Water pollution monitoring system and methods

Abstract

This invention discloses a water pollution monitoring system and method, relating to the field of water pollution monitoring. The system includes a region division and sampling point layout module, a deviation analysis module, a first monitoring area analysis module, and a second monitoring area analysis module. The region division and sampling point layout module analyzes the pollution source characteristics of the area to be monitored to rationally divide the monitoring area and deploy sampling points. The deviation analysis module performs deviation analysis on data from adjacent sampling points after collecting water samples to ensure data accuracy and consistency. The first monitoring area analysis module combines water quality fluorescence fingerprint analysis technology to match and trace known pollution sources, accurately identifying their locations. The second monitoring area analysis module analyzes the water sample flow path to gradually confirm the specific location of the pollution source.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of water quality pollution monitoring, in particular to a water quality pollution monitoring system and method. BACKGROUND

[0002] Current water quality pollution monitoring technology mainly relies on the detection of traditional water quality indicators (such as chemical oxygen demand, ammonia nitrogen, total phosphorus, total nitrogen, etc.). Although these indicators can identify basic pollution conditions in water quality in some cases, in the monitoring of organic pollutants, traditional technology often cannot accurately distinguish the type of pollution source, thereby limiting the effect of pollution source tracing.

[0003] In order to solve this problem, water quality fingerprint tracing technology has gradually become an important monitoring means. However, the existing technology still faces two key challenges in actual application: one is the problem of sampling point layout, especially in complex environments such as sewage treatment plant inlet, river section, etc., how to reasonably layout the sampling points to ensure that the collected water samples can truly reflect the actual situation of the pollution source; the second is the problem of water sample analysis, how to correctly identify the pollution source through data analysis is still a difficult problem faced by current technology. SUMMARY

[0004] The purpose of the present application is to provide a water quality pollution monitoring system and method to solve the problems raised in the background art.

[0005] In order to solve the above technical problems, the present application provides the following technical solutions:

[0006] The water quality pollution monitoring method comprises the following steps:

[0007] Step S100. Obtain the position information of the area to be monitored, extract the pollution source characteristics from the position information, and divide the area to be monitored into a first monitoring area and a second monitoring area according to the pollution source characteristics; based on the classification of the division of the area to be monitored, layout the sampling points for water quality monitoring;

[0008] Step S200. According to the layout of the water quality monitoring sampling points, water samples are collected in turn, so as to obtain the corresponding water quality monitoring data; the water quality monitoring data of adjacent two sampling points are analyzed for deviation, and the sampling point density is adjusted according to the deviation analysis result;

[0009] Step S300. For the first monitoring area, the water sample corresponding to the sampling point is subjected to water quality fluorescence fingerprint analysis, and is matched with the known pollution source water quality fluorescence fingerprint in the pollution source database; according to the matching result, a suspected pollution source is identified, and the position of the suspected pollution source is sent to the relevant personnel;

[0010] Step S400. For the second monitoring area, water sample flow path analysis is performed, the water quality fluorescence fingerprints of cross-section water samples at different sampling points are compared, and the suspected pollution source position is gradually confirmed according to the comparison result, and the suspected pollution source position is sent to the relevant personnel.

[0011] Further, step S100 comprises:

[0012] S101. The to-be-monitored area is divided into sub-regions according to a preset minimum region, thereby obtaining a plurality of sub-regions; the position information of the to-be-monitored area is obtained, for each sub-region, the pollution source feature is extracted from the corresponding position information, and the category of the corresponding sub-region is determined according to the pollution source feature; if there is a known pollution source in the pollution source feature of the sub-region, the sub-region is divided into a first monitoring area; if there is an unknown pollution source in the pollution source feature of the sub-region, the sub-region is divided into a second monitoring area; wherein the first monitoring area has a known pollution source, such as a sewage treatment plant, a factory sewage point, etc.; the pollution source in the second monitoring area is unknown, such as a river, a lake, etc. natural water area;

[0013] S102. For the first monitoring area, the corresponding drainage pipe network structure diagram is obtained, the sewage outlet and the drainage pipe intersection point are identified according to the drainage pipe network structure diagram; the historical sewage discharge data of the first monitoring area is obtained, and the pollution concentration Cp of each sewage outlet is calculated in combination with the historical sewage discharge data, and Cp=Mp / Qp, wherein Mp represents the total amount of pollutants discharged from the sewage outlet, and Qp represents the discharge flow of the sewage outlet; the pollution concentration Cp of all sewage outlets is summarized, and the pollution concentration Cp of all sewage outlets is compared with the preset pollution concentration threshold C1 in turn, and the sewage outlet with the pollution concentration Cp greater than or equal to the pollution concentration threshold C1 is recorded as a sampling point; the pollution concentration Cj of each drainage pipe intersection point is calculated, and Cj=∑n i=1(Qi×Ci) / ∑n i=1Qi, wherein Qi represents the flow of the i-th pipe, Ci represents the pollution concentration of the i-th pipe, and n represents the number of pipes connected at the intersection point; the pollution concentration Cj of all drainage pipe intersection points is summarized, and the pollution concentration Cj of all drainage pipe intersection points is compared with the preset pollution concentration threshold C2 in turn, and the drainage pipe intersection point with the pollution concentration Cj greater than or equal to the pollution concentration threshold C2 is recorded as a sampling point;

[0014] S103. For the second monitoring area, the flow direction and confluence relationship of all tributaries in the second monitoring area are obtained, thereby identifying the confluence points in the second monitoring area, and taking the confluence points as the sampling points of the second monitoring area; according to the location of each confluence point and in combination with the corresponding tributary flow direction, the sampling points are set segmentally along the tributary flow direction according to a preset minimum sampling point distance.

[0015] Further, step S200 comprises:

[0016] S201. For the first and second monitoring areas respectively, water samples are collected sequentially according to the established water quality monitoring sampling points to obtain corresponding water quality monitoring data. Based on the monitoring area and sampling point, the collected water quality monitoring data are processed sequentially to obtain the first monitoring dataset A and the second monitoring dataset B, where A = {a1, a2, ..., au} and B = {b1, b2, ..., bv}. a1 represents the water quality monitoring data of the first sampling point in the first monitoring area, a2 represents the water quality monitoring data of the second sampling point in the first monitoring area, and so on, with au representing the water quality monitoring data of the u-th sampling point in the first monitoring area. Similarly, b1 represents the water quality monitoring data of the first sampling point in the second monitoring area, b2 represents the water quality monitoring data of the second sampling point in the second monitoring area, and bv represents the water quality monitoring data of the v-th sampling point in the second monitoring area; u and v represent the number of sampling points in the first and second monitoring areas, respectively.

[0017] S202. For the first monitoring dataset A and the second monitoring dataset B respectively, calculate the deviation index R of the water quality monitoring data between two adjacent sampling points. The corresponding calculation formula is: R=|f_k-f_k+1| / d_k, where f_k represents the water quality monitoring data of the k-th sampling point in the first monitoring dataset A or the second monitoring dataset B, f_k+1 represents the water quality monitoring data of the (k+1)-th sampling point in the first monitoring dataset A or the second monitoring dataset B, and d_k represents the distance between adjacent sampling points f_k and f_k+1 in the first monitoring dataset A or the second monitoring dataset B. Compare the deviation index R of the water quality monitoring data between two adjacent sampling points with the threshold RT. If the deviation index R is greater than the threshold RT, a new sampling point needs to be inserted between the corresponding two adjacent sampling points, and the location of the area between the two adjacent sampling points is output to the relevant personnel, who then increase the sampling point density of the current area. If the deviation index R is less than the threshold RT, adjacent sampling points need to be merged, and the location of the area between the two adjacent sampling points is output to the relevant personnel, who then decrease the sampling point density of the current area.

[0018] Furthermore, step S300 includes:

[0019] S301. For the first monitoring area, perform water quality fluorescence fingerprint analysis at the corresponding sampling points to obtain water quality fluorescence fingerprint data for each sampling point; calculate the similarity between the water quality fluorescence fingerprint data of each sampling point and the water quality fluorescence fingerprint data of known pollution sources in the database; if the similarity result is greater than or equal to the similarity threshold, mark the corresponding sampling point as a suspected pollution source.

[0020] S302. For sampling points with a similarity score less than the similarity threshold, obtain the water quality fluorescence fingerprint data of the upstream sampling points and calculate the similarity. If the similarity score is greater than or equal to the similarity threshold, the upstream sampling points are taken as target sampling points. Calculate the similarity between the target sampling point and the water quality fluorescence fingerprint data of its upstream sampling points until the similarity score is less than the similarity threshold, and mark the target sampling point as a suspected pollution source. Output the location coordinates of the suspected pollution source to relevant personnel for appropriate processing. Specifically, for sampling points with a similarity score less than the similarity threshold, it is necessary to continue tracing upstream. Therefore, the upstream sampling points are taken as target sampling points. This step essentially uses similarity calculation to gradually eliminate irrelevant sampling points until an area with water quality characteristics similar to a known pollution source is found. When the similarity score is less than the similarity threshold, it indicates that continuing to trace upstream is ineffective, and the pollution source may have already been located in the target sampling point area. Therefore, the target sampling point is marked as a suspected pollution source.

[0021] Furthermore, step S400 includes:

[0022] S401. For each sampling point in the second monitoring area, perform water sample flow path analysis, name the sampling section of the sampling point as section X, name the sampling section of the upstream sampling point of the current sampling point as section Y, analyze the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to section X and section Y. If the similarity is greater than or equal to the similarity threshold, it indicates that the suspected pollution source may be upstream of section Y. Name the sampling section of the upstream sampling point of section Y as section Z1, analyze the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to section X and section Z1. If the similarity is greater than or equal to the similarity threshold, continue to analyze the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to the upstream sampling points of section X and section Z1 until the similarity is less than the similarity threshold, and take the sampling points corresponding to section Y and section Z1 as suspected pollution sources.

[0023] S402. Analyze the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to section X and section Z1. If the similarity is less than the similarity threshold, it indicates that the suspected pollution source may be between section Y and X. The sampling points corresponding to section X and section Y are taken as suspected pollution sources. The location coordinates of the suspected pollution source are output to relevant personnel for appropriate processing.

[0024] The water pollution monitoring system includes: a regional division and sampling point layout module, a deviation analysis module, a first monitoring area analysis module, and a second monitoring area analysis module;

[0025] The area division and sampling point deployment module acquires the location information of the area to be monitored, extracts pollution source characteristics from the location information, and divides the area to be monitored into a first monitoring area and a second monitoring area according to the pollution source characteristics; based on the classification of the area to be monitored, sampling points for water quality monitoring are deployed.

[0026] The deviation analysis module collects water samples sequentially from the deployed water quality monitoring sampling points to obtain corresponding water quality monitoring data; it performs deviation analysis on the water quality monitoring data of two adjacent sampling points and adjusts the sampling point density based on the deviation analysis results;

[0027] The first monitoring area analysis module performs water quality fluorescence fingerprint analysis on water samples from the corresponding sampling points in the first monitoring area and matches them with the known pollution source water quality fluorescence fingerprints in the pollution source database; based on the matching results, it identifies suspected pollution sources and sends the locations of suspected pollution sources to relevant personnel;

[0028] The second monitoring area analysis module performs water sample flow path analysis. By comparing the water quality fluorescence fingerprints of cross-sectional water samples from different sampling points, it gradually confirms the location of suspected pollution sources based on the comparison results and sends the location of suspected pollution sources to relevant personnel.

[0029] Furthermore, the region division and sampling point layout module includes a region division unit and a sampling point layout unit;

[0030] The area division unit divides the area to be monitored into multiple sub-areas according to the preset minimum area, obtains the location information of each sub-area, and extracts pollution source characteristics; based on the pollution source characteristics, it determines whether there are known pollution sources in each sub-area and classifies it as the first monitoring area or the second monitoring area; the sampling point deployment unit deploys water quality monitoring sampling points based on the classification of the area to be monitored.

[0031] Furthermore, the deviation analysis module includes a data deviation calculation unit and a sampling point density adjustment unit;

[0032] The data deviation calculation unit calculates the deviation index of water quality monitoring data between two adjacent sampling points and compares it with a preset threshold. The sampling point density adjustment unit determines whether to increase or decrease the sampling point density based on the deviation analysis results. If the deviation index is greater than the set threshold, the sampling points are increased; if the deviation index is less than the set threshold, the sampling points are decreased.

[0033] Furthermore, the first monitoring area analysis module includes a water quality fluorescence fingerprint analysis unit and a first pollution source identification unit;

[0034] The water quality fluorescence fingerprint analysis unit performs fluorescence fingerprint analysis on water samples from sampling points in the first monitoring area and obtains the corresponding water quality fluorescence fingerprint data; the pollution source identification unit matches the water quality fluorescence fingerprint data of each sampling point with the water quality fluorescence fingerprint data of known pollution sources in the database, and identifies suspected pollution sources based on the matching results, and sends the location of the pollution source to relevant personnel.

[0035] Furthermore, the second monitoring area analysis module includes a water flow path analysis unit and a second pollution source identification unit;

[0036] The water flow path analysis unit performs water sample flow path analysis for the second monitoring area; the second pollution source identification unit compares the water quality fluorescence fingerprints of cross-sectional water samples from different sampling points, and gradually confirms the location of the pollution source based on the comparison results, and sends the pollution source location to relevant personnel.

[0037] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention divides the area to be monitored into a first monitoring area and a second monitoring area based on the characteristics of the pollution source, and adopts different sampling point layout methods for different areas to ensure the targeting and accuracy of the monitoring work; for known pollution sources in the first monitoring area, a combination of sewage outlets and pipeline structure diagrams is used for precise monitoring; while for the second monitoring area, monitoring points are laid out according to flow direction and confluence relationship, thereby comprehensively covering potential pollution source areas. Through deviation analysis and data processing, the sampling point density can be dynamically adjusted according to the deviation of the monitoring data; the deviation index between adjacent sampling points is compared, and the number of sampling points is increased or decreased in a timely manner based on the results, optimizing the layout of sampling points and monitoring accuracy; through comparison of water quality fluorescence fingerprint data with known pollution sources and similarity calculation, suspected pollution sources can be effectively traced, and irrelevant sampling points can be gradually eliminated, ultimately locating the precise location of the pollution source; this step-by-step tracing method is more comprehensive than traditional single-point sampling or local area analysis, and can better cope with complex water pollution sources. Attached Figure Description

[0038] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0039] Figure 1 This is a schematic diagram of the modules of the water pollution monitoring system of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Please see Figure 1 The present invention provides the following technical solution:

[0042] The water pollution monitoring system includes: a regional division and sampling point layout module, a deviation analysis module, a first monitoring area analysis module, and a second monitoring area analysis module;

[0043] The area division and sampling point deployment module acquires the location information of the area to be monitored, extracts pollution source characteristics from the location information, and divides the area to be monitored into a first monitoring area and a second monitoring area according to the pollution source characteristics; based on the classification of the area to be monitored, sampling points for water quality monitoring are deployed.

[0044] The deviation analysis module collects water samples sequentially from the deployed water quality monitoring sampling points to obtain corresponding water quality monitoring data; it performs deviation analysis on the water quality monitoring data of two adjacent sampling points and adjusts the sampling point density based on the deviation analysis results;

[0045] The first monitoring area analysis module performs water quality fluorescence fingerprint analysis on water samples from the corresponding sampling points in the first monitoring area and matches them with the known pollution source water quality fluorescence fingerprints in the pollution source database; based on the matching results, it identifies suspected pollution sources and sends the locations of suspected pollution sources to relevant personnel;

[0046] The second monitoring area analysis module performs water sample flow path analysis. By comparing the water quality fluorescence fingerprints of cross-sectional water samples from different sampling points, it gradually confirms the location of suspected pollution sources based on the comparison results and sends the location of suspected pollution sources to relevant personnel.

[0047] The region division and sampling point layout module includes a region division unit and a sampling point layout unit;

[0048] The area division unit divides the area to be monitored into multiple sub-areas according to the preset minimum area, obtains the location information of each sub-area, and extracts pollution source characteristics; based on the pollution source characteristics, it determines whether there are known pollution sources in each sub-area and classifies it as the first monitoring area or the second monitoring area; the sampling point deployment unit deploys water quality monitoring sampling points based on the classification of the area to be monitored.

[0049] The deviation analysis module includes a data deviation calculation unit and a sampling point density adjustment unit;

[0050] The data deviation calculation unit calculates the deviation index of water quality monitoring data between two adjacent sampling points and compares it with a preset threshold. The sampling point density adjustment unit determines whether to increase or decrease the sampling point density based on the deviation analysis results. If the deviation index is greater than the set threshold, the sampling points are increased; if the deviation index is less than the set threshold, the sampling points are decreased.

[0051] The first monitoring area analysis module includes a water quality fluorescence fingerprint analysis unit and a first pollution source identification unit;

[0052] The water quality fluorescence fingerprint analysis unit performs fluorescence fingerprint analysis on water samples from sampling points in the first monitoring area and obtains the corresponding water quality fluorescence fingerprint data; the pollution source identification unit matches the water quality fluorescence fingerprint data of each sampling point with the water quality fluorescence fingerprint data of known pollution sources in the database, and identifies suspected pollution sources based on the matching results, and sends the location of the pollution source to relevant personnel.

[0053] The second monitoring area analysis module includes a water flow path analysis unit and a second pollution source identification unit.

[0054] The water flow path analysis unit performs water sample flow path analysis for the second monitoring area; the second pollution source identification unit compares the water quality fluorescence fingerprints of cross-sectional water samples from different sampling points, and gradually confirms the location of the pollution source based on the comparison results, and sends the pollution source location to relevant personnel.

[0055] Water pollution monitoring methods include the following steps:

[0056] Step S100. Obtain the location information of the area to be monitored, extract the pollution source characteristics from the location information, and divide the area to be monitored into a first monitoring area and a second monitoring area according to the pollution source characteristics; based on the classification of the area to be monitored, set up sampling points for water quality monitoring.

[0057] Step S200. Collect water samples sequentially according to the established water quality monitoring sampling points to obtain corresponding water quality monitoring data; perform deviation analysis on the water quality monitoring data of two adjacent sampling points, and adjust the sampling point density based on the deviation analysis results;

[0058] Step S300. For the first monitoring area, perform water quality fluorescence fingerprint analysis on the water samples from the corresponding sampling points and match them with the known pollution source water quality fluorescence fingerprints in the pollution source database; based on the matching results, identify suspected pollution sources and send the locations of suspected pollution sources to relevant personnel;

[0059] Step S400. For the second monitoring area, perform water sample flow path analysis, compare the water quality fluorescence fingerprints of cross-sectional water samples from different sampling points, and gradually confirm the location of suspected pollution sources based on the comparison results, and send the location of suspected pollution sources to relevant personnel.

[0060] Step S100 includes:

[0061] S101. Divide the area to be monitored into several sub-areas according to a preset minimum area; obtain the location information of the area to be monitored; for each sub-area, extract pollution source features from the corresponding location information, and determine the category of the corresponding sub-area based on the pollution source features; if there are known pollution sources in the pollution source features of the sub-area, then the sub-area is classified as the first monitoring area; if there are unknown pollution sources in the pollution source features of the sub-area, then the sub-area is classified as the second monitoring area; wherein the first monitoring area has known pollution sources, such as sewage treatment plants, factory discharge points, etc.; the pollution sources in the second monitoring area are unknown, such as natural water bodies such as rivers and lakes;

[0062] S102. For the first monitoring area, obtain the corresponding drainage network structure diagram, and identify the sewage outlets and drainage pipe intersections based on the drainage network structure diagram; obtain historical sewage discharge data for the first monitoring area, and calculate the pollutant concentration Cp for each sewage outlet based on the historical sewage discharge data, where Cp = Mp / Qp, Mp represents the total amount of pollutants discharged from the sewage outlet, and Qp represents the discharge flow rate of the sewage outlet; summarize the pollutant concentration Cp of all sewage outlets, and compare the pollutant concentration Cp of all sewage outlets with the preset pollutant concentration threshold C1 in turn, and set the pollutant concentration Cp greater than or equal to the pollutant concentration threshold C1. The sewage outlets with a pollutant concentration threshold C1 are recorded as sampling points; the pollutant concentration Cj at each drainage pipe junction is calculated, and Cj = ∑ni=1(Qi×Ci) / ∑ni=1Qi, where Qi represents the flow rate of the i-th pipe, Ci represents the pollutant concentration of the i-th pipe, and n represents the number of pipes connected at the junction; the pollutant concentration Cj of all drainage pipe junctions is summarized, and the pollutant concentration Cj of all drainage pipe junctions is compared with the preset pollutant concentration threshold C2 in turn. Drainage pipe junctions with pollutant concentration Cj greater than or equal to pollutant concentration threshold C2 are recorded as sampling points.

[0063] S103. For the second monitoring area, obtain the flow direction and confluence relationship of all tributaries in the second monitoring area, thereby identifying the confluence points in the second monitoring area, and using the confluence points as sampling points in the second monitoring area; according to the location of each confluence point and in combination with the corresponding tributary flow direction, set sampling points in segments along the tributary flow direction according to the preset minimum sampling point distance.

[0064] Step S200 includes:

[0065] S201. For the first and second monitoring areas respectively, water samples are collected sequentially according to the established water quality monitoring sampling points to obtain corresponding water quality monitoring data. Based on the monitoring area and sampling point, the collected water quality monitoring data are processed sequentially to obtain the first monitoring dataset A and the second monitoring dataset B, where A = {a1, a2, ..., au} and B = {b1, b2, ..., bv}. a1 represents the water quality monitoring data of the first sampling point in the first monitoring area, a2 represents the water quality monitoring data of the second sampling point in the first monitoring area, and so on, with au representing the water quality monitoring data of the u-th sampling point in the first monitoring area. Similarly, b1 represents the water quality monitoring data of the first sampling point in the second monitoring area, b2 represents the water quality monitoring data of the second sampling point in the second monitoring area, and bv represents the water quality monitoring data of the v-th sampling point in the second monitoring area; u and v represent the number of sampling points in the first and second monitoring areas, respectively.

[0066] S202. For the first monitoring dataset A and the second monitoring dataset B respectively, calculate the deviation index R of the water quality monitoring data between two adjacent sampling points. The corresponding calculation formula is: R=|f_k-f_k+1| / d_k, where f_k represents the water quality monitoring data of the k-th sampling point in the first monitoring dataset A or the second monitoring dataset B, f_k+1 represents the water quality monitoring data of the (k+1)-th sampling point in the first monitoring dataset A or the second monitoring dataset B, and d_k represents the distance between adjacent sampling points f_k and f_k+1 in the first monitoring dataset A or the second monitoring dataset B. Compare the deviation index R of the water quality monitoring data between two adjacent sampling points with the threshold RT. If the deviation index R is greater than the threshold RT, a new sampling point needs to be inserted between the corresponding two adjacent sampling points, and the location of the area between the two adjacent sampling points is output to the relevant personnel, who then increase the sampling point density of the current area. If the deviation index R is less than the threshold RT, adjacent sampling points need to be merged, and the location of the area between the two adjacent sampling points is output to the relevant personnel, who then decrease the sampling point density of the current area.

[0067] In this embodiment, it is assumed that for a certain water quality monitoring index in the second monitoring area, the following calculation process exists:

[0068] The first pair of sampling points: b1 and b2;

[0069] Calculate the corresponding deviation index R1 = |b1-b2| / d1 = |6.0-6.7| / 1.5 = 0.7 / 1.5 = 0.467; Compare R1 with the threshold RT = 1.5. Since 0.467 < 1.5, it is necessary to merge these two sampling points to reduce the sampling point density; the determination of RT requires specific analysis by professionals.

[0070] The second pair of sampling points: b2 and b3;

[0071] The deviation index R2 = |b2-b3| / d2 = |6.7-8.5| / 1.0 = 1.8; Comparing R2 with the threshold RT = 1.5: 1.8 > 1.51, therefore, new sampling points need to be inserted to increase the sampling point density.

[0072] Step S300 includes:

[0073] S301. For the first monitoring area, perform water quality fluorescence fingerprint analysis at the corresponding sampling points to obtain water quality fluorescence fingerprint data for each sampling point; calculate the similarity between the water quality fluorescence fingerprint data of each sampling point and the water quality fluorescence fingerprint data of known pollution sources in the database; if the similarity result is greater than or equal to the similarity threshold, mark the corresponding sampling point as a suspected pollution source.

[0074] S302. For sampling points with a similarity score less than the similarity threshold, obtain the water quality fluorescence fingerprint data of the upstream sampling points and calculate the similarity. If the similarity score is greater than or equal to the similarity threshold, the upstream sampling points are taken as target sampling points. Calculate the similarity between the target sampling point and the water quality fluorescence fingerprint data of its upstream sampling points until the similarity score is less than the similarity threshold, and mark the target sampling point as a suspected pollution source. Output the location coordinates of the suspected pollution source to relevant personnel for appropriate processing. Specifically, for sampling points with a similarity score less than the similarity threshold, it is necessary to continue tracing upstream. Therefore, the upstream sampling points are taken as target sampling points. This step essentially uses similarity calculation to gradually eliminate irrelevant sampling points until an area with water quality characteristics similar to a known pollution source is found. When the similarity score is less than the similarity threshold, it indicates that continuing to trace upstream is ineffective, and the pollution source may have already been located in the target sampling point area. Therefore, the target sampling point is marked as a suspected pollution source.

[0075] In this embodiment, source tracing water sample analysis is performed at each sampling point in the first monitoring area, and the results are compared with the existing pollution source water quality fluorescence fingerprint database. The water sample to be traced is compared with the water quality fluorescence fingerprint of known pollution sources in the database, and the suspected pollution source with the highest similarity and its similarity are given. When no similar pollution source is found in the pollution source water quality fluorescence fingerprint database, more sampling points can be added, and the water quality fluorescence fingerprint of the water sample to be traced is compared with the water quality fluorescence fingerprint of the collected pollution source water samples one by one. For example, pollution sources with a similarity of ≥60% can be regarded as suspected pollution sources. After identifying a suspected pollution source, water samples should be collected from the suspected pollution source and its surrounding area as soon as possible to confirm the pollution source. For water areas with large changes in water quality fluorescence fingerprint, water samples from relevant points should be collected as much as possible for investigation.

[0076] Step S400 includes:

[0077] S401. For each sampling point in the second monitoring area, perform water sample flow path analysis, name the sampling section of the sampling point as section X, name the sampling section of the upstream sampling point of the current sampling point as section Y, analyze the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to section X and section Y. If the similarity is greater than or equal to the similarity threshold, it indicates that the suspected pollution source may be upstream of section Y. Name the sampling section of the upstream sampling point of section Y as section Z1, analyze the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to section X and section Z1. If the similarity is greater than or equal to the similarity threshold, continue to analyze the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to the upstream sampling points of section X and section Z1 until the similarity is less than the similarity threshold, and take the sampling points corresponding to section Y and section Z1 as suspected pollution sources.

[0078] S402. Analyze the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to section X and section Z1. If the similarity is less than the similarity threshold, it indicates that the suspected pollution source may be between section Y and X. The sampling points corresponding to section X and section Y are taken as suspected pollution sources. The location coordinates of the suspected pollution source are output to relevant personnel for appropriate processing.

[0079] In this embodiment, for each sampling point within the second monitoring area, water sample flow path analysis is performed, with the water sample at the sampling point serving as the source-tracing sample. The collection section of the source-tracing sample is named section X. If the similarity of the water quality fluorescence fingerprint between the source-tracing sample and the upstream section (named section Y) is ≥60%, it indicates that the suspected pollution source may be upstream of section Y. The source-tracing sample is then compared with the water quality fluorescence fingerprint of the upstream section (named section Z1) of section Y. When the polluted river section can be identified, section Y is set at approximately the midpoint of the identified polluted river section. When the polluted river section cannot be identified, the upstream section Y is selected at approximately 1 / 2 the distance from the river source to section X. If the similarity between the source-tracing sample and the section Y is <60%, it indicates that the suspected pollution source may be between sections Y and X. Water samples were collected from a section approximately halfway between the two sections (named section Z2), and then the water quality fluorescence fingerprints of the water sample to be traced were compared with those of the water sample from section Z2.

[0080] Based on the similarity judgment results, the above method is used to continuously narrow down the investigation scope. When the investigation scope is narrowed down to about 1km, relevant personnel should go to the rainwater outlets, pollution source outlets into rivers (lakes, reservoirs) and tributary inlets into rivers (lakes, reservoirs) within the investigation scope to collect water samples, and use water quality fluorescent fingerprint comparison to locate the location where the pollution enters the river (lake, reservoir).

[0081] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0082] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of monitoring water quality pollution, characterized by: The method comprises the following steps: Step S100. Obtain the position information of the region to be monitored, extract the pollution source features from the position information, and divide the region to be monitored into a first monitoring region and a second monitoring region according to the pollution source features; based on the classification of the division of the region to be monitored, arrange the sampling points for water quality monitoring; Step S200. According to the arranged sampling points for water quality monitoring, sequentially collect water samples to obtain corresponding water quality monitoring data; perform deviation analysis on the water quality monitoring data of adjacent two sampling points, and adjust the sampling point density according to the deviation analysis result; Step S300. For the first monitoring region, perform water quality fluorescence fingerprint analysis on the water sample corresponding to the sampling point, and match it with the known pollution source water quality fluorescence fingerprint in the pollution source database; according to the matching result, identify a suspected pollution source, and send the position of the suspected pollution source to relevant personnel; Step S400. For the second monitoring region, perform water sample flow path analysis, compare the water quality fluorescence fingerprints of the section water samples of different sampling points, and gradually confirm the position of the suspected pollution source according to the comparison result, and send the position of the suspected pollution source to relevant personnel; The step S100 comprises: S101. Divide the region to be monitored according to a preset minimum region to obtain a plurality of sub-regions; obtain the position information of the region to be monitored, for each sub-region, extract the pollution source features from the corresponding position information, and determine the category of the corresponding sub-region according to the pollution source features, if there is a known pollution source in the pollution source features of the sub-region, divide this sub-region into a first monitoring region; if there is an unknown pollution source in the pollution source features of the sub-region, divide this sub-region into a second monitoring region; S102. For the first monitoring area, the corresponding sewer network structure diagram is obtained, the sewage outlet and the sewer pipeline intersection point are identified according to the sewer network structure diagram; the historical sewage discharge data of the first monitoring area is obtained, the pollutant concentration Cp of each sewage outlet is calculated in combination with the historical sewage discharge data, and Cp= Mp / Qp, wherein Mp represents the total amount of pollutants discharged from the sewage outlet, and Qp represents the discharge flow of the sewage outlet; the pollutant concentrations Cp of all sewage outlets are summarized, the pollutant concentrations Cp of all sewage outlets are compared with the preset pollutant concentration threshold C1 in turn, and the sewage outlets with the pollutant concentration Cp greater than or equal to the pollutant concentration threshold C1 are recorded as sampling points; the pollutant concentration Cj of each sewer pipeline intersection point is calculated, and wherein Qi represents the flow of the ith pipeline, Ci represents the pollutant concentration of the ith pipeline, and n represents the number of connected pipelines at the intersection point; the pollutant concentrations Cj of all sewer pipeline intersection points are summarized, the pollutant concentrations Cj of all sewer pipeline intersection points are compared with the preset pollutant concentration threshold C2 in turn, and the sewer pipeline intersection points with the pollutant concentration Cj greater than or equal to the pollutant concentration threshold C2 are recorded as sampling points; S103. For the second monitoring region, obtain the flow direction and confluence relationship of all tributaries in the second monitoring region to identify the confluence points in the second monitoring region, and take the confluence points as the sampling points of the second monitoring region; according to the position of each confluence point and combining the corresponding tributary flow direction, segmentally set the sampling points along the tributary flow direction according to a preset minimum sampling point distance; The step S200 comprises: S201. For the first monitoring region and the second monitoring region respectively, according to the arranged sampling points for water quality monitoring, sequentially collect water samples to obtain corresponding water quality monitoring data, sequentially process the collected water quality monitoring data according to the monitoring region and the sampling point to obtain a first monitoring data set A and a second monitoring data set B, and A={a1, a2,..., au}, B={b1, b2,..., bv}, a1 represents the water quality monitoring data of the first sampling point of the first monitoring region, a2 represents the water quality monitoring data of the second sampling point of the first monitoring region, and so on, and au represents the water quality monitoring data of the u-th sampling point of the first monitoring region; similarly, b1 represents the water quality monitoring data of the first sampling point of the second monitoring region, b2 represents the water quality monitoring data of the second sampling point of the second monitoring region, and bv represents the water quality monitoring data of the v-th sampling point of the second monitoring region; u and v respectively represent the number of sampling points of the first monitoring region and the second monitoring region; S202. For the first monitoring data set A and the second monitoring data set B respectively, the deviation index R of the water quality monitoring data of the adjacent two sampling points is calculated, and the corresponding calculation formula is: R = |f_k-f_k+1| / d_k, wherein f_k represents the water quality monitoring data of the kth sampling point in the first monitoring data set A or the second monitoring data set B, f_k+1 represents the water quality monitoring data of the k+1th sampling point in the first monitoring data set A or the second monitoring data set B, and d_k represents the distance between the adjacent sampling points f_k and f_k+1 in the first monitoring data set A or the second monitoring data set B; the deviation index R of the water quality monitoring data of the adjacent two sampling points is compared with the threshold value RT, if the deviation index R is greater than the threshold value RT, the region position between the adjacent two sampling points is output to the relevant personnel, and the sampling point density of the current region is increased by the relevant personnel; if the deviation index R is less than the threshold value RT, the region position between the adjacent two sampling points is output to the relevant personnel, and the sampling point density of the current region is reduced by the relevant personnel.

2. The method of claim 1, wherein: The step S300 comprises: S301. For the first monitoring region, the water quality fluorescence fingerprint analysis of the corresponding sampling point is carried out, so as to obtain the water quality fluorescence fingerprint data corresponding to each sampling point; the water quality fluorescence fingerprint data of each sampling point is subjected to similarity calculation with the water quality fluorescence fingerprint data of the known pollution source in the database, if the similarity result is greater than or equal to the similarity threshold value, the corresponding sampling point is marked as a suspected pollution source; S302. For the sampling point with the similarity less than the similarity threshold value, the water quality fluorescence fingerprint data of the sampling point upstream of the sampling point is obtained, and the similarity calculation is carried out, if the similarity is greater than or equal to the similarity threshold value, the sampling point upstream is taken as a target sampling point; the similarity between the water quality fluorescence fingerprint data of the target sampling point and the sampling point upstream thereof is calculated, until the similarity is less than the similarity threshold value, and the target sampling point is marked as a suspected pollution source; the position coordinates of the suspected pollution source are output to the relevant personnel, and the relevant personnel carry out corresponding processing.

3. The method of claim 2, wherein: The step S400 comprises: S401. For each sampling point in the second monitoring region, the water sample flow path analysis is carried out, the collection section of the sampling point is named as section X, the collection section of the upstream sampling point of the current sampling point is named as section Y, the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to the section X and the section Y is analyzed, if the similarity is greater than or equal to the similarity threshold value, the collection section of the upstream sampling point of the section Y is named as section Z1, the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to the section X and the section Z1 is analyzed, if the similarity is greater than or equal to the similarity threshold value, the similarity between the water quality fluorescence fingerprint data of the water samples corresponding to the collection section of the upstream sampling point of the section X and the section Z1 is continuously analyzed, until the similarity is less than the similarity threshold value, and the sampling points corresponding to the section Y and the section Z1 are taken as suspected pollution sources; S402. Analyzing the similarity between the water quality fluorescence fingerprint data of the water sample corresponding to section X and section Z1, if the similarity is less than the similarity threshold, it will indicate that the suspected pollution source may be between section Y and section X, and the sampling point corresponding to section X and section Y will be taken as the suspected pollution source; the position coordinates of the suspected pollution source are output to the relevant personnel for corresponding processing.

4. A water quality pollution monitoring system applied to the water quality pollution monitoring method of any one of claims 1-3, characterized in that: The system comprises a region division and sampling point layout module, a deviation analysis module, a first monitoring region analysis module, and a second monitoring region analysis module. The region division and sampling point layout module obtains the position information of the region to be monitored, extracts the pollution source features from the position information, and divides the region to be monitored into a first monitoring region and a second monitoring region according to the pollution source features; based on the classification of the division of the region to be monitored, the sampling points for water quality monitoring are laid out; The deviation analysis module sequentially collects water samples based on the laid-out sampling points for water quality monitoring, thereby obtaining corresponding water quality monitoring data; the water quality monitoring data of adjacent two sampling points are subjected to deviation analysis, and the sampling point density is adjusted according to the deviation analysis result; The first monitoring region analysis module performs water quality fluorescence fingerprint analysis on the water sample of the sampling point corresponding to the first monitoring region, and matches it with the known pollution source water quality fluorescence fingerprint in the pollution source database; according to the matching result, a suspected pollution source is identified, and the position of the suspected pollution source is sent to the relevant personnel; The second monitoring region analysis module performs water sample flow path analysis, compares the water quality fluorescence fingerprints of the water samples of different sampling points, and gradually confirms the position of the suspected pollution source according to the comparison result, and sends the position of the suspected pollution source to the relevant personnel.

5. The water quality pollution monitoring system of claim 4, wherein: The region division and sampling point layout module comprises a region division unit and a sampling point layout unit; The region division unit divides the region to be monitored into a plurality of sub-regions according to a preset minimum region, obtains the position information of each sub-region, and extracts the pollution source features; according to the pollution source features, it is judged whether there is a known pollution source in each sub-region, and it is classified as a first monitoring region or a second monitoring region; the sampling point layout unit lays out the sampling points for water quality monitoring based on the classification of the division of the region to be monitored.

6. The water quality pollution monitoring system of claim 4, wherein: The deviation analysis module comprises a data deviation calculation unit and a sampling point density adjustment unit; The data deviation calculation unit calculates the deviation index of the water quality monitoring data of adjacent two sampling points, and compares it with a preset threshold; the sampling point density adjustment unit judges whether the sampling point density needs to be increased or decreased according to the deviation analysis result; if the deviation index is greater than the set threshold, the sampling point is increased; if the deviation index is less than the set threshold, the sampling point is reduced.

7. The water quality pollution monitoring system of claim 4, wherein: The first monitoring region analysis module comprises a water quality fluorescence fingerprint analysis unit and a first pollution source identification unit; The water quality fluorescence fingerprint analysis unit performs fluorescence fingerprint analysis on the water sample of the sampling point of the first monitoring region, and obtains the corresponding water quality fluorescence fingerprint data; The pollution source identification unit matches the water quality fluorescence fingerprint data of each sampling point with the water quality fluorescence fingerprint data of known pollution sources in the database, and identifies a suspected pollution source according to a matching result, and sends the pollution source position to relevant personnel.

8. The water quality pollution monitoring system of claim 4, wherein: The second monitoring area analysis module comprises a water quality flow path analysis unit and a second pollution source identification unit; The water quality flow path analysis unit performs water sample flow path analysis for the second monitoring area; the second pollution source identification unit confirms the pollution source position step by step by comparing the water quality fluorescence fingerprints of cross-section water samples of different sampling points, and sends the pollution source position to relevant personnel.

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