Quantitative analysis method of regional talent demand based on big data

Abstract

The invention provides a big data based quantitative analysis method for regional talent demand. The method comprises the following steps: firstly, scattered recruitment information of various organizations is taken as acquisition and analysis objects, processing such as summarization, calculation, mining and the like is performed with a big data processing technology, and arguments and support are provided for making regional talent policies and plans; secondly, underlying root causes of talent demand change are tracked and searched dynamically, and switching of different macroscopic-microscopic analytic perspectives is realized; thirdly, different index systems are constructed by combining various index dimensions according to user analytical object, multi-subject analysis can be performed simultaneously on the basis of identical data, multiple analysis reports can be generated, and data utilization rate is increased and analytical cost is reduced by the aid of the information technology; finally, an analysis result is shown in a chart form, the demand for information interpretation by various users from different perspectives can be met, and information needs of multiple parties such as the government, the enterprise, the individual and the like are met simultaneously.

Description

technical field [0001] The present invention relates to a method for quantitative analysis of regional talent demand, and more specifically, a method for quantitative analysis of regional talent demand 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 analysis of talent su...

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

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