Method and system for controlling and distributing wave energy in offshore aquaculture

A method for controlling and distributing wave energy in offshore aquaculture, comprising:

• obtaining an aquaculture cycle of each aquaculture sub-zone of an offshore aquaculture zone, sorting remaining aquaculture cycles of the aquaculture sub-zones from small to large, and obtaining a plurality of work cycles according to sorting results;
• setting an input layer of a preset neural network model into three dimensions, and selecting one long short term memory (LSTM) layer and one gated recurrent unit (GRU) layer to form a hidden layer of the preset neural network model;
• and setting a plurality of first neurons and one second neuron in an output layer, wherein each of the first neurons corresponds to one group of sensors deployed offshore, output of the second neuron is a sum of output of all the first neurons, and each group of sensors is responsible for information collection of one ocean subregion;
• converting historical ocean data into a three-dimensional tensor, wherein a first dimension of the three-dimensional tensor is batch sample quantity, and the batch sample quantity is equal to a group number of sensors deployed offshore;
• a second dimension of the three-dimensional tensor is time step, and the time step is equal to a time span of the historical ocean data;
• and a third dimension of the three-dimensional tensor is feature quantity;
• inputting the three-dimensional tensor into the preset neural network model for training;
• obtaining a predicted wave energy yield of a next work cycle through the preset neural network model;
• obtaining an importance coefficient value sorting result of each aquaculture zone through a preset recursive feature elimination model, wherein the step comprises:
• collecting data of different aquaculture zones to form an aquaculture data set, and taking an aquaculture yield as a target variable;
• dividing the aquaculture data set into an aquaculture training set and an aquaculture test set;
• using the aquaculture training set to train a preset logistic regression model;
• and predicting the aquaculture test set through the logistic regression model, and obtaining importance coefficient sorting results of the plurality of aquaculture zones, wherein importance coefficients decrease sequentially, and each importance coefficient reflects an influence of the aquaculture zone on the aquaculture yield;
• collecting historical operation data of each aquaculture apparatus;
• performing feature extraction on the historical operation data of each aquaculture apparatus, and obtaining corresponding five-dimensional apparatus feature vectors of different aquaculture apparatus, wherein a first dimension of each five-dimensional apparatus feature vector is apparatus type value, a second dimension of each five-dimensional apparatus feature vector is apparatus working environment value, a third dimension of each five-dimensional apparatus feature vector is apparatus working time value, a fourth dimension of each five-dimensional apparatus feature vector is apparatus latitude and longitude value, and a fifth dimension of each five-dimensional apparatus feature vector is quarterly yield value of an aquaculture farm to which the apparatus belongs;
• and performing K-mean clustering on all the five-dimensional apparatus feature vectors, and obtaining three clusters, wherein the aquaculture apparatuses corresponding to the five-dimensional apparatus feature vectors in the first cluster are first-type aquaculture apparatuses, the aquaculture apparatuses corresponding to the five-dimensional apparatus feature vectors in the second cluster are second-type aquaculture apparatuses, and the aquaculture apparatuses corresponding to the five-dimensional apparatus feature vectors in the third cluster are third-type aquaculture apparatuses;
• and
• adjusting operation cycles and operation power of the first-type aquaculture apparatuses, the second-type aquaculture apparatuses, and the third-type aquaculture apparatuses in sequence according to an apparatus type of each aquaculture apparatus, the aquaculture zone where each aquaculture apparatus is located, the predicted wave energy yield, and the importance coefficient value sorting results, wherein the step comprises:
• counting wave energy required by the first-type aquaculture apparatuses, the second-type aquaculture apparatuses, and the third-type aquaculture apparatuses in the next work cycle, wherein the first-type aquaculture apparatuses comprise a water quality monitoring apparatus, a feeding apparatus, and a waste treatment apparatus, the second-type aquaculture apparatuses comprise an underwater camera monitoring apparatus, a water pump and filtering apparatus, and a disease prevention apparatus, and the third-type aquaculture apparatuses comprise an automatic control apparatus, a greenhouse and incubation apparatus, and an aquaculture processing apparatus;
• confirming an importance coefficient value order of the first-type aquaculture apparatuses, an importance coefficient value order of the second-type aquaculture apparatuses, and an importance coefficient value order of the third-type aquaculture apparatuses according to the aquaculture zone where each aquaculture apparatus is located and the importance coefficient value sorting results;
• adjusting, in a case where the wave energy required by all the first-type aquaculture apparatuses is greater than the predicted wave energy yield, the operation cycles and the operation power of the first-type aquaculture apparatuses in sequence according to the importance coefficient value order of the first-type aquaculture apparatuses;
• adjusting, in a case where the wave energy required by all the first-type aquaculture apparatuses is less than or equal to the predicted wave energy yield and the wave energy required by all the first-type aquaculture apparatuses and all the second-type aquaculture apparatuses is greater than the predicted wave energy yield, the operation cycles and the operation power of the second-type aquaculture apparatuses in sequence according to the importance coefficient value order of the second-type aquaculture apparatuses after operation cycle requirements and operation power requirements of all the first-type aquaculture apparatuses are satisfied;
• and
• adjusting, in a case where the wave energy required by all the first-type aquaculture apparatuses and all the second-type aquaculture apparatuses is less than or equal to the predicted wave energy yield and the wave energy required by all the first-type aquaculture apparatuses, all the second-type aquaculture apparatuses, and all the third-type aquaculture apparatuses is greater than the predicted wave energy yield, the operation cycles and the operation power of the third-type aquaculture apparatuses in sequence according to the importance coefficient value order of the third-type aquaculture apparatuses after operation cycle requirements and operation power requirements of all the first-type aquaculture apparatuses and all the second-type aquaculture apparatuses are satisfied.