An intelligent early warning system and method applied to sewer network operation anomaly

Abstract

The application discloses an intelligent early warning system and method applied to abnormal operation of a drainage pipe network, relates to the technical field of intelligent early warning of the drainage pipe network, and comprises the following steps: S10, acquiring control parameters affecting sewage pollution concentration; S20, performing segmented processing on a main drainage pipeline; S30, generating a plurality of continuous sewage flow direction lines and determining reverse tracing features of the sewage flow direction lines; and S40, selectively triggering an early warning signal. The application overcomes path interference and positioning deviation caused by the topological structure of the sewage drainage pipe network, reduces the investigation range of the abnormal operation area to short-distance segmented pipelines around the target point, and improves the investigation efficiency of the abnormal operation area.

Description

Technical Field

[0001] This invention relates to the field of intelligent early warning technology for drainage pipe networks, specifically an intelligent early warning system and method for abnormal operation of drainage pipe networks. Background Technology

[0002] In the process of urbanization, the discharge of domestic and industrial wastewater is increasing day by day. However, the actual operation of the drainage pipes that guide the wastewater discharge lacks "eyes". Under such circumstances, the relevant decisions made may deviate from the actual situation, thereby reducing the effectiveness of the drainage pipes.

[0003] In existing technologies, relying solely on liquid level monitoring, local monitoring, or manual inspection makes it difficult to achieve full coverage monitoring of sewage drainage pipe networks. This is especially true for complex topological sewage drainage pipe networks, where water flow interference and spatial positioning deviations between different sewage discharge paths significantly reduce the detection rate of abnormal operating areas in the sewage drainage pipe network. Furthermore, it is completely unable to support the analysis of pollution linkage and diffusion trends in cross-regional abnormal operating areas, and can only achieve single-point early warning for isolated abnormal operating areas. It cannot predict the risk of contiguous pollution diffusion and is difficult to support the advance prediction and graded early warning of pollution risks in sewage drainage pipe networks. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent early warning system and method for abnormal operation of drainage pipe networks, so as to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent early warning method for abnormal operation of drainage pipe networks, the method comprising: S10: Obtain control parameters that affect the concentration of wastewater pollution. These control parameters include wastewater discharge volume, wastewater discharge rate, and the inclination angle of the wastewater transmission pipeline. S20: Analyze the segmentation judgment conditions of the main drainage pipeline at each connection point, and segment the main drainage pipeline based on the analysis results; S30: Based on the water quality data and spatial coordinates at each segment location, construct a three-dimensional map of the sewage drainage network. According to the historical normal water quality baseline of various water quality data, selectively mark the segment locations. Combined with the water flow direction of the main drainage pipeline, generate several continuous sewage flow lines and determine the reverse source tracing characteristics of each sewage flow line. S40: Based on the reverse source tracing characteristics of each sewage flow path, analyze the abnormal operation of the sewage drainage network in each sewage flow path, and selectively trigger early warning signals based on the analysis results.

[0006] Furthermore, S10 includes: S101: The sewage drainage network consists of main drainage pipelines, branch drainage pipelines, and ancillary facilities. The ancillary facilities include inspection wells, drainage pumping stations, etc. The main drainage pipeline serves as the core trunk line of the sewage system and is ultimately directly connected to the sewage treatment plant. One end of each branch drainage pipeline is connected to the branch drainage pipeline, and the other end is connected to various drainage nodes within the building. The branch drainage pipeline is used to collect the sewage transported by the branch drainage pipelines and transport the collected sewage to the main drainage pipeline. S102: Based on the flow direction of sewage in the main drainage pipeline, the connections between the main drainage pipeline and each other drainage branch pipeline are numbered sequentially. The numbering result is as follows: ; m represents the total number. In the main drainage pipeline, a flow meter Gi is installed at a distance h meters from the connection numbered i. Sewage flows from the flow meter Gi to the connection numbered i. The specific value of h is less than the minimum distance between two adjacent connections. In the main drainage pipeline, a flow meter Fi is installed at a distance h meters from the connection numbered i. Sewage flows from the flow meter Fi to the connection numbered i. S103: The wastewater discharge rate corresponding to the flow rate of wastewater passing through flow meter Gi. Monitoring is performed using flow meter Fi to measure the wastewater discharge rate as it flows through the flow meter Fi. Monitoring was conducted, and the flow meter used was model TWQ. The TWQ flow meter is a THWater smart drainage monitoring flow meter, which is a dedicated flow monitoring device developed specifically for drainage scenarios. Monitoring the amount of sewage discharged is one of its core functions. Wastewater discharge and sewage discharge The sum of the values ​​between them is used to calculate the discharge amount Qi when the sewage flows through the connection numbered i. An in-situ miniature sampler Wi equipped with a GNSS positioning module is installed at the connection point numbered i. Specifically, limit buckles are installed at each connection point, and lightweight rigid rods are connected to the limit buckles. The in-situ miniature sampler is fitted onto the lightweight rigid rods via a sliding sleeve, ensuring that the in-situ miniature sampler is constrained within the same vertical plane of the corresponding water flow section at the connection point throughout its operation. This prevents the equipment from drifting due to water flow impact and ensures that the GNSS positioning module is always in an unobstructed signal reception environment. The GNSS positioning module is used to determine the water flow cross-sectional area S corresponding to the connection point numbered i. Monitoring is performed on device i, where the in-situ micro sampler is model TWS. The in-situ micro sampler has a special cavity structure, which allows it to float stably on the liquid surface, ensuring that the GNSS positioning module is in an unobstructed environment and avoiding satellite signal blockage. The GNSS positioning module is used to collect the precise latitude, longitude, and elevation coordinates of the sampling point corresponding to the connection number i in real time, so as to complete the spatial location calibration of the sampling point. The sampling point is the floating point of the in-situ micro sampler. The ratio between the discharge amount Qi and Si is calculated to obtain the transmission rate Vi corresponding to the sewage flowing through the connection number i.

[0007] Furthermore, S20 includes: S201: The discharge volume Qi corresponding to the sewage flowing through the connection numbered i is compared with the discharge volume of the main drainage pipe monitored by the flow meter G(i+1) when the sewage flows through the connection numbered i+1. Whether the difference X between the two values ​​is within the error range K1 is used as the first segment judgment condition. If the difference X is within the error range K1, then the first segment judgment condition is met; if the difference X is not within the error range, then the first segment judgment condition is not met. The error range K1 is... Where z represents the maximum allowable flow deviation of the main drainage pipeline, in units of: The maximum allowable flow deviation of the main drainage pipeline = the design rated full flow of the main drainage pipeline × the comprehensive allowable error rate of the flow meter. Specifically, the comprehensive allowable error rate for a 1.5-class industrial flow meter is 0.03. The second segment judgment condition is whether the difference Y between the transmission rate Vi when the sewage flows through connection i and the transmission rate V(i+1) when the sewage flows through connection i+1 is within the error range K2. If the difference Y is within the error range K2, the second segment judgment condition is met, and the hydraulic state is continuous and stable. If the difference Y is not within the error range, the second segment judgment condition is not met, and there is an abnormal hydraulic disturbance. The error range K2 is... Where ti represents the inclination angle of the main drainage pipe between the connection numbered i-1 and the connection numbered i, and t(i+1) represents the inclination angle of the main drainage pipe between the connection numbered i and the connection numbered i+1. In the conventional engineering design and operation and maintenance scenarios of urban gravity flow drainage main pipes, the pipe must meet the mandatory design requirement of a non-decreasing slope along the sewage flow direction, that is... , , This represents the square root difference in hydraulic gradient between the drainage main pipeline segment corresponding to connection i-1 and connection i, and the drainage main pipeline segment corresponding to connection i and connection i+1. This is equivalent to the theoretical flow velocity difference caused by the different gradients of the upstream and downstream drainage main pipelines. 0.15 represents an empirical coefficient, specifically indicating that for every 10% of the drainage volume of the main drainage pipeline flowing into it, the flow velocity at the merging point will naturally fluctuate by 1.5%. This represents the ratio between the sewage discharge from the drainage trunk line at connection number i into the main drainage line and the sewage discharge monitored by flow meter Gi. The specific value can be 0.3. In existing technologies, the error range K2 is limited by a single classical hydraulic formula or empirical threshold, which lacks adaptability to different pipe network topologies. However, by using the mandatory engineering design rule of non-decreasing slope of urban gravity flow drainage trunk pipelines, the error range can be directly converted into a quantifiable adaptive error range. This allows the error range to be dynamically adjusted according to the actual slope of the upstream and downstream pipe sections and the real-time flow ratio, which can overcome the path interference of the pipe network topology and improve the engineering adaptability of segment judgment. S202: If both the first and second segmentation judgment conditions are met, then the connection point numbered i is considered not to be a segmentation location of the main drainage pipeline; otherwise, the connection point numbered i is considered to be a segmentation location of the main drainage pipeline. The main drainage pipeline is then segmented according to the segmentation location, dividing the long and continuous main drainage pipeline into several independent pipelines with uniform hydraulic characteristics. This avoids the need for traversing the entire sewage drainage network and shortens the computational time required to investigate abnormal operating areas.

[0008] Furthermore, S30 includes: S301: Collect water quality data at the corresponding segment locations using in-situ micro samplers installed at each segment location. The water quality data includes chemical oxygen demand, ammonia nitrogen concentration, and suspended solids concentration. S302: Using the GNSS positioning module mounted on the in-situ micro sampler set at each segment location to collect spatial coordinates, the spatial coordinates are the coordinates of the sampling point, to obtain a three-dimensional map of the sewage drainage network, and to map the water quality data collected at each segment location onto the three-dimensional map of the sewage drainage network. S303: Compare the real-time water quality parameters at the segment location with the historical normal water quality baseline for that location. If any two of the following indicators—chemical oxygen demand (COD), ammonia nitrogen concentration, and suspended solids concentration—exceed the baseline fluctuation range for the corresponding indicator, mark the segment location on the 3D map of the sewage drainage network. The historical normal water quality baseline refers to the average concentration of COD, ammonia nitrogen concentration, and suspended solids concentration collected continuously for more than two weeks under normal conditions (no abnormal sewage discharge, no network damage), after removing outliers, and the calculated average concentration over different time periods and the normal fluctuation range. The normal fluctuation range is... Where B represents the standard deviation of all valid monitoring data for a certain indicator across different time periods, and A represents the average normal concentration for a certain indicator across different time periods. By statistically analyzing the average normal concentration across different time periods and the normal fluctuation range, the reasonable fluctuation of the fluctuation range between different time periods is overcome, and the effectiveness of the segmentation position marking is improved. S304: Based on the water flow direction of the main drainage pipeline, connect all marked segment positions to generate multiple continuous sewage flow lines. Determine the reverse source tracing characteristics of each sewage flow line based on the water quality data at each marked segment position. Specifically, randomly select a continuous sewage flow line. Calculate the pollution index for each segment position in the selected sewage flow line based on the water quality data mapped to them. Pollution index = Chemical Oxygen Demand (COD) score × 45% + Ammonia Nitrogen score × 35% + Suspended Solids score × 20%. The single-index score refers to the quantified value that maps the measured concentrations of COD, ammonia nitrogen, and suspended solids to a pollution level score within the 0-100 range. For example, 15 ≤ COD ≤ 50. The unit is: The score for chemical oxygen demand (COD) is between 20 and 40. If the COD is... The score for chemical oxygen demand is then... The segment point corresponding to the maximum value of the pollution index is recorded as the pollution index peak point. The downstream segment position adjacent to the pollution index peak point in the selected sewage flow route is determined. The difference between the pollution index corresponding to the pollution index peak point and the pollution index corresponding to the downstream adjacent segment position point is calculated to obtain the first concentration gradient difference. After removing the peak point of the pollution index from the selected sewage flow path, repeat the above steps to obtain several concentration gradient differences until the calculated difference is less than or equal to 0. When the calculated difference is less than or equal to 0, it means that the pollution index no longer increases along the water flow direction of the main drainage pipe and has been traced back to the upstream boundary of the pollution source. The reverse source tracing feature of the selected wastewater flow path is obtained by combining all the concentration gradient differences calculated from the set and the coordinates of the pollution index peak points corresponding to the concentration gradient differences. Specifically, the reverse source tracing feature = {(concentration gradient difference, pollution index peak point coordinates), ...}. The reverse source tracing feature refers to the set of points formed by iterating along the back direction of the water flow in the main drainage pipeline, which consists of "the difference between the pollution index value corresponding to the peak point of the pollution index and the pollution index of the downstream segment position adjacent to the peak point, and the coordinates corresponding to the peak point of the pollution index". By accurately locating abnormal operating nodes in the sewage flow path through concentration iteration and relying on continuous concentration gradient verification along the flow direction, it is not only unaffected by the complexity of the sewage drainage network topology, but also avoids misjudgments caused by accidental data fluctuations at single abnormal points. At the same time, it narrows the investigation scope to short-distance segmented pipelines around the target point. Compared with traditional methods that can only locate single pollution sources through nonlinear superposition correction models, the complete point set formed by reverse source tracing features can completely restore the entire link of pollution transmission in reverse along the pipeline network. It can effectively avoid the defects of traditional methods that miss intermediate overflow nodes. In addition, it also solves the problem that the source tracing results are not unique in multi-source superposition scenarios of traditional methods.

[0009] Furthermore, S40 includes: S401: Based on the reverse source tracing characteristics of each sewage flow path, all abnormal operating areas in the sewage drainage network are identified. These abnormal operating areas are closed regions formed by connecting all pollution index peak points where the concentration gradient difference exceeds a set threshold along the sewage drainage network. Specifically, the set threshold can be... ; S402: Based on the area of ​​each abnormal operating area and the minimum boundary distance between each abnormal operating area and its upstream adjacent abnormal operating area, the early warning index of the sewage flow path matched for each abnormal operating area is calculated. The specific calculation method is as follows: Randomly select an abnormal operating area, normalize the area u of the selected abnormal operating area, and the normalization result of the area... ,in, , These represent the maximum and minimum areas of all regions within the sewage drainage network, respectively. At that time, take ; Based on the outer contour boundary of the selected abnormal operation area and its upstream adjacent abnormal operation area, the Euclidean distance between all point pairs is calculated, and the shortest calculated Euclidean distance is taken as the minimum boundary distance v between the selected abnormal operation area and its upstream adjacent abnormal operation area. Specifically, if there is only one abnormal operation area in the sewage drainage network, v is taken as positive infinity. At this time, the early warning index is determined only by the area normalization value. according to The early warning index for the sewage flow path matched to the selected abnormal operation area is calculated, whereby... This represents the average length of all pipe segments in the sewage drainage network. Average pipe length = (Sum of the lengths of all pipe segments in the sewage drainage network) / (Total number of pipe segments in the sewage drainage network). , Both represent weighting coefficients and , This represents an exponential function with base e. By incorporating the area of ​​abnormal operation zones and the minimum boundary distance between zones into the calculation of the early warning index, regional pollution spread risk early warning can be achieved, which improves the accuracy of early warning compared to single-point early warning. S403: The warning index of the sewage flow path is compared with the warning threshold. When the warning index is greater than the warning threshold, an early warning signal is issued for the abnormal operation area corresponding to the sewage flow path mapped on the 3D map of the sewage drainage network. The early warning signal includes the warning index of the sewage flow path matching the abnormal operation area, and the warning level of the warning signal is automatically adjusted according to the warning index, which facilitates maintenance personnel to determine the maintenance priority of each abnormal operation area. By incorporating the normalized area of ​​the abnormal area and the minimum boundary distance between areas into the calculation of the warning index and introducing an exponential decay term, the objective law that "the closer the spatial distance between adjacent abnormal areas, the higher the risk of pollution linkage and diffusion" in the sewage drainage network scenario can be quantified. This is different from the inherent logic of traditional pipeline early warning that relies only on single-point water quality indicators to trigger early warnings. Furthermore, by binding spatial distance with the transmission law of pollution diffusion through the exponential decay term, it does not rely on pure hydraulic parameters, thus improving the early warning efficiency.

[0010] An intelligent early warning system for abnormal operation of drainage pipe networks, the system includes a control parameter acquisition module, a segmented processing module, a reverse tracing feature determination module, and an intelligent early warning module; The control parameter acquisition module is used to acquire control parameters that affect the concentration of wastewater pollution. The segmentation processing module is used to analyze the segmentation judgment conditions of the main drainage pipeline at each connection point, and to segment the main drainage pipeline based on the analysis results. The reverse tracing feature determination module is used to generate several continuous sewage flow lines based on the marking of each segment position in the three-dimensional map of the sewage drainage network and the water flow direction of the main drainage pipeline, and to determine the reverse tracing features of each sewage flow line. The intelligent early warning module is used to analyze the abnormal operation of the sewage drainage pipe network along each sewage flow path, and selectively trigger early warning signals based on the analysis results.

[0011] Furthermore, the control parameter acquisition module includes a monitoring device deployment unit and a control parameter monitoring unit; The monitoring device deployment unit determines the deployment locations of the flow meter and the in-situ micro sampler equipped with a GNSS positioning module based on the location of the connection between the main drainage pipe and each drainage branch pipe in the sewage drainage network. The control parameter monitoring unit monitors the sewage discharge volume corresponding to each flow meter and the cross-sectional area of ​​the sewage flow at each connection point by deploying flow meters and an in-situ miniature sampler equipped with a GNSS positioning module.

[0012] Furthermore, the segmentation processing module includes a first segmentation judgment condition analysis unit, a second segmentation judgment condition analysis unit, and a segmentation unit; The first segment judgment condition analysis unit analyzes whether the first segment judgment condition is met based on whether the difference between the corresponding discharge amounts when the sewage flows through two adjacent connection points is within the error range, and transmits the analysis results to the segmentation unit. The second segment judgment condition analysis unit analyzes whether the second segment judgment condition is met based on whether the difference between the corresponding transmission rates when the sewage flows through two adjacent connection points is within the error range, and transmits the analysis result to the segmentation unit. The segmentation unit determines the segmentation position of the main drainage pipeline based on the analysis results transmitted by the first segmentation judgment condition analysis unit and the second segmentation judgment condition analysis unit, and performs segmentation processing on the main drainage pipeline according to the determined segmentation position.

[0013] Furthermore, the reverse source tracing feature determination module includes a water quality data acquisition unit, a sewage drainage network three-dimensional map construction unit, a segmented location marking unit, and a reverse source tracing feature determination unit; The water quality data acquisition unit uses in-situ micro samplers set at each segment location to collect water quality data at the corresponding segment location. The three-dimensional map construction unit of the sewage drainage pipe network uses the GNSS positioning module mounted on the in-situ micro sampler set at each segment location to collect spatial coordinates to obtain a three-dimensional map of the sewage drainage pipe network, and maps the water quality data collected at each segment location onto the three-dimensional map of the sewage drainage pipe network. The segment location marking unit compares the real-time water quality parameters at the segment location with the historical normal water quality baseline at that location. If any two of the indicators exceed the baseline fluctuation range of the corresponding indicator, the segment location is marked in the three-dimensional map of the sewage drainage network. The reverse source tracing feature determination unit uses the water flow direction of the main drainage pipeline as a reference, connects all marked segment positions in series to generate multiple continuous sewage flow lines, and determines the reverse source tracing features of each sewage flow line based on the water quality data at each marked segment position.

[0014] Furthermore, the intelligent early warning module includes an abnormal operation area locking unit, an early warning index calculation unit, and an intelligent early warning unit; The abnormal operation area locking unit locks all abnormal operation areas in the sewage drainage pipe network based on the reverse tracing characteristics of each sewage flow path. The early warning index calculation unit calculates the early warning index of the sewage flow path matched for each abnormal operation area based on the area of ​​each abnormal operation area and the minimum boundary distance between each abnormal operation area and its upstream adjacent abnormal operation area. The intelligent early warning unit controls the abnormal operation areas corresponding to the sewage flow lines mapped in the three-dimensional map of the sewage drainage network to selectively issue early warning signals based on the comparison results between the early warning index and the early warning threshold of the sewage flow lines.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention overcomes the path interference and positioning deviation caused by the topology of the sewage drainage pipe network by using a process of segmented deployment of monitoring equipment → high-precision spatial coordinate mapping of the sewage drainage pipe network into a 3D map → dual-index baseline triggering markers → iterative reverse tracing of pollution index peak gradient differences. Traditional methods only eliminate false peaks at junctions through nonlinear superposition correction, which cannot filter out local pseudo-anomalies caused by hydraulic disturbances along the pipeline. This invention also relies on the spatial correlation screening of the early warning index to directly narrow the scope of investigation of abnormal operating areas to short-distance segmented pipes around the target point. Compared with the existing technology of carpet-style investigation of the entire pipe network, this reduces the time for anomaly investigation. At the same time, the full path backtracking capability obtained by the reverse tracing point set improves the accuracy of cross-regional diffusion anomaly identification and solves the problem that the traditional method of single-point tracing cannot cover the entire pollution transmission link.

[0016] 2. This invention analyzes the first and second segmentation judgment conditions of the main drainage pipeline at each connection point by measuring the sewage transmission rate and sewage discharge volume corresponding to the sewage flow through each connection point. This enables the segmentation processing of the main drainage pipeline, dividing the long and continuous main drainage pipeline into several independent pipelines with uniform hydraulic characteristics. This avoids the need for traversing the entire sewage drainage network and shortens the computational time required to investigate abnormal operating areas.

[0017] 3. This invention incorporates the area of ​​the abnormal operation zone and the minimum boundary distance between zones into the calculation process of the early warning index, thereby realizing a quantitative and graded early warning of regional pollution spread risk. Compared with single-point early warning, it can capture the cross-regional pollution linkage and spread trend, and the early warning grading accuracy is higher, thus realizing intelligent early warning of abnormal operation of sewage drainage pipe network. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the workflow of an intelligent early warning method for abnormal operation of drainage pipe networks according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] As shown in the figure, this invention provides a technical solution for an intelligent early warning method applied to abnormal operation of drainage pipe networks. The method includes: S10: Obtain control parameters that affect the concentration of wastewater pollution. These control parameters include wastewater discharge volume, wastewater discharge rate, and the inclination angle of the wastewater transmission pipeline. S10 includes: S101: The sewage drainage network consists of main drainage pipes, branch drainage pipes, and ancillary facilities. Ancillary facilities include inspection wells, drainage pumping stations, etc. The main drainage pipes serve as the core trunk line of the sewage system and are ultimately directly connected to the sewage treatment plant. One end of the branch drainage pipes is connected to the branch drainage pipes, and the other end is connected to various drainage nodes within the building. The branch drainage pipes are used to collect the sewage transported by the branch drainage pipes and transport the collected sewage to the main drainage pipes. S102: Based on the flow direction of sewage in the main drainage pipeline, the connections between the main drainage pipeline and each other drainage branch pipeline are numbered sequentially. The numbering result is as follows: ; m represents the total number. In the main drainage pipeline, a flow meter Gi is installed at a distance h meters from the connection numbered i. Sewage flows from the flow meter Gi to the connection numbered i. The specific value of h is less than the minimum distance between two adjacent connections. In the main drainage pipeline, a flow meter Fi is installed at a distance h meters from the connection numbered i. Sewage flows from the flow meter Fi to the connection numbered i. S103: The wastewater discharge rate corresponding to the flow rate of wastewater passing through flow meter Gi. Monitoring is performed using flow meter Fi to measure the wastewater discharge rate as it flows through the flow meter Fi. Monitoring was conducted, and the flow meter used was model TWQ. The TWQ flow meter is a THWater smart drainage monitoring flow meter, which is a dedicated flow monitoring device developed specifically for drainage scenarios. Monitoring the amount of sewage discharged is one of its core functions. Wastewater discharge and sewage discharge The sum of the values ​​between them is used to calculate the discharge amount Qi when the sewage flows through the connection numbered i. An in-situ miniature sampler Wi equipped with a GNSS positioning module is installed at the connection point numbered i. Specifically, limit buckles are installed at each connection point, and lightweight rigid rods are connected to the limit buckles. The in-situ miniature sampler is fitted onto the lightweight rigid rods via a sliding sleeve, ensuring that the in-situ miniature sampler is constrained within the same vertical plane of the corresponding water flow section at the connection point throughout its operation. This prevents the equipment from drifting due to water flow impact and ensures that the GNSS positioning module is always in an unobstructed signal reception environment. The GNSS positioning module is used to determine the water flow cross-sectional area S corresponding to the connection point numbered i. i is used for monitoring. Among them, the model of the in-situ micro sampler is TWS. The in-situ micro sampler has a special cavity structure, which can float stably on the liquid surface. This allows the GNSS positioning module to be in an unobstructed environment, avoiding the obstruction of satellite signals. The GNSS positioning module is used to collect the accurate latitude, longitude and elevation coordinates of the sampling point corresponding to the connection number i in real time, so as to complete the spatial location calibration of the sampling point. The sampling point is the floating point of the in-situ micro sampler. The ratio between the discharge Qi and Si is calculated to obtain the transmission rate Vi corresponding to the sewage flowing through the connection number i. S20: Analyze the segmentation judgment conditions of the main drainage pipeline at each connection point, and segment the main drainage pipeline based on the analysis results; S20 includes: S201: The discharge volume Qi corresponding to the sewage flowing through the connection numbered i is compared with the discharge volume of the main drainage pipe monitored by the flow meter G(i+1) when the sewage flows through the connection numbered i+1. Whether the difference X between the two values ​​is within the error range K1 is used as the first segment judgment condition. If the difference X is within the error range K1, then the first segment judgment condition is met; if the difference X is not within the error range, then the first segment judgment condition is not met. The error range K1 is... Where z represents the maximum allowable flow deviation of the main drainage pipeline, in units of: The maximum allowable flow deviation of the main drainage pipeline = the design rated full flow of the main drainage pipeline × the comprehensive allowable error rate of the flow meter. Specifically, the comprehensive allowable error rate for a 1.5-class industrial flow meter is 0.03. The second segment judgment condition is whether the difference Y between the transmission rate Vi when the sewage flows through connection i and the transmission rate V(i+1) when the sewage flows through connection i+1 is within the error range K2. If the difference Y is within the error range K2, the second segment judgment condition is met, and the hydraulic state is continuous and stable. If the difference Y is not within the error range, the second segment judgment condition is not met, and there is an abnormal hydraulic disturbance. The error range K2 is... Where ti represents the inclination angle of the main drainage pipe between the connection numbered i-1 and the connection numbered i, and t(i+1) represents the inclination angle of the main drainage pipe between the connection numbered i and the connection numbered i+1. In the conventional engineering design and operation and maintenance scenarios of urban gravity flow drainage main pipes, the pipe must meet the mandatory design requirement of a non-decreasing slope along the sewage flow direction, that is... , , This represents the square root difference in hydraulic gradient between the drainage main pipeline segment corresponding to connection i-1 and connection i, and the drainage main pipeline segment corresponding to connection i and connection i+1. This is equivalent to the theoretical flow velocity difference caused by the different gradients of the upstream and downstream drainage main pipelines. 0.15 represents an empirical coefficient, specifically indicating that for every 10% of the drainage volume of the main drainage pipeline flowing into it, the flow velocity at the merging point will naturally fluctuate by 1.5%. This represents the ratio between the sewage discharge from the drainage trunk line at connection number i into the main drainage line and the sewage discharge monitored by flow meter Gi. The specific value can be 0.3. S202: If both the first segmentation judgment condition and the second segmentation judgment condition are met, then the connection point numbered i is considered not to be a segmentation position of the main drainage pipeline; otherwise, the connection point numbered i is considered to be a segmentation position of the main drainage pipeline, and the main drainage pipeline is segmented according to the segmentation position. S30: Based on the water quality data and spatial coordinates at each segment location, construct a three-dimensional map of the sewage drainage network. According to the historical normal water quality baseline of various water quality data, selectively mark the segment locations. Combined with the water flow direction of the main drainage pipeline, generate several continuous sewage flow lines and determine the reverse source tracing characteristics of each sewage flow line. S30 includes: S301: Collect water quality data at the corresponding segment locations using in-situ micro samplers installed at each segment location. The water quality data includes chemical oxygen demand, ammonia nitrogen concentration, and suspended solids concentration. S302: Using the GNSS positioning module mounted on the in-situ micro sampler set at each segment location to collect spatial coordinates, which are the coordinates of the sampling points, a three-dimensional map of the sewage drainage network is obtained, and the water quality data collected at each segment location is mapped onto the three-dimensional map of the sewage drainage network. S303: Compare the real-time water quality parameters at the segment location with the historical normal water quality baseline for that location. If any two of the following indicators—chemical oxygen demand (COD), ammonia nitrogen concentration, and suspended solids concentration—exceed the baseline fluctuation range for the corresponding indicator, mark the segment location on the 3D map of the sewage drainage network. The historical normal water quality baseline refers to the average concentration of COD, ammonia nitrogen concentration, and suspended solids concentration collected continuously for more than two weeks under normal conditions (no abnormal sewage discharge, no network damage), after removing outliers, and the calculated average concentration over different time periods and the normal fluctuation range. The normal fluctuation range is... Where B represents the standard deviation of all valid monitoring data for a certain indicator across different time periods, and A represents the mean of the conventional concentration for a certain indicator across different time periods. S304: Based on the water flow direction of the main drainage pipeline, connect all marked segment positions to generate multiple continuous sewage flow lines. Determine the reverse source tracing characteristics of each sewage flow line based on the water quality data at each marked segment position. Specifically, randomly select a continuous sewage flow line. Calculate the pollution index for each segment position in the selected sewage flow line based on the water quality data mapped to them. Pollution index = Chemical Oxygen Demand (COD) score × 45% + Ammonia Nitrogen score × 35% + Suspended Solids score × 20%. The single-index score refers to the quantified value that maps the measured concentrations of COD, ammonia nitrogen, and suspended solids to a pollution level score within the 0-100 range. For example, 15 ≤ COD ≤ 50. The unit is: The score for chemical oxygen demand (COD) is between 20 and 40. If the COD is... The score for chemical oxygen demand is then... The segment point corresponding to the maximum value of the pollution index is recorded as the pollution index peak point. The downstream segment position adjacent to the pollution index peak point in the selected sewage flow route is determined. The difference between the pollution index corresponding to the pollution index peak point and the pollution index corresponding to the downstream adjacent segment position point is calculated to obtain the first concentration gradient difference. After removing the peak point of the pollution index from the selected sewage flow path, repeat the above steps to obtain several concentration gradient differences until the calculated difference is less than or equal to 0. When the calculated difference is less than or equal to 0, it means that the pollution index no longer increases along the water flow direction of the main drainage pipe and has been traced back to the upstream boundary of the pollution source. The reverse source tracing feature of the selected wastewater flow path is obtained by combining all the concentration gradient differences calculated from the set and the coordinates of the pollution index peak points corresponding to the concentration gradient differences. Specifically, the reverse source tracing feature = {(concentration gradient difference, pollution index peak point coordinates), ...}. The reverse source tracing feature refers to the set of points obtained by iterating along the water flow in the reverse direction of the main drainage pipeline, which consists of "the difference between the pollution index value corresponding to the peak point of the pollution index and the pollution index of the downstream segment point adjacent to the peak point, and the coordinates corresponding to the peak point of the pollution index". S40: Based on the reverse source tracing characteristics of each sewage flow path, analyze the abnormal operation of the sewage drainage network in each sewage flow path, and selectively trigger early warning signals based on the analysis results; S40 includes: S401: Based on the reverse source tracing characteristics of each sewage flow path, all abnormal operating areas in the sewage drainage network are identified. An abnormal operating area is a closed area formed by connecting all pollution index peaks where the concentration gradient difference exceeds a set threshold along the sewage drainage network. Specifically, the set threshold can be... ; S402: Based on the area of ​​each abnormal operating area and the minimum boundary distance between each abnormal operating area and its upstream adjacent abnormal operating area, the early warning index of the sewage flow path matched for each abnormal operating area is calculated. The specific calculation method is as follows: Randomly select an abnormal operating area, normalize the area u of the selected abnormal operating area, and the normalization result of the area... ,in, , These represent the maximum and minimum areas of all regions within the sewage drainage network, respectively. At that time, take ; Based on the outer contour boundary of the selected abnormal operation area and its upstream adjacent abnormal operation area, the Euclidean distance between all point pairs is calculated. The point pair refers to the coordinate point pair formed by pairing any point on the outer contour boundary of the selected abnormal operation area with any point on the outer contour boundary of the upstream adjacent abnormal operation area. The shortest calculated Euclidean distance is taken as the minimum boundary distance v between the selected abnormal operation area and its upstream adjacent abnormal operation area. Specifically, if there is only one abnormal operation area in the sewage drainage network, v is taken as positive infinity. according to The early warning index for the sewage flow path matched to the selected abnormal operation area is calculated, whereby... This represents the average length of all pipe segments in the sewage drainage network. Average pipe length = (Sum of the lengths of all pipe segments in the sewage drainage network) / (Total number of pipe segments in the sewage drainage network). , Both represent weighting coefficients and , This represents an exponential function with base e. ; S403: The warning index of the sewage flow path is compared with the warning threshold. When the warning index is greater than the warning threshold, the abnormal operation area corresponding to the sewage flow path is mapped in the three-dimensional map of the sewage drainage network and a warning signal is issued. The warning signal contains the warning index of the sewage flow path that matches the abnormal operation area. The warning level of the warning signal is automatically adjusted according to the warning index, so as to facilitate maintenance personnel to determine the maintenance priority of each abnormal operation area.

[0021] An intelligent early warning system for abnormal operation of drainage pipe networks is provided. The system includes a control parameter acquisition module, a segmented processing module, a reverse tracing feature determination module, and an intelligent early warning module. The control parameter acquisition module is used to acquire control parameters that affect the concentration of wastewater pollution. The control parameter acquisition module includes a monitoring device deployment unit and a control parameter monitoring unit; The monitoring device deployment unit determines the deployment locations of flow meters and in-situ micro samplers equipped with GNSS positioning modules based on the location of the connection points between the main drainage pipes and various drainage trunk pipes in the sewage drainage network. The control parameter monitoring unit monitors the sewage discharge corresponding to each flow meter and the cross-sectional area of ​​the sewage flow at each connection point by deploying flow meters and an in-situ miniature sampler equipped with a GNSS positioning module. The segmentation processing module is used to analyze the segmentation judgment conditions of the main drainage pipeline at each connection point, and to segment the main drainage pipeline based on the analysis results. The segmentation processing module includes a first segmentation judgment condition analysis unit, a second segmentation judgment condition analysis unit, and a segmentation unit; The first segment judgment condition analysis unit analyzes whether the first segment judgment condition is met based on whether the difference between the corresponding discharge amounts when the sewage flows through two adjacent connection points is within the error range, and transmits the analysis results to the segmentation unit. The second segment judgment condition analysis unit analyzes whether the second segment judgment condition is met based on whether the difference between the corresponding transmission rates when the sewage flows through two adjacent connection points is within the error range, and transmits the analysis results to the segmentation unit. The segmentation unit determines the segmentation position of the main drainage pipeline based on the analysis results transmitted by the first segmentation judgment condition analysis unit and the second segmentation judgment condition analysis unit, and performs segmentation processing on the main drainage pipeline according to the determined segmentation position. The reverse source tracing feature determination module is used to generate several continuous sewage flow lines based on the marking of each segment location in the three-dimensional map of the sewage drainage network and the water flow direction of the main drainage pipeline, and to determine the reverse source tracing features of each sewage flow line. The reverse source tracing feature determination module includes a water quality data acquisition unit, a sewage drainage network 3D map construction unit, a segmented location marking unit, and a reverse source tracing feature determination unit. The water quality data acquisition unit uses in-situ miniature samplers set at each segment location to collect water quality data at the corresponding segment location; The three-dimensional map construction unit of the sewage drainage pipe network uses the spatial coordinates collected by the GNSS positioning module mounted on the in-situ micro sampler set at each segment location to obtain a three-dimensional map of the sewage drainage pipe network, and maps the water quality data collected at each segment location onto the three-dimensional map of the sewage drainage pipe network. The segment location marking unit compares the real-time water quality parameters at the segment location with the historical normal water quality baseline at that location. If any two of the indicators exceed the baseline fluctuation range of the corresponding indicator, the segment location is marked in the three-dimensional map of the sewage drainage network. The reverse source tracing feature determination unit uses the water flow direction of the main drainage pipeline as a reference, connects all marked segment positions in series, generates multiple continuous sewage flow lines, and determines the reverse source tracing features of each sewage flow line based on the water quality data at each marked segment position. The intelligent early warning module is used to analyze the abnormal operation of the sewage drainage pipe network along each sewage flow path, and selectively trigger early warning signals based on the analysis results; The intelligent early warning module includes an abnormal operation area locking unit, an early warning index calculation unit, and an intelligent early warning unit; The abnormal operation area locking unit locks down all abnormal operation areas in the sewage drainage pipe network based on the reverse tracing characteristics of each sewage flow path; The early warning index calculation unit calculates the early warning index of the sewage flow path matched to each abnormal operation area based on the area of ​​each abnormal operation area and the minimum boundary distance between each abnormal operation area and its upstream adjacent abnormal operation area. Based on the comparison between the warning index and the warning threshold of the sewage flow path, the intelligent early warning unit controls the abnormal operation area corresponding to the sewage flow path mapped in the three-dimensional map of the sewage drainage network to selectively issue early warning signals.

[0022] Example 1: When there is only a single abnormal operating area in the sewage drainage network, let the area of ​​the single abnormal operating area be 30. , , At this point, the normalized result of the region area If v is positive infinity, then the early warning index for the sewage flow path matched in a single abnormal operation area is: ; When there are at least two abnormal operating areas in the sewage drainage network, let the area of ​​the selected abnormal operating area be 30. , , , , ; If the minimum boundary distance between the selected abnormal operation area and its upstream adjacent abnormal operation area is selected... At this point, the normalized result of the area of ​​the selected abnormal operating region is: The early warning index for the selected abnormal operation area matching the sewage flow route is: ; If the minimum boundary distance between the selected abnormal operation area and its upstream adjacent abnormal operation area is selected... , The early warning index for the selected abnormal operation area matching the sewage flow route is: ; If the warning threshold is set to 0.3, since 0.23 < 0.3, the risk of pollution spread in the selected abnormal operation area is determined to be extremely low, and no warning signal is issued for the selected abnormal operation area in the three-dimensional map of the sewage drainage network. Since 0.46 is slightly higher than 0.3, a regular early warning signal is issued for a single abnormal operating area or a selected abnormal operating area in the three-dimensional map of the sewage drainage network. Since 0.6 is significantly higher than 0.3, a high-level early warning signal will be issued for a single abnormal operating area or a selected abnormal operating area in the three-dimensional map of the sewage drainage network.

[0023] Example 2: Assume that the discharge volume Qi corresponding to the sewage flowing through the connection numbered i is 300. The discharge volume of the main drainage pipe monitored by flow meter G(i+1) when sewage flows through connection number i+1. 330 ,but: The calculated result of the difference X is 30. ; The design rated full-pipe flow rate of the main drainage pipeline is 500. If the overall permissible error rate of the flow meter is 0.03, then the error range K1 is... ; Since the difference X is not within the error range K1, it indicates that the first segment judgment condition is not met.

[0024] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An intelligent early warning method for abnormal operation of drainage pipe networks, characterized in that: The method includes: S10: Obtain control parameters that affect the concentration of wastewater pollution. These control parameters include wastewater discharge volume, wastewater discharge rate, and the inclination angle of the wastewater transmission pipeline. S20: Analyze the segmentation judgment conditions of the main drainage pipeline at each connection point, and segment the main drainage pipeline based on the analysis results; S30: Based on the water quality data and spatial coordinates at each segment location, construct a three-dimensional map of the sewage drainage network. According to the historical normal water quality baseline of various water quality data, selectively mark the segment locations. Combined with the water flow direction of the main drainage pipeline, generate several continuous sewage flow lines. Iteratively calculate the pollution index gradient value between the peak point of the pollution index and the downstream segment location adjacent to the peak point along the opposite direction of the water flow of each sewage flow line until the gradient difference is less than or equal to 0. The resulting set of several data groups consisting of the concentration gradient difference and the coordinates of the peak point of the pollution index is used as the reverse source tracing feature of the corresponding sewage flow line. S40: Based on the reverse source tracing characteristics of each sewage flow path, analyze the abnormal operation of the sewage drainage network in each sewage flow path, and selectively trigger early warning signals based on the analysis results.

2. The intelligent early warning method for abnormal operation of drainage pipe networks according to claim 1, characterized in that: S10 includes: S101: The sewage drainage network consists of a main drainage pipeline, a branch drainage pipeline, and ancillary facilities. The main drainage pipeline serves as the core trunk line of the sewage system and is ultimately directly connected to the sewage treatment plant. One end of the branch drainage pipeline is connected to the branch drainage pipeline, and the other end is connected to various drainage nodes within the building. The branch drainage pipeline is used to collect the sewage transported by the branch drainage pipeline and transport the collected sewage to the main drainage pipeline. S102: Based on the flow direction of sewage in the main drainage pipeline, the connections between the main drainage pipeline and each other drainage branch pipeline are numbered sequentially. The numbering result is as follows: ; m represents the total number. In the main drainage pipeline, a flow meter Gi is installed at a distance h meters from the connection numbered i. Sewage flows from the flow meter Gi to the connection numbered i. The specific value of h is less than the minimum distance between two adjacent connections. In the main drainage pipeline, a flow meter Fi is installed at a distance h meters from the connection numbered i. Sewage flows from the flow meter Fi to the connection numbered i. S103: The wastewater discharge rate corresponding to the flow rate of wastewater passing through flow meter Gi. Monitoring is performed using flow meter Fi to measure the wastewater discharge rate as it flows through the flow meter Fi. Conduct monitoring; Wastewater discharge and sewage discharge The sum of the values ​​between them is used to calculate the discharge amount Qi when the sewage flows through the connection numbered i. An in-situ miniature sampler Wi equipped with a GNSS positioning module is installed at the connection point numbered i. The cross-sectional area Si of the sewage flowing through the connection point numbered i is monitored by the GNSS positioning module. The ratio between the discharge amount Qi and Si is calculated to obtain the transmission rate Vi of the sewage flowing through the connection point numbered i.

3. The intelligent early warning method for abnormal operation of drainage pipe networks according to claim 2, characterized in that: S20 includes: S201: The discharge volume Qi corresponding to the sewage flowing through the connection numbered i is compared with the discharge volume of the main drainage pipe monitored by the flow meter G(i+1) when the sewage flows through the connection numbered i+1. Whether the difference X between the two values ​​is within the error range K1 is used as the first segment judgment condition. If the difference X is within the error range K1, then the first segment judgment condition is met; if the difference X is not within the error range, then the first segment judgment condition is not met. The error range K1 is... Where z represents the maximum allowable flow deviation of the main drainage pipeline; The second segmentation judgment condition is whether the difference Y between the transmission rate Vi when the sewage flows through connection i and the transmission rate V(i+1) when the sewage flows through connection i+1 is within the error range K2. If the difference Y is within the error range K2, the second segmentation judgment condition is met; if the difference Y is not within the error range, the second segmentation judgment condition is not met. The error range K2 is... ; S202: If both the first segmentation judgment condition and the second segmentation judgment condition are met, then the connection point numbered i is considered not to be a segmentation position of the main drainage pipeline; otherwise, the connection point numbered i is considered to be a segmentation position of the main drainage pipeline, and the main drainage pipeline is segmented according to the segmentation position.

4. The intelligent early warning method for abnormal operation of drainage pipe networks according to claim 3, characterized in that: S30 includes: S301: Collect water quality data at the corresponding segment locations using in-situ micro samplers installed at each segment location. The water quality data includes chemical oxygen demand, ammonia nitrogen concentration, and suspended solids concentration. S302: Using the spatial coordinates collected by the GNSS positioning module mounted on the in-situ micro sampler set at each segment location, a three-dimensional map of the sewage drainage network is obtained, and the water quality data collected at each segment location is mapped onto the three-dimensional map of the sewage drainage network. S303: Compare the real-time water quality parameters at the segment location with the historical normal water quality baseline at that location. If any two of the chemical oxygen demand, ammonia nitrogen concentration, and suspended solids concentration exceed the baseline fluctuation range of the corresponding indicators, mark the segment location on the three-dimensional map of the sewage drainage network. S304: Based on the water flow direction of the main drainage pipeline, connect all marked segment positions in series to generate multiple continuous sewage flow lines. Based on the water quality data at each marked segment position, determine the reverse source tracing characteristics of each sewage flow line.

5. The intelligent early warning method for abnormal operation of drainage pipe networks according to claim 4, characterized in that: S40 includes: S401: Based on the reverse source tracing characteristics of each sewage flow path, lock all abnormal operating areas in the sewage drainage pipe network. The abnormal operating area is a closed area formed by connecting all pollution index peak points with a concentration gradient difference greater than a set threshold along the sewage drainage pipe network. S402: Calculate the early warning index of the sewage flow path matched for each abnormal operation area based on the area of ​​each abnormal operation area and the minimum boundary distance between each abnormal operation area and its upstream adjacent abnormal operation area; S403: Compare the warning index of the sewage flow path with the warning threshold, and when the warning index is greater than the warning threshold, issue a warning signal to the abnormal operation area corresponding to the sewage flow path in the three-dimensional map of the sewage drainage network.

6. A smart early warning system for abnormal operation of a drainage pipe network applied to the method described in any one of claims 1-5, characterized in that: The system includes a control parameter acquisition module, a segmented processing module, a reverse tracing feature determination module, and an intelligent early warning module. The control parameter acquisition module is used to acquire control parameters that affect the concentration of wastewater pollution. The segmentation processing module is used to analyze the segmentation judgment conditions of the main drainage pipeline at each connection point, and to segment the main drainage pipeline based on the analysis results. The reverse tracing feature determination module is used to generate several continuous sewage flow lines based on the marking of each segment position in the three-dimensional map of the sewage drainage network and the water flow direction of the main drainage pipeline, and to determine the reverse tracing features of each sewage flow line. The intelligent early warning module is used to analyze the abnormal operation of the sewage drainage pipe network along each sewage flow path, and selectively trigger early warning signals based on the analysis results.

7. The intelligent early warning system for abnormal operation of drainage pipe networks according to claim 6, characterized in that: The control parameter acquisition module includes a monitoring device deployment unit and a control parameter monitoring unit; The monitoring device deployment unit determines the deployment locations of the flow meter and the in-situ micro sampler equipped with a GNSS positioning module based on the location of the connection between the main drainage pipe and each drainage branch pipe in the sewage drainage network. The control parameter monitoring unit monitors the sewage discharge volume corresponding to each flow meter and the cross-sectional area of ​​the sewage flow at each connection point by deploying flow meters and an in-situ miniature sampler equipped with a GNSS positioning module.

8. The intelligent early warning system for abnormal operation of drainage pipe networks according to claim 7, characterized in that: The segmentation processing module includes a first segmentation judgment condition analysis unit, a second segmentation judgment condition analysis unit, and a segmentation unit; The first segment judgment condition analysis unit analyzes whether the first segment judgment condition is met based on whether the difference between the corresponding discharge amounts when the sewage flows through two adjacent connection points is within the error range, and transmits the analysis results to the segmentation unit. The second segment judgment condition analysis unit analyzes whether the second segment judgment condition is met based on whether the difference between the corresponding transmission rates when the sewage flows through two adjacent connection points is within the error range, and transmits the analysis result to the segmentation unit. The segmentation unit determines the segmentation position of the main drainage pipeline based on the analysis results transmitted by the first segmentation judgment condition analysis unit and the second segmentation judgment condition analysis unit, and performs segmentation processing on the main drainage pipeline according to the determined segmentation position.

9. The intelligent early warning system for abnormal operation of drainage pipe networks according to claim 8, characterized in that: The reverse source tracing feature determination module includes a water quality data acquisition unit, a sewage drainage network three-dimensional map construction unit, a segmented location marking unit, and a reverse source tracing feature determination unit. The water quality data acquisition unit uses in-situ micro samplers set at each segment location to collect water quality data at the corresponding segment location. The three-dimensional map construction unit of the sewage drainage pipe network uses the GNSS positioning module mounted on the in-situ micro sampler set at each segment location to collect spatial coordinates to obtain a three-dimensional map of the sewage drainage pipe network, and maps the water quality data collected at each segment location onto the three-dimensional map of the sewage drainage pipe network. The segment location marking unit compares the real-time water quality parameters at the segment location with the historical normal water quality baseline at that location. If any two of the indicators exceed the baseline fluctuation range of the corresponding indicator, the segment location is marked in the three-dimensional map of the sewage drainage network. The reverse source tracing feature determination unit uses the water flow direction of the main drainage pipeline as a reference, connects all marked segment positions in series to generate multiple continuous sewage flow lines, and determines the reverse source tracing features of each sewage flow line based on the water quality data at each marked segment position.

10. The intelligent early warning system for abnormal operation of drainage pipe networks according to claim 9, characterized in that: The intelligent early warning module includes an abnormal operation area locking unit, an early warning index calculation unit, and an intelligent early warning unit; The abnormal operation area locking unit locks all abnormal operation areas in the sewage drainage pipe network based on the reverse tracing characteristics of each sewage flow path. The early warning index calculation unit calculates the early warning index of the sewage flow path matched for each abnormal operation area based on the area of ​​each abnormal operation area and the minimum boundary distance between each abnormal operation area and its upstream adjacent abnormal operation area. The intelligent early warning unit controls the abnormal operation areas corresponding to the sewage flow lines mapped in the three-dimensional map of the sewage drainage network to selectively issue early warning signals based on the comparison results between the early warning index and the early warning threshold of the sewage flow lines.

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