School of General Education, Hunan University of Information Technology, Hunan Changsha 410151, China.
School of Foreign Language, Changsha Normal University, Hunan Changsha 410100, China.
Comput Intell Neurosci. 2022 May 19;2022:6788813. doi: 10.1155/2022/6788813. eCollection 2022.
Today, we have entered an era of big data. Under the background of global economization, communication in all walks of life is becoming more and more frequent and cross-language communication is inevitable. Cross-language communication is difficult for many people. The online translation system can greatly reduce the communication barriers between people of different languages. As an efficient tool, the translation system can realize the translation of different languages under the conditions of retaining the original semantics equivalent conversion. The article adopts the Internet of Things technology and big data model to build an English intelligent translation system, which can realize intelligent translation between multiple languages and English. The research results of the article show the following: (1) For samples with high semantic feature values, the correlation coefficient and similarity coefficient will be higher. Therefore, it can be concluded that for different semantics, the similarity is generally positively correlated with its feature value and correlation coefficient. The translation speed of the system proposed in the article is the fastest among the three translation systems. When the number of sentences is 10,000, the translation speed of the translation system proposed in the article is 5.89 seconds, the translation speed of the network multilingual translation system is 6.74 seconds, and the translation speed of the traditional translation system is 10.53 seconds. (2) The translation accuracy of the big data intelligent translation model proposed in the article is the highest among the three models. The translation accuracy of simple sentences can reach 99%. The translation accuracy of general sentences is 98%, and the translation accuracy of complex sentences is 95%. The BLEU value of the method in this paper is basically the same as that of the RNN cyclic neural network translation model. When translating general sentences, the BLEU value of the method in this paper is slightly higher than that of the RNN cyclic neural network translation model; especially when translating complex sentences by machine, the BLEU value of the method in this paper is far higher than that of the RNN translation model. (3) The average response time will increase with the increase in the number of tests, and the success rate generally remains above 98%, close to 100%, indicating that the response time of the system operation is normal. The number of designed test cases for the data processing module is 90, the number of executed test cases is 90, and the execution rate can reach 100%. Normal operation means that in the process of operation, no fault occurs. In the system load test, the load of serial number 1 is normal, the average delay is 38 seconds, the average delay of serial number 2 is 48 seconds, the average delay of serial number 3 is 59 seconds, the average delay of serial number 4 is 62 seconds, and the average delay of serial number 5 is 47 seconds. The delay of data packets under all kinds of loads can meet the standard requirements.
今天，我们已经进入了大数据时代。在全球化经济化的背景下，各行各业的交流越来越频繁，跨语言交流不可避免。跨语言交流对很多人来说都很困难。在线翻译系统可以大大降低不同语言之间的沟通障碍。作为一种高效的工具，该翻译系统可以在保留原语义等效转换的条件下实现不同语言之间的翻译。本文采用物联网技术和大数据模型构建英语智能翻译系统，可以实现多语言与英语之间的智能翻译。文章的研究结果表明：（1）对于语义特征值较高的样本，相关系数和相似系数会更高。因此，可以得出结论，对于不同的语义，相似度通常与特征值和相关系数呈正相关。本文提出的系统的翻译速度是三个翻译系统中最快的。当句子数量为 10000 时，本文提出的翻译系统的翻译速度为 5.89 秒，网络多语言翻译系统的翻译速度为 6.74 秒，传统翻译系统的翻译速度为 10.53 秒。（2）本文提出的大数据智能翻译模型的翻译精度在三个模型中最高。简单句的翻译准确率可达 99%。一般句的翻译准确率为 98%，复杂句的翻译准确率为 95%。本文方法的 BLEU 值与 RNN 循环神经网络翻译模型基本相同。在翻译一般句时，本文方法的 BLEU 值略高于 RNN 循环神经网络翻译模型；特别是在机器翻译复杂句时，本文方法的 BLEU 值远高于 RNN 翻译模型。（3）平均响应时间会随着测试次数的增加而增加，成功率一般保持在 98%以上，接近 100%，表明系统运行的响应时间正常。数据处理模块的设计测试用例数为 90，执行测试用例数为 90，执行率可达 100%。正常运行意味着在运行过程中不会发生故障。在系统负载测试中，序列号 1 的负载正常，平均延迟为 38 秒，序列号 2 的平均延迟为 48 秒，序列号 3 的平均延迟为 59 秒，序列号 4 的平均延迟为 62 秒，序列号 5 的平均延迟为 47 秒。各种负载下的数据报的延迟都能满足标准要求。
