Aquaculture intelligent monitoring management system based on Internet of Things technology

Abstract

The invention provides an aquaculture intelligent monitoring management system based on the Internet of Things technology, and relates to the technical field of aquaculture, and the system comprises a laying module which is used for determining a key position in an aquaculture environment, and laying a plurality of sensor nodes at the key position; the communication module is used for being in wireless communication connection with the plurality of sensor nodes so as to send the breeding data monitored by the sensor nodes; the data analysis module is connected with the communication module and used for receiving and analyzing the breeding data sent by the communication module and determining a dynamic correction value; and adjusting the position of the current sensor node by using the dynamic correction value to obtain a final position node. The intelligent level of aquaculture can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture, and in particular to an intelligent monitoring and management system for aquaculture based on the Internet of Things technology. Background Art

[0002] With the rapid development of aquaculture, the demand for refined and intelligent management of aquaculture environment is growing. However, some traditional aquaculture monitoring and management solutions have some shortcomings. Specifically, traditional technical solutions have the following defects: For example, traditional aquaculture usually relies on manual regular sampling and laboratory testing to obtain key parameters such as water quality and weather. This method is not only time-consuming and laborious, but also difficult to ensure the real-time and accuracy of the data. In addition, there is a lack of effective means to monitor the growth status of farmed organisms, and it is impossible to detect abnormal situations in a timely manner.

[0003] Due to the lack of advanced early warning systems, traditional aquaculture solutions are somewhat slow to respond to emergencies such as water quality deterioration, which can cause unnecessary losses. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent monitoring and management system for aquaculture based on Internet of Things technology, which can improve the intelligence level of aquaculture.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows: In the first aspect, an intelligent monitoring and management system for aquaculture based on Internet of Things technology comprises: A deployment module is used to determine key locations in an aquaculture environment and deploy multiple sensor nodes at the key locations; A communication module is used to wirelessly connect with multiple sensor nodes to send the farming data monitored by each sensor node; A data analysis module is connected to the communication module, and is used to receive and analyze the farming data sent by the communication module, and determine a dynamic correction value; using the dynamic correction value, the current sensor node position is adjusted to obtain a final position node; A construction module is used to obtain the aquaculture data monitored by each sensor node according to the final location node, wherein the aquaculture data includes the monitored water quality parameters, meteorological parameters and growth status parameters of the aquaculture organisms; the aquaculture data of each sensor node are integrated to form a comprehensive data set, and key features reflecting the state and change trend of the aquaculture environment are extracted from the comprehensive data set; a prediction model for simulating the water flow dynamics, water quality distribution, meteorological conditions and growth status of the aquaculture organisms in the aquaculture pond is constructed according to the key features; The early warning module is used to generate corresponding early warning information according to the prediction results and preset thresholds; The user interaction interface is connected to the warning module and is used to display warning information to the user and receive the user's operation instructions.

[0006] Furthermore, key locations in the aquaculture environment are identified, including: Evaluate the overall layout of the aquaculture environment, water flow dynamics, shape and size of the aquaculture pond to obtain environmental assessment results; Based on the environmental assessment results, determine the preliminary site selection, which includes the intersection of water flows, the edge of the aquaculture pond or near the aeration equipment; Construct a two-dimensional matrix to represent the plan layout of the aquaculture environment, where each element of the matrix corresponds to a specific location in the aquaculture environment; According to the preliminary selected points, the weight values ​​corresponding to the corresponding elements in the position matrix are calculated; Create a feature matrix that contains various factors that affect the selection of key locations, including the rate of change of water quality parameters, the stability of meteorological parameters, and the distribution density of aquaculture organisms. Each factor corresponds to a column in the feature matrix. Perform matrix multiplication of the position matrix and the feature matrix, and get a comprehensive value for each position; According to the comprehensive value obtained by the matrix multiplication operation, all positions are sorted according to the size of the comprehensive value to obtain the corresponding key position.

[0007] Furthermore, wireless communication is connected with multiple sensor nodes to send the farming data monitored by each sensor node, including: According to the key positions, the location, quantity and type of sensor nodes are determined, and the initial parameters of the genetic algorithm are set, including population size, crossover rate, mutation rate and number of iterations; The wireless communication path is represented by binary coding, and a population is initialized, which includes multiple individuals, each of which represents a wireless communication scheme; Define a fitness function to evaluate the pros and cons of each wireless communication scheme; According to the fitness function, the corresponding individuals are selected to enter the next generation, two individuals are randomly selected to exchange the corresponding partial genes to generate new individuals, and the individuals in the population, i.e. the wireless communication scheme, are continuously optimized through multiple iterations; after each iteration, the pros and cons of the individuals in the current population are evaluated according to the fitness function, and selection and genetic operations are performed. When the preset number of iterations is reached, the iteration is stopped, and the final wireless communication scheme, i.e. the corresponding individual, is output; Configure sensor nodes and wireless communication network according to the final wireless communication scheme; According to the configured sensor nodes and wireless communication network, the breeding data monitored by each sensor node is sent.

[0008] Furthermore, the calculation formula of the dynamic correction value is: ; in, Indicates the dynamic correction value; represents the distance from the i-th sensor node to the target location; represents the real-time monitoring value of the i-th sensor node; represents the reference value of the i-th sensor node; represents the standard deviation of the Gaussian distribution; represents the number of sensor nodes; and Represent the signal strength of the i-th sensor and the j-th sensor respectively; i and j are indexes used to represent different sensors.

[0009] Furthermore, the current sensor node position is adjusted using the dynamic correction value to obtain the final position node, including: Determine the original sensor node position. The position of each sensor node is represented by three-dimensional coordinates (x, y, z), where x and y represent the position on the horizontal plane and z represents the water depth; Define the position adjustment step size according to the size and shape of the monitoring area and the current distribution of sensor nodes; Decompose the dynamic correction value into three dynamic correction components, corresponding to the three directions of the x-axis, y-axis and z-axis respectively. Multiply the dynamic correction component in each direction by the adjustment step length to obtain a three-dimensional adjustment vector. The dynamic correction value is decomposed into (dx, dy, dz), and the adjustment vector is (dx×step length, dy×step length, dz×step length); The calculated adjustment vector is added to the original three-dimensional coordinates of each sensor node to obtain the final position node, that is, the final x coordinate = original x coordinate + dx × step length; the final y coordinate = original y coordinate + dy × step length; the final z coordinate = original z coordinate + dz × step length.

[0010] Furthermore, the dynamic correction value is decomposed into three dynamic correction components, corresponding to the three directions of the x-axis, y-axis and z-axis, including: Collect a dataset containing sensor node locations and readings; Preprocess the data set and generate corresponding dx, dy, and dz components as training labels based on the position changes of sensor nodes in historical data; Construct a convolutional neural network model and define the input layer and output layer. The input layer contains sensor readings and position coordinates; the output layer has three nodes, corresponding to the predicted values ​​of the three components dx, dy, and dz, respectively. Set the hidden layer and determine the corresponding ReLU function. Divide the dataset into training, validation and test sets; Use the training set to train the convolutional neural network model, and optimize the parameters of the convolutional neural network model through the back propagation algorithm and the gradient descent algorithm to obtain the final convolutional neural network model; The reading and position coordinates of the current sensor node are input into the final convolutional neural network model, and the predicted dx, dy, and dz component values ​​are obtained through the forward propagation calculation of the final convolutional neural network model.

[0011] Furthermore, corresponding warning information is generated based on the prediction results and preset thresholds, including: Obtain key indicators of simulation data from the prediction model in real time, including predicted values ​​of water flow dynamics, water quality distribution, meteorological conditions and growth status of aquaculture organisms; Based on historical data analysis, set corresponding warning thresholds for each key indicator; Compare key indicators with warning thresholds to determine whether each key indicator exceeds or approaches the warning threshold to obtain comparison results; Based on the comparison results, determine whether to trigger an early warning.

[0012] In the second aspect, an intelligent monitoring and management method for aquaculture based on Internet of Things technology includes: Identify key locations in an aquaculture environment and deploy multiple sensor nodes at key locations; Wirelessly connect with multiple sensor nodes to send farming data monitored by each sensor node; Receive and analyze the farming data sent by the communication module, and determine the dynamic correction value; use the dynamic correction value to adjust the current sensor node position to obtain the final position node; According to the final location node, the aquaculture data monitored by each sensor node is obtained, wherein the aquaculture data includes the monitored water quality parameters, meteorological parameters and growth status parameters of the aquaculture organisms; the aquaculture data of each sensor node are integrated to form a comprehensive data set, and the key features reflecting the state and change trend of the aquaculture environment are extracted from the comprehensive data set; based on the key features, a prediction model is constructed to simulate the water flow dynamics, water quality distribution, meteorological conditions and growth status of the aquaculture organisms in the aquaculture pond; Generate corresponding warning information based on the prediction results and preset thresholds.

[0013] According to a third aspect, a computing device includes: one or more processors; The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0014] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0015] The above solution of the present invention includes at least the following beneficial effects: By accurately determining key locations in the aquaculture environment and deploying sensor nodes, comprehensive real-time monitoring of water quality parameters, meteorological parameters, and parameters of the growth status of aquaculture organisms is achieved. This deployment strategy ensures the comprehensiveness and accuracy of the data, providing a solid foundation for subsequent data analysis and decision-making.

[0016] The communication module interacts with multiple sensor nodes through wireless communication connections, realizing real-time and efficient collection of aquaculture data. This not only reduces the cost and time of manual data collection, but also greatly improves the timeliness and reliability of the data, providing timely data support for aquaculture managers.

[0017] The data analysis module conducts in-depth analysis of the collected breeding data, determines dynamic correction values, and adjusts the positions of sensor nodes based on these values. This dynamic adjustment mechanism makes the monitoring system more intelligent and flexible, and can optimize the layout of monitoring points according to actual conditions, thereby improving the accuracy and effectiveness of monitoring.

[0018] The building module forms a comprehensive data set by fusing the data from each sensor node, and extracts key features from it to build a prediction model. This comprehensive analysis and modeling method not only helps managers fully understand the current status and changing trends of the breeding environment, but also provides a scientific basis for predicting future situations.

[0019] The early warning module generates early warning information based on the prediction model and preset thresholds, realizing timely discovery and early warning of potential problems. This early warning mechanism can effectively reduce breeding risks, help managers take timely measures to deal with abnormal situations, and ensure the smooth progress of breeding activities.

[0020] The user interface provides managers with an intuitive and convenient platform to view warning information and receive operation instructions, which not only improves the transparency and readability of information, but also enhances the interactivity and user-friendliness of the system, enabling managers to manage and control the breeding process more efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of an intelligent monitoring and management method for aquaculture based on Internet of Things technology provided by an embodiment of the present invention.

[0022] Figure 2It is a schematic diagram of an intelligent monitoring and management system for aquaculture based on Internet of Things technology provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0024] like Figure 1 As shown, the embodiment of the present invention proposes an intelligent monitoring and management system for aquaculture based on the Internet of Things technology, including: A deployment module is used to determine key locations in an aquaculture environment and deploy multiple sensor nodes at the key locations; A communication module is used to wirelessly connect with multiple sensor nodes to send the farming data monitored by each sensor node; A data analysis module is connected to the communication module, and is used to receive and analyze the farming data sent by the communication module, and determine a dynamic correction value; using the dynamic correction value, the current sensor node position is adjusted to obtain a final position node; A construction module is used to obtain the aquaculture data monitored by each sensor node according to the final location node, wherein the aquaculture data includes the monitored water quality parameters, meteorological parameters and growth status parameters of the aquaculture organisms; the aquaculture data of each sensor node are integrated to form a comprehensive data set, and key features reflecting the state and change trend of the aquaculture environment are extracted from the comprehensive data set; a prediction model for simulating the water flow dynamics, water quality distribution, meteorological conditions and growth status of the aquaculture organisms in the aquaculture pond is constructed according to the key features; The early warning module is used to generate corresponding early warning information according to the prediction results and preset thresholds; The user interaction interface is connected to the warning module and is used to display warning information to the user and receive the user's operation instructions.

[0025] In the embodiment of the present invention, by deploying multiple sensor nodes at key positions, the system can comprehensively and in real time monitor various parameters in the aquaculture environment, such as water quality, weather and the growth status of cultured organisms. This distributed monitoring method not only improves the accuracy and precision of the data, but also greatly enhances the efficiency and response speed of monitoring. The data analysis module can receive and analyze sensor data in real time, and adjust the position of the sensor node through dynamic correction values. This dynamic adjustment mechanism can ensure the flexibility and adaptability of the monitoring system, so that it can be optimized according to the actual changes in the aquaculture environment, thereby more accurately reflecting the true state of the aquaculture environment. The construction module uses the integrated data set after fusion to extract key features and build a prediction model, which can simulate and predict the water flow dynamics, water quality distribution, meteorological conditions and growth status of aquaculture ponds. This prediction capability provides important decision-making support for aquaculture managers, helping them to take necessary measures in advance, optimize aquaculture conditions and improve aquaculture benefits. The early warning module can generate corresponding early warning information based on the prediction results and preset thresholds, which helps to timely discover potential risks and problems, such as water quality deterioration, meteorological disasters, etc. By displaying these early warning information to users in a timely manner, the system can help users take timely countermeasures, thereby effectively preventing and controlling risks and reducing losses. The user interaction interface not only displays rich monitoring data and early warning information to users, but also receives users' operation instructions, providing users with an intuitive and friendly operation platform. This interactive design greatly improves the user experience and convenience, allowing users to manage and control the entire breeding process more easily.

[0026] In a preferred embodiment of the present invention, determining a key location in an aquaculture environment comprises: The overall layout of the aquaculture environment, water flow dynamics, shape and size of the breeding pond are evaluated to obtain the environmental assessment results, which specifically include: collecting relevant information on the aquaculture environment, including design drawings of the breeding pond, historical data on water flow dynamics, meteorological records, etc.; conducting on-site inspections of the breeding ponds to observe water flow conditions, pond shapes, sizes and the layout of existing equipment; analyzing the collected data to understand the main flow paths of the water flow, speed changes, and water quality differences in different areas of the pond; and determining the overall characteristics and key areas of the breeding environment based on the analysis and survey results.

[0027] Based on the results of the environmental assessment, preliminary selection points are determined. The preliminary selection points include the intersection of water flows, the edge areas of the culture pond or the vicinity of the aeration equipment. Specifically, based on the environmental assessment report, identify the areas where water flows converge, mix or generate eddies, which are key points for water quality changes; mark the edge areas of the culture pond, especially those boundaries where the external environment exchanges matter and energy; select monitoring points around factors that may have a significant impact on local water quality, such as aeration equipment, and mark these identified preliminary selection points on the floor plan of the culture pond, and record their coordinates and characteristics.

[0028] A two-dimensional matrix is ​​constructed to represent the plan layout of the aquaculture environment, and each element of the matrix corresponds to a specific location in the aquaculture environment. Specifically, the plan view of the aquaculture pond is divided into a fine grid, and each grid represents a specific location; a coordinate value is assigned to each grid, and a two-dimensional coordinate system (x, y) is used to represent it; based on the coordinates of the grid, a two-dimensional matrix is ​​constructed, and each element in the matrix corresponds to a specific location in the aquaculture pond.

[0029] According to the preliminary selected points, the weight values ​​corresponding to the corresponding elements in the position matrix are calculated, where the weight values The calculation formula is: ; in, , , and They are the weight coefficients of water flow dynamics, water quality changes, the impact of aeration equipment, and the impact of edge areas; It's location The water velocity at It is the maximum velocity of water flow in the entire breeding pond; It's location The rate of change of water quality at the location; is the maximum rate of change of water quality at all locations in the aquaculture pond; It's location Distance to aeration equipment; It's location Distance to the edge of the culture pond.

[0030] Create a feature matrix that contains various factors that affect the selection of key locations, including the rate of change of water quality parameters, the stability of meteorological parameters, and the distribution density of aquaculture organisms. Each factor corresponds to a column of the feature matrix. Specifically, select the main factors that affect the selection of key locations, such as the rate of change of water quality parameters, the stability of meteorological parameters, and the distribution density of aquaculture organisms. For each factor, collect or measure relevant data at different locations in the aquaculture pond. Extract values ​​or indicators that can represent the characteristics of each factor from the collected data. Arrange the extracted eigenvalues ​​according to the factors to form columns of the feature matrix. Each row represents a feature combination of a location.

[0031] Perform matrix multiplication on the position matrix and the feature matrix, and obtain a comprehensive value for each position. Specifically, it includes: ensuring that the number of rows of the position matrix and the feature matrix is ​​the same so as to perform matrix multiplication, multiplying the weight value of the position matrix with the eigenvalue of the corresponding position in the feature matrix, and summing them to obtain the comprehensive value of each position. This process is equivalent to weighted summation, where the weight reflects the importance of the initial point selection and the eigenvalue reflects the degree of influence of each factor.

[0032] According to the comprehensive value obtained by matrix multiplication operation, all positions are sorted according to the size of the comprehensive value to obtain the corresponding key positions, specifically including: sorting the comprehensive values ​​of all positions from high to low or from low to high, and according to the sorting results, selecting several positions with the highest comprehensive values ​​as key positions. These positions are representative or special in the aquaculture environment and are the key areas for subsequent monitoring and management.

[0033] In an embodiment of the present invention, by comprehensively evaluating the overall layout, water flow dynamics, shape and size of the aquaculture environment, the method can scientifically determine the preliminary selection point, and then use matrix operations to accurately locate the key position. This layout and positioning method ensures the representativeness and effectiveness of the monitoring point, and lays a solid foundation for subsequent data collection and analysis. When constructing the feature matrix, the method comprehensively considers various factors such as the rate of change of water quality parameters, the stability of meteorological parameters, and the distribution density of cultured organisms. This diversified consideration makes the determination of the key position more comprehensive and accurate, and can truly reflect the complexity and dynamics of the culture environment. Through matrix multiplication operations, the method calculates a comprehensive value for each position, thereby realizing the quantitative analysis of the key position. This quantitative method is not only convenient for comparing and sorting different positions, but also provides intuitive decision-making basis for culture managers, which helps them optimize the layout and resource allocation of monitoring points. The accurate determination of the key position helps to improve the efficiency and effect of monitoring. By arranging sensor nodes at key positions, important information and change trends in the culture environment can be captured to the maximum extent, and the collection of redundant data can be reduced, thereby improving the response speed and accuracy of the entire monitoring system. By optimizing the layout of monitoring points, problems in the breeding environment can be discovered and solved in a timely manner, reducing breeding risks and improving breeding efficiency.

[0034] In a preferred embodiment of the present invention, wireless communication is connected with a plurality of sensor nodes to send the farming data monitored by each sensor node, including: According to the key positions, the positions, quantity and types of sensor nodes are determined, and the initial parameters of the genetic algorithm are set, including population size, crossover rate, mutation rate and number of iterations. Specifically, the following are carried out: survey the breeding environment to determine the key areas that need to be monitored, such as locations where environmental factors such as temperature, humidity and light change greatly; select suitable sensor nodes according to the type of parameters that need to be monitored at each key position (such as temperature, humidity, pH value, etc.); comprehensively consider the monitoring accuracy, cost budget and system complexity, and determine the number and specific location of sensor nodes placed at each key position; according to the complexity of the problem and computing resources, set the appropriate population size, that is, the number of individuals in each generation, set the probability of crossover operation to control the speed of new individuals, set the probability of mutation operation to increase the diversity of the population, and set a reasonable number of iterations as the condition for algorithm termination according to the accuracy and time requirements of the problem.

[0035] The wireless communication path is represented by binary coding, and a population is initialized, which includes multiple individuals, and each individual represents a wireless communication scheme. Specifically, the selection problem of the wireless communication path is converted into a binary string representation by binary coding, and an initial population is randomly generated according to the coding scheme, and each individual (i.e., binary string) represents a possible wireless communication scheme.

[0036] A fitness function is defined to evaluate the pros and cons of each wireless communication scheme, where the calculation formula of the fitness function is: ; in, represents the fitness function; Represents individuals (wireless communication schemes, including node locations and communication paths between nodes); , , and Represents the weight factor, which adjusts the importance of each indicator; Indicates the effective coverage area; Indicates the total area of ​​the target area; represents the distance between the i-th and j-th nodes; represents the transmission delay of the i-th node (including processing time and link delay); represents the transmission failure probability of the i-th node; i and j represent two different nodes respectively; n is the total number of nodes.

[0037] According to the fitness function, the corresponding individuals are selected to enter the next generation, two individuals are randomly selected to exchange the corresponding partial genes to generate new individuals, and the individuals in the population, i.e., the wireless communication scheme, are continuously optimized through multiple iterations; after each iteration, the fitness function is used to evaluate the quality of individuals in the current population, and selection and genetic operations are performed. When the preset number of iterations is reached, the iteration is stopped, and the final wireless communication scheme, i.e., the corresponding individuals, is output, specifically including: according to the evaluation results of the fitness function, excellent individuals are selected to enter the next generation, and the commonly used selection methods include roulette selection, tournament selection, etc.; two individuals are randomly selected, and partial genes are exchanged according to the set crossover rate and crossover mode (such as single-point crossover, multi-point crossover, etc.) to generate new individuals; the newly generated individuals are randomly mutated according to the set mutation rate, i.e., the values ​​of certain genes are changed to increase the diversity of the population, and the above selection, crossover and mutation operations are repeated until the preset number of iterations is reached. After each iteration, the fitness function is used to evaluate the quality of individuals in the current population.

[0038] According to the final wireless communication scheme, the sensor nodes and the wireless communication network are configured, specifically including: when the preset number of iterations is reached, the iteration is stopped, the individual with the highest fitness is selected from the current population as the final wireless communication scheme, and the selected individual (binary string) is decoded into a specific wireless communication path and configuration parameters.

[0039] According to the configured sensor nodes and wireless communication network, the aquaculture data monitored by each sensor node is sent, specifically including: according to the decoded plan, the sensor nodes and wireless communication equipment are deployed in the aquaculture environment, and the parameters of the wireless communication network, such as communication frequency, transmission rate, etc., are configured to ensure that the sensor nodes can smoothly access the network and send data. Each sensor node collects the monitoring data of the aquaculture environment at the set frequency, and the collected data is sent to the data center or the designated receiving end in real time through the configured wireless communication network.

[0040] In an embodiment of the present invention, by determining the location, number and type of sensor nodes through key position analysis, it can be ensured that key areas in the breeding environment are effectively monitored, thereby improving the comprehensiveness and accuracy of the data. The application of genetic algorithms can search for better wireless communication path solutions, reduce signal interference and transmission delays, and improve the efficiency and stability of data transmission. Through algorithm optimization, unnecessary sensor nodes and communication equipment can be reduced while meeting monitoring requirements, thereby reducing the hardware cost and maintenance cost of the entire system. The genetic algorithm used in this solution has good flexibility and scalability, and can adapt to breeding scenarios of different scales, as well as possible future increases in sensor nodes or adjustments to the network structure. The optimized wireless communication solution can ensure the real-time and accuracy of breeding data.

[0041] In a preferred embodiment of the present invention, the calculation formula of the dynamic correction value is: ; in, Indicates the dynamic correction value; represents the distance from the i-th sensor node to the target location; represents the real-time monitoring value of the i-th sensor node; represents the reference value of the i-th sensor node; represents the standard deviation of the Gaussian distribution; represents the number of sensor nodes; and Represent the signal strength of the i-th sensor and the j-th sensor respectively; i and j are indexes used to represent different sensors.

[0042] In the embodiment of the present invention, the formula can make weighted corrections in real time based on the monitoring value of the sensor node and its distance to the target location by introducing a dynamic correction value. This correction method helps to reduce data errors caused by sensor location, environmental interference or other factors, thereby improving the data accuracy and reliability of the entire monitoring system. The Gaussian distribution function is used in the formula to consider the distance from the sensor node to the target location, which means that the sensor node closer to the target will be given a higher weight in the correction value calculation. At the same time, by introducing the signal strength and The formula can also reflect the relative importance of different sensor nodes, further enhancing the accuracy of the correction. Since the formula is dynamic, it can update the correction value as the real-time monitoring value of the sensor node changes, thereby ensuring the real-time performance of the system. By comparing the real-time monitoring value with the reference value, the formula can identify and suppress the influence of abnormal values ​​to a certain extent. When the monitoring value of a sensor node deviates from the normal range, its correction value will be adjusted accordingly, thereby reducing the impact of abnormal data on the overall result. By comprehensively considering multiple factors (such as distance, signal strength, real-time monitoring value, etc.), the formula can enhance the robustness of the entire monitoring system. Even if some sensor nodes fail or the data is abnormal, it can still rely on other normally working nodes to provide relatively accurate correction values.

[0043] In a preferred embodiment of the present invention, the current sensor node position is adjusted using the dynamic correction value to obtain the final position node, including: Determine the original sensor node position. The position of each sensor node is represented by three-dimensional coordinates (x, y, z), where x and y represent the position on the horizontal plane and z represents the water depth. Specifically, it includes: obtaining the original position information of all sensor nodes, which is achieved through GPS and underwater positioning system; recording the position of each sensor node as three-dimensional coordinates (x, y, z), where x and y represent the projected position of the sensor on the horizontal plane, represented by latitude and longitude or relative coordinate system; z represents the depth of the sensor underwater.

[0044] Define the position adjustment step size based on the size and shape of the monitoring area and the current distribution of sensor nodes, including: analyzing the size and shape of the monitoring area, understanding the scope of the entire monitoring environment, and evaluating the current distribution of sensor nodes, including node density and whether there are coverage blind spots. Based on the above analysis, set a suitable adjustment step size that should ensure the accuracy of the adjustment without causing over-adjustment. The size of the step size may require multiple tests and optimizations based on actual conditions.

[0045] Decompose the dynamic correction value into three dynamic correction components, corresponding to the three directions of the x-axis, y-axis and z-axis respectively. Multiply the dynamic correction component in each direction by the adjustment step length to obtain a three-dimensional adjustment vector. The dynamic correction value is decomposed into (dx, dy, dz), and the adjustment vector is (dx×step length, dy×step length, dz×step length); The calculated adjustment vector is added to the original three-dimensional coordinates of each sensor node to obtain the final position node, that is, the final x coordinate = original x coordinate + dx × step length; the final y coordinate = original y coordinate + dy × step length; the final z coordinate = original z coordinate + dz × step length, specifically including: multiplying the dynamic correction component of each direction with the adjustment step length defined in step 2 to obtain the adjustment amount in three directions. That is, dx × step length, dy × step length, dz × step length, these three values ​​constitute a three-dimensional adjustment vector; the calculated adjustment vector is added to the original three-dimensional coordinates of each sensor node. Specifically, dx × step length is added to the original x coordinate, dy × step length is added to the original y coordinate, and dz × step length is added to the original z coordinate, so as to obtain the adjusted new coordinates.

[0046] In the embodiment of the present invention, by introducing a dynamic correction value and decomposing it into correction components in three directions, the position of each sensor node can be adjusted more accurately. This fine-grained adjustment method helps to reduce the error in the original position coordinates, thereby improving the positioning accuracy of the entire sensor network. The method can flexibly define the position adjustment step according to the size, shape and distribution of sensor nodes in different monitoring areas. This adaptability enables the method to be widely used in various scenarios, and effective position adjustment can be achieved in both small and large monitoring areas. By dynamically adjusting the position of the sensor nodes, the distribution of nodes in the monitoring area can be optimized, which helps to ensure that the key areas are adequately covered, while avoiding excessive deployment of nodes in non-key areas, thereby improving the efficiency and performance of the entire monitoring system. When some sensor nodes cause data abnormalities due to faults or environmental factors, the method can identify and adjust the positions of these nodes through dynamic correction values. This adjustment helps to reduce the impact of abnormal nodes on the entire system, thereby enhancing the robustness and stability of the system. Since the dynamic correction value is calculated in real time, this method can adjust the position of the sensor node in real time. This real-time nature enables the system to respond quickly to changes in the environment and adjust the node position in time to maintain the best monitoring effect. At the same time, the real-time feedback mechanism also helps to promptly discover and correct problems in the system.

[0047] In a preferred embodiment of the present invention, the dynamic correction value is decomposed into three dynamic correction components, corresponding to the three directions of the x-axis, y-axis and z-axis respectively, including: Collect a data set containing the locations and readings of sensor nodes, including: determining the data source, determining the type and deployment location of the sensor nodes, ensuring that data containing the locations and readings of the sensor nodes can be obtained; collecting data through the sensor network, recording the location coordinates (x, y, z) and corresponding readings of each sensor node, and organizing the collected data into a standard data set format for subsequent processing and analysis.

[0048] The data set is preprocessed, and the corresponding dx, dy, and dz components are generated as training labels based on the position changes of the sensor nodes in the historical data. Specifically, outliers, missing values, or duplicate data in the data set are removed to ensure data quality. Features related to the position changes of sensor nodes, such as timestamps and reading change rates, are extracted from the data set. Based on the position changes of the sensor nodes in the historical data, the true values ​​of the three components dx, dy, and dz are calculated and used as training labels, which usually involves differential operations on the position coordinates of adjacent time points.

[0049] Construct a convolutional neural network model and define the input layer and output layer. The input layer contains sensor readings and position coordinates; the output layer has three nodes, corresponding to the predicted values ​​of the three components dx, dy, and dz, respectively. Set the hidden layer and determine the corresponding ReLU function. Specifically, design the structure of the convolutional neural network, including the input layer, hidden layer, and output layer. The input layer should contain sensor readings and position coordinates. The output layer should have three nodes corresponding to the predicted values ​​of dx, dy, and dz, respectively. Determine the number of hidden layers and the number of neurons in each layer, and select a suitable activation function (such as the ReLU function). Initialize the weights and biases of the model. Usually, random initialization or a pre-trained model is used for parameter initialization.

[0050] Divide the dataset into training, validation, and test sets. Specifically, randomly divide the sorted dataset into training, validation, and test sets. The usual ratio is 70%:15%:15%. Prepare a data loader for model training to ensure that data can be loaded efficiently in batches.

[0051] Use the training set to train the convolutional neural network model, and optimize the parameters of the convolutional neural network model through the back propagation algorithm and the gradient descent algorithm to obtain the final convolutional neural network model, including: selecting a suitable optimizer (such as Adam) and learning rate, defining the loss function of the model, usually using the mean square error (MSE) to measure the gap between the predicted value and the true value, using the training set to train the model for multiple iterations, and optimizing the parameters of the model through the back propagation algorithm and the gradient descent algorithm. After each training cycle, use the validation set to evaluate the performance of the model, and adjust the parameters or improve the model as needed. When the model achieves satisfactory performance on the validation set, save the parameters and structure of the model for subsequent use.

[0052] The reading and position coordinates of the current sensor node are input into the final convolutional neural network model, and the predicted dx, dy, and dz component values ​​are obtained through the forward propagation calculation of the final convolutional neural network model, specifically including: loading the trained final convolutional neural network model, organizing the reading and position coordinates of the current sensor node into a format that matches the model input layer, passing the input data to the model, and obtaining the predicted dx, dy, and dz component values ​​through the forward propagation calculation of the model, and outputting the prediction results for subsequent position adjustment.

[0053] In the embodiment of the present invention, by constructing a convolutional neural network model and using historical data for training, the complex relationship between sensor readings and position changes can be learned. This method can more accurately predict the three components of dx, dy, and dz, thereby improving the accuracy of position adjustment. Using the CNN model for prediction realizes the automation and intelligence of position adjustment. Once the model training is completed, the required position adjustment amount can be automatically calculated based on the current sensor readings and position coordinates without manual intervention. Since the CNN model has a strong feature extraction capability, it can adapt to different environmental conditions and sensor configurations. Even if the environment changes or the sensor is replaced, as long as the model is retrained, it can continue to provide accurate position adjustment suggestions. The forward propagation calculation speed of the CNN model is fast, and the position adjustment amount can be calculated in real time based on the current sensor data; through a large amount of historical data for training, the CNN model can learn the position change rules under various circumstances, so that it has a certain robustness, and can provide relatively accurate position adjustment suggestions even in the face of noise data or abnormal situations. This method can be easily extended to a larger-scale sensor network. It only needs to collect more historical data and adjust the structure and parameters of the CNN model accordingly to adapt to a larger-scale network.

[0054] In a preferred embodiment of the present invention, the aquaculture data monitored by each sensor node is obtained according to the final location node, wherein the aquaculture data includes the monitored water quality parameters, meteorological parameters and growth status parameters of the aquaculture organisms; the aquaculture data of each sensor node are fused to form a comprehensive data set, and key features reflecting the state and change trend of the aquaculture environment are extracted from the comprehensive data set; a prediction model for simulating the water flow dynamics, water quality distribution, meteorological conditions and growth status of the aquaculture organisms in the aquaculture pond is constructed according to the key features, specifically including: Determine the location node. First, determine that the "final location node" refers to the accurate location of the sensor node determined after dynamic correction; based on these location node information, obtain aquaculture data from each sensor node in real time or regularly through a preset data interface or communication protocol; analyze the acquired data to extract water quality parameters (such as dissolved oxygen, ammonia nitrogen, pH value, etc.), meteorological parameters (such as temperature, humidity, wind speed, light intensity, etc.) and growth status parameters of the aquaculture organisms (such as body length, weight, food intake, etc.).

[0055] Define a unified data format, which is usually a structured data model, which can be a database table structure, CSV file format or a data object model. This format should contain all necessary fields, such as timestamp, sensor ID, monitoring parameter type (such as water quality, meteorology, etc.) and monitoring value. Store the parsed data in the corresponding data storage system according to the defined format, such as a relational database (such as MySQL, PostgreSQL, etc.), a NoSQL database (such as MongoDB, Cassandra, etc.) or a distributed file system (such as HDFS). Since different sensors may use different clock sources, it is necessary to ensure that the timestamps of all data are synchronized, which is usually achieved through NTP (Network Time Protocol) to ensure that the time of all sensor nodes and data centers is consistent. If the data collection frequency of some sensor nodes is lower than that of other nodes, or the data arrival time is different due to network delays, etc., the data may need to be interpolated to ensure that all data are aligned in time. The interpolation method can be linear interpolation, nearest neighbor interpolation, etc. Kalman filter is an effective data fusion algorithm. It can estimate the state of dynamic systems in a recursive way in the presence of noise. For the same type of monitoring data (such as temperature, humidity, etc.), Kalman filter can be used to fuse data from different sensor nodes. Implementing Kalman filter includes initializing the state vector and covariance matrix, and then updating the state vector and covariance matrix according to the measured value and predicted value of each time step. This process will gradually reduce the noise in the data and improve the accuracy of the data. According to the actual application, adjust the parameters of Kalman filter, such as the covariance of process noise and measurement noise, to achieve the best fusion effect; in the data fusion process, set thresholds to detect outliers, which may be caused by sensor failure, transmission error or external interference; once an outlier is detected, a variety of strategies can be adopted to deal with it, such as ignoring the outlier, replacing it with the previous valid value or average value, or filling it with interpolation methods, etc. The specific strategy should be selected according to the actual situation and data characteristics; the data after fusion and outlier processing are integrated into a unified data set, which contains the monitoring data of all sensor nodes and the comprehensive data after fusion processing.

[0056] Basic statistical analysis is performed on various parameters in the comprehensive data set (such as water quality parameters, meteorological parameters, etc.), including calculation of mean, median, standard deviation, maximum value, minimum value, etc., to understand the distribution and fluctuation range of the data. For time series data, the data can be converted from the time domain to the frequency domain by Fourier transform (FFT) or wavelet transform, and the periodicity and frequency characteristics of the data can be analyzed, which helps to extract key features related to the state and change trend of the aquaculture environment. Based on the results of statistical analysis and time-frequency conversion, key features that can reflect the state and change trend of the aquaculture environment are extracted, including statistics, periodic components, trend terms, etc. of certain parameters. The correlation between different features is analyzed using correlation coefficients (such as Pearson correlation coefficient) or mutual information, and those features that have a significant impact on the aquaculture environment are identified, which helps to understand the relationship between features and provide a basis for subsequent feature selection. Based on the results of correlation analysis, the most representative feature subset is selected, and feature selection algorithms (such as feature importance evaluation based on tree models, etc.) can be used to assist this process. The selected features are standardized (such as z-score standardization) or normalized (such as minimum-maximum normalization) to eliminate the impact of dimensional differences on model training, which helps ensure that different features have the same weight and influence in the model. According to the complexity of the problem and the characteristics of the data, the long short-term memory network (LSTM) is selected as the prediction model. LSTM is a deep learning model suitable for sequence data that can capture long-term dependencies in the sequence. Using the extracted key features as input, the water flow dynamics, water quality distribution, meteorological conditions and the growth status of aquaculture organisms in historical data as the target output, the LSTM model is constructed and trained. During the training process, the model performance is optimized by adjusting the model parameters (such as the number of layers, the number of units, the learning rate, etc.) and the learning algorithm (such as optimizer selection, loss function selection, etc.). The trained LSTM model is verified using the validation data set to evaluate the model's performance indicators such as prediction accuracy and generalization ability. The mean square error (MSE) can be used to measure the model's prediction performance. According to the verification results, the model is adjusted and optimized as necessary, including adjusting the model structure (such as increasing or decreasing the number of layers, changing the number of units, etc.), adjusting hyperparameters (such as learning rate, batch size, etc.) or trying different learning algorithms and loss functions. The prediction performance of the model is improved through continuous iteration and optimization; the trained and tuned LSTM model is deployed in the actual application environment to ensure that the model can predict and analyze the breeding environment in real time or regularly.

[0057] In a preferred embodiment of the present invention, corresponding warning information is generated according to the prediction result and the preset threshold, including: The key indicators of the simulation data are obtained from the prediction model in real time. The key indicators include the predicted values ​​of water flow dynamics, water quality distribution, meteorological conditions and the growth status of the farmed organisms. Specifically, the early warning system regularly (such as every second, every minute) pulls the latest simulation data from the prediction model through the data interface, and parses the obtained simulation data into specific key indicators, including water flow dynamics (such as flow velocity, flow direction), water quality distribution (such as dissolved oxygen, pH value), meteorological conditions (such as temperature, humidity, wind speed) and the growth status of the farmed organisms (such as growth rate, health status prediction value).

[0058] Based on historical data analysis, corresponding warning thresholds are set for each key indicator, including: collecting a large amount of historical data, including key indicator values ​​under normal and abnormal conditions, using statistical methods to analyze historical data, understanding the fluctuation range of each key indicator under normal conditions and the characteristics under abnormal conditions, and setting reasonable warning thresholds for each key indicator based on the analysis results. These thresholds can be single values ​​(such as upper or lower limits), and using a portion of historical data to verify the set thresholds to ensure their accuracy and effectiveness.

[0059] Compare key indicators with warning thresholds to determine whether each key indicator exceeds or approaches the warning threshold to obtain comparison results, specifically including: comparing the key indicator values ​​obtained in real time with the preset warning thresholds one by one, and judging whether each key indicator exceeds the warning threshold (indicating an abnormal situation) or approaches the warning threshold (indicating potential risks) based on the comparison results, and record the comparison results in the database, including key indicator values, warning thresholds, comparison time and status (normal, approaching warning, exceeding warning).

[0060] Based on the comparison results, determine whether to trigger an early warning, including: defining the logical rules for triggering early warnings, such as when one or more key indicators exceed the early warning threshold for multiple consecutive times, or when a key indicator suddenly deviates significantly from the normal value, etc.; based on the comparison results and early warning logical rules, determine whether an early warning needs to be triggered; if it is determined that an early warning is triggered, generate early warning information (including early warning type, level, trigger time, and recommended measures, etc.) and send it to relevant personnel via SMS, email, or other means; and record the triggered early warning information in the database for subsequent query and analysis.

[0061] In the embodiment of the present invention, by real-time monitoring and comparing key indicators with warning thresholds, the system can issue warnings in time when key indicators approach or exceed the safety range, which helps relevant personnel to respond quickly and prevent potential problems from worsening. The early warning system can identify potential risks in advance, such as abnormal water flow, deteriorating water quality, unfavorable meteorological conditions or abnormal growth of aquaculture organisms, and can greatly reduce the impact of these risks on aquaculture or other related activities through early intervention. The early warning information provides an important basis for managers to make decisions. After receiving the warning, managers can formulate corresponding response measures according to the type and level of the warning, thereby optimizing resource allocation and improving management efficiency. Through the early warning system, operators can more accurately grasp the operating status, adjust the operating strategy in time, reduce unnecessary waste, and improve overall operating efficiency. As part of the entire monitoring system, the early warning mechanism enhances the robustness of the system. Even in the face of emergencies or abnormal situations, the system can detect problems and respond in time through the early warning mechanism.

[0062] like Figure 2 As shown, an embodiment of the present invention further provides an intelligent monitoring and management method for aquaculture based on Internet of Things technology, comprising: Identify key locations in an aquaculture environment and deploy multiple sensor nodes at key locations; Wirelessly connect with multiple sensor nodes to send farming data monitored by each sensor node; Receive and analyze the farming data sent by the communication module, and determine the dynamic correction value; use the dynamic correction value to adjust the current sensor node position to obtain the final position node; According to the final location node, the aquaculture data monitored by each sensor node is obtained, wherein the aquaculture data includes the monitored water quality parameters, meteorological parameters and growth status parameters of the aquaculture organisms; the aquaculture data of each sensor node are integrated to form a comprehensive data set, and the key features reflecting the state and change trend of the aquaculture environment are extracted from the comprehensive data set; based on the key features, a prediction model is constructed to simulate the water flow dynamics, water quality distribution, meteorological conditions and growth status of the aquaculture organisms in the aquaculture pond; Generate corresponding warning information based on the prediction results and preset thresholds.

[0063] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0064] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0065] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

Claims

1. An intelligent monitoring and management system for aquaculture based on Internet of Things technology, characterized in that: include: A deployment module is used to determine key locations in an aquaculture environment and deploy multiple sensor nodes at the key locations; A communication module is used to wirelessly connect with multiple sensor nodes to send the farming data monitored by each sensor node; A data analysis module, connected to the communication module, for receiving and analyzing the breeding data sent by the communication module, and determining a dynamic correction value; Using the dynamic correction value, the current sensor node position is adjusted to obtain the final position node; A construction module is used to obtain the aquaculture data monitored by each sensor node according to the final location node, wherein the aquaculture data includes the monitored water quality parameters, meteorological parameters and growth status parameters of the aquaculture organisms; the aquaculture data of each sensor node are integrated to form a comprehensive data set, and key features reflecting the state and change trend of the aquaculture environment are extracted from the comprehensive data set; a prediction model is constructed based on the key features to simulate the water flow dynamics, water quality distribution, meteorological conditions and growth status of the aquaculture organisms in the aquaculture pond; and the future aquaculture environment is predicted in real time based on the prediction model and real-time aquaculture data to obtain prediction results; The early warning module is used to generate corresponding early warning information based on the prediction results and preset thresholds.

2. According to claim 1, the intelligent monitoring and management system for aquaculture based on Internet of Things technology is characterized in that: Identify critical locations within the aquaculture environment, including: Evaluate the overall layout of the aquaculture environment, water flow dynamics, shape and size of the aquaculture pond to obtain environmental assessment results; Based on the environmental assessment results, determine the preliminary site selection, which includes the intersection of water flows, the edge of the aquaculture pond or near the aeration equipment; Construct a two-dimensional matrix to represent the plan layout of the aquaculture environment, where each element of the matrix corresponds to a specific location in the aquaculture environment; According to the preliminary selected points, the weight values ​​corresponding to the corresponding elements in the position matrix are calculated; Create a feature matrix that contains various factors that affect the selection of key locations, including the rate of change of water quality parameters, the stability of meteorological parameters, and the distribution density of aquaculture organisms. Each factor corresponds to a column in the feature matrix. Perform matrix multiplication of the position matrix and the feature matrix, and get a comprehensive value for each position; According to the comprehensive value obtained by the matrix multiplication operation, all positions are sorted according to the size of the comprehensive value to obtain the corresponding key position.

3. The intelligent monitoring and management system for aquaculture based on Internet of Things technology according to claim 2 is characterized in that: Wirelessly connect to multiple sensor nodes to send farming data monitored by each sensor node, including: According to the key positions, the location, quantity and type of sensor nodes are determined, and the initial parameters of the genetic algorithm are set, including population size, crossover rate, mutation rate and number of iterations; The wireless communication path is represented by binary coding, and a population is initialized, which includes multiple individuals, each of which represents a wireless communication scheme; Define a fitness function to evaluate the pros and cons of each wireless communication scheme; According to the fitness function, the corresponding individuals are selected to enter the next generation, two individuals are randomly selected to exchange the corresponding partial genes to generate new individuals, and the individuals in the population, i.e. the wireless communication scheme, are continuously optimized through multiple iterations; after each iteration, the pros and cons of the individuals in the current population are evaluated according to the fitness function, and selection and genetic operations are performed. When the preset number of iterations is reached, the iteration is stopped, and the final wireless communication scheme, i.e. the corresponding individual, is output; Configure sensor nodes and wireless communication network according to the final wireless communication scheme; According to the configured sensor nodes and wireless communication network, the breeding data monitored by each sensor node is sent.

4. The intelligent monitoring and management system for aquaculture based on Internet of Things technology according to claim 3 is characterized in that: The calculation formula of dynamic correction value is: ； in, Indicates the dynamic correction value; represents the distance from the i-th sensor node to the target location; represents the real-time monitoring value of the i-th sensor node; represents the reference value of the i-th sensor node; represents the standard deviation of the Gaussian distribution; represents the number of sensor nodes; and Represent the signal strength of the i-th sensor and the j-th sensor respectively; i and j are indexes used to represent different sensors.

5. The aquaculture intelligent monitoring and management system based on Internet of Things technology according to claim 4 is characterized in that: Using the dynamic correction value, the current sensor node position is adjusted to obtain the final position node, including: Determine the original sensor node position. The position of each sensor node is represented by three-dimensional coordinates (x, y, z), where x and y represent the position on the horizontal plane and z represents the water depth; Define the position adjustment step size according to the size and shape of the monitoring area and the current distribution of sensor nodes; Decompose the dynamic correction value into three dynamic correction components, corresponding to the three directions of the x-axis, y-axis and z-axis respectively. Multiply the dynamic correction component in each direction by the adjustment step length to obtain a three-dimensional adjustment vector. The dynamic correction value is decomposed into (dx, dy, dz), and the adjustment vector is (dx×step length, dy×step length, dz×step length); The calculated adjustment vector is added to the original three-dimensional coordinates of each sensor node to obtain the final position node, that is, the final x coordinate = original x coordinate + dx × step length; the final y coordinate = original y coordinate + dy × step length; the final z coordinate = original z coordinate + dz × step length.

6. The aquaculture intelligent monitoring and management system based on Internet of Things technology according to claim 5 is characterized in that: The dynamic correction value is decomposed into three dynamic correction components, corresponding to the three directions of the x-axis, y-axis and z-axis, including: Collect a dataset containing sensor node locations and readings; Preprocess the data set and generate corresponding dx, dy, and dz components as training labels based on the position changes of sensor nodes in historical data; Construct a convolutional neural network model and define the input layer and output layer. The input layer contains sensor readings and position coordinates; the output layer has three nodes, corresponding to the predicted values ​​of the three components dx, dy, and dz, respectively. Set the hidden layer and determine the corresponding ReLU function. Divide the dataset into training, validation and test sets; Use the training set to train the convolutional neural network model, and optimize the parameters of the convolutional neural network model through the back propagation algorithm and the gradient descent algorithm to obtain the final convolutional neural network model; The reading and position coordinates of the current sensor node are input into the final convolutional neural network model, and the predicted dx, dy, and dz component values ​​are obtained through the forward propagation calculation of the final convolutional neural network model.

7. The aquaculture intelligent monitoring and management system based on Internet of Things technology according to claim 6 is characterized in that: Generate corresponding warning information based on the prediction results and preset thresholds, including: Obtain key indicators of simulation data from the prediction model in real time, including predicted values ​​of water flow dynamics, water quality distribution, meteorological conditions and growth status of aquaculture organisms; Based on historical data analysis, set corresponding warning thresholds for each key indicator; Compare key indicators with warning thresholds to determine whether each key indicator exceeds or approaches the warning threshold to obtain comparison results; Based on the comparison results, determine whether to trigger an early warning.

8. An intelligent monitoring and management method for aquaculture based on Internet of Things technology, characterized in that: The method is used to execute the system according to any one of claims 1 to 7, comprising: Identify key locations in an aquaculture environment and deploy multiple sensor nodes at key locations; Wirelessly connect with multiple sensor nodes to send farming data monitored by each sensor node; Receive and analyze the farming data sent by the communication module, and determine the dynamic correction value; use the dynamic correction value to adjust the current sensor node position to obtain the final position node; According to the final location node, the aquaculture data monitored by each sensor node is obtained, wherein the aquaculture data includes the monitored water quality parameters, meteorological parameters and growth status parameters of the aquaculture organisms; the aquaculture data of each sensor node are integrated to form a comprehensive data set, and the key features reflecting the state and change trend of the aquaculture environment are extracted from the comprehensive data set; based on the key features, a prediction model is constructed to simulate the water flow dynamics, water quality distribution, meteorological conditions and growth status of the aquaculture organisms in the aquaculture pond; Generate corresponding warning information based on the prediction results and preset thresholds.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to claim 8 when executed by a processor.

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