Water quality index response control system for high-density aquaculture

Abstract

The invention relates to the technical field of automatic control, in particular to a water quality index response control system for high-density aquaculture, which comprises a data processing module, a feature extraction module, a grade identification module, a proportion conversion module and a putting control module. According to the method, the water quality data of a plurality of depth points in the culture pond are continuously recorded, and the change rate of the water quality data in adjacent time is calculated, so that the change from paying attention to a water quality static threshold value to insight into a water quality dynamic evolution trend is realized, and the fluctuation amplitude and trend direction difference of water quality indexes among different depth points are further judged; according to the method, the local water quality deterioration risk caused by aggravation of water layering can be recognized in advance, the abnormal types are recognized and the risk levels are divided based on fluctuation characteristics and trend differences, microecological preparation combinations with matched functions can be screened out, the adjustment coefficient weights of the preparations can be dynamically adjusted according to the severity of the abnormal levels, and the accuracy of the microecological preparations is improved. And an accurate combination ratio is generated.

Description

Technical Field

[0001] This invention relates to the field of automation control technology, and in particular to a water quality index response control system for high-density aquaculture. Background Technology

[0002] The field of automation control technology involves the regulation and control of various dynamic systems, and covers technologies for real-time monitoring and regulation of physical processes through computer hardware and software.

[0003] The water quality index response control system for high-density aquaculture uses automated means to monitor and adjust water quality indicators in real time to reduce stress on aquatic animals caused by changes in water quality. It mainly includes water quality sensors, a data processing unit, and a control execution system. It uses real-time monitoring technology to acquire water quality data (such as dissolved oxygen, pH, and ammonia nitrogen concentration) and automatically adjusts water quality parameters through a feedback control mechanism.

[0004] Current technologies rely on real-time monitoring of absolute water quality indicators and trigger feedback control through fixed thresholds. This operating mode makes it significantly lagging in dealing with gradual water quality deterioration. In high-density aquaculture, water bodies often exhibit stratification, leading to significant differences in water quality parameters between upper and lower layers. Single or averaged data collection methods cannot effectively capture the heterogeneity of water bodies in the vertical direction. For example, the bottom water body may show a rapid increase in ammonia nitrogen concentration and a continuous decrease in dissolved oxygen due to the decomposition of uneaten feed and feces, but the water quality in the middle and upper layers has not changed significantly. This causes the system to judge the water quality as normal based on average values ​​or single-point values, thus missing the best time for intervention. Its control execution system usually adopts uniform adjustment methods and lacks refined response strategies for different causes of deterioration. This adjustment method may not only waste resources but also sometimes fail to solve the problem at its root, ultimately leading to sudden changes in water quality and causing irreversible stress or losses to the aquaculture species. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a water quality index response control system for high-density aquaculture.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a water quality index response control system for high-density aquaculture includes:

[0007] The data processing module continuously records water quality sampling data at multiple depth points in the aquaculture pond at fixed time intervals, calculates the rate of change of each water quality sampling data in adjacent time periods, and generates a water quality change trend dataset.

[0008] The feature extraction module, based on the water quality change trend dataset, determines the difference in fluctuation amplitude and trend direction of each water quality sampling data at each depth point, and generates a water quality fluctuation feature set.

[0009] The level identification module identifies the abnormal type of the current aquaculture pond water quality through the water quality fluctuation feature set, classifies the corresponding water quality abnormality level, and generates water quality abnormality level labels.

[0010] The proportion conversion module filters microecological preparation combinations based on the water quality anomaly level labels, extracts the conversion coefficient and regulation coefficient corresponding to each microecological preparation in the combination, and generates microecological preparation combination proportion data by setting the weighting ratio of the coefficients according to the level labels.

[0011] The dosing control module configures the proportion data of the microecological preparation combination onto the automatic dosing device to perform time-sharing and quantitative dosing of the microecological preparation combination. It compares the data with the water quality sampling data before dosing to determine whether the condition of consistency of water quality index change direction is met, and obtains the index response control result.

[0012] As a further aspect of the present invention, the water quality change trend dataset specifically includes the time-series change rate of water quality parameters, the sampling point identifier of depth points, and the sampling time label; the water quality fluctuation feature set includes the difference in fluctuation amplitude between depths, the trend direction comparison result, and the change consistency judgment label; the water quality anomaly level label specifically refers to the water quality anomaly classification category and the water quality risk level number; the microecological preparation combination ratio data includes the microecological preparation type identifier, the corresponding adjustment ratio value of the preparation, and the ratio conversion weight coefficient; and the indicator response control result includes the change direction consistency judgment result, the response confirmation status label, and the control trigger judgment identifier.

[0013] As a further aspect of the present invention, the data processing module includes:

[0014] The multi-point water quality sampling data acquisition submodule continuously records water quality sampling data at multiple depth points in the middle and bottom layers of the aquaculture pond at fixed time intervals. The water quality sampling data includes raw measurement data of ammonia nitrogen concentration, dissolved oxygen concentration and pH value. The water quality sampling data from multiple depth points are integrated and arranged according to the recording time to establish multi-point time-sharing water quality sampling data.

[0015] The water quality change rate calculation submodule calculates the change rate of ammonia nitrogen concentration and dissolved oxygen concentration in the multi-point time-division water quality sampling data at adjacent times, and determines the positive or negative change direction of pH value, and integrates them into water quality sampling data change rate information;

[0016] The water quality trend dataset generation submodule generates a water quality change trend dataset by combining the recording time and depth point information in the multi-point time-division water quality sampling data with the corresponding water quality sampling data change rate information in a structured manner.

[0017] As a further aspect of the present invention, the feature extraction module includes:

[0018] The interlayer trend extraction submodule extracts the rate of change of ammonia nitrogen concentration and dissolved oxygen concentration at multiple depth points within the same time window of the water quality change trend dataset, integrates the positive and negative changes of pH value within the same time period and classifies them accordingly, and establishes stratified water quality trend data.

[0019] The fluctuation amplitude calculation submodule calls the stratified water quality trend data to calculate the difference between the change rate of ammonia nitrogen concentration and the change rate of dissolved oxygen concentration between adjacent depth points, and compares the difference between the maximum fluctuation value and the average change rate of the two within the same time window to obtain the interlayer water quality fluctuation amplitude.

[0020] The water quality feature integration submodule, based on the interlayer water quality fluctuation amplitude and combined with the pH value trend direction difference at each depth point in the stratified water quality trend data, structurally combines the fluctuation amplitude and trend direction difference of each water quality sampling data at each depth point to generate a water quality fluctuation feature set.

[0021] As a further aspect of the present invention, the grade identification module includes:

[0022] The feature recognition submodule integrates the differences in the ammonia nitrogen concentration change rate, dissolved oxygen concentration change rate, and pH value trend direction from the water quality fluctuation feature set as features to be determined.

[0023] The anomaly level determination submodule calls the two indicators, ammonia nitrogen concentration change rate and dissolved oxygen concentration change rate, in the features to be determined. It compares the values ​​of the two indicators with the preset grading threshold range. When the value of the indicator exceeds the grading threshold range, it is considered to trigger an abnormal state and obtain the preliminary anomaly level determination result.

[0024] The grade label generation submodule identifies the abnormal type and classifies the corresponding grade of the current aquaculture pond water quality based on the preliminary judgment result of the abnormality level and the difference in the trend direction of pH value in the feature to be judged, and generates water quality abnormality grade labels.

[0025] As a further aspect of the present invention, the ratio conversion module includes:

[0026] The formulation combination screening submodule screens microecological formulation combinations with corresponding regulatory functions according to the water quality anomaly level label, extracts the conversion coefficient and regulation coefficient corresponding to each microecological formulation in the combination, including ammonia nitrogen conversion coefficient, dissolved oxygen regulation coefficient and pH regulation coefficient, and establishes a set of formulation functional coefficients.

[0027] The coefficient weight configuration submodule, in combination with the level of the water quality abnormality level label, uses the level label as the overall weight adjustment factor for the adjustment coefficient of each microecological preparation in the combination ratio, and sets the corresponding multiplication weight ratio for each conversion coefficient and adjustment coefficient in the preparation functional coefficient set to obtain the weighted adjustment coefficient value.

[0028] The combination ratio generation submodule performs a weighted conversion on the coefficient of each microecological preparation in the combination based on the weighted adjustment coefficient value, and normalizes the converted result to determine the ratio of each microecological preparation, thereby generating microecological preparation combination ratio data.

[0029] As a further aspect of the present invention, the process of screening microecological preparation combinations with corresponding regulatory functions specifically involves selecting one or more combinations from a preset library containing EM bacteria, nitrifying bacteria, Bacillus, photosynthetic bacteria, and actinomycetes, based on the abnormality type of ammonia nitrogen concentration, dissolved oxygen concentration, or pH value indicated by the water quality abnormality level label.

[0030] As a further aspect of the present invention, the delivery control module includes:

[0031] The parameter conversion submodule converts the proportion of each microecological preparation in the microecological preparation combination ratio data into the rotation speed setting value and single opening duration parameter of the target feeding channel on the automatic feeding device, and configures the parameters in the automatic feeding device to establish the device feeding setting parameters.

[0032] The time-sharing quantitative dosing submodule, according to the device dosing setting parameters, sequentially controls the dosing device to start each channel to dosing the microecological preparation combination in a time-sharing quantitative manner within a fixed dosing cycle, and collects ammonia nitrogen concentration, dissolved oxygen concentration and pH value data after dosing to obtain water quality data after dosing;

[0033] The indicator response judgment submodule compares the change direction of the corresponding water quality indicators in the multi-point time-sharing water quality sampling data before deployment with the water quality data after deployment, determines whether the consistency condition of the change direction of water quality indicators is met, confirms whether the water quality indicators have generated a response, and generates indicator response control results.

[0034] As a further aspect of the present invention, the process of determining whether the consistency condition of the direction of change of water quality indicators is met specifically involves: comparing the changing trends of ammonia nitrogen concentration, dissolved oxygen concentration, and pH value in the water quality data after the injection with the changing trends reflected in the multi-point time-segmented water quality sampling data before the injection; when the ammonia nitrogen concentration in the water quality data after the injection shows a decreasing trend, it is determined that the changing direction of ammonia nitrogen concentration meets the consistency condition; when the dissolved oxygen concentration in the water quality data after the injection shows an increasing trend, it is determined that the changing direction of dissolved oxygen concentration meets the consistency condition; and when the changing direction of pH value in the water quality data after the injection is opposite to or tends to be stable compared with the changing direction before the injection, it is determined that the changing direction of pH value meets the consistency condition.

[0035] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0036] This invention achieves a shift from focusing on static water quality thresholds to understanding dynamic water quality evolution trends by continuously recording water quality data at multiple depth points in aquaculture ponds and calculating their rate of change over adjacent time periods. It further assesses the differences in fluctuation amplitude and trend direction of water quality indicators between different depth points, enabling early identification of localized water quality deterioration risks caused by intensified water stratification. Based on fluctuation characteristics and trend differences, it identifies anomaly types and classifies risk levels. This not only allows for the selection of functionally compatible microecological preparation combinations but also enables dynamic adjustment of the adjustment coefficient weights of the preparations according to the severity of the anomaly level, generating precise combination ratios. Finally, through time-slotted quantitative administration and consistent judgment of post-administration water quality change direction, a control process from trend warning and precise diagnosis to closed-loop verification is formed. This significantly improves the predictability and accuracy of water quality control, effectively preventing sudden water quality deterioration and ensuring the stability of high-density aquaculture environments. Attached Figure Description

[0037] Figure 1 This is a system flowchart of the present invention;

[0038] Figure 2 This is a flowchart of the data processing module of the present invention;

[0039] Figure 3 This is a flowchart of the feature extraction module of the present invention;

[0040] Figure 4 This is a flowchart of the grade identification module of the present invention;

[0041] Figure 5 This is a flowchart of the proportional conversion module of the present invention;

[0042] Figure 6 This is a flowchart of the deployment control module of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0044] Please see Figure 1 A water quality index response control system for high-density aquaculture includes:

[0045] The data processing module continuously records water quality sampling data at multiple depth points in the aquaculture pond at fixed time intervals, calculates the rate of change of each water quality sampling data in adjacent time periods, and generates a water quality change trend dataset.

[0046] The feature extraction module, based on the water quality change trend dataset, determines the difference in fluctuation amplitude and trend direction of each water quality sampling data at each depth point, and generates a water quality fluctuation feature set.

[0047] The level identification module identifies the abnormal type of the current aquaculture pond water quality through the water quality fluctuation feature set, classifies the corresponding water quality abnormality level, and generates water quality abnormality level labels.

[0048] The proportion conversion module filters microecological preparation combinations based on water quality anomaly level labels, extracts the conversion coefficient and regulation coefficient corresponding to each microecological preparation in the combination, and generates microecological preparation combination proportion data by setting the multiplication ratio of the coefficients according to the level label.

[0049] The dosing control module configures the proportion data of the microecological preparation combination on the automatic dosing device to carry out time-sharing and quantitative dosing of the microecological preparation combination. It compares the data with the water quality sampling data before dosing to determine whether the condition of consistency of water quality index change direction is met, and obtains the index response control result.

[0050] The water quality change trend dataset specifically includes the time-series change rate of water quality parameters, sampling point identifiers at depths, and sampling time labels. The water quality fluctuation feature set includes the difference in fluctuation amplitude between depths, trend direction comparison results, and change consistency judgment labels. The water quality anomaly level labels specifically refer to the water quality anomaly classification category and water quality risk level number. The microecological preparation combination ratio data includes the microecological preparation type identifier, the corresponding adjustment ratio value of the preparation, and the ratio conversion weight coefficient. The indicator response control results include the change direction consistency judgment results, response confirmation status labels, and control trigger judgment identifiers.

[0051] Please see Figure 2 The data processing module includes:

[0052] The multi-point water quality sampling data acquisition submodule continuously records water quality sampling data at multiple depth points in the middle and bottom layers of the aquaculture pond at fixed time intervals. The water quality sampling data includes raw measurement data of ammonia nitrogen concentration, dissolved oxygen concentration and pH value. The water quality sampling data from multiple depth points are integrated and arranged according to the recording time to establish multi-point time-sharing water quality sampling data.

[0053] First, water quality sensors deployed in the middle and bottom layers of the aquaculture pond were activated. The middle layer monitoring point was set at a depth of 1.5 meters, and the bottom layer monitoring point at a depth of 3.0 meters. This depth selection was chosen because the middle layer represents the main condition of the aquaculture water, while the bottom layer, adjacent to sediment, is the area of ​​most intense material decomposition and exchange. Monitoring these two key layers can comprehensively reflect the vertical differentiation characteristics of the water body. Subsequently, the data acquisition interval was set to 30 minutes. This interval was chosen to capture short-term fluctuations in key water quality indicators caused by the diurnal rhythm activities of aquatic organisms (such as feeding and respiration) and the metabolism of aquatic microorganisms, while avoiding data redundancy caused by excessively high acquisition frequency. Starting at 8:00 AM, the sensor array continuously recorded raw measurement data of ammonia nitrogen concentration, dissolved oxygen concentration, and pH value. At 8:00 AM, water quality data collected at the monitoring point at a depth of 1.5 meters were: ammonia nitrogen concentration 0.45 mg / L, dissolved oxygen concentration 6.8 mg / L, and pH 8.2; data collected at the monitoring point at a depth of 3.0 meters were: ammonia nitrogen concentration 0.52 mg / L, dissolved oxygen concentration 6.2 mg / L, and pH 8.1. At 8:30 AM, the data from the 1.5-meter depth monitoring point was updated to: ammonia nitrogen concentration 0.48 mg / L, dissolved oxygen concentration 6.6 mg / L, and pH 8.1; data from the 3.0-meter depth monitoring point was updated to: ammonia nitrogen concentration 0.56 mg / L, dissolved oxygen concentration 6.0 mg / L, and pH 8.0. The data acquisition submodule continued to perform this operation, acquiring a series of temporally continuous data points. Subsequently, the water quality sampling data collected from different depths were aligned and integrated according to the recorded time points. The specific integration process involves pairing water quality data records from all depth points under the same collection timestamp as a single data unit, and then arranging these data units in chronological order to form a time-synchronized, multi-dimensional water quality data sequence. For example, integrating the data from 8:00 and 8:30 results in the following data structures: [Time: 8:00, {Depth 1.5m: {Ammonia Nitrogen: 0.45, Dissolved Oxygen: 6.8, pH: 8.2}, Depth 3.0m: {Ammonia Nitrogen: 0.52, Dissolved Oxygen: 6.2, pH: 8.1}}], [Time: 8:30, {Depth 1.5m: {Ammonia Nitrogen: 0.48, Dissolved Oxygen: 6.6, pH: 8.1}, Depth 3.0m: {Ammonia Nitrogen: 0.56, Dissolved Oxygen: 6.0, pH: 8.0}}]. Through continuous recording and integration, a structured, multi-point, time-division water quality sampling data set is ultimately established.

[0054] The water quality change rate calculation submodule calculates the change rate of ammonia nitrogen concentration and dissolved oxygen concentration in adjacent time periods in multi-point time-sharing water quality sampling data, and determines the positive or negative direction of pH value change, integrating them into water quality sampling data change rate information;

[0055] Data from two consecutive time points recorded in multi-point time-segmented water quality sampling data are retrieved to calculate the rate of change of water quality indicators. Compared with absolute values, the rate of change can more sensitively reveal the early dynamic trend of water quality deterioration. The calculation process for ammonia nitrogen concentration and dissolved oxygen concentration is achieved by subtracting the measurement value of the previous time point from the measurement value of the later time point, and then dividing the difference by the time interval (30 minutes) between the two time points. Taking the data from a 1.5-meter depth monitoring point between 8:00 and 8:30 as an example, the rate of change of ammonia nitrogen concentration is calculated as (0.48 mg / L - 0.45 mg / L) / 30 minutes, resulting in a value of +0.001 mg / L / minute. Similarly, the rate of change of dissolved oxygen concentration is calculated as (6.6 mg / L - 6.8 mg / L) / 30 minutes, resulting in a value of -0.0067 mg / L / minute. For the monitoring point at a depth of 3.0 meters, the change rate of ammonia nitrogen concentration was (0.56 mg / L - 0.52 mg / L) / 30 minutes = +0.0013 mg / L / minute, and the change rate of dissolved oxygen concentration was (6.0 mg / L - 6.2 mg / L) / 30 minutes = -0.0067 mg / L / minute. For pH changes, the direction of increase or decrease was directly determined without calculating the specific rate of change, because the logarithmic nature of pH means that even small changes can represent significant shifts in the acid-base balance of the water body; focusing on the direction of change is more crucial. At a depth of 1.5 meters, the pH value changed from 8.2 to 8.1, showing a downward trend, and this direction of change was recorded as negative. At a depth of 3.0 meters, the pH value changed from 8.1 to 8.0, also showing a downward trend, and this direction of change was also recorded as negative. To facilitate subsequent structured processing and interpretation, positive changes were quantified as a value of 1, negative changes as a value of -1, and no change as a value of 0. This quantification process transforms continuous pH changes into discrete trend categories, simplifying the complexity of subsequent trend comparisons. Therefore, the pH change direction at both depth points is -1. After completing calculations for all monitoring points and time periods, the quantified values ​​of ammonia nitrogen concentration change rate, dissolved oxygen concentration change rate, and pH change direction are integrated to form water quality sampling data change rate information.

[0056] The water quality trend dataset generation submodule combines the recording time and depth information from multi-point time-sharing water quality sampling data with the corresponding water quality sampling data change rate information in a structured manner to generate a water quality change trend dataset.

[0057] The process involves retrieving multi-point, time-segmented water quality sampling data and combining it with corresponding water quality sampling data change rate information. This is a structured composition operation aimed at creating a more informative data record. This record not only contains a "static" snapshot of water quality at a specific time and depth but also includes "dynamic" characteristics describing how water quality evolves over time. Specifically, the submodule extracts the recording time and depth information from each multi-point, time-segmented water quality sampling data as an index. For example, it extracts the information "Time: 8:30" and "Depth: 1.5m". Then, based on this index, it finds the corresponding water quality sampling data change rate information, namely, the ammonia nitrogen concentration change rate +0.001 mg / L / min, the dissolved oxygen concentration change rate -0.0067 mg / L / min, and the pH value change direction -1. Finally, this dynamic information is combined with the original static measurements into a new, unified data structure. This structure contains the original measurements (ammonia nitrogen concentration 0.48 mg / L, dissolved oxygen concentration 6.6 mg / L, pH 8.1) along with calculated rates of change and direction information. This process is repeated for all time and depth points. For example, for "Time: 8:30" and "Depth: 3.0m", the combined data unit will contain the original measurements (ammonia nitrogen concentration 0.56 mg / L, dissolved oxygen concentration 6.0 mg / L, pH 8.0) along with the corresponding rates of change (ammonia nitrogen concentration change rate +0.0013 mg / L / min, dissolved oxygen concentration change rate -0.0067 mg / L / min, pH change direction -1). By performing this structured combination of data from all collected time periods, a complete dataset of water quality change trends is ultimately generated.

[0058] Please see Figure 3 The feature extraction module includes:

[0059] The interlayer trend extraction submodule extracts the rate of change of ammonia nitrogen concentration and dissolved oxygen concentration at multiple depth points within the same time window of the water quality change trend dataset, integrates the positive and negative changes of pH value within the same time period and classifies them accordingly, and establishes stratified water quality trend data.

[0060] Data within a specific time window was extracted from the water quality change trend dataset. The time window was set at 2 hours, consisting of four consecutive 30-minute sampling cycles. This time window was chosen based on the study of the daily variation cycle of physicochemical factors in aquaculture water. The 2-hour length is sufficient to smooth out instantaneous disturbances and effectively reflect trend changes driven by factors such as light and feeding. For example, from 8:00 AM to 10:00 AM. Within this time window, the ammonia nitrogen concentration change rate and dissolved oxygen concentration change rate at all depth points (1.5 meters and 3.0 meters) were extracted. Assuming that between 8:00 and 10:00, the ammonia nitrogen concentration change rate sequence at a depth of 1.5 meters is [+0.0010, +0.0011, +0.0009, +0.0012] mg / L / min, and the ammonia nitrogen concentration change rate sequence at a depth of 3.0 meters is [+0.0013, +0.0015, +0.0016, +0.0014] mg / L / min, the pH value change direction sequence at both depths is extracted, for example, [-1, -1, 0, -1] at 1.5 meters and [-1, -1, -1, -1] at 3.0 meters. Subsequently, the positive and negative pH value changes within the same time period are integrated and categorized. Specifically, the number of times each depth point records a positive change (+1), a negative change (-1), or no change (0) within the time window is counted. When the number of negative changes significantly exceeds the number of positive changes (e.g., negative changes account for more than 70% of all non-zero changes), the overall pH trend at that depth level is classified as "decreasing." At a depth of 1.5 meters, there were 3 instances of negative changes, 1 instance of no change, and 0 instances of positive changes, meeting the criteria for a decreasing trend; therefore, this depth level is classified as "decreasing." At a depth of 3.0 meters, there were 4 instances of negative changes, also classified as "decreasing." By integrating the data from these two depths, stratified water quality trend data for this time window were finally established.

[0061] The fluctuation amplitude calculation submodule calls the stratified water quality trend data, calculates the difference between the change rate of ammonia nitrogen concentration and the change rate of dissolved oxygen concentration between adjacent depth points, and compares the difference between the maximum fluctuation value and the average change rate of the two within the same time window to obtain the interlayer water quality fluctuation amplitude.

[0062] By calling stratified water quality trend data, the difference in the rate of change of various indicators between adjacent depth points is calculated. This difference directly quantifies the degree of heterogeneity of the water body in the vertical direction. Taking 8:30 as an example, the ammonia nitrogen concentration change rate is +0.0010 mg / L / min at a depth of 1.5 meters and +0.0013 mg / L / min at a depth of 3.0 meters. The difference between the two is |+0.0010-(+0.0013)|=0.0003 mg / L / min. The same calculation is performed for the dissolved oxygen concentration change rate. This calculation is performed once at each time point within the set time window (8:00-10:00) to obtain a difference sequence reflecting the dynamic changes in interlayer differences. Next, to comprehensively evaluate the fluctuation amplitude, an interlayer fluctuation index is introduced for calculation. This index combines the difference between the maximum fluctuation value and the average rate of change within the time window. Its calculation method is as follows: The specific explanation of each letter in the formula is as follows:

[0063] : Indicates a specific water quality indicator The interlayer fluctuation index is a comprehensive quantitative indicator used to assess the degree of difference in the trend of this water quality indicator between the middle and bottom layers of a water body within a set time window. In this context, it specifically refers to ammonia nitrogen concentration or dissolved oxygen concentration. The dimensions of this index are consistent with the dimensions of the rate of change, which is mg / L / min.

[0064] : Indicates water quality indicators The maximum value of the difference in the rate of change between layers within the time window. It is the largest absolute value of the interlayer difference calculated from all sampling time points within the time window of 8:00 to 10:00, representing the instantaneous state of the most intense water stratification.

[0065] : Indicates water quality indicators The arithmetic mean of the differences in the rates of change between layers within a time window. It is obtained by summing the absolute values ​​of the interlayer differences calculated at all sampling time points within the time window and dividing by the number of samplings, reflecting the average or normal level of water stratification.

[0066] : Represents the maximum amplitude weighting coefficient, a dimensionless parameter between 0 and 1. Its setting is based on the principle that in early warning of sudden water quality events, the instantaneous maximum risk is more indicative than the long-term average risk. When the differences between water layers increase sharply in a short period of time (i.e., When the concentration is very high (e.g., very high), it is often a strong signal that water quality is about to deteriorate. Therefore, assigning... A larger weighting can increase the final volatility index. More sensitive to such emergencies. (Settings) A value of 0.7 means that in the final comprehensive evaluation, the contribution of the instantaneous maximum difference accounts for 70%, while the average difference accounts for 30%, thus highlighting the early warning role of the maximum amplitude.

[0067] Taking ammonia nitrogen concentration as an example, its interlayer difference sequence is [0.0003, 0.0004, 0.0007, 0.0002], therefore The value was 0.0007 mg / L / min. The value is (0.0003 + 0.0004 + 0.0007 + 0.0002) / 4 = 0.0004 mg / L / min. Substitute the values ​​into the formula for calculation: The calculated interlayer ammonia nitrogen fluctuation index was 0.00061 mg / L / min. The same calculation was performed on the interlayer difference in dissolved oxygen concentration change rate. The interlayer fluctuation index obtained through these calculations serves as the core quantitative result for the amplitude of interlayer water quality fluctuations.

[0068] The water quality feature integration submodule, based on the fluctuation amplitude of water quality between layers and combined with the difference in pH trend direction of each depth point in the stratified water quality trend data, structurally combines the fluctuation amplitude and trend direction difference of each water quality sampling data between each depth point to generate a water quality fluctuation feature set.

[0069] Based on the amplitude of water quality fluctuations between layers and combined with the differences in pH trend direction at each depth point recorded in the stratified water quality trend data, a structured combination is performed. The purpose of this integration process is to correlate the fluctuations of physicochemical indicators (ammonia nitrogen, dissolved oxygen) with the macroscopic manifestations of biochemical processes (pH trend), thereby constructing a feature set that can more deeply reveal the root causes of water quality problems. First, various results of the amplitude of water quality fluctuations between layers are extracted, such as the maximum change rate of ammonia nitrogen concentration of 0.0007 mg / L / min, the average change rate difference of 0.0004 mg / L / min, and the comprehensive interlayer fluctuation index of 0.00061. Second, the trend direction of pH values ​​in the stratified water quality trend data is extracted. In the previous steps, the pH trend at a depth of 1.5 meters was classified as "decreasing," and the pH trend at a depth of 3.0 meters was also classified as "decreasing." Comparing the trends of the two layers, since the pH trend directions of the two layers are the same, the difference in trend direction is judged as "no difference." The structured combination process involves correlating the fluctuation range (maximum amplitude, average rate of change difference, interlayer fluctuation index) of each water quality sampling data point (ammonia nitrogen, dissolved oxygen) across different depths with the difference in pH trend direction (whether there is a difference or not). An example data entry in the final water quality fluctuation feature set is: {Indicator: Ammonia nitrogen concentration, Maximum amplitude: 0.0007 mg / L / min, Average rate of change difference: 0.0004 mg / L / min, pH trend direction difference: No difference}. The same operation is performed on dissolved oxygen concentration to create another data entry, together forming the water quality fluctuation feature set.

[0070] Please see Figure 4 The grade recognition module includes:

[0071] The feature recognition submodule integrates the differences in the trend direction of ammonia nitrogen concentration change rate, dissolved oxygen concentration change rate and pH value from the water quality fluctuation feature set as features to be determined.

[0072] By integrating various data from the water quality fluctuation feature set, the maximum inter-layer variation in ammonia nitrogen concentration rate, the maximum inter-layer variation in dissolved oxygen concentration rate, and the difference in pH trend direction are considered as the features to be determined. This process is a feature selection step, aiming to select the core features most indicative of water quality anomalies from all calculated indicators, thereby simplifying subsequent judgment logic and improving accuracy. For example, the maximum variation in ammonia nitrogen concentration is 0.0007 mg / L / min, the maximum variation in dissolved oxygen concentration is 0.0015 mg / L / min, and the difference in pH trend direction is identified as "no difference" from the water quality fluctuation feature set. This set of features [0.0007, 0.0015, no difference] will be used for subsequent anomaly level determination.

[0073] The anomaly level determination submodule calls the two indicators, ammonia nitrogen concentration change rate and dissolved oxygen concentration change rate, in the feature to be determined. It compares the values ​​of the two indicators with the preset classification threshold range. When the value of the indicator exceeds the classification threshold range, it is considered to trigger an abnormal state and obtain the preliminary anomaly level determination result.

[0074] The module retrieves the maximum fluctuation range of ammonia nitrogen concentration change rate (0.0007 mg / L / min) and dissolved oxygen concentration change rate (0.0015 mg / L / min) from the features to be judged. The submodule compares these two values ​​with preset grading threshold ranges. The grading thresholds are set based on statistical analysis of historical water quality data from over 500 similar aquaculture ponds over two complete aquaculture cycles (approximately 24 months). By identifying the fluctuation characteristics in the 24 hours prior to a water quality deterioration event, critical values ​​for different risk levels were determined. To ensure the reliability and reproducibility of the thresholds, further verification experiments were conducted: 20 pilot-scale aquaculture tanks were used, with 10 serving as a control group maintaining optimal aquaculture conditions, and the other 10 serving as an experimental group simulating the water quality deterioration process by gradually increasing feeding and decreasing aeration. Water quality fluctuation characteristics were continuously monitored and calculated, and thresholds were fine-tuned until the judgment method could issue an early warning at least 12 hours before irreversible deterioration of water quality occurred in the experimental group, and the false alarm rate was less than 5% in the entire cycle of the control group experiment. Based on the above process, the threshold range for the maximum amplitude of ammonia nitrogen concentration change rate was finally set as follows: 0 to 0.002 mg / L / min was "normal", 0.0021 to 0.005 mg / L / min was "Level 1 abnormality", and more than 0.005 mg / L / min was "Level 2 abnormality". The threshold range for the maximum amplitude of dissolved oxygen concentration change rate was set as follows: 0 to 0.004 mg / L / min was "normal", 0.0041 to 0.010 mg / L / min was "Level 1 abnormality", and more than 0.010 mg / L / min was "Level 2 abnormality". In this example, the maximum fluctuation of ammonia nitrogen concentration (0.0007 mg / L / min) is within the "normal" range, as is the maximum fluctuation of dissolved oxygen concentration (0.0015 mg / L / min). When either indicator exceeds its "normal" threshold, it is considered to trigger an abnormal state. Since neither indicator exceeds the threshold, the initial assessment of the abnormality level is "normal." If the maximum fluctuation of ammonia nitrogen concentration were 0.003 mg / L / min, it would trigger a Level 1 abnormality.

[0075] The grade label generation submodule identifies the abnormal type and classifies the corresponding grade of the current aquaculture pond water quality based on the preliminary judgment result of the abnormality level and the difference in the trend direction of pH value in the feature to be judged, and generates water quality abnormality grade labels.

[0076] Based on the initial anomaly level assessment result of "normal," and combined with the pH trend direction difference being "no difference," the final level classification is performed. The logic of this step is to combine the "severity" of the problem (determined by the anomaly level) and the "type" of the problem (revealed by the pH trend difference) to generate a more instructive diagnostic label. In this case, since the initial assessment result is "normal," the submodule directly generates the level label "Water Quality Status Normal." As another example, if the initial anomaly level assessment result is "Level 1 Anomaly" (e.g., triggered by a maximum ammonia nitrogen concentration fluctuation of 0.003 mg / L / minute), and the pH trend direction difference is "different" (e.g., pH rising in the middle layer and falling in the bottom layer, indicating a significant differentiation between photosynthesis in the upper layer and decomposition in the lower layer), then the submodule will identify the anomaly type as "local water quality deterioration caused by intensified water stratification," and, combined with the Level 1 anomaly assessment, generate the level label "Level 1 Anomaly - Stratification Deterioration." If the initial assessment identifies it as a "Level 1 Anomaly" and the pH trend shows "no difference" (pH decreases synchronously across all levels, indicating a consistent acidification trend throughout the water body, possibly stemming from excessive respiration or organic matter decomposition), then the anomaly type is identified as "Increased Metabolic Load Across the Pool," and a "Level 1 Anomaly - Overall Deterioration" grade label is generated. Through this combination, a water quality anomaly grade label that reflects the degree of anomaly and its underlying cause is ultimately generated.

[0077] Please see Figure 5 The ratio conversion module includes:

[0078] The formulation combination screening submodule screens microecological formulation combinations with corresponding regulatory functions based on water quality anomaly level labels. It extracts the conversion coefficient and regulation coefficient corresponding to each microecological formulation in the combination, including ammonia nitrogen conversion coefficient, dissolved oxygen regulation coefficient and pH regulation coefficient, and establishes a set of formulation functional coefficients. The specific process of screening microecological formulation combinations with corresponding regulatory functions is as follows: based on the anomaly type of ammonia nitrogen concentration, dissolved oxygen concentration or pH value indicated by the water quality anomaly level label, one or more combinations are selected from a preset library containing EM bacteria, nitrifying bacteria, Bacillus, photosynthetic bacteria and actinomycetes.

[0079] The system receives a water quality anomaly label of "Level 1 Anomaly - Overall Deterioration." Based on the abnormally rising ammonia nitrogen concentration and abnormally decreasing dissolved oxygen concentration indicated by this label, the submodule selects from a pre-set microbial preparation library. This library is established based on the principles of microbial ecology and extensive aquaculture application practices. It stores detailed descriptions and applicable conditions of the water quality index regulation functions of five preparations: EM bacteria, nitrifying bacteria, Bacillus, photosynthetic bacteria, and actinomycetes. For the increased ammonia nitrogen concentration, nitrifying bacteria, which have a highly efficient ammonia nitrogen degradation function, are selected; for the decreased dissolved oxygen and organic matter accumulation, photosynthetic bacteria, which can perform photosynthesis to produce oxygen and decompose organic matter, are selected. Therefore, the selected preparation combination is "nitrifying bacteria + photosynthetic bacteria." Subsequently, the conversion coefficient and regulation coefficient corresponding to these two preparations are extracted. These coefficients are obtained through calibration experiments conducted under standard laboratory conditions (water temperature 28℃, salinity 15ppt, pH 8.0) on the effect of a unit concentration of the preparation on water quality indicators over 24 hours. For example, to determine the ammonia nitrogen conversion coefficient of nitrifying bacteria, aquaculture water with known ammonia nitrogen concentrations was added to multiple parallel reactors, and nitrifying bacteria preparations at gradient concentrations were inoculated. The decrease curve of ammonia nitrogen concentration was continuously monitored over 24 hours, and the amount of ammonia nitrogen converted per unit of preparation per unit time was calculated using linear regression analysis. Experimental results showed that the ammonia nitrogen conversion coefficient of nitrifying bacteria was 0.85 (i.e., each unit of preparation could convert 0.85 units of ammonia nitrogen), the dissolved oxygen adjustment coefficient was -0.1 (consuming a small amount of oxygen), and the pH adjustment coefficient was -0.2 (acid production during nitrification). The ammonia nitrogen conversion coefficient of photosynthetic bacteria was 0.30 (assisting in ammonia nitrogen fixation), the dissolved oxygen adjustment coefficient was +0.70 (oxygen production through photosynthesis), and the pH adjustment coefficient was +0.15 (consuming carbon dioxide). These coefficients together established a set of functional coefficients for the preparation.

[0080] The coefficient weight configuration submodule combines the level of water quality abnormality level labels and uses the level label as the overall weight adjustment factor for the adjustment coefficient of each microecological preparation in the combination ratio. It sets the corresponding multiplication ratio for each conversion coefficient and adjustment coefficient in the preparation functional coefficient set to obtain the weighted adjustment coefficient value.

[0081] The "Level 1" in the water quality anomaly level label "Level 1 Anomaly - Overall Deterioration" is used as the overall weight adjustment factor. The weight adjustment factor is set based on the urgency of water quality restoration and the required intervention intensity under different anomaly levels. Its value is calculated using the following function, which is obtained by nonlinear regression fitting based on dose-response relationship data from historical intervention cases: The specific explanation of each letter in the formula is as follows:

[0082] : Indicates the level of abnormality The weighting adjustment factor. This is a dimensionless value used to amplify or reduce the basic adjustment coefficient of the ecological agent, so that the application strategy can match the severity of the water quality problem.

[0083] : Represents the level of water quality abnormality, and is a discrete integer. In this scenario, according to the aforementioned criteria, "Level 1 Abnormality" corresponds to "Level 2 anomaly" corresponds to . The higher the value, the more serious the deviation of the water quality from the normal state.

[0084] : Represents the linear adjustment coefficient, a dimensionless constant. It determines the portion of the weighting factor that increases linearly with the severity of the anomaly. Setting this coefficient implies acknowledging that for each increase in the severity of the problem, the required intervention should also increase proportionally.

[0085] : Represents the exponential adjustment coefficient, a dimensionless constant. It determines the portion of the weighting factor that increases exponentially with the anomaly level. The rationale for introducing the exponential term is that water quality deterioration is often non-linear; a higher-level anomaly (e.g., from level one to level two) may signify an impending collapse of the aquaculture environment, with risks and recovery difficulties far exceeding a linear increase. Therefore, an exponentially increasing intervention force is needed to implement strong corrections to avoid irreversible losses. Anomaly Level The larger the exponent, the larger the exponent. The faster the growth, the more... It also increased dramatically.

[0086] : represents the natural constant, a mathematical constant approximately equal to 2.71828, which serves as the basis for exponential functions.

[0087] The constant "1" in the formula represents the base weight. This means that in the absence of any anomalies (theoretically...), When the anomaly is at its lowest level (e.g., the lowest level), the weighting adjustment baseline is 1, ensuring that the formulation's basic regulatory capacity is fully accounted for. The entire formula is based on this, with incremental adjustments made according to the anomaly level.

[0088] By fitting historical data, the determination can be made. The value is 0.2. The value is 0.0582. When the anomaly level is "Level 1 Anomaly", Take a value of 1 and substitute it into the calculation: The weighting adjustment factor corresponding to "Level 1 Anomaly" was calculated to be 1.2. This factor will be used to amplify the effect of the adjustment coefficient. Next, a corresponding weighting ratio was set for each conversion coefficient and adjustment coefficient in the formulation functional coefficient set. Specifically, the coefficients of the screened nitrifying bacteria and photosynthetic bacteria were multiplied by the weighting adjustment factor of 1.2. For nitrifying bacteria, the weighted ammonia nitrogen conversion coefficient was 0.85*1.2=1.02; the weighted dissolved oxygen adjustment coefficient was -0.1*1.2=-0.12; and the weighted pH adjustment coefficient was -0.2*1.2=-0.24. For photosynthetic bacteria, the weighted ammonia nitrogen conversion coefficient was 0.30*1.2=0.36; the weighted dissolved oxygen adjustment coefficient was +0.70*1.2=+0.84; and the weighted pH adjustment coefficient was +0.15*1.2=+0.18. Through this process, a set of weighted adjustment coefficient values ​​were obtained.

[0089] The combination ratio generation submodule performs a weighted conversion on the coefficient of each microecological preparation in the combination based on the weighted adjustment coefficient value, and normalizes the converted result to determine the ratio of each microecological preparation, thereby generating microecological preparation combination ratio data.

[0090] Based on the weighted regulation coefficient values, the proportion of each microecological agent in the combination was determined. The conversion process first focused on the most important regulation targets indicated by the current water quality anomaly level label, namely reducing ammonia nitrogen and increasing dissolved oxygen. The weighted regulation coefficient value of each agent on the main target was used as its contribution to quantify its ability to solve the core problem. The comprehensive contribution of nitrifying bacteria to ammonia nitrogen and dissolved oxygen was defined as the combined manifestation of its ability in both ammonia nitrogen conversion and dissolved oxygen regulation, calculated as the sum of the absolute values ​​of the weighted ammonia nitrogen conversion coefficient and the weighted dissolved oxygen regulation coefficient, i.e., |1.02| + |-0.12| = 1.14. The comprehensive contribution of photosynthetic bacteria was calculated in the same way, as |0.36| + |+0.84| = 1.20. Then, the two contributions were added together to obtain the total contribution of 1.14 + 1.20 = 2.34. Finally, the proportion of each formulation was determined through normalization, that is, the contribution of each formulation was divided by the total contribution, thus obtaining their proportion in the final combination. The proportion of nitrifying bacteria was 1.14 / 2.34 ≈ 48.7%. The proportion of photosynthetic bacteria was 1.20 / 2.34 ≈ 51.3%. Therefore, the resulting microecological formulation combination proportions were: nitrifying bacteria 48.7%, and photosynthetic bacteria 51.3%.

[0091] Please see Figure 6 The delivery control module includes:

[0092] The parameter conversion submodule converts the proportion of each microecological preparation in the microecological preparation combination ratio data into the rotation speed setting value and single opening duration parameter of the target feeding channel on the automatic feeding device, and configures the parameters in the automatic feeding device to establish the device feeding setting parameters.

[0093] The system receives data on the proportions of the microbial preparation combination: nitrifying bacteria: 48.7%, photosynthetic bacteria: 51.3%. First, the total dosage is determined based on the volume of the aquaculture pond (1000 cubic meters) and the anomaly level (Level 1 anomaly). The total dosage standard is preset by aquaculture operation procedures that have been validated through long-term practice. These procedures clearly state that the standard for Level 1 anomaly is 5 grams of dry powder preparation per cubic meter of water to ensure sufficient initial microbial concentration for effective water quality improvement, requiring a total dosage of 5000 grams. Based on this calculation, 5000 grams * 48.7% = 2435 grams of nitrifying bacteria and 5000 grams * 51.3% = 2565 grams of photosynthetic bacteria need to be added. The automatic dispensing device has two independent feeding channels: channel one for nitrifying bacteria and channel two for photosynthetic bacteria. To ensure accurate dispensing, a calibration experiment was conducted on the dispensing device beforehand: at a set rotation speed (e.g., 100 rpm), each channel was run continuously for 1 minute, and the amount of product dispensed was collected and weighed. This process was repeated multiple times, and the average value was taken to accurately determine the dispensing rate. The experiment determined that the dispensing rate of channel one at a set rotation speed of 100 rpm was 50 g / min; and the dispensing rate of channel two at the same rotation speed was 45 g / min. Therefore, the single-cycle operating time parameter for channel one was calculated to be 2435 g / 50 g / min = 48.7 minutes; and the single-cycle operating time parameter for channel two was 2565 g / 45 g / min ≈ 57.0 minutes. The parameters {channel one: rotation speed 100 rpm, duration 48.7 minutes} and {channel two: rotation speed 100 rpm, duration 57.0 minutes} were configured in the automatic dispensing device to establish the device's dispensing setting parameters.

[0094] The time-sharing quantitative dosing submodule controls the dosing device to start each channel in sequence to dosing the combination of microecological preparations in a time-sharing quantitative manner within a fixed dosing cycle, according to the device dosing setting parameters. After the dosing is completed, the ammonia nitrogen concentration, dissolved oxygen concentration and pH value data are collected to obtain the water quality data after dosing.

[0095] The dosing operation was performed according to the device's set parameters. A fixed dosing cycle was set, for example, starting at 10:00 AM. First, channel one of the dosing device was started, running at 100 rpm for 48.7 minutes, evenly distributing 2435 grams of nitrifying bacteria into the aquaculture pond. After channel one finished running, a 5-minute interval was observed, and then channel two was started, running at 100 rpm for 57.0 minutes, distributing 2565 grams of photosynthetic bacteria. This time-segmented dosing strategy was adopted to avoid uneven mixing or changes in physical properties of the two preparations at the dosing inlet, and to facilitate better dispersion of each in the water. The entire dosing process was completed at approximately 11:52 AM. Six hours after dosing, at 5:52 PM, the water quality sensor was activated again to collect data on ammonia nitrogen concentration, dissolved oxygen concentration, and pH value at two depths of 1.5 meters and 3.0 meters in the pond. Six hours was chosen as the observation time point because, according to microbial kinetics studies, this is the typical timeframe for most microbial agents to transition from the adaptation phase to the logarithmic growth phase and begin to have a significant impact on the environment after entering a new environment. The collected data represents the water quality data after administration. For example, the data at a depth of 1.5 meters were: ammonia nitrogen concentration 0.40 mg / L, dissolved oxygen concentration 7.2 mg / L, and pH value 8.0.

[0096] The indicator response judgment submodule compares the change direction of corresponding water quality indicators in the multi-point time-sharing water quality sampling data before and after the release, determines whether the consistency condition of the change direction of water quality indicators is met, and confirms whether the water quality indicators have generated a response, generating indicator response control results. Specifically, the process of determining whether the consistency condition of the change direction of water quality indicators is met is as follows: the change trends of ammonia nitrogen concentration, dissolved oxygen concentration and pH value in the water quality data after the release are compared with the change trends reflected by the multi-point time-sharing water quality sampling data before the release. When the ammonia nitrogen concentration in the water quality data after the release shows a decreasing trend, it is determined that the change direction of ammonia nitrogen concentration meets the consistency condition. When the dissolved oxygen concentration in the water quality data after the release shows an increasing trend, it is determined that the change direction of dissolved oxygen concentration meets the consistency condition. When the change direction of pH value in the water quality data after the release is opposite to the change direction before the release or tends to be stable, it is determined that the change direction of pH value meets the consistency condition.

[0097] Comparing the post-treatment water quality data with the pre-treatment multi-point time-sampling data creates a closed-loop feedback loop to verify the effectiveness of the intervention. Before treatment, water quality data showed an increasing trend in ammonia nitrogen concentration. The post-treatment ammonia nitrogen concentration was 0.40 mg / L, lower than the highest recorded value of 0.56 mg / L before treatment, showing a clear downward trend. Therefore, the direction of change in ammonia nitrogen concentration met the consistency condition. Before treatment, dissolved oxygen concentration showed a decreasing trend. The post-treatment dissolved oxygen concentration was 7.2 mg / L, higher than the lowest recorded value of 6.0 mg / L before treatment, showing a clear upward trend. Therefore, the direction of change in dissolved oxygen concentration also met the consistency condition. Before treatment, pH value generally showed a decreasing trend. The post-treatment pH value was 8.0, the same as the lowest value before treatment, indicating that the downward trend was contained and the pH value tended to stabilize. Therefore, the direction of change in pH value met the consistency condition. When the changes in all key indicators meet the preset consistency conditions, it is confirmed that the water quality indicators have produced the expected positive response to the applied microecological agents, generating indicator response control results, indicating that the water quality control operation is effective. If any indicator fails to produce the expected response, different control results will be generated, triggering a process of re-diagnosis or adjustment of the intervention plan.

[0098] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A water quality index response control system for high-density aquaculture, characterized in that, The system comprises: a data processing module, which continuously records water quality sampling data of multiple depth points in the culture pond at fixed time intervals, calculates the change rate of each item of water quality sampling data at adjacent times, and generates a water quality change trend data set; a feature extraction module, which judges the fluctuation amplitude and trend direction difference of each item of water quality sampling data between each depth point according to the water quality change trend data set, and generates a water quality fluctuation feature set; a grade identification module, which identifies the abnormal type of the current culture pond water quality through the water quality fluctuation feature set, divides the corresponding water quality abnormal grade, and generates a water quality abnormal grade label; a proportion conversion module, which screens a micro-ecological preparation combination according to the water quality abnormal grade label, extracts the conversion coefficient and adjustment coefficient of each micro-ecological preparation in the combination, and sets the multiplication weight proportion of the coefficient according to the high and low grade label, and generates micro-ecological preparation combination proportion data; a release control module, which configures the micro-ecological preparation combination proportion data on an automatic release device, performs time-quantitative release of the micro-ecological preparation combination, compares with the water quality sampling data before release, judges whether the water quality index change direction consistency condition is met, and obtains an index response control result.

2. The water quality index response control system for high-density aquaculture according to claim 1, characterized by The water quality change trend data set specifically refers to the time series change rate of water quality parameters, the sampling point position identifier of depth points, and the sampling time label. The water quality fluctuation feature set includes the fluctuation amplitude difference between depths, the trend direction comparison result, and the change consistency determination label. The water quality abnormal grade label specifically refers to the water quality abnormal grading category and the water quality risk grade number. The micro-ecological preparation combination proportion data includes the micro-ecological preparation type identifier, the adjustment proportion value corresponding to the preparation, and the proportion conversion weight coefficient. The index response control result includes the change direction consistency judgment result, the response confirmation state label, and the control trigger determination identifier.

3. The water quality index response control system for high-density aquaculture according to claim 1, characterized by The data processing module comprises: a multi-point water quality sampling data acquisition submodule, which continuously records water quality sampling data of multiple depth points in the middle and bottom layers of the culture pond at fixed time intervals. The water quality sampling data includes original measurement data of ammonia nitrogen concentration, dissolved oxygen concentration, and pH value. The water quality sampling data of multiple depth points is integrated and arranged according to the recording time to establish multi-point time-division water quality sampling data; a water quality change rate calculation submodule, which calculates the change rate of ammonia nitrogen concentration and dissolved oxygen concentration in the multi-point time-division water quality sampling data at adjacent times, and judges the positive and negative change direction of pH value, and integrates it into water quality sampling data change rate information; a water quality trend data set generation submodule, which combines the recording time and depth point information in the multi-point time-division water quality sampling data with the corresponding water quality sampling data change rate information for structured combination to generate a water quality change trend data set.

4. The water quality index response control system for high-density aquaculture according to claim 3, characterized by The feature extraction module comprises: an interlayer trend extraction submodule, which extracts the change rate of ammonia nitrogen concentration and dissolved oxygen concentration of multiple depth points in the water quality change trend data set at adjacent times within the same time window, and integrates the positive and negative changes of pH value within the same time period and performs corresponding classification to establish hierarchical water quality trend data; a fluctuation range calculation sub-module, which calls the stratified water quality trend data, calculates the difference between the ammonia nitrogen concentration change rate and the dissolved oxygen concentration change rate between adjacent depth points, and compares the maximum fluctuation range and the average fluctuation rate difference of the two in the same time window to obtain the interlayer water quality fluctuation range; a water quality feature integration sub-module, which, based on the interlayer water quality fluctuation range, combines the pH value trend direction difference of each depth point in the stratified water quality trend data, structures the fluctuation range and the trend direction difference of each item of water quality sampling data between each depth point, and generates a water quality fluctuation feature set; 5. The water quality index response control system for high-density aquaculture according to claim 4, characterized by the grade identification module includes: a feature identification sub-module, which integrates the ammonia nitrogen concentration change rate, the dissolved oxygen concentration change rate, and the pH value trend direction difference in the water quality fluctuation feature set as a feature to be judged; an abnormal grade determination sub-module, which calls the ammonia nitrogen concentration change rate and the dissolved oxygen concentration change rate in the feature to be judged, compares the values of the two indexes with preset grading threshold intervals, and determines that an abnormal state is triggered when the values of the indexes exceed the grading threshold intervals, thereby obtaining an abnormal grade preliminary determination result; a grade label generation sub-module, which, according to the abnormal grade preliminary determination result, combines the pH value trend direction difference in the feature to be judged, identifies the abnormal type of the water quality of the current aquaculture pond, divides the corresponding grade, and generates a water quality abnormal grade label.

6. The water quality index response control system for high-density aquaculture according to claim 5, characterized by the proportion conversion module includes: a preparation combination screening sub-module, which screens a micro-ecological preparation combination having a corresponding adjustment function according to the water quality abnormal grade label, extracts the conversion coefficient and the adjustment coefficient of each micro-ecological preparation in the combination, including the ammonia nitrogen conversion coefficient, the dissolved oxygen adjustment coefficient, and the pH adjustment coefficient, and establishes a preparation function coefficient set; a coefficient weight configuration sub-module, which, in combination with the high and low of the water quality abnormal grade label, takes the grade label as an overall weight adjustment factor of the adjustment coefficient of each micro-ecological preparation in the combination, sets a corresponding multiplication weight proportion for each conversion coefficient and adjustment coefficient in the preparation function coefficient set, and obtains a weighted adjustment coefficient value; a combination proportion generation sub-module, which, based on the weighted adjustment coefficient value, multiplies the coefficients of each micro-ecological preparation in the combination, normalizes the result of the multiplication, determines the proportion of each micro-ecological preparation, and generates micro-ecological preparation combination proportion data.

7. The water quality index response control system for high-density aquaculture according to claim 6, characterized by The process of screening a micro-ecological preparation combination having a corresponding adjustment function is specifically: according to the abnormal type of the ammonia nitrogen concentration, the dissolved oxygen concentration, or the pH value indicated by the water quality abnormal grade label, one or more combinations are selected from a preset library containing EM bacteria, nitrifying bacteria, bacillus, photosynthetic bacteria, and actinomycetes.

8. The water quality index response control system for high-density aquaculture according to claim 6, characterized by the delivery control module includes: a delivery parameter conversion sub-module, which converts the proportion value of each micro-ecological preparation in the micro-ecological preparation combination proportion data into a rotational speed setting value and a single opening duration parameter of a target feeding channel of an automatic delivery device, and configures the parameters in the automatic delivery device to establish device delivery setting parameters; The time-sharing quantitative delivery sub-module controls the delivery device to start the time-sharing quantitative delivery of the micro-ecological preparation combination in each channel according to the device delivery setting parameters in a fixed delivery period, and collects ammonia nitrogen concentration, dissolved oxygen concentration and pH value data after the delivery is completed to obtain water quality data after the delivery. The index response judgment sub-module compares the change direction of the corresponding water quality indexes in the multi-point time-sharing water quality sampling data before the delivery and the water quality data after the delivery, judges whether the water quality index change direction consistency condition is met, confirms whether the water quality index produces a response, and generates an index response control result.

9. The water quality index response control system for high-density aquaculture according to claim 8, characterized by The process of judging whether the water quality index change direction consistency condition is met is specifically: comparing the change trend of the ammonia nitrogen concentration, the dissolved oxygen concentration and the pH value in the water quality data after the delivery with the change trend reflected by the multi-point time-sharing water quality sampling data before the delivery; when the ammonia nitrogen concentration in the water quality data after the delivery presents a downward trend, it is determined that the ammonia nitrogen concentration change direction meets the consistency condition; when the dissolved oxygen concentration in the water quality data after the delivery presents an upward trend, it is determined that the dissolved oxygen concentration change direction meets the consistency condition; when the pH value change direction in the water quality data after the delivery is opposite to the change direction before the delivery or tends to be stable, it is determined that the pH value change direction meets the consistency condition.

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