基于知识图谱的水产养殖病害诊断技术研究

Research on Diagnosis Technology of Aquaculture Diseases Based on Knowledge Graph

摘要:

水产养殖病害是影响水产养殖效益的重要因素，由于水产养殖病害文本数据杂乱无章，无法快速准确定位疾病原因，从而耽误诊断和治疗时机，导致水产养殖质量和产量下降。为解决上述问题，深入知识图谱的工作原理和模型特征，采用知识图谱技术完成水产养殖病害诊断总体方案设计，建立水产病害语料库，引入H-BIO标注策略，完成标注方案设计、改进BiLSTM模型构建，进行实体关系抽取和水产病害模型训练，完成水产养殖病害知识图谱可视化设计，并进行水产病害联合抽取实验。实验结果表明：基于知识图谱的改进BiLSTM模型在实体关系抽取方面效果较好、可靠性较高，有效提高了水产病害联合抽取准确率，构建了水产养殖病害可视化知识图谱，能够辅助作业人员快速准确进行水产病害诊断和治疗，对提升水产养殖生产效益具有十分重要的作用。

Abstract:

Diseases in aquaculture are an important factor affecting the efficiency of aquaculture. Due to the disorderly text data of aquaculture diseases, it is difficult to quickly and accurately locate the causes of diseases, which delays diagnosis and treatment, leading to a decrease in the quality and yield of aquaculture. To solve the above problems, we delve into the working principles and model features of knowledge graphs, use knowledge graph technology to complete the overall design of aquaculture disease diagnosis, establish a corpus of aquaculture diseases, introduce the H-BIO annotation strategy, complete the annotation scheme design, improve the BiLSTM model construction, extract entity relationships and train aquaculture disease models, complete the visualization design of aquaculture disease knowledge graphs, and conduct experiments on joint extraction of aquaculture diseases. The experimental results show that the improved BiLSTM model based on knowledge graph has good performance and high reliability in entity relationship extraction, effectively improving the accuracy of joint extraction of aquatic diseases. A visual knowledge graph of aquatic disease has been constructed, which can assist operators in quickly and accurately diagnosing and treating aquatic diseases. It plays a very important role in improving the production efficiency of aquaculture.