In the human resource system of modern enterprises, human-post matching big data occupies an important irreplaceable position. With the deepening of the reform of state-owned enterprises, some shortcomings of human-post matching big data have become prominent. The purpose of this article is to solve the current state-owned enterprises. There are a variety of problems with big data in the enterprise, and an effective method is found that can accurately evaluate the degree of human-job matching in state-owned enterprises and provide a scientific basis for the manager of talent and resource allocation to make more rational decisions. Through the radial basis function (RBF) neural network-based big data model of human-post matching evaluation of state-owned enterprises, we scientifically and effectively evaluate the matching degree of the quality and ability of the personnel with the relevant requirements of the position and then help the company to adjust the personnel at any time changes in positions to maximize the efficiency of human resources. In this paper, considering the actual situation of the enterprise, the RBF neural network and the analytic hierarchy process (AHP) method are used comprehensively. Firstly, the AHP is used to obtain the weight of each evaluation index in the human-post matching index system. At the same time, the artificial neural network theory is self-adapting. Learning is helpful to solve the problem that the AHP method is too subjective. The two learn from each other’s strong points and combine their weaknesses organically to increase the convenience and effectiveness of evaluation.

中文翻译：

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基于径向基函数（RBF）神经网络的企业管理资源调度优化与仿真

在现代企业人力资源体系中，人岗匹配大数据占有不可替代的重要地位。随着国企改革的深入，人岗匹配大数据的一些弊端凸显出来。本文的目的就是要解决当前国有企业的问题。针对企业大数据存在的多种问题，找到了一种能够准确评估国有企业人岗匹配程度的有效方法，为人才和资源配置管理者提供科学依据。更理性的决定。通过基于径向基函数（RBF）神经网络的国有企业人岗匹配评价大数据模型，我们科学有效地评估人员素质和能力与岗位相关要求的匹配程度，进而帮助公司随时调整岗位变动的人员，实现人力资源效率的最大化。本文结合企业的实际情况，综合运用RBF神经网络和层次分析法（AHP）方法。首先利用层次分析法获得人岗匹配指标体系中各评价指标的权重。同时，人工神经网络理论具有自适应性。学习有助于解决层次分析法过于主观的问题。