A natural gas pipeline stress detection method, device, equipment and storage medium

Abstract

The application discloses a natural gas pipeline stress detection method, device, equipment and storage medium, and belongs to the technical field of natural gas pipeline safety monitoring. The method comprises the following steps: establishing a saturated magnetic field in a target detection section of a natural gas pipeline; collecting magnetic flux density, spatial difference signals, internal pressure and temperature in the saturated magnetic field; correcting and interference suppressing the collected data; inputting the processed data into a magnetic-force coupling model to establish the correlation between the pipeline stress and the magnetic flux density and the magnetization parameters; sequentially compensating the magnetic flux density based on the temperature and the strain rate; and finally combining the correlation to construct an inversion cost function, inputting the compensated data to obtain the pipeline stress distribution of the target section. The application does not need to invade the inside of the pipeline, guarantees the stable correlation between the magnetic characteristics and the stress through the saturated magnetic field, eliminates the environmental and dynamic factor interference through double compensation processing, significantly improves the stress detection accuracy, and can provide data support for the safe operation and risk early warning of the natural gas pipeline.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of natural gas pipeline safety monitoring, and in particular to a natural gas pipeline stress detection method, device, equipment and storage medium. BACKGROUND

[0002] With the continuous growth of natural gas energy demand, the construction scale of long-distance natural gas pipelines is expanding. As a key infrastructure for energy transportation, pipelines are subjected to internal pressure, medium scouring, environmental temperature changes and soil stress for a long time, which can easily cause local stress concentration or stress anomalies. If not detected in a timely and accurate manner, it may cause pipeline leakage, rupture and other safety accidents. Therefore, it is crucial to efficiently and accurately detect the stress of natural gas pipelines.

[0003] However, natural gas pipelines are mostly buried or overhead laid, and the running environment is complex. Traditional stress detection methods have obvious limitations. Some invasive detection methods (such as direct measurement after excavation) need to interrupt normal pipeline transportation, which is low in efficiency and high in cost. In non-invasive detection, ultrasonic detection, ray detection and other technologies are easily disturbed by pipeline surface corrosion and corrosion coating thickness, making it difficult to accurately capture deep stress distribution. There are also detection schemes based on the magnetic properties of pipelines, but in most cases, the magnetic properties of pipeline ferromagnetic materials are strongly nonlinearly related to stress, and the magnetic signal is easily distorted under complex working conditions, which leads to stress calculation deviation and cannot meet the demand for high-precision stress evaluation for pipeline safe operation.

[0004] Therefore, how to realize non-invasive natural gas pipeline stress detection and eliminate the interference of environmental and dynamic factors on detection data to improve stress calculation accuracy is a problem to be solved. SUMMARY

[0005] Therefore, the natural gas pipeline stress detection method, device, equipment and storage medium provided by the embodiments of the present application can realize non-invasive natural gas pipeline stress detection, eliminate the interference of environmental and dynamic factors on detection data to improve stress calculation accuracy. The natural gas pipeline stress detection method, device, equipment and storage medium provided by the embodiments of the present application are realized as follows:

[0006] A saturated magnetic field is established in a target detection section of a natural gas pipeline;

[0007] The magnetic flux density, spatial difference signal, internal pressure and temperature of the target detection section are obtained in the saturated magnetic field, and the magnetic flux density is the magnetic property response of the natural gas pipeline under the saturated magnetic field;

[0008] The magnetic flux density, spatial difference signal, internal pressure and temperature are corrected and interference suppressed to obtain processed magnetic flux density, spatial difference signal, internal pressure and temperature;

[0009] obtain a magnetic-force coupling model, input the processed magnetic flux density, the spatial difference signal, the internal pressure and the temperature into the magnetic-force coupling model to obtain a relationship between the pipeline stress and the magnetic flux density and the magnetization parameter;

[0010] perform compensation processing on the processed magnetic flux density according to the processed temperature and a temperature compensation coefficient to obtain a temperature-compensated magnetic flux density;

[0011] perform compensation processing on the temperature-compensated magnetic flux density according to the strain rate parameter and a strain rate compensation coefficient to obtain a strain rate-compensated magnetic flux density;

[0012] based on the relationship between the pipeline stress and the magnetic flux density and the magnetization parameter, construct an inversion cost function, input the compensated magnetic flux density, the internal pressure and the temperature into the inversion cost function to obtain a pipeline stress distribution of a target detection section.

[0013] In some embodiments, the obtaining a magnetic-force coupling model, inputting the processed magnetic flux density, the spatial difference signal, the internal pressure and the temperature into the magnetic-force coupling model to obtain a relationship between the pipeline stress and the magnetic flux density and the magnetization parameter comprises:

[0014] obtain a magnetic-force coupling model and a pipeline parameter database;

[0015] input the processed magnetic flux density, the spatial difference signal, the internal pressure and the temperature into the magnetic-force coupling model respectively to obtain working condition data;

[0016] obtain working condition reference data matched with the working condition data from the pipeline parameter database, calibrate magnetic-force correlation basic parameters in the magnetic-force coupling model according to the working condition reference data to obtain calibrated magnetic-force correlation basic parameters;

[0017] perform operation on the magnetic flux density, the spatial difference signal and the calibrated magnetic-force correlation basic parameters by the magnetic-force coupling model to obtain a relationship between the pipeline stress and the magnetic flux density and the magnetization parameter.

[0018] In some embodiments, the performing correction and interference suppression processing on the magnetic flux density, the spatial difference signal, the internal pressure and the temperature to obtain processed magnetic flux density, spatial difference signal, internal pressure and temperature comprises:

[0019] perform outlier rejection processing on the magnetic flux density, the spatial difference signal, the internal pressure and the temperature data to obtain processed magnetic flux density, spatial difference signal, internal pressure and temperature data after outlier rejection processing;

[0020] Interference suppression is performed on the magnetic flux density and spatial difference signals after outlier removal to obtain the magnetic flux density and spatial difference signals after interference suppression.

[0021] After outlier removal, the temperature and internal pressure are stabilized to obtain the stabilized temperature and internal pressure.

[0022] The magnetic flux density and spatial difference signal after interference suppression processing, as well as the temperature and internal pressure after stability processing, are calibrated. The processed magnetic flux density, spatial difference signal, internal pressure, and temperature are then calibrated.

[0023] In some embodiments, the step of compensating the magnetic flux density based on the processed temperature and a temperature compensation coefficient to obtain a temperature-compensated magnetic flux density includes:

[0024] The temperature compensation coefficient is obtained, and the temperature compensation coefficient is determined based on the variation law of magnetic flux density at different temperatures;

[0025] Determine the reference temperature for temperature compensation, and calculate the difference between the processed temperature and the reference temperature;

[0026] The processed magnetic flux density, the temperature difference, and the temperature compensation coefficient are corrected to obtain the temperature-compensated magnetic flux density.

[0027] In some embodiments, the step of compensating the temperature-compensated magnetic flux density based on the strain rate parameter and the strain rate compensation coefficient to obtain the strain rate-compensated magnetic flux density includes:

[0028] Obtain the strain change rate of the target detection section of the natural gas pipeline, and obtain the strain rate parameter based on the strain change rate;

[0029] Obtain the strain rate compensation coefficient, which is determined based on the degree of influence of different strain rates on magnetic flux density;

[0030] The strain rate under stable operating conditions is obtained as the reference strain rate;

[0031] The strain rate deviation is obtained by calculating the difference between the strain rate parameter and the reference strain rate.

[0032] The magnetic flux density, strain rate deviation, and strain rate compensation coefficient after temperature compensation are calculated to obtain the magnetic flux density after strain rate compensation.

[0033] In some embodiments, the step of constructing an inversion cost function based on the correlation between the pipe stress and magnetic flux density and magnetization parameters, and inputting the compensated magnetic flux density, internal pressure, and temperature into the inversion cost function to obtain the pipe stress distribution in the target detection section includes:

[0034] To obtain the correlation between pipeline stress and magnetic flux density and magnetization parameters;

[0035] The input parameters for constructing the inversion cost function are determined, including the strain-compensated magnetic flux density, the processed internal pressure, and the processed temperature.

[0036] Based on the aforementioned correlation, an inversion cost function containing deviation constraints is constructed. The deviation constraints are calculated based on the theoretical stress calculation value and the actual stress corresponding to the input parameters.

[0037] The input parameters are input into the inversion cost function to obtain the pipeline stress distribution of the target detection section.

[0038] In some embodiments, the magnetic field strength of the saturated magnetic field is greater than or equal to 1.3T.

[0039] This application provides a natural gas pipeline stress detection device, comprising:

[0040] A module was established to create a saturated magnetic field in the target detection section of a natural gas pipeline.

[0041] The acquisition module is used to acquire the magnetic flux density, spatial difference signal, internal pressure and temperature of the target detection section in the saturated magnetic field, wherein the magnetic flux density is the magnetic characteristic response of the natural gas pipeline under the saturated magnetic field.

[0042] The processing module is used to perform correction and interference suppression processing on the magnetic flux density, spatial difference signal, internal pressure and temperature to obtain the processed magnetic flux density, spatial difference signal, internal pressure and temperature.

[0043] The acquisition module is also used to acquire a magnetic-force coupling model, and input the processed magnetic flux density, spatial difference signal, internal pressure and temperature into the magnetic-force coupling model to obtain the correlation between pipeline stress and magnetic flux density and magnetization parameters;

[0044] The processing module is also used to perform compensation processing on the magnetic flux density according to the processed temperature and temperature compensation coefficient to obtain the temperature-compensated magnetic flux density.

[0045] The processing module is also used to perform compensation processing on the temperature-compensated magnetic flux density according to the strain rate parameter and the strain rate compensation coefficient to obtain the strain rate-compensated magnetic flux density.

[0046] The processing module is also used to construct an inversion cost function based on the correlation between the pipeline stress and the magnetic flux density and magnetization parameters, and input the compensated magnetic flux density, internal pressure and temperature into the inversion cost function to obtain the pipeline stress distribution of the target detection section.

[0047] The computer device provided in this application includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the method described in this application.

[0048] The computer-readable storage medium provided in this application embodiment stores a computer program thereon, which, when executed by a processor, implements the method described in this application embodiment.

[0049] This application provides a method, apparatus, equipment, and storage medium for stress detection in natural gas pipelines, comprising: establishing a saturated magnetic field in the target detection section of the natural gas pipeline; acquiring magnetic flux density, spatial differential signal, internal pressure, and temperature in the saturated magnetic field; performing correction and interference suppression processing on the acquired data; establishing a correlation between pipeline stress and magnetic flux density and magnetization parameters by inputting the processed data through a magnetic-force coupling model; compensating for magnetic flux density based on temperature and strain rate in sequence; and finally constructing an inversion cost function based on the correlation, and solving for the pipeline stress distribution in the target section by inputting the compensated data. This invention does not require intrusion into the pipeline interior, ensures a stable correlation between magnetic properties and stress through a saturated magnetic field, and eliminates environmental and dynamic interference through dual compensation processing, significantly improving stress detection accuracy. It can provide early warning for the safe operation and risks of natural gas pipelines, solving the technical problems mentioned in the background art. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A schematic diagram illustrating the implementation process of a natural gas pipeline stress detection method provided in this application embodiment;

[0052] Figure 2 This is a schematic diagram illustrating the implementation process of the correlation between pipeline stress and magnetic flux density and magnetization parameters in a natural gas pipeline stress detection method provided in this application embodiment;

[0053] Figure 3 This is a schematic diagram of the structure of a natural gas pipeline stress detection device provided in an embodiment of this application. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0055] The following description of some technologies involved in the embodiments of this application is provided to aid understanding and should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, some descriptions of well-known functions and structures are omitted in the following description.

[0056] Figure 1 This is a schematic flowchart illustrating the implementation of a natural gas pipeline stress detection method according to an embodiment of this application, including steps 101 to 107. Wherein, Figure 1 This is merely one execution order shown in the embodiments of this application and does not represent the only execution order for a natural gas pipeline stress detection method. Where the final result can be achieved, Figure 1 The steps shown can be performed in parallel or in reverse order.

[0057] The following equipment and basic information need to be prepared in advance for this application: 1. Excitation device for establishing a saturated magnetic field; 2. Detection sensor group including magnetic induction sensor, pressure sensor, temperature sensor and strain sensor; 3. Data acquisition device for synchronous signal acquisition; 4. Data processing terminal with built-in magnetic-force coupling model and natural gas pipeline parameter database; 5. Pipeline basic parameters (such as steel grade, pipe diameter, wall thickness, design internal pressure, design operating temperature, etc.).

[0058] Step 101: Establish a saturated magnetic field in the target detection section of the natural gas pipeline.

[0059] In this embodiment of the application, the pipeline section to be inspected is selected based on the natural gas pipeline operation record or risk assessment results, and the floating dust, oil stains and other impurities on the outer wall of the pipeline in that section are cleaned; if it is a buried pipeline, only the target section is excavated.

[0060] A U-shaped excitation coil is used as the excitation device. The coil frame is made of insulating and high-temperature resistant material, and the coil is made of multiple strands of conductive wire wound together. The magnetic poles of the coil are attached to the outer walls on both sides of the target section of the pipeline. The magnetic poles are in close contact with the outer wall of the pipeline through insulating fasteners to avoid the contact gap from affecting the uniformity of the magnetic field.

[0061] The excitation coil is connected to a DC power supply, and the output current is gradually adjusted. Simultaneously, a magnetic field strength meter is used to monitor the magnetic field strength of the target section of the pipeline in real time. When the magnetic field strength stabilizes and reaches the saturation requirement, and the deviation of the magnetic field strength at each monitoring point within the section is controlled within a preset range, the excitation device is kept in operation to complete the establishment of the saturated magnetic field. In this embodiment, the magnetic field strength of the saturated magnetic field is greater than or equal to 1.3T.

[0062] Step 102: Obtain the magnetic flux density, spatial differential signal, internal pressure and temperature of the target detection section in a saturated magnetic field. The magnetic flux density is the magnetic characteristic response of the natural gas pipeline under a saturated magnetic field.

[0063] In this embodiment, a magnetic induction sensor array is deployed along the pipeline axis in the target detection section, with the sensor spacing set according to the requirement of capturing local magnetic characteristic changes. Pressure sensors, temperature sensors, and strain sensors are arranged at appropriate locations in the target section.

[0064] Start the data acquisition device, set the sampling frequency to match the rate of change of pipeline stress, and simultaneously acquire the magnetic flux density signal from the magnetic induction sensor, the internal pressure signal from the pressure sensor, and the temperature signal from the temperature sensor; the acquisition duration should be based on obtaining stable dynamic data to avoid interference from instantaneous fluctuations.

[0065] The data processing terminal calculates the spatial difference signal at the corresponding position based on the magnetic flux density data of adjacent magnetic induction sensors, so as to reflect the change in magnetic characteristic gradient of adjacent detection points along the pipeline axis.

[0066] Step 103: Correct and suppress interference for magnetic flux density, spatial difference signal, internal pressure and temperature to obtain processed magnetic flux density, spatial difference signal, internal pressure and temperature.

[0067] In this embodiment of the application, an outlier threshold is set based on the reasonable range of each parameter under normal pipeline operating conditions; the data processing terminal automatically filters and removes outlier data points that exceed the threshold, and replaces the outlier values ​​with the statistical values ​​of adjacent normal data.

[0068] An appropriate filtering method (such as band-stop filtering) is used to eliminate environmental power frequency interference and equipment electromagnetic radiation interference. Then, the baseline drift of the magnetic induction sensor is eliminated through a baseline correction algorithm. Based on the corrected magnetic flux density data, the spatial differential signal after interference suppression is recalculated.

[0069] A smoothing process is used to reduce short-term jumps in temperature data and instantaneous pulse fluctuations in internal pressure data, so that the operating parameters can reflect the true stable state of the pipeline.

[0070] The magnetic flux density, internal pressure, and temperature data are calibrated using standard magnetic field, standard pressure source, and standard temperature source respectively to correct the inherent error of the sensor; the spatial difference signal is corrected synchronously with the magnetic flux density calibration, and finally the processed parameter data are obtained.

[0071] Step 104: Obtain the magnetic-force coupling model. Input the processed magnetic flux density, spatial difference signal, internal pressure, and temperature into the magnetic-force coupling model to obtain the correlation between pipeline stress and magnetic flux density and magnetization parameters.

[0072] In this embodiment, the data processing terminal calls a magnetic-force coupling model adapted to steel pipes (constructed based on the coupling relationship between the hysteresis characteristics of ferromagnetic materials and stress), and simultaneously calls a pipe parameter database covering commonly used pipe parameters (steel grade, pipe diameter, wall thickness, internal pressure).

[0073] The processed internal pressure data and pipeline basic parameters are input into the database, and the reference data that is closest to the current pipeline operating conditions (including the stress-magnetic characteristic relationship and magnetization parameter range under the corresponding operating conditions) are filtered out.

[0074] Based on the selected reference data, adjust the fundamental parameters of the magnetic-force coupling model to ensure that the model is adapted to the characteristics of the current pipeline being inspected.

[0075] The processed magnetic flux density, spatial difference signal, internal pressure, and temperature are input into the calibrated model. The model calculation outputs the correlation between pipe stress and magnetic flux density and magnetization parameters, clarifying the corresponding law between stress change and magnetic characteristic parameter change.

[0076] Step 105: Compensate the magnetic flux density based on the processed temperature and temperature compensation coefficient to obtain the temperature-compensated magnetic flux density.

[0077] In this embodiment, the compensation coefficient is obtained by calibrating a test block made of the same material as the pipeline on-site. Magnetic flux density is measured on the test block at different temperatures to quantify the effect of temperature changes on magnetic flux density, determine the temperature compensation coefficient, and pre-store it in the data processing terminal.

[0078] The normal operating temperature of the pipeline is selected as the reference temperature for temperature compensation. The difference between the processed temperature and the reference temperature is calculated to obtain the temperature deviation.

[0079] Based on the temperature deviation and temperature compensation coefficient, the processed magnetic flux density is corrected to eliminate the interference of temperature fluctuations on magnetic characteristic parameters, thus obtaining the temperature-compensated magnetic flux density.

[0080] Step 106: The magnetic flux density after temperature compensation is compensated according to the strain rate parameter and the strain rate compensation coefficient to obtain the magnetic flux density after strain rate compensation.

[0081] In this embodiment of the application, the strain rate parameter is obtained by collecting the rate of change of strain over time in the target section of the pipeline using a strain sensor.

[0082] The compensation coefficient was obtained by calibrating test blocks of the same steel grade. Different strain rates were applied to the test blocks, and the corresponding changes in magnetic flux density were measured. The effect of strain rate on magnetic flux density was quantified, and the strain rate compensation coefficient was determined and stored in advance.

[0083] The strain rate during stable pipeline operation is selected as the reference strain rate. The difference between the current strain rate and the reference strain rate is calculated to obtain the strain rate deviation.

[0084] Based on the strain rate deviation and strain rate compensation coefficient, the magnetic flux density after temperature compensation is corrected to eliminate the interference of strain rate change on magnetic property parameters, and the magnetic flux density after strain rate compensation is obtained.

[0085] Step 107: Based on the correlation between pipeline stress and magnetic flux density and magnetization parameters, construct an inversion cost function. Input the compensated magnetic flux density, internal pressure, and temperature into the inversion cost function to obtain the pipeline stress distribution of the target detection section.

[0086] In this embodiment of the application, based on the obtained correlation, an inversion cost function is constructed with the goal of "minimizing the deviation between the measured value of the compensated parameter and the theoretical value under the assumed stress", incorporating the influence constraints of internal pressure and temperature on the theoretical value.

[0087] Two termination conditions are set: 1. The relative deviation between the measured value and the theoretical value is less than the preset accuracy threshold; 2. The number of iterations is not less than the preset minimum number of iterations to avoid deviations caused by local optimal solutions.

[0088] The strain-compensated magnetic flux density, processed internal pressure, and temperature are input into the inversion cost function. The assumed stress distribution is adjusted through iterative calculation until the termination condition is met, at which point the calculation stops.

[0089] The data processing terminal outputs the final results in the form of location-stress correspondence, marks the stress concentration areas (such as welds and bends), and completes the stress detection of the target detection section.

[0090] This application's embodiments ensure continuous pipeline operation through non-invasive testing. There is no need for pipeline excavation or cutting, or interruption of media transport. Only surface treatment and excitation arrangement of the target section are required, avoiding pipeline downtime losses caused by traditional invasive testing, significantly improving testing efficiency and pipeline operational safety. A stable magnetic-stress correlation is established through a saturated magnetic field. Addressing the problem of "non-linear correlation between magnetic properties and stress due to unsaturated magnetic fields" in traditional magnetic property testing, a saturated magnetic field is constructed to ensure a stable correlation between the magnetic response (magnetic flux density) of the pipeline's ferromagnetic material and stress. Simultaneously, multi-dimensional parameters such as magnetic flux density, spatial differential signal, internal pressure, and temperature are collected. First, noise in the original data is filtered out through correction and interference suppression processing. Then, dual compensation through temperature and strain rate eliminates the interference of ambient temperature fluctuations and small pipeline vibrations on the magnetic property parameters, ensuring that the magnetic property parameters accurately reflect the stress state. By combining the correlation established by the magnetic-force coupling model, the deviation between the measured parameters and the theoretical stress is quantified by the inversion cost function. At the same time, the working condition constraints are incorporated, and the final output stress distribution is not only highly accurate, but can also accurately locate the stress concentration areas in welds, elbows and other places.

[0091] In the above Figure 1 Based on the above, this application embodiment also provides a schematic diagram of the implementation process of the correlation between pipeline stress and magnetic flux density and magnetization parameters in a natural gas pipeline stress detection method, as shown below. Figure 2 As shown, steps 201 to 204 are included:

[0092] Step 201: Obtain the magnetic-force coupling model and the pipeline parameter database.

[0093] In this embodiment, the magnetic-force coupling model built into the data processing terminal is invoked. The magnetic-force coupling model is constructed based on the magnetoelastic effect of ferromagnetic materials and can reflect the influence of stress changes on the material's magnetic permeability and magnetic flux density. At the same time, it incorporates the correction logic for the magnetic-force correlation of temperature and internal pressure. The model includes a "magnetic characteristic parameter interface", a "operating condition parameter interface", a "basic parameter storage unit", and a "computation unit", which can receive different types of input parameters and perform calculations respectively.

[0094] The pre-built pipeline parameter database is invoked. This database stores data in categories of "basic attributes - operating condition reference": 1. Basic attribute data (classified by pipeline steel grade, recording the inherent magnetic property parameters of materials corresponding to different steel grades, pipe diameters, and wall thicknesses); 2. Operating condition reference data (stored in combinations of "steel grade - pipe diameter - internal pressure - temperature", including the correlation between stress and magnetic properties under the corresponding combination, the correspondence between spatial differential signals and local stress, and the reasonable range of magnetization parameters).

[0095] Step 202: Input the processed magnetic flux density, spatial difference signal, internal pressure and temperature into the magnetic-force coupling model respectively to obtain the operating condition data.

[0096] In this embodiment, the four types of processed parameters are connected to the corresponding interfaces of the model: 1. Magnetic flux density and spatial difference signal are connected to the "magnetic characteristic parameter interface"; 2. Internal pressure and temperature are connected to the "operating condition parameter interface". During the input process, the model automatically verifies whether the parameters are within a reasonable range (such as whether the internal pressure meets the design conditions of the current steel grade pipeline). If the parameters are abnormal, the model prompts the user to re-enter the corrected data.

[0097] The model integrates and correlates the four types of input parameters to generate operating condition data containing comprehensive information on "magnetic characteristics and operating conditions". Specifically, it covers: 1. the correlation trend between magnetic flux density and internal pressure and temperature; 2. the distribution characteristics of spatial differential signals along the pipeline axis; 3. the basic attributes and real-time operating condition indicators of the currently detected pipeline; the operating condition data is temporarily stored on the terminal.

[0098] Step 203: Obtain operating condition reference data that matches the operating condition data from the pipeline parameter database, and calibrate the magnetic-force correlation basic parameters in the magnetic-force coupling model based on the operating condition reference data to obtain the calibrated magnetic-force correlation basic parameters.

[0099] In this embodiment of the application, the "pipeline basic attributes + real-time operating condition identifier" in the operating condition data is used as the search keywords. First, the dataset matching the basic attributes is filtered by steel grade and pipe diameter. Then, the operating condition reference data that is closest to the current operating condition is filtered in the dataset according to the reasonable range of internal pressure and temperature. If there are multiple sets of similar reference data, their statistical average value is taken as the final reference basis. The reference data includes the standard stress-magnetic characteristic relationship and the standard value of the magnetic-force correlation basic parameter under the operating condition.

[0100] Retrieve the initial magnetic-force correlation basic parameters (such as material coercivity and saturation permeability) corresponding to the current pipeline from the model's "basic parameter storage unit," compare them with the standard values ​​in the operating condition reference data, and calculate the parameter deviation; adjust the initial parameters using the proportional correction method according to the magnitude of the deviation; after calibration, update the basic parameters in the model.

[0101] Step 204: The magnetic flux density, spatial difference signal, and calibrated magnetic-force correlation basic parameters are calculated using the magnetic-force coupling model to obtain the correlation between pipeline stress and magnetic flux density and magnetization parameters.

[0102] In this embodiment, the model uses the calibrated basic parameters as a benchmark, substitutes the processed magnetic flux density into the "magnetic property-stress conversion logic" to obtain the overall stress benchmark of the pipeline; combines the spatial difference signal to locally correct the overall stress benchmark (the larger the spatial difference signal, the higher the corresponding local stress concentration, and adjusts the local stress value according to the correction rules in the reference data); at the same time, the magnetization parameters under the current working condition are extracted and correlated with stress and magnetic flux density.

[0103] The model outputs the correlation between pipe stress and magnetic flux density and magnetization parameters, specifically: 1. The correspondence between stress and magnetic flux density (the stress change trend corresponding to changes in magnetic flux density under different magnetization parameters); 2. The correlation rules between spatial difference signals and local stress concentration; 3. The influence range of magnetization parameters on the above correlation.

[0104] This application's embodiments achieve precise matching of operating conditions through a pipeline parameter database. Addressing the differences in magnetic properties of pipelines with different steel grades, diameters, and wall thicknesses, pre-stored reference data covering multiple operating conditions allows for rapid selection of reference data suitable for the currently inspected pipeline, avoiding correlation deviations caused by the "generalization" of the model. The basic parameters in the model are calibrated using the operating condition reference data, transforming the model from a "general template" into a "customized model adapted to the current pipeline," solving the problem of "theory and reality being disconnected" when traditional fixed-parameter models are applied to different pipelines. Incorporating spatial difference signals into the model calculations ensures that the correlation not only reflects the overall stress benchmark of the pipeline but also quantifies the stress concentration degree corresponding to local magnetic property changes, providing a more accurate correlation basis for subsequent inversion function location of stress concentration areas.

[0105] In some embodiments, the magnetic flux density, spatial difference signal, internal pressure, and temperature are corrected and interference suppressed to obtain processed magnetic flux density, spatial difference signal, internal pressure, and temperature, including: performing outlier removal processing on the magnetic flux density, spatial difference signal, internal pressure, and temperature data to obtain outlier-removed magnetic flux density, spatial difference signal, internal pressure, and temperature data.

[0106] Specifically, the data processing terminal calls the pre-stored "normal operating condition parameter range of the pipeline" and sets out the abnormal value judgment threshold for each of the four types of parameters. For example, the internal pressure threshold is determined based on the reasonable fluctuation range of the pipeline's designed internal pressure, the temperature threshold is determined based on the temperature stability range of the pipeline during daily operation, the magnetic flux density threshold is determined based on the effective magnetic response range of the pipeline material under a saturated magnetic field, and the spatial differential signal threshold is determined based on the magnetic characteristic gradient range of similar pipelines under normal operating conditions.

[0107] The data processing terminal uses a combination of "threshold comparison + trend analysis" to identify outliers: 1. First, compare each collected data point with the threshold of the corresponding parameter and directly mark obvious outliers that exceed the threshold (such as instantaneous data where the internal pressure suddenly exceeds the design range); 2. Then mark the points that do not exceed the threshold but have abnormal data trends (such as temperature dropping immediately after a short period of time without any cause, or magnetic flux density fluctuating irregularly).

[0108] For marked outlier data points, a "neighborhood normal data statistical replacement" method is used. That is, multiple normal data points adjacent to the outlier point are selected, and a correction value is obtained through statistical averaging (or trend fitting). This correction value is then used to replace the outlier data. After replacement, the data sequences of the four types of parameters are checked for integrity to ensure that there is no missing data. Finally, the magnetic flux density, spatial difference signal, internal pressure, and temperature data after outlier removal are obtained.

[0109] Furthermore, interference suppression is applied to the magnetic flux density and spatial difference signal after outlier removal to obtain the magnetic flux density and spatial difference signal after interference suppression.

[0110] Specifically, the interference between magnetic flux density and spatial differential signal mainly comes from two categories: 1. Environmental electromagnetic interference (such as power frequency interference from the power grid in the detection area and radiation interference from surrounding electrical equipment); 2. Equipment interference (such as magnetic field instability caused by current fluctuations in the excitation device and baseline drift caused by long-term operation of the sensor). Both types of interference can cause irregular fluctuations or reference shifts in the magnetic characteristic signal, which need to be suppressed in a targeted manner.

[0111] The terminal invokes its built-in filtering module to select the appropriate filtering method based on the type of interference. For example, it uses band-stop filtering to filter out electromagnetic noise within a specific frequency range for power frequency interference; and low-pass filtering to retain the effective frequency components of the magnetic signal while reducing high-frequency interference for broad-spectrum radiated interference. During the filtering process, the signal fluctuation is monitored in real time, and the filtering parameters are adjusted to ensure that the effective signal is not over-filtered.

[0112] A "dynamic baseline correction" algorithm is used to eliminate sensor baseline drift. The average value of the initial segment (or stable segment) of the data sequence after outlier removal is used as the initial baseline. Subsequently, at fixed acquisition intervals, the baseline is finely adjusted according to the current signal stability trend to ensure that the baseline always remains consistent with the true reference of the signal. For example, if the magnetic flux density signal shifts slowly with the acquisition time, the baseline is adjusted synchronously to follow the shift trend, avoiding signal deviation caused by a fixed baseline.

[0113] Since the magnetic flux density changes after interference suppression, the spatial difference signal needs to be recalculated based on the corrected magnetic flux density (calculated according to the difference in magnetic flux density between adjacent detection points) to ensure that the spatial difference signal and the corrected magnetic flux density remain logically consistent, and finally obtain the magnetic flux density and spatial difference signal after interference suppression processing.

[0114] Furthermore, the temperature and internal pressure after outlier removal are subjected to stability processing to obtain the stable temperature and internal pressure.

[0115] Specifically, fluctuations in temperature and internal pressure mainly originate from transient disturbances. For example, temperature fluctuations may arise from changes in airflow in the detection environment or short-term sunlight exposure, while internal pressure fluctuations may originate from pulses in the flow of the medium within the pipeline or transient response deviations of pressure sensors. These fluctuations are short-term, ineffective disturbances and need to be weakened through processing to reflect the true stable operating conditions of the pipeline.

[0116] The temperature and internal pressure data are processed using appropriate smoothing methods (such as moving average or trend fitting). For example, the temperature data is processed using "multi-point moving average", which uses the average value of multiple consecutive sampling points as the current temperature value to reduce short-term jumps; the internal pressure data is processed using "trend fitting smoothing", which fits a curve according to the overall trend of the data and removes pulse fluctuations that deviate from the curve.

[0117] After processing, the effect is verified by "fluctuation amplitude calculation". That is, the maximum fluctuation amplitude of the processed data sequence is calculated. If the fluctuation amplitude is within the pre-stored "operating condition stability threshold" range, the processing is deemed qualified, and the temperature and internal pressure after stability processing are obtained; if the fluctuation amplitude exceeds the standard, the smoothing parameters are readjusted and the processing is repeated until the stability requirements are met.

[0118] Furthermore, the magnetic flux density and spatial difference signal after interference suppression processing, as well as the temperature and internal pressure after stability processing, are calibrated. The processed magnetic flux density, spatial difference signal, internal pressure, and temperature are then calibrated.

[0119] Specifically, the core of calibration is to eliminate inherent errors in sensors (such as sensitivity deviation of magnetic induction sensors, zero-point offset of pressure sensors, and measurement deviation of temperature sensors), which is achieved through "standard source comparison correction". That is, a known standard value is generated using a standard calibration source, the sensor's acquired value is compared with the standard value, the deviation coefficient is calculated, and then the processed parameters are corrected using the deviation coefficient.

[0120] 1. Place the magnetic induction sensor in a known magnetic field generated by a standard magnetic field source, and record the deviation coefficient between the current output magnetic flux density value of the sensor and the standard magnetic field value. Use this coefficient to correct the magnetic flux density after interference suppression. 2. Connect the pressure sensor to a standard pressure source, and record the deviation coefficient between the output internal pressure value of the sensor and the standard pressure value. Use this coefficient to correct the internal pressure after stability processing. 3. Place the temperature sensor in a known temperature environment simulated by a standard temperature source, and record the deviation coefficient between the output temperature value of the sensor and the standard temperature value. Use this coefficient to correct the temperature after stability processing. 4. Since the spatial differential signal is derived from the magnetic flux density, it is automatically and synchronously corrected with the calibration of the magnetic flux density, without the need for additional separate calibration.

[0121] After calibration, the four types of parameters are finally verified for accuracy. Once the verification is successful, the processed magnetic flux density, spatial difference signal, internal pressure, and temperature are output.

[0122] This application's embodiments employ a dual identification method of "threshold comparison + trend analysis" to prevent abnormal data from entering subsequent calculations, ensuring data validity from the source and reducing the interference of "bad points" on the final stress results. For core interferences in magnetic flux density and spatial difference signals, adaptation methods such as band-stop filtering and dynamic baseline correction are used. This filters out environmental electromagnetic noise while avoiding over-filtering that could distort the effective magnetic characteristic signal, ensuring that the magnetic characteristic parameters accurately reflect the magnetic response corresponding to pipeline stress. Temperature and internal pressure data are processed using methods such as moving averages and trend fitting to weaken instantaneous fluctuations, enabling the operating parameters to reflect the long-term stable operating state of the pipeline.

[0123] In some embodiments, the magnetic flux density is compensated according to the processed temperature and the temperature compensation coefficient to obtain the temperature-compensated magnetic flux density, including: obtaining the temperature compensation coefficient, which is determined according to the variation law of magnetic flux density at different temperatures.

[0124] Specifically, a test block of the same steel grade and thickness as the pipe being tested is selected, and impurities on the surface of the test block are cleaned. The test block is fixed inside the temperature control device, and a magnetic induction sensor is attached to the surface of the test block. The sensor is connected to the data processing terminal.

[0125] Adjust the temperature control device according to the preset temperature gradient to cover the actual temperature range that the pipeline may face; after adjusting to a target temperature and waiting for the temperature to stabilize, record the magnetic flux density of the test block collected by the magnetic induction sensor at that temperature; repeat this process to obtain multiple sets of corresponding "temperature-magnetic flux density" data.

[0126] By analyzing multiple sets of calibration data using a data processing terminal, the correlation between temperature changes and magnetic flux density changes is explored. For example, the increase or decrease in magnetic flux density for each unit increase or decrease in temperature is determined; based on this correlation, the degree of influence of temperature on magnetic flux density is quantified, and a temperature compensation coefficient is obtained.

[0127] Furthermore, the reference temperature for temperature compensation is determined, and the difference between the processed temperature and the reference temperature is calculated.

[0128] Specifically, the reference temperature is determined based on the actual operating characteristics of the pipeline, and one of two types of temperatures is preferred: 1. The pipeline's design operating temperature; 2. The pipeline's stable temperature before testing.

[0129] Obtain the "processed temperature" (after eliminating anomalies and fluctuations, reflecting the true temperature of the current detection section), calculate the difference between it and the selected reference temperature, and record the magnitude and sign of the difference. A positive difference indicates that the current temperature is higher than the reference temperature, and a negative difference indicates that the current temperature is lower than the reference temperature.

[0130] Furthermore, the processed magnetic flux density, temperature difference, and temperature compensation coefficient are corrected to obtain the temperature-compensated magnetic flux density.

[0131] Specifically, based on the temperature compensation coefficient and temperature deviation, the direction and magnitude of magnetic flux density correction are determined as follows: 1. If the current temperature is higher than the reference temperature (positive deviation), the trend of magnetic flux density change is judged in combination with the compensation coefficient (e.g., the magnetic flux density of most steel pipe materials will decrease as the temperature rises, and at this time, the processed magnetic flux density needs to be positively supplemented and corrected); 2. If the current temperature is lower than the reference temperature (negative deviation), the correction is performed according to the opposite logic (e.g., the magnetic flux density is too high due to the temperature drop, and a reverse reduction correction is required).

[0132] The system retrieves three types of data: "processed magnetic flux density," "temperature deviation," and "temperature compensation coefficient," and performs calculations according to a preset correction logic. For example, it calculates using the formula "processed magnetic flux density + (temperature deviation × temperature compensation coefficient)" or "processed magnetic flux density - (temperature deviation × temperature compensation coefficient)." The specific operation symbol is determined based on the correction logic to ensure that the corrected magnetic flux density is equivalent to the magnetic characteristic parameters at the reference temperature.

[0133] After correction, the reasonableness of the obtained "temperature-compensated magnetic flux density" is verified. It is compared with the typical magnetic flux density range of the pipeline at the reference temperature (obtained from the pipeline parameter database or historical test data). If it is within a reasonable range, the correction is deemed effective; if it exceeds the range, the accuracy of the temperature compensation coefficient and the calculation precision of the temperature deviation are rechecked, and the correction is performed again until the result is qualified.

[0134] This application embodiment uses a test block of the same steel grade and thickness as the tested pipeline for temperature-magnetic flux density calibration. This avoids the deviation in compensation coefficients caused by the different effects of temperature on magnetic properties on different materials, ensuring that the compensation coefficient can accurately quantify the temperature-magnetic flux density correlation of the current pipeline. Depending on the requirements, either the "design operating temperature" (general scenario) or the "stable temperature before testing" (specific operating condition scenario) can be selected as the benchmark. This solves the problem of inaccurate compensation when the traditional fixed benchmark temperature is used when the pipeline deviates from the design temperature for a long time, improving the scenario adaptability of the compensation method. By calculating the difference between the current temperature and the benchmark temperature, and combining it with the compensation coefficient, the magnetic flux density is accurately corrected, making the corrected magnetic flux density equivalent to the magnetic property parameters at the benchmark temperature. This completely eliminates spurious changes in magnetic flux density caused by temperature increases / decreases (such as a decrease in magnetic flux density when the temperature of a steel pipeline increases, which can easily be misjudged as a decrease in stress), ensuring that the correlation between magnetic flux density and stress is not affected by temperature.

[0135] In some embodiments, the magnetic flux density after temperature compensation is compensated according to the strain rate parameter and the strain rate compensation coefficient to obtain the magnetic flux density after strain rate compensation, including: obtaining the strain change rate of the target detection section of the natural gas pipeline, and obtaining the strain rate parameter according to the strain change rate.

[0136] Specifically, the strain sensor is attached and fixed at key locations in the target detection section of the pipeline. Priority is given to areas prone to strain fluctuations due to changes in operating conditions (such as near welds, pipe bends and straight pipe connections), ensuring that the sensor is in close contact with the outer wall of the pipeline (this can be achieved by using insulating adhesive to prevent contact gaps from affecting strain transmission); at the same time, the sensor is connected to the data processing terminal to ensure stable signal transmission.

[0137] Start the strain sensor and data processing terminal to synchronously collect strain data of the target detection section. The acquisition time needs to cover a complete dynamic change cycle of the pipeline (such as the medium flow velocity fluctuation cycle, environmental vibration cycle) to avoid missing strain change characteristics due to insufficient acquisition time; during the acquisition process, the terminal records the strain value corresponding to each time node in real time, forming a "time-strain" data sequence.

[0138] The "time-strain" data sequence is processed. By calculating the strain difference between adjacent time nodes and dividing it by the corresponding time interval, the strain change rate within that time period is obtained. The strain change rates of multiple time periods are statistically analyzed (e.g., by taking the average or selecting the strain change rate synchronized with the magnetic flux density acquisition time), and finally, the strain rate parameter that can represent the current detection condition is determined.

[0139] Furthermore, the strain rate compensation coefficient is obtained, which is determined based on the degree of influence of different strain rates on magnetic flux density.

[0140] Specifically, a test block of the same material is fixed on a strain loading device, and a strain sensor and a magnetic induction sensor are attached to the surface of the test block. The two sensors are then connected to a data processing terminal. The strain is applied to the test block according to a preset strain rate gradient through the strain loading device.

[0141] After adjusting to a target strain rate and waiting for the strain to stabilize, the magnetic flux density of the test block collected by the magnetic induction sensor at that strain rate is recorded. This process is repeated to obtain multiple sets of "strain rate - magnetic flux density" data. These data are then analyzed through a data processing terminal to explore the influence of strain rate changes on magnetic flux density. For example, the increase or decrease in magnetic flux density corresponding to each unit increase or decrease in strain rate can be determined.

[0142] Based on the above-mentioned influence patterns, the degree of interference of strain rate on magnetic flux density is quantified, and the strain rate compensation coefficient (i.e., the amount of magnetic flux density that needs to be corrected for each unit change in strain rate) is obtained.

[0143] Furthermore, the strain rate under stable operating conditions is obtained as the reference strain rate.

[0144] Specifically, refer to pipeline design documents and operation records to clarify the criteria for determining the "stable operating state" of the pipeline. For example, if the pipeline internal pressure, medium flow velocity, and environmental vibration are all within the design-allowed stable range, and there are no significant fluctuations during the preset continuous operation time, the pipeline strain state is stable, and the strain rate can be used as a benchmark.

[0145] When the pipeline is in a stable operating state, strain data of the target detection section is continuously collected by strain sensors, and the strain rate under this state is calculated. The multiple strain rate data collected continuously are statistically analyzed (such as taking the average value) to ensure that the data is stable and without fluctuations, and finally the strain rate is determined as the reference strain rate.

[0146] Furthermore, the difference between the strain rate parameter and the reference strain rate is calculated to obtain the strain rate deviation.

[0147] Specifically, obtain the "strain rate parameter" and the "reference strain rate", and then perform a subtraction operation between the two; record the magnitude and sign of the result. A positive difference indicates that the current strain rate parameter is higher than the reference strain rate, and a negative difference indicates that the current strain rate parameter is lower than the reference strain rate. This strain rate deviation directly reflects the degree of deviation between the current strain rate and the stable operating condition.

[0148] Furthermore, the magnetic flux density, strain rate deviation, and strain rate compensation coefficient after temperature compensation are calculated to obtain the magnetic flux density after strain rate compensation.

[0149] Specifically, based on the strain rate compensation coefficient and strain rate deviation, the correction direction and magnitude of magnetic flux density are determined. For example, if the calibration test shows that "the magnetic flux density will decrease when the strain rate is higher than the reference," then when the strain rate deviation is positive (the current strain rate is too high), the magnetic flux density after temperature compensation needs to be positively corrected; if the strain rate deviation is negative (the current strain rate is too low), then the reverse logic is used for reverse reduction correction.

[0150] The calculation uses three types of data: "temperature-compensated magnetic flux density," "strain rate deviation," and "strain rate compensation coefficient," and performs the calculations according to the above correction logic. This ensures that the calculation result is equivalent to the magnetic flux density under the reference strain rate, eliminating interference caused by strain rate changes.

[0151] After correction, the obtained "strain rate compensated magnetic flux density" is verified for rationality. It is compared with the typical magnetic flux density range under stable pipeline operating conditions (reference strain rate, reference temperature) (obtained from pipeline parameter database or historical stable operating condition test data). If it is within a reasonable range, the correction is deemed effective; if it exceeds the range, the accuracy of the strain rate compensation coefficient and the calculation precision of the strain rate deviation are rechecked, and the correction is performed again until the result is qualified.

[0152] This application embodiment acquires the real-time strain change rate of the pipeline using strain sensors, directly capturing the dynamic strain generated by fluctuations in medium flow velocity, minor soil settlement, and surrounding vibrations. This solves the problems of traditional methods that "ignore the influence of dynamic strain on magnetic properties" or "have large errors in indirect strain rate estimation." In the laboratory, different strain rates are simulated using a strain loading device to obtain the correlation between strain rate and magnetic flux density and determine the compensation coefficient. This avoids deviations caused by on-site experience-based estimation of the compensation coefficient, ensuring accurate and controllable compensation amplitude. The "strain rate during stable operation" of the pipeline is selected as the benchmark (e.g., the strain rate when internal pressure and flow velocity are stable), making the compensated magnetic flux density equivalent to "magnetic property parameters under stable operating conditions." This eliminates additional changes in magnetic flux density caused by dynamic strain in the pipeline (e.g., a decrease in magnetic flux density when the strain rate increases, which can easily be misinterpreted as a decrease in stress), further improving the accuracy of the correlation between magnetic property parameters and stress.

[0153] In some embodiments, based on the correlation between pipeline stress and magnetic flux density and magnetization parameters, an inversion cost function is constructed. The compensated magnetic flux density, internal pressure, and temperature are input into the inversion cost function to obtain the pipeline stress distribution of the target detection section, including: obtaining the correlation between pipeline stress and magnetic flux density and magnetization parameters.

[0154] Specifically, obtain the correlation file. This file contains three core types of information: 1. The correspondence between "magnetic flux density and stress" under different magnetization parameters (e.g., the proportional relationship between changes in magnetic flux density and stress when magnetization parameters are fixed); 2. The correlation rules between spatial differential signals and local stress concentrations (e.g., when the spatial differential signal exceeds a certain range, the local stress needs to be corrected according to a specific ratio); 3. The influence coefficients of internal pressure and temperature on the correlation between "magnetic flux density and stress" (e.g., when internal pressure increases, the stress reference value corresponding to the same magnetic flux density needs to be adjusted synchronously).

[0155] Automatically verify the compatibility of the association relationship with the currently inspected pipeline. Compare the basic attributes (steel grade, pipe diameter) of the pipeline corresponding to the association relationship with the currently inspected pipeline. If there are slight differences (such as pipe diameters being similar but not exactly the same), the "attribute correction coefficient" in the pipeline parameter database is called to fine-tune the proportional parameters in the association relationship to ensure that the association relationship can accurately adapt to the current inspection object; if the difference is too large, a suitable association relationship is regenerated.

[0156] Furthermore, the input parameters for constructing the inversion cost function are determined, including the strain-compensated magnetic flux density, the processed internal pressure, and the processed temperature.

[0157] Specifically, the "magnetic flux density after strain rate compensation", "internal pressure after treatment", and "temperature after treatment" are obtained.

[0158] The three types of parameters are integrated according to the correspondence between "spatial location and parameter value". For example, according to the axial detection point sequence of the target pipeline detection section, the "magnetic flux density after strain rate compensation" corresponding to each detection point is organized, and the "treated internal pressure" (the internal pressure is relatively uniformly distributed along the section and can be assigned according to the average internal pressure of the section) and "treated temperature" (if the temperature distribution along the section is different, it is assigned according to the temperature corresponding to each detection point) at the location of the detection point are matched to form a structured input parameter table to ensure that the parameters correspond one-to-one with the spatial location of the pipeline.

[0159] Furthermore, based on the correlation, an inversion cost function containing deviation constraints is constructed. The deviation constraints are calculated based on the theoretical stress calculation value and the actual stress corresponding to the input parameters.

[0160] Specifically, based on the correlation, the calculation method for "input parameters → theoretical stress" is determined as follows: 1. Based on the "magnetic flux density after strain rate compensation" at a certain detection point, and combined with the proportional relationship of "magnetic flux density - stress" in the correlation, the initial theoretical stress at that point is calculated; 2. The "processed internal pressure" is multiplied by the "internal pressure influence coefficient" in the correlation to obtain the internal pressure correction value. The initial theoretical stress is then superimposed with the internal pressure correction value to obtain the corrected theoretical stress; 3. If there is a slight deviation between the "processed temperature" at that point and the reference temperature, the residual correction term of the temperature compensation logic is called to fine-tune the theoretical stress, and finally the calculated theoretical stress value at that detection point is determined.

[0161] The "actual stress" is defined as the unknown quantity to be solved, and the deviation constraint is the "difference between the theoretical stress calculation value and the actual stress at each detection point". To avoid the deviation of a single detection point from affecting the overall result, a "global deviation accumulation" constraint is adopted, that is, the sum of squares (or the sum of absolute values) of the deviations of all detection points, to ensure that the deviation constraint can reflect the rationality of the overall stress distribution.

[0162] With the goal of minimizing global bias, an inversion cost function is constructed. The independent variable of the function is the actual stress at each detection point, and the dependent variable is the global bias. At the same time, "stress rationality constraints" are incorporated (such as the actual stress must not exceed the yield strength of the pipe material, and the stress change between adjacent detection points must not exceed the gradient range allowed by material mechanics) to avoid solving for physically unreasonable stress values, and finally form an inversion cost function that combines bias minimization and physical rationality.

[0163] Furthermore, the input parameters are fed into the inversion cost function to obtain the pipeline stress distribution in the target detection section.

[0164] Specifically, to ensure the accuracy and efficiency of stress calculation, two termination conditions are set: 1. Deviation threshold constraint: the global deviation is reduced to within the preset accuracy range; 2. The number of iterations reaches the preset minimum number.

[0165] Substitute the structured input parameter table into the inversion cost function and start the iterative optimization algorithm of the data processing terminal: 1. Initially assume a set of stress distributions; 2. Calculate the global deviation corresponding to the assumed distribution and determine whether the termination condition is met; 3. If not, adjust the assumed stress distribution according to the algorithm rules; 4. Repeat the process of "calculating deviation - adjusting stress" until any termination condition is met, and stop the iteration.

[0166] After the iteration stops, the pipeline stress distribution of the target detection section is output. It is presented in the form of a table or curve of "axial position - actual stress", and stress concentration areas are marked (e.g., if the stress at a certain detection point is significantly higher than the surrounding area and exceeds the normal stress range, it is determined to be a stress concentration area). The physical rationality of the output results is verified (e.g., the maximum stress does not exceed the material yield strength, and the stress change trend is consistent with the mechanical properties of the pipeline). If the verification is qualified, it is the final stress distribution; if it is not qualified, the iteration parameters (e.g., deviation threshold, initial assumed distribution) are readjusted, and the solution is solved again until it is qualified.

[0167] This application's embodiment uses "strain rate compensated magnetic flux density (core stress-related parameter) + processed internal pressure / temperature (basic operating condition parameter)" as input, enabling the inversion process to not only be based on magnetic properties but also incorporate the influence of internal pressure on foundation stress and the residual constraints of temperature on magnetic properties. This solves the problem of traditional inversion relying solely on a single parameter and "ignoring the operating condition background." The inversion cost function aims at "minimizing global deviation" (quantifying the theoretical and actual stress deviations at all detection points) while incorporating physical constraints (such as stress not exceeding the material's yield strength and stress changes at adjacent points conforming to mechanical gradients). This avoids the problem of "outputting unreasonable stress values ​​(such as stress exceeding the yield strength) in pursuit of minimizing deviation" in traditional inversion, ensuring that the results are both accurate and consistent with engineering practice.

[0168] While this application provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in this embodiment is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the methods shown in this embodiment or the accompanying drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0169] like Figure 3 As shown in the illustration, this application also provides a natural gas pipeline stress detection device 300. The device includes:

[0170] Module 301 is established to create a saturated magnetic field in the target detection section of a natural gas pipeline.

[0171] The acquisition module 302 is used to acquire the magnetic flux density, spatial differential signal, internal pressure and temperature of the target detection section in a saturated magnetic field. The magnetic flux density is the magnetic characteristic response generated by the natural gas pipeline under a saturated magnetic field.

[0172] The processing module 303 is used to perform correction and interference suppression processing on the magnetic flux density, spatial difference signal, internal pressure and temperature to obtain the processed magnetic flux density, spatial difference signal, internal pressure and temperature.

[0173] The acquisition module 302 is also used to acquire the magnetic-force coupling model, inputting the processed magnetic flux density, spatial difference signal, internal pressure and temperature into the magnetic-force coupling model to obtain the correlation between pipeline stress and magnetic flux density and magnetization parameters.

[0174] The processing module 303 is also used to compensate the magnetic flux density according to the processed temperature and the temperature compensation coefficient to obtain the temperature-compensated magnetic flux density.

[0175] The processing module 303 is also used to perform compensation processing on the temperature-compensated magnetic flux density according to the strain rate parameter and the strain rate compensation coefficient to obtain the strain rate-compensated magnetic flux density.

[0176] The processing module 303 is also used to construct an inversion cost function based on the correlation between pipeline stress and magnetic flux density and magnetization parameters, and input the compensated magnetic flux density, internal pressure and temperature into the inversion cost function to obtain the pipeline stress distribution of the target detection section.

[0177] In some embodiments, the acquisition module 302 is also used to acquire the magnetic-force coupling model and the pipeline parameter database.

[0178] The processing module 303 is also used to input the processed magnetic flux density, spatial difference signal, internal pressure and temperature into the magnetic-force coupling model respectively to obtain the operating condition data.

[0179] The processing module 303 is also used to obtain operating condition reference data that matches the operating condition data from the pipeline parameter database, and to calibrate the magnetic-force correlation basic parameters in the magnetic-force coupling model based on the operating condition reference data to obtain the calibrated magnetic-force correlation basic parameters.

[0180] The processing module 303 is also used to calculate the magnetic flux density, spatial difference signal and calibrated magnetic-force correlation basic parameters through the magnetic-force coupling model to obtain the correlation between pipeline stress and magnetic flux density and magnetization parameters.

[0181] In some embodiments, the processing module 303 is further configured to perform outlier removal processing on the magnetic flux density, spatial difference signal, internal pressure and temperature data to obtain the outlier removal processed magnetic flux density, spatial difference signal, internal pressure and temperature data.

[0182] The processing module 303 is also used to suppress interference in the magnetic flux density and spatial difference signal after outlier removal processing, so as to obtain the magnetic flux density and spatial difference signal after interference suppression processing.

[0183] The processing module 303 is also used to perform stability processing on the temperature and internal pressure after outlier removal to obtain the stable temperature and internal pressure.

[0184] The processing module 303 is also used to calibrate the magnetic flux density and spatial difference signal after interference suppression processing, as well as the temperature and internal pressure after stability processing, and the processed magnetic flux density, spatial difference signal, internal pressure and temperature.

[0185] In some embodiments, the acquisition module 302 is further configured to acquire a temperature compensation coefficient, which is determined based on the variation law of magnetic flux density at different temperatures.

[0186] The processing module 303 is also used to determine the reference temperature for temperature compensation and to calculate the difference between the processed temperature and the reference temperature.

[0187] The processing module 303 is also used to correct the processed magnetic flux density, temperature difference and temperature compensation coefficient to obtain the temperature-compensated magnetic flux density.

[0188] In some embodiments, the acquisition module 302 is further configured to acquire the strain change rate of the target detection section of the natural gas pipeline, and obtain the strain rate parameter based on the strain change rate.

[0189] The acquisition module 302 is also used to acquire the strain rate compensation coefficient, which is determined based on the degree of influence of different strain rates on the magnetic flux density.

[0190] The acquisition module 302 is also used to acquire the strain rate under stable operating conditions as the reference strain rate.

[0191] The processing module 303 is also used to calculate the difference between the strain rate parameter and the reference strain rate to obtain the strain rate deviation.

[0192] The processing module 303 is also used to calculate the temperature-compensated magnetic flux density, strain rate deviation, and strain rate compensation coefficient to obtain the strain rate-compensated magnetic flux density.

[0193] In some embodiments, the acquisition module 302 is also used to acquire the correlation between pipeline stress and magnetic flux density and magnetization parameters.

[0194] The processing module 303 is also used to determine the input parameters for constructing the inversion cost function. The input parameters include the magnetic flux density after strain rate compensation, the internal pressure after processing, and the temperature after processing.

[0195] The processing module 303 is also used to construct an inversion cost function containing deviation constraints based on the correlation relationship. The deviation constraints are calculated based on the theoretical stress calculation value and the actual stress corresponding to the input parameters.

[0196] The processing module 303 is also used to input the input parameters into the inversion cost function to obtain the pipeline stress distribution of the target detection section.

[0197] Some modules in the apparatus described in this application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0198] The apparatus or module described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. For ease of description, the above apparatus is described by dividing it into various modules according to their functions. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0199] The methods, apparatus, or modules described in this application can be implemented in a computer-readable program code manner. The controller can be implemented in any suitable manner, such as a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of a memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code manner, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included within it for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module that implements the method or a structure within a hardware component.

[0200] This application also provides an apparatus, the apparatus comprising: a processor; a memory for storing processor-executable instructions; wherein, when the processor executes the executable instructions, it implements the method described in this application.

[0201] This application also provides a non-volatile computer-readable storage medium storing a computer program or instructions thereon, which, when executed, enables the method described in this application embodiment to be implemented.

[0202] Furthermore, in the various embodiments of the present invention, each functional module can be integrated into a processing module, or each module can exist independently, or two or more modules can be integrated into a single module.

[0203] The aforementioned storage media include, but are not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions.

[0204] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0205] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0206] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A method for stress detection in natural gas pipelines, characterized in that, include: Establish a saturated magnetic field in the target detection section of the natural gas pipeline; The magnetic flux density, spatial difference signal, internal pressure and temperature of the target detection section are obtained in the saturated magnetic field, wherein the magnetic flux density is the magnetic characteristic response of the natural gas pipeline under the saturated magnetic field. The magnetic flux density, spatial difference signal, internal pressure, and temperature are corrected and interference suppressed to obtain the processed magnetic flux density, spatial difference signal, internal pressure, and temperature. A magnetic-force coupling model is obtained. The processed magnetic flux density, spatial difference signal, internal pressure, and temperature are input into the magnetic-force coupling model to obtain the correlation between pipe stress and magnetic flux density and magnetization parameters. The magnetic flux density is compensated based on the processed temperature and temperature compensation coefficient to obtain the temperature-compensated magnetic flux density. The magnetic flux density after temperature compensation is compensated based on the strain rate parameter and strain rate compensation coefficient to obtain the strain rate compensated magnetic flux density. Based on the correlation between pipeline stress and magnetic flux density and magnetization parameters, an inversion cost function is constructed. The compensated magnetic flux density, internal pressure and temperature are input into the inversion cost function to obtain the pipeline stress distribution of the target detection section. The process of correcting and suppressing interference in the magnetic flux density, spatial difference signal, internal pressure, and temperature to obtain the processed magnetic flux density, spatial difference signal, internal pressure, and temperature includes: Outlier removal processing is performed on the magnetic flux density, spatial difference signal, internal pressure, and temperature data to obtain the outlier-removed magnetic flux density, spatial difference signal, internal pressure, and temperature data. After outlier removal, the magnetic flux density and spatial difference signals are subjected to band-stop or low-pass filtering and interference suppression by dynamic baseline correction algorithm to obtain the interference-suppressed magnetic flux density and spatial difference signals. After outlier removal, the temperature and internal pressure are stabilized to obtain the stabilized temperature and internal pressure. The magnetic flux density and spatial difference signal after interference suppression processing, as well as the temperature and internal pressure after stability processing, are calibrated to obtain the processed magnetic flux density, spatial difference signal, internal pressure, and temperature. The step of compensating the magnetic flux density based on the processed temperature and temperature compensation coefficient to obtain the temperature-compensated magnetic flux density includes: Obtain the temperature compensation coefficient based on the calibration of test blocks of the same steel grade and thickness. The temperature compensation coefficient is determined according to the variation law of magnetic flux density at different temperatures. Determine the reference temperature for temperature compensation, and calculate the difference between the processed temperature and the reference temperature; The processed magnetic flux density, the temperature difference, and the temperature compensation coefficient are corrected to obtain the temperature-compensated magnetic flux density.

2. The method according to claim 1, characterized in that, The process of obtaining the magnetic-force coupling model involves inputting the processed magnetic flux density, spatial difference signal, internal pressure, and temperature into the model to obtain the correlation between pipe stress and magnetic flux density and magnetization parameters, including: Obtain the magnetic-force coupling model and pipeline parameter database; The processed magnetic flux density, spatial difference signal, internal pressure, and temperature are respectively input into the magnetic-force coupling model to obtain the operating condition data; Obtain operating condition reference data that matches the operating condition data from the pipeline parameter database, and calibrate the magnetic-force correlation basic parameters in the magnetic-force coupling model based on the operating condition reference data to obtain the calibrated magnetic-force correlation basic parameters; The magnetic flux density, the spatial difference signal, and the calibrated magnetic-force correlation parameters are calculated using a magnetic-force coupling model to obtain the correlation between pipeline stress and magnetic flux density and magnetization parameters.

3. The method according to claim 1, characterized in that, The process of compensating the temperature-compensated magnetic flux density based on strain rate parameters and strain rate compensation coefficients to obtain the strain rate-compensated magnetic flux density includes: Obtain the strain change rate of the target detection section of the natural gas pipeline, and obtain the strain rate parameter based on the strain change rate; Obtain the strain rate compensation coefficient, which is determined based on the degree of influence of different strain rates on magnetic flux density; The strain rate under stable operating conditions is obtained as the reference strain rate; The strain rate deviation is obtained by calculating the difference between the strain rate parameter and the reference strain rate. The magnetic flux density, strain rate deviation, and strain rate compensation coefficient after temperature compensation are calculated to obtain the magnetic flux density after strain rate compensation.

4. The method according to claim 1, characterized in that, Based on the correlation between pipeline stress and magnetic flux density and magnetization parameters, an inversion cost function is constructed. The compensated magnetic flux density, internal pressure, and temperature are input into the inversion cost function to obtain the pipeline stress distribution in the target detection section, including: To obtain the correlation between pipeline stress and magnetic flux density and magnetization parameters; The input parameters for constructing the inversion cost function are determined, including the strain-compensated magnetic flux density, the processed internal pressure, and the processed temperature. Based on the aforementioned correlation, an inversion cost function containing deviation constraints is constructed. The deviation constraints are calculated based on the theoretical stress calculation value and the actual stress corresponding to the input parameters. The input parameters are input into the inversion cost function to obtain the pipeline stress distribution of the target detection section.

5. The method according to claim 1, characterized in that, The magnetic field strength of the saturated magnetic field is greater than or equal to 1.3T.

6. A stress detection device for natural gas pipelines, characterized in that, include: A module was established to create a saturated magnetic field in the target detection section of a natural gas pipeline. The acquisition module is used to acquire the magnetic flux density, spatial difference signal, internal pressure and temperature of the target detection section in the saturated magnetic field, wherein the magnetic flux density is the magnetic characteristic response of the natural gas pipeline under the saturated magnetic field. The processing module is used to perform correction and interference suppression processing on the magnetic flux density, spatial difference signal, internal pressure and temperature to obtain the processed magnetic flux density, spatial difference signal, internal pressure and temperature. The acquisition module is also used to acquire a magnetic-force coupling model, and input the processed magnetic flux density, spatial difference signal, internal pressure and temperature into the magnetic-force coupling model to obtain the correlation between pipeline stress and magnetic flux density and magnetization parameters; The processing module is also used to perform compensation processing on the magnetic flux density according to the processed temperature and temperature compensation coefficient to obtain the temperature-compensated magnetic flux density. The processing module is also used to perform compensation processing on the temperature-compensated magnetic flux density according to the strain rate parameter and the strain rate compensation coefficient to obtain the strain rate-compensated magnetic flux density. The processing module is also used to construct an inversion cost function based on the correlation between the pipeline stress and the magnetic flux density and magnetization parameters, and input the compensated magnetic flux density, internal pressure and temperature into the inversion cost function to obtain the pipeline stress distribution of the target detection section; The processing module is further configured to perform correction and interference suppression processing on the magnetic flux density, spatial difference signal, internal pressure, and temperature to obtain processed magnetic flux density, spatial difference signal, internal pressure, and temperature, wherein: Outlier removal processing is performed on the magnetic flux density, spatial difference signal, internal pressure, and temperature data to obtain the outlier-removed magnetic flux density, spatial difference signal, internal pressure, and temperature data. After outlier removal, the magnetic flux density and spatial difference signals are subjected to band-stop or low-pass filtering and interference suppression by dynamic baseline correction algorithm to obtain the interference-suppressed magnetic flux density and spatial difference signals. After outlier removal, the temperature and internal pressure are stabilized to obtain the stabilized temperature and internal pressure. The magnetic flux density and spatial difference signal after interference suppression processing, as well as the temperature and internal pressure after stability processing, are calibrated to obtain the processed magnetic flux density, spatial difference signal, internal pressure, and temperature. The processing module is further configured to compensate the magnetic flux density based on the processed temperature and a temperature compensation coefficient to obtain a temperature-compensated magnetic flux density, wherein: Obtain the temperature compensation coefficient based on the calibration of test blocks of the same steel grade and thickness. The temperature compensation coefficient is determined according to the variation law of magnetic flux density at different temperatures. Determine the reference temperature for temperature compensation, and calculate the difference between the processed temperature and the reference temperature; The processed magnetic flux density, the temperature difference, and the temperature compensation coefficient are corrected to obtain the temperature-compensated magnetic flux density.

7. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

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