Underground water corrosion water sample collection spacing optimization method and system

Abstract

The invention provides an underground water corrosion water sample collection spacing optimization method and system, and relates to the technical field of hydrogeology and engineering safety crossing, and the method comprises the following steps: constructing a three-dimensional partition model; identifying corrosion high-risk change points by adopting a density clustering algorithm and generating an encrypted monitoring area; the economic cost, the interpolation precision and the high-risk coverage rate are balanced through a multi-objective optimization algorithm, and the partition reference spacing is determined; the sampling density is dynamically adjusted in combination with the corrosion factor exceeding multiple, and seasonal factors are introduced. Through coupling of the three-dimensional zoning model and the intelligent algorithm, accurate optimization of the sampling spacing is realized, sampling points are reduced by more than 40% compared with a traditional method, the coverage rate of a high-risk area is improved to 93%, the interpolation error is reduced by 4.8%, the monitoring cost is remarkably reduced, and the method is suitable for large-scale popularization and application. And a dynamic and low-cost technical scheme is provided for underground water corrosion prevention and control of linear engineering.

Description

Technical Field

[0001] The present invention relates to the cross - technical field of hydrogeology and engineering safety, and particularly to a method and system for optimizing the sampling interval of groundwater corrosion water samples. Background Art

[0002] In traditional groundwater corrosivity sampling along linear projects (such as railways, highways, etc.), the work - point method or equal - interval method (usually 1 km) is mostly used. The spatial heterogeneity of factors such as geomorphic morphology, strata, groundwater dynamic processes, and human activities is not fully considered, resulting in missed inspections in high - risk areas or over - sampling in low - risk areas. The existing technologies have defects such as poor spatial adaptability, insufficient economy, and lagging dynamic response. The poor spatial adaptability is manifested in the failure to distinguish horizontal / vertical zonation differences and the inability to capture corrosion mutations in special geological units (such as seawater intrusion areas, human activity areas); the insufficient economy is manifested in resource waste caused by fixed - interval sampling. For example, the sampling density is redundant in stable plain areas, while the accuracy is insufficient in high - dynamic areas; the lagging dynamic response is manifested in the lack of seasonal adjustment and corrosion - grade feedback mechanisms, making it difficult to track pollution diffusion or the impact of engineering disturbances in a timely manner. Summary of the Invention

[0003] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a method and system for optimizing the sampling interval of groundwater corrosion water samples, which realizes geomorphic sensitivity grading, precise coverage of high - risk areas, and dynamic allocation of monitoring resources, and solves the pain points of high cost, low accuracy, and poor adaptability of traditional methods.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for optimizing the sampling interval of groundwater corrosion water samples includes:

[0006] Dividing the corrosion areas of the horizontal zonation dimension and the vertical zonation dimension according to preset conditions to establish a three - dimensional model including multiple dynamic characteristic partitions;

[0007] Performing density clustering on the corrosion factor concentration data in the dynamic characteristic partitions to identify high - risk change points and generate encrypted monitoring areas;

[0008] Constructing a multi - objective optimization function and using a multi - objective optimization algorithm to balance the economic cost, interpolation error, and high - risk change - point coverage rate to determine the sampling reference interval for each dynamic characteristic partition;

[0009] Based on the sampling reference interval, dynamically adjusting the sampling density in combination with the multiple - fold excess of the corrosion factor and introducing seasonal factors to adjust the sampling interval.

[0010] Preferably, the division method of the horizontal zonation dimension includes:

[0011] Extract the areas with terrain slope > 15° and elevation mutation zones based on the digital elevation model, and combine with remote sensing images to divide the geochemical evolution sequence of "mountain area → piedmont plain → central plain → lacustrine / marine plain";

[0012] Based on the geochemical evolution sequence, generate the distribution map of the corrosion factor concentration data by the Kriging interpolation method, and define each of the dynamic characteristic zones; the dynamic characteristic zones include: high-dynamic zone, change-point transition zone, sensitive geomorphic zone, stable plain zone, and medium-high corrosion zone.

[0013] Preferably, the corrosion factor concentration data includes Mg 2+ concentration data, SO4 2- concentration data, and Cl - concentration data.

[0014] Preferably, the high-dynamic zone is the transition zone from piedmont to central plain, and the definition condition of the high-dynamic zone is: the coefficient of variation CV of Mg 2+ , SO4 2- , Cl - concentration data ≥ 1.0; the change-point transition zone is the junction zone between mountain area and piedmont plain, and the definition condition of the change-point transition zone is: the nugget value / base value of the semivariogram of Mg 2+ , SO4 2- , Cl - concentration data > 0.5; the definition condition of the sensitive geomorphic zone is: EC / Cl- > 2.0 in the lacustrine / marine plain and the tidal influence > 30 times / year; the stable plain zone is the central plain area, and the definition condition of the stable plain zone is: the coefficient of variation CV of Mg 2+ , SO4 2- , Cl - concentration data < 0.5; the definition condition of the medium-high corrosion zone is: Mg 2+ , SO4 2- concentration data > 1000 mg / L, Cl - concentration data > 500 mg / L, pH < 5.5, and the groundwater hydraulic gradient < 0.1‰.

[0015] Preferably, the division method of the vertical zoning dimension includes:

[0016] Implement dynamic dense sampling at the shallow aquifer in the plain area; the coefficient of variation CV of SO4 2- , Cl - concentration data in the shallow aquifer of the plain area > 0.8;

[0017] Set 1 km equidistant sampling at the deep confined water in the plain area according to the autocorrelation distance > 2 km; the hydrochemical type of the deep confined water in the plain area is Cl-Na type.

[0018] Preferably, perform density clustering on the corrosion factor concentration data in the dynamic feature partition to identify high-risk change points and generate an encrypted monitoring area, including:

[0019] Perform clustering analysis on the corrosion factor concentration data in the dynamic feature partition based on the density clustering algorithm to identify density clusters with abnormal corrosion factor concentrations;

[0020] Dynamically adjust the neighborhood radius of the density clustering algorithm according to the corrosion factor concentration gradient, and set the core point determination condition based on the corrosion factor exceedance threshold; the core point determination condition is positively correlated with the erosion level;

[0021] Mark the center point of the density cluster as a high-risk change point, and generate an encrypted monitoring area around the high-risk change point;

[0022] Dynamically reduce the sampling interval within the encrypted monitoring area.

[0023] Preferably, the expression of the multi-objective optimization function is:

[0024]

[0025] where N is the total number of sampling points, Var is the variance of the Kriging interpolation error, C is the coverage rate of high-risk change points, f1 is the first objective function, f2 is the second objective function, and f3 is the third objective function; the expression of the constraint conditions of the multi-objective optimization function is: where N max is the preset maximum allowable number of sampling points.

[0026] Preferably, the multi-objective optimization algorithm is an improved NSGA-II algorithm; the expression of the adaptive crossover probability Pc of the improved NSGA-II algorithm is Pc = 0.9 - 0.5×t / T, and the expression of the mutation probability Pm is Pm = 0.1 + 0.4×t / T; where t is the current iteration number and T is the total number of iterations; in the crowding degree calculation of the improved NSGA-II algorithm, assign a weight of 2 to the crowding degree of the coverage rate of high-risk change points to preferentially retain solutions with a high coverage rate.

[0027] Preferably, based on the sampling reference interval, dynamically adjust the sampling density in combination with the corrosion factor exceedance multiple, and introduce seasonal factors to adjust the sampling interval, including:

[0028] Calculate the dynamic correction coefficient α of the corrosion level according to the corrosion factor exceedance multiple k; the calculation formula of the dynamic correction coefficient α of the corrosion level is: α = 1 + 0.5×k 0.7 ; where k is the ratio of the corrosion factor concentration to the preset limit value;

[0029] When k > 3, force α to be set to 2.0 and reduce the sampling interval to 50% of the reference interval D base ;

[0030] When 1.2 < k ≤ 3, correct the sampling interval; the corrected sampling interval D cor has the following expression:

[0031] During the rainy season, reduce the sampling interval of the sensitive landform area to 1 / 1.1 of the reference interval;

[0032] During the dry season, widen the sampling interval of the deep confined groundwater in the plain area to 1.2 times the reference interval.

[0033] An optimization system for the sampling interval of groundwater corrosion water samples, comprising:

[0034] A model construction unit, configured to divide the corrosion areas of the horizontal zoning dimension and the vertical zoning dimension according to preset conditions to establish a three-dimensional model including multiple dynamic feature partitions;

[0035] A density clustering unit, configured to perform density clustering on the corrosion factor concentration data in the dynamic feature partitions to identify high-risk change points and generate encrypted monitoring areas;

[0036] A spacing determination unit, which constructs a multi-objective optimization function and uses a multi-objective optimization algorithm to balance the economic cost, interpolation error, and high-risk change point coverage rate to determine the sampling reference interval for each of the dynamic feature partitions;

[0037] A spacing optimization unit, configured to dynamically adjust the sampling density based on the sampling reference interval, combine the corrosion factor exceeding multiple, and introduce seasonal factors to adjust the sampling interval.

[0038] According to the specific embodiments provided by the present invention, the following technical effects are disclosed by the present invention:

[0039] The present invention provides a method and system for optimizing the sampling interval of groundwater corrosion water samples. By coupling a three-dimensional zoning model with an intelligent algorithm, the sampling interval of groundwater is optimized, significantly reducing the sampling cost and redundant workload, improving the evaluation accuracy and engineering efficiency, and realizing the precision, dynamics, and low cost of groundwater corrosion monitoring for linear projects. The dynamic regulation mechanism adapts to the spatio-temporal changes of the groundwater corrosive characteristics, providing a precise and efficient solution for the prevention and control of groundwater corrosion in railway projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0041] Figure 1 It is the flowchart of the method provided by the embodiment of the present invention;

[0042] Figure 2 It is the schematic diagram of the technical route provided by the embodiment of the present invention;

[0043] Figure 3 It is the schematic diagram of the landform and geological changes along a certain section of the railway in Shanxi Province provided by the embodiment of the present invention;

[0044] Figure 4 It is the curve graph of the main corrosion factors and corrosion changes in the groundwater along a certain section of the railway in Shanxi Province provided by the embodiment of the present invention;

[0045] Figure 5 It is the system structure diagram provided by the embodiment of the present invention. Specific implementation manners

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0047] The purpose of the present invention is to provide a method and system for optimizing the sampling interval of groundwater corrosion water samples, which realizes landform sensitivity grading, precise coverage of high-risk areas, and dynamic allocation of monitoring resources, and solves the pain points of high cost, low accuracy, and poor adaptability of traditional methods.

[0048] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the drawings and specific implementation manners.

[0049] Figure 1 It is the flowchart of the method provided by the embodiment of the present invention. As Figure 1 shown, the present invention provides a method for optimizing the sampling interval of groundwater corrosion water samples, including:

[0050] Step 100: Divide the corrosion areas of the horizontal zoning dimension and the vertical zoning dimension according to preset conditions to establish a three-dimensional model including multiple dynamic feature partitions;

[0051] Step 200: Perform density clustering on the corrosion factor concentration data in the dynamic feature partition to identify high-risk change points and generate encrypted monitoring areas;

[0052] Step 300: Construct a multi-objective optimization function, and use the multi-objective optimization algorithm to balance the economic cost, interpolation error, and high-risk change point coverage rate to determine the sampling reference spacing for each dynamic feature partition;

[0053] Step 400: Based on the sampling reference spacing, dynamically adjust the sampling density in combination with the corrosion factor exceeding multiple, and introduce seasonal factors to adjust the sampling spacing.

[0054] Specifically, the preset conditions of this embodiment are geomorphic units, geological conditions, and groundwater dynamic characteristics.

[0055] Please refer to Figure 2 , this embodiment provides a schematic technical route of a method for optimizing the sampling spacing of groundwater corrosion water samples, including:

[0056] S1. Construct a three-dimensional partition model of groundwater corrosion

[0057] S11. Horizontal zoning dimension division

[0058] S111. Data preprocessing. Based on DEM data, extract the area with a terrain slope > 15° in a certain section of a railway in Shanxi (such as the Wutai Mountain section DK198+640~DK224+850), and identify the elevation mutation zone (elevation difference > 50m / km); integrate the boundary of the piedmont alluvial plain (DK242+341.40) and the alluvial-lacustrine plain (DK256+040.96) interpreted from remote sensing images to form a geochemical evolution sequence of "mountain area → piedmont alluvial plain → alluvial-lacustrine plain" (such as Figure 3 shown).

[0059] S112. Groundwater corrosion zoning. Generate existing Mg 2+ , SO4 2- , Cl - concentration distribution maps through Kriging interpolation (such as Figure 4 shown), and define the following functional partitions:

[0060] (1) High-dynamic area: In the piedmont → alluvial plain section, the SO4 2- concentration change range is 610.46~1173.85mg / L, and the Cl - concentration change range is 118.41~195.22mg / L. Due to the superposition of sedimentary facies change and human activities, at DK245+469.37 (SO4 2- =610.46mg / L, Cl - =118.41mg / L), the coefficient of variation C V is 1.8.

[0061] (2) Transition zone of change points: In the transition zone from mountainous area to piedmont plain, nugget value / sill value of semivariance = 0.6 - 0.8. For example, at DK224+850, Cl - concentration is 100.01 mg / L, and concentration gradient ΔC / ΔL = 20 mg / (km·a).

[0062] (3) Sensitive geomorphic area: In the section from DK277+082.02 to DK278+039.62 in the lacustrine plain area, EC / Cl - = 2.0 - 2.5. For example, at DK277+082.02, EC / Cl - = 2.0 (EC = 1048 μS / cm, Cl - = 524.04 mg / L), combined with the tidal influence frequency > 30 times / year, SO4 2- concentration is 1047.054 mg / L (Y2 / H2 level), and the vertical zoning characteristics are significant.

[0063] (4) Stable plain area: In the section from DK256+040.96 to DK270+098.79 in the lacustrine plain area, the coefficient of variation C 2- of SO4 and Cl - is 0.3 - 0.5. V

[0064] (5) Medium-high corrosion area: In other sections of the lacustrine plain area, such as DK273+096.41 (SO4 2- = 2523.98 mg / L, Cl - = 219.22 mg / L, hydraulic gradient = 0.08‰), DK277+082.02 (SO4 2- = 1047.05 mg / L, Cl - = 524.04 mg / L, hydraulic gradient = 0.08‰), DK281+045.13 (SO4 2- = 1921.20 mg / L, Cl - = 171.21 mg / L, hydraulic gradient = 0.08‰), etc., where SO4 2- > 1000 mg / L or Cl - > 500 mg / L.

[0065] The results of the groundwater corrosion level zoning are shown in Table 1 below:

[0066] Table 1

[0067]

[0068] S12, vertical zoning dimension division, and the division results are as follows:

[0069] (1) Shallow aquifer (<50m): DK261+094.49 (Cl - = 159.90mg / L, C V = 1.5) Due to the influence of irrigation infiltration, the dynamic sampling interval was encrypted to 2.5km (benchmark interval 5km×50%).

[0070] (2) Deep confined water (>50m): DK270+098.79 (Cl - = 188.82mg / L, water chemical type Cl-Na type) Set a 1km equal interval sampling according to the autocorrelation distance of 2.5km.

[0071] S2, Identification of corrosion high-risk change points

[0072] S21, Identify corrosion high-risk change points through the improved DBSCAN algorithm, and the specific application is as follows:

[0073] (1) Calculation of dynamic neighborhood radius, taking DK273+096.41 (SO4 2- = 2523.98mg / L, Cl - = 219.22mg / L) as an example, the concentration gradient ΔC = 2523.98mg / L / km, ΔLmax = 5km, ε = 1km×(1 + 0.3×2523.98 / 5) = 151.7km (adjusted to a 1km encrypted circle according to the actual terrain).

[0074] (2) Adjustment of core point threshold, DK261+094.49 (SO4 2- = 1517.75mg / L, Cl - = 159.90mg / L), DK270+098.79 (SO4 2- = 1071.069mg / L, Cl - = 188.82mg / L), DK273+096.41 (SO4 2- = 2523.98mg / L) trigger MinPts = 5, generate the center point of the density cluster, and the sampling interval within the circle is reduced to 1.0km (original benchmark 2.0km×50%); other areas (such as DK256+040.96: SO4 2- = 255.52mg / L, Cl - = 78.41mg / L), MinPts = 3.

[0075] S22. Generation of encrypted monitoring layers. The high-risk change points identified in this embodiment are: DK261+094.49, DK270+098.79, DK273+096.41, etc. The encrypted spacing is reduced from the original benchmarks of 5 km and 2.0 km to 2.5 km and 1.0 km.

[0076] S3. Optimization and drive by intelligent algorithms

[0077] S31. According to the actual situation and requirements along a certain section of the railway in Shanxi, construct a multi-objective optimization function, including:

[0078] (1) Economic cost. Preset N max = 86 points. After optimization, the total number of points N = 52 (a 40% reduction);

[0079] (2) Coverage accuracy. Kriging interpolation variance = 0.08 (<0.1 threshold), meeting the accuracy requirements of the high-corrosion area of DK277+082.02;

[0080] (3) Risk control. Coverage rate of high-risk change points = 93% (full coverage of key points such as DK261+094.49, DK270+098.79, DK273+096.41, etc.).

[0081] Specifically, the expression of the multi-objective optimization function in this embodiment can be:

[0082]

[0083] Among them, N is the total number of sampling points, Var is the variance of the Kriging interpolation error, C is the coverage rate of high-risk change points, f1 is the first objective function, f2 is the second objective function, and f3 is the third objective function.

[0084] Among them, the expression of the constraint conditions of the multi-objective optimization function in this embodiment is: Among them, N max is the preset maximum allowable number of sampling points.

[0085] S32. The parameters of the improved NSGA-II algorithm are set as follows: the number of iterations T = 100. At the 50th iteration, Pc = 0.65 (0.9 - 0.5×50 / 100), Pm = 0.3 (0.1 + 0.4×50 / 100), and the crowding degree weight of high-risk change points is doubled, and the coverage scheme is preferentially retained. This embodiment outputs the Pareto optimal solution set. The allocation results of the benchmark spacing are shown in Table 2 below:

[0086] Table 2

[0087]

[0088] As an alternative implementation, the running steps of the improved NSGA-II algorithm in this embodiment are as follows:

[0089] (1) Start;

[0090] (2) Initialize the population;

[0091] (3) Evaluate the fitness of the population (economic cost, interpolation error, high-risk change point coverage rate);

[0092] (4) Non-dominated sorting;

[0093] (5) Calculate the crowding distance (assign a weight of 2 to the crowding degree of the high-risk change point coverage rate);

[0094] (6) Select parent individuals (based on non-dominated sorting and crowding distance);

[0095] (7) Apply improved crossover and mutation operations, where the adaptive crossover probability Pc = 0.9 - 0.5×t / T; the adaptive mutation probability Pm = 0.1 + 0.4×t / T;

[0096] (8) Generate the offspring population;

[0097] (9) Combine the parent and offspring populations;

[0098] (10) Perform non-dominated sorting on the combined population;

[0099] (11) Select the next generation population (based on non-dominated sorting and crowding distance);

[0100] (12) Check the termination condition (such as reaching the maximum number of iterations). If so, output the result (Pareto optimal solution set, including the sampling reference spacing for each partition). If not, return to the step of selecting parent individuals.

[0101] S4, Zoning and hierarchical dynamic regulation

[0102] S41, Dynamic correction of corrosion grade. The triggering condition of this embodiment: DK278+039.62 (SO4 2- = 1056.66mg / L, Cl - = 560.05mg / L) exceeds 1.2 times the limit of Class II (SO4 2- > 300mg / L × 1.2 = 360mg / L), k = 2.94, α = 1 + 0.5×2.94^0.7 = 2.2, and the spacing is reduced to 2.0km / 2.2 = 0.91km. And this embodiment realizes forced encryption. DK273+096.41 (SO4 2- = 2523.98mg / L, k = 8.41) triggers α max = 2.0 (αmax (the maximum value of α), the spacing is forced to be reduced to 1.0 km.

[0103] S42, Seasonal factors

[0104] Rainy season: According to the objectively quantitative division of the preset rainy season and combined with the climate characteristics along a certain section of the railway in Shanxi, the main rainy season is from July to September. The sampling spacing in sensitive geomorphic areas (such as DK277+082.02) is reduced from 1.5 km to 1.36 km (1.5 / 1.1). The dry season is from November to January, and the spacing of the deep aquifer (such as DK270+098.79) is widened from 1.0 km to 1.2 km.

[0105] As an optional implementation method, the effect verification of this embodiment is as shown in Tables 3 and 4 below:

[0106] Table 3

[0107]

[0108] Table 4

[0109]

[0110]

[0111] Exemplarily, the tables in this embodiment only show the key nodes, and the intermediate points need to be supplemented according to the spacing requirements during implementation.

[0112] Corresponding to the above method, as Figure 5 shown, this embodiment also provides an optimization system for the sampling spacing of groundwater corrosion water samples, including:

[0113] A model construction unit for dividing the corrosion areas of the horizontal zoning dimension and the vertical zoning dimension according to preset conditions to establish a three-dimensional model including multiple dynamic feature partitions;

[0114] A density clustering unit for performing density clustering on the corrosion factor concentration data in the dynamic feature partitions to identify high-risk change points and generate encrypted monitoring areas;

[0115] A spacing determination unit for constructing a multi-objective optimization function and using a multi-objective optimization algorithm to balance the economic cost, interpolation error, and high-risk change point coverage rate to determine the sampling reference spacing for each of the dynamic feature partitions;

[0116] A spacing optimization unit for dynamically adjusting the sampling density based on the sampling reference spacing, combining the multiple times of exceeding the standard of the corrosion factor, and introducing seasonal factors to adjust the sampling spacing.

[0117] The beneficial effects of the present invention are as follows:

[0118] The present invention optimizes the groundwater sampling interval by coupling a three-dimensional zoning model with an intelligent algorithm, significantly reducing the sampling cost and redundant workload, improving the evaluation accuracy and engineering efficiency, and achieving precision, dynamic control, and low cost for groundwater corrosion monitoring in linear projects. The dynamic regulation mechanism adapts to the temporal and spatial changes in the corrosive characteristics of groundwater, providing a precise and efficient solution for groundwater corrosion prevention and control in railway engineering.

[0119] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0120] Specific examples are used in this article to elaborate on the principles and implementation methods of the present invention. The descriptions of the above embodiments are only used to help understand the method of the present invention and its core idea. At the same time, for those of ordinary skill in the art, there will be changes in the specific implementation methods and application scopes according to the idea of the present invention. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. An optimization method for the sampling interval of groundwater corrosion water samples, characterized in that Including: Dividing the corrosion area of the horizontal zoning dimension and the vertical zoning dimension according to preset conditions to establish a three-dimensional model including multiple dynamic feature partitions; Performing density clustering on the corrosion factor concentration data in the dynamic feature partitions to identify high-risk change points and generate encrypted monitoring areas; Constructing a multi-objective optimization function and using a multi-objective optimization algorithm to balance economic cost, interpolation error, and high-risk change point coverage rate to determine the sampling reference spacing of each dynamic feature partition; Based on the sampling reference spacing, dynamically adjusting the sampling density in combination with the corrosion factor exceeding standard multiple, and introducing seasonal factors to adjust the sampling spacing.

2. The optimized method for the sampling interval of groundwater corrosion water samples according to claim 1, wherein The division method of the horizontal zoning dimension includes: Extracting the areas with terrain slope > 15° and elevation mutation zones based on the digital elevation model, and combining remote sensing images to divide the geochemical evolution sequence of "mountain area → piedmont plain → central plain → lacustrine / marine plain"; Based on the geochemical evolution sequence, generating a distribution map of the corrosion factor concentration data through Kriging interpolation method and defining each dynamic feature partition; the dynamic feature partitions include: high-dynamic area, change point transition area, sensitive geomorphic area, stable plain area, and medium-high corrosion area.

3. The optimized method for the sampling interval of groundwater corrosion water samples according to claim 2, characterized in that, The corrosion factor concentration data includes Mg 2+ concentration data, SO4 2- concentration data, and Cl - concentration data.

4. The method for optimizing the sampling interval of groundwater corrosion water samples according to claim 3, characterized in that, The high-dynamic area is the transition zone from the front of the mountain to the central plain. The defining conditions for the high-dynamic area are: Mg 2+ , SO4 2- , Cl - The coefficient of variation CV of the concentration data ≥ 1.0; the inflection transition zone is the mountain-front plain junction zone. The defining conditions for the inflection transition zone are: Mg 2+ , SO4 2- , Cl - The nugget value / base value of the semivariogram of the concentration data > 0.5; the defining conditions for the sensitive geomorphic area are: EC / Cl- in the lacustrine / marine plain > 2.0 and the tidal influence > 30 times / year; the stable plain area is the central plain area. The defining conditions for the stable plain area are: Mg 2+ , SO4 2- , Cl - The coefficient of variation CV of the concentration data < 0.5; the defining conditions for the medium-high corrosion area are: Mg 2+ , SO4 2- The concentration data > 1000 mg / L, Cl - The concentration data > 500 mg / L, pH < 5.5, and the groundwater hydraulic gradient < 0.1‰.

5. The method for optimizing the sampling interval of groundwater corrosion water samples according to claim 4, characterized in that, The division method of the vertical zoning dimension includes: Implement dynamic and intensive sampling at the shallow aquifer in the plain area; the coefficient of variation CV of the concentration data of SO4 2- and Cl - in the shallow aquifer of the plain area is > 0.8; Sampling at equal intervals of 1 km where the autocorrelation distance > 2 km in the deep confined water of the plain area; the hydrochemical type of the deep confined water in the plain area is Cl-Na type.

6. The method for optimizing the sampling interval of groundwater corrosion water samples according to claim 1, wherein, Performing density clustering on the corrosion factor concentration data in the dynamic feature partitions to identify high-risk change points and generate encrypted monitoring areas, including: Performing cluster analysis on the corrosion factor concentration data of the dynamic feature partitions based on the density clustering algorithm to identify density clusters with abnormal corrosion factor concentrations; Dynamically adjusting the neighborhood radius of the density clustering algorithm according to the corrosion factor concentration gradient, and setting the core point determination condition based on the corrosion factor exceeding standard threshold; the core point determination condition is positively correlated with the erosion grade; Marking the center point of the density cluster as a high-risk change point and generating an encrypted monitoring area around the high-risk change point; Dynamically reducing the sampling spacing within the encrypted monitoring area.

7. The optimized method for the sampling interval of groundwater corrosion water samples according to claim 1, wherein The expression of the multi-objective optimization function is: Among them, N is the total number of sampling points, Var is the variance of the Kriging interpolation error, C is the coverage rate of high-risk change points, f1 is the first objective function, f2 is the second objective function, and f3 is the third objective function; the expression of the constraint condition of the multi-objective optimization function is: Among them, N max is the preset maximum allowable number of sampling points.

8. The groundwater corrosion water sample collection interval optimization method according to claim 1, wherein The multi-objective optimization algorithm is an improved NSGA-II algorithm; the expression of the adaptive crossover probability Pc of the improved NSGA-II algorithm is Pc = 0.9 - 0.5×t / T, and the expression of the mutation probability Pm is Pm = 0.1 + 0.4×t / T; where t is the current iteration number and T is the total iteration number; in the crowding degree calculation of the improved NSGA-II algorithm, the crowding degree of the coverage rate of high-risk change points is given a weight of 2 to preferentially retain solutions with high coverage rates.

9. The groundwater corrosion water sample collection interval optimization method according to claim 5, characterized in that Based on the sampling reference spacing, dynamically adjusting the sampling density in combination with the corrosion factor exceeding standard multiple, and introducing seasonal factors to adjust the sampling spacing, including: Calculate the dynamic correction coefficient α of the corrosion grade according to the exceeding multiple k of the corrosion factor; the calculation formula of the dynamic correction coefficient α of the corrosion grade is: α = 1 + 0.5 × k 0.7 ; where k is the ratio of the corrosion factor concentration to the preset limit value; When k > 3, force α to be set to 2.0 and reduce the sampling interval to 50% of the reference interval D base ; When 1.2 < k ≤ 3, correct the sampling interval; the corrected sampling interval D cor is expressed as: During the rainy season, reducing the sampling spacing of the sensitive geomorphic area to 1 / 1.1 of the reference spacing; During the dry season, widening the sampling spacing of the deep confined water in the plain area to 1.2 times the reference spacing.

10. An optimized system for the sampling interval of groundwater corrosion water samples, characterized in that, Including: A model construction unit, configured to divide the corrosion areas of the horizontal zoning dimension and the vertical zoning dimension according to preset conditions, so as to establish a three-dimensional model including multiple dynamic feature partitions; A density clustering unit, configured to perform density clustering on the corrosion factor concentration data in the dynamic feature partitions, so as to identify high-risk change points and generate encrypted monitoring areas; A spacing determination unit, which constructs a multi-objective optimization function and uses a multi-objective optimization algorithm to balance the economic cost, interpolation error and high-risk change point coverage rate, so as to determine the sampling reference spacing of each of the dynamic feature partitions; A spacing optimization unit, configured to dynamically adjust the sampling density based on the sampling reference spacing in combination with the multiple times of exceeding the standard of the corrosion factor, and introduce seasonal factors to adjust the sampling spacing.

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