Dynamic comprehensive monitoring and management method and system for deep-sea culture

Abstract

The invention provides a dynamic comprehensive monitoring management method and system for deep-sea culture. The method comprises the following steps: S1, parameter determination: determining natural parameters, assessment index parameters and management parameters related to a water body in a culture area; s2, a prediction model is established, wherein the prediction model comprises the change relation of the temperature T along with the time t in the breeding period Z; establishing a correlation function to obtain a fish school number, a single fish body weight and a fish total biomass related to the time t; determining the feeding amount; establishing a function relationship among the natural parameters, the management parameters and the environment index parameters, and obtaining a function relationship between the environment index parameters and time t; s3, monitoring temperature and water quality parameters in real time, and inputting the parameters into the prediction model; calculating the prediction model to obtain a change relation of assessment index parameters along with time; s4, based on the change relation of the assessment index parameters along with time, breeding management is carried out, and management parameters are adjusted.

Description

Technical Field

[0001] This invention relates to the field of aquaculture management, and in particular to a method and system for comprehensive dynamic monitoring and management of deep-sea aquaculture. Background Technology

[0002] Deep-sea aquaculture is an important means of meeting global seafood demand. Compared to coastal aquaculture, its high water exchange rate and open water environment reduce water pollution and disease transmission. However, deep-sea aquaculture faces unique challenges, including strong ocean currents, temperature fluctuations, infrastructure durability, and the management of nutrients and waste. Golden pomfret, a high-value warm-water fish, thrives in temperatures between 22°C and 28°C and needs to reach a market size of around 500 grams within 3-4 months, with a maximum weight of 1 kilogram. However, the farming cycle typically does not exceed two years to control costs.

[0003] Existing aquaculture models are mostly designed for shallow water or pond systems, with limited consideration of the high water exchange rates and complex ecological interactions in deep-sea environments. For example, logistic models are often used to simulate fish growth, but they do not adequately integrate environmental factors such as dissolved oxygen, chlorophyll, and nutrient dynamics. Furthermore, traditional models lack cumulative assessments of total nitrogen (TN) and total phosphorus (TP) emissions, making it difficult to meet the sustainable development needs of deep-sea aquaculture.

[0004] Therefore, there is an urgent need for a comprehensive model that integrates fish growth, population dynamics, water quality monitoring, and environmental impact assessment to optimize the management methods for deep-sea golden pomfret farming. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method and system for dynamic integrated monitoring and management of deep-sea aquaculture, which can integrate factors such as fish growth, population dynamics, water quality monitoring and environmental impact assessment to achieve dynamic integrated monitoring and optimize aquaculture management.

[0006] To achieve the above objectives, this invention provides a method for comprehensive monitoring and management of deep-sea aquaculture dynamics, comprising the following steps:

[0007] S1. Parameter determination:

[0008] S11. Determine the relevant natural parameters of the aquaculture area, including temperature (T) and algal biomass. and water quality parameters;

[0009] S12. Determine the assessment indicator parameters used to evaluate the aquaculture status, including fish quantity indicator parameters and environmental indicator parameters. The fish quantity indicator parameters include the weight of an individual fish (W) and the number of fish in the group (N).

[0010] S13. Determine the management parameters related to aquaculture, including feed intake. ;

[0011] S2. Establish a prediction model, including:

[0012] S21. Establish the relationship between temperature T and time t within the breeding cycle Z;

[0013] S22. Establish the functional relationship between the fish population size N and the individual fish weight W and the temperature T. Establish the functional relationship between the total fish biomass B and the fish population size N and the individual fish weight W, B=f(N,W). Combine this with the relationship between temperature T and time t to obtain the fish population size related to time t. Individual fish weight Total biomass of fish Determine the amount of feed. , where f rate The set feeding rate;

[0014] S23. Establish the functional relationships between natural parameters, management parameters, and environmental indicator parameters, and obtain the functional relationship between environmental indicator parameters and time t;

[0015] S3. Monitor temperature and water quality parameters in real time and input them into the prediction model; the prediction model calculates the relationship between the performance indicators and time.

[0016] S4. Based on the relationship between the performance indicators and time, conduct aquaculture management and adjust management parameters.

[0017] Furthermore, in step S11, the water quality parameters include dissolved oxygen concentration, total nitrogen concentration, total phosphorus concentration, and chlorophyll concentration.

[0018] Furthermore, in step S13, the environmental impact statistical parameters include the total cumulative nitrogen and total phosphorus emissions from the aquaculture area's water bodies, as well as some or all of the water quality parameters.

[0019] Furthermore, in step S21, the functional relationship between temperature T and time t is as follows: ,in, The set reference temperature, Z represents the set temperature change amplitude, and Z represents the set breeding cycle.

[0020] Furthermore, in step S22, the functional relationship between the fish population N and the temperature T is as follows: ,in, Mortality rate as a function of temperature variation, It is the set mortality rate temperature coefficient. It is the set baseline mortality rate. This is the set optimal temperature; the functional relationship between the weight W of a single fish and the temperature T is: , ,in, This indicates the growth rate as a function of temperature. The maximum individual weight is set. The set growth rate temperature coefficient, The maximum growth rate is set.

[0021] Further, step S23 includes:

[0022] Total nitrogen concentration Functional relationship: , ,in, This represents the total nitrogen input to the aquaculture area's water body per unit time, where V is the water volume of the aquaculture area. For the set nitrogen degradation rate, For the set water exchange rate, The set chlorophyll nitrogen content, The set feed nitrogen content, The set percentage of uneaten feed, The set proportion of uneaten feed

[0023] Total phosphorus concentration Functional relationship: , ,in, This represents the total phosphorus content input into the aquaculture area's water body per unit time. For the set phosphorus degradation rate, The set chlorophyll phosphorus content; The set phosphorus content in the feed;

[0024] chlorophyll concentration Functional relationship: ,in The set algae decay rate;

[0025] seaweed biomass Functional relationship: , ,in, This indicates the temperature-related chlorophyll growth rate. The set nitrogen half-saturation constant, The set phosphorus half-saturation constant, To set the maximum chlorophyll growth rate, The chlorophyll temperature coefficient, It is the set optimal temperature;

[0026] Dissolved oxygen The functional relationship is as follows: ,in, For the set oxygen production rate, The set oxygen consumption rate for fish, The set oxygen consumption rate for nitrogen decomposition, The set amount of dissolved oxygen flowing in.

[0027] Furthermore, in step S23, the functional relationship of the environmental impact statistical parameters includes: cumulative nitrogen emissions. Functional relationship: Cumulative phosphorus emissions Functional relationship: .

[0028] Further, step S3 includes: real-time monitoring of temperature T and water quality parameters, and inputting them into the prediction model; the prediction model calculates the current seaweed biomass based on the current monitored temperature T and water quality parameters. And based on the current seaweed biomass The total nitrogen concentration was obtained in relation to time t. Total phosphorus concentration chlorophyll concentration Dissolved oxygen .

[0029] Furthermore, in step S13, the management parameters also include water exchange rate. And fish density.

[0030] This invention also provides a comprehensive monitoring and management system for deep-sea aquaculture dynamics, used to execute the above-described comprehensive monitoring and management method for deep-sea aquaculture dynamics, comprising:

[0031] The real-time monitoring module is used to detect temperature and water quality parameters in the aquaculture area;

[0032] The control module, which communicates with the real-time monitoring module, contains a prediction model. This model receives and processes data measured by the real-time monitoring module to determine the relationship between the performance indicators and time.

[0033] Output module: Connected to the control module, it is used to output the relationship between the performance indicator parameters obtained by the control module and the change over time.

[0034] As described above, the integrated monitoring and management method and system for deep-sea aquaculture dynamics of the present invention has the following beneficial effects:

[0035] Integrating biological, environmental, and aquaculture operational factors, this system provides comprehensive dynamic assessment and prediction through real-time monitoring, predictive modeling, and adaptive management. It serves as a basis for aquaculture adjustments, optimizing feeding, population density, and water exchange, balancing aquaculture efficiency with environmental impact, optimizing ecological carrying capacity, and reducing environmental impact. It is suitable for deep-sea cage environments. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating the deep-sea aquaculture dynamic integrated monitoring and management method of the present invention.

[0037] Figure 2 The temperature obtained by the prediction model in this invention A schematic diagram of the curve.

[0038] Figure 3 The number of fish populations obtained by the prediction model in this invention. A schematic diagram of the curve.

[0039] Figure 4 The individual fish weight obtained by the prediction model in this invention A schematic diagram of the curve.

[0040] Figure 5 The total fish biomass obtained by the prediction model in this invention A schematic diagram of the curve.

[0041] Figure 6 The amount of feed obtained by the prediction model in this invention A schematic diagram of the curve.

[0042] Figure 7 The total nitrogen concentration obtained by the prediction model in this invention A schematic diagram of the curve.

[0043] Figure 8 The total phosphorus concentration obtained by the prediction model in this invention A schematic diagram of the curve.

[0044] Figure 9 The chlorophyll concentration obtained by the prediction model in this invention A schematic diagram of the curve.

[0045] Figure 10 Dissolved oxygen obtained by the prediction model in this invention A schematic diagram of the curve.

[0046] Figure 11 The cumulative nitrogen emissions obtained from the prediction model in this invention A schematic diagram of the curve.

[0047] Figure 12 The cumulative phosphorus emissions obtained by the prediction model in this invention A schematic diagram of the curve. Detailed Implementation

[0048] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0049] It should be understood that the structures, proportions, sizes, etc., depicted in the accompanying drawings are merely for illustrative purposes to aid those skilled in the art and to facilitate understanding. They are not intended to limit the scope of the invention and therefore have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effectiveness and objectives of the invention, should still fall within the scope of the technical content disclosed herein. Furthermore, the terms "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity and not intended to limit the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention's implementation.

[0050] It should also be noted that when a component is referred to as being "fixed to" or "set on" another component, it can be directly on the other component or may be connected to an intermediary component. When a component is referred to as being "connected to" another component, it can be directly connected to the other component or indirectly connected to the other component through an intermediary component.

[0051] Furthermore, the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed in this application.

[0052] See Figures 1 to 12 This invention provides a method for comprehensive monitoring and management of deep-sea aquaculture dynamics, comprising the following steps:

[0053] S1. Parameter determination, including:

[0054] S11. Determine the relevant natural parameters of the aquaculture area, including temperature (T) and algal biomass. Water quality parameters. Preferably, the water quality parameters include, but are not limited to, total nitrogen concentration. Total phosphorus concentration chlorophyll concentration and dissolved oxygen concentration These natural parameters can affect the breeding conditions.

[0055] S12. Determine the assessment indicators for evaluating the aquaculture status, including fish quantity indicators and environmental indicators. The fish quantity indicators include individual fish weight W and school size N. Individual fish weight W reflects the growth of individual fish and is the most important assessment indicator. School size N reflects the fish survival rate. Simultaneously, individual fish weight W and school size N determine the total fish biomass B, which can then be used as the basis for feeding amounts.

[0056] Preferably, the environmental indicator parameters include two parts: one part consists of some or all water quality parameters, i.e., the quality of the aquatic environment in the aquaculture area is evaluated through water quality parameters; the other part consists of the impact of the aquaculture area's water body on the surrounding environment, including the cumulative total nitrogen and phosphorus emissions from the aquaculture area's water body. In other embodiments, the environmental indicator parameters may also include other indicator parameters that need to be considered.

[0057] S12. Determine the management parameters related to aquaculture, including feed amount F and feed rate f. rate Preferably, it may also include a water exchange rate Q. overV And fish density (determined by the number of fish N), etc. Management parameters, as parameters that can be controlled by humans, can be adjusted to regulate the aquaculture status.

[0058] S2. Establish a prediction model, including:

[0059] S21. Establish the functional relationship between temperature T and time t within the breeding cycle Z. Preferably, in this embodiment, a cosine function is used to simulate temperature changes, and the functional relationship between temperature T and time t is:

[0060] ,

[0061] in, The set reference temperature, Both the set temperature variation amplitude and the set temperature variation amplitude are determined based on actual conditions. Z represents the rearing period, which can be in days, depending on the type of fish being raised. For example, for golden pomfret, the growth period is typically from April to October, so the rearing period Z is set to 180 days, with a reference temperature... Use 22 degrees Celsius (annual average or moderate water temperature, which can be adjusted according to actual conditions), and measure the amplitude of temperature changes. Take 8 degrees. This embodiment uses a cosine function to represent the 180-day seasonal water temperature variation, with the temperature fluctuating around 22 ± 8 degrees Celsius or 22 - 8 degrees Celsius. See [link / reference needed]. Figure 2 .

[0062] S22. Establish the functional relationship between the fish population size N and the individual fish weight W and the temperature T. Establish the functional relationship between the total fish biomass B and the fish population size N and the individual fish weight W, B=f(N,W). Combine this with the functional relationship between temperature T and time t to obtain the fish population size related to time t. Individual fish weight Total biomass of fish Determine the amount of feed. , where f rate The set feeding rate.

[0063] In this embodiment, preferably, the functional relationship between the fish population N and the temperature T is as follows:

[0064] ;

[0065] in, Indicates mortality rate as a function of temperature. It is the set baseline mortality rate. It is the set mortality rate temperature coefficient. It's the set optimal temperature. Combined with... The fish population size in relation to time t can be obtained by solving the differential equation using functions (such as the odeint function in Python and SciPy libraries). .

[0066] In this embodiment, preferably, the functional relationship between the weight W of a single fish and the temperature T is as follows:

[0067] , ,

[0068] in, This indicates the growth rate as a function of temperature. The maximum individual weight is set. The maximum growth rate is set. This is the set temperature coefficient for the growth rate. Combined with... The individual fish weight in relation to time t can be obtained by solving the differential equation using functions (such as the odeint function in Python and SciPy libraries). This embodiment uses the logistic growth model, where the growth rate is directly proportional to the weight (small fish grow faster), while also being constrained by an upper limit, with growth slowing down as the fish approaches its maximum weight. It also considers temperature-corrected growth rates, where the growth rate decreases when the temperature deviates from the optimal temperature, thus allowing for a good estimation of the weight of an individual fish.

[0069] Total biomass equals the number of fish multiplied by the weight of each individual fish. Feed input is linked to the total fish mass, therefore the functional relationship is as follows:

[0070] This allows us to determine the required feed input. The functional relationship that changes with time, the feeding rate f rate It can be set according to the actual situation and can be adjusted during the breeding process.

[0071] S23. Establish the functional relationships between natural parameters, management parameters, and environmental indicator parameters, and obtain the functional relationship between environmental indicator parameters and time t.

[0072] In this embodiment, the environmental indicator parameters consist of two parts: water quality parameters and environmental impact statistics parameters, including the cumulative total nitrogen and phosphorus emissions from the aquaculture area. The changes in these environmental indicator parameters over time need to be based on natural parameters, management parameters, and feed input rates. Calculated. Preferably, the functional relationship includes the following:

[0073] Total nitrogen concentration Functional relationship:

[0074] , ,

[0075] in, This represents the total amount of nitrogen input into the aquaculture area's water body per unit time. The volume of water in the aquaculture area. For the set nitrogen degradation rate, For the set water exchange rate, To set the chlorophyll nitrogen content, algal biomass For the value that needs to be solved, The set feed nitrogen content, The set percentage of uneaten feed, The set percentage of uneaten feed, This refers to the amount of feed given.

[0076] Total phosphorus concentration Functional relationship:

[0077] ,

[0078] ,

[0079] in, This represents the total phosphorus content input into the aquaculture area's water body per unit time. The volume of water in the aquaculture area. For the set phosphorus degradation rate, For the set water exchange rate, The set chlorophyll phosphorus content; The set phosphorus content in the feed, The set percentage of uneaten feed, The set percentage of uneaten feed, This refers to the amount of feed given.

[0080] chlorophyll concentration Functional relationship:

[0081] ,

[0082] in, The set algae decay rate, The set water exchange rate.

[0083] seaweed biomass Functional relationship:

[0084] ,

[0085] ,

[0086] in To be the function that takes the smaller number, This indicates the temperature-related chlorophyll growth rate. The set nitrogen half-saturation constant, The set phosphorus half-saturation constant, The maximum chlorophyll growth rate was set. The chlorophyll temperature coefficient, It is the set optimal temperature.

[0087] Dissolved oxygen The functional relationship is as follows:

[0088] ,

[0089] in, For the set oxygen production rate, The set oxygen consumption rate for fish, The set oxygen consumption rate for nitrogen decomposition, For the set water exchange rate, The set amount of dissolved oxygen flowing in.

[0090] Nitrogen cumulative emissions Functional relationship:

[0091] ,

[0092] Nitrogen cumulative emissions This represents the total amount of nitrogen discharged from the water body from the start of aquaculture to the present, of which, For the set water exchange rate, This represents the total nitrogen concentration. This refers to the volume of water in the aquaculture area.

[0093] Cumulative phosphorus emissions Functional relationship:

[0094] ,

[0095] This shows the total amount of phosphorus discharged into the water body from the start of aquaculture to the present. For the set water exchange rate, This represents the total phosphorus concentration. This refers to the volume of water in the aquaculture area.

[0096] Through the series of functional relationships described above, the mathematical relationships between the various parameters can be established, thereby obtaining the desired prediction model. The parameters or constants involved in the functional relationships described above are shown in the table below:

[0097]

[0098]

[0099] In this embodiment, before the monitoring work begins, the relevant parameters can be set to initial values, including: initial fish population N(0) = 15,000 fish, individual fish weight W(0) = 10 grams, and initial total nitrogen concentration. = 5.0 mg / L, initial total phosphorus concentration = 0.5 mg / L, initial chlorophyll concentration C(0) = 0.5 mg / L, initial dissolved oxygen DO(0) = 8 mg / L.

[0100] S3. Input the real-time monitored water quality parameters into the prediction model; the prediction model calculates the relationship between the assessment index parameters and time. At the start of operation, based on the set initial values, the prediction model first performs calculations and initially obtains the relationship between the assessment index parameters and time. During continuous operation, based on the real-time monitored water quality parameters and temperature T, the prediction model can make timely adjustments.

[0101] In this embodiment, firstly, the prediction model obtains the temperature related to time t. See Figure 2 Meanwhile, in the continuous monitoring work, adjustments were also made based on the real-time monitored temperature. Make corrections, for example, when the measured temperature is different from the actual temperature. When the deviation is larger, the prediction model can update the functional relationship based on historical temperature data. In and This makes the prediction model It is closer to the actual temperature.

[0102] The fish quantity parameter in the assessment indicators is obtained from the correlation function relationship in step 21 of the prediction model, which yields the fish population quantity related to time t. Individual fish weight Total biomass of fish See each Figure 3 , Figure 4 and Figure 5 As shown. Furthermore, based on the total fish biomass... The amount of feed received See Figure 6 As shown.

[0103] The environmental parameters in the assessment indicators are obtained from the correlation function relationship in step 21 of the prediction model. The first part is the water quality parameters of the aquaculture area, specifically: the algal biomass during the prediction model's calculation process. For unknown values, the total nitrogen concentration is determined based on the monitored water quality parameters. and total phosphorus concentration The prediction model calculates the seaweed biomass. The value, and then through seaweed biomass Based on total nitrogen concentration Functional relationship, total phosphorus concentration Functional relationship, chlorophyll concentration Functional relationship, dissolved oxygen Functional relationship and The total nitrogen concentration related to time t can be obtained by solving the differential equation using functions (such as the odeint function in Python and SciPy libraries). Total phosphorus concentration chlorophyll concentration Dissolved oxygen See each Figure 7 , Figure 8 , Figure 9 and Figure 10 As shown. In continuous monitoring, the total nitrogen concentration is also monitored in real time. and total phosphorus concentration It can update the calculation of seaweed biomass. The value of total nitrogen concentration should be updated promptly. Total phosphorus concentration chlorophyll concentration Dissolved oxygen Furthermore, in continuous monitoring, the total nitrogen concentration obtained from the prediction model can be determined based on actual measured data. Total phosphorus concentration chlorophyll concentration Dissolved oxygen The prediction effect is used to adjust and correct the corresponding parameters or constants in the prediction model, that is, to correct the total nitrogen concentration. Functional relationship, total phosphorus concentration Functional relationship, chlorophyll concentration Functional relationship and dissolved oxygen The functional relationship is determined to make it closer to the measured data.

[0104] The second part of the environmental indicator parameters consists of environmental impact statistical parameters, based on the obtained total nitrogen concentration. and total phosphorus concentration Solve differential equations using functions (such as the odeint function in Python and SciPy libraries): and The cumulative nitrogen emissions related to time t can be obtained. and cumulative phosphorus emissions See Figure 11 and Figure 12 As shown.

[0105] S4. Based on the relationship between the performance indicators and time, conduct aquaculture management and adjust management parameters.

[0106] Specifically, the various parameters in the assessment indicators obtained through the above steps are used to predict the aquaculture status, including: individual fish weight. Whether the fish are close to the required weight (e.g., 1000 grams) within 180 days; whether the fish survival rate meets the requirements (i.e., the number of fish in the school). Does it meet the requirements? Do the water quality parameters meet aquaculture and environmental protection requirements (e.g., DO should be higher than 5 mg / L, and TN and TP concentrations should be kept reasonable)? Cumulative nitrogen emissions and cumulative phosphorus emissions Does it meet environmental protection requirements? Based on the predicted situation of the assessment indicator parameters, one or more of the management parameters are adjusted, so that the breeding status can be adjusted in a timely manner, including: 1) adjusting the feeding rate f rate 2) Adjust the water exchange rate Q by adjusting the performance of the aquaculture cages. overV 3) Fish density can be adjusted by catching or replenishing fish, thereby optimizing aquaculture production and environmental sustainability.

[0107] This invention also provides a comprehensive monitoring and management system for deep-sea aquaculture dynamics, used to execute the above-described comprehensive monitoring and management method for deep-sea aquaculture dynamics, comprising:

[0108] The real-time monitoring module is used to detect the temperature and water quality parameters of the aquaculture area.

[0109] The control module, which communicates with the real-time monitoring module, contains a prediction model. This model receives and processes data measured by the real-time monitoring module to determine the relationship between the performance indicators and time. The control module may include a memory and a processor. The memory stores a computer program based on the prediction model, and the processor communicates with the memory, executing the prediction model's calculations when the computer program is called.

[0110] Output module: Connected to the control module, it outputs the relationship between the performance indicator parameters obtained by the control module and time. The output module may include a display screen to show the results, displaying a graph of the performance indicator parameters over time. The display screen can also show the various functional relationships in the prediction model for easy adjustment.

[0111] The input module, connected to the control module, is used to set the relevant parameters and constants of each function relationship in the prediction model, and to adjust the prediction model.

[0112] The method and system for comprehensive monitoring and management of deep-sea aquaculture dynamics of the present invention have the following beneficial effects:

[0113] Integrating biological, environmental, and aquaculture operational factors, this system provides comprehensive dynamic assessment and prediction through real-time monitoring, predictive modeling, and adaptive management. It serves as a basis for aquaculture adjustments, optimizing feeding, population density, and water exchange, balancing aquaculture efficiency with environmental impact, optimizing ecological carrying capacity, and reducing environmental impact. It is suitable for deep-sea cage environments.

[0114] In summary, this invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0115] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for comprehensive monitoring and management of deep-sea aquaculture dynamics, comprising the following steps: S1. Parameter determination: S11. Determine the relevant natural parameters of the aquaculture area, including temperature (T) and algal biomass. and water quality parameters; S12. Determine the assessment indicator parameters used to evaluate the aquaculture status, including fish quantity indicator parameters and environmental indicator parameters. The fish quantity indicator parameters include the weight of an individual fish (W) and the number of fish in the group (N). S13. Determine the management parameters related to aquaculture, including feed intake. ; S2. Establish a prediction model, including: S21. Establish the relationship between temperature T and time t within the breeding cycle Z; S22. Establish the functional relationship between the fish population size N and the individual fish weight W and the temperature T. Establish the functional relationship between the total fish biomass B and the fish population size N and the individual fish weight W, B=f(N,W). Combine this with the relationship between temperature T and time t to obtain the fish population size related to time t. Individual fish weight Total biomass of fish Determine the amount of feed. , where f rate The set feeding rate; S23. Establish the functional relationships between natural parameters, management parameters, and environmental indicator parameters, and obtain the functional relationship between environmental indicator parameters and time t; S3. Monitor temperature and water quality parameters in real time and input them into the prediction model; the prediction model calculates the relationship between the performance indicators and time. S4. Adjust management parameters based on the relationship between performance indicator parameters and time.

2. The method for comprehensive monitoring and management of deep-sea aquaculture dynamics according to claim 1, characterized in that: In step S11, the water quality parameters include dissolved oxygen concentration, total nitrogen concentration, total phosphorus concentration, and chlorophyll concentration.

3. The method for comprehensive monitoring and management of deep-sea aquaculture dynamics according to claim 1 or 2, characterized in that: In step S12, the environmental indicator parameters include environmental impact statistical parameters and some or all of the water quality parameters. The environmental impact statistical parameters include the total cumulative nitrogen discharge and total cumulative phosphorus discharge from the aquaculture area.

4. The method for comprehensive monitoring and management of deep-sea aquaculture dynamics according to claim 1, characterized in that: In step S21, the functional relationship between temperature T and time t is as follows: ,in, The set reference temperature, Z represents the set temperature change amplitude, and Z represents the set breeding cycle.

5. The method for comprehensive monitoring and management of deep-sea aquaculture dynamics according to claim 1 or 4, characterized in that: In step S22, the functional relationship between the fish population N and the temperature T is as follows: ,in, Mortality rate as a function of temperature variation, It is the set mortality rate temperature coefficient. It is the set baseline mortality rate. This is the set optimal temperature; the functional relationship between the weight W of a single fish and the temperature T is: , ,in, This indicates the growth rate as a function of temperature. The maximum individual weight is set. The set growth rate temperature coefficient, The maximum growth rate is set.

6. The method for comprehensive monitoring and management of deep-sea aquaculture dynamics according to claim 3, characterized in that: Step S23 includes: Total nitrogen concentration Functional relationship: , ,in, This represents the total nitrogen input to the aquaculture area's water body per unit time, where V is the water volume of the aquaculture area. For the set nitrogen degradation rate, For the set water exchange rate, The set chlorophyll nitrogen content, The set feed nitrogen content, The set percentage of uneaten feed, The set proportion of uneaten feed Total phosphorus concentration Functional relationship: , ,in, This represents the total phosphorus content input into the aquaculture area's water body per unit time. For the set phosphorus degradation rate, The set chlorophyll phosphorus content; The set phosphorus content in the feed; chlorophyll concentration Functional relationship: ,in The set algae decay rate, seaweed biomass Functional relationship: , ,in, This indicates the temperature-related chlorophyll growth rate. The set nitrogen half-saturation constant, The set phosphorus half-saturation constant, To set the maximum chlorophyll growth rate, The chlorophyll temperature coefficient, It is the set optimal temperature; Dissolved oxygen The functional relationship is as follows: ;in, For the set oxygen production rate, The set oxygen consumption rate for fish, The set oxygen consumption rate for nitrogen decomposition, The set amount of dissolved oxygen flowing in.

7. The method for comprehensive monitoring and management of deep-sea aquaculture dynamics according to claim 6, characterized in that: In step S23, the functional relationship of the environmental impact statistical parameters includes: Nitrogen cumulative emissions Functional relationship: ; Cumulative phosphorus emissions Functional relationship: .

8. The method for comprehensive monitoring and management of deep-sea aquaculture dynamics according to claim 1, characterized in that: Step S3 includes: real-time monitoring of temperature T and water quality parameters, and inputting them into the prediction model; the prediction model calculates the current seaweed biomass based on the current monitored temperature and water quality parameters. And based on the current seaweed biomass The total nitrogen concentration was obtained in relation to time t. Total phosphorus concentration chlorophyll concentration Dissolved oxygen .

9. The method for comprehensive monitoring and management of deep-sea aquaculture dynamics according to claim 1, characterized in that: In step S13, the management parameters also include water exchange rate. And fish density.

10. A comprehensive monitoring and management system for deep-sea aquaculture dynamics, characterized in that: The method for implementing the comprehensive monitoring and management of deep-sea aquaculture dynamics as described in any one of claims 1 to 9 includes: The real-time monitoring module is used to detect temperature and water quality parameters in the aquaculture area; The control module, which communicates with the real-time monitoring module, contains a prediction model. This model receives and processes data measured by the real-time monitoring module to determine the relationship between the performance indicators and time. Output module: Connected to the control module, it is used to output the relationship between the performance indicator parameters obtained by the control module and the change over time.

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