In order to achieve optimal radiation protection, rapid and accurate reconstruction of radiation field has vital significance in the selection of working paths during the overhaul of nuclear power plants and the decommissioning of nuclear facilities. The radiation field is usually reconstructed by various interpolation methods, but the reconstruction accuracy of such methods is insufficient, With the improvement of AI technology, neural networks have great potential in radiation field reconstruction, but conventional neural networks is prone to local minima and vanishing grandient problem. This paper aims to develop a radiation field reconstruction method based on an adaptive Back-propagation (BP) neural network neural network method with learning rate decay and a corresponding sampling method for multisampling in places where flux gradient changes drastically, and verify its accuracy and feasibility. The proposed method achieves global optimality and avoids vanishing grandient problem by virtue of adaptive algorithm and learning rate decay, ensuring that the radiation field is reconstructed with the smallest relative average error when the sampling point is determined, moreover, the proposed sampling method can greatly improve the accuracy of radiation field reconstruction. The accuracy of the proposed method was tested with three MC simulated radiation fields with simpler cases, and the feasibility of the proposed method was further validated with two MC simulated, more complex and realistic scenes. The results of the proposed method show that the errors of the three test cases are 1.7%, 6.8%, and 7.8%, and the errors of the two validated cases are 8.8% and 7.7%, respectively. The merit of this method was preliminarily verified, further validation is underway to validate its application in real world scenarios.

为了实现最佳的辐射防护，快速准确地重建辐射场对于核电厂检修和核设施退役过程中工作路径的选择具有重要意义。辐射场重建通常采用各种插值方法，但此类方法的重建精度不足，随着人工智能技术的进步，神经网络在辐射场重建方面具有巨大潜力，但传统神经网络容易出现局部极小值和消失的盛大问题。本文旨在开发一种基于具有学习率衰减的自适应反向传播（BP）神经网络方法和相应采样方法的辐射场重建方法，用于在通量梯度变化剧烈的地方进行多重采样，并验证其准确性和可行性。 . 该方法通过自适应算法和学习率衰减实现了全局最优，避免了消失的盛大问题，保证了在确定采样点时以最小的相对平均误差重建辐射场，并且该采样方法可以大大提高辐射场重建的准确性。所提方法的准确性通过三个 MC 模拟辐射场与更简单的情况进行了测试，并通过两个 MC 模拟的更复杂和逼真的场景进一步验证了所提出方法的可行性。所提方法的结果表明，三个测试用例的误差分别为1.7%、6.8%和7.8%，两个验证用例的误差分别为8.8%和7.7%。该方法的优点得到了初步验证，正在进一步验证以验证其在实际场景中的应用。