Tracing and positioning method and device for sewage with imbalance of carbon-nitrogen ratio

Abstract

The invention relates to the technical field of sewage traceability positioning, and discloses a sewage traceability positioning method and device for carbon nitrogen ratio imbalance. The method comprises the following steps: performing carbon-nitrogen ratio standard curve deviation calculation and data dimension reduction processing on carbon-nitrogen space-time attenuation data to obtain carbon-nitrogen imbalance feature vector data; performing pipe network connectivity calculation and transmission path analysis on the coordinate data of the multi-level sewage system to obtain migration trajectory data of carbon and nitrogen pollutants; performing multi-dimensional data fusion on the carbon-nitrogen imbalance feature vector data and the carbon-nitrogen pollutant migration trajectory data to obtain spatial distribution data of the carbon-nitrogen imbalance pipe section; and carrying out pollution contribution calculation and region boundary identification on the spatial distribution data to obtain position region data of a carbon-nitrogen ratio abnormal source. According to the method, correlation analysis of the carbon and nitrogen space-time attenuation characteristics and the pipe network connectivity is established, and data dimension reduction and space analysis technologies are combined, so that the problem region range can be quickly reduced, and a more accurate tracing positioning result can be provided.

Description

Technical Field

[0001] The present application relates to the field of sewage source tracing and positioning, and in particular to a method and device for sewage source tracing and positioning with an imbalanced carbon-nitrogen ratio. Background Art

[0002] In the field of urban sewage treatment, the imbalance of carbon-nitrogen ratio in the influent water quality of sewage treatment plants seriously affects the treatment effect and operating costs. Existing technologies mainly use online monitoring equipment to monitor the influent water quality in real time, investigate pipe network problems through manual inspections and pipe network censuses, use water quality models to simulate the migration and transformation of pollutants in the pipe network, and manage the spatial information of the pipe network in combination with the GIS platform. These methods can detect the imbalance of carbon-nitrogen ratio and trace the source to a certain extent.

[0003] However, the relevant technology has the following shortcomings: First, the traditional dragnet inspection method is time-consuming and costly, and it is difficult to quickly and accurately locate the problem area; second, a single water quality monitoring data cannot reflect the spatial distribution characteristics of the carbon-nitrogen ratio imbalance, and lacks quantitative analysis of the migration paths of pollutants; third, the existing traceability methods often rely on empirical judgment and lack systematic data processing and analysis methods, making it difficult to establish a correlation between the carbon-nitrogen ratio imbalance problem and the characteristics of the pipeline network. Summary of the invention

[0004] The present application provides a method and device for tracing the source of sewage with an imbalanced carbon-nitrogen ratio, which is used to quickly narrow the scope of the problem area and provide more accurate tracing and positioning results by establishing a correlation analysis between the spatiotemporal attenuation characteristics of carbon and nitrogen and the connectivity of the pipe network, combined with data dimensionality reduction and spatial analysis technology.

[0005] In the first aspect, the present application provides a method for tracing and locating the source of sewage with an imbalanced carbon-nitrogen ratio, and the method for tracing and locating the source of sewage with an imbalanced carbon-nitrogen ratio comprises: performing spatial relationship integration processing on pre-collected basic information of drainage users, pipeline network GIS data, sewage treatment plant topology structure and environmental factor data to obtain multi-level sewage system coordinate data; performing time series correlation analysis on the inlet carbon-nitrogen ratio parameters and water quality monitoring original data of the drainage user's drainage outlet and key nodes of the pipeline network to obtain carbon-nitrogen spatiotemporal attenuation data characterizing the carbon source concentration and nitrogen source concentration; performing carbon-nitrogen ratio standard curve deviation calculation and data dimensionality reduction processing on the carbon-nitrogen spatiotemporal attenuation data to obtain carbon-nitrogen imbalance feature vector data; performing pipeline network connectivity calculation and transmission path analysis on the multi-level sewage system coordinate data to obtain carbon-nitrogen pollutant migration trajectory data; performing multi-dimensional data fusion on the carbon-nitrogen imbalance feature vector data and the carbon-nitrogen pollutant migration trajectory data to obtain spatial distribution data of carbon-nitrogen imbalance pipe sections; performing pollution contribution calculation and regional boundary identification on the spatial distribution data of the carbon-nitrogen imbalance pipe sections to obtain location area data of the source of the abnormal carbon-nitrogen ratio.

[0006] In a second aspect, the present application provides a sewage source tracing and locating device for carbon-nitrogen ratio imbalance, the sewage source tracing and locating device for carbon-nitrogen ratio imbalance comprising: The integration module is used to integrate the spatial relationship of the pre-collected basic information of drainage users, pipe network GIS data, sewage treatment plant topology structure and environmental factor data to obtain multi-level sewage system coordinate data; The correlation module is used to perform time-series correlation analysis on the carbon-nitrogen ratio parameters of the inlet water at the drainage outlets of drainage users and key nodes of the pipe network and the original data of water quality monitoring, and obtain the carbon-nitrogen spatiotemporal attenuation data representing the carbon source concentration and the nitrogen source concentration; A calculation module, used for calculating the deviation of the carbon-nitrogen ratio standard curve and performing data dimensionality reduction processing on the carbon-nitrogen spatiotemporal attenuation data to obtain carbon-nitrogen imbalance feature vector data; An analysis module, used to perform pipe network connectivity calculation and transfer path analysis on the multi-level sewage system coordinate data to obtain carbon and nitrogen pollutant migration trajectory data; A fusion module is used to perform multi-dimensional data fusion on the carbon-nitrogen imbalance feature vector data and the carbon-nitrogen pollutant migration trajectory data to obtain spatial distribution data of the carbon-nitrogen imbalance pipe section; The identification module is used to calculate the pollution contribution and identify the regional boundaries of the spatial distribution data of the carbon-nitrogen imbalance pipe section to obtain the location area data of the source of the carbon-nitrogen ratio anomaly.

[0007] In the technical solution provided by this application, by integrating the spatial relationship of drainage user basic information, pipe network GIS data, sewage treatment plant topological structure and environmental factor data, a complete multi-level sewage system coordinate data is established, which provides comprehensive spatial basic data support for the tracing of carbon-nitrogen ratio anomalies; by performing time-series correlation analysis on the inlet carbon-nitrogen ratio parameters and water quality monitoring raw data of drainage user drainage outlets and key nodes of the pipe network, the spatiotemporal attenuation characteristics of carbon source concentration and nitrogen source concentration are accurately grasped, and the quantitative characterization of the migration and transformation laws of pollutants in the pipe network is realized; based on the spatiotemporal attenuation data of carbon and nitrogen, the spatial and temporal attenuation characteristics of carbon and nitrogen sources are analyzed. The deviation of the carbon-nitrogen ratio standard curve is calculated and the data dimension reduction is processed to extract the key features reflecting the carbon-nitrogen imbalance and reduce the data redundancy; the pipe network connectivity calculation and transmission path analysis are performed on the multi-level sewage system coordinate data to achieve the accurate tracking of carbon-nitrogen pollutants in the pipe network; the corresponding relationship between the degree of carbon-nitrogen imbalance and the spatial position is established by multi-dimensional data fusion of carbon-nitrogen imbalance feature vector data and carbon-nitrogen pollutant migration trajectory data; finally, the pollution contribution calculation and regional boundary identification of the spatial distribution data of the carbon-nitrogen imbalance pipe section are performed to achieve the accurate positioning of the source of the abnormal carbon-nitrogen ratio. The whole method avoids the blindness of the traditional dragnet investigation, quickly locks the problem area in a data-driven way, and significantly improves the efficiency and accuracy of traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0009] Figure 1 This is a schematic diagram of an embodiment of a method for tracing the source of sewage with an imbalanced carbon-nitrogen ratio in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a sewage source tracing and locating device for carbon-nitrogen ratio imbalance in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiments of the present application provide a method and device for tracing the source of sewage with an imbalanced carbon-nitrogen ratio. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of a method for tracing the source of sewage with an imbalanced carbon-nitrogen ratio includes: Step S101, performing spatial relationship integration processing on pre-collected basic information of drainage users, pipe network GIS data, sewage treatment plant topology structure and environmental factor data to obtain multi-level sewage system coordinate data; Step S102: performing time series correlation analysis on the carbon-nitrogen ratio parameters of the water inlet of the drainage user and the key nodes of the pipe network and the original data of water quality monitoring to obtain carbon-nitrogen spatiotemporal attenuation data representing the carbon source concentration and the nitrogen source concentration; Step S103, calculating the carbon-nitrogen ratio standard curve deviation and performing data dimensionality reduction processing on the carbon-nitrogen spatiotemporal attenuation data to obtain carbon-nitrogen imbalance feature vector data; Step S104, performing pipe network connectivity calculation and transmission path analysis on the multi-level sewage system coordinate data to obtain carbon and nitrogen pollutant migration trajectory data; Step S105, multi-dimensional data fusion is performed on the carbon-nitrogen imbalance characteristic vector data and the carbon-nitrogen pollutant migration trajectory data to obtain the spatial distribution data of the carbon-nitrogen imbalance pipe section; Step S106, calculate the pollution contribution and identify the regional boundaries of the spatial distribution data of the carbon-nitrogen imbalance pipe section to obtain the location area data of the source of the carbon-nitrogen ratio anomaly.

[0012] It is understandable that the execution subject of the present application may be a sewage source tracing and positioning device for carbon-nitrogen ratio imbalance, or a terminal or a server, which is not specifically limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0013] Optionally, by classifying and arranging the basic information of drainage users, the drainage users in the study area are coded according to four categories: residential land, office land, commercial land and industrial enterprises, and the basic parameters such as spatial coordinates and daily average water consumption of each type of user are recorded. At the same time, the attribute information such as the starting and ending coordinates of the pipe section, pipe diameter, burial depth, material, etc. in the pipe network GIS data are collected. Combined with the topological structure of the inlet pipe network of the sewage treatment plant and the environmental elements such as the surrounding river and lake water system and groundwater level, a multi-level digital base map of the sewage system is established through spatial overlay analysis. For the drainage user's drainage outlet and key nodes of the pipe network, online monitoring equipment is used to obtain 72 consecutive hours of inlet water quality data, record the concentration changes of indicators such as COD, BOD, and ammonia nitrogen, and calculate the carbon-nitrogen ratio parameters. By performing time series analysis on the monitoring data, the daily variation law and mutation characteristics of the carbon-nitrogen ratio are identified, and a mathematical model describing the migration and transformation of pollutants in the pipe network is established.

[0014] Based on the acquired carbon-nitrogen spatiotemporal attenuation data, we first establish a carbon-nitrogen ratio benchmark curve under standard working conditions, and use a dynamic time warping algorithm to calculate the degree of deviation between the measured curve and the standard curve, extract characteristic parameters reflecting carbon-nitrogen imbalance, including peak deviation, fluctuation period, attenuation rate, etc., and use the principal component analysis method to reduce the dimension of the characteristic parameters to obtain a characteristic vector that characterizes the degree of carbon-nitrogen imbalance. For the constructed multi-level sewage system coordinate data, we use graph theory to analyze the connectivity of the pipe network, take the inspection wells as nodes, and the pipe sections as edges, establish a directed graph structure, and calculate the hydraulic connection between each pipe section. Based on the depth-first search algorithm, we track the transmission path of pollutants from the source to the end, consider factors such as the flow rate and slope of the pipe section, and quantify the migration time and attenuation characteristics of pollutants on different paths.

[0015] For the obtained carbon-nitrogen imbalance characteristic vector data and pollutant migration trajectory data, spatial analysis methods are used for data fusion. First, the characteristic vectors are spatially interpolated to generate a distribution heat map of carbon-nitrogen imbalance, and then the migration trajectory data are superimposed and analyzed to identify the pipeline network areas with significant correlation, and the key pipeline sections with carbon-nitrogen imbalance problems are determined by setting thresholds. Finally, the source analysis of the determined carbon-nitrogen imbalance pipeline sections is carried out, and the pollution contribution of each pipeline section to the downstream monitoring point is calculated. The boundary range of the pollution cluster is identified based on the spatial autocorrelation analysis method, and the specific location area of ​​the abnormal source is determined in combination with the pipeline network topology relationship.

[0016] For example, there are 458 different types of drainage users in the service area of ​​a sewage treatment plant, including residential areas, commercial areas, and industrial parks. Through spatial relationship integration processing, a digital base map containing 2145 pipe network nodes and 3267 pipe sections was established. After continuous monitoring for 72 hours at the drainage outlet of an industrial park, the COD concentration was observed to fluctuate between 250-450 mg / L, and the ammonia nitrogen concentration varied between 25-45 mg / L. The calculated carbon-nitrogen ratio was significantly lower than the sewage treatment plant inlet requirements. Through systematic monitoring and data analysis of 27 pipe network nodes within 1.2 kilometers of the area, the source of the abnormal carbon-nitrogen ratio was finally located in three drainage subsystems within about 300 meters northeast of the industrial park.

[0017] In the embodiment of the present application, by integrating the spatial relationship of drainage user basic information, pipe network GIS data, sewage treatment plant topological structure and environmental factor data, a complete multi-level sewage system coordinate data is established, which provides comprehensive spatial basic data support for the tracing of carbon-nitrogen ratio anomalies; by performing time-series correlation analysis on the inlet carbon-nitrogen ratio parameters and water quality monitoring raw data of drainage user drainage outlets and key nodes of the pipe network, the spatiotemporal attenuation characteristics of carbon source concentration and nitrogen source concentration are accurately grasped, and the quantitative characterization of the migration and transformation laws of pollutants in the pipe network is realized; carbon is analyzed based on the spatiotemporal attenuation data of carbon and nitrogen. The calculation of the deviation of the nitrogen ratio standard curve and the data dimensionality reduction process extract the key features reflecting the carbon-nitrogen imbalance and reduce the data redundancy; the pipe network connectivity calculation and transmission path analysis of the multi-level sewage system coordinate data are carried out to achieve the accurate tracking of carbon-nitrogen pollutants in the pipe network; through the multi-dimensional data fusion of the carbon-nitrogen imbalance feature vector data and the carbon-nitrogen pollutant migration trajectory data, the corresponding relationship between the carbon-nitrogen imbalance degree and the spatial position is established; finally, by calculating the pollution contribution and identifying the regional boundaries of the spatial distribution data of the carbon-nitrogen imbalance pipe section, the source of the abnormal carbon-nitrogen ratio is accurately located. The whole method avoids the blindness of the traditional dragnet investigation, quickly locks the problem area in a data-driven way, and significantly improves the efficiency and accuracy of traceability.

[0018] In an optional embodiment, the process of executing step S101 may specifically include the following steps: (1) Classify the land use types and extract the spatial coordinates of the basic information of drainage users to obtain the drainage user distribution layer data, and calculate the water consumption weight of the drainage user distribution layer data to obtain the zoned drainage load data; (2) Analyze the connection relationship between the start and end points of the pipeline network GIS data to obtain the pipeline network topology data, and extract the pipe diameter, slope and burial depth parameters from the pipeline network topology data to obtain the pipeline network characteristic parameter data; (3) The topological structure of the sewage treatment plant is divided into watershed boundaries to obtain the zoned pipe network ownership data, and the pipe network connectivity is calculated for the zoned pipe network ownership data to obtain the zoned pipe network connectivity relationship data; (4) Perform interpolation operations on the terrain elevation in the environmental element data to obtain the digital elevation data of the study area, and perform slope analysis on the digital elevation data of the study area to obtain the surface runoff direction data; (5) Through spatial overlay analysis, the regional drainage load data, pipe network characteristic parameter data, regional pipe network connectivity data and surface runoff direction data are spatially matched to obtain multi-level sewage system coordinate data.

[0019] Optionally, the basic information of drainage users is processed, and the land use types are divided into four categories: residential land, office land, commercial land and industrial enterprises. Each type of land is assigned a unique code, and the spatial coordinate information (longitude and latitude) of each drainage user is extracted to generate a digital drainage user spatial distribution layer. According to the water metering data of each type of user, the drainage per unit area is calculated, and the water consumption weight value is obtained in combination with the land area, thereby forming the drainage load data for each region. For the pipe section information in the pipe network GIS data, the starting and ending coordinates of each pipe section are extracted, and the connection relationship matrix between the pipe sections is established to construct a complete pipe network topology. At the same time, the pipe network attribute data is read, including the pipe diameter (unit: mm), the buried slope (unit: ‰) and the buried depth (unit: m). These parameters directly affect the flow characteristics of sewage and the migration and transformation process of carbon and nitrogen substances.

[0020] In the topological structure analysis of the sewage treatment plant, the service area is divided into several relatively independent catchment areas according to the terrain characteristics and drainage zoning requirements. By tracing the flow relationship of each pipe section, the partition number of each pipe network is determined, and a pipe network attribution database is established. The pipe network connectivity is calculated using graph theory methods, the pipe network connection strength and interconnectivity between each partition are analyzed, and the partition pipe network connectivity relationship matrix is ​​generated.

[0021] The terrain data in the study area is processed, and the Kriging interpolation method is used to perform spatial interpolation operations on discrete elevation points to generate a continuous digital elevation model. Based on the digital elevation data, the slope angle (0-360°) and slope value of each grid unit are calculated to determine the natural flow direction of surface water flow, providing a basis for identifying external water intrusion. Finally, through spatial overlay analysis, the partition drainage load data, pipe network characteristic parameter data, partition pipe network connectivity relationship data and surface runoff direction data are unified into the same coordinate system for matching analysis. The specific execution process includes spatial registration, layer overlay, attribute association and other operations, and finally a sewage system coordinate data set containing multi-level information is formed.

[0022] For example, in the service area of ​​a sewage treatment plant, 156 residential areas, 45 office areas, 78 commercial areas, and 23 industrial enterprise areas were identified through land use type classification. The average daily drainage volume of residential areas is 280L / person / day, office areas are 45L / person / day, and commercial areas are 95L / person / day. The daily drainage volume of industrial enterprises is between 150-1200 tons according to the industry type. The pipe network system includes 498 main pipes, 1245 secondary pipes, and 2367 branch pipes. The pipe diameter ranges from 200-1200mm, the buried depth is 1.5-6.8m, and the average slope is 2.5‰. The area is divided into 5 catchment areas based on the terrain characteristics, and the pipe network connectivity analysis shows that there are 47 connection points between the sub-areas. For the study area covering 25 square kilometers, 1235 elevation points were collected, and a digital elevation model with a resolution of 5m×5m was formed through interpolation. The main surface runoff direction in the area was calculated to be northeast-southwest, with an average slope of 8.6°. Through spatial overlay analysis, a multi-level sewage system coordinate dataset containing more than 150,000 spatial units was established.

[0023] In an optional embodiment, the process of executing step S102 may specifically include the following steps: (1) Detect the carbon and nitrogen content of water samples from the drainage outlet of the drainage user to obtain the original data of carbon and nitrogen concentration at the drainage outlet, and perform seasonal fluctuation analysis on the original data of carbon and nitrogen concentration at the drainage outlet to obtain seasonal variation data of carbon and nitrogen concentration; (2) The water quality monitoring data of key nodes of the pipeline network are divided into time periods and counted to obtain the time series data of carbon and nitrogen concentrations at the nodes. The outliers in the time series data of carbon and nitrogen concentrations at the nodes are removed to obtain the valid data of carbon and nitrogen concentrations at the nodes. (3) Perform upstream and downstream correlation calculations on the effective data of node carbon and nitrogen concentrations to obtain the spatial transfer data of carbon and nitrogen concentrations, and perform flow-weighted processing on the spatial transfer data of carbon and nitrogen concentrations to obtain the spatial distribution data of carbon and nitrogen concentrations; (4) Performing spatiotemporal interpolation operations on the seasonal variation data of carbon and nitrogen concentration and the spatial distribution data of carbon and nitrogen concentration to obtain the carbon and nitrogen concentration distribution surface data, and performing multi-scale decomposition of the carbon and nitrogen concentration distribution surface data through wavelet transform to obtain the carbon and nitrogen concentration characteristic scale data; (5) The attenuation law of the characteristic scale data of carbon and nitrogen concentrations is extracted to obtain the spatiotemporal attenuation data of carbon and nitrogen that characterize the carbon source concentration and nitrogen source concentration.

[0024] Optionally, photoelectric sensors are used to monitor the influent water quality in real time. The detection indicators include total organic carbon (TOC), chemical oxygen demand (COD), and biochemical oxygen demand (BOD) as carbon source indicators, and total nitrogen (TN), ammonia nitrogen (NH4-N), and nitrate nitrogen (NO3-N) as nitrogen source indicators. The monitoring data collection frequency is set to 2 hours / time, with a total of 12 data points per day, and the continuous monitoring period is more than 72 hours to ensure the continuity and representativeness of the data. The acquired raw data is first seasonally decomposed, and the time series is decomposed into three parts: trend term, seasonal term, and random term. Seasonal fluctuation analysis adopts a combination of additive model and multiplicative model, and its mathematical expression is: ; in is the seasonal fluctuation value, is the seasonal factor weight, is the seasonal component, is the trend factor weight, is the trend component, is the random factor weight, is the random component. By calculating the weight coefficient of each component, the influence of different factors on the change of carbon and nitrogen concentration is quantified.

[0025] For the water quality monitoring data of key nodes of the pipe network, the time window is first divided according to the basic unit of 24 hours, and then seasonal segmentation is performed according to the dry season (April to September) and the rainy season (October to March). The data in each time window is statistically analyzed to calculate the characteristic values ​​such as the mean, standard deviation, and coefficient of variation. The improved box plot method is used for outlier identification, and the judgment criteria are: ; in and are the upper and lower thresholds, is the outlier discrimination coefficient (1.5-3.0 according to the data distribution characteristics), and are the upper and lower quartiles of the data, respectively. Data points that exceed the threshold range are marked and removed. For the upstream and downstream correlation analysis of the effective data of the node carbon and nitrogen concentration, a spatial attenuation model considering the influence of hydraulic conditions is established. First, the concentration transfer relationship between adjacent monitoring points is calculated: ; in is the concentration value at a distance L from the source, is the source concentration, is the attenuation coefficient, is the flow influence coefficient, is the unit flow rate, is the hydraulic slope influence coefficient, is the hydraulic slope. The model parameters are fitted by the least squares method to obtain the accurate spatial attenuation law. When interpolating the spatiotemporal distribution characteristics of carbon and nitrogen concentrations, the Kriging interpolation method considering spatial autocorrelation is used, and the time weight factor is introduced to construct a spatiotemporal interpolation model: ; in is the concentration value of the point to be determined, is the concentration value of the known point, is the spatial weight coefficient, is the time weight coefficient, is the space-time distance function.

[0026] For example, a multi-parameter water quality online monitoring device was installed at the drainage outlet of an industrial park, and the monitoring was continuous for 72 hours, with a sampling interval of 2 hours, and a total of 36 sets of complete water quality data were obtained. The original monitoring results showed that the TOC concentration range was 280-420mg / L, the COD concentration range was 520-780mg / L, the BOD concentration range was 220-380mg / L, the TN concentration range was 35-55mg / L, and the NH4-N concentration range was 25-42mg / L. After seasonal decomposition analysis, it was found that the carbon source index showed obvious periodic fluctuations in spring (March-May), with the TOC amplitude of ±15%, the COD amplitude of ±18%, and the BOD amplitude of ±12%; while the nitrogen source index was relatively stable, with the TN fluctuation range within ±8% and the NH4-N fluctuation range within ±10%. The 72-hour monitoring data of the five key monitoring nodes downstream were used to identify outliers. The TOC outlier threshold was determined to be 450 mg / L and the TN outlier threshold was determined to be 60 mg / L through box plot analysis. A total of three groups of data points with obvious deviations were eliminated. Through upstream and downstream correlation calculations, the spatial attenuation coefficient of carbon source pollutants in the study area was determined to be 0.15 / km, the flow influence coefficient was 0.08, and the hydraulic slope influence coefficient was 0.05. Finally, the wavelet transform was used to decompose the concentration distribution surface data into four scales, extract the characteristic scale information reflecting the carbon-nitrogen imbalance, and establish a complete carbon-nitrogen spatiotemporal attenuation data set.

[0027] In an optional embodiment, the process of executing step S103 may specifically include the following steps: (1) Perform data standardization on the carbon-nitrogen spatiotemporal decay data to obtain standardized carbon-nitrogen time series data, and segment the standardized carbon-nitrogen time series data into time windows to obtain segmented carbon-nitrogen data sequences; (2) Screening the segmented carbon-nitrogen data sequence under normal operating conditions to obtain benchmark carbon-nitrogen ratio curve data, and smoothing the benchmark carbon-nitrogen ratio curve data to obtain a standard carbon-nitrogen ratio reference curve; (3) Calculate the difference between the standardized carbon-nitrogen time series data and the standard carbon-nitrogen ratio reference curve to obtain the carbon-nitrogen ratio deviation data, and perform spectrum analysis on the carbon-nitrogen ratio deviation data to obtain the deviation characteristic frequency data; (4) Extracting the principal components of the deviation characteristic frequency data to obtain the main characteristic data of carbon-nitrogen imbalance, and normalizing the main characteristic data of carbon-nitrogen imbalance to obtain carbon-nitrogen characteristic weight data; (5) The carbon-nitrogen characteristic weight data are combined and constructed to obtain the carbon-nitrogen imbalance characteristic vector data.

[0028] Optionally, the acquired carbon-nitrogen spatiotemporal attenuation data is subjected to standardized preprocessing. The standardized processing uses the Z-score method to convert carbon source indicators (including COD, BOD, TOC) and nitrogen source indicators (including TN, NH4-N, NO3-N) of different dimensions to the same scale to eliminate the impact of the dimension on subsequent analysis. After completing the standardized processing, the time window is divided according to 24 hours as the basic unit, and each time window contains 12 sampling points to form a segmented carbon-nitrogen data sequence. The segmented data sequence is screened for normal operating conditions, and the normal range of the carbon-nitrogen ratio is set between 8:1 and 12:1 according to the requirements of the sewage treatment process for the influent water quality. For the data segments that meet the normal operating conditions, the sliding average method is used to calculate the benchmark carbon-nitrogen ratio curve, and the sliding window size is set to 6 data points. The smooth carbon-nitrogen ratio change trend line is obtained by point-by-point calculation as the standard carbon-nitrogen ratio reference curve.

[0029] The degree of deviation between the standardized carbon-nitrogen time series data and the standard carbon-nitrogen ratio reference curve is quantified using Euclidean distance. The difference between the measured value and the reference value at each time point is statistically analyzed to obtain the carbon-nitrogen ratio deviation data. The deviation data is then subjected to spectral analysis, and the time domain signal is converted to the frequency domain using the fast Fourier transform method to extract the main frequency components of the deviation characteristics, including high-frequency fluctuations, medium-frequency changes, and low-frequency trends. Principal component analysis is performed on the obtained deviation characteristic frequency data to extract the key features reflecting carbon-nitrogen imbalance. First, a feature correlation matrix is ​​constructed, the eigenvalues ​​and eigenvectors are calculated, and the principal components with a cumulative contribution rate of more than 85% are selected as the main features. The extracted principal component features are then normalized, and the eigenvalues ​​of different scales are mapped to the [0, 1] interval to obtain comparable carbon-nitrogen characteristic weight data.

[0030] Finally, the normalized feature weights are linearly combined with the corresponding feature vectors to construct the carbon-nitrogen imbalance feature vector. The dimension of the feature vector is determined by the number of main features determined by principal component analysis, and usually contains 3-5 dimensions, which reflect the absolute deviation degree of the carbon-nitrogen ratio, fluctuation frequency characteristics, change trend characteristics and other key information.

[0031] For example, 12 online monitoring points are set up in the service area of ​​a sewage treatment plant. Each monitoring point collects water quality data for 7 consecutive days with a sampling interval of 2 hours. The original data shows that the COD concentration ranges from 180 to 650 mg / L and the TN concentration ranges from 20 to 85 mg / L. After Z-score standardization, the data is converted to a standard normal distribution with a mean of 0 and a standard deviation of 1. The data is segmented according to a 24-hour window, and a total of 84 time window data sequences are obtained. Through normal operating conditions screening, it is identified that the period of abnormal carbon-nitrogen ratio is mainly concentrated between 2 am and 6 am, accounting for 18% of the total monitoring time. The data of these abnormal periods are analyzed by spectrum, and it is found that there are mainly two characteristic cycles of 4 hours and 12 hours. The results of principal component analysis show that the cumulative contribution rate of the first three principal components reaches 89.6%, which reflects the baseline shift, intraday fluctuation and mutation characteristics of the carbon-nitrogen ratio respectively. The final constructed feature vector contains information of these three dimensions, which provides an important basis for subsequent traceability analysis. Using this method, three drainage sections with persistent carbon-nitrogen ratio imbalance were successfully identified. The carbon-nitrogen ratio of one section was lower than 6:1 for a long time, while the other two sections showed obvious periodic fluctuations.

[0032] In an optional embodiment, the process of executing step S104 may specifically include the following steps: (1) The connectivity relationships of the pipe network nodes in the multi-level sewage system coordinate data are extracted to obtain the pipe network node connection table data, and the graph structure of the pipe network node connection table data is constructed to obtain the pipe network directed graph data; (2) Perform upstream and downstream hierarchical identification on the directed graph data of the pipe network to obtain the hierarchical partition data of the pipe network, and perform pipe section flow direction calibration on the hierarchical partition data of the pipe network to obtain the sewage flow direction sequence data; (3) Perform pipe segment hydraulic calculation on the sewage flow sequence data to obtain pipe segment flow velocity distribution data, and perform transmission time calculation on the pipe segment flow velocity distribution data to obtain pollutant transmission time data; (4) Track the propagation path of pollutant transfer time data to obtain pollutant migration path data, and perform propagation distance statistics on pollutant migration path data to obtain path length weight data; (5) Analyze the migration process of the path length weight data to obtain the migration trajectory data of carbon and nitrogen pollutants.

[0033] Optionally, when analyzing the migration trajectory of carbon and nitrogen pollutants, the coordinate data of the multi-level sewage system is first processed in depth to extract the connectivity relationship between the pipe network nodes. Each pipe network node contains basic information such as a unique node number, spatial coordinates (X, Y, Z), node type (inspection well, pump station, drain, etc.). By analyzing the connection relationship between adjacent nodes, a node connection table is established to record information such as the start node, the end node, and the number of the connecting pipe segment. Based on the node connection table, a directed graph structure is constructed using a graph theory method, with each node as a vertex of the graph and the connecting pipe segment as a directed edge to construct a complete pipe network topological structure expression. Based on the directed graph of the pipe network, a depth-first search algorithm is used to identify the upstream and downstream levels. Starting from each drain outlet node, trace upstream along the directed edge, and record the node level number at the same time. The level number reflects the positional relationship of the node in the entire drainage system. For nodes at the same level, the actual flow direction of the pipe segment is determined according to its elevation difference and the slope of the pipe segment, and the sewage flow direction sequence data containing flow direction information is generated.

[0034] The hydraulic calculation of the pipe section is carried out based on the sewage flow sequence data, and the actual flow velocity in the pipe section is calculated using the Manning formula: ; in is the flow velocity (m / s), is the Manning roughness coefficient, R is the hydraulic radius (m), J is the hydraulic gradient. Based on the flow velocity calculation results, the transmission time of pollutants in the pipe section is further calculated: ; Where t is the transmission time (s), M is the length of the pipe section (m), is the delay coefficient (taking into account factors such as pipeline deposition and biodegradation). The calculated pollutant transfer time data is used for propagation path tracking analysis. Starting from the monitoring point with abnormal carbon-nitrogen ratio, all possible upstream transmission paths are identified using the backtracking method according to the directed graph structure of the pipeline network. The time accumulation calculation is performed on each transmission path, and the key nodes, pipe section length, transmission time and other information on the path are recorded at the same time to form a complete pollutant migration path data set.

[0035] Based on the migration path data, the comprehensive weight of each path is calculated. The weight calculation takes into account multiple factors such as path length, transmission time, and pipe network connectivity. The larger the weight value, the higher the possibility that the path is a pollution source. By sorting all possible paths by weight, the most likely pollutant migration path is identified, and finally the migration trajectory data of carbon and nitrogen pollutants is generated.

[0036] For example, the pipe network system in the service area of ​​a sewage treatment plant contains 458 inspection well nodes and 567 pipe sections. Through the analysis of node connectivity, a complete directed graph structure was constructed, and a total of 35 drain outlet nodes were identified. Depth-first search was used for hierarchical division, and the nodes were divided into 6 levels, including 78 first-level nodes (directly connected to the drain outlet), 156 second-level nodes, 124 third-level nodes, 67 fourth-level nodes, 22 fifth-level nodes, and 11 sixth-level nodes. The hydraulic calculation of the pipe section showed that the average flow velocity of the main pipe network was 0.8m / s, the average slope of the pipe network was 2.5‰, and the Manning roughness coefficient was 0.013. The propagation path of a carbon-nitrogen ratio anomaly point was tracked, and a total of 12 possible transmission paths were identified, with path lengths ranging from 280-1560m and transmission times ranging from 15-85 minutes. Through weight analysis, the most likely migration path was determined: the total length of the path is 867m, passing through 8 inspection well nodes, with an average transmission time of 42 minutes, and passing through 3 industrial enterprise drainage outlets. The carbon-nitrogen ratio of one drainage outlet was long-term low (the average value was 5.2:1), which was highly consistent with the carbon-nitrogen ratio characteristics of the downstream abnormal point.

[0037] In an optional embodiment, the process of executing step S105 may specifically include the following steps: (1) Perform spatial coordinate mapping on the carbon-nitrogen imbalance feature vector data to obtain feature point distribution data, and perform density calculation on the feature point distribution data to obtain feature cluster area data; (2) Analyze the path overlap of carbon and nitrogen pollutant migration trajectory data to obtain key transmission path data, and assign flow weights to the key transmission path data to obtain path importance data; (3) Divide the feature clustering area data into buffer zones to obtain feature influence range data, and spatially superimpose the feature influence range data with the path importance data to obtain regional pipe section association data; (4) Calculate the carbon-nitrogen contribution rate of the regional pipe section correlation data to obtain the pipe section influence data, and perform threshold classification on the pipe section influence data to obtain the unbalanced pipe section classification data; (5) The spatial relationship of the unbalanced pipe section classification data is reconstructed to obtain the spatial distribution data of the carbon-nitrogen imbalanced pipe section.

[0038] Optionally, in the process of tracing and locating the carbon-nitrogen ratio imbalance, the carbon-nitrogen imbalance feature vector data is first mapped to the actual spatial coordinate system. In the spatial coordinate mapping, each feature vector corresponds to the geographic coordinates (X, Y) and elevation information (Z) of a monitoring point, forming a three-dimensional spatial distribution point set. For all monitoring points, the kernel density analysis method is used to calculate the spatial aggregation degree of the feature points. By setting the search radius (usually 2-3 times the average spacing of the pipe network), the point density within the unit area is counted, so as to identify the high-incidence areas and concentrated distribution areas of the carbon-nitrogen imbalance characteristics. For the migration trajectory data of carbon and nitrogen pollutants obtained in the previous step, the path overlap analysis is performed. First, all possible migration paths are superimposed on the same spatial reference system, and the number of times each section of the pipe network is covered by different migration paths is counted. The more times it is covered, the higher the importance of the pipe section in the pollutant transmission process. For the identified key transmission paths, the weight assignment is combined with the actual flow data of the pipe section. The flow weight calculation takes into account factors such as pipe diameter, flow velocity, and water level, reflecting the transmission capacity and influence of the pipe section in the entire drainage system.

[0039] The processing of feature clustering area data adopts a multi-level buffer analysis method. Taking the center point of the clustering area as the benchmark, multiple buffer distances (such as 50m, 100m, and 200m) are set outward to form a nested influence range circle. The setting of the buffer zone takes into account the diffusion characteristics and attenuation laws of pollutants in the pipe network. Different buffer zones correspond to different influence intensities. The buffer zone analysis results are spatially superimposed with the path importance data to identify the pipe network sections that are both within the feature influence range and have high transmission importance, and generate regional pipe segment association data.

[0040] For the regional pipeline segment association data, a comprehensive scoring method is used to calculate the contribution of each pipeline segment to the carbon-nitrogen imbalance. The calculation formula for the carbon-nitrogen contribution rate is: ; Where G is the contribution rate of the pipe section, is the traffic weight, is the path importance weight, is the buffer impact weight, is the number of influencing factors. According to the calculation results, the pipe sections are graded, usually divided into four levels: high impact (contribution rate>0.8), moderate impact (0.5-0.8), slight impact (0.2-0.5) and weak impact (<0.2). Finally, the spatial relationship of the pipe sections is reconstructed based on the classification results, and the pipe sections with the same impact level and adjacent spatial positions are merged to form a continuous impact area. Through the reconstruction of spatial relationships, the distribution pattern and concentration degree of the carbon-nitrogen imbalance pipe sections are clearly displayed, providing a spatial reference for subsequent precise governance.

[0041] For example, 85 water quality monitoring points were set up within the service scope of a sewage treatment plant. Through the feature vector space mapping, three obvious characteristic clustering areas were found around the industrial park. The kernel density analysis results showed that the highest density area reached 8 points / square kilometer. The pollutant migration trajectory analysis identified 35 possible transmission paths, of which 15 paths overlapped on the main pipeline network on the east side of the park, with the highest overlap reaching 12 times. Through the multi-level buffer zone analysis with an interval of 100m, combined with the distribution of the main pipeline network with a diameter of DN600 or above, the key focus pipe section with a total length of about 2.8 kilometers was determined. The carbon-nitrogen contribution rate calculation showed that the contribution rate of the pipe section of about 850 meters exceeded 0.85, which was a highly affected area; the contribution rate of the pipe section of 1200 meters was between 0.6-0.75, which was a moderately affected area; and the contribution rate of the remaining pipe sections was less than 0.5. After the spatial relationship reconstruction, two major problem areas were formed, both of which were directly connected to the drainage system of the industrial park. The larger problem area was about 0.5 square kilometers in area and contained three drainage outlets with abnormal carbon-nitrogen ratios.

[0042] In an optional embodiment, the process of executing step S106 may specifically include the following steps: (1) The spatial distribution data of the carbon-nitrogen imbalance pipe section are counted by pipe section length to obtain unit pipe section index data, and the unit pipe section index data are normalized to obtain standardized pipe section parameters; (2) Calculate the pollutant concentration attenuation of the standardized pipe section parameters to obtain the pollution load transfer data, and perform upstream and downstream cumulative statistics on the pollution load transfer data to obtain the pipe section contribution data; (3) Perform spatial autocorrelation analysis on the contribution data of the pipe sections to obtain the boundary data of the pollution clusters, and make regional connectivity judgment on the boundary data of the pollution clusters to obtain the associated regional data; (4) Extract boundary features from the associated area data to obtain abnormal boundary contour data, and calculate the area of ​​the abnormal boundary contour data to obtain core location range data; (5) Extract the spatial coordinates of the core area range data to obtain the location area data of the source of the carbon-nitrogen ratio anomaly.

[0043] Optionally, the spatial distribution data of the identified carbon-nitrogen imbalance pipe sections are analyzed in detail. According to the spatial distribution characteristics of the pipe sections, the actual length of each pipe section is counted, and the carbon-nitrogen load index per unit length is calculated by considering factors such as pipe diameter and flow rate. In order to eliminate the differences between pipe sections of different scales, the maximum and minimum value standardization method is used to normalize the unit pipe section index, and the values ​​of each index are mapped to the interval [0, 1] to ensure the comparability between different characteristics. For the standardized pipe section parameters, a pollutant concentration attenuation model considering hydraulic conditions and biochemical reactions is established. The mathematical expression for concentration attenuation calculation is: ; in is the pollutant concentration at a distance x from the source, is the initial concentration, is the comprehensive attenuation coefficient, is the inflow flow along the way, is the initial flow rate, is the background concentration. Based on the calculation results of the attenuation model, the contribution of each pipe section to the overall pollution load is obtained through the upstream and downstream cumulative statistics. For the pipe section contribution data, the Moran's I index is used to perform spatial autocorrelation analysis to identify pipe section groups with significant correlation. The spatial relationship between adjacent pipe sections is determined using a dual standard based on distance threshold and connectivity to ensure that the identified pollution clusters have actual hydraulic connections. For each pollution cluster, its boundary range and internal connectivity are calculated, focusing on the breakpoints and connected nodes at the boundary to provide a basis for determining the associated area.

[0044] The processing of the associated region data adopts the boundary extraction method based on morphological features. First, the key inflection points and curvature features of the region boundary are extracted to construct a mathematical description of the boundary contour. The region area is calculated using the polygon area calculation formula: ; Where S is the area of ​​the region, ( , ) are the coordinates of the boundary vertices, and n is the number of vertices. The abnormal area is quantitatively described through the area calculation results, and the spatial range of the core location is determined by combining the boundary characteristics. Finally, based on the core location range data, the regional center coordinates and boundary coordinate point sequences are extracted, and the final abnormal source location area is determined by combining the carbon-nitrogen ratio anomaly characteristics identified in the early stage. The determination of the source area comprehensively considers multiple factors such as spatial distribution characteristics, pollution load contribution, and regional connectivity, providing accurate spatial positioning results.

[0045] For example, the drainage system of an industrial park has a carbon-nitrogen ratio imbalance problem. After preliminary analysis, a total of 28 abnormal pipe sections were identified, with a total length of about 3.2 kilometers. The unit length index of these pipe sections was calculated, and the standardized pipe section parameters were obtained by considering the pipe diameter range (DN300-DN1000) and the measured flow rate (15-280L / s). Pollutant concentration attenuation analysis showed that the comprehensive attenuation coefficient of the carbon source index was 0.12km^-1, and the background concentration was 85mg / L. Through spatial autocorrelation analysis, three major pollution clusters were identified at a significance level of 0.05, of which the largest cluster contained 12 pipe sections with a total length of 1.8 kilometers. After boundary feature extraction, it was determined that the core area of ​​the largest cluster was 0.38 square kilometers. There were 5 industrial enterprise drainage outlets distributed in the area, of which the carbon-nitrogen ratio of two drainage outlets was continuously lower than 6:1, and it was highly consistent with the abnormal characteristics of the downstream monitoring points. The final source location results showed that the problem area was mainly concentrated in the northeast of the industrial park, with the center coordinates deviating from the sewage treatment plant inlet by about 2.5 kilometers. The drainage users in the area were mainly chemical and food processing companies, with an average daily discharge of about 3,200 tons. The distribution of characteristic pollutant concentrations had a significant spatial correlation with the industrial layout of the park.

[0046] The above describes the method for tracing the source of sewage with an imbalanced carbon-nitrogen ratio in the embodiment of the present application. The following describes the device for tracing the source of sewage with an imbalanced carbon-nitrogen ratio in the embodiment of the present application. Figure 2 In the embodiments of the present application, an embodiment of a sewage source tracing and locating device for carbon-nitrogen ratio imbalance includes: Integration module 201 is used to perform spatial relationship integration processing on pre-collected drainage user basic information, pipe network GIS data, sewage treatment plant topology structure and environmental factor data to obtain multi-level sewage system coordinate data; The correlation module 202 is used to perform time series correlation analysis on the carbon-nitrogen ratio parameters of the water inlet of the drainage user and the key nodes of the pipe network and the original data of water quality monitoring to obtain the carbon-nitrogen spatiotemporal attenuation data representing the carbon source concentration and the nitrogen source concentration; A calculation module 203 is used to calculate the deviation of the carbon-nitrogen ratio standard curve and perform data dimension reduction processing on the carbon-nitrogen spatiotemporal attenuation data to obtain carbon-nitrogen imbalance feature vector data; An analysis module 204 is used to perform pipe network connectivity calculation and transfer path analysis on the multi-level sewage system coordinate data to obtain carbon and nitrogen pollutant migration trajectory data; A fusion module 205 is used to perform multi-dimensional data fusion on the carbon-nitrogen imbalance feature vector data and the carbon-nitrogen pollutant migration trajectory data to obtain spatial distribution data of the carbon-nitrogen imbalance pipe section; The identification module 206 is used to calculate the pollution contribution and identify the regional boundaries of the spatial distribution data of the carbon-nitrogen imbalance pipe section to obtain the location area data of the abnormal carbon-nitrogen ratio source.

[0047] Through the collaborative cooperation of the above-mentioned components, by integrating the spatial relationship of drainage user basic information, pipe network GIS data, sewage treatment plant topological structure and environmental factor data, a complete multi-level sewage system coordinate data was established, providing comprehensive spatial basic data support for the tracing of carbon-nitrogen ratio anomalies; through the time series correlation analysis of the inlet carbon-nitrogen ratio parameters and water quality monitoring raw data at the drainage user's drainage outlet and the key nodes of the pipe network, the spatiotemporal attenuation characteristics of the carbon source concentration and the nitrogen source concentration were accurately grasped, and the quantitative characterization of the migration and transformation laws of pollutants in the pipe network was achieved; based on the carbon-nitrogen spatiotemporal attenuation data The deviation of the carbon-nitrogen ratio standard curve is calculated and the data is reduced in dimension, and the key features reflecting the carbon-nitrogen imbalance are extracted to reduce data redundancy; the pipe network connectivity is calculated and the transmission path is analyzed for the coordinate data of the multi-level sewage system, and the accurate tracking of carbon-nitrogen pollutants in the pipe network is realized; by multi-dimensional data fusion of carbon-nitrogen imbalance feature vector data and carbon-nitrogen pollutant migration trajectory data, the corresponding relationship between the degree of carbon-nitrogen imbalance and the spatial position is established; finally, by calculating the pollution contribution and identifying the regional boundaries of the spatial distribution data of the carbon-nitrogen imbalance pipe section, the source of the abnormal carbon-nitrogen ratio is accurately located. The whole method avoids the blindness of the traditional dragnet investigation, quickly locks the problem area in a data-driven way, and significantly improves the efficiency and accuracy of traceability.

[0048] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for tracing the source of sewage with an imbalanced carbon-nitrogen ratio, characterized in that: The method for tracing and locating the source of sewage with an imbalanced carbon-nitrogen ratio comprises: The pre-collected basic information of drainage users, pipe network GIS data, sewage treatment plant topology and environmental factor data are processed through spatial relationship integration to obtain multi-level sewage system coordinate data; Conduct time series correlation analysis on the carbon-nitrogen ratio parameters of the inlet water at the drainage outlets and key nodes of the pipe network and the original data of water quality monitoring to obtain the carbon-nitrogen spatiotemporal attenuation data representing the carbon source concentration and the nitrogen source concentration. The carbon-nitrogen spatiotemporal attenuation data are subjected to carbon-nitrogen ratio standard curve deviation calculation and data dimension reduction processing to obtain carbon-nitrogen imbalance characteristic vector data; Performing pipe network connectivity calculation and transmission path analysis on the multi-level sewage system coordinate data to obtain carbon and nitrogen pollutant migration trajectory data; Performing multi-dimensional data fusion on the carbon-nitrogen imbalance feature vector data and the carbon-nitrogen pollutant migration trajectory data to obtain spatial distribution data of the carbon-nitrogen imbalance pipe section; The pollution contribution amount is calculated and the regional boundary is identified for the spatial distribution data of the carbon-nitrogen imbalance pipe section to obtain the location area data of the source of the carbon-nitrogen ratio anomaly.

2. The method for tracing the source of sewage with an imbalanced carbon-nitrogen ratio according to claim 1 is characterized in that: The spatial relationship integration processing of the pre-collected basic information of drainage users, pipe network GIS data, sewage treatment plant topology structure and environmental factor data is performed to obtain multi-level sewage system coordinate data, including: The basic information of the drainage users is processed by land use type classification and spatial coordinate extraction to obtain drainage user distribution layer data, and the water consumption weight is calculated on the drainage user distribution layer data to obtain zoned drainage load data; Analyzing the connection relationship between the starting and ending points of the pipe network GIS data to obtain the pipe network topological structure data, and extracting the pipe diameter, slope and buried depth parameters from the pipe network topological structure data to obtain the pipe network characteristic parameter data; Dividing the topological structure of the sewage treatment plant into water catchment zone boundaries to obtain zoned pipe network affiliation data, and calculating the pipe network connectivity of the zoned pipe network affiliation data to obtain zoned pipe network connectivity relationship data; Performing interpolation operation on the terrain elevation in the environmental element data to obtain digital elevation data of the study area, and performing slope analysis on the digital elevation data of the study area to obtain surface runoff direction data; The spatial matching processing is performed on the partition drainage load data, the pipe network characteristic parameter data, the partition pipe network connectivity relationship data and the surface runoff direction data through spatial superposition analysis to obtain the coordinate data of the multi-level sewage system.

3. The method for tracing the source of sewage with an imbalanced carbon-nitrogen ratio according to claim 1, characterized in that: The carbon-nitrogen ratio parameters of the water inlet at the drainage outlet of the drainage user and the key nodes of the pipe network and the original data of water quality monitoring are subjected to time series correlation analysis to obtain carbon-nitrogen spatiotemporal attenuation data representing the carbon source concentration and the nitrogen source concentration, including: Conduct carbon and nitrogen content testing on water samples from the drainage outlet of the drainage user to obtain original data of carbon and nitrogen concentration at the drainage outlet, and conduct seasonal fluctuation analysis on the original data of carbon and nitrogen concentration at the drainage outlet to obtain seasonal variation data of carbon and nitrogen concentration; The water quality monitoring data of key nodes of the pipe network are divided into time periods for statistics to obtain the time series data of carbon and nitrogen concentration of the nodes, and the outliers of the time series data of carbon and nitrogen concentration of the nodes are eliminated to obtain the valid data of carbon and nitrogen concentration of the nodes; Performing upstream and downstream correlation calculation on the effective data of the carbon and nitrogen concentration of the node to obtain the spatial transfer data of the carbon and nitrogen concentration, and performing flow weighting processing on the spatial transfer data of the carbon and nitrogen concentration to obtain the spatial distribution data of the carbon and nitrogen concentration; Performing spatiotemporal interpolation operations on the carbon-nitrogen concentration seasonal variation data and the carbon-nitrogen concentration spatial distribution data to obtain carbon-nitrogen concentration distribution surface data, and performing multi-scale decomposition on the carbon-nitrogen concentration distribution surface data by wavelet transform to obtain carbon-nitrogen concentration characteristic scale data; The attenuation law is extracted from the characteristic scale data of carbon and nitrogen concentration to obtain the carbon and nitrogen spatiotemporal attenuation data characterizing the carbon source concentration and the nitrogen source concentration.

4. The method for tracing the source of sewage with an imbalanced carbon-nitrogen ratio according to claim 1, characterized in that: The carbon-nitrogen ratio standard curve deviation calculation and data dimensionality reduction processing are performed on the carbon-nitrogen spatiotemporal attenuation data to obtain carbon-nitrogen imbalance feature vector data, including: Performing data standardization processing on the carbon-nitrogen spatiotemporal attenuation data to obtain standardized carbon-nitrogen time series data, and performing time window segmentation on the standardized carbon-nitrogen time series data to obtain a segmented carbon-nitrogen data sequence; Performing normal operating condition screening on the segmented carbon-nitrogen data sequence to obtain reference carbon-nitrogen ratio curve data, and performing smoothing processing on the reference carbon-nitrogen ratio curve data to obtain a standard carbon-nitrogen ratio reference curve; Performing difference calculation on the standardized carbon-nitrogen time series data and the standard carbon-nitrogen ratio reference curve to obtain carbon-nitrogen ratio deviation data, and performing spectrum analysis on the carbon-nitrogen ratio deviation data to obtain deviation characteristic frequency data; Performing principal component extraction on the deviation characteristic frequency data to obtain main characteristic data of carbon-nitrogen imbalance, and performing normalization processing on the main characteristic data of carbon-nitrogen imbalance to obtain carbon-nitrogen characteristic weight data; The carbon-nitrogen characteristic weight data is subjected to characteristic combination construction to obtain the carbon-nitrogen imbalance characteristic vector data.

5. The method for tracing the source of sewage with an imbalanced carbon-nitrogen ratio according to claim 1, characterized in that: The pipe network connectivity calculation and transfer path analysis are performed on the multi-level sewage system coordinate data to obtain carbon and nitrogen pollutant migration trajectory data, including: Extracting connectivity relationships of pipe network nodes in the multi-level sewage system coordinate data to obtain pipe network node connection table data, and constructing a graph structure for the pipe network node connection table data to obtain pipe network directed graph data; Performing upstream and downstream level identification on the pipe network directed graph data to obtain pipe network level partition data, and performing pipe section flow direction calibration on the pipe network level partition data to obtain sewage flow direction sequence data; Performing pipe segment hydraulic calculation on the sewage flow direction sequence data to obtain pipe segment flow velocity distribution data, and performing transmission time calculation on the pipe segment flow velocity distribution data to obtain pollutant transmission time data; Tracing the propagation path of the pollutant transfer time data to obtain pollutant migration path data, and performing propagation distance statistics on the pollutant migration path data to obtain path length weight data; The migration process of the path length weight data is analyzed to obtain the migration trajectory data of the carbon-nitrogen pollutants.

6. The method for tracing the source of sewage with an imbalanced carbon-nitrogen ratio according to claim 1, characterized in that: The multi-dimensional data fusion of the carbon-nitrogen imbalance feature vector data and the carbon-nitrogen pollutant migration trajectory data is performed to obtain the spatial distribution data of the carbon-nitrogen imbalance pipe section, including: Performing spatial coordinate mapping on the carbon-nitrogen imbalance feature vector data to obtain feature point distribution data, and performing density calculation on the feature point distribution data to obtain feature aggregation area data; Performing path overlap analysis on the carbon-nitrogen pollutant migration trajectory data to obtain key transmission path data, and assigning flow weights to the key transmission path data to obtain path importance data; Dividing the feature aggregation area data into buffer zones to obtain feature influence range data, and spatially superimposing the feature influence range data with the path importance data to obtain regional pipe section association data; Calculating the carbon-nitrogen contribution rate of the regional pipe section associated data to obtain pipe section influence data, and performing threshold classification on the pipe section influence data to obtain unbalanced pipe section classification data; The spatial relationship of the unbalanced pipe section classification data is reconstructed to obtain the spatial distribution data of the carbon-nitrogen unbalanced pipe section.

7. The method for tracing the source of sewage with an imbalanced carbon-nitrogen ratio according to claim 1, characterized in that: The calculation of pollution contribution and regional boundary identification of the spatial distribution data of the carbon-nitrogen imbalance pipe section to obtain the location area data of the source of the carbon-nitrogen ratio anomaly includes: Performing pipe segment length statistics on the spatial distribution data of the carbon-nitrogen imbalance pipe segment to obtain unit pipe segment index data, and normalizing the unit pipe segment index data to obtain standardized pipe segment parameters; Performing pollutant concentration attenuation calculation on the standardized pipe section parameters to obtain pollution load transfer data, and performing upstream and downstream cumulative statistics on the pollution load transfer data to obtain pipe section contribution data; Performing spatial autocorrelation analysis on the pipe section contribution data to obtain pollution cluster boundary data, and performing regional connectivity judgment on the pollution cluster boundary data to obtain associated region data; Extracting boundary features from the associated area data to obtain abnormal boundary contour data, and calculating the area of ​​the abnormal boundary contour data to obtain core location range data; The spatial coordinates of the core area range data are extracted to obtain the location area data of the source of the carbon-nitrogen ratio anomaly.

8. A device for tracing and locating the source of sewage with an imbalanced carbon-nitrogen ratio, used to implement the method for tracing and locating the source of sewage with an imbalanced carbon-nitrogen ratio as described in any one of claims 1 to 7, characterized in that: The sewage source tracing and positioning device for carbon-nitrogen ratio imbalance comprises: The integration module is used to integrate the spatial relationship of the pre-collected basic information of drainage users, pipe network GIS data, sewage treatment plant topology structure and environmental factor data to obtain multi-level sewage system coordinate data; The correlation module is used to perform time-series correlation analysis on the carbon-nitrogen ratio parameters of the inlet water at the drainage outlets of drainage users and key nodes of the pipe network and the original data of water quality monitoring, and obtain the carbon-nitrogen spatiotemporal attenuation data representing the carbon source concentration and the nitrogen source concentration; A calculation module, used for calculating the deviation of the carbon-nitrogen ratio standard curve and performing data dimensionality reduction processing on the carbon-nitrogen spatiotemporal attenuation data to obtain carbon-nitrogen imbalance feature vector data; An analysis module, used to perform pipe network connectivity calculation and transfer path analysis on the multi-level sewage system coordinate data to obtain carbon and nitrogen pollutant migration trajectory data; A fusion module is used to perform multi-dimensional data fusion on the carbon-nitrogen imbalance feature vector data and the carbon-nitrogen pollutant migration trajectory data to obtain spatial distribution data of the carbon-nitrogen imbalance pipe section; The identification module is used to calculate the pollution contribution and identify the regional boundaries of the spatial distribution data of the carbon-nitrogen imbalance pipe section to obtain the location area data of the source of the carbon-nitrogen ratio anomaly.

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