Systematic treatment method for sewage concentration improvement and multi-source external water reduction

Abstract

The present application provides a multi-source external water reduction system management method for improving the concentration of sewage into the plant, collects sewage pipe network water quality monitoring data, flow monitoring data and pipe network space topology data, and constructs a multi-source external water analysis data set; according to the water quality conductivity deviation degree and the chemical oxygen demand concentration gradient, an external water identification characteristic vector is constructed, the external water source type is identified through the characteristic distance and the matching degree, and the external water influence pipe section is determined along the sewage flow direction; according to the river and lake water inflow, the water supply pipeline leakage, the clear water inflow, the groundwater infiltration and the pipe network structure defect, the external water reduction engineering measures are matched; combined with the water quality and flow data before and after the treatment, the improvement contribution value of the chemical oxygen demand concentration and the biochemical oxygen demand concentration is calculated; according to the improvement contribution value, the engineering implementation priority is determined, and the external water identification parameter, the external water reduction proportion and the engineering measure are updated, so that the closed-loop management of multi-source external water identification, treatment, evaluation and feedback update is realized.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment, and in particular to a systematic treatment method for reducing external water sources and increasing the concentration of wastewater entering the treatment plant. Background Technology

[0002] The concentration of pollutants in the influent to wastewater treatment plants is a crucial factor affecting the stable operation of biological treatment systems. In some areas, wastewater collection networks are affected by factors such as backflow from rivers and lakes, leaks in water supply pipelines, misconnection of clean water, groundwater infiltration, and structural defects in municipal pipe networks. This results in a large influx of low-pollutant-concentration external water into the wastewater network, reducing the chemical oxygen demand (COD) and biochemical oxygen demand (BOD) concentrations in the wastewater treatment plant's influent. Existing treatment methods typically target and repair single pipe sections or single sources of external water. However, there is a lack of effective correlation between water quality monitoring data, flow monitoring data, pipe network topology data, and pipe defect detection data, making it difficult to accurately identify the type of external water source, the location of its entry, and the extent of its impact.

[0003] Existing technology CN119041537A discloses a method for precise pollution control and target attainability analysis to improve the quality and efficiency of wastewater systems. This method acquires data on water quality and quantity from wastewater treatment facilities, drainage network data, groundwater levels, and river and lake levels to construct the drainage network topology. It then conducts water balance analysis and investigates external water infiltration. Furthermore, it reduces external water infiltration through measures such as pipeline repair, separation of clean and wastewater, and backflow prevention. Simultaneously, it analyzes the influent concentration of the wastewater treatment plant after external water removal. While this existing technology can investigate and improve the quality of wastewater systems, it primarily relies on water balance and on-site investigation results to determine external water issues. It lacks the technical means to classify and identify different external water sources using changes in water conductivity and chemical oxygen demand (COD) concentration gradients, and it does not establish a closed-loop process for continuously updating external water identification parameters based on actual treatment results.

[0004] Existing technology CN113111478B discloses a method and equipment for assessing the degree of mixed connections, inflow, and infiltration in drainage system networks. It establishes an evaluation set of water bodies from different sources by collecting water quality characteristic factors such as chemical oxygen demand, conductivity, total nitrogen, and hardness, and uses fuzzy mathematics to determine the source and degree of inflow and infiltration of mixed water bodies in the drainage network. While this existing technology can identify the impact of groundwater, surface water, and other water bodies on sewage networks using water quality characteristic factors, it is mainly used for evaluating the degree of mixed connections, inflow, and infiltration. It does not continuously correlate the results of identifying external water sources with the network spatial topology, abnormal start and end nodes, classified treatment projects, concentration increase contribution evaluation, and treatment parameter feedback updates.

[0005] Furthermore, existing external water treatment schemes typically determine the implementation sequence based on human experience, and there is a lack of unified quantitative evaluation methods for water quality changes before and after treatment. This makes it difficult to accurately determine the actual contribution of each external water reduction project to the improvement of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) concentrations. When the treatment effect does not meet expectations, it is also difficult to reverse-correct the external water reduction ratio, external water characteristic center, and external water identification threshold based on actual measurement results. This leads to a disconnect between external water identification, project implementation, and effect verification, affecting the accuracy and sustainability of external water treatment in sewage pipe networks. Summary of the Invention

[0006] The main objective of this invention is to provide a systematic treatment method for reducing multi-source external water to increase the concentration of wastewater entering the plant. The technical problem solved by this application is: how to accurately identify the types of multi-source external water in the wastewater pipe network and the pipe sections affected by the external water, and evaluate the treatment contribution, update the identification parameters and the engineering implementation sequence based on the changes in water quality and flow before and after treatment, so as to achieve closed-loop treatment to increase the concentration of wastewater entering the plant.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a multi-source external water reduction system treatment method for increasing the concentration of wastewater entering the treatment plant, comprising the following steps: S1. Collect sewage pipe network water quality monitoring data, flow monitoring data and pipe network spatial topology data, and construct a multi-source external water analysis dataset; S2. Identify the types of external water sources based on changes in water conductivity and chemical oxygen demand concentration gradients, and determine the pipe sections affected by external water in the spatial topology of the pipe network. S3. Determine external water reduction engineering measures based on the type of external water source and the affected pipeline section, and implement external water reduction engineering in the affected pipeline section; S4. Collect influent water quality and flow data before and after the implementation of the external water reduction project, and calculate the contribution of the external water reduction project to the increase of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) concentrations. S5. Update the external water identification parameters and the implementation sequence of external water reduction projects based on the contribution value, and carry out closed-loop reduction of multi-source external water in the sewage pipe network.

[0008] In the preferred embodiment, step S1 includes: Based on the manholes, pipe connections, and sewage flow direction in the spatial topology of the sewage pipe network, the sewage pipe network is divided into multiple pipe network analysis units; Water conductivity, chemical oxygen demand (COD) concentration, and biochemical oxygen demand (BOD) concentration are collected by water quality testing devices installed at monitoring nodes of the pipeline analysis unit, and wastewater flow rate is collected by flow detection devices. Acquire spatial location data of rivers and lakes, spatial location data of water supply pipelines, location data of clean water access points, groundwater level data, and pipeline defect detection data; Water quality monitoring data, flow monitoring data, spatial location data, and pipeline defect detection data are synchronized in time and mapped to the corresponding pipeline network analysis units according to the location of monitoring nodes to form a multi-source external water analysis dataset.

[0009] In the preferred scheme, in step S2, the monitoring nodes are calculated according to the following formula. Water conductivity deviation : ; Calculate the monitoring nodes according to the following formula. Relative to upstream adjacent monitoring nodes Chemical oxygen demand concentration gradient : ; in, For monitoring nodes Water conductivity during the current analysis period, For monitoring nodes Baseline water conductivity during historically stable operating periods For monitoring nodes Chemical oxygen demand concentration, For upstream adjacent monitoring nodes Chemical oxygen demand concentration, For monitoring nodes With monitoring nodes The length of the pipe between them; Based on the degree of deviation of water conductivity and chemical oxygen demand concentration gradient Build monitoring nodes External water identification feature vector .

[0010] In the preferred scheme, an external water feature database is established in advance, which includes external water feature centers corresponding to river and lake water inflow types, water supply pipeline leakage types, clean water inflow types, groundwater infiltration types, and municipal pipeline network structural defect types. In step S2, the monitoring nodes are calculated according to the following formula. Types of external water sources Feature distance between : ; Calculate the monitoring nodes according to the following formula. Types of external water sources Matching degree between : ; in, External water source type The corresponding deviation of water conductivity from the center value, External water source type The corresponding center value of the chemical oxygen demand concentration gradient, and These are the weights for the deviation in water conductivity and the gradient in chemical oxygen demand concentration, respectively. ; The type of external water source corresponding to the maximum matching degree is determined as the monitoring node. The candidate external water source types are identified, and the candidate external water source types are output when the maximum matching degree reaches the external water identification threshold.

[0011] In the preferred scheme, step S2, determining the pipe section affected by external water flow, includes: The matching degree of adjacent monitoring nodes is compared sequentially along the sewage flow direction. The monitoring node whose matching degree first reaches the external water identification threshold is determined as the external water anomaly starting node, and the monitoring node located downstream of the external water anomaly starting node and whose matching degree recovers to below the external water identification threshold is determined as the external water anomaly ending node. The pipeline between the starting node and the ending node of the external water anomaly is identified as the candidate pipeline segment affected by external water. Based on the spatial distance between the candidate external water-affected pipe sections and river / lake water bodies, water supply pipelines and clean water access points, as well as the groundwater level data and pipeline defect detection data corresponding to the candidate external water-affected pipe sections, the candidate external water source types are corrected. The corrected external water source type and its corresponding candidate external water-affected pipe section are determined as the external water identification result.

[0012] In the preferred embodiment, step S3 includes: When the external water source is river or lake water inflow, interception facilities should be set up at the connection point between the river / lake water body and the sewage pipe network, or the clean water and sewage should be separated in the river / lake water drainage channel and the sewage transportation channel. When the external water source is a water supply pipeline leakage type, leak detection is carried out on the water supply pipeline adjacent to the affected pipeline section, and the leaks found in the water supply pipeline are sealed or the pipeline section is replaced. When the external water source is clean water inflow, trace the clean water access point along the affected pipeline section and implement measures such as blocking, reconnection, or diversion at the clean water access point; When the external water source is groundwater infiltration or municipal pipeline network structural defects, the pipeline repair method is determined based on the location, length, and severity of the defect. For pipelines that meet the conditions for trenchless repair, in-situ solidification is used for repair; for pipelines that do not meet the conditions for trenchless repair, excavation and replacement are used for repair.

[0013] In the preferred embodiment, in step S4, the pollutant type is... The predicted influent concentration before the implementation of the external water reduction project is calculated according to the following formula. : ; The predicted influent concentration after the implementation of the external water reduction project is calculated using the following formula. : ; The impact of external water reduction projects on pollutant types is calculated using the following formula. The predicted concentration increase contribution value : ; Among them, pollutant types Chemical oxygen demand (COD) or biochemical oxygen demand (BOD) To prevent external water from affecting the sewage flow rate in the pipeline section, Types of pollutants in wastewater concentration, To prevent the inflow of external water from affecting the external water flow rate of the pipeline section, Types of pollutants in external water concentration, The proportion of external water reduction in the external water reduction project.

[0014] In the preferred embodiment, step S4 further includes: Select water quality assessment periods before and after the implementation of the external water reduction project, and match the operating conditions of the wastewater treatment plant and the wastewater discharge periods corresponding to the two water quality assessment periods. Calculate the median concentrations of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) for the two water quality assessment periods, respectively. The difference between the median concentration after the implementation of the external water reduction project and the median concentration before implementation is determined as the measured concentration increase contribution value; When the predicted concentration increase contribution is not zero, a contribution correction coefficient is determined based on the ratio of the measured concentration increase contribution to the predicted concentration increase contribution, and this contribution correction coefficient is used to update the external water reduction ratio. .

[0015] In the preferred embodiment, in step S5, the first... For each candidate external water reduction project, the project implementation priority index is calculated according to the following formula. : ; in, For the first The contribution of each candidate external water reduction project to the increase in chemical oxygen demand (COD) concentration. For the first The contribution of each candidate external water reduction project to the increase in biochemical oxygen demand (BOD) concentration. For the first The external water flow corresponding to each candidate external water reduction project For the first The defect levels of the pipeline sections affected by external water reduction projects corresponding to the candidate external water reduction projects. For normalization function, , , and As weight, and ; A list of external water reduction projects and their implementation schedule are generated in descending order of project implementation priority index, and then sent to the smart water management platform.

[0016] In the preferred embodiment, step S5 further includes: The smart water management platform associates and stores monitoring node identifiers, pipe section identifiers affected by external water, types of external water sources, external water reduction engineering measures, predicted concentration increase contribution values, and measured concentration increase contribution values. When the measured concentration increase contribution value is lower than the corresponding target contribution value, the water quality monitoring data and flow monitoring data of the pipe section affected by external water are collected again, and the external water identification feature vector, external water feature center, external water identification threshold and contribution correction coefficient are updated. Based on the updated parameters, the types of external water sources are re-identified, the affected pipe sections are re-determined, and external water reduction engineering measures are adjusted until the measured concentration increase contribution value reaches the target contribution value.

[0017] This invention provides a systematic treatment method for reducing multi-source external water by increasing the concentration of wastewater entering the plant. This application collects wastewater pipeline water quality monitoring data, flow monitoring data, and pipeline spatial topology data, and associates monitoring information from different sources with corresponding pipeline analysis units, providing a unified data foundation for identifying multi-source external water and locating the affected pipeline sections.

[0018] This application is based on the degree of deviation of water conductivity. and chemical oxygen demand concentration gradient Constructing external water identification feature vectors and through feature distance and matching degree Identify the types of external water sources, and combine abnormal starting nodes, abnormal ending nodes in the pipeline network, and surrounding spatial data to determine the pipe sections affected by external water, thereby improving the accuracy of identifying different external water sources and locating pipe sections.

[0019] Based on different types of external water sources, such as river and lake water inflow, water supply pipeline leakage, clean water inflow, groundwater infiltration, and municipal pipeline network structural defects, this application matches engineering measures such as clean and sewage diversion, interception to prevent backflow, leak repair, clean water reconnection, in-situ solidification repair, or excavation and replacement, so that the external water identification results can be directly transformed into corresponding engineering treatment methods.

[0020] This application calculates and predicts the contribution value of concentration increase based on water quality and flow data before and after treatment. The measured concentration increase contribution value was used to adjust the external water reduction ratio based on the relationship between the two. This allows the effectiveness of governance to be quantified and verified, reducing the problem of relying solely on human experience to judge the effectiveness of governance.

[0021] This application calculates the project implementation priority index based on the contribution value of the increase in chemical oxygen demand concentration, the contribution value of the increase in biochemical oxygen demand concentration, the external water flow rate, and the defect level of the pipeline section. Based on the actual measurement results of the treatment, the external water characteristic center, external water identification threshold and contribution correction coefficient are updated, thus forming a closed-loop treatment process in which external water identification, pipe section location, engineering treatment, effect evaluation and parameter update are interconnected. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is the overall flow chart of the multi-source external water reduction system for improving the concentration of wastewater entering the plant, as described in this invention.

[0023] Figure 2 This is a flowchart illustrating the multi-source external water identification process of this invention.

[0024] Figure 3 This is a flowchart illustrating the process of determining and correcting pipe sections affected by external water flow in this invention.

[0025] Figure 4 This is a flowchart of the matching and contribution evaluation of external water reduction projects in this invention.

[0026] Figure 5 This is a flowchart of the priority ranking and closed-loop update process for the present invention. Detailed Implementation

[0027] Example 1 like Figure 1 As shown, a systematic treatment method for reducing external water pollution from multiple sources to increase the concentration of wastewater entering the treatment plant includes the following steps: S1. Collect sewage pipe network water quality monitoring data, flow monitoring data and pipe network spatial topology data, and construct a multi-source external water analysis dataset; S2. Identify the types of external water sources based on changes in water conductivity and chemical oxygen demand concentration gradients, and determine the pipe sections affected by external water in the spatial topology of the pipe network. S3. Determine external water reduction engineering measures based on the type of external water source and the affected pipeline section, and implement external water reduction engineering in the affected pipeline section; S4. Collect influent water quality and flow data before and after the implementation of the external water reduction project, and calculate the contribution of the external water reduction project to the increase of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) concentrations. S5. Update the external water identification parameters and the implementation sequence of external water reduction projects based on the contribution value, and carry out closed-loop reduction of multi-source external water in the sewage pipe network.

[0028] In the preferred embodiment, step S1 includes: Based on the manholes, pipe connections, and sewage flow direction in the spatial topology of the sewage pipe network, the sewage pipe network is divided into multiple pipe network analysis units; Water conductivity, chemical oxygen demand (COD) concentration, and biochemical oxygen demand (BOD) concentration are collected by water quality testing devices installed at monitoring nodes of the pipeline analysis unit, and wastewater flow rate is collected by flow detection devices. Acquire spatial location data of rivers and lakes, spatial location data of water supply pipelines, location data of clean water access points, groundwater level data, and pipeline defect detection data; Water quality monitoring data, flow monitoring data, spatial location data, and pipeline defect detection data are synchronized in time and mapped to the corresponding pipeline network analysis units according to the location of monitoring nodes to form a multi-source external water analysis dataset.

[0029] In the preferred scheme, in step S2, the monitoring nodes are calculated according to the following formula. Water conductivity deviation : ; Calculate the monitoring nodes according to the following formula. Relative to upstream adjacent monitoring nodes Chemical oxygen demand concentration gradient : ; in, For monitoring nodes Water conductivity during the current analysis period, For monitoring nodes Baseline water conductivity during historically stable operating periods For monitoring nodes Chemical oxygen demand concentration, For upstream adjacent monitoring nodes Chemical oxygen demand concentration, For monitoring nodes With monitoring nodes The length of the pipe between them; Based on the degree of deviation of water conductivity and chemical oxygen demand concentration gradient Build monitoring nodes External water identification feature vector .

[0030] In the preferred scheme, an external water feature database is established in advance, which includes external water feature centers corresponding to river and lake water inflow types, water supply pipeline leakage types, clean water inflow types, groundwater infiltration types, and municipal pipeline network structural defect types. In step S2, the monitoring nodes are calculated according to the following formula. Types of external water sources Feature distance between : ; Calculate the monitoring nodes according to the following formula. Types of external water sources Matching degree between : ; in, External water source type The corresponding deviation of water conductivity from the center value, External water source type The corresponding center value of the chemical oxygen demand concentration gradient, and These are the weights for the deviation in water conductivity and the gradient in chemical oxygen demand concentration, respectively. ; The type of external water source corresponding to the maximum matching degree is determined as the monitoring node. The candidate external water source types are identified, and the candidate external water source types are output when the maximum matching degree reaches the external water identification threshold.

[0031] In the preferred scheme, step S2, determining the pipe section affected by external water flow, includes: The matching degree of adjacent monitoring nodes is compared sequentially along the sewage flow direction. The monitoring node whose matching degree first reaches the external water identification threshold is determined as the external water anomaly starting node, and the monitoring node located downstream of the external water anomaly starting node and whose matching degree recovers to below the external water identification threshold is determined as the external water anomaly ending node. The pipeline between the starting node and the ending node of the external water anomaly is identified as the candidate pipeline segment affected by external water. Based on the spatial distance between the candidate external water-affected pipe sections and river / lake water bodies, water supply pipelines and clean water access points, as well as the groundwater level data and pipeline defect detection data corresponding to the candidate external water-affected pipe sections, the candidate external water source types are corrected. The corrected external water source type and its corresponding candidate external water-affected pipe section are determined as the external water identification result.

[0032] In the preferred embodiment, step S3 includes: When the external water source is river or lake water inflow, interception facilities should be set up at the connection point between the river / lake water body and the sewage pipe network, or the clean water and sewage should be separated in the river / lake water drainage channel and the sewage transportation channel. When the external water source is a water supply pipeline leakage type, leak detection is carried out on the water supply pipeline adjacent to the affected pipeline section, and the leaks found in the water supply pipeline are sealed or the pipeline section is replaced. When the external water source is clean water inflow, trace the clean water access point along the affected pipeline section and implement measures such as blocking, reconnection, or diversion at the clean water access point; When the external water source is groundwater infiltration or municipal pipeline network structural defects, the pipeline repair method is determined based on the location, length, and severity of the defect. For pipelines that meet the conditions for trenchless repair, in-situ solidification is used for repair; for pipelines that do not meet the conditions for trenchless repair, excavation and replacement are used for repair.

[0033] In the preferred embodiment, in step S4, the pollutant type is... The predicted influent concentration before the implementation of the external water reduction project is calculated according to the following formula. : ; The predicted influent concentration after the implementation of the external water reduction project is calculated using the following formula. : ; The impact of external water reduction projects on pollutant types is calculated using the following formula. Contribution to predicted concentration increase : ; Among them, pollutant types This refers to chemical oxygen demand or biochemical oxygen demand. To prevent external water from affecting the sewage flow rate in the pipeline section, Types of pollutants in wastewater concentration, To prevent the inflow of external water from affecting the external water flow rate of the pipeline section, Types of pollutants in external water concentration, The proportion of external water reduction in the external water reduction project.

[0034] In the preferred embodiment, step S4 further includes: Select water quality assessment periods before and after the implementation of the external water reduction project, and match the operating conditions of the wastewater treatment plant and the wastewater discharge periods corresponding to the two water quality assessment periods. Calculate the median concentrations of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) for the two water quality assessment periods, respectively. The difference between the median concentration after the implementation of the external water reduction project and the median concentration before implementation is determined as the measured concentration increase contribution value; When the predicted concentration increase contribution is not zero, a contribution correction coefficient is determined based on the ratio of the measured concentration increase contribution to the predicted concentration increase contribution, and this contribution correction coefficient is used to update the external water reduction ratio. .

[0035] In the preferred embodiment, in step S5, the first... For each candidate external water reduction project, the project implementation priority index is calculated according to the following formula. : ; in, For the first The contribution of each candidate external water reduction project to the increase in chemical oxygen demand (COD) concentration. For the first The contribution of each candidate external water reduction project to the increase in biochemical oxygen demand (BOD) concentration. For the first The external water flow corresponding to each candidate external water reduction project For the first The defect levels of the pipeline sections affected by external water reduction projects corresponding to the candidate external water reduction projects. For normalization function, , , and As weight, and ; A list of external water reduction projects and their implementation schedule are generated in descending order of project implementation priority index, and then sent to the smart water management platform.

[0036] In the preferred embodiment, step S5 further includes: The smart water management platform associates and stores monitoring node identifiers, pipe section identifiers affected by external water, types of external water sources, external water reduction engineering measures, predicted concentration increase contribution values, and measured concentration increase contribution values. When the measured concentration increase contribution value is lower than the corresponding target contribution value, the water quality monitoring data and flow monitoring data of the pipe section affected by external water are collected again, and the external water identification feature vector, external water feature center, external water identification threshold and contribution correction coefficient are updated. Based on the updated parameters, the types of external water sources are re-identified, the affected pipe sections are re-determined, and external water reduction engineering measures are adjusted until the measured concentration increase contribution value reaches the target contribution value.

[0037] Example 2 Further explanation in conjunction with Example 1, such as Figure 1 As shown, this embodiment provides a systematic treatment method for reducing external water from multiple sources to increase the concentration of wastewater entering the treatment plant. This method does not simply involve formulating a treatment plan or relying on manual experience to arrange engineering. Instead, it utilizes water quality monitoring devices, flow monitoring devices, pipeline spatial data, and data processing equipment to identify, locate, treat, and verify the effects of external water in the wastewater pipeline network. During implementation, water quality monitoring data, flow monitoring data, and pipeline spatial topology data such as pipeline connections, manhole locations, and wastewater flow direction are collected from different monitoring nodes in the wastewater pipeline network. The collected data are then correlated according to monitoring time and location to form a multi-source external water analysis dataset. The data processing equipment identifies the types of external water sources entering the wastewater pipeline network based on changes in water conductivity and chemical oxygen demand (COD) concentration along the wastewater flow direction, and determines the specific pipe sections affected by the external water. Based on the identified external water source types, external water reduction engineering measures such as clean water / wastewater separation, leak repair, clean water reconnection, in-situ solidification repair, or excavation and replacement are determined. After the external water reduction project is implemented, influent water quality and flow data are collected again. The chemical oxygen demand (COD) and biochemical oxygen demand (BOD) concentrations before and after the project are compared to calculate the concentration increase contribution of the external water reduction project. Finally, the external water identification parameters and the subsequent project implementation sequence are updated based on the actual treatment effect, forming a continuous closed loop of external water identification, engineering treatment, and effect evaluation.

[0038] Step S1 establishes the data foundation required for identifying external water. Water quality monitoring data includes at least water conductivity, chemical oxygen demand (COD) concentration, and biochemical oxygen demand (BOD) concentration. Flow monitoring data includes the sewage flow rate of the corresponding pipe section at each monitoring node during the designated monitoring period. Pipeline topology data includes the connection relationships between manholes, pipe length, pipe flow direction, pipe burial depth, and upstream / downstream relationships. Step S2 utilizes the ability of water conductivity to characterize differences in ionic composition from different water sources and the ability of COD concentration changes to characterize the sewage dilution process to determine the source and scope of external water influence. Step S3 transforms the external water identification results into actionable engineering remediation measures. Step S4 evaluates whether the project has truly reduced external water flow using measured data before and after remediation. Step S5 updates model parameters and the project sequence based on the evaluation results to avoid external water remediation remaining at the stage of one-time investigation or one-time repair.

[0039] The sewage pipe network is first divided into multiple pipe network analysis units according to spatial topology. A pipe network analysis unit can consist of pipes between adjacent manholes or multiple continuous pipe segments with clear upstream and downstream relationships. The division is based on the principles of continuous sewage flow, the ability of monitoring data to be mapped to specific pipe segments, and the ability to trace the source of external water step by step. Each pipe network analysis unit is assigned a unique identifier and records the corresponding upstream node, downstream node, pipe length, pipe diameter, pipe material, and spatial coordinates, thus enabling data from different sources to be mapped to the same spatial object.

[0040] Water quality monitoring devices are installed at inspection wells, branch manifold inlets, main manifold inlets, pump station inlets, or sewage treatment plant inlets to collect water conductivity, chemical oxygen demand (COD) concentration, and biochemical oxygen demand (BOD) concentration. Flow monitoring devices are installed at corresponding monitoring nodes to collect instantaneous and cumulative flow rates. The spatial location of rivers and lakes is obtained through water system geographic information data; the spatial location of water supply pipelines is obtained through water supply network data; the location of clean water access points is obtained through on-site inspection data and pipeline connection information; groundwater level data is obtained through groundwater monitoring devices or regional groundwater monitoring data; and pipeline defect detection data is obtained through closed-circuit television (CCTV) inspection, sonar inspection, pipeline periscope inspection, or other pipeline inspection methods.

[0041] Different monitoring devices may have inconsistent sampling times when collecting data. Therefore, it is necessary to unify water quality monitoring data, flow monitoring data, and spatial data to the same time reference. Time synchronization can be achieved by resampling data according to a fixed sampling period and associating data within the same time window into the same set of monitoring records. For spatial association, water quality data, flow data, groundwater level data, and pipeline defect data are written into the corresponding pipeline network analysis unit based on the coordinates of monitoring nodes, manhole identifiers, and pipe segment identifiers. This forms a multi-source external water analysis dataset that reflects the water quality, water quantity, surrounding water bodies, and structural status of each pipeline network analysis unit.

[0042] Regarding claim 3, the monitoring node Water conductivity deviation Calculate according to the following formula: ; In the formula, Indicates the first in the sewage pipe network One monitoring node. Indicates monitoring node The degree of deviation of the current water conductivity from the historical baseline water conductivity. It is a dimensionless numerical value. Indicates monitoring node Water conductivity data collected during the current analysis period. Indicates monitoring node The baseline water conductivity is determined within a historically stable operating period. This historically stable operating period is preferably a time when there is no significant rainfall impact on the pipe network, no abnormal drainage connections, no pipeline construction, and no abnormal pump station operation. The baseline water conductivity can be the median of water conductivity from multiple historically stable operating periods to reduce the impact of individual abnormal detection values ​​on the baseline result. The absolute value sign is used to eliminate the influence of deviation direction, ensuring that changes in water conductivity above or below the baseline can be identified. The larger the value, the more significant the change in the ionic composition of the current water body relative to normal sewage, and the higher the possibility of external water bodies entering.

[0043] Monitoring nodes Relative to upstream adjacent monitoring nodes Chemical oxygen demand concentration gradient Calculate according to the following formula: ; In the formula, Indicates the upstream adjacent monitoring node To monitoring nodes The degree of variation in chemical oxygen demand concentration along the length of the pipeline. Indicates upstream adjacent monitoring nodes Chemical oxygen demand concentration, Indicates monitoring node Chemical oxygen demand concentration, Indicates monitoring node With monitoring nodes The length of the pipeline between them. Monitoring nodes. With monitoring nodes Based on the direction of sewage flow, monitoring nodes are determined. Located at monitoring node Upstream of. and Data from the same monitoring period were used and included in the calculation after correction for flow direction propagation time. When the value is positive and relatively large, it indicates that wastewater is flowing from the monitoring node. Flow to monitoring nodes During the process, the concentration of chemical oxygen demand decreases significantly, which may be due to the entry of low-concentration external water and dilution of the sewage. When the value is close to zero, it indicates that the chemical oxygen demand concentration changes little between adjacent monitoring nodes. When the value is negative, it indicates that the downstream chemical oxygen demand concentration is higher than the upstream concentration, which may indicate the presence of high-concentration wastewater. Therefore, it should not be used as the primary criterion for determining the dilution of low-concentration external water.

[0044] External water identification feature vector Based on the degree of deviation of water conductivity and chemical oxygen demand concentration gradient It is composed in a fixed order. Indicates monitoring node The corresponding comprehensive external water identification features include a first dimension reflecting the change in the water's ionic composition relative to its historical normal state, and a second dimension reflecting the dilution degree of chemical oxygen demand (COD) concentration along the wastewater flow direction. Using two types of detection features with different physical meanings to jointly identify external water can reduce the possibility of misjudgment based solely on a single water quality indicator.

[0045] The external water source database is established based on historical monitoring data of confirmed external water sources. It includes at least the following: river and lake water inflow types, water supply pipeline leakage types, clean water inflow types, groundwater infiltration types, and municipal pipeline network structural defects types. Each type of external water source corresponds to a median value for the deviation of water conductivity and a median value for the chemical oxygen demand (COD) concentration gradient. External water source types Indicating the first in the external water feature reservoir Types of extraspecific water sources. Indicates the type of external water source The corresponding deviation of water conductivity from the center value, Indicates the type of external water source The corresponding central value of the chemical oxygen demand (COD) concentration gradient. Both central values ​​can be calculated from similar external water samples that have been confirmed on-site, and serve as representative characteristics of this type of external water source.

[0046] Monitoring nodes Types of external water sources Feature distance between Calculate according to the following formula: ; In the formula, Indicates monitoring node External water identification feature vector Types of external water sources The weighted distance between feature centers. Indicates monitoring node The degree of deviation of water conductivity, Indicates the type of external water source The corresponding deviation of water conductivity from the center value, Indicates monitoring node The chemical oxygen demand concentration gradient, Indicates the type of external water source The corresponding center value of the chemical oxygen demand concentration gradient. This indicates the weight of the deviation in water conductivity in the identification of external water. This represents the weight of the chemical oxygen demand (COD) concentration gradient in external water identification and satisfies... . and The accuracy of monitoring equipment, regional water quality differences, and historical identification results are pre-determined. When water conductivity has a stronger effect on distinguishing different water bodies in a region, it can be improved. When the chemical oxygen demand (COD) concentration gradient has a stronger characterizing effect on the dilution process, it can improve... The weights remain constant throughout the same round of external water identification to ensure the comparability of calculation results from different monitoring nodes.

[0047] Monitoring nodes Types of external water sources Matching degree between Calculate according to the following formula: ; In the formula, Indicates monitoring node Types of external water sources The degree of matching between them. The smaller the value, the more likely it is to be a monitoring node. The closer the water quality characteristics are to the type of external water source Feature centers, calculated The larger. When it is zero, One indicates a monitoring node. Water quality characteristics and types of external water sources The feature centers are consistent. This applies to the same monitoring node. The matching degree between the data and each external water source type is calculated, and the external water source type corresponding to the highest matching degree is determined as the candidate external water source type. When the highest matching degree reaches the preset external water identification threshold, the candidate external water source type is output. When the highest matching degree does not reach the external water identification threshold, the current monitoring data is not directly determined as a clear external water source, and subsequent monitoring data continues to be collected for analysis.

[0048] The affected pipeline section is determined based on changes in the matching degree of adjacent monitoring nodes along the sewage flow direction. For the identified candidate external water source types, the corresponding matching degrees are compared sequentially from upstream to downstream monitoring nodes. When the matching degree of a monitoring node changes from below the external water identification threshold to reaching the external water identification threshold, that monitoring node is identified as the external water anomaly initiation node. When the matching degree of a monitoring node located downstream of the external water anomaly initiation node decreases back below the external water identification threshold, that monitoring node is identified as the external water anomaly termination node. The continuous pipeline between the external water anomaly initiation node and the external water anomaly termination node is identified as the candidate external water affected pipeline section.

[0049] When branch pipes converge in a sewage pipe network, the upstream monitoring nodes corresponding to each branch pipe are tracked according to the spatial topology of the pipe network, and the direction of the abnormal external water source is determined based on the change in the matching degree of each branch pipe. After the candidate external water-affected pipe section is formed, it is also necessary to combine spatial location data, groundwater level data, and pipe defect detection data to correct the candidate external water source type. When the candidate external water-affected pipe section is adjacent to a river or lake, and the external water anomaly intensifies with the rise of the river or lake water level, the reliability of judging the river or lake water inflow type can be improved. When the candidate external water-affected pipe section is adjacent to a water supply pipe, and the water supply pipe has leakage signals, the reliability of judging the water supply pipe leakage type can be improved. When the candidate external water-affected pipe section has a clear clean water access point, it can be identified as a clean water inflow type. When the groundwater level is higher than the bottom of the pipe or higher than the location of the pipe defect, and the pipe has misaligned joints, cracks, leaks, or holes, it can be identified as a groundwater infiltration type or a municipal pipe network structural defect type. By correcting with water quality characteristics and spatial structure data, misjudgments of external water sources caused by relying solely on water quality similarity can be reduced.

[0050] Different types of external water sources require different external water reduction engineering measures. River and lake water inflow mainly occurs at the connection points between river / lake drainage channels and sewage pipe networks, overflow outlets, intercepting wells, or sections with backflow risk. During treatment, interception facilities, backflow prevention facilities, or independent clean water delivery channels can be installed at the connection points, and river / lake water drainage channels can be separated from sewage delivery channels to prevent river / lake water from entering the sewage pipe network.

[0051] The target of remediation for water supply pipeline leaks is the water supply pipeline near the section affected by external water. During implementation, the leak point is first identified through flow balancing, pressure monitoring, correlation instrument detection, or on-site excavation. Then, the leak point is sealed, the connection is repaired, or the pipe section is replaced. After the water supply pipeline leak is repaired, the flow rate and water conductivity of the corresponding sewage pipe section are monitored to verify whether the flow rate of clean water entering the sewage network has decreased.

[0052] The treatment targets for clean water inflow are roof drains, site drains, spring water, equipment cooling drainage, or other water bodies with low pollutant concentrations that are mistakenly connected to the sewage network. During implementation, the clean water inflow point is traced step by step along the candidate external water inflow section, and the clean water is introduced into the rainwater system, independent drainage system, or discharge channel that is allowed to receive the clean water through sealing, reconnection, or diversion.

[0053] The type of groundwater infiltration and the type of structural defects in municipal pipe networks require different repair methods based on the defect location, length, severity, degree of pipe deformation, and traffic conditions. When the overall pipe structure remains stable and the defects are mainly cracks, joint leaks, or localized damage, in-situ curing can be used for trenchless repair. In in-situ curing involves inserting a resin-impregnated tube into the pipe to be repaired and curing the resin using hot water, steam, or ultraviolet light, forming a continuous inner lining layer inside the original pipe. When the pipe experiences severe collapse, overall fracture, excessive deformation, or when in-situ curing is not feasible, excavation and replacement methods are used to restore the pipe structure and its airtightness.

[0054] Predicted influent concentration before implementation of external water reduction project Calculate according to the following formula: ; In the formula, Indicates the type of pollutant. It is either chemical oxygen demand or biochemical oxygen demand. This indicates the types of pollutants present after the wastewater and external water are mixed before the implementation of the external water reduction project. The predicted concentration of the substance entering the plant. This indicates the flow rate of sewage in the pipe section that has not been diluted by external water. Indicates the types of pollutants in wastewater that have not been diluted by external water. The concentration. This indicates the flow rate of external water entering and affecting the pipe section. Indicates the type of pollutants in external water The concentration of molecules. Indicates the types of pollutants carried by sewage and external water. Total mass flow rate, denominator This represents the total flow rate after the wastewater and external water are mixed. The formula calculates the mixed concentration based on the conservation relationship between flow rate and pollutant mass within the same analysis period.

[0055] Predicted influent concentration after implementation of external water reduction project Calculate according to the following formula: ; In the formula, This indicates the types of pollutants formed by the mixing of residual external water and sewage after the implementation of the external water reduction project. The predicted concentration of the substance entering the plant. This indicates the proportion of external water reduction achieved by the external water reduction project. The value of is not less than zero and not greater than one. A value of zero indicates that the external water reduction project has not reduced the external water flow. "For a time" means that all identified external water has been reduced. This indicates the remaining external water flow after the implementation of the external water reduction project. Indicates the types of pollutants carried by the remaining external water. The mass flow rate. Calculate. hour, , and Adopted and The same statistical caliber is used to ensure that the prediction results before and after the project implementation are comparable.

[0056] External water reduction projects for different types of pollutants Contribution to predicted concentration increase Calculate according to the following formula:

[0057] In the formula, This indicates the predicted increase in concentration after the implementation of the external water reduction project compared to before implementation. This indicates the predicted concentration entering the plant after the project is implemented. This indicates the predicted influent concentration before project implementation. (The type of pollutant in the external water is also considered.) The concentration is lower than that of the pollutants in the wastewater. The concentration, and the proportion of external water reduction. When the value is greater than zero, the dilution effect on the mixed water body after the reduction of external water is weakened. Typically greater than zero. Pollutant types are categorized separately. By setting the values ​​to chemical oxygen demand (COD) and biochemical oxygen demand (BOD), the predicted increase in the concentrations of these two pollutants by external water reduction projects can be obtained.

[0058] To verify whether the predicted increase in concentration is consistent with the actual treatment effect, water quality assessment periods before and after the implementation of the external water reduction project need to be selected. The two assessment periods should have similar wastewater treatment plant operating conditions, upstream discharge periods, weather conditions, and wastewater flow ranges. If the assessment period before the project is during a period of significant rainfall, while the assessment period after the project is during a period of continuous dryness, the data from the two periods should not be directly compared to avoid the influence of changes in rainfall, industrial wastewater discharge, or differences in domestic wastewater discharge cycles on the assessment of treatment effectiveness.

[0059] Within each water quality assessment period, multiple chemical oxygen demand (COD) and biochemical oxygen demand (BOD) concentration measurements were obtained, and the median of these values ​​was calculated. Using the median reduces the impact of instantaneous discharge fluctuations and individual abnormal measurements on the assessment results. The difference between the median concentration after the implementation of the external water reduction project and the median concentration before implementation was used as the contribution value for the increase in measured concentration. The contribution values ​​for the increase in measured concentration were calculated separately for COD and BOD, and the concentrations of the two pollutants were not combined into a single value.

[0060] When the predicted concentration increase contribution is not zero, the ratio between the measured concentration increase contribution and the corresponding predicted concentration increase contribution is calculated, and this ratio is used as the basis for calculating the contribution correction coefficient. If the measured concentration increase contribution is lower than the predicted concentration increase contribution, it indicates that the original external water reduction ratio is low. The estimate may be too high, and the proportion of external water reduction needs to be reduced based on the contribution correction factor. When the measured concentration increase contribution value is higher than the predicted concentration increase contribution value, it indicates that the original external water reduction ratio is higher. The estimate may be too low; the proportion of external water reduction can be increased based on the contribution correction factor. Updated external water reduction ratio This model is used to predict concentration increases in subsequent candidate external water reduction projects, allowing the prediction model to gradually approach the actual pipeline network operation state.

[0061] No. Project implementation priority index of candidate external water reduction projects Calculate according to the following formula: ; In the formula, This indicates the serial number of the candidate external water reduction project. Indicates the first The project implementation priority index of the candidate external water reduction projects. The larger the value, the higher the priority should be given to the candidate external water reduction project. Indicates the first The contribution of each candidate external water reduction project to the prediction of chemical oxygen demand concentration. Indicates the first The contribution of each candidate external water reduction project to the predicted improvement of biochemical oxygen demand concentration. Indicates the first The external water flow corresponding to each candidate external water reduction project. Indicates the first The defect level of each candidate external water reduction project corresponds to the external water impact section.

[0062] This represents the normalization function, used to convert the contribution values ​​of chemical oxygen demand (COD) concentration increase, biochemical oxygen demand (BOD) concentration increase, external water flow rate, and defect level into dimensionless values ​​within the same dimension range. During normalization, the same normalization benchmark is used for the same batch of candidate external water reduction projects. All indicators are treated as positive indicators; that is, the larger the contribution value of COD concentration increase, the larger the contribution value of BOD concentration increase, the larger the external water flow rate, or the higher the defect level, the larger the normalized value. For pipe section defect levels recorded using textual grades, they are converted into incremental values ​​according to the severity of the defect from low to high before normalization.

[0063] This indicates the weight of the contribution value of the increase in chemical oxygen demand concentration. This indicates the weight of the contribution value of the increase in biochemical oxygen demand concentration. The weight representing the external water flow rate, The weights of the defect levels in the pipe section are indicated, and the following conditions are met: Each weight remains unchanged during the ranking of candidate external water reduction projects within the same batch. When it is necessary to prioritize increasing the influent pollutant concentration of wastewater treatment plants, the weight can be increased. and When it is necessary to prioritize reducing large-volume external water flows, the efficiency can be increased. When prioritizing pipe sections with higher structural safety risks, the efficiency can be increased. Data processing equipment according to Candidate external water reduction projects are ranked from largest to smallest, and a list of external water reduction projects is generated, which includes the affected pipe sections, the type of external water source, engineering measures, the predicted concentration increase contribution value, and the order of implementation.

[0064] The smart water management platform links and stores data from the external water identification and treatment process. Each monitoring node, affected pipe section, and candidate external water reduction project is assigned a unique identifier. The platform writes the monitoring node identifier, affected pipe section identifier, external water source type, external water reduction project measures, predicted concentration increase contribution value, and measured concentration increase contribution value into the same treatment record, enabling subsequent tracing of the original monitoring data, project implementation content, and actual treatment effect corresponding to a specific external water identification result.

[0065] When the measured concentration increase contribution value is lower than the corresponding target contribution value, it indicates that the current project may have incomplete identification of external water sources, deviation in the positioning of pipeline sections affected by external water, insufficient reduction in the degree of project reduction, or insufficient external water reduction ratio. The problem was due to inaccurate estimations. The smart water management platform re-acquired water quality and flow monitoring data for the affected pipe section and its upstream and downstream monitoring nodes, and recalculated the degree of deviation in water conductivity. Chemical oxygen demand concentration gradient External water identification feature vector Feature distance and matching degree .

[0066] When updating the external water characteristics center, monitoring data that has been verified and confirmed through engineering will be added to the corresponding type of sample data to re-determine the source of external water. and When updating the external water identification threshold, the threshold is adjusted based on confirmed correct and incorrect identification results to increase the credibility of external water sources for monitoring nodes that reach the threshold. When updating the contribution correction coefficient, the external water reduction ratio is adjusted based on the relationship between the latest measured concentration increase contribution value and the predicted concentration increase contribution value. The updated parameters continue to be used for identifying external water sources, locating pipe sections affected by external water, and predicting the contribution of concentration increases.

[0067] Based on the updated , , , 、 、 、 and The process involves re-identifying the types of external water sources and the affected pipe sections, and adjusting external water reduction engineering measures based on the re-identification results. After each round of adjustment, a matching water quality assessment period is selected, and the measured concentration increase contribution value is calculated and compared with the target contribution value. When the measured concentration increase contribution value reaches the target contribution value, the closed-loop treatment of the currently affected pipe section is completed. When the measured concentration increase contribution value still does not reach the target contribution value, data acquisition, parameter updates, external water identification, engineering adjustments, and effect verification continue, thereby achieving continuous correction and closed-loop control of the multi-source external water reduction process.

[0068] Example 3 To further illustrate this point, in conjunction with Example 1, this example focuses on a wastewater treatment plant's service area. The chemical oxygen demand (COD) and biochemical oxygen demand (BOD) concentrations in the influent to this area have consistently been low. The surrounding area includes rivers and lakes, rural water supply pipelines, and areas with high groundwater levels. Some wastewater pipelines also exhibit structural defects such as misaligned joints, cracks, and leaks. A multi-source external water reduction system, designed to increase the concentration of influent wastewater, is employed to identify and address external water sources within the area, including river and lake inflows, leaking water supply pipelines, clean water inflows, groundwater infiltration, and structural defects in the municipal pipeline network.

[0069] like Figure 1 As shown, step S1 is executed first, installing water quality monitoring devices and flow monitoring devices at the inlets of branch manifolds, main manifolds, manholes, pump station inlets, and sewage treatment plant inlets of the sewage pipe network. The water quality monitoring devices collect data on water conductivity, chemical oxygen demand (COD) concentration, and biochemical oxygen demand (BOD) concentration, while the flow monitoring devices collect the instantaneous and cumulative flow rates of the corresponding pipe sections. Simultaneously, the locations of manholes, pipe connections, pipe lengths, diameters, burial depths, and sewage flow directions are retrieved from the pipe network geographic information system to form spatial topology data of the sewage pipe network.

[0070] Based on the connection relationships between inspection wells and the sewage flow direction, the sewage pipe network is divided into multiple pipe network analysis units. Each pipe network analysis unit consists of pipe segments between adjacent inspection wells and is assigned a unique pipe segment identifier. Further data is obtained on the spatial locations of rivers and lakes, water supply pipelines, clean water inlet points, groundwater levels, and pipe defect detection data. Pipeline defect detection data is acquired using closed-circuit television (CCTV) equipment and includes at least information on defects such as misaligned joints, cracks, breaks, leaks, and pipe deformation.

[0071] Water quality monitoring data and flow monitoring data are synchronized in time, and data within the same monitoring period are mapped to the corresponding pipeline analysis unit according to the monitoring node coordinates and pipe segment identifiers. For data with inconsistent sampling times, resampling is performed according to a unified sampling cycle, and data from equipment maintenance, pipeline construction, and abnormal discharge periods are removed to form a multi-source external water analysis dataset.

[0072] like Figure 2 As shown, step S2 is executed to identify multiple external water sources for each monitoring node. (Based on monitoring nodes...) For example, read the water conductivity during the current analysis period. And obtain the benchmark water conductivity from historical stable operating data. Historically stable operating periods are selected during times of no significant rainfall, no pumping station malfunctions, no pipeline construction, and stable sewage discharge.

[0073] Monitoring nodes Water conductivity deviation Calculate according to the following formula: ; In this embodiment, the monitoring node Water conductivity for Reference water conductivity for The degree of deviation of water conductivity for: ; Water conductivity deviation Used to characterize monitoring nodes The degree of change in the current ionic composition of water bodies relative to historical normal wastewater. The larger the value, the more significant the difference in conductivity between the current water body and normal sewage.

[0074] Read monitoring nodes Chemical oxygen demand concentration Upstream adjacent monitoring nodes Chemical oxygen demand concentration and the length of the pipeline between the two monitoring nodes. Monitoring nodes Relative to upstream adjacent monitoring nodes Chemical oxygen demand concentration gradient Calculate according to the following formula: ; In this embodiment, the upstream adjacent monitoring node Chemical oxygen demand concentration for Monitoring nodes Chemical oxygen demand concentration for Pipe length for The chemical oxygen demand concentration gradient for: ; Chemical oxygen demand concentration gradient A positive value indicates that the wastewater originates from an adjacent upstream monitoring node. Flow direction monitoring node During the process, the concentration of chemical oxygen demand decreases. The larger the value, the more significant the dilution of wastewater between adjacent monitoring nodes.

[0075] Based on the degree of deviation of water conductivity and chemical oxygen demand concentration gradient Build monitoring nodes External water identification feature vector External water identification feature vector The first dimension is used to characterize the relative change in water conductivity, and the second dimension is used to characterize the change in chemical oxygen demand concentration along the wastewater flow direction.

[0076] An external water source characteristic database was established based on historical data from which on-site tracing and confirmation of external water source types have been completed. The database includes river and lake water inflow types, water supply pipeline leakage types, clean water inflow types, groundwater infiltration types, and municipal pipeline network structural defect types. Each type of external water source... All are configured with the center value of the deviation of water conductivity. and the center value of the chemical oxygen demand concentration gradient .

[0077] In this embodiment, the center value of the deviation of water conductivity corresponding to the inflow type of river and lake water is used. The central value of the chemical oxygen demand concentration gradient is 0.35. The weight is 0.12. (This refers to the weighting of the deviation in water conductivity.) Set to 0.6, for the chemical oxygen demand concentration gradient weighting. Take 0.4, and satisfy: ; Monitoring nodes Types of external water sources Feature distance between Calculate according to the following formula: ; Substituting the data from this embodiment, we get: ; Calculated monitoring nodes Characteristic distance between river and lake inflow types It is approximately 0.015.

[0078] Monitoring nodes Types of external water sources Matching degree between Calculate according to the following formula: ; feature distance Substitute the values ​​to obtain the monitoring nodes. Matching degree with river and lake inflow type Approximately 0.985. Calculate the monitoring nodes separately. The matching degree between the water supply pipeline leakage type, clean water inflow type, groundwater infiltration type, and municipal pipeline network structural defect type is highest for the river and lake water inflow type.

[0079] In this embodiment, the threshold for identifying external water is set to 0.97. Since the maximum matching degree corresponding to the river / lake water inflow type reaches the external water identification threshold, the river / lake water inflow type is determined as the monitoring node. The candidate external water source types are identified. If the maximum matching degree does not reach the external water identification threshold, water quality monitoring data and flow monitoring data are collected again, and the external water source identification is performed again.

[0080] like Figure 3 As shown, the matching degree of river and lake water inflow types corresponding to adjacent monitoring nodes is compared sequentially along the sewage flow direction. The matching degree of upstream monitoring nodes is lower than the external water identification threshold, and the matching degree from monitoring nodes... Initially, the matching degree reached the external water identification threshold for the first time, therefore the monitoring node was... It was identified as the starting point of the external water anomaly.

[0081] Continue tracking the matching degree changes of each monitoring node downstream. When the matching degree of a downstream monitoring node recovers to below the external water identification threshold, that monitoring node is identified as the external water anomaly termination node. The continuous pipeline between the external water anomaly initiation node and the external water anomaly termination node is identified as the candidate external water impact section.

[0082] Spatially correlated the candidate pipeline sections affected by external water pollution with the spatial locations of rivers and lakes, water supply pipelines, clean water access points, groundwater level data, and pipeline defect detection data. Analysis showed that the candidate pipeline sections affected by external water pollution are approximately [distance missing] from the boundary of the river or lake. An intercepting well connected to a river and lake drainage channel is located upstream of the pipeline section. During periods of rising river and lake water levels, the sewage flow in this section significantly increases, water conductivity decreases, and chemical oxygen demand (COD) concentration declines simultaneously. No pressure anomalies or leakage signals were found in the adjacent water supply pipeline, and closed-circuit television monitoring of the pipeline also failed to detect any structural defects sufficient to cause significant groundwater infiltration. Therefore, the candidate external water source type was corrected and confirmed as river and lake water inflow, and the pipeline section between the external water anomaly initiation node and the external water anomaly termination node was identified as the external water affected section.

[0083] like Figure 4 As shown, external water reduction engineering measures are determined based on the inflow type of river and lake water and the affected pipe sections. Clean water and sewage are separated in river and lake drainage channels and sewage conveying channels. Backflow prevention facilities are installed at the original intercepting wells, and normal river and lake drainage is diverted to an independent clean water discharge channel. The backflow prevention facilities close the channel for river and lake water to enter the sewage pipe network when the river and lake water level is higher than the sewage level in the intercepting well, and keep the sewage conveying channel unobstructed during normal sewage discharge.

[0084] For other pipeline analysis units, when the leak is identified as a water supply pipeline leak, leak detection, interface repair, or pipe section replacement are carried out on the adjacent water supply pipeline; when the leak is identified as a clean water inflow, the clean water inlet point is sealed, reconnected, or diverted; when the leak is identified as a groundwater infiltration or municipal pipeline network structural defect, in-situ solidification repair or excavation and replacement are carried out according to the defect location, defect length, and defect level.

[0085] Before implementing the external water reduction project, obtain the sewage flow rate in the affected pipe section. Types of pollutants in wastewater concentration External water flow and types of pollutants in external water concentration Types of pollutants It is either chemical oxygen demand or biochemical oxygen demand.

[0086] In this embodiment, the sewage flow rate for External water flow for External water reduction ratio of external water reduction projects The initial value is 0.75.

[0087] When pollutant type When the chemical oxygen demand is 1, the types of pollutants in the wastewater concentration for Types of pollutants in external water concentration for Predicted influent concentration before implementation of external water reduction project. Calculate according to the following formula: ; Substituting the data, we get: ; Predicted influent concentration after implementation of external water reduction project Calculate according to the following formula: ; Substituting the data, we get: ; The predicted influent concentration of chemical oxygen demand after the implementation of the external water reduction project was calculated. Approximately .

[0088] Contribution of external water reduction projects to the predicted increase in chemical oxygen demand (COD) concentration. Calculate according to the following formula: ; Substituting the data, we get: ; When pollutant type When the biochemical oxygen demand is used, the types of pollutants in the wastewater concentration for Types of pollutants in external water concentration for Using the same calculation method, the predicted influent concentration of biochemical oxygen demand (BOD) before the implementation of the external water reduction project was obtained. for Predicted influent concentration of biochemical oxygen demand after the implementation of the external water reduction project Approximately The corresponding predicted concentration increase contribution value Approximately .

[0089] After the external water reduction project was completed, water quality assessment periods were selected before and after the project implementation. The two assessment periods shared similar operating conditions of the wastewater treatment plants, domestic sewage discharge cycles, industrial wastewater discharge status, and weather conditions. The median concentrations of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) were calculated for each of the two assessment periods.

[0090] Before the project was implemented, the median chemical oxygen demand (COD) concentration was: The median biochemical oxygen demand concentration was After the project was implemented, the median chemical oxygen demand (COD) concentration was... The median biochemical oxygen demand concentration was Therefore, the contribution value of the measured increase in chemical oxygen demand concentration is... The contribution value of the measured concentration increase of biochemical oxygen demand is .

[0091] The ratios between the measured concentration increase contribution and the corresponding predicted concentration increase contribution were calculated, and both ratios were approximately 0.915. The external water reduction ratio was then adjusted based on this ratio. Reduce the proportion of external water The ratio has been adjusted from 0.75 to approximately 0.686. This represents the adjusted external water reduction ratio. Used for predicting the concentration increase contribution of subsequent similar external water source types and similar engineering measures.

[0092] like Figure 5 As shown, the river and lake water inflow treatment project in this embodiment is prioritized and implemented together with other candidate external water reduction projects. Regarding the first... One candidate external water reduction project was selected to obtain the contribution value of the increase in chemical oxygen demand concentration. Contribution value of biochemical oxygen demand concentration increase External water flow and pipe section defect level .

[0093] The indicators were normalized, and the project implementation priority index was calculated according to the following formula. : ; In this embodiment, the weight of the contribution value of the increase in chemical oxygen demand concentration is... The weight of the contribution value to the increase in biochemical oxygen demand concentration is set to 0.35. The weight of the external water flow rate is set to 0.25. The weight of the defect level in the pipe section is set to 0.25. Take 0.15, and satisfy: ; The river and lake water inflow treatment project in this embodiment corresponds to It is 0.92. It is 0.90. It is 0.88. The value is 0.60. Substitute these data into the project implementation priority index. The calculation formula yields: ; The project implementation priority index of the river and lake water inflow treatment project was calculated. The value is 0.857. The smart water management platform ranks each candidate external water reduction project according to its implementation priority index. Generate a list of external water reduction projects and their implementation schedule, from largest to smallest.

[0094] The smart water management platform associates and stores monitoring node identifiers, pipe section identifiers affected by external water, types of external water sources, external water reduction engineering measures, predicted concentration increase contribution values, and measured concentration increase contribution values. The platform establishes a link between each external water source identification result and the corresponding original water quality monitoring data, flow monitoring data, pipe network spatial topology data, and engineering implementation records.

[0095] When the measured concentration increase contribution value reaches the target contribution value, the closed-loop treatment of the affected pipe section is deemed complete. When the measured concentration increase contribution value is lower than the target contribution value, water quality monitoring data and flow monitoring data of the affected pipe section and its upstream and downstream monitoring nodes are collected again, and the deviation of water conductivity is recalculated. Chemical oxygen demand concentration gradient External water identification feature vector Feature distance and matching degree .

[0096] The smart water management platform will add data on external water sources that have been verified and confirmed through engineering to the corresponding external water characteristic database, and update the center value of the deviation of water conductivity. and the center value of the chemical oxygen demand concentration gradient The system adjusts the external water identification threshold based on both correct and incorrect identification results. Simultaneously, it updates the contribution correction coefficient and external water reduction ratio based on the latest measured and predicted concentration increase contributions. .

[0097] The updated parameters are used to re-identify the types of external water sources and the affected pipe sections, and the external water reduction engineering measures are adjusted accordingly. After the engineering adjustments are completed, water quality monitoring data and flow monitoring data are collected again, and the contribution value of the measured concentration increase is recalculated. When the measured concentration increase contribution value reaches the target contribution value, the treatment of the current external water-affected pipe section ends; when the measured concentration increase contribution value is still lower than the target contribution value, the process of parameter updating, external water identification, engineering adjustment, and effect verification continues, thus forming a closed-loop treatment process of multi-source external water identification, external water-affected pipe section location, engineering measure matching, concentration increase contribution evaluation, and parameter feedback updating.

[0098] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A systematic treatment method for reducing external water pollution from multiple sources to increase the concentration of wastewater entering the treatment plant, characterized by: Includes the following steps: S1. Collect sewage pipe network water quality monitoring data, flow monitoring data and pipe network spatial topology data, and construct a multi-source external water analysis dataset; S2. Identify the types of external water sources based on changes in water conductivity and chemical oxygen demand concentration gradients, and determine the pipe sections affected by external water in the spatial topology of the pipe network. S3. Determine external water reduction engineering measures based on the type of external water source and the affected pipeline section, and implement external water reduction engineering in the affected pipeline section; S4. Collect influent water quality and flow data before and after the implementation of the external water reduction project, and calculate the contribution of the external water reduction project to the increase of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) concentrations. S5. Update the external water identification parameters and the implementation sequence of external water reduction projects based on the contribution value, and carry out closed-loop reduction of multi-source external water in the sewage pipe network.

2. The systematic treatment method for reducing the concentration of wastewater entering the treatment plant from multiple sources, as described in claim 1, is characterized by the following steps: S1 includes: Based on the manholes, pipe connections, and sewage flow direction in the spatial topology of the sewage pipe network, the sewage pipe network is divided into multiple pipe network analysis units; Water conductivity, chemical oxygen demand (COD) concentration, and biochemical oxygen demand (BOD) concentration are collected by water quality testing devices installed at monitoring nodes of the pipeline analysis unit, and wastewater flow rate is collected by flow detection devices. Acquire spatial location data of rivers and lakes, spatial location data of water supply pipelines, location data of clean water access points, groundwater level data, and pipeline defect detection data; Water quality monitoring data, flow monitoring data, spatial location data, and pipeline defect detection data are synchronized in time and mapped to the corresponding pipeline network analysis units according to the location of monitoring nodes to form a multi-source external water analysis dataset.

3. The systematic treatment method for reducing external water pollution from multiple sources to increase the concentration of wastewater entering the treatment plant, as described in claim 2, is characterized by: In step S2, the monitoring nodes are calculated according to the following formula. Water conductivity deviation : ； Calculate the monitoring nodes according to the following formula. Relative to upstream adjacent monitoring nodes Chemical oxygen demand concentration gradient : ； in, For monitoring nodes Water conductivity during the current analysis period, For monitoring nodes Baseline water conductivity during historically stable operating periods For monitoring nodes Chemical oxygen demand concentration, For upstream adjacent monitoring nodes Chemical oxygen demand concentration, For monitoring nodes With monitoring nodes The length of the pipe between them; Based on the degree of deviation of water conductivity and chemical oxygen demand concentration gradient Build monitoring nodes External water identification feature vector .

4. The multi-source external water reduction system treatment method for increasing the concentration of wastewater entering the treatment plant according to claim 3, characterized in that: An external water feature database is pre-established, which includes external water feature centers corresponding to river and lake water inflow types, water supply pipeline leakage types, clean water inflow types, groundwater infiltration types, and municipal pipeline network structural defect types. In step S2, the monitoring nodes are calculated according to the following formula. Types of external water sources Feature distance between : ； Calculate the monitoring nodes according to the following formula. Types of external water sources Matching degree between : ； in, External water source type The corresponding deviation of water conductivity from the center value, External water source type The corresponding center value of the chemical oxygen demand concentration gradient, and These are the weights for the deviation in water conductivity and the gradient in chemical oxygen demand concentration, respectively. ; The type of external water source corresponding to the maximum matching degree is determined as the monitoring node. The candidate external water source types are identified, and the candidate external water source types are output when the maximum matching degree reaches the external water identification threshold.

5. The multi-source external water reduction system treatment method for increasing the concentration of wastewater entering the treatment plant according to claim 4, characterized in that: Step S2 identifies the pipe sections affected by external water flow, including: The matching degree of adjacent monitoring nodes is compared sequentially along the sewage flow direction. The monitoring node whose matching degree first reaches the external water identification threshold is determined as the external water anomaly starting node, and the monitoring node located downstream of the external water anomaly starting node and whose matching degree recovers to below the external water identification threshold is determined as the external water anomaly ending node. The pipeline between the starting node and the ending node of the external water anomaly is identified as the candidate pipeline segment affected by external water. Based on the spatial distance between the candidate external water-affected pipe sections and river / lake water bodies, water supply pipelines and clean water access points, as well as the groundwater level data and pipeline defect detection data corresponding to the candidate external water-affected pipe sections, the candidate external water source types are corrected. The corrected external water source type and its corresponding candidate external water-affected pipe section are determined as the external water identification result.

6. The multi-source external water reduction system treatment method for increasing the concentration of wastewater entering the treatment plant according to claim 5, characterized in that: Step S3 includes: When the external water source is river or lake water inflow, interception facilities should be set up at the connection point between the river / lake water body and the sewage pipe network, or the clean water and sewage should be separated in the river / lake water drainage channel and the sewage transportation channel. When the external water source is a water supply pipeline leakage type, leak detection is carried out on the water supply pipeline adjacent to the affected pipeline section, and the leaks found in the water supply pipeline are sealed or the pipeline section is replaced. When the external water source is clean water inflow, trace the clean water access point along the affected pipeline section and implement measures such as blocking, reconnection, or diversion at the clean water access point; When the external water source is groundwater infiltration or municipal pipeline network structural defects, the pipeline repair method is determined based on the location, length, and severity of the defect. For pipelines that meet the conditions for trenchless repair, in-situ solidification is used for repair; for pipelines that do not meet the conditions for trenchless repair, excavation and replacement are used for repair.

7. The multi-source external water reduction system treatment method for increasing the concentration of wastewater entering the treatment plant according to claim 6, characterized in that: In step S4, the type of pollutant is determined. The predicted influent concentration before the implementation of the external water reduction project is calculated according to the following formula. : ； The predicted influent concentration after the implementation of the external water reduction project is calculated using the following formula. : ； The impact of external water reduction projects on pollutant types is calculated using the following formula. The predicted concentration increase contribution value : ； Among them, pollutant types Chemical oxygen demand (COD) or biochemical oxygen demand (BOD) To prevent external water from affecting the sewage flow rate in the pipeline section, Types of pollutants in wastewater concentration, To prevent the inflow of external water from affecting the external water flow rate of the pipeline section, Types of pollutants in external water concentration, The proportion of external water reduction in the external water reduction project.

8. The multi-source external water reduction system treatment method for increasing the concentration of wastewater entering the treatment plant according to claim 7, characterized in that: Step S4 also includes: Select water quality assessment periods before and after the implementation of the external water reduction project, and match the operating conditions of the wastewater treatment plant and the wastewater discharge periods corresponding to the two water quality assessment periods. Calculate the median concentrations of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) for the two water quality assessment periods, respectively. The difference between the median concentration after the implementation of the external water reduction project and the median concentration before implementation is determined as the measured concentration increase contribution value; When the predicted concentration increase contribution is not zero, a contribution correction coefficient is determined based on the ratio of the measured concentration increase contribution to the predicted concentration increase contribution, and this contribution correction coefficient is used to update the external water reduction ratio. .

9. The multi-source external water reduction system treatment method for increasing the concentration of wastewater entering the treatment plant according to claim 8, characterized in that: In step S5, for the first For each candidate external water reduction project, the project implementation priority index is calculated according to the following formula. : ； in, For the first The contribution of each candidate external water reduction project to the increase in chemical oxygen demand (COD) concentration. For the first The contribution of each candidate external water reduction project to the increase in biochemical oxygen demand (BOD) concentration. For the first The external water flow corresponding to each candidate external water reduction project For the first The defect levels of the pipeline sections affected by external water reduction projects corresponding to the candidate external water reduction projects. For normalization function, , , and As weight, and ; A list of external water reduction projects and their implementation schedule are generated in descending order of project implementation priority index, and then sent to the smart water management platform.

10. The systematic treatment method for reducing and reducing multi-source external water to increase the concentration of wastewater entering the treatment plant according to claim 9, characterized in that: Step S5 also includes: The smart water management platform associates and stores monitoring node identifiers, pipe section identifiers affected by external water, types of external water sources, external water reduction engineering measures, predicted concentration increase contribution values, and measured concentration increase contribution values. When the measured concentration increase contribution value is lower than the corresponding target contribution value, the water quality monitoring data and flow monitoring data of the pipe section affected by external water are collected again, and the external water identification feature vector, external water feature center, external water identification threshold and contribution correction coefficient are updated. Based on the updated parameters, the types of external water sources are re-identified, the affected pipe sections are re-determined, and external water reduction engineering measures are adjusted until the measured concentration increase contribution value reaches the target contribution value.

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