Shrimp culture yield increasing method and system based on staged growth management

Abstract

The invention discloses a shrimp breeding yield increasing method and system based on staged growth management. By acquiring behavior and physiological change data of shrimps in different growth stages, the resistance of the shrimps to environmental change is evaluated, so that refined growth management stages are divided. Further combining the release characteristics of the bottom mud released substances of the target culture pond, performing correlation analysis with the environmental resistance of the shrimps in each stage, constructing a corresponding matrix of the management stage and the bottom mud influence, and quantifying the fitness index of the shrimps to the bottom mud released substances in each stage. When the fitness index is lower than a safety threshold value, bottom mud state information is obtained, a bottom mud regulation and control mode is determined, then bottom mud regulation and control strategies of all stages are formulated, and dynamic regulation and control and optimization of the breeding environment are achieved. The method can effectively improve the growth efficiency and culture yield of the shrimps, and has high practical value and popularization prospect.

Description

Technical Field

[0001] This invention relates to the field of aquaculture technology, and in particular to a method and system for increasing shrimp production based on phased growth management. Background Technology

[0002] Shrimp, such as the Pacific white shrimp, has become one of the main economic species in my country's aquaculture due to its advantages such as fast growth rate, high market value, and strong adaptability. However, with the increasing level of intensive farming, the problems exposed in shrimp farming have become increasingly prominent, especially in terms of environmental stress, growth stage management, and bottom sediment pollution.

[0003] Currently, shrimp farming exhibits significant differences in its sensitivity to the farming environment at different growth stages. Changes in environmental factors such as water temperature, dissolved oxygen, ammonia nitrogen, and nitrite all have varying degrees of impact on shrimp behavior, physiological state, and immune function. As a crucial site for material deposition and transformation in the shrimp farming system, pond sediment continuously releases harmful substances such as ammonia nitrogen, hydrogen sulfide, and organic acids throughout the entire farming cycle, posing a potential threat to the aquatic environment and shrimp health. Especially in the later stages of farming, the accumulation of feed residue and feces intensifies the anaerobic properties of the sediment, resulting in high spatiotemporal heterogeneity in the types and concentrations of sediment releases. The lack of systematic research and management strategies regarding the pathogenic risks posed by these dynamic changes in releases to shrimp at different growth stages further exacerbates the risks associated with shrimp farming.

[0004] Some existing studies have attempted to improve the sediment environment by adding sediment conditioners, such as oxidants and biological agents. However, these methods are mostly extensive and lack a precise control mechanism that matches the sediment condition characteristics with the aquaculture stage. This can easily lead to problems such as over-addition, insufficient depth of action, or short-lived effects, which not only increase aquaculture costs but may also create new environmental burdens.

[0005] Therefore, there is an urgent need for a technical system that can scientifically divide the growth and management stages based on the shrimp's resistance to environmental changes at different stages of the farming cycle, and construct a dynamic control model by combining the dynamic changes of substances released from pond bottom sediment, so as to achieve efficient and healthy shrimp farming. Summary of the Invention

[0006] To address at least one of the aforementioned technical problems, this invention proposes a method and system for increasing shrimp farming yield based on phased growth management.

[0007] The first aspect of this invention provides a method for increasing shrimp farming yield based on phased growth management, comprising:

[0008] Acquire behavioral and physiological change data of shrimp during their growth process to assess their resistance to environmental changes, and divide the growth management stages based on the environmental change resistance.

[0009] The release characteristics of sediment released from the target shrimp farming pond were obtained. Correlation analysis was performed between the release characteristics and the environmental change resistance of farmed shrimp at each growth management stage to determine the impact of sediment release on shrimp growth at different growth management stages and to construct a management stage-sediment impact matrix.

[0010] The fitness index of shrimp to sediment releases at each growth management stage is calculated based on the management stage-sediment influence matrix. If the fitness index is lower than the safety threshold, the sediment state information of the target aquaculture pond is obtained, and the sediment control mode is determined based on the sediment state information.

[0011] Based on the bottom sediment regulation model, bottom sediment regulation strategies for different growth management stages of target aquaculture ponds are constructed, and shrimp farming is optimized for increased production based on the bottom sediment regulation strategies.

[0012] In this solution, the acquisition of behavioral change data, physiological change data, and aquaculture environment data during the shrimp's growth process is used to assess their resistance to environmental changes. Based on this resistance, growth management stages are then defined, specifically as follows:

[0013] Based on underwater camera devices, data on changes in swimming activity and feeding activity of shrimp during the farming and growth process are obtained to obtain behavioral change data. Data on changes in hemolymphatic immunity, molting frequency, and metabolic enzyme activity of shrimp during the growth process are also obtained to obtain physiological change data.

[0014] The behavioral change dataset and physiological change dataset are input into a pre-trained environmental change resistance assessment model. The assessment model extracts behavioral change features through a convolutional neural network and extracts physiological change temporal features through a long short-term memory network. The behavioral change features and physiological change temporal features are weighted and fused to determine the shrimp's vitality. Based on the vitality, an environmental change resistance score is output.

[0015] Based on the changing trend of the environmental change resistance score, the dynamic time warping algorithm is used to identify the inflection point of the resistance change, and the breeding cycle between adjacent inflection points is divided into a growth management stage.

[0016] In this scheme, the release characteristics of sediment released from the target shrimp farming pond are obtained. Correlation analysis is then performed between these release characteristics and the environmental change resistance of farmed shrimp at each growth management stage to determine the impact of sediment release on shrimp growth at different growth management stages. A management stage-sediment impact matrix is ​​then constructed, specifically as follows:

[0017] Obtain pond structure data of the target shrimp farming pond, obtain pond bottom shape data based on the pond structure data, draw a pond bottom mud structure diagram based on the bottom shape data, and divide the pond into grids of a preset size based on the bottom mud structure diagram;

[0018] Data on the types of sediment released from the bottom sediment at the contact surface between the bottom sediment and the pond water in each grid during the shrimp farming and growth process are obtained according to a preset time period, and the concentration information of each sediment release is obtained. The sediment release type data and concentration information for each preset time period are mapped to the corresponding grid to construct a time variation distribution map of sediment release.

[0019] The release characteristics of sediment releases are extracted from the time variation distribution map of sediment releases, including spatial distribution characteristics, type characteristics of released substances, and concentration variation characteristics.

[0020] Historical aquaculture environment data and shrimp disease occurrence data for each growth management stage are obtained. Correlation analysis is performed between the environmental change resistance corresponding to each growth management stage and the historical aquaculture environment data and shrimp disease occurrence data to determine the disease probability of shrimp at different growth management stages in different aquaculture environments, and a growth management stage-aquaculture environment-disease probability mapping table is obtained.

[0021] Based on the mapping table and release characteristics, the probability of pathogenic impact on shrimp growth at different growth management stages is determined, and a management stage-sediment impact matrix is ​​constructed.

[0022] In this scheme, the fitness index of farmed shrimp to sediment releases at each growth management stage is calculated based on the management stage-sediment influence matrix. If the fitness index is lower than a safety threshold, the sediment state information of the target aquaculture pond is obtained, and the sediment control mode is determined based on the sediment state information. Specifically:

[0023] Extract the characteristics of sediment release types and release concentration changes corresponding to each growth management stage in the sediment influence matrix, calculate the concentration change gradient of each sediment release within a preset time period, and determine the diffusion rate of sediment release based on the concentration change gradient.

[0024] Obtain water depth distribution data and bottom sediment thickness distribution data of the target aquaculture pond; calculate the vertical diffusion distance of the bottom sediment release in the water body based on the diffusion rate and water depth distribution data; calculate the horizontal diffusion distance of the bottom sediment release in the bottom sediment based on the bottom sediment thickness distribution data.

[0025] Based on the pathogenicity probability of shrimp to sediment releases at each growth management stage in the management stage-sediment influence matrix, and combined with the vertical diffusion distance and horizontal diffusion distance, the concentration attenuation coefficient of sediment releases when they reach the shrimp activity area is calculated.

[0026] The effective concentration is obtained by multiplying the concentration decay coefficient by the initial concentration of the sediment release. The fitness index is obtained by calculating the ratio between the effective concentration and the shrimp's environmental change resistance score at the corresponding growth management stage.

[0027] When the fitness index is lower than the safety threshold, the redox potential profile data and porosity distribution data of the bottom sediment of the target aquaculture pond are obtained. The dissolved oxygen gradient change curve at the bottom sediment-water interface is measured by microelectrode array. The organic matter content of the bottom sediment is obtained and the bottom sediment state information is obtained. A three-dimensional map of bottom sediment biochemical activity is constructed based on the bottom sediment state information.

[0028] The vertical distribution data of bottom sediment moisture content and the metabolic activity data of bottom sediment microbial community were obtained from the target aquaculture pond. The composition characteristics of organic matter functional groups in the bottom sediment were analyzed by Fourier transform infrared spectroscopy.

[0029] Based on the vertical distribution data of sediment moisture content and the composition characteristics of organic matter functional groups, the ratio of hydrophobic to hydrophilic components in the sediment was calculated. Combined with the metabolic activity data of microbial communities, the infiltration rate of liquid amendments in sediments at different depths and the coverage and diffusion rate of solid amendments in the surface sediment were analyzed.

[0030] Based on the three-dimensional biochemical activity spectrum of the sediment and the penetration rate of the liquid amendment, the effective depth of the liquid amendment in the sediment was calculated. Based on the coverage and diffusion rate of the solid amendment and the surface porosity of the sediment, the surface coverage improvement efficiency of the solid amendment was calculated.

[0031] When the ratio of the effective depth of the liquid amendment to the thickness of the main sediment-producing layer is greater than the set ratio, the liquid amendment control mode is used; otherwise, the solid amendment control mode is used.

[0032] In this scheme, the construction of bottom sediment control strategies for different growth management stages of the target aquaculture pond based on the bottom sediment control model, and the optimization of shrimp farming for increased production based on the bottom sediment control strategies, specifically includes:

[0033] If a liquid amendment control mode is used, the extreme points of redox potential and the peak points of organic matter content at each depth layer are obtained based on the three-dimensional map of biochemical activity of bottom sediment in the target aquaculture pond. The spatial coordinates of the extreme points and peak points are coupled and analyzed to determine the three-dimensional distribution of anaerobic metabolic hotspots in the bottom sediment.

[0034] The theoretical infiltration time of the liquid amendment from the water-mud interface to each anaerobic metabolic hotspot area is calculated based on the infiltration rate of the liquid amendment in the sediment. Combined with the sediment release concentration change cycle of the corresponding grid in the sediment release time change distribution map, the optimal application time window of the liquid amendment is determined.

[0035] Based on sediment porosity distribution data and organic matter functional group composition characteristics, the reaction equivalence relationship between oxidizing components and reducing substances in the liquid amendment was analyzed. The effective component loading of the amendment required per unit volume of sediment was calculated based on the organic matter content detection results of each anaerobic metabolic hot spot area. Combined with the actual penetration and diffusion range of the liquid amendment in the corresponding depth layer, the application concentration gradient distribution of the liquid amendment was calculated.

[0036] Based on the optimal application time window and the application concentration gradient distribution, a liquid amendment sediment regulation strategy was constructed.

[0037] In this scheme, the step of constructing bottom sediment control strategies for different growth management stages of the target aquaculture pond based on the bottom sediment control model further includes:

[0038] If a solid amendment control mode is adopted, the solid amendment is tested in the pore water environment corresponding to the bottom sediment of the target aquaculture pond to obtain the ion release curve of the solid amendment in the simulated bottom sediment pore water environment. Based on the ion release curve, the dissolution characteristic parameters of the solid amendment are extracted. The dissolution characteristic parameters include the initial dissolution delay time, the duration of the maximum dissolution rate, and the inflection point of dissolution decay.

[0039] The effective dissolution radius and diffusion rate of the solid amendment in the bottom sediment of the target aquaculture pond are calculated based on the dissolution characteristic parameters and the bottom sediment porosity distribution data.

[0040] Based on the release characteristics of the sediment releases, the effective dissolution radius, and the diffusion rate of the dissolved substances, the burial location and amount of solid amendment in the target pond sediment are determined, thus obtaining a solid amendment sediment control strategy.

[0041] A second aspect of the present invention also provides a shrimp farming production enhancement system based on phased growth management, comprising: a memory and a processor, wherein the memory is used to store a program, and the processor is used to execute the program stored in the memory, wherein when the program stored in the memory is executed, a shrimp farming production enhancement method based on phased growth management as described in any of the preceding claims is implemented.

[0042] This invention discloses a method and system for increasing shrimp farming yield based on phased growth management. By acquiring data on the behavioral and physiological changes of shrimp at different growth stages, their resistance to environmental changes is assessed, thereby dividing the growth management into refined stages. Furthermore, by combining the release characteristics of sediment from the target aquaculture pond with the environmental resistance of shrimp at each stage, a correlation analysis is performed to construct a matrix corresponding to the management stages and the impact of sediment, quantifying the fitness index of shrimp to sediment release at each stage. When the fitness index is below a safe threshold, sediment state information is acquired and a sediment control mode is determined, thereby formulating sediment control strategies for each stage to achieve dynamic control and optimization of the aquaculture environment. This invention can effectively improve shrimp growth efficiency and aquaculture yield, and has strong practical value and promising prospects for promotion. Attached Figure Description

[0043] Figure 1 A flowchart of a shrimp farming yield-increasing method based on phased growth management according to the present invention is shown;

[0044] Figure 2 A flowchart illustrating the liquid modifier sediment regulation strategy of the present invention is shown;

[0045] Figure 3 A flowchart illustrating the solid amendment sediment regulation strategy of the present invention is shown;

[0046] Figure 4 A block diagram of a shrimp farming yield-increasing system based on phased growth management according to the present invention is shown. Detailed Implementation

[0047] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0049] Figure 1 The flowchart of a shrimp farming yield increase method based on phased growth management according to the present invention is shown.

[0050] like Figure 1 As shown, the first aspect of the present invention provides a method for increasing shrimp farming yield based on phased growth management, comprising:

[0051] S102, acquire behavioral and physiological change data of shrimp during their growth process to assess their resistance to environmental changes, and divide the growth management stage according to the environmental change resistance.

[0052] S104, Obtain the release characteristics of bottom sediment released by shrimp in the target shrimp farming pond, perform correlation analysis between the release characteristics and the environmental change resistance of farmed shrimp at each growth management stage, determine the impact of bottom sediment released on shrimp growth at different growth management stages, and construct a management stage-bottom sediment impact matrix.

[0053] S106, calculate the fitness index of shrimp to sediment releases at each growth management stage according to the management stage-sediment influence matrix. If the fitness index is lower than the safety threshold, obtain the sediment state information of the target aquaculture pond and determine the sediment control mode according to the sediment state information.

[0054] S108, construct bottom sediment control strategies for different growth management stages of the target aquaculture pond according to the bottom sediment control mode, and optimize shrimp farming production based on the bottom sediment control strategies.

[0055] It should be noted that by collecting data on shrimp behavior and physiological changes, a deep learning model is used to assess their resistance to environmental changes and to define precise growth management stages. The spatial distribution, type, and concentration changes of substances released from pond sediment are combined to analyze their correlation with shrimp resistance at each stage, constructing a management stage-sediment impact matrix. Furthermore, based on the diffusion characteristics of sediment releases and the concentration decay in shrimp activity areas, the shrimp's fitness index to sediment impact is calculated. If it is below a threshold, sediment redox potential, organic matter content, porosity, and other state information are obtained to construct a three-dimensional sediment activity map, comprehensively evaluating liquid or solid amendment control modes. Finally, based on the control mode, a matching sediment improvement strategy is formulated, including application timing, concentration gradient, and coverage location, to achieve precise, phased control of the sediment ecological environment, improve shrimp survival rate and yield, and achieve intelligent production optimization.

[0056] According to an embodiment of the present invention, the step of acquiring behavioral change data, physiological change data, and aquaculture environment data during the shrimp growth process to assess environmental change resistance, and dividing the growth management stages according to the environmental change resistance, specifically includes:

[0057] Based on underwater camera devices, data on changes in swimming activity and feeding activity of shrimp during the farming and growth process are obtained to obtain behavioral change data. Data on changes in hemolymphatic immunity, molting frequency, and metabolic enzyme activity of shrimp during the growth process are also obtained to obtain physiological change data.

[0058] The behavioral change dataset and physiological change dataset are input into a pre-trained environmental change resistance assessment model. The assessment model extracts behavioral change features through a convolutional neural network and extracts physiological change temporal features through a long short-term memory network. The behavioral change features and physiological change temporal features are weighted and fused to determine the shrimp's vitality. Based on the vitality, an environmental change resistance score is output.

[0059] Based on the changing trend of the environmental change resistance score, the dynamic time warping algorithm is used to identify the inflection point of the resistance change, and the breeding cycle between adjacent inflection points is divided into a growth management stage.

[0060] It should be noted that shrimp exhibit significant differences in their response to environmental stress at different growth stages. For example, juvenile shrimp, whose immune systems are not fully developed, have low tolerance to water quality fluctuations. While adult shrimp possess strong metabolic capabilities, frequent molting can lead to a short-term decrease in resistance. By monitoring shrimp swimming and feeding activity using underwater cameras, combined with physiological data such as hemolymphatic immune indicators, molting frequency, and metabolic enzyme activity, a comprehensive assessment of shrimp vitality and their adaptability to environmental changes can be achieved. Using convolutional neural networks to extract behavioral features and analyzing physiological temporal changes through long short-term memory networks, a more accurate quantification of shrimp's environmental resistance score can be obtained. Based on the dynamic trend of this score, a dynamic time warping algorithm can be used to identify inflection points in resistance changes, enabling the scientific classification of shrimp growth management stages. This allows for the development of differentiated aquaculture management strategies tailored to the resistance characteristics of different stages, ultimately leading to increased shrimp production.

[0061] According to an embodiment of the present invention, the step of obtaining the release characteristics of sediment released from the target shrimp farming pond, performing correlation analysis between the release characteristics and the environmental change resistance of farmed shrimp at each growth management stage, determining the impact of sediment release on shrimp growth at different growth management stages, and constructing a management stage-sediment impact matrix are specifically as follows:

[0062] Obtain pond structure data of the target shrimp farming pond, obtain pond bottom shape data based on the pond structure data, draw a pond bottom mud structure diagram based on the bottom shape data, and divide the pond into grids of a preset size based on the bottom mud structure diagram;

[0063] Data on the types of sediment released from the bottom sediment at the contact surface between the bottom sediment and the pond water in each grid during the shrimp farming and growth process are obtained according to a preset time period, and the concentration information of each sediment release is obtained. The sediment release type data and concentration information for each preset time period are mapped to the corresponding grid to construct a time variation distribution map of sediment release.

[0064] The release characteristics of sediment releases are extracted from the time variation distribution map of sediment releases, including spatial distribution characteristics, type characteristics of released substances, and concentration variation characteristics.

[0065] Historical aquaculture environment data and shrimp disease occurrence data for each growth management stage are obtained. Correlation analysis is performed between the environmental change resistance corresponding to each growth management stage and the historical aquaculture environment data and shrimp disease occurrence data to determine the disease probability of shrimp at different growth management stages in different aquaculture environments, and a growth management stage-aquaculture environment-disease probability mapping table is obtained.

[0066] Based on the mapping table and release characteristics, the probability of pathogenic impact on shrimp growth at different growth management stages is determined, and a management stage-sediment impact matrix is ​​constructed.

[0067] It should be noted that by acquiring pond sediment structure data and dividing it into grids, combined with time-series monitoring of sediment release types and concentration changes, a comprehensive understanding of the spatial distribution patterns and release dynamics of sediment pollutants can be achieved. Further analysis of the correlation between shrimp's resistance to environmental changes at different growth management stages and historical farming environment and disease occurrence data can reveal the association between sediment releases and shrimp health risks. For example, juvenile shrimp may be sensitive to ammonia nitrogen, while adult shrimp are more susceptible to hydrogen sulfide; this differentiated pathogenicity probability is visually presented in a matrix format. The construction of this matrix solves the problem of overly general sediment impact assessments in traditional methods, enabling farmers to predict the potential hazards of sediment releases based on the resistance characteristics of shrimp at different growth stages and take early intervention measures. For areas where technology is not fully disclosed, such as sediment release monitoring methods, microelectrode sensing technology can be used to collect real-time chemical parameters at the sediment-water interface, combined with spectral analysis to determine organic matter composition, thereby ensuring the accuracy and timeliness of data collection. This shifts sediment management from experience-based judgment to data-driven approaches, effectively reducing shrimp disease risks and improving farming efficiency.

[0068] According to an embodiment of the present invention, the step of calculating the fitness index of farmed shrimp to sediment releases at each growth management stage based on the management stage-sediment influence matrix, and if the fitness index is lower than a safety threshold, obtaining the sediment state information of the target aquaculture pond, and determining the sediment control mode based on the sediment state information, specifically includes:

[0069] Extract the characteristics of sediment release types and release concentration changes corresponding to each growth management stage in the sediment influence matrix, calculate the concentration change gradient of each sediment release within a preset time period, and determine the diffusion rate of sediment release based on the concentration change gradient.

[0070] Obtain water depth distribution data and bottom sediment thickness distribution data of the target aquaculture pond; calculate the vertical diffusion distance of the bottom sediment release in the water body based on the diffusion rate and water depth distribution data; calculate the horizontal diffusion distance of the bottom sediment release in the bottom sediment based on the bottom sediment thickness distribution data.

[0071] Based on the pathogenicity probability of shrimp to sediment releases at each growth management stage in the management stage-sediment influence matrix, and combined with the vertical diffusion distance and horizontal diffusion distance, the concentration attenuation coefficient of sediment releases when they reach the shrimp activity area is calculated.

[0072] The effective concentration is obtained by multiplying the concentration decay coefficient by the initial concentration of the sediment release. The fitness index is obtained by calculating the ratio between the effective concentration and the shrimp's environmental change resistance score at the corresponding growth management stage.

[0073] It should be noted that the fitness index, calculated by comparing the effective concentration of sediment-released substances with the environmental change resistance score of shrimp at the corresponding growth stage, can scientifically assess the shrimp's survival adaptability in the current sediment environment. The effective concentration reflects the actual concentration of harmful substances reaching the shrimp's activity area, while the environmental change resistance score characterizes the shrimp's tolerance threshold to that concentration of pollutants. The ratio of the two directly reflects the dynamic balance between environmental pressure and biological tolerance. The core value of calculating the fitness index lies in quantifying the complex sediment-shrimp interaction into a comparable standardized indicator, enabling farmers to quickly determine whether the current environment exceeds the shrimp's physiological tolerance range. When the fitness index is below the safe threshold, it indicates that the environmental pressure caused by sediment releases has exceeded the shrimp's compensatory capacity, potentially leading to growth inhibition or disease outbreaks. In this case, targeted sediment control measures must be taken.

[0074] When the fitness index is lower than the safety threshold, the redox potential profile data and porosity distribution data of the bottom sediment of the target aquaculture pond are obtained. The dissolved oxygen gradient change curve at the bottom sediment-water interface is measured by microelectrode array. The organic matter content of the bottom sediment is obtained and the bottom sediment state information is obtained. A three-dimensional map of bottom sediment biochemical activity is constructed based on the bottom sediment state information.

[0075] The vertical distribution data of bottom sediment moisture content and the metabolic activity data of bottom sediment microbial community were obtained from the target aquaculture pond. The composition characteristics of organic matter functional groups in the bottom sediment were analyzed by Fourier transform infrared spectroscopy.

[0076] Based on the vertical distribution data of sediment moisture content and the composition characteristics of organic matter functional groups, the ratio of hydrophobic to hydrophilic components in the sediment was calculated. Combined with the metabolic activity data of microbial communities, the infiltration rate of liquid amendments in sediments at different depths and the coverage and diffusion rate of solid amendments in the surface sediment were analyzed.

[0077] Based on the three-dimensional biochemical activity spectrum of the sediment and the penetration rate of the liquid amendment, the effective depth of the liquid amendment in the sediment was calculated. Based on the coverage and diffusion rate of the solid amendment and the surface porosity of the sediment, the surface coverage improvement efficiency of the solid amendment was calculated.

[0078] When the ratio of the effective depth of the liquid amendment to the thickness of the main sediment-producing layer is greater than the set ratio, the liquid amendment control mode is used; otherwise, the solid amendment control mode is used.

[0079] It is important to note that the core of determining the control mode based on sediment state information lies in ensuring that the amendment can accurately target the most heavily polluted areas of the sediment, thereby effectively inhibiting the release of harmful substances. When the effective depth of the liquid amendment can cover the main layer where sediment releases occur, it indicates that the liquid amendment can penetrate to the depth of the pollution source and directly decompose organic pollutants and reducing substances in the sediment through oxidation-reduction reactions, fundamentally blocking the release pathways of harmful substances. If the liquid amendment cannot reach the main pollution layer, it indicates that the sediment structure may be too dense or have poor permeability. In this case, a solid amendment is more suitable because it can form a physical covering layer on the surface, preventing the upward diffusion of pollutants from the bottom layer through adsorption and isolation. This intelligent selection mechanism based on sediment state information avoids the inefficiency caused by blindly using amendments in traditional aquaculture, ensuring a precise match between the amendment measures and the sediment pollution characteristics. By constructing a three-dimensional biochemical activity spectrum of the sediment and combining it with the penetration and diffusion characteristics of the amendment, the optimal spatial distribution of the amendment can be achieved, fully utilizing both the deep purification effect of chemical oxidation and the barrier effect of physical covering. It significantly improved the targeting and effectiveness of sediment improvement and reduced the waste of chemicals.

[0080] Figure 2 A flowchart illustrating the liquid modifier sediment regulation strategy of this invention is shown.

[0081] According to an embodiment of the present invention, the step of constructing bottom sediment regulation strategies for different growth management stages of the target aquaculture pond based on the bottom sediment regulation mode, and optimizing shrimp farming production based on the bottom sediment regulation strategies, specifically includes:

[0082] S202, if the liquid amendment control mode is adopted, the extreme points of redox potential and the peak points of organic matter content of each depth layer are obtained according to the three-dimensional map of biochemical activity of bottom sediment in the target aquaculture pond. The spatial coordinates of the extreme points and the peak points are coupled and analyzed to determine the three-dimensional distribution of the anaerobic metabolism hotspot area in the bottom sediment.

[0083] S204. Based on the infiltration rate of the liquid amendment in the sediment, calculate the theoretical infiltration time of the amendment from the water-sludge interface to each anaerobic metabolic hot spot area. Combined with the sediment release concentration change cycle of the corresponding grid in the sediment release time change distribution map, determine the optimal application time window of the liquid amendment.

[0084] S206. Based on sediment porosity distribution data and organic matter functional group composition characteristics, the reaction equivalence relationship between oxidizing components and reducing substances in the liquid amendment is analyzed. The loading of effective components of the amendment required per unit volume of sediment is calculated based on the organic matter content detection results of each anaerobic metabolic hot spot area. Combined with the actual penetration and diffusion range of the liquid amendment in the corresponding depth layer, the application concentration gradient distribution of the liquid amendment is calculated.

[0085] S208, Based on the optimal application time window and the application concentration gradient distribution, a liquid amendment sediment control strategy is constructed.

[0086] It should be noted that by using a three-dimensional biochemical activity map of the sediment to pinpoint anaerobic metabolic hotspots, the amendment is ensured to act directly on the most heavily polluted core areas, avoiding the problem of unreasonable agent distribution that exists in traditional uniform application methods. The optimal application time window, determined by combining the permeation rate and the sediment release concentration variation cycle, allows the amendment to reach the target depth before the peak of pollutant release, achieving pollution prevention rather than post-contamination remediation. The amendment concentration gradient distribution calculated based on porosity and organic matter characteristics ensures both sufficient reaction between oxidizing and reducing components and avoids agent residue and cost waste caused by excessive use.

[0087] Figure 3 A flowchart illustrating the solid amendment sediment regulation strategy of the present invention is shown.

[0088] According to an embodiment of the present invention, the step of constructing a bottom sediment regulation strategy for different growth management stages of the target aquaculture pond based on the bottom sediment regulation mode further includes:

[0089] S302, if the solid amendment control mode is adopted, the solid amendment is tested in the pore water environment corresponding to the bottom mud of the target aquaculture pond, and the ion release curve of the solid amendment in the simulated bottom mud pore water environment is obtained. Based on the ion release curve, the dissolution characteristic parameters of the solid amendment are extracted. The dissolution characteristic parameters include the initial dissolution delay time, the duration of the maximum dissolution rate, and the dissolution decay inflection point.

[0090] S304, calculate the effective dissolution radius and dissolved substance diffusion rate of the solid amendment in the bottom mud of the target aquaculture pond based on the dissolution characteristic parameters and the bottom mud porosity distribution data;

[0091] S306, Based on the release characteristics of the sediment releases, the effective dissolution radius, and the diffusion rate of the dissolved substances, determine the burial location and amount of solid amendment in the target pond sediment, and obtain the solid amendment sediment control strategy.

[0092] It should be noted that the ion release curves obtained through simulation experiments reveal the dissolution kinetics of solid amendments in real sediment environments. The effective dissolution radius and diffusion rate calculated based on dissolution characteristic parameters and sediment porosity can accurately assess the influence range and intensity of individual amendment particles, thus avoiding the problems of local over-dosing or insufficient coverage caused by traditional empirical application. Combining the spatiotemporal distribution characteristics of sediment releases, the optimized design of burial locations and dosages enables the amendments to form a reasonable spatial distribution network, ensuring both sufficient interception and neutralization of pollutants and optimized allocation of reagent resources. The sediment control strategies include liquid amendment sediment control strategies and solid amendment sediment control strategies.

[0093] Figure 4 A block diagram of a shrimp farming yield-increasing system based on phased growth management according to the present invention is shown.

[0094] A second aspect of the present invention also provides a shrimp farming production enhancement system based on phased growth management, comprising: a memory 401, a processor 402, and a communication interface 403. The memory is used to store a program, the processor is used to execute the program stored in the memory, and the communication interface is used for data connection and communication between the memory and the processor. When the program stored in the memory is executed, a shrimp farming production enhancement method based on phased growth management as described in any of the above claims is implemented.

[0095] This invention discloses a method and system for increasing shrimp farming yield based on phased growth management. By acquiring data on the behavioral and physiological changes of shrimp at different growth stages, their resistance to environmental changes is assessed, thereby dividing the growth management into refined stages. Furthermore, by combining the release characteristics of sediment from the target aquaculture pond with the environmental resistance of shrimp at each stage, a correlation analysis is performed to construct a matrix corresponding to the management stages and the impact of sediment, quantifying the fitness index of shrimp to sediment release at each stage. When the fitness index is below a safe threshold, sediment state information is acquired and a sediment control mode is determined, thereby formulating sediment control strategies for each stage to achieve dynamic control and optimization of the aquaculture environment. This invention can effectively improve shrimp growth efficiency and aquaculture yield, and has strong practical value and promising prospects for promotion.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0097] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0098] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0099] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, 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 cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0101] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for increasing shrimp farming yield based on phased growth management, characterized in that, Includes the following steps: Acquire behavioral and physiological change data of shrimp during their growth process to assess their resistance to environmental changes, and divide the growth management stages based on the environmental change resistance. The release characteristics of sediment released from the target shrimp farming pond were obtained. Correlation analysis was performed between the release characteristics and the environmental change resistance of farmed shrimp at each growth management stage to determine the impact of sediment release on shrimp growth at different growth management stages and to construct a management stage-sediment impact matrix. The fitness index of shrimp to sediment releases at each growth management stage is calculated based on the management stage-sediment influence matrix. If the fitness index is lower than the safety threshold, the sediment state information of the target aquaculture pond is obtained, and the sediment control mode is determined based on the sediment state information. Based on the bottom sediment regulation model, bottom sediment regulation strategies for different growth management stages of target aquaculture ponds are constructed, and shrimp farming is optimized for increased production based on the bottom sediment regulation strategies.

2. The method for increasing shrimp farming yield based on phased growth management according to claim 1, characterized in that, The process involves acquiring behavioral, physiological, and aquaculture environment data during shrimp growth to assess their resistance to environmental changes. Based on this resistance, growth management stages are defined. Specifically: Based on underwater camera devices, data on changes in swimming activity and feeding activity of shrimp during the farming and growth process are obtained to obtain behavioral change data. Data on changes in hemolymphatic immunity, molting frequency, and metabolic enzyme activity of shrimp during the growth process are also obtained to obtain physiological change data. The behavioral change dataset and physiological change dataset are input into a pre-trained environmental change resistance assessment model. The assessment model extracts behavioral change features through a convolutional neural network and extracts physiological change temporal features through a long short-term memory network. The behavioral change features and physiological change temporal features are weighted and fused to determine the shrimp's vitality. Based on the vitality, an environmental change resistance score is output. Based on the changing trend of the environmental change resistance score, the dynamic time warping algorithm is used to identify the inflection point of the resistance change, and the breeding cycle between adjacent inflection points is divided into a growth management stage.

3. The method for increasing shrimp farming yield based on phased growth management according to claim 1, characterized in that, The release characteristics of sediment released from target shrimp farming ponds are obtained. Correlation analysis is then performed between these release characteristics and the environmental change resistance of farmed shrimp at each growth management stage to determine the impact of sediment release on shrimp growth at different growth management stages. A management stage-sediment impact matrix is ​​then constructed. Specifically: Obtain pond structure data of the target shrimp farming pond, obtain pond bottom shape data based on the pond structure data, draw a pond bottom mud structure diagram based on the bottom shape data, and divide the pond into grids of a preset size based on the bottom mud structure diagram; Data on the types of sediment released from the bottom sediment at the contact surface between the bottom sediment and the pond water in each grid during the shrimp farming and growth process are obtained according to a preset time period, and the concentration information of each sediment release is obtained. The sediment release type data and concentration information for each preset time period are mapped to the corresponding grid to construct a time variation distribution map of sediment release. The release characteristics of sediment releases are extracted from the time variation distribution map of sediment releases, including spatial distribution characteristics, type characteristics of released substances, and concentration variation characteristics. Historical aquaculture environment data and shrimp disease occurrence data for each growth management stage are obtained. Correlation analysis is performed between the environmental change resistance corresponding to each growth management stage and the historical aquaculture environment data and shrimp disease occurrence data to determine the disease probability of shrimp at different growth management stages in different aquaculture environments, and a growth management stage-aquaculture environment-disease probability mapping table is obtained. Based on the mapping table and release characteristics, the probability of pathogenic impact on shrimp growth at different growth management stages is determined, and a management stage-sediment impact matrix is ​​constructed.

4. The method for increasing shrimp farming yield based on phased growth management according to claim 1, characterized in that, The fitness index of farmed shrimp to sediment releases at each growth management stage is calculated based on the management stage-sediment influence matrix. If the fitness index is lower than a safety threshold, the sediment state information of the target aquaculture pond is obtained, and the sediment control mode is determined based on the sediment state information. Specifically: Extract the characteristics of sediment release types and release concentration changes corresponding to each growth management stage in the sediment influence matrix, calculate the concentration change gradient of each sediment release within a preset time period, and determine the diffusion rate of sediment release based on the concentration change gradient. Obtain water depth distribution data and bottom sediment thickness distribution data of the target aquaculture pond; calculate the vertical diffusion distance of the bottom sediment release in the water body based on the diffusion rate and water depth distribution data; calculate the horizontal diffusion distance of the bottom sediment release in the bottom sediment based on the bottom sediment thickness distribution data. Based on the pathogenicity probability of shrimp to sediment releases at each growth management stage in the management stage-sediment influence matrix, and combined with the vertical diffusion distance and horizontal diffusion distance, the concentration attenuation coefficient of sediment releases when they reach the shrimp activity area is calculated. The effective concentration is obtained by multiplying the concentration decay coefficient by the initial concentration of the sediment release. The fitness index is obtained by calculating the ratio between the effective concentration and the shrimp's environmental change resistance score at the corresponding growth management stage. When the fitness index is lower than the safety threshold, the redox potential profile data and porosity distribution data of the bottom sediment of the target aquaculture pond are obtained. The dissolved oxygen gradient change curve at the bottom sediment-water interface is measured by microelectrode array. The organic matter content of the bottom sediment is obtained and the bottom sediment state information is obtained. A three-dimensional map of bottom sediment biochemical activity is constructed based on the bottom sediment state information. The vertical distribution data of bottom sediment moisture content and the metabolic activity data of bottom sediment microbial community were obtained from the target aquaculture pond. The composition characteristics of organic matter functional groups in the bottom sediment were analyzed by Fourier transform infrared spectroscopy. Based on the vertical distribution data of sediment moisture content and the composition characteristics of organic matter functional groups, the ratio of hydrophobic to hydrophilic components in the sediment was calculated. Combined with the metabolic activity data of microbial communities, the infiltration rate of liquid amendments in sediments at different depths and the coverage and diffusion rate of solid amendments in the surface sediment were analyzed. Based on the three-dimensional biochemical activity spectrum of the sediment and the penetration rate of the liquid amendment, the effective depth of the liquid amendment in the sediment was calculated. Based on the coverage and diffusion rate of the solid amendment and the surface porosity of the sediment, the surface coverage improvement efficiency of the solid amendment was calculated. When the ratio of the effective depth of the liquid amendment to the thickness of the main sediment-producing layer is greater than the set ratio, the liquid amendment control mode is used; otherwise, the solid amendment control mode is used.

5. The method for increasing shrimp farming yield based on phased growth management according to claim 4, characterized in that, The process involves constructing bottom sediment control strategies for different growth management stages of the target aquaculture pond based on the bottom sediment control model, and optimizing shrimp farming yield based on these strategies. Specifically: If a liquid amendment control mode is used, the extreme points of redox potential and the peak points of organic matter content at each depth layer are obtained based on the three-dimensional map of biochemical activity of bottom sediment in the target aquaculture pond. The spatial coordinates of the extreme points and peak points are coupled and analyzed to determine the three-dimensional distribution of anaerobic metabolic hotspots in the bottom sediment. The theoretical infiltration time of the liquid amendment from the water-mud interface to each anaerobic metabolic hotspot area is calculated based on the infiltration rate of the liquid amendment in the sediment. Combined with the sediment release concentration change cycle of the corresponding grid in the sediment release time change distribution map, the optimal application time window of the liquid amendment is determined. Based on sediment porosity distribution data and organic matter functional group composition characteristics, The reaction equivalence between oxidizing components and reducing substances in the sediment was analyzed. Based on the organic matter content detection results of each anaerobic metabolic hot spot area, the required loading of effective components of the amendment per unit volume of sediment was calculated. Combined with the actual penetration and diffusion range of the liquid amendment in the corresponding depth layer, the application concentration gradient distribution of the liquid amendment was calculated. Based on the optimal application time window and the application concentration gradient distribution, a liquid amendment sediment regulation strategy was constructed.

6. The method for increasing shrimp farming yield based on phased growth management according to claim 5, characterized in that, The method for constructing bottom sediment control strategies for different growth management stages of the target aquaculture pond based on the bottom sediment control model also includes: If a solid amendment control mode is adopted, the solid amendment is tested in the pore water environment corresponding to the bottom sediment of the target aquaculture pond to obtain the ion release curve of the solid amendment in the simulated bottom sediment pore water environment. Based on the ion release curve, the dissolution characteristic parameters of the solid amendment are extracted. The dissolution characteristic parameters include the initial dissolution delay time, the duration of the maximum dissolution rate, and the inflection point of dissolution decay. The effective dissolution radius and diffusion rate of the solid amendment in the bottom sediment of the target aquaculture pond are calculated based on the dissolution characteristic parameters and the bottom sediment porosity distribution data. Based on the release characteristics of the sediment releases, the effective dissolution radius, and the diffusion rate of the dissolved substances, the burial location and amount of solid amendment in the target pond sediment are determined, thus obtaining a solid amendment sediment control strategy.

7. A shrimp farming yield-increasing system based on phased growth management, characterized in that, include: A memory and a processor, wherein the memory is used to store a program, and the processor is used to execute the program stored in the memory, wherein when the program stored in the memory is executed, a method for increasing shrimp production based on staged growth management as described in any one of claims 1-6 is implemented.

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