Artificial intelligence-based marine ranching digital construction method and system

Abstract

The invention relates to a marine ranching digital construction method and system based on artificial intelligence, and the method comprises the steps: monitoring plankton in a marine ranching in real time, obtaining a water body image through an image recognition technology, and building a dynamic database, so as to regularly store and update the related data of the plankton. Based on monitoring data, a water quality evaluation model is established, and the correlation among plankton species, density and water quality parameters is analyzed. Factors such as water flow velocity and flow direction are integrated, a flow change model is established, and the influence of water flow on plankton is judged. A primary culture optimization scheme is formulated by simulating the interaction between cultured organisms and planktons, and the culture trend is predicted in real time and the management strategy is adjusted by integrating the water quality evaluation and the output result of the flow change model. According to the scheme, the intelligent management level of the marine ranching is effectively improved, dynamic and scientific management of the culture environment is achieved, powerful support is provided for healthy growth of cultured organisms, and sustainable development of the marine ranching is promoted.

Description

Technical Field

[0001] The present invention relates to the field of marine ranching, and specifically to a method and system for digital construction of marine ranching based on artificial intelligence. Background Art

[0002] With the continuous increase in global demand for aquaculture, marine ranching, as a new type of sustainable aquaculture model, has received increasing attention. However, traditional marine ranching management methods mainly rely on manual monitoring and empirical judgment, making it difficult to achieve scientific and systematic management. This method is not only inefficient but also often leads to problems such as resource waste and ecological imbalance. With the development of technology, especially the progress of artificial intelligence and data analysis technologies, the management of marine ranching urgently needs to transform towards digitalization and intelligentization to improve aquaculture efficiency and ecological health.

[0003] Currently, the monitoring of marine ranches mostly uses manual sampling and observation, making it difficult to obtain and analyze real-time data. In addition, the changes in water quality parameters have a direct impact on the growth and reproduction of aquaculture organisms, but the understanding of their change laws often relies on experience and lacks scientific data support. At the same time, the impact of water flow patterns on the distribution of plankton and the aquaculture environment is often ignored, resulting in the inability to effectively adjust aquaculture management strategies. Therefore, although marine ranching plays an important role in sustainable aquaculture, traditional management means are no longer sufficient to meet the needs of modern and intelligent development. Summary of the Invention

[0004] The present invention aims at the technical problems existing in the prior art and provides a method and system for digital construction of marine ranching based on artificial intelligence.

[0005] The technical solution for the present invention to solve the above technical problems is as follows: A method for digital construction of marine ranching based on artificial intelligence, the method comprising: Real-time monitoring of plankton-related data in a marine ranch based on image recognition, and establishing a dynamic database, and regularly storing and updating the plankton-related data obtained in the dynamic database; Based on the monitored plankton-related data, establishing a water quality assessment model, analyzing the mutual relationship between plankton species, density and water quality parameters, and at the same time analyzing the water flow pattern, establishing a flow change model, and judging the impact of water flow on plankton; Simulating the interaction between aquaculture organisms and plankton in a marine ranch, and establishing a primary aquaculture optimization plan only for the control of plankton; Integrating the output results of the water quality assessment model and the flow change model, predicting the current aquaculture trend in the marine ranch in real time, integrating the primary aquaculture optimization plan, analyzing the development and changes of aquaculture organisms, and adjusting the primary aquaculture optimization plan according to the results of the development and changes of aquaculture organisms to generate a final aquaculture optimization plan.

[0006] As a further solution of the present invention, the real-time monitoring of the relevant data of plankton in the marine ranch and the establishment of a dynamic database specifically include: Regularly obtain water body images and add time stamps to the water body images according to the collection time; Analyze the obtained images, identify the plankton in the images, and analyze the species and density conditions; Establish a dynamic database architecture for the needs of the marine ranch, and store the obtained plankton-related data together with the time stamps of the corresponding water body images in the dynamic database.

[0007] As a further solution of the present invention, the establishment of a water quality assessment model, the analysis of the mutual relationship between plankton species, density and water quality parameters, and at the same time the analysis of the water body flow pattern, the establishment of a flow change model, and the judgment of the impact of water flow on plankton specifically include: Establish a water quality assessment model, analyze the relationship between plankton species, density and water quality parameters, judge how plankton species and density affect water quality parameters, and construct an ecological network model between plankton and water quality parameters; Integrate water body flow data and combine it with plankton monitoring data to establish a flow change model and evaluate the impact of water flow on plankton; Combine the results of the water quality assessment model with the output results of the flow change model to identify the current trend of water quality change and the changes in plankton species and density, and generate a water quality status report.

[0008] As a further solution of the present invention, the analysis of the relationship between plankton species, density and water quality parameters, and the judgment of how plankton species and density affect water quality parameters are specifically: ; Wherein, represents the comprehensive impact score, reflecting the impact degree of plankton on water quality parameters, is the total number of plankton species, is the th plankton density, is the th plankton species index, with a value of , is the th plankton's basic impact coefficient on the target water quality parameter, is the impact index of plankton density, is the impact index of plankton species; The evaluation of the impact of water flow on plankton is specifically: ; ; Among them, is the change rate of the density of the th type of plankton, is the inherent growth rate of the th type of plankton, is the environmental carrying capacity of the th type of plankton, is the influence function of water flow on plankton, determined by the speed and direction of the water flow, is a constant, representing the benchmark of the influence of water flow on plankton, represents the non - linear exponent of the influence of water flow intensity on plankton growth, representing the velocity field of the water flow, is the sign function, used to indicate the directionality of the water flow, that is: .

[0009] As a further solution of the present invention, the interaction between the cultured organisms and plankton in the simulated marine ranch is considered, and a primary aquaculture optimization plan only for plankton control is established, specifically including: Extract plankton monitoring data, water quality data, and cultured organism data from the dynamic database; Establish several simulation scenarios, considering different plankton densities, species, and water quality conditions, and record the growth, reproduction, and health status of cultured organisms under different simulation scenarios; Extract the simulation results, analyze the growth rate, reproduction rate, and health index of cultured organisms, and identify the influence of different plankton densities on cultured organisms, including positive and negative influences; Based on the identification results, formulate a primary aquaculture optimization plan to optimize the growth environment of cultured organisms by controlling plankton.

[0010] As a further solution of the present invention, the analysis of the growth rate, reproduction rate, and health index of cultured organisms to identify the influence of different plankton densities on cultured organisms is specifically as follows: Calculate the growth rate : ; Among them, represents the inherent growth rate, reflecting the maximum growth potential of cultured organisms under ideal conditions, is the density of the current cultured organisms, is the environmental carrying capacity, representing the maximum density of cultured organisms under specific environmental conditions; Calculate the reproduction rate : ; Among them, is the basic reproduction rate coefficient, representing the reproduction ability under optimized conditions, is the density of plankton, is the optimized plankton density, representing the optimal plankton density for improving the reproduction rate of cultured organisms; Calculate the health index : ; Among them, is the basic health index, representing the health status of cultured organisms in an ideal state, is the weight coefficient, representing the relative contributions of growth rate and reproduction rate to the health index; Evaluate the overall impact score of plankton density on the growth, reproduction and health of cultured organisms : ; Among them, is the overall impact score, reflecting the comprehensive impact of plankton on cultured organisms; are weight coefficients, respectively representing the contribution degrees of growth rate, reproduction rate and health index to the overall impact score.

[0011] As a further aspect of the present invention, the breeding trend of the current marine ranch is predicted in real time, the primary breeding optimization plan is integrated, the development and changes of cultured organisms are analyzed, and the primary breeding optimization plan is adjusted according to the results of the development and changes of cultured organisms to generate a final breeding optimization plan, which specifically includes: Extract the latest output results of the water quality assessment model and the flow change model, and extract real-time monitoring data of cultured organisms; Establish a breeding trend prediction model, capture the relationship between cultured organisms and water quality parameters, predict and analyze the breeding trend in the current and future time periods, and judge the growth changes and reproduction ability changes of cultured organisms; Integrate the prediction results with the primary breeding optimization plan, comprehensively consider the mutual influence relationships among cultured organisms, plankton, water body flow and water quality parameters, identify the parts that need to be adjusted in the primary breeding optimization plan, and generate a final breeding optimization plan.

[0012] As a further aspect of the present invention, the prediction and analysis of the breeding trend in the current and future time periods, and the judgment of the growth changes and reproduction ability changes of cultured organisms are specifically as follows: Predict the density of cultured organisms at time : : ; Among them, Initial density of cultured organisms; Calculate the reproduction rate of cultured organisms based on the plankton density at time : : ; Combine the growth and reproduction of cultured organisms to form a comprehensive prediction model: ; Among them, represents the rate of change of the cultured organism density with time . If , it means the reproduction rate is higher than the mortality rate. If , it means the reproduction rate is lower than the mortality rate.

[0013] As a further solution of the present invention, comprehensively consider the mutual influence relationships among cultured organisms, plankton, water body flow, and water quality parameters, and identify the parts that need to be adjusted in the primary aquaculture optimization plan, specifically: Analyze the water quality parameters at time : ; Among them, is the basic water quality parameter, is the parameter related to water body flow, are the water quality influence coefficients corresponding to cultured organisms, plankton, and flow patterns respectively; Calculate the comprehensive minimum loss at the th step: ; Among them, is the water quality parameter at the th step, is the desired target water quality parameter, is the cultured organism density at the th step, is the desired target cultured organism density, is the reproduction rate of cultured organisms at the th step, is the desired target reproduction rate of cultured organisms, is the plankton density at the th step, is the weight coefficient.

[0014] Set a loss threshold. If it is greater than the loss threshold, it means that the step needs to be adjusted.

[0015] Another object of the present invention is to provide a digital construction system for marine ranching based on artificial intelligence, and the system includes: A plankton monitoring module, which is used to monitor the data related to plankton in the marine ranch in real time based on image recognition, establish a dynamic database, and regularly store and update the data related to plankton obtained in the dynamic database; A water quality analysis and water body flow pattern analysis module, which is used to establish a water quality assessment model based on the monitored data related to plankton, analyze the mutual relationship between the types and densities of plankton and water quality parameters, and at the same time analyze the water body flow pattern, establish a flow change model, and judge the impact of water flow on plankton; A primary aquaculture optimization plan simulation module, which is used to simulate the interaction between the aquaculture organisms and plankton in the marine ranch and establish a primary aquaculture optimization plan only for plankton control; A final optimization plan adjustment module, which is used to comprehensively predict the aquaculture trend of the current marine ranch in real time based on the output results of the water quality assessment model and the flow change model, integrate the primary aquaculture optimization plan, analyze the development and changes of aquaculture organisms, and adjust the primary aquaculture optimization plan according to the results of the development and changes of aquaculture organisms to generate a final aquaculture optimization plan.

[0016] The beneficial effects of the present invention are: Through artificial intelligence and digital technology, this solution constructs a comprehensive marine ranch management system aimed at realizing dynamic and efficient management of the aquaculture environment. In terms of real-time monitoring, the solution uses image recognition technology to regularly obtain water body images and identify plankton, forming a dynamic database. This mechanism ensures the timeliness and accuracy of data, enabling aquaculture managers to make scientific decisions based on the latest environmental conditions. Further, by establishing a water quality assessment model, analyzing the impact of the types and densities of plankton on water quality parameters, and constructing an ecological network, the relationship between plankton and water quality is revealed, providing an important basis for the health of aquaculture organisms. In addition, by integrating the influence of the water flow pattern, managers can understand the effect of water flow on the distribution of plankton and further optimize the aquaculture environment.

[0017] In terms of interaction simulation, the solution simulates the relationship between aquaculture organisms and plankton, identifies the positive or negative impact of plankton density on aquaculture organisms, enabling managers to formulate specific control strategies, improve aquaculture efficiency, and balance ecological and economic benefits. Combining the output of the water quality assessment and flow change models, the solution realizes real-time prediction of the aquaculture trend, establishes an aquaculture trend prediction model, enabling managers to better grasp the growth changes of aquaculture organisms and the relationship between water quality parameters. This prediction ability helps managers adjust aquaculture strategies in a timely manner in a dynamic environment, ensuring the sustainability and ecological health of aquaculture. Description of the Drawings

[0018] Figure 1 Flow chart of the digital construction method of the marine ranch based on artificial intelligence provided by the embodiment of the present invention; Figure 2 Flow chart of the real-time monitoring of the data related to plankton in the marine ranch and the establishment of a dynamic database provided by the embodiment of the present invention; Figure 3 Flow chart of analyzing the mutual relationship between the types and densities of plankton and water quality parameters and judging the influence of water flow on plankton provided by the embodiment of the present invention; Figure 4 Flow chart of establishing a primary aquaculture optimization plan for plankton control only provided by the embodiment of the present invention; Figure 5 Flow chart of generating the final aquaculture optimization plan provided by the embodiment of the present invention; Figure 6 Block diagram of the structure of the digital construction system of the marine ranch based on artificial intelligence provided by the embodiment of the present invention. Detailed implementation manners

[0019] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0020] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more unless otherwise specifically defined.

[0021] In the description of the present application, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present application is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but rather to be in line with the broadest scope consistent with the principles and features disclosed in the present application.

[0022] Figure 1 The flowchart of the method for digital construction of an ocean ranch based on artificial intelligence provided by an embodiment of the present invention is as Figure 1 shown, and the method includes: S100, based on image recognition, monitor the data related to plankton in the ocean ranch in real time, and establish a dynamic database, and regularly store and update the data related to plankton obtained in the dynamic database; In this step, by regularly acquiring water body images and adding time stamps to each image, the timeliness and traceability of the monitoring data are ensured. The automation of this process makes data collection more efficient, reduces the possibility of manual intervention, and thus reduces the risk of human error. Then, the acquired images are analyzed, and advanced image recognition algorithms are used to achieve accurate recognition of plankton in the images. Through the application of deep learning technology, the system can continuously improve the recognition accuracy to adapt to the changes in different water body environments and plankton species.

[0023] Establishing a dynamic database architecture that meets the requirements of the ocean ranch is a key link in this step. In the dynamic database, not only the data related to plankton are stored, but also the corresponding water body images and their time stamps are included, thus providing multi-dimensional data support. This structured information storage method makes subsequent data analysis and model construction more efficient and accurate. The regular storage and update mechanism of the dynamic database ensures the timeliness and accuracy of the data, enabling decision-makers to formulate management strategies based on the latest monitoring results.

[0024] Through efficient image recognition and data storage mechanisms, comprehensive monitoring of the species, quantity, and distribution of plankton has been achieved, providing a solid data foundation for subsequent water quality assessment and aquaculture optimization. The establishment of a dynamic database not only provides long-term data accumulation for ecological health assessment but also lays a good foundation for future intelligent analysis and decision-making systems. At the same time, real-time monitoring provides timely feedback to aquaculture managers, enabling them to quickly adjust aquaculture strategies in the face of sudden environmental changes to ensure the health and stability of the aquaculture environment, thereby improving aquaculture efficiency and ecological sustainability.

[0025] Such as Figure 2 shown, the real-time monitoring of plankton-related data in the marine ranch and the establishment of a dynamic database specifically include: S110, regularly obtain water body images and add time stamps to the water body images according to the collection time; S120, analyze the obtained images to identify plankton in the images and analyze the species and density conditions; S130, establish a dynamic database architecture for the needs of the marine ranch and store the obtained plankton-related data together with the time stamps of the corresponding water body images in the dynamic database.

[0026] S200, based on the monitored plankton-related data, establish a water quality assessment model, analyze the mutual relationship between plankton species, density and water quality parameters, and at the same time analyze the water body flow pattern, establish a flow change model, and judge the impact of water flow on plankton; This step provides a scientific basis for the management of subsequent aquaculture organisms by establishing an ecological network model to clarify the impact of plankton on water quality parameters (such as nitrogen, phosphorus content, and dissolved oxygen, etc.).

[0027] In actual operation, the water quality assessment model integrates the monitoring data of plankton, identifies the species and quantity of various plankton, and through the establishment of a mathematical model, quantitatively analyzes how plankton density affects water quality parameters. The core of this model lies in quantifying the interaction between plankton and water quality through the combination of experiments and field data, enabling managers to timely identify potential risks of water quality changes.

[0028] At the same time, it also integrates water body flow data such as water flow velocity and direction, and combines it with plankton monitoring data to establish a flow change model. This model can be used to evaluate the impact of water flow on plankton, especially the impact on the distribution and reproduction of plankton under different environmental conditions (water temperature, salinity, wind force, etc.). This comprehensive analysis makes the relationship between the ecological behavior of plankton and water quality changes clearer.

[0029] In addition, combining the results of the water quality assessment model with the output of the flow change model helps to identify current trends in water quality changes and record changes in plankton species and density. By generating detailed water quality status reports, it can provide early warning for the management of aquaculture organisms and ensure a healthy and stable aquaculture environment.

[0030] The formula proposed in this step is actually a dynamic model used to analyze the relationship between plankton species, density and water quality parameters. The construction of this formula is not only a mathematical expression, but also an important reflection of the interaction between various factors in the ecosystem.

[0031] The key part of this model is to reflect the impact of plankton on water quality parameters through comprehensive impact assessment. Specifically, by quantifying the types, density and water quality parameters of plankton in the formula, managers can clearly understand how plankton affects water quality under different environmental conditions. For example, when high density of plankton exists, it may lead to eutrophication of water bodies, thus affecting the stability of water quality and the living environment of farmed organisms. This analysis not only identifies potential water quality problems, but also provides a basis for the implementation of regulatory measures.

[0032] By adjusting and analyzing the parameters of the formula, managers can evaluate changes in water quality in real time. For example, changes in the diversity and density of plankton species will directly affect the concentration of water quality parameters such as nitrogen and phosphorus, thereby affecting the health of the aquaculture environment. The results of the formula can help managers detect abnormal fluctuations in water quality in a timely manner and take corresponding aquaculture control measures, such as adjusting feed input and aquaculture density, to ensure that the aquaculture organisms are in a good growth environment.

[0033] In addition, the incorporation of a flow change model brings a deeper meaning to this analysis. The speed and direction of water flow will affect the distribution of plankton and its interaction with water quality. Through this dynamic model, managers can more comprehensively understand how water flow affects the survival and reproduction of plankton under different water conditions. This global perspective not only enhances environmental sensitivity, but also provides strong support for the formulation of ecological management strategies.

[0034] Through scientific model construction and data analysis, managers can monitor the ecological status of marine ranches in real time and take appropriate measures in a timely manner to deal with potential ecological crises. This dynamic management method not only improves the sustainability of marine ranches, but also provides solid data support for decision-making, enhances the ability to adapt to environmental changes, thereby optimizing aquaculture efficiency and ensuring ecological health. At the same time, with a deep understanding of the relationship between water quality and plankton, it can provide guidance for future aquaculture management and environmental protection, and promote the intelligent development of marine ranches.

[0035] likeFigure 3 As shown, the water quality assessment model is established to analyze the mutual relationship between plankton species, density and water quality parameters, and at the same time analyze the water body flow pattern, establish a flow change model, and judge the impact of water flow on plankton, specifically including: S210, establish a water quality assessment model, analyze the relationship between plankton species, density and water quality parameters, judge how plankton species and density affect water quality parameters, and construct an ecological network model between plankton and water quality parameters; S220, integrate the water body flow data, combine it with the plankton monitoring data, establish a flow change model, and evaluate the impact of water flow on plankton; S230, combine the results of the water quality assessment model with the output results of the flow change model, identify the current trend of water quality change and the change of plankton species and density, and generate a water quality status report.

[0036] In this step, the analysis of the relationship between plankton species, density and water quality parameters, and the judgment of how plankton species and density affect water quality parameters are specifically as follows: ; Among them, represents the comprehensive impact score, reflecting the impact degree of plankton on water quality parameters, is the total number of plankton species, is the th plankton density, is the th plankton species index, with a value of , is the th basic impact coefficient of plankton on the target water quality parameter, is the impact index of plankton density, is the impact index of plankton species; The evaluation of the impact of water flow on plankton is specifically as follows: ; ; Among them, is the th plankton density change rate, is the th inherent growth rate of plankton, is the th environmental carrying capacity of plankton, is the impact function of water flow on plankton, determined by the speed and direction of water flow, is a constant, representing the benchmark of the impact of water flow on plankton, represents the non - linear index of the influence of water flow intensity on plankton growth, and represents the velocity field of the water flow, is the sign function, used to indicate the directionality of the water flow, that is: 。

[0037] S300, simulate the interaction between cultured organisms and plankton in the marine ranch, and establish a primary aquaculture optimization plan only for plankton control; The core of this step lies in the analysis of the growth rate, reproduction rate and health index, and uses formulas to quantitatively evaluate these key parameters.

[0038] By considering different plankton densities, species and water quality conditions, the system can effectively simulate the growth, reproduction and health status of cultured organisms in various environments. This simulation is not limited to the analysis of static data, but also allows dynamic factors (such as water temperature, salinity, etc.) to be taken into account, so as to more realistically reflect the complexity of the actual aquaculture environment.

[0039] After extracting the simulation results, it is crucial to analyze the growth rate, reproduction rate and health index of cultured organisms. By comparing the growth performance of cultured organisms under different plankton densities, managers can identify the positive or negative impacts of plankton on cultured organisms. For example, high - density plankton may promote the nutrient supply of water quality, and vice versa, too high density may lead to increased competition and affect the health of cultured organisms. This detailed analysis can help managers make more scientific aquaculture decisions to ensure that cultured organisms can grow in the best environment.

[0040] Based on the identification results, through in - depth analysis of the simulation results, managers can formulate specific control strategies for different environmental conditions, such as adjusting the quantity, species of plankton or applying specific nutrients to optimize aquaculture conditions. This plan not only helps to improve aquaculture efficiency, but also reduces environmental risks and ensures the sustainability of the ecosystem.

[0041] By establishing a dynamic simulation model, managers can quickly respond to environmental changes and adjust aquaculture strategies. This real - time decision - making support system enables managers to take necessary measures in a timely manner according to the latest data and simulation results, thereby reducing environmental risks and improving the growth efficiency of cultured organisms. In addition, the dynamic control strategy for plankton can effectively reduce the possible ecological problems in the aquaculture process, achieve the dual optimization of ecology and economy, and inject new vitality into the intelligent management of marine ranches.

[0042] Such as Figure 4As shown, the interaction between the cultured organisms and plankton in the simulated marine ranch is considered, and a primary aquaculture optimization plan for controlling only plankton is established, specifically including: S310, Extract plankton monitoring data, water quality data, and cultured organism data from the dynamic database; S320, Establish a number of simulation scenarios, considering different plankton densities, species, and water quality conditions, and record the growth, reproduction, and health status of cultured organisms under different simulation scenarios; S330, Extract the simulation results, analyze the growth rate, reproduction rate, and health index of cultured organisms, and identify the effects of different plankton densities on cultured organisms, including positive and negative effects; S340, Based on the identification results, formulate a primary aquaculture optimization plan to optimize the growth environment of cultured organisms by controlling plankton.

[0043] In this step, the analysis of the growth rate, reproduction rate, and health index of cultured organisms to identify the effects of different plankton densities on cultured organisms is specifically as follows: Calculate the growth rate : ; Among them, represents the intrinsic growth rate, reflecting the maximum growth potential of cultured organisms under ideal conditions, is the current density of cultured organisms, is the environmental carrying capacity, indicating the maximum density of cultured organisms under specific environmental conditions; Calculate the reproduction rate : ; Among them, is the basic reproduction rate coefficient, indicating the reproduction ability under optimized conditions, is the density of plankton, is the optimized plankton density, indicating the optimal plankton density to increase the reproduction rate of cultured organisms; Calculate the health index : ; Among them, is the basic health index, indicating the health status of cultured organisms under ideal conditions, is the weight coefficient, indicating the relative contribution of the growth rate and reproduction rate to the health index; Evaluate the overall impact score of plankton density on the growth, reproduction, and health of cultured organisms : ; Among them, For the overall impact score, it reflects the comprehensive impact of plankton on cultured organisms; Are weight coefficients, respectively representing the contribution degrees of the growth rate, reproduction rate, and health index to the overall impact score.

[0044] S400, based on the output results of the comprehensive water quality assessment model and the flow change model, predicts the current aquaculture trends in the marine ranch in real time, integrates the primary aquaculture optimization plan, analyzes the development and changes of cultured organisms, and adjusts the primary aquaculture optimization plan according to the results of the development and changes of cultured organisms to generate the final aquaculture optimization plan.

[0045] In this step, by extracting the latest output results of the water quality assessment model and the flow change model, real-time monitoring data of cultured organisms can be obtained. These data will provide a basis for establishing an aquaculture trend prediction model. The establishment of the prediction model means that it is possible to conduct a forward-looking analysis of the future aquaculture environment, the growth and reproduction capabilities of cultured organisms, so as to better formulate management strategies. The dynamic change relationship between the density of cultured organisms and time in the formula, combined with the influence of flow and water quality parameters, can accurately predict the performance of cultured organisms under different environmental conditions.

[0046] The prediction model enables managers to clearly understand the growth potential of cultured organisms and the environmental carrying capacity. Such model-based prediction not only helps to identify the possible risks of over-aquaculture, but also guides managers to make timely adjustments in actual operations.

[0047] Regarding the density of cultured organisms, managers can clearly understand the growth potential of cultured organisms under different environmental conditions, and then judge whether it is necessary to adjust the aquaculture scale. This dynamic prediction ability enables managers to identify potential risks at the early stage of the aquaculture process. For example, too high aquaculture density may lead to resource competition and ecological imbalance.

[0048] In addition, the analysis of the interaction between flow and water quality parameters in the model enables managers to identify the optimal aquaculture strategies under different environmental conditions. By calculating the loss function, managers can quantify the deviation between the performance of cultured organisms and the target state, so as to achieve dynamic adjustment in aquaculture management. This quantitative analysis not only improves the scientific nature of decision-making, but also can more effectively cope with the challenges brought by environmental changes.

[0049] The calculation of the loss function quantifies the deviation between the actual performance of cultured organisms and the target state. Through the systematic comparison of the actual growth of cultured organisms with the target growth, managers can accurately identify the parts that need to be adjusted. This feedback mechanism makes aquaculture management more targeted and improves the scientific nature and timeliness of decision-making.

[0050] Through integrating multiple data sources and models, managers can adjust aquaculture strategies in real time in a dynamically changing environment to ensure the health of aquaculture organisms and the sustainability of the ecosystem. The construction of a predictive scheduling system enables managers to utilize resources more efficiently, reduce costs, and improve the overall efficiency of aquaculture during the aquaculture process. At the same time, this data-driven decision-making mechanism provides strong support for the intelligent management of marine ranches and promotes the process of digital transformation.

[0051] As Figure 5 shown, the method for predicting the aquaculture trend of the current marine ranch in real time, integrating the primary aquaculture optimization plan, analyzing the development and changes of aquaculture organisms, and adjusting the primary aquaculture optimization plan according to the results of the development and changes of aquaculture organisms to generate the final aquaculture optimization plan specifically includes: S410, extracting the latest output results of the water quality assessment model and the flow change model, and extracting real-time aquaculture organism monitoring data; S420, establishing an aquaculture trend prediction model to capture the relationship between aquaculture organisms and water quality parameters, predicting and analyzing the aquaculture trend in the current and future time periods, and judging the growth changes and reproductive capacity changes of aquaculture organisms; S430, integrating the prediction results with the primary aquaculture optimization plan, comprehensively considering the mutual influence relationships among aquaculture organisms, plankton, water body flow, and water quality parameters, identifying the parts that need to be adjusted in the primary aquaculture optimization plan, and generating the final aquaculture optimization plan.

[0052] In this step, the predicting and analyzing the aquaculture trend in the current and future time periods and judging the growth changes and reproductive capacity changes of aquaculture organisms specifically means: Predicting the density of aquaculture organisms at time : ; wherein, initial density of aquaculture organisms; Calculating the reproductive rate of aquaculture organisms based on the density of plankton at time : ; Combining the growth and reproduction of aquaculture organisms to form a comprehensive prediction model: ; wherein, represents the rate of change of the density of aquaculture organisms with time . If , it means that the reproductive rate is higher than the mortality rate. If , it means that the reproductive rate is lower than the mortality rate.

[0053] In this step, comprehensively consider the mutual influence relationships among cultured organisms, plankton, water body flow, and water quality parameters, and identify the parts that need to be adjusted in the primary aquaculture optimization plan. Specifically: Analysis time Water quality parameters at the moment : ; Among them, is the basic water quality parameter, is the parameter related to water body flow, are the water quality influence coefficients corresponding to cultured organisms, plankton, and flow patterns respectively; Calculate the comprehensive minimum loss in the th step: ; Among them, is the water quality parameter at the th step, is the expected target water quality parameter, is the density of cultured organisms at the th step, is the expected target density of cultured organisms, is the reproduction rate of cultured organisms at the th step, is the expected target reproduction rate of cultured organisms, is the density of plankton at the th step, is the weight coefficient.

[0054] Set a loss threshold. If it is greater than the loss threshold, it means that step needs to be adjusted.

[0055] Figure 6 is the structural block diagram of the digital construction system of an ocean ranch based on artificial intelligence provided by an embodiment of the present invention. As Figure 6 shown, the system includes: A plankton monitoring module 100, which is used to monitor plankton-related data in the ocean ranch in real time based on image recognition, establish a dynamic database, and regularly store and update the plankton-related data obtained in the dynamic database; A water quality analysis and water body flow pattern analysis module 200, which is used to establish a water quality assessment model based on the monitored plankton-related data, analyze the mutual relationship between plankton species, density and water quality parameters, and at the same time analyze the water body flow pattern, establish a flow change model, and judge the impact of water flow on plankton; The primary aquaculture optimization scheme simulation module 300 is used to simulate the interaction between aquaculture organisms and plankton in the marine ranch, and establish a primary aquaculture optimization scheme only for the control of plankton; The final optimization scheme adjustment module 400 is used to comprehensively evaluate the output results of the water quality assessment model and the flow change model, predict the aquaculture trend of the current marine ranch in real time, integrate the primary aquaculture optimization scheme, analyze the development and change of aquaculture organisms, and adjust the primary aquaculture optimization scheme according to the results of the development and change of aquaculture organisms to generate the final aquaculture optimization scheme.

[0056] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0057] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0058] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0059] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0060] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, causing a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process, such that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for implementing the functions specified in one block or a plurality of blocks.

[0061] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0062] It is apparent that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An artificial intelligence-based digital construction method for marine ranch, characterized in that The method includes: Based on image recognition, real-time monitor the data related to plankton in the marine ranch, and establish a dynamic database to regularly store and update the data related to plankton obtained in the dynamic database; Based on the monitored data related to plankton, establish a water quality assessment model to analyze the mutual relationship between the types, density of plankton and water quality parameters, and at the same time analyze the water flow pattern, establish a flow change model to judge the impact of water flow on plankton; Simulate the interaction between the cultured organisms and plankton in the marine ranch, and establish a primary aquaculture optimization plan only for the control of plankton; Integrate the output results of the water quality assessment model and the flow change model, predict the aquaculture trend of the current marine ranch in real time, integrate the primary aquaculture optimization plan, analyze the development and change of the cultured organisms, and adjust the primary aquaculture optimization plan according to the results of the development and change of the cultured organisms to generate the final aquaculture optimization plan.

2. The method according to claim 1, wherein The real-time monitoring of the data related to plankton in the marine ranch and the establishment of a dynamic database specifically include: Regularly obtain water body images and add time stamps to the water body images according to the collection time; Analyze the obtained images to identify the plankton in the images and analyze the species and density; Establish a dynamic database architecture for the needs of the marine ranch, and store the data related to plankton obtained together with the time stamps of the corresponding water body images in the dynamic database.

3. The method according to claim 2, wherein The establishment of a water quality assessment model to analyze the mutual relationship between the types, density of plankton and water quality parameters, and at the same time analyze the water flow pattern, establish a flow change model to judge the impact of water flow on plankton specifically includes: Establish a water quality assessment model to analyze the relationship between the types, density of plankton and water quality parameters, judge how the types and density of plankton affect the water quality parameters, and construct an ecological network model between plankton and water quality parameters; Integrate the water flow data and combine it with the plankton monitoring data to establish a flow change model to evaluate the impact of water flow on plankton; Combine the results of the water quality assessment model with the output results of the flow change model to identify the current trend of water quality change and the changes in the types and density of plankton, and generate a water quality status report.

4. The method according to claim 3, wherein The analysis of the relationship between the types, density of plankton and water quality parameters, and the judgment of how the types and density of plankton affect the water quality parameters are specifically: ； Among them, represents the comprehensive impact score, reflecting the impact degree of plankton on water quality parameters, is the total number of plankton species, is the density of the th plankton species, is the species index of the th plankton species, and the value is is the basic impact coefficient of the th plankton species on the target water quality parameter, is the impact index of plankton density, is the impact index of plankton species; The evaluation of the impact of water flow on plankton is specifically: ； ； in, For the The rate of change of plankton density, For the The intrinsic growth rate of the plankton species, For the The environmental carrying capacity of plankton species, is the function of the effect of water flow on plankton, which is determined by the speed and direction of the water flow. is a constant, representing the benchmark for the effect of water flow on plankton. It represents the nonlinear index that affects the growth of plankton due to the intensity of water flow, and it represents the velocity field of water flow. is a sign function used to indicate the directionality of water flow, namely: 。 5. The method according to claim 4, characterized in that The simulation of the interaction between the cultured organisms and plankton in the marine ranch, and the establishment of a primary aquaculture optimization plan only for the control of plankton specifically include: Extract the plankton monitoring data, water quality data and cultured organism data from the dynamic database; Establish several simulation scenarios, consider different plankton densities, species and water quality conditions, and record the growth, reproduction and health status of the cultured organisms under different simulation scenarios; Extract the simulation results, analyze the growth rate, reproduction rate and health index of the cultured organisms, and identify the impact of different plankton densities on the cultured organisms, including positive and negative impacts; Based on the identification results, formulate a primary aquaculture optimization plan to optimize the growth environment of the cultured organisms by controlling plankton.

6. The method according to claim 4, characterized in that Analyze the growth rate, reproduction rate, and health index of the cultured organisms, and identify the impact of different plankton densities on the cultured organisms, specifically: Calculate the growth rate : ； Among them, represents the intrinsic growth rate, reflecting the maximum growth potential of the cultured organisms under ideal conditions, is the density of the current cultured organisms, is the environmental carrying capacity, indicating the maximum density of the cultured organisms under specific environmental conditions; Calculate the reproduction rate : ； Among them, is the basic reproduction rate coefficient, representing the reproductive ability under optimized conditions, is the density of plankton, is the optimized plankton density, representing the optimal plankton density for increasing the reproductive rate of cultured organisms; Calculate the health index : ； Among them, is the basic health index, indicating the health status of the cultured organisms under ideal conditions, is the weight coefficient, indicating the relative contributions of the growth rate and the reproduction rate to the health index; Evaluate the overall impact score of plankton density on the growth, reproduction, and health of cultured organisms : ； Among them, is the overall impact score, reflecting the comprehensive impact of plankton on cultured organisms; is the weight coefficient, respectively representing the contribution degrees of the growth rate, reproduction rate, and health index to the overall impact score.

7. The method according to claim 5, wherein Predict the current aquaculture trends in the marine ranch in real time, integrate the primary aquaculture optimization plan, analyze the development and changes of the cultured organisms, and adjust the primary aquaculture optimization plan based on the results of the development and changes of the cultured organisms to generate the final aquaculture optimization plan, specifically including: Extract the latest output results of the water quality assessment model and the flow change model, and extract the real-time monitoring data of the cultured organisms; Establish an aquaculture trend prediction model to capture the relationship between the cultured organisms and water quality parameters, predict and analyze the aquaculture trends in the current and future time periods, and judge the growth changes and changes in reproductive ability of the cultured organisms; Integrate the prediction results with the primary aquaculture optimization plan, comprehensively consider the mutual influence relationship among the cultured organisms, plankton, water body flow, and water quality parameters, identify the parts that need to be adjusted in the primary aquaculture optimization plan, and generate the final aquaculture optimization plan.

8. The method according to claim 7, wherein Predict and analyze the aquaculture trends in the current and future time periods, and judge the growth changes and changes in reproductive ability of the cultured organisms, specifically: Predict the density of cultured organisms at time : ； Among them, Initial density of cultured organisms; Calculate the breeding rate of cultured organisms based on plankton density at time : ​ ； Combine the growth and reproduction of the cultured organisms to form a comprehensive prediction model: ； Among them, represents the rate of change of the density of cultured organisms over time If , it means that the reproduction rate is higher than the mortality rate. If , it means that the reproduction rate is lower than the mortality rate.

9. The method according to claim 8, wherein Comprehensively consider the mutual influence relationship among the cultured organisms, plankton, water body flow, and water quality parameters, and identify the parts that need to be adjusted in the primary aquaculture optimization plan, specifically: Analysis time Water quality parameters at a moment : ； Among them, is the basic water quality parameter, is the parameter related to water body flow, are the water quality influence coefficients corresponding to the cultured organisms, plankton, and flow patterns, respectively; Calculate the comprehensive minimum loss of the th step: ； Among them, is the water quality parameter at the th step, is the desired target water quality parameter, is the density of cultured organisms at the th step, is the desired target density of cultured organisms, is the reproduction rate of cultured organisms at the th step, is the desired target reproduction rate of cultured organisms, is the density of plankton at the th step, is the weight coefficient; Set a loss threshold, If it is greater than the loss threshold, it means that step Needs to be adjusted.

10. An artificial intelligence-based digital construction system for ocean ranching, characterized in that, The system includes: A plankton monitoring module for real-time monitoring of plankton-related data in the marine ranch based on image recognition, establishing a dynamic database, and regularly storing and updating the plankton-related data obtained in the dynamic database; A water quality analysis and water body flow pattern analysis module for establishing a water quality assessment model based on the monitored plankton-related data, analyzing the mutual relationship between plankton species, density, and water quality parameters, analyzing the water body flow pattern at the same time, establishing a flow change model, and judging the impact of water flow on plankton; A primary aquaculture optimization plan simulation module for simulating the interaction between the cultured organisms and plankton in the marine ranch and establishing a primary aquaculture optimization plan only for plankton control; A final optimization plan adjustment module for comprehensively considering the output results of the water quality assessment model and the flow change model, predicting the current aquaculture trends in the marine ranch in real time, integrating the primary aquaculture optimization plan, analyzing the development and changes of the cultured organisms, and adjusting the primary aquaculture optimization plan based on the results of the development and changes of the cultured organisms to generate the final aquaculture optimization plan.

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