The invention relates to the technical field of elevator early warning, and discloses a multi-modal feature fusion elevator early warning method in a big data environment, which comprises the following steps: step S1, collecting video feature data, voiceprint feature data and operation feature data in an elevator operation process; s2, video feature extraction: extracting a video feature vector of a key video frame from the collected video feature data by using a video feature extraction model; and S3, voiceprint feature extraction: extracting voiceprint feature vectors from the collected voiceprint feature data by using a Mel frequency cepstrum coefficient. According to the method, the video feature data, the voiceprint feature data and the operation feature data are fused, so that the problems of low single-mode data recognition rate and high false alarm rate of a traditional mode are solved, the accuracy, reliability and robustness of early warning are improved, accurate prediction of elevator operation risks can be realized, and the safety of elevator operation is improved. Therefore, the quality safety of the elevator is effectively improved through preventive maintenance measures.
本发明涉及电梯预警技术领域,公开了一种大数据环境下多模态特征融合的电梯预警方法,包括以下步骤:步骤S1:采集电梯运行过程中的视频特征数据、声纹特征数据以及运行特征数据;步骤S2:视频特征提取,利用视频特征提取模型从采集的视频特征数据中提取关键视频帧的视频特征向量;步骤S3:声纹特征提取,利用梅尔频率倒谱系数从采集的声纹特征数据中提取声纹特征向量。本发明通过融合视频特征数据、声纹特征数据及运行特征数据,解决传统方式单模态数据识别率低、误报率高的不足问题,提高了预警的准确性、可靠性和鲁棒性,能够实现对电梯运行风险的精准预测,从而通过预防性维护措施有效提升电梯质量安全。
Multi-modal feature fusion elevator early warning method in big data environment
一种大数据环境下多模态特征融合的电梯预警方法
2025-02-28
Patent
Electronic Resource
Chinese
IPC: | B66B Aufzüge , ELEVATORS |
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