The goal of entity relation extraction is to extract entities and the relations between entities from unstructured texts. Most of the existing research approaches are oriented towards common entity labels in general-purpose domains (e.g., time, place, person, institution, etc.) and simple texts in specialized domains (texts consisting of single sentences with low knowledge density). The existence of long-distance dependencies of entity pairs (cross-sentence entity pairs) and the phenomenon of overlapping relations (different relations sharing the same entity) in complex texts are ignored. However, complex texts are common in practical applications, especially in professional fields such as power technology standards, where the knowledge density of texts is high and the phenomenon of entity-pair cross-sentence dependency is significant. To solve the above problems, this paper proposes a novel multi-hop automatic question-and-answer-based entity relation extraction method, which combines the current well-established machine reading comprehension framework with the automatic question construction mechanism proposed in this paper, and uses the a priori knowledge provided by the question as the extraction type guide, and uses the multi-hop question-and-answer mechanism to reason about the answer span of the question, effectively alleviating the phenomena of overlapping relations and entity dependence on crosssentences in complex texts. We conducted extensive comparative experiments on the power technology standard dataset self-constructed in this paper, and the results show that the MT-auQA model proposed in this paper achieves optimal performance
The existing dynamic monitoring methods of low-voltage distribution network leakage detection device have been unable to meet the needs of the distribution network in China. Therefore, a dynamic monitoring method of low-voltage distribution network leakage detection device based on Internet of Things technology is proposed. Based on the introduction of the Internet of Things technology sensor sensing technology, the sensor is installed on the leakage detection device to obtain the operation data of the leakage detection device and preprocess it with noise reduction and normalization. At the same time, the statistical analysis of partial discharge signal is carried out to extract the characteristics of fast waveform signal (energy parameters, sample entropy and modal components). Based on the operation data features of the leakage detection device extracted above, the state diagnosis framework of the leakage detection device is built to diagnose the state of the leakage detection device, and the dynamic monitoring of the leakage detection device in low-voltage distribution network is realized. The experimental results show that: compared with the existing methods, the proposed method has stronger anti-interference ability and smaller monitoring error, which fully proves that the proposed method has better application effect.
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