A mangrove soil carbon dynamic simulation method and related device

Abstract

The application discloses a mangrove soil carbon dynamic simulation method and related equipment, and the method comprises the following steps: acquiring a data set collected by a mangrove station, and preprocessing the collected data set to obtain a model input data set; inputting the obtained model input data set into a preset simulation model for calculation to obtain a core differential equation set, and performing product calculation on soil data in the model input data set to obtain an exogenous carbon input amount; performing simulation calculation on the basis of the calculated exogenous carbon input amount, the collected model input data set and the calculated core differential equation set to obtain soil carbon pool data; and analyzing the soil carbon pool data to determine an endogenous carbon contribution ratio and an exogenous carbon contribution ratio in soil carbon dynamics as a simulation result of carbon dynamics; the embodiment of the application can accurately simulate carbon dynamics. The application can be widely applied to the technical field of carbon cycle simulation.

Description

Technical Field

[0001] This application relates to the field of carbon cycle simulation technology, and in particular to a method and related equipment for simulating the dynamic carbon of mangrove soil. Background Technology

[0002] Mangroves are among the ecosystems with the highest carbon density and are the core carriers of long-term carbon sequestration. Soil carbon research on mangroves can provide support for understanding the long-term evolution mechanism of mangrove carbon pools and their impact on environmental changes. Existing soil carbon pool studies usually employ field surveys and statistics or establish corresponding carbon cycle models to simulate and analyze changes in mangrove soil carbon cycles. However, the simulation accuracy of existing technical solutions is low. Summary of the Invention

[0003] The main objective of this application is to propose a method and related equipment for simulating carbon dynamics in mangrove soil, which can improve the accuracy of carbon dynamics simulation.

[0004] To achieve the above objectives, one aspect of this application proposes a method for simulating mangrove soil carbon dynamics, the method comprising: Obtain the site dataset and preprocess it to obtain the model input dataset; The core differential equations are obtained by substituting the input dataset of the model and the preset simulation model into the calculation, and the exogenous carbon input amount is obtained by multiplying the input dataset of the model. Soil carbon pool data are obtained by simulation calculation based on the exogenous carbon input, the model input dataset, and the core differential equations. Based on the soil carbon pool data, analytical calculations were performed to obtain the carbon dynamics simulation results.

[0005] In some embodiments, the step of substituting the model input dataset and the preset simulation model into calculations to obtain the core set of differential equations specifically includes: Data is extracted from the model input dataset to obtain primary net productivity data; the surface input quantity is estimated based on the primary net productivity data. The endogenous carbon input data is obtained by calculating the surface input amount and the first parameter in the preset simulation model; the environmental regulation factor is obtained by calculating the preset surface coefficient and the second parameter in the preset simulation model. The core set of differential equations is obtained by calculating based on the endogenous carbon input, the environmental regulation factor, and the preset simulation model.

[0006] In some embodiments, the step of performing product calculations based on the model input dataset to obtain the amount of exogenous carbon input specifically includes: Data extraction was performed on the model input dataset to obtain sedimentation rate data, sediment organic carbon content data, and soil bulk density data; The amount of exogenous carbon input is obtained by multiplying the sedimentation rate data, the sediment organic carbon content data, and the soil bulk density data.

[0007] In some embodiments, the step of performing simulation calculations based on the exogenous carbon input, the model input dataset, and the core differential equation system to obtain soil carbon pool data specifically includes: Soil layer data is determined by calculation based on the preset simulation depth and preset soil depth; and sedimentation rate data is obtained by extracting data from the model input dataset. Simulations are performed based on the deposition rate data and the soil layer data to obtain the current carbon pool data. The soil carbon pool is then updated based on the current carbon pool data. The current simulation duration is recorded, and the current simulation duration is compared with the preset simulation duration. If the current simulation duration is less than the preset simulation duration, the model input dataset is updated, and the simulation based on the deposition rate data and the soil layer data is returned to obtain the current carbon pool data. If the current simulation duration is greater than or equal to the preset simulation duration, the soil carbon pool will be used as the soil carbon pool data.

[0008] In some embodiments, the step of simulating based on the deposition rate data and the soil layer data to obtain the current carbon pool data specifically includes: Based on the deposition rate data and the soil layer data, complete soil layer data and residual thickness data are obtained; and based on the preset humification coefficient and the complete soil layer data, a ratio calculation is performed to obtain the first carbon pool data; wherein, the first carbon pool data includes young carbon pool data and ancient carbon pool data. The allocation weight is calculated proportionally based on historical deposition data and the deposition rate data. The first carbon pool data is then updated based on the allocation weight and the residual thickness data to obtain the second carbon pool data. Parameters are extracted based on a preset simulation model to obtain endogenous carbon input data and environmental adjustment factors. The second carbon pool data is then updated based on the endogenous carbon input data and the environmental adjustment factors to obtain the current carbon pool data.

[0009] In some embodiments, the step of performing analytical calculations based on the soil carbon pool data to obtain carbon dynamics simulation results specifically includes: Data extraction is performed on the soil carbon pool data according to a preset time period to obtain a steady-state carbon pool dataset. The steady-state carbon pool dataset is parsed to obtain several sets of young carbon pool data and several sets of ancient carbon pool data; and data extraction is performed on the several sets of young carbon pool data and several sets of ancient carbon pool data to obtain several sets of young endogenous carbon input data, several sets of young exogenous carbon input data, several sets of ancient endogenous carbon input data and several sets of ancient exogenous carbon input data. The proportion of endogenous carbon contribution is obtained by averaging several sets of young endogenous carbon input data and several sets of ancient endogenous carbon input data; the proportion of exogenous carbon contribution is obtained by averaging several sets of young exogenous carbon input data and several sets of ancient exogenous carbon input data. The carbon dynamics simulation results are obtained by integrating the proportions of endogenous carbon contribution and the proportions of exogenous carbon contribution.

[0010] In some embodiments, the method further includes: Data is extracted from the soil carbon pool data according to a preset time period to obtain steady-state carbon pool data. The mean is calculated based on the steady-state carbon pool data and the model input dataset to obtain the simulated soil carbon content. Data is extracted from the model input dataset to obtain organic carbon content profile data. The difference between the simulated soil carbon content and the organic carbon content profile data is calculated to obtain the carbon content deviation function. The carbon content deviation function is minimized iteratively to obtain the optimal parameter set, and the preset simulation model is updated according to the optimal parameter set.

[0011] To achieve the above objectives, another aspect of this application proposes a mangrove soil carbon dynamics simulation system, the system comprising: The collection module is used to acquire site datasets and preprocess the site datasets to obtain model input datasets; The calculation module is used to perform substitution calculations based on the model input dataset and the preset simulation model to obtain the core differential equation system, and to perform product calculations based on the model input dataset to obtain the amount of exogenous carbon input. The simulation module is used to perform simulation calculations based on the exogenous carbon input, the model input dataset, and the core differential equations to obtain soil carbon pool data. The analysis module is used to perform analytical calculations based on the soil carbon pool data to obtain carbon dynamic simulation results.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a method, system, electronic device, storage medium, and program product for simulating mangrove soil carbon dynamics. This solution obtains a dataset collected from mangrove sites and preprocesses the collected dataset to obtain a model input dataset. The obtained model input dataset is then input into a preset simulation model for calculation, resulting in a core set of differential equations. The exogenous carbon input is calculated by multiplying the soil data in the model input dataset. Simulation calculations are performed based on the calculated exogenous carbon input, the collected model input dataset, and the calculated core set of differential equations to obtain soil carbon pool data. The soil carbon pool data is analyzed to determine the proportions of endogenous and exogenous carbon contributions in soil carbon dynamics, which serve as the simulation results of carbon dynamics. By analyzing and calculating the exogenous and endogenous carbon in the soil carbon pool to simulate the soil carbon dynamic cycle, the soil carbon dynamics are quantitatively analyzed, thereby improving the accuracy of carbon dynamics simulation. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method for simulating mangrove soil carbon dynamics provided in an embodiment of this application; Figure 2 yes Figure 1 The flowchart of step S102 in the document; Figure 3 yes Figure 1 Another flowchart in step S102; Figure 4 yes Figure 1 The flowchart of step S103 in the process; Figure 5 yes Figure 4 The flowchart of step S402 in the document; Figure 6 yes Figure 1 The flowchart of step S104 in the process; Figure 7 This is a flowchart illustrating parameter updates in a mangrove soil carbon dynamics simulation method provided in this application embodiment; Figure 8 This is a flowchart of a specific embodiment provided in this application; Figure 9 This is a schematic diagram of the structure of a simulation model in a specific embodiment provided in this application. Figure 10 This is a schematic diagram illustrating the changes in soil organic carbon, endogenous carbon, and exogenous carbon with depth in a specific embodiment provided in this application. Figure 11 This is a schematic diagram of the structure of a mangrove soil carbon dynamics simulation system provided in an embodiment of this application; Figure 12 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0017] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0018] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] This application provides a method for simulating mangrove soil carbon dynamics, relating to the field of information technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited thereto. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the mangrove soil carbon dynamics simulation method, but is not limited to the above forms.

[0021] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0022] Figure 1 This is an optional flowchart of a mangrove soil carbon dynamics simulation method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0023] Step S101: Obtain the site dataset and preprocess the site dataset to obtain the model input dataset; Step S102: Substitute the model input dataset and the preset simulation model into the calculation to obtain the core differential equation set, and perform product calculation based on the model input dataset to obtain the amount of exogenous carbon input. Step S103: Perform simulation calculations based on the amount of exogenous carbon input, the model input dataset, and the core differential equation set to obtain soil carbon pool data; Step S104: Perform analytical calculations based on soil carbon pool data to obtain carbon dynamic simulation results.

[0024] Steps S101 to S104 as illustrated in this embodiment involve acquiring multi-source measured data from mangrove sites, including sediment data and soil profile data of the mangrove area; performing data cleaning, depth normalization, and unit unification preprocessing on the acquired site dataset to generate a model input dataset; inputting the obtained model input dataset into a pre-constructed simulation model for calculation; splitting the carbon input data into endogenous carbon input and exogenous carbon input, and classifying the carbon content in the soil into young carbon pools and ancient carbon pools; based on the simulation model, and according to the endogenous carbon... Input and exogenous carbon inputs update young and ancient carbon pools, thereby simulating the dynamic cycle of soil carbon. By inputting the model input dataset into the pre-constructed simulation model, a set of core differential equations for carbon pool simulation is obtained. Based on the data in the model input dataset, calculations are performed to determine the exogenous carbon input data. Then, by combining the model input dataset and the obtained core differential equations, simulations are performed to obtain carbon pool data for different soil layers. By analyzing and calculating the obtained carbon pool data, the quantitative results of the impact of different types of carbon inputs on the carbon pools of each soil layer are determined, serving as the carbon dynamic simulation results.

[0025] Please see Figure 2 In some embodiments, step S102 may include, but is not limited to, steps S201 to S203: Step S201: Extract data from the model input dataset to obtain primary net productivity data; estimate the surface input quantity based on the primary net productivity data. Step S202: Calculate the endogenous carbon input data based on the surface input amount and the first parameter in the preset simulation model; calculate the environmental regulation factor based on the preset surface coefficient and the second parameter in the preset simulation model. Step S203: Calculate the core differential equations based on the endogenous carbon input, environmental regulation factors, and the preset simulation model.

[0026] In step S201 of some embodiments, the data collected by the site includes primary net productivity, soil bulk density data, sediment organic carbon content, organic carbon deposition rate, organic carbon content profile data, clay content, and climate data. The system extracts data from the collected site dataset to obtain primary net productivity data. Based on the primary net productivity data, it estimates and determines the surface input amount, which is used to calculate the endogenous carbon input amount, i.e., the soil carbon content generated by the mangroves themselves.

[0027] In step S202 of some embodiments, as soil depth increases, the amount of carbon input from the surface layer entering the soil and the amount undergoing corresponding physicochemical changes, such as mineralization, will decrease. The system extracts parameters from the constructed preset simulation model to determine the relevant attenuation parameters of soil carbon content with depth. Then, based on the estimated surface input amount and the extracted attenuation index, it calculates the endogenous carbon input data and the environmental impact factors affecting carbon mineralization. In this embodiment, the calculation formula is as follows: , in, In depth Annual endogenous carbon input at the location. For surface intake, The carbon input decays with depth as an exponent. In depth Environmental impact factors at the location This represents the surface organic carbon mineralization rate coefficient. This is the coefficient for the decrease in mineralization rate with depth.

[0028] In step S203 of some embodiments, the system inputs the calculated endogenous carbon input data and environmental factors affecting carbon mineralization into a preset simulation model to calculate the input model input dataset, thereby obtaining a set of core differential equations characterizing the changes in the soil carbon pool. In this embodiment, the expression of the set of core differential equations is as follows: , in, For the content of young carbon pools in depth The rate of change at that point For ancient carbon pools in depth The rate of change at that point In depth The young carbon pool reserves at that location In depth Ancient carbon reserves at the location, In depth Annual endogenous carbon input at the location. In depth Environmental impact factors at the location The humification coefficient is... The decomposition rate constant of the young carbon pool, The decomposition rate constant of the ancient carbon pool, For exogenous carbon input at depth The allocation amount at the location.

[0029] Please see Figure 3In some embodiments, step S102 may also include, but is not limited to, steps S301 to S302: Step S301: Extract data from the model input dataset to obtain sedimentation rate data, sediment organic carbon content data, and soil bulk density data; Step S302: Calculate the exogenous carbon input by multiplying the sedimentation rate data, sediment organic carbon content data, and soil bulk density data.

[0030] In step S301 of some embodiments, the carbon content in the soil is influenced not only by the carbon input from mangrove products themselves, but also by dynamic geomorphic deposition and exogenous carbon input, such as animals brought about by geomorphic changes. The system is coupled based on the dynamic geomorphic product process to calculate the exogenous carbon input flux. The system extracts data from the model input dataset to obtain sedimentation rate data, sediment organic carbon content data, and soil bulk density data, which are used to calculate the exogenous carbon input content to the soil.

[0031] In step S302 of some embodiments, the system performs a product calculation based on the extracted sedimentation rate data, sediment organic carbon content data, and soil bulk density data to obtain the exogenous carbon input flux. In this embodiment, sedimentation rate and sediment organic carbon content are used as control variables for exogenous carbon input so that the exogenous carbon input flux for the corresponding simulation year can be calculated based on the sedimentation rate and sediment organic carbon content of different years during subsequent simulation calculations. The calculation formula is as follows: , in, For the first Annual exogenous carbon input flux For the first Annual deposition rate, For soil bulk density data, This refers to the organic carbon content of the sediment.

[0032] Please see Figure 4 In some embodiments, step S103 may also include, but is not limited to, steps S401 to S404: Step S401: Calculate and determine soil layer data based on preset simulation depth and preset soil depth; and extract data from the model input dataset to obtain deposition rate data. Step S402: Simulate based on sedimentation rate data and soil layer data to obtain current carbon pool data, update the soil carbon pool based on the current carbon pool data, record the current simulation duration, and compare the current simulation time with the preset simulation duration. Step S403: If the current simulation duration is less than the preset simulation duration, update the model input dataset and return the simulation based on the deposition rate data and soil layer data to obtain the current carbon pool data. Step S404: If the current simulation duration is greater than or equal to the preset simulation duration, the soil carbon pool is used as soil carbon pool data.

[0033] In step S401 of some embodiments, after calculating the exogenous carbon input amount and the endogenous carbon input amount based on the model input dataset, the system solves the core differential equations obtained earlier to simulate the carbon pool storage data of each soil layer. The system divides the soil into layers according to the depth data to be simulated and the thickness of each layer, so as to simulate the carbon pool storage change of each layer. Then, according to the simulation duration, the system extracts the deposition rate data corresponding to the duration from the model input data to simulate the carbon content change of each soil layer.

[0034] In step S402 of some embodiments, the system simulates and calculates the carbon pool storage of each soil layer based on the extracted sedimentation rate data, and corrects the carbon pool storage by combining the model input dataset, thereby obtaining the current carbon pool data corresponding to the current simulation duration; the system updates the soil layer carbon pool based on the current carbon pool data obtained from the simulation calculation, which records carbon pool data of different simulation durations and different soil layers; the system records the current simulation duration and compares it with the preset total duration to determine whether the simulation calculation needs to be terminated.

[0035] In step S403 of some embodiments, if the current simulation duration is less than the preset total duration, the system updates the model input dataset according to the simulation duration, thereby updating data such as the deposition rate; the system recalculates the corresponding soil carbon pool data based on the updated deposition rate and other data.

[0036] In step S404 of some embodiments, if the current simulation duration is greater than or equal to the preset total duration, the system will record the obtained soil carbon pool data as soil carbon pool data.

[0037] Please see Figure 5 In some embodiments, step S402 may also include, but is not limited to, steps S501 to S503: Step S501: Calculate the complete soil layer data and residual thickness data based on the sedimentation rate data and soil layer data; and calculate the proportion based on the preset humification coefficient and the complete soil layer data to obtain the first carbon pool data; wherein, the first carbon pool data includes young carbon pool data and ancient carbon pool data. Step S502: Calculate the allocation weights based on historical sedimentation data and sedimentation rate data, and update the first carbon pool data based on the allocation weights and residual thickness data to obtain the second carbon pool data. Step S503: Extract parameters according to the preset simulation model to obtain endogenous carbon input data and environmental adjustment factors, and update the second carbon pool data according to the endogenous carbon input data and environmental adjustment factors to obtain the current carbon pool data.

[0038] In step S501 of some embodiments, the system calculates the number of newly completed soil layers and the residual thickness based on the extracted sedimentation rate and the number of soil layers to be simulated. The residual thickness indicates that the thickness of the remaining sediment is less than the thickness of the complete soil layer. The system allocates the calculated number of complete soil layers to the young carbon pool and the ancient carbon pool in each soil layer according to the set humification coefficient, and updates the storage data therein.

[0039] In step S502 of some embodiments, the system calculates the ratio of the original surface carbon and newly deposited carbon in the current soil according to the thickness, and then performs a weighted allocation on the calculated residual thickness based on the calculated ratio, thereby updating the young carbon pool and ancient carbon pool in each soil layer.

[0040] In step S503 of some embodiments, for the young carbon pool and ancient carbon pool of each soil layer calculated above, the system calculates the endogenous carbon input generated by the mangrove itself based on the data from the model input dataset, as well as the environmental impact factors of carbon mineralization in the soil, and adjusts and updates the calculated young carbon pool and ancient carbon pool of each soil layer to obtain the current carbon pool data.

[0041] Please see Figure 6 In some embodiments, step S104 may also include, but is not limited to, steps S601 to S604: Step S601: Extract soil carbon pool data according to a preset time period to obtain a steady-state carbon pool dataset; Step S602: Analyze the steady-state carbon pool dataset to obtain several sets of young carbon pool data and several sets of ancient carbon pool data; and extract data from the several sets of young carbon pool data and several sets of ancient carbon pool data to obtain several sets of young endogenous carbon input data, several sets of young exogenous carbon input data, several sets of ancient endogenous carbon input data and several sets of ancient exogenous carbon input data. Step S603: Calculate the average of several sets of young endogenous carbon input data and several sets of ancient endogenous carbon input data to obtain the endogenous carbon contribution ratio; calculate the average of several sets of young exogenous carbon input data and several sets of ancient exogenous carbon input data to obtain the exogenous carbon contribution ratio. Step S604: Integrate the proportions of endogenous carbon contribution and exogenous carbon contribution to obtain the carbon dynamic simulation results.

[0042] In step S601 of some embodiments, after a long period of simulation calculation, the system tends to a stable state. The system can select data of any simulation duration from the recorded soil carbon pool data to calculate the relative contribution ratio of endogenous carbon input and exogenous carbon input to the soil carbon pool under that simulation duration. Since the carbon pool storage fluctuates slightly from year to year, in order to characterize the long-term equilibrium state of the system and eliminate randomness, the system selects data of a preset steady-state time period for analysis, thereby calculating the relative contribution ratio of endogenous carbon input and exogenous carbon input to the soil carbon pool.

[0043] In step S602 of some embodiments, after the system extracts soil carbon pool data for a steady-state time period, it analyzes the soil carbon pool data to obtain soil carbon pool data corresponding to different soil layers at different times within the steady-state time period, including young carbon pool storage data and ancient carbon pool storage data. Then, the extracted soil carbon pool data is further analyzed and data extracted to divide the carbon pool data at different times and different soil layers into endogenous carbon input data and exogenous carbon input data, that is, endogenous carbon input data and exogenous carbon input data of young carbon pool at different times and different soil layers, and the corresponding endogenous carbon input data and exogenous carbon input data of ancient carbon pool. The system calculates the relative contribution ratio of endogenous carbon input and exogenous carbon input to soil carbon pool storage based on the extracted multiple sets of endogenous carbon input data and exogenous carbon input data.

[0044] In step S603 of some embodiments, the system sums and statistically analyzes the endogenous carbon input data of young and ancient carbon pools in different soil layers at different times, and sums and statistically analyzes the exogenous carbon input data of young and ancient carbon pools in different soil layers at different times, to obtain the total endogenous carbon input and the total exogenous carbon input within the steady-state time period; then, it calculates the mean of the total endogenous carbon input and the total exogenous carbon input within the steady-state time period respectively; it calculates the ratio of the mean of the total endogenous carbon input to the sum of the two means as the relative contribution ratio of the endogenous carbon input; and it calculates the ratio of the mean of the total exogenous carbon input to the sum of the two means as the relative contribution ratio of the exogenous carbon input.

[0045] In step S604 of some embodiments, the system integrates the calculated relative contribution ratio of endogenous carbon input and the relative contribution ratio of exogenous carbon input as the cyclic simulation result of soil carbon dynamics in the soil region.

[0046] Please see Figure 7 In some embodiments, the mangrove soil carbon dynamics simulation method provided in this application may also include, but is not limited to, steps S701 to S703: Step S701: Extract soil carbon pool data according to a preset time period to obtain steady-state carbon pool data, and calculate the mean based on steady-state carbon pool data and model input dataset to obtain simulated soil carbon content. Step S702: Extract data from the model input dataset to obtain organic carbon content profile data. Calculate the difference between the simulated soil carbon content and the organic carbon content profile data to obtain the carbon content deviation function. Step S703: Minimize the carbon content deviation function to obtain the optimal parameter set, and update the parameters of the preset simulation model according to the optimal parameter set.

[0047] In step S701 of some embodiments, the system calibrates the preset simulation model based on the soil carbon pool data obtained from the simulation calculation, thereby ensuring the accuracy and precision of the system's simulation calculation. In this embodiment, an improved ICBM model is constructed as the simulation model, wherein the parameters to be calibrated are the decay index of carbon input with depth and the decay index of mineralization rate with depth. The system extracts the soil carbon pool data for the steady-state time period obtained from the simulation calculation, and calculates the average carbon pool storage data of each soil layer at different times in the soil carbon pool data for this time period to obtain the average carbon pool storage of different soil layers in this time period, and sums them to obtain the total organic carbon storage of each soil layer. Then, based on the soil bulk density data and the thickness of each soil layer in the model input dataset, the carbon content percentage of different soil layers is calculated. In this embodiment, the carbon content percentage of different soil layers is calculated according to the following formula: , in, For the first Percentage of carbon content in the soil layer For the first The amount of endogenous carbon input to the young carbon pool in the soil layer. For the first The amount of exogenous carbon input to the young carbon pool in the soil layer. For the first Endogenous carbon input to the ancient carbon pool in the soil layers. For the first Exogenous carbon input to the ancient carbon pool in soil layers For soil bulk density data, This refers to the thickness of the soil layer.

[0048] In step S702 of some embodiments, the system extracts data from the model input dataset collected during the steady-state time period to determine the organic carbon content profile data at the corresponding time. The system calculates the carbon content deviation function for different soil layers based on the extracted organic carbon content profile data and the calculated percentage of soil carbon content at the corresponding time. The system then performs nonlinear least squares calculation on this function to calibrate the model parameters. In this embodiment, the function expression is as follows: , in, For the first Deviation in carbon content between soil layers For the first Measured carbon content of soil layers For the first Percentage of carbon content in the soil layer.

[0049] In step S703 of some embodiments, the system iteratively calculates the obtained function to minimize the deviation value and obtain the optimal parameters. The system updates the simulation model based on the obtained optimal parameters to ensure the accuracy and precision of the system simulation calculation.

[0050] The following is a detailed description and explanation of the solutions in the embodiments of the present invention, using specific application examples: Please see Figure 8 , Figure 8 This is a flowchart illustrating a mangrove soil carbon dynamics simulation method provided in this application embodiment, applied in a specific embodiment. The method involves collecting station monitoring data, including primary net productivity, soil bulk density, organic carbon deposition rate, clay content, and climate data, within a depth of 0 to 1 meter within the mangrove study area, and performing corresponding preprocessing to obtain model input data. This model input data is then input into a constructed improved ICBM model for simulation calculations. The model structure diagram is shown below. Figure 9 As shown, the model sets the decomposition rate constant for young carbon pools. =0.8, ancient carbon pool decomposition rate parameter =0.006, humification coefficient =0.089, environmental factor baseline adjustment coefficient =2.07, with an average annual temperature of 24 degrees Celsius; the model decomposes the input data into endogenous carbon input and exogenous carbon input; the coupled dynamic geomorphological sedimentation module in the simulation system calculates carbon pool data for different soil layers based on the sedimentation rate and the exogenous carbon input flux calculated from the model input data; specifically, the soil profile is discretized into layers of equal thickness, each layer with a thickness of 0.01 meters, a total simulation depth of 1 meter, and divided into 100 layers; the simulation time is set to 101 years, with an additional extension of 2000 years as a model warm-up period to allow the simulation system to reach a steady state; the system calculates the endogenous carbon input data and exogenous carbon input data for different soil layers based on the input data, updates the carbon pool data for different soil layers, and can also draw schematic diagrams based on the simulation results and measured results, as shown in the figure. Figure 10 As shown, the simulation curves of total soil organic carbon, endogenous organic carbon, and exogenous organic carbon with profile depth can be intuitively obtained, and the results are compared with the measured organic carbon. It can be seen that the simulation results are in good agreement with the measured data. The simulation calculation system constructs an objective function based on the simulated carbon pool data and the measured data in the model input data, and uses the nonlinear least squares method to localize and calibrate the model parameters, obtaining a mineralization rate depth attenuation coefficient of 0.01, a mangrove root carbon input depth attenuation coefficient of 2.3, and a deposition rate of 0.4035. At the same time, the simulation calculation system calculates the average carbon stock based on the carbon pool data after the model stabilizes, analyzes the proportion of endogenous and exogenous contributions, and obtains the analysis results of soil carbon dynamics in the mangrove area. With the model parameters in the simulation calculation system as the core, through gradient adjustment simulation experiments, sensitivity analysis of mangrove soil carbon turnover rate and carbon pool stability can be performed to identify the dominant factors affecting soil carbon dynamics and their action pathways.

[0051] The embodiments of this application include at least the following beneficial effects: This application provides a method, system, electronic device, storage medium, and program product for simulating mangrove soil carbon dynamics. This solution obtains a dataset collected from mangrove sites and preprocesses the collected dataset to obtain a model input dataset. The obtained model input dataset is then input into a preset simulation model for calculation, resulting in a core set of differential equations. The exogenous carbon input is calculated by multiplying the soil data in the model input dataset. Simulation calculations are performed based on the calculated exogenous carbon input, the collected model input dataset, and the calculated core set of differential equations to obtain soil carbon pool data. The soil carbon pool data is analyzed to determine the proportions of endogenous and exogenous carbon contributions in soil carbon dynamics, which serve as the simulation results of carbon dynamics. By analyzing and calculating the exogenous and endogenous carbon in the soil carbon pool to simulate the soil carbon dynamic cycle, the soil carbon dynamics are quantitatively analyzed, thereby improving the accuracy of carbon dynamics simulation.

[0052] Please see Figure 11This application also provides a mangrove soil carbon dynamics simulation system, which can implement the above-mentioned method. The system includes: The collection module is used to acquire site datasets and preprocess the site datasets to obtain model input datasets; The calculation module is used to perform substitution calculations based on the model input dataset and the preset simulation model to obtain the core differential equation system, and to perform product calculations based on the model input dataset to obtain the amount of exogenous carbon input. The simulation module is used to perform simulation calculations based on the exogenous carbon input, the model input dataset, and the core differential equations to obtain soil carbon pool data. The analysis module is used to perform analytical calculations based on the soil carbon pool data to obtain carbon dynamic simulation results.

[0053] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0054] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0055] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0056] Please see Figure 12 , Figure 12 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 1201 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1202 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1202 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1202 and is called and executed by the processor 1201 using the methods described in the embodiments of this application. The input / output interface 1203 is used to implement information input and output; The communication interface 1204 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1205 transmits information between various components of the device (e.g., processor 1201, memory 1202, input / output interface 1203, and communication interface 1204); The processor 1201, memory 1202, input / output interface 1203 and communication interface 1204 are connected to each other within the device via bus 1205.

[0057] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0058] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0059] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0060] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0061] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0062] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0063] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0064] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0066] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0067] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0069] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0071] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for mangrove soil carbon dynamics, characterized in that, The method includes: Obtain the site dataset and preprocess it to obtain the model input dataset; The core differential equations are obtained by substituting the input dataset of the model and the preset simulation model into the calculation, and the exogenous carbon input amount is obtained by multiplying the input dataset of the model. Soil carbon pool data are obtained by simulation calculation based on the exogenous carbon input, the model input dataset, and the core differential equations. Based on the soil carbon pool data, analytical calculations were performed to obtain the carbon dynamics simulation results.

2. The method according to claim 1, characterized in that, The step of substituting the input dataset and the preset simulation model into the calculation to obtain the core set of differential equations specifically includes: Data is extracted from the model input dataset to obtain primary net productivity data; the surface input quantity is estimated based on the primary net productivity data. The endogenous carbon input data is obtained by calculating the surface input amount and the first parameter in the preset simulation model; the environmental regulation factor is obtained by calculating the preset surface coefficient and the second parameter in the preset simulation model. The core set of differential equations is obtained by calculating based on the endogenous carbon input, the environmental regulation factor, and the preset simulation model.

3. The method according to claim 1, characterized in that, The step of performing a product calculation based on the model input dataset to obtain the amount of exogenous carbon input specifically includes: Data extraction was performed on the model input dataset to obtain sedimentation rate data, sediment organic carbon content data, and soil bulk density data; The amount of exogenous carbon input is obtained by multiplying the sedimentation rate data, the sediment organic carbon content data, and the soil bulk density data.

4. The method according to claim 1, characterized in that, The process of obtaining soil carbon pool data through simulation calculations based on the exogenous carbon input, the model input dataset, and the core differential equation system specifically includes: Soil layer data is determined by calculation based on the preset simulation depth and preset soil depth; and sedimentation rate data is obtained by extracting data from the model input dataset. Simulations are performed based on the deposition rate data and the soil layer data to obtain the current carbon pool data. The soil carbon pool is then updated based on the current carbon pool data. The current simulation duration is recorded, and the current simulation duration is compared with the preset simulation duration. If the current simulation duration is less than the preset simulation duration, the model input dataset is updated, and the simulation based on the deposition rate data and the soil layer data is returned to obtain the current carbon pool data. If the current simulation duration is greater than or equal to the preset simulation duration, the soil carbon pool will be used as the soil carbon pool data.

5. The method according to claim 4, characterized in that, The process of simulating based on the deposition rate data and the soil layer data to obtain the current carbon pool data specifically includes: Based on the deposition rate data and the soil layer data, complete soil layer data and residual thickness data are obtained; and based on the preset humification coefficient and the complete soil layer data, a ratio calculation is performed to obtain the first carbon pool data; wherein, the first carbon pool data includes young carbon pool data and ancient carbon pool data. The allocation weight is calculated proportionally based on historical deposition data and the deposition rate data. The first carbon pool data is then updated based on the allocation weight and the residual thickness data to obtain the second carbon pool data. Parameters are extracted based on a preset simulation model to obtain endogenous carbon input data and environmental adjustment factors. The second carbon pool data is then updated based on the endogenous carbon input data and the environmental adjustment factors to obtain the current carbon pool data.

6. The method according to claim 1, characterized in that, The step of performing analytical calculations based on the soil carbon pool data to obtain carbon dynamic simulation results specifically includes: Data extraction is performed on the soil carbon pool data according to a preset time period to obtain a steady-state carbon pool dataset. The steady-state carbon pool dataset is parsed to obtain several sets of young carbon pool data and several sets of ancient carbon pool data; and data extraction is performed on the several sets of young carbon pool data and several sets of ancient carbon pool data to obtain several sets of young endogenous carbon input data, several sets of young exogenous carbon input data, several sets of ancient endogenous carbon input data and several sets of ancient exogenous carbon input data. The proportion of endogenous carbon contribution is obtained by averaging several sets of young endogenous carbon input data and several sets of ancient endogenous carbon input data; the proportion of exogenous carbon contribution is obtained by averaging several sets of young exogenous carbon input data and several sets of ancient exogenous carbon input data. The carbon dynamics simulation results are obtained by integrating the proportions of endogenous carbon contribution and the proportions of exogenous carbon contribution.

7. The method according to claim 1, characterized in that, The method further includes: Data is extracted from the soil carbon pool data according to a preset time period to obtain steady-state carbon pool data. The mean is calculated based on the steady-state carbon pool data and the model input dataset to obtain the simulated soil carbon content. Data is extracted from the model input dataset to obtain organic carbon content profile data. The difference between the simulated soil carbon content and the organic carbon content profile data is calculated to obtain the carbon content deviation function. The carbon content deviation function is minimized iteratively to obtain the optimal parameter set, and the preset simulation model is updated according to the optimal parameter set.

8. A mangrove soil carbon dynamics simulation system, characterized in that, The system includes: The collection module is used to acquire site datasets and preprocess the site datasets to obtain model input datasets; The calculation module is used to perform substitution calculations based on the model input dataset and the preset simulation model to obtain the core differential equation system, and to perform product calculations based on the model input dataset to obtain the amount of exogenous carbon input. The simulation module is used to perform simulation calculations based on the exogenous carbon input, the model input dataset, and the core differential equations to obtain soil carbon pool data. The analysis module is used to perform analytical calculations based on the soil carbon pool data to obtain carbon dynamic simulation results.

9. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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