An automatic control system for a microfilter for aquaculture

Abstract

This invention relates to the field of recirculating aquaculture technology, specifically to an automatic control system for a microfilter used in aquaculture. The system includes a data acquisition module for simultaneously acquiring water quality parameters of the aquaculture water, operating parameters of the microfilter, and current aquaculture process parameters, and performing outlier removal and smoothing filtering preprocessing on the acquired raw data. This invention integrates the dynamic pollution load of the aquaculture water, the real-time operating conditions of the microfilter, and key parameters of the aquaculture process to construct a multi-parameter control model. This replaces passive control methods based on single turbidity monitoring or fixed time intervals, enabling the microfilter to dynamically match parameter changes during the aquaculture process and solving the problem of the filtration effect being out of sync with actual needs.

Description

Technical Field

[0001] This invention relates to the field of industrialized recirculating aquaculture technology, specifically to an automatic control system for a microfiltration machine used in aquaculture. Background Technology

[0002] In factory-scale recirculating aquaculture systems, microfilters are the core equipment for water pretreatment. They are mainly used to remove suspended particulate matter such as uneaten feed and manure from the water, prevent clogging of subsequent biological filters, and avoid deterioration of aquaculture water quality. Their operating status is directly related to the water quality stability and energy consumption level of the entire system, and is a key piece of equipment to ensure the smooth operation of aquaculture production.

[0003] The existing control methods for microfiltration machines still have the following problems: they fail to organically integrate the dynamic pollution load of the aquaculture water, the real-time operating conditions of the microfiltration machine itself, and key parameters in the aquaculture process; they also lack effective linkage with other equipment in the recirculating water system; and fault response relies on manual inspection. During the aquaculture process, the degree of water pollution changes dynamically with changes in biomass and feeding amount. The filter cartridge clogging and motor load of the microfiltration machine also change in real time. However, the existing control relies solely on monitoring water turbidity or adjusting operation at fixed time intervals, which cannot dynamically match these changes. This leads to a disconnect between the filtration effect and actual needs. At the same time, problems such as filter cartridge clogging and motor failure cannot be detected and dealt with in a timely manner, which can easily cause sudden deterioration of water quality and affect the survival of aquaculture organisms. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an automatic control system for microfiltration machines used in aquaculture, which solves the problems of passive control, single monitoring, lack of equipment linkage, and delayed fault response in existing microfiltration machines.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an automatic control system for a microfiltration machine used in aquaculture, comprising: The data acquisition module is used to simultaneously collect water quality parameters of aquaculture water bodies, operating parameters of microfilters, and current aquaculture process parameters, and to perform outlier removal and smoothing filtering preprocessing on the collected raw data. The control decision module includes a microprocessor, a control model library, and a threshold database. It is used to perform logical cross-validation on the preprocessed parameters to confirm the validity of the data. After confirming the validity of the data, it combines the species-specific thresholds in the threshold database, determines the operating status of the microfilter through the control model library, and outputs the corresponding control commands. The execution control module includes a frequency converter, a solenoid valve group, and a relay, which are used to receive the control commands and adjust the operating frequency of the microfilter motor, the start and stop of the backwash pump and the drain valve, and the backwash pressure accordingly. The collaborative linkage module is used to establish signal communication between the microfilter and the biological filter and aeration device in the circulating water system, and to synchronously adjust the water distribution of the biological filter and the output power of the aeration device according to the changes in the filtration rate of the microfilter and the backwashing status. The control decision module calculates the deviation between the actual operating effect and the expected target based on the actual operating status data fed back by the execution control module and the water quality change data fed back by the coordination and linkage module, and calibrates the weight parameters and judgment logic in the control model library based on the deviation value.

[0006] Furthermore, the data acquisition module includes an optical turbidity sensor installed at the inlet and outlet of the microfilter, a differential pressure sensor installed inside and outside the filter cartridge, a current sensor for monitoring the load of the drive motor, a binocular vision device for monitoring the surface covering of the filter cartridge, and a water flow rate sensor for monitoring the circulating water flow rate. The water quality parameters include influent and effluent turbidity and suspended particulate matter concentration; the operating parameters include the pressure difference between the filter cartridge inlet and outlet and the motor operating current; and the aquaculture process parameters include aquaculture biomass, daily feed amount, and circulating water flow rate.

[0007] Furthermore, the parameter cross-validation mechanism performed by the control decision module specifically involves: identifying and eliminating false data and false detection signals by establishing logical relationships between water quality parameters, analyzing the matching degree between operating parameters and the current operating status of the microfilter, and establishing the correspondence between process parameters and pollution load. When a parameter conflict is detected, the control decision module sends a sensor self-test command to the data acquisition module and uses historical data from the same period or simulated data generated by the prediction model as a temporary substitute to maintain the continuity of the control process.

[0008] Furthermore, the control model library includes a start / stop determination model, a filter rate adjustment model, a backwashing determination model, and a fault early warning model; The start / stop determination model makes a comprehensive logical determination based on influent turbidity, suspended particulate matter concentration, and pollution load prediction values ​​calculated based on biomass and feeding amount. The filtration rate adjustment model calculates the motor speed adjustment based on the influent turbidity change rate and biomass density, and limits the filtration rate within a preset mechanical load range through a frequency converter.

[0009] Furthermore, the backwashing determination model determines whether a backwashing command is triggered by real-time monitoring of the filter cartridge pressure difference, the continuous operating time of the microfilter, and the turbidity difference between the inlet and outlet water. During backwashing, the execution control module performs the following timing actions: first, the filter cartridge rotation is stopped, then the backwash pump is started, and after a preset time delay, the drain valve is opened. The flushing process continues until the filter cartridge pressure differential recovers to the preset filter cartridge cleaning pressure differential threshold. Finally, the pump is stopped, the valve is closed, and the filter cartridge is restarted in sequence. The output pressure of the backwash pump is non-linearly adjusted based on the pressure difference of the filter cartridge before rinsing.

[0010] Furthermore, the fault early warning model adopts a three-level early warning mechanism: When the monitored motor current or differential pressure parameters show a first-level deviation, the system sends an early warning message to the preset terminal and automatically fine-tunes the operating parameters; When the deviation reaches the secondary threshold, the system will forcibly start the enhanced backwashing procedure and increase the monitoring frequency; When a preset severe fault type is detected, a level 3 early warning is triggered, and the relay is immediately controlled to cut off the motor power and stop the machine. At the same time, a load reduction command is sent to the circulating water system through the coordinated linkage module.

[0011] Furthermore, the collaborative linkage module includes a multi-protocol compatible data interaction interface and a linkage control unit; The linkage control unit synchronously increases the opening of the biofilter inlet valve according to the filtration rate increase signal from the microfilter execution control module, in order to maintain the hydraulic retention time in the biofilter. After receiving the backwash start signal, the linkage control unit anticipates the drop in dissolved oxygen caused by water disturbance and increases the variable frequency operation power of the oxygenation device in advance.

[0012] Furthermore, the collaborative linkage module is also used to receive load status information fed back from the biofilter; When the treatment load of the biofilter exceeds the preset maximum treatment load threshold of the biofilter, the collaborative linkage module sends a feedback signal to the control decision module, which then adjusts the target filtration rate of the microfilter or increases the backwashing frequency to reduce the total amount of pollutants entering the subsequent treatment unit.

[0013] Furthermore, the calibration process of the control model library by the control decision module includes: The system collects data on the action response time of the execution unit under different commands, the rate at which water quality indicators recover to the set range, and the energy consumption data for treating a unit volume of aquaculture water. It then uses machine learning algorithms to dynamically correct the weights of various features in the judgment model and automatically adjusts the operating limits in the threshold database according to changes in the aquaculture cycle.

[0014] Furthermore, the system also includes a visualization monitoring module, which communicates bidirectionally with the control decision module through the factory farming big data management and control platform to display the operating parameters, equipment health status and early warning records collected by each module in real time. The visualization monitoring module is equipped with a remote manual intervention interface. Emergency operation commands issued manually have the highest priority and can bypass the control decision module and be directly responded to by the execution control module.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention integrates dynamic pollution load in aquaculture water, real-time operating conditions of the microfilter, and key parameters of the aquaculture process to construct a multi-parameter control model. This replaces passive control methods based on single turbidity monitoring or fixed time intervals, enabling the microfilter to dynamically match parameter changes during the aquaculture process and solving the problem of filtration effect not meeting actual needs. A collaborative linkage module establishes effective linkage between the microfilter, biological filter, and aeration device, achieving synchronized adaptation of the operating status of each device and avoiding overall system incoordination. Through a three-level fault early warning mechanism and automatic handling strategy, it monitors and responds quickly to anomalies such as filter cartridge blockage and motor failure in real time, replacing manual inspections and solving the problem of delayed fault response leading to sudden water quality deterioration, ensuring the survival of aquaculture organisms and improving the system's water quality stability and operational reliability. Attached Figure Description

[0016] Figure 1 This is a diagram of the overall system architecture of the present invention; Figure 2 This is a data processing flowchart of the present invention; Figure 3 This is a flowchart illustrating the core control decision-making process of the present invention. Figure 4 This is a flowchart of the backwashing sequence of the present invention; Figure 5 This is a flowchart of the three-level fault early warning processing of the present invention; Figure 6 This is a flowchart illustrating the collaborative linkage process of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1

[0019] Please see Figures 1-6This invention provides an automatic control system for a microfiltration machine used in aquaculture, comprising: The data acquisition module is used to simultaneously collect water quality parameters of aquaculture water bodies, operating parameters of microfiltration machines, and current aquaculture process parameters, and to perform outlier removal and smoothing filtering preprocessing on the collected raw data.

[0020] Specifically, the data acquisition module includes optical turbidity sensors installed at the inlet and outlet of the microfilter, differential pressure sensors installed inside and outside the filter cartridge, current sensors monitoring the load of the drive motor, binocular vision devices monitoring the surface covering of the filter cartridge, and water flow rate sensors monitoring the circulating water flow rate.

[0021] The system includes optical turbidity sensors installed at the inlet and outlet of the microfilter, with an accuracy of ±0.5 NTU and a range of 0-50 NTU, for precise monitoring of influent and effluent turbidity; differential pressure sensors installed inside and outside the filter cartridge, with a range of 0-20 kPa, for real-time capture of the pressure difference between the inlet and outlet of the filter cartridge; a current sensor integrated into the microfilter motor distribution box for monitoring the drive motor load; a binocular vision device with a resolution of 1920×1080, which uses underwater biomass detection technology combined with sampling by an inspection robot to obtain the biomass of sub-areas within the aquaculture pond; and a water flow velocity sensor for monitoring the circulating water flow rate to ensure that the influent flow rate matches the filtration rate.

[0022] The collected water quality parameters specifically include influent and effluent turbidity and suspended particulate matter concentration; operating parameters include the pressure difference between the filter cartridge inlet and outlet and the motor operating current; and aquaculture process parameters include aquaculture biomass, daily feed intake, and circulating water flow rate. Data was collected every 2 minutes. After collection, outliers were first removed using the Laida criterion, and then smoothed and filtered using a moving average method to ensure data accuracy and stability, providing a reliable basis for subsequent control decisions.

[0023] Furthermore, the control decision module includes a microprocessor, a control model library, and a threshold database. It is used to perform logical cross-validation on the preprocessed parameters to confirm the validity of the data. After confirming the validity of the data, it combines the species-specific thresholds in the threshold database with the control model library to determine the operating status of the microfilter and output the corresponding control commands.

[0024] The microprocessor uses a PLC that supports multi-parameter parallel calculation. The control model library has built-in start-stop determination model, filtration rate adjustment model, backwash determination model and fault early warning model. The threshold database stores the specific thresholds of different aquaculture species. These thresholds are set according to the growth characteristics of the aquaculture species, water quality tolerance requirements and aquaculture practice data. For example, the turbidity threshold NTUset for freshwater perch is set to 15 NTU, the turbidity threshold NTUset for whiteleg shrimp is set to 12 NTU, and the suspended particulate matter concentration threshold Cset is uniformly set to 10 mg / L.

[0025] When performing parameter cross-validation, the parameter cross-validation mechanism performed by the control decision module is as follows: by establishing logical relationships between water quality parameters, analyzing the matching degree between operating parameters and the current operating status of the microfilter, and the correspondence between process parameters and pollution load, false data and false detection signals are identified and eliminated. When a parameter conflict is detected, the control decision module sends a sensor self-check command to the data acquisition module and temporarily replaces it with historical data from the same period or simulated data generated by the predictive model to maintain the continuity of the control process. For example, when the optical turbidity sensor detects a significant increase in influent turbidity, but the binocular vision device does not detect a synchronous increase in suspended particulate matter concentration, and the pollution load calculated based on the daily feed amount and biomass does not change significantly, the system determines that there is a parameter conflict, immediately sends a self-check command to the data acquisition module, and simultaneously uses water quality data from the same period of the past 7 days as a temporary replacement to ensure that the control process is not interrupted.

[0026] Furthermore, the various models in the control model library work together according to preset logic. The control model library includes start-stop determination model, filter rate adjustment model, backwashing determination model, and fault early warning model. The start-stop determination model makes a comprehensive logical judgment based on influent turbidity, suspended particulate matter concentration, and pollution load prediction values ​​calculated based on biomass and feeding amount. The filtration rate adjustment model calculates the motor speed adjustment based on the influent turbidity change rate and biomass density, and limits the filtration rate within the preset mechanical load range through a frequency converter.

[0027] Specifically, the start-stop decision-making logic of the model is to start the microfiltration unit if any condition is met, and to stop it if none is met. The pollution load prediction based on biomass and feed amount is calculated using the formula... Calculate, where This is the predicted pollution load value. The average biomass over the culture cycle. This refers to the daily feeding amount. This is the biomass weighting coefficient, with a value of 0.3 (determined based on the characteristics of manure emissions from the farmed species). This is the weighting coefficient for the amount of feed, with a value of 0.6 (determined based on the characteristics of the residual feed rate). This is the residual bait coefficient, with a value of 0.18 (the average value of a residual bait rate of 15%-20%). ( When the biological filter's treatment capacity is set (based on the day's parameters), the start / stop condition is triggered. The suspended particulate matter concentration is then determined by the formula... The conversion yields, where The formula for influent turbidity is derived from fitting a large amount of experimental data, ensuring consistent dimensions and accurate conversion.

[0028] The filtration rate adjustment model is based on the formula. Calculate the motor speed adjustment amount, where For the target filtration rate, Basic filtration rate (freshwater bass³²) South American white shrimp³² ), This is the turbidity influence coefficient, with a value of 0.4 (determined based on the degree of influence of turbidity on filtration requirements). The biomass impact coefficient is set to 0.3 (determined based on the contribution of biomass to the pollution load). This represents the current biomass. The calculated filtration rate must be limited to a preset mechanical load range, i.e. When the calculated value is within this range, adjust the motor speed according to the calculated value; when the calculated value is lower... When the calculated value is higher than the lower threshold, it operates at the lower limit to maintain low-load energy-saving filtration; when the calculated value is higher than the lower limit, it operates at the lower limit threshold to maintain low-load energy-saving filtration. When the device is overloaded, it will operate at the upper limit threshold and trigger an early warning.

[0029] Furthermore, the backwashing judgment model determines whether a backwashing command is triggered by real-time monitoring of the filter cartridge pressure difference, the continuous operating time of the microfilter, and the turbidity difference between the influent and effluent. During backwashing, the control module executes the following timing actions: first, the filter cartridge rotation is stopped, then the backwash pump is started, and after a preset time delay, the drain valve is opened. The flushing process continues until the filter cartridge pressure differential recovers to the preset filter cartridge cleaning pressure differential threshold. Finally, the pump is stopped, the valve is closed, and the filter cartridge is restarted in sequence. Specifically, the output pressure of the backwash pump is non-linearly adjusted based on the filter cartridge pressure differential before backwashing. The backwashing trigger conditions include the filter cartridge pressure differential. Microfilter continuous operating time effluent turbidity With influent turbidity Difference Backwashing is initiated when any one of the conditions is met. The preset time delay is set to 30 seconds, which is determined based on the pipeline pressure stabilization requirements; the filter cartridge cleaning differential pressure threshold is set to 1 kPa, determined based on the measured differential pressure under clean filter cartridge conditions. The output pressure of the backwash pump is determined by the formula... Nonlinear adjustment is performed, where To output pressure, Based on a base pressure of 0.3 MPa, The pressure regulation coefficient is 0.15 MPa. The pressure difference of the filter cartridge before rinsing is given. This formula can dynamically adjust the rinsing intensity according to the degree of clogging, ensuring the rinsing effect while avoiding resource waste.

[0030] Furthermore, the fault early warning model adopts a three-level early warning mechanism: When the monitored motor current or differential pressure parameters show a first-level deviation, the system sends an early warning message to the preset terminal and automatically fine-tunes the operating parameters; When the deviation reaches the secondary threshold, the system will forcibly start the enhanced backwashing procedure and increase the monitoring frequency; When a preset severe fault type is detected, a level 3 early warning is triggered, and the relay is immediately controlled to cut off the motor power and stop the machine. At the same time, a load reduction command is sent to the circulating water system through the coordinated linkage module.

[0031] Specifically, the first-level deviation is set when the motor current exceeds the rated current by 10% but ≤15%, or the filter cartridge pressure difference is between 8kPa and 10kPa. In this case, the system automatically reduces the filtration speed by 10% and sends an early warning message to the control platform; the second-level deviation is the filter cartridge pressure difference. If the motor current exceeds the rated current by 15% but is ≤20%, the system will forcibly start the enhanced backwashing program, increasing the backwashing pressure to 0.6MPa and the monitoring frequency to once every 30 seconds. If the pressure difference does not recover or the current does not drop after 3 backwashes, the system will shut down and alarm. The preset serious fault types include motor current exceeding the rated current by 20%, sensor data interruption lasting for more than 1 minute, and filter cartridge pressure difference exceeding 15kPa. After triggering the three-level warning, the motor power supply will be immediately cut off, and at the same time, a load reduction command will be sent to the circulating water system through the collaborative linkage module, such as reducing the amount of feed or reducing the water flow rate.

[0032] After the control decision module outputs control commands, the execution control module is activated. The execution control module includes a frequency converter, a solenoid valve group, and a relay, which are used to receive control commands and adjust the operating frequency of the microfilter motor, the start and stop of the backwash pump and the drain valve, and the backwash pressure accordingly.

[0033] Specifically, the frequency converter is adapted to the microfilter motor, precisely adjusting the motor's operating frequency according to the filtration speed adjustment command to achieve stepless control of the filtration speed; the solenoid valve group is responsible for controlling the start and stop of the backwash pump and the opening and closing of the drain valve, strictly following the backwash sequence; the relay, when a fault warning is triggered, performs a power cut-off operation on the motor to ensure equipment safety. For example, when the control decision module outputs a command to start backwashing, the execution control module first controls the filter cartridge to stop rotating via the relay, then starts the backwash pump via the solenoid valve group, opens the drain valve after a 30-second delay, and continues flushing until the filter cartridge pressure differential recovers to 1 kPa, then sequentially performs pump stop and valve closure operations, and finally restarts the filter cartridge via the relay. The entire process is completed automatically without manual intervention.

[0034] Furthermore, the collaborative linkage module is used to establish signal communication between the microfilter and the biological filter and aeration device in the circulating water system, and to synchronously adjust the water distribution of the biological filter and the output power of the aeration device according to the changes in the filtration rate of the microfilter and the backwashing status.

[0035] The collaborative linkage module includes a multi-protocol compatible data interaction interface and a linkage control unit; The linkage control unit synchronously increases the opening of the biofilter inlet valve according to the filtration rate increase signal from the microfilter execution control module, in order to maintain the hydraulic retention time in the biofilter. After receiving the backwash start signal, the linkage control unit anticipates the drop in dissolved oxygen caused by water disturbance and increases the variable frequency operation power of the aeration device in advance.

[0036] Specifically, the data interaction interface is compatible with common communication protocols in circulating water systems such as Modbus, ensuring smooth signal exchange; the linkage control unit has a built-in proportional adjustment algorithm, which simultaneously increases the opening of the inlet valve of the biological filter by 20% when the microfilter filtration rate increases by 20%, keeping the hydraulic retention time of the biological filter within the optimal range of 8 hours, and avoiding a decrease in treatment effect due to sudden changes in inlet flow; when the backwash start signal is received, the linkage control unit increases the variable frequency operation power of the aeration device 10 seconds in advance to offset the decrease in dissolved oxygen caused by water disturbance during backwashing, ensuring stable dissolved oxygen in the aquaculture water.

[0037] Meanwhile, the collaborative linkage module is also used to receive load status information fed back from the biofilter. When the treatment load of the biofilter exceeds the preset maximum treatment load threshold, the collaborative linkage module sends a feedback signal to the control decision module, which then adjusts the target filtration rate of the microfilter or increases the backwashing frequency to reduce the total amount of pollutants entering the subsequent treatment units.

[0038] Specifically, the maximum treatment load threshold of a biofilter is determined based on parameters such as the biofilter's volume, packing type, and biofilm thickness. For example, the maximum treatment load threshold of a biofilter in a factory-scale aquaculture workshop is set to process 5 kg of pollutants per cubic meter of packing per day. When the collaborative linkage module receives feedback from the biofilter that the treatment load exceeds this threshold, it immediately sends a signal to the control decision module. The control decision module then adjusts the target filtration rate of the microfilter by 10%-15% or adjusts the backwashing frequency from once every 8 hours to once every 6 hours, based on the current operating status, thereby reducing the total amount of pollutants entering the biofilter and preventing the biofilter from overloading and causing a decrease in treatment efficiency.

[0039] Furthermore, to ensure continuous optimization of system control accuracy, the control decision module calculates the deviation between the actual operating effect and the expected target based on the actual operating status data fed back by the execution control module and the water quality change data fed back by the coordination module, and calibrates the weight parameters and judgment logic in the control model library based on the deviation value.

[0040] The calibration process of the control decision module for the control model library includes: collecting the action response time of the execution unit under different instructions, the rate at which water quality indicators recover to the set range, and the energy consumption data for treating a unit volume of aquaculture water. The module then uses machine learning algorithms to dynamically correct the weights of various features in the decision model and automatically adjusts the operating limits in the threshold database according to changes in the aquaculture cycle.

[0041] Specifically, the machine learning algorithm used is gradient descent, with the objective function being to minimize the deviation between the actual operating effect and the expected target. This is applied to the weight coefficients (such as...) in the start / stop decision model and the filtration rate adjustment model. , , , (etc.) Dynamic adjustments are made. For example, during the seedling stage of culture, when biomass is small and pollution load is low, the algorithm automatically reduces the biomass weight coefficient based on collected operational data. The value of turbidity threshold is adjusted, and the turbidity threshold is appropriately increased. After entering the growth period, the biomass increases and the pollution load increases. The algorithm will then adjust the weight coefficient and threshold to adapt to the current breeding state and ensure that the control model is always in the optimal working state.

[0042] In addition, the system is also equipped with a visual monitoring module. The visual monitoring module communicates bidirectionally with the control decision module through the big data management and control platform for factory farming, and is used to display the operating parameters, equipment health status and early warning records collected by each module in real time. The visualization monitoring module is equipped with a remote manual intervention interface. Emergency operation commands issued manually have the highest priority and can bypass the control decision module and be directly responded to by the execution control module.

[0043] Specifically, the status display unit of the visualization monitoring module displays key operating parameters of the microfilter in real time, such as filtration rate, filter cartridge pressure difference, motor current, influent and effluent turbidity, and suspended particulate matter concentration, as well as equipment health status scores and early warning records. It also supports historical data queries and control command traceability. In case of emergencies, such as the presence of a large amount of abnormal suspended particulate matter in the aquaculture water, staff can issue emergency operation commands through the remote manual intervention interface of the factory aquaculture big data management and control platform. These commands have the highest priority and are directly responded to by the execution control module, such as urgently starting backwashing or adjusting the filtration rate to quickly respond to emergencies.

[0044] Through the coordinated operation of the above modules, this invention effectively solves the problems of passive control, single parameter monitoring, lack of coordinated regulation capabilities, and delayed fault response in traditional microfilter control methods. It achieves precise automatic control of microfilter operation, improves filtration efficiency and water quality stability, reduces system energy consumption, strengthens the coordination between devices, and shortens fault response time.

[0045] Example 2

[0046] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0047] This embodiment selects a factory-scale freshwater bass farming workshop as the application scenario to verify the practical application of the microfiltration automatic control system used for aquaculture. The farming workshop covers an area of ​​1,000 square meters and has 10 farming ponds, each with a volume of 50 cubic meters. The farming density is 20 fish per cubic meter, and the farming cycle is 12 months. The circulating water system includes 3 microfiltration machines, 1 biological filter, and 5 aeration devices. After deployment, the system runs continuously for 6 months to verify its practical application effect.

[0048] During system deployment, each module component is configured according to the technical solution in Example 1. The optical turbidity sensor, differential pressure sensor, current sensor, binocular vision device, and water flow velocity sensor of the data acquisition module are installed in preset positions. The PLC of the control decision module is connected to each sensor and actuator through communication lines. The collaborative linkage module establishes signal communication with the biological filter and aeration device through the Modbus protocol. The visualization monitoring module is deployed in the central control room of the aquaculture workshop, and staff can view the system operation status in real time.

[0049] During the six-month operation, the system collected and processed various parameters at a preset frequency. The control decision module dynamically adjusted the operating status of the microfilter based on parameter changes, the coordination module synchronously controlled the biological filter and aeration device, and the fault warning module responded promptly to various abnormal situations. To more clearly demonstrate the system's operation at different stages of aquaculture, the key data and control strategies are presented in Table 1 below: During operation, the system successfully handled three minor faults and one moderate fault: In the second month, the motor current of one microfilter exceeded the rated current by 12%, triggering a level one warning. The system automatically reduced the filtration rate by 10% and sent a warning message to the central control room. After inspection, the staff found that there were slight impurities adhering to the surface of the filter cartridge, which did not require shutdown. The system maintained normal operation through automatic adjustment. In the fifth month, the filter cartridge pressure difference of one microfilter reached 11 kPa, triggering a level two warning. The system started an enhanced backwashing program. After three backwashes, the filter cartridge pressure difference returned to 3 kPa, and the fault was resolved. In the sixth month, the data of one optical turbidity sensor was interrupted. The system immediately called up historical data from the same period to temporarily replace it and sent a sensor fault warning. The staff replaced the sensor within 2 hours. During this period, the system did not experience any abnormal operation.

[0050] Through practical application verification in this factory-scale freshwater bass farming workshop, the automatic control system of the microfilter can adapt to the needs of different farming stages, realize precise control of the microfilter and coordinated operation of the recirculating water system equipment, effectively avoid the phenomena of "over-filtration" and "under-filtration", ensure the stability of the water quality of the farming water, reduce the impact of equipment failure on farming production, and is convenient to operate and reliable in operation, fully meeting the practical application needs of factory-scale recirculating aquaculture.

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An automatic control system for a microfiltration machine used in aquaculture, characterized in that, include: The data acquisition module is used to simultaneously collect water quality parameters of aquaculture water bodies, operating parameters of microfilters, and current aquaculture process parameters, and to perform outlier removal and smoothing filtering preprocessing on the collected raw data. The control decision module includes a microprocessor, a control model library, and a threshold database. It is used to perform logical cross-validation on the preprocessed parameters to confirm the validity of the data. After confirming the validity of the data, it combines the species-specific thresholds in the threshold database, determines the operating status of the microfilter through the control model library, and outputs the corresponding control commands. The execution control module includes a frequency converter, a solenoid valve group, and a relay, which are used to receive the control commands and adjust the operating frequency of the microfilter motor, the start and stop of the backwash pump and the drain valve, and the backwash pressure accordingly. The collaborative linkage module is used to establish signal communication between the microfilter and the biological filter and aeration device in the circulating water system, and to synchronously adjust the water distribution of the biological filter and the output power of the aeration device according to the changes in the filtration rate of the microfilter and the backwashing status. The control decision module calculates the deviation between the actual operating effect and the expected target based on the actual operating status data fed back by the execution control module and the water quality change data fed back by the coordination and linkage module, and calibrates the weight parameters and judgment logic in the control model library based on the deviation value.

2. The automatic control system for a microfiltration machine used in aquaculture according to claim 1, characterized in that, The data acquisition module includes an optical turbidity sensor installed at the inlet and outlet of the microfilter, a differential pressure sensor installed inside and outside the filter cartridge, a current sensor for monitoring the load of the drive motor, a binocular vision device for monitoring the surface covering of the filter cartridge, and a water flow rate sensor for monitoring the circulating water flow rate. The water quality parameters include influent and effluent turbidity and suspended particulate matter concentration; the operating parameters include the pressure difference between the filter cartridge inlet and outlet and the motor operating current; and the aquaculture process parameters include aquaculture biomass, daily feed amount, and circulating water flow rate.

3. The automatic control system for a microfiltration machine used in aquaculture according to claim 1, characterized in that, The parameter cross-validation mechanism performed by the control decision module is as follows: by establishing logical relationships between water quality parameters, analyzing the matching degree between operating parameters and the current operating status of the microfilter, and the correspondence between process parameters and pollution load, false data and false detection signals are identified and eliminated. When a parameter conflict is detected, the control decision module sends a sensor self-test command to the data acquisition module and uses historical data from the same period or simulated data generated by the prediction model as a temporary substitute to maintain the continuity of the control process.

4. The automatic control system for a microfiltration machine used in aquaculture according to claim 1, characterized in that, The control model library includes start-stop determination model, filtration rate adjustment model, backwashing determination model, and fault early warning model. The start / stop determination model makes a comprehensive logical determination based on influent turbidity, suspended particulate matter concentration, and pollution load prediction values ​​calculated based on biomass and feeding amount. The filtration rate adjustment model calculates the motor speed adjustment based on the influent turbidity change rate and biomass density, and limits the filtration rate within a preset mechanical load range through a frequency converter.

5. The automatic control system for a microfiltration machine used in aquaculture according to claim 4, characterized in that, The backwashing determination model determines whether a backwashing command is triggered by real-time monitoring of filter cartridge pressure difference, microfilter continuous operating time, and inlet and outlet turbidity difference. During backwashing, the execution control module performs the following timing actions: first, the filter cartridge rotation is stopped, then the backwash pump is started, and after a preset time delay, the drain valve is opened. The flushing process continues until the filter cartridge pressure differential recovers to the preset filter cartridge cleaning pressure differential threshold. Finally, the pump is stopped, the valve is closed, and the filter cartridge is restarted in sequence. The output pressure of the backwash pump is non-linearly adjusted based on the pressure difference of the filter cartridge before rinsing.

6. The automatic control system for a microfiltration machine used in aquaculture according to claim 4, characterized in that, The fault early warning model adopts a three-level early warning mechanism: When the monitored motor current or differential pressure parameters show a first-level deviation, the system sends an early warning message to the preset terminal and automatically fine-tunes the operating parameters; When the deviation reaches the secondary threshold, the system will forcibly start the enhanced backwashing procedure and increase the monitoring frequency; When a preset severe fault type is detected, a level 3 early warning is triggered, and the relay is immediately controlled to cut off the motor power and stop the machine. At the same time, a load reduction command is sent to the circulating water system through the coordinated linkage module.

7. The automatic control system for a microfiltration machine used in aquaculture according to claim 1, characterized in that, The collaborative linkage module includes a multi-protocol compatible data interaction interface and a linkage control unit; The linkage control unit synchronously increases the opening of the biofilter inlet valve according to the filtration rate increase signal from the microfilter execution control module, in order to maintain the hydraulic retention time in the biofilter. After receiving the backwash start signal, the linkage control unit anticipates the drop in dissolved oxygen caused by water disturbance and increases the variable frequency operation power of the oxygenation device in advance.

8. The automatic control system for a microfiltration machine used in aquaculture according to claim 1, characterized in that, The collaborative linkage module is also used to receive load status information fed back from the biofilter. When the treatment load of the biofilter exceeds the preset maximum treatment load threshold of the biofilter, the collaborative linkage module sends a feedback signal to the control decision module, which then adjusts the target filtration rate of the microfilter or increases the backwashing frequency to reduce the total amount of pollutants entering the subsequent treatment unit.

9. The automatic control system for a microfiltration machine used in aquaculture according to claim 1, characterized in that, The calibration process of the control model library by the control decision module includes: The system collects data on the action response time of the execution unit under different commands, the rate at which water quality indicators recover to the set range, and the energy consumption data for treating a unit volume of aquaculture water. It then uses machine learning algorithms to dynamically correct the weights of various features in the judgment model and automatically adjusts the operating limits in the threshold database according to changes in the aquaculture cycle.

10. The automatic control system for a microfiltration machine used in aquaculture according to claim 1, characterized in that, The system also includes a visualization monitoring module, which communicates bidirectionally with the control decision module through the factory farming big data management and control platform to display the operating parameters, equipment health status and early warning records collected by each module in real time. The visualization monitoring module is equipped with a remote manual intervention interface. Emergency operation commands issued manually have the highest priority and can bypass the control decision module and be directly responded to by the execution control module.

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