Real time Chinese English translation cultural appropriateness assessment using deep learning methods based on wireless sensor networks

Aiming at the problem that real-time Chinese-English translation evaluation in the context of Industry 4.0 overly relies on text semantics and is difficult to reflect the context and cultural appropriateness of the industrial field, a wireless sensor network-driven method for evaluating the cultural appropriateness of real-time Chinese-English translation is proposed. Based on scenarios such as production line operation, equipment failure alarm, remote engineering collaboration and industrial safety training, a multimodal sample set including Chinese source text, English translation, cultural pragmatics annotation, and industrial field perception data is constructed. The model uses a bidirectional encoder representations from Transformers—bidirectional long short-term memory network—attention mechanism for the fusion modeling of text, culture, and field perception data, and combines edge computing for low-latency inference. Experiments show that the proposed method achieves an accuracy of 93.84%, an F1-score of 93.26%, a root mean square error reduced to 0.083, and a cloud-edge collaborative inference delay of 19.31 ms in the cultural suitability level identification task. The findings reveal that with the introduction of wireless sensor network field data and cultural pragmatic features, the model can more accurately identify the cultural suitability level, scoring bias, and risk type of the translation, providing real-time evaluation support for cross-language collaboration, security alerts, and multilingual human–computer interaction in the context of Industry 4.0.