Water quality monitoring, regulating and controlling system and method for abalone culture

Abstract

The invention discloses a water quality monitoring, regulating and controlling system and method for abalone culture, and relates to the technical field of water quality monitoring, regulating and controlling. The invention aims to solve the problems of poor real-time performance, low intelligent level, insufficient system adaptability and the like of the existing abalone culture water quality monitoring and regulating system. The problems are effectively solved by fusing a multi-parameter water quality sensor, cloud intelligent analysis modeling, an automatic control response mechanism and a green energy power supply system. The system has high-frequency and multi-dimensional data acquisition capability, trend prediction and grade response branch control are realized in combination with a cloud modeling algorithm, linkage equipment can be actively triggered for intervention when water quality data are abnormal, and the intelligence and the strain capability of the system are improved; meanwhile, a solar energy and storage battery power supply mode is adopted, and stable operation of the system in a bay and other commercial power-free areas is ensured; and a graphical management interface and an early warning mechanism are matched, so that the scientificity and efficiency of breeding management are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring and control, and in particular to a water quality monitoring and control system and method for abalone farming. Background Art

[0002] With the continuous development of the aquaculture industry, abalone has become one of the key aquaculture species due to its high economic and nutritional value. However, abalone is extremely sensitive to water quality and environment. Its growth, reproduction and survival rate are significantly affected by a variety of water quality parameters such as temperature, dissolved oxygen, pH value, salinity, ammonia nitrogen, flow rate, etc. Traditional water quality monitoring methods mostly rely on manual sampling and testing, which is not only inefficient and has poor real-time performance, but also difficult to detect water quality abnormalities in a timely manner, which in turn affects the healthy growth of abalone and the breeding efficiency. Therefore, there is an urgent need for an integrated and intelligent water quality monitoring and control system that can collect, analyze and control the parameters of the aquaculture water body in real time, realize dynamic management of water quality, and thus improve the scientific and automated level of abalone farming.

[0003] The existing water quality monitoring and control system still has many shortcomings in actual abalone farming applications; it is difficult to achieve comprehensive perception and dynamic adjustment of the farming environment; the control system usually relies on manual operation and lacks an intelligent response mechanism linked to real-time data, making it difficult to respond to sudden environmental changes in a timely manner, affecting abalone health and farming production. Summary of the Invention

[0004] The present invention aims to solve the problems mentioned in the above background technology and proposes a water quality monitoring and control system and method for abalone farming.

[0005] The object of the present invention can be achieved by the following technical solutions: A water quality monitoring and control system for abalone farming, comprising: a control device and an analysis and control module;

[0006] The control device automatically adjusts the aquaculture environment according to the water quality data feedback; after adjusting the aquaculture environment, the water quality data is re-tested, and an analysis control command is generated and sent to the analysis control module;

[0007] The analysis and control module is configured to, upon receiving the analysis and control command, analyze water quality data within the same water level to obtain instructions for adjusting the cage depth, starting the aerator pump, and adjusting the feeding strategy; then substitute the water quality data within the same water level into a preset model, analyze the interaction mechanism between the water quality data, and obtain an environmental cross-impact execution strategy; perform a comprehensive evaluation of the aquaculture environment based on the water quality data, and obtain a comprehensive evaluation coefficient of the aquaculture environment through multivariate fusion modeling analysis; and classify the aquaculture environment according to the comprehensive evaluation coefficient.

[0008] Based on the comprehensive evaluation coefficient of the aquaculture environment and the environmental cross-impact execution strategy, the cage depth control command, the feeding reduction control command, the floating plant supplementation control command, the feeding stop control command and the oxygenation control command are analyzed and output.

[0009] As a preferred embodiment of the present invention, the process of the analysis control module analyzing the water quality data within the same water level is as follows:

[0010] Analyze water temperature, pH value, dissolved oxygen, salinity, ammonia nitrogen, and flow rate within the same water level:

[0011] Compare the water temperature with the set range for the optimal growth of abalone. If the temperature does not fall within the set range, the cage depth adjustment instruction is triggered;

[0012] Record the real-time pH value. When the pH value is less than 7.5 or greater than 8.5, an indication of water quality deterioration risk is generated and sent to the corresponding smart terminal for prompting.

[0013] Continuously monitor the dissolved oxygen concentration in the water and analyze its relationship with time, temperature, and feeding activities to obtain the oxygen consumption risk index; if the current dissolved oxygen concentration is lower than 5mg / L or the oxygen consumption risk index is greater than or equal to 0.8, generate a command to start the aeration pump;

[0014] Continuously monitor the salinity in the water body. If the salinity is greater than 35‰ or less than 27‰ within a fixed time period after real-time, an abnormal water salinity indication is generated and sent to the corresponding smart terminal for prompting;

[0015] The ammonia nitrogen risk index is obtained by analyzing the correlation between the ammonia nitrogen fluctuation trend and the feeding amount and water exchange frequency parameters. If the current ammonia nitrogen concentration is greater than 0.2 mg / L and continues to exceed the standard for more than the preset time threshold or the ammonia nitrogen risk index is greater than or equal to 0.7, the feeding strategy is adjusted.

[0016] The water flow velocity V is continuously monitored. If the current flow velocity is greater than 0.8 m / s or less than 0.01 m / s and the duration exceeds the preset time threshold or the flow velocity risk index is greater than or equal to 0.8, a cage depth adjustment strategy is generated.

[0017] As a preferred embodiment of the present invention, the specific process of analyzing the interaction mechanism between water quality data to obtain the environmental cross-impact execution strategy is as follows:

[0018] S001: Obtain real-time water quality data stream and parameter relationship modeling; define the parameter vector at the current moment;

[0019] S002: Analyze the cross-influence between water quality data;

[0020] S201: Analyze water temperature and dissolved oxygen: Obtain the relationship between water temperature and dissolved oxygen through the model;

[0021] S202: Analyze the relationship between feeding amount, ammonia nitrogen increase, and dissolved oxygen decrease: obtain the relationship between feeding amount, ammonia nitrogen, and dissolved oxygen through the model;

[0022] S203: Analyze photosynthesis, the increase in pH value, and the decrease in dissolved oxygen: if the daily variation in pH value is small, photosynthesis is weak;

[0023] S204: Analyze sudden changes in salinity and the increased sensitivity of all water quality data: If the sudden change in salinity causes biological stress, accelerated respiration, decreased dissolved oxygen, and increased ammonia nitrogen;

[0024] S205: When the dissolved oxygen is less than the threshold, oxygenation measures are automatically activated;

[0025] S206: Integrate the cross-impacts between different water quality data to generate an environmental cross-impact execution strategy;

[0026] As a preferred embodiment of the present invention, the comprehensive evaluation coefficient of the aquaculture environment is obtained by multivariate fusion modeling analysis, specifically:

[0027] S301: Constructing a comprehensive evaluation model for water quality data fusion:

[0028] Input parameter vector: X = [T, pH, DO, S, NH3, V];

[0029] The target output is the comprehensive evaluation coefficient of the breeding environment: Score∈[0,1];

[0030] Select the weighted scoring model as the backbone model;

[0031] S302: Setting the ideal range and weight coefficient of each water quality data;

[0032] S303: using different normalization strategies to process water quality data; normalization strategies include interval normalization and one-side limit normalization;

[0033] S304: Calculate the comprehensive coefficient to obtain the comprehensive evaluation coefficient of the breeding environment.

[0034] As a preferred embodiment of the present invention, the aquaculture environment is divided into grades according to the comprehensive evaluation coefficient of the aquaculture environment; specifically:

[0035] If the comprehensive evaluation coefficient of the aquaculture environment is greater than or equal to 0.85, it is excellent; if the comprehensive evaluation coefficient of the aquaculture environment is greater than or equal to 0.70 and less than 0.85, it is good; if the comprehensive evaluation coefficient of the aquaculture environment is 0.50 or greater than or equal to less than 0.70, it is fair; if the comprehensive evaluation coefficient of the aquaculture environment is less than 0.50, it is poor.

[0036] As a preferred embodiment of the present invention, the comprehensive evaluation coefficient of the breeding environment and the environmental cross-impact execution strategy are specifically as follows:

[0037] S401: Enter different response branches according to the comprehensive evaluation coefficient level of the breeding environment:

[0038] If the comprehensive evaluation coefficient of the aquaculture environment is excellent, the status quo is maintained and no intervention is required; if the comprehensive evaluation coefficient of the aquaculture environment is good, the system enters the light intervention mode; if the comprehensive evaluation coefficient of the aquaculture environment is fair, the system enters the moderate intervention mode and activates the cross-mechanism analysis; if the comprehensive evaluation coefficient of the aquaculture environment is poor, the system enters the strong intervention mode and the system is linked in real time.

[0039] S402: Conduct water quality data mechanism linkage analysis:

[0040] S421: When the water temperature is greater than 22°C and the dissolved oxygen is less than 5mg / L, a cage depth control command is generated; the electric winch is activated; the cage is slowly moved down 0.5-2 meters and automatically stops when it reaches the bottom, locking the position; the water temperature and dissolved oxygen changes during the dive are monitored in real time; the new depth, new water temperature, and new dissolved oxygen are recorded, and the change trend is observed within 1 hour;

[0041] S422: When the feeding amount and ammonia nitrogen increase and dissolved oxygen decrease, if the ammonia nitrogen is greater than 0.2 and the dissolved oxygen is less than 5 mg / L dissolved oxygen, a feed reduction control command is generated;

[0042] S423: When photosynthesis is insufficient and pH fluctuation is small; when the absolute pH value is less than 0.2 for consecutive days, a floating plant supplementation control command is generated;

[0043] S424: When the sensitivity of salinity and water quality data increases, when the salinity is less than 27 or greater than 35, and ammonia nitrogen or dissolved oxygen fluctuates violently, a feed stop control command is generated;

[0044] S425: In the case of dissolved oxygen being less than the threshold and in linkage control, when the dissolved oxygen is less than 5 mg / L, the ammonia nitrogen toxicity rises to the point of inhibiting feeding and the pH value drops, an oxygenation control command is generated.

[0045] As a preferred embodiment of the present invention, the control device automatically adjusts the aquaculture environment based on water quality data feedback; specifically:

[0046] Receive cage depth control commands, feed reduction control commands, floating plant supplement control commands, feeding stop control commands and oxygenation control commands;

[0047] When receiving the cage depth control command, the electric winch or hydraulic lifting system is activated; the cage moves down 0.5 to 2 meters, then automatically stops and locks the position; the water temperature and dissolved oxygen changes during the dive are monitored in real time; the new depth, new water temperature, and new dissolved oxygen are recorded, and the change trend is observed within 1 hour;

[0048] When receiving a feed reduction control command, control the feeding equipment to avoid feeding during periods of high temperature or low oxygen;

[0049] When receiving the floating plant supplement control command, the algae feeding equipment is controlled to add algae seed bags;

[0050] When receiving the feeding stop control command, feeding is temporarily stopped and water dilution or fresh water injection is performed;

[0051] When receiving the oxygenation control command, the oxygenation pump is automatically enabled. If it is a partition management system, the oxygenation device in the target area is started; local high-efficiency oxygenation equipment is given priority, and the startup time defaults to 30 minutes to 1 hour, during which dissolved oxygen data feedback is performed every 10 minutes; if the dissolved oxygen has not yet recovered to ≥6.0 mg / L, the working cycle is automatically extended or the backup oxygenation equipment is started.

[0052] Another aspect of the present invention provides a water quality monitoring and control method for abalone farming, the method comprising:

[0053] Step 1: Collect water quality data, process it through the embedded controller, and then remotely synchronize it to the cloud;

[0054] Step 2: Compare and evaluate the collected data, set risk thresholds based on the ideal growth range of abalone, trigger an alert when the threshold is exceeded, and recommend adjusting cage depth, increasing oxygen, diluting, or optimizing feeding strategies;

[0055] Step 3: Construct the interaction mechanism between water quality data and obtain the environmental cross-impact implementation strategy;

[0056] Step 4: Normalize and weight the water quality data according to their ideal ranges and weights to derive a comprehensive evaluation coefficient for the aquaculture environment, determine the water quality level, and guide the response strategy.

[0057] Step 5: Automatically generate control commands based on the linkage between the scoring level and water quality data to achieve cage depth adjustment, intelligent food reduction, oxygenation start-up, algae supplementation intervention and active water quality control.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. This invention integrates multiple technologies, including multi-parameter sensing and monitoring, intelligent analysis and modeling, automatic control response, and green energy power supply. The system can collect water quality data such as water temperature, pH, dissolved oxygen, salinity, ammonia nitrogen, and flow rate in real time, and perform data modeling and trend prediction through a cloud-based analysis module, achieving accurate perception and dynamic regulation of nitrogen in the aquaculture environment. According to environmental changes, the system can automatically adjust the cage depth, control the oxygenation equipment, and optimize the feeding strategy, significantly improving the intelligence, automation, and risk resistance of abalone farming.

[0060] 2. This invention offers significant advantages in real-time performance, intelligence, and system adaptability. By constructing a multivariable fusion model to comprehensively score the aquaculture environment and divide it into hierarchical response branches, this system ensures rapid coordinated control of equipment intervention in abnormal situations. Furthermore, the system utilizes a combination of solar energy and batteries for power supply, ensuring stable operation even in environments without mains electricity. A graphical interface and early warning mechanism provide scientific decision-making support for aquaculture managers, improving aquaculture efficiency and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0062] Figure 1 It is a principle block diagram of the present invention;

[0063] Figure 2 The figure is a flowchart of the method steps of the present invention. DETAILED DESCRIPTION

[0064] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0065] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0066] It should also be understood that the terminology used in this disclosure is for the purpose of describing specific embodiments only and is not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should be further understood that the term "and / or" as used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0067] See also Figure 1 As shown, the present invention provides a water quality monitoring and control system for abalone farming, including: a water quality monitoring device, a control device, an analysis and control module, and an energy supply module, which is suitable for abalone ecological farming scenarios in floating cages in the bay.

[0068] The water quality monitoring device is composed of multi-parameter water quality sensors that monitor water quality data including water temperature, pH value, dissolved oxygen, salinity, ammonia nitrogen, flow rate, etc. The sensor components are anti-corrosion encapsulated and suitable for long-term operation in seawater environments. The monitoring data is collected and processed by an embedded acquisition controller (such as STM32 or ESP32), and supports wired (such as RS485) or wireless (such as LoRa, NB-IoT, Wi-Fi) transmission of data to a server or cloud platform, realizing real-time synchronization of remote data.

[0069] The control device is used to automatically adjust the aquaculture environment based on water quality data feedback. Specifically, it controls the water quality sensor's data collection depth to monitor water quality in different water layers; adjusts the cage's sinking and floating depth and abalone aquaculture density to respond to changes in the marine environment; and controls the start and stop of equipment such as aerators and water pumps based on dissolved oxygen concentration.

[0070] The analysis and control module is deployed on the cloud or local server for the storage, analysis and modeling of water quality data. The system can present the changing trends of water quality data through a visual interface, and supports historical data comparison and anomaly detection. If any anomaly occurs, an early warning will be immediately issued to management personnel via SMS, APP, etc., facilitating timely intervention and reducing aquaculture risks.

[0071] The energy supply module uses a combination of solar panels and batteries to power the system, ensuring continuous operation at sea or in areas without mains electricity. Solar power is used during the day and batteries are used to supplement power at night, ensuring that the system can operate uninterruptedly around the clock and providing stable energy support for water quality monitoring and control.

[0072] The analysis and control module is deployed on the cloud platform or local server, and is responsible for the data processing and intelligent decision-making functions of the entire system. It mainly includes five sub-modules: data acquisition, storage, modeling, analysis and early warning;

[0073] The system can display the changing trends of water quality data (such as water temperature, pH value, dissolved oxygen, salinity, ammonia nitrogen, etc.) in real time through a graphical visual interface. It supports historical data backtracking and multi-period comparative analysis to help managers determine the patterns of water quality changes and potential risks.

[0074] The specific monitoring water quality data analysis is as follows:

[0075] Collect water temperature, pH value, dissolved oxygen, salinity, and ammonia nitrogen within the same water level;

[0076] Comparing water temperature with the optimal growth range for abalone (e.g., 16°C to 22°C). If the temperature deviates from the set range, an alert will be triggered, suggesting adjusting the cage depth or activating a water exchange mechanism.

[0077] Record real-time pH readings and combine them with daily variation curves to determine whether there are photosynthesis anomalies or ammonia nitrogen accumulation problems. When the pH value is less than 7.5 or greater than 8.5, an indication of water quality deterioration risk is generated and sent to the corresponding smart terminal for prompting.

[0078] Continuously monitor dissolved oxygen concentration in water and analyze its relationship with time, temperature, and feeding activities:

[0079] Divide 24 hours into multiple time periods (e.g., early morning, morning, noon, afternoon, evening, and nighttime), calculate the mean, minimum, and rate of change of dissolved oxygen concentration in each time period, and identify the periodic patterns of daily changes in dissolved oxygen concentration; for example, early morning and noon are high-risk periods when dissolved oxygen is prone to decrease;

[0080] By constructing a temperature-dissolved oxygen scatter plot and fitting curve, we analyzed the stability and decline rate of dissolved oxygen under different water temperatures (e.g., 15°C to 35°C). It was determined that water temperatures (e.g., greater than 30°C) generally lead to a faster decline in dissolved oxygen, especially during the high temperature and high light period at noon.

[0081] The specific time and amount of each feeding were recorded. Using a sliding window analysis method, the drop in dissolved oxygen within 30 minutes after feeding was calculated to assess the impact of feeding on oxygen consumption. For example, if the average drop in dissolved oxygen exceeded 1.5 mg / L after a large amount of feeding, this period was marked as a high-risk response period.

[0082] Based on the periodic law of daily changes in bait supply, water temperature and dissolved oxygen concentration, the oxygen consumption risk index in the current environment is calculated in real time. Through the model: Output oxygen consumption risk index DOFX, ranging from 0 to 1, with higher values indicating greater oxygen consumption risk;

[0083] Among them, F t is the current feeding amount, Fmax T is the maximum single feeding amount set by the system. t is the current water temperature, T opt The optimal water temperature for aquaculture is T max is the set critical high temperature value (such as 35℃), DOt is the current dissolved oxygen concentration, D avg (t) is the average dissolved oxygen concentration at the current time point in the same period in the past (a reference value for daily variation); A1, A2, and A3 are weight coefficients, satisfying A1+A2+A3=1, which can be optimized based on actual experience or training; for example: A1=0.4, A2=0.3, A3=0.3;

[0084] If the current dissolved oxygen concentration is lower than 5 mg / L or the oxygen consumption risk index is greater than or equal to 0.8, an instruction to start the oxygen pump is generated, and the event is recorded as a reference for subsequent modeling optimization;

[0085] Continuously monitor the salinity in the water body. If the salinity is greater than 35‰ or less than 27‰ within a fixed time period after real-time, an abnormal water salinity indication is generated and sent to the corresponding smart terminal for prompting;

[0086] The correlation between ammonia nitrogen fluctuation trends and parameters such as feeding amount and water exchange frequency was analyzed to construct a "farming activity-ammonia nitrogen response" model. In this model, abnormal increases in ammonia nitrogen concentrations are generally associated with high feeding intensity, low water replacement rate, and increased temperature. The system uses multivariate correlation analysis and trend fitting algorithms to establish the following risk assessment mechanism:

[0087] Output ammonia nitrogen risk index ARI; the higher the value, the greater the risk of ammonia nitrogen accumulation;

[0088] Among them, F avg W is the recommended daily feeding amount corresponding to the set unit breeding density; f is the current water exchange frequency; W opt The minimum reasonable water exchange frequency recommended for the system; NH base is the safety benchmark ammonia nitrogen concentration in the aquaculture standard; B1, B2 and B3 are all preset weight coefficients, and the sum of B1, B2 and B3 is equal to 1 (such as B1 = 0.4, B2 = 0.3, B3 = 0.3. The specific values can be determined by technicians using reasonable values based on actual conditions, such as by subjective assignment method); if the current ammonia nitrogen concentration is greater than 0.2 mg / L and continues to exceed the standard for more than a preset time threshold (such as 6 hours) or the ammonia nitrogen risk index is greater than or equal to 0.7, an adjustment feeding strategy is generated; for example: according to the current feeding plan, the feeding amount is automatically reduced by 10% to 30%, the main daily feeding time is adjusted to avoid the high ammonia nitrogen period; at the same time, the water exchange frequency is increased or the aeration / overflow device is turned on to accelerate the dilution of ammonia nitrogen;

[0089] The collected water temperature, pH, dissolved oxygen, salinity, ammonia nitrogen, and flow rate parameters are then input into a preset model to analyze the interaction mechanism between water quality data, determine whether there are any anomalies or imbalances, and then make intelligent responses;

[0090] Finally, a comprehensive evaluation of the aquaculture environment was conducted in combination with water quality data, and a comprehensive evaluation coefficient of the aquaculture environment was obtained through multivariate fusion modeling analysis.

[0091] The specific process of analyzing the interaction mechanism between water quality data is as follows:

[0092] S001: Model the relationship between real-time data flow and parameters; define the parameter vector at the current moment as:

[0093]

[0094] Where: T is water temperature, pH is water acidity, DO is dissolved oxygen, S is salinity, NH3 is ammonia nitrogen concentration, V is flow rate, F is feed amount, A is the aeration pump status;

[0095] S002: Analyze the cross-influence between water quality data;

[0096] S201: Analyze water temperature → dissolved oxygen: through the model: DO(t) = DO0-α×T(t) (the higher the water temperature, the lower the dissolved oxygen capacity of the water);

[0097] S202: Analyze the feeding amount F→ammonia nitrogen NH3↑+dissolved oxygen DO↓: Through the model: NH3(t)=NH 3,0 +β×F(t)-λ×E(t), (the more you are fed, the more uneaten food and excrement there are, and the ammonia nitrogen increases);

[0098] At the same time, oxygen consumption will be increased: DO(t) = DO(t) - δ × F(t);

[0099] S203: Analyze photosynthesis (daytime) → pH↑, DO↑: If the daily pH variation is small, it may be due to weak photosynthesis (absence of phytoplankton or pollution); Analysis model:

[0100]

[0101] S204: Analyze the sensitivity of all water quality data to the sudden change in salinity S: sudden change in salinity → biological stress → accelerated respiration → decreased dissolved oxygen and increased ammonia nitrogen;

[0102] S205: When dissolved oxygen is less than the threshold, oxygenation measures are automatically activated to alleviate other problems: low dissolved oxygen will increase ammonia nitrogen toxicity and inhibit feeding, resulting in a decrease in pH and an increase in NH3.

[0103] S206: Convection velocity V → dissolved oxygen DO↑ → ammonia nitrogen NH3↓ → pH fluctuation regulation → balanced distribution of water temperature T. Increasing fluidity helps the water body fully exchange oxygen, alleviate local hypoxia, and improve the overall DO level; through dilution and material diffusion, it reduces local high concentrations of ammonia nitrogen and slows the accumulation rate of ammonia nitrogen. Flow improves phytoplankton distribution and light uniformity, indirectly improving photosynthesis efficiency; eliminates temperature stratification of water layers, avoiding overheating of the surface layer or hypoxia of the bottom layer;

[0104] S207: Integrate the cross-impacts between different water quality data to generate an environmental cross-impact execution strategy;

[0105] The comprehensive evaluation coefficient of the aquaculture environment obtained by multivariate fusion modeling analysis is specifically:

[0106] S301: Constructing a comprehensive evaluation model for water quality data fusion:

[0107] Define the input parameter vector: X = [T, pH, DO, S, NH3, V];

[0108] The target output is the comprehensive evaluation coefficient of the breeding environment: Score∈[0,1];

[0109] The weighted scoring model is selected as the backbone model; it can be subsequently expanded to a machine learning model (such as SVM and RandomForest) for training optimization;

[0110] S302: Setting the ideal range and weight coefficient; it should be noted that the specific recommended weight is obtained by the mortality sensitivity of various water quality data of the experimental abalone, and the ideal range is set according to the ideal growth environment of the corresponding species;

[0111] The reference ranges and recommended weights of various water quality data are as follows:

[0112] Water quality data Ideal range Weight Water temperature T 16～22℃ 0.10 pH 7.5～8.5 0.20 Dissolved oxygen (DO) ≥5mg / L 0.25 Salinity S 27～35‰ 0.15 <![CDATA[Ammonia nitrogen NH3]]> ≤0.2mg / L 0.25 Flow rate V 5~15cm / 0.05

[0113] S303: using different normalization processing strategies for water quality data; the strategies include: interval normalization and limit normalization;

[0114] The water temperature, pH value and salinity in the water quality data were processed using interval normalization;

[0115] Taking pH as an example, the ideal range is [L i ,U i ]=[7.5,8.5]:

[0116]

[0117] Dissolved oxygen and ammonia nitrogen in water quality data were processed using confined normalization;

[0118] DO:

[0119]

[0120] NH2:

[0121]

[0122] S303: Calculate the comprehensive coefficient; record all normalized coefficients as Score i , then the comprehensive evaluation coefficient of the breeding environment is:

[0123]

[0124] Among them, Score T 、Score DO 、Score S 、Score PH 、Score NH3 and Score V They are recorded as Score1, Score2, Score3, Score4, Score5 and Score6 respectively;

[0125] S304: Classify the aquaculture environment into different levels according to the comprehensive evaluation coefficient Score of the aquaculture environment:

[0126] If Score ≥ 0.85 → Excellent: Suitable for breeding, the system maintains the status quo; If 0.70 ≤ Score < 0.85 → Good: Breeding can continue, and slight adjustments are recommended; If 0.50 ≤ Score < 0.70 → Fair: Breeding risk increases, the system prompts attention; If Score < 0.50 → Poor: There is abnormal water quality, the system issues a high-level warning.

[0127] Then, based on the cross-influence between the final score and the water quality data, further reasoning and control commands are output; specifically:

[0128] S401: Enter different response branches according to the score level:

[0129] If the final score is excellent, the status quo is maintained without intervention; if the final score is good, the system enters a mild intervention mode; if the final score is fair, the system enters a moderate intervention mode and activates cross-mechanism analysis; if the final score is poor, the system enters a strong intervention mode and uses a real-time linkage control system.

[0130] S402: Water quality data mechanism linkage analysis:

[0131] S421: Water temperature T → dissolved oxygen DO. When T > 22°C and DO < 5DO, generate a cage depth control command; activate the electric winch or hydraulic lifting system; slowly move the cage downward 0.5 to 2 meters. The specific depth is set based on the previous water profile monitoring data (for example, the target is the 20-21°C water layer); monitor the changes in water temperature and DO in real time during the dive; automatically stop and lock the position after reaching the predetermined target layer; record the new depth, new temperature, and new DO, and observe the change trend within 1 hour;

[0132] S422: Feeding amount F → ammonia nitrogen NH3↑, dissolved oxygen DO↓, when NH3>0.2, DO<5DO, a feed reduction control command is generated; the system supports "intelligent feeding": control feeding equipment to avoid feeding during high temperature or low oxygen periods;

[0133] S423: Insufficient photosynthesis → small pH fluctuation; when ΔpH < 0.2 for consecutive days, a floating plant supplement control command is generated; the algae seeding equipment is controlled to add algae seed bags at all times;

[0134] S424: Salinity mutation S → Water quality data sensitivity ↑. When S<27 or S>35, and NH3 or DO fluctuates sharply, a feeding stop control command is generated; temporarily stop feeding (e.g., stop feeding for 2 hours) and perform water source dilution or fresh water injection.

[0135] S425: DO < threshold (<5) → linkage control; when DO < 5 → NH3 toxicity ↑ → feeding inhibition → pH ↓, generate an oxygenation control command; automatically enable the aeration pump; if it is a zone management system, start the aeration device in the target area (such as micro-nano aeration pump, wind aeration system, waterwheel aerator, etc.); give priority to local high-efficiency aeration equipment, and the default startup time is 30 minutes to 1 hour, during which DO data feedback is performed every 10 minutes; if DO still has not recovered to ≥6.0 mg / L, automatically extend the working cycle or start the backup aeration equipment.

[0136] S426: Continuously monitor the water flow velocity V. If the current flow velocity is greater than 0.8 m / s or less than 0.01 m / s and the duration exceeds the preset time threshold or the flow velocity risk index is greater than or equal to 0.8, generate a cage depth adjustment strategy.

[0137] See also Figure 2 As shown, another aspect of the present invention provides a water quality monitoring and control method for abalone farming, comprising:

[0138] Step 1: Multi-parameter water quality sensors deployed within the bay's floating cages collect real-time water quality data, including temperature (T), pH, dissolved oxygen (DO), salinity (S), ammonia nitrogen (NH3), and flow rate (V). The sensors are packaged in corrosion-resistant packaging to withstand long-term operation in seawater. Data is collected and processed by an embedded acquisition controller and sent to a server or cloud platform via wired or wireless methods (such as LoRa or NB-IoT), enabling remote real-time data synchronization.

[0139] Step 2: The collected data is processed by the analysis and control module. Combined with the ideal growth environment of abalone, the water quality data is compared and analyzed, and risk thresholds are set for judgment: if T exceeds 16-22°C, pH is less than 7.5 or greater than 8.5, DO is lower than 5mg / L, salinity is less than 27‰ or greater than 35‰, and NH3 is higher than 0.2mg / L, the corresponding risk warning is triggered, and it is recommended to adjust the cage depth, increase oxygen, dilute or adjust the feeding, so as to achieve preliminary identification of water quality abnormalities;

[0140] Step 3: Based on the data collection and analysis, this method further constructs a model of the mutual influence between various water quality data: for example, increased water temperature → decreased DO, increased feeding → increased NH3 and decreased DO, enhanced photosynthesis → increased pH and DO, sudden changes in salinity → increased sensitivity of water quality data; a time series relationship model is established based on the current parameter vector [X = T, pH, DO, S, NH3] and the management action vector (e.g., F = feeding amount, A = oxygenation status) to deeply understand the mechanism of water quality fluctuations and provide a decision basis for response control;

[0141] Step 4: By setting the ideal range and weight for each water quality data point, normalizing it and performing a weighted calculation, a comprehensive score (Score∈[0,1)) for the aquaculture environment is obtained. Based on the score results, the environmental grade is divided into four levels: "Excellent, Good, Fair, and Poor," with a score ≥ 0.85 considered excellent and a score < 0.50 considered poor. This score is used to determine the current aquaculture water quality status and provide a basis for subsequent control response selection. The scoring weights and algorithm performance can be continuously optimized in conjunction with machine learning models.

[0142] Step 5: Based on the scoring level and parameter cross-influence model, the system automatically generates a response control strategy and issues control commands, including: controlling the cage to dive when the water temperature T is high and DO is low; adjusting the feeding plan when NH3 is high and DO is low; triggering algae supplementation measures when photosynthesis is abnormal; temporarily stopping feeding and performing water dilution when salinity changes suddenly; automatically starting the oxygenation equipment and dynamically adjusting the working time when DO is lower than the set threshold; through linkage control, active intervention in water quality fluctuations is achieved to ensure the stability and efficiency of the abalone farming environment.

[0143] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A water quality monitoring and control system for abalone farming, comprising: The control device and analysis and control module are characterized by: The control device automatically adjusts the aquaculture environment according to the water quality data feedback; after adjusting the aquaculture environment, the water quality data is re-tested, and an analysis control command is generated and sent to the analysis control module; The analysis and control module is configured to, upon receiving the analysis and control command, analyze water quality data within the same water level to obtain instructions for adjusting the cage depth, starting the aerator pump, and adjusting the feeding strategy; then substitute the water quality data within the same water level into a preset model, analyze the interaction mechanism between the water quality data, and obtain an environmental cross-impact execution strategy; perform a comprehensive evaluation of the aquaculture environment based on the water quality data, and obtain a comprehensive evaluation coefficient of the aquaculture environment through multivariate fusion modeling analysis; and classify the aquaculture environment according to the comprehensive evaluation coefficient. Based on the comprehensive evaluation coefficient of the aquaculture environment and the environmental cross-impact execution strategy, the cage depth control command, the feeding reduction control command, the floating plant supplementation control command, the feeding stop control command and the oxygenation control command are analyzed and output.

2. A water quality monitoring and control system for abalone farming according to claim 1, characterized in that, The process of analyzing the water quality data within the same water level by the analysis control module is as follows: Analyze water temperature, pH value, dissolved oxygen, salinity, ammonia nitrogen, and flow rate within the same water level: Compare the water temperature with the set range for the optimal growth of abalone. If the temperature does not fall within the set range, the cage depth adjustment instruction is triggered; Record the real-time pH value. When the pH value is less than 7.5 or greater than 8.5, an indication of water quality deterioration risk is generated and sent to the corresponding smart terminal for prompting. Continuously monitor the dissolved oxygen concentration in the water and analyze its relationship with time, temperature, and feeding activities to obtain the oxygen consumption risk index; If the current dissolved oxygen concentration is lower than 5 mg / L or the oxygen consumption risk index is greater than or equal to 0.8, a command to start the oxygen pump is generated; Continuously monitor the salinity in the water body. If the salinity is greater than 35‰ or less than 27‰ within a fixed time period after real-time, an abnormal water salinity indication is generated and sent to the corresponding smart terminal for prompting; The ammonia nitrogen risk index is obtained by analyzing the correlation between the ammonia nitrogen fluctuation trend and the feeding amount and water exchange frequency parameters. If the current ammonia nitrogen concentration is greater than 0.2 mg / L and continues to exceed the standard for more than the preset time threshold or the ammonia nitrogen risk index is greater than or equal to 0.7, an adjustment feeding strategy is generated. The water flow velocity V is continuously monitored. If the current flow velocity is greater than 0.8 m / s or less than 0.01 m / s and the duration exceeds the preset time threshold or the flow velocity risk index is greater than or equal to 0.8, a cage depth adjustment strategy is generated.

3. a kind of water quality monitoring and control system for abalone farming according to claim 2, is characterized in that, The specific process of analyzing the interaction mechanism between water quality data to obtain the environmental cross-impact execution strategy is as follows: S001: Obtain real-time water quality data stream and parameter relationship modeling; define the parameter vector at the current moment; S002: Analyze the cross-influence between water quality data; S201: Analyze water temperature and dissolved oxygen: Obtain the relationship between water temperature and dissolved oxygen through the model; S202: Analyze the relationship between feeding amount, ammonia nitrogen increase, and dissolved oxygen decrease: obtain the relationship between feeding amount, ammonia nitrogen, and dissolved oxygen through the model; S203: Analyze photosynthesis, the increase in pH value, and the decrease in dissolved oxygen: if the daily variation in pH value is small, photosynthesis is weak; S204: Analyze sudden changes in salinity and the increased sensitivity of all water quality data: If the sudden change in salinity causes biological stress, accelerated respiration, decreased dissolved oxygen, and increased ammonia nitrogen; S205: When the dissolved oxygen is less than the threshold, oxygenation measures are automatically activated; S206: Integrate the cross-impacts between different water quality data to generate an environmental cross-impact execution strategy.

4. A water quality monitoring and control system for abalone farming according to claim 3, characterized in that, The comprehensive evaluation coefficient of the aquaculture environment obtained by multivariate fusion modeling analysis is specifically: S301: Constructing a comprehensive evaluation model for water quality data fusion: Input parameter vector: X = [T, pH, DO, S, NH3, V]; The target output is the comprehensive evaluation coefficient of the breeding environment: Score∈[0,1]; Select the weighted scoring model as the backbone model; S302: Setting the ideal range and weight coefficient of each water quality data; S303: using different normalization strategies to process water quality data; normalization strategies include interval normalization and one-side limit normalization; S304: Calculate the comprehensive coefficient to obtain the comprehensive evaluation coefficient of the breeding environment.

5. A water quality monitoring and control system for abalone farming according to claim 4, characterized in that, The breeding environment is divided into levels according to the comprehensive evaluation coefficient of the breeding environment; specifically: If the comprehensive evaluation coefficient of the breeding environment is greater than or equal to 0.85, it is excellent; if the comprehensive evaluation coefficient of the breeding environment is greater than or equal to 0.70 and less than 0.85, it is good; If the comprehensive evaluation coefficient of the aquaculture environment is 0.50 or greater and less than 0.70, it is fair; if the comprehensive evaluation coefficient of the aquaculture environment is less than 0.50, it is poor.

6. A water quality monitoring and control system for abalone farming according to claim 5, characterized in that, The comprehensive evaluation coefficient of the breeding environment and the implementation strategy of the environmental cross-impact are specifically as follows: S401: Enter different response branches according to the comprehensive evaluation coefficient level of the breeding environment: If the comprehensive evaluation coefficient of the aquaculture environment is excellent, the status quo is maintained and no intervention is required; if the comprehensive evaluation coefficient of the aquaculture environment is good, the light intervention mode is entered; if the comprehensive evaluation coefficient of the aquaculture environment is fair, the moderate intervention mode is entered and the cross-mechanism analysis is activated; If the comprehensive evaluation coefficient of the breeding environment is poor, it will enter the strong intervention mode and link the control system in real time; S402: Conduct water quality data mechanism linkage analysis: S421: When the water temperature is greater than 22°C and the dissolved oxygen is less than 5mg / L, a cage depth control command is generated; the electric winch is activated; the cage is slowly moved down 0.5-2 meters and automatically stops when it reaches the bottom, locking the position; the water temperature and dissolved oxygen changes during the dive are monitored in real time; the new depth, new water temperature, and new dissolved oxygen are recorded, and the change trend is observed within 1 hour; S422: When the feeding amount and ammonia nitrogen increase and dissolved oxygen decrease, if the ammonia nitrogen is greater than 0.2 and the dissolved oxygen is less than 5 mg / L dissolved oxygen, a feed reduction control command is generated; S423: Insufficient photosynthesis and small pH fluctuation; When the absolute pH value is less than 0.2 for consecutive days, a floating plant supplementation control command is generated; S424: When the sensitivity of salinity and water quality data increases, when the salinity is less than 27 or greater than 35, and ammonia nitrogen or dissolved oxygen fluctuates violently, a feed stop control command is generated; S425: In the case of dissolved oxygen being less than the threshold and in linkage control, when the dissolved oxygen is less than 5 mg / L, the ammonia nitrogen toxicity rises to the point of inhibiting feeding and the pH value drops, an oxygenation control command is generated.

7. A water quality monitoring and control system for abalone farming according to claim 1, characterized in that, The control device automatically adjusts the aquaculture environment based on water quality data feedback; specifically: Receive cage depth control commands, feed reduction control commands, floating plant supplement control commands, feeding stop control commands and oxygenation control commands; When receiving the cage depth control command, the electric winch or hydraulic lifting system is activated; the cage moves down 0.5 to 2 meters, then automatically stops and locks the position; the water temperature and dissolved oxygen changes during the dive are monitored in real time; the new depth, new water temperature, and new dissolved oxygen are recorded, and the change trend is observed within 1 hour; When receiving a feed reduction control command, control the feeding equipment to avoid feeding during periods of high temperature or low oxygen; When receiving the floating plant supplement control command, the algae feeding equipment is controlled to add algae seed bags; When receiving the feeding stop control command, feeding is temporarily stopped and water dilution or fresh water injection is performed; When receiving the oxygenation control command, the oxygenation pump is automatically enabled. If it is a partition management system, the oxygenation device in the target area is started; local high-efficiency oxygenation equipment is given priority, and the startup time defaults to 30 minutes to 1 hour, during which dissolved oxygen data feedback is performed every 10 minutes; if the dissolved oxygen has not yet recovered to ≥6.0 mg / L, the working cycle is automatically extended or the backup oxygenation equipment is started.

8. A water quality monitoring and control method for abalone farming, characterized in that, Applied to implement a water quality monitoring and control system for abalone farming as described in any one of claims 1 to 7, the method comprises: Step 1: Collect water quality data, process it through the embedded controller, and then remotely synchronize it to the cloud; Step 2: Compare and evaluate the collected data, set risk thresholds based on the ideal growth range of abalone, trigger an alert when the threshold is exceeded, and recommend adjusting cage depth, increasing oxygen, diluting, or optimizing feeding strategies; Step 3: Construct the interaction mechanism between water quality data and obtain the environmental cross-impact implementation strategy; Step 4: Normalize and weight the water quality data according to their ideal ranges and weights to derive a comprehensive evaluation coefficient for the aquaculture environment, determine the water quality level, and guide the response strategy. Step 5: Automatically generate control commands based on the linkage between the scoring level and water quality data to achieve cage depth adjustment, intelligent food reduction, oxygenation start-up, algae supplementation intervention and active water quality control.

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