Method and system for comprehensive environmental risk assessment and zoning of regional groundwater

Abstract

The present application relates to the technical field of environmental risk assessment, in particular to a regional groundwater comprehensive environmental risk assessment and zoning method and system, comprising the following steps: laying out monitoring points to collect pollution characteristic records, screening out incomplete information, analyzing spatial co-occurrence relationship to identify observation characteristic combinations, judging continuous distribution characteristics to delimit independent regions, modifying boundary trends to adjust spatial ranges, and finally outputting regional groundwater risk assessment and zoning atlas. In the present application, an index set is constructed based on pollution characteristic records, feature merging and combination are realized through spatial co-occurrence relationship, a set of risk areas with continuous distribution is generated, boundary overlap and fracture problems are corrected, the boundary trend of the region is adjusted in combination with the migration direction of groundwater and the stratum structure, the pollution information is accurately anchored to the actual spatial unit, the labeling relationship between the graphical boundary and the pollution elements is constructed, and the spatial expression clarity of risk assessment and the positioning accuracy of regional governance are improved.

Description

Technical Field

[0001] This invention relates to the field of environmental risk assessment technology, and in particular to a method and system for comprehensive environmental risk assessment and zoning of regional groundwater. Background Technology

[0002] The field of environmental risk assessment technology involves the technical process of systematically identifying, quantitatively analyzing, and predicting the potential negative impacts on the natural environment and its related systems after being disturbed by natural or human activities. Its core aspects include environmental sensitivity identification, pollution source identification, risk factor analysis, exposure path determination, risk level classification, and regional management. It is widely used in the management and treatment of multiple environmental media such as soil, groundwater, atmosphere, and water bodies. By establishing an assessment indicator system, determining evaluation models, conducting monitoring and data analysis, and constructing risk maps, it scientifically assesses and dynamically manages environmental risks, aiming to provide support for environmental protection planning, prioritization of pollution control, and ecological restoration. Among them, the traditional regional groundwater comprehensive environmental risk assessment and zoning method refers to a method system that, based on the identification of groundwater pollution source types, distribution and migration patterns within a certain geographical area, identifies the potential pollution risks faced by the groundwater system by determining evaluation units, selecting risk factors, constructing a risk assessment index system, collecting groundwater quality and surrounding human activity data, and using quantitative assessment methods such as weighted scoring or hierarchical analysis. This system classifies the potential pollution risks faced by the groundwater system and manages the area according to administrative boundaries, hydrogeological conditions or land use types, thereby forming a spatial distribution pattern of groundwater environmental risks.

[0003] Existing technologies rely excessively on pre-set evaluation units and indicator systems in groundwater risk assessment, making it difficult to take into account the heterogeneity of pollution phenomena and their actual distribution. The assessment system lacks sensitivity to perceived dynamic characteristics of pollution, resulting in risk identification remaining at the static deduction stage. Spatial zoning is mainly based on artificial boundaries, which cannot effectively reflect the real path of pollution diffusion. The process of dividing areas lacks a response mechanism to groundwater migration trends, making it difficult to achieve accurate fitting of spatial boundaries. Pollution characteristics fail to form a clear correspondence with specific spatial areas, limiting the positioning effectiveness and intervention targeting of risk information in regional governance. Summary of the Invention

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a method for comprehensive environmental risk assessment and zoning of regional groundwater, comprising the following steps: S1: Set up monitoring points in the target area to collect records of groundwater depth changes, water color changes, odor of effluent, surface subsidence, and soil seepage traces. Screen out those missing in the time dimension and spatial location field to construct a set of observation indicators. S2: Analyze the spatial co-occurrence relationship of the groundwater depth change records, water color change records, effluent odor records and surface subsidence records in the set of observation indicators, identify and merge observation feature combinations with consistent spatial locations, and construct a pollution association grouping table; S3: Determine the continuous distribution characteristics of pollution association groups in the pollution association grouping table, delineate pollution association groups with continuous coverage characteristics as independent regions, and generate a set of environmental risk distribution areas; S4: Adjust the boundary overlap and breakage of adjacent risk distribution areas in the set of environmental risk distribution areas, and correct the boundary orientation by combining the groundwater migration direction and the relationship between the stratigraphic structure distribution, and construct a graphic set of risk zoning boundary ranges; S5: Construct the pollution association features of the risk zones in the graphic set of the risk zone boundary range, label the spatial range of the risk zones with the pollution association features, and output the regional groundwater risk assessment and zoning map.

[0005] As a further aspect of the present invention, the set of observation indicators includes groundwater depth variation characteristics, water color variation characteristics, effluent odor characteristics, surface subsidence characteristics, and soil seepage trace characteristics; the pollution association grouping table includes spatial co-occurrence feature combinations, pollution phenomenon classification labels, and spatial location information; the environmental risk distribution area set includes independent risk area units, boundary description attributes, and continuous distribution feature identifiers; the risk zoning boundary range graphic set includes closed boundary lines, spatial coverage graphics, and geological and hydrological correction information; and the regional groundwater risk assessment and zoning map includes spatial zoning identifiers, pollution feature annotation information, and risk level classification results.

[0006] As a further aspect of the present invention, the filtering and removal of data with missing time and spatial location fields refers to removing observation data that is missing or corrupted in both time and spatial dimensions.

[0007] As a further aspect of the present invention, the designation of pollution-associated groups with continuous coverage characteristics as independent regions refers to identifying combinations of pollution characteristics that are continuously distributed in space as single, independent environmental risk areas.

[0008] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Obtain information on monitoring points deployed within the target area, call up five types of records: groundwater depth, water color, odor of effluent, surface subsidence, and soil seepage traces, match the recorded data frames according to time and space fields, remove data with incomplete time markers and missing spatial locations, and generate a monitoring record dataset. S102: Based on the monitoring record dataset, extract numerical fields and character fields. The numerical fields are compared according to the effective value range of the pre-set monitoring indicators, and the character fields are judged according to keywords. Records with an interval exceeding the preset continuity threshold between adjacent records are removed to generate a monitoring record set that meets the indicator requirements. S103: Call the monitoring record set that meets the indicator requirements, classify and cluster the fields according to the record type, extract the attribute fields corresponding to the five types of monitoring data as observation items, integrate them, and establish a set of observation indicators.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the set of observation indicators, extract the spatial location fields of four types of records: burial depth change, water color, effluent odor, and surface subsidence. Perform cross-matching on data frames with the same spatial location identifier, filter record groups with consistent spatial locations, and obtain spatial co-occurrence record groups. S202: Call the spatial co-occurrence record group, perform combination discrimination on the monitoring type field in each group of records, filter according to whether the indicator field in the group has observation values ​​at the same time, and label the record group that meets the conditions based on the observation type in the group to obtain the co-occurrence feature combination set; S203: Based on the co-occurrence feature combination set, and according to the pre-set observation type association determination rules, compare the association relationships between different observation types in the combination, aggregate and group the combinations with pollution co-cause relationships into a unified label, and establish a mapping relationship for all the merging results to establish a pollution association grouping table.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the pollution association grouping table, extract the spatial coordinate field of the association group, perform clustering judgment on the spatial points according to the latitude and longitude adjacency, and aggregate the groups that meet the preset continuous spatial coverage requirements into a single spatial region to generate a continuously distributed grouping set; S302: Call the continuous distribution group set, perform boundary point extraction operation on the edge point set of each aggregation region, calculate the adjacency relationship between boundary points and perform connection processing to obtain the boundary contour structure set; S303: Based on the boundary contour structure set, map the boundary structure to the corresponding associated group, and construct a data structure frame including spatial boundary information and pollution group index to establish a set of environmental risk distribution areas.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the set of environmental risk distribution areas, extract the coordinates of the boundary contour lines of the areas, perform overlap matching and boundary discontinuity judgment on the boundary points of adjacent areas, and if there are cases where the overlap of adjacent boundaries of two areas exceeds the preset contact threshold and the boundary distance is less than the discontinuity threshold, then connect and repair the corresponding boundary contour lines to obtain a set of continuous boundary line segments. S402: Call the continuous boundary line segment set, extract the boundary direction vector sequence corresponding to the area in the line segment coordinate data, determine the direction offset of the vector sequence in combination with the regional groundwater migration direction and the stratigraphic structure distribution parameters, and reconstruct the vector at the boundary node according to the angle adjustment strategy to generate the corrected boundary structure set. S403: Based on the modified boundary structure set, re-encode the coordinate nodes in each group of boundary structures and connect them in the encoding order to form a closed contour graphic. At the same time, establish the mapping relationship between the boundary graphic and the risk area index to construct a risk partition boundary range graphic set.

[0012] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the risk zoning boundary range graphic set, extract the spatial coding field corresponding to the graphic structure, call the pollution feature labels that have been merged in the pollution association grouping table, and collect them according to the spatial matching relationship between the points within the graphic boundary and the pollution feature records to generate a spatial pollution association list. S502: Based on the spatial pollution association list, the pollution type field in each spatial graphic unit is summarized, and the pollution type, corresponding indicator range and location code are combined to form a feature identifier, which is then marked on the corresponding spatial unit to obtain the partitioned pollution feature annotation set. S503: Call the partition pollution feature annotation set, integrate the pollution feature annotation information with the graphic boundary structure, establish a unified visualization configuration frame, and obtain the layer rendering sequence according to the spatial location index order to establish a regional groundwater risk assessment and zoning map.

[0013] The regional groundwater integrated environmental risk assessment and zoning system includes: The observation index acquisition and preprocessing module is used to achieve S1: setting up monitoring points in the target area, collecting records of groundwater depth changes, water color changes, outflow odors, surface subsidence, and soil seepage traces, filtering out those missing in the time dimension and spatial location fields, and constructing a set of observation indicators; The pollution feature identification and merging module is used to implement S2: analyze the spatial co-occurrence relationship of the groundwater depth change record, the water color change record, the effluent odor record and the surface subsidence record in the set of observation indicators, identify and merge observation feature combinations with consistent spatial locations, and construct a pollution association grouping table; The pollution area identification and division module is used to achieve S3: determine the continuous distribution characteristics of pollution association groups in the pollution association grouping table, delineate pollution association groups with continuous coverage characteristics as independent areas, and generate a set of environmental risk distribution areas; The risk zone boundary optimization module is used to achieve S4: adjust the boundary overlap and break of adjacent risk distribution zones in the set of environmental risk distribution zones, correct the boundary orientation by combining the groundwater migration direction and the distribution relationship of the stratigraphic structure, and construct a risk zone boundary range graphic set; The groundwater risk assessment and mapping generation module is used to implement S5: construct the pollution association features of the risk zones in the graphic set of the risk zone boundary range, label the spatial range of the risk zones with the pollution association features, and output the regional groundwater risk assessment and zoning map.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, an indicator set is constructed based on pollution feature records. Feature merging and combination are achieved through spatial co-occurrence relationships to generate a continuously distributed risk area set. This corrects boundary overlap and fracture issues. The orientation of regional boundaries is adjusted by combining the direction of groundwater migration and stratigraphic structure, so that pollution information is accurately anchored to actual spatial units. The annotation relationship between graphic boundaries and pollution elements is constructed, thereby improving the spatial expression clarity of risk assessment and the positioning accuracy of regional governance. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1 This invention provides a method for comprehensive environmental risk assessment and zoning of regional groundwater, including the following steps: S1: By setting up monitoring points in the target area, records of groundwater depth changes, water color changes, outflow odors, surface subsidence, and soil seepage traces are collected. The collected records are then screened to remove those with incomplete time stamps and uncertain spatial locations, thus constructing a set of observation indicators. S2: Based on the set of observation indicators, analyze the co-occurrence relationship of burial depth change records, water color change records, effluent odor records, and surface subsidence records in spatial distribution, identify the observation feature combinations with consistent spatial locations, and merge them according to the correlation of pollution phenomena to construct a pollution correlation grouping table. S3: Based on the pollution association grouping table, determine the continuous distribution of association groups in geographic space, delineate association groups with continuous spatial coverage characteristics as independent distribution areas, form an information structure with boundary descriptions, and generate a set of environmental risk distribution areas. S4: For the set of environmental risk distribution areas, adjust the overlap and fracture of the boundaries between adjacent distribution areas, and modify the boundary orientation of the areas in combination with the regional groundwater migration direction and the distribution of stratigraphic structure to form a continuous and closed spatial range, and construct a graphic set of risk zoning boundary ranges. S5: Based on the risk zoning boundary map set, construct the pollution association features corresponding to the zoning, label the spatial range with the pollution features, and output the regional groundwater risk assessment and zoning map.

[0023] The set of observation indicators includes characteristics of groundwater depth changes, water color changes, odor characteristics of effluent, surface subsidence characteristics, and soil seepage traces. The pollution association grouping table includes combinations of spatial co-occurrence characteristics, pollution phenomenon classification labels, and spatial location information. The set of environmental risk distribution areas includes independent risk area units, boundary description attributes, and continuous distribution characteristic identifiers. The risk zoning boundary range graphic set includes closed boundary lines, spatial coverage graphics, and geological and hydrological correction information. The regional groundwater risk assessment and zoning map includes spatial zoning identifiers, pollution characteristic labeling information, and risk level classification results.

[0024] Please see Figure 2 The specific steps of S1 are as follows: S101: Obtain information on monitoring points deployed within the target area, call up five types of records: groundwater depth, water color, odor of effluent, surface subsidence, and soil seepage traces, match the recorded data frames according to time and space fields, remove data with incomplete time markers and missing spatial locations, and generate a monitoring record dataset. The data acquisition server is connected to 120 multi-parameter monitoring wells (numbered MW-001 to MW-120) deployed within the park, executing data acquisition commands. The server polls each monitoring terminal via the Modbus TCP protocol to extract raw data frames from the period 08:00 to 18:00. Each data frame contains a unique identifier for the monitoring point, a timestamp, longitude, latitude, and readings from five types of sensors: pressure level gauge readings (converted to groundwater depth in meters), multispectral water quality sensor readings (RGB values ​​of water color), electronic nose sensor array response values ​​(odor of effluent in OU / cubic meter), displacement sensor readings (surface subsidence in millimeters), and soil moisture sensor readings (water seepage traces in percentage). After receiving the data stream, the time field is parsed first, and records that do not conform to the standard format (i.e., not in the year-month-day hour:minute:second format) or have an empty timestamp are marked as invalid and deleted. Next, spatial location verification was performed, setting the longitude boundary of the target area to 114.3000 to 114.3500 E and the latitude boundary to 30.5500 to 30.6000 N. Records with empty longitude and latitude fields or values ​​outside the above closed intervals were removed. For example, in the data frame returned by monitoring point MW-045 at 10:15, the longitude and latitude fields showed empty values, so this record was immediately deleted; the timestamp of monitoring point MW-012 showed an error code, so the same deletion operation was performed. After data cleaning, 550 invalid data were removed from the original 14,400 records, and finally 13,850 valid records were retained to generate the monitoring record dataset.

[0025] S102: Based on the monitoring record dataset, extract numerical fields and character fields. The numerical fields are compared according to the effective value range of the pre-set monitoring indicators, and the character fields are judged according to keywords. Records with an interval exceeding the preset continuity threshold between adjacent records are removed to generate a monitoring record set that meets the indicator requirements. Numerical fields (groundwater depth, surface subsidence, soil seepage moisture) and character fields (water color description code, effluent odor label) are processed separately. For numerical fields, strict physical thresholds are set for comparison. Based on the historical extreme groundwater depth of 1.8 meters (corresponding to high water level) and 38.5 meters (historical extreme groundwater depth during the alluvial fan aquifer where the park is located) during the wet season, a safety margin is introduced to expand the threshold. Specifically, for the extreme value during the wet season, the threshold is expanded by 1.5 meters along the direction of decreasing groundwater depth, resulting in a lower threshold of 0.3 meters. For the extreme value during the dry season, the threshold is expanded by 1.5 meters along the direction of increasing groundwater depth, resulting in an upper threshold of 40.0 meters. Thus, the effective threshold range for groundwater depth is set to 0.3 meters to 40.0 meters; the threshold for surface subsidence is set to 0 to 500 millimeters; and the threshold for seepage moisture is set to 10% to 100%. For example, the groundwater depth recorded at monitoring point MW-023 is 0.5 meters, which falls within the set effective threshold range of 0.3 meters to 40.0 meters. This value is considered a valid record and retained. If the groundwater depth recorded is less than 0.3 meters, it is considered abnormal data exceeding a reasonable physical range and is removed. For character fields, a preset keyword library is loaded. Water color keywords include clear, slightly yellow, black, red, and blue; odor keywords include odorless, sulfurous, solvent-like, and metallic. The description field in the records is scanned; if a keyword not in the library appears (such as unknown or garbled characters), the field is considered invalid. Subsequently, the time series continuity of the same monitoring point is checked, with a maximum allowable time interval of 30 minutes. If the interval between two adjacent records at a monitoring point exceeds 30 minutes, the time field is considered discontinuous, and fragmented record sequences shorter than three records before and after the breakpoint are removed. After the above numerical and logical filtering, the data volume is refined to 12,500 records, generating a monitoring record set that meets the indicator requirements.

[0026] S103: Call the monitoring record set that meets the indicator requirements, classify and cluster the fields according to the record type, extract the attribute fields corresponding to the five types of monitoring data as observation items, integrate them, and establish a set of observation indicators; Field classification and clustering are performed based on record type. Five originally scattered attribute fields (buried depth, color, odor, subsidence, and humidity) are mapped to unified observation item objects. A five-dimensional vector space is constructed, and for each record, it is transformed into a set of observation indicators. During this process, non-numerical data is encoded. If an indicator is within the normal background value range in the original record (e.g., odor is odorless or subsidence is 0), it is marked as invalid in the set, retaining only significant abnormal features. For example, if a record contains only abnormal data indicating reddish water color, while all other indicators are normal, then only the color dimension has valid values ​​in this set. Through this step, all single-dimensional monitoring data are integrated into structured objects with multi-dimensional attributes, establishing a set of observation indicators.

[0027] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the set of observation indicators, extract the spatial location fields of four types of records: burial depth change, water color, effluent odor, and surface subsidence. Cross-match the data frames with the same spatial location identifier, filter the record groups with consistent spatial locations, and obtain the spatial co-occurrence record groups. Spatial coordinate fields were extracted for four key indicators: changes in burial depth, water color, unusual odor in effluent, and surface subsidence. Assuming the dataset contains N observation objects, the spatial coordinates of all objects were extracted. Using a spatial hashing algorithm, the spatial coordinates, accurate to four decimal places (with an error range controlled within 10 meters), were used as keys to group data frames with the same coordinate key value into the same group. For example, monitoring point A, with coordinates of 114.32°E, 30.58°N, uploaded three records at different times or from different sensors: record 1 showed a sudden change in burial depth, record 2 showed yellowing of the water, and record 3 showed slight ground subsidence. These three records were identified as having the same spatial key value and grouped together. Groups with two or more records within each group were selected, indicating multiple anomalies of different types at the same location. If a location had only one record of a single type, it was excluded from the co-occurrence analysis. This step output 120 groups, obtaining the spatial co-occurrence record groups.

[0028] S202: Call the spatial co-occurrence record group, perform combination discrimination on the monitoring type field in each group of records, filter according to whether the indicator field in the group has observation values ​​at the same time, and label the record group that meets the conditions based on the observation type in the group to obtain the co-occurrence feature combination set; For each monitoring type field within a group, a combination judgment is performed. It checks whether the group simultaneously contains valid observations of both groundwater depth and surface subsidence, or valid values ​​of both water color and effluent odor. The filtering logic is set as follows: if the number of valid abnormal indicators in a group is less than two, the group is filtered. Taking group 5 as an example, this group contains a depth of 18 meters, subsidence of 0 millimeters, clear color, and no odor. Although it is a co-occurrence record, only the depth value is valid, while the rest are background values; therefore, this group is discarded. Taking group 12 as an example, this group contains a depth of 5 meters (abnormally shallow), subsidence of 20 millimeters, red color, and solvent odor; four valid abnormal indicators exist simultaneously in this group. Based on the observation type within the group, a feature code "depth-subsidence-color-odor" is generated, resulting in a co-occurrence feature combination set.

[0029] S203: Based on the co-occurrence feature combination set, and according to the pre-set observation type association judgment rules, compare the association relationship between different observation types in the combination, aggregate and group the combinations with pollution co-cause relationship into a unified label, and establish a mapping relationship for all the merging results to establish a pollution association grouping table; Perform comparative analysis of pollution correlation levels. A pre-defined pollution common cause relationship matrix is ​​established, defining the correlation weights between different phenomena. Correlation rules are set: if a combination includes abnormal water color and an odor from organic solvents, it is classified as a chemical leak correlation; if a combination includes drastic fluctuations in burial depth and surface subsidence, it is classified as a geological instability correlation. The combination set is traversed. For combinations with the feature code "burial depth + subsidence," the merged label is physical risk level I; for combinations with the feature code "color + odor," the merged label is chemical risk level II; for combinations with all elements, the merged label is composite severe risk. After establishing the mapping relationship, the spatial coordinates of each data set are bound to the merged unified label to create a pollution correlation grouping table.

[0030] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the pollution association grouping table, extract the spatial coordinate field of the association group, perform clustering judgment on the spatial points according to the latitude and longitude adjacency, and aggregate the groups that meet the preset continuous spatial coverage requirements into a single spatial region to generate a continuously distributed group set; Extract the spatial coordinate field of the associated groups. Convert the latitude and longitude coordinates to a Cartesian coordinate system (unit: meters) for Euclidean distance calculation. Use density-based spatial clustering logic to perform neighborhood search on the points. Set the distance threshold for continuous spatial coverage to 80 meters. This threshold is determined based on the average spacing of monitoring wells within the park (50 meters) and the theoretical maximum diffusion radius of groundwater pollution plumes within a single monitoring period (75 meters), taking the coverage range of both and rounding up. Calculate the straight-line distance between points. If the distance is less than 80 meters, the two points are considered to belong to the same spatial connectivity domain. For example, if point A has plane coordinates (100, 200) and point B has plane coordinates (150, 240), the square root of the sum of the squares of the differences in their x and y coordinates yields a distance of approximately 64.03 meters. Since 64.03 meters is less than 80 meters, points A and B are grouped into the same cluster. After clustering, the originally discrete 120 co-occurrence record groups are aggregated into 8 independent regions, generating a continuously distributed group set.

[0031] S302: Call the continuous distribution group set, perform boundary point extraction operation on the edge point set of each aggregation region, calculate the adjacency relationship between boundary points and perform connection processing to obtain the boundary contour structure set; For each aggregated region, its geometric edge points are extracted. Convex hull or concave hull algorithms are used to construct the boundary. The maximum edge length limit for the generated concave hull is set to 1.5 times the distance threshold, i.e., 120 meters. The outermost points within the cluster are connected; if the distance between adjacent outer points is less than 120 meters, they are connected; otherwise, concave points are searched to avoid incorrectly including non-risk areas. Taking cluster 1 as an example, the extreme points located at the northernmost, southernmost, easternmost, and westernmost points are identified, and adjacent points are searched in a counter-clockwise direction. The adjacent vector relationships between boundary points are calculated, and the extracted points are connected sequentially to form a closed polygon, obtaining the boundary contour structure set.

[0032] S303: Based on the boundary contour structure set, map the boundary structure to the corresponding associated grouping, and construct a data structure frame including spatial boundary information and pollution grouping index to establish a set of environmental risk distribution areas; Geometric boundaries are mapped to corresponding associated groups using attribute mapping. A data structure frame is constructed, with each frame containing two parts: spatial boundary information (polygon vertex coordinate sequence) and a pollution group index (all original group IDs and their corresponding merged labels within the region). If a region contains multiple risk types, the primary risk type is determined based on a preset risk priority (compound severity > chemical risk > physical risk). For example, if a region contains 3 physical risk points and 1 chemical risk point, it is indexed as a region with chemical risk as the primary risk based on priority. Finally, a set of environmental risk distribution regions is established.

[0033] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the set of environmental risk distribution areas, extract the coordinates of the boundary contour lines of the areas, perform overlap matching and boundary discontinuity judgment on the boundary points of adjacent areas, and if there are cases where the overlap of adjacent boundaries of two areas exceeds the preset contact threshold and the boundary spacing is less than the discontinuity threshold, then connect and repair the corresponding boundary contour lines to obtain a set of continuous boundary line segments. Extract the coordinates of the boundary outlines of the zones, and perform overlap matching and boundary discontinuity judgment for adjacent zones. Set the contact threshold to 20 meters and the discontinuity threshold to 15 meters. Calculate the nearest distance and the parallel overlap length of the boundaries between any two adjacent zones. If the nearest distance between two zones is less than 15 meters, it is considered a geologically continuous but monitoring-discontinuous area, triggering bridging repair, i.e., connecting the nearest points with a straight line. If two zones have overlapping boundaries, and the projected length of the overlapping part exceeds 20 meters, it is considered a diffusion of the same risk source, triggering fusion repair, i.e., taking the union of the two areas. For example, if the distance between the eastern boundary of zone 1 and the western boundary of zone 2 is 12.5 meters, which is less than 15 meters, and the two zones have the same risk attribute, perform a connection repair operation, merging the outlines of the two zones to obtain a set of continuous boundary line segments.

[0034] S402: Call the continuous boundary line segment set, extract the boundary orientation vector sequence corresponding to the area in the line segment coordinate data, determine the direction offset of the vector sequence in combination with the regional groundwater migration direction and stratigraphic structure distribution parameters, and reconstruct the vector at the boundary node according to the angle adjustment strategy to generate the corrected boundary structure set. Extract the boundary orientation vector sequence. Combine the regional groundwater migration direction (obtained from hydrogeological exploration and set as a constant unit vector) with the stratigraphic permeability coefficient (range 0.1 to 1.5 m / day) to determine the directional offset. An angle adjustment strategy is set: if the angle between the boundary line segment vector and the groundwater flow direction is less than 90 degrees (i.e., a downstream trend), the boundary expands outward, with the expansion magnitude proportional to the permeability coefficient. The calculation formula is: new coordinates equal old coordinates plus the time evolution weight coefficient multiplied by the permeability coefficient and then multiplied by the flow direction vector. Assuming the time evolution weight coefficient is 2.0 days, the permeability coefficient at a certain boundary node is 1.2 m / day, and the flow direction is southeast (unit vector components are all 0.707), the calculated offset distance is 2.0 multiplied by 1.2 equals 2.4 meters. That is, the node extends outward approximately 2.4 meters in the southeast direction. Through this process, a corrected boundary structure set is generated.

[0035] S403: Based on the modified boundary structure set, re-encode the coordinate nodes in each group of boundary structures and connect them in the encoding order to form a closed contour graphic. At the same time, establish the mapping relationship between the boundary graphic and the risk area index to construct a risk partition boundary range graphic set. The coordinate nodes within each boundary structure are re-encoded. The corrected node sequence is arranged in counter-clockwise order to ensure that the first and last nodes are closed. A closed contour graphic is constructed, and a unique risk zone ID is assigned to each graphic. Simultaneously, a mapping table is established to associate the risk zone ID with the pollution risk index. For example, zone 01 is associated with the chemical risk level II index and contains 50 corrected boundary coordinate points. Finally, a set of risk zone boundary extent graphics is constructed.

[0036] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the risk zoning boundary range graphic set, extract the spatial coding field corresponding to the graphic structure, call the pollution feature labels that have been merged in the pollution association grouping table, and collect them according to the spatial matching relationship between the points within the graphic boundary and the pollution feature records to generate a spatial pollution association list. Extract the spatial encoding field corresponding to the graphic structure. Retrieve the merged pollution feature labels from the pollution association grouping table. Perform a spatial inclusion query, traversing all original monitoring points to determine if they are located within the boundary of a certain risk zone graphic. If a point is inside the graphic, then the specific pollution value carried by that point (e.g., benzene concentration of 0.5 mg / L) is added to the attribute list of that graphic. Through this process, a spatial pollution association list is generated.

[0037] S502: Based on the spatial pollution association list, the pollution type field in each spatial graphic unit is summarized, and the pollution type, corresponding indicator range and location code are combined to form a feature identifier, which is then marked on the corresponding spatial unit to obtain the partitioned pollution feature annotation set. The pollution type field in each spatial graphic unit is summarized. All indicator values ​​within the partition are extracted, and their maximum values ​​are calculated. The feature identifier generation rule is set as: main pollution type + highest exceedance multiple level + center location code. For example, for partition 01, the main pollution type is identified as organic matter. The maximum measured benzene concentration in the region is 0.5 mg / L, and the corresponding environmental quality standard benchmark value is 0.01 mg / L. The exceedance multiple is calculated by subtracting the benchmark value from the measured value and then dividing by the benchmark value, i.e., (0.5-0.01) / 0.01 equals 49 times. The level classification rule is set as follows: 10 to 50 times is considered high risk level (Level 3). The center location code is the abbreviation of the region's centroid coordinates (e.g., WX4). The generated feature identifier is "Organic Matter-L3@WX4". This identifier is labeled on the corresponding spatial unit to obtain the partition pollution feature annotation set.

[0038] S503: Call the regional pollution feature annotation set, integrate the pollution feature annotation information with the graphic boundary structure, establish a unified visualization configuration frame, and obtain the layer rendering sequence according to the spatial location index order to establish a regional groundwater risk assessment and zoning map. Pollution feature annotations are integrated with the graphic boundary structure to establish a unified visualization configuration frame. Rendering rules are defined as follows: for physical risk level I, the fill color is set to yellow with 30% transparency; for chemical risk level II, the fill color is set to orange with 50% transparency; for combined severe risk, the fill color is set to red with 70% transparency, and a diagonal shadow is overlaid. A layer rendering sequence is generated according to the spatial location index order (from north to south, from west to east), ensuring that high-risk layers are placed on top and not obscured. Finally, the geometry with corrected boundaries, fill color, and feature text annotations are combined to output a regional groundwater risk assessment and zoning map.

[0039] Please see Figure 7 The regional groundwater comprehensive environmental risk assessment and zoning system includes: The observation index acquisition and preprocessing module is used to achieve S1: setting up monitoring points in the target area, collecting records of groundwater depth changes, water color changes, outflow odors, surface subsidence, and soil seepage traces, filtering out those missing in the time dimension and spatial location fields, and constructing a set of observation indicators; The pollution feature identification and merging module is used to realize S2: analyze the spatial co-occurrence relationship of groundwater depth change records, water color change records, effluent odor records and surface collapse records in the set of observation indicators, identify and merge observation feature combinations with consistent spatial locations, and construct a pollution association grouping table. The pollution area identification and division module is used to implement S3: determine the continuous distribution characteristics of pollution association groups in the pollution association grouping table, delineate pollution association groups with continuous coverage characteristics as independent areas, and generate a set of environmental risk distribution areas; The risk zone boundary optimization module is used to implement S4: adjust the boundary overlap and fracture of adjacent risk distribution zones in the environmental risk distribution zone set, correct the boundary orientation by combining the groundwater migration direction and the stratigraphic structure distribution relationship, and construct a risk zone boundary range graphic set; The groundwater risk assessment and mapping generation module is used to implement S5: constructing risk zoning boundary range graphics, collecting pollution association features of risk zoning zones, marking the spatial range of risk zoning zones with pollution association features, and outputting regional groundwater risk assessment and zoning maps.

[0040] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the technical solution.

Claims

1. A method for comprehensive environmental risk assessment and zoning of regional groundwater, characterized in that, Includes the following steps: S1: Set up monitoring points in the target area to collect records of groundwater depth changes, water color changes, odor of effluent, surface subsidence, and soil seepage traces. Screen out those missing in the time dimension and spatial location field to construct a set of observation indicators. S2: Analyze the spatial co-occurrence relationship of the groundwater depth change records, water color change records, effluent odor records and surface subsidence records in the set of observation indicators, identify and merge observation feature combinations with consistent spatial locations, and construct a pollution association grouping table; S3: Determine the continuous distribution characteristics of pollution association groups in the pollution association grouping table, delineate pollution association groups with continuous coverage characteristics as independent regions, and generate a set of environmental risk distribution areas; S4: Adjust the boundary overlap and breakage of adjacent risk distribution areas in the set of environmental risk distribution areas, and correct the boundary orientation by combining the groundwater migration direction and the relationship between the stratigraphic structure distribution, and construct a graphic set of risk zoning boundary ranges; S5: Construct the pollution association features of the risk zones in the graphic set of the risk zone boundary range, label the spatial range of the risk zones with the pollution association features, and output the regional groundwater risk assessment and zoning map.

2. The method for comprehensive environmental risk assessment and zoning of regional groundwater according to claim 1, characterized in that, The set of observation indicators includes groundwater depth variation characteristics, water color variation characteristics, effluent odor characteristics, surface subsidence characteristics, and soil seepage trace characteristics. The pollution association grouping table includes spatial co-occurrence feature combinations, pollution phenomenon classification labels, and spatial location information. The set of environmental risk distribution areas includes independent risk area units, boundary description attributes, and continuous distribution feature identifiers. The risk zoning boundary range graphic set includes closed boundary lines, spatial coverage graphics, and geological and hydrological correction information. The regional groundwater risk assessment and zoning map includes spatial zoning identifiers, pollution feature annotation information, and risk level classification results.

3. The method for comprehensive environmental risk assessment and zoning of regional groundwater according to claim 1, characterized in that, The filtering and removal of data with missing time and spatial location fields refers to removing observation data that is missing or corrupted in both time and spatial dimensions.

4. The method for comprehensive environmental risk assessment and zoning of regional groundwater according to claim 1, characterized in that, The designation of pollution-related groups with continuous coverage characteristics as independent regions refers to identifying combinations of pollution characteristics that are continuously distributed in space as single, independent environmental risk areas.

5. The method for comprehensive environmental risk assessment and zoning of regional groundwater according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain information on monitoring points deployed within the target area, call up five types of records: groundwater depth, water color, odor of effluent, surface subsidence, and soil seepage traces, match the recorded data frames according to time and space fields, remove data with incomplete time markers and missing spatial locations, and generate a monitoring record dataset. S102: Based on the monitoring record dataset, extract numerical fields and character fields. The numerical fields are compared according to the effective value range of the pre-set monitoring indicators, and the character fields are judged according to keywords. Records with an interval exceeding the preset continuity threshold between adjacent records are removed to generate a monitoring record set that meets the indicator requirements. S103: Call the monitoring record set that meets the indicator requirements, classify and cluster the fields according to the record type, extract the attribute fields corresponding to the five types of monitoring data as observation items, integrate them, and establish a set of observation indicators.

6. The method for comprehensive environmental risk assessment and zoning of regional groundwater according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the set of observation indicators, extract the spatial location fields of four types of records: burial depth change, water color, effluent odor, and surface subsidence. Perform cross-matching on data frames with the same spatial location identifier, filter record groups with consistent spatial locations, and obtain spatial co-occurrence record groups. S202: Call the spatial co-occurrence record group, perform combination discrimination on the monitoring type field in each group of records, filter according to whether the indicator field in the group has observation values ​​at the same time, and label the record group that meets the conditions based on the observation type in the group to obtain the co-occurrence feature combination set; S203: Based on the co-occurrence feature combination set, and according to the pre-set observation type association determination rules, compare the association relationships between different observation types in the combination, aggregate and group the combinations with pollution co-cause relationships into a unified label, and establish a mapping relationship for all the merging results to establish a pollution association grouping table.

7. The method for comprehensive environmental risk assessment and zoning of regional groundwater according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the pollution association grouping table, extract the spatial coordinate field of the association group, perform clustering judgment on the spatial points according to the latitude and longitude adjacency, and aggregate the groups that meet the preset continuous spatial coverage requirements into a single spatial region to generate a continuously distributed grouping set; S302: Call the continuous distribution group set, perform boundary point extraction operation on the edge point set of each aggregation region, calculate the adjacency relationship between boundary points and perform connection processing to obtain the boundary contour structure set; S303: Based on the boundary contour structure set, map the boundary structure to the corresponding associated group, and construct a data structure frame including spatial boundary information and pollution group index to establish a set of environmental risk distribution areas.

8. The method for comprehensive environmental risk assessment and zoning of regional groundwater according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the set of environmental risk distribution areas, extract the coordinates of the boundary contour lines of the areas, perform overlap matching and boundary discontinuity judgment on the boundary points of adjacent areas, and if there are cases where the overlap of adjacent boundaries of two areas exceeds the preset contact threshold and the boundary distance is less than the discontinuity threshold, then connect and repair the corresponding boundary contour lines to obtain a set of continuous boundary line segments. S402: Call the continuous boundary line segment set, extract the boundary direction vector sequence corresponding to the area in the line segment coordinate data, determine the direction offset of the vector sequence in combination with the regional groundwater migration direction and the stratigraphic structure distribution parameters, and reconstruct the vector at the boundary node according to the angle adjustment strategy to generate the corrected boundary structure set. S403: Based on the modified boundary structure set, re-encode the coordinate nodes in each group of boundary structures and connect them in the encoding order to form a closed contour graphic. At the same time, establish the mapping relationship between the boundary graphic and the risk area index to construct a risk partition boundary range graphic set.

9. The method for comprehensive environmental risk assessment and zoning of regional groundwater according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the risk zoning boundary range graphic set, extract the spatial coding field corresponding to the graphic structure, call the pollution feature labels that have been merged in the pollution association grouping table, and collect them according to the spatial matching relationship between the points within the graphic boundary and the pollution feature records to generate a spatial pollution association list. S502: Based on the spatial pollution association list, the pollution type field in each spatial graphic unit is summarized, and the pollution type, corresponding indicator range and location code are combined to form a feature identifier, which is then marked on the corresponding spatial unit to obtain the partitioned pollution feature annotation set. S503: Call the partition pollution feature annotation set, integrate the pollution feature annotation information with the graphic boundary structure, establish a unified visualization configuration frame, and obtain the layer rendering sequence according to the spatial location index order to establish a regional groundwater risk assessment and zoning map.

10. A regional groundwater integrated environmental risk assessment and zoning system, characterized in that, The system is used to implement the regional groundwater integrated environmental risk assessment and zoning method according to any one of claims 1-9, and the system includes: The observation index acquisition and preprocessing module is used to achieve S1: setting up monitoring points in the target area, collecting records of groundwater depth changes, water color changes, outflow odors, surface subsidence, and soil seepage traces, filtering out those missing in the time dimension and spatial location fields, and constructing a set of observation indicators; The pollution feature identification and merging module is used to implement S2: analyze the spatial co-occurrence relationship of the groundwater depth change record, the water color change record, the effluent odor record and the surface subsidence record in the set of observation indicators, identify and merge observation feature combinations with consistent spatial locations, and construct a pollution association grouping table; The pollution area identification and division module is used to achieve S3: determine the continuous distribution characteristics of pollution association groups in the pollution association grouping table, delineate pollution association groups with continuous coverage characteristics as independent areas, and generate a set of environmental risk distribution areas; The risk zone boundary optimization module is used to achieve S4: adjust the boundary overlap and fracture of adjacent risk distribution zones in the set of environmental risk distribution zones, correct the boundary orientation by combining the groundwater migration direction and the distribution relationship of the stratigraphic structure, and construct a risk zone boundary range graphic set; The groundwater risk assessment and mapping generation module is used to achieve S5: constructing the pollution association features of the risk zones in the graphic set of the risk zone boundary range, marking the spatial range of the risk zones with the pollution association features, and outputting the regional groundwater risk assessment and zoning map.

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