A method for predicting the dosage of potassium peroxodisulfate composite salt in the factory culture of litopenaeus vannamei

Abstract

This invention discloses a method for predicting the dosage of potassium persulfate compound salt in the factory farming of Litopenaeus vannamei, belonging to the field of intelligent disinfection control in aquaculture. The method includes: acquiring the biomechanical parameters of potassium persulfate compound salt's inactivation of pathogenic Vibrio, reactive oxygen species attenuation parameters, Vibrio growth kinetic parameters, and shrimp safety threshold; constructing a microbial growth sub-model, a disinfectant inactivation sub-model, and a disinfectant concentration attenuation sub-model, respectively; and dynamically coupling these three sub-models into a dosage prediction model in system dynamics software. Real-time physicochemical parameters of the aquaculture water are input into the model, and the initial dosage concentration of potassium persulfate compound salt, effective sterilization duration, pathogen regrowth warning, and dosing interval are dynamically output. This invention can dynamically and accurately predict the disinfectant dosage based on the water quality parameters at the aquaculture site, achieving efficient Vibrio control while ensuring shrimp safety, and is suitable for intelligent disinfection management in factory shrimp farming.

Description

Technical Field

[0001] This invention belongs to the field of intelligent disinfection control technology in aquaculture, and particularly relates to a method for predicting the dosage of potassium persulfate compound salt in the factory farming of Litopenaeus vannamei. Background Technology

[0002] Litopenaeus vannamei, due to its rapid growth, strong environmental adaptability, and high economic value, has become the world's largest farmed shrimp species. Factory farming, with its high density, high yield, controllable environment, and resource conservation, has gradually become the mainstream method of shrimp farming, significantly improving production efficiency and reducing dependence on natural water bodies. However, high-density farming environments easily lead to the accumulation of organic matter and microecological imbalance in the water, providing favorable conditions for the proliferation of pathogenic microorganisms. Among them, Vibrio parahaemolyticus, Vibrio alginolyticus, Vibrio campbellii, and Vibrio harveyi are the main pathogens causing red body disease, gill rot, and acute hepatopancreatic necrosis in shrimp. Outbreaks of these diseases often lead to mass shrimp mortality and serious economic losses. To effectively control Vibrio diseases, disinfection has become an indispensable part of factory farming. Potassium monopersulfate compound (PMS), as a non-chlorine strong oxidant, releases highly reactive oxidizing substances when its active component, peroxymonosulfate ions, dissolves in water. It achieves efficient sterilization by directly oxidizing key bacterial cell structures and metabolic functions, and has good environmental compatibility. Therefore, it is widely used for disinfection of aquaculture water bodies and equipment, playing a positive role in ensuring the biosafety of aquaculture organisms.

[0003] Although PMS (Probiotic Monitoring System) is widely used in the intensive farming of Litopenaeus vannamei, its actual dosage largely relies on empirical judgment or static test results. Current technologies primarily determine dosage based on single-tested Vibrio-killing effects or simplified safe concentration ranges, lacking a dynamic quantitative decision-making tool that simultaneously integrates the biomechanics of pathogenic Vibrio killing, the decay pattern of effective reactive oxygen species in the aquaculture water, and the shrimp's safety threshold. This leads to situations where insufficient dosage in actual production results in incomplete sterilization and rapid pathogen rebound, while excessive dosage increases production costs, disrupts the microecological balance of the aquaculture water, and causes acute or subacute toxicity to juvenile Litopenaeus vannamei, affecting survival rates and product quality. Therefore, existing PMS dosage decision-making methods suffer from poor dynamic adaptability and difficulty in synergistically controlling Vibrio and ensuring shrimp biosafety. There is an urgent need for a solution that can accurately predict PMS dosage, effective sterilization duration, and dosing intervals based on real-time environmental parameters at the aquaculture site. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method for predicting the dosage of potassium persulfate compound salt in the industrialized farming of Litopenaeus vannamei, thereby resolving the issues existing in the prior art.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for predicting the dosage of potassium peroxymonosulfate compound salt in the industrialized farming of Litopenaeus vannamei, comprising: Step 1: Obtain the inactivation kinetic parameters of potassium persulfate compound salt on four pathogenic Vibrio species, the attenuation parameters of effective reactive oxygen species in the aquaculture water, the growth kinetic parameters of the four pathogenic Vibrio species under different environmental factors, and the acute toxicity parameters and safety threshold of potassium persulfate compound salt on juvenile Litopenaeus vannamei. Step 2: Based on the parameters obtained in Step 1, construct the microbial growth sub-model, the disinfectant inactivation sub-model, and the disinfectant concentration decay sub-model, respectively; Step 3: Dynamically couple the microbial growth sub-model, disinfectant inactivation sub-model, and disinfectant concentration decay sub-model in the system dynamics software to form a potassium persulfate compound salt dosage prediction model; Step 4: Input the real-time physicochemical parameters of the Litopenaeus vannamei farmed water into the potassium persulfate compound salt dosage prediction model. The model dynamically outputs the initial concentration of potassium persulfate compound salt, the effective sterilization duration, the early warning of pathogen regrowth, and the dosing interval.

[0006] Preferably, in step one, the four pathogenic Vibrio species include Vibrio parahaemolyticus, Vibrio alginolyticus, Vibrio campbellii, and Vibrio harveyi.

[0007] Preferably, in step one, the kinetic parameters of the inactivation activity are obtained by fitting the Chick-Watson model; the kinetic parameters of the growth are obtained by fitting the modified Gompertz model; and the attenuation parameters of the effective reactive oxygen species are obtained by fitting the first-order kinetic model.

[0008] Preferably, in step one, the environmental factors involved in the growth kinetic parameters include at least one of temperature, pH, chemical oxygen demand, total suspended solids, dissolved oxygen, and salinity; the environmental factors involved in the attenuation parameters of effective active oxygen include at least one of temperature, pH, chemical oxygen demand, total suspended solids, and dissolved oxygen.

[0009] Preferably, in step one, the acute toxicity parameters include the median lethal concentrations at 24h, 48h, 72h, and 96h; the safety threshold is a safe concentration determined based on the 96h median lethal concentration as a model constraint boundary.

[0010] Preferably, in step two, the microbial growth sub-model is used to describe the process of Vibrio count increasing over time when no disinfectant is added or the disinfectant concentration is insufficient; the disinfectant inactivation sub-model is used to describe the inactivation rate of Vibrio by the disinfectant concentration; and the disinfectant concentration decay sub-model is used to describe the process of effective active oxygen of the disinfectant decaying over time in the aquaculture water.

[0011] Preferably, in step three, the system dynamics software is Stella Architect.

[0012] Preferably, in step three, the dynamic coupling process includes: The effective concentration of disinfectant calculated by the disinfectant concentration attenuation sub-model is input into the disinfectant inactivation sub-model in real time to calculate the sterilization rate coefficient at the current moment. The sterilization rate coefficient is then input into the microbial growth sub-model as a reduction flow rate for the number of Vibrio bacteria, which, together with the Vibrio bacteria's own growth flow rate, determines the dynamic change in the number of Vibrio bacteria.

[0013] Preferably, in step four, the real-time physicochemical parameters include temperature, pH, salinity, chemical oxygen demand, total suspended solids, and dissolved oxygen.

[0014] In a second aspect, the present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0015] Compared with the prior art, the present invention has the following advantages and technical effects: This invention systematically acquires the biomechanical parameters of PMS inactivation, reactive oxygen species attenuation parameters, Vibrio growth kinetic parameters, and shrimp safety threshold through steps one through three. Based on these parameters, it constructs sub-models for microbial growth, disinfectant inactivation, and disinfectant concentration attenuation. These three sub-models are then dynamically coupled in system dynamics software to form a PMS dosage prediction model. This technology enables the model to dynamically simulate the real-time attenuation process of Vibrio count changes and effective PMS concentration in aquaculture water based on the interaction of multiple factors, thereby outputting a precise initial dosage concentration. This fundamentally changes the traditional method of determining dosage based on experience or a single static experiment.

[0016] This invention, through step four, inputs real-time physicochemical parameters of the Litopenaeus vannamei culture water into the dosage prediction model. This allows the model to automatically adjust its output based on dynamic changes in on-site water quality parameters such as temperature, pH, chemical oxygen demand, total suspended solids, and dissolved oxygen. This feature makes the disinfection scheme no longer fixed but can be adapted in real time to the actual state of the culture water, significantly improving its applicability and responsiveness under different culture conditions, seasons, or water quality fluctuations.

[0017] In step one, this invention clearly obtains the acute toxicity parameters and safety thresholds of PMS on juvenile Litopenaeus vannamei, and in the output of step four, it includes the effective sterilization duration and dosing interval. Based on this technical feature, the model always adheres to the safe concentration limits for shrimp when recommending the initial PMS dosage and subsequent dosing intervals, avoiding acute or subacute toxicity to shrimp due to overdosing. At the same time, by outputting the effective sterilization duration and pathogen regrowth warning, it ensures that sufficient sterilization effect is achieved under safe conditions, thus synergistically achieving the unity of efficient Vibrio control and the safety protection of farmed animals.

[0018] This invention dynamically outputs early warnings of pathogen regrowth and medication intervals through a step-four model. This feature is based on the dynamic balance between Vibrio growth and inactivation rates in a system dynamics coupling model: when the effective concentration of PMS decays to a point where the bactericidal rate is lower than the Vibrio growth rate, the model can promptly issue a regrowth warning and recommend the next medication interval accordingly. This allows aquaculture managers to proactively grasp the critical point of disinfection failure, avoiding blind and frequent medication or delayed replenishment, forming a closed-loop disinfection decision-making process of "prediction—administration—monitoring—early warning—re-administration," significantly improving the scientific and refined level of disinfection management in factory farming. Attached Figure Description

[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram illustrating the dynamic changes in the abundance of four pathogenic Vibrio species in aquaculture water under different concentrations of potassium persulfate according to an embodiment of the present invention. It includes the curves showing the changes in the number of viable bacteria of Vibrio parahaemolyticus, Vibrio alginolyticus, Vibrio campbellii, and Vibrio harveyi over time in the control group and different PMS treatment groups. Figure 2 This invention describes the time-concentration-dependent acute toxicity effects of gradient concentration PMS on juvenile Litopenaeus vannamei in an embodiment of the present invention. Figure 3This is a schematic diagram of the microbial growth-disinfectant inactivation coupling model in an embodiment of the present invention. It is used to illustrate the interaction path between Vibrio growth and PMS inactivation within the framework of system dynamics, as well as the correlation between microbial quantity, growth rate, inactivation rate, and environmental correction factor. Figure 4 This is a schematic diagram of the reactive oxygen species (ROS) decay kinetic model of disinfectant in an embodiment of the present invention. It is used to represent the dynamic decay process of PMS concentration in aquaculture water, and the synergistic regulatory relationship between temperature (T), pH, chemical oxygen demand (COD), dissolved oxygen (DO), and total suspended solids (TSS) on the ROS decay rate. Figure 5 The figure shows the model prediction results of the change of the number of four Vibrio species over time under the fixed PMS dosing condition in an embodiment of the present invention. Figure 6 This is a schematic diagram showing the comparison between the model-predicted and measured values ​​of active oxygen concentration in water after PMS addition, and the average relative error (ARE) in an embodiment of the present invention. Figure 7 This is a schematic diagram showing the comparison between simulated and measured values ​​of four types of Vibrio bacteria under the action of PMS, and the mean relative error (ARE). Figure 8 This is a flowchart illustrating a method for predicting the dosage of potassium persulfate compound salt in the industrialized farming of Litopenaeus vannamei, according to an embodiment of the present invention. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0022] Example 1 like Figure 8 As shown, this embodiment provides a method for predicting the dosage of potassium peroxymonosulfate compound salt in the industrialized farming of Litopenaeus vannamei, including: Step 1: Obtain the inactivation kinetic parameters of potassium persulfate compound salt on four pathogenic Vibrio species, the attenuation parameters of effective reactive oxygen species in the aquaculture water, the growth kinetic parameters of the four pathogenic Vibrio species under different environmental factors, and the acute toxicity parameters and safety threshold of potassium persulfate compound salt on juvenile Litopenaeus vannamei. Furthermore, in step one, the four pathogenic Vibrio species include Vibrio parahaemolyticus, Vibrio alginolyticus, Vibrio campbellii, and Vibrio harveyi.

[0023] Furthermore, in step one, the extinguishing mechanical parameters are obtained by fitting the Chick-Watson model.

[0024] Specifically, the process of obtaining the biomechanical parameters for the inactivation of pathogenic Vibrio by PMS includes: selecting Vibrio parahaemolyticus, Vibrio alginolyticus, Vibrio campbellii, and Vibrio harveyi as target pathogens. After activation, each tested Vibrio is adjusted to the logarithmic growth phase, and an initial Vibrio concentration of 2 × 10⁻⁶ is prepared using aquaculture pond water. 3 -10 4 Simulated aquaculture water samples at CFU / mL were used. The experiment included four treatment groups: control group (CK, no PMS added), low concentration group (LC, PMS 0.50 mg / L), medium concentration group (MC, PMS 1.0 mg / L), and high concentration group (HC, PMS 2.0 mg / L), with three biological replicates for each group.

[0025] PMS powder was dissolved in sterile water and serially diluted to prepare a 200 mg / L PMS stock solution. Subsequently, PMS stock solution was added according to the required experimental concentrations to achieve final disinfectant concentrations of 0.5 mg / L, 1.0 mg / L, and 2.0 mg / L. All treatments were incubated at 28℃ and 150 r / min under isothermal shaking conditions. Samples were taken at 0, 1, 3, 6, 9, and 12 h, with 0.1 mL of water sample taken each time. The sample was evenly spread on the surface of TCBS selective solid medium and incubated upside down in a 28℃ incubator for 24 h. Colony counting was then performed, and the viable cell count (CFU / mL) was recorded at each time point. To quantitatively characterize the killing kinetics of PMS against Vibrio in aquaculture water, the results were fitted using the Chick–Watson model, and relevant kinetic parameters such as the killing rate constant were calculated.

[0026] The results are as follows Figure 1As shown in Table 1 (fitting parameters of the Chick-Watson model), PMS has a rapid, concentration-dependent killing effect on Vibrio in water, but the intensity and persistence vary for different species. At 1 h, Vibrio parahaemolyticus and Vibrio harveyi showed high sensitivity to PMS, with 0.5 mg / L achieving kill rates of 85.7% and 82.4%, respectively. When the concentration increased to 2 mg / L, the kill rates of Vibrio parahaemolyticus and Vibrio harveyi reached 98.9% and 98.0%, respectively, and the bacterial count decreased to 1.22 and 1.60 log10 CFU / mL. The Chick-Watson model was used to fit the killing kinetics; the Chick-Watson models for *Cambella campestris*, *Harveyi*, and *Vibrio alginolyticus* showed good fit (R0.0). 2 =0.995, R 2 =0.951, R 2 =0.875); Vibrio parahaemolyticus had poor fit, so empirical graphical functions were used to describe it in combination with measured data, and these functions were used as input parameters for the subsequent system dynamics model.

[0027] Table 1 Furthermore, in step one, the attenuation parameters of the effective reactive oxygen species are obtained by fitting a first-order kinetic model, and the environmental factors involved in the attenuation parameters of the effective reactive oxygen species include at least one of temperature, pH, chemical oxygen demand, total suspended solids, and dissolved oxygen.

[0028] Specifically, the process of obtaining PMS reactive oxygen species (ROS) attenuation parameters under different environmental factors includes: taking actual aquaculture water from factory-scale aquaculture ponds for ROS attenuation experiments under different environmental conditions. Based on the experiments, the water samples were adjusted for temperature (T), pH, chemical oxygen demand (COD), dissolved oxygen (DO), and total suspended solids (TSS).

[0029] The specific experimental groups are as follows: Group T: 20, 25, 30, 37℃; pH groups: 6.0, 7.0, 8.0, 9.0; COD group: 5, 10, 20, 40 mg / L; TSS group: 10, 50, 100, 300 mg / L; DO group: 5, 5.65, 6.3, 7 mg / L.

[0030] Each group was set up in triplicate for each condition, with a pure water control included for COD and TSS experiments. Before the experiment, the PMS stock solution was diluted to the target concentration. Samples were taken at 0 h, 4 h, 8 h, 12 h, and 18 h after adding PMS. Before each sampling, the water was gently mixed, and 50 mL of water was drawn from the middle of the beaker. The N-diethyl-p-phenylenediamine spectrophotometric method was used, and glycine was added to remove interference from reactive oxygen species. The reactive oxygen species concentration of each water sample was measured to analyze the kinetics of PMS reactive oxygen species decay under different environmental factors.

[0031] The fitting parameters for reactive oxygen species (ROS) decay under different environmental conditions are shown in Table 2. The effective ROS decay process of PMS conforms to a first-order kinetic model. Increasing temperature significantly accelerates PMS decay; the decay rate constant k at 37℃ is 0.1045 h⁻¹. -1 Elevated TSS and COD levels also accelerate the decay of effective reactive oxygen species (ROS), with k=0.1232 h when TSS is 300 mg / L. -1 When COD is 40 mg / L, k is 0.1332 h. -1 The k-values ​​showed relatively small changes within the pH and DO experimental ranges. These findings provide PMS reactive oxygen species decay parameters for model construction.

[0032] Table 2 Furthermore, in step one, the acute toxicity parameters include the median lethal concentrations at 24h, 48h, 72h, and 96h; the safety threshold is a safe concentration determined based on the 96h median lethal concentration as a model constraint boundary.

[0033] Specifically, the process for determining the acute toxicity and safety threshold of PMS to juvenile Litopenaeus vannamei included: using 100L culture tanks as exposure containers, and using actual production water from factory-scale culture ponds with water quality conditions consistent with the culture site. The water temperature was maintained at 28 ± 3℃ throughout the experiment, with continuous aeration to ensure dissolved oxygen (DO) concentration remained within the range of 6.5-7.2 mg / L. PMS concentrations were set at five logarithmically spaced gradients: 5 mg / L, 8.13 mg / L, 13.22 mg / L, 21.5 mg / L, and 35 mg / L. Each concentration group had three replicates, and a blank control group without PMS was also included. Twenty healthy, uniformly sized juvenile shrimp were stocked in each culture tank. No water changes were performed during the experiment; only small amounts of feed were added based on feeding behavior. The cumulative mortality rate for each group was recorded at 24, 48, 72, and 96 hours after exposure to calculate the median lethal concentration (LC50) at different exposure times. 50) and safe concentration (SC).

[0034] The results are as follows Figure 2 As shown, the mortality rate of shrimp at different concentrations generally increased with time, and the higher the concentration, the faster the mortality rate increased. During the acute exposure process of 24–96 h, the mortality rate of juvenile shrimp in the control group (0 mg / L) remained at 0%. At 24 h, the mortality rate of the 5 mg / L group was low, while the mortality rate of the 8.13–21.5 mg / L groups increased significantly (p<0.05), and the 35 mg / L group reached 100% mortality. At 48–96 h, the mortality rate of the high concentration groups (13.22–35 mg / L) remained at a high level, significantly higher than that of the 8.13 mg / L and 5 mg / L groups (p<0.05). The calculated LC50 values ​​of PMS for juvenile Litopenaeus vannamei at 24 h, 48 h, 72 h, and 96 h were approximately 8.918, 7.297, 6.897, and 6.365 mg / L, respectively, and the 96 h-SC was 0.6365 mg / L.

[0035] Furthermore, in step one, the growth kinetic parameters are obtained by fitting a modified Gompertz model; the environmental factors involved in the growth kinetic parameters include at least one of temperature, pH, chemical oxygen demand, total suspended solids, dissolved oxygen, and salinity. Specifically, the strain used in this invention is the same. The process of obtaining Vibrio growth parameters under different environmental factors includes: using a single-factor experimental design, with the culture substrate being the water from a Litopenaeus vannamei culture pond sterilized by high temperature and high pressure. Six core environmental factors—temperature, pH, salinity, TSS, DO, and COD—were selected to evaluate their effects on the growth of four types of Vibrio. The specific experimental settings are as follows: Temperature: 20℃, 25℃, 30℃, 37℃; pH: 6, 7, 8, 9; COD: 40 mg / L, 20 mg / L, 10 mg / L, 5 mg / L; TSS: 300 mg / L, 100 mg / L, 50 mg / L, 10 mg / L; Salinity: 8‰, 13‰, 19‰, 25‰, 31‰; DO: 5, 5.65, 6.3, 7 mg / L. Each experimental group adopted the single variable principle. The activated and adjusted Vibrio bacterial suspension was inoculated into water bodies with different environmental conditions to achieve a final concentration of 1×10⁻⁶. 5 -10 6 CFU / mL. Samples were taken at 0 h, 3 h, 6 h, 9 h, 12 h, 20 h, and 24 h, respectively. The bacterial concentration was determined by plate counting, and the growth kinetic parameters were fitted and analyzed using a modified Gompertz model.

[0036] The modified Gompertz model parameters under different environmental factors are shown in Table 3. The modified Gompertz model can accurately describe the growth process of four Vibrio strains under different environmental conditions, with a fitting determination coefficient R² greater than 0.90. 30-37℃, pH 7-8, higher salinity, and TSS / COD within a suitable range are more conducive to Vibrio proliferation. The effect of DO on different strains is strain-specific. All of the above parameters were included as input variables in the microbial growth sub-model.

[0037] Table 3 Step 2: Based on the parameters obtained in Step 1, construct the microbial growth sub-model, the disinfectant inactivation sub-model, and the disinfectant concentration decay sub-model, respectively; Furthermore, in step two, the microbial growth sub-model is used to describe the process of Vibrio count increasing over time when no disinfectant is added or the disinfectant concentration is insufficient; the disinfectant inactivation sub-model is used to describe the inactivation rate of Vibrio by the disinfectant concentration; and the disinfectant concentration decay sub-model is used to describe the process of effective active oxygen of the disinfectant decaying over time in the aquaculture water.

[0038] Specifically, this invention uses biomechanical parameters of disinfection, reactive oxygen species attenuation parameters, and Vibrio growth parameters as core data, combined with shrimp safety thresholds, to construct microbial growth sub-models, disinfectant inactivation sub-models, and disinfectant concentration attenuation sub-models, respectively.

[0039] In this embodiment, the microbial growth sub-model is used to describe the process of Vibrio count increasing over time when no PMS is added or the PMS concentration is insufficient. It can be written as: (1) In the formula: N i The number of Vibrio i at time t (CFU / mL) is given, and K is the environmental carrying capacity of the aquaculture water for Vibrio (CFU / mL). U mef This represents the effective specific growth rate under the current environmental factors. K c This represents the inactivation rate coefficient at the current disinfectant concentration.

[0040] Effective specific growth rate U mef calculate: (2) In the formula: R m For the maximum specific growth rate, f(T), f(S), f(pH), f(TSS), f(COD), f(DO)These are the correction factors for the effects of temperature, salinity, pH, total suspended matter, chemical oxygen demand, and dissolved oxygen, respectively, while Lag(t) is a function representing the lag phase.

[0041] In this embodiment, the formula for the disinfectant inactivation sub-model is: (3) Or written as: (4) In the formula: kd C represents the sensitivity coefficient of Vibrio to disinfectants. dis The concentration of the disinfectant at time t. z This represents the kinetic order of the disinfectant reaction.

[0042] In this embodiment, the PMS reactive oxygen species concentration decay sub-model is used to describe the decay process of PMS in aquaculture water over time: (5) Its integral form is: (6) In the formula: C 0 represents the initial mass concentration of the disinfectant (mg / L). C t The concentration of the disinfectant (mg / L) at time t. t For time (h), k ef The apparent decay rate coefficient (h) -1 ).

[0043] Apparent decay rate coefficient: (7) In the formula: k d This represents the degradation rate constant under baseline conditions. fa(T), fa(pH), fa(TSS), fa(COD), fa (DO) These are the correction factors for the decay rate based on temperature, pH, total suspended solids, chemical oxygen demand, and dissolved oxygen, respectively.

[0044] Step 3: Dynamically couple the microbial growth sub-model, disinfectant inactivation sub-model, and disinfectant concentration decay sub-model in the system dynamics software to form a potassium persulfate compound salt dosage prediction model; Furthermore, in step three, the system dynamics software is Stella Architect.

[0045] Furthermore, in step three, the dynamic coupling process includes: inputting the effective concentration of disinfectant calculated by the disinfectant concentration attenuation sub-model into the disinfectant inactivation sub-model in real time to calculate the sterilization rate coefficient at the current moment; and then inputting the sterilization rate coefficient into the microbial growth sub-model as the reduction flow of Vibrio count, which, together with the Vibrio count itself, determines the dynamic change of Vibrio count.

[0046] Specifically, this embodiment completes the coupling of the three major sub-models on the system dynamics platform, realizing multi-dimensional interactive simulation of microbial dynamics, disinfectant effects, and environmental factors.

[0047] In the potassium persulfate compound salt dosage prediction model, the number of Vibrio bacteria and the effective concentration of PMS in the water are defined as core state variables that change dynamically over time. These are the core outputs of the model simulation and are mathematically represented through ordinary differential equations. To realize the simulation calculation and analysis of the model, the Stella Architect system dynamics software is used to transform the above mathematical model into a visualized and operable computer model. Among them, the microbial growth prediction and disinfectant inactivation model and the disinfectant effective concentration decay kinetic model are as follows: Figure 3 , Figure 4 As shown. During the modeling process, a modular structure was constructed based on the ecological functions of each state variable in the aquaculture environment, and the quantitative mathematical relationships between them were clarified. To maintain... Figure 3 and Figure 4 To ensure the clarity and simplicity of the model, all intermediate variables and parameters are identified using standardized symbols with specific prefixes. The meanings of the model symbols are detailed in Table 4.

[0048] Table 4 Within the system dynamics framework, the microbial growth sub-model, disinfectant inactivation sub-model, and disinfectant concentration decay sub-model are dynamically coupled through state variables, flow variables, and auxiliary variables. Specifically, the disinfectant concentration decay sub-model uses the effective concentration of disinfectant (DC) as the core state variable. DC gradually decreases over time according to the disinfectant effective concentration decay kinetics, and its change is jointly regulated by environmental factors such as temperature, pH, TSS, COD, and DO. The dynamic change result of DC is further input into the disinfectant inactivation sub-model to calculate the sterilization rate coefficient at the current moment. K c As DC continues to decay, K c Correspondingly, the inhibitory effect on Vibrio gradually weakens. The bactericidal rate coefficient calculated by the disinfectant inactivation model... K cIn the microbial growth model, the reduced flow rate of Vibrio, along with its own growth flow rate, jointly determines the dynamic change of the Vibrio population N. When the effective concentration of PMS is high, the reduced flow rate of Vibrio is greater than or close to the growth flow rate, and the Vibrio population decreases or is inhibited; as the effective concentration of PMS decays over time, the bactericidal rate coefficient... K c When the Vibrio growth flow rate is greater than the reduced flow rate, the Vibrio count will rise again. The model uses this to determine the risk of pathogen regrowth and provides a basis for predicting subsequent drug administration time and intervals.

[0049] Step 4: Input the real-time physicochemical parameters of the Litopenaeus vannamei farmed water into the potassium persulfate compound salt dosage prediction model. The model dynamically outputs the initial concentration of potassium persulfate compound salt, the effective sterilization duration, the early warning of pathogen regrowth, and the dosing interval.

[0050] Furthermore, in step four, the real-time physicochemical parameters include temperature, pH, salinity, chemical oxygen demand, total suspended solids, and dissolved oxygen.

[0051] Specifically, the potassium persulfate complex salt dosage prediction model was validated as follows: Under baseline conditions, the simulation duration was set to 12 hours, with initial PMS concentration of 1.0 mg / L, COD of 15 mg / L, DO of 6.5 mg / L, pH of 8, temperature of 30℃, TSS of 50 mg / L, and salinity of 25‰. The model results are as follows: Figure 5 , Figure 6 As shown, the model as a whole can effectively reflect the dynamic response characteristics of Vibrio counts after PMS application. In the initial stage of disinfectant action, the Vibrio count rapidly decreases, and the model predictions and measured data are consistent in the direction of change. As the active ingredient in PMS gradually decays, the Vibrio count gradually recovers from a low level, and the model can continuously describe the trend during this stage, comprehensively reproducing the sterilization process of PMS and its subsequent effects. Simulation results for different Vibrio strains show that the sterilization intensity and duration of action of PMS vary among strains. The minimum bacterial count level predicted by the model and its occurrence time differ among strains, reflecting the varying sensitivities of different Vibrio strains to PMS. Figure 7 This diagram illustrates the comparison between simulated and measured values ​​of four Vibrio species under PMS treatment, along with the mean relative error (ARE). The ARE values ​​for the model validation of the four Vibrio strains were -0.01, -0.04, 0.12, and -0.14, respectively, while the ARE value for reactive oxygen species (ROS) under PMS was -0.13. This indicates that the model has good predictive ability for the killing effect of Vibrio, regrowth trend, and ROS decay. The mean relative error (ARE) is close to 0, suggesting that the model can be used for dynamic prediction of PMS dosage and dosing interval in the industrialized farming of Litopenaeus vannamei.

[0052] This embodiment provides a method for predicting the dosage of potassium peroxymonosulfate compound salt, including: first, inputting real-time physicochemical parameters of the water in the factory farming of Litopenaeus vannamei, including temperature, pH, salinity, COD, TSS, and DO, and inputting the initial bacterial count of the target Vibrio and the initial concentration of the disinfectant; then, the model automatically calls the corresponding growth kinetic parameters of Vibrio based on the input environmental parameters to calculate the effective growth rate of Vibrio under the current water quality conditions; simultaneously, the model calls the reactive oxygen species (PMS) decay parameter to simulate the change of the effective concentration of PMS over time at the current initial dosage, and calculates its inactivation rate against the target Vibrio at that concentration. Based on this, the model introduces a safe concentration threshold for PMS in juvenile Litopenaeus vannamei, and outputs the recommended dosage, effective sterilization duration, pathogen regrowth warning, and recommended dosing interval, provided that the shrimp safety threshold is met.

[0053] Example 2 This embodiment also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0054] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the dosage of potassium persulfate compound salt in the industrialized farming of Litopenaeus vannamei, characterized in that, Includes the following steps: Step 1: Obtain the inactivation kinetic parameters of potassium persulfate compound salt on four pathogenic Vibrio species, the attenuation parameters of effective reactive oxygen species in the aquaculture water, the growth kinetic parameters of the four pathogenic Vibrio species under different environmental factors, and the acute toxicity parameters and safety threshold of potassium persulfate compound salt on juvenile Litopenaeus vannamei. Step 2: Based on the parameters obtained in Step 1, construct the microbial growth sub-model, the disinfectant inactivation sub-model, and the disinfectant concentration decay sub-model, respectively; Step 3: Dynamically couple the microbial growth sub-model, disinfectant inactivation sub-model, and disinfectant concentration decay sub-model in the system dynamics software to form a potassium persulfate compound salt dosage prediction model; Step 4: Input the real-time physicochemical parameters of the Litopenaeus vannamei farmed water into the potassium persulfate compound salt dosage prediction model. The model dynamically outputs the initial concentration of potassium persulfate compound salt, the effective sterilization duration, the early warning of pathogen regrowth, and the dosing interval.

2. The prediction method according to claim 1, characterized in that, In step one, the four pathogenic Vibrio species include Vibrio parahaemolyticus, Vibrio alginolyticus, Vibrio campbellii, and Vibrio harveyi.

3. The prediction method according to claim 1, characterized in that, In step one, the kinetic parameters of the inactivation activity are obtained by fitting the Chick-Watson model; the kinetic parameters of the growth are obtained by fitting the modified Gompertz model; and the attenuation parameters of the effective reactive oxygen species are obtained by fitting the first-order kinetic model.

4. The prediction method according to claim 1, characterized in that, In step one, the environmental factors involved in the growth kinetic parameters include at least one of temperature, pH, chemical oxygen demand, total suspended solids, dissolved oxygen, and salinity; the environmental factors involved in the attenuation parameters of effective active oxygen include at least one of temperature, pH, chemical oxygen demand, total suspended solids, and dissolved oxygen.

5. The prediction method according to claim 1, characterized in that, In step one, the acute toxicity parameters include the median lethal concentrations at 24h, 48h, 72h, and 96h; the safety threshold is a safe concentration determined based on the 96h median lethal concentration as a model constraint boundary.

6. The prediction method according to claim 1, characterized in that, In step two, the microbial growth sub-model is used to describe the process of Vibrio count increasing over time when no disinfectant is added or the disinfectant concentration is insufficient; the disinfectant inactivation sub-model is used to describe the inactivation rate of Vibrio by the disinfectant concentration; and the disinfectant concentration decay sub-model is used to describe the process of effective reactive oxygen species in the disinfectant decaying over time in the aquaculture water.

7. The prediction method according to claim 1, characterized in that, In step three, the system dynamics software is Stella Architect.

8. The prediction method according to claim 1, characterized in that, In step three, the dynamic coupling process includes: The effective concentration of disinfectant calculated by the disinfectant concentration attenuation sub-model is input into the disinfectant inactivation sub-model in real time to calculate the sterilization rate coefficient at the current moment. The sterilization rate coefficient is then input into the microbial growth sub-model as a reduction flow rate for the number of Vibrio bacteria, which, together with the Vibrio bacteria's own growth flow rate, determines the dynamic change in the number of Vibrio bacteria.

9. The prediction method according to claim 1, characterized in that, In step four, the real-time physicochemical parameters include temperature, pH, salinity, chemical oxygen demand, total suspended solids, and dissolved oxygen.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-5.

Images