A method for identifying early warnings of water pollutants in urban river water bodies in plain river networks

Abstract

The present invention discloses a method for identifying early warnings of river water pollutants in urban areas of plain river networks, including automatic water quality detection warnings at monitoring stations and AI intelligent discrimination of monitoring videos; the specific steps are as follows: Step 1: Conduct a primary warning and forecast of water quality monitoring at an automatic monitoring station of a certain monitoring section in the plain river network; Step 2: Perform preprocessing on the monitoring video images at a fixed angle at the moment of the primary warning of the automatic monitoring station, and generate the image data to be discriminated after preprocessing; Step 3: Use the density peak clustering algorithm for clustering analysis and determination, and divide the classification results into 4 categories, namely the background state of the river channel boundary, the state of floating aquatic organisms in the river channel, the abnormal state of the water color in the river channel, and the normal state of the river surface; Step 4: The system determines whether there is an instantaneous sudden high-concentration pollution event and whether to issue a system-assigned water quality warning. The present invention has the characteristics of strong practicability.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy ecological management, and specifically provides a method for identifying early warnings of water body pollutants in urban areas of plain river networks. Background Art

[0002] Ecological water conservancy is a way and means of scientific utilization of water resources, aiming to respect and maintain the ecological environment, develop water conservancy, and develop the economy to serve the sustainable development of the economic society. During the process of water body pollution, it is necessary to accurately identify the water quality, including measures such as multiple samplings, so that the water quality monitoring process can reflect the real results as much as possible.

[0003] Existing products are unable to perform secondary intelligent identification on the early warning judgment of river water body quality, which is prone to false alarms and missed reports of water quality, resulting in unsuccessful water quality early warnings and poor practicability. Therefore, it is necessary to design a method for identifying early warnings of water body pollutants in urban areas of plain river networks with strong practicability. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for identifying early warnings of water body pollutants in urban areas of plain river networks to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solution: A method for identifying early warnings of water body pollutants in urban areas of plain river networks, including automatic water quality detection and early warning of monitoring stations and AI intelligent discrimination of monitoring videos; the specific steps are as follows:

[0006] Step 1: Conduct a primary early warning forecast on the water quality of an automatic monitoring station at a certain monitoring section of the plain river network.

[0007] Step 2: Perform preprocessing on the monitoring video images at a fixed angle at the moment of the primary early warning of the automatic monitoring station, and generate the image data to be discriminated after preprocessing.

[0008] Step 3: Use the density peak clustering algorithm for clustering analysis and determination, and divide the classification results into 4 categories, namely the background state of the river channel boundary, the state of floating aquatic organisms in the river channel, the abnormal state of the river water color, and the normal state of the river surface.

[0009] Step 4: The system determines whether there is an instantaneous sudden high-concentration pollution event and whether to issue a system-assigned water quality early warning.

[0010] Step 5: Construct a monitoring network centered on the automatic monitoring station of the first water quality early warning and surrounded by adjacent automatic monitoring

[0011] stations, and use the water body pollutant tracing method to predict the possible spatio-temporal orientation and emission amount of pollutants.

[0012] Step 6: The system determines whether there is a continuous low-concentration pollution event and whether to issue a water quality warning or equipment warning through system dispatch.

[0013] According to the above technical solution, the automatic water quality detection and warning of the monitoring station uses the density peak clustering algorithm to perform clustering analysis and determination on the monitoring video image data at a fixed angle, and the classification results are divided into 4 categories;

[0014] The specific working method of the AI intelligent discrimination of the monitoring video is that, combined with the water quality detection and warning conditions of the automatic monitoring station, the AI intelligent discrimination of the monitoring video is for the state of floating aquatic organisms in the river or the abnormal state of the river water color, that is, the system dispatches a water quality alarm and manual on-site rapid handling;

[0015] The four specific classifications of the classification results are: the background state of the river boundary, the state of floating aquatic organisms in the river, the abnormal state of the river water color, and the normal state of the river surface.

[0016] According to the above technical solution, the specific steps of the AI intelligent discrimination of the monitoring video also include that for continuous low-concentration pollution events, since the change in the river water body state is not significant and the change in the state of aquatic organisms or the river water color and transparency is not clear, that is, the AI intelligent discrimination of the monitoring video cannot synchronously determine whether the automatic water quality detection and warning of the monitoring station is correct. Therefore, a method of tracing the source of water pollutants is adopted for secondary determination and warning.

[0017] According to the above technical solution, the method of using the water pollutant source tracing method for secondary determination and warning includes:

[0018]

[0019] Among them, x refers to the distance of the pollution source from the automatic station along the river flow direction, with the unit of m;

[0020] y refers to the distance of the pollution source from the automatic station along the river transverse direction, with the unit of m;

[0021] z refers to the distance of the pollution source from the automatic station along the river vertical direction, with the unit of m;

[0022] t refers to time, with the unit of s;

[0023] u refers to the water flow velocity along the river flow direction, with the unit of m / s;

[0024] v refers to the water flow velocity along the river transverse direction, with the unit of m / s;

[0025] w refers to the water flow velocity along the river vertical direction, with the unit of m / s;

[0026] C refers to the concentration of a certain pollutant in the river water body, with the unit of mg / L;

[0027] Dx refers to the dispersion coefficient along the upstream direction of the river, with the unit of m2 / s;

[0028] Dy refers to the dispersion coefficient along the transverse direction of the river, with the unit of m2 / s;

[0029] Dz refers to the dispersion coefficient along the vertical direction of the river, with the unit of m2 / s;

[0030] k refers to the degradation coefficient corresponding to a certain pollutant in the river water body, with the unit of 1 / s.

[0031] According to the above technical solution, the method for secondary determination and early warning of the water body pollutant tracing method further includes: for the general conditions of rivers in the plain river network urban area, z ≤ 4m, so the pollutant migration concentration condition in the vertical direction of the river is considered less, and the formula of this method is simplified to:

[0032]

[0033] According to the above technical solution, the method for secondary determination and early warning of the water body pollutant tracing method further includes: assuming that the origin (0, 0) is the coordinate of the continuous low-concentration pollution source, at t = 0, the corresponding continuous flow rate is Q, and the continuous pollutant concentration is C0; then when reaching the position of the automatic monitoring station (x, y), the pollutant monitoring and early warning concentration is C(x, y, t), and we can get:

[0034]

[0035] Among them, the constant

[0036]

[0037] According to the above technical solution, the derivative of the above concentration with respect to time is obtained as:

[0038]

[0039]

[0040]

[0041] After arrangement, we get

[0042]

[0043]

[0044] For The difference method is used to replace the fitting solution.

[0045] According to the above technical solution, the method for secondary determination and early warning of the water body pollutant tracing method further includes: considering that in the rivers of the plain river network urban area, for the river section adjacent to the automatic monitoring station in the upstream direction, x≥3 - 5 km, and the deviation of the corresponding flow velocity x between the two stations is not large under normal conditions, the average value obtained during the monitoring and early warning of the automatic monitoring station within the selected time interval can be used for substitution.

[0046] According to the above technical solution, at the same time, the general river width y≤100 m, and the corresponding flow velocity v is defaulted to be in a uniform state within the time interval, and the value obtained during the monitoring and early warning of the corresponding automatic monitoring station is read. Dx, Dy, and k are all corresponding pollutant constants. Then, the approximate coordinates of the continuous low-concentration pollution source, as well as the corresponding emission duration and emission quantity Q, can be traced and inversely deduced.

[0047] Combined with the water quality monitoring and early warning of the automatic monitoring station and the early warning conditions of the water body pollutant tracing method of the adjacent automatic monitoring station, that is, when the system dispatches a water quality alarm, manual on-site rapid handling is carried out;

[0048] If the water quality of the automatic monitoring station is monitored and early warned, but the AI intelligent discrimination of the monitoring video cannot identify it, and the water body pollutant tracing method of the adjacent automatic monitoring station does not give an early warning, that is, the system determines that there is a single false alarm in the automatic monitoring result and records it on file; if the false alarms are continuously accumulated or exceed 3 times within the monitoring time interval of 2 hours, that is, the system determines that there is a failure in the automatic monitoring equipment, and dispatches an equipment alarm, and manual on-site handling is carried out within the day.

[0049] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention realizes the secondary confirmation of the early warning of river water body pollutants in the plain river network urban area, making it not restricted by factors such as monitoring time and monitoring instrument disturbance for discrimination. The present invention solves the problem of excessive human input cost caused by false alarms in the water quality monitoring and early warning of automatic monitoring stations under the current plain river network. Without a large amount of cost, the operation efficiency of automatic monitoring stations under the plain river network is greatly improved. Description of the Drawings

[0050] The drawings are used to provide further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0051] Figure 1 It is a flowchart of a method for tracing and managing sudden water body pollutants and response in a plain river network disclosed by the present invention;

[0052] Figure 2 It is a framework diagram of a method for tracing and managing sudden water body pollutants and response in a plain river network disclosed by the present invention. Detailed Embodiments

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] Please refer to Figure 1 - Figure 2 , the present invention provides a technical solution: an identification method for early warning of river water pollutants in urban areas of plain river networks, including automatic water quality detection early warning of monitoring stations and AI intelligent discrimination of monitoring videos; the specific steps are as follows:

[0055] Step 1: Conduct a primary early warning and forecast of the water quality of an automatic monitoring station at a certain monitoring section of the plain river network.

[0056] Step 2: Perform preprocessing on the monitoring video images at a fixed angle at the moment of the primary early warning of the automatic monitoring station, and generate the image data to be discriminated after preprocessing.

[0057] Step 3: Use the density peak clustering algorithm for clustering analysis and determination, and divide the classification results into 4 categories, namely the background state of the river channel boundary, the state of floating aquatic organisms in the river channel, the abnormal state of the river water color, and the normal state of the river surface.

[0058] Step 4: The system determines whether there is an instantaneous sudden high-concentration pollution event and whether to issue a system dispatch water quality early warning.

[0059] Step 5: Build a monitoring network centered on the automatic monitoring station of the first water quality early warning, surrounded by adjacent automatic monitoring

[0060] stations, and use the method of tracing the source of water pollutants to predict the possible spatial and temporal positions and emissions of pollutants.

[0061] Step 6: The system determines whether there is a continuous low-concentration pollution event and whether to issue a system dispatch water quality early warning and equipment early warning.

[0062] The automatic water quality detection early warning of the monitoring station uses the density peak clustering algorithm to perform clustering analysis and determination on the monitoring video image data at a fixed angle, and divides the classification results into 4 categories;

[0063] The specific working method of the AI intelligent discrimination of the monitoring video is that, combined with the water quality detection early warning conditions of the automatic monitoring station, when the AI intelligent discrimination of the monitoring video is the state of floating aquatic organisms in the river channel or the abnormal state of the river water color, the system will dispatch a water quality alarm and manual on-site rapid processing;

[0064] The classification results are defined as four categories, specifically: the background state of the river channel boundary, the state of floating aquatic organisms in the river channel, the abnormal state of the river water color, and the normal state of the river surface;

[0065] The specific steps of AI intelligent discrimination of monitoring videos also include, for continuous low-concentration pollution events, since the change in the river water body state is not significant and the change in the state of aquatic organisms or the water color and transparency of the river is not clear, that is, the AI intelligent discrimination of monitoring videos cannot simultaneously determine whether the automatic water quality detection and early warning at the monitoring station is correct. Therefore, a method of tracing the source of water body pollutants is adopted for secondary determination and early warning;

[0066] The method of tracing the source of water body pollutants for secondary determination and early warning includes:

[0067]

[0068] Among them, x refers to the distance of the pollution source from the automatic station along the river flow direction, with the unit of m;

[0069] y refers to the distance of the pollution source from the automatic station along the river transverse direction, with the unit of m;

[0070] z refers to the distance of the pollution source from the automatic station along the river vertical direction, with the unit of m;

[0071] t refers to time, with the unit of s;

[0072] u refers to the water flow velocity along the river flow direction, with the unit of m / s;

[0073] v refers to the water flow velocity along the river transverse direction, with the unit of m / s;

[0074] w refers to the water flow velocity along the river vertical direction, with the unit of m / s;

[0075] C refers to the concentration of a certain pollutant in the river water body, with the unit of mg / L;

[0076] Dx refers to the dispersion coefficient along the river flow direction, with the unit of m2 / s;

[0077] Dy refers to the dispersion coefficient along the river transverse direction, with the unit of m2 / s;

[0078] Dz refers to the dispersion coefficient along the river vertical direction, with the unit of m2 / s;

[0079] k refers to the degradation coefficient corresponding to a certain pollutant in the river water body, with the unit of 1 / s;

[0080] The method of tracing the source of water body pollutants for secondary determination and early warning also includes: for the general situation of rivers in the plain river network urban area, z ≤ 4m, so the pollutant migration concentration situation in the river vertical direction is weakly considered, and the formula of this method is simplified to:

[0081]

[0082] The method for secondary determination and early warning of the water pollutant tracing method also includes: assuming that the origin (0, 0) is the coordinate of the continuous low-concentration pollution source. At t = 0, the corresponding continuous flow rate is Q, and the continuous pollutant concentration is C0. Then, when reaching the position of the automatic monitoring station (x, y), the pollutant monitoring and early warning concentration is C(x, y, t), and we can get:

[0083]

[0084] Among them, the constant

[0085]

[0086] Performing derivative processing of the above concentration with respect to time, we get:

[0087]

[0088]

[0089]

[0090] After arrangement, we get

[0091]

[0092]

[0093] For The difference method is used to replace fitting for solution;

[0094] The method for secondary determination and early warning of the water pollutant tracing method also includes: considering the rivers in the plain river network urban area, adjacent to the automatic monitoring station, in the river flow direction, x≥3 - 5 km, and the deviation of the corresponding flow velocity x between the two stations is not large under normal conditions. The average value obtained during the monitoring and early warning of the automatic monitoring station within the selected time interval can be used for substitution;

[0095] At the same time, the general river width y≤100 m, and the corresponding flow velocity v is defaulted to be in a uniform state within the time interval, and the value obtained during the monitoring and early warning of the corresponding automatic monitoring station is read. Dx, Dy, and k are all corresponding pollutant constants. Then, the approximate coordinates of the continuous low-concentration pollution source, as well as the corresponding emission duration and emission volume Q, can be traced and deduced.

[0096] Combined with the water quality monitoring and early warning of the automatic monitoring station and the early warning conditions of the water pollutant tracing method of the adjacent automatic monitoring station, that is, when the system dispatches a water quality alarm, it is quickly processed manually on-site;

[0097] If the automatic monitoring station issues a water quality monitoring warning, but the surveillance video AI intelligent judgment cannot identify it, and the water pollutant tracing method of the nearby automatic monitoring station does not issue an early warning, the system will determine that there is a single false alarm in the automatic monitoring result and record it on file; if the false alarms accumulate continuously or exceed 3 times within the monitoring time interval of 2 hours, the system will determine that there is a fault in the automatic monitoring equipment and dispatch a device alarm, and manual on-site processing will be carried out within the same day;

[0098] Example 1:

[0099] 1. When a water quality monitoring indicator at an automatic monitoring station on the Pingyuan River exceeds the preset upper limit of the assessment monitoring section, an early warning forecast signal is triggered;

[0100] 2. For the automatic monitoring station that generates a warning signal, the system connects the fixed-angle monitoring video deployed at the automatic monitoring station and selects retrospective video screenshots at intervals of 5 minutes starting from the warning moment for image preprocessing;

[0101] 3. The system uses a density peak clustering algorithm to perform cluster analysis on the above video screenshots. AI intelligent segmentation is used to first determine the background state of the river boundary to ensure that the monitoring video is in normal operation at a fixed angle. It then further determines whether the river in the monitoring section is in the following three categories: floating aquatic organisms in the river, abnormal water color in the river, and normal river surface state;

[0102] 4. If the system determines that the river in the monitored section is in a state of floating aquatic organisms, the river water color is abnormal, or both, the system will assume that an instantaneous sudden high-concentration pollution event has occurred in the river in the monitored section, automatically triggering a secondary water quality warning, and reporting the warning information to the river chief and the superior management unit of the river section by phone / text message / APP, requesting manual on-site rapid processing; if the system determines that the river in the monitored section is in a normal state, the system will construct a monitoring network centered on the automatic monitoring station of the first water quality warning and surrounded by nearby automatic monitoring stations, repeating the above steps 2-4 to confirm and determine whether an instantaneous sudden high-concentration pollution event has occurred;

[0103] 5. If the system determines that the river surface in the monitored section is normal, it will use the water pollutant source tracing method to estimate the temporal and spatial location of possible pollutants and their discharge amount. At the same time, it will assume that it is a continuous low-concentration pollution source, and use this as the origin to calculate the pollution concentration impact value and possible spread time of other nearby automatic monitoring stations.

[0104] 6. If the system calculates that the pollutant concentration of the adjacent automatic monitoring station approaches or exceeds the actual monitoring concentration within the preset impact time range, that is, the system confirms and determines that a continuous low-concentration pollution event has occurred, it will automatically trigger a secondary water quality warning, and report the warning information of the pollutant emission and spatial information to the river chief of this river section and the superior management unit through telephone / text message / APP, etc., and request rapid on-site manual handling; if the system calculates that the pollutant concentration of the adjacent automatic monitoring station is continuously stable and less than the actual monitoring concentration within the preset impact time range, and the primary water quality warning of the automatic monitoring station has not been eliminated continuously (false alarms accumulate continuously or exceed 3 times within the monitoring time interval of 2 hours), that is, the system confirms and determines that there is a fault in the automatic monitoring station equipment, it will automatically trigger an automatic monitoring equipment warning, and report it to the river chief of this river section and the operation and maintenance staff through telephone / text message / APP, etc., and request on-site manual handling within the day; if the system calculates that the pollutant concentration of the adjacent automatic monitoring station is continuously stable and less than the actual monitoring concentration within the preset impact time range, and the primary water quality warning of the automatic monitoring station is eliminated independently, that is, the system confirms and determines that there is a false alarm in the automatic monitoring station equipment, only record and file it for handling.

[0105] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0106] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used 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 perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for identifying early warnings of water pollutants in urban river water bodies in plain river networks, characterized in that: It includes automatic water quality detection and early warning of monitoring sites and AI intelligent discrimination of monitoring videos; the specific steps are as follows: Step 1: Conduct a primary early warning and prediction of the water quality of an automatic monitoring site at a certain monitoring section of the plain river network; Step 2: Perform preprocessing on the monitoring video images at a fixed angle at the moment of the primary early warning of the automatic monitoring site, and generate the image data to be discriminated after preprocessing; Step 3: Use the density peak clustering algorithm for clustering analysis and determination, and divide the classification results into 4 categories, namely the background state of the river channel boundary, the state of floating aquatic organisms in the river channel, the state of abnormal water color in the river channel, and the normal state of the river surface; Step 4: The system determines whether there is an instantaneous sudden high-concentration pollution event and whether to issue a system-assigned water quality early warning; Step 5: Construct a monitoring network centered on the automatic monitoring site with the first water quality early warning and surrounded by adjacent automatic monitoring sites, and use the method of tracing the water body pollutants to predict the possible spatial and temporal positions and emissions of the pollutants; Step 6: The system determines whether there is a continuous low-concentration pollution event and whether to issue a system-assigned water quality early warning and equipment early warning; The specific steps of the AI intelligent discrimination of the monitoring video also include that for continuous low-concentration pollution events, since the change of the river water body state is not significant and the change of the state of aquatic organisms or the water color and transparency of the river is not clear, that is, the AI intelligent discrimination of the monitoring video cannot synchronously determine whether the automatic water quality detection early warning of the monitoring site is correct, so the method of tracing the water body pollutants is adopted for secondary determination and early warning; The method of using the water body pollutant tracing method for secondary determination and early warning includes: ； Among them, x refers to the distance of the pollution source from the automatic site along the river flow direction, with the unit of m; y refers to the distance of the pollution source from the automatic site along the river transverse direction, with the unit of m; z refers to the distance of the pollution source from the automatic site along the river vertical direction, with the unit of m; t refers to time, with the unit of s; u refers to the water flow velocity along the river flow direction, with the unit of m / s; v refers to the water flow velocity along the river transverse direction, with the unit of m / s; w refers to the water flow velocity along the river vertical direction, with the unit of m / s; C refers to the concentration of a certain pollutant in the river water body, with the unit of mg / L; Dx refers to the dispersion coefficient along the river flow direction, with the unit of m2 / s; Dy refers to the dispersion coefficient along the river transverse direction, with the unit of m2 / s; Dz refers to the dispersion coefficient along the river vertical direction, with the unit of m2 / s; k refers to the degradation coefficient corresponding to a certain pollutant in the river water body, with the unit of 1 / s.

2. An identification method for warning of water body pollutants in urban area river channels in the plain river network according to claim 1, characterized in that: The automatic water quality detection early warning of the monitoring site uses the density peak clustering algorithm to conduct clustering analysis and determination on the monitoring video image data at a fixed angle, and divides the classification results into 4 categories; The specific working method of the AI intelligent discrimination of the monitoring video is that, combined with the water quality detection early warning conditions of the automatic monitoring site, when the AI intelligent discrimination of the monitoring video is the state of floating aquatic organisms in the river channel or the state of abnormal water color in the river channel, that is, the system issues a water quality alarm and manual on-site rapid processing; The classification results are defined as four categories, specifically: the background state of the river channel boundary, the state of floating aquatic organisms in the river channel, the abnormal state of the river water color, and the normal state of the river surface.

3. The identification method for early warning of river water pollutants in urban areas of plain river networks according to claim 2, characterized in that: The method for secondary determination and early warning of the water pollutant tracing method further includes: for the general situation of rivers in the plain river network urban area, z ≤ 4m, so the pollutant migration concentration situation in the vertical direction of the river is weakly considered, and the formula of this method is simplified as: 。 4. A method for identifying early warnings of river water pollutants in urban areas of plain river networks according to claim 3, characterized in that: The method for secondary determination and early warning of the water pollutant tracing method further includes: assuming that the origin (0, 0) is the coordinate of the continuous low-concentration pollution source. At t = 0, the corresponding continuous flow rate is Q, and the continuous pollutant concentration is C0; then when reaching the position of the automatic monitoring station (x, y), the pollutant monitoring and early warning concentration is C(x, y, t), and we can get: ； Among them, 。 5. The identification method for early warning of river water pollutants in urban areas of plain river networks according to claim 4, characterized in that: The derivative of the above concentration with respect to time is obtained as: ； After arrangement, we get For , , , the difference method is used to replace the fitting solution.

6. The identification method for early warning of river water pollutants in urban areas of plain river networks according to claim 5, characterized in that: The method for secondary determination and early warning of the water pollutant tracing method further includes: considering the rivers in the plain river network urban area, for the river flow direction adjacent to the automatic monitoring station, x ≥ 3 - 5 km, and the deviation of the corresponding flow velocity x between two stations is not large under normal conditions, the average value obtained during the monitoring and early warning of the automatic monitoring station within the selected time interval can be used for substitution; At the same time, the general river width y ≤ 100m, and the corresponding flow velocity v is defaulted to be in a uniform state within the time interval. Read the values obtained during the monitoring and early warning of the corresponding automatic monitoring station. Dx, Dy, and k are all corresponding pollutant constants; then the approximate coordinates of the continuous low-concentration pollution source, as well as the corresponding emission duration and emission volume Q, can be traced and deduced; combined with the water quality monitoring and early warning of the automatic monitoring station and the early warning conditions of the water pollutant tracing method of the adjacent automatic monitoring station, that is, the system dispatches a water quality alarm, and manual on-site rapid processing is carried out; If the water quality of the automatic monitoring station is monitored and early warned, but the AI intelligent discrimination of the monitoring video cannot identify it, and the water pollutant tracing method of the adjacent automatic monitoring station does not give an early warning, that is, the system determines that there is a single false alarm in the automatic monitoring result and records it on file; if the false alarms are continuously accumulated or exceed 3 times within the monitoring time interval of 2h, that is, the system determines that there is a fault in the automatic monitoring equipment, and dispatches an equipment alarm, and manual on-site processing is carried out within the day.

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