Method and system for simulating litter decomposition function feedback under salinity gradient

Abstract

The invention discloses a method and system for simulating litter decomposition function feedback under salinity gradient, and belongs to the technical field of ecological environment simulation testing, the method comprises the following steps: obtaining a target salinity gradient curve, and controlling a multi-channel layered salt liquid supply device to form a dynamic salinity gradient environment in a decomposition reactor; litters are placed in a rotatable litter bearing net basket, dynamic coupling of the litters and the salt environment is achieved through rotation, and motion trail data are generated; environment parameters are collected and degradation product parameters are obtained through an in-situ sensor array in combination with litter movement track data; generating a salinity regulation and control instruction through multi-modal data fusion and collaborative feedback analysis; and dynamically adjusting the output of the salt liquid supply device to correct the salinity gradient environment. According to the method, a closed-loop control strategy of dynamic coupling, in-situ sensing, cooperative feedback and self-adaptive correction is adopted, and simulation and quantitative analysis of the bidirectional coupling relation between the decomposition process and the environmental factors can be achieved.

Description

Technical Field

[0001] This invention relates to the field of ecological environment simulation testing technology, and in particular to a method and system for simulating the functional feedback of litter decomposition under a salinity gradient. Background Technology

[0002] Litter decomposition is a core component of ecosystem material cycling and energy flow, and its rate and efficiency directly affect the biogeochemical cycles of key elements such as carbon and nitrogen. In ecotones such as estuaries and coastal wetlands, salinity, as an important environmental stressor, significantly influences the activity and function of litter-decomposing microbial communities through its spatiotemporal dynamics. Therefore, studying litter decomposition processes under salinity gradients is crucial for accurately assessing the service functions and health status of such ecosystems, and falls within the scope of analytical research using specific methods on biological materials.

[0003] Currently, research on litter decomposition mainly relies on in-situ experiments and indoor simulation experiments. In-situ experiments, such as litter decomposition bag methods, while reflecting the overall decomposition rate under natural conditions, struggle to isolate the coupling effects of salinity with other environmental factors such as temperature and nutrients, and lack the ability for precise process control and high-frequency monitoring. Indoor simulation experiments are typically conducted in microcosms or mesocosms, using static culture systems with varying salinity levels to study the impact of salinity. Some studies also employ simple programmed controls to simulate periodic changes in salinity.

[0004] However, existing research methods have significant limitations in simulating the complex salinity dynamics in nature and their interaction with decomposition processes. Static or pre-set open-loop control methods cannot reflect the feedback of the biodecomposition process itself to the environment; that is, the release of decomposition products may alter the local microenvironment, thereby affecting the subsequent decomposition rate. Furthermore, most of these methods neglect the physical migration of litter in natural water bodies via water flow and tides, leading to a severe simplification of the actual salinity history and exposure patterns experienced by litter. In addition, traditional monitoring methods rely heavily on low-frequency, destructive sampling and analysis, making it difficult to capture the rapid response of the decomposition process to environmental changes. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method and system for simulating the functional feedback of litter decomposition under salinity gradients. It employs a closed-loop control strategy that integrates litter dynamic coupling, multimodal in-situ sensing, collaborative feedback analysis, and adaptive correction to the salinity environment. This approach enables accurate simulation and quantitative analysis of the bidirectional coupling relationship between the decomposition process and environmental factors.

[0006] The above objectives can be achieved through the following approach: A method and system for simulating the functional feedback of litter decomposition under a salinity gradient includes: acquiring a preset target salinity gradient curve; controlling a multi-channel stratified brine supply device according to the target salinity gradient curve to form a dynamic salinity gradient environment in a decomposition reactor; placing the litter to be decomposed in a rotatable litter support basket within the decomposition reactor and initiating rotation; simulating the dynamic coupling effect between the litter and the dynamic salinity gradient environment to generate litter motion trajectory data; collecting environmental parameters within the dynamic salinity gradient environment and obtaining degradation product parameters generated by the litter decomposition through an in-situ sensor array combined with the litter motion trajectory data; performing multimodal data fusion and collaborative feedback analysis based on the environmental parameters and the degradation product parameters to generate salinity control commands; and dynamically adjusting the output parameters of the multi-channel stratified brine supply device according to the salinity control commands to correct the dynamic salinity gradient environment.

[0007] Optionally, forming a dynamic salinity gradient environment in the decomposition reactor includes: resolving target salinity values ​​at different time points from the target salinity gradient curve; calculating and generating brine supply flow rate parameters for each channel by combining the target salinity values ​​with the pre-set salinity information of high-salinity and low-salinity solutions; and sending the brine supply flow rate parameters for each channel to the multi-channel stratified brine supply device to form a dynamic salinity gradient environment in the decomposition reactor.

[0008] Optionally, generating litter motion trajectory data includes: controlling the rotatable litter-carrying basket to rotate via a drive unit, and recording control parameters including the rotational speed, direction of rotation, and timestamp of the rotatable litter-carrying basket; combining the control parameters to capture the spatial position and attitude changes of the litter in real time and record the three-dimensional motion trajectory; and spatiotemporally correlating the three-dimensional motion trajectory with the dynamic salinity gradient environment to generate litter motion trajectory data.

[0009] Optionally, the step of collecting environmental parameters within the dynamic salinity gradient environment by combining the litter movement trajectory data with the in-situ sensor array includes: determining the real-time spatial region of the litter in the decomposition reactor based on the litter movement trajectory data; activating the in-situ sensor array at the corresponding location based on the real-time spatial region, and collecting environmental parameters including salinity distribution data, temperature distribution data, dissolved oxygen content data, and pH value data, wherein the in-situ sensor array includes a salinity sensor, a temperature sensor, a dissolved oxygen sensor, and a pH sensor.

[0010] Optionally, obtaining the degradation product parameters generated by the decomposition of the litter includes: real-time monitoring of the characteristic absorption spectrum of the water body surrounding the litter using a miniature fiber optic spectral probe; and calculating the dissolved organic carbon concentration by combining the characteristic absorption spectrum with the preset absorption coefficient and optical path length, as a degradation product parameter. Optionally, the generation of salinity regulation instructions includes: simulating microbial metabolic activity based on the environmental parameters to generate a microbial activity index; calculating the actual decomposition rate based on the time change rate of the degradation product parameters; performing a correlation analysis between the actual decomposition rate and the microbial activity index to obtain the microbial metabolic driving efficiency; comparing the microbial metabolic driving efficiency with a preset standard decomposition curve to generate decomposition feedback parameters; and performing decomposition and salinity coupling feedback analysis based on the decomposition feedback parameters to generate salinity regulation instructions.

[0011] Optionally, the step of performing decomposition and salinity coupling feedback analysis based on the decomposition feedback parameters to generate salinity regulation instructions includes: identifying key threshold intervals for salinity inhibition or promotion of decomposition based on the decomposition feedback parameters; performing difference analysis based on the key threshold intervals and the salinity distribution data to determine the direction and magnitude of salinity gradient adjustment; determining the salinity exposure history of litter in the reactor by combining the litter movement trajectory data, evaluating the spatiotemporal necessity of salinity regulation, and obtaining a spatiotemporal assessment result of salinity exposure; and generating salinity regulation instructions adapted to the current decomposition stage and microbial metabolic state by combining the direction and magnitude of the salinity gradient adjustment and the spatiotemporal assessment result of salinity exposure.

[0012] Optionally, the method further includes: monitoring the growth status of the microbial film on the surface of the litter using an impedance sensing device to obtain physical biomass parameters; verifying the correlation between the physical biomass parameters and the microbial activity index to generate a verification result; and triggering calibration of the contribution of the environmental parameters to correct the calculation of the microbial activity index when the verification result deviates from a preset correlation threshold.

[0013] Optionally, the correction of the dynamic salinity gradient environment includes: adjusting the flow rate of each controllable flow rate pump according to the salinity control command to generate an adjusted mixed brine solution; dispersing the adjusted mixed brine solution to generate a uniform brine flow; and injecting the uniform brine flow into the decomposition reactor to smoothly correct the dynamic salinity gradient environment.

[0014] Based on the same inventive concept, this invention also provides a system for simulating the functional feedback of litter decomposition under a salinity gradient. The system includes: a salinity gradient control module, used to acquire a preset target salinity gradient curve and control a multi-channel stratified brine supply device according to the target salinity gradient curve to form a dynamic salinity gradient environment in the decomposition reactor; a litter dynamic coupling module, used to place the litter to be decomposed in a rotatable litter carrying basket within the decomposition reactor and initiate rotation, simulating the dynamic coupling effect between the litter and the dynamic salinity gradient environment to generate litter motion trajectory data; a multi-parameter acquisition module, used to acquire environmental parameters within the dynamic salinity gradient environment through an in-situ sensor array combined with the litter motion trajectory data, and to acquire parameters of degradation products generated by the litter decomposition; a multi-modal data analysis module, used to perform multi-modal data fusion and collaborative feedback analysis combining the environmental parameters and the degradation product parameters to generate salinity control commands; and a salinity environment correction module, used to dynamically adjust the output parameters of the multi-channel stratified brine supply device according to the salinity control commands to correct the dynamic salinity gradient environment.

[0015] Compared with the prior art, the present invention has the following advantages: 1. This invention achieves dynamic simulation of natural ecological processes by constructing a closed-loop system integrating environmental simulation, process monitoring, functional evaluation, and feedback regulation. Compared with traditional static or open-loop experimental methods, this invention can accurately reproduce the spatiotemporal dynamic changes of salinity gradients in natural water bodies at the laboratory scale and simulate the dynamic coupling process of litter within them, thereby improving the ecological realism of the experimental environment and the reliability of the simulation results.

[0016] 2. This invention establishes a high-resolution, multi-parameter collaborative acquisition mechanism based on target tracking, improving the effectiveness and relevance of data acquisition. By linking the real-time movement trajectory of litter with the data acquisition of the in-situ sensor array, it can accurately capture the instantaneous microenvironmental parameters and decomposition product concentrations experienced by the litter, reducing the shortcomings of insufficient spatial sampling representativeness and time response lag in traditional methods, and providing a high-quality data foundation for analyzing the instantaneous interaction between environmental factors and biological functions.

[0017] 3. This invention introduces a functional feedback control logic based on actual decomposition function, enabling the simulation system to possess adaptive adjustment capabilities similar to those of living systems. The system no longer simply executes preset programs, but can assess in real time the difference between the actual performance and theoretical potential of the decomposition function, and generate control commands accordingly to correct environmental conditions. This allows the system to proactively explore and identify key environmental thresholds affecting the decomposition function, revealing a two-way feedback mechanism between the environment and biological functions.

[0018] 4. This invention verifies and calibrates the microbial activity model by introducing independent physical biomass monitoring methods, ensuring the long-term accuracy and robustness of the system analysis model. This dual monitoring and calibration mechanism can effectively capture and correct model prediction biases caused by the adaptive evolution of microbial communities, ensuring the reliability of system evaluation in long-term simulation experiments, and making the entire simulation and regulation process always based on a more realistic biological situation.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for simulating the functional feedback of litter decomposition under a salinity gradient according to an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram illustrating the relationship between target salinity and flow rate in an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of the three-dimensional motion trajectory of fallen objects according to an embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram of the model calibration mechanism in an embodiment of the present invention.

[0025] Figure 5 This is a schematic diagram of the structure of a system for simulating the decomposition function feedback of litter under a salinity gradient according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Reference Figure 1 One embodiment of the present invention proposes a method for simulating the functional feedback of litter decomposition under a salinity gradient. It adopts a closed-loop feedback control scheme that combines the construction of a dynamic salinity gradient environment, tracking the movement trajectory of litter, in-situ acquisition of multiple parameters, and collaborative analysis of multimodal data. It can analyze and correct environmental conditions in real time according to the actual functional response of litter decomposition, and realize a high-fidelity simulation and exploration of the interaction between biological materials and their chemical environment.

[0028] The method described in this embodiment specifically includes: Obtain a preset target salinity gradient curve, and control the multi-channel stratified brine supply device according to the target salinity gradient curve to form a dynamic salinity gradient environment in the decomposition reactor. The litter to be decomposed is placed in a rotatable litter carrying basket inside the decomposition reactor and the rotation is started. Based on the dynamic coupling effect between the litter and the dynamic salinity gradient environment, the litter motion trajectory data is generated. By combining the in-situ sensor array with the litter movement trajectory data, environmental parameters within the dynamic salinity gradient environment are collected, and parameters of degradation products generated by the decomposition of the litter are obtained. By combining the environmental parameters and the degradation product parameters, multimodal data fusion and collaborative feedback analysis are performed to generate salinity regulation instructions; The output parameters of the multi-channel stratified brine supply device are dynamically adjusted according to the salinity control command to correct the dynamic salinity gradient environment.

[0029] Specifically, the first step is to obtain a pre-defined target salinity gradient curve, which forms the basis for subsequent simulation and control. High-frequency online monitoring sensors, such as temperature-salinity-depth profilers, can be deployed in the target study water area to continuously collect data on water depth, salinity, and temperature at different depth levels. After preprocessing the collected time-series data through filtering, interpolation, and smoothing, a salinity spatiotemporal distribution model reflecting typical environmental characteristics of the area, such as tidal cycles and seasonal saltwater intrusion, is constructed, thereby generating the target salinity gradient curve. Based on this target salinity gradient curve, a multi-channel stratified brine supply device is controlled to create a dynamic salinity gradient environment in the decomposition reactor, while simultaneously simulating the dynamic physical movement of litter within this environment, thus generating litter trajectory data. By combining the litter trajectory data for targeted monitoring, the environmental parameters experienced by the litter and the parameters of the degradation products actually produced by decomposition are simultaneously acquired. Subsequently, these two different modalities of data are fused and analyzed to assess the actual impact of the current salinity environment on the decomposition function in real time, thereby forming a feedback signal to judge the suitability of the current salinity environment, and generating salinity control instructions based on this signal. This salinity regulation command then acts on the brine supply device, correcting the dynamic salinity gradient environment in real time, thus forming a complete and dynamic cycle from environmental simulation, process monitoring, functional assessment to environmental feedback correction. By establishing a closed-loop feedback mechanism between the environment and biological functions, this method surpasses traditional one-way, pre-set environmental simulations, enabling experiments to adaptively adjust environmental conditions based on the actual functional performance of litter decomposition. This enhances the ecological realism and process controllability of the simulation experiments. Furthermore, it proactively reveals the key impact thresholds and dynamic response patterns of salinity gradient changes on decomposition function, rather than merely passively observing. This provides a powerful and more refined experimental research tool for a deeper understanding of the material cycling and functional maintenance mechanisms of ecosystems under salinity fluctuations.

[0030] Optionally, the formation of a dynamic salinity gradient environment in the decomposition reactor includes: The target salinity values ​​at different time points are analyzed from the target salinity gradient curve; By combining the target salinity value with the pre-set salinity information of high-salinity and low-salinity solutions, the brine supply flow rate parameters for each channel are calculated and generated. The flow rate parameters of the brine supply for each channel are sent to the multi-channel stratified brine supply device to form a dynamic salt gradient environment in the decomposition reactor.

[0031] Specifically, the first step is to digitize and parameterize the preset simulation target. This process begins with interpreting the target salinity gradient curve, which mathematically or graphically defines the ideal pattern of salinity changes over time at different spatial levels within the decomposition reactor during the experiment, such as simulating tidal periodic salinity fluctuations. This continuous or high-resolution discrete target salinity gradient curve is first analyzed into a series of target salinity values ​​at specific time points, corresponding to each channel in the multi-channel stratified brine supply device. Then, based on the principles of mass conservation and mixing, the analyzed target salinity values ​​for each channel are converted into executable brine supply flow rate parameters for each channel. This calculation process requires combining two known preset conditions: the salinity information of the high-salinity solution and the salinity information of the low-salinity solution. The following mixing ratio calculation formula can be used to generate the brine supply flow rate parameters driving each channel in the multi-channel stratified brine supply device. For calculations at specific time points... , No. The flow rate of the high-salinity solution supplied through each channel Flow rate with low salinity solutions ,have: ; in, This is to maintain the stability of the fluid dynamics within the reactor. The total supply flow rate set for each channel is a preset stable value; It is the value extracted from the target salinity gradient curve at time points. No. Target salinity values ​​for each channel; and These are the pre-defined salinity values ​​for the high-salinity and low-salinity solutions, which are known constants. Finally, the calculated values ​​for all channels at each time point will be used... and The flow rate parameters of the brine supply for each channel are sent as control commands to the multi-channel stratified brine supply device. The precision fluid pumps within this device adjust the extraction rates of the high-salinity and low-salinity solutions based on the received flow rate parameters, ensuring thorough mixing before the solutions enter the decomposition reactor. The mixtures are then injected into different stratified locations within the reactor through pipelines corresponding to each channel. Figure 2 As shown, the two subgraphs have the same x-coordinate. The subplot represents the target salinity value of a channel as it changes over time, simulating periodic fluctuations similar to tides. The diagram clearly illustrates the dynamic adjustment of the flow rates of the high-salt solution (dashed line) and the low-salt solution (solid line) over time: as the target salinity increases, the flow rate of the high-salt solution increases, while the flow rate of the low-salt solution decreases, and vice versa. By continuously executing this series of operations in both time and space, a dynamic salinity gradient environment highly consistent with the target salinity gradient curve can be successfully constructed and maintained within the decomposition reactor. By resolving the macroscopic environmental change target curve into microscopic, precisely executable brine supply flow rate parameters for each channel, a high-precision, automated, and dynamic construction of the spatiotemporal distribution of the salinity gradient within the decomposition reactor is achieved. This provides a stable, repeatable experimental environment foundation highly correlated with real ecological scenarios for subsequent litter decomposition simulation research, enhancing the scientific rigor and reliability of the entire simulation method.

[0032] Optionally, the generation of litter trajectory data includes: The rotatable litter-carrying basket is controlled to rotate by a drive unit, and control parameters including the rotation speed, direction of rotation and timestamp of the rotatable litter-carrying basket are recorded. The spatial position and attitude changes of the fallen object are captured in real time by combining the control parameters, and the three-dimensional motion trajectory is recorded. The three-dimensional motion trajectory is spatiotemporally correlated with the dynamic salinity gradient environment to generate litter motion trajectory data.

[0033] Specifically, the process begins with precise motion control of a rotatable litter-carrying basket placed within the decomposition reactor via a drive unit, typically a precision stepper motor or servo motor, which executes rotation commands according to a pre-defined simulation scheme. During this process, a series of detailed control parameters are recorded in real time. These parameters constitute the raw digital record of the motion, primarily including the instantaneous rotational speed, direction of rotation, and corresponding timestamp of the rotatable litter-carrying basket. Next, based on the recorded control parameters, combined with the known geometry of the rotatable litter-carrying basket and its fixed installation position within the decomposition reactor, the real-time spatial position and attitude changes of the litter samples are captured, thereby constructing their three-dimensional motion trajectory. This process can be achieved using a kinematic model. A three-dimensional Cartesian coordinate system can be established within the decomposition reactor, and the litter samples move in time... The position can be calculated through rotational transformation. For example, to calculate the coordinates of a trajectory point rotating around the Z-axis. ,have: ; in, It is the distance from the point to the center of rotation, which is a geometric constant; It is the initial angle of that point; At a certain point in time The angular velocity is obtained directly from the rotational speed and steering information in the control parameters; Indicates from time arrive The integral of angular velocity represents the time... The total angle rotated inward. By integrating over time, the angle rotated by a point at any given time can be obtained, thus determining its three-dimensional coordinates within the reactor. Performing this calculation on multiple key points on the litter allows for the reconstruction of the entire litter sample's three-dimensional trajectory during its movement. Finally, the generated three-dimensional trajectory is spatiotemporally correlated with the established dynamic salinity gradient environment. This means matching the three-dimensional spatial coordinates of the litter at each timestamp with environmental parameters, especially salinity values, at the same time and coordinate point. This correlation operation transforms isolated physical motion data into environmentally meaningful exposure history data, ultimately generating a structured dataset containing information such as time, three-dimensional coordinates, attitude, and real-time salinity at that location—i.e., litter trajectory data. Figure 3 As shown, this figure simulates the process of generating litter trajectory data in a decomposition reactor. This trajectory is spatiotemporally correlated with a pre-set vertical salinity gradient within the reactor. The background color gradually changes from light to dark from bottom to top, representing salinity increasing from low to high. The color intensity of the trajectory points dynamically maps to their real-time salinity values ​​at that spatiotemporal coordinate, thus generating "litter trajectory data" that includes time, spatial location, and salinity exposure history. By no longer treating litter as a static reactant, but simulating its tumbling and displacement in the water body due to dynamic factors such as wave currents and tides, the salinity change history experienced by litter in a non-uniform environment can be quantified. This dynamically coupled simulation method enhances the ecological realism of the experiment, providing a crucial data foundation for accurately assessing the instantaneous and cumulative impacts of salinity gradient changes on the decomposition process. This allows subsequent analyses to be based on the actual environmental exposure history of litter, rather than static or averaged environmental conditions.

[0034] Optionally, the step of collecting environmental parameters within the dynamic salinity gradient environment by combining the in-situ sensor array with the litter movement trajectory data includes: The real-time spatial region of the litter in the decomposition reactor is determined based on the litter's trajectory data. Based on the real-time spatial region, the in-situ sensor array at the corresponding location is activated to collect environmental parameters including salinity distribution data, temperature distribution data, dissolved oxygen content data, and pH value data. The in-situ sensor array includes a salinity sensor, a temperature sensor, a dissolved oxygen sensor, and a pH sensor.

[0035] Specifically, the generated litter trajectory data is first received and processed in real time, providing the position coordinates of the litter in the three-dimensional space of the decomposition reactor at each moment. Based on these coordinates, a real-time spatial region centered on the litter is dynamically determined. The range of this real-time spatial region is pre-defined, designed to cover the volume around the litter where the most direct physicochemical and biological interactions occur. Subsequently, the coordinate range of this real-time spatial region is compared with the fixed spatial coordinates of all sensors in the in-situ sensor array. Through a logical judgment program, all sensors whose physical locations fall within the current real-time spatial region are automatically identified and activated, forming a dynamically changing in-situ sensor array. This in-situ sensor array only includes sensors currently adjacent to the litter, while sensors in other locations remain in standby mode. Once the in-situ sensor array is activated, it immediately sends acquisition commands to these selected salinity, temperature, dissolved oxygen, and pH sensors. These sensors then synchronously perform measurements and transmit the raw data back. Because the in-situ sensor array contains multiple sensors, the obtained data is not a single value, but a set of discrete data points within the real-time spatial region. This collection of data points constitutes high-resolution environmental parameters, specifically including salinity distribution data reflecting spatial variations in salinity within the micro-region, temperature distribution data reflecting thermal conditions, dissolved oxygen content data characterizing the redox environment, and pH value data indicating acidity or alkalinity. This process is continuously and cyclically executed as litter continues to move, thereby achieving dynamic and high-precision tracking and acquisition of microenvironmental parameters experienced by the litter. By combining the macroscopic movement of litter with fixed-point sensing of the microenvironment, an intelligent and efficient data acquisition mechanism has been established. This realizes the transformation from environmental monitoring to target microenvironment tracking, improves the relevance and effectiveness of the collected data, ensures that the acquired environmental parameters most accurately reflect the actual environmental pressure experienced by the litter at a specific moment, and avoids data redundancy and dilution of key information caused by full-area monitoring.

[0036] Optionally, obtaining the parameters of the degradation products generated by the decomposition of the litter includes: The characteristic absorption spectrum of the water body surrounding the litter was monitored in real time using a miniature fiber optic spectral probe. By combining the characteristic absorption spectrum with the preset absorption coefficient and optical path, the concentration of dissolved organic carbon is calculated and used as a parameter for degradation products; Specifically, this is achieved firstly through one or more miniature fiber optic spectrometers deployed near a rotatable litter-carrying basket, which simultaneously acquire data. These miniature fiber optic spectrometers continuously emit light within a specific wavelength range into the surrounding microenvironment water and receive the light signal after it passes through the water, thereby acquiring the characteristic absorption spectrum of the water in real time. During the decomposition of litter, various organic compounds are released into the water, with dissolved organic carbon (DOC) being the core product, exhibiting strong light absorption characteristics in the ultraviolet band. The absorbance value at a specific wavelength is extracted from the real-time acquired characteristic absorption spectrum; this wavelength is pre-selected based on the representative absorption peak of DOC. Subsequently, the concentration of DOC is calculated according to the Beer-Lambert law, combined with two other known parameters: the preset absorption coefficient and the optical path length of the probe. For the calculation... Dissolved organic carbon concentration at any time ,have: ; in, for The absorbance measured in real time at a specific wavelength by a miniature fiber optic spectral probe is a dimensionless measurement value. The absorbance coefficient of dissolved organic carbon at this specific wavelength, expressed in L·mg⁻ 1 ·cm⁻ 1 This is a physical constant characterizing the light absorption capacity of a substance, obtained through a pre-calibration experiment on a standard concentration of dissolved organic carbon solution; The optical path length, measured in centimeters, represents the distance light travels through the water sample at the probe's measuring end. This is a known, fixed value determined by the probe's structure. The calculated dissolved organic carbon concentration can be used as a parameter for degradation products. By continuously performing the above measurement and calculation process, a time-varying curve of dissolved organic carbon concentration can be generated, thereby dynamically quantifying the decomposition output of litter. Compared to traditional discrete sampling and offline analysis methods, this method avoids interference with the experiment and can capture the instantaneous response of decomposition rate caused by abrupt changes in environmental parameters with high temporal resolution. This provides a direct and reliable quantitative basis for subsequently establishing the real-time feedback relationship between decomposition function and environmental factors.

[0037] Optionally, the salt content control command includes: Microbial metabolic activity was simulated based on the environmental parameters to generate a microbial activity index. The actual decomposition rate is calculated based on the time-varying rate of the degradation product parameters. The actual decomposition rate is correlated with the microbial activity index to obtain the microbial metabolic driving efficiency. Based on the comparison between the microbial metabolic driving efficiency and the preset standard decomposition curve, decomposition feedback parameters are generated. Based on the decomposition feedback parameters, a decomposition and salinity coupling feedback analysis is performed to generate salinity control commands.

[0038] Specifically, firstly, based on environmental parameters obtained from an in-situ sensor array—namely, salinity distribution data, temperature distribution data, dissolved oxygen content data, and pH data—microbial metabolic activity is simulated to generate a comprehensive microbial activity index. This microbial activity index aims to quantify the theoretical metabolic potential of the microbial community under the current environment. It is typically calculated using a multi-factor model, which pre-determines the weights and response functions of each environmental parameter on metabolic activity based on microbial physiology. Simultaneously, the actual decomposition rate is calculated based on time-series data of degradation product parameters, namely, dissolved organic carbon concentration. For the calculation... Actual decomposition rate at time ,have: ; in, Indicates the previous time point The concentration of dissolved organic carbon; The time interval is defined as the actual decomposition rate. This actual decomposition rate directly reflects the amount of organic carbon actually released into the water body by litter per unit time, serving as a direct quantitative indicator of decomposition function. Next, a correlation analysis is performed between the actual decomposition rate, characterizing the actual decomposition intensity, and the microbial activity index, characterizing the theoretical decomposition potential, to generate the microbial metabolic driving efficiency. This microbial metabolic driving efficiency value is obtained by dividing the actual decomposition rate by the microbial activity index; it reveals the extent to which the theoretical metabolic potential is converted into actual decomposition function under current environmental conditions. Subsequently, this microbial metabolic driving efficiency is compared in real-time with a pre-set standard decomposition curve. This standard decomposition curve represents the target trajectory of the microbial metabolic driving efficiency evolving with the decomposition process under ideal or reference conditions. Comparing the deviation between the two generates a decomposition feedback parameter. The value and sign of this decomposition feedback parameter directly indicate whether the current decomposition efficiency is higher, lower, or meets the expected level. Finally, based on this decomposition feedback parameter, an in-depth decomposition and salinity coupling feedback analysis is conducted. The core of this analysis process is to establish a causal relationship between decomposition efficiency deviation and salinity conditions. When the decomposition feedback parameters indicate that the efficiency is not up to standard, the analysis module reviews recent salinity distribution data and litter movement trajectory data to determine whether the current salinity level is within the range that inhibits microbial metabolism. Based on this judgment, a specific salinity regulation instruction is generated. This instruction clarifies what adjustments need to be made to the salinity gradient to bring decomposition efficiency back to the target level, such as increasing or decreasing salinity overall, or adjusting the salinity of a specific layer. By constructing a complete closed loop from environmental perception and functional assessment to feedback regulation, intelligent management of the simulation experiment process is achieved. This mechanism can proactively explore and identify key salinity thresholds affecting decomposition function and simulate the complex interactions and feedback regulation processes between the environment and biological functions within the ecosystem.

[0039] Optionally, the step of performing decomposition and salinity coupling feedback analysis based on the decomposition feedback parameters to generate salinity control commands includes: The key threshold ranges for salt inhibition or decomposition are identified based on the decomposition feedback parameters. Based on the difference analysis between the key threshold range and the salinity distribution data, the direction and magnitude of the salinity gradient adjustment are determined. By combining the litter movement trajectory data, the salt exposure history of the litter in the reactor is determined, the spatiotemporal necessity of salt regulation is evaluated, and the spatiotemporal evaluation results of salt exposure are obtained. By combining the direction and magnitude of the salt gradient adjustment with the spatiotemporal assessment results of salt exposure, a salt regulation instruction adapted to the current decomposition stage and microbial metabolic state is generated.

[0040] Specifically, the process begins with a continuous time-series correlation analysis of the decomposition feedback parameters and the salinity distribution data experienced by the litter. Through machine learning or statistical modeling, strong correlation intervals between salinity values ​​and decomposition efficiency deviations can be dynamically identified. This identifies which salinity range(s) significantly inhibit or promote the decomposition process; these ranges are defined as critical threshold intervals. After identifying these critical threshold intervals, the currently collected real-time salinity distribution data around the litter is immediately compared to these intervals using a difference analysis. If the current salinity value falls within the identified inhibitory critical threshold interval, the direction of salinity gradient adjustment is determined to be reducing salinity; conversely, to promote decomposition, the salinity may need to be adjusted to a known promoting interval. The adjustment magnitude is initially determined based on the difference between the current salinity value and the target optimization interval, such as the center value of the promoting interval or the edge value of the inhibiting interval. This step provides a clear target and a quantified adjustment amount for salinity regulation. Before executing the adjustment, a spatiotemporal necessity assessment is performed, which is closely integrated with the litter movement trajectory data. The process involves retrospectively analyzing the salinity exposure history of litter to determine whether the litter has been exposed to the unfavorable salinity environment for a prolonged period or only briefly, while also predicting its future movement trends based on its motion patterns. If the litter is only briefly exposed or will soon move to a more suitable salinity area, then salinity regulation may not be necessary in terms of time and space; conversely, if the litter will remain in the unfavorable environment, the necessity of regulation will increase. This time-space necessity assessment process ultimately generates a salinity exposure time-space assessment result, adding temporal and spatial dimensions to the regulation decision. Finally, by combining the direction and magnitude of the salinity gradient adjustment with the salinity exposure time-space assessment result, the final salinity regulation command is generated. The generation of this salinity regulation command is a multi-dimensional decision-making process that not only considers the adjustment needs at the physicochemical level but also fully incorporates an understanding of the dynamics of biological processes. Even if the spatiotemporal assessment of salinity exposure indicates a need for significant salinity adjustment, the rate and method of adjustment are determined by considering the current decomposition stage (e.g., initial microbial colonization or later decomposition of recalcitrant substances) and the microbial metabolic state, thus avoiding the impact of drastic environmental changes on the microbial community. This generates refined and dynamic salinity regulation instructions tailored to the specific coupling state between the organism and its environment. By introducing dynamic identification of key thresholds, assessment of salinity exposure history, and adaptive consideration of biological stages, the intelligence level of feedback control is enhanced. This transforms salinity environment correction from a simple mechanical correction of deviations from the target into a complex, predictive, and adaptive environmental regulation process that simulates natural ecosystems, revealing more deeply the intrinsic mechanism of dynamic feedback between salinity gradients and ecological functions.

[0041] Optionally, the method further includes: The growth status of the microbial film on the surface of the litter is monitored by an impedance sensor to obtain physical biomass parameters. The correlation between the physical biomass parameters and the microbial activity index was verified, and verification results were generated. When the verification result deviates from the preset correlation threshold, the calibration of the contribution of the environmental parameters is triggered to correct the calculation of the microbial activity index.

[0042] Specifically, this is achieved through an impedance sensor installed on the litter surface, which can non-destructively monitor the growth status of the microbial film in real time. The microbial film, or biofilm, alters the electrical properties of the litter surface, such as resistance and capacitance, during its growth. The impedance sensor quantifies these changes in electrical properties by applying a weak alternating current signal and measuring its response. These measurement data are processed through a pre-defined electro-biomass conversion model to generate a physical biomass parameter that directly reflects the physical accumulation of the microbial film. This parameter is typically obtained using regression methods such as linear or machine learning models, fitted and trained using known biomass samples (e.g., weighed or stained quantitative data) and corresponding impedance values ​​from prior calibration experiments. Key parameters include the frequency-dependent impedance change and the fitting coefficient, used to convert the measured electrical signal into a microbial biomass estimate in real time without loss of quality. Subsequently, this physical biomass parameter obtained through direct physical measurement is used to verify its correlation with the microbial activity index obtained through indirect simulation calculation based on environmental parameters in real time. During most stages of microbial growth, a positive correlation is expected between biomass and metabolic activity. The correlation coefficient or ratio between these two parameters is continuously calculated and compared to a pre-defined correlation threshold. This threshold defines the acceptable range of correlation between the two, based on historical experimental data or theoretical microbial growth-metabolic relationships. When validation results show that the relationship between physical biomass parameters and the microbial activity index significantly deviates from the pre-defined correlation threshold—for example, if physical biomass continues to increase while the calculated microbial activity index stagnates or decreases—a calibration procedure is triggered. This deviation usually indicates that the microbial community may have undergone adaptive evolution to its environment, causing the initial multi-factor model used to calculate the microbial activity index to no longer accurately reflect its true metabolic potential. The calibration procedure re-evaluates and adjusts the contributions of various environmental parameters in the multi-factor model, such as salinity and temperature, i.e., adjusts their weighting coefficients. The adjustment is based on re-establishing a correlation between the corrected microbial activity index and the measured physical biomass parameters that meets the correlation threshold. In this way, the dynamic correction of the microbial activity index calculation model is completed. Figure 4As shown, the solid line on the left axis represents the physical biomass parameter monitored by an impedance sensor, directly reflecting the actual growth of the microbial film on the litter surface. The dashed line on the right axis represents the microbial activity index indirectly calculated based on environmental parameters. Due to microbial community adaptation, the calculated microbial activity index fails to accurately reflect the continuously increasing physical biomass parameter, causing the correlation between the two to deviate from the correlation threshold. At this point, a "calibration event" is triggered, indicated by the dashed line at 70 days in the figure, adjusting the contribution of environmental parameters in the model to ensure that the corrected microbial activity index is consistent with the changing trend of the physical biomass parameter. By introducing direct physical measurement of microbial biomass, an independent validation and calibration benchmark is provided for indirect simulation assessment based on environmental parameters. This approach can capture and correct model prediction biases caused by long-term adaptive changes in the microbial community, ensuring that subsequent functional feedback and environmental regulation decisions are always based on an assessment that more closely approximates biological reality.

[0043] Optionally, the modification of the dynamic salinity gradient environment includes: The flow rate of each controllable flow rate pump is adjusted according to the salinity control command to generate the adjusted mixed brine solution; The adjusted mixed salt solution is dispersed to generate a uniform salt solution flow; The uniform brine stream is injected into the decomposition reactor to smoothly correct the dynamic salinity gradient environment.

[0044] Specifically, the high-level salinity control commands are first interpreted as execution parameters for each controllable flow rate pump in the multi-channel stratified brine supply device. This means that based on the salinity gradient adjustment direction and magnitude determined in the commands, the required supply flow rates of high-salinity and low-salinity solutions for each channel are recalculated, and the output flow rates of each controllable flow rate pump are adjusted accordingly. By precisely changing the supply ratio of high- and low-salinity solutions, an adjusted mixed brine solution conforming to the new target salinity value is generated in the pipeline. Next, to ensure the high homogeneity of the brine solution injected into the decomposition reactor and avoid local salinity deviations due to insufficient mixing, the adjusted mixed brine solution passes through a dispersion treatment unit, such as a static mixer or a small premixing chamber, before entering the reactor. Through its special internal structure, the fluid is forced to cut, rotate, and re-merge, thereby achieving thorough mixing at the molecular level in a short time and generating a uniform brine flow with consistent physicochemical properties. Finally, this uniform brine flow is precisely injected into the designated stratification location in the decomposition reactor. The injection process was designed to be smooth, meaning the pump flow rate adjustment was not instantaneous but followed a pre-defined rate change curve, gradually reaching the new set value over a certain period. This smooth transition effectively avoids hydraulic shocks or drastic salinity abrupt changes within the reactor, thus gently and gradually correcting the existing dynamic salinity gradient environment, allowing it to evolve into the new state required by the salinity control command. By translating the control command into precise fluid manipulation, a stable and low-disturbance environmental correction process was achieved, reducing the physical interference of the environmental correction operation itself on the experiment, especially on the microbial community. This gentle correction method ensures that the observed changes in litter decomposition function can be more realistically attributed to changes in the chemical environmental factor of salinity, rather than abrupt changes in fluid dynamic conditions.

[0045] Based on the same inventive concept, such as Figure 5 As shown, the present invention also provides a system for simulating litter decomposition function feedback under a salinity gradient, the system comprising: The salinity gradient control module is used to acquire a preset target salinity gradient curve and control the multi-channel stratified brine supply device according to the target salinity gradient curve to form a dynamic salinity gradient environment in the decomposition reactor. The litter dynamic coupling module is used to place the litter to be decomposed in a rotatable litter carrying basket in the decomposition reactor and start the rotation, and simulate the dynamic coupling effect between the litter and the dynamic salinity gradient environment to generate litter motion trajectory data. The multi-parameter acquisition module is used to collect environmental parameters within the dynamic salinity gradient environment by combining the in-situ sensor array with the litter movement trajectory data, and to obtain the degradation product parameters generated by the decomposition of the litter. The multimodal data analysis module is used to perform multimodal data fusion and collaborative feedback analysis by combining the environmental parameters and the degradation product parameters to generate salinity regulation instructions; The salinity environment correction module is used to dynamically adjust the output parameters of the multi-channel stratified brine supply device according to the salinity control command, thereby correcting the dynamic salinity gradient environment.

[0046] To verify the feasibility of this invention in practice, it was applied to a research project at a coastal wetland ecological research center. This center aims to accurately simulate the decomposition process of mangrove litter, such as tung tree leaves, under tidal influences in estuaries, in order to assess the actual impact of salinity fluctuations on wetland carbon cycle functions. Traditional laboratory simulation methods often employ static or stepped salinity settings, which cannot realistically reproduce the continuous and dynamic changes in salinity gradients in nature, nor can they simulate the physical process of litter tumbling with water flow, leading to significant discrepancies between research results and field observations. The center hopes to use this invention to construct an intelligent experimental platform capable of simulating bidirectional feedback between litter and the dynamic salinity environment.

[0047] In this embodiment, the research center used the system of the present invention to conduct a 30-day simulation experiment on the decomposition of tung tree litter. First, a target salinity gradient curve simulating a semi-diurnal tidal cycle was input through the salinity gradient control module, with a cycle of 12.4 hours and a salinity range of 5-25 PSU. The litter dynamic coupling module placed pre-treated tung tree leaves in a rotatable litter-bearing basket within the decomposition reactor and started rotating at a variable speed of 0.5-2 rpm to simulate water flow disturbance. The multi-parameter acquisition module tracked and collected the salinity, temperature, dissolved oxygen, and pH of the microenvironment using an in-situ sensor array combined with litter movement trajectory data. Simultaneously, a miniature fiber optic spectral probe monitored and acquired the concentration of dissolved organic carbon (DOC) in the decomposition products in real time. The multi-modal data analysis module fused and analyzed the collected data, evaluated the decomposition function in real time, and generated salinity control commands. The salinity environment correction module dynamically corrected the salinity gradient environment according to the commands, forming a closed-loop feedback.

[0048] To verify the beneficial effects of this invention, a control group was set up in this embodiment. A conventional static salinity of 15 PSU (average tidal cycle value) was used for synchronous experiments via a microcosmic culture method. Data were recorded at multiple key time points during the experiment, and the specific analysis and effect verification results are as follows.

[0049] On the 10th day of the simulation experiment, the system, through its multi-mode data analysis module, discovered that when the salinity of the litter microenvironment exceeded 22 PSU, the actual decomposition rate calculated based on DOC concentration changes showed a significant decrease of 35% compared to the predicted value of the microbial activity index. Based on this, the system identified "salinity > 22 PSU" as a critical inhibition threshold range and generated a negative decomposition feedback parameter. Based on this decomposition feedback parameter, and combined with litter movement trajectory data analysis, the system assessed that the litter would be continuously exposed to this inhibitory salinity environment for more than 2 hours in each tidal cycle, thus making salinity regulation both temporally and spatially necessary. The system then generated a salinity regulation command, smoothly reducing the peak salinity in subsequent tidal cycles from 25 PSU to 21.5 PSU. Data showed that on the 12th day after the regulation was implemented, the actual decomposition rate at the same high tide level and time point increased by approximately 30% compared to the 10th day, essentially returning to a level consistent with the microbial activity index, demonstrating the effectiveness of the feedback regulation.

[0050] Furthermore, on day 21 of the experiment, the system monitored a steady increase in the physical biomass parameters of the microbial film using an impedance sensor on the litter surface. However, the microbial activity index calculated based on environmental parameters tended to level off, and the correlation between the two deviated from the preset threshold by more than 20%. The system automatically triggered a calibration procedure, reassessed the contribution of each environmental parameter to microbial metabolic activity, and corrected the calculation model of the microbial activity index. After calibration, the microbial activity index and physical biomass parameters regained the expected strong positive correlation, ensuring the accuracy of subsequent functional feedback assessments.

[0051] Data shows that, using the dynamic feedback simulation system of this invention, the total mass loss rate of litter in the experimental group reached 28.6% within 30 days, higher than the 19.8% in the control group, and closer to the 31.2% decomposition rate measured by the research center in in-situ field experiments. The system of this invention can respond in real time, with a response time of less than 5 minutes, adapting to changes in decomposition function and making environmental corrections, a dynamic feedback mechanism that traditional methods cannot achieve. This system simulates the inhibitory effect of salt stress on decomposition function and the adaptive response of microbial communities, providing an experimental means for understanding the functions of coastal wetland ecosystems.

[0052] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0053] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for simulating the functional feedback of litter decomposition under a salinity gradient, characterized in that, The method includes: Obtain a preset target salinity gradient curve, and control the multi-channel stratified brine supply device according to the target salinity gradient curve to form a dynamic salinity gradient environment in the decomposition reactor. The litter to be decomposed is placed in a rotatable litter carrying basket inside the decomposition reactor and the rotation is started. Based on the dynamic coupling effect between the litter and the dynamic salinity gradient environment, the litter motion trajectory data is generated. By combining the in-situ sensor array with the litter movement trajectory data, environmental parameters within the dynamic salinity gradient environment are collected, and parameters of degradation products generated by the decomposition of the litter are obtained. By combining the environmental parameters and the degradation product parameters, multimodal data fusion and collaborative feedback analysis are performed to generate salinity regulation instructions; The output parameters of the multi-channel stratified brine supply device are dynamically adjusted according to the salinity control command to correct the dynamic salinity gradient environment.

2. The method for simulating litter decomposition function feedback under a salinity gradient according to claim 1, characterized in that, The formation of a dynamic salinity gradient environment in the decomposition reactor includes: The target salinity values ​​at different time points are analyzed from the target salinity gradient curve; By combining the target salinity value with the pre-set salinity information of high-salinity and low-salinity solutions, the brine supply flow rate parameters for each channel are calculated and generated. The flow rate parameters of the brine supply for each channel are sent to the multi-channel stratified brine supply device to form a dynamic salt gradient environment in the decomposition reactor.

3. The method for simulating litter decomposition function feedback under a salinity gradient according to claim 1, characterized in that, The generated litter trajectory data includes: The rotatable litter-carrying basket is controlled to rotate by a drive unit, and control parameters including the rotation speed, direction of rotation and timestamp of the rotatable litter-carrying basket are recorded. The spatial position and attitude changes of the fallen object are captured in real time by combining the control parameters, and the three-dimensional motion trajectory is recorded. The three-dimensional motion trajectory is spatiotemporally correlated with the dynamic salinity gradient environment to generate litter motion trajectory data.

4. The method for simulating litter decomposition function feedback under a salinity gradient according to claim 1, characterized in that, The process of collecting environmental parameters within the dynamic salinity gradient environment by combining an in-situ sensor array with litter movement trajectory data includes: The real-time spatial region of the litter in the decomposition reactor is determined based on the litter's trajectory data. Based on the real-time spatial region, the in-situ sensor array at the corresponding location is activated to collect environmental parameters including salinity distribution data, temperature distribution data, dissolved oxygen content data, and pH value data. The in-situ sensor array includes a salinity sensor, a temperature sensor, a dissolved oxygen sensor, and a pH sensor.

5. The method for simulating litter decomposition function feedback under a salinity gradient according to claim 1, characterized in that, The method of obtaining the degradation product parameters generated by the decomposition of the litter includes: The characteristic absorption spectrum of the water body surrounding the litter was monitored in real time using a miniature fiber optic spectral probe. By combining the characteristic absorption spectrum with the preset absorption coefficient and optical path, the concentration of dissolved organic carbon is calculated and used as a parameter for degradation products.

6. The method for simulating litter decomposition function feedback under a salinity gradient according to claim 4, characterized in that, The salt content regulation command includes: Microbial metabolic activity was simulated based on the environmental parameters to generate a microbial activity index. The actual decomposition rate is calculated based on the time-varying rate of the degradation product parameters. The actual decomposition rate is correlated with the microbial activity index to obtain the microbial metabolic driving efficiency. Based on the comparison between the microbial metabolic driving efficiency and the preset standard decomposition curve, decomposition feedback parameters are generated. Based on the decomposition feedback parameters, a decomposition and salinity coupling feedback analysis is performed to generate salinity control commands.

7. The method for simulating litter decomposition function feedback under a salinity gradient according to claim 6, characterized in that, The step of performing decomposition and salinity coupling feedback analysis based on the decomposition feedback parameters to generate salinity control commands includes: The key threshold ranges for salt inhibition or decomposition are identified based on the decomposition feedback parameters. Based on the difference analysis between the key threshold range and the salinity distribution data, the direction and magnitude of the salinity gradient adjustment are determined. By combining the litter movement trajectory data, the salt exposure history of the litter in the reactor is determined, the spatiotemporal necessity of salt regulation is evaluated, and the spatiotemporal evaluation results of salt exposure are obtained. By combining the direction and magnitude of the salt gradient adjustment with the spatiotemporal assessment results of salt exposure, a salt regulation instruction adapted to the current decomposition stage and microbial metabolic state is generated.

8. The method for simulating litter decomposition function feedback under a salinity gradient according to claim 6, characterized in that, The method further includes: The growth status of the microbial film on the surface of the litter is monitored by an impedance sensor to obtain physical biomass parameters. The correlation between the physical biomass parameters and the microbial activity index was verified, and verification results were generated. When the verification result deviates from the preset correlation threshold, the calibration of the contribution of the environmental parameters is triggered to correct the calculation of the microbial activity index.

9. The method for simulating litter decomposition function feedback under a salinity gradient according to claim 1, characterized in that, The modification of the dynamic salinity gradient environment includes: The flow rate of each controllable flow rate pump is adjusted according to the salinity control command to generate the adjusted mixed brine solution; The adjusted mixed salt solution is dispersed to generate a uniform salt solution flow; The uniform brine stream is injected into the decomposition reactor to smoothly correct the dynamic salinity gradient environment.

10. A system for simulating litter decomposition function feedback under a salinity gradient, applied to a method for simulating litter decomposition function feedback under a salinity gradient as described in any one of claims 1-9, characterized in that, The system includes: The salinity gradient control module is used to acquire a preset target salinity gradient curve and control the multi-channel stratified brine supply device according to the target salinity gradient curve to form a dynamic salinity gradient environment in the decomposition reactor. The litter dynamic coupling module is used to place the litter to be decomposed in a rotatable litter carrying basket in the decomposition reactor and start rotation, and simulate the dynamic coupling effect between the litter and the dynamic salinity gradient environment to generate litter motion trajectory data. The multi-parameter acquisition module is used to collect environmental parameters within the dynamic salinity gradient environment by combining the litter movement trajectory data with an in-situ sensor array, and to obtain the degradation product parameters generated by the decomposition of the litter. The multimodal data analysis module is used to perform multimodal data fusion and collaborative feedback analysis by combining the environmental parameters and the degradation product parameters to generate salinity regulation instructions; The salinity environment correction module is used to dynamically adjust the output parameters of the multi-channel stratified brine supply device according to the salinity control command, thereby correcting the dynamic salinity gradient environment.

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