Monitoring system for aquaculture tail water treatment

Abstract

The invention discloses a monitoring system for aquaculture tail water treatment, and belongs to the technical field of aquaculture tail water treatment, the monitoring system comprises a data center and a control center, the control center comprises an acquisition module, a real-time database, a visualization module and a feedback control module, the data center comprises a data preprocessing module, a data fusion module, a data analysis module and a judgment module, the system can monitor the treatment conditions of a plurality of aquaculture tail water treatment ponds, the situation that one aquaculture tail water treatment pond is matched with one monitoring system is avoided, the cost is reduced, meanwhile, treatment equipment can be synchronously and rapidly adjusted, and the treatment efficiency is improved. And abnormal points can be monitored for multiple times, errors during data acquisition are avoided, error data can be corrected, meaningless adjustment is avoided, cost consumption is reduced, and the quality of tail water can be fully guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture tail water treatment, and in particular to a monitoring system for aquaculture tail water treatment. Background Art

[0002] Aquaculture effluent usually contains pollutants such as organic matter, ammonia nitrogen, and phosphorus. If it is discharged directly into the water body without treatment, it will cause water pollution, deterioration of water quality, and endanger the health of the aquatic ecosystem. Therefore, aquaculture effluent needs to be treated.

[0003] During the treatment of aquaculture effluent, the monitoring platform needs to monitor the changes in pollutant content in the aquaculture effluent at every moment, so as to evaluate the treatment of aquaculture effluent according to the changes in pollutants and make corresponding treatment measures in a timely manner.

[0004] After searching, Chinese patent number CN117575140B discloses a monitoring system for aquaculture effluent treatment. Although the compression efficiency of the monitoring data is high and the loss is low, which ensures the timeliness and accuracy of the assessment of aquaculture effluent treatment, it cannot avoid errors in data collection, so that erroneous data is involved in the adjustment, which not only produces meaningless adjustments and increases costs, but also fails to fully guarantee the quality of the effluent. Summary of the invention

[0005] The purpose of the present invention is to solve the defects in the prior art and to propose a monitoring system for aquaculture tail water treatment.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A monitoring system for aquaculture tailwater treatment, comprising a data center and a control center, wherein the control center comprises a collection module, a real-time database, a visualization module and a feedback control module, and the data center comprises a data preprocessing module, a data fusion module, a data analysis module and a judgment module;

[0008] The acquisition module uses a variety of sensors to collect various parameters in aquaculture tail water in real time. The specific process is as follows:

[0009] S1. Set two groups of sensors of the same type, record them as group A sensors and group B sensors, number the sensors of group A and group B sensors to form group A sensor numbers and group B sensor numbers, then preset the cycle time of the timing switch, control the group A sensors through the timing switch, set the recheck switch, and control the group B sensors through the recheck switch;

[0010] S2. Evenly distribute the sensors of group A and group B in each aquaculture tailwater treatment pool. At the same time, the sensors of group A correspond to the sensors of group B one by one, and the numbers also correspond accordingly, such as A-001 corresponds to B-001, and they are placed side by side;

[0011] S3. The various parameters obtained after the sensors of group A detect the aquaculture tail water are combined with the sensor number and the acquisition time of group A to form the real-time parameters of the tail water;

[0012] S4, compress the tailwater real-time parameters using a revolving door compression algorithm to reduce the amount of data transmission while retaining the key features of the data, and then transmit the tailwater real-time parameters to a real-time database and data preprocessing module through wireless communications such as WiFi, LoRa, and NB-IoT;

[0013] The data preprocessing module decompresses the real-time parameters of the tail water, and then cleans, unifies the format, detects and deletes the outliers of the real-time parameters of the tail water to form the parameters to be integrated; the data fusion module receives all the parameters to be integrated, uses the data analysis algorithm to conduct in-depth analysis of the parameters to be integrated, extracts the key water quality parameters in each aquaculture tail water treatment pool, summarizes and integrates the same parameters in different aquaculture tail water treatment pools to form a comprehensive water quality monitoring data set, and constructs a monitoring curve based on the water quality monitoring data set; the data analysis module calculates the credibility of the data point according to the trend of the data point on the monitoring curve, and optimizes the water quality monitoring data set, specifically:

[0014] SS1. If the credibility of the data point is high, the data point is ignored. If the credibility of the data point is low, the tailwater real-time parameter of the data point is extracted, and the sensor number of group A in the tailwater real-time parameter is extracted;

[0015] SS2, generate a re-test instruction, and send it to the acquisition module in combination with the sensor number of group A. The acquisition module converts the sensor number of group A into the corresponding sensor number of group B. The re-test switch starts the sensor of group B corresponding to the sensor number of group B according to the sensor number of group B. The corresponding sensor of group B detects the corresponding water quality parameter to form the re-test parameter.

[0016] SS3, the acquisition module sends the re-inspection parameters to the data analysis module, and the re-inspection parameters replace the corresponding tailwater real-time parameters, construct a new monitoring curve, and repeat the above operation. If the data point is credible, the monitoring curve is sent to the judgment module. If it is still untrustworthy, the above operation is repeated, and the monitoring curve is sent to the judgment module if it is still untrustworthy.

[0017] The judgment module sets a preset threshold, combines the monitoring curve with the preset threshold to form a judgment chart, and judges whether the data point on the monitoring curve is within the preset threshold. If so, it is ignored; if not, an alarm instruction is generated and sent to the feedback control module in combination with the tail water real-time parameter or re-inspection parameter of the data point.

[0018] Furthermore, the multiple sensors include a pH sensor, a dissolved oxygen sensor, a temperature sensor, a turbidity sensor and a conductivity sensor.

[0019] Furthermore, the trend calculation of the data point is specifically as follows:

[0020] a. Construct a mathematical equation for the monitoring curve, calculate the first-order derivative of the mathematical equation at the data point, and obtain the slope at the data point;

[0021] b. Compare the slopes of adjacent data points. If the slope changes suddenly and significantly, the data point corresponding to the slope is unreliable, otherwise it is credible.

[0022] Furthermore, the mathematical equation of the monitoring curve can be constructed by using the matplotlib library of MATLAB or Python.

[0023] Furthermore, the visualization module supports cross-platform interconnection and displays judgment charts through terminal devices; the feedback control module automatically sends out warning signals according to alarm instructions, and automatically controls the operation of related equipment according to real-time parameters or re-inspection parameters of tail water; the real-time database stores judgment charts and real-time parameters of tail water, and establishes correlation.

[0024] Furthermore, the related equipment supports three control modes: manual control, automatic control and remote control.

[0025] Furthermore, the feedback control module uses an STM32 single-chip microcomputer as a main control board, combined with hardware devices such as wireless communication to build a transmission system.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. The present invention receives all the parameters to be fused through the data fusion module, uses the data analysis algorithm to conduct in-depth analysis of the parameters to be fused, extracts the key water quality parameters in each aquaculture tailwater treatment pool, summarizes and integrates similar parameters in different aquaculture tailwater treatment pools to form a comprehensive water quality monitoring data set, and constructs a monitoring curve based on the water quality monitoring data set, so that the system can monitor the treatment conditions of multiple aquaculture tailwater treatment pools, avoids the need for one monitoring system for each aquaculture tailwater treatment pool, reduces costs, and can synchronously and quickly adjust the treatment equipment.

[0028] 2. The data analysis module of the present invention calculates the credibility of the data point according to the trend of the data point on the monitoring curve, and optimizes the water quality monitoring data set. If the credibility of the data point is high, the data point is ignored. If the credibility of the data point is low, the tail water real-time parameter of the data point is extracted, and the A group sensor number in the tail water real-time parameter is extracted. The acquisition module converts the A group sensor number into the corresponding B group sensor number. The re-inspection switch starts the B group sensor corresponding to the number according to the B group sensor number, and the corresponding B group sensor detects the corresponding water quality parameter to form a re-inspection parameter. The re-inspection parameter is then used to replace the corresponding tail water real-time parameter to construct a new monitoring curve. Repeat the above operation. If the data point is credible, the monitoring curve is sent to the judgment module. If it is still not credible, the above operation is repeated. If it is still not credible, the monitoring curve is sent to the judgment module, so that abnormal points can be monitored multiple times to avoid errors during data acquisition, so that erroneous data can be corrected, which not only avoids meaningless adjustments and reduces cost consumption, but also fully guarantees the quality of tail water. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0030] Figure 1 The present invention provides a module flow chart of a monitoring system for aquaculture tailwater treatment. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0032] In the description of the present invention, it is necessary to understand that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0033] Embodiment 1:

[0034] See also Figure 1 , the present invention provides a technical solution: a monitoring system for aquaculture tailwater treatment, including a data center and a control center, the control center includes a collection module, a real-time database, a visualization module and a feedback control module, the data center includes a data preprocessing module, a data fusion module, a data analysis module and a judgment module;

[0035] The acquisition module uses a variety of sensors to collect various parameters in aquaculture tail water in real time. The specific process is as follows:

[0036] S1. Set two groups of sensors of the same type, record them as group A sensors and group B sensors, number the sensors of group A and group B sensors to form group A sensor numbers and group B sensor numbers, then preset the cycle time of the timing switch, control the group A sensors through the timing switch, set the recheck switch, and control the group B sensors through the recheck switch;

[0037] S2. Evenly distribute the sensors of group A and group B in each aquaculture tailwater treatment pool. At the same time, the sensors of group A correspond to the sensors of group B one by one, and the numbers also correspond accordingly, such as A-001 corresponds to B-001, and they are placed side by side;

[0038] S3. The various parameters obtained after the sensors of group A detect the aquaculture tail water are combined with the sensor number and the acquisition time of group A to form the real-time parameters of the tail water;

[0039] S4, compress the tailwater real-time parameters using a revolving door compression algorithm to reduce the amount of data transmission while retaining the key features of the data, and then transmit the tailwater real-time parameters to a real-time database and data preprocessing module through wireless communications such as WiFi, LoRa, and NB-IoT;

[0040] The data preprocessing module decompresses the real-time parameters of the tailwater, and then cleans, unifies the format, detects and deletes outliers to form parameters to be merged; the data fusion module receives all the parameters to be merged, uses the data analysis algorithm to conduct in-depth analysis of the parameters to be merged, extracts the key water quality parameters in each aquaculture tailwater treatment pool, summarizes and integrates similar parameters in different aquaculture tailwater treatment pools to form a comprehensive water quality monitoring data set, and constructs a monitoring curve based on the water quality monitoring data set.

[0041] Specifically, in the process of aquaculture tailwater treatment and monitoring, a variety of sensors are used to collect various parameters in aquaculture tailwater in real time. The specific process is: S1. Two groups of the same sensors are set, recorded as group A sensors and group B sensors, and the group A sensors and group B sensors are numbered to form group A sensor numbers and group B sensor numbers, and then the cycle time of the timing switch is preset, the group A sensors are controlled by the timing switch, and a re-inspection switch is set to control the group B sensors by the re-inspection switch; S2. The group A sensors and group B sensors are evenly distributed in each aquaculture tailwater treatment pool. At the same time, the group A sensors correspond to the group B sensors one by one, and the numbers also correspond accordingly, such as A-001 corresponds to B-001, and they are placed side by side; S3. The various parameters obtained after the group A sensors detect the aquaculture tailwater are combined with the group A sensor numbers and the collection time to form the real-time parameters of the tailwater; S4. The real-time parameters of the tailwater are compressed using the revolving door compression algorithm The data is compressed to reduce the amount of data transmission while retaining the key features of the data. The real-time parameters of the tailwater are then transmitted to the real-time database and data preprocessing module through wireless communications such as WiFi, LoRa and NB-IoT. The real-time parameters of the tailwater are then decompressed by the data preprocessing module, and then the real-time parameters of the tailwater are cleaned, formatted, detected and deleted for outliers to form parameters to be fused. The data fusion module then receives all the parameters to be fused, uses the data analysis algorithm to conduct in-depth analysis of the parameters to be fused, extracts the key water quality parameters in each aquaculture tailwater treatment pool, summarizes and integrates similar parameters in different aquaculture tailwater treatment pools to form a comprehensive water quality monitoring data set, and constructs a monitoring curve based on the water quality monitoring data set, so that the system can monitor the treatment of multiple aquaculture tailwater treatment pools, avoids the need for one monitoring system for each aquaculture tailwater treatment pool, and can simultaneously and quickly adjust the treatment equipment while reducing costs.

[0042] Embodiment 2:

[0043] See also Figure 1 The present invention provides a technical solution: a monitoring system for aquaculture tailwater treatment, a data analysis module calculates the credibility of data points according to the trend of data points on the monitoring curve, and optimizes the water quality monitoring data set, specifically:

[0044] SS1. If the credibility of the data point is high, the data point is ignored. If the credibility of the data point is low, the tailwater real-time parameter of the data point is extracted, and the sensor number of group A in the tailwater real-time parameter is extracted;

[0045] SS2, generate a re-test instruction, and send it to the acquisition module in combination with the sensor number of group A. The acquisition module converts the sensor number of group A into the corresponding sensor number of group B. The re-test switch starts the sensor of group B corresponding to the sensor number of group B according to the sensor number of group B. The corresponding sensor of group B detects the corresponding water quality parameter to form the re-test parameter.

[0046] SS3, the acquisition module sends the re-inspection parameters to the data analysis module, and the re-inspection parameters replace the corresponding tailwater real-time parameters, construct a new monitoring curve, and repeat the above operation. If the data point is credible, the monitoring curve is sent to the judgment module. If it is still untrustworthy, the above operation is repeated, and the monitoring curve is sent to the judgment module if it is still untrustworthy.

[0047] The judgment module sets a preset threshold, combines the monitoring curve with the preset threshold, forms a judgment chart, and judges whether the data point on the monitoring curve is within the preset threshold. If it is, it is ignored. If not, an alarm instruction is generated and sent to the feedback control module in combination with the tail water real-time parameters or re-inspection parameters of the data point. The trend calculation of the data point is specifically as follows:

[0048] a. Construct a mathematical equation for the monitoring curve, calculate the first-order derivative of the mathematical equation at the data point, and obtain the slope at the data point;

[0049] b. Compare the slopes of adjacent data points. If the slope changes suddenly and significantly, the data point corresponding to the slope is unreliable, otherwise it is credible.

[0050] Specifically, in the process of determining the credibility of the data point, the credibility of the data point is calculated according to the trend of the data point on the monitoring curve through the data analysis module, and the water quality monitoring data set is optimized, specifically: SS1. If the credibility of the data point is high, the data point is ignored. If the credibility of the data point is low, the tail water real-time parameters of the data point are extracted, and the group A sensor number in the tail water real-time parameters is extracted; SS2. A re-inspection instruction is generated and sent to the acquisition module in combination with the group A sensor number. The acquisition module converts the group A sensor number into the corresponding group B sensor number. The re-inspection switch starts the group B sensor corresponding to the group B sensor number according to the group B sensor number, and the corresponding group B sensor detects the corresponding water quality parameters to form re-inspection parameters; SS3. The acquisition module sends the re-inspection parameters to the data acquisition module. According to the analysis module, the corresponding real-time parameters of the tail water are replaced by the re-inspection parameters, a new monitoring curve is constructed, and the above operation is repeated. If the data point is credible, the monitoring curve is sent to the judgment module. If it is still not credible, the above operation is looped. If it is still not credible, the monitoring curve is sent to the judgment module. The judgment module then sets a preset threshold, combines the monitoring curve with the preset threshold, forms a judgment chart, and judges whether the data point on the monitoring curve is within the preset threshold. If so, it is ignored. If not, an alarm instruction is generated and sent to the feedback control module in combination with the real-time parameters of the tail water or the re-inspection parameters of the data point. In this way, abnormal points can be monitored multiple times to avoid errors during data collection, so that erroneous data can be corrected. This not only avoids meaningless adjustments and reduces cost consumption, but also fully guarantees the quality of the tail water.

[0051] The working principle and use process of the present invention are as follows: in the process of aquaculture tailwater treatment and monitoring, a variety of sensors are used to collect various parameters in aquaculture tailwater in real time, and the specific process is as follows: S1, two groups of the same type of sensors are set, recorded as group A sensors and group B sensors, and the group A sensors and group B sensors are numbered to form group A sensor numbers and group B sensor numbers, and then the cycle time of the timing switch is preset, the group A sensors are controlled by the timing switch, and a re-inspection switch is set to control the group B sensors by the re-inspection switch; S2, the group A sensors and group B sensors are evenly distributed in each aquaculture tailwater treatment pool, and at the same time, the group A sensors correspond to the group B sensors one by one, and the numbers also correspond accordingly, such as A-001 corresponds to B-001, and are placed side by side; S3, the various parameters obtained after the group A sensors detect the aquaculture tailwater are combined with the group A sensor numbers and the collection time to form the real-time parameters of the tailwater; S4, the revolving door compression algorithm is used to compress the tailwater real-time parameters. The real-time parameters are compressed to reduce the amount of data transmission while retaining the key features of the data. The real-time parameters of the tailwater are then transmitted to the real-time database and data preprocessing module through wireless communications such as WiFi, LoRa and NB-IoT. The real-time parameters of the tailwater are then decompressed by the data preprocessing module, and then the real-time parameters of the tailwater are cleaned, formatted, detected and deleted for outliers to form parameters to be fused. The data fusion module receives all the parameters to be fused, uses the data analysis algorithm to conduct in-depth analysis of the parameters to be fused, extracts the key water quality parameters in each aquaculture tailwater treatment pool, summarizes and integrates the same parameters in different aquaculture tailwater treatment pools to form a comprehensive water quality monitoring data set, and constructs a monitoring curve based on the water quality monitoring data set, so that the system can monitor the treatment of multiple aquaculture tailwater treatment pools, avoiding the need for one aquaculture tailwater treatment pool to be equipped with a monitoring system, while reducing costs and being able to synchronize and quickly adjust the treatment equipment;

[0052] The data analysis module then calculates the credibility of the data point according to the trend of the data point on the monitoring curve, and optimizes the water quality monitoring data set, specifically: SS1. If the credibility of the data point is high, ignore the data point; if the credibility of the data point is low, extract the tail water real-time parameters of the data point, and extract the A group sensor number in the tail water real-time parameters; SS2. Generate a re-inspection instruction, and send it to the acquisition module in combination with the A group sensor number. The acquisition module converts the A group sensor number into the corresponding B group sensor number. The re-inspection switch starts the B group sensor corresponding to the B group sensor number according to the B group sensor number, and the corresponding B group sensor detects the corresponding water quality parameters to form re-inspection parameters; SS3. The acquisition module sends the re-inspection parameters to the data analysis module, and the re-inspection module sends the re-inspection parameters to the data analysis module. Replace the corresponding tail water real-time parameters with the re-inspection parameters, construct a new monitoring curve, repeat the above operation, if the data point is credible, the monitoring curve is sent to the judgment module, if it is still not credible, loop the above operation, if it is still not credible, the monitoring curve is sent to the judgment module, and then the judgment module sets a preset threshold, combines the monitoring curve with the preset threshold, forms a judgment chart, and judges whether the data point on the monitoring curve is within the preset threshold, if so, ignores it, if not, generates an alarm instruction, and sends it to the feedback control module in combination with the tail water real-time parameters or re-inspection parameters of the data point, so that abnormal points can be monitored multiple times, errors in data collection can be avoided, and erroneous data can be corrected, which not only avoids meaningless adjustments and reduces cost consumption, but also fully guarantees the quality of tail water.

[0053] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A monitoring system for aquaculture tailwater treatment, characterized in that: It includes a data center and a control center, wherein the control center includes a collection module, a real-time database, a visualization module and a feedback control module, and the data center includes a data preprocessing module, a data fusion module, a data analysis module and a judgment module; The acquisition module uses a variety of sensors to collect various parameters in aquaculture tail water in real time. The specific process is as follows: S1. Set two groups of sensors of the same type, record them as group A sensors and group B sensors, number the sensors of group A and group B sensors to form group A sensor numbers and group B sensor numbers, then preset the cycle time of the timing switch, control the group A sensors through the timing switch, set the recheck switch, and control the group B sensors through the recheck switch; S2. Evenly distribute the sensors of group A and group B in each aquaculture tailwater treatment pool. At the same time, the sensors of group A correspond to the sensors of group B one by one, and the numbers also correspond accordingly, such as A-001 corresponds to B-001, and they are placed side by side; S3. The various parameters obtained after the sensors of group A detect the aquaculture tail water are combined with the sensor number and the acquisition time of group A to form the real-time parameters of the tail water; S4, compress the tailwater real-time parameters using a revolving door compression algorithm to reduce the amount of data transmission while retaining the key features of the data, and then transmit the tailwater real-time parameters to a real-time database and data preprocessing module through wireless communications such as WiFi, LoRa, and NB-IoT; The data preprocessing module decompresses the real-time parameters of the tail water, and then cleans, unifies the format, detects and deletes the outliers of the real-time parameters of the tail water to form the parameters to be fused; the data fusion module receives all the parameters to be fused, uses the data analysis algorithm to perform in-depth analysis on the parameters to be fused, extracts the key water quality parameters in each aquaculture tail water treatment pool, summarizes and integrates the same parameters in different aquaculture tail water treatment pools to form a comprehensive water quality monitoring data set, and constructs a monitoring curve based on the water quality monitoring data set; The data analysis module calculates the credibility of the data points according to the trend of the data points on the monitoring curve and optimizes the water quality monitoring data set, specifically: SS1. If the credibility of the data point is high, the data point is ignored. If the credibility of the data point is low, the tailwater real-time parameter of the data point is extracted, and the sensor number of group A in the tailwater real-time parameter is extracted; SS2, generate a re-test instruction, and send it to the acquisition module in combination with the sensor number of group A. The acquisition module converts the sensor number of group A into the corresponding sensor number of group B. The re-test switch starts the sensor of group B corresponding to the sensor number of group B according to the sensor number of group B. The corresponding sensor of group B detects the corresponding water quality parameter to form the re-test parameter. SS3, the acquisition module sends the re-inspection parameters to the data analysis module, and the re-inspection parameters replace the corresponding tailwater real-time parameters, construct a new monitoring curve, and repeat the above operation. If the data point is credible, the monitoring curve is sent to the judgment module. If it is still untrustworthy, the above operation is repeated, and the monitoring curve is sent to the judgment module if it is still untrustworthy. The judgment module sets a preset threshold, combines the monitoring curve with the preset threshold to form a judgment chart, and judges whether the data point on the monitoring curve is within the preset threshold. If so, it is ignored; if not, an alarm instruction is generated and sent to the feedback control module in combination with the tail water real-time parameter or re-inspection parameter of the data point.

2. A monitoring system for aquaculture tailwater treatment according to claim 1, characterized in that: The various sensors include pH sensors, dissolved oxygen sensors, temperature sensors, turbidity sensors, and conductivity sensors.

3. A monitoring system for aquaculture tailwater treatment according to claim 1, characterized in that: The trend calculation of the data point is specifically as follows: a. Construct a mathematical equation for the monitoring curve, calculate the first-order derivative of the mathematical equation at the data point, and obtain the slope at the data point; b. Compare the slopes of adjacent data points. If the slope changes suddenly and significantly, the data point corresponding to the slope is unreliable, otherwise it is credible.

4. A monitoring system for aquaculture tailwater treatment according to claim 3, characterized in that: The mathematical equation of the monitoring curve can be constructed by using the matplotlib library of MATLAB or Python.

5. A monitoring system for aquaculture tailwater treatment according to claim 1, characterized in that: The visualization module supports cross-platform interconnection and displays judgment charts through terminal devices; the feedback control module automatically sends out warning signals according to alarm instructions, and automatically controls the operation of related equipment according to real-time parameters or re-inspection parameters of tail water; the real-time database stores judgment charts and real-time parameters of tail water, and establishes correlation.

6. A monitoring system for aquaculture tailwater treatment according to claim 5, characterized in that: The related equipment supports three control modes: manual control, automatic control and remote control.

7. A monitoring system for aquaculture tailwater treatment according to claim 5, characterized in that: The feedback control module uses an STM32 single-chip microcomputer as the main control board and combines hardware devices such as wireless communication to build a transmission system.

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