A method and system for investigating ocean carbon sink data

Abstract

This application provides a method and system for marine carbon sink data survey, relating to the field of carbon sink management survey technology. It acquires initial data on marine carbon sink ecosystems, analyzes carbon sink elements based on this initial data to determine carbon storage and influencing factors, constructs a dynamic change model based on the initial data and influencing factors, calculates initial carbon sink prediction results, constructs a temperature feedback model to correct the obtained carbon sink prediction results, and, based on the corrected carbon sink prediction results, introduces biological density and competition feedback mechanisms to construct a multidimensional dynamic carbon sink optimization model, deriving the final optimized carbon sink prediction results. By introducing a dynamic change model, a temperature feedback model, and biological density and population competition feedback mechanisms, it not only considers the static storage of carbon sinks in marine ecosystems but also integrates multidimensional dynamic feedback factors, enabling adjustments to carbon sink prediction results at different time steps.

Description

Technical Field

[0001] This invention relates to the field of carbon sink management survey technology, specifically to a method and system for marine carbon sink data survey. Background Technology

[0002] Marine carbon sinks, also known as blue carbon, are the processes, activities, and mechanisms by which marine activities and marine life absorb carbon dioxide from the atmosphere and fix and store it in marine ecosystems. The ocean possesses abundant carbon sink resources such as salt marshes, seagrass beds, algae cultivation, and shellfish farming, making it the largest active carbon reservoir. Studies show that approximately one-third of the carbon dioxide emitted by humans each year is absorbed by the ocean, indicating its enormous carbon sequestration potential.

[0003] Current methods for surveying marine carbon sink data are mainly based on simple carbon storage statistics and temperature feedback models. However, these methods usually only consider the initial data on carbon storage and basic environmental factors, such as the impact of temperature on carbon sinks, and fail to fully consider dynamic feedback mechanisms such as biological density and population competition, making it difficult to accurately reflect the complex interactions within the marine ecosystem.

[0004] Furthermore, existing methods have a relatively simple time step setting for carbon sink changes, which cannot adapt to the multi-dimensional and multi-scale dynamic changes in marine ecosystems, resulting in low accuracy and applicability of the prediction results.

[0005] Therefore, there is an urgent need to propose a method and system for investigating marine carbon sink data. Summary of the Invention

[0006] This invention provides a method and system for marine carbon sink data investigation, aiming to solve the technical problem of accurately reflecting the complex interactions within the marine ecosystem in related technologies, thereby avoiding the practical problem of being unable to adapt to the multi-dimensional and multi-scale dynamic changes in the marine ecosystem, and thus avoiding subsequent problems of low accuracy and applicability of prediction results.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for investigating marine carbon sink data includes the following:

[0009] Step S100: Obtain initial data on marine carbon sink ecosystems, and conduct carbon sink element analysis based on the initial data to determine carbon storage and influencing factors;

[0010] Step S200: Based on the initial data and influencing factors in the marine carbon sink ecosystem, construct a dynamic change model and calculate the initial carbon sink prediction results;

[0011] Step S300: Construct a temperature feedback model correction model to correct the carbon sink prediction results obtained in step S200 using temperature feedback.

[0012] Step S400: Based on the corrected carbon sink prediction results in step S300, biological density and competitive feedback mechanisms are introduced to construct a multidimensional dynamic carbon sink optimization model, and the final optimized carbon sink prediction results are obtained.

[0013] This application obtains initial data on marine carbon sink ecosystems and analyzes carbon sink elements based on this data to determine carbon storage and influencing factors. Based on the initial data and influencing factors, a dynamic change model is constructed to calculate initial carbon sink prediction results. A temperature feedback model is then constructed to correct the obtained carbon sink prediction results. Based on the corrected carbon sink prediction results, a biological density and competition feedback mechanism are introduced to construct a multidimensional dynamic carbon sink optimization model, yielding the final optimized carbon sink prediction results. By introducing a dynamic change model, a temperature feedback model, and biological density and population competition feedback mechanisms, the accuracy and reliability of carbon sink prediction are significantly improved. Compared with disclosed technologies, this invention not only considers the static storage of carbon sinks in marine ecosystems but also integrates multidimensional dynamic feedback factors, enabling adjustments to carbon sink prediction results at different time steps. Furthermore, by optimizing the output mechanism, this invention can more accurately reflect the dynamic changing trend of marine carbon sinks, effectively solving the problems of inaccurate prediction results and poor adaptability in existing technologies.

[0014] As a preferred embodiment of the present invention, initial data on marine carbon sink ecosystems are obtained, and carbon sink element analysis is performed based on the initial data to determine carbon storage and influencing factors, specifically including:

[0015] Step S101: Initial data in marine carbon sink ecosystems include aboveground carbon storage data, underground carbon storage data, and sediment carbon storage data.

[0016] Step S102, carbon sink element analysis, specifically:

[0017] Aboveground carbon storage analysis, calculating the carbon storage of the aboveground parts of the marine ecosystem:

[0018] ;

[0019] In the formula, For aboveground biomass, The carbon content coefficient, This refers to above-ground carbon storage data.

[0020] Subsurface carbon storage analysis to calculate the subsurface carbon storage in marine ecosystems:

[0021] ;

[0022] In the formula, For underground biomass, The carbon content coefficient, This is data on underground carbon storage.

[0023] Sediment carbon storage analysis to calculate the carbon storage of sediments in marine ecosystems:

[0024] ;

[0025] In the formula, For sediment density, For sediment depth, The organic carbon content of the sediment. This represents data on carbon storage in sediments.

[0026] Step S103: Based on the acquired initial data and the analysis results of carbon sink elements, use statistical regression analysis to determine the influencing factors of carbon reserves.

[0027] As a preferred embodiment of the present invention, a dynamic change model is constructed based on initial data and influencing factors in the marine carbon sink ecosystem to calculate the initial carbon sink prediction results, specifically including:

[0028] Step S201: Before constructing the dynamic change model, the respiration rate and primary productivity in the marine ecosystem are calculated.

[0029] Among them, the respiration rate in marine ecosystems represents the respiration consumption process of marine carbon sink ecosystems. The respiration rate in marine ecosystems varies with changes in temperature, salinity, and dissolved oxygen concentration.

[0030] The respiration rate in marine ecosystems is calculated as follows:

[0031] ;

[0032] In the formula, Represents the respiratory rate coefficient. A function representing the effect of temperature on respiration rate. A function representing the combined effect of salinity and dissolved oxygen concentration. This indicates the rate of respiration in marine ecosystems.

[0033] Among them, primary productivity in marine ecosystems refers to the carbon storage capacity increased by marine carbon sink ecosystems through photosynthesis and nutrient utilization.

[0034] Calculating primary productivity in marine ecosystems, specifically:

[0035] ;

[0036] In the formula, Represents primary productivity in marine ecosystems. Represents the primary productivity coefficient. Represents the effect of temperature. The function representing the effect of nutrient concentration.

[0037] Step S202: Based on the initial data and influencing factors in the marine carbon sink ecosystem from step S100, a dynamic change model is constructed by inputting the primary productivity and respiration rate in the marine ecosystem. Specifically:

[0038] ;

[0039] In the formula, Initial carbon sink forecast results, Indicates the initial carbon reserves. Primary productivity in marine ecosystems The respiratory rate in marine ecosystems is represented by t, which represents the prediction time step.

[0040] Step S203: Calculate the carbon sink change within each time step based on the dynamic change model obtained in step S202, and accumulate the results to the initial carbon storage to obtain the initial carbon sink prediction result.

[0041] As a preferred embodiment of the present invention, the carbon sink change within each time step is calculated based on the dynamic change model obtained in step S202, and the results are accumulated to the initial carbon storage to obtain a preliminary prediction result of the carbon sink. Specifically:

[0042] ;

[0043] In the formula, This indicates the initial carbon sink forecast results. This indicates the carbon sequestration at the previous time step. Indicates the time step as The net change in carbon reserves. Indicates the time step.

[0044] In the dynamic change model, the time step is set based on the time range and precision of the study. The event step size of this dynamic change model is set to seven days as a time step to calculate and accumulate the increase or decrease in carbon storage.

[0045] As a preferred embodiment of the present invention, a temperature feedback model correction model is constructed to correct the carbon sink prediction results obtained in step S200 using temperature feedback, specifically including:

[0046] Step S301: Obtain the current temperature data, set the optimal temperature, and calculate the temperature deviation value. The optimal temperature is determined based on the ecological type and historical data.

[0047] The optimal temperature is the best temperature value in the marine carbon sink ecosystem. At this temperature, primary productivity and respiration rate are at their best, and carbon sink capacity reaches its maximum.

[0048] Calculate the temperature deviation, and compare the current temperature T with the optimal temperature. The deviations between them are as follows:

[0049] ;

[0050] In the formula, T represents the optimal temperature, and T represents the current temperature. This indicates temperature deviation.

[0051] Among them, if A smaller value indicates that the temperature is close to the optimal value and the carbon sequestration capacity is close to the maximum value. If the temperature is too high, the temperature deviates from the optimal value, and the carbon sequestration capacity decreases.

[0052] Step S302: Construct a temperature feedback model, specifically as follows:

[0053] ;

[0054] In the formula, This indicates the revised carbon sink forecast results. This represents the initial carbon sink prediction result, where T represents the ambient temperature at the current time step. Indicates the optimal temperature. This represents the temperature sensitivity coefficient.

[0055] Step S303: Feedback Correction. Based on the temperature feedback model obtained in step S302, the initial carbon sink prediction result obtained in step S200 is corrected, and the temperature deviation is adjusted. Substituting these values ​​into the temperature feedback model, the initial carbon sink prediction results obtained in step S200 are corrected as follows:

[0056] ;

[0057] In the formula, This indicates the revised carbon sink forecast results. This indicates the initial carbon sink forecast results. This represents the temperature sensitivity coefficient, where e represents the base of the natural logarithm. This indicates temperature deviation.

[0058] Step S304: Based on step S303, obtain the revised carbon sink prediction results.

[0059] Among them, when the temperature is close to the optimal temperature At that time, the result was corrected. Close to the initial prediction result If the temperature deviates from the optimal temperature, the correction result will be smaller than the optimal temperature. This reflects the negative impact of temperature deviation on carbon sequestration capacity.

[0060] As a preferred embodiment of the present invention, the temperature sensitivity coefficient is obtained through MATLAB. The function performs regression analysis on the initial data of marine carbon sink ecology obtained in step S100, fits the carbon sink change data at different temperatures, and finally determines the temperature sensitivity coefficient.

[0061] As a preferred embodiment of the present invention, based on the corrected carbon sink prediction results in step S300, a multidimensional dynamic carbon sink optimization model is constructed by introducing biological density and a competitive feedback mechanism to obtain the final optimized carbon sink prediction results, specifically including:

[0062] Step S401: Obtain biological density data and calculate the biological density feedback factor, specifically as follows:

[0063] ;

[0064] In the formula, Indicates the current biological density. This indicates the maximum permissible value for biological density. This represents the adjustment coefficient.

[0065] Among them, when the biological density is close to hour, It will approach 0, indicating a decline in carbon sequestration capacity.

[0066] Step S402: Obtain inter-population competition data and calculate the population competition feedback factor, specifically as follows:

[0067] ;

[0068] In the formula, , , This represents the carbon storage of different biological populations. This represents the population competition feedback factor.

[0069] Among them, when a certain species is highly competitive and dominates, carbon sequestration capacity will tilt towards that species, but the overall carbon sequestration capacity may be affected by the decline in biodiversity, so this feedback mechanism is introduced.

[0070] Step S403: Based on the carbon sink prediction corrected by temperature feedback obtained in step S300, a multidimensional dynamic carbon sink optimization model is constructed by inputting the population competition feedback factor and the biological density feedback factor, and the final carbon sink prediction result is output based on the model, specifically:

[0071] ;

[0072] In the formula, This represents the predicted carbon sink amount after temperature feedback correction. Represents biological density feedback factor. This represents the population competition feedback factor. This indicates the final carbon sink forecast result.

[0073] This application also provides a marine carbon sink data survey system, including the following:

[0074] The analysis and determination unit is used to obtain initial data in the marine carbon sink ecosystem, and to conduct carbon sink element analysis based on the initial data to determine carbon storage and influencing factors;

[0075] The preliminary prediction unit is used to construct a dynamic change model based on initial data and influencing factors in the marine carbon sink ecosystem, and to calculate the initial carbon sink prediction results;

[0076] The feedback correction unit constructs a temperature feedback model correction model to correct the obtained carbon sink prediction results based on temperature feedback.

[0077] The optimized output unit is used to perform multidimensional dynamic carbon sink optimization based on the corrected carbon sink prediction results, by introducing biological density and competitive feedback mechanisms, and to obtain the final optimized carbon sink prediction results.

[0078] Compared with the prior art, the beneficial effects of the present invention are:

[0079] 1. This invention can accurately calculate the changes in marine carbon sinks by collecting initial data of marine carbon sink ecosystems and combining dynamic analysis and environmental correction models. Specifically, by combining and optimizing multiple formulas, external factors affecting carbon sinks are incorporated into the analysis, further improving the prediction accuracy. Compared with traditional methods that only use static data, this method more accurately reflects the changes in marine carbon sinks under complex ecological environments.

[0080] 2. This invention goes beyond simply acquiring and predicting carbon sink data. It further optimizes the prediction results of carbon sink volume by introducing an internal feedback mechanism. The internal feedback mechanism allows ecological feedback effects to be incorporated into data analysis, thereby simulating the dynamic changes of actual marine carbon sink systems.

[0081] 3. By applying this method to different marine carbon sink environments, this invention can acquire and analyze carbon sink data under different ecological environments. Furthermore, this method has additional scalability and can adapt to different ecological environments. Attached Figure Description

[0082] Figure 1A flowchart illustrating a method for investigating marine carbon sink data, provided as an embodiment of this application.

[0083] Figure 2 This is a schematic diagram of a marine carbon sink data survey system provided in an embodiment of this application.

[0084] Figure reference numerals: 230, Analysis and determination unit; 231, Preliminary prediction unit; 232, Feedback correction unit; 233, Optimization output unit. Detailed Implementation

[0085] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. Please refer to... Figure 1 , Figure 1 This document provides a flowchart of a method for investigating marine carbon sink data, which is an embodiment of this application.

[0086] In this embodiment, a method for investigating marine carbon sink data may include steps S100, S200, S300, and S400.

[0087] Step S100: Obtain initial data on marine carbon sink ecosystems and conduct carbon sink element analysis based on the initial data to determine carbon storage and influencing factors.

[0088] Specifically, this includes:

[0089] Step S101: Initial data in marine carbon sink ecosystems include aboveground carbon storage data, underground carbon storage data, and sediment carbon storage data.

[0090] Step S102, carbon sink element analysis, specifically:

[0091] Aboveground carbon storage analysis, calculating the carbon storage of the aboveground components of the marine ecosystem:

[0092] ;

[0093] In the formula, For aboveground biomass, The carbon content coefficient, This refers to aboveground carbon storage data.

[0094] Subsurface carbon storage analysis to calculate the carbon storage in the subsurface portion of the marine ecosystem:

[0095] ;

[0096] In the formula, For underground biomass, The carbon content coefficient, This is data on underground carbon storage.

[0097] Sediment carbon storage analysis to calculate the carbon storage of sediments in marine ecosystems:

[0098] ;

[0099] In the formula, For sediment density, For sediment depth, The organic carbon content of the sediment. This represents data on carbon storage in sediments.

[0100] Step S103: Based on the acquired initial data and the analysis results of carbon sink elements, use statistical regression analysis to determine the influencing factors of carbon reserves.

[0101] Step S200: Based on the initial data and influencing factors in the marine carbon sink ecosystem, construct a dynamic change model and calculate the initial carbon sink prediction results.

[0102] Specifically, this includes:

[0103] Step S201: Before constructing the dynamic change model, the respiration rate and primary productivity in the marine ecosystem are calculated.

[0104] The respiration rate in marine ecosystems is calculated as follows:

[0105] ;

[0106] In the formula, Represents the respiratory rate coefficient. A function representing the effect of temperature on respiration rate. A function representing the combined effect of salinity and dissolved oxygen concentration. This indicates the rate of respiration in marine ecosystems.

[0107] Calculating primary productivity in marine ecosystems, specifically:

[0108] ;

[0109] In the formula, Represents primary productivity in marine ecosystems. Represents the primary productivity coefficient. Represents the effect of temperature. The function representing the effect of nutrient concentration.

[0110] Step S202: Based on the initial data and influencing factors in the marine carbon sink ecosystem from step S100, a dynamic change model is constructed by inputting the primary productivity and respiration rate in the marine ecosystem. Specifically:

[0111] ;

[0112] In the formula, Initial carbon sink forecast results, Indicates the initial carbon reserves. Primary productivity in marine ecosystems The value represents the respiration rate in marine ecosystems, and t represents the prediction time step.

[0113] Step S203: Based on the dynamic change model obtained in step S202, calculate the carbon sequestration change within each time step, and accumulate the results to the initial carbon storage to obtain the initial carbon sequestration prediction result, specifically:

[0114] ;

[0115] In the formula, This indicates the initial carbon sink forecast results. This indicates the carbon sequestration at the previous time step. Indicates the time step as The net change in carbon reserves. Indicates the time step.

[0116] Step S300: Construct a temperature feedback model to correct the carbon sink prediction results obtained in step S200.

[0117] Specifically, this includes:

[0118] Step S301: Obtain the current temperature data, set the optimal temperature, and calculate the temperature deviation value. The optimal temperature is determined based on the ecological type and historical data.

[0119] Calculate the temperature deviation, and compare the current temperature T with the optimal temperature. The deviations between them are as follows:

[0120] ;

[0121] In the formula, T represents the optimal temperature, and T represents the current temperature. This indicates temperature deviation.

[0122] Step S302: Construct a temperature feedback model, specifically as follows:

[0123] ;

[0124] In the formula, This indicates the revised carbon sink forecast results. This represents the initial carbon sink prediction result, where T represents the ambient temperature at the current time step. Indicates the optimal temperature. This represents the temperature sensitivity coefficient.

[0125] Step S303: Feedback Correction. Based on the temperature feedback model obtained in step S302, the initial carbon sink prediction result obtained in step S200 is corrected, and the temperature deviation is adjusted. Substituting these values ​​into the temperature feedback model, the initial carbon sink prediction results obtained in step S200 are corrected as follows:

[0126] ;

[0127] In the formula, This indicates the revised carbon sink forecast results. This indicates the initial carbon sink forecast results. This represents the temperature sensitivity coefficient, where e represents the base of the natural logarithm. This indicates temperature deviation.

[0128] Step S304: Based on step S303, obtain the revised carbon sink prediction results.

[0129] It should be noted that the temperature sensitivity coefficient is obtained through MATLAB. The function performs regression analysis on the initial data of marine carbon sink ecology obtained in step S100, fits the carbon sink change data at different temperatures, and finally determines the temperature sensitivity coefficient.

[0130] In step S400, based on the corrected carbon sink prediction results in step S300, biological density and competitive feedback mechanisms are introduced to construct a multidimensional dynamic carbon sink optimization model, and the final optimized carbon sink prediction results are obtained.

[0131] Specifically, this includes:

[0132] Step S401: Obtain biological density data and calculate the biological density feedback factor, specifically as follows:

[0133] ;

[0134] In the formula, Indicates the current biological density. This indicates the maximum permissible value for biological density. This represents the adjustment coefficient.

[0135] Step S402: Obtain inter-population competition data and calculate the population competition feedback factor, specifically as follows:

[0136] ;

[0137] In the formula, , , This represents the carbon storage of different biological populations. This represents the population competition feedback factor.

[0138] Step S403: Based on the carbon sink prediction corrected by temperature feedback obtained in step S300, a multidimensional dynamic carbon sink optimization model is constructed by inputting the population competition feedback factor and the biological density feedback factor, and the final carbon sink prediction result is output based on the model, specifically:

[0139] ;

[0140] In the formula, This represents the predicted carbon sink amount after temperature feedback correction. Indicates biological density feedback factor, This represents the population competition feedback factor. This indicates the final carbon sink forecast result.

[0141] For example, the following set of experimental data is used to illustrate the specific process of the method.

[0142] First, we obtained initial data on the marine carbon sink ecosystem. The initial dataset is as follows:

[0143] Aboveground carbon storage data is The underground carbon storage data is Sediment carbon storage data are The sediment depth is 50 cm, and the organic carbon content is... It is 2%.

[0144] The calculation is performed by substituting the data, specifically:

[0145] Aboveground carbon storage is:

[0146] ;

[0147] The underground carbon reserves are:

[0148] ;

[0149] The carbon reserves in the sediments are:

[0150] ;

[0151] A dynamic change model is constructed to calculate the initial carbon sink prediction results, assuming the primary productivity and respiration rate are as follows:

[0152] Primary productivity .

[0153] respiratory rate .

[0154] The initial carbon sink prediction result of the dynamic change model is:

[0155] ;

[0156] Construct a temperature feedback model for correction, assuming the current temperature is... The optimal temperature is Then the temperature deviation A temperature feedback model is adopted, specifically:

[0157] ;

[0158] In the formula, .

[0159] ;

[0160] Based on the revised results, a biological density and population competition feedback mechanism are introduced to optimize carbon sink prediction, specifically:

[0161] Let the current biological density be... Maximum biological density Calculate the biological density feedback factor:

[0162] ;

[0163] The biological density feedback factor value was obtained.

[0164] Suppose there are currently two main populations with carbon storage of:

[0165] and ;

[0166] Calculate the population competition feedback factor:

[0167] ;

[0168] The numerical values ​​of the population competition feedback factor were obtained.

[0169] Substituting the population competition feedback factor and the biological density feedback factor into the formula, the final optimized carbon sink prediction result is as follows:

[0170] ;

[0171] The system obtains the carbon sink prediction results after the prediction area is optimized. After obtaining the values, it compares them with the actual calculated values ​​and generates a survey report based on the comparison results, thus realizing the carbon sink data survey and processing.

[0172] Please see Figure 2 , Figure 2 This is a flowchart of a marine carbon sink data survey system provided in an embodiment of this application.

[0173] In this embodiment, a marine carbon sink data survey system may include:

[0174] Analysis and determination unit 230 is used to acquire initial data in marine carbon sink ecosystems and to conduct carbon sink element analysis based on the initial data to determine carbon storage and influencing factors.

[0175] The preliminary prediction unit 231 is used to construct a dynamic change model based on initial data and influencing factors in the marine carbon sink ecosystem and calculate the initial carbon sink prediction results.

[0176] Feedback correction unit 232 constructs a temperature feedback model correction model to correct the obtained carbon sink prediction results based on temperature feedback.

[0177] The optimized output unit 233 is used to perform multidimensional dynamic carbon sink optimization based on the corrected carbon sink prediction results, by introducing biological density and competitive feedback mechanisms, and to obtain the final optimized carbon sink prediction results.

[0178] The system employs several key components. First, an analysis unit acquires initial data on marine carbon sink ecology and analyzes carbon sink elements to determine carbon storage and influencing factors. This unit connects to a database via a sensor network to collect basic data on surface, subsurface, and sediment carbon storage. Based on the preliminary prediction unit, a dynamic change model is constructed to calculate initial carbon sink prediction results. The system operates according to the set model, automatically calculating carbon sink changes at each time step. After obtaining the initial carbon sink prediction results, a feedback correction unit performs feedback correction based on actual environmental conditions. A temperature feedback model is constructed to correct the prediction results. This feedback correction unit obtains temperature data from an external meteorological database and compares it with a preset optimal temperature to correct the carbon sink prediction results. Finally, an optimization output unit, based on the corrected prediction results, introduces a biological density and competition feedback mechanism to construct a multidimensional dynamic carbon sink optimization model and outputs the final carbon sink prediction results. The system automatically adjusts parameters such as biological density and population competition to optimize the final results. After obtaining the numerical values, the system compares these values ​​with the actual calculated values ​​and generates a survey report based on the comparison results, thus realizing carbon sink data survey and processing.

[0179] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for investigating marine carbon sink data, characterized in that, Including the following: S100. Obtain initial data on marine carbon sink ecosystems and conduct carbon sink element analysis based on the initial data to determine carbon storage and influencing factors. S200: Based on initial data and influencing factors in marine carbon sink ecosystems, a dynamic change model is constructed to calculate the initial carbon sink prediction results; S300: Construct a temperature feedback model to correct the carbon sink prediction results obtained in S200. Based on the modified carbon sink prediction results in S300, S400 introduces biological density and competitive feedback mechanisms to construct a multidimensional dynamic carbon sink optimization model, and obtains the final optimized carbon sink prediction results. To obtain initial data on marine carbon sink ecosystems and conduct carbon sink element analysis based on this data to determine carbon storage and influencing factors, specifically including: S101, The initial data in the marine carbon sink ecosystem includes aboveground carbon storage data, underground carbon storage data, and sediment carbon storage data; S102, Carbon Sequestration Element Analysis, specifically: Aboveground carbon storage analysis, calculating the carbon storage of the aboveground parts of the marine ecosystem: C above =B above ·f c In the formula, B above For aboveground biomass, f c CD is the carbon content coefficient. above Data on aboveground carbon storage; Subsurface carbon storage analysis to calculate the subsurface carbon storage in marine ecosystems: C below =B below ·f c In the formula, B below For underground biomass, f c C is the carbon content coefficient. below Data on underground carbon storage; Sediment carbon storage analysis to calculate the carbon storage of sediments in marine ecosystems: C sediment =ρ sediment ·d sediment ·f oc In the formula, ρ sediment d represents the sediment density. sediment f represents the sediment depth. oc C represents the organic carbon content of sediments. sediment This represents data on sediment carbon reserves; S103. Based on the initial data obtained and the analysis results of carbon sink elements, statistical regression analysis is used to determine the influencing factors of carbon storage. Based on initial data and influencing factors in marine carbon sink ecosystems, a dynamic change model is constructed to calculate initial carbon sink prediction results, specifically including: S201. Before constructing the dynamic change model, the respiration rate and primary productivity of the marine ecosystem are calculated. The respiration rate in marine ecosystems is calculated as follows: R eco (T,S,O)=β·h(T)·k(S,O) In the formula, β represents the respiration rate coefficient, h(t) represents the effect function of temperature on respiration rate, k(S, O) represents the combined effect function of salinity and dissolved oxygen concentration, and R... eco (T, S, O) represents the respiration rate in marine ecosystems; Calculating primary productivity in marine ecosystems, specifically: P g (T,N)=α·f(T)·g(N) In the formula, P g (T, N) represents primary productivity in marine ecosystems, α represents the primary productivity coefficient, f(T) represents the temperature effect function, and g(N) represents the nutrient concentration effect function. S202. Based on the initial data and influencing factors of marine carbon sink ecosystems in S100, a dynamic change model is constructed by inputting primary productivity and respiration rate in marine ecosystems. Specifically: In the formula, C t Initial carbon sink forecast results, where C0 represents initial carbon reserves, P g (T, N) Primary productivity in marine ecosystems, R eco (T, S, O) represents the respiratory rate in marine ecosystems, and t represents the prediction time step. S203. Based on the dynamic change model obtained in S202, calculate the carbon sink change in each time step, and accumulate the results to the initial carbon storage to obtain the initial carbon sink prediction result. Based on the dynamic change model obtained from S202, the carbon sink change within each time step is calculated, and the results are accumulated to the initial carbon storage to obtain the preliminary prediction results of the carbon sink, specifically: C t =C t-Δt +(P g (T,N)-R eco (T,S,O))·Δt In the formula, C t This represents the initial carbon sink forecast result, C. t-Δt This represents the carbon sequestration at the previous time step, (P) g (T, N)-R eco (T, S, O)) represents the net change in carbon reserves at a time step of Δt, where Δt represents the time step. A temperature feedback model is constructed to correct the carbon sink prediction results obtained in S200. Specifically, this includes: S301. Obtain the current temperature data, set the optimal temperature, and calculate the temperature deviation value. The optimal temperature is determined based on the ecological type and historical data. Calculate the temperature deviation, and compare the current temperature T with the optimal temperature T. opt The deviations between them are as follows: ΔT=T-T opt In the formula, T opt ΔT represents the optimal temperature, T represents the current temperature, and ΔT represents the temperature deviation. S302. Construct a temperature feedback model, specifically as follows: In the formula, C feedback (T) represents the revised carbon sink forecast result, C t This represents the initial carbon sink prediction result, where T represents the ambient temperature at the current time step. opt This represents the optimal temperature, and γ represents the temperature sensitivity coefficient. S303, Feedback Correction: Based on the temperature feedback model obtained in S302, the initial carbon sink prediction results obtained in S200 are corrected by substituting the temperature deviation ΔT into the temperature feedback model, specifically: In the formula, C feedback (T) represents the revised carbon sink forecast result, C t This represents the initial carbon sink prediction result, γ represents the temperature sensitivity coefficient, e represents the base of the natural logarithm, and ΔT represents the temperature deviation. S304. Based on S303, the revised carbon sink prediction results are obtained.

2. The method for investigating marine carbon sink data according to claim 1, characterized in that, The temperature sensitivity coefficient was determined by performing regression analysis on the initial data of marine carbon sink ecology obtained from S100 using the fitnlm function in MATLAB, fitting the carbon sink change data at different temperatures, and finally determining the temperature sensitivity coefficient.

3. The method for investigating marine carbon sink data according to claim 2, characterized in that, Based on the revised carbon sink prediction results in S300, a multidimensional dynamic carbon sink optimization model is constructed by introducing biological density and competitive feedback mechanisms, yielding the final optimized carbon sink prediction results, specifically including: S401. Obtain biological density data and calculate the biological density feedback factor, specifically: In the formula, ρ current ρ represents the current biological density. max This represents the maximum permissible value for biological density, and α represents the adjustment coefficient. S402. Obtain inter-population competition data and calculate the population competition feedback factor, specifically: In the formula, C species1 C species2 ... represent the carbon storage of different biological populations, F competition Indicates the population competition feedback factor; S403. Based on the carbon sink prediction corrected by temperature feedback obtained in S300, a multidimensional dynamic carbon sink optimization model is constructed by inputting population competition feedback factor and biological density feedback factor, and the final carbon sink prediction result is output based on the model, specifically: C optimized =C feedback (T)·F densuty ·F competition In the formula, C feedback (T) represents the predicted carbon sequestration amount after temperature feedback correction, F density F represents the biological density feedback factor. competition C represents the population competition feedback factor. optimized This indicates the final carbon sink forecast result.

4. A marine carbon sink data survey system, employing a marine carbon sink data survey method as described in any one of claims 1-3, characterized in that, Including the following: The analysis and determination unit is used to obtain initial data in the marine carbon sink ecosystem, and to conduct carbon sink element analysis based on the initial data to determine carbon storage and influencing factors; The preliminary prediction unit is used to construct a dynamic change model based on initial data and influencing factors in the marine carbon sink ecosystem, and to calculate the initial carbon sink prediction results; The feedback correction unit constructs a temperature feedback model correction model to correct the obtained carbon sink prediction results based on temperature feedback. The optimized output unit is used to perform multidimensional dynamic carbon sink optimization based on the corrected carbon sink prediction results, by introducing biological density and competitive feedback mechanisms, and to obtain the final optimized carbon sink prediction results.

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