A water quality prediction and intelligent control system based on machine learning

Abstract

The present invention provides a water quality prediction and intelligent control system based on machine learning, comprising: a real-time acquisition module for collecting current water quality data of aquaculture ponds in real time; a water quality prediction module for determining water quality prediction results based on a pre-trained water quality prediction model and current water quality data; and an intelligent control module for adaptively controlling future water quality adjustment plans and feed feeding plans for the aquaculture ponds based on the water quality prediction results. The present invention trains a water quality prediction model based on a machine learning algorithm, determines water quality prediction results based on the water quality prediction model and current water quality data, and adaptively controls future water quality adjustment plans and feed feeding plans for the aquaculture ponds based on the water quality prediction results, thereby achieving automated and precise water quality management and feed feeding management for the aquaculture ponds, reducing labor costs, achieving real-time and precise control, and improving aquaculture efficiency and quality.

Description

Technical Field

[0001] The present invention relates to the field of machine learning technology, and in particular to a water quality prediction and intelligent control system based on machine learning. Background Art

[0002] At present, in the aquaculture industry, the water quality of breeding ponds not only directly affects the growth, reproduction and health of aquatic animals, but is also closely related to the ecological balance of the breeding environment. The feed for aquatic animals is also determined by the water quality.

[0003] Traditional water quality management and bait delivery management mainly rely on manual monitoring and manual experience judgment. The operation is cumbersome, the labor cost is high, and it is difficult to achieve real-time and precise regulation, which has a great impact on breeding efficiency and aquatic product quality.

[0004] Therefore, a solution is urgently needed. Summary of the Invention

[0005] One of the purposes of the present invention is to provide a water quality prediction and intelligent control system based on machine learning. Based on the machine learning algorithm, a water quality prediction model is trained, and the current water quality data of the breeding pond is collected in real time. Based on the water quality prediction model, the water quality prediction result is determined according to the current water quality data. Based on the water quality prediction result, the future water quality adjustment plan and feed feeding plan of the breeding pond are adaptively controlled to realize automated and precise water quality management and feed feeding management of the breeding pond, reduce labor costs, realize real-time and precise control, and improve aquaculture efficiency and aquaculture quality.

[0006] An embodiment of the present invention provides a water quality prediction and intelligent control system based on machine learning, comprising:

[0007] Real-time data collection module, used to collect the current water quality data of the breeding pond in real time;

[0008] The water quality prediction module is used to determine the water quality prediction results based on the pre-trained water quality prediction model and the current water quality data;

[0009] Intelligent control module, used to adaptively control the future water quality adjustment plan and bait feeding plan of the aquaculture pond based on the water quality prediction results;

[0010] Among them, the pre-training steps of the water quality prediction model are as follows:

[0011] Obtain a large amount of historical water quality data from different aquaculture ponds;

[0012] Preprocess historical water quality data;

[0013] Based on the machine learning algorithm, the water quality prediction model is trained according to the preprocessed historical water quality data.

[0014] Optionally, the real-time acquisition module collects the current water quality data of the aquaculture pond in real time, including:

[0015] The current water quality data of the breeding pond is collected in real time through the sensor network arranged in the breeding pond.

[0016] Optionally, the current water quality data includes at least: salinity, temperature, pH value, and dissolved oxygen.

[0017] Optionally, the preprocessing of historical water quality data includes:

[0018] The historical water quality data are processed in sequence by removing missing values, removing outliers and standardizing.

[0019] Optional machine learning-based water quality prediction and intelligent control system also includes:

[0020] Optimization modules for:

[0021] Obtain the error situation determined by the water quality prediction results;

[0022] Based on the error situation, the water quality prediction model is optimized.

[0023] Optionally, the intelligent control module adaptively controls the future water quality adjustment plan and feed feeding plan of the aquaculture pond based on the water quality prediction results, including:

[0024] Perform feature description on the water quality prediction result to obtain the first feature description vector;

[0025] Determine the plan control scheme corresponding to the first feature description vector from the plan control scheme library;

[0026] Based on the planned control plan, the future water quality adjustment plan and bait feeding plan of the breeding pond are regulated.

[0027] Optional machine learning-based water quality prediction and intelligent control system also includes:

[0028] Auxiliary modules for:

[0029] Generate a digital model based on current water quality data, water quality forecasts, and process information for adaptively controlling future water quality and feeding plans for the aquaculture ponds;

[0030] When a user brings a smart terminal close to the aquaculture pond, the user's movement trajectory is continuously obtained through the smart terminal;

[0031] Determine whether the movement route indicates a capture prompt time;

[0032] If the answer is yes, the user is prompted to take a photo of the intended area of ​​the aquaculture pond via the smart terminal;

[0033] When the user starts to shoot the aquaculture pond, the user's shooting trajectory is continuously obtained through the smart terminal;

[0034] When the user is filming the aquaculture pond, the smart terminal determines whether it has reached the camera flipping time based on the movement trajectory and the filming trajectory;

[0035] When the answer is yes, control the smart terminal to flip the camera;

[0036] After the user finishes photographing the aquaculture pond, the user's intended photographing area is obtained;

[0037] Determine the slice model of the intended area from the digitized model;

[0038] Determine regulatory recommendations based on the slicing model;

[0039] Display slicing models and control suggestions to users;

[0040] When the user inputs a control plan, the breeding pond is controlled in a relay manner based on the control plan.

[0041] Optionally, the auxiliary module determines whether the moving route indicates entering a shooting prompt time, including:

[0042] Determine a target sub-pond from the aquaculture pond; wherein the shortest distance between the center position of the target sub-pond and the moving route does not exceed a distance threshold;

[0043] When the ratio of the total number of target sub-pools to the total number of sub-pools in the breeding pond exceeds a ratio threshold or at least two target sub-pools have at least one standard association relationship, determining that the movement route indicates entering a shooting prompt timing;

[0044] Otherwise, it is determined that the movement route does not indicate a shooting prompt opportunity.

[0045] Optionally, the auxiliary module determines whether the smart terminal enters a camera flipping opportunity based on the movement trajectory and the shooting trajectory, including:

[0046] Determine whether the movement trajectory and the shooting trajectory meet the timing entry constraint. If so, determine the smart terminal enters the camera flip timing;

[0047] The timing entry constraints include:

[0048] Constraint 1: The average moving speed of the second target segment within the first target segment on the shooting trajectory exceeds the first speed threshold; the first target segment is the part of the shooting trajectory generated within the first time; the second target segment is the part of the trajectory of the previous target within the first target segment;

[0049] as well as,

[0050] Constraint 2: The average moving speed of the third target segment within the first target segment on the shooting trajectory does not exceed the second speed threshold; the third target segment is the portion of the trajectory within the first target segment excluding the second target segment; the second speed threshold is less than the first speed threshold;

[0051] as well as,

[0052] Constraint 3: The maximum matching degree between the fourth target segment on the trajectory and each standard trajectory in the standard trajectory library exceeds the matching degree threshold; the fourth target segment is the portion of the trajectory generated within the most recent second time; and the second time is less than the first time.

[0053] Optionally, the auxiliary module determines control suggestions based on the slicing model, including:

[0054] Perform feature description on the slice model to obtain a second feature description vector;

[0055] Determine the control suggestion corresponding to the second feature description vector from the control suggestion library.

[0056] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0059] Figure 1 Schematic diagram of a water quality prediction and intelligent control system based on machine learning in an embodiment of the present invention;

[0060] Figure 2 This is another schematic diagram of a water quality prediction and intelligent control system based on machine learning in an embodiment of the present invention;

[0061] Figure 3 This is another schematic diagram of a water quality prediction and intelligent control system based on machine learning in an embodiment of the present invention;

[0062] Figure 4 This is a flowchart of specific operation steps performed by the auxiliary module in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0064] The embodiment of the present invention provides a water quality prediction and intelligent control system based on machine learning, such as Figure 1 As shown, including:

[0065] Real-time collection module 1, used to collect the current water quality data of the breeding pond in real time;

[0066] Water quality prediction module 2, used to determine the water quality prediction result based on the pre-trained water quality prediction model and the current water quality data;

[0067] Intelligent control module 3 is used to adaptively control the future water quality adjustment plan and bait feeding plan of the aquaculture pond based on the water quality prediction results;

[0068] Among them, the pre-training steps of the water quality prediction model are as follows:

[0069] Obtain a large amount of historical water quality data from different aquaculture ponds;

[0070] Preprocess historical water quality data;

[0071] Based on machine learning algorithms, water quality prediction models are trained based on pre-processed historical water quality data;

[0072] The real-time acquisition module collects the current water quality data of the aquaculture pond in real time, including:

[0073] The current water quality data of the aquaculture pond is collected in real time through the sensor network arranged in the aquaculture pond;

[0074] The current water quality data includes at least: salinity, temperature, pH value, and dissolved oxygen;

[0075] The pre-processing of historical water quality data includes:

[0076] The historical water quality data are processed in sequence by removing missing values, removing outliers and standardizing.

[0077] In the above technical solution, the real-time acquisition module monitors and collects the current water quality data of the aquaculture pond in real time through the sensor network deployed in the aquaculture pond. The sensor network includes at least: salinity sensor, temperature sensor, pH sensor, dissolved oxygen sensor, etc.; when collecting a large amount of historical water quality data from different aquaculture ponds, historical data of various water quality parameters (such as salinity, temperature, pH value, dissolved oxygen, etc.) are collected from different aquaculture ponds; then data preprocessing is performed to handle missing values ​​by interpolation, filling or discarding, identify and eliminate outliers in the data (such as sudden sensor errors or extreme water quality fluctuations), and standardize the data (such as normalization or z-score standardization) to ensure that the dimensions of each eigenvalue are consistent; then based on the machine learning algorithm, the data is analyzed and analyzed. The machine learning algorithm trains a water quality prediction model based on pre-processed historical water quality data. The trained water quality prediction model can determine the water quality prediction results based on the current water quality data. The water quality prediction results include the water quality change trend data of the aquaculture pond in the future (such as 24 hours, 48 ​​hours, etc.); finally, based on the water quality prediction results, the future water quality adjustment plan and bait feeding plan of the aquaculture pond can be adaptively regulated. For example, if the dissolved oxygen level is predicted to drop, the intelligent control module will start the oxygenation device in advance or adjust the water flow to ensure that the aquatic animals in the aquaculture pond have sufficient oxygen. For example, when the system predicts that the dissolved oxygen level is low and the ammonia nitrogen concentration is high, the system can appropriately reduce the amount of bait fed to avoid further deterioration of water quality due to excessive bait.

[0078] This application is based on a machine learning algorithm to train a water quality prediction model, collect current water quality data of the aquaculture pond in real time, and determine the water quality prediction results based on the water quality prediction model and the current water quality data. Based on the water quality prediction results, the future water quality adjustment plan and feed feeding plan of the aquaculture pond are adaptively regulated to achieve automated and precise water quality management and feed feeding management of the aquaculture pond, reduce labor costs, achieve real-time and precise regulation, and improve aquaculture efficiency and quality.

[0079] In one embodiment, Figure 2 As shown, the water quality prediction and intelligent control system based on machine learning also includes:

[0080] Optimization module 4 is used to:

[0081] Obtain the error situation determined by the water quality prediction results;

[0082] Based on the error situation, the water quality prediction model is optimized.

[0083] Compare the water quality prediction results with the subsequent actual water quality data collected to determine the error situation; based on the error situation, optimize the water quality prediction model, such as adaptively adjusting the hyperparameters of the machine learning model according to the error situation.

[0084] In one embodiment, the intelligent control module adaptively controls the future water quality adjustment plan and bait feeding plan of the aquaculture pond based on the water quality prediction results, including:

[0085] Perform feature description on the water quality prediction result to obtain the first feature description vector;

[0086] Determine the plan control scheme corresponding to the first feature description vector from the plan control scheme library;

[0087] Based on the planned control plan, the future water quality adjustment plan and bait feeding plan of the breeding pond are regulated.

[0088] When performing feature description, key features in the water quality prediction results are extracted, such as: dissolved oxygen change trend, temperature fluctuation amplitude, pH stability, etc.; the extracted key features are converted into numerical vectors to obtain the first feature description vector; the planned control scheme library contains pre-set planned control schemes corresponding to different first feature description vectors, and the planned control schemes indicate how to control the future water quality adjustment plan and bait feeding plan of the aquaculture pond.

[0089] In one embodiment, Figure 3 As shown, the water quality prediction and intelligent control system based on machine learning also includes:

[0090] Auxiliary module 5, such as Figure 4 As shown, it is used for:

[0091] S1. Generate a digital model based on current water quality data, water quality prediction results, and process information for adaptively controlling future water quality adjustment plans and feed feeding plans for the aquaculture ponds;

[0092] In S1, the process information for adaptively regulating the future water quality adjustment plan and bait feeding plan of the aquaculture pond includes at least: the adjustment results of the water quality adjustment plan and the bait feeding plan, and the comparison information before and after the adjustment; the digital model is a digital three-dimensional model that visually displays the current water quality data, water quality prediction results, and process information;

[0093] S2. When the user brings the smart terminal close to the aquaculture pond, the user's movement trajectory is continuously obtained through the smart terminal;

[0094] In S2, the smart terminal may be a smart phone, etc.; the smart terminal has a built-in positioning device and a millimeter-wave radar. The positioning device collects the real-time position of the smart terminal, and the millimeter-wave radar collects the real-time relative position relationship between the user and the smart terminal. The real-time position of the user can be determined based on the real-time position of the smart terminal and the real-time relative position relationship between the user and the smart terminal. The real-time recording of the real-time position of the user forms the user's movement trajectory.

[0095] S3, determining whether the moving route indicates that a shooting prompt has been entered;

[0096] In S3, the shooting prompt timing refers to the timing of prompting the user to shoot the intended area of ​​the aquaculture pond. The intended area can be the aquaculture pond area where the user needs to re-determine the control plan, the aquaculture pond area where the user needs to obtain control suggestions from the system, etc.

[0097] S4. If the answer is yes, prompt the user to take a photo of the intended area of ​​the aquaculture pond via the smart terminal;

[0098] In S4, when the answer is yes, the user is prompted accordingly, and the prompt may be made by pushing relevant prompt information to the smart terminal;

[0099] S5. When the user starts to shoot the aquaculture pond, the user's shooting trajectory is continuously obtained through the smart terminal;

[0100] In S5, when the user receives the prompt, they will start to shoot the aquaculture pond. When they start shooting, the user's shooting trajectory is continuously obtained through the smart terminal. The smart terminal is equipped with a front camera and a rear camera. As the user adjusts the shooting area of ​​the front camera or the rear camera, the center position of the shooting area will continue to change. The trajectory formed by the continuous change of the center position of the shooting area is the shooting trajectory;

[0101] S6. When the user is photographing the aquaculture pond, determining whether the smart terminal has entered a camera flipping time based on the movement trajectory and the photographing trajectory;

[0102] In S6, the camera flip timing refers to the timing of controlling the smart terminal to flip the camera;

[0103] S7. When the answer is yes, control the smart terminal to flip the camera;

[0104] In S6, when the camera flip opportunity comes, the smart terminal is controlled to flip the camera;

[0105] S8. After the user finishes photographing the aquaculture pond, the user's intended photographing area is obtained;

[0106] In S8, when obtaining the intended area photographed by the user, the entire area photographed by the smart terminal from the time the user starts photographing the aquaculture pond to the time the user finishes photographing the aquaculture pond is taken as the intended area;

[0107] S9, determining a slice model of the intended area from the digital model;

[0108] In S9, the intended area is a geographical location area in the aquaculture pond, and information related to the geographical location area will be displayed in the digital model, and the part of the model displaying the related information will be used as a slice model;

[0109] S10. Determine control recommendations based on the slice model;

[0110] In S10, the user may want to obtain regulation suggestions related to the slice model, therefore, the disadvantage of regulation suggestions based on the slice model;

[0111] S11. Displaying the slicing model and control suggestions to the user;

[0112] In S11, the slicing model and the control suggestions are displayed to the user. The user can view the slicing model to understand the situation of the intended area and make a decision on a new control plan based on the control suggestions.

[0113] S12. When the user inputs a control plan, the breeding pond is controlled in a relay manner based on the control plan.

[0114] In S12, when the user decides on a new control plan, the breeding pond is relay-controlled based on the control plan; relay control means that based on the water quality prediction results, the future water quality control plan and bait feeding plan of the breeding pond are adaptively controlled, and the water quality control plan and bait feeding plan are continued to be controlled.

[0115] Generally, when the system performs intelligent regulation, the user (the manager of the breeding pond) may want to make appropriate manual intervention in the regulation of the breeding pond. He will approach the breeding pond and observe the working conditions of relevant sensors and the actual water quality. At this time, the system can make regulation suggestions, but the premise of the suggestion is to know which breeding pond areas the user wants to make regulation decisions on, that is, how to determine the intention.

[0116] Typically, when a user wants to select an intended area, they need to select a frame on a map of the aquaculture pond, which is rather cumbersome. The embodiment of the present invention attempts to assist the user in photographing the intended area, but the following two problems may arise:

[0117] 1. There are a large number of sub-pools in the aquaculture pond, which are arranged in several rows and columns. When shooting the intended area, the user may need to shoot at least two sub-pools in opposite directions (such as front and back). However, if the user wants to shoot at least two sub-pools, it is not possible to shoot continuously. To avoid irrelevant areas in the shooting area, the user needs to manually pause, which is quite cumbersome.

[0118] 2. The space available for users to walk between different pools is small. If the user manually pauses the camera and then controls the camera to face the next pool to be photographed, the movement is cumbersome.

[0119] The embodiments of the present invention can solve the above two problems. When the user is shooting the breeding pond, it determines whether the smart terminal has entered the camera flipping time based on the movement trajectory and the shooting trajectory. When the camera flipping time is entered, the smart terminal is controlled to flip the camera. The user does not need to manually pause shooting to continuously shoot at least two sub-pool areas that the user wants to be the intended area. The user does not need to turn the body over and move, which suggests convenience and is suitable for smaller moving spaces between different sub-pools.

[0120] Secondly, the embodiment of the present invention determines whether the moving route indicates entering a shooting prompt time. If so, the user is prompted to shoot the intended area of ​​the breeding pond through the smart terminal, accurately prompting the user to shoot the intended area, which is humane.

[0121] In one embodiment, the auxiliary module determines whether the movement route indicates entering a shooting prompt opportunity, including:

[0122] S31. Determine a target sub-pond from the aquaculture pond; wherein the shortest distance between the center position of the target sub-pond and the moving route does not exceed a distance threshold;

[0123] In S31, the distance threshold may be 50 meters; if the shortest distance between the center position of the target sub-pool and the moving route does not exceed the distance threshold, it means that the user has viewed the target sub-pool during the movement;

[0124] S32: When the ratio of the total number of target sub-pools to the total number of sub-pools in the breeding pond exceeds a ratio threshold or at least two target sub-pools have at least one standard association relationship, determining that the movement route indicates entering a shooting prompt timing;

[0125] In S31, the ratio threshold may be, for example, 3 / 10, and the standard association relationship may be that the aquatic animals cultured in the target sub-pools are the same. When the ratio of the total number of target sub-pools to the total number of sub-pools in the breeding pond exceeds the ratio threshold or at least two target sub-pools have at least one standard association relationship, it indicates that the user has viewed a large number of sub-pools of the breeding pond or that the viewed sub-pools of the breeding pond indicate that the user needs to make a control decision, and the moving route is determined to indicate that a shooting prompt timing has been entered;

[0126] S33: Otherwise, determine that the moving route does not indicate entering a shooting prompting opportunity.

[0127] The embodiment of the present invention accurately determines whether the moving route indicates entering a shooting prompt opportunity, thereby improving the suitability of the shooting prompt and greatly improving the applicability of the system.

[0128] In one embodiment, the auxiliary module determines whether the smart terminal enters a camera flipping opportunity based on the movement trajectory and the shooting trajectory, including:

[0129] S61, determining whether the movement trajectory and the shooting trajectory meet the timing entry constraint, and if so, determining that the smart terminal enters the camera flip timing;

[0130] The timing entry constraints include:

[0131] Constraint 1: The average moving speed of the second target segment within the first target segment on the shooting trajectory exceeds the first speed threshold; the first target segment is the part of the shooting trajectory generated within the first time; the second target segment is the part of the trajectory of the previous target within the first target segment;

[0132] as well as,

[0133] Constraint 2: The average moving speed of the third target segment within the first target segment on the shooting trajectory does not exceed the second speed threshold; the third target segment is the portion of the trajectory within the first target segment excluding the second target segment; the second speed threshold is less than the first speed threshold;

[0134] as well as,

[0135] Constraint 3: The maximum matching degree between the fourth target segment on the trajectory and each standard trajectory in the standard trajectory library exceeds the matching degree threshold; the fourth target segment is the portion of the trajectory generated within the most recent second time; and the second time is less than the first time.

[0136] In Constraints 1 and 2, the first speed threshold represents a user's faster movement speed; the average movement speed of the second target segment can be obtained by dividing the total travel length of the second target segment by the total duration of the second target segment; the most recent first time can be, for example, the last 100 seconds; the previous target ratio can be, for example, the previous 4 / 5; the average movement speed of the third target segment can be obtained by dividing the total travel length of the third target segment by the total duration of the third target segment; and the second speed threshold represents a user's slower movement speed. When a user wants to capture at least two sub-pool areas in opposite directions, they will first use the rear camera of the smart terminal to capture one of the sub-pool areas. When capturing, the initial movement speed is relatively fast. If the smart terminal camera is to be flipped, the movement speed will drop sharply and the system will automatically flip the camera. This satisfies Constraints 1 and 2.

[0137] In constraint three, the second most recent time can be, for example, the last 20 seconds. The standard trajectory in the standard trajectory library represents the movement trajectory generated when the user wants to flip the camera of the smart terminal. For example, when the user starts shooting with the rear camera of the smart terminal and wants to flip the camera, the user moves from the original position facing the front camera of the smart terminal to the position facing the front camera sideways to the smart terminal (moving away so as not to block the shooting area of ​​the front camera). The matching degree threshold can be 85%. When the maximum matching degree between the fourth target segment and each standard trajectory in the standard trajectory library exceeds the matching degree threshold, it indicates that the user wants to flip the camera of the smart terminal.

[0138] By setting constraints one, two, and three, it is possible to accurately determine whether the smart terminal has entered the camera flipping time, which greatly improves the intelligence level of the system.

[0139] In one embodiment, the auxiliary module determines the control suggestion based on the slice model, including:

[0140] Perform feature description on the slice model to obtain a second feature description vector;

[0141] Determine the control suggestion corresponding to the second feature description vector from the control suggestion library.

[0142] When performing feature description, the system will extract features related to the status of the aquaculture pond in the slice model, such as: the average value and variation range of dissolved oxygen, the fluctuation of pH value, the change trend of water temperature, etc.; then the feature is represented in vector form to obtain the second feature description vector; there are control suggestions corresponding to different second feature description vectors in the control suggestion library, so the library is checked to determine the control suggestion.

[0143] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A water quality prediction and intelligent control system based on machine learning, characterized in that: include: Real-time data collection module, used to collect the current water quality data of the breeding pond in real time; The water quality prediction module is used to determine the water quality prediction results based on the pre-trained water quality prediction model and the current water quality data; Intelligent control module, used to adaptively control the future water quality adjustment plan and bait feeding plan of the aquaculture pond based on the water quality prediction results; Among them, the pre-training steps of the water quality prediction model are as follows: Obtain a large amount of historical water quality data from different aquaculture ponds; Preprocess historical water quality data; Based on machine learning algorithms, water quality prediction models are trained based on pre-processed historical water quality data; Auxiliary modules for: Generate a digital model based on current water quality data, water quality forecasts, and process information for adaptively controlling future water quality and feeding plans for the aquaculture ponds; When a user brings a smart terminal close to the aquaculture pond, the user's movement trajectory is continuously obtained through the smart terminal; Determine whether the movement route indicates a capture prompt time; If the answer is yes, the user is prompted to take a photo of the intended area of ​​the aquaculture pond via the smart terminal; When the user starts to shoot the aquaculture pond, the user's shooting trajectory is continuously obtained through the smart terminal; When the user is filming the aquaculture pond, the smart terminal determines whether it has reached the camera flipping time based on the movement trajectory and the filming trajectory; When the answer is yes, control the smart terminal to flip the camera; After the user finishes photographing the aquaculture pond, the user's intended photographing area is obtained; Determine the slice model of the intended area from the digitized model; Determine regulatory recommendations based on the slicing model; Display slicing models and control suggestions to users; When the user inputs a control plan, the breeding pond is controlled in relay mode based on the control plan; The auxiliary module determines whether the smart terminal enters a camera flipping time based on the movement trajectory and the shooting trajectory, including: Determine whether the movement trajectory and the shooting trajectory meet the timing entry constraint. If so, determine the smart terminal enters the camera flip timing; The timing entry constraints include: Constraint 1: The average moving speed of the second target segment within the first target segment on the shooting trajectory exceeds the first speed threshold; the first target segment is the part of the shooting trajectory generated within the first time; the second target segment is the part of the trajectory of the previous target within the first target segment; as well as, Constraint 2: The average moving speed of the third target segment within the first target segment on the shooting trajectory does not exceed the second speed threshold; the third target segment is the portion of the trajectory within the first target segment excluding the second target segment; the second speed threshold is less than the first speed threshold; as well as, Constraint 3: The maximum matching degree between the fourth target segment on the trajectory and each standard trajectory in the standard trajectory library exceeds the matching degree threshold; the fourth target segment is the portion of the trajectory generated within the most recent second time; and the second time is less than the first time.

2. The water quality prediction and intelligent control system based on machine learning according to claim 1, characterized in that: The real-time acquisition module collects the current water quality data of the aquaculture pond in real time, including: The current water quality data of the breeding pond is collected in real time through the sensor network arranged in the breeding pond.

3. The water quality prediction and intelligent control system based on machine learning according to claim 1, characterized in that: The current water quality data at least includes: salinity, temperature, pH value, and dissolved oxygen.

4. The water quality prediction and intelligent control system based on machine learning according to claim 1, characterized in that: The preprocessing of historical water quality data includes: The historical water quality data are processed in sequence by removing missing values, removing outliers and standardizing.

5. The water quality prediction and intelligent control system based on machine learning according to claim 1, characterized in that: Also includes: Optimization modules for: Obtain the error situation determined by the water quality prediction results; Based on the error situation, the water quality prediction model is optimized.

6. The water quality prediction and intelligent control system based on machine learning according to claim 1, characterized in that: The intelligent control module adaptively controls the future water quality adjustment plan and feed feeding plan of the aquaculture pond based on the water quality prediction results, including: Perform feature description on the water quality prediction result to obtain the first feature description vector; Determine the plan control scheme corresponding to the first feature description vector from the plan control scheme library; Based on the planned control plan, the future water quality adjustment plan and bait feeding plan of the breeding pond are regulated.

7. The water quality prediction and intelligent control system based on machine learning according to claim 1, characterized in that: The auxiliary module determines whether the moving route indicates entering a shooting prompt time, including: Determine a target sub-pond from the aquaculture pond; wherein the shortest distance between the center position of the target sub-pond and the moving route does not exceed a distance threshold; When the ratio of the total number of target sub-pools to the total number of sub-pools in the breeding pond exceeds a ratio threshold or at least two target sub-pools have at least one standard association relationship, determining that the movement route indicates entering a shooting prompt timing; Otherwise, it is determined that the movement route does not indicate a shooting prompt opportunity.

8. The water quality prediction and intelligent control system based on machine learning according to claim 1, characterized in that: The auxiliary module determines control suggestions based on the slice model, including: Perform feature description on the slice model to obtain a second feature description vector; Determine the control suggestion corresponding to the second feature description vector from the control suggestion library.

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