Unlike Artificial Neural Networks (ANN), Spiking Neural Networks (SNN), as a representative of the third generation of neural network technologies, perform computations based on biological neuron mechanisms, using sequences of spike signals to transmit information. This exhibits significant energy efficiency advantages and high-speed processing capabilities for massive data. However, due to the complex dynamics of spiking neurons and the non-differentiability of spike computations, the current direct training methods for SNNs are not very effective, hindering their widespread application. At present, converting high-precision ANN to SNN is considered one of the most promising methods for generating SNN. However, mainstream ANN-to-SNN conversion methods have their limitations: first, they do not support negative spikes, making it difficult to represent negative spikes from dynamic vision sensor cameras; second, low latency and high precision cannot be achieved simultaneously during the conversion process. To address these issues, this paper proposes a novel spiking neuron that can represent the entire range of values, supporting both positive and negative values in traditional ANN as well as the positive and negative polarities of DVS (Dynamic Vision Sensor) spikes. Additionally, this paper proposes a step-wise Leaky ReLU activation function and a regional convergence testing algorithm to achieve zero-error conversion from ANN to SNN. With these methods, we realize a high-precision, low-latency, and robust ANN-to-SNN conversion. Our method demonstrates outstanding performance on the CIFAR10 and CIFAR100 datasets.