Sensor-based pipeline defect location method and system

Abstract

Provided are a sensor-based pipeline defect location method and a system, which accurately locate the location of a pipeline defect point by means of multi-parameter fusion, related to the technical f

Description

[0002] The present disclosure relates to the technical field of oil and gas pipeline maintenance, and in particular, to a sensor-based pipeline defect locating method and system. BACKGROUND OF THE INVENTION

[0003] Oil and gas pipeline transportation plays an important role in the energy sector. Pipeline transportation, as an efficient, safe and economical transportation way, is essential to meet the global energy needs. Since oil and natural gas are the main energy sources of modem industrial society, smooth operation of pipeline transportation systems is essential to maintaining social stability and economic development. If oil and gas pipelines are defective and leak, there may be serious impacts and consequences.

[0004] It is thus particularly critical to prevent and respond to oil and gas pipeline leaks. However, in terms of preventing pipeline leaks, the most important thing is to accurately locate the pipeline defects and repair the defects. But it is not easy to locate the defects on pipelines that are several kilometers long or even on the seabed. Conventional excavation verification methods are not only impractical, but also consume a lot of manpower, material resources and time.

[0005] Therefore, it is of great significance to accurately locate the pipeline defects so as to carry out repair work in a timely manner. SUMMARY OF THE INVENTION

[0006] The present disclosure provides a sensor-based pipeline defect locating method and system, which can accurately locate the position of pipeline defect points.

[0007] In a first aspect, a sensor-based pipeline defect locating method is provided, including: 1

[0008] inputting a raw data of a tri-channel odometer wheel of a magnetic flux leakage detection sensor to an inertial measurement unit sensor, after the magnetic flux leakage detection sensor completes magnetic flux leakage detection on a pipeline;

[0009] performing, via the inertial measurement unit sensor, data solving on the raw data to obtain a first positioning data, which includes at least one first time point, and longitude and latitude corresponding to each first time point;

[0010] obtaining a second positioning data recorded by at least one ground marker positioning sensor, after the magnetic flux leakage detection sensor performs magnetic flux leakage detection on the pipeline, wherein the at least one ground marker positioning sensor is buried along a route direction of the pipeline; the second positioning data includes at least one second time point, and longitude and latitude corresponding to each second time point; and the second time point is a time point at which the magnetic flux leakage detection sensor passes the ground marker positioning sensor when running in the pipeline;

[0011] fusing the first positioning data and the second positioning data, using temporal synchronization processing and spatial localization inference processing, to obtain a fused positioning data;

[0012] analyzing and visualizing the raw data via an inline inspection data analysis software;

[0013] determining defect data of the pipeline based on content visualized by the inline inspection data analysis software, wherein the defect data includes inspection time information and mileage information corresponding to each defect point; and

[0014] fitting and comparing the defect data and the fused positioning data to obtain longitude and latitude of each defect point.

[0015] In a second aspect, a sensor-based pipeline defect locating system is provided, including:

[0016] a basic information import module which is configured to input a raw data of a tri-channel odometer wheel of a magnetic flux leakage detection sensor to an inertial measurement unit sensor after the magnetic flux leakage detection sensor completes magnetic flux leakage detection on the pipeline, wherein

[0017] the basic information import module is configured to use the inertial measurement unit sensor to perform data solving on the raw data to obtain a first positioning data, where the first positioning data includes at least one first time point and longitude and latitude corresponding to each first time point,

[0018] the basic information import module is configured to obtain a second positioning data recorded by at least one ground marker positioning sensor after the magnetic flux leakage detection sensor completes magnetic flux leakage detection on the pipeline, wherein the at least one ground marker positioning sensor is buried along a route direction of the pipeline, the second positioning data includes at least one second time point and longitude and latitude corresponding to each second time point, and the second time point is a time point at which the magnetic flux leakage detection sensor passes the ground marker positioning sensor when running in the pipeline;

[0019] a first fusion algorithm module which is configured to fuse the first positioning data and the second positioning data using temporal synchronization processing and spatial localization inference processing to obtain a fused positioning data;

[0020] a display module which is configured to analyze and visualize the raw data by using an inline inspection data analysis software;

[0021] a defect data determination module which is configured to determine defect data of the pipeline according to contents visualized by the inline inspection data analysis software, wherein the defect data includes inspection time information and mileage information corresponding to each defect point; and

[0022] a second fusion algorithm module which is configured to fit and compare the defect data and the fused positioning data to obtain longitude and latitude of each defect point.

[0023] The raw data output by the tri-channel odometer wheel of the magnetic flux leakage detection sensor is greatly affected by the pipeline environment, which affects the accuracy of the first positioning data output by the inertial measurement unit sensor. Therefore, the accuracy of the position of the defect point determined according to the first positioning data is low. hi order to improve the accuracy of locating pipeline defects, in the present invention, a second positioning data recorded by a ground marker positioning sensor is obtained, temporal synchronization processing and spatial localization inference processing are used, the first positioning data is fused and corrected by using the second positioning data, and the accuracy of the first positioning data is improved through fusion of multi parameters from the sensors. The fused and corrected first positioning data is the fused positioning data. Therefore, by fitting and comparing the defect data and the fused positioning data, accurate longitude and latitude of each defect point can be obtained, which facilitates timely repair work. BRIEF DESCRIPTION OF THE DRAWINGS [00241 In order to more clearly illustrate the technical solution of the present disclosure, the drawings required for use in the embodiments are briefly introduced below. Apparently, other drawings may be obtained by those skilled in the art based on these drawings without paying any creative work. [00251 FIG. 1 is a schematic flow chart of a sensor-based pipeline defect locating method provided according to an exemplary embodiment of the present disclosure.

[0026] FIG. 2 is a schematic diagram of a sensor-based pipeline defect locating system provided according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] Technical solutions of embodiments of the present disclosure will be clearly and fully described below in combination with the drawings of the embodiments of the present disclosure. Of course, these embodiments are only part, not all, of embodiments of the present disclosure. All other embodiments obtained by ordinary technicians in this field based on the embodiments described in the present disclosure without making creative work are within the protection scope of the present disclosure.

[0028] At present, traditional detection technologies (such as ultrasonic and magnetic flaw detection) are usually used to locate pipeline defects. These traditional methods usually require manual operation, are time-consuming and labor-intensive, and the accuracy of positioning is greatly affected by human factors. In order to accurately locate the position of pipeline defects, the present disclosure adopts a multi-parameter fusion method to achieve accurate positioning of pipeline defects, which solves the problems of resource waste and time delay caused by using traditional detection technologies for pipeline maintenance.

[0029] Fig. 1 is a schematic flow chart of a sensor-based pipeline defect locating method provided according to an exemplary embodiment of the present disclosure. As shown in FIG. 1, the method includes:

[0030] SI 10, inputting raw data of a tri-channel odometer wheel of a magnetic flux leakage detection sensor to an inertial measurement unit sensor, after the magnetic flux leakage detection sensor completes magnetic flux leakage detection on a pipeline.

[0031] When the magnetic flux leakage detection sensor performs magnetic flux leakage detection on the pipeline, the odometer wheel of the magnetic flux leakage detection sensor will output 4096 pulses for each rotation of the odometer wheel, and 256 pulses of the 4096 pulses will be transmitted to the inertial measurement unit sensor through the 422 serial port in a form of digital signal.

[0032] S120, performing, via the inertial measurement unit sensor, data solving on the raw data to obtain a first positioning data.

[0033] Where, the first positioning data includes at least one first time point, and longitude and latitude corresponding to each first time point.

[0034] The inertial measurement unit sensor adopts the inertial measurement unit (IMU) technology, which can fuse the information from the tri-channel odometer wheel through a solution algorithm and output one channel of positioning data (i.e. the first positioning data).

[0035] Exemplarily, the first positioning data further includes information such as elevation information corresponding to each first time point, mileage information from an initial point, and a timestamp.

[0036] It should be noted that the model of the magnetic flux leakage detection sensor is not limited in this disclosure.

[0037] Wherein, each first time point is a time point at which the IMU collects data once. For example, the IMU collects data once every 6|1S, and the first time point corresponding to the first data collection is 0.0000058301 second. The longitude and latitude corresponding to each first time point are respectively the longitude and latitude of the position of the magnetic flux leakage detection sensor at the first time point.

[0038] SI30, obtaining a second positioning data recorded by at least one ground marker positioning sensor, after the magnetic flux leakage detection sensor completes the magnetic flux leakage detection on the pipeline.

[0039] Where, the at least one ground marker positioning sensor is buried along the route direction of the pipeline, and the second positioning data includes at least one second time point, and longitude and latitude corresponding to each second time point. The second time point is the time point at which the magnetic flux leakage detection sensor passes the ground marker positioning sensor when it is running in the pipeline.

[0040] Exemplarily, the second positioning data is read and exported by a host computer software, wherein the host computer software is a host computer software of the ground marker positioning sensor.

[0041] Before the magnetic flux leakage detection sensor performs magnetic flux leakage detection on the pipeline, it is necessary to bury several ground marker positioning sensors along the route direction of the pipeline. For example, one ground marker positioning sensor is placed every kilometer along the laying direction of the pipeline.

[0042] S140, fusing the first positioning data and the second positioning data by using temporal synchronization processing and spatial localization inference processing to obtain a fused positioning data.

[0043] In a feasible design, the following method is employed to achieve the fusion of the first positioning data and the second positioning data by using temporal synchronization processing and spatial localization inference processing to obtain the fused positioning data:

[0044] S141, inputting the first positioning data and the second positioning data into a first neural network model.

[0045] The network architecture used by the first neural network model may be selected according to actual needs, and the present disclosure does not limit this. For example, the first neural network model may adopt a Long Short-Term Memory (LSTM) network architecture.

[0046] S142, performing temporal synchronization processing on the first positioning data and the second positioning data by identifying temporal features in the first positioning data and the second positioning data via the first neural network model, to align the time point in the second positioning data with the time point in the first positioning data.

[0047] After the first positioning data and the second positioning data are input into the first neural network model, the first neural network model can identify the temporal features in the first positioning data and the second positioning data by learning the time information in the data. Then, the first neural network model performs temporal synchronization processing on the first positioning data and the second positioning data according to the temporal features in the first positioning data and the second positioning data to ensure that the two sets of data correspond to each other at the same time point.

[0048] For example, one ground marker positioning sensor is arranged at every kilometer on the pipeline, and there is one kilometer between adjacent points. If a first time point output by a first ground marker positioning sensor is 12:50:07.195 on January 24, 2024, while a first time point output by the IMU, which collects one data every 6jlS, is 0.0000058301 second, it thus can be seen that the two sets of data are organized according to their own time series, and one cannot see any corresponding relationship between the corresponding time points in the two sets of data, that is, the two sets of data are not synchronized. After synchronization, the two sets of data correspond to each other at the same time point.

[0049] Reference may be made to the following time point corresponding formula (i.e., formula (1)) as for the corresponding same time point of the two sets of data after synchronization:

[0050] tsync — arg mint||t-ti||, formula (1);

[0051] Wherein, t is a second time point in the second positioning data, tsync is a corresponding same time point of the first positioning data and the second positioning data after temporal synchronization processing, ti is the i-th first time point in the first positioning data, “arg mint” represents a parameter that makes the function value reach the minimum value, and “|| ||” represents norm calculation, which is used to calculate the absolute value.

[0052] It can be seen that the formula (1) is a mathematical implementation for calculating the corresponding same time point of the two sets of data, which is used to help understand the concept of the same time point. Formula (1) means that, after a second time point in the second positioning data which has the smallest time difference from ti is determined, tsync is determined as the time point commonly corresponded by ti and the second time point which has the smallest time difference from ti. tsync may be ti, that is, each second time point in the second positioning data is replaced by a first time point with the smallest time difference therewith, thereby realizing temporal synchronization processing of the two sets of data.

[0053] The above example is based on the following considerations, and the time information of the second positioning data is re-determined based on the time information in the first positioning data:

[0054] (1) The second positioning data is subsequently used to perform interpolation processing on the first positioning data. The time information in the first positioning data is used as the basis. The time information of the second positioning data is re-determined in order to prepare for the subsequent interpolation processing;

[0055] (2) Compared with the first positioning data, the second positioning data includes second time points that are relatively discontinuous, and the interval between two adjacent second time points is too long to be used as a basis for temporal synchronization processing.

[0056] It should be understood that in a case that the first positioning data includes elevation information corresponding to each first time point and mileage information from the initial point, the fused positioning data will also include elevation information corresponding to each time point and mileage information from the initial point.

[0057] S142, predicting, via a second neural network model, a longitude function and a latitude function based on the first positioning data.

[0058] Among them, the longitude function is used to represent the corresponding relationship between the time point sequence and the longitude, and the latitude function is used to represent the corresponding relationship between the time point sequence and the latitude.

[0059] The network architecture used by the second neural network model may be selected according to actual needs, and the present disclosure does not limit this. For example, the second neural network model may adopt a long short-term memory network architecture.

[0060] S143, correcting, via the second neural network model, the first positioning data according to the longitude function and the latitude function.

[0061] As for a first time point ti in the first positioning data, longitude and latitude of the time period [ti-i, ti] may be predicted by the second neural network model. The second neural network model uses the learned rules, historical data and context information to infer the longitude and latitude in the time series, and then predicts the estimated values of the longitude loiii and the latitude lati. The second neural network model predicts the estimated value of the longitude as shown in the following formula (2):

[0062] loni =fNN(IMUdata, ti), formula (2);

[0063] The second neural network model predicts the estimated value of the latitude as shown in the following formula (3):

[0064] lati =gNN(IMUdata, ti), formula (3);

[0065] Among them, Inn and gNN respectively represent the longitude function and the latitude function calculated by the second neural network model, and IMUdata represents the first positioning data.

[0066] In this example, the second neural network model re-predicts the longitude corresponding to each first time point ti according to the longitude function, re-predicts the latitude corresponding to each first time point ti according to the latitude function, and then updates the longitude and latitude of the first positioning data at each first time point to correct the first positioning data.

[0067] S144, performing linear interpolation on the corrected first positioning data by using the second positioning data to obtain a fused positioning data.

[0068] Exemplarily, the longitude of the second positioning data is used to perform linear interpolation on the longitude of the corrected first positioning data, as shown in the following formula (4):

[0069] loninterp=loniiMu+(loniMK-loniiMu)x(ti-ti-i) / (ti+i-ti-i), formula (4);

[0070] The latitude of the second positioning data is used to perform linear interpolation on the latitude of the corrected first positioning data, as shown in the following formula (5):

[0071] latinterp=latiiMu+(latiMK-latiiMu)x(ti-tj-i) / (ti+i-ti-i), formula (5);

[0072] Wherein, loniiMu is an estimated longitude value of the corrected first positioning data at the first time point ti, latiiMu is the estimated latitude value of the corrected first positioning data at the first time point ti, loniMK is the actually recorded longitude value of the second positioning data after temporal synchronization processing at the second time point ti, latiMK is the actually recorded latitude value of the second positioning data after temporal synchronization processing at the second time point ti, loninterp is the interpolated longitude value at the first time point ti in the first positioning data, and latinterp is the interpolated latitude value at the first time point ti in the first positioning data.

[0073] After a long period of use, a lot of dirt and impurities, such as sediment, rust and sand, have been accumulated in the pipeline being inspected, and these dirts continue to accumulate over time, forming a solid scaling layer, which reduces the flow efficiency of the pipeline. Affected by the environment inside the pipeline, the odometer wheel on the magnetic flux leakage detection sensor running in the pipeline is prone to slippage. However, the accuracy of the IMU output data depends largely on whether the odometer wheel is operating normally. Therefore, relying only on IMU data to locate the actual position of the defect on the pipeline may lead to inaccurate locating problems, and thus the data recorded by the ground marker positioning sensor are need for the fusion and correction. [00741 In the above example, the first neural network model can accurately perform temporal synchronization processing on the first positioning data and the second positioning data to ensure that the two sets of data correspond at the same time point, so as to facilitate the subsequent use of the second positioning data to interpolate the first positioning data and realize fusion and correction of the first positioning data. Since the first positioning data output by the IMU is scattered data point, and the accuracy is greatly affected by the environment in the pipeline, the present disclosure uses the second neural network model to predict the longitude function and the latitude function, and re-estimate the longitude and latitude corresponding to each first time point to realize the correction of the first positioning data, thereby further improving the accuracy of the first positioning data. On this basis, the second positioning data is used to perform linear interpolation on the corrected first positioning data to obtain accurate fused positioning data.

[0075] In a feasible design, before the first positioning data and the second positioning data are fused by using temporal synchronization processing and spatial localization inference processing, the first positioning data and / or the second positioning data are subjected to a first preprocessing, and the first preprocessing includes one or more of the following manners: data cleaning processing, time calibration and synchronization processing, and format conversion processing, to ensure the accuracy and consistency of the data.

[0076] Where, the data cleaning processing is to remove redundant data. In the first positioning data output by the IMU, every 6LIS corresponds to a set of time data, which includes mileage data, longitude data, latitude data and elevation data. Because the odometer wheel may undergo uncertain situations on the pipeline, such as slipping, the data recorded by the odometer wheel in this case is invalid. In addition, there may be several consecutive sets of data that are same, in which case, except for the first set of data, the following sets of data are redundant and need to be eliminated. Furthermore, one ground marker positioning sensor may record several data at the same time, and it is also necessary to select the most accurate data and eliminate useless data.

[0077] The format conversion processing refers to converting the unit of the second time point in the second positioning data into the time length from the magnetic flux leakage detection sensor entering the pipeline in seconds, in order to make the unit of the second time point in the second positioning data the same as the unit of the first time point in the first positioning data. For example, the magnetic flux leakage detection sensor starts detection at 12:50, and the second time point output by the first ground marker positioning sensor is 12:52. In other words, it takes 2 minutes for the magnetic flux leakage detection sensor to pass the first ground marker positioning sensor, and the second time point "12:52" recorded by the first ground marker positioning sensor is changed to 120 seconds. [00781 The time calibration and synchronization processing includes performing precise sorting and deduplication operations, as well as time stamp correction operation, on the time stamp data of the first positioning data and the time stamp data of the second positioning data.

[0079] In order to ensure the temporal sequence integrity and accuracy of the first positioning data and the second positioning data, precise sorting and deduplication operations are performed on the timestamp data of the two sets of data. This step is the basis for mitigating potential data conflicts and data duplications.

[0080] The purpose of timestamp correction operation is to identify and correct possible time deviations that may arise from a variety of factors, such as clock drift or time delays caused by signal transmission delays. The implementation of timestamp correction ensures that time information from different data sources can be synchronized under the same time reference.

[0081] The timestamp correction operation includes:

[0082] (1) Performing timestamp verification on the first positioning data and the second positioning data to verify the consistency and integrity of the data.

[0083] (2) Checking whether the time intervals between timestamps conform to the expected time series pattern, and filtering out abnormal or incomplete data.

[0084] (3) Performing timestamp consistency check to ensure that the time information provided by the IMU and ground marker positioning sensor are not misaligned or erroneously aligned during the matching process.

[0085] The purpose of time calibration and synchronization processing is to ensure the consistency of the time series of the two sets of data, and to prepare for the subsequent temporal synchronization processing by the first neural network model on the two sets of data.

[0086] Exemplarily, the first positioning data is subjected to a coordinate system transformation to eliminate data noise and errors.

[0087] Exemplarily, the format of the second positioning data is standardized to facilitate subsequent calculation and analysis.

[0088] The above example can effectively ensure that the time information of different data sources can be correctly matched and aligned through the first preprocessing, thereby achieving subsequent accurate data fusion and extraction of pipeline trend information.

[0089] In one possible design, the method includes:

[0090] Inputting the longitude and latitude in the fused positioning data as an observation into the first Kalman filter, and inputting the corrected first positioning data as another observation into the first Kalman filter;

[0091] Fitting the fused positioning data and the first positioning data by the first Kalman filter to obtain fitted fused positioning data.

[0092] The fused positioning data obtained by linear interpolation is used as the first observation in the Kalman filter, and the corrected first positioning data is used as the second observation, and the resulting observation matrix is constructed as H= [first observation; second observation], where H represents the observation matrix, the first observation is used to update the state estimate of the Kalman filter system. The Kalman filter outputs complete data after the two observations are compared and redundant data is removed.

[0093] The above example simultaneously considers the correlation between the two observations, optimizes the estimation of the state of the Kalman filter system, and forms a complete set of fused positioning data by fitting the fused positioning data and the first positioning data, thereby improving the accuracy of the fused positioning data.

[0094] SI50, analyzing and visualizing the raw data by an inline inspection data analysis software.

[0095] The inline inspection data analysis software is used to analyze signals (i.e. raw data) of various types of magnetic flux leakage detection sensors, to make it easier for data readers to view the raw data and mark pipeline characteristic signals such as defect signals.

[0096] In one possible design, before analyzing and visualizing the raw data by the inline inspection data analysis software, the method includes:

[0097] Performing synchronous processing on the tri-channel odometer wheel by applying synchronous processing mechanism of the odometer wheel to the inline inspection data analysis software.

[0098] Exemplarily, the synchronous processing mechanism of the odometer wheel is implemented in the following manner to synchronize the tri-channel odometer wheel:

[0099] Mileage of each of the three odometer wheels is calculated every 5 seconds, and data of one odometer wheel corresponding to the maximum mileage is taken as data of the other two odometer wheels.

[00100] When the magnetic flux leakage detection sensor is used for detection in the pipeline, in order to minimize impact of odometer wheel jam on the detection, each detector usually deploys three odometer wheels. Therefore, the three odometer wheels need to be synchronized in the inline inspection data software to ensure consistency and accuracy of the data.

[00101] In the above example, the raw data generated by the magnetic flux leakage detection sensor during operation may be obtained in a time sampling manner. According to the odometer wheel synchronization processing mechanism, data of the tri-channel odometer wheel in the raw data are synchronously processed for subsequent data analysis and interpretation.

[00102] For example, the synchronously processed raw data is spread out in the inline inspection data analysis software, after difference analysis, to enable further data analysis and visualization. Through the spread out data, users can intuitively view the changing trend of the odometer wheel data in time and space, helping to identify possible problems or abnormalities in the pipeline.

[00103] Among them, difference analysis refers to converting the time-based collected mileage information into mileage-sorted data. Presenting this data in the software serves the purpose of mileage-based concatenation. This allows multiple scattered files to be merged into a single continuous file based on their mileage information, enabling data browsing.

[00104] SI 60, determining defect data of the pipeline according to the content visualized by the inline inspection data analysis software.

[00105] Wherein, the defect data includes inspection time information and mileage information corresponding to each defect point. The inspection time information includes time information such as timestamp.

[00106] Specifically, after a person marks a defect signal in the inline inspection data analysis software, the inspection time information and mileage information corresponding to the marked defect point are exported from the software to obtain the defect data.

[00107] S170, fitting and comparing the defect data and the fused positioning data to obtain longitude and latitude of each defect point.

[00108] In a feasible design, before aligning the defect data and the fused positioning data using the timestamps of the defect data and the fused positioning data, the method includes:

[00109] Performing a second preprocessing on the defect data, the second preprocessing includes one or more of the followings: format conversion processing, standardization and normalization processing.

[00110] Where, the format conversion processing is used to convert the unit of the time information of the defect data into the same unit as the unit of the time information of the fused positioning data.

[00111] For example, the standardization and normalization processing uses the following formula (6):

[00112] x' =(x- y ) / o, formula (6);

[00113] Where x is the original defect data, y is mean value of the defect data, g is standard deviation of the defect data, and x' is defect data after standardization and normalization processing.

[00114] In the above example, by performing standardization and normalization processing on the data, dimensional differences between different features may be eliminated.

[00115] In a feasible design, the defect data and the fused positioning data are fitted and compared in the following way to obtain the longitude and latitude of each defect point:

[00116] Aligning the defect data and the fused positioning data by using their timestamps;

[00117] Performing cubic spline interpolation fitting on each mileage interval where a defect point is located by using the fused positioning data and the mileage information of each defect point, to obtain the longitude and latitude of each defect point.

[00118] Specifically, the defect data and the positioning data are aligned according to the timestamps in the defect data and the fused positioning data, the time point in the fused positioning data that is closest to each defect point in time can be found, that is, the tp that satisfies the condition |td-tp| to be the smallest is found, where “tp=arg mintp |td-tp|”, td is the time point corresponding to the d-th defect point, tp is the p-th time point in the fused positioning data, and “arg mintp” represents the parameter that makes the value of the function the minimum. After the defect data and the fused positioning data are aligned, for each defect point, cubic spline interpolation fitting is performed in the mileage interval where it is located.

[00119] The mileage interval is a distance between an upstream ground marker positioning sensor and a downstream ground marker positioning sensor of the defect point.

[00120] A starting position of the mileage interval where the defect point is located is defined as a position of an upstream ground marker positioning sensor adjacent to the defect point. The upstream ground marker positioning sensor refers to the nearest ground marker positioning sensor buried before the defect point in the extension direction of the pipeline.

[00121] An end position of the mileage interval where the defect point is located is defined as a position of a downstream ground marker positioning sensor adjacent to the defect point. The downstream ground marker positioning sensor refers to the nearest ground marker positioning sensor buried after the defect point in the extension direction of the pipeline.

[00122] When detecting pipeline defects, considering that the defect distribution on a good quality pipeline is very sparse, and the cubic spline interpolation fitting requires at least 4 parameters, the mileage interval cannot be set too small. And because the ground marker positioning sensor is usually arranged one per kilometer, the mileage information of the output defect point needs to include the distance between the defect point and the ground marker positioning sensor. Therefore, the mileage interval is set to be the position interval between the upstream ground marker positioning sensor and the downstream ground marker positioning sensor of the defect point.

[00123] For example, the following formula (7) and formula (8) are used to implement cubic spline interpolation fitting for each mileage interval where a defect point is located:

[00124] Taking a longitude interpolation function corresponding to the d-th defect point as S_lon_d(m), and a latitude interpolation function as S_lat_d(m), where:

[00125] S__lon_d(m)=aidm3+a2dm2+a3dm+a4d, formula (7);

[00126] S_Jat_d (m)=bidm3 +b2dm2 +b3dm+b4d, formula (8);

[00127] Where, aid, aad, aid, a4d, bid, b2d. baa, bad are obtained by fitting and fusing the positioning data, and m represents the mileage of each defect point.

[00128] Two linear equation systems may be constructed according to the mileage information, longitude information and latitude information in the fused positioning data, each of which contains N equations corresponding to data at N time points. The parameters of the two linear equations are solved to obtain aid, a2d, aaa, a^t. and bid, b2d, bad, b4d.

[00129] The interpolated value of the mileage of each defect point obtained by formula (7) is used as the longitude corresponding to the defect point, and the interpolated value of the mileage of each defect point obtained by formula (8) is used as the latitude corresponding to the defect point.

[00130] In the above example, the defect data and the fused positioning data are first aligned to facilitate the interpolation of the mileage data of the defect point. Then, the present disclosure uses cubic spline interpolation to segmentally fit the curves between the data corresponding to each time point in the fused positioning data (referred to as data points) into a set of cubic polynomials, and requires that these cubic polynomials have the same first-order derivatives and second-order derivatives at adjacent data points to ensure smoothness of the interpolation curve. Thus, a smooth data point curve is obtained, which can better fit the data points of the fused positioning data and the mileage data of the defect point, and provide high-precision interpolation results. By using high-precision interpolation results as the longitude and latitude of the defect point, the accuracy of defect point detection can be further improved.

[00131] In one possible design, the method includes:

[00132] Inputting the longitude and latitude of each defect point into the second Kalman filter as observations, that is, Z=[S_lon_d(m), S_lat_d(m)] (Z represents the matrix composed of the longitude and latitude of each defect point after cubic spline interpolation fitting), and the fused positioning data is input into the second Kalman filter as the predicted value of the system state;

[00133] Fitting the fused positioning data and the longitude and the latitude of each defect point by the second Kalman filter, to obtain fitted longitude and latitude of each defect point.

[00134] Since the fused positioning data comes from the IMU sensor, it may be affected by the IMU sensor noise and external environmental factors, and the longitude and latitude of the defect point obtained by spline interpolation may be limited by the density and quality of data sampling. In addition, the second Kalman filter can estimate the state of the dynamic system from a series of incomplete and noisy measurements. Therefore, the present disclosure uses the second Kalman filter to process and fuse the longitude and latitude data of the defect point and the fused positioning data obtained by spline interpolation to optimize the longitude estimate, latitude estimate and fused positioning data of the defect point. In addition, since the present disclosure can estimate the route direction information of the pipeline based on the fused positioning data and realize trajectory tracking, it can also improve the accuracy of the estimated route direction information of the pipeline.

[00135] In one possible design, the method includes:

[00136] Inputting the fitted longitude and latitude of each defect point, as observations, into the second Kalman filter again, and inputting the fused positioning data into the second Kalman filter as a predicted value of the system state;

[00137] The longitude and latitude of each defect point after fitting and the fused positioning data are fitted by the second Kalman filter again, and the longitude and latitude of each defect point after fitting are corrected.

[00138] In the above example, by employing the second Kalman fdter, combined with the fused positioning data, to re-correct the longitude and latitude of the fitted defect point, the positioning accuracy of the defect point is further improved.

[00139] Exemplarily, the method comprises:

[00140] Determining route direction information of the pipeline based on the fused positioning data;

[00141] Displaying a pipeline route direction map based on the route direction information.

[00142] Exemplarily, an elevation information map is displayed based on the elevation information of the fused positioning data.

[00143] Exemplarily, specific location information of the defect point and the pipeline route direction are displayed in the map, and the location information includes longitude information, latitude information, mileage information and elevation information.

[00144] The raw data output by the tri-channel odometer wheel of the magnetic flux leakage detection sensor is greatly affected by the pipeline environment, which affects the accuracy of the first positioning data output by the inertial measurement unit sensor. Therefore, the accuracy of the position of the defect point determined based on the first positioning data is low. In order to improve the accuracy of locating pipeline defects, the present disclosure obtains the second positioning data recorded by the ground marker positioning sensor, adopts temporal synchronization processing and spatial localization inference processing, uses the second positioning data to fuse and correct the first positioning data, and improves the accuracy of the first positioning data by fusing multi parameters from the sensors. The fused and corrected first positioning data is the fused positioning data. Therefore, by fitting and comparing the defect data and the fused positioning data, accurate longitude and latitude of each defect point can be obtained, which facilitates to carry out the repair work in a timely manner.

[00145] As shown in FIG. 2, the present disclosure also provides a sensor-based pipeline defect locating system, comprising:

[00146] a basic information import module which is configured to input a raw data of a trichannel odometer of a magnetic flux leakage detection sensor to an inertial measurement unit sensor after the magnetic flux leakage detection sensor completes magnetic flux leakage detection on a pipeline;

[00147] The basic information import module is configured to use the inertial measurement unit sensor to perform data solving on the raw data to obtain a first positioning data, where the first positioning data includes at least one first time point and a longitude and a latitude corresponding to each first time point;

[00148] The basic information import module is configured to obtain a second positioning data recorded by at least one ground marker positioning sensor after the magnetic flux leakage detection sensor completes the magnetic flux leakage detection on the pipeline, wherein the at least one ground marker positioning sensor is buried along a route direction of the pipeline; and the second positioning data includes at least one second time point, and a longitude and a latitude corresponding to each second time point; and the second time point is a time point at which the magnetic flux leakage detection sensor passes the ground marker positioning sensor when running in the pipeline;

[00149] A first fusion algorithm module which is configured to fuse the first positioning data and the second positioning data using temporal synchronization processing and spatial localization inference processing to obtain a fused positioning data;

[00150] A display module which is configured to analyze and visualize the raw data using an inline inspection data analysis software;

[00151] A defect data determination module which is configured to determine defect data of the pipeline based on content visualized by the inline inspection data analysis software, where the defect data includes inspection time information and mileage information corresponding to each defect point;

[00152] A second fusion algorithm module which is configured to fit and compare the defect data and the fused positioning data to obtain longitude and latitude of each defect point.

[00153] In a feasible design, the system includes a project processing module configured to perform project initialization, project data storage, and historical project loading. The project refers to a magnetic flux leakage detection performed by the magnetic flux leakage detection sensor on a pipeline.

[00154] Exemplarily, the project processing module includes a project display interface for displaying newly created project data and historical project data. The project data includes data obtained in any process of the disclosed embodiment such as the first positioning data, the second positioning data, the fused positioning data, the raw defect data, etc. The data can be viewed in real time through the project display interface.

[00155] Exemplarily, the system includes a picture display module configured to display a pipeline route direction map and an elevation information map.

[00156] Exemplarily, the system includes a map display module configured to display the pipeline route direction and the location information of the defect point.

[00157] Exemplarily, the map display module is configured to display and record in real time the location information of the defect point in the pipeline system and other related information. By inputting longitude information and latitude information into the map display module, the user can accurately locate a problem area in the pipeline system and take necessary maintenance measures in a timely manner. In addition, when clicking on any point on the pipeline route on the map, the system will quickly display the longitude information and latitude information (i.e., longitude and latitude information) of the point. The present disclosure integrates the map display and the longitude and latitude information display functions, which not only makes the monitoring of the pipeline system more intuitive and efficient, but also provides users with more comprehensive data support.

[00158] In a feasible design, the first fusion algorithm module is implemented in the following manner using temporal synchronization processing and spatial localization inference processing to fuse the first positioning data and the second positioning data to obtain a fused positioning data:

[00159] Inputting the first positioning data and the second positioning data into the a first neural network model;

[00160] Performing temporal synchronization processing on the first positioning data and the second positioning data by identifying temporal features in the first positioning data and the second positioning data via the first neural network model, to align a time point in the second positioning data with a time point in the first positioning data;

[00161] Predicting a longitude function and a latitude function, via a second neural network model, based on the first positioning data, wherein the longitude function is used to represent a corresponding relationship between time point sequence and the longitude, and the latitude function is used to represent a corresponding relationship between the time point sequence and the latitude;

[00162] Correcting, via the second neural network model, the first positioning data based on the longitude function and the latitude function;

[00163] Performing linear interpolation on the corrected first positioning data using the second positioning data, to obtain a fused positioning data.

[00164] In a feasible design, the first fusion algorithm module optimizes the fused positioning data in the following manner:

[00165] Inputting the longitude and latitude in the fused positioning data, as an observation, into a first Kalman filter, and inputting the corrected first positioning data, as another observation, into the first Kalman filter;

[00166] Fitting the fused positioning data and the first positioning data by the first Kalman filter to obtain a fitted fused positioning data.

[00167] In a feasible design, the second fusion algorithm module is implemented in the following way, fitting and comparing the defect data and the fused positioning data to obtain the longitude and latitude of each defect point, including:

[00168] Aligning the defect data and the fused positioning data using their timestamps;

[00169] Performing cubic spline interpolation fitting on each mileage segment interval where each defect point is located, by using mileage information of each defect point and the fused positioning data, to obtain longitude and latitude of each defect point.

[00170] In a feasible design, the second fusion algorithm module is implemented by fitting the longitude and latitude of each defect point and the fused positioning data in the following manner:

[00171] Inputting the longitude and the latitude of each defect point into the second Kalman filter as observations, and inputting the fused positioning data into the second Kalman filter as a predicted value of system state;

[00172] Fitting, via the second Kalman filter, the longitude and the latitude of each defect point and the fused positioning data to obtain longitude and latitude of each defect point after fitting.

[00173] In a feasible design, the second fusion algorithm module is implemented in the following way to correct the longitude and latitude of each defect point after fitting:

[00174] Inputting the longitude and latitude of each defect point after fitting, as observations, into the second Kalman filter again, and inputting the fused positioning data into the second Kalman filter as the predicted value of the system state;

[00175] Fitting, by the second Kalman filter, the longitude and latitude of each defect point after fitting and the fused positioning data again, to correct the longitude and latitude of each defect point after fitting.

[00176] In a feasible design, the system includes a data preprocessing module which is configured to perform a first preprocessing on the first positioning data and / or the second positioning data, and the first preprocessing includes one or more of the following methods: data cleaning processing, time calibration and synchronization processing, and format conversion processing.

[00177] In a feasible design, the data preprocessing module is configured to perform a second preprocessing on the defect data, and the second preprocessing includes one or more of the following methods: format conversion processing, and standardization and normalization processing.

[00178] In a feasible design, the system includes a tri-channel odometer wheel synchronization module, which is configured to apply odometer wheel synchronization processing mechanism to the inline inspection data analysis software, before using the inline inspection data analysis software to analyze and visualize the raw data, to synchronize the tri-channel odometer wheel.

[00179] For other implementations and effects of the above system, please refer to the description of the embodiment of the sensor-based pipeline defect locating method, which will not be repeated herein.

[00180] The basic principle of the present disclosure is described above in conjunction with specific embodiments. However, it should be noted that the benefits, advantages, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these benefits, advantages, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[00181] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a portion of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time point, but can be executed at different time points, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[00182] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems may be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and may be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and may be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and may be used interchangeably therewith.

[00183] It should also be noted that, in the apparatus, device and method of the present disclosure, each component or each step may be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[00184] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be 5 applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[00185] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the 10 forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A sensor-based pipeline defect locating method, comprising:inputting raw data of a tri-channel odometer wheel of a magnetic flux leakage detection sensor to an inertial measurement unit sensor, after the magnetic flux leakage detection sensor completes magnetic flux leakage detection on a pipeline;performing, via the inertial measurement unit sensor, data solving on the raw data to obtain a first positioning data, wherein the first positioning data comprises at least one first time point, and longitude and latitude corresponding to each first time point;obtaining, after the magnetic flux leakage detection sensor completes magnetic flux leakage detection on the pipeline, a second positioning data recorded by at least one ground marker positioning sensor, wherein the at least one ground marker positioning sensor is buried along a route direction of the pipeline; the second positioning data comprises at least one second time point, and longitude and latitude corresponding to each second time point; and the second time point is a time point at which the magnetic flux leakage detection sensor passes the ground marker positioning sensor when running in the pipeline;fusing the first positioning data and the second positioning data using temporal synchronization processing and spatial localization inference processing to obtain a fused positioning data;analyzing and visualizing the raw data by an inline inspection data analysis software;determining defect data of the pipeline according to content visualized by the inline inspection data analysis software, wherein the defect data comprises inspection time information and mileage information corresponding to each defect point; andfitting and comparing the defect data and the fused positioning data to obtain longitude and latitude of each defect point.

2. The method according to claim 1. wherein the fusing the first positioning data and the second positioning data using temporal synchronization processing and spatial localizationinference processing to obtain the fused positioning data comprises:inputting the first positioning data and the second positioning data into a first neural network model;performing temporal synchronization processing on the first positioning data and the second positioning data by identifying temporal features in the first positioning data and the second positioning data via the first neural network model, to align a time point in the second positioning data with a time point in the first positioning data;predicting a longitude function and a latitude function based on the first positioning data via a second neural network model, the longitude function being used to represent a corresponding relationship between a time point sequence and longitude, and the latitude function being used to represent a corresponding relationship between the time point sequence and latitude;correcting, via the second neural network model, the first positioning data based on the longitude function and the latitude function; andperforming linear interpolation on a corrected first positioning data by using the second positioning data to obtain the fused positioning data.

3. The method according to claim 2, wherein the method comprises:inputting longitude and latitude in the fused positioning data as an observation into a first Kalman filter, and inputting the corrected first positioning data as another observation into the first Kalman filter;fitting, via the first Kalman filter, the fused positioning data and the first positioning data to obtain a fitted fused positioning data.

4. The method according to any one of claims 1 to 3, wherein the fitting and comparing the defect data and the fused positioning data to obtain longitude and latitude of each defect point comprises:aligning the defect data with the fused positioning data using timestamps of the defect data25and the fused positioning data;performing cubic spline interpolation fitting on each mileage segment interval where a defect point is located by using the fused positioning data and the mileage information of each defect point, to obtain the longitude and latitude of each defect point.

5. The method according to claim 4, wherein the method comprises:inputting the longitude and latitude of each defect point as observations into the second Kalman filter, and inputting the fused positioning data as a predicted value of a system state into the second Kalman filter;fitting the longitude and latitude of each defect point and the fused positioning data, via the second Kalman filter, to obtain fitted longitude and latitude of each defect point.

6. The method according to claim 5, wherein the method comprises:inputting the fitted longitude and latitude of each defect point as observations into the second Kalman filter again, and inputting the fused positioning data into the second Kalman filter as the predicted value of the system state;fitting, via the second Kalman filter, the fitted longitude and latitude of each defect point and the fused positioning data again, and correcting the fitted longitude and latitude of each defect point.

7. The method according to any one of claims 1 to 3, wherein before fusing the first positioning data and the second positioning data using temporal synchronization processing and spatial localization inference processing, the method comprises:performing a first preprocessing on the first positioning data and / or the second positioning data, wherein the first preprocessing comprises one or more of the followings:data cleaning processing, time calibration and synchronization processing, and formatconversion processing.

8. The method according to claim 5 or 6, wherein before aligning the defect data with the fused positioning data using timestamps of the defect data and the fused positioning data, the JL A A 'method comprises:performing a second preprocessing on the defect data, wherein the second preprocessing comprises one or more of the following methods:format conversion processing, standardization and normalization processing.

9. The method according to any one of claims 1 to 3, wherein before analyzing and visualizing the raw data by the inline inspection data analysis software, the method comprises:applying odometer wheel synchronous processing mechanism to the inline inspection data analysis software to perform synchronous processing on the tri-channel odometer wheel.

10. A sensor-based pipeline defect locating system, comprising:a basic information import module, wherein the basic information import module is configured to input raw data of a tri-channel odometer wheel of a magnetic flux leakage detection sensor to an inertial measurement unit sensor, after the magnetic flux leakage detection sensor completes magnetic flux leakage detection on a pipeline; the basic information import module is configured to use the inertial measurement unit sensor to perform data solving on the raw data to obtain a first positioning data, the first positioning data comprising at least one first time point and longitude and latitude corresponding to each first time point; and the basic information import module is configured to obtain a second positioning data recorded by at least one ground marker positioning sensor after the magnetic flux leakage detection sensor completes magnetic flux leakage detection on the pipeline, the at least one ground marker positioning sensor being buried along a route direction of the pipeline, the second positioning data comprising at least one second time point and longitude and latitude corresponding to each second time point, and the second timepoint is a time point at which the magnetic flux leakage detection sensor passes the ground marker positioning sensor when running in the pipeline;a first fusion algorithm module configured to fuse the first positioning data and the second positioning data using temporal synchronization processing and spatial localization inference 5 processing to obtain a fused positioning data;a display module configured to analyze and visualize the raw data using an inline inspection data analysis software;a defect data determination module configured to determine defect data of the pipeline based on content visualized by the inline inspection data analysis software, the defect data comprising 10 inspection time information and mileage information corresponding to each defect point; anda second fusion algorithm module configured to fit and compare the defect data and the fused positioning data to obtain longitude and latitude of each defect point.PATENT COOPERATION TREATYPCTINTERNATIONAL SERACH REPORT(PCT Article 18 and Rules 43 and 44)Applicant’s or agent’s file reference PCT2420952Y FOR FURTHER ACTION See Form PCT / ISA / 220 as well as, where applicable, item 5 below International application No. PCT / CN2025 / 082076 International filing date (day / month / year) 12 March 2025 (Earliest) Priority Date (day / month / year) 18 July 2024 ApplicantSINOMACH SENSING TECHNOLOGY CO., LTD., et al.This international search report has been prepared by this International Searching Authority and is transmitted to the applicant according to Article 18. A copy is being transmitted to the International Bureau.This international report consists of a total of 5 sheets.CH It is also accompanied by a copy of each prior art document cited in this report.

1. Basis of reporta. With regard to the language, the international search was carried out on the basis of:S the international application in the language in which it was filed□ a translation of the international application into, which is the language of a translation furnished for the purposes of international search (Rule 12.3 (a) and 23.1 (b))b. □ This international search report has been established taking into account the rectification of an obvious mistake authorized by or notified to this Authority under Rule 91 (Rule 43.6 bis (a)).c. O With regard to any nucleotide and / or amino acid sequence disclosed in the international application, see Box No. 1.

2. □ Certain claims were found unsearchable (see Box No.II)3. □ Unity of invention is lacking (see Box No. Ill)4. With regard to the title,the text is approved as submitted by the applicantQ the text has been determined by this Authority to read as follows:

5. With regard to the abstract,□ the text is approved as submitted by the applicantthe text has been established, according to Rule 38.2(b), by this Authority as it appears in Box No. IV. The applicant may, within one month from the date of mailing of this international search report, submit comments to this Authority6. With regard to the drawings,a. the figure of the drawings to be published with the abstract is Figure No. 1 as suggested by the applicant□ as selected by this Authority, because the applicant failed to suggest a figure□ as selected by this Authority, because this figure better characterizes the inventionb. □ none of the figures is to be published with the abstractInternational application No.PCT / CN2025 / 082076Box No. IV Text of the abstract (continuation of item 5 on page 1)Provided is a sensor-based pipeline defect positioning method and system, which accurately locates the position of pipeline defect points by multi-parameter fusion, and relates to the technical field of oil and gas pipeline maintenance. The method includes: after the magnetic flux leakage detection sensor completes the magnetic flux leakage detection on the pipeline, the original data of the three-way odometer of the magnetic flux leakage detection sensor is input into the inertial measurement unit sensor, and the first positioning data output by the inertial measurement unit sensor through data solution of the original data is obtained; the second positioning data recorded by at least one ground marker positioning sensor buried along the direction of the pipeline is obtained; the first positioning data and the second positioning data are fused by time synchronization processing and space inference positioning processing to obtain fused positioning data; the pipeline defect data is determined according to the content of the visual display of the original data by the internal detection data analysis software; the defect data and the fused positioning data are fitted and compared to obtain the longitude and latitude of each defect point.International application No.PCT / CN2025 / 082076A. CLASSIFICATION OF SUBJECT MATTER G01N27 / 83(2006.01)i According to International Patent Classification (IPC) or to both national classification and IPC B. FIELDS SEARCHED Minimum documentation searched (classification system and classification numbers should be stated) IPC: G01N27 F17D5 Documentation searched other than minimum documentation to the extent that such documents are included in the fields searched Electronic data base consulted during the international search (name of data base and, where practicable, search terms used) CNTXT,ENTXTC,VEN,CJFD,CNKI: positioning, pipeline, leakage magnetic, defect, inertia, fusion, time, space, synchronization, longitude, latitude, position, positioning, correction, neural network, pipeline, position, defect, magnetic leakage, neural network C. DOCUMETNS CONSIDERED TO BE RELEVANT Category* Citation of document, with indication, where appropriate, of the relevant passages Relevant to claim No. PX CN 118501248 A (SINOMACH SENSING TECHNOLOGY CO., LTD., SHENYANG ACADEMY OF INSTRUMENTATION SCIENCE CO., LTD.) August 16, 2024 (2024-08-16) Claims 1-10 1-10 A CN 118330019 A (Beijing Dichuan International Energy Services Co., Ltd. Dichuan Energy Management (Beijing) Co,, Ltd.) July 12, 2024 (2024-07-12) Paragraphs 5-16 of the Specification and Figures 1 -2 1-10 A CN 101986095 A (Tianjin University) March 16, 2011 (2011-03-16) foil text 1-10 A CN 107228662 A (Harbin Engineering University Shanghai Hangshi Marine Technology Co., Ltd.) October 3, 2017 (2017-10-03) full text 1-10 A CN 117570381 A (Qingdao Tianshi Intelligent Aviation Technology Co., Ltd., Northern China University of Technology, Qingdao Xinglinuo New Energy Technology Co., Ltd.) February 20, 2024 (2024-02-20) full text 1-10 A CN 103697886 A (PetroChina Co., Ltd.) April 2, 2014 (2014-04-02) full text 1-10 S Further documents are listed in the continuation of Box C. See patent family annexInternational application No.PCT / CN2025 / 082076* Special categories of cited documents: “I” later document published after the international „, , , . , , „ , filing date or priority date and not in conflict with the A document defining the general state of the art ., , , _ ., , , application but cited to understand the principle or which is not considered to be of particular relevance . , , , ,. , , theory underlying the invention D document cited by the applicant m the international application “E” earlier application or patent but published on or “X” document of particular relevance; the claimed after the international filing date invention cannot be considered novel or cannot be considered to involve an inventive step when the document is taken alone “L” document which may throw doubts on priority “Y” document of particular relevance; the claimed claim (s) or which is cited to establish the publication invention cannot be considered novel or cannot be date of another citation or other special reason (as considered to involve an inventive step when the specified) document is combined with one or more other such “0” document referring to an oral disclosure, use, documents, such combination being obvious to a exhibition or other means person skilled in the art “P” document published prior to the international filing document member of the same patent family date but later than the priority date claimed Date of the actual completion of the international search 3 April 2025 Date of mailing the international search report 22 April 2025 Name and mailing address of the ISA / CN State Intellectual Property Office of People’s Republic of China No. 6, Xitucheng Lu, Jimenqiao Haidian District, Beijing City, 100088 Authorized officer: ZHANG Huaping Tel. No.: 86-27-59371511International application No.PCT / CN2025 / 082076C (CONTINUT1ON). DOCUMENTS CONSIDERED TO BE RELEVANT Category* Citation of document, with indication, where appropriate, of the relevant passages Relevant to claim No. A CN 111912897 A (China University of Petroleum (Beijing)) November 10, 2020 (2020-11-10) fall text 1-10 A US 2020210826 Al (UNIV NORTHEASTERN) July 2, 2020 (2020-07-02) full text 1-10 A CN 111815561 A (CNOOC (China) Co., Ltd. CNOOC Energy Development Equipment Technology Co., Ltd. CNOOC Technology Testing Co., Ltd.) October 23, 2020 (2020-10-23) full text 1-10INTERNATIONAL SERACH REPORTInformation on patent family membersInternational application No.PCT / CN2025 / 082076Patent document cited in search report Publication date Patent family member (s) Publication date CN 118501248A August 16,2024 CN 118501248B September 13,2024 CN 118330019 A July 12, 2024 CN 118330019 B August 16, 2024 CN 101986095 A March 16,2011 CN 101986095 B January 11,2012 CN 107228662 A October 3, 2017 CN 107228662 B June 23, 2020 CN 117570381 A February 20, 2024 no CN 103697886 A April 2,2014 no CN 111912897 A November 10, 2020 no US 2020210826 Al July 2, 2020 US 11488010 B2 November 1, 2022 CN109783906A May 21,2019 WO 2020133639 Al July 2, 2020 CN 109783906 B July 7, 2023 CN 111815561 A October 23, 2020 CN 111815561 B April 16, 2024

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