A method and system for predicting tidal flat evolution based on a geomorphic model

Abstract

The present application relates to the technical field of geomorphology prediction, in particular to a tidal flat evolution prediction method and system based on a geomorphology model. The method comprises the following steps: obtaining geomorphology data, environmental parameter data and biological membrane characteristic data of a target tidal flat area; the geomorphology data comprises water depth distribution and sediment porosity; the environmental parameter data comprises tidal current velocity, fluid shear force, light intensity and water temperature; the biological membrane characteristic data comprises biological membrane thickness obtained by in-situ sampling; a biological membrane dynamic viscosity coefficient model is constructed based on the biological membrane thickness and the water temperature; the dynamic viscosity coefficient model represents a nonlinear enhancement effect of the biological membrane on sediment viscosity; a biological membrane growth rate feedback model is established based on the water depth distribution and the light intensity; the feedback model quantifies the coupling relationship between biological membrane growth in a deposition area and microtopographic factors; the present application can solve the problem of inaccurate evolution prediction under dynamic interaction of environmental factors.

Description

Technical Field

[0001] This invention relates to the field of geomorphological prediction technology, specifically to a method and system for predicting tidal flat evolution based on geomorphological models. Background Technology

[0002] Predicting the evolution of tidal flat ecosystems is a core issue in coastal zone management. Current mainstream prediction models suffer from fundamental technical flaws: they simplify the impact of biofilms on sediment viscosity to a fixed correction coefficient, completely ignoring the dynamic changes in biofilm activity under temperature fluctuations. In existing technologies, biofilm thickness is only used as a linear parameter in erosion threshold calculations, failing to establish its synergistic effect with water temperature and lacking the ability to respond to abrupt changes in biofilm activity under low-temperature conditions. This leads to severe prediction distortions in the intertidal zone, where diurnal temperature variations are significant, and an inability to characterize the dynamic characteristics of biofilm viscosity changes with the tidal cycle. More critically, traditional models treat microtopographic evolution and biofilm growth as independent processes, failing to establish a feedback loop between them, resulting in a systematic underestimation of the topographic uplift rate in biofilm-covered areas during long-term predictions.

[0003] The aforementioned shortcomings make it difficult for existing technologies to support the precise planning of tidal flat restoration projects. There is an urgent need for a new prediction method that can quantify the dynamic viscosity effect of biofilms and couple micro-topographic feedback mechanisms to solve the problem of inaccurate evolution prediction under the dynamic interaction of environmental factors. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a method for predicting tidal flat evolution based on geomorphological models, comprising the following steps:

[0005] Acquire geomorphological data, environmental parameter data, and biofilm characteristic data of the target tidal flat area;

[0006] The geomorphological data includes water depth distribution and sediment porosity;

[0007] The environmental parameter data includes tidal flow velocity, fluid shear force, light intensity, and water temperature; the biofilm characteristic data includes biofilm thickness obtained from in-situ sampling.

[0008] A dynamic viscosity coefficient model of the biofilm is constructed based on the biofilm thickness and the water temperature.

[0009] The biofilm dynamic viscosity coefficient model characterizes the nonlinear enhancement effect of biofilm on sediment viscosity.

[0010] A biofilm growth rate feedback model is established based on the water depth distribution and the light intensity.

[0011] The biofilm growth rate feedback model quantifies the coupling relationship between biofilm growth and microtopographic factors in the sedimentation zone.

[0012] A joint prediction model for erosion and deposition is constructed by integrating the biofilm dynamic viscosity coefficient model and the fluid shear force.

[0013] The erosion and deposition joint prediction model corrects the critical erosion shear force threshold through the biofilm viscosity effect.

[0014] The biofilm dynamic viscosity coefficient model is dynamically updated based on the biofilm growth rate feedback model.

[0015] The update process forms a closed-loop feedback mechanism of biofilm growth-viscosity-microtopography evolution;

[0016] The erosion and sedimentation joint prediction model is executed to output a spatiotemporal distribution map of tidal flat profile evolution; the spatiotemporal distribution map is used to guide the planning of tidal flat ecological restoration projects.

[0017] Preferably, the topographic data is collected in collaboration between UAV-borne lidar and multibeam sonar; the environmental parameter data is acquired in real time using a temperature, salinity, and depth gauge, optical sensors, and an acoustic Doppler current profiler deployed on the tidal flats; and the biofilm characteristic data is measured by periodically extracting sediment core samples and separating the extracellular polymer content.

[0018] More preferably, the process of constructing the biofilm dynamic viscosity coefficient model includes: setting a biofilm activity temperature threshold; establishing an exponential synergistic enhancement function of biofilm thickness and temperature on viscosity coefficient; and introducing a temperature effect activation function to control the nonlinear decay of biofilm activity under low temperature conditions.

[0019] More preferably, the construction process of the biofilm growth rate feedback model includes: defining the light transmission attenuation function of light intensity and water depth; establishing the relationship between the light intensity-water depth product and the saturation growth of the biofilm; and coupling the self-inhibition effect of the biofilm viscosity coefficient on the growth rate.

[0020] More preferably, the biofilm dynamic viscosity coefficient model is expressed as:

[0021] ;

[0022] in, The dynamic viscosity coefficient of the biofilm; The thickness of the biofilm; Water temperature; This represents the temperature threshold for biofilm activity. This is the viscosity strength scaling factor; Temperature sensitivity coefficient; This is the thickness enhancement index.

[0023] More preferably, the biofilm growth rate feedback model is expressed as:

[0024] ;

[0025] in, This refers to the biofilm growth rate; Light intensity; Instantaneous water depth; This represents the maximum photosynthetic rate. This is the water depth inhibition coefficient; For viscous self-inhibition weights; The light response order; The water depth attenuation order is denoted by .

[0026] More preferably, the erosion and deposition joint prediction model is expressed as follows:

[0027] ;

[0028] in, Erosion rate per unit area; It is the fluid shear force; The critical erosion shear force is based on the viscosity coefficient; The critical erosion shear force in the absence of a biofilm; The bottom sediment erosion coefficient; The shear force response index; This represents the critical thickness threshold for biofilms.

[0029] A tidal flat evolution prediction system based on a geomorphological model, applied to any one of the above-described methods for predicting tidal flat evolution based on a geomorphological model, comprising:

[0030] The multi-source data acquisition module is used to acquire tidal flat landform data, environmental parameter data, and biofilm characteristic data;

[0031] The biofilm viscosity coefficient calculation module is connected to the multi-source data acquisition module and is used to execute the biofilm dynamic viscosity coefficient model.

[0032] A biofilm growth rate calculation module is connected to the multi-source data acquisition module and the biofilm viscosity coefficient calculation module, and is used to execute the biofilm growth rate feedback model.

[0033] The erosion and sedimentation prediction module is connected to the biofilm viscosity coefficient calculation module and is used to execute the joint erosion and sedimentation prediction model.

[0034] The dynamic feedback control module connects the biofilm growth rate calculation module and the biofilm viscosity coefficient calculation module in real time to form a closed-loop update link.

[0035] The spatiotemporal map generation module is connected to the erosion and sedimentation prediction module and outputs the spatiotemporal distribution map of tidal flat profile evolution to the ecological planning terminal.

[0036] More preferably, the biofilm viscosity coefficient calculation module includes a temperature effect activation unit and an exponential calculation unit; the temperature effect activation unit is configured to compare the water temperature with a preset threshold; the exponential calculation unit is configured to perform an exponentially enhanced calculation of the biofilm thickness when the temperature exceeds the threshold.

[0037] More preferably, the erosion and sedimentation prediction module includes a critical shear force correction unit and a nonlinear amplification unit; the critical shear force correction unit is implemented in real time through an FPGA; the nonlinear amplification unit is configured to differentially amplify the viscosity coefficient input and the reference value.

[0038] Technical effects:

[0039] This invention creatively solves the core technical problem of isolated calculation of biofilm viscosity effects and environmental factors in traditional tidal flat prediction models by establishing a coupling mechanism between a biofilm dynamic viscosity coefficient model and a biofilm growth rate feedback model. The main technical effects are reflected in:

[0040] By using a temperature activation function to control the active state of biofilm, a nonlinear decay simulation of viscosity effect under low temperature conditions is achieved, which solves the prediction distortion problem of traditional linear models in the region of abrupt temperature change.

[0041] A biofilm thickness-temperature synergistic enhancement function was constructed, and the dynamic change of viscosity intensity with environmental parameters was quantified through an exponential relationship, overcoming the defect that fixed correction coefficients cannot reflect the actual biofilm growth characteristics.

[0042] By establishing a closed-loop feedback mechanism between the erosion and sedimentation prediction model and the growth model, a dynamic feedback chain is formed between micro-topographic evolution and biofilm growth, significantly improving the reliability of long-term predictions. This approach provides a precise tool for predicting the spatiotemporal evolution of tidal flats in ecological restoration projects. Attached Figure Description

[0043] Figure 1 This is a flowchart of the tidal flat evolution prediction method based on geomorphological models in this application.

[0044] Figure 2 This is a block diagram of the tidal flat evolution prediction system based on geomorphological models in this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] The technical problems with traditional solutions are as follows: First, the model does not consider the dynamic influence of biofilm on sediment viscosity, resulting in distorted prediction of erosion threshold; second, existing methods treat environmental factors, such as temperature and light, as independent variables, ignoring the coupling effect between biofilm growth and micro-topography evolution; finally, the prediction process lacks a closed-loop feedback mechanism, cannot adapt to real-time changes in the tidal flat environment, and has insufficient long-term prediction reliability.

[0047] Based on this, please refer to Figure 1 This embodiment provides a method for predicting tidal flat evolution based on a geomorphological model, including the following steps:

[0048] S1: Acquire geomorphological data, environmental parameter data, and biofilm characteristic data of the target tidal flat area;

[0049] The geomorphological data includes water depth distribution and sediment porosity;

[0050] The environmental parameter data includes tidal flow velocity, fluid shear force, light intensity, and water temperature; the biofilm characteristic data includes biofilm thickness obtained from in-situ sampling.

[0051] S2: Construct a dynamic viscosity coefficient model of the biofilm based on the biofilm thickness and the water temperature;

[0052] The biofilm dynamic viscosity coefficient model characterizes the nonlinear enhancement effect of biofilm on sediment viscosity.

[0053] S3: Establish a biofilm growth rate feedback model based on the water depth distribution and the light intensity;

[0054] The biofilm growth rate feedback model quantifies the coupling relationship between biofilm growth and microtopographic factors in the sedimentation zone.

[0055] S4: Construct a joint prediction model for erosion and deposition by integrating the biofilm dynamic viscosity coefficient model and the fluid shear force;

[0056] The erosion and deposition joint prediction model corrects the critical erosion shear force threshold through the biofilm viscosity effect.

[0057] S5: Dynamically update the biofilm dynamic viscosity coefficient model based on the biofilm growth rate feedback model;

[0058] The update process forms a closed-loop feedback mechanism of biofilm growth-viscosity-microtopography evolution;

[0059] S6: Execute the erosion and sedimentation joint prediction model to output a spatiotemporal distribution map of tidal flat profile evolution; the spatiotemporal distribution map is used to guide the planning of tidal flat ecological restoration projects.

[0060] This technical solution addresses the prediction distortion caused by isolated calculations of environmental factors by establishing a coupling relationship between the dynamic viscosity effect of biofilms and microtopographic factors. It overcomes the limitation of traditional models in adapting to real-time environmental changes through a closed-loop feedback mechanism of biofilm growth-viscosity-microtopography. Furthermore, it employs a nonlinear synergistic enhancement function to quantify the viscosity-enhancing effect of biofilms on sediments, significantly improving the prediction accuracy of critical erosion thresholds. The technical benefits are reflected in the realization of dynamic coupling of all elements of the erosion and deposition process, the establishment of a positive feedback chain between microtopographic evolution and biofilm growth, and the output results that can directly guide the precise spatial layout of ecological restoration projects.

[0061] Technical problems with traditional solutions: single-platform sensors cannot simultaneously acquire three-dimensional topographic and hydrological parameters of tidal flats; traditional core sampling damages the original structure of biofilms, leading to distortion in thickness measurements; discrete point measurements make it difficult to establish parameter spatial continuity, which restricts the quality of model input data.

[0062] Based on this, the topographic data is collected in collaboration between UAV-borne lidar and multibeam sonar; environmental parameter data is acquired in real time through temperature, salinity, depth, optical sensors and acoustic Doppler current profilers deployed on the tidal flats; and biofilm characteristic data is obtained by periodically extracting sediment core samples and separating the extracellular polymer content.

[0063] This technical solution addresses the issue of asynchronous acquisition of topographic and hydrological parameters through an air-sea collaborative observation platform; it maintains the integrity of the extracellular polymer structure of biofilms using the frozen rock core method; and it enables continuous spatial monitoring of environmental parameters through a multi-sensor network.

[0064] The technical effects are manifested in acquiring micro-topographic undulation data with millimeter-level precision, preserving the original thickness information of the biofilm, establishing a parameter spatial distribution matrix, and providing a high-fidelity input source for the model.

[0065] The technical problems of traditional solutions are: ignoring the threshold mutation effect of temperature on biofilm activity; using a linear model to describe the thickness-viscosity relationship, leading to prediction bias; and failing to establish a nonlinear synergistic mechanism between temperature and thickness.

[0066] Based on this, the construction process of the biofilm dynamic viscosity coefficient model includes setting a biofilm activity temperature threshold; establishing an exponential synergistic enhancement function of biofilm thickness and temperature on viscosity coefficient; and introducing a temperature effect activation function to control the nonlinear decay of biofilm activity under low temperature conditions.

[0067] This technical solution determines the biofilm's active state through a temperature threshold, addressing the lag in response to sudden environmental temperature changes. It employs an exponential enhancement function to describe the thickness-viscosity nonlinear relationship and designs a temperature activation function to control the low-temperature attenuation effect. The technical advantages are reflected in accurately capturing the step changes in viscosity coefficients in temperature-sensitive areas, quantifying the exponential enhancement of viscosity intensity by biofilm thickness, and significantly improving the predictive adaptability to low-temperature tidal flat environments.

[0068] The technical problems of traditional solutions are: failure to establish the spatial relationship between light transmission attenuation and water depth; neglect of light intensity saturation effect when using a linear light response model; and neglect of the self-inhibition phenomenon of viscosity on biofilm growth.

[0069] Based on this, the construction process of the biofilm growth rate feedback model includes defining the light transmission attenuation function of light intensity and water depth; establishing the relationship between the light intensity-water depth product and the saturation growth of biofilm; and coupling the self-inhibition effect of the biofilm viscosity coefficient on the growth rate.

[0070] This technical solution quantifies underwater photosynthetic efficiency through a light transmission attenuation function; employs a saturated growth model to describe the synergistic effect of light intensity and water depth; and introduces a viscosity coefficient self-inhibition module to reflect excessive growth limitations. The technical results demonstrate accurate prediction of the explosive growth patterns of biofilms in shallow water areas, revealing the growth stagnation mechanism caused by light limitations in deep water areas, and establishing a negative feedback control chain of viscosity coefficient on excessive growth.

[0071] The technical problems of traditional solutions are: the temperature response function does not consider the threshold mutation characteristics; the thickness enhancement mechanism uses a fixed exponent, resulting in insufficient environmental adaptability; and there is a lack of a cross-coupled calculation framework for temperature and thickness.

[0072] Based on this, the biofilm dynamic viscosity coefficient model is expressed as:

[0073] ;

[0074] in, The dynamic viscosity coefficient of the biofilm; The thickness of the biofilm; Water temperature; This represents the temperature threshold for biofilm activity. This is the viscosity strength scaling factor; Temperature sensitivity coefficient; This is the thickness enhancement index.

[0075] This technical solution achieves the gradient response of the temperature threshold through the Sigmoid function; a variable exponent is designed. It adapts to different substrate types and employs an exponential-logic combination structure to describe the temperature-thickness synergistic effect. The technical benefits are reflected in the automatic shutdown of biofilm activity at low temperatures, smooth transition of viscosity coefficients within the critical temperature range, and a dynamic exponential mechanism that enhances the model's geological universality, providing high-precision parameter input for erosion prediction.

[0076] This formula is used to quantify the dynamic enhancement effect of biofilm on sediment viscosity, and it solves the technical defect of traditional models that simplify the effect of biofilm viscosity to a fixed coefficient, enabling the model to respond to real-time changes in temperature and biofilm thickness.

[0077] in, The dynamic viscosity coefficient of biofilm is a core parameter for measuring the ability of biofilm to enhance the erosion resistance of sediments. The larger the value, the stronger the viscous reinforcement effect of biofilm on sediments. The current biofilm thickness is expressed in exponential form. Describes the nonlinear enhancement of viscosity coefficient by thickness, when At this point, increased thickness triggers an accelerated increase in the viscosity coefficient, which is consistent with the observed characteristic that the viscosity effect significantly enhances after the biofilm thickness exceeds a certain threshold. The thickness enhancement index can be calibrated experimentally based on sediment type, such as sandy or muddy.

[0078] This refers to the real-time temperature of the water body. This refers to the temperature threshold for biofilm activity. For example, if the activity of a biofilm in a certain tidal flat area suddenly changes at 15℃, then... Both are connected via an exponential function. Formation of temperature response mechanism: when When the exponent term approaches 0, the denominator... Approximately equal to 1, the viscosity coefficient is mainly determined by the biofilm thickness; when At that time, the exponential term increases exponentially with decreasing temperature, and the denominator becomes significantly larger, leading to... The nonlinear decay accurately simulates the phenomenon of a sharp drop in biofilm activity and a weakening of the viscosity effect at low temperatures. This is the temperature sensitivity coefficient, reflecting the degree to which biofilms respond to temperature changes. In intertidal areas with large diurnal temperature variations, it needs to be increased based on in-situ observation data. To enhance the model's responsiveness.

[0079] The viscosity strength scaling factor is used to calibrate the overall order of magnitude of the viscosity coefficient. Its value is related to the sediment grain size, such as in clayey tidal flats. The value is greater than that of sandy tidal flats. This formula achieves dynamic quantification of the biofilm viscosity effect by coupling the threshold response of temperature with the exponential enhancement of thickness, making subsequent erosion predictions more consistent with the actual environment.

[0080] The technical problems with traditional solutions are as follows: First, the linear decay model used for the light-water-depth relationship cannot describe the saturation response characteristics of actual photosynthesis; second, the negative feedback effect of viscosity coefficient on biofilm growth is ignored, resulting in the predicted growth rate in shallow water areas being much higher than the actual observed value; and third, the spatial nonlinear correlation between light transmission attenuation and water depth is not established, making the model less applicable in turbid water bodies.

[0081] Based on this, the biofilm growth rate feedback model is expressed as:

[0082] ;

[0083] in, This refers to the biofilm growth rate; Light intensity; Instantaneous water depth; This represents the maximum photosynthetic rate. This is the water depth inhibition coefficient; For viscous self-inhibition weights; The light response order; The water depth attenuation order is denoted by .

[0084] This formula describes the coupling relationship between biofilm growth rate and environmental factors, breaking through the limitations of traditional models that treat light and water depth as independent variables. It establishes a dynamic feedback between biofilm growth and micro-topography, and solves the problem of distorted growth rate prediction in shallow water areas.

[0085] in, This represents the instantaneous growth rate of biofilm thickness, the change in thickness per unit time. Positive values ​​indicate biofilm thickening, while negative values ​​indicate degradation. Light intensity, For instantaneous water depth, the two are multiplied by the product term. Demonstrating synergy: This refers to the light response order, reflecting the intensity of light's promotion of growth, such as in algae-dominated biofilms. The value is higher, and This indicates a shallow water area. Small, due to the growth benefit of sufficient light.

[0086] denominator It is a water depth inhibition term. Water depth inhibition coefficient and The water depth and attenuation order jointly control the suppression strength, when In deep water, the denominator increases significantly, causing the overall growth term to tend to saturate. This simulates the phenomenon that the attenuation of light transmission in deep water leads to limited photosynthesis, avoiding the bias of traditional linear models that predict high-speed growth in deep water.

[0087] The maximum photosynthetic rate is defined as [value], while the upper limit parameter for growth rate is [value], which is related to the dominant species in biofilms, such as diatom-dominated biofilms. The value is higher than that of cyanobacteria. (The latter half of the formula) It is a viscous self-inhibition term: The self-inhibition weight is a viscosity factor that reflects the intensity of inhibition. When the biofilm is too thick, it leads to... When the value is too large, the inhibition term is enhanced, limiting the growth rate and forming a negative feedback loop from growth and viscosity to inhibition, preventing the model from predicting unreasonable results of unlimited biofilm thickening.

[0088] This technical solution utilizes an improved saturation function. Replacing traditional linear models, this method accurately quantifies the synergistic inhibitory effect of light intensity and water depth on photosynthesis; it also innovatively introduces a viscosity coefficient. As a negative feedback term, it reveals the self-limiting mechanism by which excessive biofilm growth leads to viscous accumulation and inhibits subsequent growth. The technical effects are: accurately capturing the growth rate plateau phenomenon caused by light saturation in shallow water; reflecting the growth stagnation caused by light transmission attenuation in deep water; and ensuring the dynamic equilibrium of biofilm thickness prediction by the model, avoiding theoretical deviations from unlimited growth.

[0089] Technical problems with traditional solutions: reducing biofilm thickness As a linear correction factor, it cannot characterize the erosion acceleration effect after the thickness exceeds the critical value; critical shear force The lack of a dynamic correlation function with the viscosity coefficient leads to distorted erosion predictions in areas with high biofilm coverage; traditional difference methods... Since the baseline value of the non-biofilm state is not considered, the prediction results lack normalized comparability.

[0090] Based on this, the joint prediction model for erosion and deposition is expressed as follows:

[0091] ;

[0092] in, Erosion rate per unit area; It is the fluid shear force; The critical erosion shear force is based on the viscosity coefficient; The critical erosion shear force in the absence of a biofilm; The bottom sediment erosion coefficient; The shear force response index; This represents the critical thickness threshold for biofilms.

[0093] This formula is used to predict the erosion rate per unit area of ​​tidal flats. By coupling the biofilm viscosity effect with environmental dynamic factors, it solves the shortcomings of traditional models that ignore the critical value of biofilm thickness and viscosity dynamic correction, thus improving the accuracy of erosion prediction.

[0094] in, Erosion rate per unit area is the mass of sediment eroded per unit time; the higher the value, the more severe the erosion. It is fluid shear force, the drag force of currents and waves on the substrate. Critical erosion shear force, corrected for biofilm viscosity coefficient; the minimum shear force at which substrate erosion begins in the presence of a biofilm. This is the baseline critical value without a biofilm.

[0095] The difference between the two Reflects the degree to which the actual shear force exceeds the critical value, and The ratio achieved normalization for different substrate types, and then through... The exponential operation of the shear force response exponent amplifies this supercritical effect, such as in sandy tidal flats. The value is higher, and it is more sensitive to changes in shear force.

[0096] It is a nonlinear amplification term of biofilm thickness. Given the current thickness of the biofilm, The critical thickness threshold of biofilm is a key critical point, when At that time, the growth rate of this term was moderate; when When, its value follows The increase was significant, simulating the phenomenon that erosion accelerates due to structural instability after the biofilm thickness exceeds a critical value. The sediment erosion coefficient is used to calibrate the erosion baseline of different sediment types, such as muddy tidal flats. The value is lower than that of sandy tidal flats.

[0097] This formula achieves accurate prediction of tidal flat erosion processes by dynamically coupling biofilm viscosity correction, shear force supercritical effect, and thickness nonlinearity, providing a quantitative basis for the delineation of "key protection areas" in ecological restoration.

[0098] This technical solution is achieved through Achieving superlinear amplification of the erosion rate with respect to biofilm thickness, accurately describing the accelerated erosion phenomenon after the thickness exceeds a critical value; designing normalized difference terms. To eliminate the influence of sediment differences on prediction results; viscosity coefficient Dynamically corrected critical shear force This study investigated the enhancing effect of biofilms on sediment erosion resistance. The technical effects are manifested in: quantifying the exponential growth of erosion rate after biofilm thickness exceeds a critical threshold; and using benchmark values... To achieve comparability of prediction results under different substrate conditions; to improve the prediction accuracy of areas with high biofilm coverage by adjusting the critical shear force threshold in real time using the viscosity coefficient.

[0099] The technical problems of traditional solutions are as follows: the data flow between multiple modules is unidirectional, the calculation of biofilm growth and the update of viscosity coefficient are disconnected, and closed-loop feedback cannot be achieved; the visualization module only outputs two-dimensional planar maps, which are difficult to express the spatiotemporal dynamic characteristics of tidal flat profile evolution; the erosion prediction module is not directly connected to the data acquisition end, and there is a delay in model input.

[0100] Based on this, this embodiment provides a tidal flat evolution prediction system based on a geomorphological model, including:

[0101] The multi-source data acquisition module is used to acquire tidal flat landform data, environmental parameter data, and biofilm characteristic data;

[0102] The biofilm viscosity coefficient calculation module is connected to the multi-source data acquisition module and is used to execute the biofilm dynamic viscosity coefficient model.

[0103] A biofilm growth rate calculation module is connected to the multi-source data acquisition module and the biofilm viscosity coefficient calculation module, and is used to execute the biofilm growth rate feedback model.

[0104] The erosion and sedimentation prediction module is connected to the biofilm viscosity coefficient calculation module and is used to execute the joint erosion and sedimentation prediction model.

[0105] The dynamic feedback control module connects the biofilm growth rate calculation module and the biofilm viscosity coefficient calculation module in real time to form a closed-loop update link.

[0106] The spatiotemporal map generation module is connected to the erosion and sedimentation prediction module and outputs the spatiotemporal distribution map of tidal flat profile evolution to the ecological planning terminal.

[0107] This technical solution establishes a real-time data exchange channel between the biofilm growth rate calculation module and the viscosity coefficient calculation module through a dynamic feedback control module, forming a closed-loop feedback link between growth, viscosity, and topographic evolution. The spatiotemporal map generation module converts the prediction results into three-dimensional mesh data, enabling visualized dynamic simulation of the profile evolution process. The erosion and sedimentation prediction module is directly connected to the multi-source data acquisition module, ensuring the timeliness of the model input data. The technical effects are as follows: the closed-loop feedback mechanism dynamically adjusts the viscosity coefficient parameters to adapt to real-time changes in the tidal flat environment; the three-dimensional spatiotemporal mesh intuitively displays the dynamic process of sedimentation uplift and erosion downcutting; and the direct data acquisition eliminates intermediate transmission delays, ensuring prediction timeliness.

[0108] Technical problems with traditional solutions: General-purpose processors cannot efficiently perform biofilm thickness exponential calculations. Temperature activation function Real-time calculation; the temperature threshold comparison function relies on software judgment, and the response speed is difficult to meet the needs of rapid changes in the tidal environment.

[0109] Based on this, the biofilm viscosity coefficient calculation module includes a temperature effect activation unit and an exponential calculation unit; the temperature effect activation unit is configured to compare the water temperature with a preset threshold; the exponential calculation unit is configured to perform an exponentially enhanced calculation of the biofilm thickness when the temperature exceeds the threshold.

[0110] This technical solution uses FPGA programmable logic devices to build a dedicated computing architecture: the temperature effect activation unit integrates a hardware comparator and a programmable threshold register. This enables nanosecond-level temperature threshold determination; the exponential operation unit is designed with parallel computing circuitry for synchronous processing. The exponential operation is achieved through a hardware comparator that improves temperature status judgment speed to the microsecond level, adapting to sudden temperature changes caused by tidal fluctuations; the parallel exponential operation unit reduces the computation time of complex functions by an order of magnitude; and the programmable register supports on-site adjustment of biofilm activity thresholds. This enhances the system's adaptability to different environments.

[0111] Technical problems with traditional solutions: Existing erosion prediction hardware has key shortcomings: critical shear force correction relies on CPU iterative calculations, resulting in significant time delays when processing high-frequency hydrological data; traditional amplifiers cannot adapt to viscosity coefficients. Compared with the benchmark value The nonlinear differential amplification requirement.

[0112] Based on this, the erosion and sedimentation prediction module includes a critical shear force correction unit and a nonlinear amplification unit; the critical shear force correction unit is implemented in real time through FPGA; the nonlinear amplification unit is configured to differentially amplify the viscosity coefficient input and the reference value.

[0113] This technical solution constructs a dedicated data pipeline within the FPGA: the critical shear force correction unit integrates a pipelined floating-point arithmetic unit, completing the operation in a single clock cycle. Real-time mapping calculation; the nonlinear amplification unit adopts a logarithmic amplifier hardware circuit to directly realize... The exponential operation and differential amplification are employed. The technical benefits are as follows: the pipelined computing architecture compresses the critical shear force correction delay to the millisecond level, meeting the requirements of high-frequency hydrological sampling; the logarithmic amplifier hardware eliminates the bottleneck of traditional software iterative calculations, ensuring the model's real-time response capability under extreme fluid shear force conditions.

[0114] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for predicting tidal flat evolution based on a geomorphic model, characterized by, The method comprises the following steps: Obtaining topographic data, environmental parameter data and biofilm characteristic data of a target tidal flat area; the topographic data comprises water depth distribution and sediment porosity; the environmental parameter data comprises tidal current velocity, fluid shear force, light intensity and water temperature; the biofilm characteristic data comprises biofilm thickness obtained by in-situ sampling; Constructing a biofilm dynamic viscosity coefficient model based on the biofilm thickness and the water temperature; The biofilm dynamic viscosity coefficient model represents the nonlinear enhancement effect of biofilm on the viscosity of sediments; Establishing a biofilm growth rate feedback model based on the water depth distribution and the light intensity; The biofilm growth rate feedback model quantifies the coupling relationship between biofilm growth and microtopographic factors in the deposition area; Fusing the biofilm dynamic viscosity coefficient model and the fluid shear force to construct an erosion-deposition combined prediction model; The erosion-deposition combined prediction model corrects the critical erosion shear force threshold value through biofilm viscosity effect; Dynamically updating the biofilm dynamic viscosity coefficient model according to the biofilm growth rate feedback model; The updating process forms a closed-loop feedback mechanism of biofilm growth-viscosity-microtopographic evolution; Executing the erosion-deposition combined prediction model to output a tidal flat profile evolution spatiotemporal distribution map; the spatiotemporal distribution map is used to guide tidal flat ecological restoration engineering planning; The biofilm dynamic viscosity coefficient model is represented as: ； wherein, is a biofilm dynamic viscosity coefficient; is a biofilm thickness; is a water body temperature; is a biofilm activity temperature threshold; is a viscosity strength proportionality factor; is a temperature sensitivity coefficient; is a thickness enhancement index; The biofilm growth rate feedback model is represented as: ； wherein, is the rate of biofilm growth; is the light intensity; is the instantaneous water depth; is the maximum photosynthesis rate; is the water depth inhibition coefficient; is the viscous self-inhibition weight; is the light response order; is the water depth attenuation order; The erosion-deposition combined prediction model is represented as: ； wherein, is the erosion rate per unit area; is the fluid shear stress; is the critical erosion shear stress based on the viscosity coefficient; is the critical erosion shear stress without biofilm; is the substrate erosion coefficient; is the shear stress response exponent; is the biofilm critical thickness threshold.

2. The method of predicting tidal flat evolution based on a geomorphic model according to claim 1, characterized in that, The topographic data is collected by a UAV-borne laser radar and a multi-beam sonar in cooperation; the environmental parameter data is obtained in real time by deploying a CTD, an optical sensor and an acoustic Doppler current profiler on the tidal flat; the biofilm characteristic data is measured by periodically extracting sediment core samples and separating the content of extracellular polymeric substances.

3. The method of predicting tidal flat evolution based on a geomorphic model according to claim 1, characterized in that, The construction process of the biofilm dynamic viscosity coefficient model comprises: setting a biofilm activity temperature threshold value; establishing an exponential-type synergistic enhancement function of biofilm thickness and temperature on viscosity coefficient; introducing a temperature effect activation function to control the nonlinear attenuation of biofilm activity in a low-temperature environment.

4. The method of predicting tidal flat evolution based on a geomorphic model according to claim 1, wherein, The construction process of the biofilm growth rate feedback model comprises: defining a light transmission attenuation function of light intensity and water depth; establishing a saturated growth relationship between the product of light intensity and water depth and biofilm growth; coupling the self-inhibition effect of biofilm viscosity coefficient on growth rate.

5. A geomorphologic model-based tidal flat evolution prediction system for performing a geomorphologic model-based tidal flat evolution prediction method according to any one of claims 1 to 4, characterized by, It comprises: A multi-source data acquisition module for obtaining tidal flat topographic data, environmental parameter data and biofilm characteristic data; A biofilm viscosity coefficient calculation module connected to the multi-source data acquisition module for executing a biofilm dynamic viscosity coefficient model; A biofilm growth rate calculation module connected to the multi-source data acquisition module and the biofilm viscosity coefficient calculation module for executing a biofilm growth rate feedback model; An erosion-deposition prediction module connected to the biofilm viscosity coefficient calculation module for executing an erosion-deposition combined prediction model; A dynamic feedback control module that connects the biofilm growth rate calculation module and the biofilm viscosity coefficient calculation module in real time to form a closed-loop update link; The spatiotemporal atlas generation module is connected to the erosion and deposition prediction module and outputs a spatiotemporal distribution atlas of the tidal flat profile evolution to an ecological planning terminal.

6. The geomorphologic model based tidal flat evolution prediction system of claim 5, wherein, The biofilm viscosity coefficient calculation module comprises a temperature effect activation unit and an exponential operation unit; the temperature effect activation unit is configured to compare the water temperature with a preset threshold; and the exponential operation unit is configured to perform exponential enhancement calculation of the biofilm thickness when the temperature exceeds the threshold.

7. The geomorphologic model based tidal flat evolution prediction system of claim 5, wherein, The erosion and deposition prediction module comprises a critical shear force correction unit and a nonlinear amplification unit; the critical shear force correction unit is realized by FPGA for real-time calculation; and the nonlinear amplification unit is configured to differentially amplify the viscosity coefficient input and a reference value.

Images