The reasonable allocation and use of human resources is an important content in the process of complex system analysis and design. This paper studies the human resource allocation model of Petri net based on artificial intelligence and neural network. In this paper, combined with the characteristics of human resource scheduling, human resource mobility, concurrency, and obvious classification characteristics, the human resource allocation model based on Petri net is implemented. In this paper, the model is trained with the python version of human resource analysis data set. The training parameters are 100, the error coefficient is 0.001, and the learning speed is 0.01. First, the coding rules of human resource data are established. Then, the parameters are input into the model, and the human resource data are trained in the model. Finally, the results of the model output layer are analyzed. The research study shows that the average prediction accuracy of this model is 78.85%. Model training requires the addition of 25 neurons for every 0.01 increase to improve the accuracy of predicting dynamic data of human resources. If the accuracy rate exceeds 75%, the increase in the number of neurons cannot be compensated for by the increase in the accuracy rate, but it is most efficient when the amount of data for human resource scheduling is 2000 to 4000. Therefore, this system can effectively allocate small- and medium-sized human resources and has a high accuracy.

中文翻译：

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基于人工智能和神经网络的人力资源Petri网配置模型

人力资源的合理配置和使用是复杂系统分析设计过程中的重要内容。本文研究了基于人工智能和神经网络的Petri网人力资源配置模型。本文结合人力资源调度、人力资源流动性、并发性、分类特征明显等特点，实现了基于Petri网的人力资源配置模型。本文使用python版本的人力资源分析数据集训练模型。训练参数为100，误差系数为0.001，学习速度为0.01。首先，建立了人力资源数据的编码规则。然后将参数输入到模型中，在模型中训练人力资源数据。最后，分析模型输出层的结果。研究表明，该模型的平均预测准确率为78.85%。模型训练需要每增加 0.01 增加 25 个神经元，以提高预测人力资源动态数据的准确性。如果准确率超过75%，神经元数量的增加无法通过准确率的提高来补偿，但在人力资源调度的数据量为2000到4000时效率最高。 因此，本系统能有效配置中小型人力资源，准确率高。01 增加提高预测人力资源动态数据的准确性。如果准确率超过75%，神经元数量的增加无法通过准确率的提高来补偿，但在人力资源调度的数据量为2000到4000时效率最高。 因此，本系统能有效配置中小型人力资源，准确率高。01 增加提高预测人力资源动态数据的准确性。如果准确率超过75%，神经元数量的增加并不能通过准确率的提高来补偿，但在人力资源调度的数据量为2000到4000时效率最高。 因此，本系统能有效配置中小型人力资源，准确率高。