Rh estimation method and system based on simulation box and environmental parameter inversion

Abstract

This invention discloses a wetland RH estimation method and system based on a simulation chamber and environmental parameter inversion. The method includes: first, obtaining a simulation chamber for simulating the wetland to be estimated, and constructing a corresponding digital twin system for RH estimation of the wetland based on the simulation chamber; then, collecting target parameters of the wetland to be estimated; using the digital twin system for RH estimation of the wetland to be estimated, constructing an initial RH inversion model based on a hybrid algorithm, and using a data interaction layer to transmit the target parameters to a virtual model layer to drive iterative updates of the initial RH inversion model, generating a target RH inversion model based on the hybrid algorithm; finally, calculating the RH value of the wetland to be estimated based on the target RH inversion model based on the hybrid algorithm, generating RH spatiotemporal distribution data. It can achieve deep integration of physical monitoring and virtual simulation through digital twins, improving the accuracy and application scope of RH estimation.

Description

Technical Field

[0001] This invention relates to the field of soil respiration measurement technology in wetland ecosystems, specifically to a wetland RH estimation method and system based on simulation chamber and environmental parameter inversion. Background Technology

[0002] Microbial respiration (RH) in wetland soil is a crucial link in the carbon cycle of wetland ecosystems, and its dynamic changes are controlled by multiple factors coupled together, including water level, water content, salinity, and temperature. Traditional RH estimation mainly relies on temperature-based empirical models, but the unique tidal, waterlogged, and anaerobic environments of wetlands cause RH to fluctuate periodically, making it difficult for traditional models to accurately characterize this complex dynamic. Among existing technologies, the static box method is a commonly used RH monitoring method, but this method cannot achieve long-term continuous monitoring and is difficult to invert regional-scale RH distribution, which has become a pressing technical problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide a wetland RH estimation method and system based on simulation chamber and environmental parameter inversion, to overcome the shortcomings of traditional technologies. It provides a wetland RH estimation method based on digital twin simulation chamber and environmental parameter inversion, achieving deep integration of physical monitoring and virtual simulation through digital twins, thereby improving the accuracy and application scope of RH estimation.

[0004] One embodiment of this application provides a wetland RH estimation method based on simulation chamber and environmental parameter inversion, the method comprising: A simulation chamber is obtained to simulate the wetland to be estimated, and a corresponding digital twin system for estimating the RH of the wetland to be estimated is constructed based on the simulation chamber; wherein, the digital twin system includes a simulation chamber device layer, a virtual model layer and a data interaction layer; Collect target parameters of the wetland to be estimated; wherein, the target parameters include Emission flux data and environmental factor data, including water level, soil temperature, soil moisture content, salinity, redox potential and air temperature; Using a digital twin system for estimating the RH of the wetland to be estimated, an initial RH inversion model based on a hybrid algorithm is constructed. The target parameters are transmitted to the virtual model layer through the data interaction layer to drive the initial RH inversion model to perform iterative updates and generate a target RH inversion model based on a hybrid algorithm. The target model for RH inversion based on a hybrid algorithm calculates the RH value of the wetland to be estimated and generates spatiotemporal distribution data of RH.

[0005] Optionally, the simulation chamber for simulating the wetland environment to be estimated includes: A gate-type tide level synchronization simulation box or a U-shaped tube weak open-circuit water level synchronization simulation box; the simulation box has a built-in water level adjustment module for real-time response to water level changes.

[0006] Optionally, the step of constructing a corresponding digital twin system for estimating the RH of the wetland to be estimated based on the simulation chamber includes: Simulation boxes are set up at preset intervals in the wetland area to be estimated; the simulation boxes include infrared sensors. Analyzer and / or Analyzer, multi-parameter sensing module, and wireless communication module; We utilized the respiratory mechanism of wetland soil microorganisms and constructed a digital twin parameter mapping model based on random forest, XGBoost, and LSTM algorithms. Based on the digital twin parameter mapping model, a two-way communication between the simulation box device layer and the data interaction layer is established, and the target parameter synchronization frequency is set to 5-30 minutes each time. An HMI visualization interface is constructed to generate a digital twin system for estimating the RH of the wetland to be estimated; wherein, the visualization interface includes a target parameter display module, a model iteration adjustment module, and an RH result output function module.

[0007] Optionally, the Emission flux data were obtained through bin method calculation, using the following formula: in, This represents the change in gas concentration per unit time. For the monitoring time interval, To simulate the box volume, The area of ​​soil covering the simulation box.

[0008] Optionally, the RH inversion initial model based on the hybrid algorithm includes: The system comprises a cascaded data preprocessing module, a mechanism modeling module, a machine learning module, a parameter fusion module, and a dynamic update module. The data preprocessing module is used to receive target parameters and perform preprocessing on the target parameters; The mechanism model module is used to construct the correlation between RH and various environmental factor data using a wetland soil microbial respiration mechanism model and pre-processed target parameters; The machine learning module is used to mine the nonlinear coupling relationships between various environmental factor data based on a hybrid algorithm. The parameter fusion module is used to fuse the correlation and nonlinear coupling relationships of various environmental factor data to generate monitoring data; The dynamic update module is used to receive and use the monitoring data to update the training parameters of the wetland soil microbial respiration mechanism model, so as to realize the dynamic adaptation of the RH inversion initial model based on the hybrid algorithm to the changes in the wetland environment.

[0009] Optionally, the RH inversion target model based on the hybrid algorithm calculates the RH value of the wetland to be estimated and generates RH spatiotemporal distribution data, including: Based on the target parameters retrieved by digital twin, and using digital elevation model and regional water level model to drive the RH retrieval target model based on hybrid algorithm to expand the regional scale, so as to output RH spatial distribution data with a grid resolution of not less than 1km×1km.

[0010] Another embodiment of this application provides a wetland RH estimation system based on simulation chamber and environmental parameter inversion, the system comprising: The module is used to obtain a simulation box for simulating the wetland to be estimated, and to construct a corresponding digital twin system for estimating the RH of the wetland to be estimated based on the simulation box; wherein, the digital twin system includes a simulation box device layer, a virtual model layer and a data interaction layer; The data acquisition module is used to acquire target parameters of the wetland to be estimated; wherein, the target parameters include... Emission flux data and environmental factor data, including water level, soil temperature, soil moisture content, salinity, redox potential and air temperature; The module is used to construct an initial RH inversion model based on a hybrid algorithm using a digital twin system for estimating the RH of the wetland to be estimated. The target parameters are transmitted to the virtual model layer using the data interaction layer to drive the initial RH inversion model to perform iterative updates and generate a target RH inversion model based on a hybrid algorithm. The generation module is used to calculate the RH value of the wetland to be estimated based on the RH inversion target model of the hybrid algorithm, and generate RH spatiotemporal distribution data.

[0011] Optionally, the obtaining module includes: A simulation unit is used to deploy simulation boxes at preset intervals in the wetland area to be estimated; the simulation box includes an infrared sensor. Analyzer and / or Analyzer, multi-parameter sensing module, and wireless communication module; The building blocks are used to utilize the respiratory mechanism of wetland soil microorganisms and construct digital twin parameter mapping models based on random forest, XGBoost and LSTM algorithms; Establish a unit to build bidirectional communication between the simulation box device layer and the data interaction layer based on the digital twin parameter mapping model, and set the target parameter synchronization frequency to 5-30 minutes each time; The generation unit is used to construct an HMI visualization interface to generate a digital twin system for estimating the RH of the wetland to be estimated; wherein, the visualization interface includes a target parameter display module, a model iteration adjustment module, and an RH result output function module.

[0012] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to implement the method described in any of the above-described embodiments when running.

[0013] Another embodiment of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement the method described in any of the above embodiments.

[0014] Compared with existing technologies, this invention first obtains a simulation chamber for simulating the wetland to be estimated, and then constructs a corresponding digital twin system for RH estimation of the wetland based on the simulation chamber. Next, it collects target parameters of the wetland to be estimated. Using the digital twin system for RH estimation, it constructs an initial RH inversion model based on a hybrid algorithm. The target parameters are transmitted to the virtual model layer via a data interaction layer to drive iterative updates of the initial RH inversion model, generating a target RH inversion model based on the hybrid algorithm. Finally, it calculates the RH value of the wetland to be estimated based on the target RH inversion model using the hybrid algorithm, generating spatiotemporal RH distribution data. This invention enables deep integration of physical monitoring and virtual simulation through digital twins, improving the accuracy and application scope of RH estimation. Attached Figure Description

[0015] Figure 1 A hardware structure block diagram of a computer terminal for a wetland RH estimation method based on simulation chamber and environmental parameter inversion provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a wetland RH estimation method based on simulation chamber and environmental parameter inversion provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a wetland RH estimation system based on simulation chamber and environmental parameter inversion, provided in an embodiment of the present invention. Detailed Implementation

[0016] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0017] This invention first provides a wetland RH estimation method based on simulation chamber and environmental parameter inversion. This method can be applied to electronic devices, such as computer terminals, specifically ordinary computers, tablets, etc.

[0018] The following detailed explanation uses a computer terminal as an example. Figure 1 This is a hardware block diagram of a computer terminal for a wetland RH estimation method based on simulation chamber and environmental parameter inversion, provided as an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0019] The non-volatile storage medium can store the operating system and computer program. The computer program includes program instructions that, when executed, cause the processor to perform any wetland RH estimation method based on simulation chamber and environmental parameter inversion.

[0020] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0021] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to perform any wetland RH estimation method based on the simulation chamber and environmental parameter inversion.

[0022] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0023] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0024] See Figure 2 , Figure 2 A flowchart illustrating a wetland RH estimation method based on simulation chamber and environmental parameter inversion provided by an embodiment of the present invention may include the following steps: S201: Obtain a simulation chamber for simulating the wetland to be estimated, and construct a corresponding digital twin system for estimating the RH of the wetland to be estimated based on the simulation chamber; wherein, the digital twin system includes a simulation chamber device layer, a virtual model layer and a data interaction layer.

[0025] Specifically, the simulation chamber used to simulate the wetland environment to be estimated may include: A gate-type tide level synchronization simulation box or a U-shaped tube weak open-circuit water level synchronization simulation box; the simulation box has a built-in water level adjustment module for real-time response to water level changes.

[0026] The digital twin system for estimating the RH of the wetland to be estimated, constructed based on the simulation chamber, may include: Simulation boxes are set up at preset intervals in the wetland area to be estimated; the simulation boxes include infrared sensors. Analyzer and / or Analyzer, multi-parameter sensing module, and wireless communication module; We utilized the respiratory mechanism of wetland soil microorganisms and constructed a digital twin parameter mapping model based on random forest, XGBoost, and LSTM algorithms. Based on the digital twin parameter mapping model, a two-way communication between the simulation box device layer and the data interaction layer is established, and the target parameter synchronization frequency is set to 5-30 minutes each time. An HMI visualization interface is constructed to generate a digital twin system for estimating the RH of the wetland to be estimated; wherein, the visualization interface includes a target parameter display module, a model iteration adjustment module, and an RH result output function module.

[0027] Specifically, depending on the type of wetland to be estimated, such as coastal wetland tidal flats or inland freshwater wetlands, either a gate-type tidal level synchronous simulation box or a U-shaped tube weak open-circuit water level synchronous simulation box can be selected. The simulation box has a built-in water level regulation module, which responds in real time to changes in the external wetland water level through the opening and closing of the gate or the hydraulic balance principle of the U-shaped tube, maintaining the soil hydrological environment inside the box consistent with the original location.

[0028] For example, simulation boxes are set up at preset intervals in the wetland area to be estimated, and an infrared sensor is installed on top of each simulation box. Analyzer or optional The analyzer can be equipped with multi-parameter sensing modules, such as water level sensors, soil temperature sensors, water content sensors, salinity sensors, and redox potential sensors, and can also be equipped with a Wi-Fi wireless communication module to form a simulation chamber device layer with multi-parameter acquisition and data transmission capabilities.

[0029] Based on the physicochemical mechanisms of microbial respiration in wetland soil, this study integrates substrate decomposition kinetics and gas diffusion mass transfer principles to establish an intrinsic correlation model between RH and core factors such as water level, temperature, and salinity, laying the physical foundation for parameter mapping. Three algorithms—random forest, XGBoost, and LSTM—are integrated to construct a digital twin parameter mapping model. Random forest and XGBoost are used to capture nonlinear interactions between factors, while LSTM is used to mine dynamic changes in time-series monitoring data. Weighted ensemble optimization is used to improve model prediction accuracy. Finally, a bidirectional data link between the simulation chamber device layer and the data interaction layer is constructed based on the TCP / IP protocol. The simulation chamber uploads collected gas flux and environmental parameters to the data interaction layer in real time via a wireless communication module. The data interaction layer feeds back model optimization commands to the simulation chamber's sensing module. The target parameter synchronization frequency is set to 5-30 minutes / time, with the gas flux parameter synchronization frequency set to 10-30 minutes / time and the environmental factor parameter synchronization frequency set to 5-20 minutes / time, ensuring that data timeliness matches model response speed.

[0030] In one alternative implementation, a web-based HMI visualization interface can be developed, integrating three core functional modules: a target parameter display module for real-time display of gas flux, environmental parameters, and model predictions; a model iteration and adjustment module for supporting manual correction of algorithm weights or importing offline data to optimize the model; and an RH result output module for supporting the export of single-point RH time-series curves and regional RH statistical reports.

[0031] For example, by activating the monitoring function of the simulation box device layer, the stability and integrity of sensor data transmission are verified; the initial parameters of the digital twin parameter mapping model are calibrated using historical measured data to ensure that the model prediction deviation is small; and the bidirectional communication delay of the data interaction layer is adjusted to ensure that the synchronization error is no more than 30 seconds, so as to complete the construction of the digital twin system for estimating the RH of the wetland to be estimated.

[0032] S202: Collect target parameters of the wetland to be estimated; wherein, the target parameters include Emission flux data and environmental factor data, including water level, soil temperature, soil moisture content, salinity, redox potential and air temperature.

[0033] Specifically, based on the analog box device layer of the digital twin system, the infrared... Analyzer or optional The installation positions of the analyzer and multi-parameter sensing module should ensure unobstructed detection paths between the sensors and the soil or environment inside the simulation chamber, and the connection and debugging between the sensors and the data interaction layer should be completed through the wireless communication module.

[0034] Start the infrared sensor installed on the top of the simulation box. The analyzer can continuously monitor changes in gas concentration inside the chamber at a frequency of 10-30 minutes, calculate the flux value in real time based on the chamber method formula, and upload the collected data to the data interaction layer of the digital twin system in real time. Emission flux data were obtained through bin method calculation, using the following formula: in, This represents the change in gas concentration per unit time. For the monitoring time interval, To simulate the box volume, The area of ​​soil covering the simulation box.

[0035] The simulation chamber utilizes a built-in water level sensor to monitor water level changes in real time, simultaneously acquiring in-situ water level data of the wetland to be estimated. Water level data can be collected every 5-20 minutes, recording peak and trough values ​​and their duration. The data is then calibrated and uploaded to the data exchange layer. Soil temperature, soil moisture, salinity, and redox potential sensors are inserted into the soil at a preset depth within the simulation chamber, simultaneously collecting soil temperature, soil moisture, salinity, and redox potential data every 5-30 minutes. Finally, an air temperature sensor is installed on the upper exterior of the simulation chamber, avoiding direct sunlight and water accumulation, to collect ambient air temperature data of the wetland to be estimated every 5-30 minutes, ensuring the data reflects the actual atmospheric temperature conditions of the wetland.

[0036] It should be noted that all collected target parameters, after noise removal by the Kalman filter noise reduction unit and outlier removal unit filtering of valid data in the data interaction layer, are transmitted to the virtual model layer of the digital twin system at a set synchronization frequency to provide input data for the RH inversion model. The acquisition status of each sensor can be viewed in real time through the HMI visualization interface of the digital twin system. If data interruption or abnormal fluctuations occur, the system automatically sends alarm commands, prompting operators to check sensor connections or replace faulty equipment, ensuring the continuity and integrity of target parameter acquisition.

[0037] S203: Using the digital twin system for estimating the RH of the wetland to be estimated, construct an initial RH inversion model based on a hybrid algorithm, and use the data interaction layer to transmit the target parameters to the virtual model layer to drive the initial RH inversion model to perform iterative updates and generate an initial RH inversion target model based on a hybrid algorithm.

[0038] Specifically, the initial RH inversion model based on the hybrid algorithm may include: The system comprises a cascaded data preprocessing module, a mechanism modeling module, a machine learning module, a parameter fusion module, and a dynamic update module. The data preprocessing module is used to receive target parameters and perform preprocessing on the target parameters; The mechanism model module is used to construct the correlation between RH and various environmental factor data using a wetland soil microbial respiration mechanism model and pre-processed target parameters; The machine learning module is used to mine the nonlinear coupling relationships between various environmental factor data based on a hybrid algorithm. The parameter fusion module is used to fuse the correlation and nonlinear coupling relationships of various environmental factor data to generate monitoring data; The dynamic update module is used to receive and use the monitoring data to update the training parameters of the wetland soil microbial respiration mechanism model, so as to realize the dynamic adaptation of the RH inversion initial model based on the hybrid algorithm to the changes in the wetland environment.

[0039] For example, the initial RH inversion model based on the hybrid algorithm is first constructed by building the model framework in a cascade structure, and then integrating the data preprocessing module, the mechanism model module, the machine learning module, the parameter fusion module and the dynamic update module in sequence. Each module realizes information flow through the data interaction interface.

[0040] Firstly, through the data interaction layer of the digital twin system, the collected data... Target parameters such as emission flux, water level, soil temperature, soil moisture content, salinity, redox potential, and air temperature are transmitted to the virtual model layer in real time. Then, the data preprocessing module performs standardization processing on the target parameters to eliminate dimensional differences. At the same time, the Kalman filter algorithm is used to remove outliers and fill in missing values ​​to ensure data quality.

[0041] Based on the respiratory mechanism of wetland soil microorganisms, a linear correlation model between RH and the target parameters after pretreatment is established, which can be expressed by the following formula: This represents the RH estimate output by the mechanistic model. These represent the initial values ​​of the mechanistic model coefficients determined based on the thermodynamic characteristics of wetland soil respiration and the kinetics of substrate decomposition. This represents the standardized parameters of soil temperature. This represents the standardized parameter of soil moisture content. Indicates the water level standardization parameter. This represents the salinity standardization parameter. This represents the normalized parameter of redox potential.

[0042] A hybrid algorithm combining Random Forest (RF), XGBoost, and LSTM is employed, using preprocessed target parameters as input, to uncover nonlinear coupling features between factors and output RH prediction values. .

[0043] For example, firstly, ensemble learning is performed using multiple decision trees, as shown in the formula: in, For the number of decision trees, Let m be the prediction function of the m-th decision tree. For decision tree parameters, To standardize the set of target parameters.

[0044] Then, the prediction accuracy is optimized based on a gradient boosting strategy, as shown in the formula. in, For the number of classifiers, Let t be the output of the t-th classifier.

[0045] Finally, the dynamic correlation of time-series data is captured, the state is updated through the gating unit, and the time-series prediction value is output. .

[0046] The predicted RH value was obtained by weighted fusion. Represented as: in, , , The sum is 1, and the weights are determined after cross-validation.

[0047] It should be noted that the linear correlation result of the fusion mechanism model and the nonlinear coupling result of the machine learning module are expressed as follows: in, For the merged monitoring data, To fuse weights, the validation set error is minimized. Sure.

[0048] In one alternative implementation, the dynamic update module receives the fused monitoring data. Combined with the actual RH value measured offline Calculation error .

[0049] The mechanistic model coefficients and machine learning model parameters are updated based on error feedback, where the formula for updating the mechanistic model coefficients is: in, The machine learning model updates the decision tree split threshold, gradient boosting step size, and LSTM network weights using the backpropagation algorithm, with the learning rate as the threshold. Repeating these steps allows the model to iterate using new target parameters transmitted in real-time from the data interaction layer at a preset synchronization frequency, until the model's prediction error converges, thus generating a hybrid algorithm-based RH inversion target model.

[0050] S204: The target model for RH inversion based on a hybrid algorithm calculates the RH value of the wetland to be estimated and generates spatiotemporal distribution data of RH.

[0051] Specifically, the RH inversion target model based on the hybrid algorithm calculates the RH value of the wetland to be estimated and generates RH spatiotemporal distribution data, including: Based on the target parameters retrieved by digital twin, and using digital elevation model and regional water level model to drive the RH retrieval target model based on hybrid algorithm to expand the regional scale, so as to output RH spatial distribution data with a grid resolution of not less than 1km×1km.

[0052] Specifically, the inversion target parameters are first extracted from the virtual model layer of the digital twin system, including the RH single-point estimate optimized by a hybrid algorithm and standardized environmental factor data. The parameters are then checked for spatiotemporal consistency to ensure consistent data format and synchronized timestamps. Next, the digital elevation model (DEM) data of the wetland to be estimated and the regional water level model results are acquired. The DEM data undergoes topographic correction and resolution unification. The spatialized water level data output from the regional water level model is overlaid with the DEM for analysis, generating regional hydrological baseline data that includes topographic relief features. Finally, based on the fused regional hydrological baseline data, the RH inversion target model is spatially adapted and optimized by embedding topographic correction coefficients. in For terrain slope, The altitude is used to correct the RH estimation weights for different terrain regions, thereby improving the model's adaptability to regional heterogeneity.

[0053] Using 1km×1km as the grid unit, the fused regional hydrological data, remote sensing inversion data, and target parameters from the digital twin inversion are input into the RH inversion target model. Spatial interpolation algorithms can be used to supplement parameter values ​​in areas without simulation chambers within the grid, driving the model to calculate RH values ​​grid by grid. By integrating the RH calculation results of each grid unit and combining them with time-series data from long-term monitoring of the simulation chambers, a spatiotemporal distribution dataset of RH containing both time and spatial dimensions is generated. The dataset can include grid RH mean, fluctuation range, and confidence level information.

[0054] In one optional implementation, cross-validation can be used to select some in-situ measured RH data of wetlands that were not involved in model training to verify the accuracy of the spatial distribution data of RH in the region, ensuring that the relative error is not greater than a preset value. After the verification is passed, the data is output in the target format to support compatibility with geographic information system software and meet the needs of subsequent applications such as wetland carbon sink assessment.

[0055] As can be seen, this invention first obtains a simulation chamber for simulating the wetland to be estimated, and then constructs a corresponding digital twin system for RH estimation of the wetland based on the simulation chamber. Next, it collects the target parameters of the wetland to be estimated. Using the digital twin system for RH estimation, it constructs an initial RH inversion model based on a hybrid algorithm. The target parameters are transmitted to the virtual model layer using a data interaction layer to drive iterative updates of the initial RH inversion model, generating a target RH inversion model based on the hybrid algorithm. Finally, it calculates the RH value of the wetland to be estimated based on the target RH inversion model using the hybrid algorithm, generating spatiotemporal RH distribution data. This invention enables deep integration of physical monitoring and virtual simulation through digital twins, improving the accuracy and application scope of RH estimation.

[0056] Another embodiment of this application provides a wetland RH estimation system based on simulation chamber and environmental parameter inversion, such as... Figure 3 The diagram shows a structural schematic of a wetland RH estimation system based on simulation chamber and environmental parameter inversion. The system includes: The module 301 is used to obtain a simulation box for simulating the wetland to be estimated, and to construct a corresponding digital twin system for estimating the RH of the wetland to be estimated based on the simulation box; wherein, the digital twin system includes a simulation box device layer, a virtual model layer and a data interaction layer; Acquisition module 302 is used to acquire target parameters of the wetland to be estimated; wherein, the target parameters include Emission flux data and environmental factor data, including water level, soil temperature, soil moisture content, salinity, redox potential and air temperature; The construction module 303 is used to construct an initial RH inversion model based on a hybrid algorithm using a digital twin system for estimating the RH of the wetland to be estimated. The target parameters are transmitted to the virtual model layer using the data interaction layer to drive the initial RH inversion model to perform iterative updates and generate an RH inversion target model based on a hybrid algorithm. The generation module 304 is used to calculate the RH value of the wetland to be estimated based on the RH inversion target model of the hybrid algorithm and generate RH spatiotemporal distribution data.

[0057] Specifically, the obtaining module 301 includes: A simulation unit is used to deploy simulation boxes at preset intervals in the wetland area to be estimated; the simulation box includes an infrared sensor. Analyzer and / or Analyzer, multi-parameter sensing module, and wireless communication module; The building blocks are used to utilize the respiratory mechanism of wetland soil microorganisms and construct digital twin parameter mapping models based on random forest, XGBoost and LSTM algorithms; Establish a unit to build bidirectional communication between the simulation box device layer and the data interaction layer based on the digital twin parameter mapping model, and set the target parameter synchronization frequency to 5-30 minutes each time; The generation unit is used to construct an HMI visualization interface to generate a digital twin system for estimating the RH of the wetland to be estimated; wherein, the visualization interface includes a target parameter display module, a model iteration adjustment module, and an RH result output function module.

[0058] Compared with existing technologies, this invention first obtains a simulation chamber for simulating the wetland to be estimated, and then constructs a corresponding digital twin system for RH estimation of the wetland based on the simulation chamber. Next, it collects target parameters of the wetland to be estimated. Using the digital twin system for RH estimation, it constructs an initial RH inversion model based on a hybrid algorithm. The target parameters are transmitted to the virtual model layer via a data interaction layer to drive iterative updates of the initial RH inversion model, generating a target RH inversion model based on the hybrid algorithm. Finally, it calculates the RH value of the wetland to be estimated based on the target RH inversion model using the hybrid algorithm, generating spatiotemporal RH distribution data. This invention enables deep integration of physical monitoring and virtual simulation through digital twins, improving the accuracy and application scope of RH estimation.

[0059] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to implement the steps in the above method embodiments when running.

[0060] Specifically, in this embodiment, the storage medium can be configured to store a computer program for performing the following steps: S201: Obtain a simulation chamber for simulating the wetland to be estimated, and construct a corresponding digital twin system for estimating the RH of the wetland to be estimated based on the simulation chamber; wherein, the digital twin system includes a simulation chamber device layer, a virtual model layer and a data interaction layer; S202: Collect target parameters of the wetland to be estimated; wherein, the target parameters include Emission flux data and environmental factor data, including water level, soil temperature, soil moisture content, salinity, redox potential and air temperature; S203: Using the digital twin system for estimating the RH of the wetland to be estimated, construct an initial RH inversion model based on a hybrid algorithm, and use the data interaction layer to transmit the target parameters to the virtual model layer to drive the initial RH inversion model to perform iterative updates and generate an RH inversion target model based on a hybrid algorithm; S204: The target model for RH inversion based on a hybrid algorithm calculates the RH value of the wetland to be estimated and generates spatiotemporal distribution data of RH.

[0061] Specifically, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0062] Compared with existing technologies, this invention first obtains a simulation chamber for simulating the wetland to be estimated, and then constructs a corresponding digital twin system for RH estimation of the wetland based on the simulation chamber. Next, it collects target parameters of the wetland to be estimated. Using the digital twin system for RH estimation, it constructs an initial RH inversion model based on a hybrid algorithm. The target parameters are transmitted to the virtual model layer via a data interaction layer to drive iterative updates of the initial RH inversion model, generating a target RH inversion model based on the hybrid algorithm. Finally, it calculates the RH value of the wetland to be estimated based on the target RH inversion model using the hybrid algorithm, generating spatiotemporal RH distribution data. This invention enables deep integration of physical monitoring and virtual simulation through digital twins, improving the accuracy and application scope of RH estimation.

[0063] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps described in the method embodiments above.

[0064] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0065] Specifically, in this embodiment, the processor can be configured to perform the following steps via a computer program: S201: Obtain a simulation chamber for simulating the wetland to be estimated, and construct a corresponding digital twin system for estimating the RH of the wetland to be estimated based on the simulation chamber; wherein, the digital twin system includes a simulation chamber device layer, a virtual model layer and a data interaction layer; S202: Collect target parameters of the wetland to be estimated; wherein, the target parameters include Emission flux data and environmental factor data, including water level, soil temperature, soil moisture content, salinity, redox potential and air temperature; S203: Using the digital twin system for estimating the RH of the wetland to be estimated, construct an initial RH inversion model based on a hybrid algorithm, and use the data interaction layer to transmit the target parameters to the virtual model layer to drive the initial RH inversion model to perform iterative updates and generate an RH inversion target model based on a hybrid algorithm; S204: The target model for RH inversion based on a hybrid algorithm calculates the RH value of the wetland to be estimated and generates spatiotemporal distribution data of RH.

[0066] Compared with existing technologies, this invention first obtains a simulation chamber for simulating the wetland to be estimated, and then constructs a corresponding digital twin system for RH estimation of the wetland based on the simulation chamber. Next, it collects target parameters of the wetland to be estimated. Using the digital twin system for RH estimation, it constructs an initial RH inversion model based on a hybrid algorithm. The target parameters are transmitted to the virtual model layer via a data interaction layer to drive iterative updates of the initial RH inversion model, generating a target RH inversion model based on the hybrid algorithm. Finally, it calculates the RH value of the wetland to be estimated based on the target RH inversion model using the hybrid algorithm, generating spatiotemporal RH distribution data. This invention enables deep integration of physical monitoring and virtual simulation through digital twins, improving the accuracy and application scope of RH estimation.

[0067] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0068] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0069] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus 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 devices or units may be electrical or other forms.

[0070] 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.

[0071] Furthermore, the functional units in the various embodiments of the present invention 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.

[0072] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this invention, 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 memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0073] The embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A wetland RH estimation method based on simulation chamber and environmental parameter inversion, characterized in that, The method includes: A simulation chamber is obtained to simulate the wetland to be estimated, and a corresponding digital twin system for estimating the RH of the wetland to be estimated is constructed based on the simulation chamber; wherein, the digital twin system includes a simulation chamber device layer, a virtual model layer and a data interaction layer; Collect target parameters of the wetland to be estimated; wherein, the target parameters include Emission flux data and environmental factor data, including water level, soil temperature, soil moisture content, salinity, redox potential and air temperature; Using a digital twin system for estimating the RH of the wetland to be estimated, an initial RH inversion model based on a hybrid algorithm is constructed. The target parameters are transmitted to the virtual model layer through the data interaction layer to drive the initial RH inversion model to perform iterative updates and generate a target RH inversion model based on a hybrid algorithm. The target model for RH inversion based on a hybrid algorithm calculates the RH value of the wetland to be estimated and generates spatiotemporal distribution data of RH.

2. The method according to claim 1, characterized in that, The simulation chamber used to simulate the wetland environment to be estimated includes: A gate-type tide level synchronization simulation box or a U-shaped tube weak open-circuit water level synchronization simulation box; the simulation box has a built-in water level adjustment module for real-time response to water level changes.

3. The method according to claim 2, characterized in that, The digital twin system for estimating the RH of the wetland to be estimated, constructed based on the simulation chamber, includes: Simulation boxes are set up at preset intervals in the wetland area to be estimated; the simulation boxes include infrared sensors. Analyzer and / or Analyzer, multi-parameter sensing module, and wireless communication module; We utilized the respiratory mechanism of wetland soil microorganisms and constructed a digital twin parameter mapping model based on random forest, XGBoost, and LSTM algorithms. Based on the digital twin parameter mapping model, a two-way communication between the simulation box device layer and the data interaction layer is established, and the target parameter synchronization frequency is set to 5-30 minutes each time. An HMI visualization interface is constructed to generate a digital twin system for estimating the RH of the wetland to be estimated; wherein, the visualization interface includes a target parameter display module, a model iteration adjustment module, and an RH result output function module.

4. The method according to claim 3, characterized in that, The Emission flux data were obtained through bin method calculation, using the following formula: ， in, This represents the change in gas concentration per unit time. For monitoring time intervals, To simulate the volume of the box, The area of ​​soil covering the simulated test chamber.

5. The method according to claim 4, characterized in that, The initial RH inversion model based on the hybrid algorithm includes: The system comprises a cascaded data preprocessing module, a mechanism modeling module, a machine learning module, a parameter fusion module, and a dynamic update module. The data preprocessing module is used to receive target parameters and perform preprocessing on the target parameters; The mechanism model module is used to construct the correlation between RH and various environmental factor data using a wetland soil microbial respiration mechanism model and pre-processed target parameters; The machine learning module is used to mine the nonlinear coupling relationships between various environmental factor data based on a hybrid algorithm. The parameter fusion module is used to fuse the correlation and nonlinear coupling relationships of various environmental factor data to generate monitoring data; The dynamic update module is used to receive and use the monitoring data to update the training parameters of the wetland soil microbial respiration mechanism model, so as to realize the dynamic adaptation of the RH inversion initial model based on the hybrid algorithm to the changes in the wetland environment.

6. The method according to claim 5, characterized in that, The RH inversion target model based on the hybrid algorithm calculates the RH value of the wetland to be estimated and generates RH spatiotemporal distribution data, including: Based on the target parameters retrieved by digital twin, and using digital elevation model and regional water level model to drive the RH retrieval target model based on hybrid algorithm to expand the regional scale, so as to output RH spatial distribution data with a grid resolution of not less than 1km×1km.

7. A wetland RH estimation system based on simulation chamber and environmental parameter inversion, characterized in that, The system includes: The module is used to obtain a simulation box for simulating the wetland to be estimated, and to construct a corresponding digital twin system for estimating the RH of the wetland to be estimated based on the simulation box; wherein, the digital twin system includes a simulation box device layer, a virtual model layer and a data interaction layer; The data acquisition module is used to acquire target parameters of the wetland to be estimated; wherein, the target parameters include... Emission flux data and environmental factor data, including water level, soil temperature, soil moisture content, salinity, redox potential and air temperature; The module is used to construct an initial RH inversion model based on a hybrid algorithm using a digital twin system for estimating the RH of the wetland to be estimated. The target parameters are transmitted to the virtual model layer using the data interaction layer to drive the initial RH inversion model to perform iterative updates and generate a target RH inversion model based on a hybrid algorithm. The generation module is used to calculate the RH value of the wetland to be estimated based on the RH inversion target model of the hybrid algorithm, and generate RH spatiotemporal distribution data.

8. The system according to claim 7, characterized in that, The obtaining module includes: A simulation unit is used to deploy simulation boxes at preset intervals in the wetland area to be estimated; the simulation box includes an infrared sensor. Analyzer and / or Analyzer, multi-parameter sensing module, and wireless communication module; The building blocks are used to utilize the respiratory mechanism of wetland soil microorganisms and construct digital twin parameter mapping models based on random forest, XGBoost and LSTM algorithms; Establish a unit to build bidirectional communication between the simulation box device layer and the data interaction layer based on the digital twin parameter mapping model, and set the target parameter synchronization frequency to 5-30 minutes each time; The generation unit is used to construct an HMI visualization interface to generate a digital twin system for estimating the RH of the wetland to be estimated; wherein, the visualization interface includes a target parameter display module, a model iteration adjustment module, and an RH result output function module.

9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to implement the method of any one of claims 1 to 6 when it is run.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to implement the method of any one of claims 1 to 6.

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