A method and system for intelligent control of aerators in aquaculture fish ponds

Abstract

The present application relates to the technical field of intelligent aquaculture, in particular to an intelligent control method and system for an oxygenator of an aquaculture pond, the method comprising: collecting real-time aquaculture pond environment data and oxygenator operation data, preprocessing the collected data, obtaining general characteristic time series and target characteristic time series; implementing first data analysis to determine oxygen concentration basic consumption coefficients and general variation parameters of general characteristic indexes; implementing second data analysis to determine characteristic variation parameters of target characteristic indexes and calculate decay coefficients of each general characteristic index; constructing an oxygen concentration prediction model based on the decay coefficients and general variation parameters, generating control optimization instructions for the oxygenator according to the prediction results output by the oxygen concentration prediction model, determining control logic of the oxygenator and controlling the operation of the oxygenator. The present application realizes dynamic control of dissolved oxygen concentration and optimization control of oxygenator related parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent aquaculture, and particularly relates to an intelligent control method and system for an aquaculture fish pond oxygenator. BACKGROUND

[0002] In the process of aquaculture, dissolved oxygen is a key factor affecting the growth and health of cultured products. Traditional oxygenator control methods are usually based on fixed schedules or simple threshold controls, which cannot be intelligently adjusted according to the dynamic changes of the fish pond environment, easily leading to excessively high or low dissolved oxygen concentrations, affecting the growth of cultured products, such as Macrobrachium rosenbergii, which has high requirements for water environmental conditions, including suitable water temperature, dissolved oxygen, pH value, and good water quality management. Especially under high-density culture conditions, water quality management and dissolved oxygen control become key factors affecting the healthy growth of Macrobrachium rosenbergii and the efficiency of aquaculture. In the case of poor management of fish pond water quality indicators, the efficiency of Macrobrachium rosenbergii culture will be greatly reduced, resulting in high economic losses. In addition, the operation energy consumption of the oxygenator is high, and how to reduce energy consumption while ensuring stable dissolved oxygen concentration is a problem to be solved in the current aquaculture field.

[0003] In the prior art, there is a lack of an intelligent control system that combines environmental data prediction and optimization algorithms to achieve dynamic control of dissolved oxygen concentration and optimized control of oxygenator-related parameters. SUMMARY

[0004] The present application aims to solve the problems in the background art and proposes an intelligent control method and system for an aquaculture fish pond oxygenator.

[0005] The technical solution of the present application is an intelligent control method for an aquaculture fish pond oxygenator, comprising the following steps:

[0006] Real-time collection of fish pond environmental data and oxygenator operation data, preprocessing of the collected data, determination of a target characterization index and a plurality of general characterization indexes, acquisition of a target characterization time series corresponding to the target characterization index and a general characterization time series corresponding to each of the plurality of general characterization indexes based on the operation timestamp;

[0007] First data analysis is performed in combination with the plurality of general characterization time series and the oxygenator operation data to determine an oxygen concentration basic consumption coefficient and general variation parameters of the general characterization indexes;

[0008] Second data analysis is performed in combination with the target characterization time series and the oxygenator operation data to determine characteristic variation parameters of the target characterization index, and the decay coefficients of each general characterization index are calculated based on the general variation parameters and the characteristic variation parameters;

[0009] The oxygen concentration prediction model is constructed based on the attenuation coefficient and the general change parameter, the control optimization instruction for the aerator is generated according to the prediction result output by the oxygen concentration prediction model, and the aerator is controlled and optimized according to the control optimization instruction.

[0010] Preferably, the fish pond environment data includes seven characteristic indexes of water temperature, dissolved oxygen concentration, atmospheric pressure, potential, water transparency, pH value and sunlight intensity; and the aerator operation data includes operation time and operation power.

[0011] The dissolved oxygen concentration is marked as a target characteristic index, and other characteristic indexes are marked as general characteristic indexes; the target characteristic time sequence and the general characteristic time sequences corresponding to the plurality of general characteristic indexes are obtained based on the operation time stamp.

[0012] Preferably, the oxygen concentration basic consumption coefficient is determined by the method comprising:

[0013] The monitoring period is defined, the general characteristic index change range is obtained according to the general characteristic time sequence, the general characteristic index change parameter is calculated based on the general characteristic index change range, the index change set is constructed according to the plurality of general characteristic index change parameters, and the index change set of different time dimensions is obtained.

[0014] The general change parameter Cp is calculated by the following formula k,i :

[0015]

[0016] In the formula, D k.i is the change value of the general characteristic index in the monitoring period; Rd k,i is the general characteristic index change range span value; k is the general characteristic index change parameter number, k is a positive integer; i is the index change set number.

[0017] Preferably, the similar index change set is determined by using the constraint model, and the expression of the constraint model is as follows:

[0018]

[0019] In the formula, k [1, n], n is the total number of general characteristic index change parameters; S is the similarity threshold value;

[0020] The index change set i and the index change set j that meet the constraint model are both defined as similar index change sets, the time dimension information and the corresponding aerator operation data of the two similar index change sets are obtained respectively, the span length and the oxygenation amount of the similar index change set are determined, and the oxygen concentration basic consumption coefficient is determined by the following expression:

[0021]

[0022] In the formula, z is the basic oxygen consumption coefficient; L1 is the first oxygenation amount; L2 is the second oxygenation amount; t1 is the first span duration; t2 is the second span duration; and c is the oxygen concentration conversion coefficient.

[0023] Preferably, the standard range of oxygen concentration is obtained, and the characteristic change parameter Cp of the target characterization index within the monitoring period is calculated using the following formula. tar :

[0024]

[0025] In the formula, D tar The change values ​​of the target characterization index at the beginning and end of the monitoring period; L T The oxygenation rate during the monitoring period is represented by T, which represents the duration of the monitoring period; Rd represents the oxygenation rate during the monitoring period. tar t represents the range of change of the target indicator. real To monitor the actual operating time of the aerator during the monitoring period.

[0026] Preferably, within the monitoring period, the real-time general change parameters of each general characterization indicator and the characteristic change parameters of the target characterization indicator are obtained, and the ratio of each real-time general change parameter to the characteristic change parameter of the target characterization indicator is calculated to obtain the attenuation coefficient of the general characterization indicator to the target characterization indicator.

[0027] The real-time decay coefficients of all general characterization indicators are normalized by min-max to determine the decay weight of each general characterization indicator.

[0028] An oxygen concentration prediction model is constructed based on the attenuation coefficient and general variation parameters. The expression of the oxygen concentration prediction model is as follows:

[0029]

[0030] In the formula, O y The predicted oxygen concentration for the y-th monitoring period; O realtime This represents the real-time oxygen concentration; ω h The decay weight for general characterization indicators; Cpf h,y This refers to the general characteristic parameter representing the change of a general characteristic indicator in the y-th monitoring period.

[0031] Preferably, the method for generating control optimization instructions for the aerator based on the prediction results output by the oxygen concentration prediction model includes:

[0032] Determine the predicted oxygen concentration O y If the oxygen concentration falls within the standard range, a waiting instruction is generated; if the predicted oxygen concentration is O... y If the value is less than the minimum value within the standard range for oxygen concentration, a control command is generated.

[0033] Preferably, the method for optimizing the control of the aerator according to the control optimization command includes:

[0034] The oxygenator is kept in standby mode according to the waiting command;

[0035] Control the aerator operation according to control commands, and calculate the required oxygenation volume L from now until the y-th monitoring cycle in the future. pre,y The calculation formula is as follows:

[0036]

[0037] In the formula, O std This is the median value of the standard range for oxygen concentration; L pre,y To calculate the oxygenation rate for the y-th monitoring period, based on the calculated oxygenation rate L... pre,y Controlling the aerator includes calculating the maximum aeration duration from the present to the y-th future monitoring period using the following formula:

[0038] t max,y = (y-1)×T.

[0039] Preferably, the standard duration t for a single oxygenation session of the aerator is obtained. single and the duration of a single maintenance session t wait And calculate the predicted number of oxygenation cycles N required from now until the y-th future monitoring cycle. pre,y It is calculated using the following formula;

[0040]

[0041] For the predicted number of oxygenation times N pre,y Round down and update;

[0042] The oxygenation capacity (L) of the aerator is calculated using the following formula. pre,y Required average oxygenation power P avg,y :

[0043]

[0044] In the formula, λ is the conversion coefficient between work done and oxygen increase, which is calibrated based on big data testing;

[0045] Based on the predicted number of oxygenation times N pre,y and average oxygenation power P avg,y The calculated oxygenation volume L required by the aerator from now until the y-th future monitoring cycle. pre,y To take control.

[0046] This invention also discloses an intelligent control system for aerators in aquaculture ponds, which applies the aforementioned intelligent control method for aerators in aquaculture ponds, specifically including:

[0047] The data acquisition and preprocessing module is used to collect fishpond environmental data and aerator operation data in real time, preprocess the collected data, determine the target characterization index and multiple general characterization indexes, and obtain the target characterization time series corresponding to the target characterization index and the general characterization time series corresponding to the multiple general characterization indexes based on the operation timestamp.

[0048] The first data analysis module is used to perform first data analysis by combining multiple general characterization time series and aerator operation data to determine the basic consumption coefficient of oxygen concentration and the general change parameters of general characterization indicators.

[0049] The second data analysis module is used to perform second data analysis by combining the target characterization time series and aerator operation data, determine the characteristic change parameters of the target characterization index, and calculate the decay coefficient of each general characterization index based on the general change parameters and characteristic change parameters.

[0050] The prediction and optimization control module is used to construct an oxygen concentration prediction model based on the attenuation coefficient and general changing parameters, generate control optimization instructions for the aerator based on the prediction results output by the oxygen concentration prediction model, and optimize the control of the aerator according to the control optimization instructions.

[0051] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects:

[0052] (1) By dividing target characterization indicators and general characterization indicators, multidimensional time series data is constructed. Combined with phased data analysis, dynamic coupling modeling of the influence of environmental factors on oxygen concentration is realized, overcoming the one-sidedness of traditional methods that rely solely on the isolated determination of dissolved oxygen.

[0053] (2) An attenuation coefficient calculation mechanism is introduced. By analyzing the ratio of real-time general changing parameters to target characteristic parameters and determining the weights by min-max normalization, a dynamic attenuation oxygen concentration prediction model is constructed, which solves the problem of insufficient adaptability of existing models due to fixed weights or empirical formulas. Based on the prediction results, the oxygenation amount, duration, power and number of operations in the future monitoring cycle are accurately calculated. Parameters such as "maximum oxygenation duration" and "average oxygenation power" are introduced to optimize the start-stop strategy, realizing refined and adaptive control of fishpond oxygenation. This solves the problem that existing technologies are difficult to combine with environmental data to predict oxygen concentration and optimize the control of aerators.

[0054] (3) By monitoring key parameters such as water temperature, dissolved oxygen concentration, atmospheric pressure, potential, water transparency, and pH value in real time, the system dynamically adjusts the operation of the aerator by combining prediction algorithms and particle swarm optimization, improves water circulation, reduces the accumulation of harmful substances, optimizes the breeding environment of giant freshwater prawns, and improves breeding efficiency. By intelligently adjusting the start-up time, running time and power of the aerator, the system significantly reduces energy consumption and breeding costs. Attached Figure Description

[0055] Figure 1 This is a block diagram of the method according to Embodiment 1 of the present invention;

[0056] Figure 2 This is a flowchart of the modules in Embodiment 2 of the present invention. Detailed Implementation

[0057] Example 1, as Figure 1 As shown, the present invention proposes an intelligent control method for aerators in aquaculture ponds, comprising the following steps:

[0058] Real-time data collection of fishpond environmental data and aerator operation data; preprocessing of the collected data to determine target characterization indicators and multiple general characterization indicators; obtaining the target characterization time series corresponding to the target characterization indicators and the general characterization time series corresponding to the multiple general characterization indicators based on the operation timestamps; data preprocessing also includes data cleaning and formatting of fishpond environmental data and aerator operation data, specifically handling missing values ​​in the collected data and filling in missing values ​​using linear interpolation methods; formatting the data to adapt to the input requirements of the time series prediction model;

[0059] Fishpond environmental data includes seven characterizing indicators: water temperature, dissolved oxygen concentration, atmospheric pressure, electrical potential, water transparency, pH value, and solar irradiance; aerator operating data includes operating time and operating power.

[0060] Data analysis was conducted by combining multiple general characterization time series and aerator operation data to determine the basic oxygen consumption coefficient. The methods included:

[0061] Define the monitoring period, obtain the range of change of general characterization indicators based on the general characterization time series, calculate the change parameters of general characterization indicators based on the range of change of general characterization indicators, construct the indicator change set based on several general characterization indicator change parameters, and obtain indicator change sets of several different time dimensions.

[0062] The general variation parameter Cp is calculated using the following formula. k,i :

[0063]

[0064] In the formula, Dk.i Rd represents the change in a general characterization indicator during the monitoring period. k,i is the range of change of a general characterizing index; k is the parameter number of the general characterizing index change, k is a positive integer; i is the index change set number;

[0065] The constraint model is used to determine the set of changes in similar indicators. The expression of the constraint model is as follows:

[0066]

[0067] In the formula, k∈[1,n], n is the total number of parameters representing changes in general indicators; S is the similarity threshold;

[0068] Both the index change set i and index change set j that satisfy the constraint model are defined as similar index change sets. The time dimension information and corresponding aerator operation data of the two similar index change sets are obtained respectively. The span duration and oxygenation amount of the similar index change sets are determined. The basic oxygen consumption coefficient is determined by the following expression:

[0069]

[0070] In the formula, z is the basic oxygen consumption coefficient; L1 is the first oxygenation rate; L2 is the second oxygenation rate; t1 is the first span duration; t2 is the second span duration; and c is the oxygen concentration conversion coefficient.

[0071] Obtain the standard range of oxygen concentration, and calculate the characteristic change parameter Cp of the target characterization index within the monitoring period using the following formula. tar :

[0072]

[0073] In the formula, D tar The change values ​​of the target characterization index at the beginning and end of the monitoring period; L T The oxygenation rate during the monitoring period is represented by T, which represents the duration of the monitoring period; Rd represents the oxygenation rate during the monitoring period. tar t represents the range of change of the target indicator. real To monitor the actual operating time of the aerator during the monitoring period;

[0074] During the monitoring period, the real-time general change parameters of each general characterization indicator and the characteristic change parameters of the target characterization indicator are obtained. The ratio of each real-time general change parameter to the characteristic change parameter of the target characterization indicator is calculated to obtain the attenuation coefficient of the general characterization indicator to the target characterization indicator.

[0075] The real-time attenuation coefficients of all general characterization indicators are normalized using the min-max method to determine the attenuation weight of each general characterization indicator. It should be noted that the attenuation weight is updated after every m monitoring cycles, where m can be set to 2.

[0076] An oxygen concentration prediction model is constructed based on the attenuation coefficient and general variation parameters. The expression of the oxygen concentration prediction model is as follows:

[0077]

[0078] In the formula, O y The predicted oxygen concentration for the y-th monitoring period; O realtime This represents the real-time oxygen concentration; ω h The decay weight for general characterization indicators; Cpf h,y Let y be the general characteristic parameter of the general indicator in the future monitoring period y; it should be noted that the general change parameter can be implemented by time series processing technology based on deep learning. Time series processing is an existing technology, which will not be elaborated on here.

[0079] The method for generating control optimization commands for the aerator based on the prediction results output by the oxygen concentration prediction model includes:

[0080] Determine the predicted oxygen concentration O y If the oxygen concentration falls within the standard range, a waiting instruction is generated; if the predicted oxygen concentration is O... y If the oxygen concentration is less than the minimum value within the standard range, a control command is generated.

[0081] The aerator is optimized according to the control optimization instructions. The methods include:

[0082] The oxygenator is kept in standby mode according to the waiting command;

[0083] Control the aerator operation according to control commands, and calculate the required oxygenation volume L from now until the y-th monitoring cycle in the future. pre,y The calculation formula is as follows:

[0084]

[0085] In the formula, O std This is the median value of the standard range for oxygen concentration; L pre,y To calculate the oxygenation rate for the y-th monitoring period, based on the calculated oxygenation rate L... pre,y Controlling the aerator includes calculating the maximum aeration duration from the present to the y-th future monitoring period using the following formula:

[0086] t max,y = (y-1)×T;

[0087] Obtain the standard duration t of a single oxygenation session from the aerator. single and the duration of a single maintenance session t wait And calculate the predicted number of oxygenation cycles N required from now until the y-th future monitoring cycle. pre,y It is calculated using the following formula;

[0088]

[0089] For the predicted number of oxygenation times N pre,y Round down and update;

[0090] The oxygenation capacity (L) of the aerator is calculated using the following formula. pre,y Required average oxygenation power P avg,y :

[0091]

[0092] In the formula, λ is the conversion coefficient between work done and oxygen increase, which is calibrated based on big data testing;

[0093] Based on the predicted number of oxygenation times N pre,y and average oxygenation power P avg,y The calculated oxygenation volume L required by the aerator from now until the y-th future monitoring cycle. pre,y To take control.

[0094] Example 2, as Figure 2 As shown, the present invention proposes an intelligent control system for aquaculture pond aerators, which is applied to the intelligent control method for aquaculture pond aerators proposed in Example 1, and specifically includes:

[0095] The data acquisition and preprocessing module is used to collect fishpond environmental data and aerator operation data in real time, preprocess the collected data, determine the target characterization index and multiple general characterization indexes, and obtain the target characterization time series corresponding to the target characterization index and the general characterization time series corresponding to the multiple general characterization indexes based on the operation timestamp.

[0096] The first data analysis module is used to perform first data analysis by combining multiple general characterization time series and aerator operation data to determine the basic consumption coefficient of oxygen concentration and the general change parameters of general characterization indicators.

[0097] The second data analysis module is used to perform second data analysis by combining the target characterization time series and aerator operation data, determine the characteristic change parameters of the target characterization index, and calculate the decay coefficient of each general characterization index based on the general change parameters and characteristic change parameters.

[0098] The prediction and optimization control module is used to construct an oxygen concentration prediction model based on the attenuation coefficient and general changing parameters, generate control optimization instructions for the aerator based on the prediction results output by the oxygen concentration prediction model, and optimize the control of the aerator according to the control optimization instructions.

[0099] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. An intelligent control method for an oxygenation machine for an aquaculture pond, characterized by, The method comprises the following steps: Real-time acquisition of fish pond environment data and aerator operation data, preprocessing of the collected data, determination of a target characterization index and a plurality of general characterization indexes, acquisition of a target characterization time sequence corresponding to the target characterization index and a plurality of general characterization time sequences corresponding to the plurality of general characterization indexes based on a running timestamp; The fish pond environment data comprises seven characterization indexes of water temperature, dissolved oxygen concentration, atmospheric pressure, potential, water transparency, pH value and sunlight intensity; and the aerator operation data comprises running time and running power; The dissolved oxygen concentration is marked as the target characterization index, and the other characterization indexes are marked as the general characterization indexes; the target characterization time sequence and the plurality of general characterization time sequences corresponding to the plurality of general characterization indexes are acquired based on the running timestamp; First data analysis is performed in combination with the plurality of general characterization time sequences and the aerator operation data, to determine an oxygen concentration basic consumption coefficient and general change parameters of the general characterization indexes; The method for determining the oxygen concentration basic consumption coefficient comprises: A monitoring period is defined, a general characterization index change range is acquired according to the general characterization time sequence, a general characterization index change parameter is calculated based on the general characterization index change range, an index change set is constructed according to a plurality of general characterization index change parameters, and a plurality of index change sets of different time dimensions are acquired; The general variation parameters are calculated by the following equation : ； wherein is the value of the general characteristic indicator change during the monitoring period; is the general characteristic indicator change range span value; k is the general characteristic indicator change parameter number, k is a positive integer; i is the indicator change set number; A similar index change set is determined by using a constraint model, and the expression of the constraint model is as follows: ； In the formula, k [1, n], n is the total number of general characterization index change parameters; S is a similarity threshold; The index change set i and the index change set j that satisfy the constraint model are both defined as similar index change sets, time dimension information and corresponding aerator operation data of the two similar index change sets are respectively acquired, a span length and an oxygenation amount of the similar index change set are determined, and the oxygen concentration basic consumption coefficient is determined by the following expression: ； In the formula, z is the oxygen concentration basic consumption coefficient; L1 is the first oxygenation amount; L2 is the second oxygenation amount; t1 is the first span length; t2 is the second span length; c is the oxygen concentration conversion coefficient; Second data analysis is performed in combination with the target characterization time sequence and the aerator operation data, to determine a characteristic change parameter of the target characterization index, and an attenuation coefficient of each general characterization index is calculated according to the general change parameter and the characteristic change parameter; an oxygen concentration prediction model is constructed based on the attenuation coefficient and the general change parameter, a control optimization instruction for the aerator is generated according to a prediction result output by the oxygen concentration prediction model, and the aerator is controlled and optimized according to the control optimization instruction.

2. The intelligent control method of the oxygenation machine for an aquaculture pond according to claim 1, characterized in that, Obtaining the oxygen concentration standard range, calculating the characteristic change parameter of the target characterization index in the monitoring period through the following formula : ； In the formula, is the change value of the target characteristic index at the beginning and end of the monitoring period; is the oxygen increase amount in the monitoring period; T is the monitoring period length; is the target characteristic index change range span value.

3. The intelligent control method of the oxygenation machine for an aquaculture pond according to claim 2, characterized in that, In the monitoring period, real-time general change parameters of each general characterization index and a characteristic change parameter of the target characterization index are acquired, each real-time general change parameter is compared with the characteristic change parameter of the target characterization index, and an attenuation coefficient of the general characterization index to the target characterization index is obtained by ratio calculation; Real-time attenuation coefficients of all general characterization indexes are subjected to min-max normalization processing, to determine an attenuation weight of each general characterization index; The oxygen concentration prediction model is constructed based on the attenuation coefficient and the general change parameter, and the expression of the oxygen concentration prediction model is as follows: ； wherein is the predicted oxygen concentration for the future yth monitoring period; is the real-time oxygen concentration; is the decay weight of the general characterization index; is the general characterization change parameter of the general characterization index for the future yth monitoring period.

4. The intelligent control method of the oxygenation machine for aquaculture ponds according to claim 3, characterized in that, The method comprises the following steps: determining whether the predicted oxygen concentration falls within a standard range of oxygen concentrations, and if the predicted oxygen concentration falls within the standard range of oxygen concentrations, generating a wait command, and if the predicted oxygen concentration is less than the minimum value of the standard range of oxygen concentrations, generating a control command.

5. The intelligent control method of the oxygenation machine for an aquaculture pond according to claim 4, characterized in that, The method comprises the following steps: The method comprises the following steps: According to the control instruction, the oxygenator is controlled to operate, and the required calculation oxygenation amount of the oxygenator in the present and future in the yth monitoring period is calculated , and the calculation formula is as follows: ； wherein, is the oxygen concentration standard range median value; is the future calculated oxygen increase amount at the yth monitoring period, based on the calculated oxygen increase amount controlling the oxygen generator, including calculating the maximum duration of oxygen increase from the present to the yth monitoring period by the following formula: 。 6. The intelligent control method of an oxygenation machine for an aquaculture pond according to claim 5, characterized in that, Obtaining the single oxygen-increasing standard duration of the oxygen-increasing machine and the single maintenance duration and calculating the predicted oxygen-increasing times required from the current to the future yth monitoring period , which is obtained through the following formula; ； to predict the number of times of oxygen increase round down and update; The oxygen increasing amount is calculated by the following equation The required average oxygen increasing power : ; In the formula, is the conversion coefficient between work and oxygen increase, based on big data test calibration; Based on the predicted number of oxygenation times And the average oxygenation power The calculated oxygenation amount required by the oxygenation machine in the current to the future yth monitoring period Control.

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