Buried pipeline stress monitoring system and method

Abstract

The present application provides a buried pipeline stress monitoring system and method, relating to the field of pipeline monitoring technology. The system includes: a TMR sensor; a full-bridge strain gauge group, the full-bridge strain gauge group being configured to obtain stress data of a pipeline to be measured; and a monitoring module, the monitoring module being configured to: obtain historical data; train a linear model using historical environmental magnetic field data and historical stress data to obtain an initial magnetic field stress model; update model parameters of the initial magnetic field stress model using a least squares method to obtain a target magnetic field stress model; and input the environmental magnetic field data and stress data into the target magnetic field stress model to obtain target stress data. This addresses the problem that current pipeline stress monitoring processes primarily utilize a single sensor for measurement, which is susceptible to interference from factors such as temperature drift and magnetic field fluctuations during the measurement process, resulting in monitoring accuracy failing to meet actual requirements and difficulty in reflecting the true stress state of the pipeline.

Description

Technical Field

[0001] The present application relates to the technical field of pipeline monitoring, and in particular to a buried pipeline stress monitoring system and method. Background Art

[0002] Because buried pipelines are in a complex environment for a long time, affected by multiple factors such as soil pressure, geological activities, and corrosion, they are very prone to safety hazards such as structural failure. Therefore, stress monitoring of pipelines to prevent structural failure has become a key link in ensuring the safe operation of pipelines.

[0003] Existing technologies primarily use single sensors for pipeline stress monitoring, such as strain gauges or magnetic measurement. Strain gauges reflect pipeline stress by measuring changes in strain on the pipeline surface, while magnetic measurement monitors stress based on changes in the magnetic field. For real-time monitoring of the passing status of magnetic flux leakage internal detectors, marker boxes are currently the primary method for monitoring the passing status of magnetic flux leakage internal detectors. Marker boxes are placed along the pipeline. When a magnetic flux leakage internal detector passes through, the marker box generates a corresponding signal, enabling real-time monitoring of the passing status of the magnetic flux leakage internal detector.

[0004] However, a single sensor is susceptible to interference from external factors such as temperature drift and magnetic field fluctuations. Over long-term monitoring, the errors caused by these interference factors gradually accumulate, making it difficult to accurately reflect the pipeline's true stress state and failing to meet monitoring accuracy requirements. While Marker boxes can provide real-time monitoring of the ball passing state of magnetic flux leakage detectors, a large number of Marker boxes must be relocated along the pipeline before each test. For example, a Marker box must be deployed every 1-2 kilometers. For a 100-kilometer pipeline, the cost of a single deployment is extremely high, deployment is extremely time-consuming, and deployment and recovery are time-consuming and labor-intensive. Summary of the Invention

[0005] The present application provides a buried pipeline stress monitoring system and method to address the technical problem that the current pipeline stress monitoring process mainly uses a single sensor for measurement. The measurement process is easily interfered by external factors such as temperature drift and magnetic field fluctuations, resulting in the monitoring accuracy failing to meet actual needs and making it difficult to accurately reflect the actual stress state of the pipeline.

[0006] A first aspect of the present application provides a buried pipeline stress monitoring system, comprising:

[0007] A TMR sensor is provided on the outer wall of the pipeline to be tested along the length direction of the pipeline to be tested; the TMR sensor is provided with a strong magnetic mode and a weak magnetic mode; when the TMR sensor is in the strong magnetic mode, the TMR sensor obtains the environmental magnetic field data of the pipeline to be tested at a first preset resolution; when the TMR sensor is in the weak magnetic mode, the TMR sensor obtains the environmental magnetic field data of the pipeline to be tested at a second preset resolution; the range of the second preset resolution is greater than the range of the first preset resolution;

[0008] a full-bridge strain gauge group, the full-bridge strain gauge group being disposed between the TMR sensor and the pipeline to be measured; the full-bridge strain gauge group being configured to obtain stress data of the pipeline to be measured;

[0009] A monitoring module is connected to the TMR sensor and the full-bridge strain gauge group, and is configured to:

[0010] Acquiring historical data; the historical data includes: historical environmental magnetic field data and historical stress data;

[0011] Downsampling the historical environmental magnetic field data;

[0012] Using the historical environmental magnetic field data and the historical stress data to train a linear model to obtain an initial magnetic field stress model;

[0013] Using the least squares method, the model parameters of the initial magnetic field stress model are updated to obtain a target magnetic field stress model;

[0014] The environmental magnetic field data and the stress data are input into the target magnetic field stress model to obtain target stress data.

[0015] In some embodiments, the number of the TMR sensors located on the same horizontal plane of the pipeline to be measured is 3; and the angle between adjacent TMR sensors along the outer wall of the pipeline to be measured is 120°.

[0016] In some embodiments, the full-bridge strain gauge group includes: a first strain gauge and a second strain gauge; the first strain gauge is arranged between the TMR sensor and the pipeline to be measured along the axial direction of the pipeline to be measured; the second strain gauge is arranged between the TMR sensor and the pipeline to be measured along the circumferential direction of the pipeline to be measured; the full-bridge strain gauge group is fixed to the outside of the pipeline to be measured by epoxy resin glue, and the first strain gauge and the second strain gauge are arranged in the epoxy resin glue.

[0017] In some embodiments, the system further comprises:

[0018] A demagnetization module is provided on the outer wall of the pipeline to be tested along the length direction of the pipeline to be tested; the demagnetization module is configured to:

[0019] Output demagnetization current in exponential decay mode to reduce the intensity of the ambient magnetic field;

[0020] The TMR sensor is configured as:

[0021] Determine whether the change value of the ambient magnetic field strength within the first preset time is greater than a preset value, and if so, determine whether the ambient magnetic field strength is greater than 1 mT, the stress value is a constant value, and the ambient magnetic field strength increases;

[0022] If so, after the ambient magnetic field strength decreases, the ambient magnetic field data and the corresponding time point for obtaining the ambient magnetic field data are sent to the setting device and the demagnetization module is turned on until the ambient magnetic field strength recovers to the target ambient magnetic field strength, and then the demagnetization module is turned off; the setting device is an electronic device that can receive the ambient magnetic field strength data and the corresponding time point for obtaining the ambient magnetic field strength.

[0023] In some embodiments, after the step of determining whether the ambient magnetic field strength is greater than 1 mT, the stress value is a constant value, and the ambient magnetic field strength increases, the method further includes:

[0024] If so, the device information and time information of the TMR sensor are sent to the setting device; the device information includes: the location information and model information of the TMR sensor.

[0025] In some embodiments, the TMR sensor is further configured to:

[0026] After the ambient magnetic field strength returns to the target ambient magnetic field strength, calculating an average value of the ambient magnetic field strength within a second preset time;

[0027] Based on the average value of the ambient magnetic field strength, a target ambient magnetic field strength is determined.

[0028] In some embodiments, the system further comprises:

[0029] A power supply module, the power supply module comprising: a solar panel and a battery; the solar panel is configured to convert solar energy into electrical energy and store it in the battery; the battery is configured to store electrical energy and supply power to the TMR sensor, full-bridge strain gauge group, monitoring module, and demagnetization module.

[0030] In some embodiments, the step of updating the model parameters of the initial magnetic field stress model using the least squares method to obtain the target magnetic field stress model includes:

[0031] Initialize the algorithm to obtain the parameter vector and covariance matrix;

[0032] The parameter vector is: ;

[0033] The covariance matrix is: ;

[0034] Where, ; X is the historical stress data; Y is the historical environmental magnetic field data; To set the value; I is the identity matrix, the dimension of which is consistent with the dimension of the parameter vector; T is the transpose;

[0035] Based on the parameter vector and the covariance matrix, updating model parameters of the initial magnetic field stress model;

[0036] Based on the model parameters, a residual value is calculated; the residual value is:

[0037] ;

[0038] Where, σ is the historical stress value; H TMR，k is the historical environmental magnetic field value; a and b are model parameters; k is any positive integer;

[0039] Based on the residual value, the initial magnetic field stress model is calibrated to obtain a target magnetic field stress model.

[0040] In some embodiments, the step of calibrating the initial magnetic field stress model based on the residual value to obtain a target magnetic field stress model includes:

[0041] Calculating an average of a preset number of consecutive residual values, and determining whether the average residual value is greater than a preset value;

[0042] If so, the historical environmental magnetic field data and the historical stress data are used to determine the gain parameter and offset of the initial magnetic field stress model; the gain parameter and the offset are:

[0043] ;

[0044] Where, K is the gain parameter; m is the offset; H is the historical environmental magnetic field data; ;

[0045] Based on the gain parameter and the offset, a target magnetic field stress model is obtained; the target magnetic field stress model is:

[0046] .

[0047] A second aspect of the present application provides a buried pipeline stress monitoring method, which is applied to a buried pipeline stress monitoring system according to any one of the first aspects above, comprising:

[0048] Acquiring historical data; the historical data includes: historical environmental magnetic field data and historical stress data;

[0049] Downsampling the historical environmental magnetic field data;

[0050] Using the historical environmental magnetic field data and the historical stress data to train a linear model to obtain an initial magnetic field stress model;

[0051] Using the least squares method, the model parameters of the initial magnetic field stress model are updated to obtain a target magnetic field stress model;

[0052] The environmental magnetic field data and the stress data are input into the target magnetic field stress model to obtain target stress data.

[0053] The present application provides a buried pipeline stress monitoring system and method, the system comprising: a TMR sensor, the TMR sensor being arranged on the outer wall of the pipeline to be measured along the length direction of the pipeline to be measured; the TMR sensor being provided with a strong magnetic mode and a weak magnetic mode; when the TMR sensor is in the strong magnetic mode, the TMR sensor acquires the environmental magnetic field data of the pipeline to be measured at a first preset resolution; when the TMR sensor is in the weak magnetic mode, the TMR sensor acquires the environmental magnetic field data of the pipeline to be measured at a second preset resolution; the range of the second preset resolution is greater than the range of the first preset resolution; a full-bridge strain gauge group, the full-bridge strain gauge group being arranged between the TMR sensor and the pipeline to be measured; the full-bridge strain gauge group being configured to acquire the environmental magnetic field data of the pipeline to be measured stress data; a monitoring module connected to the TMR sensor and the full-bridge strain gauge group, the monitoring module being configured to: acquire historical data; the historical data including: historical environmental magnetic field data and historical stress data; downsample the historical environmental magnetic field data; train a linear model using the historical environmental magnetic field data and the historical stress data to obtain an initial magnetic field stress model; update the model parameters of the initial magnetic field stress model using the least squares method to obtain a target magnetic field stress model; input the environmental magnetic field data and the stress data into the target magnetic field stress model to obtain target stress data, so as to realize joint measurement by the TMR sensor and the strain gauge during pipeline stress monitoring, realize data cross-calibration, and eliminate measurement errors of a single sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] Figure 1 This is a schematic diagram of the structure of the buried pipeline stress monitoring system in this application;

[0056] Figure 2 This is a schematic diagram of the structure of the distribution of various sensors on the pipeline to be tested in this application;

[0057] Figure 3 Schematic diagram of the structure of the full-bridge strain gauge group in this application;

[0058] Figure 4 Flowchart for outputting target stress data in this application.

[0059] Description of reference numerals:

[0060] 1-TMR sensor; 2-full-bridge strain gauge group; 21-first strain gauge; 22-second strain gauge; 23-epoxy resin glue; 3-monitoring module; 4-demagnetization module; 5-power supply module. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the technical solutions in this application, 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 only 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 should fall within the scope of protection of this application.

[0062] For example, the following problems exist in the current pipeline stress monitoring process:

[0063] 1. Insufficient monitoring accuracy of pipeline stress (pressure inside and outside the pipeline): Currently, the use of a single sensor (strain gauge or magnetic measurement method) is susceptible to interference such as temperature drift and magnetic field fluctuations, and long-term monitoring errors gradually accumulate.

[0064] 2. The issue of repeated deployment of magnetic flux leakage detectors (MFLDs) for real-time monitoring of ball-passing status. Currently, MFLD status monitoring is performed using ball-passing indicators. However, before each inspection, a large number of these indicators must be relocated along the pipeline (e.g., one every 1-2 kilometers). Deploying and retrieving these indicators is time-consuming and labor-intensive. (For a 100-kilometer pipeline, for example, a single deployment would be costly and take 1-2 days.) Ball-passing indicators are typically buried only 0.3-0.5 meters deep and are susceptible to damage from surface vehicles, farming activities, and other factors. This leads to a false trigger rate of up to 15%-20%. To avoid damaging the pipeline, ball-passing indicators are placed more than 1 meter from the outer wall, resulting in signal attenuation (a drop in sensitivity of over 50%). This makes it difficult to detect subtle magnetic field variations, and the indicators rely heavily on the experience of construction personnel. This can lead to significant positioning errors, including offset and tilted positioning. Environmental magnetic interference, including low-frequency magnetic noise generated by surface vehicle motors and high-voltage power lines, can also cause misjudgments.

[0065] 3. The destructive effect of residual magnetic fields on long-term monitoring: The weak magnetic method requires measuring the micromagnetic changes caused by stress (on the order of 0.1-1μT) under a constant background magnetic field. However, the residual magnetic field (up to 10mT) after the leakage magnetic detector passes through will completely mask the stress signal.

[0066] In some technologies, pipeline stress monitoring mainly uses a single sensor for measurement. The measurement process is easily interfered by external factors such as temperature drift and magnetic field fluctuations, making the monitoring accuracy unable to meet actual needs and difficult to accurately reflect the actual stress state of the pipeline. To solve the above technical problems, this application provides a buried pipeline stress monitoring system and method. The buried pipeline stress monitoring system and method are described below:

[0067] like Figure 1 FIG. 1 is a schematic diagram of the structure of the buried pipeline stress monitoring system in this application.

[0068] A first aspect of the present application provides a buried pipeline stress monitoring system, comprising:

[0069] A TMR sensor 1 is provided on the outer wall of the pipeline to be tested along the length direction of the pipeline to be tested; the TMR sensor 1 is provided with a strong magnetic mode and a weak magnetic mode; when the TMR sensor 1 is in the strong magnetic mode, the TMR sensor 1 obtains the environmental magnetic field data of the pipeline to be tested at a first preset resolution; when the TMR sensor 1 is in the weak magnetic mode, the TMR sensor 1 obtains the environmental magnetic field data of the pipeline to be tested at a second preset resolution; the range of the second preset resolution is greater than the range of the first preset resolution.

[0070] Specifically, TMR sensor 1 is configured as a dual-mode TMR sensor array: the weak magnetic mode (0.1μT resolution) monitors stress-induced micromagnetic changes; the strong magnetic mode (±50mT range) captures the magnetic field signal passing through the magnetic flux leakage detector; the signal switching logic is as follows: the sensor operates in the weak magnetic mode by default to monitor stress-related micromagnetic changes; when the magnetic field strength suddenly increases above 1mT, the sensor automatically switches to the strong magnetic mode (±50nT) to record the full waveform of the magnetic flux leakage detector.

[0071] Exemplarily, the TMR sensor 1 can optimize detection efficiency by being configured with two modes. The TMR sensor 1 defaults to weak magnetic mode, which reduces unnecessary high magnetic mode operation time, reduces power consumption, and extends the service life of the sensor. It automatically switches to strong magnetic mode: when the magnetic flux leakage detector passes, it quickly responds to changes in magnetic field intensity to ensure accurate recording of full waveform data. In weak magnetic mode: high resolution (0.1μT) is suitable for stress monitoring, ensuring accurate capture of micro-magnetic changes. In strong magnetic mode: a wide range (±50mT) is suitable for magnetic flux leakage detection, ensuring complete recording of strong magnetic field signals. It adapts to complex detection environments and can simultaneously meet the needs of stress monitoring and magnetic flux leakage detection. It is suitable for complex detection environments such as long-distance oil and gas pipelines.

[0072] like Figure 3 , which is a structural diagram of the full-bridge strain gauge group 2 in this application.

[0073] A full-bridge strain gauge group 2 is provided between the TMR sensor 1 and the pipeline to be measured; the full-bridge strain gauge group 2 is configured to obtain stress data of the pipeline to be measured; the full-bridge strain gauge group 2 includes: a first strain gauge 21 and a second strain gauge 22; the first strain gauge 21 is provided between the TMR sensor 1 and the pipeline to be measured along the axial direction of the pipeline to be measured; the second strain gauge 22 is provided between the TMR sensor 1 and the pipeline to be measured along the circumferential direction of the pipeline to be measured.

[0074] Specifically, by placing the first strain gauge 21 and the second strain gauge 22 axially and circumferentially between the TMR sensor 1 and the pipeline under test, respectively, omnidirectional stress monitoring is possible. The first strain gauge 21 and the second strain gauge 22 monitor the pipeline's stress changes in the axial and circumferential directions, respectively, forming a comprehensive stress monitoring system. This provides a more comprehensive understanding of the pipeline's stress state, providing more accurate data support for pipeline safety assessment and risk management. The axial and circumferential strain gauges, used in conjunction with the TMR sensor, can mutually verify and supplement monitoring data, improving detection accuracy and reliability and reducing the risk of misjudgment due to single sensor failure or error.

[0075] like Figure 2 , which is a schematic diagram of the structure of the distribution of various sensors (TMR sensor 1, full-bridge strain gauge group 2, demagnetization module 4) in this application on the pipeline to be tested.

[0076] The full-bridge strain gauge assembly 2 is fixed to the outside of the pipe to be measured via epoxy resin glue 23, and the first strain gauge 21 and the second strain gauge 22 are disposed within the epoxy resin glue 23. It is understood that the epoxy resin glue 23 firmly secures the full-bridge strain gauge assembly 2 to the outer wall of the pipe to be measured, preventing the full-bridge strain gauge assembly 2 from moving or falling off during the measurement process. Simultaneously, the epoxy resin glue 23 also protects the full-bridge strain gauge assembly 2 from erosion or damage from the external environment. The epoxy resin glue 23 also has excellent insulating properties, preventing short circuits between the full-bridge strain gauge assembly 2 and other conductive materials. The epoxy resin glue 23 also has certain moisture-proof properties, protecting the full-bridge strain gauge assembly 2 from the effects of humid environments.

[0077] Specifically, the full-bridge strain gauge group 2 uses epoxy resin glue 23 to encapsulate the first strain gauge 21 and the second strain gauge 22 through a patch process. Two strain gauges (axially and circumferentially arranged) are arranged directly below each group of TMR sensors 1 and are spatially aligned with the magnetic field signal.

[0078] like Figure 4 As shown in FIG, it is a flow chart of outputting target stress data in this application.

[0079] Monitoring module 3, the monitoring module 3 is connected to the TMR sensor 1 and the full-bridge strain gauge group 2, and the monitoring module 3 is configured as follows:

[0080] Obtain historical data; the historical data includes: historical environmental magnetic field data and historical stress data; if the system is installed for the first time, the historical environmental magnetic field data and historical stress data are input into the system through an external device to obtain target stress data; if the system has been installed for a certain period of time, the environmental magnetic field data and stress data obtained by the TMR sensor 1 and the full-bridge strain gauge group 2 are used as historical data.

[0081] The historical environmental magnetic field data is downsampled. Since the sampling rates of the strain gauge (10 Hz) and the TMR sensor (100 Hz) are different, data alignment and downsampling are required: the average of every 10 data points in the 100 Hz data of TMR sensor 1 is taken, or the last point is taken as the 10 Hz data, to synchronize with the strain gauge.

[0082] .

[0083] The historical environmental magnetic field data and the historical stress data are used to train a linear model to obtain an initial magnetic field stress model.

[0084] The linear model is obtained by using the linear relationship between historical environmental magnetic field data and historical stress data. The linear model is:

[0085] ;

[0086] Where, is the error value.

[0087] Solve a and b using the least squares method:

[0088] ;

[0089] Where, , ;

[0090] The model parameters of the initial magnetic field stress model are updated by using the least square method to obtain a target magnetic field stress model.

[0091] The step of using the least squares method to update the model parameters of the initial magnetic field stress model to obtain the target magnetic field stress model includes the following sub-steps:

[0092] Initialize the algorithm to obtain the parameter vector and covariance matrix;

[0093] The parameter vector is: ;

[0094] The covariance matrix is: ;

[0095] Where, ; X is the historical stress data; Y is the historical environmental magnetic field data; To set the value, it is a small positive number (usually 0.01 or smaller) used to initialize the covariance matrix; I is the unit matrix, the dimension of which is consistent with the dimension of the parameter vector; T is the transpose.

[0096] Based on the parameter vector and the covariance matrix, the model parameters of the initial magnetic field stress model are updated to compensate for temperature drift and interference; specifically, based on the parameter vector and the covariance matrix, the model parameters of the initial magnetic field stress model are updated using an update equation; the update equation is:

[0097] , , ;

[0098] Where, ; λ is the forgetting factor, which ranges from 0.95 to 1.

[0099] Based on the model parameters, a residual value is calculated; the residual value is:

[0100] ;

[0101] Where, σ is the historical stress value; H TMR，k is the historical environmental magnetic field value; a and b are model parameters; k is any positive integer.

[0102] Based on the residual value, the initial magnetic field stress model is calibrated to obtain a target magnetic field stress model.

[0103] The step of calibrating the initial magnetic field stress model based on the residual value to obtain a target magnetic field stress model includes the following sub-steps:

[0104] Calculate the average of a preset number of consecutive residual values ​​and determine whether the residual average is greater than a preset value; specifically, calculate the average of 5 consecutive residual values ​​and determine whether the residual average is greater than a preset value, which is set based on the experiment (such as 3 times the standard deviation); if not, use the following target magnetic field stress model to input the target stress data; if so, start the calibration process. The following is the calibration process:

[0105] If so, the historical environmental magnetic field data and the historical stress data are used to determine the gain parameters and offset of the initial magnetic field stress model; the output parameters (gain parameters and offset) of the initial magnetic field stress model are adjusted, and the output model after adjustment is:

[0106] ;

[0107] Where, H cal is the environmental magnetic field data;

[0108] Make the adjusted output model satisfy:

[0109] ;

[0110] Solve using the least squares method K and m , construct the system of equations:

[0111] ;

[0112] Solve the above equation K and m , the gain parameter and the offset are:

[0113] ;

[0114] Where, K is the gain parameter; m is the offset; H is the historical environmental magnetic field data;

[0115] ;

[0116] Based on the gain parameter and the offset, a target magnetic field stress model is obtained; the target magnetic field stress model is:

[0117] .

[0118] The environmental magnetic field data and the stress data are input into the target magnetic field stress model to obtain target stress data. Once the model parameters are confirmed, the target stress data can be determined by inputting the real-time environmental magnetic field data into the target magnetic field stress model. The stress data is used as historical stress data to determine whether to update the model parameters, thereby maintaining the accuracy of the target stress data at a high level.

[0119] This application provides a buried pipeline stress monitoring system. By pre-installing monitoring equipment such as a TMR sensor 1 and a full-bridge strain gauge set 2, the system continuously monitors magnetic field changes while measuring pipeline stress, directly replacing the function of a ball indicator without the need for repeated deployment. Once installed, the equipment can be reused throughout its lifecycle, significantly reducing operation and maintenance costs and improving efficiency. By combining weak magnetic field measurement with strain gauge measurements, data cross-calibration is achieved, eliminating single-sensor errors.

[0120] For example, three TMR sensors 1 are located on the same horizontal plane of the pipeline to be tested; the angle between adjacent TMR sensors 1 along the outer wall of the pipeline to be tested is 120°. This layout places three sensors every 120° along the pipeline, keeping them close to the outer wall to prevent magnetic field attenuation and enable comprehensive collection of ambient magnetic field data.

[0121] For example, after the magnetic flux leakage detector passes, the local residual magnetic field in the pipeline will change the background magnetic field environment of the weak magnetic method, resulting in distortion of subsequent stress monitoring data. This application adds a demagnetization module 4 to ensure that after the magnetic flux leakage detector passes, the background magnetic field remains at the same level as before the detector passes, thereby improving the stress monitoring accuracy of the full-bridge strain gauge group 2.

[0122] In this embodiment, the system further comprises:

[0123] The demagnetization module 4 is provided on the outer wall of the pipeline to be tested along the length direction of the pipeline to be tested; the demagnetization module 4 is configured as follows:

[0124] The demagnetization current is output in an exponential decay pattern to reduce the ambient magnetic field strength. The drive circuit of the demagnetization module 4 is based on an H-bridge topology, outputting an adjustable sine wave from 1 to 10 kHz with a peak current of 0 to 5A. The demagnetization coil is wound with multiple strands of Litz wire and arranged axially around the pipeline to cover the monitoring area. The demagnetization current parameters of the demagnetization module 4 are: 10 kHz high-frequency AC, an initial current of 5A, exponentially decaying for 10 seconds. After demagnetization, the following operations are performed: 30 seconds of magnetic field data is collected using the TMR sensor 1 to calculate a new background magnetic field value. Based on this background magnetic field value and the strain gauge data, the stress calculation baseline is reset.

[0125] The TMR sensor 1 is configured as follows:

[0126] Determine whether the change value of the ambient magnetic field intensity within the first preset time is greater than a preset value. If so, determine whether the ambient magnetic field intensity is greater than 1mT, the stress value is a constant value, and the ambient magnetic field intensity increases; if so, after the ambient magnetic field intensity decreases, send the ambient magnetic field data and the corresponding time point for obtaining the ambient magnetic field data to the setting device and turn on the demagnetization module 4 until the ambient magnetic field intensity returns to the target ambient magnetic field intensity, and then turn off the demagnetization module 4; the setting device is an electronic device that can receive the ambient magnetic field intensity data and the corresponding time point for obtaining the ambient magnetic field intensity.

[0127] Specifically, the demagnetization control logic is that when the TMR sensor 1 detects that the magnetic field strength is greater than 1mT and the strain gauge value does not change, the magnetic field strength first increases and then decreases, it is considered that a leakage magnetic internal detector has passed at this time. The device activates the demagnetization current through the rear demagnetization module 4, outputs the demagnetization current in an exponential decay mode, eliminates the magnetostrictive effect, and verifies the background magnetic field in real time through the TMR sensor 1 after demagnetization until the background magnetic field drops to the level before the leakage magnetic internal detector passes.

[0128] Specifically, when the magnetic flux leakage internal detector passes, the data from full-bridge strain gauge group 2 is stable or the stress remains essentially unchanged, but the signal from TMR sensor 1 changes dramatically, with a characteristic waveform (rising first and then falling), triggering a ball-passing event. TMR sensor 1 records the data and time at this moment and publishes it to the IoT cloud platform via a 4G wireless module, enabling construction personnel to monitor the internal detector's passing status and analyze and locate defects after internal inspection.

[0129] In this embodiment, after the step of determining whether the ambient magnetic field strength is greater than 1 mT, the stress value is a constant value, and the ambient magnetic field strength increases, the following steps are further included:

[0130] If so, the device information and time information of the TMR sensor 1 are sent to the setting device; the device information includes: the location information and model information of the TMR sensor 1. It is understood that the magnetic flux leakage internal detector is used to detect defects in the pipeline. After the detection is completed, a pipeline defect information table is generated. The pipeline defect information table shows the internal defects of the pipeline detected by the magnetic flux leakage internal detector at a specific time. Currently, the location of the pipeline defect detected by the magnetic flux leakage internal detector is roughly determined by the travel speed of the magnetic flux leakage internal detector, but the obtained location is not accurate and requires users to spend a lot of time to troubleshoot. In this application, when the TMR sensor 1 detects that the ambient magnetic field strength is greater than 1mT, the stress value is constant, and the ambient magnetic field strength increases, it means that the magnetic flux leakage internal detector has passed within the detection range of the corresponding TMR sensor 1. Therefore, by sending the device information and time information of the TMR sensor 1 to the setting device, the user can view the device information and match the time information through the setting device to find the corresponding TMR sensor 1. The exact location of the pipeline defect can be quickly confirmed by the location information and model information of the TMR sensor 1.

[0131] In this embodiment, the TMR sensor 1 is further configured as follows:

[0132] After the ambient magnetic field strength returns to the target ambient magnetic field strength, the average ambient magnetic field strength over a second preset time period is calculated; based on this average ambient magnetic field strength, the target ambient magnetic field strength is determined. After the magnetic flux leakage detector passes through, the TMR sensor 1 calculates the average ambient magnetic field strength over a second preset time period, such as collecting 30 seconds of ambient magnetic field data. The average ambient magnetic field strength of the ambient magnetic field data is then calculated and used as the new ambient magnetic field determination value to initiate the demagnetization control logic.

[0133] In this embodiment, the system further comprises:

[0134] The power supply module 5 includes a solar panel and a battery. The solar panel is configured to convert solar energy into electrical energy and store it in the battery. The battery is configured to store the electrical energy and supply power to the TMR sensor 1, the full-bridge strain gauge assembly 2, the monitoring module 3, and the demagnetization module 4. The solar panel and battery reduce power consumption of the buried pipeline stress monitoring system, and the battery's ability to store energy prevents the system from ceasing operation during power outages.

[0135] Exemplarily, the system provided in the present application also includes: a 4G wireless communication module (using the MQTT protocol to periodically send sensor-collected signal values ​​and send them to the Internet of Things server).

[0136] This application provides a buried pipeline stress monitoring system, which has the following technical effects:

[0137] 1. Multi-sensor collaborative mechanism:

[0138] The weak magnetic method and the spatial alignment layout of strain gauges (axial / circumferential orthogonal distribution) achieve dual perception and complementary calibration of stress data; the dynamic calibration algorithm eliminates temperature drift and magnetic field interference, and the combined error is smaller than the measurement error of a single sensor.

[0139] 2. Magnetic leakage detector ball passing judgment logic:

[0140] When the magnetic flux leakage internal detector passes, the strain gauge data is stable or the stress remains essentially unchanged, but the signal from TMR sensor 1 changes dramatically. The characteristic waveform (rising first, then falling) triggers a ball-passing event marker. This data and time are recorded and published to the IoT cloud platform via a 4G wireless module, enabling construction personnel to monitor the internal detector's passing status and analyze and locate defects after the internal inspection is complete.

[0141] 3. Intelligent demagnetization and data reset:

[0142] After the magnetic flux leakage detector passes through, it will automatically trigger high-frequency demagnetization (1-10kHz alternating magnetic field) to reduce background magnetic field fluctuations; after demagnetization, the magnetic field identification value will be recalibrated to maintain long-term monitoring accuracy.

[0143] Among them, compared with the separate solutions of strain gauge stress measurement, weak magnetic method and ball indicator, this application achieves a leap in measurement accuracy through joint calibration of TMR sensor and strain gauge, dynamic demagnetization and pre-buried integrated design: joint calibration suppresses temperature drift and magnetic field interference, and stress monitoring error is greatly reduced; zero repetitive cost: through lifelong reuse of pre-buried equipment, it replaces the repeated deployment of ball indicators, and the operation and maintenance efficiency is greatly improved; anti-interference closed loop: the leakage magnetic internal detector is automatically demagnetized after passing, eliminating the residual magnetic interference of the weak magnetic method; deep integration of functions: a single device simultaneously realizes stress monitoring, detector positioning and damage warning, and the efficiency of data correlation analysis is greatly improved, providing full-cycle protection for pipeline safety from "perception" to "maintenance".

[0144] A second aspect of the present application provides a buried pipeline stress monitoring method, which is applied to a buried pipeline stress monitoring system described in any of the above embodiments, comprising:

[0145] Acquiring historical data; the historical data includes: historical environmental magnetic field data and historical stress data;

[0146] Downsampling the historical environmental magnetic field data;

[0147] Using the historical environmental magnetic field data and the historical stress data to train a linear model to obtain an initial magnetic field stress model;

[0148] Using the least squares method, the model parameters of the initial magnetic field stress model are updated to obtain a target magnetic field stress model;

[0149] The environmental magnetic field data and the stress data are input into the target magnetic field stress model to obtain target stress data.

[0150] It is worth noting that the effects of the above method embodiments can be found in the effects of the above system embodiments, which will not be described in detail here.

[0151] The above specific implementation methods further explain in detail the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above are only specific implementation methods of the embodiments of the present application and are not intended to limit the scope of protection of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. A buried pipeline stress monitoring system, characterized in that: include: A TMR sensor (1), wherein the TMR sensor (1) is arranged on the outer wall of the pipeline to be measured along the length direction of the pipeline to be measured; the TMR sensor (1) is provided with a strong magnetic mode and a weak magnetic mode; when the TMR sensor (1) is in the strong magnetic mode, the TMR sensor (1) obtains the environmental magnetic field data of the pipeline to be measured at a first preset resolution; when the TMR sensor (1) is in the weak magnetic mode, the TMR sensor (1) obtains the environmental magnetic field data of the pipeline to be measured at a second preset resolution; the range of the second preset resolution is greater than the range of the first preset resolution; A full-bridge strain gauge group (2), the full-bridge strain gauge group (2) being arranged between the TMR sensor (1) and the pipeline to be measured; the full-bridge strain gauge group (2) being configured to obtain stress data of the pipeline to be measured; A monitoring module (3), the monitoring module (3) is connected to the TMR sensor (1) and the full-bridge strain gauge group (2), and the monitoring module (3) is configured as follows: Acquiring historical data; the historical data includes: historical environmental magnetic field data and historical stress data; Downsampling the historical environmental magnetic field data; Using the historical environmental magnetic field data and the historical stress data to train a linear model to obtain an initial magnetic field stress model; Using the least squares method, the model parameters of the initial magnetic field stress model are updated to obtain a target magnetic field stress model; Inputting the environmental magnetic field data and the stress data into the target magnetic field stress model to obtain target stress data; The system further comprises: A demagnetization module (4), the demagnetization module (4) being arranged on the outer wall of the pipeline to be tested along the length direction of the pipeline to be tested; the demagnetization module (4) is configured as follows: Output demagnetization current in exponential decay mode to reduce the intensity of the ambient magnetic field; The TMR sensor (1) is configured as: Determine whether the change value of the ambient magnetic field strength within the first preset time is greater than a preset value, and if so, determine whether the ambient magnetic field strength is greater than 1 mT, the stress value is a constant value, and the ambient magnetic field strength increases; If so, after the ambient magnetic field intensity decreases, the ambient magnetic field data and the corresponding time point for obtaining the ambient magnetic field data are sent to a setting device and the demagnetization module (4) is turned on until the ambient magnetic field intensity returns to the target ambient magnetic field intensity, and the demagnetization module (4) is turned off; the setting device is an electronic device capable of receiving the ambient magnetic field intensity data and the corresponding time point for obtaining the ambient magnetic field intensity.

2. A buried pipeline stress monitoring system according to claim 1, characterized in that: The number of the TMR sensors (1) located on the same horizontal plane of the pipeline to be measured is three; and the angle between adjacent TMR sensors (1) along the outer wall of the pipeline to be measured is 120°.

3. The buried pipeline stress monitoring system according to claim 1, characterized in that: The full-bridge strain gauge group (2) comprises: a first strain gauge (21) and a second strain gauge (22); the first strain gauge (21) is arranged between the TMR sensor (1) and the pipeline to be measured along the axial direction of the pipeline to be measured; the second strain gauge (22) is arranged between the TMR sensor (1) and the pipeline to be measured along the circumferential direction of the pipeline to be measured; the full-bridge strain gauge group (2) is fixed to the outside of the pipeline to be measured by epoxy resin glue (23), and the first strain gauge (21) and the second strain gauge (22) are arranged in the epoxy resin glue (23).

4. A buried pipeline stress monitoring system according to claim 1, characterized in that: After the step of determining whether the environmental magnetic field strength is greater than 1 mT, the stress value is a constant value, and the environmental magnetic field strength increases, the method further includes: If yes, the device information and time information of the TMR sensor (1) are sent to the setting device; the device information includes: the location information and model information of the TMR sensor (1).

5. The buried pipeline stress monitoring system according to claim 1, characterized in that: The TMR sensor (1) is further configured to: After the ambient magnetic field strength returns to the target ambient magnetic field strength, calculating an average value of the ambient magnetic field strength within a second preset time; Based on the average value of the ambient magnetic field strength, a target ambient magnetic field strength is determined.

6. The buried pipeline stress monitoring system according to claim 1, characterized in that: The system further comprises: A power supply module (5), the power supply module (5) comprising: a solar panel and a storage battery; the solar panel is configured to convert solar energy into electrical energy and store it in the storage battery; the storage battery is configured to store electrical energy and supply power to the TMR sensor (1), the full-bridge strain gauge group (2), the monitoring module (3), and the demagnetization module (4).

7. The buried pipeline stress monitoring system according to claim 1, characterized in that: The step of using the least squares method to update the model parameters of the initial magnetic field stress model to obtain the target magnetic field stress model includes: Initialize the algorithm to obtain the parameter vector and covariance matrix; The parameter vector is: ; The covariance matrix is: ; Where, ; X is the historical stress data; Y is the historical environmental magnetic field data; To set the value; I is the identity matrix, the dimension of which is consistent with the dimension of the parameter vector; T is the transpose; Based on the parameter vector and the covariance matrix, updating model parameters of the initial magnetic field stress model; Based on the model parameters, a residual value is calculated; the residual value is: ； Where, σ is the historical stress value; H TMR，k is the historical environmental magnetic field value; a and b are model parameters; k is any positive integer; Based on the residual value, the initial magnetic field stress model is calibrated to obtain a target magnetic field stress model.

8. The buried pipeline stress monitoring system according to claim 7, characterized in that: The step of calibrating the initial magnetic field stress model based on the residual value to obtain a target magnetic field stress model includes: Calculating an average value of a preset number of consecutive residual values, and determining whether the average value of the residual values ​​is greater than a preset value; If yes, then the historical environmental magnetic field data and the historical stress data are used to determine the gain parameter and offset of the initial magnetic field stress model; the gain parameter and the offset are: ； Where, K is the gain parameter; m is the offset; H is the historical environmental magnetic field data; ; Based on the gain parameter and the offset, a target magnetic field stress model is obtained; the target magnetic field stress model is: 。 9. A buried pipeline stress monitoring method, applied to a buried pipeline stress monitoring system according to any one of claims 1 to 8, characterized in that: include: Acquiring historical data; the historical data includes: historical environmental magnetic field data and historical stress data; Downsampling the historical environmental magnetic field data; Using the historical environmental magnetic field data and the historical stress data to train a linear model to obtain an initial magnetic field stress model; Using the least squares method, the model parameters of the initial magnetic field stress model are updated to obtain a target magnetic field stress model; Inputting the environmental magnetic field data and the stress data into the target magnetic field stress model to obtain target stress data; The method further comprises: Determine whether the change value of the ambient magnetic field strength within the first preset time is greater than a preset value, and if so, determine whether the ambient magnetic field strength is greater than 1 mT, the stress value is a constant value, and the ambient magnetic field strength increases; If so, after the ambient magnetic field strength decreases, the ambient magnetic field data and the corresponding time point for obtaining the ambient magnetic field data are sent to the setting device and the demagnetization module is turned on until the ambient magnetic field strength recovers to the target ambient magnetic field strength, and then the demagnetization module is turned off; the setting device is an electronic device that can receive the ambient magnetic field strength data and the corresponding time point for obtaining the ambient magnetic field strength; the demagnetization module is arranged on the outer wall of the pipeline to be tested along the length direction of the pipeline to be tested; the demagnetization module is configured to: output a demagnetization current in an exponential decay mode to reduce the ambient magnetic field strength.

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