Circulating aquaculture process optimization method and system based on digital twinning

Abstract

The invention discloses a recirculating aquaculture process optimization method and system based on digital twinning, and particularly relates to the technical field of aquaculture optimizing.The method comprises the steps that a high-precision sensor network is deployed in an aquaculture system and used for collecting key aquaculture parameter data in real time and recording aquaculture parameter data of a recirculating aquaculture system under different set parameters; constructing a digital twinborn model of the recirculating aquaculture system based on the preprocessed data, including an oxygen balance equation and a regression equation of the oxygen consumption rate, obtaining oxygen information in the recirculating aquaculture process, and determining the oxygen consumption rate of the recirculating aquaculture system by collecting the water flow direction in the recirculating aquaculture system and judging the fluctuation of the water flow direction. And by collecting suspended particles in the recirculating aquaculture system, determining water body information in the recirculating aquaculture process, constructing an aquaculture evaluation model and comparing and judging threshold values, determining whether an optimized early warning signal is generated or not, the aquaculture system is helped to be timely guided to adjust oxygenation equipment and water flow parameters, the aquaculture efficiency is improved, and loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture optimization, and more specifically, to a method and system for optimizing the circulating water aquaculture process based on digital twin. Background Art

[0002] With the rapid development of the aquaculture industry, especially the widespread application of circulating water aquaculture systems, traditional aquaculture systems face many challenges in operation. Especially during the aquaculture process, fluctuations in parameters such as water quality, oxygen concentration, and water flow rate will directly affect the stability of the aquaculture environment and aquaculture benefits;

[0003] Traditional aquaculture environment monitoring relies on manual sampling and regular analysis, and cannot obtain and feedback key information during the aquaculture process in real time, nor can it achieve dynamic adjustment. This leads to delays in the adjustment of oxygenation equipment, water flow rate, etc., and cannot respond in time to water quality fluctuations and changes in aquaculture needs. Therefore, it is not easy to predict and evaluate the performance of aquaculture systems under different conditions, resulting in waste of resources.

[0004] To solve the above defects, a technical solution is provided now. Summary of the Invention

[0005] To overcome the above defects of the prior art, embodiments of the present invention provide a method and system for optimizing the circulating water aquaculture process based on digital twin to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for optimizing the circulating water aquaculture process based on digital twin specifically includes the following steps:

[0008] S1: Deploy a high-precision sensor network in the aquaculture system to collect key aquaculture parameter data in real time and record the aquaculture parameter data of the circulating aquaculture system under different setting parameters;

[0009] S2: Build a digital twin model of the circulating water aquaculture system based on the preprocessed data, including an oxygen balance equation and a regression equation of oxygen consumption rate, to obtain the oxygen information of the circulating water aquaculture process;

[0010] S3: Determine the water body information of the circulating water aquaculture process by collecting the water flow direction in the circulating aquaculture system, judging the volatility of the water flow direction, and collecting suspended particles in the circulating aquaculture system;

[0011] S4: Conduct a comprehensive analysis of the oxygen information and water body information of the circulating water aquaculture process, build an aquaculture evaluation model, and determine whether to generate an optimization warning signal through threshold comparison and judgment.

[0012] In a preferred embodiment, the oxygen information in the recirculating aquaculture process includes:

[0013] Represent the oxygen information in the recirculating aquaculture process through the dissolved oxygen impact assessment coefficient;

[0014] The acquisition logic of the dissolved oxygen impact assessment coefficient is as follows:

[0015] Based on the oxygen-consuming organisms existing in the aquaculture area during the recirculating aquaculture process, where the oxygen-consuming organisms include fish, algae, and microorganisms, obtain the oxygen consumption rate and species quantity of the oxygen-consuming organisms, and use the oxygen consumption rate and species quantity of the oxygen-consuming organisms as input variables to establish a regression equation for predicting the oxygen consumption rate;

[0016] The regression equation for the oxygen consumption rate is:

[0017] Among them, HY v is the oxygen consumption rate of the oxygen-consuming organisms existing in the aquaculture area during the recirculating aquaculture process, α1, α2, α3, ……, α n are the weights of the oxygen-consuming organisms existing in the aquaculture area, YL1, YL2, YL3, ……, YL n are the quantities of each type of oxygen-consuming organism in the aquaculture area, SL1, SL2, SL3, ……, SL n are the oxygen consumption rates of each type of oxygen-consuming organism in the aquaculture area, and 1, 2, 3 ……, n are the species numbers of the oxygen-consuming organisms;

[0018] Determine the aquaculture parameters in the actual aquaculture process. The aquaculture parameters include but are not limited to water temperature, water flow rate, and oxygen supplementation rate of the oxygenation equipment, and determine the oxygen balance equation for the recirculating aquaculture process based on the aquaculture parameters;

[0019] The calculation formula of the oxygen balance equation is: Among them, ND is the actual dissolved oxygen concentration, ND(T) is the dissolved oxygen saturation concentration at temperature T, k is the oxygen transfer coefficient, and φ is the oxygen supplementation rate of the oxygenation equipment;

[0020] According to the regression equation of the oxygen consumption rate and the oxygen balance equation, determine the dissolved oxygen impact assessment coefficient. The calculation formula of the dissolved oxygen impact assessment coefficient is: Among them, YX yq is the dissolved oxygen impact assessment coefficient.

[0021] In a preferred embodiment, the water body information in the recirculating aquaculture process includes:

[0022] Represent the water body information in the recirculating aquaculture process through the water direction volatility coefficient and the suspended particle anomaly coefficient;

[0023] The acquisition logic of the water direction volatility coefficient is as follows: By deploying water flow sensors in the aquaculture area, time series data of the water flow direction is obtained, and the time series data of the water flow direction is marked as: θ i , i = 1, 2, 3, ……, I, where I is a positive integer and i is the number of different water flow directions;

[0024] Using the preprocessed water flow direction data, the average water flow direction and the volatility index are calculated, and the average water flow direction and the standard deviation of the water flow direction are marked as: θ avg and θ std , where

[0025] The standard deviation of the water flow direction is compared with the average water flow direction to calculate the water direction volatility coefficient. The calculation formula is: where BD sx is the water direction volatility coefficient.

[0026] In a preferred embodiment, the acquisition logic of the water direction volatility coefficient is:

[0027] By deploying water flow sensors in the aquaculture area, time series data of the water flow direction is obtained, and the time series data of the water flow direction is marked as: θ i , i = 1, 2, 3, ……, I, where I is a positive integer and i is the number of different water flow directions;

[0028] Using the preprocessed water flow direction data, the average water flow direction and the volatility index are calculated, and the average water flow direction and the standard deviation of the water flow direction are marked as: θ avg and θ std , where

[0029] The standard deviation of the water flow direction is compared with the average water flow direction to calculate the water direction volatility coefficient. The calculation formula is: where BD sx is the water direction volatility coefficient.

[0030] In a preferred embodiment, a farming evaluation model is constructed, including:

[0031] The dissolved oxygen impact evaluation coefficient, the water direction volatility coefficient, and the suspended particle anomaly coefficient are used to construct a farming evaluation model through a BP neural network to generate a farming evaluation coefficient. The calculation formula of the farming evaluation coefficient is: where PG yz is the farming evaluation coefficient, and β1, β2, and β3 are the proportionality coefficients of the dissolved oxygen impact evaluation coefficient, the water direction volatility coefficient, and the suspended particle anomaly coefficient respectively, and β1, β2, and β3 are all greater than 0.

[0032] In a preferred embodiment, determining whether to generate an optimization warning signal includes:

[0033] Set a breeding evaluation coefficient threshold, obtain the breeding evaluation coefficient of the recirculating aquaculture system under real-time data, compare the breeding evaluation coefficient of the recirculating aquaculture system under real-time data with the breeding evaluation coefficient threshold. If the breeding evaluation coefficient is greater than the breeding evaluation coefficient threshold, generate a breeding warning signal. If the breeding evaluation coefficient is less than the breeding evaluation coefficient threshold, do not generate a breeding warning signal.

[0034] In a preferred embodiment, a recirculating aquaculture process optimization system based on digital twin includes a data acquisition module, a digital twin model construction module, a water body information analysis module, and a breeding evaluation module, with signal connections between the modules;

[0035] The data acquisition module is used to collect various key parameter data in the aquaculture system in real time, including dissolved oxygen concentration, water flow rate, temperature, and suspended particle concentration;

[0036] The digital twin model construction module is used to construct a digital twin model of the recirculating aquaculture system based on the collected data, and simulate and predict the oxygen balance in the aquaculture process;

[0037] The water body information analysis module is used to analyze the water flow direction volatility and suspended particle concentration in the water body environment, and evaluate the cleanliness and health status of the water body;

[0038] The breeding evaluation module is used to comprehensively analyze the oxygen information and water body information, and determine whether the aquaculture process needs to adjust the aeration equipment and water flow parameters through the set threshold, and generate an optimization warning signal.

[0039] The technical effects and advantages of the present invention:

[0040] The present invention collects key aquaculture parameter data in real time through high-precision sensors, constructs a digital twin model, provides a comprehensive evaluation of the aquaculture environment through multiple water quality indicators such as oxygen, water flow direction, and suspended particle concentration. Through threshold comparison, the system can automatically generate an optimization warning signal. The present invention helps to timely guide the aquaculture system to adjust the aeration equipment and water flow parameters, improve aquaculture efficiency and reduce losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings;

[0042] Figure 1 It is a flow chart of the optimization method for the recirculating aquaculture process based on digital twin of the present invention;

[0043] Figure 2This is a schematic structural diagram of the circulating water aquaculture process optimization system based on digital twin of the present invention. Detailed implementation manners

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] Embodiment 1

[0046] Figure 1 This is a schematic flowchart of the circulating water aquaculture process optimization method based on digital twin of the present invention, which specifically includes the following steps:

[0047] S1: Deploy a high-precision sensor network in the aquaculture system to collect key aquaculture parameter data in real time and record the aquaculture parameter data of the circulating aquaculture system under different setting parameters;

[0048] S2: Build a digital twin model of the circulating water aquaculture system based on the preprocessed data, including the oxygen balance equation and the regression equation of the oxygen consumption rate, to obtain the oxygen information of the circulating water aquaculture process;

[0049] S3: Determine the water body information of the circulating water aquaculture process by collecting the water flow direction in the circulating aquaculture system, judging the volatility of the water flow direction, and collecting the suspended particles in the circulating aquaculture system;

[0050] S4: Conduct a comprehensive analysis of the oxygen information and water body information of the circulating water aquaculture process, build an aquaculture evaluation model, and determine whether to generate an optimization warning signal through threshold comparison and judgment.

[0051] Arrange water quality sensors at different depths and different areas in the aquaculture pond to obtain balanced water quality data, prevent local water quality deviation from affecting the overall judgment, install flow sensors at key positions such as the water inlet, drain outlet, and circulating water outlet to optimize the water flow control strategy, and install status monitoring sensors near oxygenation equipment, filtration equipment, etc. to keep track of the equipment operation status in real time.

[0052] Adopt LoRa, Zigbee or Wi-Fi transmission methods for the aquaculture parameter data, and transmit the data to the central data processing system in real time. Use wired transmission for key equipment (such as water pumps and aerators) to ensure a stable data transmission rate.

[0053] Among them, the oxygenation equipment is the key equipment to maintain the dissolved oxygen level. Different oxygenation strategies will affect the water body oxygen balance. By adjusting the impeller speed, the efficiency of oxygen mixing into the water body can be controlled, and the injection rate of oxygen or air can be controlled. Too low a speed may lead to local hypoxia, and too high a speed may lead to an increase in water body disturbance. Insufficient oxygen supply will cause hypoxia in cultured organisms, and excessive oxygen supply may waste energy.

[0054] Water flow control affects suspended particles, dissolved oxygen distribution, metabolite discharge rate, etc. Reasonable setting can optimize the aquaculture environment. By adjusting the water pump flow rate, the total flow rate of circulating water can be controlled. Too high a flow rate may exacerbate water body disturbance, and too low a flow rate may lead to uneven water quality.

[0055] Through a high-precision sensor network, the oxygen information and water body information in the circulating water aquaculture process are determined. The oxygen information is represented by the dissolved oxygen impact assessment coefficient, and the water body information is represented by the water direction volatility coefficient and the suspended particle anomaly coefficient.

[0056] The role of the dissolved oxygen impact assessment coefficient is as follows:

[0057] The dissolved oxygen impact assessment coefficient can directly reflect the balance state between dissolved oxygen supply and biological oxygen consumption demand in the system by performing a ratio operation on the oxygen supply capacity (including natural transfer and mechanical oxygenation) and the total oxygen consumption rate of oxygen-consuming organisms.

[0058] In the digital twin platform, the system can collect water temperature, water flow rate, actual dissolved oxygen concentration, and oxygen-consuming organism data in real time. Using the dissolved oxygen impact assessment coefficient, the platform can timely detect abnormal states in the system operation and automatically or semi-automatically adjust the oxygenation equipment, water flow parameters, etc. according to the index changes, so as to achieve precise feedback control.

[0059] As a quantitative index, the dissolved oxygen impact assessment coefficient can be used as the objective function or constraint condition of the optimization algorithm in the digital twin system. Based on this, the system can not only predict the current water quality status, but also simulate and warn the future operation status, guide the adoption of effective measures in the actual aquaculture process, and thus improve the aquaculture efficiency and system stability.

[0060] The acquisition logic of the said dissolved oxygen impact assessment coefficient is as follows:

[0061] According to the oxygen-consuming organisms existing in the aquaculture area during the circulating water aquaculture process, the oxygen-consuming organisms include fish, algae, and microorganisms. Obtain the oxygen consumption rate and species quantity of the oxygen-consuming organisms. Take the oxygen consumption rate and species quantity of the oxygen-consuming organisms as input variables and establish a regression equation for predicting the oxygen consumption rate.

[0062] The regression equation of the oxygen consumption rate is:

[0063] Among them, HY vis the oxygen consumption rate of oxygen-consuming organisms in the aquaculture area during the recirculating aquaculture process, α1, α2, α3, ……, α n is the weight of oxygen-consuming organisms in the aquaculture area, YL1, YL2, YL3, ……, YL n is the quantity of each type of oxygen-consuming organism in the aquaculture area, SL1, SL2, SL3, ……, SL n is the oxygen consumption rate of each type of oxygen-consuming organism in the aquaculture area, 1, 2, 3……, n are the species numbers of oxygen-consuming organisms;

[0064] It should be noted that in some embodiments, the oxygen-consuming organisms can be limited to one or more of fish, or algae, or microorganisms. The data of various oxygen-consuming organisms collected are normalized to determine the standardized unit oxygen consumption contribution of each oxygen-consuming organism. The normalization process may include converting the oxygen consumption rate and the quantity of species into dimensionless parameters, and determining the contribution ratio of each type of organism to the overall oxygen consumption rate according to the preset weight.

[0065] Determine the aquaculture parameters in the actual aquaculture process. The aquaculture parameters include but are not limited to water temperature, water flow rate, and oxygen supplementation rate of the oxygenation equipment. Based on the aquaculture parameters, determine the oxygen balance equation for the recirculating aquaculture process;

[0066] The calculation formula of the oxygen balance equation is: where ND is the actual dissolved oxygen concentration, ND(T) is the dissolved oxygen saturation concentration at temperature T, k is the oxygen transfer coefficient, and φ is the oxygen supplementation rate of the oxygenation equipment;

[0067] It should be noted that the oxygen transfer coefficient measures the ability to transfer oxygen through the gas-liquid interface in water per unit volume per unit time. It is affected by factors such as water flow, stirring, and system structure, that is, affected by the set recirculating water rate.

[0068] According to the regression equation of the oxygen consumption rate and the oxygen balance equation, determine the dissolved oxygen impact assessment coefficient. The calculation formula of the dissolved oxygen impact assessment coefficient is: where YX yq is the dissolved oxygen impact assessment coefficient.

[0069] It can be seen from the formula that the larger the dissolved oxygen impact assessment coefficient, the more the oxygen supply ability in the recirculating water exceeds the total oxygen consumption demand of the oxygen-consuming organisms. At this time, the system has relatively sufficient dissolved oxygen conditions, which is beneficial to the growth and health of the aquaculture organisms. On the contrary, it means that the oxygen supply is insufficient and cannot meet the demand of the oxygen-consuming organisms. The system may face the risk of hypoxia and measures need to be taken to optimize the oxygen supply, including increasing the oxygen supplementation rate and increasing the recirculating water rate to improve the oxygen transfer coefficient, etc.

[0070] The role of the water fluctuation coefficient is:

[0071] The stability of the water flow direction is directly related to the water mixing effect. If the water flow direction fluctuates frequently, it may lead to uneven mixing of the water, forming local static or dead corners, which may have problems such as uneven distribution of dissolved oxygen, abnormal temperature or water quality changes. Homogeneous water helps to ensure that the oxygen provided by the aeration equipment can be evenly distributed throughout the breeding area, thereby providing a stable growth environment for the breeding organisms;

[0072] The fluctuation of water flow direction also reflects the operating status of water pumps and other circulation equipment. When the water flow direction fluctuates abnormally, it may indicate problems such as unstable equipment operation, pipe blockage or reduced pump efficiency. Monitoring these fluctuations can provide a basis for early warning and maintenance of equipment, ensuring that the system always operates in the best condition.

[0073] The logic for obtaining the water direction volatility coefficient is as follows: by deploying water flow sensors in the aquaculture area, the time series data of the water flow direction is obtained, and the time series data of the water flow direction is marked as: θ i , i=1, 2, 3, ..., I, I is a positive integer, i is the number of different water flow directions;

[0074] Using the preprocessed water flow direction data, the average water flow direction and volatility index are calculated, and the average water flow direction and the standard deviation of the water flow direction are marked as: θ avg and θ std ,in,

[0075] It should be noted that, since the water flow direction data is usually periodic (angle data), when processing the angle data, the circular mean and circular standard deviation can be used to eliminate the periodic effect.

[0076] The standard deviation of the water flow direction is compared with the average water flow direction to calculate the water direction volatility coefficient. The calculation formula is: Among them, BD sx is the water volatility coefficient.

[0077] It can be seen from the formula that the larger the water direction volatility coefficient is, the more drastic the change in water flow direction is, and the instability of water flow increases. For example, fish are more sensitive to sudden changes in water flow. Excessive water direction volatility will cause unstable swimming of fish schools, increase stress response, and may reduce feeding rate and affect growth. In addition, drastic changes in water flow direction may cause aquaculture waste, feed residues and other particles to be suspended or abnormally settled, affecting water quality and even increasing the burden of system filtration. Therefore, if the water direction volatility is too large, it may be necessary to adjust the working mode of the oxygenation equipment and optimize water flow parameters (such as pump power, water outlet angle, etc.) to stabilize the water flow and improve the utilization rate of dissolved oxygen.

[0078] The role of the suspended particle anomaly coefficient is:

[0079] Excessive suspended particles (such as feed residues, excrement, and microbial metabolites) will intensify the decomposition of organic matter and consume dissolved oxygen (DO), thereby affecting the working efficiency of the aeration equipment. Excessive particles may cause local hypoxia, especially in areas with slow water flow, affecting the normal growth of fish and microorganisms.

[0080] Excessive suspended particles may irritate fish gills, affect breathing efficiency, and even cause breathing difficulties or disease infection in fish. Increased particulate matter may reduce water transparency, affect fish feeding efficiency, and reduce feed utilization;

[0081] In the recirculating aquaculture system, the biological filter needs to process a large amount of particulate matter. Excessive suspended particles will cause the filter to become clogged, reducing the purification efficiency of the system. Increased particulate matter will increase the burden on the solid-liquid separation equipment and sedimentation tank, affecting water quality management.

[0082] The acquisition logic of the suspended particle anomaly coefficient is: monitor the real-time concentration of suspended particles in the aquaculture water body through the water quality sensor, record the suspended particle concentration sequence at different time points, and mark the suspended particle concentration sequence as: KL m , m = 1, 2, 3, ..., M, M is a positive integer, and m is the number of the suspended particle concentration at different time points;

[0083] Set the suspended particle concentration threshold, compare the data in the suspended particle concentration sequence with the suspended particle concentration threshold, obtain data greater than the suspended particle concentration threshold, and re-mark the data in the suspended particle concentration sequence greater than the suspended particle concentration threshold as: CX q , q=1, 2, 3, ..., Q, Q is a positive integer, and q is the data number that is greater than the suspended particle concentration threshold;

[0084] Calculate the suspended particle anomaly coefficient using the following formula: Among them, YC kl is the suspended particle anomaly coefficient.

[0085] It can be seen from the formula that the larger the suspended particle anomaly coefficient is, the more suspended particle concentration data points that exceed the set threshold value will increase, that is, in the collected time series, the frequency of abnormal suspended particle concentration will be higher, which may mean that the aquaculture water is polluted by external factors, fish activity is enhanced, or water quality treatment equipment (such as filtration system, sedimentation system) fails. Therefore, it is possible to consider enhancing water filtration or adding sedimentation equipment to improve water purification efficiency.

[0086] The dissolved oxygen impact assessment coefficient, water volatility coefficient and suspended particle anomaly coefficient are used to construct an aquaculture assessment model through a BP neural network to generate an aquaculture assessment coefficient. The calculation formula of the aquaculture assessment coefficient is: Among them, PG yzis the breeding evaluation coefficient, and β1, β2, and β3 are the proportionality coefficients of the dissolved oxygen impact evaluation coefficient, water direction volatility coefficient, and suspended particle anomaly coefficient, respectively. β1, β2, and β3 are all greater than 0.

[0087] It can be seen from the formula that the larger the dissolved oxygen impact evaluation coefficient, the smaller the breeding evaluation coefficient; the smaller the water direction volatility coefficient and the suspended particle anomaly coefficient, the smaller the breeding evaluation coefficient, indicating that the current recirculating aquaculture system performs well, and the current set parameters may be more conducive to fish farming. On the contrary, the smaller the oxygen impact evaluation coefficient, the larger the breeding evaluation coefficient; the larger the water direction volatility coefficient and the suspended particle anomaly coefficient, the larger the breeding evaluation coefficient, indicating that the current recirculating aquaculture system performs poorly and the set parameters may need to be changed.

[0088] Set the threshold of the breeding evaluation coefficient, obtain the breeding evaluation coefficient of the recirculating aquaculture system under real-time data, and compare the breeding evaluation coefficient of the recirculating aquaculture system under real-time data with the threshold of the breeding evaluation coefficient. If the breeding evaluation coefficient is greater than the threshold of the breeding evaluation coefficient, a breeding warning signal is generated, indicating that the parameters set in the current recirculating aquaculture system are unreasonable and the relevant equipment parameters need to be adjusted. The scenarios where the breeding evaluation coefficient is greater than the threshold under different settings can be determined through digital twin simulation. If the breeding evaluation coefficient is less than the threshold of the breeding evaluation coefficient, no breeding warning signal is generated.

[0089] The present invention collects key aquaculture parameter data in real time through high-precision sensors, constructs a digital twin model, provides a comprehensive evaluation of the aquaculture environment through multiple water quality indicators such as oxygen, water flow direction, and suspended particle concentration, and through threshold comparison, the system can automatically generate an optimization warning signal. The present invention helps to timely guide the aquaculture system to adjust the oxygenation equipment and water flow parameters, improve the aquaculture efficiency and reduce losses.

[0090] Embodiment 2

[0091] Figure 1 is the structural schematic diagram of the recirculating aquaculture process optimization system based on digital twin of the present invention, which specifically includes a data acquisition module, a digital twin model construction module, a water body information analysis module, and a breeding evaluation module, and the modules are signal-connected;

[0092] The data acquisition module is used to collect various key parameter data in the aquaculture system in real time, including dissolved oxygen concentration, water flow rate, temperature, and suspended particle concentration;

[0093] The digital twin model construction module is used to construct a digital twin model of the recirculating aquaculture system based on the collected data, and simulate and predict the oxygen balance in the aquaculture process;

[0094] A water body information analysis module for analyzing the flow direction volatility and suspended particle concentration in the water body environment to evaluate the cleanliness and health status of the water body;

[0095] A breeding evaluation module for comprehensively analyzing oxygen information and water body information, and judging whether the oxygenation equipment and water flow parameters need to be adjusted during the breeding process through set thresholds, and generating an optimization warning signal.

[0096] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0097] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more sets of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0098] It should be understood that in various embodiments of the present application, the order numbers of the above processes do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0099] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0100] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0101] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0102] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs, and other media that can store program codes.

[0103] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application and should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for optimizing the circulating water aquaculture process based on digital twin, characterized in that, Specifically, it includes the following steps: S1: Deploy a high-precision sensor network in the aquaculture system to collect key aquaculture parameter data in real time and record the aquaculture parameter data of the recirculating aquaculture system under different setting parameters; S2: Build a digital twin model of the recirculating aquaculture system based on the preprocessed data, including the oxygen balance equation and the regression equation of the oxygen consumption rate, to obtain the oxygen information during the recirculating aquaculture process; S3: Determine the water body information during the recirculating aquaculture process by collecting the water flow direction in the recirculating aquaculture system, judging the volatility of the water flow direction, and collecting the suspended particles in the recirculating aquaculture system; S4: Conduct a comprehensive analysis of the oxygen information and water body information during the recirculating aquaculture process, build an aquaculture evaluation model, and determine whether to generate an optimization warning signal through threshold comparison judgment.

2. The method for optimizing the circulating water aquaculture process based on digital twin according to claim 1, characterized in that, The oxygen information during the recirculating aquaculture process includes: Express the oxygen information during the recirculating aquaculture process through the dissolved oxygen impact evaluation coefficient; The acquisition logic of the dissolved oxygen impact evaluation coefficient is: Based on the oxygen-consuming organisms existing in the aquaculture area during the recirculating aquaculture process, the oxygen-consuming organisms include fish, algae, and microorganisms, obtain the oxygen consumption rate and the number of species of the oxygen-consuming organisms, use the oxygen consumption rate and the number of species of the oxygen-consuming organisms as input variables, and establish a regression equation for predicting the oxygen consumption rate; The regression equation of the oxygen consumption rate is: Among them, HY v is the oxygen consumption rate of oxygen-consuming organisms existing in the aquaculture area during the recirculating aquaculture process, and α1, α2, α3, ……, α n are the weights of oxygen-consuming organisms existing in the aquaculture area, and YL1, YL2, YL3, ……, YL n are the quantities of each type of oxygen-consuming organism in the aquaculture area, and SL1, SL2, SL3, ……, SL n are the oxygen consumption rates of each type of oxygen-consuming organism in the aquaculture area, and 1, 2, 3, ……, n are the species numbers of oxygen-consuming organisms; Determine the aquaculture parameters in the actual aquaculture process, the aquaculture parameters include but are not limited to water temperature, water flow rate, and oxygen supplementation rate of the oxygenation equipment, and determine the oxygen balance equation during the recirculating aquaculture process based on the aquaculture parameters; The calculation formula for the oxygen balance equation is as follows: where ND is the actual dissolved oxygen concentration, ND(T) is the saturated dissolved oxygen concentration at temperature T, k is the oxygen transfer coefficient, and φ is the oxygen replenishment rate of the oxygenation equipment; According to the regression equation of oxygen consumption rate and the oxygen balance equation, determine the dissolved oxygen impact evaluation coefficient. The calculation formula for the dissolved oxygen impact evaluation coefficient is as follows: where, YX yq is the dissolved oxygen impact evaluation coefficient.

3. The method for optimizing the circulating water aquaculture process based on digital twin according to claim 2, wherein, The water body information during the recirculating aquaculture process includes: Express the water body information during the recirculating aquaculture process through the water flow direction volatility coefficient and the suspended particle anomaly coefficient; The acquisition logic of the water direction volatility coefficient is as follows: By deploying water flow sensors in the aquaculture area, time series data of the water flow direction is obtained, and the time series data of the water flow direction is marked as: θ i , where i = 1, 2, 3, ……, I, I is a positive integer, and i is the number of different water flow directions; Using the preprocessed water flow direction data, calculate the average water flow direction and the volatility index, and mark the average water flow direction and the standard deviation of the water flow direction as: θ avg and θ std , where Compare the standard deviation of the water flow direction with the average water flow direction to calculate the water direction volatility coefficient. The calculation formula is as follows: where BD sx is the water direction volatility coefficient.

4. The method for optimizing the circulating water aquaculture process based on digital twin according to claim 3, wherein, The acquisition logic of the water flow direction volatility coefficient is: By deploying water flow sensors in the aquaculture area, time series data of the water flow direction is obtained, and the time series data of the water flow direction is marked as: θ i , i = 1, 2, 3, ……, I, where I is a positive integer and i is the number of different water flow directions; Using the preprocessed water flow direction data, calculate the average water flow direction and volatility index, and mark the average water flow direction and the standard deviation of the water flow direction as: θ avg and θ std , where The standard deviation of the water flow direction is compared with the average water flow direction to calculate the water direction volatility coefficient, and the calculation formula is as follows: where BD sx is the water direction volatility coefficient.

5. The method for optimizing the circulating water aquaculture process based on digital twin according to claim 4, wherein, Build an aquaculture evaluation model, including: Construct a breeding evaluation model through a BP neural network with the dissolved oxygen impact evaluation coefficient, water direction volatility coefficient, and suspended particle anomaly coefficient to generate a breeding evaluation coefficient. The calculation formula for the breeding evaluation coefficient is as follows: Among them, PG yz is the breeding evaluation coefficient, and β1, β2, and β3 are the proportionality coefficients of the dissolved oxygen impact evaluation coefficient, water direction volatility coefficient, and suspended particle anomaly coefficient respectively. β1, β2, and β3 are all greater than 0.

6. The method for optimizing the circulating water aquaculture process based on digital twin according to claim 5, wherein, Determine whether to generate an optimization warning signal, including: Set the threshold of the aquaculture evaluation coefficient, obtain the aquaculture evaluation coefficient of the recirculating aquaculture system under real-time data, compare the aquaculture evaluation coefficient of the recirculating aquaculture system under real-time data with the threshold of the aquaculture evaluation coefficient. If the aquaculture evaluation coefficient is greater than the threshold of the aquaculture evaluation coefficient, generate an aquaculture warning signal. If the aquaculture evaluation coefficient is less than the threshold of the aquaculture evaluation coefficient, no aquaculture warning signal is generated.

7. A circulating water aquaculture process optimization system based on digital twin, which is used to implement the digital-twin-based circulating water aquaculture process optimization method according to any one of claims 1-6, characterized in that, It includes a data acquisition module, a digital twin model construction module, a water body information analysis module, and an aquaculture evaluation module, and the modules are signal-connected; The data acquisition module is used to collect various key parameter data in the aquaculture system in real time, including dissolved oxygen concentration, water flow rate, temperature, and suspended particle concentration; The digital twin model construction module is used to build a digital twin model of the recirculating aquaculture system based on the collected data, and simulate and predict the oxygen balance during the aquaculture process; The water body information analysis module is used to analyze the volatility of the water flow direction and the concentration of suspended particles in the water body environment, and evaluate the cleanliness and health status of the water body; The aquaculture evaluation module is used to comprehensively analyze the oxygen information and water body information, and judge whether the aquaculture process needs to adjust the oxygenation equipment and water flow parameters through the set threshold, and generate an optimization warning signal.

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