Method, device, storage medium and program product for adjusting water exchange angle of a fish farm vessel

Abstract

The application discloses a culture work ship water exchange angle adjusting method and device, a storage medium and a program product, relates to the culture work ship technical field, and the water exchange angle adjusting method comprises the following steps: obtaining water quality parameters in a culture net cage, a heading parameter of a culture work ship and external environment parameters; it is judged whether the water quality parameters meet the preset water quality standard; in the case that the water quality parameters are lower than the preset water quality standard, then the water quality parameters, the heading parameter and the external environment parameters are processed by using a water exchange angle optimization model to obtain the best water exchange angle and the best maintenance time; the attitude of the culture work ship is adjusted, so that the included angle between the heading of the culture work ship and the external water flow direction is the best water exchange angle, and the attitude is kept for the best maintenance time.The application uses the water exchange angle optimization model to process the related parameters and obtain the best water exchange angle and the best maintenance time when the water quality parameters are lower than the preset water quality standard, so that the attitude of the culture work ship is adjusted to the best water exchange angle, and the water exchange efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of aquaculture vessel technology, and in particular to methods, equipment, storage media and program products for adjusting the water exchange angle of aquaculture vessels. Background Technology

[0002] In aquaculture operations, to reduce energy consumption, the aquaculture vessel spends most of its time in a head-on flow. Under this condition, the exchange efficiency between the water in the aquaculture cages and natural water is at its lowest, and prolonged head-on flow can negatively impact water quality in the cages. Therefore, regular water changes are necessary during the aquaculture period. During these changes, the bow of the aquaculture vessel should form a certain angle with the direction of the water flow and be maintained for a period of time. However, the optimal angle for water changes is currently undetermined.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a method, equipment, storage medium and program product for adjusting the water exchange angle of an aquaculture vessel, aiming to solve the technical problem of how to obtain the optimal water exchange angle of the aquaculture vessel.

[0005] To achieve the above objectives, this application proposes a method for adjusting the water exchange angle of an aquaculture vessel, wherein the aquaculture vessel includes a bow, a midship section, and a stern section connected in sequence, the midship section is equipped with an aquaculture net cage, and the aquaculture net cage is connected to an external water body;

[0006] The method for adjusting the water exchange angle of the aquaculture vessel includes:

[0007] The water quality parameters inside the aquaculture cage, the heading parameters of the aquaculture vessel, and the external environmental parameters are obtained; wherein, the external environmental parameters include flow velocity and flow direction parameters and wind speed and wind direction parameters;

[0008] Determine whether the water quality parameters meet the preset water quality standards;

[0009] If the water quality parameters are lower than the preset water quality standard, the water quality parameters, the heading parameters, and the external environmental parameters are processed using a water exchange angle optimization model to obtain the optimal water exchange angle and the optimal maintenance time; wherein, the water exchange angle optimization model consists of a trained BP neural network model and a genetic algorithm;

[0010] The attitude of the aquaculture vessel is adjusted so that the angle between the bow of the aquaculture vessel and the direction of the external water flow is the optimal water exchange angle, and the attitude is maintained for the optimal maintenance time.

[0011] In one embodiment, the step of obtaining the water quality parameters inside the aquaculture cage, the heading parameters of the aquaculture vessel, and the external environmental parameters includes:

[0012] The water quality parameters, heading parameters, external environmental parameters, power consumption during water exchange, water exchange angle, and water exchange duration are obtained.

[0013] The water quality parameters, heading parameters, external environmental parameters, power consumption during water exchange, water exchange angle, and water exchange duration are compiled into a first parameter set.

[0014] The first parameter set is preprocessed to obtain the preprocessed target parameter set;

[0015] Correspondingly, the step of using a water exchange angle optimization model to process the water quality parameters, the heading parameters, and the external environmental parameters when the water quality parameters are lower than the preset standard to obtain the optimal water exchange angle and the optimal maintenance time includes:

[0016] When the water quality parameters are lower than the preset standard, the water quality parameters, heading parameters, and external environmental parameters in the target parameter set are processed using the water exchange energy delay product model in the water exchange angle optimization model to obtain the energy delay product function; wherein, the water exchange energy delay product model in the water exchange angle optimization model is trained by a BP neural network;

[0017] The energy delay product function is iteratively optimized using the genetic algorithm in the water exchange angle optimization model to obtain the optimal water exchange angle and the optimal maintenance time.

[0018] In one embodiment, both the water quality parameters and the external environmental parameters are parameter sequences during the water exchange period;

[0019] The step of preprocessing the first parameter set to obtain the preprocessed target parameter set includes:

[0020] Data cleaning and data fusion are performed on each parameter in the first parameter set to obtain the second parameter set;

[0021] The water quality parameters in the second parameter set are filtered, and the water quality parameters at the start and end times are retained as the basis for determining the start and end of the water exchange task.

[0022] A filtering algorithm based on the Kalman filter framework processes the external environment parameters in the second parameter set to obtain parameters characterizing the external environment during water exchange.

[0023] The target parameter set is obtained by combining the water quality parameters at the start and end times with the parameters of the external environment during the water exchange.

[0024] In one embodiment, before the step of using a water exchange angle optimization model to process the water quality parameters, the heading parameters, and the external environmental parameters to obtain the optimal water exchange angle and the optimal maintenance time when the water quality parameters are lower than the preset water quality standard, the method includes:

[0025] Obtain the historical parameter set of the aquaculture vessel during past water exchange periods; wherein the historical parameter set includes at least historical water quality parameters, historical external environmental parameters, and historical heading parameters;

[0026] A water exchange dataset for aquaculture boats was created based on the aforementioned set of historical parameters.

[0027] Construct a BP neural network model;

[0028] The BP neural network model was trained and tested based on the aquaculture vessel water exchange dataset to obtain the water exchange energy delay product model.

[0029] Construct a genetic algorithm;

[0030] Based on the genetic algorithm, the water exchange energy delay product model is optimized and iterated to obtain the water exchange angle optimization model.

[0031] In one embodiment, the step of constructing the BP neural network model includes:

[0032] Construct the BP neural network model, set the activation function, loss function and backpropagation algorithm of the BP neural network model, and randomly initialize the weights and biases of the BP neural network model.

[0033] In one embodiment, the step of training and testing the BP neural network model based on the water exchange dataset from the aquaculture vessel to obtain the water exchange energy delay product model includes:

[0034] The aquaculture vessel water exchange dataset was divided into a training set and a test set.

[0035] The training steps involve training the BP neural network model multiple times using the training set, and backpropagating the loss function according to the backpropagation algorithm to update the model parameters of the BP neural network model, thereby obtaining the trained BP neural network model; wherein, in different training rounds, the training set and the test set are re-partitioned.

[0036] The testing steps involve evaluating the trained BP neural network model using a test set to obtain model metrics for the trained BP neural network model; wherein, the model metrics include at least one of accuracy and precision.

[0037] If the model index reaches the preset model performance standard, then training and testing will end, and the water exchange energy delay product model will be obtained.

[0038] If the model index is lower than the preset model performance standard, the hyperparameters of the trained BP neural network model are adjusted, and the training and testing steps are repeated until the model index reaches the preset model performance standard to obtain the water exchange energy delay product model.

[0039] In one embodiment, the step of optimizing and iterating the water exchange energy delay product model based on the genetic algorithm to obtain the water exchange angle optimization model includes:

[0040] Construct a genetic algorithm framework, set the initial population and evolution termination conditions, and define the fitness function, selection function, crossover function and mutation function.

[0041] Furthermore, to achieve the above objectives, this application also proposes a water exchange angle adjustment device for aquaculture vessels, the water exchange angle adjustment device comprising:

[0042] The acquisition module is used to acquire water quality parameters inside the aquaculture cage, heading parameters of the aquaculture vessel, and external environmental parameters; wherein, the external environmental parameters include flow velocity and direction parameters and wind speed and direction parameters;

[0043] The judgment module is used to determine whether the water quality parameters meet the preset water quality standards;

[0044] The processing module is used to process the water quality parameters, the heading parameters, and the external environmental parameters using a water exchange angle optimization model when the water quality parameters are lower than the preset water quality standard, so as to obtain the optimal water exchange angle and the optimal maintenance time; wherein, the water exchange angle optimization model consists of a trained BP neural network model and a genetic algorithm;

[0045] An adjustment module is used to adjust the attitude of the aquaculture vessel so that the angle between the bow of the aquaculture vessel and the direction of the external water flow is the optimal water exchange angle, and to maintain the attitude for the optimal maintenance time.

[0046] In addition, to achieve the above objectives, this application also proposes a device for adjusting the water exchange angle of an aquaculture vessel, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the water exchange angle adjustment method for an aquaculture vessel as described above.

[0047] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the water exchange angle adjustment method for aquaculture boats as described above.

[0048] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the water exchange angle adjustment method for aquaculture boats as described above.

[0049] One or more technical solutions proposed in this application have at least the following technical effects:

[0050] This application employs a method for adjusting the water exchange angle of an aquaculture vessel. The aquaculture vessel comprises a bow, midships, and stern connected in sequence. The midships houses aquaculture cages, which are connected to external water bodies. The method includes: acquiring water quality parameters within the aquaculture cages, the bow direction parameters of the aquaculture vessel, and external environmental parameters; wherein the external environmental parameters include flow velocity and direction parameters and wind speed and direction parameters; determining whether the water quality parameters meet preset water quality standards; if the water quality parameters are lower than the preset standards, then using a water exchange angle optimization model to process the water quality parameters, bow direction parameters, and external environmental parameters to obtain the optimal water exchange angle and optimal maintenance time; wherein the water exchange angle optimization model consists of a trained BP neural network model and a genetic algorithm; adjusting the attitude of the aquaculture vessel so that the angle between the bow direction of the aquaculture vessel and the external water flow direction is the optimal water exchange angle, and maintaining the attitude for the optimal maintenance time.

[0051] This application achieves automatic water exchange detection by acquiring parameters such as water quality, heading, and external environmental parameters, and automatically detecting whether the water quality parameters meet preset water quality standards. When the water quality parameters are lower than the preset standards, a water exchange angle optimization model is used to process the water quality, external environmental, and heading parameters to obtain the optimal water exchange angle and optimal maintenance time. This facilitates control of the aquaculture vessel's attitude and significantly improves the water exchange efficiency. Traditional calculation methods are complex for dealing with the interplay of multiple factors such as external environmental, heading, and water quality parameters, often making it difficult to obtain the optimal water exchange angle efficiently and accurately. The water exchange angle optimization model used in this solution utilizes a BP neural network model to extract and generalize the feature patterns of external environmental, heading, and water quality parameters, obtaining a water exchange energy delay product model. This model is then iteratively optimized using a genetic algorithm to finally arrive at the optimized water exchange angle model. Therefore, the optimized water exchange angle model can achieve excellent prediction results for the optimal water exchange angle and optimal water exchange time.

[0052] Traditional methods for calculating the energy delay product struggle to reflect the influence of multiple factors, including external environmental parameters, heading parameters, and water quality parameters. Furthermore, traditional methods often face challenges in efficiently and accurately determining the optimal parameter combination when dealing with the interactions of these factors. In contrast, a BP neural network-based model leverages the powerful feature extraction and generalization capabilities of neural networks to deeply analyze the complex relationships between various parameters, providing a more accurate energy delay product function for water exchange. This is then further optimized using a genetic algorithm to predict the optimal water exchange angle and time. Moreover, compared to traditional calculation methods, the water exchange angle optimization model in this application not only improves prediction accuracy but also exhibits strong adaptability and robustness under different conditions. By learning from a large amount of historical data, the neural network and genetic algorithm-based water exchange angle optimization model can automatically identify key factors and their interaction patterns among various parameters, while the genetic algorithm can automatically iterate and optimize to obtain the optimal solution, thus solving complex multi-parameter decision-making problems more efficiently and accurately. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating Embodiment 1 of the method for adjusting the water exchange angle of an aquaculture vessel in this application.

[0056] Figure 2 This is a flowchart illustrating Embodiment 3 of the method for adjusting the water exchange angle of an aquaculture vessel in this application.

[0057] Figure 3 This is a schematic diagram of the module structure of the aquaculture vessel water exchange angle adjustment device according to an embodiment of this application;

[0058] Figure 4 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the water exchange angle adjustment method of the aquaculture vessel in the embodiments of this application.

[0059] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0060] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0061] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0062] The main solution of this application embodiment is as follows: An aquaculture vessel water exchange angle adjustment method is adopted. The aquaculture vessel includes a bow, midships, and stern connected in sequence. The midships are equipped with aquaculture net cages, which are connected to external water bodies. The aquaculture vessel water exchange angle adjustment method includes: acquiring water quality parameters within the aquaculture net cages, the bow direction parameters of the aquaculture vessel, and external environmental parameters; wherein, the external environmental parameters include flow velocity and direction parameters and wind speed and direction parameters; determining whether the water quality parameters meet preset water quality standards; if the water quality parameters are lower than the preset water quality standards, then using a water exchange angle optimization model to process the water quality parameters, bow direction parameters, and external environmental parameters to obtain the optimal water exchange angle and optimal maintenance time; wherein, the water exchange angle optimization model consists of a trained BP neural network model and a genetic algorithm; adjusting the attitude of the aquaculture vessel so that the angle between the bow direction of the aquaculture vessel and the external water flow direction is the optimal water exchange angle, and maintaining the attitude for the optimal maintenance time.

[0063] In this embodiment, for ease of description, the platform computing terminal will be used as the execution subject in the following description.

[0064] Currently, in aquaculture operations, to reduce energy consumption, aquaculture vessels spend most of their time in a head-on flow. In this state, the exchange efficiency between the water in the aquaculture cages and natural water is at its lowest, and prolonged head-on flow can negatively impact water quality in the cages. Therefore, regular water changes are necessary during the aquaculture period. During these changes, the bow of the aquaculture vessel should form a certain angle with the direction of the water flow and be maintained for a period of time. However, the optimal angle for water changes is still undetermined.

[0065] Based on this, this application provides a solution that employs a method for adjusting the water exchange angle of an aquaculture vessel. The aquaculture vessel includes a bow, midships, and stern connected in sequence. The midships are equipped with aquaculture cages, which are connected to external water bodies. The method for adjusting the water exchange angle of the aquaculture vessel includes: acquiring water quality parameters within the aquaculture cages, the bow direction parameters of the aquaculture vessel, and external environmental parameters; wherein, the external environmental parameters include flow velocity and direction parameters and wind speed and direction parameters; determining whether the water quality parameters meet preset water quality standards; if the water quality parameters are lower than the preset water quality standards, then using a water exchange angle optimization model to process the water quality parameters, bow direction parameters, and external environmental parameters to obtain the optimal water exchange angle and optimal maintenance time; wherein, the water exchange angle optimization model consists of a trained BP neural network model and a genetic algorithm; adjusting the attitude of the aquaculture vessel so that the angle between the bow direction of the aquaculture vessel and the direction of external water flow is the optimal water exchange angle, and maintaining the attitude for the optimal maintenance time.

[0066] This application achieves automatic water exchange detection by acquiring parameters such as water quality, heading, and external environmental parameters, and automatically detecting whether the water quality parameters meet preset water quality standards. When the water quality parameters are lower than the preset standards, a water exchange angle optimization model is used to process the water quality, external environmental, and heading parameters to obtain the optimal water exchange angle and optimal maintenance time. This facilitates control of the aquaculture vessel's attitude and significantly improves the water exchange efficiency. Traditional calculation methods are complex for dealing with the interplay of multiple factors such as external environmental, heading, and water quality parameters, often making it difficult to obtain the optimal water exchange angle efficiently and accurately. The water exchange angle optimization model used in this solution utilizes a BP neural network model to extract and generalize the feature patterns of external environmental, heading, and water quality parameters, obtaining a water exchange energy delay product model. This model is then iteratively optimized using a genetic algorithm to finally arrive at the optimized water exchange angle model. Therefore, the optimized water exchange angle model can achieve excellent prediction results for the optimal water exchange angle and optimal water exchange time.

[0067] Traditional methods for calculating the energy delay product struggle to reflect the influence of multiple factors, including external environmental parameters, heading parameters, and water quality parameters. Furthermore, traditional methods often face challenges in efficiently and accurately determining the optimal parameter combination when dealing with the interactions of these factors. In contrast, a BP neural network-based model leverages the powerful feature extraction and generalization capabilities of neural networks to deeply analyze the complex relationships between various parameters, providing a more accurate energy delay product function for water exchange. This is then further optimized using a genetic algorithm to predict the optimal water exchange angle and time. Moreover, compared to traditional calculation methods, the water exchange angle optimization model in this application not only improves prediction accuracy but also exhibits strong adaptability and robustness under different conditions. By learning from a large amount of historical data, the neural network and genetic algorithm-based water exchange angle optimization model can automatically identify key factors and their interaction patterns among various parameters, while the genetic algorithm can automatically iterate and optimize to obtain the optimal solution, thus solving complex multi-parameter decision-making problems more efficiently and accurately.

[0068] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or platform computing terminal capable of performing the above functions. The following description uses a platform computing terminal as an example to illustrate this embodiment and the subsequent embodiments.

[0069] Based on this, this application provides a method for adjusting the water exchange angle of an aquaculture vessel, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for adjusting the water exchange angle of an aquaculture vessel according to this application.

[0070] In this embodiment, the aquaculture vessel includes a bow, a midship section, and a stern section connected in sequence. The midship section is equipped with aquaculture net cages, which are connected to the external water body.

[0071] The method for adjusting the water exchange angle of the aquaculture vessel includes steps S10 to S50:

[0072] Step S10: Obtain the water quality parameters inside the aquaculture cage, the heading parameters of the aquaculture vessel, and the external environmental parameters; wherein, the external environmental parameters include flow velocity and flow direction parameters and wind speed and wind direction parameters;

[0073] It should be noted that water quality parameters can include dissolved oxygen content, temperature, pH value, salinity, turbidity, nitrate, heavy metals, ammonia nitrogen, phosphorus, and other parameters crucial to the survival of aquaculture organisms, and may also include other factors; no restrictions are placed here. The heading parameter reflects the direction the bow of the aquaculture vessel is pointing. The heading can be obtained through various methods such as compass, inertial navigation system, GPS, and automatic identification system. Among external environmental parameters, flow velocity and direction, as well as wind speed and direction, have a significant impact on water exchange efficiency. Therefore, external environmental parameters must include at least one of these two parameters; preferably, they include both. Other parameters can also be obtained, but will not be elaborated upon here.

[0074] Step S20: Determine whether the water quality parameters meet the preset water quality standards;

[0075] The preset water quality standard can be a specific numerical parameter, such as a dissolved oxygen content standard of A mg / L; or it can be a numerical range, such as a dissolved oxygen content standard within the range of [a, b] mg / L. The preset water quality standard can be preset in the platform computing terminal of the aquaculture vessel, or it can be obtained through communication methods such as servers or the Internet, without limitation.

[0076] Step S40: If the water quality parameters are lower than the preset water quality standard, the water quality parameters, the heading parameters, and the external environment parameters are processed using the water exchange angle optimization model to obtain the optimal water exchange angle and the optimal maintenance time; wherein, the water exchange angle optimization model consists of a trained BP neural network model and a genetic algorithm.

[0077] A BP neural network is a multi-layer feedforward artificial neural network model, also known as a backpropagation neural network. Its basic components include an input layer, hidden layers, and an output layer. Each layer has multiple nodes, and each node is connected to all nodes in the next layer, with each connection having its own weight. The training process of a BP neural network has two phases: forward propagation and backward propagation.

[0078] After data is input from the input layer, it undergoes weighted summation and activation function processing in the hidden layers before being passed to the output layer to form the output. In the output layer, the data is compared with a preset expected value to calculate the loss function; this is called forward propagation. If the loss function value is too large, it needs to be propagated forward layer by layer. Each layer adjusts its weights based on the propagated error value, and this weight adjustment and update is performed using the backpropagation algorithm; this is called backpropagation. One forward and one backpropagation cycle constitutes one iteration. Repeating this cycle a preset number of times trains the relevant BP neural network model.

[0079] Step S50: Adjust the attitude of the aquaculture vessel so that the angle between the bow of the aquaculture vessel and the direction of the external water flow is the optimal water exchange angle, and maintain the attitude for the optimal maintenance time.

[0080] This embodiment provides a method for adjusting the water exchange angle of an aquaculture vessel. By acquiring parameters such as water quality parameters, heading parameters, and external environmental parameters, and automatically detecting whether the water quality parameters meet preset water quality standards, automatic water exchange detection is achieved. When the water quality parameters are lower than the preset water quality standards, the optimal water exchange angle and optimal maintenance time are obtained by using a water exchange angle optimization model to process the water quality parameters, external environmental parameters, and heading parameters. This facilitates the control of the aquaculture vessel's attitude and significantly improves the water exchange efficiency of the aquaculture vessel. Traditional calculation methods are complex for dealing with the interplay of multiple factors such as external environmental parameters, heading parameters, and water quality parameters, often making it difficult to obtain the optimal water exchange angle efficiently and accurately. However, the water exchange angle optimization model used in this scheme utilizes a BP neural network model to extract and generalize the feature patterns of external environmental parameters, heading parameters, and water quality parameters to obtain a water exchange energy delay product model. Then, a genetic algorithm is used to iteratively optimize the water exchange energy delay product model, ultimately resulting in this optimized water exchange angle model. Therefore, the optimized water exchange angle model can achieve good prediction results for the optimal water exchange angle and optimal water exchange time.

[0081] Traditional methods for calculating the energy delay product struggle to reflect the influence of multiple factors, including external environmental parameters, heading parameters, and water quality parameters. Furthermore, traditional methods often face challenges in efficiently and accurately determining the optimal parameter combination when dealing with the interactions of these factors. In contrast, a BP neural network-based model leverages the powerful feature extraction and generalization capabilities of neural networks to deeply analyze the complex relationships between various parameters, providing a more accurate energy delay product function for water exchange. This is then further optimized using a genetic algorithm to predict the optimal water exchange angle and time. Moreover, compared to traditional calculation methods, the water exchange angle optimization model in this application not only improves prediction accuracy but also exhibits strong adaptability and robustness under different conditions. By learning from a large amount of historical data, the neural network and genetic algorithm-based water exchange angle optimization model can automatically identify key factors and their interaction patterns among various parameters, while the genetic algorithm can automatically iterate and optimize to obtain the optimal solution, thus solving complex multi-parameter decision-making problems more efficiently and accurately.

[0082] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, step S10 includes steps S11 to S13:

[0083] Step S11: Obtain the water quality parameters, the heading parameters, the external environment parameters, the power consumption during water exchange, the water exchange angle, and the duration of water exchange.

[0084] It should be noted that the duration of the water change includes the start and end times of the water change task.

[0085] Step S12: The water quality parameters, heading parameters, external environmental parameters, power consumption during water exchange, water exchange angle, and water exchange duration are compiled into a first parameter set.

[0086] Step S13: Preprocess the first parameter set to obtain the preprocessed target parameter set;

[0087] Correspondingly, step S40 includes steps S41 to S42:

[0088] Step S41: When the water quality parameters are lower than the preset standard, the water quality parameters, heading parameters, and external environment parameters in the target parameter set are processed using the water exchange energy delay product model in the water exchange angle optimization model to obtain the energy delay product function; wherein, the water exchange energy delay product model in the water exchange angle optimization model is trained by a BP neural network;

[0089] It should be noted that the energy delay product function is used to reflect the relationship between water exchange energy consumption and water exchange angle and water exchange maintenance time.

[0090] Step S42: The energy delay product function is iteratively optimized using the genetic algorithm in the water exchange angle optimization model to obtain the optimal water exchange angle and the optimal maintenance time.

[0091] In this embodiment, by compiling the collected parameters into a first parameter set, the data becomes more organized, easier to manage and access. This helps maintain data consistency and integrity. By preprocessing the parameters in the first parameter set, the parameters in the resulting target parameter set better conform to the learning process of the water exchange angle optimization model, thereby improving the training efficiency of the model and enhancing the prediction accuracy and generalization ability of the final water exchange angle optimization model. By using the water exchange energy delay product model in the water exchange angle optimization model to process the water quality parameters, heading parameters, and external environmental parameters in the target parameter set, an energy delay product function reflecting the relationship between water exchange energy consumption, water exchange angle, and water exchange maintenance time is obtained. This function can then be iteratively optimized using the genetic algorithm in the water exchange angle optimization model to obtain the accurate optimal water exchange angle and optimal maintenance time.

[0092] Furthermore, in one feasible implementation, both the water quality parameters and the external environmental parameters are parameter sequences during the water exchange period;

[0093] Step S13 may include steps S131 to S134:

[0094] Step S131: Perform data cleaning and data fusion on each parameter in the first parameter set to obtain the second parameter set;

[0095] Data cleaning includes cleaning missing values, standardizing format and content, cleaning logical problems, and deleting unnecessary data; data fusion refers to the fusion of multi-source data, which can use Kalman filtering as one data fusion method, or it can be combined with other methods to achieve better fusion results.

[0096] Step S132: Filter the water quality parameters in the second parameter set and retain the water quality parameters at the start and end times as the basis for determining the start and end of the water exchange task;

[0097] Step S133: Based on the Kalman filter framework, the filtering algorithm processes the external environment parameters in the second parameter set to obtain parameters characterizing the external environment during water exchange.

[0098] Step S134: Combine the water quality parameters at the start and end times with the parameters of the external environment during the water exchange to obtain the target parameter set.

[0099] In this embodiment, by cleaning and fusing the parameters in the first parameter set, noisy parameters are filtered out to avoid outlier parameters affecting model training. Data fusion processing enables the unification of parameters collected from different sensors and locations. By filtering the parameters in the second parameter set and making them into a data format conducive to model training, it is ensured that there is a correct correspondence between the water quality parameters, heading parameters, and external environmental parameters learned by the model, so that the model can grasp the correct patterns and have a more accurate ability to predict the optimal water exchange angle.

[0100] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Before step S40, the method for adjusting the water exchange angle of the aquaculture vessel further includes steps S31 to S36:

[0101] Step S31: Obtain the historical parameter set of the aquaculture vessel during past water exchange periods; wherein the historical parameter set includes at least historical water quality parameters, historical external environmental parameters, and historical heading parameters;

[0102] Step S32: Create a water exchange dataset for aquaculture boats based on the historical parameter set;

[0103] In this process, the historical parameter set is made into a dataset so that each parameter conforms to the input format of the BP neural network model, so that the subsequent BP neural network model can efficiently read the training set and the test set.

[0104] Step S33: Construct the BP neural network model;

[0105] Furthermore, in one feasible implementation, step S33 may include step S331:

[0106] Step S331: Construct the BP neural network model, set the activation function, loss function and backpropagation algorithm of the BP neural network model, and randomly initialize the weights and biases of the BP neural network model.

[0107] The activation function of the BP neural network model can be the sigmoid function, ReLU, Tanh, Softmax, etc. The loss function can be the mean squared error, cross-entropy loss, log-likelihood loss, etc., preferably the cross-entropy loss function. The backpropagation algorithm of the BP neural network model can be stochastic gradient descent, batch gradient descent, mini-batch gradient descent, momentum, RMSprop, Adam, etc. Preferably, the optimization algorithm can be the Adam algorithm. The Adam algorithm combines the advantages of both Momentum and RMSprop, can simultaneously consider first-order moment estimation and second-order moment estimation, and provides bias correction, thus having good performance and ease of use. Therefore, in this embodiment, Adam is preferably used as the optimization algorithm for the BP neural network model.

[0108] In this embodiment, by setting an activation function for the BP neural network model, nonlinear factors can be introduced into the neurons of the BP neural network model, enabling the model to achieve good fitting even for nonlinear function data. By setting an appropriate loss function, the model can update its weights by calculating the loss function and using backpropagation, allowing for iterative optimization. By setting an optimization algorithm, the model parameters can be adjusted to minimize the loss function, thereby improving the model's performance. Random initialization allows the model to obtain random weights and biases initially, facilitating subsequent updates to the model's weights and biases during training.

[0109] Step S34: Train and test the BP neural network model based on the water exchange dataset of the aquaculture boat to obtain the water exchange energy delay product model.

[0110] Furthermore, in one feasible implementation, step S34 may include steps S341 to S345:

[0111] Step S341: Divide the aquaculture vessel water exchange dataset into a training set and a test set;

[0112] Step S342, training step: The BP neural network model is trained multiple times using the training set, and the model parameters of the BP neural network model are updated according to the loss function to obtain the trained BP neural network model; wherein, in different training rounds, the training set and the test set are re-divided.

[0113] It should be noted that, in order to prevent overfitting during the training of the BP neural network model, the training set and the test set are re-divided in different training rounds.

[0114] Step S343, testing step: The trained BP neural network model is evaluated and tested using a test set to obtain the model index of the trained BP neural network model; wherein, the model index includes at least one of accuracy and precision;

[0115] In addition to accuracy and precision, model metrics may also include recall, F1 score, and area under the AUC-ROC curve. No specific limitations are imposed on model metrics here.

[0116] Step S344: If the model index reaches the preset model performance standard, then the training and testing will end, and the water exchange energy delay product model will be obtained.

[0117] Step S345: If the model index is lower than the preset model performance standard, adjust the hyperparameters of the trained BP neural network model and repeat the training and testing steps until the model index reaches the preset model performance standard to obtain the water exchange energy delay product model.

[0118] In this embodiment, a preset number of training rounds are used to train the BP neural network model, ensuring that the model parameters are fully iterated and updated over multiple training rounds. This makes the model's predictive performance closer to reality, improving its overall performance. The model is then tested using a test set to obtain model metrics. These metrics are used to determine whether the trained BP neural network model meets the preset performance standards. If the metrics meet the preset standards, training and testing can be terminated, resulting in the final water exchange energy delay product model. If the metrics do not meet the preset standards, the model's hyperparameters are adjusted, and the training and testing steps are repeated until the metrics meet the preset standards, yielding the water exchange energy delay product model. The water exchange energy delay product model obtained through steps S341-S345 can efficiently reflect the relationship between water exchange energy consumption, water exchange angle, and water exchange duration based on input water quality parameters, heading parameters, and external environmental parameters, providing a more accurate optimization objective function for subsequent genetic algorithms.

[0119] Step S35: Construct a genetic algorithm;

[0120] In one optional implementation, step S35 includes step S351:

[0121] Step S351: Construct the genetic algorithm framework, set the initial population and evolution termination conditions, and define the fitness function, selection function, crossover function and mutation function.

[0122] It should be noted that during water exchange on aquaculture vessels, both practicality and low energy consumption need to be considered. Therefore, the fitness function is chosen as the energy-delay product function. In the field of computer science, the energy-delay product is one of the more important indicators for measuring CPU performance; it is the product of the energy and time consumed by the CPU to complete an instruction or task. However, during water exchange on aquaculture vessels, lower energy consumption is not always better, because while the energy consumption of the aquaculture vessel is lowest when it is in the headwaters, this is also the state with the lowest water exchange efficiency. Water exchange on aquaculture vessels must consider not only energy consumption but also water exchange efficiency. Therefore, this application uses the energy-delay product as the water exchange index for aquaculture vessels, sets it as the fitness function of a genetic algorithm, and performs iterative optimization to obtain the optimal water exchange angle and the optimal water exchange duration.

[0123] Step S36: Based on the genetic algorithm, optimize and iterate the water exchange energy delay product model to obtain the water exchange angle optimization model.

[0124] In this embodiment, historical water quality parameters, historical heading parameters, and historical external environmental parameters of the aquaculture vessel during past water exchanges are obtained and compiled into training and testing sets. This allows the parameters to be converted into a training and testing set format that can be input into the BP neural network model, and this format is more suitable for the model's learning process. By constructing a BP neural network model and training and testing the model using the training and testing sets, a water exchange energy delay product model is obtained. Then, a genetic algorithm is used to optimize and iterate the water exchange energy delay product model, ultimately obtaining a water exchange angle optimization model.

[0125] This application also provides a device for adjusting the water exchange angle of an aquaculture vessel; please refer to [reference needed]. Figure 3 The water exchange angle adjustment device for the aquaculture vessel includes:

[0126] The acquisition module 10 is used to acquire water quality parameters inside the aquaculture cage, heading parameters of the aquaculture vessel, and external environmental parameters; wherein, the external environmental parameters include flow velocity and direction parameters and wind speed and direction parameters;

[0127] The judgment module 20 is used to determine whether the water quality parameters meet the preset standards;

[0128] The processing module 40 is used to process the water quality parameters, the heading parameters, and the external environmental parameters using a water exchange angle optimization model when the water quality parameters are lower than the preset standard, so as to obtain the optimal water exchange angle and the optimal maintenance time; wherein, the water exchange angle optimization model consists of a trained BP neural network model and a genetic algorithm;

[0129] The adjustment module 50 is used to adjust the attitude of the aquaculture vessel so that the angle between the bow of the aquaculture vessel and the direction of the external water flow is the optimal water exchange angle, and to maintain the attitude for the optimal maintenance time.

[0130] The aquaculture vessel water exchange angle adjustment device provided in this application, employing the aquaculture vessel water exchange angle adjustment method described in the above embodiments, can solve the technical problem of how to obtain the optimal water exchange angle for the aquaculture vessel. Compared with the prior art, the beneficial effects of the aquaculture vessel water exchange angle adjustment device provided in this application are the same as those of the aquaculture vessel water exchange angle adjustment method provided in the above embodiments, and other technical features in the aquaculture vessel water exchange angle adjustment device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0131] This application provides a device for adjusting the water exchange angle of an aquaculture vessel. The device includes: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the aquaculture vessel water exchange angle adjustment method described in Embodiment 1 above.

[0132] The following is for reference. Figure 4 The diagram illustrates a structural schematic suitable for implementing the aquaculture vessel water exchange angle adjustment device in the embodiments of this application. The aquaculture vessel water exchange angle adjustment device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The aquaculture vessel water exchange angle adjustment device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0133] like Figure 4As shown, the aquaculture vessel water exchange angle adjustment device may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the aquaculture vessel water exchange angle adjustment device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the aquaculture vessel water exchange angle adjustment equipment to exchange data wirelessly or via wired communication with other devices. Although the figure shows an aquaculture vessel water exchange angle adjustment equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0134] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0135] The aquaculture vessel water exchange angle adjustment device provided in this application, employing the aquaculture vessel water exchange angle adjustment method described in the above embodiments, can solve the technical problem of how to obtain the optimal water exchange angle for the aquaculture vessel. Compared with the prior art, the beneficial effects of the aquaculture vessel water exchange angle adjustment device provided in this application are the same as those of the aquaculture vessel water exchange angle adjustment method provided in the above embodiments, and other technical features of this aquaculture vessel water exchange angle adjustment device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0136] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0137] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0138] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the aquaculture boat water exchange angle adjustment method in the above embodiments.

[0139] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0140] The aforementioned computer-readable storage medium may be included in the water exchange angle adjustment equipment of the aquaculture vessel; or it may exist independently and not be assembled into the water exchange angle adjustment equipment of the aquaculture vessel.

[0141] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the aquaculture vessel water exchange angle adjustment device, the device performs the following actions: acquires water quality parameters within the aquaculture cage, the bow parameters of the aquaculture vessel, and external environmental parameters; wherein the external environmental parameters include flow velocity and direction parameters and wind speed and direction parameters; determines whether the water quality parameters meet preset water quality standards; if the water quality parameters are lower than the preset water quality standards, it uses a water exchange angle optimization model to process the water quality parameters, bow parameters, and external environmental parameters to obtain the optimal water exchange angle and optimal maintenance time; wherein the water exchange angle optimization model consists of a trained BP neural network model and a genetic algorithm; and adjusts the attitude of the aquaculture vessel so that the angle between the bow of the aquaculture vessel and the external water flow direction is the optimal water exchange angle, and maintains the attitude for the optimal maintenance time.

[0142] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0143] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0144] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0145] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for adjusting the water exchange angle of an aquaculture vessel, thereby solving the technical problem of how to obtain the optimal water exchange angle of the aquaculture vessel. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the aquaculture vessel water exchange angle adjustment method provided in the above embodiments, and will not be repeated here.

[0146] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for adjusting the water exchange angle of an aquaculture vessel.

[0147] The computer program product provided in this application can solve the technical problem of how to obtain the optimal water exchange angle of an aquaculture vessel. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the aquaculture vessel water exchange angle adjustment method provided in the above embodiments, and will not be repeated here.

[0148] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for adjusting the water exchange angle of an aquaculture vessel, characterized in that, The aquaculture vessel includes a bow, a midship section, and a stern section connected in sequence. The midship section is equipped with aquaculture net cages, which are connected to the external water body. The method for adjusting the water exchange angle of the aquaculture vessel includes: The water quality parameters inside the aquaculture cage, the heading parameters of the aquaculture vessel, and the external environmental parameters are obtained; wherein, the external environmental parameters include flow velocity and flow direction parameters and wind speed and wind direction parameters; Determine whether the water quality parameters meet the preset water quality standards; If the water quality parameters are lower than the preset water quality standard, the water quality parameters, the heading parameters, and the external environmental parameters are processed using a water exchange angle optimization model to obtain the optimal water exchange angle and the optimal maintenance time; wherein, the water exchange angle optimization model consists of a trained BP neural network model and a genetic algorithm; The attitude of the aquaculture vessel is adjusted so that the angle between the bow of the aquaculture vessel and the direction of the external water flow is the optimal water exchange angle, and the attitude is maintained for the optimal maintenance time. The steps of obtaining the water quality parameters inside the aquaculture cage, the heading parameters of the aquaculture vessel, and the external environmental parameters include: The water quality parameters, heading parameters, external environmental parameters, power consumption during water exchange, water exchange angle, and water exchange duration are obtained. The water quality parameters, heading parameters, external environmental parameters, power consumption during water exchange, water exchange angle, and water exchange duration are compiled into a first parameter set. The first parameter set is preprocessed to obtain the preprocessed target parameter set; Correspondingly, the step of using a water exchange angle optimization model to process the water quality parameters, the heading parameters, and the external environmental parameters when the water quality parameters are lower than the preset standard to obtain the optimal water exchange angle and the optimal maintenance time includes: When the water quality parameters are lower than the preset standard, the water quality parameters, heading parameters, and external environmental parameters in the target parameter set are processed using the water exchange energy delay product model in the water exchange angle optimization model to obtain the energy delay product function; wherein, the water exchange energy delay product model in the water exchange angle optimization model is trained by a BP neural network; The energy delay product function is iteratively optimized using the genetic algorithm in the water exchange angle optimization model to obtain the optimal water exchange angle and the optimal maintenance time.

2. The method as described in claim 1, characterized in that, Both the water quality parameters and the external environmental parameters are parameter sequences during the continuous water exchange period; The step of preprocessing the first parameter set to obtain the preprocessed target parameter set includes: Data cleaning and data fusion are performed on each parameter in the first parameter set to obtain the second parameter set; The water quality parameters in the second parameter set are filtered, and the water quality parameters at the start and end times are retained as the basis for determining the start and end of the water exchange task. A filtering algorithm based on the Kalman filter framework processes the external environment parameters in the second parameter set to obtain parameters characterizing the external environment during water exchange. The target parameter set is obtained by combining the water quality parameters at the start and end times with the parameters of the external environment during the water exchange.

3. The method as described in claim 1, characterized in that, Before the step of using a water exchange angle optimization model to process the water quality parameters, the heading parameters, and the external environmental parameters to obtain the optimal water exchange angle and the optimal maintenance time when the water quality parameters are lower than the preset water quality standard, the method includes: Obtain the historical parameter set of the aquaculture vessel during past water exchange periods; wherein the historical parameter set includes at least historical water quality parameters, historical external environmental parameters, and historical heading parameters; A water exchange dataset for aquaculture boats was created based on the aforementioned set of historical parameters. Construct a BP neural network model; The BP neural network model was trained and tested based on the aquaculture vessel water exchange dataset to obtain the water exchange energy delay product model. Construct a genetic algorithm; Based on the genetic algorithm, the water exchange energy delay product model is optimized and iterated to obtain the water exchange angle optimization model.

4. The method as described in claim 3, characterized in that, The steps for constructing the BP neural network model include: Construct the BP neural network model, set the activation function, loss function and backpropagation algorithm of the BP neural network model, and randomly initialize the weights and biases of the BP neural network model.

5. The method as described in claim 3, characterized in that, The step of training and testing the BP neural network model based on the aquaculture vessel water exchange dataset to obtain the water exchange energy delay product model includes: The aquaculture vessel water exchange dataset was divided into a training set and a test set. The training steps involve training the BP neural network model multiple times using the training set, and backpropagating the loss function according to the backpropagation algorithm to update the model parameters of the BP neural network model, thereby obtaining the trained BP neural network model; wherein, in different training rounds, the training set and the test set are re-partitioned. The testing steps involve evaluating the trained BP neural network model using a test set to obtain model metrics for the trained BP neural network model; wherein, the model metrics include at least one of accuracy and precision. If the model index reaches the preset model performance standard, then training and testing will end, and the water exchange energy delay product model will be obtained. If the model index is lower than the preset model performance standard, the hyperparameters of the trained BP neural network model are adjusted, and the training and testing steps are repeated until the model index reaches the preset model performance standard to obtain the water exchange energy delay product model.

6. The method as described in claim 3, characterized in that, The steps for constructing the genetic algorithm include: Construct a genetic algorithm framework, set the initial population and evolution termination conditions, and define the fitness function, selection function, crossover function and mutation function.

7. A device for adjusting the water exchange angle of an aquaculture vessel, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for adjusting the water exchange angle of an aquaculture vessel as described in any one of claims 1 to 6.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for adjusting the water exchange angle of an aquaculture vessel as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the method for adjusting the water exchange angle of an aquaculture vessel as described in any one of claims 1 to 6.

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