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东华复试OJ二刷复盘11

news 2026/5/12 14:29:25

进阶11：给定两个仅由大写字母或小写字母组成的字符串(长度介于1到10之间)，它们之间的关系是以下4种情况之一：
1：两个字符串长度不等。比如 Beijing 和 Hebei
2：两个字符串不仅长度相等，而且相应位置上的字符完全一致(区分大小写)，比如 Beijing 和 Beijing
3：两个字符串长度相等，相应位置上的字符仅在不区分大小写的前提下才能达到完全一致（也就是说，它并不满足情况2）。比如 beijing 和 BEIjing
4：两个字符串长度相等，但是即使是不区分大小写也不能使这两个字符串一致。比如 Beijing 和 Nanjing
编程判断输入的两个字符串之间的关系属于这四类中的哪一类，给出所属的类的编号。

#include <bits/stdc++.h> using namespace std; bool Check(string s1,string s2){ for(int i=0;i<s1.size();i++){ if(s1[i] == s2[i] || s1[i]+32 == s2[i] || s1[i] == s2[i]+32){ continue; } else return false; } return true; } int main(){ string s1,s2;cin>>s1>>s2; if(s1.size()!=s2.size()){ cout<<1; } if(s1.size()==s2.size()){ if(s1==s2){ cout<<2; } else{ if(Check(s1,s2)){ cout<<3; } else{ cout<<4; } } } system("pause"); }

The Transformer model is a neural network architecture based on the attention mechanism and has achieved great success in the field of natural language processing. Unlike traditional recurrent neural networks, Transformers do not rely on step-by-step sequence processing. Instead, they use self-attention mechanisms to process the entire sequence simultaneously. This architecture not only improves the model’s ability to perform parallel computation but also enables it to capture long-range dependencies more effectively. In machine translation tasks, the Transformer model can dynamically assign attention weights according to the relationships between different words in a sentence, thereby producing more accurate translations. In addition, Transformer architectures have been widely applied to tasks such as text generation, speech recognition, and even image processing. In recent years, most large-scale pre-trained language models have been built upon the Transformer architecture, which has significantly accelerated the development of artificial intelligence technologies.

Transformer 模型是基于注意力机制的神经网络架构，已经在自然语言处理领域取得了巨大成功。不同于传统的循环神经网络，Transformer 模型不依赖于逐步的序列处理。相反，其使用自我注意力机制来同时地处理整个序列。这个架构不仅提高了模型执行并行计算的能力，还使其能更高效地捕捉长距离依赖。在机器翻译任务，Transformer模型能动态地分配注意力权重根据句子中不同单词之间的关系，从而生成更准确的翻译。另外，Transformer架构已广泛应用于一些任务例如文本生成、语音识别甚至是图片处理。近些年，大多数预训练语言模型已经在Transformer架构之上来构建，这极大地加速了人工智能技术的发展。
recurrent neural networks 循环神经网络、 step-by-step sequence processing 逐步序列处理、long-range dependencies 长距离依赖
self-attention自注意力机制
simultaneously同时spontaneously 自发
parallel computation并行计算