A water pollution source tracing monitoring method and monitoring platform based on water balance

Abstract

The present invention discloses a water pollution source tracing monitoring method and monitoring platform based on water balance. By setting a flow meter in the maintenance well of the rainwater / sewage channel, relying on the GIS system to divide the detection area with the flow meter as the reference point, using the sliding window algorithm to screen the stable time period of rainwater flow, based on the flow meter data in this period, the total sewage inflow flow rate of the area and the actual sewage / rainwater total flow rate are calculated, and the balance value is used to judge whether the area is normal. In the event of an abnormality, the abnormal judgment value is used to distinguish the type of mixed connection or leakage, and a coincidence control with flow as the diameter is generated on the monitoring platform to intuitively display the pollution load difference. The redundant configuration of the flow meter is also used to ensure data continuity. The abnormal diffusion path prediction map and thermal map are generated by the wavelet transform algorithm to assist in precise positioning. The monitoring platform is composed of a readable storage medium and a processor. The execution of the above method realizes accurate monitoring of sewage seepage, intelligent classification of abnormalities and visual early warning, thereby improving operation and maintenance efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of water pollution source tracing monitoring, and in particular to a water pollution source tracing monitoring method and a monitoring platform based on water balance. Background Art

[0002] As an important part of urban infrastructure, the operating status of pipelines directly affects water environment safety and public health. Traditional sewage pipe network seepage monitoring mainly relies on manual inspections and regular testing, which has the defects of low efficiency and high missed detection rate. Although existing automated monitoring technologies have been gradually applied, the core pain point of pollutant tracing has not been effectively solved - the pollutant composition in urban sewage is complex and has little regional difference. The method of identifying pollution sources through water quality fingerprint characteristics such as full spectrum and fluorescence spectrum is difficult to distinguish the pollution characteristics of different regions or points, resulting in the failure of direct tracing. In addition, existing technologies cannot adapt to the characteristics of flow fluctuations, lack the ability to accurately distinguish abnormal types (such as leakage and mixing) and visual diffusion path prediction, and are difficult to support the needs of refined management and control. Therefore, there is an urgent need for an intelligent monitoring solution that can achieve pollution tracing through indirect flow analysis. Summary of the Invention

[0003] The purpose of the present invention is to provide a seepage water pollution monitoring method and monitoring platform based on big data analysis, especially for the problem that it is difficult to directly trace the pollution source through water quality characteristics in complex urban environments. Through side flow analysis, it is possible to accurately locate the pollution abnormality (leakage or mixing) area, realize the pollution path tracing, and solve the above-mentioned problem.

[0004] In order to solve the above technical problems, the present invention adopts the following solutions:

[0005] A water pollution source tracing monitoring method based on water balance includes a rainwater channel and a sewage channel, and rainwater flow meters and sewage flow meters are set at the maintenance wells of the rainwater channel and sewage channel. The rainwater channel or sewage channel is divided into detection areas according to the rainwater branch channel or sewage branch channel between the two flow meters. The monitoring platform is set with a balance judgment interval and an abnormality type judgment interval, and is connected to the total water consumption historical data of each community in the detection area, the historical data of the rainwater flow meter, and the historical data of the sewage flow meter. The specific steps are as follows:

[0006] S1. Obtain historical data from the rainwater flow meter in the current detection area and use a sliding window to find a stable time period with stable flow in the rainwater channel;

[0007] S2. Obtain the historical data of the total daily water consumption of each community in the current detection area during the stable period and add them together to obtain the total sewage inflow flow rate in the area. At the same time, obtain the historical data of the rainwater flow meter and the sewage flow meter corresponding to the stable period in the detection area and add them together to calculate the total sewage flow rate and the total rainwater flow rate of the flow meter;

[0008] S3. Subtract the total sewage flow and rainwater flow of the flow meter corresponding to the current detection area from the total sewage flow and the total rainwater flow of the flow meter corresponding to the previous detection area to obtain the actual total sewage flow and the actual total rainwater flow of the current detection area;

[0009] S4. Compare the actual total sewage flow rate with the total regional sewage inflow flow rate to obtain a balance quotient, and determine whether the balance value is within the preset balance determination value range. If so, it indicates that the detection area is normal and proceed to S6; otherwise, proceed to S5;

[0010] S5. Subtract the actual total sewage flow from the total regional sewage inflow, and then subtract the actual total rainwater flow to obtain the abnormality judgment value. Determine whether the abnormality judgment value is within the abnormality type judgment interval. If it is, the abnormality type is mixed connection and the process proceeds to S7. If not, the abnormality type is leakage and the process proceeds to S8.

[0011] S6. The monitoring platform generates a control page associated with the current detection area, and automatically generates two colorless controls on the control page based on the total sewage inflow flow rate and the actual total sewage flow rate of the current detection area as the diameter, overlaps them, and adds green to the controls to end the detection.

[0012] S7. The monitoring platform generates a control page associated with the current detection area, and automatically generates two colorless controls on the control page based on the total sewage inflow flow rate and the actual total sewage flow rate of the current detection area as the diameter, overlaps them, and adds yellow to the controls to end the detection.

[0013] S8. The monitoring platform generates a control page associated with the current detection area, and automatically generates two colorless controls on the control page based on the total sewage inflow flow of the current detection area and the actual total sewage flow as the diameter, overlaps them, and adds red to the controls to end the detection.

[0014] Further,

[0015] The division of the detection area is specifically as follows:

[0016] The monitoring platform relies on the GIS system to divide the residential area connected to the sewage branch channel connected to the sewage channel between the two reference points into detection areas based on the maintenance well where the sewage channel or rainwater channel passes or the rainwater flow meter or sewage flow meter at the maintenance well as the reference point, and the detection area of ​​the latter reference point is based on the direction of the sewage channel.

[0017] Further,

[0018] The stable time period identification step includes:

[0019] S20. Set the sliding window size to 24 hours and calculate the traffic mean of N sliding windows;

[0020] S21. When the internal flow rate of each sliding window in N consecutive sliding windows is greater than 95% of the flow average and less than 105% of the flow average, the time period corresponding to the N sliding windows is determined to be a flow stable time period.

[0021] Further,

[0022] The flow meter adopts a redundant configuration scheme: a main flow meter and a backup flow meter are set in parallel at the maintenance wells of the sewage channel and the rainwater channel, and when the data of the main flow meter is abnormal, it automatically switches to the backup flow meter.

[0023] Further,

[0024] The step S5 further includes:

[0025] S51. When the current detection area is determined to be abnormal, the monitoring platform automatically retrieves the flow meter data of the area for the previous 72 hours and generates a flow change trend curve;

[0026] S52. Identify mutation points in the trend curve using a wavelet transform algorithm and generate an abnormal diffusion path prediction map based on the spatial coordinates of the maintenance well;

[0027] S53. The probability distribution of abnormal diffusion is displayed in the form of a heat map on the monitoring platform interface, and areas with probability values ​​≥ 90% are automatically marked as key inspection areas.

[0028] Further,

[0029] The preset value interval for balance judgment is [0.9, 1], and the preset value interval for balance judgment can be adjusted according to actual conditions.

[0030] Further,

[0031] The abnormality type judgment interval is (0, 10], and the abnormality type judgment interval can be adjusted according to actual conditions.

[0032] Further,

[0033] The two colorless controls on the pages have the same shape and coincide with the center point of the controls. The colorless control with the actual total sewage flow as the diameter can block the colorless control with the regional total sewage inflow flow as the diameter. The difference between the two colorless controls represents the pollution load difference. When the two colorless controls indicate that the detection area is normal, the colorless control with the regional total sewage inflow flow as the diameter blocks the colorless control with the actual total sewage flow as the diameter.

[0034] Further,

[0035] A water pollution source tracing monitoring platform based on water balance includes a readable storage medium and a processor. The computer program in the readable storage medium is executed by the processor to implement the above-mentioned water pollution source tracing monitoring method based on water balance.

[0036] The present invention has the beneficial effects:

[0037] 1. A sliding window algorithm was used to screen stable time periods, eliminating rainfall interference and ensuring the reliability of basic data. Based on stable period data, a multi-layered calculation logic was constructed: "total regional sewage inflow - actual total sewage flow - lost sewage flow." This enabled accurate classification and identification of two typical issues: leakage and mixing, avoiding subjective errors often associated with manual judgment.

[0038] 2. An overlap control is generated using the inflow flow rate and the actual total sewage flow rate as the diameter. The flow difference and pollution type are intuitively displayed through color coding (green for normal, yellow for mixed connections, and red for leakage) and occlusion relationships. Operators can quickly determine the type and severity of pollution anomalies and identify problem areas by first observing the color and then the ring width. This transforms the complex process of flow data analysis and pollution source tracing into an intuitive and interactive visualization, significantly lowering the technical threshold and improving the efficiency of pollution incident response and investigation decision-making.

[0039] 3. To address the pain point of high similarity in urban sewage characteristic maps, making it difficult to accurately trace the source directly through water quality testing, this invention uses lateral flow balance analysis (total regional domestic water inflow - actual total sewage flow - actual total rainwater flow) to indirectly identify pollution anomalies (leakage or mixing) and accurately locate problem areas, achieving effective tracing of pollution paths and sources in complex urban environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a logic flow chart of the present invention;

[0041] Figure 2 This is a schematic diagram of the scenario of the present invention;

[0042] Figure 3 Select a graph for the stable time period in the present invention;

[0043] Figure 4 A schematic diagram of the three states of the dual control. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below with reference to the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0045] A water pollution source tracing monitoring method based on water balance includes a rainwater channel and a sewage channel, and rainwater flow meters and sewage flow meters are set at the maintenance wells of the rainwater channel and sewage channel. The rainwater channel or sewage channel is divided into detection areas according to the rainwater branch channel or sewage branch channel between the two flow meters. The monitoring platform is set with a balance judgment interval and an abnormality type judgment interval, and is connected to the total water consumption historical data of each community in the detection area, the historical data of the rainwater flow meter, and the historical data of the sewage flow meter. The specific steps are as follows:

[0046] S1. Obtain historical data from the rainwater flow meter in the current detection area and use a sliding window to find a stable time period with stable flow in the rainwater channel;

[0047] S2. Obtain the historical data of the total daily water consumption of each community in the current detection area during the stable period and add them together to obtain the total sewage inflow flow rate in the area. At the same time, obtain the historical data of the rainwater flow meter and the sewage flow meter corresponding to the stable period in the detection area and add them together to calculate the total sewage flow rate and the total rainwater flow rate of the flow meter;

[0048] S3. Subtract the total sewage flow and rainwater flow of the flow meter corresponding to the current detection area from the total sewage flow and the total rainwater flow of the flow meter corresponding to the previous detection area to obtain the actual total sewage flow and the actual total rainwater flow of the current detection area;

[0049] S4. Compare the actual total sewage flow rate with the total regional sewage inflow flow rate to obtain a balance quotient, and determine whether the balance value is within the preset balance determination value range. If so, it indicates that the detection area is normal and proceed to S6; otherwise, proceed to S5;

[0050] S5. Subtract the actual total sewage flow from the total regional sewage inflow, and then subtract the actual total rainwater flow to obtain the abnormality judgment value. Determine whether the abnormality judgment value is within the abnormality type judgment interval. If it is, the abnormality type is mixed connection and the process proceeds to S7. If not, the abnormality type is leakage and the process proceeds to S8.

[0051] S6. The monitoring platform generates a control page associated with the current detection area, and automatically generates two colorless controls on the control page based on the total sewage inflow flow of the current detection area and the actual total sewage flow as the diameter, and overlaps them, and adds green color to the controls to end the detection.

[0052] S7. The monitoring platform generates a control page associated with the current detection area, and automatically generates two colorless controls on the control page based on the total sewage inflow flow of the current detection area and the actual total sewage flow as the diameter, and overlaps them, and adds yellow color to the controls to end the detection.

[0053] S8. The monitoring platform generates a control page associated with the current detection area, and automatically generates two colorless controls on the control page based on the total sewage inflow flow rate and the actual total sewage flow rate of the current detection area as the diameter, overlaps them, and adds red color to the controls to end the detection.

[0054] The added color can be replaced with other colors.

[0055] Further,

[0056] The division of the detection area is specifically as follows:

[0057] The monitoring platform relies on the GIS system to divide the residential area connected to the sewage branch channel connected to the sewage channel between the two reference points into detection areas based on the maintenance well where the sewage channel or rainwater channel passes or the rainwater flow meter or sewage flow meter at the maintenance well as the reference point, and the detection area of ​​the latter reference point is based on the direction of the sewage channel.

[0058] The GIS system divides the inspection area based on the maintenance well, precisely aligning monitoring units with the physical structure of the channel, facilitating rapid location of problem areas. Dividing the area by channel orientation allows for an orderly connection of monitoring areas, avoiding omissions or overlaps, and improving monitoring efficiency and refined management.

[0059] In one embodiment, a residential sewage channel passes through maintenance wells A, B, and C in sequence. The monitoring platform uses A and B as base points and divides the residential area connected by the sewage branch channel between these two base points into one detection area. Then, using B and C as base points, the corresponding area is divided into the next detection area. If flow anomalies occur in a detection area, the cells within that area can be quickly identified, narrowing the scope of investigation to, for example, look for leaks or mixed connections at the junctions of the sewage branch channels within the area.

[0060] Further,

[0061] The stable time period identification step includes:

[0062] S20. Set the sliding window size to 24 hours and calculate the traffic mean of N sliding windows;

[0063] S21. When the internal flow rate of each sliding window in N consecutive sliding windows is greater than 95% of the flow average and less than 105% of the flow average, the time period corresponding to the N sliding windows is determined to be a flow stable time period.

[0064] By setting a 24-hour sliding window and calculating the flow mean, combined with the flow fluctuation range of N consecutive windows (95%~105%), it is possible to effectively identify time periods with stable flow, eliminate interference from sudden flow, ensure the accuracy of subsequent analysis, adapt to changes in different seasons or water use habits, and improve monitoring reliability.

[0065] In one example, suppose a sewage channel in a certain area experiences significant flow fluctuations during the rainy season. If the system uses a sliding window analysis to find that the mean flow rate for three consecutive days (N=3) is stable within a ±5% range, then that period is considered stable. The flow data during this period is used to calculate runoff, preventing outliers caused by rainfall from influencing the calculation.

[0066] Further,

[0067] The flow meter adopts a redundant configuration scheme: a main flow meter and a backup flow meter are set in parallel at the maintenance wells of the sewage channel and the rainwater channel, and when the data of the main flow meter is abnormal, it automatically switches to the backup flow meter.

[0068] Main and backup flow meters are set up in parallel at the maintenance well. When the data of the main flow meter is abnormal due to failure, blockage, etc., it will automatically switch to the backup flow meter to ensure the continuity of flow data collection, avoid monitoring interruption due to equipment failure, ensure the reliability and stability of the monitoring system, and provide complete data support for subsequent data analysis.

[0069] In one embodiment, a sensor failure caused data jumps on the primary flowmeter in a maintenance well's sewage channel. Upon detecting the abnormal data from the primary flowmeter, the system automatically switched to the backup flowmeter. The backup flowmeter continued to collect flow data. For example, during the primary flowmeter failure, the backup flowmeter recorded flow rates of 480 m³ / day, 475 m³ / day, and so on. This ensured normal sewage flow monitoring at the maintenance well, enabling the monitoring platform to monitor flow rates in real time without affecting abnormality assessments in the detection area.

[0070] Further,

[0071] The step S5 further includes:

[0072] S51. When the current detection area is determined to be abnormal, the monitoring platform automatically retrieves the flow meter data of the area for the previous 72 hours and generates a flow change trend curve;

[0073] S52. Identify mutation points in the trend curve using a wavelet transform algorithm and generate an abnormal diffusion path prediction map based on the spatial coordinates of the maintenance well;

[0074] S53. The probability distribution of abnormal diffusion is displayed in the form of a heat map on the monitoring platform interface, and areas with probability values ​​≥ 90% are automatically marked as key inspection areas.

[0075] When the detection area is determined to be mixed or leaking, the flow data of the previous 72 hours is retrieved to generate a trend curve, which can intuitively show the change pattern of flow over time; the mutation point is identified by the wavelet transform algorithm, and the time node when the abnormality occurs can be accurately located; the abnormal diffusion path prediction map is generated by combining the spatial coordinates of the maintenance well and the probability distribution is displayed in a heat map, which can help operation and maintenance personnel quickly grasp the abnormal diffusion range and key inspection areas, improve the efficiency and accuracy of fault detection, and realize the transition from passive monitoring to active prediction.

[0076] In one embodiment, after a detection area is determined to be mixed, the monitoring platform automatically retrieves the flow meter data of the previous 72 hours in the area and generates a flow change trend curve. The wavelet transform algorithm finds that the curve has a mutation point in the 48th hour. Combined with the spatial coordinates of the maintenance well in the area, an abnormal diffusion path prediction map is generated, showing that the abnormality may spread from maintenance well X to the northeast. After being displayed in the form of a heat map on the monitoring platform interface, areas with a probability value ≥90% are marked as key inspection areas. Operation and maintenance personnel can give priority to inspecting the connection between the rainwater branch channel and the sewage branch channel in the area to quickly find the mixed connection location, such as the case where a rainwater branch channel in a certain community is mistakenly connected to a sewage branch channel.

[0077] Further,

[0078] The preset value interval for balance judgment is [0.9, 1], and the preset value interval for balance judgment can be adjusted according to actual conditions.

[0079] Further,

[0080] The abnormality type judgment interval is (0, 10], and the abnormality type judgment interval can be adjusted according to actual conditions.

[0081] Further,

[0082] The two colorless controls on the pages have the same shape and coincide with the center point of the controls. The colorless control with the actual total sewage flow as the diameter can block the colorless control with the regional total sewage inflow flow as the diameter. The difference between the two colorless controls represents the pollution load difference. When the two colorless controls indicate that the detection area is normal, the colorless control with the regional total sewage inflow flow as the diameter blocks the colorless control with the actual total sewage flow as the diameter.

[0083] The difference in pollution load is intuitively displayed through the occlusion relationship between two concentric circle controls (with the regional total sewage inflow and the actual total sewage flow as the diameter). Color coding (green / yellow / red) simplifies problem identification, allowing operators to quickly determine the regional status and reduce the risk of misjudgment. When the detection area is normal, the regional total sewage inflow and the actual total sewage flow are very close, so the two controls with the regional total sewage inflow and the actual total sewage flow as the diameters basically overlap. To enhance the difference between the controls under normal conditions and those under mixed connection and infiltration conditions, the colorless control with the regional total sewage inflow as the diameter occludes the colorless control with the actual total sewage flow as the diameter, thus forming a circle.

[0084] In one embodiment, a 100px diameter control for the total sewage inflow in the detection area corresponds to 500 m³ / day, while an 80px diameter control for the actual total sewage flow corresponds to 400 m³ / day. When the control centers overlap, the unobstructed 20px ring area represents a loss of 100 m³ / day. If the loss exceeds the threshold, the control for the total sewage inflow in the area turns red, indicating a leakage risk.

[0085] In another embodiment, a percentage pie chart is used to intuitively display the relationship between the total sewage inflow rate in the region, the actual total sewage flow rate and the pollution load difference.

[0086] Further,

[0087] A water pollution source tracing monitoring platform based on water balance includes a readable storage medium and a processor. The computer program in the readable storage medium is executed by the processor to implement the above-mentioned sewage seepage monitoring method based on big data analysis.

[0088] It will be understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present invention, and the present invention is not limited thereto. Persons skilled in the art will readily appreciate that various modifications and improvements can be made without departing from the spirit and substance of the present invention, and such modifications and improvements are considered within the scope of protection of the present invention.

Claims

1. A water pollution source tracing monitoring method based on water balance, characterized in that: The system includes rainwater channels and sewage channels, and rainwater flow meters and sewage flow meters are installed at the maintenance wells of the rainwater channels and sewage channels, with the maintenance wells as monitoring points. The detection area is divided by the rainwater branch channel or sewage branch channel between the two flow meters to the residential area. The monitoring platform sets the balance judgment interval and the abnormal type judgment interval, and accesses the total water consumption historical data of each residential area in the detection area, the historical data of the rainwater flow meter, and the historical data of the sewage flow meter. The specific steps are as follows: S1. Obtain historical data from the rainwater flow meter in the current detection area and use a sliding window to find a stable time period with stable flow in the rainwater channel; S2. Obtain the historical data of the total daily water consumption of each community in the current detection area during the stable period and add them together to obtain the total sewage inflow flow rate in the area. At the same time, obtain the historical data of the rainwater flow meter and the sewage flow meter corresponding to the stable period in the detection area and add them together to calculate the total sewage flow rate and the total rainwater flow rate of the flow meter; S3. Subtract the total sewage flow and rainwater flow of the flow meter corresponding to the current detection area from the total sewage flow and the total rainwater flow of the flow meter corresponding to the previous detection area to obtain the actual total sewage flow and the actual total rainwater flow of the current detection area; S4. Compare the actual total sewage flow rate with the total sewage inflow flow rate of the area to obtain a balance value, and determine whether the balance value is within the preset value range for balance determination. If so, it indicates that the detection area is normal and the process goes to S6; otherwise, the process goes to S5; S5. Subtract the actual total sewage flow from the total regional sewage inflow, and then subtract the actual total rainwater flow to obtain the abnormality judgment value. Determine whether the abnormality judgment value is within the abnormality type judgment interval. If it is, the abnormality type is mixed connection and the process proceeds to S7. If not, the abnormality type is leakage and the process proceeds to S8. S6. The monitoring platform generates a control page associated with the current detection area. On the control page, it automatically creates two colorless controls based on the total sewage inflow and actual sewage flow rate of the current detection area. The controls are overlapped and colored green, ending the detection. S7. The monitoring platform generates a control page associated with the current detection area. On the control page, the platform automatically generates two colorless controls based on the total sewage inflow and the actual total sewage flow rate of the current detection area. The controls are overlapped and colored yellow, ending the detection. S8. The monitoring platform generates a control page associated with the current detection area, and automatically generates two colorless controls on the control page based on the total sewage inflow flow of the current detection area and the actual total sewage flow as the diameter, overlaps them, and adds red to the controls to end the detection.

2. The water pollution source tracing monitoring method based on water balance according to claim 1 is characterized in that: The division of detection areas is specifically as follows: The monitoring platform relies on the GIS system to divide the residential area connected to the sewage branch channel connected to the sewage channel between the two reference points into detection areas based on the maintenance well where the sewage channel or rainwater channel passes or the rainwater flow meter or sewage flow meter at the maintenance well as the reference point, and the detection area of ​​the latter reference point is based on the direction of the sewage channel.

3. The water pollution source tracing monitoring method based on water balance according to claim 1 is characterized in that: The stable time period identification step includes: S20. Set the sliding window size to 24 hours and calculate the traffic mean of N sliding windows; S21. When the internal flow rate of each sliding window in N consecutive sliding windows is greater than 95% of the flow average and less than 105% of the flow average, the time period corresponding to the N sliding windows is determined to be a flow stable time period.

4. The water pollution source tracing monitoring method based on water balance according to claim 1 is characterized in that: The flow meter adopts a redundant configuration scheme: a main flow meter and a backup flow meter are set in parallel at the maintenance wells of the sewage channel and the rainwater channel, and when the data of the main flow meter is abnormal, it automatically switches to the backup flow meter.

5. The water pollution source tracing monitoring method based on water balance according to claim 1 is characterized in that: The step S5 further includes: S51. When the current detection area is determined to be abnormal, the monitoring platform automatically retrieves the flow meter data of the area for the previous 72 hours and generates a flow change trend curve; S52. Identify mutation points in the trend curve using a wavelet transform algorithm and generate an abnormal diffusion path prediction map based on the spatial coordinates of the maintenance well; S53. The probability distribution of abnormal diffusion is displayed in the form of a heat map on the monitoring platform interface, and areas with probability values ​​≥ 90% are automatically marked as key inspection areas.

6. The water pollution source tracing monitoring method based on water balance according to claim 1 is characterized in that: The preset value interval for balance judgment is [0.9, 1], and the preset value interval for balance judgment can be adjusted according to actual conditions.

7. The water pollution source tracing monitoring method based on water balance according to claim 1 is characterized in that: The abnormality type judgment interval is (0, 10], and the abnormality type judgment interval can be adjusted according to actual conditions.

8. The water pollution source tracing monitoring method based on water balance according to claim 1 is characterized in that: The two colorless controls on the pages have the same shape and coincide with the center point of the controls. The colorless control with the actual total sewage flow as the diameter can block the colorless control with the regional total sewage inflow flow as the diameter. The difference between the two colorless controls represents the pollution load difference. When the two colorless controls indicate that the detection area is normal, the colorless control with the regional total sewage inflow flow as the diameter blocks the colorless control with the actual total sewage flow as the diameter.

9. A water pollution source tracing monitoring platform based on water balance, characterized by: It includes a readable storage medium and a processor, wherein the computer program in the readable storage medium is executed by the processor to implement a water pollution source tracing monitoring method based on water balance as described in any one of claims 1 to 8.

Images