![]() ![]() The Sliding Time Window (STW) method belongs to sequence segmentation, which divides the data into multiple time windows in time order each time window contains multiple time points and selects the data in the current time window as the analysis data set. Currently, the feature extraction methods of time series are mainly based on statistical features, prediction models, feature transformation and sequence segmentation. ![]() Therefore, it is particularly important to detect outliers in the collected data. In addition, due to system software and hardware failure and other reasons, the sensor or transmission network will be disturbed by noise, resulting in certain abnormal data and missing data, which causes certain difficulties in monitoring and analysis. The actual situation is that during the experiment, the process of transmitting data from the sensor to the processor will consume a certain amount of time, resulting in random delays. The current common form of monitoring and warning is issuing a water quality parameter outside the set range after the alarm. The various monitoring methods or technologies mentioned in the above aquaculture monitoring systems are avant-garde to an extent, but the abnormal monitoring of water quality monitoring systems and how to deal with effective early warnings brought by data anomalies are less studied. Therefore, many aquaculture monitoring systems have been designed and developed, with the advantages of low cost, multi-perception, convenient deployment, durable endurance, intelligent parameter prediction and long sensor life, which help aquaculture farmers understand enough environmental information. The wireless monitoring system has been the mainstream of development, where the wireless sensor network incorporates sensing technology, embedded computing technology, modern network technology, wireless communication technology and distributed intelligent information processing technology, etc., to work in a long-term unattended state. The support capacity to respond to the sustainable development of the aquaculture environment and adjustable demand is weak. In addition, the aquaculture ecological environment in many countries has long been marginalized, fragmented and subordinated, lacking a shared level of aquaculture major research database, research data and research results. However, the current small-scale aquaculture cannot meet the production supply, and the pursuit of too intensive culture environment will inevitably deteriorate water quality, which will lead to fish mortality, environmental degradation, and other adverse effects. By 2014, world aquaculture production had surpassed fishery catch production. The survey data released by the Food and Agriculture Organization of the United Nations (FAO) shows that aquaculture is one of the fastest-growing food production areas in the world. The platform uses WebSocket technology to interact with the server, and combined with the surveillance camera, it can monitor the aquaculture environment and perform data monitoring and analysis in a real-time, accurate and comprehensive manner, which can provide theoretical reference and technical support for sustainable development of aquaculture. Further, a fuzzy control algorithm is adopted to specify the warning information, and a software platform is developed based on data visualization. The detection results show that the algorithm can accurately identify abnormal subsequences and outliers, and the accuracy, recall and F1-Score are 87.71%, 82.58% and 85.06%, respectively, which verifies the usability of the proposed method. ![]() A detection method based on time series sliding window density clustering (STW-DBSCAN) is proposed for anomaly detection, using the confidence interval distance radius of slope to extract subsequence timing features and identify the suspected abnormal subsequences and then further determine the anomalous value by the DBSCAN clustering method. The device is capable of collecting five aquaculture environment factors such as water temperature, pH, salinity, dissolved oxygen and light intensity throughout the day by wireless data transmission via 4G DTU with a communication success rate of 92.08%. This paper designs a set for an IoT-based aquaculture environment monitoring device. The current aquaculture environment anomaly monitoring system is limited in function, making it difficult to provide overall technical support for the sustainable development of aquaculture ecosystems. ![]()
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