Method and device for tracing and positioning sewage with imbalanced carbon-nitrogen ratio

Abstract

The present application relates to the technical field of sewage source tracing and positioning, and discloses a method and device for sewage source tracing and positioning for carbon-nitrogen ratio imbalance. The method includes: calculating the deviation degree of the carbon-nitrogen ratio standard curve and performing data dimensionality reduction processing on the carbon-nitrogen spatio-temporal attenuation data to obtain carbon-nitrogen imbalance eigenvector data; calculating the pipe network connectivity and analyzing the transfer path for 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 eigenvector data and the carbon-nitrogen pollutant migration trajectory data to obtain the spatial distribution data of carbon-nitrogen imbalance pipe segments; calculating the pollution contribution amount and identifying the regional boundary for the spatial distribution data to obtain the location area data of the carbon-nitrogen ratio abnormal source. Through the establishment of the correlation analysis between the carbon-nitrogen spatio-temporal attenuation characteristics and the pipe network connectivity, and by combining the data dimensionality reduction and spatial analysis technologies, the present disclosure can quickly narrow down the scope of the problem area and provide a more accurate source tracing and positioning result.

Description

Technical Field

[0001] This application relates to the field of sewage source tracing and positioning, and particularly to a method and device for sewage source tracing and positioning for carbon-nitrogen ratio imbalance. Background Art

[0002] In the field of urban sewage treatment, the problem of carbon-nitrogen ratio imbalance in the influent water quality of sewage treatment plants seriously affects the treatment effect and operation cost. The existing technologies mainly use on-line monitoring equipment to monitor the influent water quality in real time, check the pipeline network problems through manual inspection and pipeline network census, etc., simulate the migration and transformation law of pollutants in the pipeline network by using water quality models, and manage the pipeline network spatial information in combination with the GIS platform. These methods can, to a certain extent, discover the carbon-nitrogen ratio imbalance problem and conduct source tracing and positioning.

[0003] However, the related technologies have the following deficiencies: First, the traditional grid-by-grid inspection method takes a long time and has high costs, and it is difficult to quickly and accurately locate the problem area; second, the single water quality monitoring data cannot reflect the spatial distribution characteristics of carbon-nitrogen ratio imbalance, and there is a lack of quantitative analysis of the pollutant migration path; third, the existing source tracing methods often rely on empirical judgment, lack systematic data processing and analysis methods, and it is difficult to establish the correlation between the carbon-nitrogen ratio imbalance problem and the pipeline network characteristics. Summary of the Invention

[0004] This application provides a method and device for sewage source tracing and positioning for carbon-nitrogen ratio imbalance. By establishing the correlation analysis between the carbon-nitrogen spatio-temporal attenuation characteristics and the pipeline network connectivity, and combining data dimensionality reduction and spatial analysis technologies, it can quickly narrow down the scope of the problem area and provide more accurate source tracing and positioning results.

[0005] In the first aspect, this application provides a method for sewage source tracing and positioning for carbon-nitrogen ratio imbalance. The method for sewage source tracing and positioning for carbon-nitrogen ratio imbalance includes: performing spatial relationship integration processing on the pre-collected basic information of drainage users, pipeline network GIS data, topological structure of sewage treatment plants, and environmental element data to obtain multi-level sewage system coordinate data; performing time-series correlation analysis on the carbon-nitrogen ratio parameters of the drainage outlets of drainage users and the inlet water of key nodes of the pipeline network and the original water quality monitoring data to obtain carbon-nitrogen spatio-temporal attenuation data representing the carbon source concentration and nitrogen source concentration; calculating the deviation degree of the carbon-nitrogen ratio standard curve and performing data dimensionality reduction processing on the carbon-nitrogen spatio-temporal attenuation data to obtain carbon-nitrogen imbalance feature vector data; performing pipeline network connectivity calculation and transfer 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 segments; calculating the pollution contribution amount and identifying the regional boundary of the spatial distribution data of the carbon-nitrogen imbalance pipe segments to obtain location area data of the carbon-nitrogen ratio abnormal source.

[0006] In a second aspect, the present application provides a sewage tracing and positioning device for carbon-nitrogen ratio imbalance. The sewage tracing and positioning device for carbon-nitrogen ratio imbalance includes:

[0007] An integration module for performing spatial relationship integration processing on pre-collected basic information of drainage users, pipe network GIS data, topological structure of sewage treatment plants, and environmental element data to obtain multi-level sewage system coordinate data;

[0008] A correlation module for performing time-series correlation analysis on the carbon-nitrogen ratio parameters of the drainage outlets of drainage users and the inlet of key nodes of the pipe network and the original water quality monitoring data to obtain carbon-nitrogen spatio-temporal attenuation data characterizing the carbon source concentration and nitrogen source concentration;

[0009] A calculation module for calculating the deviation degree of the carbon-nitrogen ratio standard curve and performing data dimensionality reduction processing on the carbon-nitrogen spatio-temporal attenuation data to obtain carbon-nitrogen imbalance characteristic vector data;

[0010] An analysis module for performing pipe network connectivity calculation and transfer path analysis on the multi-level sewage system coordinate data to obtain carbon-nitrogen pollutant migration trajectory data;

[0011] A fusion module for performing multi-dimensional data fusion on the carbon-nitrogen imbalance characteristic vector data and the carbon-nitrogen pollutant migration trajectory data to obtain spatial distribution data of carbon-nitrogen imbalance pipe segments;

[0012] An identification module for calculating the pollution contribution amount and identifying the regional boundary of the spatial distribution data of the carbon-nitrogen imbalance pipe segments to obtain location area data of the carbon-nitrogen ratio abnormal source.

[0013] In the technical solution provided by this application, through the spatial relationship integration processing of the basic information of drainage users, the pipe network GIS data, the topological structure of sewage treatment plants, and environmental element data, a complete multi-level sewage system coordinate data is established, providing comprehensive spatial basic data support for the carbon-nitrogen ratio anomaly traceability; through the time-series correlation analysis of the influent carbon-nitrogen ratio parameters and the original water quality monitoring data of the drainage outlets of drainage users and the key nodes of the pipe network, the spatio-temporal attenuation characteristics of the carbon source concentration and the nitrogen source concentration are accurately grasped, and the quantitative characterization of the migration and transformation law of pollutants in the pipe network is realized; based on the carbon-nitrogen spatio-temporal attenuation data, the deviation degree calculation of the carbon-nitrogen ratio standard curve and data dimensionality reduction processing are carried out, and the key features reflecting the carbon-nitrogen imbalance are extracted, reducing the data redundancy; the pipe network connectivity calculation and transfer path analysis are carried out on the multi-level sewage system coordinate data, realizing 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 degree of carbon-nitrogen imbalance and the spatial position is established; finally, through the calculation of the pollution contribution amount and the identification of the regional boundary of the spatial distribution data of the carbon-nitrogen imbalance pipe section, the precise positioning of the source of the carbon-nitrogen ratio anomaly is realized. The whole method avoids the blindness of the traditional grid-by-grid investigation, quickly locks the problem area through a data-driven method, and significantly improves the traceability efficiency and accuracy. Brief Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 It is a schematic diagram of an embodiment of the sewage traceability and positioning method for carbon-nitrogen ratio imbalance in the embodiments of this application;

[0016] Figure 2 It is a schematic diagram of an embodiment of the sewage traceability and positioning device for carbon-nitrogen ratio imbalance in the embodiments of this application. Detailed Embodiments

[0017] The embodiments of the present application provide a method and device for tracing and locating sewage with imbalanced carbon-nitrogen ratio. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 One embodiment of the method for tracing and locating sewage with imbalanced carbon-nitrogen ratio in the embodiments of the present application includes:

[0019] Step S101, perform spatial relationship integration processing on the pre-collected basic information of drainage users, pipe network GIS data, topological structure of sewage treatment plants and environmental element data to obtain multi-level sewage system coordinate data;

[0020] Step S102, perform time-series correlation analysis on the raw water quality monitoring data of the carbon-nitrogen ratio parameters at the drainage outlets of drainage users and key nodes of the pipe network to obtain carbon-nitrogen spatio-temporal attenuation data characterizing the carbon source concentration and nitrogen source concentration;

[0021] Step S103, calculate the deviation degree of the carbon-nitrogen ratio standard curve and perform data dimensionality reduction processing on the carbon-nitrogen spatio-temporal attenuation data to obtain carbon-nitrogen imbalance eigenvector data;

[0022] Step S104, perform pipe network connectivity calculation and transfer path analysis on the multi-level sewage system coordinate data to obtain carbon-nitrogen pollutant migration trajectory data;

[0023] Step S105, perform multi-dimensional data fusion on the carbon-nitrogen imbalance eigenvector data and the carbon-nitrogen pollutant migration trajectory data to obtain spatial distribution data of carbon-nitrogen imbalance pipe segments;

[0024] Step S106, calculate the pollution contribution amount and identify the regional boundary for the spatial distribution data of the carbon-nitrogen imbalance pipe segments to obtain location area data of the source of abnormal carbon-nitrogen ratio.

[0025] It can be understood that the execution subject of the present application can be a device for tracing and locating sewage with imbalanced carbon-nitrogen ratio, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present application are described by taking the server as the execution subject as an example.

[0026] Optionally, by classifying and organizing the basic information of drainage users, the drainage users in the study area are coded into four categories: residential land, office land, commercial land, and industrial enterprises. The basic parameters such as the 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, pipe diameter, buried depth, and material of the pipe segments in the pipe network GIS data is collected. Combining the topological structure of the influent pipe network of the sewage treatment plant and environmental element data such as the surrounding river and lake water systems and groundwater levels, a multi-level digital base map of the sewage system is established through spatial overlay analysis. For the drainage outlets of drainage users and key nodes of the pipe network, on-line monitoring equipment is used to obtain the influent water quality data for 72 consecutive hours, record the concentration changes of indicators such as COD, BOD, and ammonia nitrogen, and calculate the carbon-nitrogen ratio parameter. Through time series analysis of 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.

[0027] Based on the obtained carbon-nitrogen spatio-temporal attenuation data, first establish a carbon-nitrogen ratio reference curve under standard conditions, use the dynamic time warping algorithm to calculate the deviation degree between the measured curve and the standard curve, and extract the characteristic parameters reflecting carbon-nitrogen imbalance, including peak deviation, fluctuation period, attenuation rate, etc. The characteristic parameters are dimensionally reduced by the principal component analysis method to obtain the characteristic vector representing the degree of carbon-nitrogen imbalance. For the constructed multi-level sewage system coordinate data, use graph theory methods to analyze the connectivity of the pipe network, take inspection wells as nodes and pipe segments as edges to establish a directed graph structure, and calculate the hydraulic connection between each pipe segment. Based on the depth-first search algorithm, trace the transmission path of pollutants from the source to the end, consider factors such as the flow velocity and slope of the pipe segment, and quantify the migration time and attenuation characteristics of pollutants on different paths.

[0028] For the obtained carbon-nitrogen imbalance characteristic vector data and pollutant migration trajectory data, a spatial analysis method is used for data fusion. First, perform spatial interpolation on the characteristic vector to generate a distribution heat map of carbon-nitrogen imbalance, and then overlay and analyze the migration trajectory data to identify the pipe network areas with significant correlation. Determine the key pipe segments with carbon-nitrogen imbalance problems by setting thresholds. Finally, conduct a source tracing analysis on the determined carbon-nitrogen imbalance pipe segments, calculate the pollution contribution amount of each pipe segment to the downstream monitoring point, identify the boundary range of the pollution cluster based on the spatial autocorrelation analysis method, and determine the specific location area of the abnormal source in combination with the pipe network topological relationship.

[0029] For example: There are 458 drainage users of different types such as residential communities, commercial areas, and industrial parks in the service area of a sewage treatment plant. Through spatial relationship integration processing, a digital base map containing 2,145 pipeline network nodes and 3,267 pipeline segments is established. Continuous monitoring is carried out for 72 hours at the drainage outlet of an industrial park, and it is observed that the COD concentration fluctuates between 250 - 450 mg / L, and the ammonia nitrogen concentration varies between 25 - 45 mg / L. The calculated carbon-nitrogen ratio is significantly lower than the inlet requirement of the sewage treatment plant. Through systematic monitoring and data analysis of 27 pipeline network nodes within a range of 1.2 kilometers in this area, the source of the abnormal carbon-nitrogen ratio is finally located in 3 drainage subsystems within a range of approximately 300 meters northeast of the industrial park.

[0030] In the embodiment of the present application, through spatial relationship integration processing of the basic information of drainage users, pipeline network GIS data, the topological structure of the sewage treatment plant, and environmental element data, complete multi-level sewage system coordinate data is established, providing comprehensive spatial basic data support for tracing the source of abnormal carbon-nitrogen ratio; through time-series correlation analysis of the inlet carbon-nitrogen ratio parameters and the original water quality monitoring data of the drainage outlets of drainage users and key nodes of the pipeline network, the spatio-temporal attenuation characteristics of carbon source concentration and nitrogen source concentration are accurately grasped, and the quantitative characterization of the migration and transformation law of pollutants in the pipeline network is realized; based on the carbon-nitrogen spatio-temporal attenuation data, the deviation degree calculation of the carbon-nitrogen ratio standard curve and data dimensionality reduction processing are carried out, extracting the key features reflecting carbon-nitrogen imbalance and reducing data redundancy; the pipeline network connectivity calculation and transfer path analysis are carried out on the multi-level sewage system coordinate data to achieve precise tracking of carbon-nitrogen pollutants in the pipeline network; through 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 degree of carbon-nitrogen imbalance and spatial position is established; finally, through the calculation of pollution contribution amount and regional boundary identification of the spatial distribution data of carbon-nitrogen imbalance pipeline segments, the precise location of the source of abnormal carbon-nitrogen ratio is realized. The whole method avoids the blindness of traditional grid-by-grid investigation, quickly locks the problem area through a data-driven method, and significantly improves the efficiency and accuracy of source tracing.

[0031] In an optional embodiment, the process of executing step S101 may specifically include the following steps:

[0032] (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 partition drainage load data;

[0033] (2) Analyze the connection relationship between the starting and ending points of pipeline segments of the pipeline network GIS data to obtain the pipeline network topological structure data, and extract the pipe diameter, slope, and buried depth parameters of the pipeline network topological structure data to obtain the pipeline network characteristic parameter data;

[0034] (3)Divide the boundary of the catchment area for the topological structure of the sewage treatment plant to obtain the data on the attribution of the pipeline network in each area, and calculate the connectivity of the pipeline network in each area to obtain the data on the connectivity relationship of the pipeline network in each area;

[0035] (4)Perform interpolation operations on the terrain elevation in the environmental element data to obtain the digital elevation data of the study area, and conduct slope direction analysis on the digital elevation data of the study area to obtain the surface runoff direction data;

[0036] (5)Perform spatial matching processing on the subarea drainage load data, pipeline network characteristic parameter data, subarea pipeline network connectivity relationship data, and surface runoff direction data through spatial overlay analysis to obtain the multi-level sewage system coordinate data.

[0037] Optionally, process the basic information of drainage users, divide the land use types 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, latitude) of each drainage user is extracted to generate a digitalized spatial distribution layer of drainage users. According to the water consumption measurement data of various users, calculate the drainage volume per unit area, and combine with the land area to obtain the water consumption weight value, and then form the drainage load data of each region. For the pipe segment information in the pipeline network GIS data, extract the starting and ending coordinates of each pipeline segment, establish a connection relationship matrix between pipeline segments, and construct a complete pipeline network topological structure. At the same time, read the pipeline network attribute data, including the pipe diameter size (unit: millimeter), burial slope (unit: ‰), and burial depth (unit: meter). These parameters directly affect the flow characteristics of sewage and the migration and transformation process of carbon and nitrogen substances.

[0038] In the topological structure analysis of the sewage treatment plant, divide the service area into several relatively independent catchment areas according to the terrain characteristics and drainage area requirements. By tracing the flow direction relationship of each pipeline segment, determine the subarea number to which each pipeline network belongs, and establish a pipeline network attribution database. Use graph theory methods to calculate the pipeline network connectivity, analyze the pipeline network connection strength and intercommunication degree between each subarea, and generate a subarea pipeline network connectivity relationship matrix.

[0039] Process the terrain data in the study area, use the Kriging interpolation method to perform spatial interpolation operations on discrete elevation points to generate a continuous digital elevation model. Based on the digital elevation data, calculate the slope direction angle (0 - 360°) and slope value of each grid unit, determine the natural flow direction of surface water, and provide a basis for identifying the invasion of external water bodies. Finally, through spatial overlay analysis, unify the subarea drainage load data, pipeline network characteristic parameter data, subarea pipeline network connectivity relationship data, and surface runoff direction data to the same coordinate system for matching analysis. The specific execution process includes operations such as spatial registration, layer overlay, and attribute association, and finally forms a sewage system coordinate data set containing multi-level information.

[0040] 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.

[0041] In an optional embodiment, the process of executing step S102 may specifically include the following steps:

[0042] (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;

[0043] (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.

[0044] (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;

[0045] (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;

[0046] (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.

[0047] Optionally, an optoelectronic sensor is 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 once every 2 hours, 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 obtained raw data is first seasonally decomposed, and the time series is decomposed into three parts: a trend term, a seasonal term, and a random term. The seasonal fluctuation analysis adopts a combination of an additive model and a multiplicative model, and its mathematical expression is:

[0048] ;

[0049] where 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 coefficients of each component, the influence degree of different factors on the change of carbon and nitrogen concentrations is quantified.

[0050] For the water quality monitoring data of key nodes in the pipe network, the time window is first divided based on 24 hours as the basic unit, and on this basis, it is seasonally segmented according to the dry season (April - September) and the rainy season (October - March). Statistical analysis is performed on the data within each time window, and characteristic values such as the mean, standard deviation, and coefficient of variation are calculated. The identification of outliers adopts an improved box plot method, and the discrimination criterion is:

[0051] ;

[0052] where and are the upper and lower threshold values respectively, is the outlier discrimination coefficient (taking values of 1.5 - 3.0 according to the data distribution characteristics), and are the upper quartile and lower quartile of the data respectively. The data points outside the threshold range are marked and removed. For the upstream and downstream correlation analysis of the effective data of node carbon and nitrogen concentrations, a spatial attenuation model considering the influence of hydraulic conditions is established. First, the concentration transfer relationship between adjacent monitoring points is calculated:

[0053] ;

[0054] where is the concentration value at a distance L from the source, is the source concentration, is the attenuation coefficient, is the flow rate influence coefficient, is the unit flow rate, is the hydraulic gradient influence coefficient, is the hydraulic gradient. The model parameters are fitted by the least squares method to obtain the accurate spatial attenuation law. When performing interpolation analysis on the spatio-temporal distribution characteristics of carbon and nitrogen concentrations, the Kriging interpolation method considering spatial autocorrelation is adopted, and a time weight factor is introduced to construct a spatio-temporal interpolation model:

[0055] ;

[0056] where 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 spatio-temporal distance function.

[0057] For example: Multi-parameter water quality on-line monitoring equipment is installed at the drainage outlet of an industrial park, and continuous monitoring is carried out for 72 hours with a sampling interval of 2 hours, and a total of 36 groups of complete water quality data are obtained. The original monitoring results show that the TOC concentration range is 280 - 420 mg / L, the COD concentration range is 520 - 780 mg / L, the BOD concentration range is 220 - 380 mg / L, the TN concentration range is 35 - 55 mg / L, and the NH4-N concentration range is 25 - 42 mg / L. After seasonal decomposition analysis, it is found that the carbon source index shows obvious periodic fluctuations in spring (March - May), where the TOC amplitude is ±15%, the COD amplitude is ±18%, and the BOD amplitude is ±12%; while the nitrogen source index is relatively stable, the TN fluctuation range is within ±8%, and the NH4-N fluctuation range is within ±10%. Outlier identification is performed on the 72-hour monitoring data of 5 key monitoring nodes downstream. Through box plot analysis, the TOC outlier discrimination threshold is determined to be 450 mg / L, and the TN outlier discrimination threshold is 60 mg / L. A total of 3 groups of significantly deviated data points are removed. Through upstream and downstream correlation calculations, the spatial attenuation coefficient of carbon source pollutants in the research area is determined to be 0.15 / km, the flow rate influence coefficient is 0.08, and the hydraulic gradient influence coefficient is 0.05. Finally, wavelet transform is used to decompose the concentration distribution surface data at 4 scales, and the characteristic scale information reflecting the carbon-nitrogen imbalance is extracted, and a complete carbon-nitrogen spatio-temporal attenuation data set is established.

[0058] In an alternative embodiment, the process of performing step S103 may specifically include the following steps:

[0059] (1)Perform data standardization on the carbon-nitrogen spatio-temporal decay data to obtain standardized carbon-nitrogen time-series data, and segment the standardized carbon-nitrogen time-series data by time window to obtain segmented carbon-nitrogen data sequences;

[0060] (2)Screen the segmented carbon-nitrogen data sequences for normal operating conditions to obtain baseline carbon-nitrogen ratio curve data, and smooth the baseline carbon-nitrogen ratio curve data to obtain a standard carbon-nitrogen ratio reference curve;

[0061] (3)Calculate the difference between the standardized carbon-nitrogen time-series data and the standard carbon-nitrogen ratio reference curve to obtain carbon-nitrogen ratio deviation data, and perform spectral analysis on the carbon-nitrogen ratio deviation data to obtain deviation characteristic frequency data;

[0062] (4)Extract the principal components from the deviation characteristic frequency data to obtain the main characteristic data of carbon-nitrogen imbalance, and normalize the main characteristic data of carbon-nitrogen imbalance to obtain carbon-nitrogen characteristic weight data;

[0063] (5)Construct a feature combination from the carbon-nitrogen characteristic weight data to obtain carbon-nitrogen imbalance characteristic vector data.

[0064] Optionally, perform standardized preprocessing on the obtained carbon-nitrogen spatio-temporal decay data. The standardization process uses the Z-score method to convert carbon source indicators (including COD, BOD, TOC) and nitrogen source indicators (including TN, NH4-N, NO3-N) with different dimensions to the same scale, eliminating the influence of dimensions on subsequent analysis. After completing the standardization process, divide the time window with 24 hours as the basic unit. Each time window contains 12 sampling points, forming a segmented carbon-nitrogen data sequence. For the segmented data sequence, screen for normal operating conditions. According to the requirements of the sewage treatment process for the influent water quality, set the normal range of the carbon-nitrogen ratio between 8:1 and 12:1. For data segments that meet the normal operating conditions requirements, calculate the baseline carbon-nitrogen ratio curve using the moving average method, set the moving window size to 6 data points, and obtain a smooth carbon-nitrogen ratio change trend line by point-by-point calculation as the standard carbon-nitrogen ratio reference curve.

[0065] The deviation degree between the standardized carbon-nitrogen time series data and the standard carbon-nitrogen ratio reference curve is quantitatively calculated using the Euclidean distance. The differences between the measured values and the reference values at each time point are statistically analyzed to obtain the carbon-nitrogen ratio deviation data. Subsequently, spectral analysis is performed on the deviation data. The fast Fourier transform method is used to convert the time-domain signal to the frequency domain, and the main frequency components of the deviation characteristics are extracted, including high-frequency fluctuations, medium-frequency variations, and low-frequency trends. Principal component analysis is performed on the obtained deviation characteristic frequency data to extract the key characteristics reflecting carbon-nitrogen imbalance. First, a characteristic 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 characteristics. Then, the extracted principal component characteristics are normalized, and the characteristic values of different scales are mapped to the interval [0, 1] to obtain comparable carbon-nitrogen characteristic weight data.

[0066] Finally, the normalized characteristic weights and the corresponding eigenvectors are linearly combined to construct a carbon-nitrogen imbalance characteristic vector. The dimension of the characteristic vector is determined by the number of main characteristics determined by principal component analysis, usually including 3 - 5 dimensions, which respectively reflect the key information such as the absolute deviation degree of the carbon-nitrogen ratio, the fluctuation frequency characteristics, and the change trend characteristics.

[0067] For example: There are 12 on-line monitoring points set in the service area of a sewage treatment plant. The water quality data of each monitoring point is continuously collected for 7 days, and the sampling interval is 2 hours. The original data shows that the COD concentration range is between 180 - 650 mg / L, and the TN concentration range is between 20 - 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 into time window sequences with a 24-hour window, and a total of 84 time window data sequences are obtained. Through normal operating condition screening, it is identified that the periods with abnormal carbon-nitrogen ratio mainly concentrate in the period from 2 am to 6 am, accounting for 18% of the total monitoring time. Spectral analysis of the data in these abnormal periods reveals that there are mainly two characteristic periods 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 respectively reflect the baseline shift, intra-day fluctuation, and mutation characteristics of the carbon-nitrogen ratio. The finally constructed characteristic vector contains the information of these three dimensions, providing an important basis for subsequent source tracing analysis. Through this method, 3 drainage sections with persistent carbon-nitrogen ratio imbalance are successfully identified. The carbon-nitrogen ratio of one section is continuously lower than 6:1, and the other two sections show obvious periodic fluctuations.

[0068] In an alternative embodiment, the process of performing step S104 may specifically include the following steps:

[0069] (1)Extract the connection relationships of the pipeline network nodes in the multi-level sewage system coordinate data to obtain the pipeline network node connection table data, and construct a graph structure for the pipeline network node connection table data to obtain the pipeline network directed graph data;

[0070] (2)Identify the upstream and downstream levels of the pipeline network directed graph data to obtain the pipeline network level partition data, and calibrate the flow direction of the pipeline segments for the pipeline network level partition data to obtain the sewage flow sequence data;

[0071] (3)Conduct hydraulic calculations on the pipeline segments for the sewage flow sequence data to obtain the pipeline segment velocity distribution data, and calculate the transmission time for the pipeline segment velocity distribution data to obtain the pollutant transfer time data;

[0072] (4)Trace the propagation path for the pollutant transfer time data to obtain the pollutant migration path data, and statistically analyze the propagation distance for the pollutant migration path data to obtain the path length weight data;

[0073] (5)Analyze the migration process for the path length weight data to obtain the carbon and nitrogen pollutant migration trajectory data.

[0074] Optionally, when performing the analysis of the carbon and nitrogen pollutant migration trajectory, first deeply process the multi-level sewage system coordinate data to extract the connection relationships between the pipeline network nodes. Each pipeline network node contains basic information such as a unique node number, spatial coordinates (X, Y, Z), and node type (inspection well, pumping station, drainage outlet, etc.). By analyzing the connection relationships between adjacent nodes, establish a node connection table to record information such as the starting node, ending node, and connected pipeline segment number. Based on the node connection table, use graph theory methods to construct a directed graph structure, with each node as the vertex of the graph and the connected pipeline segment as the directed edge to construct a complete expression of the pipeline network topology. On the basis of the pipeline network directed graph, use the depth-first search algorithm to identify the upstream and downstream levels. Starting from each drainage outlet node, trace upstream along the directed edge while recording the level number of the node, and the level number reflects the positional relationship of the node in the entire drainage system. For nodes at the same level, determine the actual flow direction of the pipeline segment according to their elevation difference and pipeline segment slope to generate the sewage flow sequence data containing flow direction information.

[0075] For the sewage flow sequence data, conduct hydraulic calculations on the pipeline segments, and use the Manning formula to calculate the actual flow velocity in the pipeline segment:

[0076] ;

[0077] where is the flow velocity (m / s), is the Manning roughness coefficient, R is the hydraulic radius (m), Jis the hydraulic gradient. Based on the calculated flow velocity results, further calculate the transport time of pollutants in the pipe section:

[0078] ;

[0079] where t is the transport time (s), M is the pipe section length (m), is the delay coefficient (considering factors such as pipeline sedimentation and biodegradation). Conduct a propagation path tracking analysis on the calculated pollutant transfer time data. Starting from the monitoring point with abnormal carbon-nitrogen ratio, according to the directed graph structure of the pipe network, use the backtracking method to identify all possible upstream transfer paths. Perform time accumulation calculation for each transfer path, and at the same time record information such as key nodes, pipe section length, and transport time on the path to form a complete pollutant migration path dataset.

[0080] According to the migration path data, calculate the comprehensive weight of each path. The weight calculation considers multiple factors such as path length, transport time, and pipe network connectivity. The larger the weight value, the higher the possibility that the path is the pollution source. By sorting the weights of all possible paths, identify the most likely pollutant migration path, and finally generate the migration trajectory data of carbon and nitrogen pollutants.

[0081] For example: The pipe network system within the service area of a sewage treatment plant contains 458 inspection well nodes and 567 pipe sections. Through the analysis of node connectivity relationships, a complete directed graph structure is constructed, and a total of 35 drainage outlet nodes are identified. Using depth-first search for hierarchical division, the nodes are divided into 6 levels, among which there are 78 first-level nodes (directly connected to the drainage outlets), 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 sections shows that the average flow velocity of the main pipe network is 0.8 m / s, the average slope of the pipe network is 2.5‰, and the Manning roughness coefficient is taken as 0.013. Conduct a propagation path tracking on an abnormal carbon-nitrogen ratio point, and a total of 12 possible transfer paths are identified. The path length ranges from 280 to 1560 m, and the transport time is between 15 and 85 minutes. Through weight analysis, a most likely migration path is determined: The total length of this path is 867 m, passing through 8 inspection well nodes, with an average transport time of 42 minutes, and passing through 3 industrial enterprise drainage outlets. The carbon-nitrogen ratio of one of the drainage outlets is chronically low (average value is 5.2:1), which highly coincides with the carbon-nitrogen ratio characteristics of the downstream abnormal point.

[0082] In an optional embodiment, the process of performing step S105 may specifically include the following steps:

[0083] (1) Perform spatial coordinate mapping on the carbon-nitrogen imbalance eigenvector data to obtain the characteristic point distribution data, and calculate the density of the characteristic point distribution data to obtain the characteristic aggregation region data;

[0084] (2) Analyze the path overlap degree of the carbon and nitrogen pollutant migration trajectory data to obtain the key transmission path data, and assign flow weights to the key transmission path data to obtain the path importance data;

[0085] (3) Divide the buffer zones for the characteristic aggregation area data to obtain the characteristic influence range data, and perform spatial superposition on the characteristic influence range data and the path importance data to obtain the regional pipe segment association data;

[0086] (4) Calculate the carbon and nitrogen contribution rate for the regional pipe segment association data to obtain the pipe segment influence degree data, and perform threshold grading on the pipe segment influence degree data to obtain the unbalanced pipe segment grading data;

[0087] (5) Reconstruct the spatial relationship of the unbalanced pipe segment grading data to obtain the spatial distribution data of the carbon and nitrogen unbalanced pipe segments.

[0088] Optionally, during the carbon-nitrogen ratio imbalance tracing and positioning process, first map the carbon-nitrogen imbalance feature vector data to the actual spatial coordinate system. During the spatial coordinate mapping, each feature vector corresponds to the geographical 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 taking a value of 2-3 times the average pipe network spacing), the point density within the unit area is statistically calculated to identify the high-incidence areas and concentrated distribution areas of the carbon-nitrogen imbalance characteristics. For the carbon and nitrogen pollutant migration trajectory data obtained in the previous steps, perform path overlap degree analysis. First, superimpose all possible migration paths onto the same spatial reference system, and count the number of times each pipe network segment is covered by different migration paths. The more times it is covered, the higher the importance of the pipe segment during the pollutant transmission process. For the identified key transmission paths, weight assignment is combined with the actual flow data of the pipe segments. The flow weight calculation considers factors such as pipe diameter, flow velocity, and water level, reflecting the conveying capacity and influence degree of the pipe segment in the entire drainage system.

[0089] The processing of the characteristic aggregation area data adopts the multi-level buffer analysis method. Based on the center point of the aggregation area, multiple buffer distances (such as 50m, 100m, 200m) are set to expand outward to form nested influence range circles. The setting of the buffer zones considers the diffusion characteristics and attenuation laws of pollutants in the pipe network. Different buffer zones correspond to different influence intensities. Perform spatial superposition operation on the buffer zone analysis results and the path importance data to identify the pipe network sections that are both within the characteristic influence range and have high transmission importance, and generate the regional pipe segment association data.

[0090] For the regional pipe segment association data, a comprehensive scoring method is used to calculate the contribution degree of each pipe segment to the carbon-nitrogen imbalance. The calculation formula for the carbon and nitrogen contribution rate is:

[0091] ;

[0092] where G is the contribution rate of the pipe segment, is the flow weight, is the path importance weight, is the buffer impact weight, is the number of influencing factors. The pipe segments are classified according to the calculation results, usually divided into four levels: highly influential (contribution rate > 0.8), moderately influential (0.5 - 0.8), slightly influential (0.2 - 0.5), and weakly influential (< 0.2). Finally, based on the classification results, the spatial relationship of the pipe segments is reconstructed. The pipe segments with the same influence level and adjacent spatial positions are merged to form continuous influence areas. Through the reconstruction of the spatial relationship, the distribution law and concentration degree of the carbon-nitrogen imbalance pipe segments are clearly shown, providing a spatial reference for subsequent precise treatment.

[0093] For example: 85 water quality monitoring points are set within the service scope of a sewage treatment plant. Through the mapping of the eigenvector space, 3 obvious characteristic aggregation areas are formed around the industrial park. The results of the kernel density analysis show that the highest density area reaches 8 points per square kilometer. The pollutant migration trajectory analysis identifies 35 possible transmission paths, among which 15 paths overlap on the main pipe network on the east side of the park, and the highest overlap degree reaches 12 times. Through the multi-level buffer analysis at 100m intervals, combined with the distribution of the main pipe network with a diameter of more than DN600, the key pipe segments with a total length of about 2.8 kilometers are determined. The calculation of the carbon-nitrogen contribution rate shows that the contribution rate of about 850 meters of the pipe segments exceeds 0.85, belonging to the highly influential area; the contribution rate of 1200 meters of the pipe segments is between 0.6 and 0.75, belonging to the moderately influential area; the contribution rate of the remaining pipe segments is less than 0.5. After the reconstruction of the spatial relationship, 2 main problem areas are formed, both of which are directly connected to the drainage system of the industrial park. The larger of the two problem areas has an area of about 0.5 square kilometers and contains 3 drainage outlets with abnormal carbon-nitrogen ratios.

[0094] In an alternative embodiment, the process of executing step S106 may specifically include the following steps:

[0095] (1) Statistically calculate the pipe segment lengths of the spatial distribution data of the carbon-nitrogen imbalance pipe segments to obtain the unit pipe segment index data, and normalize the unit pipe segment index data to obtain the standardized pipe segment parameters;

[0096] (2) Calculate the pollutant concentration attenuation of the standardized pipe segment 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 segment contribution data;

[0097] (3) Perform spatial autocorrelation analysis on the contribution data of pipe segments to obtain pollution cluster boundary data, and judge the regional connectivity of the pollution cluster boundary data to obtain associated region data;

[0098] (4) Extract boundary features from the associated region data to obtain abnormal boundary contour data, and calculate the regional area of the abnormal boundary contour data to obtain the core location range data;

[0099] (5) Extract spatial coordinates from the core location range data to obtain the location area data of the source of abnormal carbon-nitrogen ratio.

[0100] Optionally, perform refined analysis on the spatial distribution data of the identified carbon-nitrogen imbalance pipe segments. According to the spatial distribution characteristics of the pipe segments, the actual length of each pipe segment is statistically calculated. Considering factors such as pipe diameter and flow rate, the carbon-nitrogen load index per unit length is calculated. To eliminate the differences between pipe segments of different scales, the maximum-minimum normalization method is used to normalize the unit pipe segment index, and each index value is mapped to the interval [0, 1] to ensure the comparability between different characteristics. For the standardized pipe segment parameters, a pollutant concentration attenuation model considering hydraulic conditions and biochemical reactions is established. The mathematical expression for concentration attenuation calculation is:

[0101] ;

[0102] where is the pollutant concentration at a distance x from the source, is the initial concentration, is the comprehensive attenuation coefficient, is the flow rate flowing in 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 segment to the overall pollution load is obtained through cumulative statistics of upstream and downstream. For the pipe segment contribution data, Moran's I index is used for spatial autocorrelation analysis to identify groups of pipe segments with significant correlations. The spatial relationship determination of adjacent pipe segments adopts a dual criterion based on distance threshold and connectivity to ensure that the identified pollution clusters have actual hydraulic connections. For each pollution cluster, calculate its boundary range and internal connectivity, and focus on the breakpoints and connection nodes at the boundary to provide a basis for determining the associated region.

[0103] The processing of the associated region data adopts a boundary extraction method based on morphological features. First, extract the key inflection points and curvature features of the region boundary to construct a mathematical description of the boundary contour. The regional area calculation adopts the polygon area calculation formula:

[0104] ;

[0105] where S is the regional area, ( , ) are the boundary vertex coordinates, and n is the number of vertices. The abnormal area is quantitatively described based on the area calculation results, and the spatial range of the core location is determined by combining the boundary features. Finally, based on the core location range data, the regional center coordinates and the boundary coordinate point sequence are extracted, and the final abnormal source location area is determined by combining the abnormal characteristics of the carbon-nitrogen ratio 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 an accurate spatial positioning result.

[0106] For example: There is a problem of carbon-nitrogen ratio imbalance in the drainage system of an industrial park. After preliminary analysis, a total of 28 abnormal pipe sections were identified, with a total length of about 3.2 kilometers. The unit length indicators of these pipe sections were calculated, considering the pipe diameter range (DN300 - DN1000) and the measured flow rate (15 - 280 L / s), and the standardized pipe section parameters were obtained. The pollutant concentration attenuation analysis shows that the comprehensive attenuation coefficient of the carbon source index is 0.12 km^-1, and the background concentration is 85 mg / L. Through spatial autocorrelation analysis, 3 main pollution clusters were identified at a significance level of 0.05. The largest cluster contains 12 pipe sections with a total length of 1.8 kilometers. After extracting the boundary features, the core area of the largest cluster is determined to be 0.38 square kilometers. There are 5 industrial enterprise drainage outlets distributed in this area, and the carbon-nitrogen ratio of 2 of the drainage outlets is continuously lower than 6:1, and it highly coincides with the abnormal characteristics of the downstream monitoring points. The final source location result shows that the problem area is mainly concentrated in the northeast of the industrial park. The center coordinates deviate about 2.5 kilometers from the inlet of the sewage treatment plant. The drainage users in the involved area are mainly chemical and food processing enterprises, with a daily average drainage volume of about 3,200 tons. The distribution of the characteristic pollutant concentration has a significant spatial correlation with the industrial layout of the park.

[0107] The above describes the sewage source tracing and positioning method for carbon-nitrogen ratio imbalance in the embodiments of the present application. Next, the sewage source tracing and positioning device for carbon-nitrogen ratio imbalance in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the sewage source tracing and positioning device for carbon-nitrogen ratio imbalance in the embodiments of the present application includes:

[0108] An integration module 201, configured to perform spatial relationship integration processing on the pre-collected basic information of drainage users, pipe network GIS data, topological structure of sewage treatment plants, and environmental element data to obtain multi-level sewage system coordinate data;

[0109] An association module 202, configured to perform time-series association analysis on the carbon-nitrogen ratio parameters of the drainage outlets of drainage users and the key nodes of the pipe network and the original water quality monitoring data to obtain carbon-nitrogen spatio-temporal attenuation data characterizing the carbon source concentration and nitrogen source concentration;

[0110] A calculation module 203 for calculating the deviation degree of the carbon-nitrogen ratio standard curve and performing data dimensionality reduction processing on the carbon-nitrogen spatio-temporal attenuation data to obtain carbon-nitrogen imbalance eigenvector data;

[0111] An analysis module 204 for calculating the pipe network connectivity and analyzing the transfer path of the multi-level sewage system coordinate data to obtain carbon-nitrogen pollutant migration trajectory data;

[0112] A fusion module 205 for performing multi-dimensional data fusion on the carbon-nitrogen imbalance eigenvector data and the carbon-nitrogen pollutant migration trajectory data to obtain the spatial distribution data of carbon-nitrogen imbalance pipe segments;

[0113] An identification module 206 for calculating the pollution contribution amount and identifying the regional boundary of the spatial distribution data of the carbon-nitrogen imbalance pipe segments to obtain the location area data of the carbon-nitrogen ratio anomaly source.

[0114] Through the collaborative cooperation of the above-mentioned various components, by performing spatial relationship integration processing on the basic information of drainage users, pipe network GIS data, the topological structure of sewage treatment plants, and environmental element data, a complete multi-level sewage system coordinate data is established, providing comprehensive spatial basic data support for the carbon-nitrogen ratio anomaly traceability; through the time-series correlation analysis of the carbon-nitrogen ratio parameters and the original water quality monitoring data at the drainage outlets of drainage users and the key nodes of the pipe network, the spatio-temporal attenuation characteristics of the carbon source concentration and the nitrogen source concentration are accurately grasped, and the quantitative characterization of the migration and transformation law of pollutants in the pipe network is realized; based on the carbon-nitrogen spatio-temporal attenuation data, the deviation degree of the carbon-nitrogen ratio standard curve is calculated and data dimensionality reduction processing is performed to extract the key features reflecting the carbon-nitrogen imbalance and reduce the data redundancy; by calculating the pipe network connectivity and analyzing the transfer path of the multi-level sewage system coordinate data, the accurate tracking of carbon-nitrogen pollutants in the pipe network is realized; through the multi-dimensional data fusion of the carbon-nitrogen imbalance eigenvector data and the 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 amount and identifying the regional boundary of the spatial distribution data of the carbon-nitrogen imbalance pipe segments, the precise positioning of the carbon-nitrogen ratio anomaly source is realized. The whole method avoids the blindness of the traditional grid-by-grid inspection, quickly locks the problem area through a data-driven method, and significantly improves the traceability efficiency and accuracy.

[0115] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate 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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