Aquaculture water quality dissolved oxygen prediction method based on data fusion

Abstract

The invention relates to an aquaculture water quality dissolved oxygen prediction method based on data fusion, mainly comprising the steps of S1, obtaining a plurality of dissolved oxygen values and meteorological data, and performing normalization processing on the dissolved oxygen values; S2, processing normalized data by an RBF network optimized through a K-clustering algorithm to obtain more accurate dissolved oxygen values; S3, re-combining the more accurate dissolved oxygen values with the meteorological data in S1 to form a sample prediction training sample, utilizing the training sample to train a least squares support vector machine model, and obtaining an optimal least squares support vector machine prediction model; and S4, collecting the dissolved oxygen values and meteorological environment data of the aquaculture ecological environment on line in real time, and utilizing the optimal least square support vector machine prediction model to obtain a dissolved oxygen predicted value. The method can realize dissolved oxygen accurate and efficient prediction, and lay the foundation of aquaculture on-line prediction early warning and intelligent control.

Description

technical field [0001] The invention belongs to the technical field of aquaculture, and in particular relates to a method for predicting dissolved oxygen in aquaculture water quality based on data fusion. Background technique [0002] In the process of pond aquaculture, dissolved oxygen is an important indicator for the survival of aquatic products. Timely and accurate grasp of the change trend of dissolved oxygen concentration is the key to ensuring high-density aquaculture. If the change trend of dissolved oxygen concentration in aquaculture ponds cannot be accurately grasped, the remediation of oxygen increase is not timely, the stress environment of low oxygen or severe hypoxia will not only restrict the health of aquatic products and even lead to the death of large areas of aquatic products, bringing huge economic losses to users. It also seriously affects the healthy and sustainable development of the aquaculture industry. To this end, based on intelligent information...

AI Technical Summary

Problems solved by technology

[0003] At present, the existing water quality dissolved oxygen prediction model, the input parameters mostly come from a single sensor, which is prone to problems such as missing data and abnormal data. The accuracy and reliability of the predicted dissolved oxygen value are low, which cannot meet the requirements of aquaculture need

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