Method and system for analyzing the impact of a coastal engineering on biodiversity

Abstract

The present application relates to the technical field of monitoring system, and more particularly to a method and system for analyzing the impact of coastal engineering on biodiversity, comprising: collecting micro-topographic parameters, water salinity parameters, bottom material parameters and biological larva attachment parameters of the coastal engineering area in real time through a sensor module; the micro-topographic parameters include engineering structure height, median diameter of silt, tidal period and average water depth of intertidal zone, the water salinity parameters include initial salinity before engineering and water temperature, the bottom material parameters include initial organic carbon content and porosity of bottom material, and the biological larva attachment parameters include effective area of larva attachment base; inputting the collected parameters into a data processing module, and the data processing module sequentially calculates micro-topographic disturbance intensity, salinity stratification intensity, organic carbon mineralization rate of bottom material and larva attachment success rate based on a micro-topographic salinity bottom material cascade effect model; and the present application can adapt to complex terrain and dynamic environment, and realize efficient data processing and stable operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of monitoring systems, in particular to a method and system for analyzing the impact of coastal engineering on biodiversity. BACKGROUND

[0002] In the field of alpine wetland ecological monitoring, the existing technology mostly uses a single fixed-angle spectral monitoring method, which is difficult to adapt to the differences in observation angles caused by complex wetland topography, and lacks real-time dynamic correction mechanisms for environmental factors such as temperature, humidity, and atmospheric scattering. This makes the collected red and near-infrared spectral data susceptible to topographic undulations and environmental interference, resulting in errors in reflectance calculation and a decrease in the accuracy of cover type discrimination. In addition, the data processing of traditional systems relies on remote terminals, which have insufficient real-time performance, and the energy management strategy is simple. In the alpine environment with unstable light and extreme temperature, unreasonable energy distribution often leads to monitoring interruption. At the same time, a single data transmission mode is easily affected by signal shielding in complex terrain, making it difficult to ensure the continuous and complete transmission of monitoring data. These problems seriously restrict the long-term and accurate monitoring of the surface cover type of alpine wetlands.

[0003] Based on the above problems, there is an urgent need for a technical solution that can adapt to complex terrain and dynamic environment, achieve efficient data processing and stable operation, to meet the needs of alpine wetland ecological protection and research. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art and to propose a method for analyzing the impact of coastal engineering on biodiversity, comprising:

[0005] S1: Real-time acquisition of micro-topographic parameters, water salinity parameters, bottom parameters and biological larva attachment parameters in the coastal engineering area through a sensor module,

[0006] The micro-topographic parameters include engineering structure height, median particle size of silt, tidal period and average water depth of intertidal zone, the water salinity parameters include initial salinity and water temperature before engineering, the bottom parameters include initial organic carbon content and porosity of bottom, and the biological larva attachment parameters include effective area of larva attachment base;

[0007] S2: Input the collected parameters into a data processing module, the data processing module calculates the micro-topographic disturbance intensity, salinity stratification intensity, bottom organic carbon mineralization rate and larva attachment success rate in turn based on a micro-topographic salinity bottom cascade effect model,

[0008] S3: the cascade effect model realizes dynamic conduction between parameters through progressive formula; the data processing module calculates the biodiversity index according to the larva attachment success rate and the original species base of the region, and combines the environmental resistance coefficient to quantify the influence of the coastal engineering on the biodiversity;

[0009] S4: the data processing module transmits the calculation result to the visualization module, and the visualization module displays the biodiversity index and the influence path of each parameter in a graphical manner.

[0010] Preferably, the sensor module comprises a laser terrain scanner, a salinity sensor, a bottom material sampler and a biological image acquisition device, the laser terrain scanner is used to obtain the engineering structure height and the intertidal zone micro-terrain data, the salinity sensor monitors the water salinity and temperature in real time and outputs an analog signal, the bottom material sampler collects the bottom material samples and analyzes the organic carbon content and porosity through the built-in detection unit, and the biological image acquisition device records the distribution and state of the larva attachment base through a high-definition camera and generates a digital image signal.

[0011] Further preferably, the data processing module comprises a microprocessor, a memory and an analog-to-digital converter, the analog-to-digital converter converts the analog signal output by the sensor module into a digital signal and transmits it to the microprocessor, the microprocessor calls the cascade effect model program stored in the memory to process and calculate the digital signal and the digital image signal output by the biological image acquisition device, and the memory is also used to temporarily store the collected data and the calculation result.

[0012] Further preferably, the visualization module comprises a graphics processor, a display and a data storage unit, the graphics processor receives the calculation result output by the data processing module, converts the biodiversity index into three-dimensional spatial distribution graph data, and converts the change trend of each parameter into line graph data, the display is used to display the above-mentioned graph data, and the data storage unit is connected with the graphics processor through a data interface to store the historical monitoring data and the calculation result.

[0013] Further preferably, the micro-terrain disturbance intensity is calculated by the following formula:

[0014] ;

[0015] In the formula, H is the height of the coastal engineering structure, k is the roughness coefficient of the engineering material, is the median particle size of the intertidal zone sediment, is the tidal period, is the average water depth of the intertidal zone; and D is the micro-terrain disturbance coefficient.

[0016] Further preferably, the salinity stratification intensity is calculated based on the micro-terrain disturbance intensity formula as follows:

[0017] ;

[0018] wherein S0 is the initial salinity before the project, T is the water temperature, and S is the salinity stratification intensity.

[0019] Further preferably, the mineralization rate of the organic carbon in the substrate is calculated based on the salinity stratification intensity, and the formula is as follows:

[0020] ;

[0021] wherein C is the initial organic carbon content of the substrate, P is the porosity of the substrate, and M is the mineralization rate of the organic carbon in the substrate.

[0022] Further preferably, the method further comprises a data transmission step of transmitting the data collected by the sensor module to the data processing module in real time through a communication module, the communication module adopts a LoRa wireless communication protocol, the transmitting end of the communication module is electrically connected to the signal output end of the sensor module, the receiving end of the communication module is electrically connected to the signal input end of the data processing module, and the communication module is further used for transmitting the calculation result of the data processing module to a terminal device through a wireless link.

[0023] Further preferably, the method further comprises a model optimization step of storing historical environmental data, biological species data, and calculation model parameters of the coastal engineering area in a database module, calling the historical data in the database module by the data processing module, dynamically optimizing the parameter weight of the cascade effect model through a machine learning algorithm, and transmitting the optimized model parameters to a memory through a data bus to update the model program.

[0024] A system for analyzing the impact of a coastal engineering on biodiversity, applied to the method for analyzing the impact of a coastal engineering on biodiversity according to any one of the preceding embodiments, comprising a sensor module, a data processing module, a visualization module, and an early warning module.

[0025] The sensor module is used for collecting microtopography parameters, water salinity parameters, substrate parameters, and biological larva attachment parameters of the coastal engineering area, and the signal output end of the sensor module is electrically connected to the signal input end of the data processing module.

[0026] The data processing module is built-in with a microtopography-salinity-substrate cascade effect model, which is used for calculating the microtopography disturbance intensity, the salinity stratification intensity, the mineralization rate of the organic carbon in the substrate, the larva attachment success rate, and the biodiversity index according to the collected parameters, and the data output end of the data processing module is respectively connected to the input end of the visualization module and the early warning module.

[0027] The visualization module is used to display the biodiversity index and parameter influence path in a graphical manner; the early warning module is provided with a biodiversity index threshold value, and when the received biodiversity index is lower than the threshold value, an early warning signal is output through the built-in sound and light alarm unit.

[0028] Technical effects:

[0029] The technical point of the present application is to construct a micro-topography salinity substrate cascade effect model, and to realize dynamic conduction of parameters through progressive formulas, covering the collection and quantification of hidden parameters such as micro-topography, salinity, etc. The technical solution solves the main problem that the existing technology in the background technology cannot quantify the hidden cascade effect of micro-topography disturbance salinity stratified substrate carbon cycle, can capture the deep impact of engineering on biodiversity, and improves the scientificity and accuracy of evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 A flow chart of a method for analyzing the influence of a coastal engineering on biodiversity is provided in the present application.

[0031] Figure 2 A block diagram of a system for analyzing the influence of a coastal engineering on biodiversity is provided in the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0033] In the traditional analysis of the influence of coastal engineering on biodiversity, the existing technology focuses on macro factors and ignores the hidden influence of micro-topography salinity substrate cascade effect, resulting in the inability to quantify the progressive effect of engineering on the microenvironment, and the parameters collected are not comprehensive, the calculation model lacks dynamic conduction, and finally the evaluation result is not accurate enough, which is difficult to support engineering ecological optimization.

[0034] Based on this, please refer to Figure 1 The present embodiment provides a method for analyzing the influence of a coastal engineering on biodiversity, comprising the following steps:

[0035] S1: Real-time collection of micro-topography parameters, water salinity parameters, substrate parameters and biological larva attachment parameters in the coastal engineering area through a sensor module.

[0036] The micro-topography parameters include engineering structure height, median diameter of silt, tidal period and average water depth of intertidal zone, the water salinity parameters include initial salinity and water temperature before engineering, the substrate parameters include initial organic carbon content and porosity of substrate, and the biological larva attachment parameters include effective area of larva attachment base;

[0037] S2: input the collected parameters into a data processing module, and the data processing module sequentially calculates the microtopographic disturbance intensity, the salinity stratification intensity, the organic carbon mineralization rate of the bottom material, and the larva attachment success rate based on a microtopographic salinity bottom material cascade effect model,

[0038] S3: the cascade effect model realizes dynamic conduction among the parameters through progressive formulas; the data processing module calculates the biodiversity index according to the larva attachment success rate and the regional original species base, and combines the environmental resistance coefficient to calculate the influence of the coastal engineering on the biodiversity;

[0039] S4: the data processing module transmits the calculation results to a visualization module, and the visualization module displays the biodiversity index and the influence path of each parameter in a graphical manner.

[0040] The scheme covers hidden factors such as microtopography and salinity through multi-dimensional parameter collection, realizes dynamic conduction of parameters by means of a cascade effect model, solves the problem of insufficient quantification of hidden influences in traditional technologies, can completely capture the conduction chain of engineering parameters, microenvironment and biological processes, provides technical support for accurate assessment of the influence of biodiversity, and visualized display is convenient for intuitive understanding of the influence logic of each parameter, and provides clear basis for ecological optimization of engineering.

[0041] In the monitoring of coastal engineering, the traditional sensor module has the problems of single type of monitoring equipment, non-uniform data output form, insufficient parameter collection pertinence, etc., resulting in low microtopographic data precision, untimely salinity and bottom material parameter collection, fuzzy biological larva attachment state record, and inability to provide reliable original data for subsequent calculation.

[0042] Therefore, in the analysis method, the sensor module includes a laser topographic scanner, a salinity sensor, a bottom material sampler, and a biological image acquisition device, the laser topographic scanner is used to obtain the engineering structure height and the intertidal zone microtopographic data, the salinity sensor monitors the water salinity and temperature in real time and outputs an analog signal, the bottom material sampler collects bottom material samples and analyzes the organic carbon content and porosity through the built-in detection unit, and the biological image acquisition device records the distribution and state of the larva attachment base through a high-definition camera and generates a digital image signal.

[0043] The scheme configures special sensors for different parameter characteristics, the laser terrain scanner ensures high precision of micro-topographic data, the salinity sensor outputs analog signals for subsequent conversion processing, the bottom sampler has a built-in detection unit to realize on-site rapid analysis, the biological image acquisition device ensures clear recording of larval state through a high-definition camera, solves the problem of single monitoring dimension and insufficient data reliability of traditional sensors, realizes accurate and timely collection of multiple types of parameters, provides high-quality raw data input for the data processing module, and lays a foundation for data interaction between modules.

[0044] The existing data processing module has problems such as lack of coordination of hardware configuration, loss of precision caused by direct processing of analog signals, confusion of data storage and calling, etc. in the analysis of coastal engineering biodiversity, and cannot efficiently process multi-type sensor data, nor can it ensure the accuracy of cascade effect model calculation, affecting the reliability of biodiversity index.

[0045] Therefore, in the analysis method, the data processing module includes a microprocessor, a memory and an analog-to-digital converter, the analog-to-digital converter converts the analog signal output by the sensor module into a digital signal and transmits it to the microprocessor, the microprocessor calls the cascade effect model program stored in the memory to process and calculate the digital signal and the digital image signal output by the biological image acquisition device, and the memory is also used to temporarily store the collected data and the calculation results.

[0046] The scheme solves the problem of low precision of analog signal processing through an analog-to-digital converter, converts the analog signal such as salinity into a digital signal, and then processes it with a microprocessor, improving data processing precision; the microprocessor and the memory work together to ensure stable calling of the cascade effect model program and orderly storage of data, avoiding data confusion; the clear division of labor of hardware modules and signal interaction logic solve the problem of insufficient hardware coordination of traditional processing modules, realize efficient processing and calculation of multi-type data, and ensure the calculation accuracy of intermediate results such as micro-topographic disturbance intensity and salinity stratification intensity, and biodiversity index, providing reliable data support for subsequent visualization.

[0047] The traditional visualization module has problems such as weak data conversion capability, single display form, disordered management of historical data, etc. in the analysis of coastal engineering biodiversity, cannot intuitively present the biodiversity index and parameter influence path, and is difficult to realize effective tracing of historical data, affecting the analysis and summary of the influence law of the project.

[0048] Based on this, the visualization module in the analysis method includes a graphics processor, a display, and a data storage unit. The graphics processor receives the calculation results output by the data processing module, converts the biodiversity index into three-dimensional spatial distribution graph data, and converts the parameter change trend into line graph data. The display is used to display the above-mentioned graph data. The data storage unit is connected with the graphics processor through a data interface to store historical monitoring data and calculation results.

[0049] The scheme realizes accurate conversion of data to graphics through the graphics processor, converts the abstract biodiversity index into a three-dimensional spatial distribution graph, and converts the parameter change trend into a line graph, solving the problem of single display form and insufficient intuitiveness of traditional modules. The display clearly presents the graph data, making it easy to quickly grasp the biodiversity distribution and parameter influence law. The data storage unit realizes ordered storage of historical data through interface connection, solving the problem of disordered management of historical data, realizing data traceability, providing convenience for long-term analysis of the influence trend of coastal engineering on biodiversity, and improving the application value of the analysis results.

[0050] The prior art cannot quantify the hidden factor of microtopographic disturbance when evaluating the influence of coastal engineering on biodiversity, lacks a correlation model between engineering structure parameters and microtopographic changes, and cannot determine the specific disturbance degree of engineering on intertidal microtopography, thereby affecting the analysis of subsequent salinity stratification, bottom carbon cycle, and other progressive influences, making the biodiversity evaluation lack a microscopic basis.

[0051] Based on this, the microtopographic disturbance intensity in the analysis method is calculated by the following formula:

[0052] ;

[0053] In the formula, H is the height of the coastal engineering structure, k is the roughness coefficient of the engineering material, is the median particle size of the intertidal sediment, is the tidal period, is the average water depth of the intertidal zone; D is the microtopographic disturbance coefficient, and the larger the value of D, the more significant the difference between the microtopography.

[0054] The formula quantifies the dynamic disturbance degree of coastal engineering on intertidal microtopography through multi-dimensional parameter coupling. The core design logic is based on the combination of the terrain-water flow interaction principle in fluid mechanics and the periodicity of tides. The physical meaning and synergistic effect of each parameter in the formula are as follows:

[0055] The height of the engineering structure H is the basic driving parameter of the formula, which represents the vertical height of the coastal engineering such as the dam and the revetment, and directly determines the velocity gradient of the water flow. When H increases, the shear stress of the water flow on the surface of the engineering structure is enhanced, which leads to the decrease of the incipient velocity of the sediment particles, thus intensifying the alternation of the erosion and deposition of the terrain.

[0056] For example, during the tidal fluctuation, the higher dam will form a stronger water-logging effect, which increases the turbulence intensity of the water flow in the nearshore area, and promotes the resuspension and redistribution of the sediment.

[0057] The roughness coefficient k of the engineering material is a dimensionless parameter, which is in the range of 0-1, and reflects the roughness of the surface of the engineering structure. The roughness affects the sediment transport by changing the flow characteristics of the boundary layer of the water flow: when k tends to 1, such as the uneven concrete blocks, more vortexes are formed on the surface of the structure, which enhances the local turbulent energy and further increases the probability of the incipient motion of the sediment particles; on the contrary, when k tends to 0, such as the smooth metal plate, the water flow is mainly in the form of laminar flow, and the disturbance to the terrain is weak. The introduction of this parameter enables the formula to distinguish the differential influence of different engineering materials on the microtopography.

[0058] The median particle size of the intertidal sediment wherein represents the average size of the sediment particles, which is involved in the calculation of the formula through the square root term The larger the sediment particles, the higher the critical incipient velocity, so under the same water flow conditions, the larger the D, the more difficult the sediment is transported, and the disturbance intensity D of the terrain decreases accordingly.

[0059] For example, when the median particle size of the sediment increases from 0.1 mm to 1 mm, from 0.316 to 1, which leads to the denominator term The inhibitory effect of D is enhanced, and finally the value of D is reduced by about 68%.

[0060] The tidal period and the average water depth of the intertidal zone , reflect the time interval of the tidal fluctuation, and the reciprocal of which is positively correlated with the periodic scouring frequency of the water flow.

[0061] For example, the scouring frequency of the semidiurnal tide is twice that of the diurnal tide , so under the same H and k conditions, the value of D in the semidiurnal tide area will increase significantly.

[0062] The exponential term The water flow adjusts the depth of the impact on the terrain. When the water depth increases, more energy of the water flow is absorbed by the water body, and the direct disturbance to the bottom terrain is weakened.

[0063] For example, when From 1 m to 10 m, From 1 to 2.0, the denominator increases, and the D value decreases by about 50%.

[0064] The sine fluctuation term This dynamic compensation term is used to simulate the modulation effect of the tidal periodicity on the micro-terrain disturbance. Among them, is the dimensionless phase angle, reflecting the matching degree of the engineering height and the tidal period. When H and satisfy a certain proportional relationship, such as , the sine function reaches the maximum value 0.05, and at this time the scouring-silting cycle effect of the tidal fluctuation on the terrain is the strongest. For example, at high tide, the water flows over the dam to form strong scouring, while at low tide, the sediment is quickly deposited in the still water area, and this periodic change is quantitatively reflected by the positive and negative fluctuations of the sine term.

[0065] The formula quantifies the micro-terrain disturbance intensity D as a dimensionless value between 0 and 2 through the composite structure of (engineering parameter x sediment characteristics) / (tidal characteristics x water depth adjustment) + tidal phase modulation. When D tends to 2, it indicates that the terrain concave-convex difference caused by the engineering structure reaches a significant level, and complex landforms such as deep grooves and sand dams may be formed; when D tends to 0, the terrain disturbance can be ignored. Those skilled in the art can obtain H and by laser terrain scanner, determine d s by sediment sieving experiment, and obtain combined with tidal prediction data, so as to realize the actual calculation of the formula.

[0066] The derivation of the formula is based on the combination of Shields' incipient curve in fluid mechanics and tidal dynamics model, ensuring its implementability in the field of coastal engineering.

[0067] The formula builds a quantitative model of engineering micro-terrain disturbance by coupling engineering structure parameters H, k and natural environment parameters , , , solving the problem of traditional technology that cannot quantify micro-terrain disturbance; the sine term in the formula considers the dynamic influence of tidal periodicity on micro-terrain, improving the dynamics and accuracy of the calculation; the clear physical meaning of D value makes the micro-terrain disturbance degree directly measurable, providing a key input parameter for the subsequent calculation of salinity stratification intensity, laying a micro foundation for the cascade effect analysis, making the biodiversity assessment from macro to micro, and enhancing the scientificity and accuracy of the assessment.

[0068] Traditional techniques for calculating salinity stratification intensity have the following technical problems: they do not consider the driving effect of micro-topographic disturbance on salinity stratification, estimate salinity based on a single parameter such as initial salinity or water depth, ignore the dynamic relationship between micro-topographic undulation differences and salinity vertical distribution, and do not incorporate the influence of temperature on salinity stratification. As a result, the calculation results cannot reflect the actual salinity stratification state and cannot provide a reliable basis for subsequent sediment carbon cycle analysis.

[0069] Based on this, in the aforementioned analysis method, the salinity stratification intensity is calculated based on the intensity of micro-topographic disturbance, as shown in the following formula:

[0070] ;

[0071] In the formula, S0 represents the initial salinity before engineering, T represents the water temperature, and S represents the salinity stratification intensity. A larger S value indicates a more significant difference in salinity between the upper and lower water layers. This scheme uses the micro-topographic disturbance intensity D as the core driving parameter, combined with water depth... This study quantifies the impact of micro-topography on salinity stratification and introduces an exponential term for temperature T to reflect the regulatory effect of temperature on water density and thus on salinity stratification. This solves the problem that traditional calculation methods ignore the coupling effect between micro-topography and temperature.

[0072] Based on the relationship between salinity, temperature, and density in oceanography, this formula innovatively uses micro-topographic disturbance D and temperature T as core driving parameters to quantify the dynamic changes in vertical salinity differences in water bodies.

[0073] The formula's design logic is as follows: The initial salinity S0 before the project is used as a baseline parameter, representing the natural salinity state before construction. For example, in estuary areas, S0 may vary seasonally, being lower during the rainy season and higher during the dry season. However, the formula uses the product term of S0 as a fundamental factor to ensure that the calculation results are correlated with local natural conditions. Those skilled in the art can use a salinity sensor, such as the YSI6600, to sample at multiple points in the project area and take the average value as the initial value of S0.

[0074] Micro-topographic disturbance intensity D and water depth Coupling effect The term is the core innovation of the formula. D affects the mixing efficiency of water flow by altering the degree of topographic relief: as D increases, the topographic undulation intensifies, leading to stronger turbulent mixing of water flow between channels and sand ridges, thus weakening salinity stratification. However... The introduction of [a specific technology] modulates the depth of this effect: in shallow water areas, such as... , At this point, the influence of D is amplified; while in deep water, such as , The influence of D is suppressed.

[0075] For example, when D=1 and hour, This makes the salinity stratification intensity S more than the reference value. Increase by 100%; while when At that time, the value only increased by 50%.

[0076] The formula for the exponential adjustment term of temperature T is obtained through This reflects the nonlinear effect of temperature on salinity stratification. T+273 converts Celsius temperature to Kelvin temperature to ensure the physical meaning of the exponential calculation. As temperature increases, water density decreases, weakening the stability of salinity stratification, i.e., S decreases. For example,

[0077] When temperature (T) increases from 10°C to 20°C, 1 / (T+273) decreases from 0.0033 to 0.0032, and the exponential term increases from 0.9967 to 0.9968, resulting in a decrease in the salinity (S) value of approximately 0.1%. Although this change seems small, its cumulative effect can significantly alter the salinity stratification state over long-term monitoring. The formula quantifies the salinity stratification intensity (S) to a per-th order value using a structure of initial salinity × (micro-topography-depth coupling effect) × temperature exponent correction. An increase in the S value indicates a greater salinity difference between the upper and lower water layers, potentially leading to the formation of a halocline and inhibiting the exchange of substances between the sediment and the water. Those skilled in the art can simultaneously obtain S0 and T using a CTD profiler and combine this with data obtained from a laser topography scanner. The value enables real-time calculation of the formula. The derivation of the formula is based on Turner's layered stability theory in oceanography. The traditional model is improved by introducing a micro-topographic disturbance parameter D to ensure its applicability in coastal engineering scenarios.

[0078] The explicit physical meaning of each parameter in the formula, such as S0 as the initial salinity benchmark, ensures a clear calculation logic. The quantitative result of the S value can be directly used to determine the degree of salinity stratification, providing a key input for calculating the mineralization rate of organic carbon in the substrate. This transforms salinity stratification analysis from static description to dynamic quantification, enhancing the ability to capture changes in the microenvironment.

[0079] Traditional techniques for calculating the rate of organic carbon mineralization in the substrate have the following technical problems: they do not consider the inhibitory effect of salinity stratification on the exchange of substances between the substrate and the water, and rely solely on static estimation based on the characteristics of the substrate itself. They neglect the synergistic effects of salinity stratification, micro-topographic disturbance, and temperature on the mineralization process, resulting in a large deviation between the calculated results and the actual carbon cycle state, and failing to accurately reflect the food supply capacity of benthic organisms.

[0080] Based on this, in the analytical method, the mineralization rate of the substrate organic carbon is calculated based on the salinity stratification intensity, as shown in the following formula:

[0081] ;

[0082] where C is the initial organic carbon content of the substrate, and P is the porosity of the substrate; M is the organic carbon mineralization rate of the substrate, and the higher the value of M, the greater the amount of organic carbon mineralized by the substrate. The scheme couples the salinity stratification intensity S, the microtopographic disturbance intensity D, the temperature T, and the characteristics C and P of the substrate through the formula, wherein the salinity stratification is reflected by the term (30-S) to reflect its influence on the oxygen supply of the substrate, the stronger the salinity stratification, the weaker the oxygen exchange, and the mineralization rate decreases; the temperature is reflected by the term 1+0.03T to reflect its promoting effect on the microbial activity; the microtopographic disturbance is reflected by the term -0.02D to reflect its indirect influence on the structure of the substrate, thereby solving the problem of single parameter and ignoring the synergistic effect of multiple factors in the traditional calculation method.

[0083] The value of M has a clear physical meaning, i.e., the mineralization amount per kilogram of substrate per day, which can be directly related to the food supply of benthic organisms, providing a reliable basis for the calculation of the success rate of juvenile attachment, and making the analysis of substrate carbon cycle more in line with the actual ecological process.

[0084] The formula combines the principles of microbial ecology and biogeochemistry, and innovatively takes the salinity stratification S, the temperature T, and the microtopographic disturbance D as synergistic influencing factors to quantify the degradation rate of organic carbon in the substrate.

[0085] The initial organic carbon content C and the porosity P of the substrate, C represents the total amount of organic carbon that can be decomposed by microorganisms in the substrate, and is the material basis for the mineralization rate.

[0086] For example, in mangrove sediments, C can be as high as 50 g / kg, while in sandy substrates, it can be less than 10 g / kg.

[0087] P reflects the degree of void between particles in the substrate, and indirectly regulates the mineralization process by affecting the transmission efficiency of oxygen and nutrients. When P increases, the permeability of the substrate increases, promoting the aerobic respiration of microorganisms, thereby accelerating the mineralization rate. For example, when P increases from 30% to 50%, the mineralization rate can increase by about 20%.

[0088] Inhibitory effect of salinity stratification intensity S The term is the core innovation point of the formula.

[0089] wherein 30-S reflects the inhibitory degree of salinity stratification on the material exchange between the substrate and the water body: when S increases, i.e., the salinity stratification increases, 30-S decreases, resulting in a decrease in the value of the factor, and the mineralization rate M decreases accordingly.

[0090] For example, when , 30-S=10, the value of the factor is 0.3, and the mineralization rate is 3 times the baseline value ; while when When the factor value drops to 0.1, the mineralization rate is equal to the baseline value. This design reflects the condition of high salinity stratification, where the dissolved oxygen in the pore water of the substrate is limited, forcing the microorganisms to turn to the anaerobic metabolic pathway with lower efficiency.

[0091] The promoting effect of temperature T (1+0.03T) term is based on the Arrhenius equation, which quantifies the impact of temperature on microbial enzyme activity. When T increases by 10°C, the microbial metabolic rate usually increases by about 2-3 times, i.e., the Q10 effect.

[0092] The coefficient of 0.03 in the formula is a linear approximation of this effect, for example, when T increases from 10°C to 20°C, 0.03T increases from 0.3 to 0.6, resulting in a 30% increase in mineralization rate. The introduction of this parameter enables the formula to dynamically reflect the impact of seasonal changes on substrate carbon cycling.

[0093] The indirect effect of microtopographic disturbance D -0.02D term reflects the inhibitory effect of microtopographic disturbance on mineralization rate. When D increases, the mechanical disturbance of substrate particles increases, which may destroy the spatial structure of microbial communities, leading to reduced enzyme activity.

[0094] For example, when D=1, the mineralization rate decreases by ; when D=2, it decreases by . This effect is particularly significant in the early stages of engineering construction, but the impact will weaken as the substrate gradually stabilizes.

[0095] The formula quantifies the mineralization rate M as a series value through the organic carbon storage x porosity x (salinity-temperature synergistic effect); the structure loss by topographic disturbance.

[0096] The skilled person can determine C by elemental analysis, P by mercury porosimetry, and S by a salinity sensor to realize the actual calculation of the formula. The derivation of the formula is based on the Monod growth model in microbial ecology, which improves the traditional model by introducing salinity and topographic parameters to ensure its implementability in coastal engineering scenarios.

[0097] The traditional data transmission method has the following technical problems in coastal engineering biodiversity monitoring: lack of clear communication protocol and hardware connection logic, data transmission is easily affected by complex coastal environments such as tides and electromagnetic interference, sensor data cannot be transmitted to the data processing module in real time, and the stability of remote transmission of calculation results is insufficient, leading to monitoring lag and affecting timely response to biodiversity changes.

[0098] Based on this, in the analysis method, the method further comprises a data transmission step: transmitting the data collected by the sensor module to the data processing module in real time through the communication module, the communication module adopts the LoRa wireless communication protocol, the transmitting end of the communication module is electrically connected with the signal output end of the sensor module, the receiving end is electrically connected with the signal input end of the data processing module, and the communication module is also used for transmitting the calculation result of the data processing module to the terminal device through the wireless link.

[0099] The scheme clearly adopts the LoRa wireless communication protocol, uses the characteristics of low power consumption and strong anti-interference ability to adapt to the complex environment of the coast, and ensures the stability of the signal transmission path through the hardware interaction logic of the electrical connection between the transmitting end and the sensor module and the electrical connection between the receiving end and the data processing module. At the same time, it covers the bidirectional transmission demand of sensor data uploading and calculation result downloading, and solves the problems of unclear protocol, poor anti-interference ability and untimely transmission of traditional transmission methods.

[0100] Real-time uploading of sensor data is realized, which ensures that the data processing module can calculate based on the latest data; stable remote transmission of calculation results enables the terminal device to obtain the biodiversity index in time, which provides support for the timeliness of engineering ecological evaluation, and the clear hardware connection relationship makes the scheme highly implementable.

[0101] In the analysis of coastal engineering biodiversity using the traditional cascade effect model, the following technical problems exist: the model parameters are fixed and cannot be dynamically adjusted according to the long-term changes of the coastal environment, such as changes in tidal rules and evolution of bottom characteristics, leading to a gradual deviation of the calculation results from the actual situation, and there is no mechanism for checking and optimizing the model based on historical data, affecting the long-term reliability of the biodiversity index.

[0102] Based on this, in the analysis method, the method further comprises a model optimization step: the database module stores the historical environmental data, biological species data and calculation model parameters of the coastal engineering area, the data processing module calls the historical data in the database module, and dynamically optimizes the parameter weight of the cascade effect model through a machine learning algorithm. The optimized model parameters are transmitted to the storage through the data bus to update the model program.

[0103] The scheme stores historical data through the database module system to provide a data basis for model optimization; the data processing module calls the historical data and combines a machine learning algorithm to dynamically adjust the parameter weight of the cascade effect model instead of fixing the parameters; the optimized parameters are transmitted to the storage through the data bus to update the program, forming a closed-loop optimization logic, which solves the problem of fixed parameters in traditional models that cannot adapt to environmental changes.

[0104] The cascade effect model can be self-optimized with the change of the environment, the long-term accuracy of the calculation of the micro-topographic disturbance intensity, the salinity stratification intensity and the like is improved, the accumulation and calling of the historical data provide reliable basis for the model optimization and blind adjustment is avoided, the clear parameter updating path ensures that the optimized model can take effect immediately, and the long-term reliability of the calculation of the biodiversity index is supported.

[0105] The traditional coastal engineering biodiversity impact analysis system has the following technical problems: the system module composition is fuzzy, the function division and connection relationship of the sensor module, the data processing module and the like are not clear, the system cannot form a correspondence with the analysis method, and lacks collaborative design with the method scheme, so that the system cannot stably realize the biodiversity impact analysis, and the signal interaction logic between the modules is missing, affecting the effective calculation of the cascade effect.

[0106] Based on this, please refer to Figure 2 The embodiment provides a coastal engineering biodiversity impact analysis system, which comprises a sensor module, a data processing module, a visualization module and an early warning module; the sensor module is used for collecting micro-topographic parameters, water salinity parameters, bottom material parameters and biological larva attachment parameters in a coastal engineering area, and a signal output end of the sensor module is electrically connected with a signal input end of the data processing module; the data processing module is internally provided with a micro-topographic salinity bottom material cascade effect model, which is used for calculating micro-topographic disturbance intensity, salinity stratification intensity, bottom material organic carbon mineralization rate, larva attachment success rate and a biodiversity index according to the collected parameters, and a data output end of the data processing module is connected with input ends of the visualization module and the early warning module; the visualization module is used for displaying the biodiversity index and parameter influence path in a graphical manner; and the early warning module is provided with a biodiversity index threshold value, and outputs an early warning signal through an internally provided sound-light alarm unit when the received biodiversity index is lower than the threshold value.

[0107] The scheme clearly defines the hardware composition and functions of each module, such as the parameter collection of the sensor module and the internally provided cascade effect model of the data processing module, and ensures the collaborative work of the modules through the electrical connection and data interaction logic of the sensor module, the data processing module, the visualization module and the early warning module; the core lies in the internally provided cascade effect model, which solves the problems of fuzzy system module in the traditional system and insufficient collaboration with the method.

[0108] The hardware support for the above-mentioned method is realized, each module completes parameter collection, calculation, display and early warning according to the logical division; the clear connection relationship and signal interaction enable the system to stably operate, and guarantee the orderly development of the biodiversity impact analysis; the addition of the early warning module expands the system function, and enables the biodiversity to be timely warned when it is lower than the threshold value, thereby providing support for ecological risk prevention and control.

[0109] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application in other forms. Any skilled person in the art can modify or change the disclosed technical content into equivalent embodiments with equivalent changes, and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application, without departing from the technical solution content of the present application, still falls within the protection scope of the present application.

Claims

1. A method for analyzing the impact of coastal engineering on biodiversity, characterized in that, include: S1: Real-time acquisition of micro-topographic parameters, water salinity parameters, bottom sediment parameters, and organism larval attachment parameters of the coastal engineering area through sensor modules; The micro-topographic parameters include the height of the engineering structure, the median particle size of the sediment, the tidal cycle and the average water depth in the intertidal zone; the water salinity parameters include the initial salinity and water temperature before the project; the substrate parameters include the initial organic carbon content and porosity of the substrate; and the biological larvae attachment parameters include the effective area of ​​the larval attachment substrate. S2: Input the collected parameters into the data processing module. The data processing module is based on the micro-topography salinity bottom sediment cascade effect model and calculates the micro-topography disturbance intensity, salinity stratification intensity, bottom sediment organic carbon mineralization rate and larval attachment success rate in sequence. S3: The cascade effect model realizes the dynamic transmission between parameters through a progressive formula; the data processing module calculates the biodiversity index based on the larval attachment success rate and the original species population in the region, combined with the environmental resistance coefficient, to quantify the impact of coastal engineering on biodiversity. S4: The data processing module transmits the calculation results to the visualization module, which graphically displays the biodiversity index and the impact paths of each parameter.

2. The method for analyzing the impact of coastal engineering on biodiversity according to claim 1, characterized in that, The sensor module includes a laser topography scanner, a salinity sensor, a sediment sampler, and a biological image acquisition device. The laser topography scanner is used to acquire data on the height of engineering structures and intertidal micro-topography. The salinity sensor monitors water salinity and temperature in real time and outputs analog signals. The sediment sampler collects sediment samples and analyzes organic carbon content and porosity through a built-in detection unit. The biological image acquisition device records the distribution and state of larval attachment substrates through a high-definition camera and generates digital image signals.

3. The method for analyzing the impact of coastal engineering on biodiversity according to claim 1, characterized in that, The data processing module includes a microprocessor, a memory, and an analog-to-digital converter. The analog-to-digital converter converts the analog signals output by the sensor module into digital signals and transmits them to the microprocessor. The microprocessor calls the cascade effect model program stored in the memory to process and calculate the digital signals and the digital image signals output by the biological image acquisition device. The memory is also used to temporarily store the acquired data and calculation results.

4. The method for analyzing the impact of coastal engineering on biodiversity according to claim 1, characterized in that, The visualization module includes a graphics processor, a display, and a data storage unit. The graphics processor receives the calculation results output by the data processing module, converts the biodiversity index into three-dimensional spatial distribution graphic data, and converts the changing trends of various parameters into line graph data. The display is used to display the above graphic data. The data storage unit is connected to the graphics processor through a data interface to store historical monitoring data and calculation results.

5. The method for analyzing the impact of coastal engineering on biodiversity according to claim 1, characterized in that, The intensity of the micro-topography disturbance is calculated using the following formula: ； In the formula, H is the height of the coastal engineering structure, and k is the roughness coefficient of the engineering material. This represents the median grain size of intertidal sediment. For the tidal cycle, denoted as , where is the average water depth in the intertidal zone; D is the micro-topographic disturbance coefficient.

6. The method for analyzing the impact of coastal engineering on biodiversity according to claim 5, characterized in that, The salinity stratification intensity is calculated based on the micro-topographic disturbance intensity using the following formula: ； In the formula, S0 is the initial salinity before the project, T is the water temperature, and S is the salinity stratification intensity.

7. The method for analyzing the impact of coastal engineering on biodiversity according to claim 6, characterized in that, The mineralization rate of organic carbon in the substrate is calculated based on the salinity stratification intensity, using the following formula: ； In the formula, C is the initial organic carbon content of the substrate, P is the substrate porosity, and M is the substrate organic carbon mineralization rate.

8. The method for analyzing the impact of coastal engineering on biodiversity according to claim 1, characterized in that, The method further includes a data transmission step: transmitting the data collected by the sensor module to the data processing module in real time through the communication module. The communication module adopts the LoRa wireless communication protocol, and its transmitter is electrically connected to the signal output terminal of the sensor module, and its receiver is electrically connected to the signal input terminal of the data processing module. The communication module is also used to transmit the calculation results of the data processing module to the terminal device through a wireless link.

9. The method for analyzing the impact of coastal engineering on biodiversity according to claim 1, characterized in that, The method also includes a model optimization step: the database module stores historical environmental data, biological species data and calculation model parameters of the coastal engineering area, the data processing module calls the historical data in the database module, and dynamically optimizes the parameter weights of the cascade effect model through machine learning algorithms. The optimized model parameters are transmitted to the memory through the data bus to update the model program.

10. An analysis system for the impact of coastal engineering on biodiversity, applied to the analysis method for the impact of coastal engineering on biodiversity as described in any one of claims 1-9, characterized in that, include: Sensor module, data processing module, visualization module, and early warning module; The sensor module is used to collect micro-topographic parameters, water salinity parameters, bottom sediment parameters, and organism larval attachment parameters of the coastal engineering area. Its signal output terminal is electrically connected to the signal input terminal of the data processing module. The data processing module has a built-in micro-topography salinity-substrate cascade effect model, which is used to calculate the micro-topography disturbance intensity, salinity stratification intensity, substrate organic carbon mineralization rate, larval attachment success rate and biodiversity index based on the collected parameters. Its data output end is connected to the input end of the visualization module and the early warning module, respectively. The visualization module is used to graphically display the biodiversity index and the impact path of parameters. The early warning module is preset with a biodiversity index threshold. When the received biodiversity index is lower than the threshold, an early warning signal is output through the built-in audible and visual alarm unit.

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