the model deviates, multiple iterations of model building, data analysis, and report writing are required, which invisibly increases the time-consuming analysis

[0008] 3. High cost

[0009] A talent supply and demand analysis requires a lot of manual intervention at each stage, resulting in high human and financial costs for a single analysis

Even with a series of historical analysis reports, due to the different personnel composition, target data, and model functions of each analysis, problems such as data fragmentation and single target model have resulted, and the results, data, parameters, indicators, It is difficult to reuse models, etc., and it is difficult to reduce the cost of each analysis

Images