Buried pipeline stress monitoring system and method

Abstract

The invention provides a buried pipeline stress monitoring system and method, and relates to the technical field of pipeline monitoring, and the system comprises a TMR sensor; the full-bridge strain gauge group is configured to acquire stress data of the to-be-measured pipeline; the monitoring module is configured to obtain historical data; training the linear model by using the historical environment magnetic field data and the historical stress data to obtain an initial magnetic field stress model; updating model parameters of the initial magnetic field stress model by using a least square method to obtain a target magnetic field stress model; and inputting the environment magnetic field data and the stress data into the target magnetic field stress model to obtain target stress data so as to solve the problems that a single sensor is mainly adopted for measurement in the current pipeline stress monitoring process, and the measurement process is easily interfered by factors such as temperature drift and magnetic field fluctuation, so that the monitoring precision cannot meet the actual demand, and the measurement accuracy is poor. And the real stress state of the pipeline is difficult to reflect.

Description

Technical Field

[0001] This application relates to the technical field of pipeline monitoring, and particularly to a buried pipeline stress monitoring system and method. Background Art

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

[0003] For pipeline stress monitoring, single sensors such as strain gauges or magnetic measurement methods are mainly used in the prior art. Strain gauges reflect pipeline stress by measuring the strain changes on the pipeline surface, while magnetic measurement methods monitor stress based on the principle of magnetic field change. In terms of real-time monitoring of the passing state of the magnetic flux leakage internal detector, a Mark box is mainly used to monitor the passing state of the magnetic flux leakage internal detector at present. The Mark box is arranged along the pipeline. When the magnetic flux leakage internal detector passes, the Mark box can generate corresponding signals, so as to realize the real-time monitoring of the passing state of the magnetic flux leakage internal detector.

[0004] However, single sensors are easily interfered by external factors such as temperature drift and magnetic field fluctuation. During long-term monitoring, the errors caused by these interference factors will gradually accumulate, making the monitoring accuracy unable to meet the actual requirements and difficult to accurately reflect the true stress state of the pipeline. In terms of real-time monitoring of the passing state of the magnetic flux leakage internal detector, although the Mark box can realize the monitoring function, a large number of Mark boxes need to be rearranged along the pipeline before each detection. For example, a Mark box needs to be arranged every 1-2 kilometers. For a 100-kilometer pipeline, the single deployment cost is extremely high, and the arrangement is extremely time-consuming. The deployment and recovery work are time-consuming and laborious. Summary of the Invention

[0005] This application provides a buried pipeline stress monitoring system and method to solve the technical problem that in the current pipeline stress monitoring process, a single sensor is mainly used for measurement, and it is easily interfered by external factors such as temperature drift and magnetic field fluctuation during the measurement process, resulting in the monitoring accuracy being unable to meet the actual requirements and difficult to accurately reflect the true stress state of the pipeline.

[0006] In the first aspect of this application, a buried pipeline stress monitoring system is provided, including: A TMR sensor, the TMR sensor 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 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 acquires the environmental magnetic field data of the pipeline to be measured with 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 with 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 is arranged between the TMR sensor and the pipeline to be measured; the full-bridge strain gauge group is configured to acquire the stress data of the pipeline to be measured; A monitoring module, the monitoring module is connected to the TMR sensor and the full-bridge strain gauge group, and the monitoring module is configured to: Acquire historical data; the historical data includes: historical environmental magnetic field data and historical stress data; Perform downsampling processing on the historical environmental magnetic field data; Use the historical environmental magnetic field data and the historical stress data to train a linear model to obtain an initial magnetic field stress model; Use the least squares method to update the model parameters of the initial magnetic field stress model 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.

[0007] In some embodiments, the number of TMR sensors located on the same horizontal plane of the pipeline to be measured is 3; the angle between adjacent TMR sensors along the outer wall of the pipeline to be measured is 120°.

[0008] 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.

[0009] In some embodiments, the system further includes: A demagnetization module, the demagnetization module is arranged on the outer wall of the pipeline to be measured along the length direction of the pipeline to be measured; the demagnetization module is configured to: Output a demagnetization current according to an exponential decay mode to reduce the environmental magnetic field intensity; The TMR sensor is configured to: 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 1 mT, 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 time point corresponding to obtaining the ambient magnetic field data to a set device and activate the demagnetization module until the ambient magnetic field intensity returns to the target ambient magnetic field intensity, and then turn off the demagnetization module; the set device is an electronic device capable of receiving the ambient magnetic field intensity data and the time point corresponding to obtaining the ambient magnetic field intensity.

[0010] In some embodiments, after the step of determining whether the ambient magnetic field intensity is greater than 1 mT, the stress value is a constant value, and the ambient magnetic field intensity increases, the following is further included: If so, send the device information and time information of the TMR sensor to a set device; the device information includes: the position information and model information of the TMR sensor.

[0011] In some embodiments, the TMR sensor is further configured to: After the ambient magnetic field intensity returns to the target ambient magnetic field intensity, calculate the average value of the ambient magnetic field intensity within the second preset time. Based on the average value of the ambient magnetic field intensity, determine the target ambient magnetic field intensity.

[0012] In some embodiments, the system further includes: A power supply module, the power supply module includes: 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, the full-bridge strain gauge group, the monitoring module, and the demagnetization module.

[0013] In some embodiments, 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: Perform algorithm initialization processing to obtain a parameter vector and a covariance matrix. The parameter vector is: ; The covariance matrix is: ; Wherein, ; X is historical stress data; Y is historical ambient magnetic field data; is a set value; I is an identity matrix, the dimension of which is the same as the dimension of the parameter vector; T is the transpose; Update the model parameters of the initial magnetic field stress model based on the parameter vector and the covariance matrix; Calculate a residual value based on the model parameters; the residual value is: ; wherein, σ is the historical stress value; H TMR，k is the historical environmental magnetic field value; a and b are the model parameters; k is any positive integer; Calibrate the initial magnetic field stress model based on the residual value to obtain a target magnetic field stress model.

[0014] 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: Calculate the average value of a continuous preset number of residual values, and determine whether the average value of the residual values is greater than a preset value; If so, use the historical environmental magnetic field data and the historical stress data to determine the gain parameter and the offset of the initial magnetic field stress model; the gain parameter and the offset are: ; wherein, K is the gain parameter; m is the offset; H is the historical environmental magnetic field data; ; Obtain a target magnetic field stress model based on the gain parameter and the offset; the target magnetic field stress model is: .

[0015] A second aspect of the present application provides a method for monitoring the stress of a buried pipeline, which is applied to a system for monitoring the stress of a buried pipeline according to any one of the above first aspects, and includes: Obtain historical data; the historical data includes: historical environmental magnetic field data and historical stress data; Perform downsampling processing on 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.

[0016] The present application provides a buried pipeline stress monitoring system and method. The system includes: a TMR sensor disposed on the outer wall of a pipeline to be measured along the length direction of the pipeline to be measured; 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 acquires environmental magnetic field data of the pipeline to be measured with a first preset resolution; when the TMR sensor is in the weak magnetic mode, the TMR sensor acquires environmental magnetic field data of the pipeline to be measured with 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 disposed between the TMR sensor and the pipeline to be measured; the full-bridge strain gauge group is configured to acquire stress data of the pipeline to be measured; a monitoring module connected to the TMR sensor and the full-bridge strain gauge group, and the monitoring module is configured to: acquire historical data; the historical data includes: historical environmental magnetic field data and historical stress data; perform downsampling processing on 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 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 the pipeline stress monitoring process, realize data cross-calibration, and eliminate measurement errors of a single sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0018] Figure 1 is a schematic structural diagram of the buried pipeline stress monitoring system in the present application; Figure 2 is a schematic structural diagram of the distribution of each sensor on the pipeline to be measured in the present application; Figure 3 is a schematic structural diagram of the full-bridge strain gauge group in the present application; Figure 4 is a flow chart of outputting target stress data in the present application.

[0019] Description of the reference numerals: 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 OF THE EMBODIMENTS

[0020] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0021] For example, the following problems exist in the current pipeline stress monitoring process: 1. Insufficient monitoring accuracy of pipeline stress (pressure inside and outside the pipeline): Currently, the single sensor (strain gauge or magnetic measurement method) is easily affected by temperature drift, magnetic field fluctuations, etc., and long-term monitoring errors gradually accumulate.

[0022] 2. The problem of repeated deployment of real-time monitoring of the ball passing status of the magnetic leakage internal detector (detection of defects in the pipeline, determined by magnetic field, mobile detection): At present, the passing status of the magnetic leakage internal detector is monitored by the ball passing indicator, but before each detection, a large number of ball passing indicators need to be rearranged along the pipeline (such as one every 1-2 kilometers), and the deployment and recovery of the ball passing indicators are time-consuming and labor-intensive (taking a 100-kilometer pipeline as an example, the cost of a single deployment is high and it takes 1-2 days). Among them, the ball passing indicator is usually buried only 0.3-0.5 meters deep, and is easily damaged by surface vehicles, farming activities, etc., with a false trigger rate of up to 15%-20%. In order to avoid damaging the pipeline, the ball passing indicator is arranged more than 1 meter away from the outer wall of the pipeline, resulting in attenuation of the detection signal (sensitivity decreases by more than 50%), making it difficult to capture weak magnetic field changes, relying on the experience of construction personnel, and there are problems such as layout position offset and angle tilt, resulting in relatively large positioning errors; environmental magnetic field interference, surface vehicle motors, high-voltage lines, etc. generate low-frequency magnetic field noise that can lead to misjudgment.

[0023] 3. The destructive effect of residual magnetic field on long-term monitoring: The weak magnetic method needs to measure the micromagnetic changes caused by stress (on the order of 0.1-1μT) under a constant background magnetic field, but the residual magnetic field (up to 10mT) after the leakage magnetic detector passes through will completely mask the stress signal.

[0024] In some technologies, a single sensor is mainly used for measurement during pipeline stress monitoring, which is easily disturbed by external factors such as temperature drift and magnetic field fluctuations during the measurement process, so that the monitoring accuracy cannot meet the actual needs and it is difficult to accurately reflect the real stress state of the pipeline. In order to solve the above technical problems, the present application provides a buried pipeline stress monitoring system and method. The buried pipeline stress monitoring system and method are described below: like Figure 1 As shown, it is a structural schematic diagram of the buried pipeline stress monitoring system in this application.

[0025] In the first aspect of the present application, a buried pipeline stress monitoring system is provided, including: A TMR sensor 1, which 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 acquires the ambient magnetic field data of the pipeline to be measured with a first preset resolution; when the TMR sensor 1 is in the weak magnetic mode, the TMR sensor 1 acquires the ambient magnetic field data of the pipeline to be measured with a second preset resolution; the range of the second preset resolution is greater than the range of the first preset resolution.

[0026] Specifically, the TMR sensor 1 is set as a dual-mode TMR sensor array: the weak magnetic mode (0.1 μT resolution) monitors the micro-magnetic changes caused by stress; the strong magnetic mode (±50 mT range) captures the magnetic field signals passed by the magnetic flux leakage detector; among them, the signal switching logic is: by default, it works in the weak magnetic mode to monitor the stress-related micro-magnetic changes. When the magnetic field intensity suddenly increases by more than 1 mT, it automatically switches to the strong magnetic mode (±50 nT) to record the full waveform of the magnetic flux leakage detector.

[0027] Exemplarily, by setting two modes, the TMR sensor 1 can optimize the detection efficiency. The TMR sensor 1 defaults to the weak magnetic mode: reducing the unnecessary operation time of the strong magnetic mode, reducing power consumption, and extending the service life of the sensor. Automatically switch to the strong magnetic mode: quickly respond to the change of the magnetic field intensity when the magnetic flux leakage detector passes, and ensure the accurate recording of the full waveform data. In the weak magnetic mode: high resolution (0.1 μT) is suitable for stress monitoring to ensure the precise capture of micro-magnetic changes. In the strong magnetic mode: wide range (±50 mT) is suitable for magnetic flux leakage detection to ensure the complete recording of strong magnetic field signals. It can adapt to complex detection environments, can meet the requirements of stress monitoring and magnetic flux leakage detection at the same time, and is suitable for complex detection environments such as long-distance oil and gas pipelines.

[0028] As Figure 3 shown, it is a schematic structural diagram of the full-bridge strain gauge group 2 in the present application.

[0029] A full-bridge strain gauge group 2, which is arranged between the TMR sensor 1 and the pipeline to be measured; the full-bridge strain gauge group 2 is configured to acquire the 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 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.

[0030] Specifically, by axially and circumferentially arranging the first strain gauge 21 and the second strain gauge 22 between the TMR sensor 1 and the pipeline to be measured, stress monitoring can be carried out in all directions. The first strain gauge 21 and the second strain gauge 22 respectively monitor the stress changes of the pipeline in the axial and circumferential directions, forming an all-round stress monitoring system. It can more comprehensively understand the stress state of the pipeline and provide more accurate data support for the safety assessment and risk management of the pipeline. The axial and circumferential strain gauges are used in combination with the TMR sensor, which can mutually verify and supplement the monitoring data; improve the detection accuracy and reliability, and reduce the risk of misjudgment caused by the failure or error of a single sensor.

[0031] As Figure 2 shown, it is a schematic structural diagram of the distribution of each sensor (TMR sensor 1, full-bridge strain gauge group 2, demagnetization module 4) on the pipeline to be measured in this application.

[0032] The full-bridge strain gauge group 2 is fixed to the outside of the pipeline to be measured through epoxy resin glue 23, and the first strain gauge 21 and the second strain gauge 22 are arranged in the epoxy resin glue 23. It can be understood that the epoxy resin glue 23 firmly fixes the full-bridge strain gauge group 2 on the outer wall of the pipeline to be measured, preventing the full-bridge strain gauge group 2 from moving or falling off during the measurement process; at the same time, the epoxy resin glue 23 also plays a role in protecting the full-bridge strain gauge group 2, preventing it from being eroded or damaged by the external environment. The epoxy resin glue 23 also has excellent insulation performance, which can prevent short circuits between the full-bridge strain gauge group 2 and other conductive substances. The epoxy resin glue 23 also has certain moisture-proof performance, which can protect the full-bridge strain gauge group 2 from the influence of a humid environment.

[0033] Specifically, the full-bridge strain gauge group 2 encapsulates the first strain gauge 21 and the second strain gauge 22 with epoxy resin glue 23 through a chip bonding process. Two strain gauges (axially arranged and circumferentially arranged) are arranged directly below each TMR sensor 1, and are spatially aligned with the magnetic field signal.

[0034] As Figure 4 shown, it is a flowchart of outputting the target stress data in this application.

[0035] The 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 to: Obtain historical data; the historical data includes: historical environmental magnetic field data and historical stress data; if the system is in the first installation state, the historical environmental magnetic field data and historical stress data are input into the system through an external device for obtaining the 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.

[0036] Downsample the historical environmental magnetic field data; since the sampling rates of the strain gauge (10 Hz) and the TMR sensor (100 Hz) are different, data alignment and downsampling are required: take the average of every 10 data points or the last point from the 100 Hz data of TMR sensor 1 as the 10 Hz data to synchronize with the strain gauge.

[0037] 。

[0038] Train a linear model using the historical environmental magnetic field data and the historical stress data to obtain an initial magnetic field stress model.

[0039] Among them, the linear model is obtained from the linear relationship between the historical environmental magnetic field data and the historical stress data, and the linear model is: ; In the formula, is the error value.

[0040] Solve for a and b using the least squares method: ; In the formula, , ; Using the least squares method, update the model parameters of the initial magnetic field stress model to obtain the target magnetic field stress model.

[0041] 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: Perform algorithm initialization processing to obtain a parameter vector and a covariance matrix; The parameter vector is: ; The covariance matrix is: ; In the formula, ;X is the historical stress data; Y is the historical environmental magnetic field data; is a set value, a small positive number (usually taken as 0.01 or smaller), used to initialize the covariance matrix; I is the identity matrix, with the same dimension as the parameter vector; T is the transpose.

[0042] Based on the parameter vector and the covariance matrix, update the model parameters of the initial magnetic field stress model to compensate for temperature drift and interference; specifically, based on the parameter vector and the covariance matrix, use the update equation to update the model parameters of the initial magnetic field stress model; the update equation is: , , ; Wherein, ; λ is the forgetting factor, and its value range is within the interval from 0.95 to 1.

[0043] Based on the model parameters, calculate the residual value; the residual value is: ; Wherein, 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.

[0044] Based on the residual value, calibrate the initial magnetic field stress model to obtain the target magnetic field stress model.

[0045] The step of calibrating the initial magnetic field stress model based on the residual value to obtain the target magnetic field stress model includes the following sub-steps: Calculate the average value of a continuous preset number of residual values, and determine whether the average value of the residual values is greater than a preset value; specifically, calculate the average value of 5 consecutive residual values, and determine whether the average value of the residual values is greater than the preset value, and the preset value is set according to experiments (such as 3 times the standard deviation); if not, then input the target stress data using the following target magnetic field stress model; if so, then start the calibration process. The following is the calibration process: If so, then use the historical environmental magnetic field data and the historical stress data to determine the gain parameter and offset of the initial magnetic field stress model; adjust the output parameters (gain parameter and offset) of the initial magnetic field stress model, and the adjusted output model is: ; Wherein, H cal is the environmental magnetic field data; Make the adjusted output model satisfy: ; Use the least squares method to solve K and m , and construct a system of equations: ; Based on the above equations, solve K and m , and the gain parameter and the offset are: ; Wherein, K is the gain parameter; mis the offset; H is the historical ambient 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: .

[0046] Input the ambient magnetic field data and the stress data into the target magnetic field stress model to obtain the target stress data. After the model parameters are confirmed, by inputting the ambient magnetic field data obtained in real time into the target magnetic field stress model, the target stress data can be determined; wherein, the stress data is used as historical stress data to determine whether to update the model parameters, so that the accuracy of the target stress data is always at a relatively high value.

[0047] This application provides a buried pipeline stress monitoring system. By pre-burying monitoring devices such as TMR sensors 1 and full-bridge strain gauge groups 2, while measuring the pipeline stress, it continuously monitors the magnetic field change, directly replacing the function of the sphere passing indicator, without the need for repeated deployment. The device can be reused for life after one-time installation, significantly reducing the operation and maintenance cost and improving the efficiency. Through the combined measurement of the weak magnetic method and the strain gauge, data cross-calibration is realized to eliminate the error of a single sensor.

[0048] Exemplarily, the number of the TMR sensors 1 on the same horizontal plane of the pipeline to be measured is 3; the angle between adjacent TMR sensors 1 along the outer wall of the pipeline to be measured is 120°. The layout method is to arrange a group of sensors every 120° along the pipeline (a total of 3 groups), close to the outer wall of the pipeline, avoiding magnetic field attenuation, and being able to comprehensively collect the ambient magnetic field data.

[0049] Exemplarily, after the magnetic flux leakage internal detector passes through, the local residual magnetic field of the pipeline will change the background magnetic field environment of the weak magnetic method, resulting in distortion of the subsequent stress monitoring data. This application improves the stress monitoring accuracy of the full-bridge strain gauge group 2 by adding a demagnetization module 4 to ensure that the background magnetic field remains at the same level as before the detector passes through after the magnetic flux leakage internal detector passes through.

[0050] In this embodiment, the system further includes: A demagnetization module 4, the demagnetization module 4 is arranged on the outer wall of the pipeline to be measured along the length direction of the pipeline to be measured; the demagnetization module 4 is configured to: Output the demagnetization current according to the exponential decay mode to reduce the ambient magnetic field strength; the drive circuit of the demagnetization module 4 is based on the H-bridge topology and outputs an adjustable sine wave of 1~10KHz with a peak current of 0~5A. The demagnetization coil is wound with multiple strands of litz wire and arranged axially around the pipeline to cover the monitoring area. Among them, the demagnetization current parameters of the demagnetization module 4 are: 10KHz high-frequency alternating current, an initial current of 5A, decaying according to an exponential curve, lasting for 10s. After demagnetization, the following operations are performed: Re-collect the magnetic field data for 30s through the TMR sensor 1, calculate the new background magnetic field value, and reset the stress calculation reference based on the background magnetic field value and the strain gauge data.

[0051] The TMR sensor 1 is configured to: Judge whether the change value of the ambient magnetic field strength within the first preset time is greater than the preset value. If so, judge whether the ambient magnetic field strength is greater than 1mT, the stress value is a constant value, and the ambient magnetic field strength increases. If so, after the ambient magnetic field strength decreases, send the ambient magnetic field data and the time point corresponding to obtaining the ambient magnetic field data to the setting device and turn on the demagnetization module 4 until the ambient magnetic field strength returns to the target ambient magnetic field strength, and then turn off the demagnetization module 4; the setting device is an electronic device capable of receiving the ambient magnetic field strength data and the time point corresponding to obtaining the ambient magnetic field strength.

[0052] 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, and the magnetic field strength first increases and then decreases, it is considered that there is a magnetic flux leakage internal detector passing through at this time. After the device passes, the demagnetization module 4 activates the demagnetization current, outputs the demagnetization current according to the exponential decay mode, eliminates the magneto-optical effect, and after demagnetization, the background magnetic field is verified in real time through the TMR sensor 1 until the background magnetic field drops to the level before the magnetic flux leakage internal detector passes through.

[0053] Specifically, when the magnetic flux leakage internal detector passes through, the data of the full-bridge strain gauge group 2 is stable or the stress basically does not change, but the signal of the TMR sensor 1 changes violently, and the characteristic waveform (first rising and then falling) triggers the ball passing event mark. The TMR sensor 1 records the data and time point at this time and publishes them to the Internet of Things cloud platform through the 4G wireless module, so as to facilitate the construction personnel to understand the passing state of the internal detector and the analysis and positioning of defects after the internal detection is completed.

[0054] In this embodiment, after the step of judging whether the ambient magnetic field strength is greater than 1mT, the stress value is a constant value, and the ambient magnetic field strength increases, the following steps are further included: If so, send the device information and time information of the TMR sensor 1 to the set device; the device information includes: the position information and model information of the TMR sensor 1. It can be understood that the magnetic flux leakage internal detector is used to detect whether there are defects in the pipeline. After the detection is completed, a pipeline defect information table will be generated. The pipeline defect information table will show that the magnetic flux leakage internal detector has found internal defects in the pipeline at a specific time. Currently, the position where the magnetic flux leakage internal detector detects pipeline defects is roughly judged by the traveling speed of the magnetic flux leakage internal detector, but the obtained position is not accurate, and users still need to spend a lot of time for investigation. After the TMR sensor 1 detects that the environmental magnetic field intensity is greater than 1 mT, the stress value is a constant value, and the environmental magnetic field intensity increases, it means that the magnetic flux leakage internal detector has passed through 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 set device, the user can view the device information through the set device and correspond the time information to find the corresponding TMR sensor 1. Through the position information and model information of the TMR sensor 1, the accurate position where the pipeline has defects can be quickly confirmed.

[0055] In this embodiment, the TMR sensor 1 is further configured to: After the environmental magnetic field intensity returns to the target environmental magnetic field intensity, calculate the average value of the environmental magnetic field intensity within a second preset time; based on the average value of the environmental magnetic field intensity, determine the target environmental magnetic field intensity. After the magnetic flux leakage internal detector passes by, the TMR sensor 1 calculates the average value of the environmental magnetic field intensity within a second preset time, such as collecting environmental magnetic field data for 30 s, so as to calculate the average value of the environmental magnetic field intensity of the environmental magnetic field data, and thus start the demagnetization control logic as a new environmental magnetic field determination value.

[0056] In this embodiment, the system further includes: A power supply module 5, the power supply module 5 includes: 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. Through the setting of the solar panel and the storage battery, the power consumption of the buried pipeline stress monitoring system is reduced, and the storage battery can store electricity to prevent the buried pipeline stress monitoring system from stopping running during a power outage.

[0057] Exemplarily, the system provided by the present application further includes: a 4G wireless communication module (using the MQTT protocol, periodically sending the signal values collected by the sensor and sending them to the Internet of Things server).

[0058] The present application provides a buried pipeline stress monitoring system, having the following technical effects: 1. Multi-sensor collaborative mechanism: The weak magnetic method and the strain gauge are spatially aligned (orthogonal distribution in the axial / circumferential direction) to achieve dual sensing and complementary calibration of stress data; a dynamic calibration algorithm is used to eliminate temperature drift and magnetic field interference, and the combined error is less than the measurement error of a single sensor.

[0059] 2. Magnetic flux leakage detector passing ball judgment logic: When the magnetic flux leakage internal detector passes through, the strain gauge data is stable or the stress basically does not change, but the signal of TMR sensor 1 changes violently, and the characteristic waveform (rising first and then falling) triggers the passing ball event marker. Record the data and time point at this time and publish them to the Internet of Things cloud platform through the 4G wireless module, so as to facilitate the construction personnel to understand the passing state of the internal detector and the analysis and positioning of defects after the internal detection is completed.

[0060] 3. Intelligent demagnetization and data reset: After the magnetic flux leakage internal detector passes through, it automatically triggers high-frequency demagnetization (1-10 kHz alternating magnetic field) to reduce the background magnetic field fluctuation; after demagnetization, the magnetic field identification value is recalibrated to maintain long-term monitoring accuracy.

[0061] Among them, compared with the separate solutions of strain gauges for stress measurement, weak magnetic methods, and passing ball indicators, the present application realizes a leap in measurement accuracy through the combined calibration of TMR sensors and strain gauges, dynamic demagnetization, and embedded integrated design: combined calibration suppresses temperature drift and magnetic field interference, and the stress monitoring error is greatly reduced; zero repeated cost: the embedded device is reused for life, replacing the repeated deployment of passing ball indicators, and the operation and maintenance efficiency is greatly improved; anti-interference closed loop: automatic demagnetization after the magnetic flux leakage internal detector passes through to eliminate the residual magnetic interference of the weak magnetic method; deep integration of functions: a single device synchronously realizes stress monitoring, detector positioning, and damage warning, and the data correlation analysis efficiency is greatly improved, providing full-cycle protection for pipeline safety from "perception" to "maintenance".

[0062] The 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, and includes: Obtain historical data; the historical data includes: historical environmental magnetic field data and historical stress data; Perform downsampling processing on the historical environmental magnetic field data; Use the historical environmental magnetic field data and the historical stress data to train a linear model to obtain an initial magnetic field stress model; Use the least squares method to update the model parameters of the initial magnetic field stress model 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.

[0063] It should be noted that for the effects of the above method embodiments, reference can be made to the effects of the above system embodiments, which will not be elaborated here.

[0064] The above specific embodiments have further elaborated on the objectives, technical solutions, and beneficial effects of the embodiments of the present application. It should be understood that the above are only the specific embodiments of the embodiments of the present application and are not used to limit the protection scope 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 shall be included within the protection scope of the embodiments of the present application.

Claims

1. An underground pipeline stress monitoring system, characterized in that, Including: A TMR sensor (1), which 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) acquires the environmental magnetic field data of the pipeline to be measured with a first preset resolution; when the TMR sensor (1) is in the weak magnetic mode, the TMR sensor (1) acquires the environmental magnetic field data of the pipeline to be measured with 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), which is arranged between the TMR sensor (1) and the pipeline to be measured; the full-bridge strain gauge group (2) is configured to acquire the stress data of the pipeline to be measured; A monitoring module (3), which is connected to the TMR sensor (1) and the full-bridge strain gauge group (2), and the monitoring module (3) is configured to: Acquire historical data; the historical data includes: historical environmental magnetic field data and historical stress data; Perform downsampling processing on 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.

2. The stress monitoring system for buried pipelines 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 3; 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) includes: 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 through an epoxy resin adhesive (23), and the first strain gauge (21) and the second strain gauge (22) are arranged in the epoxy resin adhesive (23).

4. The stress monitoring system for buried pipelines according to claim 1, characterized in that, The system further includes: A demagnetization module (4), which is arranged on the outer wall of the pipeline to be measured along the length direction of the pipeline to be measured; the demagnetization module (4) is configured to: Output a demagnetization current in an exponential decay mode to reduce the environmental magnetic field intensity; The TMR sensor (1) is configured to: Judge whether the change value of the environmental magnetic field intensity within the first preset time is greater than a preset value, and if so, judge whether the environmental magnetic field intensity is greater than 1 mT, the stress value is a constant value, and the environmental magnetic field intensity increases; If so, after the ambient magnetic field strength decreases, send the ambient magnetic field data and the corresponding time point when the ambient magnetic field data is obtained to the set device and turn on the demagnetization module (4) until the ambient magnetic field strength returns to the target ambient magnetic field strength, and then turn off the demagnetization module (4); the set device is an electronic device capable of receiving the ambient magnetic field strength data and the corresponding time point when the ambient magnetic field strength is obtained.

5. The buried pipeline stress monitoring system according to claim 4, characterized in that, 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: If so, send the device information and time information of the TMR sensor (1) to the set device; the device information includes: the position information and model information of the TMR sensor (1).

6. The stress monitoring system for buried pipelines according to claim 4, wherein The TMR sensor (1) is further configured to: After the ambient magnetic field strength returns to the target ambient magnetic field strength, calculate the average value of the ambient magnetic field strength within a second preset time. Based on the average value of the ambient magnetic field strength, determine the target ambient magnetic field strength.

7. A buried pipeline stress monitoring system according to claim 1, characterized in that, The system further includes: A power supply module (5), the power supply module (5) includes: 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).

8. A buried pipeline stress monitoring system according to claim 1, characterized in that, The step of updating the model parameters of the initial magnetic field stress model by using the least squares method to obtain the target magnetic field stress model includes: Performing algorithm initialization processing to obtain a parameter vector and a covariance matrix. The parameter vector is as follows: ; The covariance matrix is as follows: ; In the formula, ; X is historical stress data; Y is historical environmental magnetic field data; is a set value; I is an identity matrix with a dimension consistent with that of the parameter vector; T is the transpose; Based on the parameter vector and the covariance matrix, update the model parameters of the initial magnetic field stress model. Based on the model parameters, calculate a residual value; the residual value is: ； In the formula, 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, calibrate the initial magnetic field stress model to obtain the target magnetic field stress model.

9. The buried pipeline stress monitoring system according to claim 8, wherein, The step of calibrating the initial magnetic field stress model based on the residual value to obtain the target magnetic field stress model includes: Calculate the average value of a continuous preset number of residual values and determine whether the average value of the residual values is greater than a preset value. If so, use the historical ambient magnetic field data and the historical stress data to determine the gain parameter and offset of the initial magnetic field stress model; the gain parameter and the offset are: ； In the formula, K is the gain parameter; m is the offset; H is the historical ambient magnetic field data; ; Based on the gain parameter and the offset, obtain the target magnetic field stress model; the target magnetic field stress model is: 。 10. A method for monitoring the stress of buried pipelines, which is applied to a buried pipeline stress monitoring system described in any one of the above claims 1 to 9, and is characterized in that, Including: Obtain historical data; the historical data includes: historical ambient magnetic field data and historical stress data. Perform downsampling processing on the historical ambient magnetic field data. Use the historical ambient magnetic field data and the historical stress data to train a linear model to obtain an initial magnetic field stress model. Use the least squares method to update the model parameters of the initial magnetic field stress model to obtain the target magnetic field stress model. Input the ambient magnetic field data and the stress data into the target magnetic field stress model to obtain the target stress data.

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