Urban drainage pipe network siltation diagnosis method based on multi-factor analysis
Through multi-factor analysis and support vector machine model, combined with genetic algorithm optimization and denoising autoencoder data cleaning, the problems of low efficiency and high cost in urban drainage network sedimentation diagnosis were solved, and high-precision sedimentation diagnosis and management optimization were achieved.
Patent Information
- Application Number
- CN202510683496.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-26
- Publication Date
- 2025-09-12
AI Technical Summary
Existing technologies for diagnosing urban drainage network siltation have the problems of low efficiency, high cost, strong lag, and fail to fully consider multi-dimensional influencing factors such as pipeline properties, land use and socio-economic factors.
Multi-factor analysis combined with machine learning algorithms was used to construct an intelligent diagnostic model based on support vector machines. Big data technology was used to quantitatively analyze the multidimensional influencing factors of pipeline siltation. The model prediction accuracy was optimized through genetic algorithms, and data cleaning was performed using a denoising autoencoder. ArcGIS was used to divide the sub-catchment areas to achieve high-precision diagnosis.
It improves the accuracy and stability of pipe network sedimentation diagnosis, can more comprehensively reveal the sedimentation formation mechanism, reduce maintenance costs, and enhance the intelligent management level of urban drainage systems.
Smart Images
Figure CN120633389A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of urban infrastructure maintenance, and in particular to a method and system for diagnosing and predicting the siltation state of urban drainage pipe networks by utilizing multi-factor data analysis in combination with a machine learning algorithm, and specifically to a method for diagnosing siltation in urban drainage pipe networks based on multi-factor analysis. Background Art
[0002] With the acceleration of global urbanization, urban drainage networks, as critical infrastructure, are responsible for transporting and discharging rainwater and sewage. However, over long periods of operation, surface runoff often carries large amounts of sediment, garbage, and organic matter into the pipes, leading to increasingly serious siltation. Siltation in drainage networks not only reduces the pipes' water transport capacity but can also cause urban flooding, pollute the water environment, and even cause pipe blockages, increasing desilting and maintenance costs. Traditional pipe network management relies primarily on manual inspections and regular desilting, which are subject to low efficiency, high costs, and significant lags. Therefore, accurately and efficiently diagnosing pipe network siltation and optimizing desilting strategies have become key challenges in current urban drainage management.
[0003] In the existing technology, common pipeline siltation diagnosis methods mainly include computational models based on empirical formulas, physical simulation experiments, remote sensing monitoring technology, and methods based on statistical analysis. However, these methods all have certain limitations. For example, empirical formulas are restricted by idealized assumptions and are difficult to adapt to complex actual situations; physical experiments are costly and have poor scalability; remote sensing monitoring technology relies on expensive equipment and is difficult to achieve large-scale, full-coverage data collection; traditional statistical methods mostly use single-factor analysis and fail to fully consider multi-dimensional influencing factors such as pipeline properties, land use, and socio-economic factors. In recent years, machine learning technology has been widely used in the field of infrastructure management. By constructing intelligent prediction models, the diagnostic accuracy of pipeline siltation status has been improved. However, current research still has problems such as single data dimension, insufficient model generalization ability, and weak ability to adapt to complex environments. Summary of the Invention
[0004] To address the above technical issues, the present invention proposes a multi-factor analysis-based method for diagnosing urban drainage network siltation. This method combines pipeline attributes, land use characteristics, and socioeconomic factors to construct an intelligent diagnostic model based on a support vector machine. This method utilizes big data technology and machine learning algorithms to quantitatively analyze the multi-dimensional factors influencing pipeline siltation. Through model training, it optimizes prediction accuracy, improving diagnostic accuracy and stability. Compared to traditional methods, this method can more comprehensively reveal the formation mechanisms of pipeline siltation, enhance the intelligent level of pipeline network maintenance, and provide technical support for optimized management and scientific decision-making of urban drainage systems.
[0005] In order to achieve the above technical features, the purpose of the present invention is to achieve the following: a method for diagnosing siltation in urban drainage pipe networks based on multi-factor analysis, comprising: Data collection and preprocessing: Collect data related to the urban drainage network, including pipeline attributes, land use information, socioeconomic factors, hydrological and meteorological factors, and construction activities around the drainage network; The collected relevant data are preprocessed using the denoising autoencoder data cleaning algorithm; Subcatchment division: Combined with the actual distribution of water collection nodes in the pipelines in the study area, the water collection nodes in the study area were mapped using ArcGIS, and the sub-catchment areas were generated using the Thiessen polygon method. Model construction: A diagnostic model for urban drainage network sedimentation is constructed using a support vector machine algorithm optimized by genetic algorithms. Model evaluation and application: The model was evaluated using a set ratio of training and test sets, and the diagnostic accuracy was compared using the coefficient of determination and root mean square error; Applying the model to diagnose siltation in uninspected pipelines; Pipeline siltation warning: Utilize the manual pipeline inspection data provided by the Urban Municipal Administration Bureau to update the pipeline sedimentation data of the sedimentation model in real time, so as to connect the drainage network sedimentation diagnosis with the smart city management platform and share data and resources.
[0006] Preferably, the pipe attributes in the drainage pipe network related data include pipe diameter, pipe density, pipe network level and layout mode; The land use information includes NDVI value, underlying surface type and slope; The socioeconomic factors include population density, functional zoning, and waste conditions; The hydrometeorology includes the average precipitation over many years; If there is any construction activity information around the drainage network, the value is 1, and if not, the value is 0.
[0007] Preferably, the preprocessing of the collected relevant data using the denoising autoencoder data cleaning algorithm specifically includes: The input data is processed by adding noise, and then the noisy data is passed to the encoder, and the reconstructed data is generated by the decoder; Use the loss function to calculate the difference between the original data and the reconstructed data; The model parameters are updated through the back-propagation algorithm to minimize the loss function.
[0008] Preferably, in the denoising autoencoder, the original input is assumed to be , the input after adding noise is ,Right now: ; In the formula, the goal of the model is to obtain the reconstructed input by removing noise , and make Close to original clean input ; For noise; Loss Function The most commonly used loss function is the mean square error, which is the difference between the input data and the reconstructed data. It is used to measure the average square difference between the original input and the reconstructed input. The formula is: ; Where, is the dimension of the input data, The original data elements, is the reconstructed data elements; The collected data is normalized as shown below: ; Where, is the data after normalization. is the minimum value in the data sample, is the maximum value in the data sample.
[0009] Preferably, in the process of dividing the sub-catchment areas, the sub-catchment areas are divided into residential areas, commercial areas, administrative and scientific research areas, school areas, hospital areas and park areas according to their functions; Draw all water collection nodes through ArcGIS to obtain the collection of water collection nodes , where each point It is a point on a two-dimensional plane. Use the "Extract" tool under "Analysis Tools" and select the "VoronoiPolygons" tool to construct the Thiessen polygon, that is, the catchment area of the water collection node. The principle is that the dividing line between any two water collection nodes is the perpendicular bisector between them, as shown in the formula: ; The pipe length and pipe density within the subcatchment can be calculated.
[0010] Preferably, the construction of the urban drainage network sedimentation diagnosis model using a support vector machine algorithm optimized based on a genetic algorithm specifically includes: The data inputs include 11 factors, including pipe diameter, pipe density, pipe network level, layout method, NDVI value, underlying surface type, slope, population density, functional area division, garbage situation, average precipitation over many years, and whether there is construction activity information. The data output is pipe siltation degree. Among them, the genetic algorithm is combined with support vector regression to improve its regression performance by optimizing the hyperparameters of support vector regression. In this process, the genetic algorithm acts as an optimizer to search the parameter space and find the best hyperparameter combination; Dataset division: Multiple pipelines in the study area were selected as the overall sample, 80% of which were used as training sets and the remaining 20% as test sets to test the accuracy of the model; Parameter optimization: Dynamic adjustment is performed by observing the convergence of the algorithm and the quality of the solution after 100 generations.
[0011] Preferably, the pipeline siltation degree is the ratio of siltation thickness to pipeline diameter.
[0012] Preferably, the model evaluation and application specifically include: The model was evaluated using the set ratio of training set to test set, and the diagnostic accuracy was compared by the coefficient of determination R² and root mean square error RMSE. The calculation formulas of R² and RMSE are as follows: ; ; Where: is the true value, is the predicted value, is the mean of the true values, is the sample size; Apply the model to untested pipelines to verify its adaptability and accuracy in different areas, thereby providing a scientific basis for maintenance and desilting decisions of urban drainage systems; The LIME algorithm is used to explain the model's prediction results, helping users understand the model's decision-making basis and enhancing the model's credibility and practicality.
[0013] Preferably, the pipeline siltation early warning specifically includes: Use the detection model to update the incoming manual detection data in real time; Compare and analyze manual inspection data with historical model predictions and sensor data, and determine the credibility of the data through rule or model verification mechanisms; The real-time data is input into the existing support vector machine model as new training samples, and the weights are adjusted and the model is optimized through incremental learning methods. The model is retrained on the full data at regular intervals to improve the long-term stability and generalization ability of the model.
[0014] Preferably, the pipeline siltation early warning further includes: According to the Technical Regulations on Operation, Maintenance and Safety of Urban Drainage Pipes and Pumping Stations, the siltation degree of the drainage network is divided into four levels: <0.2, 0.2-0.3, 0.3-0.5 and >0.5. Based on this, pipelines with predicted siltation >0.5 are retrieved and reported to relevant management personnel to ensure that the pipelines are desilted in time before heavy rain disasters.
[0015] The present invention has the following beneficial effects: 1. The present invention achieves high-precision diagnosis of the siltation status of urban drainage pipe networks by comprehensively considering multiple factors such as pipeline properties, land use, and socio-economic information and using a support vector machine learning algorithm.
[0016] 2. The support vector machine sedimentation prediction model based on genetic algorithm optimization in the present invention has strong robustness to noise data, can effectively prevent overfitting, and is suitable for applications in complex environments.
[0017] 3. Comparative experiments have shown that the method of the present invention has significant diagnostic effects in actual data and has high engineering practical value.
[0018] 4. The present invention can provide decision support for the optimized design and refined management of urban drainage networks, reduce maintenance costs, and enhance urban flood prevention capabilities. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The present invention will be further described below with reference to the accompanying drawings and examples.
[0020] Figure 1 The present invention is a flow chart of the method of the present invention.
[0021] Figure 2 This is the X5 pipeline inspection robot used in the pipeline inspection process of the present invention.
[0022] Figure 3 This is the pipeline siltation diagnosis result of the support vector machine optimized based on the genetic algorithm of the present invention.
[0023] Figure 4 Schematic diagram comparing the diagnosis results of the present invention with the actual test results. DETAILED DESCRIPTION
[0024] In order to make the purpose, innovation, application field and operation method of the present invention clearer and more transparent, the present invention is further described in detail below with reference to the accompanying drawings.
[0025] See also Figure 1 The present invention provides a method for diagnosing siltation in urban drainage networks based on multi-factor analysis, which specifically includes: S1. Data collection and preprocessing: Collect data related to the urban drainage network, including pipe attributes (diameter, density, network hierarchy, layout, etc.), land use information (NDVI value, underlying surface type, slope), socioeconomic factors (population density, functional zoning, garbage levels), and hydrometeorological data (average annual precipitation). Additionally, collect information on construction activities surrounding the drainage network.
[0026] The Denoising Autoencoder data cleaning algorithm automatically identifies and removes abnormal data, reducing manual intervention and improving data cleaning efficiency. The data is then standardized for subsequent model training.
[0027] S2. Sub-catchment division: Based on the actual distribution of water collection nodes in the study area, ArcGIS was used to map the water collection nodes and generate subcatchments using the Thiessen polygon method. These subcatchments can be divided into residential, commercial, administrative and scientific research, school, hospital, and park areas according to their function.
[0028] S3, model construction: The support vector machine (SVR) algorithm based on genetic algorithm optimization is used to construct a diagnostic model for urban drainage network sedimentation: Support vector regression (SVR) is an extension of support vector machines (SVM) and is primarily used for regression problems. Unlike traditional regression methods, SVR attempts to find an optimal hyperplane in a high-dimensional feature space that minimizes the prediction error for most data points. SVR introduces a tolerance band (ε) to allow for a certain error and a penalty factor (C) to control model complexity. The primary goal of SVR is to minimize model complexity and prediction error.
[0029] A genetic algorithm (GA) is an optimization algorithm based on the principles of natural selection and genetics. By simulating the process of natural selection, GA generates multiple candidate solutions and iterates them, gradually improving the quality of the solutions.
[0030] Among them, the main parameters are: population size defines the number of individuals in each generation. A larger population may increase the search space, but the computational cost will also increase; crossover probability controls the probability of individuals in the genetic algorithm performing a crossover operation. The crossover operation exchanges genetic information between parents to generate new individuals; mutation probability controls the probability of individual gene mutation. Mutation operation can introduce new solution space and prevent the algorithm from falling into local optimal solution; selection strategy is used to determine which individuals will be selected to enter the next generation. Common selection strategies include roulette wheel selection and tournament selection. Combining genetic algorithms with SVR aims to improve its regression performance by optimizing SVR hyperparameters (such as C, ε, and kernel function parameters). Specifically, the genetic algorithm acts as an optimizer in this process, searching the parameter space to find the best hyperparameter combination.
[0031] S4. Model evaluation and application: The two models were evaluated using a set ratio of training set to test set, and the diagnostic accuracy was compared using the coefficient of determination (R²) and root mean square error (RMSE).
[0032] The model was applied to siltation diagnosis of uninspected pipes to verify its adaptability and accuracy in different areas (including areas with low pipe density and wide drainage areas), thus providing a scientific basis for maintenance and desilting decisions of urban drainage systems.
[0033] At the same time, the LIME (Local Interpretable Model-agnostic Explanations) algorithm is used to explain the model's prediction results, helping users understand the model's decision-making basis and enhancing the model's credibility and practicality.
[0034] S5. Pipeline siltation warning: By using the manual pipeline inspection data provided by the Urban Municipal Administration Bureau, the pipeline sedimentation data of the sedimentation model can be updated in real time, so that the drainage network sedimentation diagnosis system and the smart city management platform can be seamlessly connected to share data and resources.
[0035] Example 2: See also Figure 1-4 The present invention provides a method for diagnosing siltation in urban drainage networks based on multi-factor analysis, which specifically includes: Step S1: Data collection and preprocessing: In step S1, the specific steps of data collection and preprocessing are as follows: Step S11: Collect data related to the urban drainage network, including pipeline attributes (diameter, density, network hierarchy, and layout); land use information (NDVI, underlying surface type, and slope); socioeconomic factors (population density, functional zoning, and garbage levels); and hydrometeorological information (average precipitation over several years). Additionally, information on construction activities surrounding the drainage network is collected, with a value of 1 if any is present and a value of 0 if not.
[0036] Step S12: A denoising autoencoder (DAE) is a variant of an autoencoder designed to recover the original data from noisy input. Its training goal is to enable the model to learn to recover clean input data under noisy conditions. The loss function of a denoising autoencoder typically calculates the reconstruction error, which is the difference between the network's predicted output and the original input. The denoising autoencoder (DAE) first adds noise to the input data, passes the noisy data to the encoder, and generates reconstructed data through the decoder. Next, a loss function (such as MSE or cross-entropy) is used to calculate the difference between the original and reconstructed data. Finally, the model parameters are updated using the backpropagation algorithm to minimize the loss function.
[0037] Among them, in the denoising autoencoder, it is assumed that the original input is , the input after adding noise is ,Right now: ; In the formula, the goal of the model is to obtain the reconstructed input by removing noise , and make Try to get as close to the original clean input as possible , For noise.
[0038] Loss Function It is usually the difference between the input data and the reconstructed data. The most commonly used loss function is the mean square error (MSE), which is used to measure the average square difference between the original input and the reconstructed input. The formula is: ; Where, is the dimension of the input data, The original data elements, is the reconstructed data elements.
[0039] Step S13: Normalize the collected data to ensure data consistency and accuracy during subsequent model training, as shown in the following formula.
[0040] ; Where, is the normalized data, is the minimum value in the data sample, is the maximum value in the data sample.
[0041] Step S2: Subcatchment division: In step S2, the specific steps of sub-catchment division are as follows: Step S21: Based on the actual distribution of water collection nodes in Zhengzhou's pipelines, the study area was mapped using ArcGIS, and subcatchments were generated using the Thiessen polygon method. Subcatchments can be divided into residential areas, commercial areas, administrative and scientific research areas, school areas, hospital areas, and park areas according to their functions.
[0042] Draw all water collection nodes through ArcGIS to obtain the collection of water collection nodes , where each point is a point on a two-dimensional plane. Using the "Extract" tool under "Analysis Tools," select the "VoronoiPolygons" tool to construct Thiessen polygons, which are the catchment areas of the catchment nodes. The principle is that the dividing line between any two catchment nodes is the perpendicular bisector between them, as shown in the following formula: ; The pipe length (in kilometers) and pipe density (in kilometers per square kilometer) within the subcatchment can be calculated.
[0043] Step S3: Model construction: In step S3, the specific steps of model construction are as follows: Step S31: A support vector machine (SVR) algorithm based on genetic algorithm optimization is used to construct an urban drainage network sedimentation diagnosis model: 11 factors, including network density, pipeline hierarchy, pipeline layout method, pipeline diameter, NDVI, slope, catchment area properties, population density, sediment type, multi-year average precipitation, and whether there is construction, are used as data input, and pipeline sedimentation degree (ratio of sedimentation thickness to pipeline diameter × 100%) is used as data output.
[0044] Support vector regression (SVR), an extension of support vector machines (SVM), is primarily used for regression problems. Unlike traditional regression methods, SVR attempts to find an optimal hyperplane in a high-dimensional feature space to minimize the prediction error for most data points. SVR introduces a tolerance band ε to allow for a certain error and a penalty factor C to control model complexity. The primary goal of SVR is to minimize model complexity and prediction error.
[0045] A genetic algorithm (GA) is an optimization algorithm based on the principles of natural selection and genetics. By simulating the process of natural selection, GA generates multiple candidate solutions and iterates them, gradually improving the quality of the solutions.
[0046] Combining the genetic algorithm with SVR aims to improve its regression performance by optimizing SVR hyperparameters, such as C, ε, and kernel function parameters. Specifically, the genetic algorithm acts as an optimizer in this process, searching the parameter space to find the best hyperparameter combination.
[0047] Step S32: Dataset division: 460 pipelines in the study area were selected as the overall sample, 80% of which were used as the training set and the remaining 20% as the test set to test the model accuracy.
[0048] Step S33: Parameter optimization. Dynamic adjustments are made by observing the convergence of the algorithm over 100 generations and the quality of the solution. In this paper, each generation is set to have 50 individuals. The crossover probability is 0.6, and the mutation probability is 0.1. Roulette wheel selection is used as the selection strategy.
[0049] Step S4: Model evaluation and application: In step S4, the specific steps of model evaluation and application are as follows: Step S41: Evaluate the two models using the set ratio of training set to test set, and compare the diagnostic accuracy by using the coefficient of determination R² and root mean square error RMSE. The calculation formulas are: ; ; Where: is the true value, is the predicted value, is the mean of the true values, is the sample size.
[0050] Step S42: The model is applied to the 57 untested pipelines to verify its adaptability and accuracy in different areas (including areas with low pipeline density and wide drainage areas), thereby providing a scientific basis for maintenance and desilting decisions of urban drainage systems.
[0051] Step S43: Use the LIME (Local Interpretable Model-agnostic Explanations) algorithm to explain the model's prediction results, helping users understand the model's decision-making basis and enhancing the model's credibility and practicality.
[0052] Step S5: Pipeline siltation update and early warning: In step S5, the specific steps of pipeline siltation early warning are as follows: Step S51: Using the aforementioned deep learning-based anomaly detection model, the received manual detection data is updated in real time. Step S52: Compare and analyze the manual detection data with the historical model prediction value and sensor data, and determine the credibility of the data through a rule or model verification mechanism.
[0053] Step S53: Input the real-time data as new training samples into an existing support vector machine (SVR) model or other machine learning model, and perform weight adjustment and model optimization using incremental learning (online learning). The model is retrained on the full data at regular intervals (annually) to improve the model's long-term stability and generalization capabilities.
[0054] Therefore, the 10 pipelines that were recently inspected were input into the sedimentation diagnosis model optimized based on the genetic algorithm. The diagnostic accuracy of the model was compared (see Table 1), and it was found that the accuracy was still reliable.
[0055] Table 1 Model accuracy before and after data update
[0056] Step S54: According to the "Technical Regulations for Operation, Maintenance, and Safety of Urban Drainage Pipelines and Pumping Stations," drainage network siltation can be classified into four levels: <0.2, 0.2-0.3, 0.3-0.5, and >0.5. Therefore, pipelines with predicted siltation >0.5 are searched and reported to relevant management personnel to ensure timely desilting before a rainstorm disaster.
[0057] The present invention has thus far described the technical methods of the present invention in detail through the accompanying drawings and specific flow charts. However, the scope of protection of the present invention is not limited to the specific embodiments described. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and such modified or substituted technical solutions will still fall within the scope of protection of the present invention.
Claims
1. A method for diagnosing siltation in urban drainage pipe networks based on multi-factor analysis, characterized in that: include: Data collection and preprocessing: Collect data related to the urban drainage network, including pipeline attributes, land use information, socioeconomic factors, hydrological and meteorological factors, and construction activities around the drainage network; The collected relevant data are preprocessed using the denoising autoencoder data cleaning algorithm; Subcatchment division: Combined with the actual distribution of water collection nodes in the pipelines in the study area, the water collection nodes in the study area were mapped using ArcGIS, and the sub-catchment areas were generated using the Thiessen polygon method. Model construction: A diagnostic model for urban drainage network sedimentation is constructed using a support vector machine algorithm optimized by a genetic algorithm. Model evaluation and application: The model was evaluated using a set ratio of training and test sets, and the diagnostic accuracy was compared using the coefficient of determination and root mean square error; Applying the model to diagnose siltation in uninspected pipelines; Pipeline siltation warning: Utilize the manual pipeline inspection data provided by the Urban Municipal Administration Bureau to update the pipeline sedimentation data of the sedimentation model in real time, so as to connect the drainage network sedimentation diagnosis with the smart city management platform and share data and resources.
2. The method for diagnosing urban drainage network siltation based on multi-factor analysis according to claim 1, characterized in that: The pipe attributes in the drainage network related data include pipe diameter, pipe density, pipe network level and layout method; The land use information includes NDVI value, underlying surface type and slope; The socioeconomic factors include population density, functional zoning, and waste conditions; The hydrometeorology includes the average precipitation over many years; If there is any construction activity information around the drainage network, the value is 1, and if not, the value is 0.
3. The method for diagnosing urban drainage network siltation based on multi-factor analysis according to claim 2, characterized in that: The preprocessing of the collected relevant data using the denoising autoencoder data cleaning algorithm specifically includes: The input data is processed by adding noise, and then the noisy data is passed to the encoder, and the reconstructed data is generated by the decoder; Use the loss function to calculate the difference between the original data and the reconstructed data; The model parameters are updated through the back-propagation algorithm to minimize the loss function.
4. The method for diagnosing urban drainage network siltation based on multi-factor analysis according to claim 3, characterized in that: In the denoising autoencoder, assuming the original input is , the input after adding noise is ,Right now: ； In the formula, the goal of the model is to obtain the reconstructed input by removing noise , and make Close to original clean input ; For noise; Loss Function The most commonly used loss function is the mean square error, which is the difference between the input data and the reconstructed data. It is used to measure the average square difference between the original input and the reconstructed input. The formula is: ； Where, is the dimension of the input data, The original data elements, is the reconstructed data elements; The collected data is normalized as shown below: ； Where, is the data after normalization. is the minimum value in the data sample, is the maximum value in the data sample.
5. The method for diagnosing urban drainage network siltation based on multi-factor analysis according to claim 4, characterized in that: In the process of dividing the sub-catchment areas, the sub-catchment areas are divided into residential areas, commercial areas, administrative and scientific research areas, school areas, hospital areas and park areas according to their functions; Draw all water collection nodes through ArcGIS to obtain the collection of water collection nodes , where each point is a point on a two-dimensional plane. Use the "Extract" tool under "Analysis Tools" and select the "VoronoiPolygons" tool to construct Thiessen polygons, that is, the catchment area of the water collection node. The principle is that the dividing line between any two water collection nodes is the perpendicular bisector between them, as shown in the formula: ； The pipe length and pipe density within the subcatchment can be calculated.
6. The method for diagnosing siltation in urban drainage pipe networks based on multi-factor analysis according to claim 5, characterized in that: The method of constructing a city drainage network sedimentation diagnosis model using a support vector machine algorithm based on genetic algorithm optimization specifically includes: The data inputs include 11 factors, including pipe diameter, pipe density, pipe network level, layout method, NDVI value, underlying surface type, slope, population density, functional area division, garbage situation, average precipitation over many years, and whether there is construction activity information. The data output is pipe siltation degree. Among them, the genetic algorithm is combined with support vector regression to improve its regression performance by optimizing the hyperparameters of support vector regression. In this process, the genetic algorithm acts as an optimizer to search the parameter space and find the best hyperparameter combination; Dataset division: Multiple pipelines in the study area were selected as the overall sample, 80% of which were used as training sets and the remaining 20% as test sets to test the accuracy of the model; Parameter optimization: Dynamic adjustment is performed by observing the convergence of the algorithm and the quality of the solution after 100 generations.
7. The method for diagnosing urban drainage network siltation based on multi-factor analysis according to claim 6, characterized in that: The pipeline siltation degree is the ratio of siltation thickness to pipeline diameter.
8. The method for diagnosing siltation in urban drainage networks based on multi-factor analysis according to claim 6, characterized in that: The model evaluation and application specifically include: The model was evaluated using the set ratio of training set to test set, and the diagnostic accuracy was compared by the coefficient of determination R² and root mean square error RMSE. The calculation formulas of R² and RMSE are as follows: ； ； Where: is the true value, is the predicted value, is the mean of the true values, is the sample size; Apply the model to untested pipelines to verify its adaptability and accuracy in different areas, thereby providing a scientific basis for maintenance and dredging decisions of urban drainage systems; The LIME algorithm is used to explain the model's prediction results, helping users understand the model's decision-making basis and enhancing the model's credibility and practicality.
9. The method for diagnosing urban drainage network siltation based on multi-factor analysis according to claim 8, characterized in that: The pipeline siltation early warning specifically includes: Use the detection model to update the incoming manual detection data in real time; Compare and analyze manual inspection data with historical model predictions and sensor data, and determine the credibility of the data through rule or model verification mechanisms; The real-time data is input into the existing support vector machine model as new training samples, and the weights are adjusted and the model is optimized through incremental learning methods. The model is retrained on the full data at regular intervals to improve the long-term stability and generalization ability of the model.
10. The method for diagnosing urban drainage network siltation based on multi-factor analysis according to claim 9, characterized in that: The pipeline siltation early warning also includes: According to the Technical Regulations on Operation, Maintenance and Safety of Urban Drainage Pipes and Pumping Stations, the siltation degree of the drainage network is divided into four levels: <0.2, 0.2-0.3, 0.3-0.5 and >0.
5. Based on this, pipelines with predicted siltation >0.5 are retrieved and reported to relevant management personnel to ensure that the pipelines are desilted in time before heavy rain disasters.