A method and system for evaluating the vulnerability of a beach, and a medium

Abstract

The present application relates to a kind of barrier island vulnerability evaluation method, system and medium, method includes S1.Multiple-source data perception, obtains hydrology, geology, ecology and interference data;S2.Dynamically partitioned and index construction, according to water level variation range barrier island is divided into constant exposure area, drawdown area and constant flooding area, constructs index system;S3.Physical parameter inversion, based on measured data inversion key physical parameters;S4.Mechanism model quantification, using nonlinear physical response model calculates each single index vulnerability;S5.Comprehensive weighted evaluation, combined with AHP weight, calculate comprehensive vulnerability index and grade.The present application realizes the accurate, dynamic classification of barrier island vulnerability by constructing nonlinear response model based on physical process.

Description

Technical Field

[0001] This invention relates to the field of ecological environment assessment technology, specifically a method, system, and medium for assessing the vulnerability of sandbars. Background Technology

[0002] Shoals and shoals possess a unique dual nature, with their ecological stability constrained by hydrodynamic processes, soil erosion resistance, and vegetation energy dissipation. Traditional assessments are mostly used for terrestrial ecosystems (emphasizing vegetation and human activities) or river flood control (emphasizing flood discharge capacity), lacking a specific assessment system for the characteristics of the shoal-shoal transition zone.

[0003] Existing formulas are mostly empirical linear transformations (such as y = kx + b), which cannot reflect the interaction between water and soil (such as the hydraulic mechanism of bank collapse) and the nonlinear energy dissipation mechanism of vegetation.

[0004] Existing technologies often use annual averages for evaluation, which masks the extreme vulnerability during periods of rapid flood rise and fall (the most dangerous moments), resulting in evaluation results that are "peak-shaving and valley-filling" and have low sensitivity.

[0005] Existing technologies can only assess "how much damage has already occurred" and cannot predict "the risk of impending damage" based on real-time monitoring parameters (such as soil dielectric constant and flow velocity). Summary of the Invention

[0006] The purpose of this invention is to provide a method, system and medium for assessing the vulnerability of sandbars, which breaks through the limitations of traditional single hydrological or water quality assessments. It introduces specific indicators such as "inundation tolerance coefficient" and "soil dielectric constant" to reflect the ecological health status of sandbars more realistically and comprehensively.

[0007] To achieve the above objectives, in a first aspect, the present invention provides a method for assessing the vulnerability of sandbars, comprising the following specific steps:

[0008] S1. Multi-source data sensing to acquire hydrological, geological, ecological, and disturbance data;

[0009] S2. Dynamic zoning and indicator construction: Based on the range of water level changes, the sandbars are divided into the exposed zone, the drawdown zone, and the submerged zone, and an indicator system is constructed.

[0010] S3. Physical parameter inversion: Inversion of key physical parameters based on measured data;

[0011] S4. Mechanism model quantification: Calculate the vulnerability of each individual indicator using a nonlinear physical response model;

[0012] S5. Comprehensive weighted assessment, combining AHP weights, calculate the comprehensive vulnerability index and classify it.

[0013] The multi-source data sensing acquires hydrological, geological, ecological, and disturbance data, specifically hydrodynamic data, including the annual water level process line Z(t), especially the water level drop rate Vdrop during the flood receding period; and the nearshore velocity field distribution u(x,y).

[0014] Soil data was collected to obtain soil particle size distribution curves and to obtain real-time variation θ(t) of soil volumetric water content using a dielectric constant sensor.

[0015] Vegetation data were used to obtain vegetation biomass B and root depth distribution at different elevation zones.

[0016] The dynamic zoning and indicator construction divides the sandbars into perpetually exposed areas, drawdown areas, and perpetually submerged areas based on the range of water level fluctuations. The specific indicator system is as follows:

[0017] Establish an evaluation index system and construct an evaluation system comprising four dimensions:

[0018] Hydrodynamic factors include the pore water pressure dissipation hysteresis index and flow shear vulnerability.

[0019] Vegetation ecological factors, including vegetation energy dissipation efficiency index and elevation-weighted resilience;

[0020] Soil environmental factors, including the shear strength attenuation coefficient under wet-dry cycles;

[0021] Human-induced interference factors should be eliminated, and traditional indicators such as development intensity and pollution load should be maintained.

[0022] The physical parameter inversion, based on measured data, specifically involves calculating intermediate physical parameters before evaluation.

[0023] Permeability coefficient K soil Based on the soil particle size characteristic value D10, the permeability coefficient K is estimated using the empirical formula Hazen. soil =C(D 10 ) 2 ,

[0024] C stands for an empirical coefficient, reflecting the influence of soil particle shape, density, and uniformity on permeability.

[0025] Value range: This coefficient is not fixed and usually fluctuates between 0.4 and 1.5.

[0026] Critical starting shear stress τ c Determine the critical stress at which soil particles begin to move based on the Shields curve.

[0027] The mechanism model is quantified, and the vulnerability of each individual index is calculated using a nonlinear physical response model.

[0028] (1) Mechanistic model of hydrodynamic factors

[0029] A. Quantification formula for the pore water pressure dissipation hysteresis index Ihydro:

[0030] in: This represents the measured maximum water discharge rate; Porosity of the soil; This is a dimensionless correction factor; The function is used to map large ratios to the 0-1 interval;

[0031] Quantification formula for coupling vulnerability of water flow shear resistance

[0032] ,

[0033] in This refers to the shear stress at the bottom of the water flow. This represents the critical resistance of the soil.

[0034] Control the steepness of the curve. This is the critical threshold coefficient;

[0035] (2) Mechanistic model of vegetation ecological factors

[0036] C. Elevation-weighted flood stress resilience index Quantification formula:

[0037] ,

[0038] Where: i represents the elevation zone; This refers to the coverage of the band. This refers to the duration of the inundation of this area over the years; The flooding stress attenuation factor;

[0039] D. Iveg's formula for quantifying vegetation energy dissipation efficiency index:

[0040] ,

[0041] in Aboveground biomass density.

[0042] (3) Mechanistic model of soil environmental factors

[0043] E. Quantification formula for the shear strength attenuation coefficient under wet-dry cycles:

[0044] ,

[0045] in These are soil damage parameters. They reflect the process by which the soil structure gradually loosens with increasing cycles of wetting and drying.

[0046] Step S5: Comprehensive weighted assessment, combining AHP weights, calculate the comprehensive vulnerability index and classify it. Specifically, use the AHP method to determine the weight W of each dimension, and combine it with the above quantitative score Si to calculate the comprehensive vulnerability index VI:

[0047] ,

[0048] Finally, based on The value is to divide the shoals into: stable zone, slightly vulnerable zone, moderately vulnerable zone, and extremely vulnerable zone.

[0049] Secondly, the present invention provides a beach vulnerability assessment system, the system comprising: a memory and a processor, the memory including a program for a beach vulnerability assessment method, wherein when the program for the beach vulnerability assessment method is executed by the processor, the steps of a beach vulnerability assessment method as described above are implemented.

[0050] Thirdly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a beach vulnerability assessment method as described above.

[0051] The beneficial effects of using the above embodiments are:

[0052] The evaluation system is comprehensive and targeted: It breaks through the limitations of traditional single hydrological or water quality evaluation, and introduces specific indicators such as "inundation tolerance coefficient" and "soil dielectric constant" to reflect the ecological health status of the sandbars more realistically and comprehensively.

[0053] The quantitative methods are scientific and objective: By constructing specific linear or piecewise quantitative formulas (such as specific attenuation formulas designed for inundation frequency, water level fluctuation, etc.), the fuzzy qualitative descriptions are replaced, the evaluation results are standardized and reproducible, and the evaluation accuracy is improved.

[0054] Precise weight allocation: An improved analytic hierarchy process was adopted and a rigorous consistency test (CR<0.1) was conducted to ensure the dominant position of core driving factors such as hydrodynamics and vegetation ecology in the evaluation, which is consistent with the evolution of sandbars.

[0055] Strong applicability and reliable verification: The method has been verified in the Minjiang River estuary eel beach, and the evaluation results (48.98 points, vulnerability level) are highly consistent with the actual ecological degradation, proving the reliability of the model. Attached Figure Description

[0056] Figure 1This is a flowchart of a method provided in an embodiment of the present invention. Detailed Implementation

[0057] 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. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0058] It should be noted that the descriptions involving "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0059] Reference Figure 1 This embodiment of the invention provides a method for assessing the vulnerability of sandbars, comprising the following specific steps:

[0060] S1. Multi-source data sensing to acquire hydrological, geological, ecological, and disturbance data;

[0061] S2. Dynamic zoning and indicator construction: Based on the range of water level changes, the sandbars are divided into the exposed zone, the drawdown zone, and the submerged zone, and an indicator system is constructed.

[0062] S3. Physical parameter inversion: Inversion of key physical parameters based on measured data;

[0063] S4. Mechanism model quantification: Calculate the vulnerability of each individual indicator using a nonlinear physical response model;

[0064] S5. Comprehensive weighted assessment, combining AHP weights, calculate the comprehensive vulnerability index and classify it.

[0065] Example:

[0066] Assessment object: The drawdown zone of a typical cross-section of the eel beach at the Minjiang River estuary.

[0067] Data source: Based on publicly published ecological research literature on the Minjiang River estuary, measured data from hydrological stations, and empirical values ​​from geotechnical engineering.

[0068] Step S1: Multi-source data perception (input parameter table)

[0069] Before performing the calculations, we set a set of engineering parameters based on the real environment.

[0070] 1. Hydrodynamic data

[0071] Water level drop rate (Vdrop): Select data from the rapid ebb tide or the receding floodwaters.

[0072] Measured value: 0.15 m / h

[0073] Background: The average tidal range at the mouth of the Minjiang River is about 4.5m, and the water level changes drastically during low tide.

[0074] Nearshore current velocity ( ): The velocity of the current during flood season or high tide.

[0075] Measured value: 1.2 m / s.

[0076] Average water depth (h): The average depth of inundation on the beach.

[0077] Measured value: 2.5 m.

[0078] Roughness (n): Consider natural riverbeds with vegetation cover.

[0079] Value: 0.035 (Empirical engineering value).

[0080] 2. Soil data

[0081] Soil particle size characteristic value (D10): The particle size that passes through 10% of the soil particle size distribution.

[0082] Measured value: 0.005 mm (i.e. 5 × 10⁻⁶ m).

[0083] Basis: The sediments at Shanyutan are mainly composed of silt and fine sand, containing clay particles.

[0084] Soil porosity (p):

[0085] Measured value: 0.50 (typical value for wetland soil).

[0086] Number of wet-dry alternations (N):

[0087] Statistical value: 120 times / year (affected by semi-diurnal tides, frequent alternation of dry and wet conditions).

[0088] 3. Vegetation data

[0089] Vegetation type: Mixed zone of short-leaved bulrush and reeds.

[0090] Aboveground biomass density (Bmass):

[0091] Measured value: 1.8 kg / m²

[0092] Vegetation coverage (Ci): 85% (i.e., 0.85).

[0093] Annual flooding duration (Tsub): 2000 hours / year.

[0094] Steps S2 & S3: Physical parameter inversion (intermediate variable calculation)

[0095] Before substituting the mechanism model, several key intermediate physical parameters need to be calculated.

[0096] 1. Permeability coefficient (K) soil Estimate

[0097] According to the patent formula:

[0098] Selection of C value: For silty soil, take the empirical coefficient C≈100 (cm). -1 s -1 Alternatively, unit conversion can be performed under the International System of Units (SI).

[0099] This section uses the Hazen formula standard unit estimation (D) 10 The unit is mm, the result is cm / s, then convert to m / s):

[0100] (Note the dimensions here; typically, the Hazen formula...) D 10 Use cm, K is in cm / s).

[0101] Corrected calculation: D 10 =0.005mm=0.0005cm

[0102]

[0103] Results: The soil has low permeability, slow drainage, and is prone to generating excess pore water pressure.

[0104] 2. Bottom shear stress (τ) b )

[0105] formula:

[0106]

[0107]

[0108] molecular:

[0109] Denominator:

[0110]

[0111] 3. Critical starting shear stress (τ) c )

[0112] According to the Shields curve, for silt / fine sand (D 50 (≈0.05mm), its erosion resistance is relatively weak.

[0113] From the table / calculation, we get: τ c ≈0.2Pa (pure soil, before the reinforcement of vegetation and root system).

[0114] Step S4: Mechanism Model Quantification (Single Indicator Calculation)

[0115] Now we will substitute the above parameters into the nonlinear formula of the core of the patent.

[0116] (1) Hydrodynamic factors

[0117] A. Pore water pressure dissipation hysteresis index (I hydro )

[0118] formula:

[0119] Physical meaning: The faster the water recedes ( Larger, slower penetration ( (Small), the higher the lag index, the easier it is for the bank to collapse.

[0120] Parameter setting: Set the dimension correction factor (Used to balance orders of magnitude)

[0121] calculate:

[0122] ratio

[0123] Corrected parameter items

[0124] tanh(0.03336)≈0.0333

[0125]

[0126] Assessment: Extremely high risk. This indicates that the receding water rate is much faster than the drainage rate, making bank collapse extremely likely.

[0127] B. Water Flow Shear Susceptibility (Sscour)

[0128] formula:

[0129] Parameter: Set steepness Threshold coefficient (That is, the turning point is when the shear stress is 10 times the resistance).

[0130] Calculate: Stress ratio (The shear force of water flow far exceeds the resistance of soil.)

[0131] Index item index:

[0132]

[0133]

[0134] Assessment: Extremely fragile. Without vegetation protection, the soil would be washed away instantly.

[0135] (2) Vegetation ecological factors

[0136] C-weighted restoring power of elevation ( )

[0137] formula:

[0138] Parameter: Attenuation coefficient Weight (Single elevation zone)

[0139] calculate:

[0140] Index item:

[0141]

[0142] (Approximately 31.3 points after normalization)

[0143] Assessment: Low resilience due to prolonged submersion (2000 hours), resulting in significant stress on the vegetation.

[0144] D. Vegetation energy dissipation efficiency index ( )

[0145] formula:

[0146] Parameter: Full score coefficient saturation coefficient .

[0147] calculate:

[0148] Index item:

[0149]

[0150]

[0151] Evaluation: The vegetation has a good energy dissipation effect and a high biomass.

[0152] (3) Soil environmental factors

[0153] E. Shear strength attenuation coefficient under wet-dry cycles ( )

[0154] formula:

[0155] Parameters: Damage parameters .

[0156] calculate:

[0157]

[0158] Damage items:

[0159]

[0160] Assessment: The soil strength has decreased to 61.6% of its original strength due to frequent alternation between wet and dry conditions.

[0161] Step S5: Comprehensive Weighted Evaluation

[0162] We used the AHP (Analytic Hierarchy Process) to set the weights for each level of indicators. Here, we use the weight distribution of a typical ecological slope protection evaluation:

[0163]

[0164] Regarding the standardization of indicator directions: In vulnerability assessment, a higher score indicates greater vulnerability (worse vulnerability).

[0165] Positive indicators (the higher the value, the better, such as vegetation energy dissipation):

[0166] Convert to vulnerability = .

[0167] Vegetation energy dissipation vulnerability = .

[0168] Vegetation resilience vulnerability = .

[0169] Inverse indicators (the larger the value, the worse, such as water flow shear): use the calculated value directly.

[0170] Intensity attenuation coefficient The original meaning is "residual strength," which is converted into vulnerability loss. .

[0171] Corrected weighted calculation:

[0172] 1. Hydrodynamic vulnerability: (Extremely high risk)

[0173] 2. Vegetation ecological vulnerability:

[0174] (Low to medium risk, vegetation played a protective role)

[0175] 3. Soil environmental vulnerability: (The structure has been damaged to some extent)

[0176] 4. Vulnerability to human interference: (Assuming moderate interference)

[0177] Comprehensive Vulnerability Index (VI) Calculation:

[0178]

[0179]

[0180]

[0181] Conclusions and Classification

[0182] According to the calculation results, VI = 64.53.

[0183] Refer to the patent classification criteria (assuming the criteria are as follows):

[0184] 0-30: Stable Zone

[0185] 30-55: Mildly Vulnerable Area

[0186] 55-75: Moderately Vulnerable Zone

[0187] 75-100: Extremely Vulnerable Zone

[0188] Final evaluation conclusion:

[0189] The shoals at this section are in a moderately fragile state.

[0190] Results analysis:

[0191] Despite extremely harsh hydrodynamic conditions (high shear force, rapid receding water, and a hydrological vulnerability score as high as 98), the area's relatively good vegetation cover (biomass 1.8 kg / m²) and the intervention of vegetation factors (vulnerability score of only 41.7) significantly lowered the overall vulnerability score, preventing the sandbar from being rated as "extremely vulnerable." This is highly consistent with the actual observation mechanism of "vegetation protection" in the Minjiang River estuary wetlands, verifying the scientific validity of this patent's consideration of the nonlinear energy dissipation mechanism of vegetation.

[0192] This invention provides a beach vulnerability assessment system, which includes a memory and a processor. The memory includes a program for a beach vulnerability assessment method. When the program for the beach vulnerability assessment method is executed by the processor, it implements the steps of a beach vulnerability assessment method as described above.

[0193] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a beach vulnerability assessment method as described above.

[0194] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0195] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0196] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0197] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0198] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0199] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0200] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0201] Those skilled in the art can implement the present invention in various variations without departing from its scope and spirit. For example, a feature of one embodiment can be used in another embodiment to obtain yet another embodiment. Any modifications, equivalent substitutions, and improvements made within the scope of the present invention's technical concept should be within the scope of the present invention.

Claims

1. A method for assessing the vulnerability of sandbars, characterized in that, The specific steps include the following: S1. Multi-source data sensing to acquire hydrological, geological, ecological, and disturbance data; S2. Dynamic zoning and indicator construction: Based on the range of water level changes, the sandbars are divided into the exposed zone, the drawdown zone, and the submerged zone, and an indicator system is constructed. S3. Physical parameter inversion: Inversion of key physical parameters based on measured data; S4. Mechanism model quantification: Calculate the vulnerability of each individual indicator using a nonlinear physical response model; S5. Comprehensive weighted assessment, combining AHP weights, calculate the comprehensive vulnerability index and classify it; The mechanism model is quantified, and the vulnerability of each individual index is calculated using a nonlinear physical response model. (1) Mechanistic model of hydrodynamic factors A. Pore water pressure dissipation hysteresis index I hydro Quantification formula: ， in: This represents the measured maximum water discharge rate; Porosity of the soil; This is a dimensional correction factor; The function is used to map large ratios to the 0-1 interval; B. Sscour quantification formula for water flow shear-resistance coupling vulnerability: ， in This refers to the shear stress at the bottom of the water flow. The critical starting shear stress; Control the steepness of the curve. This is the critical threshold coefficient; (2) Mechanistic model of vegetation ecological factors C. Elevation-weighted flood stress resilience index Quantification formula: ， Where: i represents the elevation zone; For elevation zone i coverage; The inundation duration for elevation zones over an i-year period; The flooding stress attenuation factor; D. Iveg's formula for quantifying vegetation energy dissipation efficiency index: ， in Aboveground biomass density, (3) Mechanistic model of soil environmental factors E. Quantification formula for the shear strength attenuation coefficient under wet-dry cycles: ， in This is a soil damage parameter, reflecting the process by which the soil structure gradually loosens as the number of wet-dry cycles increases.

2. The method for assessing the vulnerability of sandbars as described in claim 1, characterized in that, The multi-source data sensing acquires hydrological, geological, ecological, and disturbance data, specifically hydrodynamic data, including the annual water level process curve Z(t) and the measured maximum water recession rate V. drop ; and the nearshore velocity field distribution u(x,y); Soil data was collected to obtain soil particle size distribution curves and to obtain real-time variation θ(t) of soil volumetric water content using a dielectric constant sensor. Vegetation data were used to obtain vegetation biomass B and root depth distribution at different elevation zones.

3. The method for assessing the vulnerability of sandbars as described in claim 1, characterized in that, The dynamic zoning and indicator construction divides the sandbars into perpetually exposed areas, drawdown areas, and perpetually submerged areas based on the range of water level fluctuations. The specific indicator system is as follows: Establish an evaluation index system and construct an evaluation system comprising four dimensions: Hydrodynamic factors include the pore water pressure dissipation hysteresis index and flow shear vulnerability. Vegetation ecological factors, including vegetation energy dissipation efficiency index and elevation-weighted resilience; Soil environmental factors, including the shear strength attenuation coefficient under wet-dry cycles; Human-induced interference factors should be eliminated, and traditional indicators such as development intensity and pollution load should be maintained.

4. The method for assessing the vulnerability of sandbars as described in claim 1, characterized in that, The physical parameter inversion, based on measured data, specifically involves calculating intermediate physical parameters before evaluation. Permeability coefficient K soil Based on soil particle size characteristic value D 10 The permeability coefficient, K, is estimated using the empirical formula Hazen's formula. soil =C(D 10 ) 2 , C stands for an empirical coefficient, reflecting the influence of soil particle shape, density, and uniformity on permeability. Critical starting shear stress τ c Determine the critical stress at which soil particles begin to move based on the Shields curve.

5. The method for assessing the vulnerability of sandbars according to claim 1, characterized in that, Step S5: Comprehensive weighted assessment, combining AHP weights, calculate the comprehensive vulnerability index and classify it. Specifically, use the AHP method to determine the weight W of each dimension. For positive indicators, normalization processing is required. S is the score corresponding to the positive indicator. Sj=100-S After converting to a vulnerability dimension, a weighted sum is performed, and combined with the aforementioned quantitative score Sj, the comprehensive vulnerability index VI is calculated: ， Finally, based on The value is to divide the shoals into: stable zone, slightly vulnerable zone, moderately vulnerable zone, and extremely vulnerable zone.

6. A vulnerability assessment system for sandbars, characterized in that, The system includes a memory and a processor, wherein the memory includes a program for a beach vulnerability assessment method, and when the program for the beach vulnerability assessment method is executed by the processor, it implements the steps of a beach vulnerability assessment method as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a beach vulnerability assessment method as described in any one of claims 1 to 5.

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