局部穿管电缆作为城市电网载流量的瓶颈位置，其温度监测至关重要。针对目前测温方法精度低、无法确定不同情况下的最佳测温点组合且输入量的选取方法不适用于“黑箱”网络的问题，提出了一种LassoNet嵌入改进BP神经网络的实时温度反演模型。首先通过LassoNet网络自主量化选定适用于局部穿管电缆的神经网络的最佳测温点组合；随后引入iCircle映射、惯性权重思想、Levy飞行混合策略对麻雀搜索算法（sparrow search algorithm，SSA）的初始分布、搜索策略以及迭代方法进行改进以提升全局寻优性能，并利用改进后的 SSA对 BP神经网络参数进行寻优，实现了多工况下热点温度的快速、高精度反演。建立了YJLW03-64/110 kV电缆局部穿管有限元仿真模型，通过与IEC标准的对比验证了该模型的准确性，随后构造了不同负荷类型下的热点温度样本数据集，基于该数据集对所提方法与典型的5种反演算法进行了对比分析，同时为了验证算法的迁移性能通过220 kV局部穿管电缆及110 kV电缆接头的温度反演进行了测试。结果表明，所提出的反演方法误差可控制在1.5 ℃以内，收敛速度快，能够系统化地选取测温点且具有更高的精度和鲁棒性。

Local tube-through cables are the bottleneck locations of urban power grid current carrying capacity, the temperature monitoring of which is crucial. A real-time temperature inversion model based on LassoNet embedding and improved BP neural network is proposed to address the problems of low accuracy of current temperature measurement methods, inability to determine the optimal combination of temperature measurement points for different situations, and unsuitable input selection methods for "black box" networks. Firstly, the LassoNet network is used to autonomously quantify and select the optimal combination of temperature measurement points suitable for local tube-through cables neural networks; Subsequently, iCircle mapping, inertia weight concept, and Levy flight hybrid strategy are introduced to improve the initial distribution, search strategy, and iterative method of sparrow search algorithm (SSA) to enhance global optimization performance. The improved SSA is used to optimize the parameters of BP neural network, achieving fast and high-precision inversion of hot spot temperature under multiple operating conditions. A finite element simulation model of YJLW03-64/110 kV cable local conduit is established, and the accuracy of the model is verified by comparing it with IEC standards. Then, a hot spot temperature sample dataset is constructed under different load types. Based on this dataset, the proposed method is compared and analyzed with five typical inverse algorithms. At the same time, in order to verify the transferability of the algorithm, temperature inversion of 220 kV local tube-through cables and 110 kV cable joints is tested. The results show that the proposed inversion method can control the error within 1.5 ℃, has a fast convergence speed, can systematically select temperature measurement points, and has higher accuracy and robustness.