A pipeline trajectory detection method, system, computer device and storage medium

Abstract

The application provides a pipeline trajectory detection method, system, computer equipment and storage medium. The pipeline trajectory detection system comprises a first automatic traction device, a second automatic traction device, a traction rope, a trajectory instrument and an upper computer. The application can detect the trajectory data of the pipeline to be detected in real time through the trajectory instrument, and can judge whether there is foreign matter in the pipeline by recording the tension data of the traction rope through the traction tension detection mechanism. The upper computer can perform trajectory processing on the trajectory data collected by the trajectory instrument sensor and the tension data, so that a high-precision trajectory unaffected by the foreign matter in the pipeline, including welding bumps and welding scars, can be obtained.

Description

Technical Field

[0001] This invention relates to the field of trenchless pipeline trajectory detection technology, specifically to a pipeline trajectory detection method, system, computer equipment, and storage medium. Background Technology

[0002] In recent years, my country's underground pipeline laying technology has developed rapidly. Running electrical wires and cables into underground pipes is both safe and saves surface space. However, with the increase in underground pipelines, it is necessary to determine whether there are already laid pipelines underground and their three-dimensional trajectory before laying new ones to ensure safe construction. Tracking instruments have emerged to meet this need. A tracking instrument is a device for measuring the trajectory of underground empty pipelines. It is placed inside the underground empty pipeline and pulled from the pipeline opening to the pipeline exit to obtain the pipeline's three-dimensional coordinate data.

[0003] However, since a complete pipeline is composed of multiple shorter pipe sections connected by thermofusion welding, weld beads and spalls are formed at the weld joints. Whenever the tracker encounters these weld beads and spalls protruding on the inner wall of the pipeline, the resistance increases instantaneously, and decreases instantaneously after passing the weld beads and spalls. The acceleration generated during this process is recorded by the instrument's internal sensors, adding unnecessary errors to the trajectory calculation. Therefore, there is an urgent need to identify and process the abnormal acceleration caused by foreign objects inside the pipeline. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a pipeline trajectory detection method, system, computer equipment, and storage medium that can improve the accuracy of trajectory calculation by a trajectory analyzer.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] One embodiment provides a pipeline trajectory detection system, which includes:

[0007] The first automatic traction device is installed at the first end of the pipeline to be inspected;

[0008] The second automatic traction device is installed at the second end of the pipeline to be inspected;

[0009] A traction rope is placed in the pipe to be inspected, with its two ends connected to the first automatic traction device and the second automatic traction device, respectively.

[0010] A tracker, mounted on the traction rope, is capable of moving from one end of the pipeline to be inspected to the other end, or from the second end to the first end; the tracker is used to detect and store the trajectory data of the pipeline in real time; and

[0011] The host computer is electrically connected to the tracker and the first automatic traction device or the second automatic traction device;

[0012] Wherein, at least one of the first automatic traction device and the second automatic traction device includes a traction force detection mechanism; the traction force detection mechanism is used to detect and store the tension data of the traction rope in real time;

[0013] The host computer is used to acquire trajectory data collected by the trajectory instrument and tension data collected by the traction force detection mechanism, and aligns the trajectory data and tension data according to the data acquisition time and labels the data. It acquires the label corresponding to the data in the tension data that is greater than a first threshold, and searches for all labels corresponding to the second threshold range before and after this label as abnormal data labels. It filters the acceleration data in the trajectory data corresponding to the abnormal data labels, and calculates the trajectory of the pipeline to be detected after returning the processed data to its original position.

[0014] In one embodiment, both the first automatic traction device and the second automatic traction device include a winch; when the first automatic traction device or the second automatic traction device includes the traction force detection mechanism, the traction force detection mechanism and the winch are fixed on the same base plate.

[0015] The traction force detection mechanism includes:

[0016] The tension gauge is fixed on the base plate;

[0017] The first pulley is disposed between the tension gauge and the winch and is fixed to the base plate;

[0018] The second pulley is disposed between the tension gauge and the pipe to be tested, and is fixed on the base plate; the center point of both the first pulley and the center point of the second pulley are lower than the vertex of the tension gauge.

[0019] In one embodiment, the winch, the first pulley, the force gauge, and the second pulley are arranged in a straight line; the traction rope extends from the pipe to be tested, passes sequentially through the bottom of the second pulley, the top of the force gauge, and the bottom of the first pulley, and is wound around the winch; the length of the traction rope is greater than the length of the pipe to be tested.

[0020] In one embodiment, the traction force detection mechanism further includes a main control chip electrically connected to the force gauge for storing the force data.

[0021] In one embodiment, the trajectory device and the traction force detection mechanism collect data at the same frequency and synchronously; the trajectory data is sensor data from the trajectory device, including acceleration data, angular velocity data, and odometer wheel data in a three-dimensional coordinate system; the first threshold is 2 to 4 times the average value of the traction force data; the second threshold is a time period or the number of data collected; the acceleration data is filtered using a one-dimensional linear Gaussian filter; the one-dimensional linear Gaussian filter process is as follows:

[0022] First, generate a one-dimensional Gaussian filter matrix according to the following formula.

[0023]

[0024] In the case of a sampling frequency of 20Hz, n = 100 and σ = 1000 are taken;

[0025] Then, taking the data outlier as the origin, take the acceleration data from -100 to 100, and let i = -100 to 100 in the following formula in sequence. After completing 201 calculations, the one-dimensional Gaussian filtering is completed.

[0026] One embodiment of this application also provides a method for pipeline trajectory detection, which includes the following steps:

[0027] The data acquisition step involves acquiring trajectory data of a tracker moving from one end of the pipe to the other, as well as the pulling force data that pulls the tracker.

[0028] The data alignment step involves aligning the trajectory data and the tension data according to the data acquisition time, and labeling the data.

[0029] The step of obtaining abnormal data labels involves obtaining the labels corresponding to the data in the tensile data that are greater than the first threshold, and then searching for all the labels corresponding to the second threshold range before and after this label as abnormal data labels.

[0030] The abnormal data processing step involves filtering the acceleration data in the trajectory data corresponding to the abnormal data label.

[0031] The pipeline trajectory calculation process involves retrieving the aforementioned data to obtain the filtered and valid data, and then calculating the trajectory of the pipeline to be detected from the valid data.

[0032] In one embodiment, during the data acquisition step, the tracker and the traction force detection mechanism acquire data at the same frequency and synchronously; the trajectory data is sensor data from the tracker, including acceleration data, angular velocity data, and odometer wheel data in a three-dimensional coordinate system; during the step of obtaining abnormal data labels, the first threshold is 2 to 4 times the average value of the traction force data; the second threshold is a time period or the number of data collected; during the abnormal data processing step, the acceleration data is filtered using a one-dimensional linear Gaussian filter; the one-dimensional linear Gaussian filter process is as follows:

[0033] First, generate a one-dimensional Gaussian filter matrix according to the following formula.

[0034]

[0035] In the case of a sampling frequency of 20Hz, n = 100 and σ = 1000 are taken;

[0036] Then, taking the data outlier as the origin, take the acceleration data from -100 to 100, and let i = -100 to 100 in the following formula in sequence. After completing 201 calculations, the one-dimensional Gaussian filtering is completed.

[0037] In one embodiment, in the step of obtaining abnormal data labels, the first threshold is 3 times the average value of the tensile data; when the second threshold is a time period, the second threshold is 5 seconds; when the second threshold is the number of data collected, the second threshold is 100 sets.

[0038] In one embodiment, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to perform the following steps:

[0039] The data acquisition step involves acquiring trajectory data of a tracker moving from one end of the pipe to the other, as well as the pulling force data that pulls the tracker.

[0040] The data alignment step involves aligning the trajectory data and the tension data according to the data acquisition time, and labeling the data.

[0041] The step of obtaining abnormal data labels involves obtaining the labels corresponding to the data in the tensile data that are greater than the first threshold, and then searching for all the labels corresponding to the second threshold range before and after this label as abnormal data labels.

[0042] The abnormal data processing step involves filtering the acceleration data in the trajectory data corresponding to the abnormal data label.

[0043] The pipeline trajectory calculation process involves retrieving the aforementioned data to obtain the filtered and valid data, and then calculating the trajectory of the pipeline to be detected from the valid data.

[0044] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the following steps:

[0045] The data acquisition step involves acquiring trajectory data of a tracker moving from one end of the pipe to the other, as well as the pulling force data that pulls the tracker.

[0046] The data alignment step involves aligning the trajectory data and the tension data according to the data acquisition time, and labeling the data.

[0047] The step of obtaining abnormal data labels involves obtaining the labels corresponding to the data in the tensile data that are greater than the first threshold, and then searching for all the labels corresponding to the second threshold range before and after this label as abnormal data labels.

[0048] The abnormal data processing step involves filtering the acceleration data in the trajectory data corresponding to the abnormal data label.

[0049] The pipeline trajectory calculation process involves retrieving the aforementioned data to obtain the filtered and valid data, and then calculating the trajectory of the pipeline to be detected from the valid data.

[0050] This invention designs a pipeline trajectory detection method, system, computer equipment, and storage medium. By setting a trajectory instrument to detect the trajectory data of the pipeline to be detected in real time, and setting a traction force detection mechanism to record the tension data of the traction rope, it can be determined whether there are foreign objects inside the pipeline. The host computer processes the trajectory data collected by the trajectory instrument sensor and the tension data to obtain a high-precision trajectory that is not affected by foreign objects inside the pipeline, including weld beads and weld scars. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram of the pipeline trajectory detection system provided by the present invention;

[0053] Figure 2 This is a schematic diagram illustrating the use of the pipeline trajectory detection system provided by the present invention;

[0054] Figure 3 This is a schematic diagram of the traction force detection mechanism provided by the present invention;

[0055] Figure 4 A flowchart of a pipeline trajectory detection method according to an embodiment of the present invention;

[0056] Figure 5 A flowchart of a pipeline trajectory detection method according to another embodiment of the present invention;

[0057] Figure 6 This is an internal structural diagram of a computer device provided in one embodiment of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] The purpose of this invention is to solve the problem of decreased trajectory measurement accuracy caused by abnormal acceleration generated when the trajectory instrument encounters weld beads or weld scars.

[0060] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] Example 1

[0062] like Figure 1 As shown, in Embodiment 1, a pipeline trajectory detection system 10 is provided, which includes: a first automatic traction device 1, a second automatic traction device 2, a traction rope 3, a trajectory meter 4, and a host computer (not shown). The pipeline trajectory detection system 10 described in this embodiment is also called a rope puller.

[0063] like Figure 1 , Figure 2As shown, a first automatic traction device 1 is installed at the first end of the pipeline 20 to be inspected; a second automatic traction device 2 is installed at the second end of the pipeline 20 to be inspected; a traction rope 3 is installed in the pipeline 20 to be inspected, with its two ends connected to the first automatic traction device 1 and the second automatic traction device 2 respectively; a tracker 4 is installed on the traction rope 3 and can move from the first end to the second end of the pipeline 20 to be inspected, or from the second end to the first end of the pipeline 20 to be inspected; the tracker 4 is used to detect and store the trajectory data of the pipeline to be inspected in real time; a host computer is electrically connected to the tracker 4 and the traction force detection mechanism 22 in the first automatic traction device 1 or the second automatic traction device 2.

[0064] like Figure 2 As shown, the first end and the second end of the pipe to be tested are not simultaneously the front end or the rear end of the pipe to be tested.

[0065] like Figure 1 , Figure 2 As shown, at least one of the first automatic traction device 1 and the second automatic traction device 2 includes a traction force detection mechanism 22; the traction force detection mechanism 22 is used to detect and store the tension data of the traction rope 3 in real time. Preferably, both the first automatic traction device 1 and the second automatic traction device 2 include a traction force detection mechanism 22 in this embodiment.

[0066] The host computer is used to acquire the trajectory data collected by the trajectory instrument 4 and the tension data collected by the traction force detection mechanism, and to align the trajectory data and the tension data according to the data acquisition time and label the data. It acquires the label corresponding to the data in the tension data that is greater than the first threshold, and searches for all labels corresponding to the second threshold range before and after this label as abnormal data labels. It filters the acceleration data in the trajectory data corresponding to the abnormal data labels, and calculates the trajectory of the pipeline 20 to be detected after returning the processed data to its original position.

[0067] like Figure 1 , Figure 2 As shown, both the first automatic traction device 1 and the second automatic traction device 2 include a winch 21; when the first automatic traction device 1 or the second automatic traction device 2 includes the traction force detection mechanism 22, the traction force detection mechanism 22 and the winch 2121 are fixed on the same base plate 12.

[0068] like Figure 3 As shown, the traction force detection mechanism 22 includes: a force gauge 11, a base plate 12, a first pulley 13, and a second pulley 14.

[0069] like Figure 3 As shown, the force gauge 11 is fixed on the base plate 12; the first pulley 13 is disposed between the force gauge 11 and the winch 21 and is fixed on the base plate 12; the second pulley 14 is disposed between the force gauge 11 and the pipe to be tested 20 and is fixed on the base plate 12; the center point of the first pulley 13 and the center point of the second pulley 14 are both lower than the vertex of the force gauge 11.

[0070] like Figure 3 As shown, the winch 21, the first pulley 13, the tension gauge 11, and the second pulley 14 are arranged in a straight line; the traction rope 3 extends from the pipe to be tested 20 and passes sequentially through the bottom of the second pulley 14, the top of the tension gauge 11, and the bottom of the first pulley 13 before winding around the winch 21; the length of the traction rope 3 is greater than the length of the pipe to be tested 20.

[0071] In this embodiment, the traction force detection mechanism 22 further includes a main control chip (not shown), which is electrically connected to the force gauge 11 and is used to store the force data.

[0072] The winch 21 has a maximum pulling force of 100 kg and a rope pulling speed of 1 m / s, and its function is to pull the tracker 4 to move inside the pipe; the tension gauge 11 is a voltage-type tension gauge with a range of 0.5-100 kg and a power supply voltage of 10V, and its function is to measure the tension of the winch 21 pulling the traction rope 3; the first pulley 13 and the second pulley 14 are used to cooperate with the tension gauge to measure the tension of the winch 21 pulling the traction rope 3; the main control chip is used to record the tension of the winch 21 pulling the traction rope 3.

[0073] In this embodiment, the trajectory device 4 and the traction force detection mechanism 22 collect data at the same frequency and synchronously, both at a frequency of 20Hz. The trajectory data is sensor data from the trajectory device 4, including acceleration data, angular velocity data, and odometer wheel data in a three-dimensional coordinate system. The acceleration data in the three-dimensional coordinate system includes acceleration data along the X, Y, and Z axes, and the angular velocity data includes angular velocity data along the X, Y, and Z axes. The first threshold is 2 to 4 times the average value of the traction force data. The second threshold is a time period or the number of data collected. The acceleration data is filtered using a one-dimensional linear Gaussian filter.

[0074] Abnormal data processing involves filtering the X-axis, Y-axis, and Z-axis acceleration data in the abnormal data segment using a one-dimensional linear Gaussian filter. The one-dimensional linear Gaussian filtering process is as follows:

[0075] First, generate a one-dimensional Gaussian filter matrix according to the following formula.

[0076]

[0077] In the case of a sampling frequency of 20Hz, n = 100 and σ = 1000 are taken;

[0078] Then, taking the data outlier as the origin, take the acceleration data from -100 to 100, and let i = -100 to 100 in the following formula in sequence. After completing 201 calculations, the one-dimensional Gaussian filtering is completed.

[0079]

[0080] The instrument experiences acceleration anomalies caused by weld beads and spalls, which cause data spikes or drops. This step smooths out the abnormal values ​​in the data. At this point, the acceleration anomalies caused by weld beads and spalls are processed, and the processed data can be input into the trajectory calculation model for calculation and plotting.

[0081] Example 2

[0082] like Figure 4 As shown, Embodiment 2 of this application also provides a method for pipeline trajectory detection, including the following steps:

[0083] Step 1: First, according to Figure 1 , Figure 2 As shown, a pipeline trajectory detection system 10 with a tensile testing mechanism 22 is constructed. Figure 3 This is a schematic diagram of the traction force detection mechanism 22, in which the signal output line of the force gauge 11 is electrically connected to the main control chip;

[0084] Step 2: Connect the two ends of the tracker 4 to two pipeline trajectory detection systems 10 with tension detection mechanisms 22 respectively, and start measuring the pipeline trajectory; the trajectory data is the sensor data inside the tracker 4, mainly acceleration data;

[0085] Step 3: Upload the pipeline data in tracker 4 and the tension data of traction rope 3 in main control chip to the host computer, find the starting point where the tension changes from zero, align this point with the starting point of the pipeline data recorded by tracker 4, and label all data.

[0086] Step 4: Calculate the average value of the tensile data and determine whether the tensile data exceeds three times the average value. If it does, record the serial number of the abnormal data. This step is to determine the abnormal acceleration location. The abnormal acceleration location is the data segment where the trajectory instrument 4 generates abnormal acceleration when it passes through foreign objects such as weld beads and weld scars. It is manifested by the tensile data of this segment exceeding three times the average value of the overall tensile data.

[0087] Step 5: Take the tracker 4 data corresponding to 5 seconds or 100 digits before and after the abnormal data sequence number, and filter the acceleration data in the tracker 4 data; specifically, filter the X-axis, Y-axis and Z-axis acceleration data in the abnormal data segment, and the filtering method used is one-dimensional linear Gaussian filtering.

[0088] Step 6: Return the processed data to its place. At this point, the abnormal acceleration data caused by weld beads and weld scars has been processed. Then, import the processed data into the trajectory calculation model to calculate the trajectory of the pipeline 20 to be inspected.

[0089] In step 5, the one-dimensional linear Gaussian filtering process is as follows:

[0090] First, generate a one-dimensional Gaussian filter matrix according to the following formula.

[0091]

[0092] In the case of a sampling frequency of 20Hz, n = 100 and σ = 1000 are taken;

[0093] Then, taking the data outlier as the origin, take the acceleration data from -100 to 100, and let i = -100 to 100 in the following formula in sequence. After completing 201 calculations, the one-dimensional Gaussian filtering is completed.

[0094]

[0095] The instrument experiences acceleration anomalies caused by weld beads and spalls, which cause data spikes or drops. This step smooths out the abnormal values ​​in the data. At this point, the acceleration anomalies caused by weld beads and spalls are processed, and the processed data can be input into the trajectory calculation model for calculation and plotting.

[0096] Example 3

[0097] like Figure 5 As shown, Embodiment 3 of this application also provides a method for pipeline trajectory detection, which includes the following steps:

[0098] S1. Data acquisition step: acquire trajectory data of a tracker 4 as it moves from one end of the pipe 20 to the other end, as well as the pulling force data of the tracker 4.

[0099] S2. Data alignment step: Align the trajectory data and the tension data according to the data acquisition time, that is, align the starting point of the tension data from 0 to the starting point of the trajectory data from 0, and label the data.

[0100] S3. Step of obtaining abnormal data labels: Obtain the labels corresponding to the data in the tensile data that are greater than the first threshold, and find all the labels corresponding to the second threshold range before and after this label as abnormal data labels.

[0101] S4. Abnormal data processing step: Filter the acceleration data in the trajectory data corresponding to the abnormal data label.

[0102] S5. Calculate the pipeline trajectory step: After the above data is returned to its original position, it becomes the effective data after filtering. Calculate the trajectory of the pipeline 20 to be detected using the effective data.

[0103] In one embodiment, in the data acquisition step S1, the tracker 4 and the traction force detection mechanism 22 acquire data at the same frequency and synchronously; the trajectory data is sensor data in the tracker 4, including acceleration data in the three-dimensional coordinate system, angular velocity data in the three-dimensional coordinate system, and odometer wheel data; in the step of obtaining abnormal data labels S3, the first threshold is 2 to 4 times the average value of the force data; the second threshold is the time period or the number of data collected; in the abnormal data processing step S4, the acceleration data is filtered using a one-dimensional linear Gaussian filter.

[0104] Abnormal data processing involves filtering the X-axis, Y-axis, and Z-axis acceleration data in the abnormal data segment using a one-dimensional linear Gaussian filter. The one-dimensional linear Gaussian filtering process is as follows:

[0105] First, generate a one-dimensional Gaussian filter matrix according to the following formula.

[0106]

[0107] In the case of a sampling frequency of 20Hz, n = 100 and σ = 1000 are taken;

[0108] Then, taking the data outlier as the origin, take the acceleration data from -100 to 100, and let i = -100 to 100 in the following formula in sequence. After completing 201 calculations, the one-dimensional Gaussian filtering is completed.

[0109]

[0110] The instrument experiences acceleration anomalies caused by weld beads and spalls, which cause data spikes or drops. This step smooths out the abnormal values ​​in the data. At this point, the acceleration anomalies caused by weld beads and spalls are processed, and the processed data can be input into the trajectory calculation model for calculation and plotting.

[0111] In one embodiment, in step S3 of obtaining abnormal data labels, the first threshold is three times the average value of the tensile data; when the second threshold is a time period, the second threshold is 5 seconds; when the second threshold is the number of data collected, the second threshold is 100 sets. That is, for tensile data, abnormal data is data that exceeds three times the average value of the tensile data, and the labels of the data within 5 seconds or 100 sets before and after that label are taken as abnormal data labels.

[0112] It should be understood that, although Figures 4-5 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figures 4-5 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0113] Specific limitations regarding the pipeline trajectory detection method can be found in the limitations of the pipeline trajectory detection system 10 described above, and will not be repeated here. Each module in the pipeline trajectory detection system 10 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0114] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and the database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores pipeline trajectory detection data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a pipeline trajectory detection method.

[0115] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0116] In one embodiment, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to perform the following steps:

[0117] The data acquisition step involves acquiring trajectory data of a tracker 4 as it moves from one end of the pipe 20 to the other, as well as the pulling force data that pulls the tracker 4.

[0118] The data alignment step involves aligning the trajectory data and the tension data according to the data acquisition time, that is, aligning the starting point of the tension data from 0 to the starting point of the trajectory data from 0, and labeling the data.

[0119] The step of obtaining abnormal data labels involves obtaining the labels corresponding to the data in the tensile data that are greater than the first threshold, and then searching for all the labels corresponding to the second threshold range before and after this label as abnormal data labels.

[0120] The abnormal data processing step involves filtering the acceleration data in the trajectory data corresponding to the abnormal data label.

[0121] The pipeline trajectory calculation step involves retrieving the above data to obtain the filtered and effective data, and then calculating the trajectory of the pipeline 20 to be detected.

[0122] For specific limitations regarding computer equipment, please refer to the limitations on the pipeline trajectory detection system 10 and the pipeline trajectory detection method mentioned above, which will not be repeated here.

[0123] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the following steps:

[0124] The data acquisition step involves acquiring trajectory data of a tracker 4 as it moves from one end of the pipe 20 to the other, as well as the pulling force data that pulls the tracker 4.

[0125] The data alignment step involves aligning the trajectory data and the tension data according to the data acquisition time, that is, aligning the starting point of the tension data from 0 to the starting point of the trajectory data from 0, and labeling the data.

[0126] The step of obtaining abnormal data labels involves obtaining the labels corresponding to the data in the tensile data that are greater than the first threshold, and then searching for all the labels corresponding to the second threshold range before and after this label as abnormal data labels.

[0127] The abnormal data processing step involves filtering the acceleration data in the trajectory data corresponding to the abnormal data label.

[0128] The pipeline trajectory calculation step involves retrieving the above data to obtain the filtered and effective data, and then calculating the trajectory of the pipeline 20 to be detected.

[0129] For specific limitations regarding computer-readable storage media, please refer to the limitations on the pipeline trajectory detection system 10 and the pipeline trajectory detection method mentioned above, which will not be repeated here.

[0130] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0131] This invention designs a pipeline trajectory detection method, system, computer equipment, and storage medium. By recording the tension data of the traction rope 3, it determines whether there are foreign objects inside the pipeline, and records the entire pulling process into the main chip. Later, the trajectory data collected by the trajectory instrument 4 sensor and the tension data are imported into the calculation software for trajectory processing, so as to obtain a high-precision trajectory that is not affected by foreign objects inside the pipeline, including weld beads and weld scars.

[0132] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A pipeline trajectory detection system, characterized in that, include: The first automatic traction device is installed at the first end of the pipeline to be inspected; The second automatic traction device is installed at the second end of the pipeline to be inspected; A traction rope is placed in the pipe to be inspected, with its two ends connected to the first automatic traction device and the second automatic traction device, respectively. A tracker, mounted on the traction rope, is capable of moving from one end of the pipe to be inspected to the other end, or from the second end to the first end; the tracker is used to detect and store the trajectory data of the pipe in real time; and The host computer is electrically connected to the tracker and the first automatic traction device or the second automatic traction device; Wherein, at least one of the first automatic traction device and the second automatic traction device includes a traction force detection mechanism; the traction force detection mechanism is used to detect and store the tension data of the traction rope in real time; The host computer is used to acquire trajectory data collected by the trajectory instrument and tension data collected by the traction force detection mechanism, and to align the trajectory data and tension data according to the data acquisition time and label the data. It acquires the label corresponding to the data in the tension data that is greater than a first threshold, and searches for all labels corresponding to the second threshold range before and after this label as abnormal data labels. It filters the acceleration data in the trajectory data corresponding to the abnormal data labels, and calculates the trajectory of the pipeline to be detected after returning the processed data to its original position. The trajectory device and the traction force detection mechanism have the same acquisition frequency and acquire data synchronously; The trajectory data is sensor data from the trajectory device, including acceleration data in the three-dimensional coordinate system, angular velocity data in the three-dimensional coordinate system, and odometer wheel data; The first threshold is 2 to 4 times the average value of the tensile force data; The second threshold is a time period or the amount of data collected; The acceleration data were filtered using a one-dimensional linear Gaussian filter. The one-dimensional linear Gaussian filtering process is as follows: First, generate a one-dimensional Gaussian filter matrix according to the following formula. ； In this case, with a sampling frequency of 20Hz, n=100 and σ=1000; g1…g n is the weighting coefficient for the first to nth groups of acceleration data corresponding to the one-dimensional Gaussian filter, and r is the coordinate of the center of the curve peak in the one-dimensional image of the Gaussian filter function; Then, taking the data outlier as the origin, the acceleration data from -100 to 100 is taken, and after 201 calculations, the one-dimensional Gaussian filtering is completed.

2. The pipeline trajectory detection system according to claim 1, characterized in that, Both the first automatic traction device and the second automatic traction device include a winch; When the first automatic traction device or the second automatic traction device includes the traction force detection mechanism, the traction force detection mechanism is fixed to the same base plate as the winch; The traction force detection mechanism includes: The tension gauge is fixed on the base plate; The first pulley is disposed between the tension gauge and the winch and is fixed to the base plate; The second pulley is disposed between the tension gauge and the pipe to be tested, and is fixed on the base plate; the center point of both the first pulley and the center point of the second pulley are lower than the vertex of the tension gauge.

3. The pipeline trajectory detection system according to claim 2, characterized in that, The winch, the first pulley, the force gauge, and the second pulley are arranged in a straight line; the traction rope extends from the pipe to be tested, passes sequentially through the bottom of the second pulley, the top of the force gauge, and the bottom of the first pulley, and is wound around the winch; the length of the traction rope is greater than the length of the pipe to be tested.

4. The pipeline trajectory detection system according to claim 2, characterized in that, The traction force detection mechanism also includes: The main control chip is electrically connected to the force gauge and is used to store the force data.

5. A method for pipeline trajectory detection, characterized in that, Includes the following steps: The data acquisition step involves acquiring trajectory data of a tracker moving from one end of the pipe to the other, as well as the pulling force data that pulls the tracker. The data alignment step involves aligning the trajectory data and the tension data according to the data acquisition time, and labeling the data. The step of obtaining abnormal data labels involves obtaining the labels corresponding to the data in the tensile data that are greater than the first threshold, and then searching for all the labels corresponding to the second threshold range before and after this label as abnormal data labels. The abnormal data processing step involves filtering the acceleration data in the trajectory data corresponding to the abnormal data label. The pipeline trajectory calculation step involves retrieving the above data and obtaining the filtered and effective data. The effective data is then used to calculate the trajectory of the pipeline to be detected. In the data acquisition step, the trajectory device and the traction force detection mechanism acquire data at the same frequency and synchronously; the trajectory data is sensor data from the trajectory device, including acceleration data in the three-dimensional coordinate system, angular velocity data in the three-dimensional coordinate system, and odometer wheel data. In the step of obtaining abnormal data labels, the first threshold is 2 to 4 times the average value of the tensile data; the second threshold is a time period or the number of data collected. In the abnormal data processing step, the acceleration data is filtered using a one-dimensional linear Gaussian filter; the one-dimensional linear Gaussian filtering process is as follows: First, generate a one-dimensional Gaussian filter matrix according to the following formula. ； In this case, with a sampling frequency of 20Hz, n=100 and σ=1000; g1…g n is the weighting coefficient for the first to nth groups of acceleration data corresponding to the one-dimensional Gaussian filter, and r is the coordinate of the center of the curve peak in the one-dimensional image of the Gaussian filter function; Then, taking the data outlier as the origin, the acceleration data from -100 to 100 is taken, and after 201 calculations, the one-dimensional Gaussian filtering is completed.

6. The method for pipeline trajectory detection according to claim 5, characterized in that, In the step of obtaining abnormal data labels, the first threshold is 3 times the average value of the tensile data; when the second threshold is a time period, the second threshold is 5 seconds; when the second threshold is the number of data collected, the second threshold is 100 sets.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method of claim 5 or 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 5 or 6.

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