A target positioning method in a steel pipeline based on double-window phase locking fusion

Abstract

This invention relates to the field of oil and gas pipeline detection and positioning technology, and discloses a target positioning method for steel pipelines based on dual-window phase-locked loop fusion. The method involves acquiring raw voltage sampling sequences and receiver motion information; after preprocessing, sliding window phase-locked loop detection is performed using short and long time windows respectively, and normalized fusion is used to obtain a fused phase-locked loop response amplitude sequence; the data is written to a rolling buffer in time blocks, and segmented sliding window local detection is performed on the fused phase-locked loop response amplitude sequence. Within each local detection window, a two-stage periodic search, local background adaptive threshold filtering, local peak refinement and merging, and periodic continuity constraint judgment are performed, outputting candidate reception event times and local detection conclusions; after merging across windows, a global candidate reception event time sequence is output; this is mapped to a candidate reception position sequence, and the target position is estimated through position clustering, and the relative distance is output. This invention can suppress false alarms caused by pure noise and achieve automatic detection and positioning of periodic signals in local time periods.

Description

Technical Field

[0001] This invention relates to the field of oil and gas pipeline inspection and positioning technology, and in particular to a target positioning method for steel pipelines based on dual-window lock-in fusion. Background Technology

[0002] In the operation and maintenance of steel oil and gas pipelines, it is often necessary to identify and locate the equipment or targets within the pipeline. For example, when a pipeline pig becomes stuck due to pipeline deformation, foreign object accumulation, structural changes, or operational abnormalities, it is necessary to quickly determine its actual location within the pipeline in order to carry out subsequent troubleshooting, repair, or recovery operations. Furthermore, in scenarios involving the identification and tracking of launching devices, detection devices, or other targets within the pipeline, there is also a need for real-time detection and location of the targets.

[0003] To address these requirements, a transmitter is typically mounted on a pipeline pig, allowing it to move alongside the pig inside the steel pipeline. When the pig becomes stuck, the transmitter stops at the corresponding position and periodically supplies a very low-frequency alternating current to its transmitting coil, generating an alternating magnetic field. An external receiving coil inducts this alternating magnetic field and outputs a voltage signal, which is then processed by a host computer for data processing, target detection, and position estimation. Because the transmitter is fixedly connected to the pig, the results of magnetic signal detection and positioning can be used to determine the pig's actual location within the pipeline.

[0004] However, steel pipes provide significant shielding and attenuation for magnetic field propagation. Furthermore, the receiving environment often suffers from superimposed power frequency interference, low-frequency drift, and random noise. Therefore, the target signal acquired by the receiver typically exhibits characteristics such as weak amplitude, unknown start time, appearance only in local time periods, and low signal-to-noise ratio, making automatic detection and localization quite challenging.

[0005] In existing technologies, Fourier transform is often used for spectrum analysis of received signals, or continuous wavelet transform is used for time-frequency analysis. Fourier transform is suitable for overall frequency domain analysis, but it is difficult to automatically output candidate reception event times for signals with unknown start times and only existing in local time periods; although continuous wavelet transform can reflect local time-frequency energy distribution, it usually relies on manual observation and auxiliary interpretation, and it is difficult to directly form stable automatic detection and automatic positioning output.

[0006] Therefore, a real-time detection and positioning method for extremely low frequency periodic magnetic signals is needed in the shielded environment of steel pipes. This method should not only be able to suppress false alarms under pure noise conditions and achieve automatic detection when target signals are present in local time periods, but also be able to combine receiver position information or velocity information to achieve target position estimation and target distance output relative to the current receiver position. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a target positioning method for steel pipes based on dual-window phase-locked fusion, which aims to suppress false alarms caused by pure noise in the shielded environment of steel pipes, achieve automatic detection of periodic magnetic signals in local time periods, and combine receiver motion information to output the estimated position and relative distance of the target in real time.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: A method for locating a target inside a steel pipe based on dual-window lock-in fusion includes the following steps: Step 1: Acquire the original voltage sampling sequence output by the receiving coil, and acquire or construct receiver motion information that is time-synchronized with the original voltage sampling sequence; Step 2: Preprocess the original voltage sampling sequence, then perform sliding window phase-locked detection using short and long time windows respectively, and normalize and fuse the obtained short window phase-locked response amplitude sequence and long window phase-locked response amplitude sequence to obtain the fused phase-locked response amplitude sequence; Step 3: Write the original voltage sampling sequence and receiver motion information into the rolling buffer continuously in time blocks, and perform segmented sliding window local detection on the fused phase-locked response amplitude sequence in the current rolling buffer. Perform two-stage period search, local background adaptive threshold screening, local peak refinement and merging, and period continuity constraint judgment in each local detection window, and output the candidate reception event time and local detection conclusion for each local detection window. Step 4: Merge the detection results of multiple local detection windows across windows, determine whether there is a target signal in the current rolling buffer, and output the global candidate reception event time sequence; Step 5: Based on the position sequence in the receiver motion information, map the global candidate reception event time sequence to a candidate reception position sequence, then perform position clustering on the candidate reception position sequence, estimate the target position, and output the distance of the target relative to the current receiver position.

[0009] In the above scheme, the receiver motion information in step 1 includes any of the following: Receiver absolute position sequence; Receive the velocity sequence and integrate it to obtain the position sequence; When position and velocity sequences are missing, a receiver position sequence is constructed based on a preset constant velocity.

[0010] In the above scheme, the preprocessing in step 2 includes, in sequence: mean removal, trend removal, high-pass filtering, power frequency notch filtering, and target frequency band bandpass filtering; wherein, the high-pass cutoff frequency is 0.2 Hz, the power frequency notch filtering frequencies include 50 Hz, 100 Hz and 150 Hz, and the bandpass frequency is set around the target transmission frequency.

[0011] In the above scheme, in step 2, the short window time span is 0.5s, the long window time span is 0.8s, the fusion weight is 0.5, and the sliding window step size is 0.05s.

[0012] In the above scheme, the segmented sliding window local detection in step 3 specifically includes: (1) The fusion phase-locked response amplitude sequence in the current rolling buffer is divided into multiple local detection windows by sliding segments with a length of 36s and a step size of 6s. (2) For each local detection window, the optimal period, optimal starting offset and theoretical period sampling time sequence are determined by a two-stage periodic search combining coarse search and fine search. (3) Select a local background interval near each theoretical period sampling point, calculate the local background median and robust standard deviation, construct a local background robust adaptive threshold, and screen effective candidate period points; (4) The effective candidate periodic points are refined by local peak refinement and candidate peak merging to obtain the refined candidate reception event time sequence; (5) Calculate the interval hit ratio, adjacent interval variation coefficient and the longest consecutive hit segment length based on the refined candidate reception event time sequence, and make dual-channel target existence judgment in combination with the optimal statistics, and output the local detection conclusion of each local detection window.

[0013] In the above scheme, the dual-channel target existence determination includes a standard detection channel and a strong signal detection channel; The decision criteria for the standard detection channel include: when the optimal statistic is greater than the adaptive score threshold, the original hit rate is greater than 0.25, the number of candidate points after refinement is not less than 4, the average local standardized response is greater than 4.0, the interval hit ratio is not less than 0.45, the coefficient of variation of adjacent intervals is not greater than 0.35, the length of the longest consecutive hit segment is not less than 4, and the deviation between the optimal period and the theoretical repetition period is less than 0.05 s, the target signal is determined to exist in the current local detection window. The method for determining a strong signal detection channel is as follows: when the robust Z-score is greater than 4.0, the number of candidate points after refinement is not less than 4, the average local normalized response is greater than 6.0, the maximum local normalized response is greater than 10.0, the interval hit ratio is not less than 0.60, the coefficient of variation of adjacent intervals is not greater than 0.20, the length of the longest consecutive hit segment is not less than 5, and the deviation between the optimal period and the theoretical repetition period is less than 0.05 s, the target signal is determined to exist in the current local detection window. If any channel meets the above conditions, it is determined that there is a target signal in the current local detection window, and the candidate reception event time sequence and local detection conclusion of the local detection window are output.

[0014] In the above scheme, in step 4, the preset threshold for determining the presence of a target signal in the current rolling buffer is 1, that is, when at least one local detection window is determined to have a target signal, the presence of a target signal is determined; the cross-window merging tolerance is set to 20%-40% of the theoretical period of the signal.

[0015] In the above scheme, in step 5, when performing position clustering on the candidate received position sequence, if the difference between two candidate positions is less than the position merging tolerance, they are merged into the same position cluster, and the maximum intensity or weighted intensity in the position cluster is used as the representative intensity of the position cluster; the target position cluster with the largest intensity is selected, and its cluster center position is used as the target estimated position.

[0016] In the above scheme, in step 5, the distance of the target relative to the current receiver position... The calculation formula is: ; in, Estimate the location of the target. This indicates the current receiver position. when When indicates that the target is in front of the current receiver, when The time indicates that the target is located behind the current receiver.

[0017] In the above scheme, in step 3, the duration of a single data block is set to 0.5s, and the detection update cycle is set to 1.0s; in the local peak refinement, the local peak search half-width is 0.2s, and the repeated peak merging threshold is 0.4s.

[0018] Through the above technical solution, the target positioning method inside a steel pipeline based on dual-window phase-locking fusion provided by the present invention has the following beneficial effects: 1. Real-time rolling detection capability: Through the rolling buffer and block online update mechanism, the present invention can process continuously received data in real time without waiting for the entire data acquisition to be completed before offline analysis, which significantly improves the response efficiency of on-site operations.

[0019] 2. Strong ability to suppress false alarms caused by pure noise: Through comprehensive means such as dual-window long-term phase-locked fusion, local background robust threshold, dual-channel decision and periodic continuity constraint, this invention can effectively suppress false alarms caused by local pseudo-peaks or pseudo-periodic peak clusters under pure noise conditions, thereby improving the reliability of detection results.

[0020] 3. Adaptable to local time period signal detection: The segmented sliding window local detection mechanism solves the problem of missed detection caused by "signal in the first half and no signal in the second half" when making a unified judgment for the whole segment. It can stably detect periodic magnetic signals that exist only in local time periods.

[0021] 4. Abundant output information: This invention can not only determine whether the target periodic signal exists, but also output a global candidate reception event time sequence. Furthermore, by combining the receiver motion trajectory information, it can obtain the estimated position of the target and the distance of the target relative to the current receiver position, providing comprehensive data support for on-site troubleshooting and positioning operations.

[0022] 5. Applicable to mobile receiving scenarios: Many existing solutions are only effective under fixed receiving conditions and are difficult to achieve position estimation when the receiver moves along the pipeline. This invention can effectively support target localization under mobile receiving conditions by synchronously mapping candidate receiving times with receiver position sequences.

[0023] 6. Strong engineering applicability: This invention is specifically designed for practical engineering needs such as steel pipe shielding environment, extremely low frequency target frequency, periodic transmission, low signal-to-noise ratio at the receiving end, and mobile receiving and positioning. The algorithm is robust and the parameters are adjustable, making it easy to implement and promote in engineering. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0025] Figure 1 This is a schematic diagram of a target positioning method for steel pipes based on dual-window lock-in fusion, as disclosed in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] This invention provides a target positioning method inside a steel pipeline based on dual-window phase-locked fusion, such as... Figure 1 As shown, it is suitable for real-time detection and positioning of extremely low frequency periodic magnetic signals emitted by the transmitter moving with the pig in a steel oil and gas pipeline shielding environment.

[0028] I. Signal Acquisition and Motion Information Acquisition First, the original voltage sampling sequence output by the receiving coil is acquired, and receiver motion information synchronized with the original voltage sampling sequence is acquired or constructed.

[0029] Specifically, the receiving coil moves along the outside of the pipe, inducing an alternating magnetic field generated by the transmitter and outputting a voltage signal. At the sampling frequency... The voltage signal is sampled to obtain the original voltage sampling sequence. The transmitter operates periodically. When an extremely low-frequency alternating current is applied, the transmitter emits a target magnetic signal. In this embodiment, the frequency of the target magnetic signal is 14 Hz. Simultaneously, this target signal repeats approximately every 3 seconds, which is the theoretical repetition period. =3.0s. The receiving end samples the output voltage of the receiving coil at a sampling frequency of 400 Hz to obtain the original voltage sampling sequence.

[0030] Simultaneously, receiver motion information is obtained through one of the following three methods: Method 1: Directly acquire the receiver's absolute position sequence using GPS or pipeline odometer. ; Method 2: Acquiring receiver speed sequence And the position sequence is obtained through numerical integration; Method 3: When position and velocity information are unavailable, assume the receiver operates at a constant speed. Move to construct a position sequence. In this embodiment, when no position or velocity column is provided in the input data, a constant velocity of 0.7 m / s is used by default to construct the receiver position sequence.

[0031] This embodiment preferably adopts method two, which uses an encoder installed on the receiver to obtain the velocity sequence, and then integrates it to obtain the position sequence, so as to ensure time synchronization with the sampling sequence.

[0032] II. Integration of Pre-processing and Double-Window Lock Original voltage sampling sequence Preprocessing is performed, and a fused phase-locked response amplitude sequence is obtained by using a dual-window long sliding window phase-locked fusion.

[0033] Preprocessing includes the following steps: Mean subtraction: Subtract the arithmetic mean of the entire sequence; Detrending: Fit the trend term of the polynomial using the least squares method and subtract it; High-pass filter: The cutoff frequency is set to 0.2 Hz to suppress low-frequency drift; Power frequency notch filtering: Notch filters are designed for 50 Hz, 100 Hz and 150 Hz respectively to suppress power frequency and its harmonic interference; Target bandpass filtering: The bandpass center frequency is set to the target magnetic signal frequency. In this embodiment ; Bandpass half-bandwidth set to Therefore, the bandpass range is 13Hz–15Hz. By performing narrowband bandpass filtering around the target magnetic signal frequency, out-of-band noise can be further suppressed, improving the signal-to-noise ratio of subsequent phase-locked detection. The preprocessed signal is then used to obtain the bandpass signal. .

[0034] Then, short time windows were used respectively. and long window For bandpass signals Perform sliding window phase-locked loop (PLL) detection. For each data segment within the detection window, construct in-phase and quadrature reference signals, and calculate the in-phase component. Orthogonal components And obtain the phase-locked response amplitude: ; Obtain the short-window phase-locked response amplitude sequence respectively and long window phase lock response amplitude sequence Then, the two are robustly normalized and then fused according to their weights to obtain the fused phase-locked response amplitude sequence. .

[0035] In this embodiment, the short window time span =0.5s, long window duration =0.8s, with fusion weights of 0.5 and 0.5 respectively, and a sliding window step size of 0.05s.

[0036] III. Rolling Buffer and Segmented Local Detection The raw data and motion information are written into the rolling buffer in time blocks, and the fused phase-locked response amplitude sequence in the buffer is subjected to segmented sliding window local detection.

[0037] The rolling buffer receives new data in time blocks, with each data block lasting 0.5 seconds and the detection update cycle being 1.0 second. The preferred length of the rolling buffer is 180 seconds. During each update, the newly arrived raw sampling data, corresponding time series, receiver position sequence, and receiver velocity sequence are written into the rolling buffer. When the length of the raw data in the buffer and the effective fusion response length simultaneously meet the detection initiation conditions, an online detection process is triggered. For long-term recorded data, the preferred local detection window length is 36 seconds, the preferred sliding step size is 6 seconds, and the preferred minimum effective detection window length is 18 seconds. When the total data length is short, the local detection window length, sliding step size, and corresponding decision threshold can be adaptively shortened to maintain local detection stability.

[0038] The purpose of this step is to avoid missed detections due to the dilution of local effective signals by the absence of signals in the non-signal section during the unified decision-making process.

[0039] For each local detection window, perform the following sub-steps: (1) Two-stage periodic search For each local detection window, a coarse search is performed first, followed by a fine search.

[0040] In the coarse search phase, candidate periods are searched within the period search range using a relatively large step size. And search for candidate starting offsets within the starting offset interval. .

[0041] In the fine search phase, a fine search is conducted around the optimal period obtained from the coarse search, using smaller step sizes.

[0042] For any set of candidate periods in the coarse search phase and the fine search phase and candidate starting offset All of them construct a periodic sampling time sequence: ; Among them, the coarse search stage For the coarse search candidate period, the fine search phase Candidate periods are obtained by further refining the search around the optimal period of the coarse search; This represents the candidate start offset within the corresponding candidate period.

[0043] And the fused phase-locked response amplitude sequence corresponding to the current local detection window Interpolation yields the response values ​​at each sampling time. And construct periodic matching statistics: ; in, This represents the number of periodic sampling points under the current candidate parameters.

[0044] The optimal period of the current local detection window is determined by comparing the statistics corresponding to each candidate parameter. Optimal starting offset and the corresponding optimal statistic .

[0045] At the same time, by the optimal period and optimal starting offset Construct the theoretical periodic sampling time sequence within the current local detection window And obtain the theoretical period sampling time in the fused phase-locked response amplitude sequence. The corresponding sampled response sequence on Among them, the optimal period Used with theoretical period The comparison is used as a reference period for subsequent continuous decisions; the optimal starting offset is used. With the optimal period Jointly determine the theoretical periodic sampling time sequence Starting position; optimal statistic It is used to characterize the overall matching strength of the target periodic structure within the current local detection window and serves as an important basis for subsequent local detection window decisions.

[0046] (2) Local background adaptive threshold filtering For the obtained theoretical periodic sampling time sequence and corresponding sampled response sequence Select a local background interval near each theoretical period sampling point (avoiding the current sampling point). For example, data within a 1-second range before and after the sampling point can be used as the local background interval. Perform robust statistics on the local background response and calculate the local background median. robust standard deviation to local background And based on this, the locally standardized response is obtained: ; Localized Response It is used to characterize the significance of the current theoretical periodic sampling point relative to the local background noise, that is, the relative intensity of the sampling point being higher than the local background.

[0047] Furthermore, based on the local background median and local background robust standard deviation Constructing a local background robust adaptive threshold: ; in, The local background robust adaptive threshold corresponding to the sampling point of the nth theoretical period; This is the threshold coefficient, used to control the tightness or looseness of local thresholds.

[0048] Based on the local background robust adaptive threshold and the local normalized response, the theoretical periodic sampling points are initially screened, and effective candidate periodic points that are significantly higher than the local background are retained.

[0049] After obtaining the local normalized responses of each theoretical period sampling point, candidate points are initially screened based on the local normalized responses. In this embodiment, if the local normalized response of a certain theoretical period sampling point is greater than 2.5, the point is retained as a valid candidate period point; otherwise, the point is considered as a local background noise point and is discarded. The threshold coefficient in the local background robust adaptive threshold is set to 2.3 to control the tightness of the local background threshold.

[0050] (3) Local peak refinement and merging For each valid candidate period point, the theoretical period sampling time sequence is obtained. For reference, local true peak values ​​are searched within a preset time range near each candidate periodic point, and the theoretical periodic sampling time is corrected to the local true peak time; for adjacent time differences less than a preset merging threshold, The repeated peaks are selected, and only the one with the larger peak value is retained, thus obtaining the refined candidate received event time sequence and its corresponding peak intensity.

[0051] In this embodiment, the local peak search half-width is 0.2s, and the repeated peak merging threshold is... It takes 0.4 seconds.

[0052] (4) Periodic continuity constraint judgment For the refined candidate reception event time sequence, calculate the time interval between adjacent candidate reception event times. Let the refined candidate reception event time sequence be... Where M is the number of candidate reception event moments after refinement, then the th Each adjacent time interval is defined as: ; in, This represents the length of the candidate received event time sequence after refinement. The theoretical launch period, To pre-determine the period tolerance, in this embodiment The value is 0.2s, which is used to limit the allowable deviation range of the interval between adjacent candidate reception events from being close to the theoretical repetition period.

[0053] The period deviation between the optimal period and the theoretical repetition period is defined as: ; Where T is the optimal period obtained from the two-stage periodic search. In this embodiment, when At that time, it is assumed that the current local detection window satisfies the periodic deviation constraint.

[0054] Further define the interval hit index function: When a certain adjacent time interval satisfies: ; If this condition is met, then the adjacent time interval is recorded as a hit; otherwise, it is recorded as a miss.

[0055] Based on the above definition, the following continuity index is constructed: First, the interval hit ratio, which is used to characterize the proportion of adjacent candidate time intervals falling within the neighborhood of the theoretical period: ; Second, the adjacent interval variation coefficient is used to characterize the dispersion of the candidate received event interval: ; in, To prevent tiny positive numbers with a denominator of zero, This represents the standard deviation of the refined sequence of time differences between adjacent candidate reception events. This is the average value of the time difference sequence of adjacent candidate reception events after refinement.

[0056] Third, the longest consecutive hit segment length is used to characterize the length of the longest candidate target response sequence that continuously satisfies the periodic constraint.

[0057] set up The longest consecutive segment containing 1s includes The longest consecutive hit segment length is defined as follows: (The original text contains several characters and symbols, which are difficult to translate accurately without context. A more accurate translation would require the full context.) ; Among them, the continuity index is directly obtained from the time sequence of the refined candidate target response, and is used to characterize the periodic consistency of the candidate response points in the current local detection window on the time axis.

[0058] A standard detection channel and a strong signal detection channel are set up in each local detection window.

[0059] The standard detection channel is determined as follows: when the optimal statistic is greater than the adaptive score threshold, the original hit rate is greater than 0.25, the number of refined candidate points is not less than 4, the average local standardized response is greater than 4.0, the interval hit ratio is not less than 0.45, the coefficient of variation of adjacent intervals is not greater than 0.35, the length of the longest consecutive hit segment is not less than 4, and the deviation between the optimal period and the theoretical repetition period is less than 0.05 s, the target signal is determined to exist in the current local detection window. In this embodiment, the adaptive score threshold is defined as the median of the statistics corresponding to all candidate parameters in the fine search stage within the current local detection window plus 4.5 times the robust standard deviation. Therefore, the adaptive score threshold for each window is somewhat different.

[0060] The method for determining a strong signal detection channel is as follows: when the robust Z-score is greater than 4.0, the number of candidate points after refinement is not less than 4, the average local normalized response is greater than 6.0, the maximum local normalized response is greater than 10.0, the interval hit ratio is not less than 0.60, the coefficient of variation of adjacent intervals is not greater than 0.20, the length of the longest consecutive hit segment is not less than 5, and the deviation between the optimal period and the theoretical repetition period is less than 0.05 s, the target signal is determined to exist in the current local detection window.

[0061] If any channel meets the above conditions, it is determined that there is a target signal in the current local detection window, and the candidate reception event time sequence and local detection conclusion of the local detection window are output.

[0062] IV. Cross-window merging and global judgment The system analyzes the detection results of multiple local detection windows. When the number of local detection windows identified as containing a target signal reaches a preset threshold, the system determines that a target signal exists within the current rolling buffer for a given time period. To prevent signal omissions during testing, the preset threshold is set to 1 by default. When the number of local detection windows identified as containing a target signal is greater than or equal to 1, the system determines that a target signal exists.

[0063] Furthermore, candidate reception event times output from multiple local detection windows are merged across windows. If the difference between two candidate times is less than the cross-window merging tolerance, they are merged into a single global candidate reception time, and the larger peak intensity is retained as the representative intensity of that time. To reduce the omission of candidate points and avoid repeated display of single candidate points, 20%-40% of the theoretical signal period is generally selected. For example, if the theoretical signal period is 3s, the cross-window merging tolerance can be set to 0.6s-1.2s.

[0064] The output of this step is: a global candidate received event time sequence and a corresponding candidate time intensity sequence.

[0065] V. Location Mapping and Target Localization According to the receiver position sequence Global candidate reception time Mapped to candidate receiving locations : ; If the collected data is a receiver velocity sequence First, construct the position sequence by summing: ; Then, the mapping from time to position is completed.

[0066] The candidate receiving location sequences are clustered along their location axes. If the difference between two candidate locations is less than the location merging tolerance (2 meters in this embodiment), they are merged into the same location cluster, and the maximum intensity or weighted intensity in that location cluster is used as the representative intensity of the location cluster.

[0067] The cluster of target locations with the highest intensity is selected, and the center of this cluster is used as the estimated target location. .

[0068] At the same time, the current receiver position is recorded as Output the distance of the target relative to the current receiver position: ; Among them, when "In real time" indicates that the target is in front of the current receiver. The time indicates that the target is behind the current receiver. The final output includes the estimated target position, the current receiver position, and the relative distance.

[0069] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for target positioning in a steel pipeline based on double-window phase-locked fusion, characterized in that, Includes the following steps: Step 1: Acquire the original voltage sampling sequence output by the receiving coil, and acquire or construct receiver motion information that is time-synchronized with the original voltage sampling sequence; Step 2: Preprocess the original voltage sampling sequence, then perform sliding window phase-locked detection using short and long time windows respectively, and normalize and fuse the obtained short window phase-locked response amplitude sequence and long window phase-locked response amplitude sequence to obtain the fused phase-locked response amplitude sequence; Step 3: Write the original voltage sampling sequence and receiver motion information into the rolling buffer continuously in time blocks, and perform segmented sliding window local detection on the fused phase-locked response amplitude sequence in the current rolling buffer. Perform two-stage period search, local background adaptive threshold screening, local peak refinement and merging, and period continuity constraint judgment in each local detection window, and output the candidate reception event time and local detection conclusion for each local detection window. Step 4: Merge the detection results of multiple local detection windows across windows, determine whether there is a target signal in the current rolling buffer, and output the global candidate reception event time sequence; Step 5: Based on the position sequence in the receiver motion information, map the global candidate reception event time sequence to a candidate reception position sequence, then perform position clustering on the candidate reception position sequence, estimate the target position, and output the distance of the target relative to the current receiver position.

2. The method of claim 1, wherein, The receiver motion information in step 1 includes any of the following: Receiver absolute position sequence; Receive the velocity sequence and integrate it to obtain the position sequence; When position and velocity sequences are missing, a receiver position sequence is constructed based on a preset constant velocity.

3. The method of claim 1, wherein, The preprocessing in step 2 includes, in sequence: mean removal, trend removal, high-pass filtering, power frequency notch filtering, and target frequency band bandpass filtering; wherein, the high-pass cutoff frequency is 0.2 Hz, the power frequency notch frequencies include 50 Hz, 100 Hz and 150 Hz, and the bandpass frequency is set around the target transmission frequency.

4. The method of claim 1, wherein, In step 2, the short window time span is 0.5s, the long window time span is 0.8s, the fusion weight is 0.5, and the sliding window step size is 0.05s.

5. The method of claim 1, wherein, The segmented sliding window local detection in step 3 specifically includes: (1) The fusion phase-locked response amplitude sequence in the current rolling buffer is divided into multiple local detection windows by sliding segments with a length of 36s and a step size of 6s. (2) For each local detection window, the optimal period, optimal starting offset and theoretical period sampling time sequence are determined by a two-stage periodic search combining coarse search and fine search. (3) Select a local background interval near each theoretical period sampling point, calculate the local background median and robust standard deviation, construct a local background robust adaptive threshold, and screen effective candidate period points; (4) The effective candidate periodic points are refined by local peak refinement and candidate peak merging to obtain the refined candidate reception event time sequence; (5) Calculate the interval hit ratio, adjacent interval variation coefficient and the longest consecutive hit segment length based on the refined candidate reception event time sequence, and make dual-channel target existence judgment in combination with the optimal statistics, and output the local detection conclusion of each local detection window.

6. The method of claim 5, wherein, The dual-channel target existence determination includes a standard detection channel and a strong signal detection channel; The decision criteria for the standard detection channel include: when the optimal statistic is greater than the adaptive score threshold, the original hit rate is greater than 0.25, the number of candidate points after refinement is not less than 4, the average local standardized response is greater than 4.0, the interval hit ratio is not less than 0.45, the coefficient of variation of adjacent intervals is not greater than 0.35, the length of the longest consecutive hit segment is not less than 4, and the deviation between the optimal period and the theoretical repetition period is less than 0.05 s, the target signal is determined to exist in the current local detection window. The method for determining a strong signal detection channel is as follows: when the robust Z-score is greater than 4.0, the number of candidate points after refinement is not less than 4, the average local normalized response is greater than 6.0, the maximum local normalized response is greater than 10.0, the interval hit ratio is not less than 0.60, the coefficient of variation of adjacent intervals is not greater than 0.20, the length of the longest consecutive hit segment is not less than 5, and the deviation between the optimal period and the theoretical repetition period is less than 0.05 s, the target signal is determined to exist in the current local detection window. If any channel meets the above conditions, it is determined that there is a target signal in the current local detection window, and the candidate reception event time sequence and local detection conclusion of the local detection window are output.

7. The method of claim 1, wherein, In step 4, the preset threshold for determining the presence of a target signal in the current rolling buffer is 1, that is, when at least one local detection window is determined to have a target signal, the presence of a target signal is determined. The cross-window merging tolerance is set to 20%-40% of the theoretical signal period.

8. The method of claim 1, wherein, In step 5, when performing position clustering on the candidate receiving position sequence, if the difference between two candidate positions is less than the position merging tolerance, they are merged into the same position cluster, and the maximum intensity or weighted intensity in the position cluster is used as the representative intensity of the position cluster. Select the cluster of target locations with the highest intensity, and use the center of the cluster as the estimated target location.

9. The method of claim 8, wherein, The step 5, the distance of the target relative to the current receiver position The calculation formula is: ； wherein, is the target estimated position, is the current receiver position; When represents that the target is in front of the current receiver, and when represents that the target is behind the current receiver.

10. The method of claim 1, wherein, In step 3, the duration of a single data block is set to 0.5s, and the detection and update cycle is set to 1.0s; in the local peak refinement, the local peak search half-width is 0.2s, and the repeated peak merging threshold is 0.4s.

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