Water conservancy project pipeline quality detection method and system based on intelligent sensor

Abstract

The application discloses a water conservancy project pipeline quality detection method and system based on an intelligent sensor, relates to the technical field of pipeline detection, realizes synchronous detection of structural defects and chemical defects by integrating an ultra-wideband (UWB) radar sensor and a terahertz spectrum sensor, collects common medium types in a pipeline, prepares medium samples with different thicknesses, obtains signal attenuation amounts, calculates attenuation coefficients, establishes the correlation between the attenuation coefficients and the medium types and the medium thicknesses, effectively solves the signal distortion problem caused by medium shielding, in addition, the spatial coordinates of the structural defects are positioned by using the corrected UWB radar signals, the chemical defect properties of the corresponding positions are analyzed based on the corrected terahertz spectrum signals, the accurate identification of the pipeline structure-chemical double-layer defects is realized, the missed judgment of hidden defects is avoided, and the complex pipeline internal environment is effectively coped with.

Description

Technical Field

[0001] This invention relates to the field of pipeline inspection technology, specifically to a method and system for quality inspection of hydraulic engineering pipelines based on intelligent sensors. Background Technology

[0002] As a key component of water resource transportation, flood control, and drainage systems, the operational status of water conservancy pipelines directly affects the overall safety and reliability of the water conservancy project. During the long-term operation of water conservancy projects, a series of problems inevitably occur on the inner wall of the pipeline, such as siltation, biofilm adhesion, and scaling. These phenomena are widespread in various types of water conservancy pipelines and become more serious with the increase of pipeline service life. These conditions on the inner wall of the pipeline not only affect the normal transportation efficiency of water resources but may also pose a potential threat to the structural integrity of the pipeline itself, thereby affecting the stable operation of the entire water conservancy project.

[0003] Currently, existing pipeline inspection technologies for water conservancy projects have many limitations. On the one hand, some inspection methods only use a single type of sensor for pipeline defect detection. For example, ultrasonic sensors have difficulty penetrating thick silt layers, making it impossible to effectively detect defects on the inner wall of the pipeline that are obscured by the silt layer. Optical sensors are easily interfered with by biofilms, and their detection signals are deviated due to the presence of biofilms, making it impossible to accurately identify the true condition of the inner wall of the pipeline, and only able to detect structural defects on the surface of the pipeline. On the other hand, although some technologies attempt to integrate multiple sensors to improve the detection effect through the complementary advantages of multiple sensors, these technologies have not effectively compensated for the signal attenuation caused by media obstruction. In actual inspection processes, various media such as silt, biofilms, and scale in the pipeline will attenuate the signals emitted by the sensors to varying degrees, causing the received signals to be distorted, which seriously affects the detection accuracy. In complex scenarios with media obstruction, this detection method without signal compensation has a large error and is very likely to miss hidden defects such as corrosion and microbial erosion, which may lead to serious safety accidents such as pipeline leaks and ruptures, causing huge losses to water conservancy projects. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and system for quality inspection of water conservancy engineering pipelines based on intelligent sensors. By integrating an ultra-wideband (UWB) radar sensor and a terahertz spectral sensor, it achieves simultaneous detection of structural and chemical defects. Simultaneously, by collecting common media types within the pipeline and preparing media samples of different thicknesses, it obtains signal attenuation and calculates the attenuation coefficient, establishing a correlation between the attenuation coefficient and the media type and thickness, effectively solving the signal distortion problem caused by media obstruction. Furthermore, by using the corrected UWB radar signal to locate the spatial coordinates of structural defects and then analyzing the chemical defect attributes at the corresponding locations based on the corrected terahertz spectral signal, it achieves accurate identification of both structural and chemical defects in the pipeline, avoiding the omission of hidden defects and effectively coping with the complex internal environment of pipelines. This provides an efficient and reliable solution for the quality inspection of water conservancy engineering pipelines.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On the one hand, a method for quality inspection of water conservancy engineering pipelines based on intelligent sensors, the method comprising the following specific steps:

[0006] S1: Fix the ultra-wideband (UWB) radar sensor and the terahertz spectral sensor to the detection end of the pipeline of the water conservancy project to be inspected, adjust the angle so that the detection direction is perpendicular to the inner wall of the pipeline and the detection area overlaps, and complete the sensor deployment.

[0007] S2: Control the UWB radar sensor to emit a continuous wave radar signal adapted to the pipe diameter, receive the reflected signal that penetrates the silt layer or scale layer, and after filtering and analysis, combine the pipe parameters and sensor recorded data to obtain the location and size parameters of structural defects.

[0008] S3: Control the terahertz spectral sensor to emit terahertz pulse signals, receive transmission signals that penetrate multiple media, extract spectral features after preprocessing, match them with a preset spectral database, and identify the type of chemical defects on the inner wall of the pipe.

[0009] S4: Prepare common media samples of different types and thicknesses inside pipes, obtain signal attenuation and attenuation coefficient through sensor detection, establish correlation to build a compensation model, and correct radar and spectral signals by combining media information at actual detection locations;

[0010] S5: Extract the corrected radar signal features and input them into the positioning model to obtain the coordinates of the structural defects. Lock the spectral signal features of the corresponding area and match them with the database to determine the chemical defect attributes. Associate and store the two types of defect information to complete the dual-layer defect identification.

[0011] Further, in step S2, the UWB radar sensor is controlled to emit continuous wave radar signals towards the inner wall of the pipe. The emission power is adaptively adjusted according to the pipe diameter. The receiving end of the UWB radar sensor collects the radar signals reflected after penetrating the silt and scale layers in real time. The reflected radar signals are filtered to remove environmental noise interference. The time delay and amplitude change data of the reflected signals are obtained through signal analysis. The propagation distance of the radar signal to the inner wall of the pipe is calculated based on the time delay and the electromagnetic wave propagation speed. The radial coordinates of the structural defect are determined in combination with the pipe diameter parameters. At the same time, the axial coordinates and circumferential coordinates are recorded according to the displacement and angle detection components equipped at the detection end to form the three-dimensional position information of the structural defect. The contour features of the structural defect are identified based on the amplitude change data. The length, width, and depth parameters of the structural defect are calculated by comparing the signal reflection area with the preset defect size calibration curve.

[0012] Furthermore, in step S2, the propagation distance of the radar signal to the inner wall of the pipe is calculated based on the time delay and the electromagnetic wave propagation speed. Specifically, the UWB radar sensor accurately records the time delay from transmitting the radar signal to receiving the radar signal reflected by the structural defect. Based on the propagation speed of electromagnetic waves in the medium inside the pipe Through formula The one-way propagation distance of the radar signal from the sensor transmitter to the location of the structural defect in the inner wall of the pipe was calculated. Pre-obtain the diameter parameters of the pipe to be inspected. From the formula Calculate the pipe radius The sensor is deployed at the central axis of the pipeline, and the distance from it to a defect-free point on the inner wall of the pipeline is the pipeline radius. Through formula Determine the radial coordinates of the structural defect radial coordinates The perpendicular distance between the location of the structural defect and the central axis of the pipe is characterized by the calculated distance. Value less than pipe radius This indicates that there is a structural defect at the corresponding location, and The value reflects the radial location of the defect.

[0013] Furthermore, in step S3, the terahertz spectral sensor is controlled to emit terahertz pulse signals to the inner wall of the pipe. The receiving end of the terahertz spectral sensor collects the transmitted terahertz spectral signals after penetrating through the silt layer, biofilm layer, and scale layer. The transmitted terahertz spectral signals are preprocessed, and baseline correction is completed using a polynomial fitting method. The signal quality is optimized through a smoothing algorithm. Then, core spectral features, including the position and intensity of characteristic peaks, are extracted from the preprocessed signals. The extracted spectral features are matched with a preset spectral database. The matching results are used to accurately identify the type of chemical defects on the inner wall of the pipe, thereby determining abnormal chemical properties of the inner wall of the pipe, such as corrosion layer composition and microbial attachment type.

[0014] Furthermore, in step S3, the spectral database is established by pre-collecting samples with different corrosion layer components and different microbial attachment types, and then using a terahertz spectral sensor to obtain the standard spectral characteristics of each sample, including sample type, characteristic peak position, and characteristic peak intensity information.

[0015] Furthermore, in step S3, the extracted spectral features are matched with a preset spectral database. The matching results are used to accurately identify the type of chemical defect on the inner wall of the pipe. The matching formula is as follows: ,in, It is the cosine similarity between the spectral features to be matched and the standard spectral features in the database, representing the degree of similarity between the two. It is the first of the spectral feature vectors of the target location extracted by the terahertz spectral sensor in this invention. One portion, , This refers to the spectral feature dimension (such as the number of key features like the position and intensity of characteristic peaks). It is the first of the standard spectral feature vectors of a certain type of chemical defect in the preset spectral database. Each component, each group Corresponding to the standard spectral characteristics of a known chemical defect, when ≥ hour, It is a preset similarity threshold, which determines whether the chemical defect type at the location to be detected is consistent with the chemical defect type corresponding to the standard spectrum.

[0016] Furthermore, in step S4, common media types inside the pipeline are collected, and media samples of different thicknesses are prepared for each media type. The sample material is consistent with the actual media inside the pipeline. UWB radar sensors and terahertz spectral sensors are used to detect media samples of different thicknesses, and the attenuation of radar signals and terahertz spectral signals under each media type and thickness is recorded. The attenuation coefficient is calculated based on the ratio of signal attenuation to media thickness. Then, with media type and media thickness as input variables and attenuation coefficient as output variables, a multiple linear regression method is used to establish the correlation between input and output variables. Based on the correlation, a multi-media penetration compensation model is constructed. By obtaining the media type and media thickness at the location to be detected, the media type and media thickness are input into the multi-media penetration compensation model to obtain the corresponding attenuation coefficient. Finally, based on the attenuation coefficient, attenuation compensation correction is performed on the received reflected radar signal and transmitted terahertz spectral signal to restore the original characteristics of the signal and eliminate the attenuation effect caused by media obstruction.

[0017] Furthermore, in step S4, a multi-media penetration compensation model is constructed based on the correlation relationship. The model is as follows: ,in This represents the attenuation coefficient of a UWB radar signal or terahertz spectral signal after it penetrates the corresponding medium, reflecting the degree of signal attenuation under a specific medium type and thickness. The media type is encoded with a unique numerical value, transforming the non-quantized media type into a quantized parameter that can participate in model calculations. For the thickness of the medium, This is a constant term, reflecting the baseline attenuation value under conditions of no medium and no influence from a specific medium type. , These are the regression coefficients corresponding to the medium type and the medium thickness, respectively, obtained through fitting and training on multiple sets of collected input and output data.

[0018] Furthermore, in step S5, feature extraction is performed on the corrected reflected radar signal. The extracted features include signal reflection time, reflection amplitude, and signal phase. The extracted features are input into the structural defect localization model, and the three-dimensional spatial coordinates of the structural defect are transmitted to the terahertz spectral signal analysis component. Based on the spatial coordinates, the corresponding region within the detection range of the terahertz spectral sensor is locked. The characteristic peak position and characteristic peak intensity of the corrected transmitted terahertz spectral signal in that region are extracted and matched with a preset spectral database to determine the chemical defect attribute at that location. Finally, the spatial coordinates and size parameters of the structural defect are associated with the corresponding chemical defect attribute and stored in the data storage device to form a complete defect detection report, thus completing the accurate identification of the pipeline structure-chemical double-layer defect.

[0019] On the other hand, a method and system for quality inspection of hydraulic engineering pipelines based on intelligent sensors, the system comprising:

[0020] Ultra-wideband (UWB) radar sensor: used to transmit radar signals to the inner wall of the pipeline of the water conservancy project to be inspected, and to receive the reflected radar signals after they have penetrated through the silt and scale layer inside the pipeline, and to obtain the location and size of structural defects in the inner wall of the pipeline based on the reflected radar signals;

[0021] Terahertz spectral sensor: used to emit terahertz spectral signals to the inner wall of the pipeline of the water conservancy project to be inspected, and to receive the transmitted terahertz spectral signals after passing through the sediment layer, biofilm layer and scale layer in the pipeline. Based on the spectral characteristics of the transmitted terahertz spectral signals, chemical defects in the inner wall of the pipeline are identified.

[0022] Compensation Model Construction Module: Used to construct a multi-media penetration compensation model, which is used to correct the attenuation effect of different media in the pipeline on UWB radar signals and terahertz spectral signals.

[0023] Layered identification module: Based on the corrected UWB radar signal, the spatial coordinates of the structural defects are first located, and then the chemical defect attributes at the corresponding position are analyzed based on the corrected terahertz spectral signal to complete the identification of the pipeline structure-chemical double layer defects.

[0024] Compared with existing technologies, this method and system for quality inspection of hydraulic engineering pipelines based on intelligent sensors has the following advantages:

[0025] I. This invention integrates a UWB radar sensor and a terahertz spectral sensor. The UWB radar sensor can penetrate sediment or scale layers, while the terahertz spectral sensor can penetrate multiple media to identify chemical characteristics, enabling simultaneous detection of structural and chemical defects. Simultaneously, a multi-media penetration compensation model is constructed to correct the signal attenuation effect of different media, effectively solving the problem of signal distortion caused by media obstruction and significantly improving detection accuracy. Furthermore, by first locating the coordinates of structural defects and then analyzing the corresponding chemical properties, it accurately identifies structural-chemical dual-layer defects, avoiding missed detection of hidden defects. It is suitable for the inspection of hydraulic engineering pipelines of different diameters, can adapt well to complex pipeline environments, and provides a reliable and efficient solution for the quality inspection of hydraulic engineering pipelines.

[0026] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0028] Figure 1 A flowchart of a quality inspection method for water conservancy engineering pipelines based on intelligent sensors;

[0029] Figure 2 This is a structural block diagram of a water conservancy engineering pipeline quality inspection system based on intelligent sensors;

[0030] Figure 3 A flowchart illustrating the steps involved in constructing a compensation model for a quality inspection method for water conservancy pipelines based on intelligent sensors. Detailed Implementation

[0031] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0032] The detection system described in this invention consists of an ultra-wideband (UWB) radar sensor, a terahertz spectral sensor, a compensation model construction module, and a hierarchical identification module, such as... Figure 2 As shown, the specific configuration and connection relationships of each part are as follows:

[0033] The ultra-wideband (UWB) radar sensor is selected to meet the requirements of industrial environment adaptability. Considering the humid and interference-prone environment at the pipeline inspection site of water conservancy projects, the sensor adopts a sealed structure design, providing waterproof and dustproof performance. The sensor supports stepless adjustment of transmission power, allowing for flexible adaptation to different pipe diameters. It also possesses sufficient signal bandwidth and sampling rate to accurately capture the time delay and amplitude changes of radar signals. The sensor has a built-in dedicated digital filtering unit that can filter noise from reflected signals in real time, reducing the impact of environmental interference on the detection results. The sensor is fixed to the central axis of the pipeline under inspection by an adjustable, corrosion-resistant metal bracket. The bracket is equipped with a precision angle adjustment mechanism and laser alignment components to ensure that the detection direction is perpendicular to the inner wall of the pipe and that the installation is stable and not easily displaced.

[0034] The terahertz spectral sensor uses a pulsed terahertz spectral sensor, whose detection wavelength range covers the key bands required for pipeline chemical defect detection. Equipped with a high-sensitivity photodetector, it can effectively receive transmission signals after penetrating multiple media such as siltation layers, biofilm layers, and scale layers. The sensor has good spectral resolution and feature detection stability, and can accurately capture the characteristic spectral differences of different chemical defects. The sensor is connected to the data processing unit through a low-loss dedicated transmission line to ensure real-time and stable transmission of spectral data and avoid signal distortion during data transmission.

[0035] The compensation model construction module uses industrial-grade computing equipment with strong anti-electromagnetic interference capabilities, making it suitable for the complex environment of water conservancy engineering sites. The equipment is equipped with a mature programming environment and machine learning-related tool libraries, specifically for training, optimizing, and calculating signal corrections for multi-media penetration compensation models. The computing equipment establishes bidirectional communication with two sensors through a standard industrial communication interface, ensuring real-time acquisition of sensor detection data and media detection information. At the same time, it feeds back correction parameters and control commands to the sensors, ensuring low latency and reliability of data transmission.

[0036] The layered identification module employs a high-performance programmable logic chip, integrating a neural network processing core and a spectral matching processing module. It supports parallel processing of 3D coordinate positioning and spectral feature matching, possessing efficient data processing capabilities to quickly analyze and process defect information. It also features a built-in large-capacity data storage component for categorizing and storing defect information, inspection reports, and raw detection data, facilitating subsequent traceability and retrieval. Furthermore, the module can connect to a high-definition display terminal to display real-time inspection progress, defect distribution heatmaps, and key inspection data, enabling operators to monitor the inspection process in real time.

[0037] Based on the above system, such as Figure 1 As shown, the specific implementation steps of the water conservancy engineering pipeline quality inspection method based on intelligent sensors provided by the present invention are as follows:

[0038] S1: Fix the ultra-wideband (UWB) radar sensor and the terahertz spectral sensor to the detection end of the pipeline of the water conservancy project to be inspected, adjust the angle so that the detection direction is perpendicular to the inner wall of the pipeline and the detection area overlaps, and complete the sensor deployment.

[0039] Pre-treatment of the pipeline to be inspected: By consulting engineering drawings or conducting on-site measurements, the basic parameters such as pipeline type and diameter are determined; then, large debris, dust and water accumulation on the surface of the inspection end are cleaned to ensure that the sensor installation area is unobstructed and free from interference, creating favorable installation conditions for sensor deployment.

[0040] Sensor Installation: The UWB radar sensor and the terahertz spectral sensor are coaxially fixed at the center axis of the detection end using an adjustable corrosion-resistant metal bracket. The laser alignment component on the bracket is used to calibrate the detection direction of the two sensors to ensure that the detection direction of both is perpendicular to the inner wall of the pipe and that the overlap of the detection areas meets the detection requirements, thus avoiding detection omissions.

[0041] Connection and debugging: Establish a stable communication connection between the sensor and the compensation model construction module and the hierarchical identification module through the industrial communication interface. Start the self-test program to check whether the sensor's transmission and reception functions are normal, whether there are packet loss or delay issues in data transmission, and verify the collaborative working status between the modules. Only after ensuring that all parts of the system are operating normally can the subsequent testing process be carried out.

[0042] S2: Control the UWB radar sensor to emit a continuous wave radar signal adapted to the pipe diameter, receive the reflected signal that penetrates the silt layer or scale layer, and after filtering and analysis, combine the pipe parameters and sensor recorded data to obtain the location and size parameters of structural defects.

[0043] Signal transmission parameter adjustment: Based on the actual diameter of the pipeline to be detected obtained in the preprocessing stage, the transmission power of the UWB radar sensor is automatically adjusted. The adjustment logic is that the larger the pipeline diameter, the higher the transmission power should be, to ensure that the radar signal can effectively penetrate the possible silt and scale layers inside the pipeline, accurately reach the inner wall of the pipeline, and form an effective reflection.

[0044] Signal acquisition and filtering: The UWB radar sensor is controlled to continuously transmit continuous wave radar signals. The receiving end acquires the radar signals reflected after penetrating the silt and scale layers in real time. Digital filtering algorithms (such as Kalman filtering, mean filtering, etc.) are used to process the reflected signals to remove noise and other clutter caused by environmental electromagnetic interference and water flow disturbance, and retain the effective signals related to structural defects.

[0045] Signal analysis and parameter calculation:

[0046] Time Delay and Propagation Distance Calculation: The sensor accurately records the time delay from the transmitted signal to the reflected signal from the receiving structural defect. Based on the actual medium type inside the pipeline, a pre-set medium electromagnetic wave propagation speed lookup table is consulted to determine the corresponding propagation speed, which is then calculated using the formula... The one-way propagation distance from the sensor to the location of the structural defect was calculated. .

[0047] Radial coordinate determination: The pipe diameter parameter is pre-input into the system, and then determined using the formula... Calculate pipe radius Since the sensor is deployed at the central axis of the pipe, its distance to the defect-free inner wall is equal to the pipe radius. Through formula Calculate the radial coordinates of structural defects If the calculated Value less than pipe radius If the value of r is positive, it is determined that there is a structural defect at that location, and the value of r can directly reflect the radial position of the defect.

[0048] Three-dimensional positioning: The displacement detection component records the sensor's movement distance along the pipe axis in real time, serving as the axial coordinate of the structural defect; the angle detection component records the sensor's rotation angle and converts it into circumferential coordinates; combined with the calculated radial coordinates... This forms the three-dimensional spatial coordinates of the structural defect, enabling precise location of the structural defect.

[0049] Size parameter calculation: Based on the amplitude change data of the filtered reflected signal, the signal reflection area feature is extracted and compared with the preset defect size calibration curve. This calibration curve is obtained by testing standard defect samples of various specifications (covering different lengths, widths, and depths) in a laboratory environment, and has good versatility. The key size parameters such as the length, width, and depth of the structural defect are calculated by interpolation algorithm, which fully characterizes the morphological features of the structural defect.

[0050] S3: Control the terahertz spectral sensor to emit terahertz pulse signals, receive transmission signals that penetrate multiple media, extract spectral features after preprocessing, match them with a preset spectral database, and identify the type of chemical defects on the inner wall of the pipe.

[0051] Terahertz signal transmission and acquisition: The terahertz spectral sensor is controlled to emit terahertz pulse signals towards the inner wall of the pipeline, and the receiving end simultaneously acquires the transmission spectral signals after penetrating through the sediment layer, biofilm layer, and scale layer. Based on the pipeline inspection accuracy requirements, the acquisition time at each detection point is adjusted appropriately to ensure that the acquired spectral signal has a sufficient signal-to-noise ratio.

[0052] Signal preprocessing:

[0053] Baseline correction: The transmission signal is corrected using a polynomial fitting method to eliminate the baseline shift caused by factors such as instrument drift and changes in ambient temperature, so that the baseline of the spectral signal tends to be stable.

[0054] Smoothing: The signal is optimized by smoothing algorithms to reduce the interference of random noise on spectral features, improve signal quality, and facilitate subsequent feature extraction.

[0055] Spectral feature extraction: Extract core spectral features from the preprocessed signal, mainly including the position and intensity of the characteristic peaks. At the same time, it can also help extract secondary features such as the full width at half maximum (FWHM) of the characteristic peaks to form a spectral feature vector to be matched.

[0056] Spectral database matching:

[0057] Pre-built spectral database: Samples of common chemical defects in pipelines are pre-collected, including different types of corrosion layer components and microbial attachment types. The samples primarily originate from failed pipelines replaced in hydraulic engineering projects. Multiple parallel samples are prepared for each type. Standard spectral characteristics are acquired using a terahertz spectral sensor under standard experimental conditions. After removing outliers, the average value is taken as the standard feature vector for that type of defect and stored in a dedicated database. The database supports rapid retrieval by keywords such as characteristic peak position and sample type, improving matching efficiency.

[0058] Similarity Calculation and Type Determination: The cosine similarity formula is used to calculate the similarity between the vector to be matched and the standard vector. : Preset a reasonable similarity threshold This threshold was determined through extensive sample testing to ensure that the risk of missed or incorrect judgments is reduced while maintaining recognition accuracy. ≥ When the chemical defect type at the detection location is determined to be consistent with the defect type corresponding to the standard vector, then... < When the defect is identified as an unknown defect type, the system will automatically prompt the operator to conduct a manual review.

[0059] S4: Prepare samples of common media inside pipes of different types and thicknesses. Detect signal attenuation and attenuation coefficients using sensors, establish correlations, and construct a compensation model. Figure 3 As shown, the radar and spectral signals are corrected by combining the medium information at the actual detection location;

[0060] Medium sample preparation: Collect common medium types in the pipeline, including sediment layer, scale layer, biofilm layer, etc. Based on the thickness range of common media in actual water conservancy engineering pipelines, prepare samples according to a reasonable thickness gradient. Prepare multiple parallel samples for each thickness. The sample material is consistent with the actual medium in the pipeline. The preparation process strictly follows the laboratory sample preparation specifications to ensure the homogeneity and representativeness of the samples and avoid affecting the model accuracy due to sample differences.

[0061] Attenuation data acquisition: UWB radar sensor and terahertz spectral sensor are used to detect media samples of different types and thicknesses. Under the same transmission parameters, the transmitted signal strength and received signal strength are recorded under each operating condition. The signal attenuation is obtained by calculating the difference between the two (attenuation = transmitted signal strength - received signal strength). The attenuation coefficient is then calculated based on the ratio of attenuation to media thickness.

[0062] Compensation Model Construction: A multiple linear regression method is used to establish a compensation model. The input variables are the medium type encoding value (a unique numerical encoding is used for different medium types, transforming non-quantitative medium types into quantifiable parameters that can participate in model calculations) and the medium thickness. The attenuation coefficient is the output variable. The model expression is as follows: ,in, Based on the baseline attenuation value, , The regression coefficients are obtained by collecting multiple sets of input and output data, dividing them into training and test sets in a reasonable proportion, using machine learning toolkits to fit and train the model, and verifying the model's prediction accuracy and generalization ability through the test set, ensuring that the model can accurately reflect the correlation between medium type, thickness and attenuation coefficient.

[0063] Actual signal correction: The medium type and thickness information of the location to be detected are acquired in real time and input into the constructed compensation model to obtain the corresponding attenuation coefficient. Based on the attenuation coefficient, the gain of the received reflected radar signal and transmitted terahertz spectrum signal is adjusted to restore the original characteristics of the signal, effectively eliminate the signal attenuation caused by medium obstruction, and provide an accurate signal basis for subsequent defect identification.

[0064] S5: Extract the corrected radar signal features and input them into the positioning model to obtain the coordinates of the structural defects. Lock the spectral signal features of the corresponding area and match them with the database to determine the chemical defect attributes. Associate and store the two types of defect information to complete the dual-layer defect identification.

[0065] Precise location of structural defect coordinates: Features are extracted again from the compensated and corrected reflected radar signal. The extracted features include key parameters such as signal reflection time, reflection amplitude, and signal phase. The extracted features are then input into a pre-trained structural defect location model.

[0066] Chemical defect attribute matching: Based on the three-dimensional coordinates of the structural defect, the detection focus of the terahertz spectral sensor is adjusted to quickly lock onto the corresponding region, ensuring that the detection area and the location of the structural defect are precisely coincident. The core features, such as the position and intensity of the characteristic peaks in the corrected transmission signal of this region, are extracted and matched again with a preset spectral database to clarify the chemical defect attributes at that location, achieving a precise correspondence between the structural defect and the chemical defect.

[0067] Defect information association storage: The three-dimensional coordinates and dimensional parameters of structural defects are associated with the corresponding chemical defect type, similarity value, and relevant data such as inspection time, pipeline number, and inspection personnel information, and stored in the data storage component. The storage format adopts a standardized data format to facilitate subsequent data query, statistics, and processing, and to ensure data traceability.

[0068] Inspection Report Generation: The system automatically generates standardized inspection reports, which include core information such as basic pipeline information, defect location distribution maps, spectral matching comparison charts, and detailed defect parameter tables. The reports support printing and can also be remotely transmitted to the engineering management platform via wired or wireless means, facilitating real-time monitoring of pipeline quality by management personnel and providing data support for subsequent repair and maintenance work.

[0069] Typical pipes commonly used in water conservancy projects, such as concrete pipes and metal pipes, are selected as test objects. Structural and chemical defects frequently occurring in actual engineering projects are pre-set within the pipes, and this invention is used for detection.

[0070] Structural defect location: It can accurately locate the three-dimensional spatial position of structural defects, and the measurement results of dimensional parameters are highly consistent with the actual defect characteristics, which fully meets the accuracy requirements of pipeline inspection in water conservancy projects;

[0071] Chemical defect identification: The spectral matching results are stable and reliable, and can accurately identify various preset chemical defect types without misjudgment or omission. The identification accuracy meets the requirements of engineering applications.

[0072] Inspection efficiency: Compared with traditional pipeline inspection methods that rely on manual operation, this invention significantly reduces the time for manual intervention through an automated inspection process, thereby significantly improving inspection efficiency and effectively reducing inspection costs;

[0073] Anti-interference capability: In simulated environments where common silt and scale layers obstruct the pipeline, the signal can still maintain good recognizability after correction by the compensation model, and the defect identification accuracy remains at a high level, demonstrating strong adaptability to practical applications.

[0074] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for quality inspection of water conservancy engineering pipelines based on intelligent sensors, characterized in that, The method includes the following specific steps: S1: Fix the ultra-wideband (UWB) radar sensor and the terahertz spectral sensor to the detection end of the pipeline of the water conservancy project to be inspected, adjust the angle so that the detection direction is perpendicular to the inner wall of the pipeline and the detection area overlaps, and complete the sensor deployment. S2: Control the UWB radar sensor to emit a continuous wave radar signal adapted to the pipe diameter, receive the reflected signal that penetrates the silt layer or scale layer, and after filtering and analysis, combine the pipe parameters and sensor recorded data to obtain the location and size parameters of structural defects. S3: Control the terahertz spectral sensor to emit terahertz pulse signals, receive transmission signals that penetrate multiple media, extract spectral features after preprocessing, match them with a preset spectral database, and identify the type of chemical defects on the inner wall of the pipe. S4: Prepare common media samples of different types and thicknesses inside pipes, obtain signal attenuation and attenuation coefficient through sensor detection, establish correlation to build a compensation model, and correct radar and spectral signals by combining media information at actual detection locations; S4 specifically includes: collecting common media types in the pipeline; preparing media samples of different thicknesses for each media type, ensuring the sample material is consistent with the actual media in the pipeline; using a UWB radar sensor and a terahertz spectral sensor to detect media samples of different thicknesses; recording the attenuation of the radar signal and terahertz spectral signal for each media type and thickness; calculating the attenuation coefficient based on the ratio of signal attenuation to media thickness; then, using media type and thickness as input variables and the attenuation coefficient as output variables, establishing the correlation between input and output variables using a multiple linear regression method; and constructing a multi-media penetration compensation model based on the correlation. The model is as follows: ,in This represents the attenuation coefficient of a UWB radar signal or terahertz spectral signal after it penetrates the corresponding medium, reflecting the degree of signal attenuation under a specific medium type and thickness. The media type is encoded with a unique numerical value, transforming the non-quantized media type into a quantized parameter that can participate in model calculations. For the thickness of the medium, This is a constant term, reflecting the baseline attenuation value under conditions of no medium and no influence from a specific medium type. , These are the regression coefficients corresponding to the medium type and the medium thickness, respectively, which are obtained by fitting and training multiple sets of collected input and output data. By obtaining the medium type and thickness at the location to be detected, the medium type and thickness are input into the multi-medium penetration compensation model to obtain the corresponding attenuation coefficient. Finally, based on the attenuation coefficient, the received reflected radar signal and transmitted terahertz spectrum signal are attenuated and corrected to restore the original characteristics of the signal and eliminate the attenuation effect caused by medium blockage. S5: Extract the corrected radar signal features and input them into the positioning model to obtain the coordinates of the structural defects. Lock the spectral signal features of the corresponding area and match them with the database to determine the chemical defect attributes. Associate and store the two types of defect information to complete the dual-layer defect identification. S5 specifically includes: extracting features from the corrected reflected radar signal, including signal reflection time, reflection amplitude, and signal phase; inputting the extracted features into the structural defect localization model; transmitting the three-dimensional spatial coordinates of the structural defect to the terahertz spectral signal analysis component; locking the corresponding region within the detection range of the terahertz spectral sensor based on the spatial coordinates; extracting the characteristic peak position and characteristic peak intensity of the corrected transmitted terahertz spectral signal in that region; matching it with a preset spectral database to determine the chemical defect attributes at the location; and finally associating and storing the spatial coordinates and size parameters of the structural defect with the corresponding chemical defect attributes in the data storage device to form a complete defect detection report, thus completing the identification of the pipeline structure-chemical dual-layer defect.

2. The method for quality inspection of water conservancy engineering pipelines based on intelligent sensors according to claim 1, characterized in that, In step S2, the UWB radar sensor is controlled to emit continuous wave radar signals towards the inner wall of the pipe. The emission power is adaptively adjusted according to the pipe diameter. The receiving end of the UWB radar sensor collects the radar signals reflected after penetrating the silt and scale layers in real time. The reflected radar signals are filtered to remove environmental noise interference. The time delay and amplitude change data of the reflected signals are obtained through signal analysis. The propagation distance of the radar signal to the inner wall of the pipe is calculated based on the time delay and the electromagnetic wave propagation speed. The radial coordinates of the structural defect are determined by combining the pipe diameter parameters. At the same time, the axial coordinates and circumferential coordinates are recorded by the displacement and angle detection components equipped at the detection end to form the three-dimensional position information of the structural defect. The contour features of the structural defect are identified based on the amplitude change data. The length, width, and depth parameters of the structural defect are calculated by comparing the signal reflection area with the preset defect size calibration curve.

3. The method for quality inspection of water conservancy engineering pipelines based on intelligent sensors according to claim 2, characterized in that, In step S2, the propagation distance of the radar signal to the inner wall of the pipe is calculated based on the time delay and the electromagnetic wave propagation speed. Specifically, the UWB radar sensor accurately records the time delay from transmitting the radar signal to receiving the radar signal reflected by the structural defect. Based on the propagation speed of electromagnetic waves in the medium inside the pipe Through formula The one-way propagation distance of the radar signal from the sensor transmitter to the location of the structural defect in the inner wall of the pipe was calculated. Pre-obtain the diameter parameters of the pipe to be inspected. From the formula Calculate the pipe radius Through formula Determine the radial coordinates of the structural defect radial coordinates The perpendicular distance between the location of the structural defect and the central axis of the pipe is characterized by the calculated distance. Value less than pipe radius This indicates that there is a structural defect at the corresponding location, and The value reflects the radial location of the defect.

4. The method for quality inspection of water conservancy engineering pipelines based on intelligent sensors according to claim 1, characterized in that, In step S3, the terahertz spectral sensor is controlled to emit terahertz pulse signals to the inner wall of the pipe. The receiving end of the terahertz spectral sensor collects the transmitted terahertz spectral signals after penetrating the sediment layer, biofilm layer, and scale layer. The transmitted terahertz spectral signals are preprocessed, and baseline correction is completed using a polynomial fitting method. The signal quality is optimized by a smoothing algorithm. Then, core spectral features, including the position and intensity of characteristic peaks, are extracted from the preprocessed signals. The extracted spectral features are matched with a preset spectral database. The matching results are used to accurately identify the type of chemical defects on the inner wall of the pipe, thereby determining the abnormal chemical properties of the inner wall of the pipe, such as the composition of the corrosion layer and the type of microbial attachment.

5. The method for quality inspection of water conservancy engineering pipelines based on intelligent sensors according to claim 4, characterized in that, In step S3, the spectral database is established by pre-collecting samples with different corrosion layer components and different microbial attachment types, and then using a terahertz spectral sensor to obtain the standard spectral characteristics of each sample. It includes information on sample type, characteristic peak position, and characteristic peak intensity.

6. The method for quality inspection of water conservancy engineering pipelines based on intelligent sensors according to claim 4, characterized in that, In step S3, the extracted spectral features are matched with a preset spectral database. The matching results are used to accurately identify the type of chemical defects on the inner wall of the pipe. The matching formula is as follows: ,in, It is the cosine similarity between the spectral features to be matched and the standard spectral features in the database, representing the degree of similarity between the two. It is the first of the spectral feature vectors of the target location extracted by the terahertz spectral sensor. One portion, , For spectral feature dimensions, It is the first of the standard spectral feature vectors of a certain type of chemical defect in the preset spectral database. One component, when ≥ hour, It is a preset similarity threshold, which determines whether the chemical defect type at the location to be detected is consistent with the chemical defect type corresponding to the standard spectrum.

7. A quality inspection system for water conservancy engineering pipelines based on intelligent sensors, the system being applicable to the quality inspection method for water conservancy engineering pipelines based on intelligent sensors as described in any one of claims 1-6, characterized in that, The system includes: Ultra-wideband (UWB) radar sensor: used to transmit radar signals to the inner wall of the pipeline of the water conservancy project to be inspected, and to receive the reflected radar signals after they have penetrated through the silt and scale layer inside the pipeline, and to obtain the location and size of structural defects in the inner wall of the pipeline based on the reflected radar signals; Terahertz spectral sensor: used to emit terahertz spectral signals to the inner wall of the pipeline of the water conservancy project to be inspected, and to receive the transmitted terahertz spectral signals after passing through the sediment layer, biofilm layer and scale layer in the pipeline. Based on the spectral characteristics of the transmitted terahertz spectral signals, chemical defects in the inner wall of the pipeline are identified. Compensation Model Construction Module: Used to construct a multi-media penetration compensation model, which is used to correct the attenuation effect of different media in the pipeline on UWB radar signals and terahertz spectral signals. Layered identification module: Based on the corrected UWB radar signal, the spatial coordinates of the structural defects are first located, and then the chemical defect attributes at the corresponding position are analyzed based on the corrected terahertz spectral signal to complete the identification of the pipeline structure-chemical double layer defects.

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