Software model simulation-based water source protected area pollution analysis method and system

Abstract

The invention provides a water source protected area pollution analysis method and system based on software model simulation, and the method comprises the steps: firstly obtaining a pollution source data set and a geographic feature data set of a target water source protected area, then calling a software model to carry out the dynamic pollution diffusion simulation processing of the pollution source data set, and obtaining a pollution diffusion feature set; and then carrying out spatial influence analysis on the pollution diffusion feature set based on the geographic feature data set, and generating a pollution influence analysis result containing a pollution migration path, an influence region boundary and a time distribution interval. And then determining a pollution protection priority set according to the result, generating a differentiated protection strategy set in combination with a preset emergency response rule, and finally feeding back the differentiated protection strategy set to the water source protection management system to trigger pollution protection operation, so that accurate and efficient pollution protection of the water source protection area can be realized.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a water source protection area pollution analysis method and system based on software model simulation. Background Art

[0002] Water source protection areas are crucial for ensuring drinking water safety and maintaining ecological balance. However, with socioeconomic development, these areas face an increasingly severe threat from pollution. Currently, traditional methods for analyzing pollution in these areas often rely on static data and simple statistical analysis. These methods have significant limitations, such as the inability to dynamically simulate the diffusion of pollution sources and the difficulty in accurately reflecting the migration and changes of pollution in complex geographical environments.

[0003] Existing pollution analyses are typically based solely on inferences from limited monitoring point data, failing to fully consider the characteristics of different pollution sources and the impact of geographical features on pollution spread. Furthermore, a lack of precise analytical tools for the scope and temporal distribution of pollution impacts leads to a one-size-fits-all approach to pollution prevention strategies, failing to tailor targeted protection measures to the actual pollution situation. This results in inefficient pollution prevention efforts at water source protection areas, making it difficult to effectively address the increasingly complex pollution landscape. Therefore, a more scientific, accurate, and efficient pollution analysis method for water source protection areas is urgently needed. Summary of the Invention

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a water source protection area pollution analysis method based on software model simulation, the method comprising:

[0005] Obtaining pollution source data sets and geographical feature data sets of target water source protection areas;

[0006] Calling a software model to perform dynamic pollution diffusion simulation processing on the pollution source data set to obtain a pollution diffusion feature set of the target water source protection area;

[0007] Performing spatial impact analysis on the pollution diffusion feature set based on the geographic feature data set to generate pollution impact analysis results including pollution migration paths, impact area boundaries, and time distribution intervals;

[0008] Determine the pollution protection priority set corresponding to the target water source protection area based on the pollution impact analysis results, and generate a differentiated protection strategy set in combination with preset emergency response rules;

[0009] The differentiated protection strategy set is fed back to the water source protection management system to trigger pollution prevention operations.

[0010] On the other hand, an embodiment of the present invention also provides a water source protection area pollution analysis system based on software model simulation, including a processor and a machine-readable storage medium, the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0011] Based on the above aspects, the embodiment of the present invention obtains the pollution source data set and geographic feature data set of the target water source protection area, calls the software model to perform dynamic pollution diffusion simulation processing, and can obtain a pollution diffusion feature set that reflects the actual situation. The pollution diffusion feature set is combined with the geographic feature data set to perform spatial impact analysis processing, and generate pollution impact analysis results including pollution migration paths, impact area boundaries and time distribution intervals. It comprehensively and accurately presents the impact of pollution in spatial and temporal dimensions, and on this basis determines the pollution protection priority set, and generates a differentiated protection strategy set in combination with preset emergency response rules. It overcomes the drawbacks of traditional one-size-fits-all protection strategies, realizes precise protection according to actual pollution conditions, and feeds back the differentiated protection strategy set to the water source protection management system to trigger pollution protection operations, which can significantly improve the efficiency and effectiveness of pollution protection work in water source protection areas and ensure the water quality safety and ecological balance of water sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of the execution flow of the water source protection area pollution analysis method based on software model simulation provided by an embodiment of the present invention.

[0013] Figure 2 It is a schematic diagram of exemplary hardware and software components of a water source protection area pollution analysis system based on software model simulation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 This is a flow chart of a water source protection area pollution analysis method based on software model simulation provided by an embodiment of the present invention. The water source protection area pollution analysis method based on software model simulation is introduced in detail below.

[0015] Step S110: Acquire the pollution source data set and geographic feature data set of the target water source protection area.

[0016] During the pollution analysis of water source protection areas, it is necessary to obtain accurate and comprehensive pollution source data sets and geographic feature data sets of the target water source protection areas. The specific process of obtaining the pollution source data sets and geographic feature data sets will be described in detail below.

[0017] Step S111: collect the pollution source type identification set, pollutant concentration distribution set and pollution source location coordinate set of the target water source protection area from the pollution monitoring equipment in real time, and perform spatiotemporal alignment processing on the pollution source type identification set, pollutant concentration distribution set and pollution source location coordinate set to generate a pollution source data set.

[0018] To obtain data on pollution sources within a target water source protection area, it is necessary to rationally deploy various types of pollution monitoring equipment within the area. These pollution monitoring devices, including but not limited to water quality sensors, air quality monitors, and soil pollution detectors, should be distributed across the water source protection area to comprehensively cover areas where pollution sources may exist.

[0019] The first step is to collect a set of pollution source type identifications. Pollution monitoring equipment will conduct a preliminary analysis of the detected pollutants and determine the type of pollution source based on information such as the chemical composition and physical properties of the pollutants. For example, if a large amount of heavy metal ions such as lead, mercury, and cadmium are detected in the water body, and there are industrial factories in the surrounding area, then the pollution source type can be identified as industrial pollution; if nutrients such as nitrogen and phosphorus are detected in the water body that exceed the standard, and there are farmlands nearby, it can be identified as agricultural non-point source pollution. The pollution source type determined at each monitoring point is represented by a unique identification symbol. The collection of these identification symbols constitutes the pollution source type identification set, denoted as A. In the actual collection process, in order to ensure the accuracy of the identification, it may be necessary to combine multiple monitoring indicators for comprehensive judgment. For example, in the case of suspected industrial pollution, in addition to detecting the composition of the pollutants, factors such as the regularity of emissions and the time of emissions can also be analyzed.

[0020] Next, a pollutant concentration distribution set is collected. Pollution monitoring equipment measures pollutant concentrations at different locations in real time. For example, water quality sensors can measure the concentrations of various pollutants in water, such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), and ammonia nitrogen. For each monitoring point, the concentrations of different pollutants are recorded and organized according to the location of the monitoring point and the type of pollutant, forming a multidimensional data set, the pollutant concentration distribution set, denoted as B. During the data collection process, attention must be paid to the accuracy and calibration of the monitoring equipment to ensure the accuracy of the measurement results. Furthermore, different measurement methods and techniques may be required for different types of pollutants.

[0021] Next, the pollution source location coordinates are collected. Using Global Positioning System (GPS) technology or Geographic Information System (GIS), the precise geographic coordinates of each pollution source can be obtained. These coordinates are typically expressed in longitude and latitude. By aggregating the coordinate information for all pollution sources, we obtain the pollution source location coordinate set, denoted as C. When locating pollution sources, it is crucial to ensure the accuracy of the coordinates to avoid bias in subsequent analysis due to positioning errors.

[0022] After completing the collection of the three datasets mentioned above, spatiotemporal alignment is required. Because different monitoring devices may have different sampling times and frequencies, the timestamps of each dataset must be adjusted based on a unified time standard. For example, taking a specific moment as the starting point, the time records of all data are converted into time differences relative to that starting point. Spatially, the datasets must be accurately matched according to their geographic location. A geographic gridding method can be used to divide the water source protection area into several small grid cells, each corresponding to a unique coordinate range. The pollution source type identification set, pollutant concentration distribution set, and pollution source location coordinate set data are classified and organized according to the grid cells, so that the data within the same grid cell correspond to each other in time and space. Through this spatiotemporal alignment process, the pollution source data set D is ultimately generated, which contains the accurate temporal and spatial correspondence between pollution source type, pollutant concentration, and pollution source location.

[0023] Step S112: Extract the terrain elevation distribution set, soil permeability coefficient distribution set and groundwater characteristic set of the target water source protection area from the geographic information database, perform coordinate normalization processing on the terrain elevation distribution set to obtain the first standardized geographic feature, perform discreteness correction processing on the soil permeability coefficient distribution set to obtain the second standardized geographic feature, perform flow direction topology reconstruction processing on the groundwater characteristic set to obtain the third standardized geographic feature, and perform spatial superposition processing on the first standardized geographic feature, the second standardized geographic feature and the third standardized geographic feature to generate a geographic feature data set.

[0024] The geographic information database is an important source for obtaining geographical feature data of target water source protection areas. The database stores a wealth of geographic information, including data on terrain elevation, soil properties, groundwater, and other aspects.

[0025] First, extract the terrain elevation distribution set E from the geographic information database. This terrain elevation distribution set reflects the topographic undulations of the water source protection area and is obtained through topographic surveying and remote sensing technology. During the extraction process, it is necessary to ensure the integrity and accuracy of the data and cover the entire scope of the target water source protection area. Because terrain elevation data in different regions may use different coordinate systems and scales, the terrain elevation distribution set needs to be normalized to facilitate subsequent analysis and processing. The specific steps for coordinate normalization are as follows:

[0026] The first step is to determine a unified coordinate system. Choose a universal coordinate system suitable for the target water source protection area, such as WGS84. Convert the terrain elevation data from the original coordinate system to this unified coordinate system.

[0027] The second step is to normalize the elevation values. The maximum and minimum values of the terrain elevation data are calculated. The minimum value is then subtracted from each elevation value, and the resultant value is divided by the difference between the maximum and minimum values to obtain the normalized elevation value. This process limits the range of elevation values to between 0 and 1, resulting in the first standardized geographic feature, E'. Coordinate normalization eliminates differences in coordinate systems and scales across regions, making terrain elevation data comparable and consistent.

[0028] Next, we extract the soil permeability coefficient distribution set F. This describes the soil's ability to penetrate water and is closely related to the vertical penetration of pollutants. In actual measurements, the soil permeability coefficient may exhibit a certain degree of dispersion. To improve data reliability and stability, the soil permeability coefficient distribution set must be corrected for dispersion. The specific method for this correction is as follows:

[0029] The first step is statistical analysis. The mean and standard deviation of the soil permeability coefficient data are calculated to assess the degree of dispersion of the data.

[0030] The second step is outlier processing. Based on the results of the statistical analysis, a reasonable threshold range is determined. Data outside this threshold range is considered an outlier and processed using appropriate methods, such as eliminating outliers or replacing them with the mean.

[0031] The third step is smoothing. Methods such as moving average and Gaussian filtering can be used to smooth the corrected soil permeability coefficient data, reducing data fluctuations and obtaining the second standardized geographic feature F'. This dispersion correction improves the quality of the soil permeability coefficient data, making it more accurately reflect the actual soil permeability characteristics.

[0032] Next, we extract the groundwater feature set G. This feature set contains information such as groundwater flow direction and velocity, which is crucial for understanding the migration patterns of pollutants in groundwater. Due to the complexity of groundwater characteristics, we need to perform flow direction topology reconstruction on the feature set. The specific steps for flow direction topology reconstruction are as follows:

[0033] The first step is data preprocessing. The collected groundwater data are cleaned and sorted to remove noise and outliers.

[0034] The second step is to construct a topological structure. Based on information such as groundwater level and flow velocity, the direction and path of groundwater flow can be determined. Using graph theory, the groundwater flow area can be abstracted into a topological structure of nodes and edges, where each node represents a groundwater monitoring point and an edge represents the direction of groundwater flow.

[0035] The third step is to optimize the topology. The constructed topology is optimized and adjusted, taking into account factors such as groundwater recharge and discharge to ensure that the topology accurately reflects the actual groundwater flow. Through flow direction topology reconstruction, the third standardized geographic feature G' is obtained.

[0036] Finally, the first standardized geographic feature E', the second standardized geographic feature F' and the third standardized geographic feature G' are spatially superimposed. The specific process of spatial superposition is as follows:

[0037] The first step is to determine the spatial reference. Choose a unified spatial reference system to ensure that the three standardized geographic features are superimposed in the same spatial coordinate system.

[0038] The second step is data matching. The data of the three standardized geographic features are matched according to their spatial locations so that different geographic feature data at the same location can be mapped together.

[0039] The third step is overlay and fusion. The matched data are overlaid and fused to generate a comprehensive geographic feature dataset, H. During the overlay and fusion process, different geographic features are weighted according to their importance to highlight the impact of certain key features. Through spatial overlay processing, the resulting geographic feature dataset, H, integrates multiple geographic information, including topography, soil, and groundwater, providing comprehensive and accurate foundational data for subsequent pollution dispersion simulations and spatial impact analysis.

[0040] Step S120: calling a software model to perform dynamic pollution diffusion simulation processing on the pollution source data set to obtain a pollution diffusion feature set of the target water source protection area.

[0041] After obtaining a dataset of pollution sources for a target water source protection area, the next step is to use a software model to perform dynamic pollution diffusion simulation on this dataset to obtain a set of pollution diffusion characteristics for the target water source protection area. This process can help predict the spread of pollutants within the water source protection area.

[0042] Step S121: inputting the pollution source type identification set into the pre-trained pollution migration pattern recognition model to generate a pollution propagation pattern identification corresponding to the pollution source data set.

[0043] First, the pollution source type identifier set A must be embedded in a vector mapping process. The purpose of embedding vector mapping is to convert discrete pollution source type identifiers into continuous high-dimensional vectors for easier model processing. Specifically, a unique vector representation is assigned to each pollution source type identifier. The dimensions of these vectors can be set according to actual conditions, for example, to 128 dimensions. Pre-trained word embedding models, such as Word2Vec or GloVe models, can be used to perform vector mapping on pollution source type identifiers. Each pollution source type identifier is input into the embedding model to obtain the corresponding high-dimensional vector. These high-dimensional vectors constitute the high-dimensional pollution source type feature vector set, denoted as A'.

[0044] The convolutional feature extraction network in the pollution migration pattern recognition model is then used to capture the spatiotemporal correlation patterns of the high-dimensional pollution source type feature vector set A'. A convolutional feature extraction network typically consists of multiple convolutional layers and pooling layers. The convolutional layer uses a sliding convolution kernel to extract local spatiotemporal correlation features on the high-dimensional pollution source type feature vector set. The pooling layer downsamples the feature maps output by the convolutional layer, reducing the feature dimensionality while retaining important feature information. Through the convolutional feature extraction network, a preliminary pollution pattern feature set, denoted as P, is generated.

[0045] The preliminary pollution pattern feature set P is then input into the attention allocation network within the pollution migration pattern recognition model. The attention allocation network determines the distribution of migration association weights between different pollution source types. The network analyzes the preliminary pollution pattern feature set and calculates the importance of each source type feature in the pollution migration process. Specifically, the attention allocation network calculates the attention score for each feature through a series of linear transformations and activation functions. These scores are then normalized to obtain the migration association weight distribution, denoted as W.

[0046] Finally, a weighted fusion process is performed on the preliminary pollution pattern feature set P based on the migration association weight distribution W. This weighted fusion process multiplies each feature vector in the preliminary pollution pattern feature set by the corresponding migration association weight. These weighted feature vectors are then concatenated to generate a pollution propagation pattern identifier, denoted as M. This method highlights the varying importance of different pollution source types in the pollution propagation process and improves the accuracy of pollution propagation pattern identification.

[0047] Step S122: determining a dynamic simulation parameter configuration strategy in the software model according to the pollution propagation mode identifier, and adjusting the fluid motion equation coefficient set and material diffusion equation constraint conditions in the software model based on the dynamic simulation parameter configuration strategy.

[0048] First, the pollution propagation mode identifier M is matched against a preset simulation parameter mapping table. This pre-built table records the initial adjustment ranges for the fluid motion equation coefficients corresponding to different pollution propagation mode identifiers. By comparing the pollution propagation mode identifier with the patterns in the mapping table, a matching mode is found and the corresponding initial adjustment range, denoted as ΔC, is obtained.

[0049] The initial adjustment amplitude ΔC is then dynamically corrected based on the statistical distribution characteristics of the pollutant concentration distribution set B. The statistical distribution characteristics of the pollutant concentration distribution set can be described by calculating statistical quantities such as its mean, variance, and skewness. These statistics can be used to determine the distribution of pollutants, such as whether they are concentrated or whether there are outliers. If the pollutant concentration distribution is relatively concentrated, the initial adjustment amplitude may need to be appropriately reduced; if there are areas of abnormally high concentration, the initial adjustment amplitude may need to be increased. This dynamic correction process generates the final set of coefficients for the fluid motion equation, denoted as C'.

[0050] The pollution propagation pattern identifier, M, is then input into a pre-trained constraint generation network. This is a trained neural network that generates a set of boundary parameters for the material diffusion equation's constraints based on the input pollution propagation pattern identifier. By learning from a large amount of historical data, the network establishes a mapping between pollution propagation patterns and boundary parameters. When the pollution propagation pattern identifier is input into the constraint generation network, the network outputs the corresponding boundary parameter set, denoted as D'.

[0051] Finally, a dynamic simulation parameter configuration strategy is constructed based on the final set of fluid motion equation coefficients C' and boundary parameter set D'. This strategy is a comprehensive configuration scheme that specifies the fluid motion equation coefficients and material diffusion equation constraints used by the software model when simulating pollution diffusion. By properly configuring these parameters, the software model can more accurately simulate pollution diffusion under different pollution propagation modes.

[0052] Step S123: Load the pollutant concentration distribution set and the pollution source location coordinate set into the adjusted software model, perform pollution diffusion simulation operations, and capture the spatial distribution change characteristics of pollutant concentration, migration rate distribution characteristics, and infiltration direction distribution characteristics of the target water source protection area in real time.

[0053] First, determine the pollution source injection point coordinate set in the software model based on the pollution source location coordinate set C. The pollution source injection point coordinate set is the specific location used to input pollutants in the software model. The pollution source location coordinate set is directly used as the pollution source injection point coordinate set, denoted as C''.

[0054] The software model's fluid motion equation solver is then initialized based on the final set of fluid motion equation coefficients C' from the dynamic simulation parameter configuration strategy. The fluid motion equation solver is the core module in the software model used to simulate fluid motion. It calculates the velocity and pressure distribution of the fluid based on the fluid motion equations. The final set of fluid motion equation coefficients is substituted into the fluid motion equation solver to complete the initialization process.

[0055] The pollutant concentration distribution set B is then loaded into the fluid motion equation solver according to the pollution source injection point coordinate set C''. Specifically, the pollutant concentration value corresponding to each pollution source injection point in the pollutant concentration distribution set is input as the initial condition into the fluid motion equation solver. Then, a three-dimensional space discretization process is performed to divide the three-dimensional space of the target water source protection area into several small grid cells. In each grid cell, the initial distribution of pollutants is determined based on the calculation results of the fluid motion equation solver, and the initial pollution distribution field is generated, which is recorded as F.

[0056] Configure the boundary condition set for the material diffusion equation constraints based on the boundary parameter set D' of the dynamic simulation parameter configuration strategy. The boundary condition set for the material diffusion equation constraints specifies the diffusion behavior of pollutants at the boundary, such as the concentration and flux at the boundary. Substitute the boundary parameter set into the material diffusion equation constraints to complete the configuration of the boundary conditions. Then, call the material diffusion solver in the software model to perform time-stepping simulation on the initial pollution distribution field F. The material diffusion solver calculates the diffusion of pollutants in each time step based on the material diffusion equation and outputs the spatial distribution change characteristics of pollutant concentration in real time, recorded as S.

[0057] The velocity field analysis module of the fluid motion equation solver is simultaneously activated. This module generates a fluid velocity vector field by iteratively solving the Navier-Stokes equations. The Navier-Stokes equations are fundamental equations describing fluid motion, accounting for factors such as fluid viscosity, pressure, and inertia. By iteratively solving these equations, the velocity vector of the fluid at each grid node is obtained. The migration velocity values for each grid node are extracted and organized according to the node's position to form a migration velocity distribution characteristic, denoted as V.

[0058] Combined with the soil permeability coefficient distribution set in the second standardized geographic feature F', the Darcy's law calculation module is introduced into the material diffusion solver. Darcy's law describes the permeation law of fluid in porous media, which is closely related to the soil permeability coefficient. According to the osmotic pressure gradient, the Darcy's law calculation module is used to calculate the underground infiltration flow direction vector set. The osmotic pressure gradient refers to the pressure difference between different locations underground, which is the main driving force for the vertical penetration of pollutants. Through vector synthesis processing, the underground infiltration flow direction vectors of each grid node are synthesized to obtain the infiltration direction distribution feature, which is denoted as O.

[0059] Step S124: performing spatiotemporal correlation analysis on the pollutant concentration spatial distribution variation characteristics, migration rate distribution characteristics, and infiltration direction distribution characteristics to generate a pollution diffusion feature set.

[0060] First, the spatial distribution variation characteristics of pollutant concentrations, S, are sliced in the time dimension. This process involves dividing the spatial distribution variation characteristics of pollutant concentrations into multiple time slices, each corresponding to a specific time period. The spatial distribution data of pollutant concentrations within each time slice is organized into an atlas, generating multiple time slice concentration distribution atlases, denoted as S'.

[0061] The migration rate distribution feature V is then subjected to velocity field overlay processing with multiple time-slice concentration distribution atlases S'. Velocity field overlay processing correlates the migration rate values in the migration rate distribution feature with the pollutant concentration values in the time-slice concentration distribution atlases. Specifically, within each time slice, the migration rate vector in the migration rate distribution feature is multiplied by the pollutant concentration value at the corresponding location to obtain the spatiotemporally correlated concentration distribution feature, denoted as T.

[0062] Next, vector direction clustering is performed on the infiltration direction distribution feature O. Vector direction clustering involves classifying the underground infiltration flow direction vectors in the infiltration direction distribution feature by direction. Clustering algorithms, such as the K-Means algorithm, can be used to classify the underground infiltration flow direction vectors into several categories, each corresponding to a primary infiltration direction. The frequency of occurrence of each primary infiltration direction is counted, and the most frequently occurring primary infiltration directions are selected as the primary infiltration directions. This forms the primary infiltration direction distribution set, denoted as O'.

[0063] Finally, the spatiotemporal correlation concentration distribution feature T is combined with the main infiltration direction distribution set O' for flow-wise weighted fusion processing. The flow-wise weighted fusion process weights the pollutant concentration values in the spatiotemporal correlation concentration distribution feature according to the main infiltration direction distribution set. Specifically, for each grid node, a weight is assigned to the corresponding pollutant concentration value based on its main infiltration direction. The weighted pollutant concentration values are concatenated to generate a pollution diffusion feature set, denoted as K. Through this spatiotemporal correlation analysis process, the concentration changes, migration rate, and infiltration direction of pollutants can be comprehensively considered to more comprehensively describe the characteristics of pollution diffusion.

[0064] Step S130: performing spatial impact analysis on the pollution diffusion feature set based on the geographic feature data set, and generating pollution impact analysis results including pollution migration paths, impact area boundaries, and time distribution intervals.

[0065] After obtaining a set of pollution diffusion characteristics for the target water source protection area, it is necessary to combine them with the geographic feature data set to perform a spatial impact analysis to generate pollution impact analysis results that include pollution migration paths, impact area boundaries, and time distribution intervals. This process can help understand the spread and impact range of pollutants in geographic space, providing an important basis for formulating pollution prevention strategies.

[0066] Step S131: performing slope correlation analysis on the first standardized geographical feature and the spatial distribution variation feature of the pollutant concentration to determine a set of migration paths of pollutants under preset terrain conditions.

[0067] The first standardized geographic feature, E', reflects the topographic slope of the target water source protection area, while the spatial distribution variation feature, S, of pollutant concentrations, shows how pollutant concentrations vary over space and time. Applying slope correlation analysis to these two features can pinpoint the set of pollutant migration pathways under predefined topographic conditions.

[0068] When conducting a slope correlation analysis, it's important to first understand how slope affects pollutant migration. Generally speaking, in areas with steeper terrain, pollutants are more likely to migrate along the slope due to gravity. In areas with shallower or flatter slopes, pollutant migration is more influenced by other factors, such as water flow and wind.

[0069] To perform slope correlation analysis, it's necessary to spatially match the first standardized geographic feature E' with the spatial distribution variation feature S of pollutant concentrations. Specifically, the target water source protection area is divided into several small grid cells, each with a corresponding terrain slope value and pollutant concentration value. For each grid cell, the likely migration direction of pollutants is determined based on the direction of the terrain slope and pollutant concentration gradient.

[0070] Suppose that within a grid cell, the terrain slope runs from point A to point B, while the pollutant concentration gradient within that grid cell runs from point C, an area of high concentration, to point D, an area of low concentration. The direction of pollutant migration within that grid cell is then influenced by the combined effects of these two directions. The degree of influence can be determined by calculating the angle between these two directions. If the angle is small, the slope and the concentration gradient are relatively aligned, and pollutants are more likely to migrate in that direction. If the angle is large, the influence of other factors on the migration direction needs to be considered.

[0071] When considering multiple grid cells, it's necessary to correlate the migration directions of adjacent grid cells. If the migration directions of adjacent grid cells are relatively consistent, these grid cells can be connected to form a possible pollution migration path. By performing this analysis and correlation on all grid cells throughout the target water source protection area, the set of pollution migration paths under the pre-defined terrain conditions can be determined, denoted as R.

[0072] Step S132: performing soil permeability matching processing on the second standardized geographical feature and the infiltration direction distribution feature to determine the vertical infiltration depth distribution feature of the pollutants.

[0073] The second standardized geographic feature, F', contains information on the distribution of soil permeability coefficients within the target water source protection area, while the infiltration direction distribution feature, O, reflects the direction of underground infiltration of pollutants. By matching these two with soil permeability, the vertical penetration depth distribution of pollutants can be determined.

[0074] The core of soil permeability matching is to consider the impact of soil permeability coefficient on the vertical penetration of pollutants. Different soil types have different permeability coefficients. The larger the permeability coefficient, the faster the pollutant will penetrate vertically through the soil, and the greater the vertical penetration depth.

[0075] First, the target water source protection area is divided into several small areas based on spatial location. Each area has a corresponding soil permeability coefficient value and permeability direction vector. For each area, the vertical penetration depth of the pollutant in that area is calculated based on the soil permeability coefficient and permeability direction vector.

[0076] Assume that in a certain area, the soil permeability coefficient is K, the permeability direction vector is V, and the initial concentration of the pollutant in that area is C0. According to Darcy's law, the permeability rate of the pollutant in the soil is proportional to the permeability coefficient and the osmotic pressure gradient. When considering vertical permeation, the osmotic pressure gradient can be simplified to the pressure difference caused by gravity. By calculating the product of the permeation rate and time, the vertical penetration depth of the pollutant in the area can be calculated.

[0077] The calculation process must account for soil heterogeneity and the diffusion characteristics of pollutants. Soil permeability coefficients may vary from region to region, and pollutants diffuse during infiltration, gradually decreasing their concentration. Therefore, these factors must be considered comprehensively when calculating vertical penetration depth.

[0078] For adjacent areas, data smoothing is required to ensure the continuity of the vertical penetration depth distribution. Interpolation algorithms, such as linear interpolation or spline interpolation, can be used to interpolate the vertical penetration depths of adjacent areas to obtain a continuous vertical penetration depth distribution feature, recorded as D.

[0079] Step S133: Perform hydrological flow direction superposition processing on the third standardized geographical feature and the migration rate distribution feature to determine the surface migration range expansion feature of the pollutant.

[0080] The third standardized geographic feature, G', encompasses the groundwater hydrological characteristics of the target water source protection area, such as groundwater flow direction and velocity, while the migration rate distribution feature, V, reflects the rate of contaminant migration across the surface. By overlaying these two features with hydrological flow direction, the surface migration range of the contaminant can be determined.

[0081] The purpose of the hydrological flow overlay is to consider the impact of groundwater on the surface migration of pollutants. Groundwater flow will affect surface water flow, thereby affecting the direction and range of pollutant migration.

[0082] First, the target water source protection area is divided into several small grid cells based on spatial location. Each grid cell has corresponding groundwater characteristics and migration rate values. For each grid cell, the comprehensive migration direction of pollutants is determined based on the groundwater flow direction and migration rate direction.

[0083] Suppose that in a grid cell, the groundwater flow direction is from point E to point F, while the pollutant migration rate within the grid cell is from point G to point H. The overall migration direction of the pollutant within the grid cell is influenced by the combined effects of these two directions. This combined migration direction can be calculated using vector synthesis.

[0084] After determining the overall migration direction, the migration distance of the pollutant within that grid cell is calculated based on the migration rate and time. By performing this calculation and analysis on all grid cells throughout the target water source protection area, the surface migration range of the pollutant at different points in time can be determined.

[0085] To characterize the expansion of surface migration, it's necessary to compare and analyze the surface migration ranges at different time points. The difference between the surface migration ranges at adjacent time points can be calculated to determine the expansion of the migration range. By collating and analyzing the expansion of the migration ranges at all time points, the surface migration range expansion characteristic of the pollutant can be determined, denoted as S'.

[0086] Step S134: Perform three-dimensional spatial fusion processing based on the migration path set, vertical penetration depth distribution characteristics and surface migration range expansion characteristics to generate a pollution migration path.

[0087] After obtaining the migration path set R, vertical penetration depth distribution characteristics D, and surface migration range expansion characteristics S', these three characteristics need to be fused in three dimensions to generate a complete pollution migration path.

[0088] The key to 3D spatial fusion processing is matching and integrating these three features in 3D space. First, the migration path set R is used as the foundation, reflecting the horizontal migration paths of pollutants on the surface. Next, the vertical penetration depth distribution feature D is correlated with the migration path set R to determine the vertical migration of pollutants. Finally, the surface migration range extension feature S' is integrated with the previous two features to account for the expansion of the pollutant's migration range at different points in time.

[0089] Specifically, the target water source protection area can be divided into a three-dimensional grid space, with each grid cell having corresponding spatial coordinates. Each migration path in the set of migration paths R is located within the three-dimensional grid space. Then, based on the vertical penetration depth distribution characteristic D, each point on each migration path is assigned a vertical penetration depth value, thereby determining the vertical migration trajectory of the pollutant.

[0090] When considering the surface migration range expansion characteristic S', the pollutant distribution in the three-dimensional grid space is updated according to the migration range expansion at different time points. By continuously updating the pollutant distribution, the dynamic migration path of pollutants in three-dimensional space can be obtained.

[0091] During the fusion process, attention must be paid to data consistency and accuracy. Overlapping areas between different features must be properly handled to avoid conflicts and contradictions. Through three-dimensional spatial fusion, the final pollution migration path is generated, denoted as P.

[0092] Step S135: performing boundary detection processing based on the vertical penetration depth distribution characteristics and the preset groundwater source protection layer depth threshold to generate the impact area boundary.

[0093] The vertical penetration depth distribution characteristic, D, reflects the vertical penetration of pollutants underground. The preset groundwater source protection layer depth threshold is a pre-set depth value used to determine whether the pollutants will affect the groundwater source. By performing boundary detection processing on the vertical penetration depth distribution characteristic and this threshold, the impact area boundary can be generated.

[0094] The boundary detection process is as follows: First, the target water source protection area is divided into several small areas, each with a corresponding vertical penetration depth value. For each area, the vertical penetration depth value is compared with the preset groundwater source protection layer depth threshold.

[0095] If the vertical penetration depth value of a certain area is greater than or equal to the preset groundwater source protection layer depth threshold, it means that the pollutants in the area may affect the groundwater source, and the area is marked as an affected area; if the vertical penetration depth value is less than the preset groundwater source protection layer depth threshold, the area is marked as an unaffected area.

[0096] After marking all areas, the boundaries of the affected and unaffected areas can be identified and extracted to generate the affected area boundary. Edge detection algorithms, such as the Canny edge detection algorithm, can be used to process the marked areas and extract boundary information.

[0097] When generating the impact zone boundary, it's important to consider the accuracy and reliability of the data. Areas near the boundary may be subject to uncertainty, requiring appropriate smoothing to ensure continuity and rationality. The resulting impact zone boundary is denoted as B.

[0098] Step S136: Perform time series comparison processing based on the spatial distribution variation characteristics of pollutant concentration and the preset safety concentration threshold to generate a time distribution interval.

[0099] The pollutant concentration spatial distribution variation characteristic S shows the concentration variation of pollutants in space and time. The preset safety concentration threshold is a pre-set concentration value used to determine whether the pollutant concentration is within the safe range. By comparing the pollutant concentration spatial distribution variation characteristic with the threshold through time series processing, the temporal distribution interval can be generated.

[0100] The specific process of time series comparison is as follows: First, the spatial distribution variation characteristics of pollutant concentrations are arranged in chronological order to form a time series. At each time point, the pollutant concentration value at that time point is compared with the preset safety concentration threshold.

[0101] If the pollutant concentration value at a certain time point is greater than or equal to the preset safety concentration threshold, it means that the pollutant concentration at that time point exceeds the safety range, and the time point is marked as a polluted time period; if the pollutant concentration value is less than the preset safety concentration threshold, the time point is marked as a safe time period.

[0102] After marking all time points, the time distribution intervals can be generated by identifying and extracting the boundaries between the polluted and safe time periods. A segmentation algorithm can be used to process the marked time series and extract the boundary information of different time periods.

[0103] When generating time distribution intervals, it's important to consider pollutant concentration fluctuations. Because pollutant concentrations can fluctuate over time, concentrations may briefly exceed safety thresholds but quickly return to safe levels. In such cases, appropriate judgment and handling must be made based on the actual situation to ensure the accuracy and reliability of the time distribution intervals. The resulting time distribution interval is denoted as T.

[0104] Step S140: Determine the pollution protection priority set corresponding to the target water source protection area based on the pollution impact analysis results, and generate a differentiated protection strategy set in combination with the preset emergency response rules.

[0105] After obtaining pollution impact analysis results that include pollution migration paths, impact area boundaries, and temporal distribution intervals, it is necessary to determine the pollution protection priority set corresponding to the target water source protection area based on these results. This step, combined with pre-set emergency response rules, generates a set of differentiated protection strategies. This step is crucial for developing reasonable and effective pollution prevention measures.

[0106] Step S141: The coordinate set of the core protection zone of the target water source protection area is normalized in the same manner as the geographic feature data set, and a gridded protection zone distribution map that matches the boundary of the impact area is generated through a spatial interpolation algorithm. The boundary of the impact area and the gridded protection zone distribution map are subjected to spatial overlap analysis to generate a first protection priority index.

[0107] The core protection zone coordinate set for the target water source protection area records the geographic location of the core protection zone. To facilitate unified processing with the geographic feature data set, the core protection zone coordinate set must be normalized in the same way as the geographic feature data set. This is done similarly to normalizing the terrain elevation distribution set: the core protection zone coordinate set is converted to a unified coordinate system and the coordinate values are normalized.

[0108] After coordinate normalization, a spatial interpolation algorithm is used to generate a gridded protected area distribution map that matches the boundaries of the affected area. Based on the known coordinates of the core protected area, this algorithm estimates the protected area information for other locations within the target water source protection area, thereby generating a continuous gridded protected area distribution map. Commonly used spatial interpolation algorithms include Kriging interpolation and inverse distance weighted interpolation.

[0109] The spatial overlap analysis of the impact area boundary and the grid protection area distribution map is performed. The purpose of the spatial overlap analysis is to determine the degree of overlap between the impact area and the core protection area, so as to assess the possibility and extent of the core protection area being affected by pollution. Specifically, the overlapping area between the impact area and each grid cell in the grid protection area distribution map is calculated, and then all overlapping areas are summarized to obtain the total overlapping area. Based on the ratio of the total overlapping area to the total area of the core protection area, the first protection priority index is generated, denoted as P1. The larger the value of this index, the higher the possibility and extent of the core protection area being affected by pollution, and the higher the protection priority.

[0110] Step S142: performing a matching analysis on the time distribution interval and the preset emergency response time window set to generate a second protection priority index.

[0111] The preset emergency response time window set is a set of predefined time ranges that specifies the emergency response measures to be taken within different time periods. A matching analysis is performed between the time distribution interval T and the preset emergency response time window set to assess the degree of match between the time of the pollution incident and the emergency response time window, thereby determining the corresponding protection priority.

[0112] The specific process for matching analysis is as follows: For each time period in the time distribution interval T, check whether it overlaps with a time window in the preset emergency response time window set. If so, record the length of the overlap. Sum up all overlapping time lengths to obtain the total overlap time. Based on the ratio of the total overlap time to the total length of the time distribution interval T, generate a second protection priority index, denoted as P2. The larger the value of this index, the more closely the pollution incident occurs and the emergency response time window matches. This indicates that protective measures need to be taken as soon as possible, and the protection priority is higher.

[0113] Step S143: Perform path intersection analysis on the pollution migration path and the sensitive ecological area distribution set of the target water source protection area to generate a third protection priority index.

[0114] The sensitive ecological zone distribution set for the target water source protection area records the geographic location of sensitive ecological areas within the area. A path intersection analysis is performed between the pollution migration path P and the sensitive ecological zone distribution set to assess whether the pollution migration path will pass through sensitive ecological areas and thus determine the corresponding protection priority.

[0115] The specific process for path intersection analysis is as follows: For each path in the pollution migration path P, check whether it intersects with a sensitive ecological area where sensitive ecological areas are concentrated. If so, record the length of the intersection. Sum up the lengths of all intersections to obtain the total intersection length. Based on the ratio of the total intersection length to the total length of the pollution migration path P, generate a third protection priority index, denoted as P3. A higher value for this index indicates a higher likelihood and degree of the pollution migration path passing through a sensitive ecological area, a greater impact on the sensitive ecological area, and a higher protection priority.

[0116] Step S144: performing weighted fusion processing on the first protection priority index, the second protection priority index, and the third protection priority index to generate a pollution protection priority set.

[0117] To comprehensively consider the impact of the first, second, and third protection priorities (P1, P2, and P3), these three indicators need to be weighted and fused. The goal of weighted fusion is to assign a weight to each indicator based on its importance, then concatenate these weighted values to generate a pollution protection priority set.

[0118] First, determine the weight of each indicator. The weights can be adjusted based on actual circumstances. For example, if the protection of the core protected area is considered more important, a higher weight can be assigned to the first priority indicator, P1. If the timeliness of the emergency response is considered more critical, a higher weight can be assigned to the second priority indicator, P2. Assume that the weight of the first priority indicator, P1, is w1, the weight of the second priority indicator, P2, is w2, and the weight of the third priority indicator, P3, is w3, and that w1 + w2 + w3 = 1.

[0119] Then, multiply each indicator by its corresponding weight to obtain the weighted indicator value. The weighted first protection priority indicator w1*P1, the weighted second protection priority indicator w2*P2, and the weighted third protection priority indicator w3*P3 are concatenated to generate the pollution protection priority set, denoted as P'.

[0120] Step S145: Match the corresponding emergency response action set from the emergency response rules according to the pollution protection priority set, perform strategy adaptation processing on the emergency response action set and the pollution diffusion feature set, and generate a differentiated protection strategy set.

[0121] The emergency response rules are a predefined set of rules that record the emergency response actions corresponding to different pollution prevention priorities. Based on the pollution prevention priority set P', the corresponding emergency response action set is matched from the emergency response rules. Specifically, for each priority value in the pollution prevention priority set, the corresponding emergency response action is searched in the emergency response rules. These actions are then aggregated into an emergency response action set, denoted as A.

[0122] The emergency response action set A is matched to the pollution diffusion feature set K through a strategy adaptation process. The purpose of strategy adaptation is to adjust and optimize the emergency response action set based on the specific characteristics of pollution diffusion, ensuring the effectiveness and relevance of emergency response measures. For example, if the pollution diffusion feature set indicates that pollutants primarily diffuse through surface water, then emergency response actions such as strengthening the interception and treatment of surface water can be selected.

[0123] When performing policy adaptation, multiple factors within the pollution diffusion feature set, such as pollutant concentration, migration rate, and penetration direction, must be considered. The emergency response action set can be categorized and adjusted based on different combinations of these factors. This policy adaptation ultimately generates a set of differentiated protection strategies, denoted as S''. Each protection strategy in this set is tailored to specific pollution conditions and protection priorities, ensuring high specificity and effectiveness.

[0124] Step S150: Feedback the differentiated protection strategy set to the water source protection management system to trigger pollution prevention operations.

[0125] After generating a set of differentiated protection strategies, they need to be fed back to the water source protection management system to trigger corresponding pollution prevention actions. This step is the ultimate goal of the entire pollution analysis method. By implementing differentiated protection strategies, the impact of pollution on water source protection areas can be effectively reduced.

[0126] For example, step S151: converting the differentiated protection strategy set into a control instruction format recognizable by the water source protection management system to generate a strategy execution instruction set.

[0127] Water source protection management systems usually have their own set control instruction formats. In order to enable the differentiated protection strategy sets to be recognized and executed by the system, they need to be converted into a control instruction format that the system can recognize.

[0128] First, analyze each protection strategy in the set of differentiated protection strategies S'' to determine its specific operational content and requirements. For example, if a protection strategy requires the installation of interception devices in a specific area, then the type, quantity, and location of the interception devices must be clearly defined.

[0129] Then, according to the control instruction format specifications of the water source protection management system, these operational contents and requirements are converted into corresponding control instructions. For example, the setting information of the interception device is converted into device deployment instructions that the system can recognize. All converted control instructions are summarized to generate a policy execution instruction set, denoted as I.

[0130] Step S152: Determine the device deployment coordinate set corresponding to the policy execution instruction set according to the boundary of the impact area, perform spatial binding processing on the device deployment coordinate set and the policy execution instruction set, and generate a geo-fence control instruction set.

[0131] The impact zone boundary B defines the scope of the pollution impact. Based on this scope, we can determine the device deployment coordinates corresponding to the policy execution instruction set. Specifically, we analyze the required device deployment locations for each instruction in the policy execution instruction set and, based on the impact zone boundary, determine the specific coordinates of these devices within the target water source protection area. The deployment coordinates of all devices are aggregated to form the device deployment coordinate set, denoted as C.

[0132] The device deployment coordinate set C is spatially bound to the policy execution set I. The purpose of spatial binding is to associate each policy execution instruction with the corresponding device deployment coordinates, so that the water source protection management system can accurately know where each instruction should be executed. The specific operation is to add the corresponding device deployment coordinate information to each instruction in the policy execution instruction set to form a new instruction set, namely the geo-fence control instruction set, denoted as G. Each instruction in the geo-fence control instruction set contains specific operation content and corresponding geographic location information. The water source protection management system can perform corresponding pollution protection operations at the specified location based on these instructions.

[0133] Step S153: Determine a time trigger condition set of the geo-fence control instruction set based on the time distribution interval, and write the time trigger condition set into the scheduling task queue of the water source protection management system to trigger the pollution protection operation.

[0134] The time distribution interval T records the temporal distribution of pollution events. Based on this information, we can determine the time trigger conditions for the geofence control command set. First, we analyze each command in the geofence control command set G to determine when it needs to be executed to achieve optimal pollution protection. By combining information such as changes in pollutant concentration and migration within the time distribution interval T, we determine an appropriate execution time range for each command.

[0135] For example, for instructions that need to be executed immediately at the beginning of pollution spread, their execution time is set to the time period at the beginning of the time distribution interval. For instructions that need to be executed after pollution reaches a certain level, the execution time is determined based on the time when the pollutant concentration reaches the set threshold in the time distribution interval. The execution time ranges corresponding to all instructions are summarized to form a time trigger condition set, recorded as Tc.

[0136] Next, the time trigger condition set Tc is written into the water source protection management system's scheduling task queue. The water source protection management system's scheduling task queue is used to manage and execute various tasks. The system executes the instructions in the geofence control instruction set sequentially according to the time sequence specified in the time trigger condition set. When the execution time for a particular instruction arrives, the system automatically triggers the corresponding pollution prevention action, such as activating interception equipment or turning on purification equipment. This ensures that pollution prevention actions are executed at the correct time and location, minimizing the impact of pollution on the target water source protection area.

[0137] In the above embodiments, the two artificial intelligence models, the pollution migration pattern recognition model and the constraint condition generation network, play a key role. The construction and training of these two artificial intelligence models and their application in the specific field scenario of water source protection area pollution analysis are described in detail below.

[0138] The pollution migration pattern recognition model primarily consists of a convolutional feature extraction network and an attention allocation network. The convolutional feature extraction network is used to capture and process the spatiotemporal correlation patterns of the input high-dimensional pollution source type feature vector set. It contains multiple convolutional layers and pooling layers. The convolutional layer extracts local features by sliding convolution kernels over the input data. Different convolution kernels can extract different types of features, such as edge features and texture features. The pooling layer downsamples the feature maps output by the convolutional layer, reducing feature dimensions while retaining important information, reducing model computational complexity, and improving training efficiency. The attention allocation network receives the preliminary pollution pattern feature set output by the convolutional feature extraction network and calculates the attention score for each feature through a series of linear transformations and activation functions. These scores reflect the importance of different pollution source types in the pollution migration process. The scores are then normalized to obtain the migration association weight distribution.

[0139] In terms of hierarchy and connectivity, the input layer receives a high-dimensional pollution source type feature vector set and connects to the first convolutional layer of the convolutional feature extraction network, passing the data to it for processing. The final pooling layer of the convolutional feature extraction network connects to the input layer of the attention allocation network, passing the preliminary pollution pattern feature set. The output layer of the attention allocation network outputs the transfer association weight distribution, based on which the preliminary pollution pattern feature set is weightedly fused to generate the pollution propagation pattern signature.

[0140] Training a pollution migration pattern recognition model requires a large amount of labeled data, including data on different types of pollution sources and corresponding pollution transmission pattern labels. The collected pollution source data is first cleaned and preprocessed to remove noise and address missing values. Next, the pollution source type identifiers are embedded in vectors to generate a high-dimensional pollution source type feature vector set. The preprocessed data is then divided into training, validation, and test sets. The training set is used for model training, the validation set is used to adjust model hyperparameters, and the test set is used to evaluate model performance. The parameters of the convolutional feature extraction network and the attention allocation network are randomly initialized. The high-dimensional pollution source type feature vectors from the training set are input into the model. The convolutional feature extraction network and the attention allocation network process the model output, the pollution transmission pattern identifier. The pollution transmission pattern identifier output by the model is compared with the actual pollution transmission pattern label to calculate a loss function, commonly using a cross-entropy loss function. Based on the loss function value, the backpropagation algorithm is used to calculate the gradient and update the model parameters. This process is repeated until satisfactory model performance is achieved. The validation set is used to monitor performance during training to avoid overfitting.

[0141] In the water source protection area pollution analysis scenario, the pollution migration pattern recognition model takes as input a set of pollution source type identifiers collected in real time from pollution monitoring equipment. This high-dimensional feature vector set of pollution source types, processed through embedding vector mapping, is then fed into the model. The model then outputs a pollution propagation pattern identifier corresponding to the pollution source data set. This identifier is then used to determine the dynamic simulation parameter configuration strategy within the software model, accurately simulating pollution diffusion. This establishes a correlation between pollution source type and pollution propagation pattern, providing crucial information for pollution diffusion simulation.

[0142] The Constraint Generation Network is a neural network-based model that generates the boundary parameter set for the material diffusion equation's constraints based on the input pollution propagation pattern identifier. It typically consists of multiple fully connected layers, connected by linear transformations and activation functions to achieve nonlinear mapping of the input data. The input layer receives the pollution propagation pattern identifier and connects to the first fully connected layer to pass data. The output of the first fully connected layer serves as the input for the next fully connected layer, and the final fully connected layer outputs the boundary parameter set for the material diffusion equation's constraints.

[0143] Training the constraint generation network also requires a large amount of labeled data. This data contains different pollution propagation mode identifiers and the corresponding boundary parameter sets for the material diffusion equation constraints. The collected pollution propagation mode identifiers and boundary parameter set data are first cleaned and preprocessed to ensure data quality, and then divided into training, validation, and test sets. The fully connected layer parameters of the constraint generation network are randomly initialized. The pollution propagation mode identifiers from the training set are input into the model. The fully connected layer processes the boundary parameter set, which is the model output. The boundary parameter set output by the model is compared with the actual boundary parameter set to calculate a loss function, typically using a mean squared error loss function. Based on the loss function value, the backpropagation algorithm is used to calculate the gradient and update the model parameters. This process is repeated until the model performance meets the required standards.

[0144] Throughout the entire process, during the data collection phase, potentially privacy-sensitive data is encrypted for transmission and storage to protect privacy and prevent data leakage. For example, data collected by pollution monitoring equipment involving companies or individuals is encrypted, ensuring that only authorized personnel can access and process this data.

[0145] Figure 2 A schematic diagram illustrates exemplary hardware and software components of a water source protection area pollution analysis system 100 based on software model simulation, which can implement the concepts of the present application, as provided in some embodiments of the present application. For example, a processor 120 can be used in the water source protection area pollution analysis system 100 based on software model simulation and be used to perform the functions of the present application.

[0146] The water source protection area pollution analysis system 100 based on software model simulation can be a general-purpose server or a special-purpose server, both of which can be used to implement the water source protection area pollution analysis method based on software model simulation of the present application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0147] For example, the water source protection ground pollution analysis system 100 based on software model simulation can include a network port 110 connected to the network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the water source protection ground pollution analysis system 100 based on software model simulation can also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The water source protection ground pollution analysis system 100 based on software model simulation also includes an I / O interface 150 between a computer and other input and output devices.

[0148] For ease of explanation, only one processor is described in the water source protection area pollution analysis system 100 based on software model simulation. However, it should be noted that the water source protection area pollution analysis system 100 based on software model simulation in the present application can also include multiple processors, so the steps performed by one processor described in the present application can also be performed jointly or individually by multiple processors. For example, if the processor of the water source protection area pollution analysis system 100 based on software model simulation executes step A and step B, it should be understood that step A and step B can also be performed jointly by two different processors or individually in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor execute steps A and B together.

[0149] In addition, an embodiment of the present invention also provides a readable storage medium, which has computer-executable instructions preset in the readable storage medium. When the processor executes the computer-executable instructions, the water source protection area pollution analysis method based on software model simulation as described above is implemented.

[0150] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A water source protection area pollution analysis method based on software model simulation, characterized in that: The method comprises: Obtaining pollution source data sets and geographical feature data sets of target water source protection areas; Calling a software model to perform dynamic pollution diffusion simulation processing on the pollution source data set to obtain a pollution diffusion feature set of the target water source protection area; Performing spatial impact analysis on the pollution diffusion feature set based on the geographic feature data set to generate pollution impact analysis results including pollution migration paths, impact area boundaries, and time distribution intervals; Determine the pollution protection priority set corresponding to the target water source protection area based on the pollution impact analysis results, and generate a differentiated protection strategy set in combination with preset emergency response rules; The differentiated protection strategy set is fed back to the water source protection management system to trigger pollution prevention operations.

2. The water source protection area pollution analysis method based on software model simulation according to claim 1 is characterized in that: The step of obtaining the pollution source data set and geographic feature data set of the target water source protection area includes: collecting the pollution source type identification set, the pollutant concentration distribution set and the pollution source location coordinate set of the target water source protection area in real time from the pollution monitoring equipment, and performing spatiotemporal alignment processing on the pollution source type identification set, the pollutant concentration distribution set and the pollution source location coordinate set to generate the pollution source data set; The terrain elevation distribution set, soil permeability coefficient distribution set and groundwater characteristic set of the target water source protection area are extracted from the geographic information database, the terrain elevation distribution set is coordinate normalized to obtain the first standardized geographic feature, the soil permeability coefficient distribution set is discretely corrected to obtain the second standardized geographic feature, the groundwater characteristic set is flow direction topology reconstructed to obtain the third standardized geographic feature, and the first standardized geographic feature, the second standardized geographic feature and the third standardized geographic feature are spatially superimposed to generate the geographic feature data set.

3. The water source protection area pollution analysis method based on software model simulation according to claim 2 is characterized in that: The calling of the software model to perform dynamic pollution diffusion simulation processing on the pollution source data set to obtain the pollution diffusion feature set of the target water source protection area includes: Inputting the pollution source type identification set into a pre-trained pollution migration pattern recognition model to generate a pollution propagation pattern identification corresponding to the pollution source data set; determining a dynamic simulation parameter configuration strategy in the software model according to the pollution propagation mode identifier, and adjusting a fluid motion equation coefficient set and a material diffusion equation constraint condition in the software model based on the dynamic simulation parameter configuration strategy; Loading the pollutant concentration distribution set and the pollution source location coordinate set into the adjusted software model, performing pollution diffusion simulation calculations, and capturing in real time the spatial distribution change characteristics of pollutant concentrations, migration rate distribution characteristics, and infiltration direction distribution characteristics of the target water source protection area; The spatial distribution change characteristics of the pollutant concentration, the migration rate distribution characteristics and the infiltration direction distribution characteristics are subjected to spatiotemporal correlation analysis to generate the pollution diffusion feature set.

4. The water source protection area pollution analysis method based on software model simulation according to claim 3 is characterized in that: The spatial impact analysis processing is performed on the pollution diffusion feature set based on the geographic feature data set to generate a pollution impact analysis result including a pollution migration path, an impact area boundary, and a time distribution interval, including: Performing slope correlation analysis on the first standardized geographical feature and the spatial distribution variation feature of the pollutant concentration to determine a set of migration paths of pollutants under preset terrain conditions; Perform soil permeability matching processing on the second standardized geographical feature and the infiltration direction distribution feature to determine the vertical infiltration depth distribution feature of the pollutant; Performing hydrological flow direction superposition processing on the third standardized geographical feature and the migration rate distribution feature to determine the surface migration range expansion feature of the pollutant; Performing three-dimensional spatial fusion processing based on the migration path set, the vertical penetration depth distribution characteristics, and the surface migration range expansion characteristics to generate the pollution migration path; Performing boundary detection processing based on the vertical penetration depth distribution characteristics and a preset groundwater source protection layer depth threshold to generate the impact area boundary; The time distribution interval is generated by performing time series comparison processing based on the spatial distribution change characteristics of the pollutant concentration and the preset safety concentration threshold.

5. The water source protection area pollution analysis method based on software model simulation according to claim 3 is characterized in that: Inputting the pollution source type identification set into a pre-trained pollution migration pattern recognition model to generate a pollution propagation pattern identification corresponding to the pollution source data set includes: Performing embedding vector mapping processing on the pollution source type identification set to generate a high-dimensional pollution source type feature vector set; Calling the convolutional feature extraction network in the pollution migration pattern recognition model to perform spatiotemporal correlation pattern capture processing on the high-dimensional pollution source type feature vector set to generate a preliminary pollution pattern feature set; Inputting the preliminary pollution pattern feature set into the attention allocation network in the pollution migration pattern recognition model to determine the migration association weight distribution between different pollution source types; The preliminary pollution pattern feature set is weightedly fused based on the migration association weight distribution to generate a pollution propagation pattern identifier.

6. The method for analyzing water source protection area pollution based on software model simulation according to claim 3 is characterized in that: Determining the dynamic simulation parameter configuration strategy in the software model according to the pollution propagation mode identifier includes: Performing pattern matching processing on the pollution propagation mode identifier and a preset simulation parameter mapping table to determine an initial adjustment range of the fluid motion equation coefficient set; Dynamically correcting the initial adjustment amplitude based on the statistical distribution characteristics of the pollutant concentration distribution set to generate a final fluid motion equation coefficient set; Inputting the pollution propagation mode identifier into a pre-trained constraint condition generation network to generate a boundary parameter set of the material diffusion equation constraint condition; The dynamic simulation parameter configuration strategy is constructed according to the final fluid motion equation coefficient set and the boundary parameter set.

7. The method for analyzing water source protection area pollution based on software model simulation according to claim 3 is characterized in that: The pollutant concentration distribution set and the pollution source location coordinate set are loaded into the adjusted software model, and a pollution diffusion simulation operation is performed to capture the spatial distribution change characteristics of the pollutant concentration, the migration rate distribution characteristics, and the infiltration direction distribution characteristics of the target water source protection area in real time, including: Determining a pollution source injection point coordinate set in the software model according to the pollution source position coordinate set; Initializing a fluid motion equation solver in the software model based on a final fluid motion equation coefficient set of the dynamic simulation parameter configuration strategy; Loading the pollutant concentration distribution set into the fluid motion equation solver according to the pollution source injection point coordinate set, performing three-dimensional space discretization processing to generate an initial pollution distribution field; configuring a boundary condition set of the material diffusion equation constraint condition according to the boundary parameter set of the dynamic simulation parameter configuration strategy, calling a material diffusion solver in the software model to perform time-stepping simulation processing on the initial pollution distribution field, and outputting the spatial distribution change characteristics of the pollutant concentration in real time; Synchronously activating a velocity field analysis module of the fluid motion equation solver, generating a fluid velocity vector field by iteratively solving the Navier-Stokes equations, and extracting the migration rate value of each grid node to form the migration rate distribution feature; Combined with the soil permeability coefficient distribution set in the second standardized geographic feature, a Darcy's law calculation module is introduced into the material diffusion solver, and an underground infiltration flow direction vector set is generated according to the infiltration pressure difference gradient. The infiltration direction distribution feature is obtained through vector synthesis processing.

8. The method for analyzing water source protection area pollution based on software model simulation according to claim 3 is characterized in that: The performing of spatiotemporal correlation analysis on the pollutant concentration spatial distribution change characteristics, the migration rate distribution characteristics, and the infiltration direction distribution characteristics to generate the pollution diffusion feature set includes: Performing time dimension slicing processing on the spatial distribution variation characteristics of the pollutant concentration to generate multiple time slice concentration distribution atlases; Performing velocity field superposition processing on the migration rate distribution feature and the multiple time slice concentration distribution atlases to generate a spatiotemporal correlation concentration distribution feature; Performing vector direction clustering processing on the infiltration direction distribution characteristics to generate a main infiltration direction distribution set; The spatiotemporal correlation concentration distribution characteristics are subjected to flow-wise weighted fusion processing with the main infiltration direction distribution set to generate the pollution diffusion feature set.

9. The water source protection area pollution analysis method based on software model simulation according to claim 1 is characterized in that: The step of determining a pollution protection priority set corresponding to the target water source protection area based on the pollution impact analysis results and generating a differentiated protection strategy set in combination with preset emergency response rules includes: Performing coordinate normalization processing on the core protection zone coordinate set of the target water source protection area in the same manner as the geographic feature data set, generating a gridded protection zone distribution map that matches the boundary of the impact area through a spatial interpolation algorithm, performing spatial overlap analysis on the boundary of the impact area and the gridded protection zone distribution map, and generating a first protection priority index; Performing a matching analysis on the time distribution interval and a preset emergency response time window set to generate a second protection priority index; Performing a path intersection analysis on the pollution migration path and the sensitive ecological area distribution set of the target water source protection area to generate a third protection priority index; Performing weighted fusion processing on the first protection priority index, the second protection priority index, and the third protection priority index to generate the pollution protection priority set; According to the pollution protection priority set, a corresponding emergency response action set is matched from the emergency response rule, and the emergency response action set is subjected to strategy adaptation processing with the pollution diffusion feature set to generate the differentiated protection strategy set.

10. A water source protection area pollution analysis system based on software model simulation, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the water source protection area pollution analysis method based on software model simulation as described in any one of claims 1 to 9.

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