Water resource quality detection method, device and electronic equipment applied to diversion project

Abstract

This disclosure presents embodiments of a method, apparatus, and electronic device for water resource quality testing applied to water diversion projects. One specific implementation of the method includes: conducting water quality testing on the water area where the water diversion project is located to obtain surface water quality datasets and groundwater quality datasets; constructing a surface data structure network based on a pre-built water area simulation model and the surface water quality datasets; constructing a groundwater data structure network; conducting water pollution testing on the water area to generate surface water quality test result sets and groundwater quality test result sets; performing correlation analysis on the surface water resources and groundwater resources of the water area to generate water pollution correlation features; identifying surface water pollution areas and groundwater pollution areas of the water area; and determining the surface water quality test result sets, groundwater quality test result sets, surface water pollution areas, groundwater pollution areas, and water pollution correlation features as the water resource quality testing results. This implementation can improve the accuracy of the overall water area quality testing results.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and more particularly to the field of water resource quality testing, specifically to water resource quality testing methods, apparatus, and electronic equipment applied to water diversion projects. Background Technology

[0002] With the acceleration of industrialization, urbanization, and agricultural modernization, the pollution pressure on water resources continues to escalate, becoming the fundamental driving force for the development of water quality testing technologies. Currently, surface water resources or groundwater resources are typically tested separately. However, this testing method does not consider the flow relationship between surface water and groundwater, resulting in problems such as fragmentation, lag, and limitations in surface water testing and groundwater testing. Consequently, the results of overall water quality testing are unsatisfactory. Summary of the Invention

[0003] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0004] Some embodiments of this disclosure propose methods, apparatuses, and electronic devices for water resource quality testing in water diversion projects to address the technical problems mentioned in the background section above.

[0005] In a first aspect, some embodiments of this disclosure provide a water resource quality detection method applied to a water diversion project. The method includes: conducting water quality detection on the water area where the water diversion project is located to obtain a surface water quality dataset and a groundwater quality dataset; constructing a surface data structure network based on a pre-built water area simulation model and the aforementioned surface water quality dataset, wherein the surface data structure network includes a set of surface water area structure points and corresponding surface water quality data; and constructing a groundwater data structure network based on the aforementioned water area simulation model and the aforementioned groundwater quality dataset, wherein the groundwater data structure network includes a set of groundwater area structure points and corresponding groundwater quality data. Based on the aforementioned surface data structure network and underground data structure network, water pollution detection is conducted on the aforementioned water areas to generate surface water quality detection result sets and groundwater quality detection result sets. Based on the aforementioned surface water quality detection result sets and groundwater quality detection result sets, correlation analysis is performed on the surface water resources and groundwater resources of the aforementioned water areas to generate water pollution correlation characteristics. Surface water pollution areas and groundwater pollution areas of the aforementioned water areas are identified. The aforementioned surface water quality detection result sets, groundwater quality detection result sets, surface water pollution areas, groundwater pollution areas, and water pollution correlation characteristics are determined as water resource quality detection results.

[0006] Secondly, some embodiments of this disclosure provide a water resource quality detection device applied to a water diversion project. The device includes: a water quality detection unit configured to perform water quality detection on the water area where the water diversion project is located, obtaining a surface water quality dataset and a groundwater quality dataset; a first construction unit configured to construct a surface data structure network based on a pre-constructed water area simulation model and the aforementioned surface water quality dataset, wherein the surface data structure network includes a set of surface water area structure points and corresponding surface water quality data; and a second construction unit configured to construct a groundwater data structure network based on the aforementioned water area simulation model and the aforementioned groundwater quality dataset, wherein the groundwater data structure network includes a set of groundwater area structure points and corresponding groundwater quality data; and water pollution detection. The unit is configured to perform water pollution detection on the aforementioned water area based on the aforementioned surface data structure network and the aforementioned underground data structure network, so as to generate a surface water quality detection result set and a groundwater quality detection result set; the correlation analysis unit is configured to perform correlation analysis on the surface water resources and groundwater resources of the aforementioned water area based on the aforementioned surface water quality detection result set and the aforementioned groundwater quality detection result set, so as to generate water pollution correlation characteristics; the first determination unit is configured to determine the surface water pollution area and the groundwater pollution area of ​​the aforementioned water area; the second determination unit is configured to determine the aforementioned surface water quality detection result set, the aforementioned groundwater quality detection result set, the aforementioned surface water pollution area, the aforementioned groundwater pollution area, and the water pollution correlation characteristics as water resource quality detection results.

[0007] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0008] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0009] The various embodiments of this disclosure have the following beneficial effects: the water resource quality detection method applied to water diversion projects through some embodiments of this disclosure can improve the accuracy of the overall water quality detection results. Specifically, the reason for the poor overall water quality detection results is that the flow relationship between surface water and groundwater is not considered, and surface water resources are detected separately or groundwater resources are detected separately, resulting in problems such as fragmentation, lag, and limitations between surface water detection and groundwater detection. Based on this, the water resource quality detection method applied to water diversion projects through some embodiments of this disclosure firstly conducts water quality detection on the water area where the water diversion project is located to obtain surface water quality datasets and groundwater quality datasets. Here, collecting surface water quality data and groundwater quality data simultaneously can be used for synchronous detection. Then, based on the pre-constructed water area simulation model and the above-mentioned surface water quality dataset, a surface data structure network is constructed, wherein the above-mentioned surface data structure network includes a set of surface water area structure points and corresponding surface water quality data. Next, based on the aforementioned water area simulation model and groundwater quality dataset, an underground data structure network is constructed. This network includes a set of underground water area structure points and corresponding groundwater quality data. Here, by constructing surface and underground data structure networks, surface and underground water area structures can be used to characterize these structures, respectively. Therefore, comprehensive detection can be performed by combining both surface and underground water area structures. Subsequently, based on these two networks, water pollution detection is conducted on the aforementioned water areas to generate surface and groundwater quality detection result sets. The introduction of surface and underground water area structures allows for water pollution detection based on the water area structure, overcoming the limitations of detecting only surface or underground water areas. Finally, based on these two result sets, a correlation analysis is performed on the surface and groundwater resources of the aforementioned water areas to generate water pollution correlation characteristics. Here, correlation analysis can be used to determine the flow relationship between surface water and groundwater within a water body, thus avoiding the disconnect between surface water and groundwater testing. Simultaneously, because water pollution correlation characteristics are generated, the degree of pollution can be determined promptly based on these characteristics, significantly reducing the lag in surface water and groundwater testing. This facilitates more accurate identification of surface water and groundwater pollution areas within the aforementioned water body. Finally, the aforementioned surface water quality testing results set, groundwater quality testing results set, surface water pollution areas, groundwater pollution areas, and water pollution correlation characteristics are defined as the water resource quality testing results. This improves the accuracy of the overall water body quality testing results. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0011] Figure 1 This is a flowchart of some embodiments of the water resource quality testing method applied to water diversion projects according to the present disclosure;

[0012] Figure 2 This is a schematic diagram of the surface water network structure;

[0013] Figure 3 This is a top-down schematic diagram of the distribution of water pollution area data;

[0014] Figure 4 These are schematic diagrams of some embodiments of a water resources quality testing device applied to water diversion projects according to the present disclosure;

[0015] Figure 5 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0016] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0017] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0018] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0019] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0020] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0021] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] Figure 1 A flow chart 100 of some embodiments of a water resource quality testing method applied to a water diversion project according to the present disclosure is shown. The water resource quality testing method applied to a water diversion project includes the following steps:

[0023] Step 101: Conduct water quality testing on the water area where the water diversion project is located to obtain surface water quality datasets and groundwater quality datasets.

[0024] In some embodiments, the implementing entity (e.g., a computing device) of the water resource quality detection method applied to a water diversion project can perform water quality detection on the water area where the water diversion project is located, obtaining surface water quality datasets and groundwater quality datasets. Surface water quality data is collected by surface sampling equipment pre-installed at surface sampling points, and groundwater quality data is collected by underground sampling equipment pre-installed in underground monitoring wells. Here, surface water quality data may include multiple detection attributes. For example, surface water quality data may include, but is not limited to, at least one of the following detection attributes: dissolved oxygen, copper, zinc, fluoride, selenium, arsenic, mercury, hexavalent chromium, lead, anionic surfactants, pH (degree of acidity or alkalinity, hydrogen ion concentration index), permanganate index, chemical oxygen demand, petroleum hydrocarbons, sulfides, cyanide, volatile phenols, total phosphorus, ammonia nitrogen, etc. Groundwater quality data may include, but is not limited to, at least one of the following properties: turbidity, total hardness, total dissolved solids, sulfate, chloride, iron, manganese, copper, zinc, aluminum, volatile phenols, anionic synthetic detergents, nitrite, nitrate, oxygen consumption, ammonia nitrogen, cyanide, fluoride, iodide, mercury, arsenic, selenium, cadmium, hexavalent chromium, lead, boron, nickel, cobalt, molybdenum, silver, etc. Additionally, each surface water quality data point corresponds to a sampling coordinate. Each groundwater quality data point corresponds to a sampling coordinate.

[0025] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (Ultra Wide Band) connections, and other currently known or future wireless connection methods.

[0026] It should be noted that the aforementioned computing devices can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed on the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0027] Step 102: Construct a surface data structure network based on the pre-built water area simulation model and surface water quality dataset.

[0028] In some embodiments, the aforementioned executing entity can construct a surface data structure network based on a pre-built water area simulation model and the aforementioned surface water quality dataset. The surface data structure network includes a set of surface water area structure points and corresponding surface water quality data. Here, the sampling coordinates can be coordinates from the water area simulation model. Furthermore, the surface data structure network can be constructed by organizing the sampling coordinates corresponding to each surface water quality data point according to the coordinate order in the water area simulation model. The water area simulation model can be a three-dimensional simulation model pre-constructed based on actual geographic data of the water area where the water diversion project is located.

[0029] In some optional implementations of certain embodiments, the aforementioned execution entity constructs a surface data structure network based on a pre-built water area simulation model and the aforementioned surface water quality dataset, including:

[0030] Step S1021: Extract the surface water confluence points from the aforementioned water area simulation model to obtain a set of surface water confluence points. The aforementioned water area simulation model includes the surface water flow direction relationships corresponding to each surface water confluence point in the aforementioned set of surface water confluence points. Here, the surface water confluence points in the aforementioned water area simulation model can be extracted using an indexing method to obtain the set of surface water confluence points. In practice, surface water confluence points can represent the confluence locations between surface water flows. Specifically, the intersections of converging river channels can be pre-marked as confluence locations in the water area simulation model. Furthermore, the surface water flow direction relationships can represent the direction of water flow from the current surface water confluence point to the next surface water confluence point.

[0031] Step S1022: Using the surface water flow direction relationships corresponding to the various surface water confluence points in the aforementioned set of surface water interaction points, an initial surface water area structure network is constructed from these confluence points. Each surface water confluence point can be designated as a surface water area structure point, and the corresponding surface water flow direction relationships can be defined as the connection relationships between these structure points, thereby constructing the initial surface water area structure network. Here, the initial surface water area structure network can be used to characterize the water flow structure of the surface water area where the water diversion project is located. Simultaneously, the node relationships in the initial surface water area structure network characterize the water flow direction.

[0032] As an example, see Figure 2 The schematic diagram of the surface water structure network shows the surface water confluence point 201. Figure 2 The arrows in the diagram can indicate the direction of water flow.

[0033] Step S1023 involves determining the surface location coordinates of each surface water quality data point in the aforementioned surface water quality dataset within the initial surface water structure network, and inserting the corresponding surface water quality data into the initial surface water structure network according to these coordinates to obtain the surface data structure network. The surface water quality data in the aforementioned surface water quality dataset includes at least one detection attribute and a corresponding surface detection attribute value. Here, the location of the sampling coordinates corresponding to the surface water quality data within the surface water structure network can be determined as the surface location coordinates. Specifically, the center point coordinates of the river channel where the sampling coordinates of the surface water quality data are located can be determined in the water simulation model. Then, the center point coordinates can be inserted as surface water structure points into the initial surface water structure network. Subsequently, the connection relationship between each surface water structure point is established according to the water flow direction in the water simulation model to obtain the surface data structure network. Simultaneously, the surface water quality data is mapped onto the corresponding surface water structure points.

[0034] As an example, see further. Figure 2 The surface water structure point 202 is shown in the surface water structure network. Here, inserting the surface water structure point 202 after the initial surface water structure network can make the resulting surface data structure network more accurately represent the actual water structure.

[0035] In practice, step S102 and its related content, by constructing an initial surface water structure network, can be used to preliminarily locate the flow relationships of surface water bodies. Then, considering the somewhat ambiguous relative positional relationship between the sampling coordinates of the surface sampling equipment and the river, and the extremely similar micro-element content within the same section of water flow due to the diffusion effect of elements, the coordinates of the river's center point corresponding to the sampling coordinates are used as nodes in the surface data structure network. This not only avoids the ambiguity of positional relationships but also allows for a precise characterization of the surface water flow structure through the generated surface data structure network. Simultaneously, surface water quality data corresponding to the surface water flow structure is inserted. This facilitates water quality testing of surface water bodies according to their structure.

[0036] Step 103: Construct an underground data structure network based on the water area simulation model and the groundwater quality dataset.

[0037] In some embodiments, the aforementioned execution entity can construct an underground data structure network based on the aforementioned water area simulation model and the aforementioned groundwater quality dataset. The underground data structure network includes a set of underground water area structure points and corresponding groundwater quality data. Furthermore, the underground data structure network can be constructed by organizing the sampling coordinates corresponding to each groundwater quality data point according to the coordinate order in the water area simulation model.

[0038] In some optional implementations of certain embodiments, the aforementioned execution entity constructs an underground data structure network based on the aforementioned water area simulation model and the aforementioned groundwater quality dataset, including:

[0039] Step S1031: Extract the groundwater confluence points from the aforementioned water area simulation model to obtain a groundwater confluence point set. The water area simulation model also includes the groundwater flow direction relationship corresponding to each groundwater confluence point in the groundwater confluence point set. Here, the groundwater confluence points in the water area simulation model can be extracted using an identifier index to obtain the groundwater confluence point set. Furthermore, groundwater confluence points can represent the convergence location between groundwater flows. Additionally, the groundwater flow direction relationship can represent the flow direction from the current groundwater confluence point to the next groundwater confluence point.

[0040] Step S1032: Using the groundwater flow direction relationships corresponding to each groundwater confluence point in the aforementioned groundwater confluence point set, an initial groundwater area structure network is constructed from these groundwater confluence points. Each groundwater confluence point can be defined as a groundwater area structure point, and the corresponding groundwater flow direction relationships can be defined as the connection relationships between these structure points, thereby constructing the initial groundwater area structure network. Here, the initial groundwater area structure network can be used to characterize the water flow structure of the underground domain where the water diversion project is located. Simultaneously, the node relationships in the initial groundwater area structure network characterize the water flow direction.

[0041] Step S1033: Determine the underground location coordinates of each groundwater quality data point in the aforementioned groundwater quality dataset within the aforementioned initial underground water structure network, and add the corresponding groundwater quality data to the aforementioned initial underground water structure network according to each underground location coordinate, thus obtaining the underground data structure network. The groundwater quality data in the aforementioned groundwater quality dataset includes at least one detection attribute and a corresponding underground detection attribute value. Here, the sampling coordinates corresponding to the groundwater quality data can be inserted into the initial underground water structure network as underground water structure points. Then, the connection relationship between each underground water structure point is established according to the water flow direction in the water simulation model, resulting in the underground data structure network. Simultaneously, the groundwater quality data is mapped onto the corresponding underground water structure points.

[0042] In practice, step S103 and its related content, by constructing an initial underground water structure network, can be used to preliminarily locate the flow relationships of underground water bodies. Then, considering the somewhat ambiguous relative positional relationship between the sampling coordinates of the underground sampling equipment and the river, the slow flow of groundwater, and the insufficient positioning accuracy of underground water bodies, the sampling coordinates corresponding to the groundwater quality data are directly determined as nodes in the underground data structure network. This not only avoids the problems of ambiguous positional relationships and insufficient positioning accuracy of underground water bodies, but also allows for a relatively concise representation of the underground water flow structure by generating a surface data structure network. Simultaneously, groundwater quality data corresponding to the underground water flow structure is inserted. This facilitates water quality testing of underground water bodies according to their structure.

[0043] Step 104: Based on the surface data structure network and the underground data structure network, water pollution detection is carried out on the water area to generate a surface water quality detection result set and a groundwater quality detection result set.

[0044] In some embodiments, the aforementioned implementing entity can perform water pollution detection on the aforementioned water area based on the aforementioned surface data structure network and the aforementioned underground data structure network to generate a surface water quality detection result set and a groundwater quality detection result set. Specifically, the existence of surface water quality data or groundwater quality data exceeding a threshold in the surface data structure network and the underground data structure network can be determined by threshold comparison. Furthermore, surface water quality data exceeding the threshold can be identified as surface water quality detection results, and groundwater quality data exceeding the threshold can be identified as groundwater quality detection results.

[0045] In some optional implementations of certain embodiments, the execution entity performs water pollution detection on the aforementioned water area based on the aforementioned surface data structure network and the aforementioned underground data structure network to generate a surface water quality detection result set and a groundwater quality detection result set, including:

[0046] Step S1041: The surface water quality data in the aforementioned surface data structure network, including at least one detection attribute whose surface detection attribute value satisfies the corresponding pollution condition, is determined as a surface water pollution attribute, thus obtaining a surface water pollution attribute group. The pollution condition corresponding to each detection attribute can be that the detection attribute value exceeds the corresponding detection range.

[0047] As an example, the pH value in the surface water quality data exceeds the acidity / alkalinity range of [6, 9]. Another example is the excessive levels of the heavy metal mercury, exceeding 0.0001 mg / L.

[0048] Step S1042: According to each surface water pollution attribute in the aforementioned surface water pollution attribute group, the surface water quality data in the aforementioned surface data structure network are reorganized to generate a set of individual pollution data linked lists. Specifically, the surface detection attribute values ​​corresponding to each surface water pollution attribute in each surface water quality data can be arranged into individual pollution data linked lists according to the arrangement of the surface data structure network. Thus, each individual pollution data linked list can represent the distribution of data corresponding to a detection attribute in surface rivers.

[0049] Step S1043: In the aforementioned surface data structure network, pollution source tracing is performed based on each individual pollution data chain in the aforementioned individual pollution data chain set to generate surface source river segment identifiers, resulting in a surface source river segment identifier set. Specifically, for each individual pollution data chain, the following source tracing steps are performed: First, pollution segments are extracted from the individual pollution data chain to obtain a pollution segment group. Here, at least one consecutive surface detection attribute value exceeding the corresponding detection interval in the individual pollution data chain can be identified as a pollution segment. Here, a pollution segment may include at least one consecutive surface location coordinate and a corresponding surface detection attribute value. Then, for each surface location coordinate in each pollution segment (except the last surface location coordinate), in response to the determination that the surface detection attribute value corresponding to the surface location coordinate is discontinuous with the next surface location coordinate, the pollution segment is split into two pollution segments from the position of the aforementioned surface location coordinate. Specifically, the first step involves using the surface detection attribute values ​​corresponding to the aforementioned surface location coordinates as a basis to predict the attribute values ​​for the next surface location coordinates using a one-dimensional water quality model. The second step involves determining that if the difference between the predicted attribute value and the surface detection attribute value corresponding to the next surface location coordinate is greater than a preset interval threshold, then although the corresponding pollution items (i.e., detection attributes) are the same between two adjacent surface location coordinates, the difference in their corresponding surface detection attribute values ​​is too large. Therefore, this indicates that the two pollution segments are caused by different pollution sources. Thus, each pollution segment after splitting and the pollution segments that do not need to be split can both be considered as split pollution segments. Then, for each split pollution segment, the surface location coordinate with the highest corresponding surface detection attribute value among the three surface location coordinates arranged according to the surface water flow direction at the earliest flowing position (i.e., the upstream position) can be identified as the surface source river section identifier (i.e., the location of the pollution source).

[0050] Step S1044: Each surface source river segment identifier in the above-mentioned surface source river segment identifier set, along with the corresponding surface water pollution attribute, surface detection attribute value, and single pollution data chain, is determined as the surface water quality detection result, thus obtaining the surface water quality detection result set.

[0051] In some optional implementations of certain embodiments, the execution entity performs water pollution detection on the aforementioned water area based on the aforementioned surface data structure network and the aforementioned underground data structure network to generate a surface water quality detection result set and a groundwater quality detection result set, further including:

[0052] Step S1045: The groundwater pollution attribute is determined as the groundwater pollution attribute whose groundwater quality data in the above-mentioned underground data structure network has a groundwater detection attribute value that meets the corresponding pollution condition. This results in a groundwater pollution attribute group.

[0053] Step S1046: According to each groundwater pollution attribute in the aforementioned groundwater pollution attribute group, the groundwater quality data in the aforementioned underground data structure network are reorganized to generate an underground data substructure network set. Specifically, the underground detection attribute values ​​corresponding to each groundwater pollution attribute in each groundwater quality data can be arranged into an underground data substructure network according to the structural relationship of the underground data structure network. Thus, each underground data substructure network can characterize the distribution of data corresponding to a detection attribute in underground rivers.

[0054] Step S1047: In the aforementioned underground data structure network, pollution source tracing is performed on each underground data substructure network in the aforementioned underground data substructure network set to generate underground source tracing river segment identifiers, thus obtaining an underground source tracing river segment identifier set. Specifically, the underground location coordinates of underground detection attribute values ​​exceeding the corresponding detection interval can be determined as underground source tracing river segment identifiers, resulting in an underground source tracing river segment identifier set. Here, the underground source tracing river segment identifier can correspond to a preset water area centered on the current underground coordinates.

[0055] Step S1048: Each underground source river segment identifier in the above underground source river segment identifier set, along with the corresponding groundwater pollution attribute, underground detection attribute value, and underground data substructure network, is determined as the groundwater quality detection result, thus obtaining the groundwater quality detection result set.

[0056] In practice, step 104 and its related content, as an inventive point of this application, can have the following beneficial effects: The above-described embodiments respectively trace surface pollution sources and underground pollution sources, which can be used to accurately locate surface or underground pollution sources. Specifically, firstly, considering that different pollutants may come from different pollution sources, each detection attribute is first split into data through data recombination, and then recombined according to the structural relationship of the surface data structure network or the underground data structure network. Thus, while ensuring that the data structure relationship remains unchanged, a single data structure is generated, which facilitates pollution source tracing separately. Then, considering the high velocity of surface water flow, in order to further accurately determine the pollution source, by generating a comparison between predicted attribute values ​​and actual attribute values, the number of pollution sources can be analyzed from the perspective of the spread of pollutants in the water flow. Thus, the pollution source and the river section (i.e., the pollution segment) affected can be further accurately located. Then, considering the slow flow of groundwater, the underground location coordinates where the underground detection attribute value exceeds the corresponding detection range are directly determined as the pollution source, thereby avoiding the omission of pollution sources due to excessive screening strategies. This allows for the rapid and accurate identification of pollution sources both on and below ground.

[0057] Step 105: Based on the surface water quality test result set and the groundwater quality test result set, conduct a correlation analysis on the surface water resources and groundwater resources of the water area to generate water pollution correlation characteristics.

[0058] In some embodiments, the aforementioned implementing entity may perform correlation analysis on the surface water resources and groundwater resources of the aforementioned water area based on the aforementioned surface water quality monitoring result set and the aforementioned groundwater quality monitoring result set, in order to generate water pollution correlation features. Specifically, the correlation analysis may identify areas where the geographical locations corresponding to the surface water quality monitoring results and the groundwater quality monitoring results in the surface data structure network and the aforementioned underground data structure network overlap as water pollution correlation features.

[0059] In some optional implementations of certain embodiments, the executing entity performs correlation analysis on the surface water resources and groundwater resources of the aforementioned water area based on the aforementioned surface water quality monitoring result set and the aforementioned groundwater quality monitoring result set, to generate water pollution correlation characteristics, including:

[0060] Step S1051: Based on the aforementioned water area simulation model, determine the water flow exchange points between the aforementioned surface data structure network and the aforementioned underground data structure network, obtaining a set of water flow exchange points. Each water flow exchange point has an exchange identifier, which is used to characterize an exposure point or infiltration point. Here, the water area simulation model pre-sets water flow exchange points that characterize exposure points or infiltration points. Therefore, the water flow exchange points between the aforementioned surface data structure network and the aforementioned underground data structure network can be extracted from the water area simulation model, resulting in a set of water flow exchange points.

[0061] Step S1052: Water flow exchange points that match the aforementioned water flow exchange point set with both the aforementioned surface water quality monitoring result set and the aforementioned groundwater quality monitoring result set are identified as pollution exchange points, thus obtaining a pollution exchange point set. Specifically, water flow exchange points located within the river segment marked by the surface water quality monitoring result set (including the surface source river segment markers) can be identified as pollution exchange points. Similarly, water flow exchange points located within the river segment marked by the groundwater quality monitoring result set (including the groundwater source river segment markers) can be identified as pollution exchange points. Therefore, a pollution exchange point set is obtained.

[0062] Step S1053: Determine the groundwater vulnerability characteristic value corresponding to each pollution exchange point in the aforementioned pollution exchange point set, thus obtaining a groundwater vulnerability characteristic value set. For each pollution exchange point, the corresponding groundwater vulnerability characteristic value can be determined using a groundwater quality vulnerability assessment model. The groundwater vulnerability characteristic value characterizes the vulnerability of groundwater resources within the corresponding underground area.

[0063] As examples, the aforementioned groundwater vulnerability assessment models can be the DRASTIC groundwater vulnerability assessment model, the GOD (Groundwater Confinement - Overlaying strata - Depth to groundwater table) assessment model, the EPIK karst aquifer groundwater vulnerability assessment model, etc.

[0064] Step S1054: Using the aforementioned set of pollution exchange points and the aforementioned set of groundwater vulnerability feature values, water pollution association features are generated. For each pollution exchange point, a corresponding impact identifier can be determined. Here, the impact identifier represents the direction of influence between surface water and groundwater. For example, if the pollution exchange point is an outflow point, then the water flow direction is from groundwater to the surface. If the pollution exchange point is an infiltration point, then the water flow direction is from the surface to the groundwater. Thus, different identifiers "0" and "1" can be used as impact identifiers representing outflow points and infiltration points, respectively. Secondly, the corresponding surface water pollution attributes and corresponding groundwater vulnerability feature values ​​and impact identifiers, and the groundwater pollution attributes and corresponding groundwater vulnerability feature values ​​and impact identifiers, can be combined in a preset order to form water pollution association features.

[0065] As an example, the data for a pollution exchange point in the water pollution association feature could be: [M1, 114, 0]. Here, M1 represents the surface water pollution attribute. "114" represents the groundwater vulnerability feature value. "0" indicates that the pollution exchange point is a leak point from groundwater into the surface.

[0066] In practice, by accurately matching the structural point sets of surface water bodies and groundwater bodies, the hydraulic connections between surface water and groundwater (such as recharge, runoff, and discharge processes) can be captured, making it easier to determine the spatial resolution of pollution detection from the "regional level" to the "point level". This makes it easier to accurately identify the correlation between groundwater pollution and surface water pollution at a certain infiltration point.

[0067] Step 106: Identify the surface water pollution area and the groundwater pollution area of ​​the water body.

[0068] In some embodiments, the aforementioned implementing entity may identify surface water pollution areas and groundwater pollution areas in the aforementioned water bodies.

[0069] In some optional implementations of certain embodiments, the aforementioned implementing entity determines the surface water pollution area and the groundwater pollution area of ​​the aforementioned water area, including:

[0070] Step S1061: Determine the pollution area corresponding to each individual pollution data list in the above-mentioned water area simulation model, thus obtaining a set of single-item pollution areas for the land area. Specifically, the area where each pollution segment under each individual pollution data list is located can be defined as a single-item pollution area for the land area, resulting in a set of single-item pollution areas for the land area. Here, the area where the pollution segment is located can be the river segment range where each surface location coordinate under the pollution segment is located.

[0071] Step S1062: The intersection of the various polluted landforms in the above-mentioned single-item polluted area group is determined as the surface water polluted area. The surface water polluted area can characterize the overall area of ​​surface water resource pollution.

[0072] Step S1063: Determine the pollution area corresponding to each underground data substructure network in the above-mentioned water simulation model, thus obtaining a group of single underground pollution areas. Specifically, the area where each underground location coordinate is located under the underground data substructure network can be defined as a single underground pollution area, resulting in a group of single underground pollution areas. The area where the underground location coordinates are located can be the area enclosed by the underground location coordinates as the center and the adjacent underground location coordinates as the boundaries.

[0073] Step S1064: The intersection of the various underground single-item pollution areas in the above-mentioned group of underground single-item pollution areas is determined as the groundwater pollution area. The above-mentioned groundwater pollution area can characterize the overall area of ​​groundwater resource pollution.

[0074] Here, considering the slow flow of groundwater, and to avoid the lag in the interaction between groundwater and surface water sources, each area involved in pollution is identified in advance as a single-item groundwater pollution area.

[0075] Step 107: The surface water quality test result set, the groundwater quality test result set, the surface water pollution area, the groundwater pollution area, and the water pollution correlation characteristics are identified as water resource quality test results.

[0076] In some embodiments, the aforementioned implementing entity may determine the aforementioned surface water quality test result set, the aforementioned groundwater quality test result set, the aforementioned surface water pollution area, the aforementioned groundwater pollution area, and water pollution correlation characteristics as water resource quality test results.

[0077] Optionally, the aforementioned implementing entity may also include:

[0078] The first step is to generate a water pollution area data distribution map in the aforementioned water area simulation model using the water resource quality monitoring results. Specifically, in the water area simulation model, each surface water pollution area and each groundwater pollution area from the water resource quality monitoring results can be marked as a whole pollution area. Therefore, the water area simulation model marked with the whole pollution area can be determined as the water pollution area data distribution map.

[0079] As an example, see Figure 3 The diagram shows a top-down view of the distribution of water pollution area data. Figure 3 The diagram illustrates the pollution levels of two pollution sources affecting surface water bodies. The first pollution source covers area 301, the second pollution source covers area 302, and the remaining areas are unpolluted.

[0080] The second step is to issue a water pollution warning based on the aforementioned water resource quality monitoring results and the data distribution map of water pollution areas. Specifically, the vulnerability level of groundwater vulnerability characteristics in the water resource quality monitoring results and the pollution level of each polluted area can be determined by referring to a table. Then, a corresponding water pollution warning is issued based on the vulnerability level and pollution level.

[0081] In practice, the above-described implementation methods of this disclosure can improve the accuracy of pollution source tracing and the accuracy of pollution area delineation. Specifically, firstly, through the correspondence between "structure points and water quality data" in the surface data structure network, surface pollution sources (such as industrial sewage outlets and agricultural ditches) can be quickly located; here, the underground data structure network can track the diffusion path of pollution in aquifers (such as through groundwater flow direction and permeability coefficient analysis). Combining the "water pollution correlation characteristics" of both, it can be determined whether the pollution is "dominated by surface infiltration," "dominated by groundwater recharge," or "bidirectional interactive pollution." For example, in the case of pollution in a chemical industrial park, the pollution source range of both the surface wastewater discharge point and the underground aquifer can be simultaneously identified. Secondly, through the spatial division of "surface water pollution area" and "groundwater pollution area," the spatial boundary of pollution can be intuitively presented, solving the problem of "pollution range relying solely on empirical estimation" in traditional detection, and providing accurate spatial basis for subsequent pollution control (such as setting up interception belts and groundwater barrier walls). In addition, groundwater pollution can be treated in advance to avoid the lag caused by groundwater pollution.

[0082] The various embodiments of this disclosure have the following beneficial effects: the water resource quality detection method applied to water diversion projects through some embodiments of this disclosure can improve the accuracy of the overall water quality detection results. Specifically, the reason for the poor overall water quality detection results is that the flow relationship between surface water and groundwater is not considered, and surface water resources are detected separately or groundwater resources are detected separately, resulting in problems such as fragmentation, lag, and limitations between surface water detection and groundwater detection. Based on this, the water resource quality detection method applied to water diversion projects through some embodiments of this disclosure firstly conducts water quality detection on the water area where the water diversion project is located to obtain surface water quality datasets and groundwater quality datasets. Here, collecting surface water quality data and groundwater quality data simultaneously can be used for synchronous detection. Then, based on the pre-constructed water area simulation model and the above-mentioned surface water quality dataset, a surface data structure network is constructed, wherein the above-mentioned surface data structure network includes a set of surface water area structure points and corresponding surface water quality data. Next, based on the aforementioned water area simulation model and groundwater quality dataset, an underground data structure network is constructed. This network includes a set of underground water area structure points and corresponding groundwater quality data. Here, by constructing surface and underground data structure networks, surface and underground water area structures can be used to characterize these structures, respectively. Therefore, comprehensive detection can be performed by combining both surface and underground water area structures. Subsequently, based on these two networks, water pollution detection is conducted on the aforementioned water areas to generate surface and groundwater quality detection result sets. The introduction of surface and underground water area structures allows for water pollution detection based on the water area structure, overcoming the limitations of detecting only surface or underground water areas. Finally, based on these two result sets, a correlation analysis is performed on the surface and groundwater resources of the aforementioned water areas to generate water pollution correlation characteristics. Here, correlation analysis can be used to determine the flow relationship between surface water and groundwater within a water body, thus avoiding the disconnect between surface water and groundwater testing. Simultaneously, because water pollution correlation characteristics are generated, the degree of pollution can be determined promptly based on these characteristics, significantly reducing the lag in surface water and groundwater testing. This facilitates more accurate identification of surface water and groundwater pollution areas within the aforementioned water body. Finally, the aforementioned surface water quality testing results set, groundwater quality testing results set, surface water pollution areas, groundwater pollution areas, and water pollution correlation characteristics are defined as the water resource quality testing results. This improves the accuracy of the overall water body quality testing results.

[0083] Further reference Figure 4As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a water resource quality testing device applied to water diversion projects. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, the water resource quality testing device applied to water diversion projects can be specifically applied to various electronic devices.

[0084] like Figure 4 As shown, a water resource quality detection device 400 applied to a water diversion project in some embodiments includes: a water quality detection unit 401, a first construction unit 402, a second construction unit 403, a water pollution detection unit 404, a correlation analysis unit 405, a first determination unit 406, and a second determination unit 407. The water quality detection unit 401 is configured to perform water quality detection on the water area where the water diversion project is located, obtaining a surface water quality dataset and a groundwater quality dataset; the first construction unit 402 is configured to construct a surface data structure network based on a pre-constructed water area simulation model and the aforementioned surface water quality dataset, wherein the surface data structure network includes a set of surface water area structure points and corresponding surface water quality data; the second construction unit 403 is configured to construct a groundwater data structure network based on the aforementioned water area simulation model and the aforementioned groundwater quality dataset, wherein the groundwater data structure network includes a set of groundwater area structure points and corresponding groundwater quality data; the water pollution detection unit 404 is configured to... The aforementioned underground data structure network performs water pollution detection on the aforementioned water area to generate a set of surface water quality detection results and a set of groundwater quality detection results. The correlation analysis unit 405 is configured to perform correlation analysis on the surface water resources and groundwater resources of the aforementioned water area based on the aforementioned set of surface water quality detection results and the aforementioned set of groundwater quality detection results to generate water pollution correlation characteristics. The first determination unit 406 is configured to determine the surface water pollution area and the groundwater pollution area of ​​the aforementioned water area. The second determination unit 407 is configured to determine the aforementioned set of surface water quality detection results, the aforementioned set of groundwater quality detection results, the aforementioned surface water pollution area, the aforementioned groundwater pollution area, and the water pollution correlation characteristics as water resource quality detection results.

[0085] It is understandable that the various units described in the water resource quality testing device 400 used in water diversion projects are similar to those in the reference system. Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method are also applicable to the water resource quality detection device 400 and its constituent units used in water diversion projects, and will not be repeated here.

[0086] The following is for reference. Figure 5 It illustrates a schematic diagram of the structure of an electronic device (such as a computing device) suitable for implementing some embodiments of the present disclosure. Figure 5The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of this disclosure. Figure 5 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any of the methods described above. The processor provides computational and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the execution of the computer program in the non-volatile storage medium; when executed by the processor, the computer program causes the processor to perform any of the methods described above. The network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the computer device to which the present disclosure is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0087] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0088] In one embodiment, the processor is configured to run a computer program stored in a memory to perform the following steps: conducting water quality testing on the water area where the water diversion project is located to obtain a surface water quality dataset and a groundwater quality dataset; constructing a surface data structure network based on a pre-built water area simulation model and the surface water quality dataset, wherein the surface data structure network includes a set of surface water area structure points and corresponding surface water quality data; and constructing a groundwater data structure network based on the water area simulation model and the groundwater quality dataset, wherein the groundwater data structure network includes a set of groundwater area structure points and corresponding groundwater quality data. Based on the aforementioned surface data structure network and underground data structure network, water pollution detection is conducted on the aforementioned water areas to generate surface water quality detection result sets and groundwater quality detection result sets. Based on the aforementioned surface water quality detection result sets and groundwater quality detection result sets, correlation analysis is performed on the surface water resources and groundwater resources of the aforementioned water areas to generate water pollution correlation characteristics. Surface water pollution areas and groundwater pollution areas of the aforementioned water areas are identified. The aforementioned surface water quality detection result sets, groundwater quality detection result sets, surface water pollution areas, groundwater pollution areas, and water pollution correlation characteristics are determined as water resource quality detection results.

[0089] This disclosure also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the method implemented when the program instructions are executed can be referred to the various embodiments of the methods described above.

[0090] The aforementioned computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. Alternatively, the aforementioned computer-readable storage medium may be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0091] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0092] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for detecting water resource quality applied to water diversion projects, characterized in that, include: Water quality testing was conducted on the water area where the water diversion project is located, resulting in surface water quality datasets and groundwater quality datasets. Based on the pre-built water area simulation model and the surface water quality dataset, a surface data structure network is constructed, wherein the surface data structure network includes a set of surface water area structure points and corresponding surface water quality data; Based on the water area simulation model and the groundwater quality dataset, an underground data structure network is constructed, wherein the underground data structure network includes a set of underground water area structure points and corresponding groundwater quality data; Based on the surface data structure network and the underground data structure network, water pollution detection is performed on the water area to generate a surface water quality detection result set and a groundwater quality detection result set; Based on the surface water quality monitoring result set and the groundwater quality monitoring result set, a correlation analysis is performed on the surface water resources and groundwater resources of the water area to generate water pollution correlation characteristics, including: Based on the water area simulation model, the water flow exchange points between the surface data structure network and the underground data structure network are determined to obtain a set of water flow exchange points. Each water flow exchange point has an exchange identifier, which is used to characterize the exposure point or infiltration point. The water exchange points that match the set of surface water quality test results and the set of groundwater quality test results are identified as pollution exchange points, thus obtaining the pollution exchange point set; Determine the groundwater vulnerability feature value corresponding to each pollution exchange point in the pollution exchange point set to obtain the groundwater vulnerability feature value set. Water pollution correlation features are generated using the pollution exchange point set and the groundwater vulnerability feature set. Identify the surface water pollution areas and groundwater pollution areas in the aforementioned water area; The surface water quality test result set, the groundwater quality test result set, the surface water pollution area, the groundwater pollution area, and the water pollution correlation characteristics are determined as the water resource quality test results.

2. The method according to claim 1, characterized in that, The method further includes: In the water area simulation model, the water resource quality detection results are used to generate a data distribution map of water pollution areas; Based on the water resource quality test results and the water pollution area data distribution map, a water pollution warning is issued.

3. The method according to claim 1, characterized in that, The step of constructing a surface data structure network based on a pre-built water area simulation model and the surface water quality dataset includes: Extract the surface water confluence points from the water area simulation model to obtain a set of surface water confluence points. The water area simulation model includes the surface water flow direction relationship corresponding to each surface water confluence point in the set of surface water confluence points. By utilizing the surface water flow direction relationship corresponding to each surface water confluence point in the aforementioned surface water interaction point set, each surface water confluence point is constructed into an initial surface water structure network; The surface location coordinates of each surface water quality data in the surface water quality dataset within the initial surface water structure network are determined, and the corresponding surface water quality data are inserted into the initial surface water structure network according to each surface location coordinate to obtain the surface data structure network. The surface water quality data in the surface water quality dataset includes at least one detection attribute and a corresponding surface detection attribute value.

4. The method according to claim 3, characterized in that, The step of constructing an underground data structure network based on the water area simulation model and the groundwater quality dataset includes: The groundwater confluence points in the water area simulation model are extracted to obtain a groundwater confluence point set. The water area simulation model also includes the groundwater flow direction relationship corresponding to each groundwater confluence point in the groundwater confluence point set. By utilizing the groundwater flow direction relationship corresponding to each groundwater confluence point in the groundwater confluence point set, each groundwater confluence point is constructed into an initial groundwater structure network; The underground location coordinates of each groundwater quality data in the groundwater quality dataset within the initial underground water area structure network are determined, and the corresponding groundwater quality data are added to the initial underground water area structure network according to each underground location coordinate to obtain an underground data structure network. The groundwater quality data in the groundwater quality dataset includes at least one detection attribute and a corresponding underground detection attribute value.

5. The method according to claim 4, characterized in that, The process of conducting water pollution detection on the water area based on the surface data structure network and the underground data structure network to generate surface water quality detection result sets and groundwater quality detection result sets includes: The surface water quality data in the surface data structure network is defined as a surface water pollution attribute if the surface detection attribute value of at least one detection attribute satisfies the corresponding pollution condition. This results in a surface water pollution attribute group. According to each surface water pollution attribute in the surface water pollution attribute group, the surface water quality data in the surface data structure network are reorganized to generate a single pollution data linked list set. In the surface data structure network, pollution source tracing is performed based on each individual pollution data list in the individual pollution data list set to generate surface source tracing river section identifiers, thus obtaining a surface source tracing river section identifier set; Each surface source river segment identifier in the surface source river segment identifier set, along with its corresponding surface water pollution attribute, surface detection attribute value, and single pollution data chain, is identified as a surface water quality detection result, thus obtaining a surface water quality detection result set.

6. The method according to claim 5, characterized in that, The method of conducting water pollution detection on the water area based on the surface data structure network and the underground data structure network to generate a surface water quality detection result set and a groundwater quality detection result set also includes: The groundwater pollution attribute is determined as a groundwater pollution attribute if the groundwater quality data in the underground data structure network contains at least one detection attribute whose underground detection attribute value satisfies the corresponding pollution condition. This results in a groundwater pollution attribute group. According to each groundwater pollution attribute in the groundwater pollution attribute group, the groundwater quality data in the underground data structure network are reorganized to generate an underground data substructure network set. In the underground data structure network, pollution source tracing is performed on each underground data substructure network set to generate underground source tracing river section identifiers, resulting in an underground source tracing river section identifier set; Each underground source river segment identifier in the underground source river segment identifier set, along with its corresponding groundwater pollution attribute, underground detection attribute value, and underground data substructure network, is identified as a groundwater quality detection result, thus obtaining a groundwater quality detection result set.

7. A water resource quality testing device applied to water diversion projects, characterized in that, include: The water quality monitoring unit is configured to monitor the water quality of the water area where the water diversion project is located, and obtain surface water quality datasets and groundwater quality datasets. The first construction unit is configured to construct a surface data structure network based on a pre-constructed water area simulation model and the surface water quality dataset, wherein the surface data structure network includes a set of surface water area structure points and corresponding surface water quality data; The second construction unit is configured to construct an underground data structure network based on the water area simulation model and the groundwater quality dataset, wherein the underground data structure network includes a set of underground water area structure points and corresponding groundwater quality data. The water pollution detection unit is configured to perform water pollution detection on the water area based on the surface data structure network and the underground data structure network, so as to generate a surface water quality detection result set and a groundwater quality detection result set; The correlation analysis unit is configured to perform correlation analysis on the surface water resources and groundwater resources of the water area based on the surface water quality test result set and the groundwater quality test result set, in order to generate water pollution correlation features, including: Based on the water area simulation model, the water flow exchange points between the surface data structure network and the underground data structure network are determined to obtain a set of water flow exchange points. Each water flow exchange point has an exchange identifier, which is used to characterize the exposure point or infiltration point. The water exchange points that match the set of surface water quality test results and the set of groundwater quality test results are identified as pollution exchange points, thus obtaining the pollution exchange point set; Determine the groundwater vulnerability feature value corresponding to each pollution exchange point in the pollution exchange point set to obtain the groundwater vulnerability feature value set. Water pollution correlation features are generated using the pollution exchange point set and the groundwater vulnerability feature set. The first determining unit is configured to determine the surface water pollution area and the groundwater pollution area of ​​the water body; The second determining unit is configured to determine the surface water quality test result set, the groundwater quality test result set, the surface water pollution area, the groundwater pollution area, and water pollution correlation features as water resource quality test results.

8. An electronic device, characterized in that, include: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium, characterized in that, It stores a computer program thereon, wherein the program, when executed by a processor, implements the method as described in any one of claims 1-6.

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