A method and device for constructing a chloride ion deposition rate profile and a medium

Abstract

The present application relates to the technical field of material performance research, and particularly relates to a method and device for constructing a distribution map of a chloride ion deposition rate and a medium. The method comprises the following steps: constructing a chloride ion deposition rate prediction model; obtaining meteorological data and geographical environment data of each to-be-tested space in a to-be-tested time period; obtaining the chloride ion deposition rate of each to-be-tested space in the to-be-tested time period through the chloride ion deposition rate prediction model; and generating a chloride ion deposition rate distribution map according to the latitude and longitude and the chloride ion deposition rate of all to-be-tested spaces. The chloride ion deposition rate of a to-be-tested space can be predicted by inputting the meteorological data and geographical environment data of the to-be-tested space into the chloride ion deposition rate prediction model, and the chloride ion deposition rate distribution map can be generated by combining the chloride ion deposition rates of a plurality of to-be-tested spaces. The chloride ion deposition rate distribution map can be used to guide the selection of appropriate materials for use in a to-be-tested region.

Description

Technical Field

[0001] This invention relates to the field of materials performance research technology, and in particular to a method, apparatus and medium for constructing a chloride ion deposition rate distribution map. Background Technology

[0002] Corrosion of materials is particularly prominent in marine atmospheric environments, with chloride ion deposition on material surfaces being one of the main causes. Chloride ions in the marine atmosphere are carried by wind and settle onto metal surfaces. These settled chloride ions are hygroscopic and, under certain humidity conditions, form a water film on the material surface, accelerating corrosion.

[0003] Currently, methods for observing chloride ion deposition rates in the atmosphere mainly involve collecting chloride ions using methods such as gauze strips, wet candles, or atmospheric sampling, followed by observation of the deposition rate using laboratory instruments like ion chromatography and spectrophotometry. The conventional gauze strip method only reflects the deposition rate of chloride ions in the atmosphere, not the actual amount deposited on different material surfaces. Additionally, X-rays can be used to collect chloride ion content. For example, the method disclosed in document CN111337527A uses X-rays emitted from an X-ray tube to excite chloride ions on a collection carrier, causing them to emit characteristic fluorescent rays that are received by a photon detector. The photon detector transmits the received chloride ion characteristic ray information to a signal processor, and the collected chloride ion information is displayed on a display terminal. Although this method can accurately and quickly collect and monitor chloride ions in the atmosphere, it is mainly suitable for collecting chloride ions at a single monitoring point and is not economical or applicable for monitoring chloride ions in the entire marine atmosphere of a certain area.

[0004] More importantly, the chloride ion deposition rate in the atmosphere is affected by various factors (such as wind direction, wind speed, distance from the sea, and wave height), leading to significant differences in chloride ion deposition rates across different regions. To address the corrosion of metallic materials caused by varying chloride ion deposition rates in different regions, it is best to adopt differentiated metal corrosion protection management methods. Therefore, it is of great significance to directly and rapidly observe the chloride ion deposition rate on material surfaces using a unified method and to construct a regional chloride ion deposition rate distribution map. However, a major technical challenge in constructing this regional chloride ion deposition rate distribution map lies in ensuring the accuracy of the map with relatively few samples and measurements. Summary of the Invention

[0005] This invention discloses a method, apparatus and medium for constructing a chloride ion deposition rate distribution map, which can fit the chloride ion deposition rate of a large area and construct a chloride ion deposition rate distribution map with a small number of samples.

[0006] To achieve the above objectives, a method for constructing a chloride ion deposition rate distribution map is provided, the specific method of which is as follows:

[0007] Construct a model for predicting chloride ion deposition rates;

[0008] Determine the latitude and longitude of several spaces to be measured, and obtain meteorological and geographical environmental data for each space during the measurement period;

[0009] The meteorological data and geographical environment data of each space to be tested during the test period are input into the chloride ion deposition rate prediction model to obtain the chloride ion deposition rate of each space to be tested during the test period.

[0010] Based on the latitude and longitude and chloride ion deposition rate of all the spaces to be measured, a chloride ion deposition rate distribution map is generated.

[0011] The advantage of this embodiment is that by inputting meteorological and geographical environmental data of the space to be tested into the chloride ion deposition rate prediction model, the chloride ion deposition rate of the space to be tested can be predicted. By combining the chloride ion deposition rates of several spaces to be tested, a chloride ion deposition rate distribution map can be generated. Based on the chloride ion deposition rate distribution map, the selection of appropriate materials for use in the area to be tested can be guided.

[0012] Optionally, the meteorological data of the space to be measured during the time period includes one or more of the following:

[0013] Average temperature, average humidity, rainfall, average wind speed, and prevailing wind direction.

[0014] Specifically, the geographical environmental data includes: distance from the sea and ocean influence transmission coefficient.

[0015] Furthermore, the ocean influence transmission coefficient is used to evaluate the sensitivity of space to the influence of ocean climate, and is given by an expert database using a nine-point scoring method.

[0016] The advantage of this embodiment is that, since the average chloride ion deposition rate is most affected by the marine climate, the geographical environment data is divided into distance from the sea and marine influence transmission coefficient. Distance from the sea can reflect the magnitude of the projection of the marine climate on the area to be measured, and the marine influence transmission coefficient can reflect the degree of acceptance of the marine climate by the space to be measured. These two conditions can directly reflect the degree of influence of the ocean on the sample.

[0017] Furthermore, the training data for the chloride ion deposition rate prediction model was obtained through experiments, the specific experimental methods of which are as follows:

[0018] Several sampling points were selected in adjacent areas of the space to be tested, and different sampling points and samples were numbered.

[0019] The sample is placed at the sampling point and exposed to the natural atmospheric environment, allowing chloride ions to freely deposit onto the sample surface during the test period.

[0020] Record the meteorological and geographical environmental data of the sampling point during the test period, as well as the average chloride ion deposition concentration of the sample, and convert the chloride ion deposition rate into chloride ion deposition rate.

[0021] Training samples were constructed using meteorological and geographical environmental data from the sampling points as variables and chloride ion deposition rate of the samples as the dependent variable.

[0022] The prediction model was trained using training samples to obtain a prediction model for chloride ion deposition rate.

[0023] The advantage of this embodiment is that it adopts a relatively simple experimental procedure and can simultaneously acquire raw data from multiple test spaces; the chloride ion deposition rate prediction model generated by a small number of training samples can be used to predict the chloride ion deposition rate in a certain area, which significantly reduces the difficulty of data acquisition compared to conducting experiments at each point.

[0024] Furthermore, the chloride ion content on the sample surface was obtained using X-ray fluorescence spectrometry, as follows:

[0025] Place the sample flat on the sample stage, and set the sample stage movement time interval, X-axis movement distance, Y-axis movement distance, X-axis total movement range, and Y-axis total movement range.

[0026] Turn on the X-ray fluorescence spectrometer, set the X-ray phototube voltage range to 5-50kV and the current range to 20-200nA, and set the X-ray fluorescence detector detection time to 20-200s;

[0027] The sample stage is controlled to move along a preset path to scan the chlorine content on the sample surface.

[0028] The advantage of this embodiment is that the X-ray fluorescence spectroscopy process for obtaining chlorine content can reduce errors caused by experimental data acquisition and facilitate data normalization.

[0029] Specifically, meteorological data of the space to be measured during the time period is obtained through meteorological data from the meteorological station.

[0030] Furthermore, the specific method for generating the chloride ion deposition rate distribution map is as follows:

[0031] In MATLAB, divide the plotted area data map into grids according to latitude and longitude.

[0032] The scatter dataset of chloride ion deposition rate in the area to be tested is located on the grid map of the area using latitude and longitude coordinates;

[0033] Chloride ion deposition rate data for the entire atmospheric environment region are obtained by fitting the scatter plot data of chloride ion deposition rate in the grid using an interpolation algorithm.

[0034] Different chloride ion deposition rates were labeled with different colors, and contour lines, coordinate axes, and image titles were added to obtain a chloride ion deposition rate distribution map.

[0035] The advantage of this embodiment is that the interpolation algorithm only needs to fit the chloride ion deposition rate, which can reduce the number of times the prediction model is used, significantly save computing power, and improve efficiency.

[0036] To achieve the above objectives, on the other hand, a device for constructing a chloride ion deposition rate distribution map is provided, comprising: a model construction module, an information acquisition module, a deposition prediction module, and a graph generation module;

[0037] The model is constructed to build a chloride ion deposition rate prediction model.

[0038] The information acquisition module determines the latitude and longitude of several spaces to be measured, and acquires meteorological data and geographical environment data for each space to be measured within the time period to be measured.

[0039] The deposition prediction module inputs the meteorological data and geographical environment data of each space to be measured within the time period to the chloride ion deposition rate prediction model to obtain the chloride ion deposition rate of each space to be measured within the time period.

[0040] The graphics generation module generates a chloride ion deposition rate distribution map based on the latitude and longitude of all the spaces to be measured and the chloride ion deposition rate.

[0041] To achieve the above objectives, another aspect is to provide a storage medium storing a plurality of instructions that are applicable to a processor for loading to execute the above-described method for constructing a chloride ion deposition rate distribution map.

[0042] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained from the following description and claims. Attached Figure Description

[0043] The accompanying drawings of this invention are described below.

[0044] Figure 1 This is a distribution map of chloride ion deposition rate on the surface of the marine atmospheric environment in Example 2.

[0045] Figure 2 This is a flowchart of Example 1. Detailed Implementation

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0047] Example 1:

[0048] A method for constructing a chloride ion deposition rate distribution map is described below:

[0049] S1. Construct a prediction model for chloride ion deposition rate.

[0050] The training data for the chloride ion deposition rate prediction model was obtained through experiments, the specific experimental methods of which are as follows:

[0051] S11. Select several sampling points in the adjacent areas of the space to be tested, and number the different sampling points and samples.

[0052] S12. Place the sample at the sampling point and expose it to the natural atmospheric environment, allowing chloride ions to freely deposit onto the sample surface during the test period.

[0053] S13. Record the meteorological and geographical environmental data of the sampling point during the test period, as well as the average chloride ion deposition concentration of the sample, and convert the chloride ion deposition rate into the chloride ion deposition rate.

[0054] The chloride ion content on the sample surface was obtained using X-ray fluorescence spectroscopy, and the specific method is as follows:

[0055] S131. Place the sample flat on the sample stage, and set the sample stage movement time interval, X-axis movement distance, Y-axis movement distance, X-axis total movement range, and Y-axis total movement range.

[0056] S132. Turn on the X-ray fluorescence spectrometer, set the X-ray phototube voltage range to 5-50kV and the current range to 20-200nA, and set the X-ray fluorescence detector detection time to 20-200s;

[0057] S133. Control the sample stage to move along the preset path and scan the chlorine content on the sample surface.

[0058] In this embodiment, an X-ray fluorescence spectrometer is used to acquire the chloride ion content of sample surfaces from different locations. The sample to be tested is placed flat on the sample stage, and the movement time interval, X-axis movement distance, Y-axis movement distance, total X-axis movement range, and total Y-axis movement range of the sample stage are set. The X-ray fluorescence spectrometer is turned on, connected to the computer software, and the X-ray phototube voltage range is set to 5-50kV, the current range to 20-200nA, and the X-ray fluorescence detector detection time is set to 20-200s, which must be consistent with the sample stage movement time interval. The sample stage is moved along a preset path to perform a full scan of the chloride content on the sample surface. To ensure the accuracy of the measurement results, no fewer than 100 data points are scanned on the sample surface. The average value of the data from the full scan of the sample surface using the X-ray fluorescence spectrometer is taken to calculate the chloride ion deposition rate on the sample surface, which is used as the chloride ion deposition rate at that measurement location.

[0059] S14. Using meteorological and geographical environmental data of the sampling points as variables and chloride ion deposition rate of the samples as the dependent variable, construct training samples.

[0060] S15. Train the prediction model using training samples to obtain the chloride ion deposition rate prediction model.

[0061] Specifically, the prediction model can be an AI prediction model such as a neural network model, a random forest model, or a Markov prediction model.

[0062] In this embodiment, taking a neural network algorithm as an example, the newff function is used to build the neural network, and the train function is used to train the neural network. The specific steps are as follows:

[0063] S151. Initialization: Set the number of hidden layers and their connections.

[0064] S152. Input training samples and calculate the output of each hidden layer;

[0065] S153. Calculate the error function;

[0066] S154. Backpropagate the error and adjust the weights of each hidden layer.

[0067] S155. Iterate until the error or number of iterations reaches the target.

[0068] In this embodiment, sampling locations are rationally arranged, and samples are placed at the selected sampling locations to expose the samples to natural atmospheric conditions for a certain period of time, allowing chloride ions to freely deposit onto the sample surface. After a period of exposure, the samples are recovered. Samples exposed at different locations are numbered, and the latitude and longitude coordinates of different sampling locations are recorded. In order to accurately reflect the chloride ion content deposited on the material surface, the samples used are material samples that are close to actual use conditions as chloride element deposition carriers, and the sample surface is flat and clean.

[0069] S2. Determine the latitude and longitude of several spaces to be measured, and obtain meteorological and geographical environmental data for each space during the measurement period.

[0070] Specifically, the meteorological data of the space to be measured during the time period includes average temperature, average humidity, rainfall, average wind speed, and prevailing wind direction.

[0071] Specifically, meteorological data of the space to be measured during the time period is obtained through meteorological data from the meteorological station.

[0072] Specifically, the geographical environmental data includes: distance from the sea and ocean influence transmission coefficient.

[0073] In this embodiment, the influence of topography on chloride ion transport and diffusion is uniformly attributed to the marine conduction influence coefficient. The main influencing factors of the marine conduction influence coefficient include: wind gaps, windward slopes, leeward slopes, mountain tops, windbreaks, etc., which are scored by an expert database based on experience.

[0074] Furthermore, the ocean influence transmission coefficient is used to evaluate the sensitivity of space to the influence of ocean climate, and is given by an expert database using a nine-point scoring method.

[0075] S3. Input the meteorological data and geographical environment data of each space to be tested within the time period into the chloride ion deposition rate prediction model to obtain the chloride ion deposition rate of each space to be tested within the time period.

[0076] Specifically, meteorological data of the space to be measured during the time period is obtained through meteorological data from the meteorological station.

[0077] S4. Generate a chloride ion deposition rate distribution map based on the latitude and longitude and chloride ion deposition rate of all the spaces to be measured.

[0078] The specific method for generating a chloride ion deposition rate distribution map is as follows:

[0079] S41. In MATLAB, divide the plotted area data map into grids according to latitude and longitude;

[0080] S42. Locate the scatter dataset of chloride ion deposition rate in the area to be tested on the grid map of the drawn area using latitude and longitude coordinates.

[0081] S43. The chloride ion deposition rate data of the entire atmospheric environment region is obtained by fitting the scatter data of chloride ion deposition rate in the grid using an interpolation algorithm.

[0082] S44. Different colors are assigned to the chloride ion deposition rates with different values, and contour lines, coordinate axes, and image titles are added to obtain a chloride ion deposition rate distribution map.

[0083] In this embodiment, the map of the plotted area is divided into grids according to latitude and longitude in MATLAB. The gridding accuracy should be as fine as possible to obtain a high-resolution chloride ion deposition rate distribution map. The gridding accuracy is not less than 0.1°×0.1°. The scatter dataset of chloride ion deposition rate in the marine atmospheric environment of the target area is located in the plotted area grid map using latitude and longitude coordinates. A two-dimensional plane is constructed using latitude and longitude coordinates. A three-dimensional coordinate system is constructed by combining the chloride ion deposition rate data of that location. Different colors are assigned to the chloride ion content of different values ​​in the grid of the regional gridded data map to indicate the chloride ion deposition rate distribution map. Contour lines, coordinate axes, image titles and other content are added to obtain the surface chloride ion deposition rate distribution map of the marine atmospheric environment.

[0084] Example 2:

[0085] Chloride ion deposition on the surface of Q235 carbon steel samples under different marine atmospheric environments in Hainan Island, my country, was investigated, and a distribution map of chloride ion deposition rate on the surface of Q235 carbon steel in the marine atmospheric environment of Hainan Island was constructed, as shown in the figure. Figure 1 As shown, the main steps include the following:

[0086] S1. Eighteen sites were selected on Hainan Island as sampling locations. These 18 sites are distributed in all directions of Hainan Island, both coastal and inland, covering various typical natural environmental areas of Hainan Island. In this example, a Q235 carbon steel sample with a size of 50mm×100mm was selected as an atmospheric exposure sample. The sample was exposed to the atmosphere for 3 months to allow chloride ions in the atmosphere to freely deposit on the surface of the sample. After 3 months, the sample was recovered and stored. The samples exposed at different locations were numbered from 1 to 18, and the latitude and longitude coordinates of different exposure points were recorded.

[0087] S2. Using an X-ray fluorescence spectrometer, the chloride ion concentration on the sample surface from different locations was scanned. The sample to be tested was placed flat on the sample stage. The sample stage movement time interval was set to 60s, the X-axis movement distance was 4mm, the Y-axis movement distance was 4mm, the total X-axis movement range was 40mm, and the total Y-axis movement range was 80mm, forming a 21×11 measurement grid. A total of 231 sites on the sample surface were measured for chloride ion deposition. The X-ray fluorescence spectrometer was turned on, the computer software was connected, the X-ray phototube voltage was set to 20kV, the current to 200nA, and the X-ray fluorescence detector detection time was set to 60s. The sample was moved along the preset path, and the chloride content on the sample surface was scanned to obtain the chloride ion deposition observation data on the sample surface.

[0088] S3. The average value of 231 data points obtained from a full scan of the sample surface using X-ray fluorescence spectrometry was calculated to obtain the average chloride ion deposition concentration on the sample surface. This concentration was then converted to the chloride ion deposition rate of the sample, which was taken as the chloride ion deposition rate at that measurement location. Full scan observations were performed on samples collected from different sampling locations using X-ray fluorescence spectrometry to obtain the chloride ion deposition rates at different sampling locations within the region.

[0089] S4. Obtain meteorological and geographical information of the sampling site during the 3-month exposure period from relevant meteorological departments. The meteorological information includes average temperature, average humidity, rainfall, average wind speed, and prevailing wind direction. The geographical information includes latitude and longitude coordinates and distance from the sea. Using the average temperature, average humidity, rainfall, average wind speed, prevailing wind direction, and distance from the sea during the exposure period of the sampling site as independent variables, and the chloride ion deposition rate on the sample surface at the sampling site as the dependent variable, a gray prediction GM(1,1) machine learning algorithm model is trained to obtain a chloride ion deposition rate prediction model for the sample surface.

[0090] S5. Meteorological and geographic information data were obtained from 123 meteorological stations on Hainan Island over a three-month exposure period through relevant meteorological departments. Meteorological information included average temperature, average humidity, rainfall, average wind speed, and prevailing wind direction. Geographic information included latitude and longitude coordinates and distance from the sea. Using the obtained meteorological and geographic data from Hainan Island meteorological stations, including average temperature, average humidity, rainfall, average wind speed, prevailing wind direction, and distance from the sea, as input, a chloride ion deposition rate prediction model was used to predict the chloride ion deposition rate on the sample surfaces of the 123 meteorological stations.

[0091] S6. Divide Hainan Island into grids with a latitude and longitude precision of 0.1°×0.1°, establish a 31×41 two-dimensional array, store the measured chloride ion deposition rate data of the sampling points and the predicted chloride ion deposition rate data of each meteorological station and their corresponding latitude and longitude into the array, and generate gridded discrete point data.

[0092] S7. Based on the scattered data of the marine atmospheric environment of Hainan Island after regional gridding, the Kriging interpolation algorithm is used to fit the discretized data in the grid to obtain the chloride ion deposition rate data of each grid point in Hainan Island.

[0093] S8. By assigning different colors to different chloride ion concentrations within the gridded data of the Hainan Island map, a chloride ion deposition rate distribution map is obtained, with chloride ion concentrations ranging from 0-25 μg / cm³. 2 The data at each grid point changes from blue to red in a gradual transition. The redder the color, the higher the chloride ion deposition rate in that area. This results in a distribution map of chloride ion deposition rate in the marine atmospheric environment of Hainan, my country.

[0094] This embodiment utilizes X-ray fluorescence spectrometry to directly observe the chloride ion deposition rate on the surface of sample materials obtained from different sampling locations. The rate is then predicted using machine learning algorithms, and finally fitted using gridding and interpolation algorithms to obtain a chloride ion deposition rate distribution map of the entire atmospheric environment. This solves the problem that existing methods cannot directly represent the differences in chloride ion deposition rates on material surfaces in different regions of the marine atmospheric environment, which is beneficial for the differentiated formulation of metal corrosion protection management methods. This embodiment only requires one X-ray fluorescence spectrometer to accommodate multiple sampling points. During sampling, samples only need to be transferred to the testing center as needed, effectively avoiding the problem of chloride ion detection accuracy being affected by sample transfer. Because materials close to actual use conditions are used as samples, the amount of chloride ions deposited on the samples is close to the amount of chloride ions deposited on the surface of the product during actual use. Compared to the traditional gauze method for observing the chloride ion content deposited on the gauze surface, this embodiment provides a more intuitive way to directly measure the chloride ion content on the surface of material samples that closely resembles actual use through X-ray fluorescence spectrometry. This embodiment uses a combination of measured and model-predicted methods to draw regional chloride ion deposition rate distribution maps, ensuring the accuracy of the distribution map drawing with fewer samples and measurements. Compared to the existing method of directly interpolating measured data to draw chloride ion deposition distribution maps after mass sampling and testing of the samples in the plotted area, this embodiment can save at least 80% of sample, facility and labor costs while ensuring high accuracy, and also has the advantages of being convenient and easy to implement.

[0095] Example 3:

[0096] A device for constructing a chloride ion deposition rate distribution map includes: a model construction module, an information acquisition module, a deposition prediction module, and a graph generation module;

[0097] The model is constructed to build a chloride ion deposition rate prediction model.

[0098] The information acquisition module determines the latitude and longitude of several spaces to be measured, and acquires meteorological data and geographical environment data for each space to be measured within the time period to be measured.

[0099] The deposition prediction module inputs the meteorological data and geographical environment data of each space to be measured within the time period to the chloride ion deposition rate prediction model to obtain the chloride ion deposition rate of each space to be measured within the time period.

[0100] The graphics generation module generates a chloride ion deposition rate distribution map based on the latitude and longitude of all the spaces to be measured and the chloride ion deposition rate.

[0101] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0105] Finally, 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 the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method of constructing a chloride deposition rate profile, characterized by, The specific method is as follows: A chlorine ion deposition rate prediction model is constructed; The latitude and longitude of a plurality of to-be-tested spaces are determined, and meteorological data and geographical environment data of each to-be-tested space in a to-be-tested time period are obtained; The meteorological data and geographical environment data of each to-be-tested space in the to-be-tested time period are input into the chlorine ion deposition rate prediction model, and the chlorine ion deposition rate of each to-be-tested space in the to-be-tested time period is obtained; According to the latitude and longitude and the chlorine ion deposition rate of all to-be-tested spaces, a chlorine ion deposition rate distribution map is generated; The meteorological data of the to-be-tested space in the to-be-tested time period includes one or more of the following contents: average temperature, average humidity, rainfall, average wind speed and dominant wind direction; The geographical environment data includes: distance from the sea and ocean influence conduction coefficient; The training data of the chlorine ion deposition rate prediction model is obtained through experiments, and the specific method of the experiment is as follows: A plurality of sample points are selected in the adjacent area of the to-be-tested space, and different sample points and samples are numbered; The sample is placed in the sample point and exposed to the natural atmospheric environment, and the chlorine ion is allowed to freely deposit on the surface of the sample in the to-be-tested time period; The meteorological data and geographical environment data of the sample point in the to-be-tested time period are recorded, and the average chlorine ion deposition concentration of the sample is recorded, and the chlorine ion deposition rate is converted into the chlorine ion deposition rate; The meteorological data and geographical environment data of the sample point are used as variables, and the chlorine ion deposition rate of the sample is used as the dependent variable to construct a training sample; The training sample is used to train the prediction model to obtain the chlorine ion deposition rate prediction model; The specific method of generating the chlorine ion deposition rate distribution map is as follows: In MATLAB, the region data map is plotted according to the latitude and longitude and is divided into grids; The chlorine ion deposition rate scatter data set data of the to-be-tested region is located in the grid map through the latitude and longitude coordinates; The chlorine ion deposition rate scatter data in the grid is fitted by an interpolation algorithm to obtain the chlorine ion deposition rate data of the entire atmospheric environment region; Different colors are assigned to different values of the chlorine ion deposition rate for marking, and contour lines, coordinate axes and picture titles are added to obtain the chlorine ion deposition rate distribution map.

2. The method of constructing a chloride deposition rate profile according to claim 1, wherein: The ocean influence conduction coefficient is used to evaluate the sensitivity of the space to the ocean climate, and is given by experts through a nine-point scoring method.

3. The method of constructing a chloride deposition rate profile according to claim 1, wherein The X-ray fluorescence spectrometer is used to obtain the chlorine ion content on the surface of the sample, and the specific method is as follows: The sample is placed on the sample stage, and the moving time interval, X-axis direction moving interval, Y-axis direction moving interval, X-axis direction moving total range and Y-axis direction moving total range of the sample stage are set; The X-ray fluorescence spectrometer is turned on, the X-ray photocell voltage range is set to 5-50kV, the current range is set to 20-200nA, and the X-ray fluorescence detector detection time is set to 20-200s; The sample stage is controlled to move according to the preset path, and the chlorine element content on the surface of the sample is scanned.

4. The method of constructing a chloride deposition rate profile according to claim 1, wherein: The meteorological data of the to-be-tested space in the to-be-tested time period is obtained through the meteorological data of the weather station.

5. A storage medium, characterized by The storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by the processor to execute the chlorine ion deposition rate distribution map construction method of any one of claims 1 to 4.

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