A rapid water pollution tracing method for automatically screening upstream risk sources

Abstract

The present invention provides a method for rapid tracing of water pollution by automatically screening upstream risk sources. The method of the present invention comprises: constructing a hydrological spatial scenario library based on DEM data, and constructing a risk source pollution feature library by analyzing the pollution characteristics of the pollution risk source; based on the spatial relationship of hydrological elements, automatically screening out the upstream risk source of the monitoring point through spatial query technology, comparing the monitoring results of the monitoring point with the pollution characteristics of the suspected risk source, and calculating the comprehensive pollution contribution rate of each suspected risk source. The greater the comprehensive pollution contribution rate of the suspected risk source, the greater the water quality pollution at the monitoring point may be affected by the risk source. The present invention can assist in the rapid screening of suspected pollution risk sources and provide a basis for water environment supervision. The tracing of the source of water environment pollution by the present invention is of great significance to the supervision of water environment pollution sources and thus to ensure the safety of the water environment. It is aimed at the problems that traditional water pollution tracing methods are time-consuming, labor-intensive, and cost-intensive, and have a high degree of manual dependence.

Description

Technical Field

[0001] The present invention relates to the field of environmental pollution control and environmental monitoring, and in particular to a method for rapid water pollution tracing by automatically screening upstream risk sources, which is used for rapid investigation and determination of suspected risk sources in water pollution incidents, and calculation of the pollution discharge contribution rate of the suspected risk sources. Background Art

[0002] Water environment safety is an important issue that affects people's lives, production, and regional economic development. Water environment control plays a vital role in the protection of the entire water environment, water pollution control, and maintaining the health of the water environment. By tracing the source of water pollution, we can effectively prevent the spread of pollutants, find the source of pollution, assist in the supervision and control of the water environment, and thus ensure the safety of the water environment.

[0003] Existing methods for tracing the source of water pollution mainly involve four categories: First, after the pollution occurs, the polluted downstream water samples and the water samples from the sewage outlets of the upstream enterprises involved are used for testing, and the results are compared and analyzed to realize the investigation, tracing, and contribution rate calculation of the pollution source, and to hold the enterprises suspected of violating the regulations accountable; the second is to use the monitoring data of the monitoring points to reversely infer the location or scope of the pollution source based on mathematical models or hydrological models; the third is to use the chemical fingerprint library or pollution feature library of the risk source established in advance, and compare it with the monitoring results of the monitoring station to determine the enterprises suspected of violating the regulations; the fourth is to start with the spectral characteristics of the polluted water body and use remote sensing image data to trace the source of water pollution. Since the first type of method requires the installation of detection equipment at each sewage outlet, the cost investment is large, the subsequent maintenance workload is large, and it is time-consuming and complicated to operate, so it is impossible to hold the companies involved accountable in a timely manner; the second type of method often requires a large amount of computing and analysis resources when tracing the source of water pollution incidents, and the model calculation speed is slow, which is difficult to meet the problem of rapid tracing of the source in engineering or ordinary users; the third type of method using remote sensing data to trace the source of water pollution relies on high-resolution and high-spectral remote sensing images, which is expensive and greatly affected by the accuracy of remote sensing interpretation methods. The existing remote sensing interpretation methods are difficult to meet the needs of rapid tracing of water pollution incidents in engineering; and the fourth type of method can greatly reduce the workload of water pollution tracing and improve the efficiency of water pollution tracing by comparing with the risk source feature library established in advance. However, the existing fourth type of method still has shortcomings. When making upstream and downstream judgments, the existing technology often determines the risk source located upstream of the monitoring point or the polluted point through visual interpretation, which leads to a low degree of automation and strong manual dependence in the process of tracing the source of water pollution incidents. Summary of the invention

[0004] According to the technical problems raised above, a method for rapid tracing of water pollution by automatically screening upstream risk sources is provided. The present invention mainly utilizes spatial query technology to construct a hydrological spatial scenario library, and analyzes the pollution discharge characteristics of pollution risk sources to construct a risk source pollution discharge characteristic library. When a water pollution incident occurs, the spatial query technology is combined to automatically screen out upstream risk sources (suspected risk sources), compare the monitoring results of the monitoring station with the pollution discharge characteristics of the suspected risk sources, and calculate the pollution discharge contribution rate of the suspected risk sources, thereby assisting in the rapid screening of suspected illegal pollution discharge risk sources. The technical means adopted by the present invention are as follows:

[0005] A method for rapid tracing of water pollution by automatically screening upstream risk sources comprises the following steps:

[0006] Step 1: Obtain the spatial relationship between water system data, monitoring point data, and risk source data, obtain the nearest runoff and nearest point coordinates corresponding to the spatial query monitoring point and risk source point, and build a hydrological spatial scenario library based on this;

[0007] Step 2: Obtain the pollutant data of the wastewater from each risk source in the target area, and establish a risk source pollution discharge feature database, which is updated in real time;

[0008] Step 3: Use spatial query technology to automatically determine the upstream and downstream relationship between monitoring points and risk sources, and preliminarily screen out risk sources located upstream of the monitoring station that may affect the water quality of the monitoring points;

[0009] Step 4. For monitoring items that exceed water quality standards or monitoring items that are under key observation, extract relevant information of suspected risk sources from the risk source pollution characteristic database based on the mapping relationship between the detection items of the monitoring points and the pollution discharge characteristics of the risk sources, calculate the pollution discharge contribution rate of the suspected risk sources affecting the water quality of the monitoring points, and finally judge the contribution degree of the suspected risk sources to the pollution of the water quality of the monitoring points based on the size of the output comprehensive pollution discharge contribution rate.

[0010] Furthermore, the step 1 specifically includes the following steps:

[0011] Step 1.1: Spatial query of the watershed where the runoff is located: Use spatial query technology to determine the watershed where each runoff section in the region is located;

[0012] Step 1.2, spatial query of the nearest runoff section and the corresponding nearest point of each monitoring point: using spatial query technology to determine the coordinates of the nearest runoff section and the corresponding nearest point of each monitoring point;

[0013] Step 1.3: Spatial query of the nearest runoff segment and the corresponding nearest point of each risk source: Use spatial query technology to determine the coordinates of the nearest runoff segment and the corresponding nearest point of each risk source.

[0014] Furthermore, in step 2, the pollutant data include pollutant types and content data, and the data updated in real time in the risk source pollution emission characteristic library include enterprise regular declaration data or irregular on-site inspection data.

[0015] Furthermore, the step 3 comprises the following steps:

[0016] Step 3.1, divide the upstream risk sources of the monitoring point into two parts according to the watershed, including the upstream risk sources in the same watershed as the monitoring point and the upstream k-order watershed risk sources, and spatially query the upstream risk sources in the same watershed. Specifically, by comparing the distance between the nearest point of the monitoring point in the nearest runoff segment and the nearest point of the nearest runoff segment of the risk source in the same watershed to the upstream cutoff point of the nearest runoff segment of the monitoring point, determine whether the risk source is located upstream of the monitoring point. If the distance from the monitoring point to the cutoff point is greater than the distance from the risk source to the cutoff point, the risk source belongs to the upstream risk source of the monitoring point and is a suspected risk source; otherwise, the risk source is judged to not affect the water quality of the monitoring point and is not a suspected risk source;

[0017] Step 3.2, spatial query of risk sources in the upstream k-order basin: by finding the basin where the first-order runoff section upstream of the monitoring point is located, and continuously searching the second, ..., k-order adjacent basins upstream, finally all risk sources located in the upstream k-order basin are also included in the set of suspected risk sources.

[0018] Furthermore, the spatial query technology of the PostgreSQL database is used to automate the process of step 3.

[0019] Furthermore, process automation specifically includes querying the upstream risk source process of the same basin and querying the risk source process within the upstream k-order basin:

[0020] The process of querying upstream risk sources in the same river basin includes:

[0021] Step 3a, using the ST_DumpPoints and ST_MakeLine functions of the PostgreSQL database to split the nearest runoff segment of the monitoring point according to the inflection point, into multiple sub-runoff segments connected in the upstream and downstream order;

[0022] Step 3b, according to the split upstream and downstream order, use the ST_Distance function to find the sub-runoff segment with a distance of 0 from the nearest point of the monitoring point, calculate the distance between the upstream cutoff point of the sub-runoff segment and the nearest point of the monitoring point, and add it with the length of all sub-runoff segments upstream of the sub-runoff segment to obtain the distance from the nearest point of the monitoring point to the upstream cutoff point of the runoff segment;

[0023] Step 3c: In the same way, the distance from the nearest point of each risk source in the same basin to the upstream cutoff point of the runoff section can be obtained. If this distance is less than or equal to the distance from the nearest point of the monitoring point to the upstream cutoff point of the runoff section, the risk source is the upstream risk source in the same basin of the monitoring point;

[0024] Step 3d, generating a set of upstream risk sources in the same river basin as the monitoring point;

[0025] The process of querying the risk sources in the upstream k-order watershed includes:

[0026] Step 3e, using the internal connection of the upstream cutoff point ID field and the downstream cutoff point ID field of the runoff table in the hydrological spatial scenario library to obtain the first-order adjacent watershed upstream of the runoff, and generate the watershed table where the first-order adjacent runoff section upstream of the runoff section is located;

[0027] Step 3f, using the table to cyclically search for the upstream k-order watersheds of the runoff section to which the monitoring point belongs, and also list the risk sources located in these upstream k-order watersheds as suspected risk sources;

[0028] Step 3g, generating a set of k-order watershed risk sources upstream of the monitoring point;

[0029] The set of upstream risk sources in the same river basin as the monitoring point queried in step 3d is merged to generate a set of suspected risk sources upstream of the monitoring point.

[0030] Furthermore, the step 4 specifically includes the following steps:

[0031] Step 4.1, determine the set of monitoring items to be compared: the monitoring items to be compared are combined with the constructed mapping relationship between the monitoring items and the pollution discharge characteristics of the risk sources, and are used to screen the pollution discharge characteristics from the risk source pollution discharge characteristic library to calculate the contribution rate;

[0032] Step 4.2, extract the preliminary screening risk sources and related pollution characteristics: using the mapping relationship between monitoring items and risk source pollution characteristics, according to the monitoring items to be compared determined in step 4.1, obtain the risk source pollution characteristics set to be compared; according to the risk source pollution characteristics set to be compared and the screened upstream risk source set obtained in step 3, search the pollution characteristics and related information of the suspected risk sources after preliminary screening from the risk source pollution characteristics database;

[0033] Step 4.3, calculate the contribution rate of single pollution discharge from suspected risk sources: Based on the suspected risk source data and monitoring data extracted in step 4.2, the contribution rate P′ of single pollution discharge from wastewater discharged from risk source a to pollutant i in the water quality at the point can be calculated. a (i)

[0034]

[0035]

[0036] The characteristic concentration of pollutant i corresponding to risk source a is C a (i) The Euclidean distance from the risk source point to the monitoring point is D a ; The concentration C(i) of pollutant i recorded at the monitoring point;

[0037] Step 4.4: Calculate the comprehensive pollution contribution rate of suspected risk sources:

[0038] According to the calculation results of step 4.3, further calculate the comprehensive pollution contribution rate Prob of risk source a that affects the water quality of the monitoring point a :

[0039]

[0040] The present invention introduces spatial query technology and GIS spatial analysis methods in the process of tracing the source of water pollution, making it easier to quickly check suspicious risk sources and calculate the contribution rate of pollution discharge. The present invention proposes a method for automatically screening upstream risk sources of polluted points and calculating the contribution rate of pollution discharge of suspected risk sources. The spatial query technology is used to automatically screen upstream risk sources, and the pollution discharge feature library of risk sources is compared with monitoring items to calculate the contribution rate of pollution discharge of risk sources. The present invention improves the efficiency of rapid tracing of water pollution incidents and can provide decision-making information for checking risk sources.

[0041] Compared with the prior art, the present invention has the following beneficial effects: the present invention can use basic DEM data and the acquired risk source information to quickly trace the source of water pollution incidents and calculate the contribution rate of pollution discharge, greatly reducing the demand for manpower and material resources; at the same time, the algorithm for preliminary screening through automatic judgment of upstream risk sources reduces manual dependence and improves the automation rate. This method of organically combining spatial query technology with water pollution source tracing reduces the workload of water pollution source tracing, reduces the cost of source tracing, and quickly and efficiently realizes source tracing, which provides convenience for ordinary researchers, users and relevant government departments to carry out water pollution source tracing. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0043] Figure 1 It is an overall flow chart of an implementation method of the present invention.

[0044] Figure 2It is a schematic diagram of "the nearest runoff and the nearest point corresponding to the monitoring point and the risk source point" in step 1 of the implementation mode of the present invention;

[0045] Figure 3 It is a schematic diagram of the runoff section from the monitoring point in sub-step 3.1 of step 3 of the embodiment of the present invention, the nearest point corresponding to the risk source in the same watershed of the monitoring point to the nearest upstream cutoff point of the runoff of the monitoring point;

[0046] Figure 4 It is a flowchart of the algorithm for spatially querying upstream risk sources in the same river basin in sub-step 3.1 of step 3 of the implementation mode of the present invention;

[0047] Figure 5 It is a schematic diagram of the k-order watershed upstream of the middle monitoring point in sub-step 3.2 of step 3 of the implementation mode of the present invention;

[0048] Figure 6 This is the algorithm flow chart of spatial query of risk sources in the upstream k-order watershed in sub-step 3.2 of step 3 of the implementation mode of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] This embodiment discloses a method for rapid water pollution source tracing by automatically screening upstream risk sources. After a pollution accident occurs or when analysts need to trace the source of pollutants, the method uses the pre-established hydrological spatial scenario library and risk source pollution feature library, combined with the detection results of the monitoring point, to automatically screen the risk sources that cause water pollution at the monitoring point and calculate the pollution contribution rate. This method can be used to quickly narrow the scope of risk source investigation and improve the efficiency of investigation. Figure 1 As shown, the following steps are included: before tracking water pollution incidents, using spatial query technology to build a hydrological spatial scenario library and establish a risk source pollution characteristic database; when investigating the risk sources of water pollution incidents, using spatial query technology to automatically query the suspected risk sources located upstream of the monitoring point; then using the mapping relationship between the risk source pollution characteristics and the monitoring items for comparison, calculate the single pollution contribution rate and comprehensive pollution contribution rate of the suspected risk source to the water quality pollution of the monitoring point, and finally determine the suspected risk source and the corresponding pollution contribution rate of the water level where the pollution monitoring point is located under the water pollution incident.

[0051] Specifically: Step 1, using spatial query technology, determine the spatial relationship between water system data, monitoring point data, and risk source data obtained based on DEM data, including the basin where the spatial query runoff is located; obtain the nearest runoff and the nearest point coordinates corresponding to the spatial query monitoring point and the risk source point, and build a hydrological spatial scenario library based on this. In this embodiment, the PostgreSQL database is used to manage the data, so as to build a hydrological spatial scenario library;

[0052] Step 2: Obtain pollutant data of wastewater collected from each risk source in the target area, manage the basic information and pollution discharge characteristics of the risk source in the form of a database to establish a risk source pollution discharge characteristic database, which is updated in real time; pollutant data include pollutant types and content data, which serve as pollution discharge characteristics of risk sources. The data updated in real time in the risk source pollution discharge characteristic database includes regular enterprise declaration data or irregular on-site inspection data, which is used as an updating method to update the risk source pollution discharge characteristic database in the region.

[0053] Step 3: Use spatial query technology to automatically determine the upstream and downstream relationship between monitoring points and risk sources, and preliminarily screen out risk sources (suspected risk sources) located upstream of the monitoring station that may affect the water quality of the monitoring points;

[0054] Step 4: For the suspected risk sources initially screened out in step 3, for monitoring items that exceed water quality standards or monitoring items that are under key observation, extract the relevant information of the suspected risk sources from the risk source pollution feature database based on the mapping relationship between the detection items of the monitoring point and the pollution discharge characteristics of the risk sources, calculate the pollution discharge contribution rate of the suspected risk sources affecting the water quality of the monitoring points, and finally judge the contribution degree of the suspected risk sources to the water quality of the monitoring points based on the output comprehensive pollution discharge contribution rate. The greater the comprehensive pollution discharge contribution rate of the suspected risk source, the greater the impact of the risk source on the water quality of the monitoring points.

[0055] Specifically, the step 1 comprises the following steps:

[0056] Step 1.1: Spatial query of the watershed where the runoff is located: Use spatial query technology to determine the watershed where each runoff section in the region is located;

[0057] Step 1.2, spatial query of the nearest runoff section and the corresponding nearest point of each monitoring point: using spatial query technology to determine the coordinates of the nearest runoff section and the corresponding nearest point of each monitoring point;

[0058] Step 1.3: Spatial query of the nearest runoff segment and the corresponding nearest point of each risk source: Use spatial query technology to determine the coordinates of the nearest runoff segment and the corresponding nearest point of each risk source. In this embodiment, the relationship between the nearest runoff and the nearest point corresponding to the monitoring point and the risk source point is as follows: Figure 2 shown.

[0059] Specifically, in step 3, the pollution discharge of the upstream risk sources in the same basin as the monitoring point and the risk sources in the basin upstream of the nearest runoff of the monitoring point may affect the water quality of the monitoring point. Therefore, in this method, the upstream risk sources of the monitoring point are divided into two parts according to the basin, including the upstream risk sources in the same basin as the monitoring point and the upstream k-order basin risk sources (k≥1), where k represents the order of the basin adjacent to the nearest runoff of the monitoring point. Usually, k is set to the highest-order basin that can be traced, so that all risk sources upstream of the monitoring point are included in the traceability range, and the upstream risk source search range can be adjusted by adjusting the k value.

[0060] Step 3 includes the following steps: Step 3.1, spatial query of upstream risk sources in the same basin, specifically, Figure 3 As shown in the figure, by comparing the distance between the nearest point of the monitoring point in the nearest runoff section (hereinafter referred to as the nearest point of the monitoring point) and the nearest point of the nearest runoff section of the risk source in the same watershed (hereinafter referred to as the nearest point of the risk source) to the upstream cutoff point of the nearest runoff section of the monitoring point, it is judged whether the risk source is located upstream of the monitoring point. If the distance from the monitoring point to the cutoff point is greater than the distance from the risk source to the cutoff point, the risk source belongs to the upstream risk source of the monitoring point and is a suspected risk source; otherwise, the risk source is judged to not affect the water quality of the monitoring point and is not a suspected risk source.

[0061] Step 3.2: Figure 5 As shown, spatial query is performed on the risk sources in the upstream k-order basin: by finding the basin where the first-order runoff section upstream of the monitoring point is located, and continuously searching the second, ..., k-order adjacent basins upstream, all risk sources in the upstream k-order basin are finally included in the set of suspected risk sources.

[0062] For automated management, the spatial query technology of the PostgreSQL database is used to automate the process of step 3.

[0063] Specifically, process automation includes querying the upstream risk source process of the same basin and querying the risk source process within the upstream k-order basin:

[0064] Among them, Figure 4 As shown in the figure, the process of querying upstream risk sources in the same river basin includes:

[0065] Step 3a, using the ST_DumpPoints and ST_MakeLine functions of the PostgreSQL database to split the nearest runoff segment of the monitoring point according to the inflection point, into multiple sub-runoff segments connected in the upstream and downstream order;

[0066] Step 3b, according to the split upstream and downstream order, use the ST_Distance function to find the sub-runoff segment with a distance of 0 from the nearest point of the monitoring point, calculate the distance between the upstream cutoff point of the sub-runoff segment and the nearest point of the monitoring point, and add it with the length of all sub-runoff segments upstream of the sub-runoff segment to obtain the distance from the nearest point of the monitoring point to the upstream cutoff point of the runoff segment;

[0067] Step 3c: In the same way, the distance from the nearest point of each risk source in the same basin to the upstream cutoff point of the runoff section can be obtained. If this distance is less than or equal to the distance from the nearest point of the monitoring point to the upstream cutoff point of the runoff section, the risk source is the upstream risk source in the same basin of the monitoring point;

[0068] Step 3d, generating a set of upstream risk sources in the same river basin as the monitoring point;

[0069] like Figure 6 As shown in the figure, the process of querying the risk source in the upstream k-order watershed includes:

[0070] Step 3e, using the internal connection of the upstream cutoff point ID field and the downstream cutoff point ID field of the runoff table in the hydrological spatial scenario library to obtain the first-order adjacent watershed upstream of the runoff, and generate the watershed table where the first-order adjacent runoff section upstream of the runoff section is located;

[0071] Step 3f, using the table to cyclically search for the upstream k-order watersheds of the runoff section to which the monitoring point belongs, and also list the risk sources located in these upstream k-order watersheds as suspected risk sources;

[0072] Step 3g, generating a set of k-order watershed risk sources upstream of the monitoring point;

[0073] The set of upstream risk sources in the same river basin as the monitoring point queried in step 3d is merged to generate a set of suspected risk sources upstream of the monitoring point.

[0074] Furthermore, the step 4 specifically includes the following steps:

[0075] Step 4.1, determine the set of monitoring items for comparison: the monitoring items for comparison are combined with the constructed mapping relationship between the monitoring items and the pollution discharge characteristics of the risk source, and are used to screen the pollution discharge characteristics from the risk source pollution discharge characteristic library to calculate the contribution rate; wherein, the set can be a set of monitoring items that the analyst focuses on observing, or a set of monitoring items that exceeds a certain water quality standard (such as the drinking water standard GB5749-2006),

[0076] Step 4.2, extract the preliminary screening risk sources and related pollution characteristics: using the mapping relationship between monitoring items and risk source pollution characteristics, according to the monitoring items to be compared determined in step 4.1, obtain the risk source pollution characteristics set to be compared; according to the risk source pollution characteristics set to be compared and the screened upstream risk source set obtained in step 3, search the pollution characteristics and related information of the suspected risk sources after preliminary screening from the risk source pollution characteristics database;

[0077] Step 4.3, calculate the contribution rate of single pollution discharge from suspected risk sources: Based on the suspected risk source data and monitoring data extracted in step 4.2, the contribution rate P′ of single pollution discharge from wastewater discharged from risk source a to pollutant i in the water quality at the point can be calculated. a (i)

[0078]

[0079]

[0080] The characteristic concentration of pollutant i corresponding to risk source a is C a (i) The Euclidean distance from the risk source point to the monitoring point is D a ; The concentration C(i) of pollutant i recorded at the monitoring point;

[0081] Step 4.4: Calculate the comprehensive pollution contribution rate of suspected risk sources:

[0082] According to the calculation results of step 4.3, further calculate the comprehensive pollution contribution rate Prob of risk source a that affects the water quality of the monitoring point a :

[0083]

[0084] Example 1

[0085] This embodiment takes a water pollution incident in a certain area of ​​Dalian City, Liaoning Province as an example to specifically illustrate the process of this method.

[0086] Step 1. Establish a hydrological spatial scenario database

[0087] The basin near a certain area in Dalian, Liaoning Province was selected as the target area, and the DEM data and administrative division data of the target area were obtained. According to the control unit refinement and decomposition technology in the "Technical Guidelines for the Preparation of Water Body Compliance Plans" (Trial), the Hydrology Modeling module in ArcGIS software was used to extract river network data and catchment area data. The basic information of monitoring points and risk sources was obtained, such as the unique number of the monitoring point, coordinates (latitude and longitude), and monitoring record information, including monitoring time, monitoring items and corresponding monitoring values; the unique number of the risk source, the corresponding enterprise name, and coordinates (latitude and longitude).

[0088] Using spatial query technology, spatial query is performed on the watershed where the runoff in the target area is located; spatial query is performed on the nearest runoff and the coordinates of the nearest points corresponding to the monitoring points and risk source points; the data is managed using the PostgreSQL database to build a hydrological spatial scenario library.

[0089] Step 1.1. Spatial query of the watershed where the runoff is located

[0090] Spatial query technology is used to determine the watershed where each runoff section in the region is located.

[0091] Step 1.2. Spatial query of the nearest runoff segment and the corresponding nearest point of each monitoring point

[0092] The spatial query technology is used to determine the nearest runoff section of each monitoring point and the corresponding coordinates of the nearest point.

[0093] Step 1.3 Spatial query of the nearest runoff segment and the corresponding nearest point of each risk source

[0094] Spatial query technology is used to determine the nearest runoff section of each risk source and the corresponding nearest point coordinates.

[0095] Step 2. Establish a risk source pollution feature database

[0096] The pollutant data collected from sewage of various risk sources in the target area, including information such as pollutant types and contents, are used as the pollution discharge characteristics of the risk sources to establish a pollution discharge characteristic database for risk sources in the target area.

[0097] Step 3. Preliminary screening of risk sources

[0098] Step 3.1. Spatial query of upstream risk sources in the same watershed

[0099] Use the spatial query technology of PostgreSQL database to write step 3.1. Spatial query of upstream risk sources in the same basin query function, automatically query the upstream risk sources in the same basin, the query results are as follows

[0100] Table 1:

[0101] Table 1 Risk sources upstream of the same basin at the monitoring point

[0102] Serial number Risk Source ID 1 d4ce86c*** 2 8cf416cf*** 3 025b3e4***

[0103] Step 3.2. Spatial query of risk sources in the upstream k-order watershed

[0104] Use the spatial query technology of PostgreSQL database to write step 3.2. Spatial query of risk source query function in upstream k-order watershed, automatically query risk sources in upstream k-order watershed. In this example, k=1 is set. The query result example is shown in Table 3:

[0105] Table 3 Risk sources in the k-order watershed upstream of the monitoring point

[0106]

[0107]

[0108] Step 4. Calculate the comprehensive pollution contribution rate of suspected risk sources

[0109] Step 4.1. Determine the set of monitoring items to be compared

[0110] This example uses the monitoring items that exceed the drinking water standard (GB5749-2006) as the set of monitoring items for comparison.

[0111] Step 4.2. Extraction of preliminary screening risk sources and related pollution characteristics

[0112] According to the intrinsic relationship between the monitoring items of the monitoring station and the pollution characteristics of the risk source, the mapping table of monitoring items and pollution characteristics is designed. The meaning of the mapping table constructed in this example is shown in Table 1. Using the mapping relationship between the detection items of the monitoring point and the pollution characteristics of the risk source, according to the monitoring items exceeding the drinking water standard (GB5749-2006) determined in step 4.1, the pollution characteristics set of the risk source for comparison and the set of upstream risk sources after screening obtained in step 3 are obtained, and the pollution characteristics and related information of the suspected risk sources after preliminary screening are found in the risk source pollution characteristics library.

[0113] Table 1 Example of mapping table between monitoring items and pollutant discharge characteristic fields

[0114] ID Monitoring Project Pollution characteristics of risk sources 1 lead Total lead outlet concentration index value_mg / L 2 cadmium Total cadmium export concentration index value_mg / L 3 mercury Total mercury outlet concentration index value_mg / L 4 Hexavalent Chromium Hexavalent chromium export concentration index value_mg / L …… …… ……

[0115] Step 4.3. Calculate the contribution rate of individual pollution discharge from suspected risk sources

[0116] Based on the suspected risk source data and monitoring data extracted in step 4.2, the single pollution discharge contribution rate of the wastewater discharged from the risk source to the pollutants in the water quality of the monitoring point can be calculated.

[0117] Step 4.4. Calculate the comprehensive pollution contribution rate of suspected risk sources

[0118] According to the calculation results of step 4.3, the comprehensive pollution discharge contribution rate of the risk source that comprehensively affects the water quality of the monitoring point is further calculated. The results of the comprehensive pollution discharge contribution rate of the suspected risk sources in this water pollution incident in this case are shown in Table 4:

[0119] Table 4 Comprehensive pollution contribution rate of suspected risk sources

[0120] Serial number Risk Source ID Comprehensive pollution contribution rate of risk sources 1 0756649b*** 0.765 2 5a432cf7*** 0.124 3 ffc51c3b*** 0.111 4 d4ce86c*** 0.000 5 8cf416cf*** 0.000 6 025b3e4*** 0.000 7 2800e0ed*** 0.000 8 8b71d9e1*** 0.000

[0121] After the above calculations are completed, the contribution degree of the suspected risk source to the water quality pollution at the monitoring point can be judged according to the size of the output comprehensive pollution discharge contribution rate. The greater the comprehensive pollution discharge contribution rate of the suspected risk source, the greater the impact of the risk source on the water quality pollution at the monitoring point.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for rapid tracing of water pollution by automatically screening upstream risk sources, characterized in that: The steps include: Step 1: Obtain the spatial relationship between water system data, monitoring point data, and risk source data, obtain the nearest runoff and nearest point coordinates corresponding to the spatial query monitoring point and risk source point, and build a hydrological spatial scenario library based on this; Step 2: Obtain the pollutant data of the wastewater from each risk source in the target area, and establish a risk source pollution discharge feature database, which is updated in real time; Step 3: Use spatial query technology to automatically determine the upstream and downstream relationship between monitoring points and risk sources, and preliminarily screen out risk sources located upstream of the monitoring station that may affect the water quality of the monitoring points; Step 4: For monitoring items that exceed water quality standards or monitoring items that are under key observation, extract relevant information about the suspected risk source from the risk source pollution feature database based on the mapping relationship between the detection items of the monitoring point and the pollution discharge characteristics of the risk source, calculate the pollution discharge contribution rate of the suspected risk source affecting the water quality of the monitoring point, and finally judge the contribution degree of the suspected risk source to the pollution of the water quality of the monitoring point based on the size of the output comprehensive pollution discharge contribution rate; The step 3 comprises the following steps: Step 3.1, divide the upstream risk sources of the monitoring point into two parts according to the watershed, including the upstream risk sources in the same watershed as the monitoring point and the upstream k-order watershed risk sources, and spatially query the upstream risk sources in the same watershed. Specifically, by comparing the distance between the nearest point of the monitoring point in the nearest runoff segment and the nearest point of the nearest runoff segment of the risk source in the same watershed to the upstream cutoff point of the nearest runoff segment of the monitoring point, determine whether the risk source is located upstream of the monitoring point. If the distance from the monitoring point to the cutoff point is greater than the distance from the risk source to the cutoff point, the risk source belongs to the upstream risk source of the monitoring point and is a suspected risk source; Otherwise, the risk source is judged to have no impact on the water quality of the monitoring point and is not considered a suspected risk source; Step 3.2, spatial query of risk sources in the upstream k-order basin: by finding the basin where the first-order runoff section upstream of the monitoring point is located, and continuously searching the second, ..., k-order adjacent basins upstream, and finally including all risk sources in the upstream k-order basin into the set of suspected risk sources; Process automation specifically includes querying the upstream risk source process of the same basin and querying the risk source process within the upstream k-order basin: The process of querying upstream risk sources in the same river basin includes: Step 3a, using the ST_DumpPoints and ST_MakeLine functions of the PostgreSQL database to split the nearest runoff segment of the monitoring point according to the inflection point, into multiple sub-runoff segments connected in the upstream and downstream order; Step 3b, according to the split upstream and downstream order, use the ST_Distance function to find the sub-runoff segment with a distance of 0 from the nearest point of the monitoring point, calculate the distance between the upstream cutoff point of the sub-runoff segment and the nearest point of the monitoring point, and add it with the length of all sub-runoff segments upstream of the sub-runoff segment to obtain the distance from the nearest point of the monitoring point to the upstream cutoff point of the runoff segment; Step 3c: In the same way, the distance from the nearest point of each risk source in the same basin to the upstream cutoff point of the runoff section can be obtained. If this distance is less than or equal to the distance from the nearest point of the monitoring point to the upstream cutoff point of the runoff section, the risk source is the upstream risk source in the same basin of the monitoring point; Step 3d, generating a set of upstream risk sources in the same river basin as the monitoring point; The process of querying the risk sources in the upstream k-order watershed includes: Step 3e, using the internal connection of the upstream cutoff point ID field and the downstream cutoff point ID field of the runoff table in the hydrological spatial scenario library to obtain the first-order adjacent watershed upstream of the runoff, and generate the watershed table where the first-order adjacent runoff section upstream of the runoff section is located; Step 3f, using the table to cyclically search for the upstream k-order watersheds of the runoff section to which the monitoring point belongs, and also list the risk sources located in these upstream k-order watersheds as suspected risk sources; Step 3g, generating a set of k-order watershed risk sources upstream of the monitoring point; Combined with the upstream risk source set of the monitoring point in step 3d, to generate the upstream suspected risk source set of the monitoring point; The step 4 specifically includes the following steps: Step 4.1, determine the set of monitoring items to be compared: the monitoring items to be compared are combined with the constructed mapping relationship between the monitoring items and the pollution discharge characteristics of the risk sources, and are used to screen the pollution discharge characteristics from the risk source pollution discharge characteristic library to calculate the contribution rate; Step 4.2, extract the preliminary screening risk sources and related pollution characteristics: using the mapping relationship between monitoring items and risk source pollution characteristics, according to the monitoring items to be compared determined in step 4.1, obtain the risk source pollution characteristics set to be compared; according to the risk source pollution characteristics set to be compared and the screened upstream risk source set obtained in step 3, search the pollution characteristics and related information of the suspected risk sources after preliminary screening from the risk source pollution characteristics database; Step 4.3, calculate the contribution rate of single pollution discharge from suspected risk sources: Based on the suspected risk source data and monitoring data extracted in step 4.2, the contribution rate P′ of single pollution discharge from wastewater discharged from risk source a to pollutant i in the water quality at the point can be calculated. a (i) The characteristic concentration of pollutant i corresponding to risk source a is C a (i) The Euclidean distance from the risk source point to the monitoring point is D a ; The concentration C(i) of pollutant i recorded at the monitoring point; Step 4.4: Calculate the comprehensive pollution contribution rate of suspected risk sources: According to the calculation results of step 4.3, further calculate the comprehensive pollution contribution rate Prob of risk source a that affects the water quality of the monitoring point a :

2. The method for rapid tracing of water pollution by automatically screening upstream risk sources according to claim 1 is characterized in that: The step 1 specifically includes the following steps: Step 1.1: Spatial query of the watershed where the runoff is located: Use spatial query technology to determine the watershed where each runoff section in the region is located; Step 1.2, spatial query of the nearest runoff section and the corresponding nearest point of each monitoring point: using spatial query technology to determine the coordinates of the nearest runoff section and the corresponding nearest point of each monitoring point; Step 1.3: Spatial query of the nearest runoff segment and the corresponding nearest point of each risk source: Use spatial query technology to determine the coordinates of the nearest runoff segment and the corresponding nearest point of each risk source.

3. The method for rapid tracing of water pollution by automatically screening upstream risk sources according to claim 1 is characterized in that: In step 2, the pollutant data include pollutant types and content data, and the data updated in real time in the risk source pollution emission characteristic database include enterprise regular declaration data or irregular on-site inspection data.

4. The method for rapid tracing of water pollution by automatically screening upstream risk sources according to claim 1 is characterized in that: The spatial query technology of the PostgreSQL database is used to automate the process of step 3.

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