Underground pipeline deformation real-time monitoring method, device, equipment and medium

Abstract

The invention relates to the field of deformation monitoring, in particular to an underground pipeline deformation real-time monitoring method and device, equipment and a medium. The method comprises the steps that through real-time connection with equipment such as an optical fiber sensor and a three-dimensional laser scanner, spectral signals can be received in real time, three-dimensional point cloud data can be collected, and data collection delay is avoided. In a data processing link, whether a strain distribution map is generated based on a spectral signal or a deformation displacement vector is determined from three-dimensional point cloud data, an efficient algorithm is adopted to quickly complete calculation and analysis, and the data processing time is greatly shortened. Moreover, the electronic equipment continuously monitors the strain distribution map and the deformation displacement vector in real time, and once it is monitored that the strain gradient and the displacement change rate of the monitoring node in the pipeline deformation field model exceed threshold values, an early warning mechanism is triggered immediately, deformation early warning information is generated, and the real-time performance of underground pipeline deformation monitoring is improved.

Description

Technical Field

[0001] The present application relates to the field of deformation monitoring, and in particular to a method, device, equipment and medium for real-time monitoring of deformation of underground pipelines. Background Art

[0002] Safety monitoring of underground pipelines is crucial for ensuring the stable operation of urban infrastructure. In recent years, with the acceleration of urbanization, underground pipeline networks have become increasingly complex, and deformation monitoring has become a critical component in ensuring public safety and minimizing economic losses. Effective monitoring methods can promptly identify potential risks and prevent accidents, thereby safeguarding the normal operation of society and protecting the lives and property of the people.

[0003] Existing technologies for monitoring underground pipeline deformation typically rely on periodic analysis of collected data to determine if any abnormalities are present. However, this method suffers from insufficient real-time monitoring, resulting in a long time interval between monitoring and feedback, making it unable to meet the demand for rapid response to underground pipeline deformation. Therefore, improving the real-time nature of monitoring is a pressing technical challenge. Summary of the Invention

[0004] In order to improve the real-time performance of underground pipeline deformation monitoring, the present application provides a method, device, equipment and medium for real-time monitoring of underground pipeline deformation.

[0005] In a first aspect, the present application provides a method for real-time monitoring of underground pipeline deformation, which adopts the following technical solutions: A method for real-time monitoring of underground pipeline deformation, comprising: Receive a spectral signal corresponding to a current area, and collect three-dimensional point cloud data of underground pipelines in the current area; generating a strain distribution map based on the spectral signal, and determining a deformation displacement vector of the underground pipeline based on the three-dimensional point cloud data; determining whether to generate deformation warning information based on the strain distribution map and the deformation displacement vector; When it is determined to generate deformation warning information, the deformation warning information is generated, and the deformation warning information, the strain distribution map, and the deformation displacement vector are transmitted to a monitoring terminal in real time.

[0006] By adopting this technical solution, real-time connections with devices such as fiber optic sensors and 3D laser scanners enable instant reception of spectral signals and acquisition of 3D point cloud data, eliminating data acquisition delays. In the data processing phase, both generating strain distribution maps based on spectral signals and determining deformation displacement vectors from 3D point cloud data utilize efficient algorithms for rapid computation and analysis, significantly reducing data processing time. Furthermore, the electronic device continuously monitors the strain distribution maps and deformation displacement vectors in real time. Once the strain gradient and displacement change rate at a monitoring node in the pipeline deformation field model exceed a threshold, an early warning mechanism is immediately triggered, generating deformation warning information. This improves the real-time nature of underground pipeline deformation monitoring. Furthermore, via a wireless communication network, deformation warning information, strain distribution maps, and deformation displacement vectors can be transmitted to the monitoring terminal in real time, enabling management personnel to immediately identify pipeline anomalies and take swift countermeasures, effectively ensuring underground pipeline safety and minimizing the time cost and potential losses associated with accidents caused by pipeline deformation.

[0007] In a possible implementation, generating a strain distribution map based on the spectral signal includes: Using a Gaussian fitting algorithm to perform peak position fitting on the spectral information to obtain the peak position corresponding to the spectral signal; Based on the peak position, a frequency shift corresponding to the spectral signal is calculated by a least squares method; An axial strain distribution of the underground pipeline in the current area is determined based on the frequency shift, so as to generate a strain distribution map based on the axial strain distribution.

[0008] In one possible implementation, determining the deformation displacement vector of the underground pipeline based on the three-dimensional point cloud data includes: Preprocessing the collected three-dimensional point cloud data to obtain preprocessed three-dimensional point cloud data; Scale-invariant feature transformation is used to match the same-name points in the preprocessed 3D point cloud data, and random sampling consistency is used to eliminate mismatched points to calculate the initial transformation matrix; Based on an iterative closest point algorithm, a minimization formula is used to solve the rotation vector and the translation vector of the initial transformation matrix; Based on the translation vector and the rotation vector, a deformation displacement vector of the underground pipeline is determined.

[0009] In a possible implementation, determining whether to generate deformation warning information based on the strain distribution map and the deformation displacement vector includes: Performing spatiotemporal registration of the strain distribution map and the deformation displacement vector, and establishing a pipeline deformation field model including a strain-displacement coupling relationship; When the strain gradient of at least one monitoring node in the pipeline deformation field model exceeds a first threshold and the displacement change rate exceeds a second threshold, it is determined to generate deformation warning information.

[0010] In a possible implementation, performing spatiotemporal registration of the strain distribution map with the deformation displacement vector includes: Using a seven-parameter transformation model, the coordinate system of the strain distribution map is unified with the coordinate system of the deformation displacement vector; To address the asynchronous sampling problem of strain data and displacement data, a linear interpolation algorithm is used to unify the strain data and displacement data into the same time interval, where the strain data is the collected data corresponding to the spectral signal, and the displacement data is the collected data corresponding to the three-dimensional point cloud data.

[0011] In a possible implementation, establishing a pipeline deformation field model including a strain-displacement coupling relationship includes: Correlating the strain distribution map with the deformation displacement vector, and establishing a strain-displacement coupling equation based on the geometric structure and material mechanical properties of the pipeline; The strain-displacement coupling equation is solved using a finite element analysis method to generate a three-dimensional visualized pipeline deformation field model.

[0012] In one possible implementation, collecting three-dimensional point cloud data of underground pipelines in the current area includes: The laser scanning control module drives the three-dimensional laser scanner to scan the pipeline surface at multiple angles; Adjust the laser emission power according to the reflectivity of the pipeline material; Based on the coordinates of abnormal sections found by optical fiber monitoring, the scanning frequency of the corresponding area is increased; In a strong vibration environment, the motion compensation algorithm is enabled to reconstruct the point cloud data to collect the three-dimensional point cloud data of the underground pipelines in the current area.

[0013] In a second aspect, the present application provides a real-time monitoring device for underground pipeline deformation, which adopts the following technical solution: A real-time monitoring device for underground pipeline deformation, comprising: A receiving module, configured to receive a spectral signal corresponding to a current area and collect three-dimensional point cloud data of underground pipelines in the current area; a generating module, configured to generate a strain distribution map based on the spectral signal, and determine a deformation displacement vector of the underground pipeline based on the three-dimensional point cloud data; a determination module, configured to determine whether to generate deformation warning information based on the strain distribution map and the deformation displacement vector; The transmission module is used to generate deformation warning information when it is determined to generate deformation warning information, and transmit the deformation warning information, the strain distribution map and the deformation displacement vector to the monitoring terminal in real time.

[0014] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the method described in any one of the first aspects above.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium, comprising: storing a computer program that can be loaded by a processor and execute any one of the methods described in the first aspect above.

[0016] In summary, this application has the following beneficial technical effects: Through real-time connections with devices such as fiber optic sensors and 3D laser scanners, spectral signals can be instantly received and 3D point cloud data collected, avoiding data collection delays. In the data processing phase, whether generating strain distribution maps based on spectral signals or determining deformation displacement vectors from 3D point cloud data, efficient algorithms are used to rapidly complete computational analysis, significantly reducing data processing time. Furthermore, the electronic equipment continuously monitors the strain distribution maps and deformation displacement vectors in real time. Once the strain gradient and displacement change rate of a monitoring node in the pipeline deformation field model exceed a threshold, an early warning mechanism is immediately triggered, generating deformation warning information and improving the real-time monitoring of underground pipeline deformation. Furthermore, through wireless communication networks, deformation warning information, strain distribution maps, and deformation displacement vectors can be transmitted in real time to the monitoring terminal, enabling management personnel to immediately identify pipeline anomalies and take swift countermeasures, effectively ensuring underground pipeline safety and minimizing the time cost and potential losses associated with accidents caused by pipeline deformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a method for real-time monitoring of underground pipeline deformation provided by an embodiment of the present application; Figure 2 1 is a block diagram of a real-time monitoring device for underground pipeline deformation provided by an embodiment of the present application; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following is combined with Figure 1 -Attached Figure 3 This application is described in further detail.

[0019] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] In order to facilitate understanding of the technical solutions proposed in this application, several elements that will be introduced in the description of this application are first introduced here. It should be understood that the following introduction is only for the convenience of understanding these elements, so as to understand the content of the embodiments of this application, and does not necessarily cover all possible situations.

[0021] Underground pipelines refer to pipelines and their ancillary facilities within a city, including those for water supply, drainage, gas, heat, electricity, communications, radio and television, and industry. They are crucial infrastructure and the "lifeline" that ensures the operation of a city. Similarly, underground pipeline data, which contains information about underground pipes and lines, can be used to better understand the distribution and utilization of underground space, providing a scientific basis for urban planning and construction.

[0022] The present application provides a method for real-time monitoring of underground pipeline deformation. Figure 1 As shown, the method provided in the embodiment of the present application is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S101 to S104, wherein: Step S101: Receive a spectral signal corresponding to a current area, and collect three-dimensional point cloud data of underground pipelines in the current area.

[0023] The spectral signal refers to the Brillouin scattered light signal generated by the interaction between light and phonons in optical fibers. Its frequency, intensity, and other characteristics contain information about the strain and temperature of the pipeline's surrounding environment, providing a crucial basis for analyzing pipeline strain. Three-dimensional point cloud data is a collection of three-dimensional coordinate points collected by equipment such as laser scanning and ground-penetrating radar. These points describe the spatial shape, position, and surface characteristics of underground pipelines and surrounding objects, and are used to analyze spatial displacement changes in pipelines.

[0024] The electronic device is connected to the optical fiber sensor pre-laid around the underground pipeline to receive the backscattered spectral signal generated by the optical fiber after the pulse light is emitted by the Brillouin optical time domain reflectometer (BOTDR) or the Brillouin optical time domain analyzer (BOTDA).

[0025] Furthermore, in this embodiment, collecting three-dimensional point cloud data of underground pipelines in the current area includes: The laser scanning control module drives the three-dimensional laser scanner to scan the pipeline surface at multiple angles; Adjust the laser emission power according to the reflectivity of the pipeline material; Based on the coordinates of abnormal sections found by optical fiber monitoring, the scanning frequency of the corresponding area is increased; In a strong vibration environment, the motion compensation algorithm is enabled to reconstruct the point cloud data to collect the three-dimensional point cloud data of the underground pipelines in the current area.

[0026] Among them, the laser scanning control module is a functional module in electronic equipment used to control the operation of the three-dimensional laser scanner. It can send instructions to adjust various parameters of the scanner (such as angle, speed, range, etc.), coordinate the scanner to perform scanning tasks according to the preset plan, and is the core control unit for achieving precise scanning.

[0027] A 3D laser scanner is a device that captures three-dimensional spatial information about an object's surface by emitting laser beams and receiving reflected light. It can quickly and accurately collect point cloud data from an object's surface and is widely used in fields such as topographic mapping, architectural modeling, and industrial inspection. It is also used to obtain 3D morphological data of underground pipelines in monitoring.

[0028] Specifically, the laser scanning control module built into the electronic device sends instructions to the 3D laser scanner based on a pre-set scanning scheme, controlling the scanner to rotate and move at multiple angles around the underground pipeline. During the scanning process, the control module precisely adjusts the scanner's scanning angle, scanning speed, and scanning range to ensure that every part of the pipeline, including the top, sides, bottom, and complex structures such as elbows and branch interfaces, are fully covered. For example, for straight pipeline segments, the scanner is controlled to perform circular scans at fixed intervals; for curved sections of the pipeline, the density of scanning angles is increased to ensure comprehensive and accurate surface data is obtained.

[0029] Furthermore, the electronic device's corresponding database pre-stores reflectivity data for common underground pipeline materials (such as metal, plastic, and concrete). Before scanning, the electronic device retrieves the reflectivity data for the corresponding material based on the pipeline's material information (which can be obtained from design data or preliminary surveys). If the pipeline material has low reflectivity (such as black plastic), the electronic device controls the laser scanning control module to appropriately increase the laser emission power to ensure sufficient reflected light returns to the scanner for clear scan data. If the pipeline material has high reflectivity (such as metal), the laser emission power is reduced to prevent excessive reflected light from distorting the data or damaging the scanner sensor. During the scanning process, the electronic device also analyzes the quality of the collected scan data in real time and dynamically fine-tunes the laser emission power to ensure accurate and stable data acquisition.

[0030] Furthermore, the electronic equipment receives underground pipeline strain data transmitted by the fiber optic monitoring system in real time and analyzes and processes the data. If the fiber optic monitoring system detects strain anomalies in a certain section (for example, strain values ​​exceeding a preset threshold, indicating a potential deformation risk), the electronic equipment obtains the precise coordinates of the abnormal section. The electronic equipment then controls the laser scanning control module to adjust the scanning strategy, setting a higher scanning frequency for the abnormal section. For example, the scanning frequency can be increased from once an hour to once every 10 minutes. This allows for more intensive collection of 3D point cloud data for the area, enabling timely detection of subtle pipeline deformations and providing more detailed data support for subsequent risk assessment and decision-making.

[0031] Sensors built into electronic devices sense the vibration of the surrounding environment in real time. When a strong vibration environment is detected (such as a nearby subway or construction pile driving), the electronic device automatically triggers a motion compensation algorithm. During the scanning process, the algorithm analyzes the position and attitude changes of the laser scanner caused by vibration in real time and corrects the collected raw point cloud data based on this data. For example, by calculating the translation and rotation parameters of the scanner during vibration, point cloud data that has been offset due to vibration is relocated to the correct spatial position. Finally, the electronic device uses the corrected point cloud data for three-dimensional reconstruction, removing noise points and erroneous data caused by vibration, and generating three-dimensional point cloud data that accurately reflects the underground pipeline shape, ensuring reliable monitoring data can still be obtained in harsh environments.

[0032] Step S102: Generate a strain distribution map based on the spectral signal, and determine the deformation displacement vector of the underground pipeline based on the three-dimensional point cloud data.

[0033] Among them, the strain distribution map is a visual chart that graphically displays the strain size and distribution at different locations of underground pipelines. The strain value is usually represented by a color gradient, with warm colors representing tensile strain and cold colors representing compressive strain, which facilitates intuitive judgment of the stress state of the pipeline.

[0034] The deformation displacement vector is a vector used to describe the position change of each point on the underground pipeline in space. It contains the direction and magnitude information of the displacement and is used to quantify the degree of deformation of the pipeline in different directions.

[0035] Specifically, in this embodiment, generating a strain distribution map based on the spectral signal includes: The Gaussian fitting algorithm is used to fit the peak position of the spectral information to obtain the peak position corresponding to the spectral signal; Based on the peak position, the frequency shift corresponding to the spectral signal is calculated by the least squares method; The axial strain distribution of the underground pipeline in the current area is determined based on the frequency shift, so as to generate a strain distribution map based on the axial strain distribution.

[0036] Specifically, after receiving the spectral signal from the fiber optic sensor, the signal is first pre-processed to remove noise caused by environmental interference or the device itself. Then, the built-in Gaussian fitting algorithm module is called to assume the intensity distribution of the spectral signal as a Gaussian function. By continuously adjusting the parameters in the Gaussian function, the function curve is made to fit the intensity distribution of the actual spectral signal as closely as possible. In this iterative adjustment process, the goal is to minimize the error between the actual signal data point and the corresponding point on the Gaussian function curve. When the error reaches a minimum or meets the set convergence condition, the center position of the Gaussian function is the peak position corresponding to the spectral signal.

[0037] After obtaining the peak position of the spectral signal, it is compared with the peak position of the spectral signal when the pipeline is in its initial state (no strain and temperature change). Using the principle of least squares, an objective function for frequency shift is established. This function measures the difference between the current peak position and the initial peak position. By repeatedly trying different frequency shift values, the corresponding objective function value is calculated, and the frequency shift value that minimizes the objective function value is found. This value is the frequency shift of the current spectral signal relative to the initial state, reflecting the physical changes in the pipeline caused by external factors.

[0038] Furthermore, the system uses a pre-defined formula for the correspondence between frequency shift and axial strain (based on the material properties and physical principles of optical fibers) to substitute the calculated frequency shift into the formula, calculating the corresponding axial strain values ​​for each fiber optic sensor installed at different locations along the underground pipeline in the current area. These discrete strain values ​​are then interpolated according to the actual distribution of the fiber optic sensors along the pipeline, generating continuous axial strain distribution data. Finally, the electronic device uses a visualization module to plot the axial strain distribution data in a color-coded format on the spatial distribution map of the pipeline, generating an intuitive strain distribution map. Different colors represent different strain levels, allowing personnel to quickly understand the strain status of the pipeline.

[0039] Furthermore, in this embodiment, determining the deformation displacement vector of the underground pipeline based on the three-dimensional point cloud data includes: Preprocessing the collected three-dimensional point cloud data to obtain preprocessed three-dimensional point cloud data; Scale-invariant feature transformation is used to match the same-name points in the preprocessed 3D point cloud data, and random sampling consistency is used to eliminate mismatched points to calculate the initial transformation matrix; Based on the iterative closest point algorithm, the rotation vector and translation vector of the initial transformation matrix are solved using the minimization formula; Based on the translation vector and the rotation vector, the deformation displacement vector of the underground pipeline is determined.

[0040] After receiving the 3D point cloud data, denoising can be performed first. Specifically, by setting a distance threshold and performing statistical analysis, isolated and outlier points that deviate significantly from the surrounding point cloud are identified and removed. These points are often caused by equipment noise and environmental interference (such as birds flying through the lidar scanning range). Voxel grid filtering technology is then used to divide the point cloud data into regular small cubes (voxels). The center of gravity of the points within each voxel is calculated and used to replace all points within the voxel. This reduces the data volume, while preserving the pipeline shape characteristics and improving subsequent computational efficiency. Finally, the electronic device normalizes the data and scales the point cloud coordinates to an appropriate numerical range for subsequent algorithm processing. Voxel grid filtering is a data reduction method that divides the 3D space into multiple small cubes (voxels) and replaces the original points by calculating the center of gravity of the points within the voxels, thereby reducing the data volume while preserving the object's geometric characteristics.

[0041] Furthermore, the Scale-Invariant Feature Transform (SIFT) algorithm can be used to extract feature points from the preprocessed 3D point cloud data from both phases. These feature points are invariant to scale, rotation, and illumination, accurately representing key parts of an object under various conditions. By calculating the Euclidean distance between the feature point descriptors, possible corresponding points in the two phases of the point cloud data can be identified. Due to data noise and complex environments, mismatches may occur during the matching process. Therefore, the electronic device uses the Random Sample Consensus (RANSAC) algorithm to randomly select some matching point pairs, assuming they conform to the correct transformation relationship. This assumed model is then used to test the remaining point pairs. If most point pairs conform to the assumed model, these pairs are retained as correct matches, and mismatched points that do not conform are discarded. Finally, based on the correctly matched point pairs, the least squares method is used to calculate the initial transformation matrix that describes the relative positional relationship between the two phases of the point cloud data. This matrix includes rotation and translation information. These points are points representing the same physical location in the 3D point cloud data collected at different times or from different viewpoints. By matching these points, changes in the position and morphology of an object can be analyzed.

[0042] After obtaining the initial transformation matrix, the iterative closest point (ICP) algorithm can be used as the starting point for optimization. Specifically, one phase of the point cloud data is used as the source point cloud and the other phase as the target point cloud. The source point cloud is initially transformed using the initial transformation matrix. Then, in the transformed source point cloud, the nearest point is found for each point in the target point cloud to establish a corresponding relationship. Then, based on these corresponding point pairs, a minimization formula (usually minimizing the sum of the squared Euclidean distances between corresponding points) can be used to calculate the new rotation vector and translation vector, further adjusting the position and posture of the source point cloud to make the source point cloud and the target point cloud more closely match. This process of finding the nearest point, calculating the transformation vector, and updating the point cloud position will continue to iterate until the distance error between the corresponding points meets the set threshold or reaches the maximum number of iterations. At this time, the rotation vector and translation vector obtained are the optimal solution.

[0043] After obtaining the optimized translation and rotation vectors, they are applied to the three-dimensional point cloud data of the underground pipeline. For each point in the point cloud, the corresponding rotation matrix can be calculated based on the rotation vector, the point can be rotated, and then the rotated point can be translated according to the translation vector. After the transformation, the difference between the new position of each point in space and the original position constitutes the displacement vector of the point. The electronic device summarizes and analyzes the displacement vectors of all points to obtain the deformation displacement vector of the entire underground pipeline. These vectors intuitively show the displacement direction and size of the pipeline at different positions, helping staff to judge the deformation of the pipeline.

[0044] Step S103: Determine whether to generate deformation warning information based on the strain distribution map and the deformation displacement vector.

[0045] After obtaining the strain distribution map and deformation displacement vector of the underground pipeline in the current area, it is possible to determine whether the pipeline in the current area has been deformed and the degree of deformation based on the strain distribution map and deformation displacement vector, and determine whether to generate deformation warning information based on the degree of deformation.

[0046] Specifically, in this embodiment, determining whether to generate deformation warning information based on the strain distribution map and the deformation displacement vector includes: The strain distribution map and the deformation displacement vector are temporally and spatially registered, and a pipeline deformation field model including the strain-displacement coupling relationship is established; When the strain gradient of at least one monitoring node in the pipeline deformation field model exceeds a first threshold and the displacement change rate exceeds a second threshold, it is determined to generate deformation warning information.

[0047] Specifically, the acquired strain distribution map and deformation displacement vector can first be spatially registered. By identifying common landmarks or reference points (such as valve wells and pipeline bends) in the strain distribution map and deformation displacement vector data, a seven-parameter transformation model is used to convert the coordinate systems of the strain distribution map and the deformation displacement vector to a unified city coordinate system or a dedicated engineering coordinate system, ensuring spatial consistency between the two. Based on material properties (such as elastic modulus and Poisson's ratio), pipe diameter, and burial depth of the underground pipeline, the strain data from the strain distribution map and the displacement data from the deformation displacement vector are fused using finite element analysis. The electronic device discretizes the pipeline into multiple cells. For each cell, the relationship between stress, strain, and displacement is calculated using constitutive and equilibrium equations from material mechanics. Ultimately, a three-dimensional pipeline deformation field model is constructed that reflects the strain-displacement coupling relationship.

[0048] Furthermore, the data changes of each monitoring node in the pipeline deformation field model are monitored in real time. For each monitoring node, the strain gradient between it and the adjacent node is calculated, that is, the strain change per unit length. At the same time, the displacement change rate of the node per unit time is calculated to reflect the speed of displacement change, and the calculated strain gradient and displacement change rate are compared with the pre-set first threshold and second threshold respectively. These thresholds are determined based on comprehensive factors such as the design standards, material properties, service life and historical monitoring data of the pipeline. When it is found that the strain gradient of at least one monitoring node exceeds the first threshold and the displacement change rate exceeds the second threshold, it is determined that there is a risk of deformation of the pipeline in the area where the monitoring node is located, and deformation warning information is then generated.

[0049] More specifically, in this embodiment, the strain distribution map and the deformation displacement vector are temporally and spatially registered, including: A seven-parameter transformation model is used to unify the coordinate system of the strain distribution map and the coordinate system of the deformation displacement vector; To address the asynchronous sampling problem of strain data and displacement data, a linear interpolation algorithm is used to unify the strain data and displacement data into the same time interval, where the strain data is the collected data corresponding to the spectral signal, and the displacement data is the collected data corresponding to the three-dimensional point cloud data.

[0050] The seven-parameter transformation model is a mathematical model used to transform between different rectangular coordinate systems. It includes three translation parameters, three rotation parameters, and one scale parameter. By calculating these seven parameters using the least squares method, the coordinates of common control points in two coordinate systems are known, achieving coordinate transformation and accurately aligning data from different sources in space.

[0051] Strain data is the collected data corresponding to the spectral signal collected by the fiber optic sensor. After processing, it can reflect the strain conditions at different locations of the underground pipeline, that is, the degree to which the pipeline is stretched or compressed. Its sampling frequency and timestamp record the time sequence of data collection.

[0052] Displacement data is collected by processing three-dimensional point cloud data. It describes the position changes of each point on the underground pipeline in space, including displacement direction and size information, and also has a corresponding timestamp to identify the collection time.

[0053] Asynchronous sampling means that strain data and displacement data have different sampling frequencies and time intervals due to reasons such as acquisition equipment and acquisition strategies, resulting in the data being unable to directly correspond in the time dimension and requiring processing for joint analysis.

[0054] Specifically, the coordinate system information contained in the strain distribution map and deformation displacement vector data is identified, and the current coordinate system type of both is determined (e.g., WGS84 geographic coordinate system, Gaussian projection plane rectangular coordinate system, etc.). Next, at least three high-precision common control points (e.g., pipeline inspection wells with known precise coordinates, fixed landmarks, etc.) are selected from each of the strain distribution map and deformation displacement vector data. Using the coordinate values ​​of these common control points in both coordinate systems, the three translation parameters, three rotation parameters, and one scale parameter in the seven-parameter transformation model are solved through iterative calculation based on the least squares principle. Once the solution is complete, the seven parameters are substituted into the transformation formula, and the entire strain distribution map and deformation displacement vector data are transformed point by point, thereby aligning the coordinate systems of both to the target coordinate system (e.g., the unified city coordinate system), achieving spatial datum consistency.

[0055] Furthermore, the timestamp information of the strain data and displacement data is read to clarify the sampling frequency and time interval difference between the two (for example, strain data is collected every 5 minutes, and displacement data is collected every 30 minutes). Then, a unified time interval is determined (such as the 5-minute interval of strain data). For each time point of the unified time interval, it can be checked whether the original strain data and displacement data exist at that time point. If there is only strain data but no displacement data at a certain time point, the electronic device will use a linear interpolation algorithm to calculate the displacement interpolation value at that time point based on the two adjacent displacement data collection moments before and after the time point and their corresponding displacement values; conversely, if there is only displacement data but no strain data, the strain interpolation value is calculated. Through interpolation calculation at each time point, the electronic device adjusts the strain data and displacement data to the same time interval to ensure that the two types of data are aligned in the time dimension.

[0056] In this embodiment, a pipeline deformation field model including a strain-displacement coupling relationship is established, including: Correlate the strain distribution map with the deformation displacement vector, and establish the strain-displacement coupling equation based on the pipeline's geometric structure and material mechanical properties; The strain-displacement coupling equation is solved using the finite element analysis method to generate a three-dimensional visualized pipeline deformation field model.

[0057] Based on the results of spatiotemporal registration, the strain data in the strain distribution map and the displacement data in the deformation displacement vector are associated one-to-one according to the actual spatial location of the underground pipeline. For example, the strain value of a pipe segment in the map is matched with the displacement vector of the corresponding point in that segment. Pre-stored pipeline geometry information is then retrieved, including data such as pipeline length, diameter, bend angle, and branch location, as well as material mechanical properties such as elastic modulus, Poisson's ratio, and yield strength. Based on theories such as Hooke's law and geometric equations in material mechanics, and combined with the actual stress conditions of the pipeline (such as internal pressure and soil pressure), mathematical equations are constructed to describe the inherent relationship between strain and displacement. During the equation construction process, the force differences at different locations along the pipeline are taken into account, and equations are established for each unit segment. Ultimately, a complete set of coupled strain-displacement equations is formed, accurately describing the relationship between strain and displacement when the pipeline is subjected to stress and deformation.

[0058] The established strain-displacement coupling equations are then imported into the built-in finite element analysis software module. Specifically, the underground pipeline is discretized into a large number of small elements (such as tetrahedral and hexahedral elements). These elements are interconnected to form a finite element model of the pipeline. Based on the pipeline's actual boundary conditions (such as fixed-end constraints and free-end conditions) and load conditions (such as internal fluid pressure and external pressure exerted by the soil), corresponding boundary conditions and load parameters are set for each element. Next, using numerical finite element analysis methods (such as Gaussian elimination and iterative methods), the strain-displacement coupling equations for each element are solved, resulting in strain and displacement values ​​for each element node. The calculated results from all elements are then integrated, and the pipeline's deformation state is displayed in a 3D graphical form using 3D modeling and visualization techniques. During visualization, different colors, textures, and transparency are used to distinguish the strain and displacement magnitudes at different locations along the pipeline. This generates an intuitive 3D visualization of the pipeline's deformation field, making it easier for engineers to observe and analyze the overall deformation of the pipeline.

[0059] Step S104: When it is determined that deformation warning information is to be generated, the deformation warning information is generated, and the deformation warning information, the strain distribution map, and the deformation displacement vector are transmitted to the monitoring terminal in real time.

[0060] Specifically, the electronic device generates deformation warning information based on a preset warning template, including detailed information such as the specific location and time of deformation, strain gradient value, displacement change rate, and threshold value exceeded. The electronic device then encrypts the deformation warning information, strain distribution map, and deformation displacement vectors and transmits them in real time to the monitoring terminal via 4G, 5G, or wireless network communication modules. During transmission, the electronic device implements data verification and retransmission mechanisms to ensure that the data reaches the monitoring terminal accurately, allowing managers to take timely measures to address pipeline deformation risks.

[0061] The present invention provides a method for real-time monitoring of underground pipeline deformation. By connecting to optical fiber sensors, 3D laser scanners, and other devices in real time, the method can instantly receive spectral signals and collect 3D point cloud data, thus avoiding data collection delays. In the data processing phase, both generating strain distribution maps based on spectral signals and determining deformation displacement vectors from 3D point cloud data utilize efficient algorithms to rapidly complete computational analysis, significantly reducing data processing time. Furthermore, the electronic device continuously monitors the strain distribution maps and deformation displacement vectors in real time. Once the strain gradient and displacement change rate of a monitoring node in the pipeline deformation field model exceed a threshold, an early warning mechanism is immediately triggered, generating deformation warning information. This improves the real-time nature of underground pipeline deformation monitoring. Furthermore, the method utilizes a wireless communication network to transmit deformation warning information, strain distribution maps, and deformation displacement vectors to a monitoring terminal in real time, enabling management personnel to immediately identify pipeline anomalies and take swift countermeasures, effectively ensuring underground pipeline safety and minimizing the time cost and potential losses associated with accidents caused by pipeline deformation.

[0062] The above embodiment introduces a method for real-time monitoring of underground pipeline deformation from the perspective of method flow. The following embodiment introduces a device for real-time monitoring of underground pipeline deformation from the perspective of a virtual module or virtual unit. Please refer to the following embodiment for details.

[0063] See also Figure 2 The underground pipeline deformation real-time monitoring device 20 may specifically include: a receiving module 201, a generating module 202, a determining module 203 and a transmitting module 204, wherein: A real-time monitoring device 20 for underground pipeline deformation, comprising: The receiving module 201 is used to receive the spectral signal corresponding to the current area and collect the three-dimensional point cloud data of the underground pipelines in the current area; A generating module 202 is configured to generate a strain distribution map based on the spectral signal and determine a deformation displacement vector of the underground pipeline based on the three-dimensional point cloud data; A determination module 203 is configured to determine whether to generate deformation warning information based on the strain distribution map and the deformation displacement vector; The transmission module 204 is used to generate deformation warning information when it is determined to generate deformation warning information, and transmit the deformation warning information, strain distribution map and deformation displacement vector to the monitoring terminal in real time. In one possible implementation of the embodiment of the present application, when generating the strain distribution map based on the spectral signal, the generation module 202 is specifically configured to: The Gaussian fitting algorithm is used to fit the peak position of the spectral information to obtain the peak position corresponding to the spectral signal; Based on the peak position, the frequency shift corresponding to the spectral signal is calculated by the least squares method; The axial strain distribution of the underground pipeline in the current area is determined based on the frequency shift, so as to generate a strain distribution map based on the axial strain distribution.

[0064] In one possible implementation of the embodiment of the present application, when determining the deformation displacement vector of the underground pipeline based on the three-dimensional point cloud data, the generation module 202 is specifically configured to: Preprocessing the collected three-dimensional point cloud data to obtain preprocessed three-dimensional point cloud data; Scale-invariant feature transformation is used to match the same-name points in the preprocessed 3D point cloud data, and random sampling consistency is used to eliminate mismatched points to calculate the initial transformation matrix; Based on the iterative closest point algorithm, the rotation vector and translation vector of the initial transformation matrix are solved using the minimization formula; Based on the translation vector and the rotation vector, the deformation displacement vector of the underground pipeline is determined.

[0065] In one possible implementation of the embodiment of the present application, when determining whether to generate deformation warning information based on the strain distribution map and the deformation displacement vector, the determination module 203 is specifically configured to: The strain distribution map and the deformation displacement vector are temporally and spatially registered, and a pipeline deformation field model including the strain-displacement coupling relationship is established; When the strain gradient of at least one monitoring node in the pipeline deformation field model exceeds a first threshold and the displacement change rate exceeds a second threshold, it is determined to generate deformation warning information.

[0066] In a possible implementation of the embodiment of the present application, when performing spatiotemporal registration of the strain distribution map with the deformation displacement vector, the determination module 203 is specifically configured to: A seven-parameter transformation model is used to unify the coordinate system of the strain distribution map and the coordinate system of the deformation displacement vector; To address the asynchronous sampling problem of strain data and displacement data, a linear interpolation algorithm is used to unify the strain data and displacement data into the same time interval, where the strain data is the collected data corresponding to the spectral signal, and the displacement data is the collected data corresponding to the three-dimensional point cloud data.

[0067] In a possible implementation of the embodiment of the present application, when establishing the pipeline deformation field model including the strain-displacement coupling relationship, the determination module 203 is specifically configured to: Correlate the strain distribution map with the deformation displacement vector, and establish the strain-displacement coupling equation based on the pipeline's geometric structure and material mechanical properties; The strain-displacement coupling equation is solved using the finite element analysis method to generate a three-dimensional visualized pipeline deformation field model.

[0068] In one possible implementation of the embodiment of the present application, when collecting three-dimensional point cloud data of underground pipelines in the current area, the receiving module 201 is specifically configured to: The laser scanning control module drives the three-dimensional laser scanner to scan the pipeline surface at multiple angles; Adjust the laser emission power according to the reflectivity of the pipeline material; Based on the coordinates of abnormal sections found by optical fiber monitoring, the scanning frequency of the corresponding area is increased; In a strong vibration environment, the motion compensation algorithm is enabled to reconstruct the point cloud data to collect the three-dimensional point cloud data of the underground pipelines in the current area.

[0069] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0070] See also Figure 3 , the embodiment of the present application also introduces an electronic device from the perspective of a physical device, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0071] Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0072] Bus 302 may include a path for transmitting information between the above components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0073] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0074] The memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0075] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and can also be servers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0076] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0077] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0078] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for real-time monitoring of underground pipeline deformation, characterized in that: include: Receive a spectral signal corresponding to a current area, and collect three-dimensional point cloud data of underground pipelines in the current area; generating a strain distribution map based on the spectral signal, and determining a deformation displacement vector of the underground pipeline based on the three-dimensional point cloud data; determining whether to generate deformation warning information based on the strain distribution map and the deformation displacement vector; When it is determined to generate deformation warning information, the deformation warning information is generated, and the deformation warning information, the strain distribution map, and the deformation displacement vector are transmitted to a monitoring terminal in real time.

2. The method for real-time monitoring of underground pipeline deformation according to claim 1, characterized in that: Generating a strain distribution map based on the spectral signal, including: Using a Gaussian fitting algorithm to perform peak position fitting on the spectral information to obtain the peak position corresponding to the spectral signal; Based on the peak position, a frequency shift corresponding to the spectral signal is calculated by a least squares method; An axial strain distribution of the underground pipeline in the current area is determined based on the frequency shift, so as to generate a strain distribution map based on the axial strain distribution.

3. The method for real-time monitoring of underground pipeline deformation according to claim 1, characterized in that: Determining a deformation displacement vector of the underground pipeline based on the three-dimensional point cloud data includes: Preprocessing the collected three-dimensional point cloud data to obtain preprocessed three-dimensional point cloud data; Scale-invariant feature transformation is used to match the same-name points in the preprocessed 3D point cloud data, and random sampling consistency is used to eliminate mismatched points to calculate the initial transformation matrix; Based on an iterative closest point algorithm, a minimization formula is used to solve the rotation vector and the translation vector of the initial transformation matrix; Based on the translation vector and the rotation vector, a deformation displacement vector of the underground pipeline is determined.

4. The method for real-time monitoring of underground pipeline deformation according to any one of claims 1 to 3, characterized in that: Determining whether to generate deformation warning information based on the strain distribution map and the deformation displacement vector includes: Performing spatiotemporal registration of the strain distribution map and the deformation displacement vector, and establishing a pipeline deformation field model including a strain-displacement coupling relationship; When the strain gradient of at least one monitoring node in the pipeline deformation field model exceeds a first threshold and the displacement change rate exceeds a second threshold, it is determined to generate deformation warning information.

5. The method for real-time monitoring of underground pipeline deformation according to claim 4, characterized in that: Performing spatiotemporal registration of the strain distribution map with the deformation displacement vector includes: Using a seven-parameter transformation model, the coordinate system of the strain distribution map is unified with the coordinate system of the deformation displacement vector; To address the asynchronous sampling problem of strain data and displacement data, a linear interpolation algorithm is used to unify the strain data and displacement data into the same time interval, where the strain data is the collected data corresponding to the spectral signal, and the displacement data is the collected data corresponding to the three-dimensional point cloud data.

6. The method for real-time monitoring of underground pipeline deformation according to claim 4, characterized in that: The pipeline deformation field model including the strain-displacement coupling relationship is established, including: Correlating the strain distribution map with the deformation displacement vector, and establishing a strain-displacement coupling equation based on the geometric structure and material mechanical properties of the pipeline; The strain-displacement coupling equation is solved using a finite element analysis method to generate a three-dimensional visualized pipeline deformation field model.

7. The method for real-time monitoring of underground pipeline deformation according to claim 1, characterized in that: Collecting three-dimensional point cloud data of underground pipelines in the current area includes: The laser scanning control module drives the three-dimensional laser scanner to scan the pipeline surface at multiple angles; Adjust the laser emission power according to the reflectivity of the pipeline material; Based on the coordinates of abnormal sections found by optical fiber monitoring, the scanning frequency of the corresponding area is increased; In a strong vibration environment, the motion compensation algorithm is enabled to reconstruct the point cloud data to collect the three-dimensional point cloud data of the underground pipelines in the current area.

8. A real-time monitoring device for underground pipeline deformation, characterized in that: include: A receiving module, configured to receive a spectral signal corresponding to a current area and collect three-dimensional point cloud data of underground pipelines in the current area; a generating module, configured to generate a strain distribution map based on the spectral signal, and determine a deformation displacement vector of the underground pipeline based on the three-dimensional point cloud data; a determination module, configured to determine whether to generate deformation warning information based on the strain distribution map and the deformation displacement vector; The transmission module is used to generate deformation warning information when it is determined to generate deformation warning information, and transmit the deformation warning information, the strain distribution map and the deformation displacement vector to the monitoring terminal in real time.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the real-time monitoring method for underground pipeline deformation according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the method for real-time monitoring of underground pipeline deformation according to any one of claims 1 to 7.

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