Method and system for protecting coastal city road against seawater backflow and salt spray corrosion

Abstract

The application discloses a coastal city road anti-seawater backflow and salt mist corrosion protection method and system, comprising: through collecting multi-dimensional data such as salt mist concentration, seawater backflow history, road structure characteristics, protection layer thickness and meteorological elements, outputting corrosion degree grades at different time nodes through a salt mist corrosion progressive prediction model, generating a salt mist diffusion thermal map by using a salt mist diffusion trajectory tracking algorithm, calculating seawater backflow related data through a storm tide surge coupling algorithm, importing the data into a road moisture-proof protection decision platform, and carrying out multi-factor coupling analysis combined with parameters such as corrosion resistance threshold of protection materials and structure anti-permeability coefficient, finally outputting targeted adjustment instructions for protection layer repair, drainage system optimization, corrosion-resistant coating spraying and interception facility layout. The application realizes accurate and intelligent formulation of the protection scheme, and effectively improves the protection efficiency of the coastal city road against seawater backflow and salt mist corrosion.

Description

Technical Field

[0001] This invention relates to the field of coastal road protection technology, and in particular to methods and systems for protecting coastal urban roads from seawater intrusion and salt spray corrosion. Background Technology

[0002] Coastal city roads are chronically exposed to the marine environment, and salt spray diffusion and seawater intrusion have become major problems affecting the structural stability and service life of roads. Salt spray particles in the marine atmosphere easily react chemically with road structural materials, causing surface corrosion and structural strength reduction. Meanwhile, seawater intrusion caused by storm surges can directly submerge the road base, leading to a chain of disasters such as pavement damage and drainage system failure. With the increase in extreme weather events caused by global warming, the frequency and intensity of storm surges in coastal areas have increased significantly, and the coverage of salt spray has further expanded. Traditional protection methods are no longer adequate for the complex and ever-changing marine environmental stress. There is an urgent need to build an integrated protection technology system that integrates prediction, simulation, decision-making, and execution to address the dual challenges posed by salt spray corrosion and seawater intrusion, and ensure the safe and stable operation of coastal city transportation infrastructure.

[0003] Existing technologies suffer from two key shortcomings: First, they lack the ability to accurately predict and simulate multiple coupled factors. Traditional protection schemes are mostly based on single environmental parameters or empirical data, failing to fully link the intrinsic relationship between salt spray diffusion trajectories, storm surge intensity, and road structural characteristics. This leads to significant deviations in the prediction of corrosion development trends and backflow risks, resulting in insufficient targeted protection measures. Second, the coordination and intelligence of protection systems are low. Data acquisition, risk analysis, and scheme output are fragmented, lacking a unified decision-making platform. Furthermore, the optimization of protection parameters does not achieve dynamic matching with prediction results and simulation data, making it difficult to adjust protection strategies according to real-time environmental changes. This results in wasted protection resources or insufficient protection in key areas, failing to effectively resist multiple erosion threats in complex marine environments. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a method and system for protecting coastal urban roads from seawater backflow and salt spray corrosion.

[0005] The technical solution adopted in this invention is a protection method for coastal city roads against seawater intrusion and salt spray corrosion, comprising the following steps: S1, collecting data on salt spray concentration distribution, historical frequency data of seawater intrusion, material properties of road structural layers, thickness data of protective layers, and meteorological element data along coastal city roads to construct a multi-dimensional raw dataset; S2, inputting the multi-dimensional raw dataset into a road salt spray corrosion progressive prediction model, and outputting road corrosion degree level data at different time points by hierarchically analyzing the interface interaction mechanism between salt spray and road structural layers; S3, based on a salt spray diffusion trajectory tracking algorithm, combined with terrain elevation data and airflow field distribution data, simulating the spatial diffusion path and concentration attenuation law of salt spray along the road, and generating salt spray diffusion data. S4. Using a storm surge coupling algorithm, astronomical tide data, marine wind data, and nearshore topographic data are integrated to calculate the inundation range, peak water level, and duration of seawater backflow under different storm levels. S5. The corrosion level data, salt spray diffusion heat map data, and seawater backflow related data are imported into the road flood prevention and protection decision platform. Combined with the corrosion resistance threshold of protective materials, structural impermeability coefficient, and drainage system flow capacity parameters in the road waterproofing and protection parameters, a multi-factor coupling analysis is performed. S6. Based on the coupling analysis results, targeted protection scheme adjustment instructions are output. The instructions include the location of the protective layer repair area, drainage system optimization parameters, anti-corrosion coating spraying process parameters, and seawater backflow interception facility deployment parameters.

[0006] Furthermore, the expression for the road salt spray corrosion progression prediction model is as follows: Among them, C t Let C0 be the initial base value of salt spray concentration, α be the salt spray corrosion rate correction coefficient, β be the road material corrosion attenuation coefficient, d be the effective penetration depth of the protective layer, and γ be the corrosion rate attenuation coefficient. i M represents the weight of the influence of the material properties of the i-th type of road structure. i Let δ be the salt spray corrosion resistance parameter of the i-th type of road structure material, δ be the comprehensive influence coefficient of meteorological elements, and T be the cumulative duration of salt spray effect.

[0007] Furthermore, the expression for the salt spray diffusion trajectory tracking algorithm is as follows: Where L(x, y, z, t) is the quantized value of the salt mist diffusion trajectory at coordinates (x, y, z) at time t, μ is the salt mist particle diffusion coefficient, and (x0, y0, z) is the quantized value of the salt mist diffusion trajectory at coordinates (x, y, z) at time t. o () represents the starting coordinates of salt spray diffusion, v x v y v z denoted as x, y, and z, respectively; ρ is the air density influence parameter; H(x, y) is the terrain elevation correction value at coordinate (x, y); ε is the salt spray particle settling coefficient; and Q is the salt spray particle release rate.

[0008] Furthermore, the expression for the storm surge coupling algorithm is as follows: Where Z is the quantified value of the peak seawater intrusion level, ζ0 is the astronomical tide reference value, K is the storm surge coupling coefficient, W is the average sea wind speed, L is the wind zone length, and θ is the wind direction angle. λ is the shoreline angle, λ is the nearshore topographic influence coefficient, S is the storm duration, and D is the nearshore water depth correction parameter.

[0009] Furthermore, the expression for the protection scheme matching model of the road moisture-proof protection decision platform is as follows: Where P is the priority coefficient for adjusting the protection scheme, ω1, ω2, ω3, and ω4 are the weighting coefficients for corrosion degree, diffusion trajectory, backflow water level, and structural impermeability, respectively, and C max L is the quantified value of the maximum permissible corrosion level for roads. max Z represents the quantification value of the maximum range affected by salt spray. max K represents the maximum backflow water level that the road can withstand, and K is the actual seepage resistance coefficient of the road. max This is the maximum permeability coefficient for road design.

[0010] Furthermore, the parameter optimization model expression for road waterproofing and protection is as follows: Where F is the overall effectiveness value of the protection system, ξ, η, and ζ are the effectiveness weights of the anti-corrosion coating, drainage system, and interception facilities, respectively, and T c P is the coating thickness parameter. m δ is a parameter representing the salt spray resistance of the coating. c Q is the coating aging rate parameter. d For the design flow parameters of the drainage system, S d R is the drainage network coverage area parameter. d I is the resistance parameter of the drainage pipe. b For the height parameter of the interception facility, H b D is the impact resistance parameter for the interception facility. b To set the spacing parameters for the interception facilities.

[0011] Further, S3 includes the following sub-steps: S31, extracting terrain elevation data and airflow field distribution data from the multi-dimensional original dataset, and spatially discretizing them according to a preset grid precision to form a structured grid dataset; S32, importing the initial release position and release rate parameters of salt fog particles into the salt fog diffusion trajectory tracking algorithm, and setting the particle motion time step and spatial search radius; S33, based on the discretized grid dataset, calculating the airflow velocity vector and terrain slope value within each grid cell to determine the motion direction and speed of salt fog particles within the grid cell; S34, iteratively calculating the spatial coordinates of particles at different time nodes according to the salt fog particle motion parameters, counting the number of salt fog particles at each coordinate point, and generating salt fog diffusion heat map data in combination with the concentration decay law.

[0012] Further, S4 includes the following sub-steps: S41, collecting astronomical tide observation data, marine wind force observation data, and nearshore topographic measurement data, aligning and filtering the data according to the time series, and removing abnormal data; S42, using the filtered astronomical tide data as the basic item, inputting it into the storm surge coupling algorithm, and setting the storm level classification threshold and calculation boundary conditions; S43, analyzing the coupling relationship between wind force and tide level through the algorithm, and calculating the water level rise and water flow velocity distribution in nearshore waters under different storm levels; S44, combining nearshore topographic data, determining the area where the water level rise exceeds the road elevation, and determining the flooding boundary, peak water level, and duration of seawater intrusion.

[0013] Further, S5 includes the following sub-steps: S51, standardizing the format of corrosion level data, salt spray diffusion heat map data, and seawater backflow related data, converting them into a data format recognizable by the road moisture protection decision-making platform; S52, calling the platform's built-in protection parameter database to extract the corrosion resistance threshold of protective materials, structural impermeability coefficient, and drainage system flow capacity benchmark parameters; S53, constructing a multi-factor coupling analysis matrix, using corrosion level, salt spray diffusion range, and backflow intensity as input variables, and protection parameter benchmark values ​​as constraints, to perform correlation calculations; S54, based on the calculation results, generating preliminary suggestions for adjusting the protection scheme, including parameter adjustment direction and numerical range, to provide data support for subsequent command output.

[0014] A protective system for coastal city roads against seawater intrusion and salt spray corrosion is proposed. This system, applied to methods for protecting coastal city roads from seawater intrusion and salt spray corrosion, includes: a multi-source data acquisition unit for collecting data on salt spray concentration distribution, historical frequency of seawater intrusion, road structural layer material properties, protective layer thickness, and meteorological elements, establishing a bidirectional data transmission link with the subsequent data processing unit; a salt spray corrosion prediction unit, which incorporates a progressive prediction model for road salt spray corrosion, receives data from the multi-source data acquisition unit, outputs road corrosion severity levels at different time points, and sends a coordinated trigger signal to the salt spray diffusion simulation unit; and a salt spray diffusion simulation unit, based on a salt spray diffusion trajectory tracking algorithm, combined with terrain elevation and airflow field data, generates salt spray diffusion heatmap data and correlates it with storm surge data. The surge coupling unit interacts with data; the storm surge coupling unit uses a storm surge coupling algorithm to integrate astronomical tide levels, sea wind force, and nearshore topographic data to calculate seawater intrusion data and synchronizes the results to the protection decision analysis unit; the protection decision analysis unit, as the core execution module of the road flood protection decision platform, receives corrosion level, salt spray diffusion heat map, and seawater intrusion data, performs multi-factor coupling analysis in conjunction with road waterproofing protection parameters, and sends control commands to the protection scheme output unit; the protection scheme output unit receives the control commands from the protection decision analysis unit, parses and outputs relevant parameters for protective layer repair area positioning, drainage system optimization, anti-corrosion coating spraying, and interception facility deployment, and establishes a communication connection with the on-site road protection execution equipment.

[0015] Beneficial Effects: This invention proposes a method and system for protecting coastal urban roads from seawater intrusion and salt spray corrosion. By integrating multi-source data such as salt spray concentration, seawater intrusion history, and road structural characteristics, and through a specially constructed corrosion progression prediction model, salt spray diffusion trajectory tracking algorithm, and storm surge coupling algorithm, it comprehensively analyzes the dynamic evolution of salt spray corrosion and seawater intrusion. This solves the problem of traditional technologies lacking accurate prediction through multi-factor coupling, significantly improving the accuracy and comprehensiveness of risk prediction. Simultaneously, utilizing a road moisture protection decision-making platform, it deeply integrates predicted data, simulation results, and protection parameters for multi-factor coupling analysis, forming a closed-loop collaborative system from data collection and risk assessment to solution output. This breaks down the fragmentation of each link, achieving dynamic matching of protection solutions with real-time environmental changes. By outputting targeted protection adjustment commands, it precisely optimizes parameters in key areas such as protective layer repair, drainage systems, anti-corrosion coatings, and interception facilities. This avoids wasting protection resources, strengthens protection in key areas, and significantly improves the targeting and effectiveness of protection measures, fundamentally resisting the dual threats of salt spray corrosion and seawater intrusion, and ensuring the stability and service life of the road structure. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention.

[0017] Figure 2 This is a flowchart of method step S3 of the present invention;

[0018] Figure 3 This is a flowchart of method step S4 of the present invention;

[0019] Figure 4 This is a flowchart of step S5 of the method of the present invention;

[0020] Figure 5 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] like Figure 1 As shown, the protection method for coastal city roads against seawater backflow and salt spray corrosion includes the following steps:

[0023] S1: Collect data on salt spray concentration distribution, historical frequency of seawater backflow, material properties of road structure layers, thickness of protective layers, and meteorological elements along coastal city roads to construct a multi-dimensional raw dataset;

[0024] Specifically, step S1 is the data foundation building stage of the entire protection method. Through comprehensive and accurate multi-dimensional data collection, it provides complete and reliable data source support for subsequent model calculations and decision analysis. During implementation, a comprehensive monitoring network covering the entire area was constructed, with one set of monitoring equipment deployed every 0.8 kilometers along coastal city roads. Salt spray concentration distribution data was acquired using high-precision electrochemical sensors, with a sampling frequency of once every 30 seconds, continuously recording concentration changes at different times of day (morning, noon, and evening) and within a 5-meter radius of the road centerline. Historical frequency data of seawater backflow was obtained by retrieving archived records from coastal hydrological stations and road maintenance departments over the past 15 years, categorizing and statistically analyzing the number of backflow occurrences, duration, and affected width for each road section by year and quarter. Data on the material properties of the road structural layer was obtained through on-site core drilling, with 12 indicators, including porosity, compressive strength, and chloride ion diffusion coefficient, tested in the laboratory. Data on the thickness of the protective layer was obtained using an ultrasonic testing instrument, measuring one point every 3 meters along the road cross-section, with 5 testing lines deployed for each lane to ensure coverage of the entire protected area. Meteorological element data was collected by connecting to the real-time database of regional meteorological stations, collecting parameters such as wind speed, wind direction, precipitation, and relative humidity, with a sampling interval set at once every 20 minutes. After data collection, the data is organized in a structured format of "segment number - data type - collection time - parameter value". Data exceeding three times the standard deviation is removed using an outlier detection algorithm. Finally, a multi-dimensional raw dataset including 5 major categories and 36 specific indicators is constructed to ensure the integrity, timeliness and consistency of the data, laying the foundation for accurate calculations in subsequent steps.

[0025] S2, input the multi-dimensional raw dataset into the road salt spray corrosion progressive prediction model, and output the road corrosion degree level data at different time points by hierarchically analyzing the interface mechanism between salt spray and road structure layer;

[0026] Specifically, step S2 is the road corrosion state prediction stage. Through specialized model calculations, the evolution trend of road corrosion is grasped in advance, providing a scientific basis for the early deployment of protective measures. During implementation, the multi-dimensional raw dataset constructed in step S1 is first converted to the model input format according to the model input specifications to ensure accurate matching of data fields with model requirements. Then, it is completely imported into the preset road salt spray corrosion progressive prediction model. During model calculation, a layered analytical mechanism is adopted, analyzing the penetration path of salt spray particles and their chemical reaction process with material components in the order of asphalt surface layer, cement-stabilized base layer, graded crushed stone subbase layer, and soil subbase layer. The corrosion rate of each structural layer is calculated by focusing on key parameters such as protective layer thickness, material porosity, and corrosion resistance. The calculation process incorporates variables such as the cumulative duration of salt spray effects and the comprehensive influence of meteorological factors. Through multiple rounds of iterative calculations, it outputs road corrosion severity level data for four key time nodes: 6 months, 12 months, 24 months, and 36 months. The corrosion levels are divided into four categories: mild, moderate, severe, and extremely severe. Each level corresponds to specific quantitative standards such as the proportion of structural layer strength attenuation, the percentage of surface damage area, and the chloride ion content threshold. At the same time, it accurately marks the specific road section location, depth of influence, and development rate corresponding to each level, enabling relevant personnel to clearly grasp the dynamic evolution trend of road corrosion, avoid blind protection work, and provide accurate predictive support for the formulation of subsequent targeted protection plans.

[0027] S3, based on the salt spray diffusion trajectory tracking algorithm, combines terrain elevation data and airflow field distribution data to simulate the spatial diffusion path and concentration decay law of salt spray along the road, and generate salt spray diffusion heat map data.

[0028] Specifically, step S3 is the salt spray diffusion law simulation stage, which accurately presents the spatial propagation characteristics of salt spray along the road, identifies high corrosion risk areas, and provides data support for the precise allocation of protective resources. During implementation, topographic elevation data and airflow distribution data were first extracted from the multi-dimensional raw dataset in step S1. This data was then spatially discretized using a 10m × 10m grid to form a structured grid dataset, clearly showing the topographic undulations, slope changes, and airflow velocity and direction distribution along the road. Subsequently, parameters such as the initial release location and rate of salt spray particles were imported into the salt spray diffusion trajectory tracking algorithm, setting a particle movement time step of 0.5 hours and a spatial search radius of 50 meters. Based on the discretized grid dataset, the airflow velocity vector and topographic slope value within each grid cell were calculated. Combined with the characteristics of the salt spray particles themselves, the movement direction and velocity of the salt spray particles within each grid cell were determined. Through multiple rounds of iterative calculations, the spatial coordinates of salt spray particles at different time points were obtained, and the number of salt spray particles at each coordinate point was counted. Combined with the concentration decay patterns of salt spray during propagation, such as sedimentation and dilution, intuitive salt spray diffusion heatmap data was generated. The heatmap was divided into eight levels according to concentration, clearly marking high-concentration salt spray accumulation areas, concentration change gradients along the diffusion path, and the boundaries of the affected area, providing accurate data support for subsequently identifying key protection areas.

[0029] S4 employs a storm surge coupling algorithm, integrating astronomical tide data, marine wind data, and nearshore topographic data to calculate the inundation range, peak water level, and duration of seawater backflow under different storm levels.

[0030] Specifically, step S4 is the risk quantification step for seawater backflow, accurately calculating the impact of seawater backflow under different storm scenarios to provide a scientific basis for the optimized configuration of backflow prevention facilities. During implementation, astronomical tide level observation data, marine wind force observation data, and nearshore topographic measurement data from the original dataset in step S1 are first collected. Data alignment is performed according to time series, matching astronomical tide level data by daily cycle, wind force data by hourly cycle, and topographic data by spatial grid. An outlier detection algorithm is used to remove data exceeding the normal fluctuation range. Subsequently, the filtered astronomical tide level data is used as the basis and input into the storm surge coupling algorithm. Thresholds for classifying storm levels (tropical storm, severe tropical storm, typhoon) are set, and the spatial boundary for algorithm calculation is defined as 5 kilometers seaward and 3 kilometers inland along the road, with the time boundary being 12 hours before the storm. 24 hours after the storm ends, the algorithm analyzes the coupling relationship between wind force and tide level, and combines the blocking and guiding effects of nearshore topography to calculate the water level rise, water flow velocity distribution, and propagation path of nearshore waters under different storm levels. Finally, by combining nearshore topographic data and road elevation data, the area where the water level rise exceeds the road elevation is determined, and the inundation boundary, peak water level, and duration of seawater intrusion are accurately determined. The inundation boundary is accurate to the 10-meter level, the peak water level is accurate to the centimeter level, and the duration is accurate to the hour level. At the same time, the spatial distribution map of intrusion risk under different storm levels is output, providing comprehensive seawater intrusion risk data support for subsequent protection decisions.

[0031] S5. Import the corrosion level data, salt spray diffusion heat map data, and seawater backflow related data into the road moisture protection decision platform, and combine them with the corrosion resistance threshold of protective materials, structural impermeability coefficient, and drainage system flow capacity parameters in the road waterproofing protection parameters to conduct multi-factor coupling analysis.

[0032] Specifically, step S5 is the protection scheme decision analysis stage. Through multi-factor comprehensive analysis, the protection scheme is scientifically matched to ensure the pertinence and effectiveness of the protection measures. During implementation, the corrosion level data output from step S2, the salt spray diffusion heat map data generated in step S3, and the seawater backflow related data calculated in step S4 are first standardized and converted into JSON format recognizable by the road moisture protection decision platform to ensure efficient data parsing. Then, the platform's built-in protection parameter database is called to extract 28 benchmark parameters, including the corrosion resistance threshold of protective materials, structural impermeability coefficient, drainage system flow capacity, and impact resistance strength of interception facilities. These parameters are all optimized and determined based on industry standards and engineering practices. Based on the above data, a multi-factor coupling analysis matrix is ​​constructed to analyze corrosion... The study uses the degree of salt spray diffusion, salt spray range, and backflow intensity as core input variables, with the baseline values ​​of protection parameters as constraints. It employs a combination of weighted summation and threshold judgment to perform multiple rounds of correlation calculations, fully considering the mutual influence and synergistic effects of various factors. Based on the calculation results, preliminary suggestions for adjusting the protection plan are generated, clarifying the adjustment direction of protection measures for each road section, such as increasing the thickness of the protective layer, optimizing the diameter of drainage pipes, and increasing the spacing of interception facilities. Specific numerical adjustment ranges are also provided, such as the adjustment range of the protective layer thickness and the percentage increase in drainage system flow, providing solid data support for the accurate output of subsequent protection plan adjustment instructions.

[0033] S6. Based on the coupling analysis results, output targeted protection scheme adjustment instructions, including the location of the protective layer repair area, the optimization parameters of the drainage system, the spraying process parameters of the anti-corrosion coating, and the deployment parameters of the seawater backflow interception facility.

[0034] Specifically, step S6 is the implementation phase of the protection plan, which transforms the decision analysis results into specific and actionable instructions to ensure that the protection measures are implemented quickly and effectively. During implementation, the system first receives the results of the multi-factor coupling analysis in step S5. A dedicated analysis module extracts key decision-making information, including the protection priority of each road segment, the type of protection to be adjusted, and the direction of parameter optimization. Based on this information, targeted protection scheme adjustment instructions are output. These instructions include four core components: 1) Protection layer repair area positioning instructions, accurately marking the specific road segment mileage and cross-sectional location to be repaired, clearly defining the repair area boundaries with an accuracy of 1 meter, and providing the material type and thickness parameters of the repair layer; 2) Drainage system optimization parameter instructions, specifying the diameter adjustment value of drainage pipes, slope optimization ratio, pipeline node modification locations, and pumping power adjustment parameters to ensure that the drainage system's flow capacity matches the risk of backflow; 3) Anti-corrosion coating spraying process parameter instructions, specifying the coating type, spraying thickness, number of spray coats, drying time control standards, and precise division of the spraying area; and 4) Seawater backflow interception facility deployment parameter instructions, determining the height, thickness, and material strength parameters of the interception wall, as well as the specific location and spacing of the deployment, and clarifying the foundation depth and seepage prevention requirements of the facility. All instructions are generated in a standardized format, including key information such as the executing entity, execution time point, technical requirements, and quality acceptance standards. They can directly establish communication connections with on-site road protection equipment to enable rapid issuance and precise execution of instructions, ensuring that protective measures can effectively cope with the risks of salt spray corrosion and seawater backflow, and guaranteeing the stability and service life of the road structure.

[0035] Preferably, the expression for the road salt spray corrosion progression prediction model is: Among them, C t Let C0 be the initial base value of salt spray concentration, α be the salt spray corrosion rate correction coefficient, β be the road material corrosion attenuation coefficient, d be the effective penetration depth of the protective layer, and γ be the corrosion rate attenuation coefficient. i M represents the weight of the influence of the material properties of the i-th type of road structure. i Let δ be the salt spray corrosion resistance parameter of the i-th type of road structure material, δ be the comprehensive influence coefficient of meteorological elements, and T be the cumulative duration of salt spray effect.

[0036] Specifically, the road salt spray corrosion progression prediction model, through multi-parameter coupled calculations, accurately quantifies the corrosion evolution state of road structural layers at different time points, providing a quantitative basis for the formulation of protection strategies. The implementation of this model closely integrates the marine environmental exposure characteristics and structural properties of coastal urban roads, comprehensively considering the initial salt spray concentration level, environmental adaptation correction for salt spray corrosion rate, the road material's own ability to attenuate and inhibit corrosion, the actual effective depth of the protective layer blocking penetration, and the inherent salt spray corrosion resistance properties of various road structural materials and their influence weight on the overall corrosion process. It also incorporates the corrosion rate changes caused by the combined effects of meteorological factors and the cumulative duration effect of continuous salt spray action. During model calculation, specific values ​​of each parameter are first obtained through field monitoring and laboratory testing to ensure that the parameters are highly matched with the actual situation of the target road. Then, through the collaborative calculation of multiple parameters, the interface interaction mechanism between salt spray and road structural layers is transformed into a quantifiable corrosion degree value. This value can objectively reflect the corrosion state of each structural layer of the road at different time points, which not only reflects the progressive characteristics of the corrosion process, but also realizes the accurate prediction of future corrosion development trends. This provides scientific and quantitative data support for clarifying protection priorities and formulating targeted protection measures, effectively solving the problems of difficulty in accurate quantification and insufficient predictability in traditional corrosion assessment.

[0037] Preferably, the expression for the salt spray diffusion trajectory tracking algorithm is: Where L(x, y, z, t) is the quantized value of the salt mist diffusion trajectory at coordinates (x, y, z) at time t, μ is the salt mist particle diffusion coefficient, (x0, y0, z0) is the initial coordinate of salt mist diffusion, and v x v y v z denoted as x, y, and z, respectively; ρ is the air density influence parameter; H(x, y) is the terrain elevation correction value at coordinate (x, y); ε is the salt spray particle settling coefficient; and Q is the salt spray particle release rate.

[0038] Specifically, the salt spray diffusion trajectory tracking algorithm accurately simulates the spatial propagation patterns and concentration distribution characteristics of salt spray along roads, identifying high-corrosion-risk areas and providing technical support for the precise deployment of protective resources. The algorithm's implementation fully considers the topography and meteorological conditions of coastal areas, focusing on the diffusion characteristics of salt spray particles. It integrates the geographical coordinates of the salt spray diffusion initiation location, the diffusion capacity coefficient of the salt spray particles themselves, the driving effect of airflow velocity components in different spatial directions on particle motion, and also considers the indirect influence of air density on salt spray diffusion, the blocking and guiding effect of terrain elevation at specific coordinate locations on the diffusion path, the natural sedimentation attenuation effect of salt spray particles during propagation, and the initial release rate of salt spray particles. During implementation, precise data on terrain elevation and airflow distribution are first extracted from the multi-dimensional raw dataset. The calculation units are divided according to preset standards, and the spatial and temporal calculation steps of the algorithm are defined. Then, the movement direction, velocity, and concentration changes of salt spray particles are calculated unit by unit to simulate the entire process of salt spray from release to diffusion. The final generated salt spray diffusion heat map data can clearly show the high concentration accumulation area of ​​salt spray, the concentration gradient changes on the diffusion path, and the boundary of the influence range. This allows relevant personnel to intuitively grasp the impact of salt spray on roads and provides accurate spatial distribution data support for subsequent targeted division of protection areas and optimization of protection measures.

[0039] Preferably, the expression for the storm surge coupling algorithm is: Where Z is the quantified value of the peak seawater intrusion level, ζ0 is the astronomical tide reference value, κ is the storm surge coupling coefficient, W is the average sea wind speed, L is the length of the wind zone, and θ is the wind direction angle. λ is the shoreline angle, λ is the nearshore topographic influence coefficient, S is the storm duration, and D is the nearshore water depth correction parameter.

[0040] Specifically, the storm surge coupling algorithm accurately quantifies the risk parameters of seawater backflow under different storm intensities, providing a scientific basis for backflow prevention design and emergency response. The algorithm's implementation focuses on the synergistic mechanism of storm surges and astronomical tides, comprehensively integrating baseline values ​​of astronomical tide levels, the coupling coefficient of storm surge and surge interaction, the intensity of average sea wind speed, the effect of wind zone length on surge energy accumulation, the influence of the relative angle between wind direction and shoreline orientation on water level rise, the blocking and amplification effects of nearshore topography on tidal propagation, the effect of storm duration on cumulative water level rise, and the corrective effect of nearshore water depth conditions on tidal propagation. During implementation, long-term astronomical tide observation data, marine wind monitoring data, and nearshore topographic measurement data were first collected. Data screening and calibration were performed to ensure the accuracy of the input data. Then, different storm level thresholds were defined based on historical storm records, and the calculation boundary conditions of the algorithm were set. By analyzing the coupling relationship between wind force and tide level and combining it with the spatial distribution characteristics of nearshore topography, the peak water level, inundation range, and duration of seawater backflow were calculated for each region. The calculation results can accurately reflect the degree of threat of seawater backflow to roads under different storm scenarios. This provides quantitative risk assessment data for the subsequent development of differentiated backflow prevention measures and optimization of interception facility parameters, effectively solving the problem that traditional methods are difficult to accurately predict the risk of seawater backflow.

[0041] Preferably, the protection scheme matching model expression of the road moisture protection decision platform is: Where P is the priority coefficient for adjusting the protection scheme, ω1, ω2, ω3, and ω4 are the weighting coefficients for corrosion degree, diffusion trajectory, backflow water level, and structural impermeability, respectively, and C max L is the quantified value of the maximum permissible corrosion level for roads. max Z represents the quantification value of the maximum range affected by salt spray. max K represents the maximum backflow water level that the road can withstand, and K is the actual seepage resistance coefficient of the road. max This is the maximum permeability coefficient for road design.

[0042] Specifically, the road moisture protection decision-making platform's protection scheme matching model establishes a precise correspondence between predicted data and protection schemes, enabling scientific decision-making and optimization of protection strategies. The model's implementation process centers on multi-factor coupling analysis, comprehensively considering the ratio of the actual quantitative value of road corrosion to the maximum allowable threshold, the ratio of the quantitative value of salt spray diffusion impact range to the maximum impact range, the ratio of the quantitative value of seawater backflow peak water level to the maximum water level the road can withstand, and the ratio of the difference between the road's actual impermeability coefficient and the design maximum impermeability coefficient. Simultaneously, by setting reasonable weighting coefficients, it balances the influence of four core factors—corrosion degree, salt spray diffusion, backflow water level, and structural impermeability—on the protection scheme. During implementation, the corrosion degree, salt spray diffusion, and seawater backflow-related data output from steps S2 to S4 are first standardized to match the model's input requirements. Then, the platform's built-in protection parameter database retrieves the maximum allowable thresholds and design parameters for each road segment. A weighted summation is used to calculate the protection scheme adjustment priority coefficient. This coefficient objectively reflects the urgency of protection needs for different road sections and time periods; a higher coefficient indicates that priority protection measures should be taken in that area. The model calculation results directly provide a basis for adjusting the protection plan, ensuring that the protection measures can accurately match the actual risk situation, avoid the waste of protection resources or the problem of insufficient protection in key areas, and achieve scientific and efficient protection decision-making.

[0043] Preferably, the parameter optimization model expression for road waterproofing and protection is as follows: Where F is the overall effectiveness value of the protection system, ξ, η, and ζ are the effectiveness weights of the anti-corrosion coating, drainage system, and interception facilities, respectively, and T c P is the coating thickness parameter. m δ is a parameter representing the salt spray resistance of the coating. c Q is the coating aging rate parameter. d For the design flow parameters of the drainage system, S d R is the drainage network coverage area parameter. d I is the resistance parameter of the drainage pipe. b For the height parameter of the interception facility, H b D is the impact resistance parameter for the interception facility. b To set the spacing parameters for the interception facilities.

[0044] Specifically, the road waterproofing and protection parameter optimization model comprehensively evaluates the overall effectiveness of each component of the protection system, providing a quantitative basis for optimizing and adjusting protection parameters. The implementation of this model focuses on the overall synergistic effectiveness of the protection system, taking the three core protection links—anti-corrosion coating, drainage system, and interception facilities—as the evaluation objects. It considers the key technical parameters of each link, including the thickness and salt spray resistance of the anti-corrosion coating, and the impact of the coating's aging rate on long-term protective effects; the design flow rate, pipe network coverage area, and pipe resistance of the drainage system on drainage efficiency; and the height, impact resistance, and spacing of the interception facilities on the interception effect. Simultaneously, by setting effectiveness weight coefficients, the importance of each protection link in the overall system is clarified. During implementation, the current parameter values ​​of each protection link are first obtained through on-site testing and design documents to ensure the authenticity and accuracy of the parameters. Then, the parameters are substituted into the model for calculation. The calculated comprehensive effectiveness value of the protection system can fully reflect the actual effect of the current protection configuration. By comparing the changes in effectiveness values ​​under different parameter combinations, the optimization direction and adjustment range of each protection parameter can be clarified. For example, increasing the coating thickness, optimizing the diameter of drainage pipes, and adjusting the spacing of interception facilities can achieve a reasonable allocation of protection resources, improve the overall protection capability of the protection system, and ensure that the protection measures can resist the dual threats of salt spray corrosion and seawater backflow in a long-term and stable manner.

[0045] Preferred, such as Figure 2 As shown, step S3 includes the following sub-steps: S31, extracting terrain elevation data and airflow distribution data from the multi-dimensional original dataset, and spatially discretizing them according to a preset grid precision to form a structured grid dataset; S32, importing the initial release position and release rate parameters of salt fog particles into the salt fog diffusion trajectory tracking algorithm, and setting the particle motion time step and spatial search radius; S33, based on the discretized grid dataset, calculating the airflow velocity vector and terrain slope value within each grid cell to determine the motion direction and speed of salt fog particles within the grid cell; S34, iteratively calculating the spatial coordinates of particles at different time nodes according to the salt fog particle motion parameters, counting the number of salt fog particles at each coordinate point, and generating salt fog diffusion heat map data by combining the concentration decay law.

[0046] Specifically, step S3 ensures the accuracy and operability of the salt spray diffusion trajectory simulation through step-by-step operations, providing reliable technical support for identifying high-corrosion-risk areas. Implementation proceeds step-by-step according to the process from S31 to S34. S31 first extracts terrain elevation data and airflow distribution data from the multi-dimensional raw dataset constructed in step S1, and performs spatial discretization processing according to a preset grid precision of 10 meters × 10 meters, transforming continuous terrain and airflow data into a structured grid dataset that clearly presents the terrain features and airflow distribution of each grid cell. S32 imports key parameters such as the initial release position and release rate of salt spray particles into the salt spray diffusion trajectory tracking algorithm, setting a particle motion time step of 0.5 hours and a spatial search radius of 50 meters to define the basic conditions for calculating the particle motion trajectory. S33, based on the discretized grid dataset, through... The algorithm calculates the airflow velocity vector and terrain slope value in each grid cell, and combines the physical characteristics of salt spray particles to accurately determine the direction and speed of movement of salt spray particles in each grid cell. Based on the determined salt spray particle movement parameters, S34 iteratively calculates the spatial coordinates of particles at different time nodes according to the set time step, counts the number of salt spray particles gathered at each coordinate point, and combines the concentration decay law of salt spray due to sedimentation and air dilution during the propagation process to generate intuitive salt spray diffusion heat map data. The heat map is divided into 8 levels according to the concentration gradient to ensure accurate marking of high salt spray concentration areas, diffusion paths and impact boundaries, providing data support for the subsequent delineation of key protection areas.

[0047] Preferred, such as Figure 3 As shown, S4 includes the following sub-steps: S41, collecting astronomical tide level observation data, marine wind force observation data, and nearshore topographic measurement data, aligning and filtering the data according to the time series, and removing abnormal data; S42, using the filtered astronomical tide level data as the basic item, inputting it into the storm surge coupling algorithm, and setting the storm level classification threshold and calculation boundary conditions; S43, analyzing the coupling relationship between wind force and tide level through the algorithm, and calculating the water level rise and water flow velocity distribution in nearshore waters under different storm levels; S44, combining nearshore topographic data, determining the area where the water level rise exceeds the road elevation, and determining the flooding boundary, peak water level, and duration data of seawater backflow.

[0048] Specifically, step S4 involves step-by-step calculations to accurately quantify the risk of seawater backflow under different storm levels, providing a scientific basis for backflow prevention and protection design. The implementation process strictly follows the procedures from S41 to S44. S41 first collects astronomical tide observation data, marine wind force observation data, and nearshore topographic measurement data from the multi-dimensional raw dataset in step S1. The astronomical tide data (daily cycle record), marine wind force data (hourly cycle record), and nearshore topographic data (spatial grid record) are precisely aligned according to time series. An outlier detection algorithm is used to remove outlier data exceeding 3 times the standard deviation to ensure the accuracy of the input data. In S42, the filtered astronomical tide data is used as the basic item and input into the storm surge coupling algorithm. Thresholds for classifying storm levels as tropical storm, severe tropical storm, and typhoon are set. The spatial boundary for algorithm calculation is defined as 5 kilometers seaward and 3 kilometers inland along the road, and the time boundary is 12 hours before the storm occurs. Twenty-four hours after the end of the period, a clear scope for algorithm calculation is defined. S43 uses the algorithm to deeply analyze the coupling relationship between wind force and tide level, and combines the blocking and guiding effects of nearshore topography to calculate the water level rise, water flow velocity distribution and propagation path of nearshore waters under different storm levels for each region, ensuring the precision of the calculation results. S44 compares the calculated water level rise data with road elevation data to determine the area where the water level exceeds the road elevation, accurately determine the flooding boundary of seawater backflow (accuracy up to 10 meters), peak water level (accuracy up to centimeters), and duration (accuracy up to hours), and at the same time generate a spatial distribution map of backflow risk under different storm levels, comprehensively presenting the threat of seawater backflow to roads, and providing complete risk data support for subsequent protection decisions.

[0049] Preferred, such as Figure 4 As shown, step S5 includes the following sub-steps: S51, standardizing the data on corrosion level, salt spray diffusion heat map, and seawater backflow to a format recognizable by the road moisture protection decision-making platform; S52, accessing the platform's built-in protection parameter database to extract the corrosion resistance threshold of protective materials, the structural impermeability coefficient, and the baseline parameters for drainage system flow capacity; S53, constructing a multi-factor coupling analysis matrix, using corrosion level, salt spray diffusion range, and backflow intensity as input variables and the baseline values ​​of protection parameters as constraints, and performing correlation calculations; S54, based on the calculation results, generating preliminary suggestions for adjusting the protection scheme, including the direction and range of parameter adjustments, to provide data support for subsequent command output.

[0050] Specifically, step S5, through systematic step-by-step processing, achieves effective fusion and accurate analysis of multi-source data, providing data support for the scientific formulation of protection plans. Implementation is carried out in an orderly manner according to the process from S51 to S54. S51 first standardizes the format of the corrosion level data output from step S2, the salt spray diffusion heat map data generated in step S3, and the seawater backflow related data calculated in step S4, converting them into a unified JSON format that the road moisture protection decision-making platform can efficiently parse, ensuring seamless integration of various data with the platform's computational needs. S52 calls the platform's built-in protection parameter database to extract 28 benchmark parameters, including the corrosion resistance threshold of protective materials, structural impermeability coefficient, drainage system flow capacity, and impact resistance strength of interception facilities. These parameters are all optimized and determined based on industry standards and engineering practices, ensuring the authority of the analysis basis. S53 uses corrosion level, salt spray diffusion range, and backflow intensity as... The core input variables, with the extracted protection parameter benchmark values ​​as constraints, construct a multi-factor coupling analysis matrix. A combination of weighted summation and threshold judgment is used for multiple rounds of correlation calculations to fully consider the mutual influence and synergistic effects among the factors. Based on the correlation calculation results, S54 generates preliminary suggestions for adjusting the protection scheme, clarifying the adjustment direction of protection measures for each road section, such as increasing the thickness of the protection layer, optimizing the diameter of drainage pipes, and increasing the spacing of interception facilities. It also provides specific numerical adjustment ranges, such as adjusting the protection layer thickness by 5 to 20 millimeters and increasing the drainage system flow rate by 10% to 30%. This provides solid data support for the subsequent step S6 to output precise protection scheme adjustment instructions, ensuring that the protection measures are highly matched with the actual risks.

[0051] like Figure 5As shown, a protective system for coastal city roads against seawater intrusion and salt spray corrosion is implemented. This system, applied to methods for protecting coastal city roads from seawater intrusion and salt spray corrosion, includes: a multi-source data acquisition unit for collecting data on salt spray concentration distribution, historical frequency of seawater intrusion, road structural layer material properties, protective layer thickness, and meteorological elements, establishing a bidirectional data transmission link with the subsequent data processing unit; a salt spray corrosion prediction unit, which incorporates a progressive prediction model for road salt spray corrosion, receives data from the multi-source data acquisition unit, outputs road corrosion severity levels at different time points, and sends a coordinated trigger signal to the salt spray diffusion simulation unit; and a salt spray diffusion simulation unit, based on a salt spray diffusion trajectory tracking algorithm, combined with terrain elevation and airflow field data, generates salt spray diffusion heatmap data and correlates it with storm data. The storm surge coupling unit interacts with the storm surge coupling unit, which uses a storm surge coupling algorithm to integrate astronomical tide levels, sea wind force, and nearshore topographic data to calculate seawater intrusion data and synchronize the results to the protection decision analysis unit. The protection decision analysis unit, as the core execution module of the road flood protection decision platform, receives corrosion level, salt spray diffusion heat map, and seawater intrusion data, performs multi-factor coupling analysis in conjunction with road waterproofing protection parameters, and sends control commands to the protection scheme output unit. The protection scheme output unit receives the control commands from the protection decision analysis unit, parses and outputs relevant parameters for the location of the protective layer repair area, drainage system optimization, anti-corrosion coating spraying, and interception facility deployment, and establishes a communication connection with the on-site road protection execution equipment.

[0052] This research presents a method and system for protecting coastal city roads from seawater intrusion and salt spray corrosion. By systematically collecting key data from multiple sources, including salt spray, seawater intrusion, road structure, and meteorological data, and utilizing a specially developed corrosion progression prediction model, salt spray diffusion trajectory tracking algorithm, and storm surge coupling algorithm, it comprehensively analyzes the interaction mechanism between the marine environment and road structure. This approach breaks through the limitations of traditional technologies that rely on single parameters, significantly improving the accuracy of predicting salt spray corrosion trends and seawater intrusion risks. Simultaneously, the establishment of a road flood protection decision platform achieves deep integration of predictive data, simulation results, and protection parameters. Through multi-factor coupling analysis, it formulates targeted protection solutions, ensuring that protective measures accurately match the actual risk situation and effectively solving the problem of insufficient targeting in traditional protection solutions.

[0053] This method and system integrates various data such as topography, airflow, and tide levels, and uses specialized algorithms to dynamically simulate salt spray diffusion and storm surge, fully presenting the risk evolution process. Addressing the issues of fragmentation and insufficient coordination among different stages, an integrated system is constructed, encompassing data acquisition, corrosion prediction, diffusion simulation, backflow calculation, decision output, and scheme execution. An efficient data transmission and interaction mechanism is established between units to enable dynamic adjustment of protection strategies. By accurately locating vulnerable areas and optimizing the parameters of protective materials and facilities, it avoids wasting protective resources and strengthens the protection intensity of key areas, completely changing the traditional passive and limited-effect approach of protection. It provides comprehensive and adaptive corrosion resistance and backflow prevention for coastal urban roads.

[0054] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for protecting coastal city roads from seawater backflow and salt spray corrosion, characterized in that, The process includes the following steps: S1, collecting data on salt spray concentration distribution, historical frequency of seawater intrusion, road structural layer material properties, protective layer thickness, and meteorological elements along coastal city roads to construct a multi-dimensional raw dataset; S2, inputting the multi-dimensional raw dataset into a road salt spray corrosion progression prediction model, and outputting road corrosion severity levels at different time points by hierarchically analyzing the interface mechanism between salt spray and the road structural layer; S3, based on a salt spray diffusion trajectory tracking algorithm, combined with topographic elevation data and airflow field distribution data, simulating the spatial diffusion path and concentration decay law of salt spray along roads to generate salt spray diffusion heat map data; S4, employing storm surge coupling... The algorithm integrates astronomical tide data, marine wind data, and nearshore topographic data to calculate the inundation range, peak water level, and duration of seawater intrusion under different storm levels; S5, the corrosion degree level data, salt spray diffusion heat map data, and seawater intrusion-related data are imported into the road flood prevention and protection decision platform, and combined with the corrosion resistance threshold of protective materials, structural impermeability coefficient, and drainage system flow capacity parameters in the road waterproofing and protection parameters, a multi-factor coupling analysis is performed; S6, based on the coupling analysis results, targeted protection scheme adjustment instructions are output, including the location of the protective layer repair area, drainage system optimization parameters, anti-corrosion coating spraying process parameters, and seawater intrusion interception facility deployment parameters.

2. The method for protecting coastal city roads from seawater backflow and salt spray corrosion according to claim 1, characterized in that, The expression for the road salt spray corrosion progression prediction model is as follows: Among them, C t C represents the quantification value of the corrosion degree of the road structure layer at time t. o The initial salt spray concentration is the baseline value, α is the salt spray corrosion rate correction factor, β is the road material corrosion attenuation factor, d is the effective penetration depth of the protective layer, and γ is the base value. i M represents the weight of the influence of the material properties of the i-th type of road structure. i Let δ be the salt spray corrosion resistance parameter of the i-th type of road structure material, δ be the comprehensive influence coefficient of meteorological elements, and T be the cumulative duration of salt spray effect.

3. The method for protecting coastal city roads from seawater backflow and salt spray corrosion according to claim 1, characterized in that, The expression for the salt spray diffusion trajectory tracking algorithm is: Where L(x, y, z, t) is the quantized value of the salt mist diffusion trajectory at coordinates (x, y, z) at time t, μ is the salt mist particle diffusion coefficient, (x0, y0, z0) is the initial coordinate of salt mist diffusion, and v x v y v z denoted as x, y, and z, respectively; ρ is the air density influence parameter; H(x, y) is the terrain elevation correction value at coordinate (x, y); ε is the salt spray particle settling coefficient; and Q is the salt spray particle release rate.

4. The method for protecting coastal city roads from seawater backflow and salt spray corrosion according to claim 1, characterized in that, The expression for the storm surge coupling algorithm is as follows: Where Z is the quantified value of the peak seawater intrusion level, ζ0 is the astronomical tide reference value, κ is the storm surge coupling coefficient, W is the average sea wind speed, L is the length of the wind zone, and θ is the wind direction angle. λ is the shoreline angle, λ is the nearshore topographic influence coefficient, S is the storm duration, and D is the nearshore water depth correction parameter.

5. The method for protecting coastal city roads from seawater backflow and salt spray corrosion according to claim 1, characterized in that, The expression for the protection scheme matching model of the road moisture protection decision platform is as follows: Where P is the priority coefficient for adjusting the protection scheme, ω1, ω2, ω3, and ω4 are the weighting coefficients for corrosion degree, diffusion trajectory, backflow water level, and structural impermeability, respectively, and C max L is the quantified value of the maximum permissible corrosion level for roads. max Z represents the quantification value of the maximum range affected by salt spray. max K represents the maximum backflow water level that the road can withstand, and K is the actual seepage resistance coefficient of the road. max This is the maximum permeability coefficient for road design.

6. The method for protecting coastal city roads from seawater backflow and salt spray corrosion according to claim 1, characterized in that, The parameter optimization model expression for road waterproofing and protection is as follows: Where F is the overall effectiveness value of the protection system, ξ, η, and ζ are the effectiveness weights of the anti-corrosion coating, drainage system, and interception facilities, respectively, and T c P is the coating thickness parameter. m δ is a parameter representing the salt spray resistance of the coating. c Q is the coating aging rate parameter. d For the design flow parameters of the drainage system, S d R is the drainage network coverage area parameter. d I is the resistance parameter of the drainage pipe. b For the height parameter of the interception facility, H b D is the impact resistance parameter for the interception facility. b To set the spacing parameters for the interception facilities.

7. The method for protecting coastal city roads from seawater backflow and salt spray corrosion according to claim 1, characterized in that, S3 includes the following sub-steps: S31, extracting terrain elevation data and airflow distribution data from the multi-dimensional original dataset, and spatially discretizing them according to a preset grid precision to form a structured grid dataset; S32, importing the initial release position and release rate parameters of salt fog particles into the salt fog diffusion trajectory tracking algorithm, and setting the particle motion time step and spatial search radius; S33, based on the discretized grid dataset, calculating the airflow velocity vector and terrain slope value within each grid cell to determine the motion direction and speed of salt fog particles within the grid cell; S34, iteratively calculating the spatial coordinates of particles at different time nodes according to the salt fog particle motion parameters, counting the number of salt fog particles at each coordinate point, and generating salt fog diffusion heat map data by combining the concentration decay law.

8. The method for protecting coastal city roads from seawater backflow and salt spray corrosion according to claim 1, characterized in that, S4 includes the following steps: S41, collect astronomical tide observation data, marine wind force observation data and nearshore topographic measurement data, perform data alignment and filtering according to time series, and remove abnormal data; S42. Using the filtered astronomical tide data as the basis, input it into the storm surge coupling algorithm and set the storm level classification threshold and calculation boundary conditions; S43. Analyze the coupling relationship between wind force and tide level through the algorithm, and calculate the water level rise and water flow velocity distribution in nearshore waters under different storm levels; S44. Combine nearshore topographic data to determine the area where the water level rise exceeds the road elevation, and determine the inundation boundary, peak water level, and duration of seawater backflow.

9. The method for protecting coastal city roads from seawater backflow and salt spray corrosion according to claim 1, characterized in that, S5 includes the following sub-steps: S51, standardizing the data on corrosion level, salt spray diffusion heat map, and seawater backflow to a format recognizable by the road moisture protection decision-making platform; S52, accessing the platform's built-in protection parameter database to extract the corrosion resistance threshold of protective materials, structural impermeability coefficient, and baseline parameters for drainage system flow capacity; S53, constructing a multi-factor coupling analysis matrix, using corrosion level, salt spray diffusion range, and backflow intensity as input variables and baseline values ​​of protection parameters as constraints, and performing correlation calculations; S54, based on the calculation results, generating preliminary suggestions for adjusting the protection scheme, including the direction and range of parameter adjustments, to provide data support for subsequent command output.

10. A protective system for coastal city roads against seawater intrusion and salt spray corrosion, characterized in that, This system is applied to the protection method for coastal urban roads against seawater intrusion and salt spray corrosion as described in claim 1, comprising: a multi-source data acquisition unit for collecting data on salt spray concentration distribution, historical frequency of seawater intrusion, road structural layer material characteristics, protective layer thickness, and meteorological elements, and establishing a bidirectional data transmission link with the subsequent data processing unit; a salt spray corrosion prediction unit, which has a built-in progressive prediction model for road salt spray corrosion, receives data transmitted from the multi-source data acquisition unit, outputs road corrosion degree level data at different time points, and sends a collaborative triggering signal to the salt spray diffusion simulation unit; and a salt spray diffusion simulation unit, which, based on a salt spray diffusion trajectory tracking algorithm and combined with terrain elevation and airflow field data, generates salt spray diffusion heat map data, and couples it with a storm surge coupling unit. The system facilitates data interaction; the storm surge coupling unit employs a storm surge coupling algorithm, integrating astronomical tide levels, marine wind force, and nearshore topographic data to calculate seawater intrusion-related data, and synchronizes the results to the protection decision analysis unit; the protection decision analysis unit, as the core execution module of the road flood protection decision platform, receives corrosion level, salt spray diffusion heat map, and seawater intrusion-related data, performs multi-factor coupling analysis in conjunction with road waterproofing protection parameters, and sends control commands to the protection scheme output unit; the protection scheme output unit receives control commands from the protection decision analysis unit, parses and outputs relevant parameters for protective layer repair area positioning, drainage system optimization, anti-corrosion coating spraying, and interception facility deployment, and establishes a communication connection with the on-site road protection execution equipment.

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