Modeling and predicting method, system and equipment of offshore salt mist evolution mechanism and medium

Abstract

The invention discloses an offshore salt mist evolution mechanism modeling and prediction method, system, device and medium, and belongs to the technical field of offshore salt mist evolution prediction.The method comprises the steps that under the preset environment condition, salt mist data and environment data are collected; establishing a first model according to the salt mist data and the environment data; obtaining a first analysis result of the salt mist particles according to the first model, and constructing a second model according to the first analysis result; acquiring a second analysis result of the salt mist particles according to the second model, and constructing a third model in combination with the salt mist data, the environmental data and the first analysis result; analyzing the salt mist particles according to the third model, and calculating salt mist parameters after deliquescence by combining the deliquescence characteristics of the salt mist; and according to the first analysis result, the second analysis result, the third analysis result and the slaked salt mist parameters, predicting the spatial distribution of the offshore salt mist. The method solves the problem that in the prior art, quantitative analysis on salt mist spatial distribution and deposited salt amount cannot be effectively achieved.

Description

Technical Field

[0001] This invention relates to the field of nearshore salt fog evolution prediction technology, specifically to the modeling and prediction methods, systems, equipment, and media for nearshore salt fog evolution mechanisms. Background Technology

[0002] The high salt spray environment in coastal areas can have a significant impact on the service life of mechanical and electrical equipment. Equipment that is exposed to high salt spray environment for a long time often suffers performance degradation due to material corrosion and aging, and its actual lifespan is often only 1 / 5 to 1 / 10 of the designed lifespan, which causes huge economic losses.

[0003] In high salt spray environments, sea salt particles serve as the primary corrosive medium. Their formation, migration, and sedimentation processes in nearshore areas are complex and influenced by various environmental factors, including wind speed, wind direction, temperature, humidity, and coastal topography.

[0004] Currently, research on marine salt spray lacks a systematic description of salt spray evolution under multiple factors. Most existing prediction techniques ignore the state changes of salt spray particles during migration, resulting in insufficient prediction accuracy. In addition, existing prediction techniques have not been able to effectively achieve quantitative analysis of the spatial distribution of salt spray and the amount of deposited salt, which limits the optimization of equipment environmental adaptability design and the formulation of corrosion protection strategies.

[0005] Therefore, there is an urgent need for an analytical method that can comprehensively consider the entire process of marine salt fog generation, migration, deposition, and deliquescence to ensure accurate prediction of nearshore salt fog concentration and distribution. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is: how to predict the evolution mechanism of nearshore salt fog by modeling and predicting the evolution mechanism of nearshore salt fog, while ensuring coverage of the entire process of salt fog generation, migration, deposition and deliquescence, improving the prediction accuracy of the spatial distribution of salt fog concentration in vertical height and horizontal distance from the shore, and achieving accurate quantification of salt fog parameters after deliquescence.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: a modeling and prediction method for the evolution mechanism of nearshore salt fog, comprising the following steps: collecting salt fog data and environmental data under preset environmental conditions; establishing a first model based on the salt fog data and environmental data; obtaining a first analysis result of salt fog particles based on the first model, and constructing a second model based on the first analysis result; obtaining a second analysis result of the salt fog particles based on the second model, and constructing a third model by combining the salt fog data, environmental data, and the first analysis result; analyzing the salt fog particles using the third model to obtain a third analysis result, and calculating the deliquescent salt fog parameters based on the deliquescence characteristics of the salt fog; and predicting the spatial distribution of nearshore salt fog based on the first analysis result, the second analysis result, the third analysis result, and the deliquescent salt fog parameters.

[0009] As a preferred embodiment of the modeling and prediction method for the nearshore salt fog evolution mechanism described in this invention, the step of establishing a third model includes: integrating the collected salt fog data, environmental data, first analysis result parameters, and second analysis results to obtain first integrated data; combining the first integrated data with the distribution of salt fog in terms of vertical height and horizontal distance from the shore in spatial dimensions to construct the third model. The beneficial effect of this preferred embodiment is that the first integrated data, obtained after integration, fuses the physical characteristics and motion patterns of salt fog particles with the measured environment and basic salt fog data, avoiding the one-sidedness in modeling; the constructed third model combines salt fog evolution with spatial distribution characteristics, enabling the third model to characterize the distribution pattern of salt fog concentration in both vertical and horizontal two-dimensional space.

[0010] As a preferred embodiment of the modeling and prediction method for the nearshore salt spray evolution mechanism described in this invention, the step of calculating the salt spray parameters after deliquescence includes: obtaining the third analysis result based on the third model to determine the salt spray-related mass data involved in deliquescence; combining the salt spray-related mass data with the molar mass of sodium chloride and water to calculate the mass percentage of sodium chloride in the deliquescence-related salt spray colloidal particles; and calculating the molar concentration of the sodium chloride solution formed by deliquescence based on the mass percentage of sodium chloride, the salt spray particle density, and the salt spray-related mass data, wherein the mass percentage of sodium chloride and the molar concentration of the sodium chloride solution together constitute the salt spray parameters after deliquescence.

[0011] As a preferred embodiment of the modeling and prediction method for the nearshore salt fog evolution mechanism described in this invention, the method involves: integrating the first analysis result, the second analysis result, the third analysis result, the sodium chloride mass percentage in the deliquescent salt fog parameters, and the molar concentration of the sodium chloride solution to construct second integrated data; matching the second integrated data with the preset environmental conditions, salt fog data, and environmental data to obtain matching results; and outputting the concentration distribution and deposition patterns of nearshore salt fog at different vertical heights and different horizontal distances from the shore based on the matching results. The beneficial effect of this preferred embodiment is that matching the second integrated data with other parameters ensures that the prediction process fully correlates the measured basic data with the model derivation results, ensuring that the matching results can truly reflect the correlation between salt fog evolution and environmental factors; and outputting the salt fog concentration distribution and deposition patterns at different vertical heights and different horizontal distances from the shore based on the matching results, thereby simulating the spatial distribution of nearshore salt fog, clarifying the concentration differences of salt fog in space, and quantifying its deposition characteristics.

[0012] As a preferred embodiment of the modeling and prediction method for the nearshore salt fog evolution mechanism described in this invention, the step of constructing the first model includes: Based on the salt spray particle radius in the salt spray data, a salt spray suspended particle index is set; Based on the salt spray suspended particulate index, the first model is constructed, and its specific form is as follows: ; In the formula, This indicates the volume of suspended particles in the salt spray. This represents the radius of the suspended salt spray particles. The beneficial effect of this preferred embodiment is that by setting the morphology of the suspended salt spray particles using the measured diameter information from the salt spray data, the construction of the first model can closely match the physical characteristics of the actually collected salt spray particles, avoiding model distortion caused by the particle morphology assumption deviating from the measured data.

[0013] As a preferred embodiment of the modeling and prediction method for the nearshore salt fog evolution mechanism described in this invention, the step of constructing the second model includes: Based on the analysis of the environmental data, the motion patterns of the salt spray particles in the air are analyzed, and the second model is constructed, specifically in the following form: ; In the formula, This indicates the velocity at which gravity causes the object to sink to the ground. Represents gravitational acceleration; Indicates particle radius; Indicates the dynamic viscosity of air; This indicates the density of salt spray particles; Indicates the density of air; Furthermore, the falling speed changes over time. The specific manifestations are as follows: ; In the formula, Indicates the falling speed over time; The relaxation time characterizes how quickly a particle reaches a steady-state settling velocity. This indicates the velocity at which gravity causes the air to fall to the ground. in, The specific manifestations are as follows: ; In the formula, Indicates the air drag coefficient; and These represent the mass of the particles and the mass of the displaced air, respectively.

[0014] As a preferred embodiment of the modeling and prediction method for the nearshore salt fog evolution mechanism described in this invention, the step of constructing the third model includes: The specific form of the third model is as follows: ; ; In the formula, This indicates the concentration of suspended salt spray particles near the coast. The correlation coefficient represents the fitting formula for salt spray height distribution; Indicates the fitting index for salt spray height distribution; Indicates vertical height; and Both represent the wind speed / wind direction correlation coefficient; Indicates a fixed coefficient; This represents the index correction factor; This indicates the concentration of salt spray particles at a horizontal distance from the shore. This indicates the concentration of salt spray at the coast; Indicates horizontal distance from the shore; This indicates the offshore distribution index of salt spray concentration; and All represent environmental and geographical factor coefficients during the propagation process; Indicates wind speed; Furthermore, regarding the parameters , , ,as well as Configure; Among them, parameters The specific form of the setting is as follows: ; In the formula, Indicates wind speed; Indicates wind direction; Indicates the direction of the coastline; Among them, parameters The specific form of the setting is as follows: ; Among them, parameters The specific form of the setting is as follows: ; In the formula, Indicates wind speed; Indicates wind direction; Indicates the direction of the coastline; Among them, parameters The specific form of the setting is as follows: ; In the formula, Indicates parameters , is the wind speed / wind direction correlation coefficient.

[0015] This invention provides a modeling and prediction system for the evolution mechanism of nearshore salt fog.

[0016] To address the aforementioned technical problems, the present invention further provides the following technical solution: a modeling and prediction system for the evolution mechanism of nearshore salt fog, comprising: a data acquisition module for acquiring salt fog data and environmental data under preset environmental conditions; a first model construction module for establishing a first model based on the salt fog data and environmental data; a second model construction module for obtaining a first analysis result of salt fog particles based on the first model and constructing a second model based on the first analysis result; a third model construction module for obtaining a second analysis result of the salt fog particles based on the second model and constructing a third model by combining the salt fog data, environmental data, and the first analysis result; a deliquescence-induced salt fog parameter calculation module, wherein the third model analyzes the salt fog particles to obtain a third analysis result and calculates the deliquescence-induced salt fog parameters based on the deliquescence characteristics of the salt fog; and a prediction analysis module for predicting the spatial distribution of nearshore salt fog based on the first analysis result, the second analysis result, the third analysis result, and the deliquescence-induced salt fog parameters.

[0017] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the modeling and prediction method for the nearshore salt fog evolution mechanism.

[0018] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the modeling and prediction method for the nearshore salt fog evolution mechanism.

[0019] The beneficial effects of this invention are as follows: By integrating the physical mechanisms of salt spray generation, migration, deposition, and deliquescence with field observation data, and taking into account the coupling effect of environmental factors, the entire evolution process of salt spray is covered, improving the prediction accuracy of nearshore salt spray spatial distribution. The prediction results can directly provide theoretical support for corrosion risk assessment, protective coating design, and maintenance cycle formulation for coastal electrical equipment and metal structures, effectively improving the reliability and service life of equipment in harsh marine environments, reducing economic losses caused by corrosion, and avoiding the limitations of traditional technologies that rely solely on independent parameters for prediction. Attached Figure Description

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

[0021] Figure 1 The overall flowchart of the modeling and prediction method for the nearshore salt fog evolution mechanism provided in one embodiment of the present invention is shown.

[0022] Figure 2 A lidar detection schematic diagram of a method for modeling and predicting the evolution mechanism of nearshore salt fog provided in an embodiment of the present invention.

[0023] Figure 3 The image shows the particle size distribution of salt fog concentration at different heights along the coast, which is part of a modeling and prediction method for the nearshore salt fog evolution mechanism provided in an embodiment of the present invention.

[0024] Figure 4 Particle radius and terminal settling velocity diagram for a modeling and prediction method of nearshore salt fog evolution mechanism provided in an embodiment of the present invention.

[0025] Figure 5 The graph shows the particle falling velocity / distance versus time variation in a modeling and prediction method for the nearshore salt fog evolution mechanism provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0027] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for modeling and predicting the evolution mechanism of nearshore salt fog, including: S100: Collect salt spray data and environmental data under preset environmental conditions.

[0028] S200: Based on salt spray data and environmental data, establish the first model.

[0029] S300: Obtain the first analysis results of salt spray particles based on the first model, and construct the second model based on the first analysis results.

[0030] S400: Obtain the second analysis results of salt spray particles based on the second model, and construct the third model by combining salt spray data, environmental data and the first analysis results.

[0031] S500: The third model analyzes salt spray particles to obtain the third analysis results, and calculates the salt spray parameters after deliquescence by combining the deliquescence characteristics of salt spray.

[0032] S600: Based on the results of the first, second, and third analyses, as well as the salt spray parameters after deliquescence, the spatial distribution of nearshore salt spray is predicted.

[0033] It should be noted that existing research on marine salt spray lacks a systematic description of salt spray evolution under multiple factors. Most existing prediction techniques ignore the state changes of salt spray particles during migration, resulting in insufficient prediction accuracy. In addition, existing prediction techniques have not been able to effectively achieve quantitative analysis of the spatial distribution of salt spray and the amount of deposited salt, which limits the optimization of equipment environmental adaptability design and the formulation of corrosion protection strategies.

[0034] Therefore, to address the issues of insufficient prediction accuracy and inability to effectively quantify the spatial distribution of salt fog and the amount of deposited salt mentioned in the existing technologies, a modeling and prediction method for the evolution mechanism of nearshore salt fog is constructed. First, salt fog data and environmental data are collected under preset environmental conditions. Second, a first model is established based on the salt fog data and environmental data. Then, the first analysis results of salt fog particles are obtained based on the first model, and a second model is constructed based on these results. Next, the second analysis results of salt fog particles are obtained based on the second model, and a third model is constructed by combining the salt fog data, environmental data, and the first analysis results. The third model analyzes the salt fog particles to obtain the third analysis results, and calculates the salt fog parameters after deliquescence based on the deliquescence characteristics. Finally, the spatial distribution of nearshore salt fog is predicted based on the first, second, and third analysis results, as well as the salt fog parameters after deliquescence.

[0035] Example 2, refer to Figures 1 to 5 This is the second embodiment of the present invention, which provides a modeling and prediction method for the evolution mechanism of nearshore salt fog.

[0036] In this embodiment of the invention, in step S100, salt spray data and environmental data are collected under preset environmental conditions.

[0037] Specifically, the preset environmental conditions are a wind angle of 60° to 90°; before data collection, a measurement section within a range of 3 to 100 meters offshore is determined, and multiple measurement points are set up vertically on the measurement section, with the height range of the measurement points covering 0 to 10 meters and the height spacing between adjacent measurement points being 1 meter; taking the end of the swell or tide as a reference, an environmental monitoring meteorological station is set up at a distance of 3 meters from the normal direction of the coastline, and the position of the meteorological station is adjusted according to the tide level changes to keep its distance from the shore constant.

[0038] Furthermore, data acquisition is performed through a measurement platform. After the measurement platform is started, fixed-point continuous measurement is performed at a sampling frequency of once per minute for at least 1 hour. A laser scattering method salt spray online detection sensor is used to measure the salt spray particle concentration and particle size.

[0039] It should be noted that a multi-factor meteorological instrument was used to measure wind speed, wind direction, significant wave height, air temperature, and relative humidity; a salt spray atmospheric sampler was used to collect salt-containing air samples once per hour.

[0040] It should be noted that the radii of the suspended salt spray particles at different distances H were detected by SAM lidar.

[0041] like Figure 2As shown, based on the Sham imaging principle, the laser emitting surface of this radar intersects with the main plane of the lens and the array sensor in a straight line; the laser emitting surface inherits three wavelengths λ1, λ2 and λ3; the detection signals at different positions of the array sensor correspond to backscattered signals of different H.

[0042] Furthermore, during the calibration process, within the distance H from h1 to hn, n distance positions are evenly divided, and salt spray suspended particles are placed at different distance positions. The peak positions of the backscattered signals are obtained on the abscissa x1 to xn of the area array sensor, and a polynomial h = a0 + a1×x + a2×x is constructed. 2 +a3×x 3 +a4×x 4 By substituting the correction values ​​of (x1,h1), ..., (xn,hn) into the above polynomial, the values ​​of a0, a1, a2, a3, and a4 are obtained using the least squares method.

[0043] Furthermore, after obtaining the distance relationship between the positions of different array sensors in the SAM lidar, salt spray suspended particles with different particle radii are placed at a distance Hk for backscatter signal correction. For salt spray suspended particles with particle radii from r1 to rm (m in total), the detected λ1 wavelength laser backscatter signals are P11 to P1m, the detected λ2 wavelength laser backscatter signals are P21 to P2m, and the detected λ3 wavelength laser backscatter signals are P31 to P3m. Based on the squared distance relationship of the light transmission types, for the q-th particle radius (q being an integer from 1 to m), the backscatter signal is preprocessed to obtain Pq1 / rq. 2 Pq2 / rq 2 Pq3 / rq 2 By processing each of the above m particle radius samples, three-wavelength scattering tables for different particle radii can be obtained.

[0044] In this embodiment of the invention, reference is made to Figure 3 In step S200, a first model is established based on salt spray data and environmental data, including the following steps A1~A2: A1: Based on the salt spray particle radius in the salt spray data, set the morphology of the suspended salt spray particles.

[0045] Specifically, the salt spray suspended particulate matter index is divided into 6 levels according to radius, and the specific manifestations are as follows: ; In the formula, This represents the radius of the suspended particles in the salt spray.

[0046] A2: Construct the first model based on the particle size and morphology of the salt spray suspended particles.

[0047] It should be noted that the first model is specifically a model of the basic physical properties of salt spray particles.

[0048] Specifically, the basic physical property model of salt spray particles is represented as follows: ; In the formula, This indicates the volume of suspended particles in the salt spray. This represents the radius of the suspended particles in the salt spray.

[0049] In one possible implementation, the first model can also be replaced by laser diffraction particle size analysis, which involves emitting a laser into a salt spray particle field, utilizing the difference in diffraction angles of particles of different sizes to convert the diffraction light signal into particle size distribution data, and then calculating the volume of individual particles and the physical property parameters of the overall particle group based on the particle size.

[0050] In another possible implementation, the first model can also be replaced by the sedimentation balance weighing method. By collecting salt spray particles onto a filter membrane of known density, measuring the total mass of the collected salt spray, and combining this with the number of salt spray particles collected simultaneously, the average mass of a single particle is calculated. Then, the volume of a single particle is deduced from the density of the salt spray particles, thereby obtaining the volume distribution of particles in different particle size ranges.

[0051] In this embodiment of the invention, reference is made to Figure 4 In step S300, the first analysis result of salt spray particles is obtained according to the first model, and the second model is constructed according to the first analysis result.

[0052] It should be noted that the second model is specifically a salt spray particle settling velocity model, and the first analysis result is specifically a salt spray particle volume.

[0053] Specifically, the salt spray particle settling velocity model is represented as follows: ; In the formula, This indicates the velocity at which gravity causes the object to sink to the ground. Represents gravitational acceleration; Indicates particle radius; Indicates the dynamic viscosity of air; This indicates the density of salt spray particles; This indicates the density of air.

[0054] Furthermore, the falling speed changes over time. The specific manifestations are as follows: ; In the formula, Indicates the falling speed over time; The relaxation time characterizes how quickly a particle reaches a steady-state settling velocity. It represents the velocity at which gravity causes air to fall to the ground.

[0055] in, The specific manifestations are as follows: ; In the formula, Indicates the air drag coefficient; and These represent the mass of the particles and the mass of the displaced air, respectively.

[0056] In one possible implementation, the second model analysis of particle motion can also be replaced by particle image velocimetry. Particle image velocimetry involves arranging a dual-pulse laser and a high-speed camera in the measurement area. The laser beam is formed by optical elements to irradiate the salt spray particle field with a thin sheet of light. The high-speed camera simultaneously captures particle images. By tracking the displacement changes of the salt spray particles, the settling velocity and motion parameters of particles of different sizes under different environmental conditions can be calculated.

[0057] In another possible implementation, the second model analysis of particle motion can be replaced by a phase Doppler particle analyzer. The phase Doppler particle analyzer emits two laser beams of different frequencies to cover the detection area. When salt spray particles pass through the detection area, they generate Doppler frequency shift signals. The particle size is calculated by analyzing the phase difference of the signals, and the instantaneous velocity of the particles is calculated by converting the frequency shift. At the same time, combined with the spatial arrangement of the laser beams, the motion direction of the particles in the vertical and horizontal directions is obtained.

[0058] In this embodiment of the invention, step S400, which involves obtaining the second analysis result of salt spray particles based on the second model, and constructing a third model by combining salt spray data, environmental data, and the first analysis result, includes the following steps B1~B2: B1: Integrate the collected salt spray data, environmental data, parameters from the first analysis result, and the second analysis result to obtain the first integrated data.

[0059] It should be noted that the second analysis result specifically refers to the motion characteristics data of salt spray particles, which are expressed as the gravitational settling threshold velocity of salt spray particles and their falling velocity over time; the first integrated data specifically includes salt spray data, environmental data, salt spray particle volume, as well as the gravitational settling threshold velocity of salt spray particles and their falling velocity over time.

[0060] Specifically, during the integration process, consistency checks are performed on various types of data, and then the data collected at the same time are matched with the corresponding first and second analysis results to form a dataset.

[0061] B2: Combine the first integrated data with the spatial dimensions of vertical height and horizontal distance from the shore to construct the third model.

[0062] It should be noted that the third model is specifically a salt spray spatial distribution model.

[0063] Specifically, the spatial distribution model of salt spray includes vertical distribution formulas and horizontal distribution formulas.

[0064] The specific form of the vertical distribution formula is as follows: ; In the formula, This indicates the concentration of suspended salt spray particles near the coast. The correlation coefficient represents the fitting formula for salt spray height distribution; Indicates the fitting index for salt spray height distribution; Indicates vertical height; and Both represent the wind speed / wind direction correlation coefficient; Indicates a fixed coefficient; This represents the index correction factor; Furthermore, regarding the parameters , , ,as well as Configure; Among them, parameters The specific form of the setting is as follows: ; In the formula, Indicates wind speed; Indicates wind direction; Indicates the direction of the coastline; Among them, parameters The specific form of the setting is as follows: ; Among them, parameters The specific form of the setting is as follows: ; In the formula, Indicates wind speed; Indicates wind direction; Indicates the direction of the coastline; Among them, parameters The specific form of the setting is as follows: ; In the formula, Indicates parameters , is the wind speed / wind direction correlation coefficient; The specific form of the horizontal distribution formula is as follows: ; In the formula, This indicates the concentration of salt spray particles at a horizontal distance from the shore. This indicates the concentration of salt spray at the coast; Indicates horizontal distance from the shore; This indicates the offshore distribution index of salt spray concentration; and All represent environmental and geographical factor coefficients during the propagation process; Indicates wind speed.

[0065] It should be noted that the technical solution adopted in this paper is... and Data for Wenchang Foguang and Lingao Huanglonggang.

[0066] In the scene depicting the Buddha's Light in Wenchang: , ; In the scene at Huanglonggang, Lingao: , ; In one possible implementation, the third model can also be constructed using a dual-inference symbolic regression framework. This framework analyzes the correlation between salt spray concentration, vertical height, and horizontal offshore distance variables, and uses the model residuals from historical cycles to locate poorly fitted data segments. Furthermore, by summarizing the model construction experience, it generates the equation skeleton for the vertical and horizontal distribution of salt spray.

[0067] In another possible implementation, the third model can also be constructed using the gradient boosting decision tree algorithm. The gradient boosting decision tree algorithm uses the collected salt fog data and environmental data as input features, and takes the salt fog concentration corresponding to different vertical heights and horizontal distances from the shore as target variables. Iteratively trains multiple regression decision trees, with each tree correcting the prediction residuals of the preceding model. The algorithm automatically learns the correlation weights between wind speed, wind direction and vertical or horizontal propagation of salt fog, and obtains the variation law of salt fog concentration in the spatial dimension.

[0068] In this embodiment of the invention, step S500 involves the third model analyzing salt spray particles to obtain a third analysis result, and calculating the salt spray parameters after deliquescence based on the salt spray deliquescence characteristics. This includes the following steps C1~C3: C1: Obtain the third analysis results based on the third model to determine the salt spray-related quality data involved in deliquescence.

[0069] It should be noted that the third analysis result is specifically the spatial distribution correlation information of salt spray, which is represented by the distribution data of salt spray concentration at different vertical heights and different horizontal distances from the shore.

[0070] Specifically, based on the third analysis results output by the third model, the salt spray particle concentration data corresponding to each spatial location is extracted, and combined with the salt spray particle volume and salt spray particle density in the first analysis results, the mass of a single salt spray particle is determined.

[0071] Furthermore, based on the salt spray particle concentration and the mass of a single salt spray particle at each spatial location, the total mass of salt spray participating in deliquescence at different spatial locations is calculated.

[0072] C2: Combine the relevant salt spray quality data with the molar masses of sodium chloride and water to calculate the mass percentage of sodium chloride in the deliquescent salt spray colloidal particles.

[0073] It should be noted that analysis has determined that the main component of salt spray particles during the deliquescence process is sodium chloride.

[0074] Furthermore, the specific form of salinity of the salt spray colloidal particles after deliquescence is as follows: ; In the formula, This indicates the mass percentage of sodium chloride in the salt spray colloidal particles after deliquescence; This indicates the mass of sodium chloride involved in the deliquescence process; This indicates the mass of water that combines with sodium chloride during the deliquescence process; This indicates the molar mass of sodium chloride.

[0075] C3: Based on the mass percentage of sodium chloride, the density of salt spray particles, and related mass data of salt spray, calculate the molar concentration of the sodium chloride solution formed by deliquescence. The mass percentage of sodium chloride and the molar concentration of the sodium chloride solution together constitute the salt spray parameters after deliquescence.

[0076] Specifically, based on the mass percentage of sodium chloride and relevant salt spray data, the actual mass of sodium chloride in the deliquescent sodium chloride solution is determined.

[0077] Specifically, the molar concentration of the sodium chloride solution formed by deliquescence is expressed as follows: ; In the formula, Indicates the molar concentration of the sodium chloride solution formed by deliquescence; This indicates the mass of sodium chloride that participated in deliquescence; This indicates the density of salt spray particles; This indicates the mass percentage of sodium chloride in the salt spray colloidal particles after deliquescence.

[0078] It should be noted that the mass percentage of sodium chloride and the molar concentration of the sodium chloride solution together constitute the salt spray parameters after deliquescence.

[0079] In one possible implementation, the salt spray parameters after deliquescence can be replaced by ion chromatography combined with gravimetric analysis. Salt spray deposition samples from different spatial locations in the nearshore area are collected by ion chromatography combined with gravimetric analysis. After removing free water by constant temperature drying, the samples are weighed to obtain the total mass of the salt spray. The samples are then dissolved in ultrapure water, and the chloride ion concentration is separated and detected using ion chromatography. The mass of sodium chloride is calculated by combining the stoichiometric relationship between chloride ions and sodium chloride, and then the mass ratio is calculated. Finally, the molar concentration is derived based on the sample solution volume, the mass of sodium chloride, and the molar mass.

[0080] In another possible implementation, the salt spray parameters after deliquescence can be replaced by microwave resonant cavity sensor technology. The microwave resonant cavity sensor emits a fixed frequency microwave signal into the salt spray monitoring area. The sodium chloride solution formed by the deliquescence of salt spray particles will change the propagation constant of microwaves. The sensor captures the resonant frequency offset. By using a preset rate offset and salt concentration calibration curve, combined with the correspondence between the dielectric properties of sodium chloride solution and concentration, the mass ratio of sodium chloride is calculated in reverse. Finally, the molar concentration is calculated by combining the volume and density data of salt spray particles.

[0081] In this embodiment of the invention, step S600, which predicts the spatial distribution of nearshore salt fog based on the first analysis result, the second analysis result, the third analysis result, and the salt fog parameters after deliquescence, includes the following steps D1~D3: D1: Integrate the results of the first, second, and third analyses, the mass percentage of sodium chloride in the salt spray parameters after deliquescence, and the molar concentration of sodium chloride solution to construct the second integrated data.

[0082] It should be noted that the second integrated data includes the volume of salt spray particles, the gravitational settling velocity of salt spray particles, the falling velocity over time, the distribution data of salt spray concentration at different vertical heights and different horizontal distances from the shore, the mass percentage of sodium chloride in the salt spray parameters after deliquescence, and the molar concentration of sodium chloride solution.

[0083] Specifically, the parameters are summarized and standardized in format, invalid data is removed, and then an association mapping is established to form a structured dataset, which is the second integrated data.

[0084] D2: Match the second integrated data with the preset environmental conditions, and the salt spray data with the environmental data to obtain the matching results.

[0085] Specifically, using spatial location as the matching dimension, the physical properties, motion patterns, spatial distribution characteristics, and core parameters after deliquescence of salt spray particles corresponding to each spatial location in the second integrated information are associated with preset environmental conditions, the original collected salt spray data, and environmental data to form an associated dataset.

[0086] D3: Based on the matching results, output the concentration distribution and deposition patterns of nearshore salt fog at different vertical heights and different horizontal distances from the shore.

[0087] It should be noted that the associated dataset is the matching result.

[0088] Specifically, based on the associated dataset, relevant data on salt fog concentration and depositional impact parameters for each spatial location are extracted. By analyzing the movement patterns, spatial distribution characteristics, and post-deliquescence parameters of salt fog particles, the variation trend and deposition intensity of salt fog concentration at different spatial locations are determined. Finally, the specific concentration values ​​of nearshore salt fog at each vertical height and each horizontal distance from the shore, as well as the deposition patterns of salt fog in different spatial regions, are output.

[0089] In summary, this invention integrates the physical mechanisms of salt spray generation, migration, deposition, and deliquescence with field observation data, and considers the coupling effect of environmental factors, covering the entire evolution process of salt spray. This improves the prediction accuracy of nearshore salt spray spatial distribution. The prediction results can directly provide theoretical support for corrosion risk assessment, protective coating design, and maintenance cycle formulation for coastal electrical equipment and metal structures. This effectively improves the reliability and service life of equipment in harsh marine environments, reduces economic losses caused by corrosion, and avoids the limitations of traditional technologies that rely solely on independent parameters for prediction.

[0090] Example 3, the third embodiment of the present invention, provides a modeling and prediction system for the evolution mechanism of nearshore salt fog, including: The data acquisition module collects salt spray data and environmental data under preset environmental conditions.

[0091] The first model construction module establishes the first model based on salt spray data and environmental data.

[0092] The second model building module obtains the first analysis results of salt spray particles based on the first model, and builds the second model based on the first analysis results.

[0093] The third model construction module obtains the second analysis results of salt spray particles based on the second model, and constructs the third model by combining salt spray data, environmental data and the first analysis results.

[0094] The salt spray parameter calculation module after deliquescence uses a third model to analyze salt spray particles and obtain the third analysis results, and calculates the salt spray parameters after deliquescence based on the deliquescence characteristics of salt spray.

[0095] The predictive analysis module predicts the spatial distribution of nearshore salt fog based on the first analysis result, the second analysis result, the third analysis result, and the salt fog parameters after deliquescence.

[0096] Example 4, the fourth embodiment of the present invention, differs from the previous three embodiments in that: if the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0098] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0099] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination of all three. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0100] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A modeling and prediction method for the evolution mechanism of nearshore salt fog, characterized in that, include: Under preset environmental conditions, salt spray data and environmental data were collected. Based on the salt spray data and environmental data, a first model is established; The first analysis results of salt spray particles are obtained based on the first model, and a second model is constructed based on the first analysis results; Based on the second model, a second analysis result of the salt spray particles is obtained, and a third model is constructed by combining the salt spray data, environmental data and the first analysis result. The third model analyzes the salt spray particles to obtain the third analysis result, and calculates the salt spray parameters after deliquescence by combining the salt spray deliquescence characteristics. Based on the first analysis result, the second analysis result, the third analysis result, and the salt spray parameters after deliquescence, the spatial distribution of the nearshore salt spray is predicted.

2. The modeling and prediction method for the nearshore salt fog evolution mechanism as described in claim 1, characterized in that... The steps to establish a third model include: By integrating the collected salt spray data, environmental data, first analysis result parameters, and second analysis results, first integrated data is obtained; The third model is constructed by combining the first integrated data with the distribution of salt spray based on the vertical height and horizontal distance from the shore in the spatial dimension.

3. The modeling and prediction method for the nearshore salt fog evolution mechanism as described in claim 2, characterized in that... The steps for calculating salt spray parameters after deliquescence include: Based on the third model, the third analysis results are obtained, and the relevant quality data of salt spray involved in deliquescence are determined. By combining the salt spray-related quality data with the molar masses of sodium chloride and water, the mass percentage of sodium chloride in the deliquescent salt spray colloidal particles was calculated. Based on the mass percentage of sodium chloride, the density of salt spray particles, and the relevant mass data of the salt spray, the molar concentration of the sodium chloride solution formed by deliquescence is calculated. The mass percentage of sodium chloride and the molar concentration of the sodium chloride solution together constitute the salt spray parameters after deliquescence.

4. The modeling and prediction method for the nearshore salt fog evolution mechanism as described in claim 3, characterized in that... The steps for predicting the spatial distribution of nearshore salt spray include: The first analysis result, the second analysis result, the third analysis result, the sodium chloride mass percentage in the salt spray parameters after deliquescence, and the molar concentration of sodium chloride solution are integrated to construct the second integrated data; The second integrated data is matched with the preset environmental conditions, salt spray data and environmental data to obtain matching results; Based on the matching results, the concentration distribution and deposition patterns of the nearshore salt spray at different vertical heights and different horizontal distances from the shore are output.

5. The modeling and prediction method for the nearshore salt fog evolution mechanism as described in claim 4, characterized in that... The steps for constructing the first model include: Based on the salt spray particle radius in the salt spray data, a salt spray suspended particle index is set; Based on the salt spray suspended particulate index, the first model is constructed, and its specific form is as follows: ； In the formula, This indicates the volume of suspended particles in the salt spray. This represents the radius of the suspended particles in the salt spray.

6. The modeling and prediction method for the nearshore salt fog evolution mechanism as described in claim 1, characterized in that... The steps for constructing the second model include: Based on the analysis of the environmental data, the motion patterns of the salt spray particles in the air are analyzed, and the second model is constructed, specifically in the following form: ； In the formula, This indicates the velocity at which gravity causes the object to sink to the ground. Represents gravitational acceleration; Indicates particle radius; Indicates the dynamic viscosity of air; This indicates the density of salt spray particles; Indicates the density of air; Furthermore, the falling speed changes over time. The specific manifestations are as follows: ； In the formula, Indicates the falling speed over time; The relaxation time characterizes how quickly a particle reaches a steady-state settling velocity. This indicates the velocity at which gravity causes the air to fall to the ground. in, The specific manifestations are as follows: ； In the formula, Indicates the air drag coefficient; and These represent the mass of the particles and the mass of the displaced air, respectively.

7. The modeling and prediction method for the nearshore salt fog evolution mechanism as described in claim 1, characterized in that... The steps for constructing the third model include: The specific form of the third model is as follows: ； ； In the formula, This indicates the concentration of suspended salt spray particles near the coast. The correlation coefficient represents the fitting formula for salt spray height distribution; Indicates the fitting index for salt spray height distribution; Indicates vertical height; and Both represent the wind speed / wind direction correlation coefficient; Indicates a fixed coefficient; This represents the index correction factor; This indicates the concentration of salt spray particles at a horizontal distance from the shore. This indicates the concentration of salt spray at the coast; Indicates horizontal distance from the shore; This indicates the offshore distribution index of salt spray concentration; and All represent environmental and geographical factor coefficients during the propagation process; Indicates wind speed; Furthermore, regarding the parameters , , ,as well as Configure; Among them, parameters The specific form of the setting is as follows: ； In the formula, Indicates wind speed; Indicates wind direction; Indicates the direction of the coastline; Among them, parameters The specific form of the setting is as follows: ； Among them, parameters The specific form of the setting is as follows: ； In the formula, Indicates wind speed; Indicates wind direction; Indicates the direction of the coastline; Among them, parameters The specific form of the setting is as follows: ； In the formula, Indicates parameters , where is the wind speed / wind direction correlation coefficient.

8. A modeling and prediction system for nearshore salt fog evolution mechanisms, employing the modeling and prediction method for nearshore salt fog evolution mechanisms as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module collects salt spray data and environmental data under preset environmental conditions; The first model construction module establishes a first model based on the salt spray data and environmental data; The second model building module obtains the first analysis results of salt spray particles based on the first model, and builds the second model based on the first analysis results; The third model construction module obtains the second analysis results of the salt spray particles based on the second model, and constructs the third model by combining the salt spray data, environmental data and the first analysis results. The salt spray parameter calculation module after deliquescence includes a third model that analyzes the salt spray particles to obtain a third analysis result and calculates the salt spray parameters after deliquescence based on the deliquescence characteristics of the salt spray. The predictive analysis module predicts the spatial distribution of nearshore salt fog based on the first analysis result, the second analysis result, the third analysis result, and the salt fog parameters after deliquescence.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the modeling and prediction method for the nearshore salt fog evolution mechanism as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the modeling and prediction method for the nearshore salt fog evolution mechanism as described in any one of claims 1 to 7.

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