Urban drainage network damage detection system based on intelligent analysis

Abstract

The present invention relates to the field of intelligent detection technology for urban infrastructure, and discloses an urban drainage pipe network damage detection system based on intelligent analysis. A multi-source data acquisition module collects static structural parameters and real-time operational monitoring data from a distributed sensor network to generate topological eigenvalues, dynamic operational status eigenvalues, and dynamic detection threshold sets for each damage type. A data preprocessing module filters noise from the monitoring data, removes outliers, and extracts time-domain and frequency-domain eigenvectors. A feature fusion module fuses the topological and operational eigenvalues ​​to generate a multidimensional fusion feature matrix. An intelligent analysis engine outputs damage type diagnosis results through pattern matching calculations. A spatial positioning module combines topological eigenvalues ​​to generate three-dimensional coordinate data for the damaged area. A dynamic learning module optimizes detection rules based on maintenance data. The system implements intelligent damage detection and precise positioning, improving detection accuracy and efficiency, adapting to complex environments, and possessing a high level of intelligence.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent detection of urban infrastructure, and in particular to an urban drainage pipe network damage detection system based on intelligent analysis. Background Art

[0002] As a vital component of urban infrastructure, the operational status of urban drainage networks is directly related to flood control and drainage, water resource utilization, and ecological and environmental protection. With the acceleration of urbanization, the scale of drainage networks continues to expand, and problems such as aging and damage are becoming increasingly prominent. Traditional manual inspections and offline testing methods, with their low efficiency, high cost, and poor accuracy, are unable to meet the demands of refined urban management.

[0003] Currently, urban drainage network inspection faces many challenges. On the one hand, the network structure is complex, containing a large number of pipe sections with different diameters, pipe materials, and laying years. Traditional inspection methods cannot comprehensively and accurately obtain the static structural parameters and dynamic operating data of the network. On the other hand, the operating environment of the network is complex and changeable. Environmental factors such as groundwater level, soil moisture content, and temperature can have a significant impact on the inspection results. The traditional fixed threshold inspection method cannot adapt to the inspection needs under different working conditions. In addition, existing inspection technologies often lack the ability to integrate and analyze multi-source data, making it difficult to extract effective damage features from massive monitoring data, resulting in low accuracy and reliability of inspection results.

[0004] The rapid development of sensor technology, the Internet of Things (IoT), and intelligent analysis technologies has provided new approaches and methods for detecting damage in urban drainage pipe networks. Deploying distributed sensor networks throughout drainage pipe networks enables real-time collection of multi-source data, including pressure, flow, and voiceprints. Using intelligent analysis algorithms to fuse and pattern-match these multi-source data enables accurate identification and spatial localization of damage types. However, existing technologies still have shortcomings in multi-source data collection and preprocessing, feature fusion and intelligent analysis, dynamic threshold adjustment, and spatial localization, requiring further optimization and improvement. Summary of the Invention

[0005] The purpose of the present invention is to provide an urban drainage pipe network damage detection system based on intelligent analysis to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an urban drainage pipe network damage detection system based on intelligent analysis, the system comprising:

[0007] A multi-source data acquisition module is used to collect static structural parameters and real-time operation monitoring data of the drainage network through a distributed sensor network, analyze and process the data to generate network topology characteristic values ​​and dynamic operation status characteristic values, and analyze and process the data to generate dynamic detection threshold sets for each damage type based on the standard parameter sets for each damage type in the historical maintenance database;

[0008] The data preprocessing module is used to receive real-time operation monitoring data and perform noise filtering and outlier removal, screen out valid monitoring data and extract the time domain feature vector and frequency domain feature vector of the valid monitoring data;

[0009] The feature fusion module fuses the time domain feature vector and the frequency domain feature vector based on the pipeline network topology feature value and the dynamic operation state feature value to generate a multi-dimensional fusion feature matrix;

[0010] The intelligent analysis engine is used to perform pattern matching calculations on the multi-dimensional fusion feature matrix and the dynamic detection threshold set of each damage type, output the matching value of each damage type, and select the damage type corresponding to the maximum matching value as the current pipeline network status diagnosis result;

[0011] The spatial positioning module is used to index the spatial distribution pattern of the corresponding damage type in the historical maintenance database according to the diagnosis results, and calculate and generate the three-dimensional coordinate positioning data of the damaged area in combination with the pipeline network topological characteristic values.

[0012] Preferably, the static structural parameters and real-time operation monitoring data of the drainage network are collected, and the specific process is as follows:

[0013] When the detection equipment is first deployed in the pipe network system, the structural parameters such as pipe diameter, pipe type, and installation year are collected. Based on the structural parameters, a pipe network topology map is established, and pressure sensors, flow meters, and acoustic detectors are activated to collect baseline data.

[0014] When there are historical inspection records for the pipe network system, the operating pressure fluctuation data, flow change data, and voiceprint feature data of the last three maintenance cycles are extracted from the historical maintenance database and recorded as the benchmark reference data set for the current real-time monitoring.

[0015] Preferably, generating a set of dynamic detection thresholds for each damage type specifically includes:

[0016] Extract standard parameter sets for each damage type from the historical maintenance database, including standard pressure gradient values ​​for leakage conditions, standard acoustic spectrum characteristics for pipe cracks, and standard flow rate mutation amplitudes for blockage conditions.

[0017] Obtain current environmental variable data, including groundwater level, soil moisture content, and temperature change parameters, and calculate and generate environmental correction coefficients;

[0018] Multiply each parameter item in the standard parameter set by the environmental correction coefficient to generate a dynamic detection threshold set that adapts to the current working conditions;

[0019] The calculation environment correction coefficient includes:

[0020] A linear regression model between groundwater level and pipe wall stress was established to obtain the correction factor for the elastic modulus of the pipe due to temperature changes.

[0021] Grey correlation analysis method is used to calculate the correlation coefficient between soil moisture content and pressure sensor reading fluctuations to generate multidimensional environmental compensation parameters.

[0022] Preferably, generating a multi-dimensional fusion feature matrix specifically includes:

[0023] Perform feature concatenation on the pressure mean, pressure fluctuation variance, and flow rate change rate contained in the time domain feature vector and the voiceprint main frequency component and harmonic energy distribution contained in the frequency domain feature vector;

[0024] Based on the pipe connection relationship in the pipe network topology eigenvalues, spatial correlation analysis is performed on the cascade features to generate a multidimensional fusion feature matrix containing 16-dimensional feature indicators.

[0025] Preferably, the noise filtering and outlier removal specifically include:

[0026] Perform sliding average filtering on the raw data sequence collected by the pressure sensor to remove sudden interference pulses;

[0027] The box plot analysis method was used to identify outliers in the flow monitoring data, and linear interpolation correction was performed on the values ​​that exceeded 1.5 times the interquartile range.

[0028] Preferably, the performing pattern matching calculation specifically includes:

[0029] Calculate the Euclidean distance between each feature dimension in the multidimensional fusion feature matrix and the dynamic detection threshold of each damage type;

[0030] After normalizing the Euclidean distances of each dimension, a weighted summation algorithm is used to generate the comprehensive matching value of each damage type.

[0031] Preferably, the method of screening out the damage type corresponding to the maximum matching value as the current pipe network status diagnosis result specifically includes:

[0032] Establish a queue with matching values ​​in descending order, and select the damage type at the top of the queue as the priority diagnosis result;

[0033] When the difference between the first two matching values ​​is less than the set tolerance threshold, the manual review mark is triggered and the pending confirmation status is recorded.

[0034] Preferably, a dynamic learning module is also included, specifically including:

[0035] After each maintenance operation is completed, obtain the actual damage type and location information confirmed by on-site survey;

[0036] Compare and analyze the actual data with the system diagnosis results to calculate the feature recognition error;

[0037] The parameter weight coefficients in the dynamic detection threshold set are adjusted according to the error amount, and the pattern matching rules in the history maintenance database are updated.

[0038] Preferably, the calculation and generation of three-dimensional coordinate positioning data of the damaged area in combination with the pipe network topological characteristic values ​​specifically includes:

[0039] Based on the pipe segment coding rules in the pipe network topology diagram, the location of the sensor node where abnormal data fluctuation occurs is determined;

[0040] According to the pipe section connection direction and spatial coordinate data, the triangulation positioning method is used to calculate the spatial position of the damaged point relative to the three nearest monitoring nodes.

[0041] Preferably, the parameter weight coefficients in the dynamic detection threshold set are adjusted according to the error amount, and the specific processing process is:

[0042] Collect statistics on the recognition accuracy of each feature dimension in the last N maintenance operations;

[0043] The feature dimensions whose accuracy is lower than the set threshold are weighted attenuated, and the attenuation amplitude is proportional to the accuracy deviation value;

[0044] Implement weight enhancement adjustments for feature dimensions whose accuracy is consistently higher than the benchmark value.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The system collects static structural parameters and real-time operational monitoring data from the drainage network through a distributed sensor network, enabling comprehensive, real-time access to network status information. During initial deployment, structural parameters such as pipe diameter, material type, and installation year are collected to establish a topological map of the network. Pressure sensors, flow meters, and acoustic detectors are also used to collect baseline data. When historical inspection records exist, operational data from the three most recent maintenance cycles is extracted as a baseline reference dataset, ensuring comprehensive data collection and a scientifically sound benchmark, providing a solid data foundation for subsequent inspections.

[0047] The data preprocessing module filters noise and eliminates outliers in real-time operation monitoring data, uses sliding average filtering to process the raw data of the pressure sensor to remove sudden interference pulses, and uses box plot analysis to identify outliers in the flow monitoring data and perform linear interpolation correction, effectively improving data quality and ensuring the accuracy of subsequent analysis.

[0048] The feature fusion module fuses time-domain and frequency-domain feature vectors based on the network's topological and dynamic operating status eigenvalues ​​to generate a multidimensional fusion feature matrix containing 16 characteristic indicators. By concatenating time-domain features such as pressure mean and pressure fluctuation variance with frequency-domain features such as the soundprint's main frequency component and harmonic energy distribution, and performing spatial correlation analysis based on pipe segment connections, this multidimensional feature fusion is achieved, enabling a more comprehensive and accurate reflection of the network's operating status and improving the ability to extract damage signatures.

[0049] The intelligent analysis engine performs pattern matching calculations on the multi-dimensional fusion feature matrix and the dynamic detection threshold set for each damage type. By calculating the Euclidean distance, normalizing it, and performing a weighted summation to generate a comprehensive matching value, it can accurately identify the type of damage in the pipeline network. Furthermore, a queue is established to sort the matching values ​​in descending order, selecting the first one as the priority diagnosis result. Manual review is triggered when the difference between the first two digits falls within the set tolerance threshold, ensuring both detection accuracy and reliability.

[0050] The generation of dynamic detection threshold sets fully considers the impact of environmental factors. Standard parameter sets for each damage type are extracted from a historical maintenance database. Environmental variable data such as groundwater level, soil moisture, and temperature fluctuations are used to calculate environmental correction coefficients. These standard parameters are multiplied by the correction coefficients to generate dynamic thresholds tailored to the current operating conditions. By establishing a linear regression model between groundwater level and pipe wall stress, deriving a correction factor for the effect of temperature on the pipe elastic modulus, and using gray correlation analysis to calculate the correlation coefficient between soil moisture and pressure sensor reading fluctuations to generate multidimensional environmental compensation parameters, the detection thresholds can adapt to environmental changes in real time, improving both the adaptability and accuracy of detection.

[0051] The spatial positioning module uses diagnostic results to index the spatial distribution patterns of corresponding damage types in the historical maintenance database and, combined with the network topology eigenvalues, calculates the three-dimensional coordinate location data for the damaged area. Pipeline segment coding rules based on the network topology map determine the location of sensor nodes with abnormal data fluctuations. Triangulation is then used based on the pipe segment connection directions and spatial coordinate data to infer the location of the damaged point. This allows for precise positioning of the damaged area, providing a clear target for pipeline network maintenance and improving maintenance efficiency.

[0052] The system also includes a dynamic learning module. After each maintenance operation, it obtains the actual damage type and location confirmed by on-site surveys, compares and analyzes these with the system's diagnostic results, calculates the error in feature recognition, adjusts the parameter weight coefficients in the dynamic detection threshold set based on this error, and updates the pattern matching rules in the historical maintenance database. Through continuous learning and optimization, the system gradually improves the accuracy and reliability of detection, achieving a continuous improvement in detection capabilities and adapting to changes in pipeline network operation and evolving detection needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a working principle diagram of the urban drainage pipe network damage detection system based on intelligent analysis according to the present invention;

[0054] Figure 2 This is the working principle diagram of the spatial positioning module;

[0055] Figure 3 Module diagram for dynamic learning;

[0056] Figure 4 It is the adjustment diagram of parameter weight coefficient. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] See also Figures 1-4 The present invention relates to an urban drainage pipe network damage detection system based on intelligent analysis. The system includes a multi-source data acquisition module, a data preprocessing module, a feature fusion module, an intelligent analysis engine, and a spatial positioning module. The specific implementation steps are as follows:

[0059] The multi-source data acquisition module collects static structural parameters and real-time operation monitoring data of the drainage network through a distributed sensor network, analyzes and processes them to generate network topology characteristic values ​​and dynamic operation status characteristic values, and analyzes and processes the standard parameter set of each damage type in the historical maintenance database to generate a dynamic detection threshold set for each damage type.

[0060] The data preprocessing module receives real-time operation monitoring data and performs noise filtering and outlier removal, screens out valid monitoring data and extracts the time domain feature vector and frequency domain feature vector of the valid monitoring data.

[0061] The feature fusion module fuses the time domain feature vector and the frequency domain feature vector based on the pipeline network topology feature value and the dynamic operation state feature value to generate a multi-dimensional fusion feature matrix.

[0062] The intelligent analysis engine performs pattern matching calculations on the multi-dimensional fusion feature matrix and the dynamic detection threshold set of each damage type, outputs the matching value of each damage type, and selects the damage type corresponding to the maximum matching value as the current pipeline network status diagnosis result.

[0063] The spatial positioning module indexes the spatial distribution pattern of the corresponding damage type in the historical maintenance database based on the diagnosis results, and calculates the three-dimensional coordinate positioning data of the damaged area in combination with the pipeline network topological characteristic values.

[0064] The present invention will be further described below in conjunction with Examples 1 to 5:

[0065] Example 1

[0066] This embodiment involves the specific process of collecting static structural parameters and real-time operational monitoring data from a drainage network within a multi-source data acquisition module. This process is divided into two different data acquisition modes, depending on whether the network is deploying detection equipment for the first time. The goal is to provide comprehensive, accurate, and comparative basic data for the entire damage detection system, ensuring the reliability and effectiveness of subsequent detection and analysis.

[0067] When the detection equipment is first deployed in the pipeline system, the multi-source data acquisition module first collects static structural parameters. Specifically, key structural parameters such as pipe diameter, pipe type, and installation age need to be obtained. Pipe diameter directly reflects the flow capacity of the pipeline network. Pipe networks with different pipe diameters have significant differences in water flow characteristics and pressure distribution; pipe type determines the physical properties of the pipeline network, such as strength and corrosion resistance. Different pipes may have different types and patterns of damage during long-term use; the installation age is related to the degree of aging of the pipeline network. Old pipeline networks are more prone to cracks, leakage, and other problems. By collecting these structural parameters, a pipeline network topology relationship map can be established. This map graphically displays the connection relationship, spatial layout, and other information of each pipe section in the pipeline network, providing a basic framework for subsequent analysis of the topological characteristic values ​​of the pipeline network.

[0068] After establishing the network topology, the module activates pressure sensors, flow meters, and acoustic detectors to collect baseline data. Pressure sensors monitor pressure changes within the network. By collecting real-time pressure data, they can reflect the network's operating load and any abnormal pressure fluctuations. Flow meters measure flow rates within the network, and changes in flow rate indirectly reflect the network's flow status, such as blockages. Acoustic detectors capture acoustic signals within the network, generating soundprint signatures. Different types of damage (such as leaks and cracks) produce distinct soundprint signatures. These three types of sensors work together to comprehensively collect real-time operational monitoring data from the initial state of the network, encompassing pressure, flow, and acoustics, to form baseline data. This baseline data represents various indicators of the network's normal operation and serves as an important benchmark for subsequent assessment of damage.

[0069] When there are historical inspection records for the pipe network system, the focus of the multi-source data acquisition module shifts to extracting relevant data from the historical maintenance database. The specific operation is to extract the operating pressure fluctuation data, flow change data, and voiceprint feature data of the three most recent maintenance cycles, and record them as the benchmark reference data set for the current real-time monitoring. The data from the three most recent maintenance cycles are selected because they can be closer to the current operating conditions of the pipe network and reduce data deviations caused by long time intervals. The operating pressure fluctuation data can reflect the changing trends and abnormal fluctuations of the pipe network pressure during the past maintenance cycle; the flow change data can reflect the fluctuation patterns of the flow in different time periods and whether there is a sudden change in flow; the voiceprint feature data records the abnormal sound characteristics that may occur in the historical operation of the pipe network. Using these data as a benchmark reference data set can provide a more timely comparison basis for the current real-time monitoring data.

[0070] During data collection, sensor deployment must adhere to certain principles. The node layout of the distributed sensor network should be optimized based on the network's topology and actual needs, ensuring comprehensive coverage of critical network locations, such as pipe joints, elbows, and areas near valves—areas prone to damage. The sensor sampling frequency must be set based on the network's operational characteristics and detection accuracy requirements, ensuring the capture of subtle anomalies while avoiding data redundancy caused by excessively high sampling frequencies, which could increase the burden on subsequent data processing.

[0071] The multi-source data acquisition module needs to perform preliminary analysis and processing on the collected static structural parameters and real-time operation monitoring data. For static structural parameters, the topological characteristic values ​​of the pipeline network are generated by integrating and calculating data such as pipe diameter, pipe type, and installation year. These characteristic values ​​can quantify the topological structural information of the pipeline network, such as pipe segment length, connection angle, and node density. For real-time operation monitoring data, the module analyzes the changing patterns and statistical characteristics of data such as pressure, flow, and soundprint to generate dynamic operation status characteristic values, such as the average, maximum, and minimum pressure values, the rate of change of flow, and the main frequency distribution of soundprints. The generation of these characteristic values ​​provides a more representative and analytically valuable data format for subsequent data preprocessing, feature fusion, and other links.

[0072] Regarding data storage, all collected data and generated feature values ​​must be stored promptly and accurately in a historical maintenance database. The database design should offer excellent scalability and query performance, enabling easy retrieval and access of data from different time periods and types. Furthermore, a data backup and recovery mechanism must be established to ensure data security and integrity, preventing data loss from impacting system operations.

[0073] Furthermore, the multi-source data acquisition module must be able to interact with other modules. After completing data acquisition and preliminary processing, it must promptly transmit network topology characteristic values, dynamic operating status characteristic values, and real-time operation monitoring data to the data preprocessing module for subsequent noise filtering and outlier removal. Furthermore, it must be able to receive feedback from other modules, such as abnormal data detected by the intelligent analysis engine during pattern matching, to adjust the focus and frequency of data acquisition, further improving the pertinence and effectiveness of data collection.

[0074] In practical applications, whether collecting baseline data during initial deployment or extracting benchmark reference datasets based on historical records, data authenticity and accuracy must be ensured. Before data collection, personnel must calibrate and debug sensor equipment to ensure that its measurement accuracy meets requirements. During the data collection process, sensor equipment should be regularly inspected and maintained to promptly identify and address equipment failures and avoid data anomalies caused by equipment problems. Furthermore, data in historical maintenance databases must be rigorously reviewed and verified to ensure data integrity and reliability, preventing historical data errors from affecting the accuracy of current test results.

[0075] Through these two data collection modes, the Multi-Source Data Acquisition Module provides comprehensive, accurate, and timely basic data for the urban drainage network damage detection system, laying a solid foundation for subsequent damage detection and diagnosis. Whether it's a newly deployed network or one with a history of inspections, this module ensures the system can promptly and accurately identify damage issues through rational data collection and processing, providing strong technical support for the maintenance and management of urban drainage networks.

[0076] Example 2

[0077] This embodiment focuses on generating dynamic detection threshold sets for each damage type. This process combines historical data with real-time environmental variables to dynamically adjust standard detection parameters to meet the needs of pipeline damage detection under different operating conditions. This process involves four core steps: extracting standard parameters, acquiring environmental variables, calculating environmental correction coefficients, and generating dynamic thresholds. These steps are closely integrated to ensure the generated threshold sets are both scientifically sound and practical.

[0078] The standard parameter sets for each damage type are extracted from the historical maintenance database. The historical maintenance database stores a large amount of inspection and maintenance data accumulated during the long-term operation of the pipeline network, covering the typical characteristic parameters of different damage types. For example, the standard parameters of the leakage state include the standard value of the pressure gradient, which reflects the typical amplitude of the pressure drop per unit length of the pipe section when a leak occurs in the pipeline network; the standard parameters of the pipe body crack include the standard characteristics of the soundprint spectrum, which can form a recognizable spectrum pattern by analyzing the frequency distribution characteristics of the sound waves generated by the cracks; the standard parameters of the blockage condition involve the standard amplitude of the flow mutation, which is used to characterize the degree of sudden decrease in flow when a blockage occurs. These standard parameter sets are based on the statistical analysis of a large number of previous damage cases, are widely representative, and provide a basic reference for current detection.

[0079] Obtain data on current environmental variables, including groundwater level, soil moisture content, and temperature variation parameters. Changes in groundwater level will generate varying hydrostatic pressures on pipe walls, thereby affecting the stress state of the pipe network. Soil moisture content can alter the soil's support and friction on the pipe network, resulting in uneven stress distribution on the pipes. Temperature fluctuations can cause pipes to expand and contract, altering their elastic modulus and internal stress distribution. To accurately capture these environmental variables, appropriate monitoring equipment can be deployed around the pipe network: using liquid level sensors to monitor groundwater level in real time, soil moisture sensors to measure soil moisture content, and temperature sensors to record ambient temperature changes. These monitoring devices must possess high accuracy and stability to ensure that the collected data truly reflects the current environmental conditions.

[0080] After obtaining the environmental variable data, the environmental correction coefficient calculation phase begins. First, a linear regression model of groundwater level and pipe wall stress is established. Using the measured values ​​of groundwater level and pipe wall stress in historical data, the linear regression analysis method is used to determine the functional relationship between the two. For example, assuming the groundwater level is The wall stress value is , obtained through regression analysis The linear equation of and is the regression coefficient. This model can quantify the impact of groundwater level changes on pipe wall stress, providing a basis for subsequent correction of pressure-related parameters.

[0081] At the same time, obtain the correction factor of the elastic modulus of the pipe due to temperature changes. The elastic modulus is an important indicator to measure the material's ability to resist elastic deformation. Temperature changes will cause the elastic modulus of the pipe to change, thereby affecting the mechanical properties of the pipe network. By consulting the physical performance parameter table of the pipe or conducting material mechanics experiments, the change ratio of the elastic modulus of the pipe in different temperature ranges is determined to form a temperature correction factor. For example, a certain pipe will change with each increase in temperature. When the elastic modulus decreases , then this ratio can be used as a temperature correction factor.

[0082] Gray correlation analysis was used to calculate the correlation coefficient between soil moisture content and pressure sensor reading fluctuations. Gray correlation analysis is a systematic analysis method used to study small amounts of data and uncertain factors. It is suitable for quantitative analysis of the complex relationship between soil moisture content and pressure fluctuations. After dimensionlessly transforming the soil moisture data series and the pressure sensor reading fluctuation data series, the correlation coefficient and degree of correlation were calculated to obtain a coefficient value reflecting the correlation between the two. A larger coefficient value indicates a more significant impact of soil moisture changes on pressure sensor reading fluctuations, while a smaller coefficient indicates a smaller impact. Through these steps, environmental compensation parameters are generated that include multi-dimensional influencing factors such as groundwater level, temperature, and soil moisture content, comprehensively reflecting the impact of environmental variables on the operating status of the pipeline network.

[0083] Finally, each parameter in the standard parameter set is multiplied by the environmental correction coefficient to generate a dynamic detection threshold set that adapts to the current working conditions. For example, for the pressure gradient standard value of the leakage state, assuming its original value is , the pressure gradient threshold under the current working conditions is calculated by the environmental correction coefficient: ,in This is a comprehensive environmental correction factor, encompassing the influence of factors such as groundwater level, temperature, and soil moisture. Similarly, parameters such as the standard acoustic spectrum characteristics of pipe cracks and the standard amplitude of flow rate mutations in blockage conditions are modified accordingly to form a complete set of dynamic detection thresholds.

[0084] In practical applications, it's important to consider the real-time and dynamic nature of environmental variables. Factors such as groundwater levels, soil moisture, and temperature can change significantly over time, season, and weather conditions. Therefore, environmental correction coefficients must be regularly updated to ensure that the dynamic detection threshold set reflects current environmental conditions. For example, when groundwater levels rise during the rainy season, environmental variable data should be recollected and the environmental correction coefficients updated to avoid biased detection results due to threshold lag.

[0085] Furthermore, the integrity and accuracy of the historical maintenance database are crucial for extracting the standard parameter set. A rigorous data entry and review mechanism must be established to ensure the authenticity and reliability of the parameter information in each damage record. Furthermore, as new damage cases accumulate, the standard parameter set should be regularly updated and expanded to incorporate characteristic parameters for new damage types, enhancing the system's ability to detect complex damage scenarios.

[0086] When calculating environmental correction factors, it's important to select appropriate analysis methods and models based on the actual layout of the pipe network and the characteristics of the pipe materials. For example, the impact of temperature on the elastic modulus may vary for pipes made of different materials (such as cast iron and plastic pipes), requiring separate correction models. In areas with complex geological conditions, the correlation between soil moisture content and pressure fluctuations may exhibit nonlinear characteristics, necessitating the use of nonlinear regression or other data analysis methods.

[0087] The generated dynamic detection threshold set must be compatible with the pattern matching algorithm of the intelligent analysis engine. When designing threshold parameters, the dimension and range of the feature dimension must be considered to ensure comparability with the feature indicators in the multidimensional fusion feature matrix. For example, the pressure gradient threshold is consistent with the dimension of time domain features such as the pressure mean and pressure fluctuation variance, and the voiceprint spectrum threshold is consistent with the dimension of frequency domain features such as the voiceprint main frequency component and harmonic energy distribution, thereby ensuring the accuracy of the pattern matching calculation.

[0088] The dynamic detection threshold set generated through the above steps not only retains the reference value of historical empirical data but also fully considers the influence of current environmental factors, enabling the detection system to maintain high sensitivity and accuracy under different operating conditions. This mechanism avoids the limitations of fixed threshold detection, improves the system's adaptability to complex environments, and provides a scientific and reliable basis for real-time damage detection in urban drainage networks.

[0089] Example 3

[0090] This embodiment focuses on the process of noise filtering and outlier removal in the data preprocessing module and the generation of a multi-dimensional fusion feature matrix in the feature fusion module. In the data preprocessing stage, the sliding average filtering method is a key step for the raw data sequence collected by the pressure sensor. This method smoothes the signal by calculating the local average value of the data points, effectively removing sudden interference pulses. Specifically, for a given raw pressure data sequence, a sliding window of fixed length is defined, and the window moves point by point on the data sequence. After each movement, the average value of the data points in the window is calculated as the filtered value of the point. This method can retain the main trend of the data while reducing the impact of random noise, making the pressure data more stable and reliable.

[0091] For flow monitoring data, a box plot analysis method is used to identify outliers. The box plot divides the data into four parts using quartiles, where the interquartile range (IQR) is defined as the difference between the upper quartile (Q3) and the lower quartile (Q1). Outliers are defined as values ​​that exceed 1.5 times the interquartile range. Linear interpolation is performed on identified outliers. Linear interpolation is a method of estimating the value of an unknown data point between two known data points. By constructing a linear function using normal data points before and after the outlier, a reasonable alternative value is calculated to ensure the accuracy and validity of the flow data.

[0092] After noise filtering and outlier removal, valid monitoring data is screened out, and the time domain eigenvectors and frequency domain eigenvectors of the valid monitoring data are extracted. The time domain eigenvectors mainly focus on the statistical characteristics of the signal in the time domain, including the pressure mean, pressure fluctuation variance, flow rate change rate, etc. The pressure mean reflects the average level of pressure in the pipeline network, the pressure fluctuation variance measures the degree of dispersion of the pressure data relative to the mean, and the flow rate change rate describes how fast the flow changes over time. The frequency domain eigenvectors convert the time domain signal to the frequency domain through Fourier transform, and analyze the frequency components of the signal, including the main frequency component of the voiceprint and the harmonic energy distribution. The main frequency component of the voiceprint is the frequency component with the strongest energy in the voiceprint signal. Different damage types often have different main frequency characteristics; the harmonic energy distribution describes the energy proportion of each harmonic, providing richer frequency domain information.

[0093] In the feature fusion module, feature concatenation is performed on the time-domain and frequency-domain feature vectors. Feature concatenation sequentially connects feature vectors from different sources into a longer vector, combining different types of feature data to form a more comprehensive feature representation. Specifically, the pressure mean, pressure fluctuation variance, and flow rate change rate features contained in the time-domain feature vector are combined with the voiceprint main frequency component and harmonic energy distribution features contained in the frequency-domain feature vector to form a high-dimensional feature vector.

[0094] Then, based on the pipe segment connectivity relationships within the network topology eigenvalues, a spatial correlation analysis is performed on the cascade features. The network topology eigenvalues ​​describe information such as the connection methods and spatial positional relationships of the various pipe segments within the network. By analyzing these relationships, the correlation between sensor data at different locations can be understood. For example, pressure and flow changes in adjacent pipe segments often exhibit a certain degree of correlation. When a pipe segment is damaged, the operating status of adjacent segments is also affected. Leveraging this spatial correlation, the cascade features are weighted, assigning higher weights to sensor data closer to the damage point and lower weights to sensor data farther away, thereby highlighting key information relevant to damage detection.

[0095] Based on spatial correlation analysis, a multidimensional fusion feature matrix containing 16 characteristic indicators is generated. These 16-dimensional characteristic indicators comprehensively consider the topological structure information of the pipeline network and the time domain and frequency domain characteristics of its operating status. For example, these indicators may include the pressure mean, pressure fluctuation variance, flow rate change rate, soundprint main frequency component, harmonic energy distribution, and other characteristics of sensors at different locations, as well as spatial correlation characteristics calculated based on the pipeline network topology. These characteristic indicators complement each other, describing the operating status of the pipeline network from multiple perspectives, and providing rich and comprehensive feature data for the intelligent analysis engine to perform pattern matching calculations.

[0096] The generation of a multidimensional fusion feature matrix requires strict adherence to the data processing workflow. First, the concatenated feature vectors are standardized to map the values ​​of each feature dimension to the same scale range, eliminating the influence of different feature dimensions and ensuring the accuracy of subsequent calculations. Then, spatial weights for each feature dimension are determined based on the network topology eigenvalues ​​and applied to the standardized feature vectors. Finally, the processed feature vectors are organized into a matrix according to specific rules to form a multidimensional fusion feature matrix.

[0097] In practical applications, the effectiveness of feature fusion is affected by a variety of factors. The density and placement of sensors can impact the quality of the acquired raw data and the accuracy of spatial correlation analysis. If sensors are too sparsely placed, subtle changes caused by localized damage may not be captured. Improper sensor placement can lead to weak correlation in the acquired data, compromising the effectiveness of feature fusion. The topological complexity of the pipe network can also pose challenges to spatial correlation analysis. Complex pipe network systems, with their intricate connections between pipe segments, require more advanced graph theory algorithms and spatial analysis methods to accurately describe and analyze these relationships.

[0098] Furthermore, the selection and combination of feature dimensions are crucial. Excessive feature dimensions can lead to data redundancy, increase computational complexity, and introduce noise. However, too few feature dimensions may fail to fully describe the operational status of the pipeline network, affecting the accuracy of damage detection. Therefore, it is necessary to rationally select and combine feature dimensions based on the actual pipeline network characteristics and detection requirements, ensuring that the multidimensional fusion feature matrix fully reflects the operational status of the pipeline network while maintaining high computational efficiency.

[0099] The collaboration between the data preprocessing module and the feature fusion module is also crucial. The quality of data preprocessing directly impacts the effectiveness of subsequent feature extraction and fusion. Incomplete noise filtering or inaccurate outlier removal can cause the extracted feature vectors to contain erroneous information, further impacting the quality of the multidimensional fused feature matrix. Therefore, the two modules must work closely together, continuously optimizing the processing flow to ensure the accuracy and reliability of the entire data processing process.

[0100] Once generated, the multidimensional fusion feature matrix serves as input data for the intelligent analysis engine and subsequent pattern matching calculations. Each feature dimension in the matrix represents an aspect of the pipeline network's operational status. By comprehensively analyzing the values ​​of these feature dimensions, it is possible to more accurately determine the presence and type of damage in the pipeline network. For example, if the main frequency component of the soundprint and the pressure fluctuation variance in the matrix show abnormal changes simultaneously, it may indicate damage such as cracks or leaks in the pipeline network.

[0101] Through noise filtering, outlier removal, and feature extraction in the data preprocessing module, and feature concatenation, spatial correlation analysis, and matrix generation in the feature fusion module, a comprehensive and accurate description of the network's operating status is achieved. This multidimensional fusion feature representation method fully utilizes information from the time, frequency, and spatial domains, improving the sensitivity and specificity of damage detection and providing strong technical support for health monitoring of urban drainage networks.

[0102] Example 4

[0103] The core of the urban drainage pipe network damage detection system is the intelligent analysis engine's pattern matching calculations and screening of diagnostic results. This process achieves intelligent identification and diagnosis of pipe network damage types through precise comparison of a multi-dimensional fusion feature matrix with a set of dynamic detection thresholds.

[0104] When performing pattern matching calculations, the intelligent analysis engine first calculates the Euclidean distance between each feature dimension in the multidimensional fusion feature matrix and the dynamic detection threshold of each damage type. Euclidean distance is a classic method to measure the distance between two points in a multidimensional space. By calculating the Euclidean distance between the feature vector and the threshold vector, the degree of difference between the two can be quantified. Specifically, for each feature vector in the multidimensional fusion feature matrix, the value of each dimension is subtracted from the corresponding dimension value of the corresponding damage type dynamic detection threshold vector, squared, summed, and then squared to obtain the Euclidean distance between the feature vector and the damage type threshold. The smaller the Euclidean distance, the higher the degree of match between the feature vector and the damage type; conversely, the lower the degree of match.

[0105] To eliminate the influence of different feature dimension dimensions and make the distances across them comparable, the calculated Euclidean distances need to be normalized. Normalization maps the Euclidean distances to the interval [0, 1]. Common normalization methods include min-max normalization and z-score normalization. In this system, min-max normalization is used. The Euclidean distance for each feature dimension is subtracted from the minimum value of that dimension, and then divided by the difference between the maximum and minimum values ​​of that dimension to obtain the normalized distance value. This process ensures that the distance values ​​across different feature dimensions are on the same scale, facilitating subsequent comprehensive calculations.

[0106] After obtaining the normalized Euclidean distance, a weighted summation algorithm is used to generate a comprehensive matching value for each damage type. Since different feature dimensions may have different importance in damage detection, it is necessary to assign different weights to each feature dimension. The determination of these weights is based on statistical analysis of historical data and the empirical knowledge of domain experts. For example, for leak detection, the pressure gradient feature may be more important than the flow rate change feature, so the pressure gradient feature is given a higher weight. The comprehensive matching value of each damage type is obtained by multiplying the normalized Euclidean distance with the weight of the corresponding feature dimension and summing them. The smaller the comprehensive matching value, the higher the degree of match between the feature vector and the damage type.

[0107] When filtering diagnostic results, first create a queue with the matching values ​​in descending order. Arrange the calculated comprehensive matching values ​​for each damage type from highest to lowest to form a queue. The first in the queue is the damage type with the highest matching value, which is the most likely damage type for the current pipe network status.

[0108] Normally, the damage type at the top of the queue is selected as the priority diagnosis result. However, when the difference between the first two matching values ​​is less than the set tolerance threshold, it means that the matching degrees of the two damage types are relatively close, and it is difficult to directly determine the exact damage type. At this time, the system will trigger the manual review mark and record the pending confirmation status. The tolerance threshold is set to avoid misjudgment due to calculation errors or data fluctuations, and to ensure the reliability of the diagnosis results. The triggering of the manual review mark reminds the staff that the test results need to be further checked and confirmed, and the final damage type is determined by combining on-site inspections or other detection methods.

[0109] In practical applications, the accuracy of pattern matching calculations is affected by a variety of factors. The rationality of the dynamic detection threshold set is directly related to the reliability of the matching results. If the threshold is set too loosely, it may lead to misjudgment, misinterpreting normal conditions as damaged. If the threshold is set too strictly, it may miss detections and fail to detect actual damage in a timely manner. Therefore, it is necessary to continuously optimize and adjust the dynamic detection threshold set based on actual pipeline network operation data and historical damage cases.

[0110] The selection and weighting of feature dimensions also significantly impact pattern matching results. If the selected feature dimensions cannot effectively distinguish between different damage types, or if the weighting is inappropriate, this can lead to inaccurate matching calculations. During system design and optimization, extensive experimentation and analysis are required to select the most representative and discriminating feature dimensions and determine a reasonable weighting scheme.

[0111] To enhance the system's intelligence and adaptability, the intelligent analysis engine can also incorporate machine learning algorithms for pattern recognition and classification. For example, algorithms such as support vector machines (SVMs) and random forests can be used to train historical data and establish a damage type classification model. The trained model then uses the multidimensional fused feature matrix as input for classification and prediction, resulting in a damage type diagnosis. This approach automatically learns the complex relationships between features and damage types, improving diagnostic accuracy and efficiency.

[0112] Computational efficiency is also a key factor to consider when processing large-scale pipeline network data. Pattern matching involves a large number of feature vector and threshold vector comparison operations. If computational efficiency is low, system response time may be excessive, affecting the effectiveness of real-time detection. To improve computational efficiency, parallel computing technology can be used to distribute computational tasks across multiple processors or computing nodes for simultaneous processing, significantly reducing computation time.

[0113] The intelligent analysis engine also collaborates closely with other modules in the system to form a complete closed-loop detection system. For example, it collaborates with the spatial positioning module to quickly determine the location of damaged areas based on diagnostic results. It also collaborates with the dynamic learning module to provide feedback on diagnostic results and actual damage to the system, continuously updating and optimizing the detection model and threshold set, thereby improving the system's detection capabilities and adaptability.

[0114] The intelligent analysis engine's pattern matching calculations and diagnostic result screening processes are key components of the urban drainage network damage detection system. Through precise Euclidean distance calculations, appropriate normalization processing, and a weighted summation algorithm, combined with a scientific diagnostic result screening mechanism, it can accurately identify damage types within the network, providing strong technical support for network maintenance and management. In practical applications, continuous optimization of algorithms and models is required to improve the system's accuracy, reliability, and intelligence to adapt to the complex and ever-changing operating environment of urban drainage networks.

[0115] Example 5

[0116] This embodiment involves the process of generating three-dimensional coordinate data for the damaged area in the dynamic learning module and the spatial positioning module. These two modules, through the perspectives of system self-optimization and precise positioning, respectively, improve the adaptability and maintenance efficiency of the detection system, forming a complete detection closed loop.

[0117] The core function of the dynamic learning module is to continuously optimize the system's detection capabilities through actual data feedback after maintenance operations. After each maintenance operation, the system first obtains information on the actual damage type and location confirmed by on-site inspections. This on-site inspection data is obtained through manual field inspections and video recording to ensure its authenticity and accuracy. For example, personnel use equipment such as pipeline endoscopes and ultrasonic detectors to conduct on-site verification of pipe sections diagnosed with leaks, recording information such as the actual damage location, the section number, and the damage morphology (such as crack length and leak aperture).

[0118] Next, the actual data is compared and analyzed with the system's diagnostic results to calculate the feature recognition error. During this comparison, the deviation between the system's predicted value and the actual value is calculated for each feature dimension (such as the mean pressure value and the dominant frequency component of the soundprint), forming an error vector. For example, if a pipe section's actual damage type is a crack, but the system diagnoses it as a leak, the error distribution of feature dimensions such as the pressure gradient and soundprint spectrum must be analyzed to identify the key features that led to the misjudgment.

[0119] Based on the error analysis, the system enters the parameter weight adjustment phase. First, it calculates the recognition accuracy data of each feature dimension in the recent N maintenance operations. The value of N can be set according to the pipe network scale and maintenance frequency, usually between 50 and 100 times, to ensure the reliability of the statistical results. The accuracy calculation method is: the ratio of the number of times a certain feature dimension correctly identifies the damage type in all maintenance operations to the total number of times. For example, if the pressure gradient feature correctly assisted in identifying the damage type 60 times in 80 maintenance operations, its accuracy is 75%.

[0120] For feature dimensions with accuracy lower than the set threshold (such as 70%), weight attenuation processing is performed, and the attenuation amplitude is proportional to the accuracy deviation value. Suppose the accuracy of a certain feature dimension is 60% and the deviation value is 10%, then its weight attenuation amplitude can be set to 10% of the initial weight. The attenuation process is achieved through a linear function, that is, the weight adjustment coefficient = 1 - k×(threshold - accuracy), where k is the attenuation coefficient (0 < k < 1). Conversely, for feature dimensions with accuracy continuously higher than the benchmark value (such as 85%), weight enhancement adjustment is implemented, and the enhancement amplitude is related to the proportion exceeding the benchmark value. For example, if it exceeds 10%, the weight increases by 5%. Through this dynamic adjustment mechanism, the system can automatically weaken the influence of redundant or inefficient features, strengthen the role of key features, and gradually optimize the parameter weight coefficients in the dynamic detection threshold set.

[0121] After the weight adjustment is completed, the system synchronously updates the actual damage data, diagnostic error information, and the adjusted weight coefficients to the historical maintenance database to form new pattern matching rules. The updated database provides a more accurate reference for subsequent detections. For example, when the weight of a certain feature dimension is enhanced, the intelligent analysis engine will pay more attention to the difference between this feature and the threshold during pattern matching, thereby improving the recognition accuracy of the corresponding damage type.

[0122] In the spatial positioning module, the process of generating the three-dimensional coordinate positioning data of the damage area is based on the pipe network topology relationship map and the layout of sensor nodes. First, according to the pipe segment coding rules in the pipe network topology relationship map, the position of the sensor node with abnormal data fluctuations is determined. The pipe segment coding rules adopt a hierarchical coding method, such as "area code - pipe segment serial number - node number", to ensure that each sensor node corresponds to a unique physical position. When the intelligent analysis engine outputs the damage diagnosis result, the system locates the specific node position by querying the sensor ID corresponding to the abnormal feature in the multi-dimensional fusion feature matrix.

[0123] Then, according to the pipe segment connection direction and spatial coordinate data, the triangulation method is used to calculate the spatial position of the damage point relative to the nearest three monitoring nodes. The principle of the triangulation method is to use three monitoring nodes (A, B, C) with known coordinates, and by measuring the time difference or signal strength difference of the signals emitted by the damage point reaching each node, calculate the coordinates of the damage point P. The specific steps are as follows:

[0124] Determine the three-dimensional coordinates of monitoring nodes A, B, and C respectively 、 、 ;

[0125] Obtain the time difference or distance difference between the damaged point signal and each node, for example (in is the signal propagation speed);

[0126] Establish a hyperbolic equation system and determine the coordinates of the damage point P by solving the equation system .

[0127] In practical applications, given the spatial distribution of underground pipelines, the spatial coordinates of monitoring nodes are obtained through a geographic information system (GIS) combined with pipeline layout drawings, with centimeter-level accuracy. The signal propagation speed is calibrated based on the pipeline medium (e.g., water, air) and sensor type (e.g., pressure sensor, acoustic detector). For example, the propagation speed of sound waves in water is approximately 1500 meters per second.

[0128] After generating three-dimensional coordinate positioning data, the system visualizes the results on a network topology map, noting the specific location of the damage point, the predicted damage type, and the confidence level. Workers can view this positioning information in real time via mobile devices or monitoring platforms. This information, combined with data such as the pipeline's buried depth and surrounding environment, helps them develop an optimal maintenance plan. For example, if a damage point is located beneath a major traffic artery, the system can automatically prompt the use of trenchless repair techniques to minimize traffic disruption.

[0129] The synergy between the dynamic learning module and the spatial positioning module is demonstrated by the following: the dynamic learning module improves diagnostic accuracy by optimizing feature weights, providing a more reliable prerequisite for spatial positioning; the precise results of the spatial positioning module, in turn, provide real-world feedback to the dynamic learning module, forming a closed loop of "detection-localization-optimization." For example, if a certain area repeatedly exhibits positioning deviations, the dynamic learning module can identify the weighting issues of the relevant feature dimensions and make targeted adjustments to improve detection and positioning accuracy in that area.

[0130] In terms of data storage and transmission, error data, weight adjustment records, and positioning results generated during the dynamic learning process are encrypted and stored in a historical maintenance database, with blockchain technology ensuring that the data cannot be tampered with. Furthermore, positioning data is transmitted in real time to maintenance personnel terminals via a dedicated communication network, supporting offline map loading and navigation, ensuring accurate navigation to damaged areas even in offline environments.

[0131] Furthermore, the system must be capable of integrating multi-source data. For example, it can incorporate geological exploration data (such as soil type and groundwater level) and pipeline design parameters (such as pipe strength and slope) to correct triangulation results. For complex pipeline structures (such as multi-branch pipe intersections and deep underground pipelines), 3D modeling technology can be used to construct a spatial model of the pipeline network. This model simulation can then assist in positioning calculations, improving positioning accuracy in complex scenarios.

[0132] Through the adaptive optimization of the dynamic learning module and the precise calculation of the spatial positioning module, the urban drainage pipe network damage detection system has achieved an upgrade from "passive detection" to "active optimization". It can not only quickly identify the type of damage, but also continuously improve the detection capability, providing key technical support for the refined management and efficient maintenance of the pipe network.

[0133] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0134] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A damage detection system for urban drainage pipe network based on intelligent analysis, characterized in that: include: A multi-source data acquisition module is used to collect static structural parameters and real-time operation monitoring data of the drainage network through a distributed sensor network, analyze and process the data to generate network topology characteristic values ​​and dynamic operation status characteristic values, and analyze and process the data to generate dynamic detection threshold sets for each damage type based on the standard parameter sets for each damage type in the historical maintenance database; The data preprocessing module is used to receive real-time operation monitoring data and perform noise filtering and outlier removal, screen out valid monitoring data and extract the time domain feature vector and frequency domain feature vector of the valid monitoring data; The feature fusion module fuses the time domain feature vector and the frequency domain feature vector based on the pipeline network topology feature value and the dynamic operation state feature value to generate a multi-dimensional fusion feature matrix; The intelligent analysis engine is used to perform pattern matching calculations on the multi-dimensional fusion feature matrix and the dynamic detection threshold set of each damage type, output the matching value of each damage type, and select the damage type corresponding to the maximum matching value as the current pipeline network status diagnosis result; The spatial positioning module is used to index the spatial distribution pattern of the corresponding damage type in the historical maintenance database based on the diagnosis results, and calculate the three-dimensional coordinate positioning data of the damaged area in combination with the pipe network topological characteristic values; Generating a set of dynamic detection thresholds for each damage type specifically includes: Extract standard parameter sets for each damage type from the historical maintenance database, including standard pressure gradient values ​​for leakage conditions, standard acoustic spectrum characteristics for pipe cracks, and standard flow rate mutation amplitudes for blockage conditions. Obtain current environmental variable data, including groundwater level, soil moisture content and temperature change parameters, and calculate environmental correction coefficients; Multiply each parameter item in the standard parameter set by the environmental correction coefficient to generate a dynamic detection threshold set that adapts to the current working conditions; The calculation environment correction coefficient includes: A linear regression model between groundwater level and pipe wall stress was established to obtain the correction factor for the elastic modulus of the pipe due to temperature changes. Grey correlation analysis method is used to calculate the correlation coefficient between soil moisture content and pressure sensor reading fluctuations to generate multidimensional environmental compensation parameters.

2. The urban drainage pipe network damage detection system based on intelligent analysis according to claim 1, characterized in that: The specific process of collecting static structural parameters and real-time operation monitoring data of the drainage network is as follows: When the detection equipment is first deployed in the pipe network system, the structural parameters such as pipe diameter, pipe type, and installation year are collected. Based on the structural parameters, a pipe network topology map is established, and pressure sensors, flow meters, and acoustic detectors are activated to collect baseline data. When there are historical inspection records for the pipe network system, the operating pressure fluctuation data, flow change data, and voiceprint feature data of the last three maintenance cycles are extracted from the historical maintenance database and recorded as the benchmark reference data set for the current real-time monitoring.

3. The urban drainage pipe network damage detection system based on intelligent analysis according to claim 1, characterized in that: Generating a multi-dimensional fusion feature matrix specifically includes: Perform feature concatenation on the pressure mean, pressure fluctuation variance, and flow rate change rate contained in the time domain feature vector and the voiceprint main frequency component and harmonic energy distribution contained in the frequency domain feature vector; Based on the pipe connection relationship in the pipe network topology eigenvalues, spatial correlation analysis is performed on the cascade features to generate a multidimensional fusion feature matrix containing 16-dimensional feature indicators.

4. The urban drainage pipe network damage detection system based on intelligent analysis according to claim 3 is characterized by: The noise filtering and outlier removal specifically include: Perform sliding average filtering on the raw data sequence collected by the pressure sensor to remove sudden interference pulses; The box plot analysis method was used to identify outliers in the flow monitoring data, and linear interpolation correction was performed on the values ​​that exceeded 1.5 times the interquartile range.

5. The urban drainage pipe network damage detection system based on intelligent analysis according to claim 3 is characterized by: The pattern matching calculation specifically includes: Calculate the Euclidean distance between each feature dimension in the multidimensional fusion feature matrix and the dynamic detection threshold of each damage type; After normalizing the Euclidean distances of each dimension, a weighted summation algorithm is used to generate the comprehensive matching value of each damage type.

6. The urban drainage pipe network damage detection system based on intelligent analysis according to claim 5, characterized in that: The damage type corresponding to the maximum matching value is selected as the current pipe network status diagnosis result, specifically including: Establish a queue with matching values ​​in descending order, and select the damage type at the top of the queue as the priority diagnosis result; When the difference between the first two matching values ​​is less than the set tolerance threshold, the manual review mark is triggered and the pending confirmation status is recorded.

7. The urban drainage pipe network damage detection system based on intelligent analysis according to claim 1, characterized in that: Also included are dynamic learning modules, including: After each maintenance operation is completed, obtain the actual damage type and location information confirmed by on-site survey; Compare and analyze the actual data with the system diagnosis results to calculate the feature recognition error; The parameter weight coefficients in the dynamic detection threshold set are adjusted according to the error amount, and the pattern matching rules in the history maintenance database are updated.

8. The urban drainage pipe network damage detection system based on intelligent analysis according to claim 1, characterized in that: The calculation and generation of three-dimensional coordinate positioning data of the damaged area in combination with the pipe network topological characteristic values ​​specifically includes: Based on the pipe segment coding rules in the pipe network topology diagram, the location of the sensor node where abnormal data fluctuation occurs is determined; According to the pipe section connection direction and spatial coordinate data, the triangulation positioning method is used to calculate the spatial position of the damaged point relative to the three nearest monitoring nodes.

9. The urban drainage pipe network damage detection system based on intelligent analysis according to claim 7, characterized in that: The parameter weight coefficients in the dynamic detection threshold set are adjusted according to the error amount. The specific processing process is as follows: Collect statistics on the recognition accuracy of each feature dimension in the last N maintenance operations; The feature dimensions whose accuracy is lower than the set threshold are weighted attenuated, and the attenuation amplitude is proportional to the accuracy deviation value; Implement weight enhancement adjustments for feature dimensions whose accuracy is consistently higher than the benchmark value.

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