Unmanned aerial vehicle-based overhead pipeline wall thickness detection method, device, medium and product

Abstract

The application relates to the technical field of thickness detection, and discloses an overhead pipeline wall thickness detection method and device based on a UAV, a medium and a product, which receive a pipeline wall thickness detection instruction and determine a pipeline to be subjected to wall thickness detection; a plurality of sub-wall thickness detection areas are divided on the pipeline to be subjected to wall thickness detection; a plurality of data detection groups are arranged in the sub-wall thickness detection areas; ultrasonic detection signals sent by an ultrasonic detector carried by the UAV to each data detection group are acquired; the acquired ultrasonic detection signals are analyzed, and a sub-wall thickness detection factor of each sub-wall thickness detection area is calculated; a comprehensive pipeline wall thickness factor of the pipeline to be subjected to wall thickness detection is calculated according to all the sub-wall thickness detection factors; and the pipeline wall thickness of the pipeline to be subjected to wall thickness detection is determined according to a mapping relationship between the comprehensive pipeline wall thickness factor and the pipeline wall thickness. The application not only improves detection efficiency and safety, but also accurately determines the pipeline wall thickness through real-time data transmission and processing, and quickly and accurately evaluates the health condition of the pipeline.

Description

Technical Field

[0001] This invention relates to the field of thickness detection technology, and more specifically, to a method, equipment, medium, and product for detecting the wall thickness of overhead pipelines based on unmanned aerial vehicles (UAVs). Background Technology

[0002] Oil and gas pipelines are critical infrastructure for energy transportation, and their safe operation is essential for ensuring energy supply and environmental security. However, due to long-term use, external environmental influences, and material aging, pipeline walls gradually thin, increasing the risk of leaks and ruptures. Therefore, regular inspection of pipeline wall thickness is of great importance in preventing accidents.

[0003] Traditional methods for inspecting pipe wall thickness often require manual intervention, with experienced personnel using handheld ultrasonic thickness gauges. An ultrasonic thickness gauge is a device that measures material thickness using the principle of ultrasonic pulse reflection. When an ultrasonic pulse emitted by the probe of the gauge travels through the object being measured and reaches the material interface, the pulse is reflected back to the probe. By measuring the time it takes for the ultrasonic wave to travel through the material, the thickness of the material can be determined. This traditional method of pipe wall thickness inspection is not only inefficient but also inherently dangerous, especially at heights or in inaccessible locations. Furthermore, these methods typically cannot provide a comprehensive and detailed inspection of the pipe. Summary of the Invention

[0004] In view of this, the present invention proposes a method, equipment, medium and product for detecting the wall thickness of overhead pipelines based on unmanned aerial vehicles (UAVs). This not only improves detection efficiency and safety, but also enables accurate determination of pipeline wall thickness and rapid and accurate assessment of pipeline health status through real-time data transmission and processing.

[0005] In a first aspect, this invention proposes a method for detecting the wall thickness of an overhead pipeline based on a drone, comprising: receiving a pipeline wall thickness detection command, analyzing the command, determining the pipeline to be detected, wherein the pipeline is divided into multiple sub-wall thickness detection areas; deploying multiple data detection groups within each sub-wall thickness detection area, wherein each data detection group includes a first data detection point and a second data detection point, the first data detection point being deployed on the outer surface of the pipeline to be detected, and the second data detection point being deployed on the inner surface of the pipeline to be detected; the pipeline to be detected includes an overhead pipeline; and acquiring ultrasonic detection data carried by the drone. The instrument sends ultrasonic detection signals to each data detection group, analyzes all acquired ultrasonic detection signals, and calculates the sub-wall thickness detection factor for each sub-wall thickness detection area based on the analysis results. The ultrasonic detection signals include a first ultrasonic detection signal and a second ultrasonic detection signal, with the first ultrasonic detection signal corresponding to a first data detection point and the second ultrasonic detection signal corresponding to a second data detection point. The instrument calculates the comprehensive pipe wall thickness factor of the pipe to be tested based on all sub-wall thickness detection factors, and determines the pipe wall thickness of the pipe to be tested based on the mapping relationship between the comprehensive pipe wall thickness factor and the pipe wall thickness.

[0006] In some embodiments, before receiving and analyzing the pipe wall thickness detection command, the method further includes: matching the pipe wall thickness detection command with the command category of the previous command, and determining whether the pipe wall thickness detection command is the same as the previous command; if the pipe wall thickness detection command matches the command category of the previous command, then the pipe wall thickness detection command is determined to be the same as the previous command, collecting the first time node of sending the pipe wall thickness detection command, and collecting the second time node of sending the previous command; calculating the time node difference between the first time node and the second time node, and determining whether to process the pipe wall thickness detection command based on the relationship between the time node difference and the preset time node difference; if the time node difference is greater than or equal to the preset time node difference, then the pipe wall thickness detection command is processed; if the time node difference is less than the preset time node difference, then the pipe wall thickness detection command is not processed, and a log reminder is generated and output; if the pipe wall thickness detection command does not match the command category of the previous command, then the pipe wall thickness detection command is determined to be different from the previous command, and the pipe wall thickness detection command is processed.

[0007] In some embodiments, multiple data detection groups within a sub-wall thickness detection area are deployed as follows: the extent and shape of the sub-wall thickness detection area are determined, and the number of data detection groups to be deployed is determined; the sub-wall thickness detection area is divided into multiple sub-detection areas based on the extent and shape of the sub-wall thickness detection area, the number of data detection groups to be deployed, and the Hilbert curve algorithm, wherein each sub-detection area is located on the outer surface of the pipe to be detected; a first data detection point is deployed at the geometric center of each sub-detection area, and a second data detection point is deployed at the corresponding position on the inner surface of the pipe to be detected, corresponding to the first data detection point; multiple data detection groups are obtained based on all the first data detection points and the corresponding second data detection points.

[0008] In some embodiments, all acquired ultrasonic detection signals are analyzed, and a sub-wall thickness detection factor for each sub-wall thickness detection region is calculated based on the analysis results. This includes: calculating multiple ultrasonic detection signal differences based on a first ultrasonic detection signal and a second ultrasonic detection signal; normalizing all ultrasonic detection signal differences to obtain corresponding signal normalized values ​​and constructing a signal normalization sequence; calculating the mean of the signal normalization sequence and extracting all signal normalized values ​​greater than or equal to the mean to generate a first signal normalization sequence; extracting all signal normalized values ​​less than the mean to generate a second signal normalization sequence; and calculating the sub-wall thickness detection factor for each sub-wall thickness detection region based on the first signal normalization sequence and the second signal normalization sequence.

[0009] In some embodiments, calculating the sub-wall thickness detection factor for each sub-wall thickness detection region based on a first signal normalization sequence and a second signal normalization sequence includes: calculating a first standard deviation of the first signal normalization sequence and a second standard deviation of the second signal normalization sequence, respectively; and calculating the sub-wall thickness detection factor for each sub-wall thickness detection region according to the following formula:

[0010]

[0011] Where W is the sub-wall thickness detection factor of the sub-wall thickness detection region, f1 is the calculated coefficient corresponding to the first signal normalization sequence, p is the mean, e1 is the number of signal normalization values ​​in the first signal normalization sequence, e2 is the number of signal normalization values ​​in the second signal normalization sequence, d1 is the first standard deviation, f2 is the calculated coefficient corresponding to the second signal normalization sequence, and d2 is the second standard deviation.

[0012] In some embodiments, the method further includes: calculating the normalized mean of the normalized sequence of the second signal; and calculating an alarm level factor for the pipe to be tested for wall thickness according to the following formula, wherein the alarm level factor is used to determine the alarm level when an alarm is issued:

[0013]

[0014] Where G is the alarm level factor of the pipeline whose wall thickness needs to be tested, and y is the normalized mean.

[0015] In some embodiments, calculating the comprehensive pipe wall thickness factor of the pipe to be tested based on all sub-wall thickness detection factors includes: extracting the same sub-wall thickness detection factor from all sub-wall thickness detection factors to obtain multiple sub-wall thickness detection factor sequences; counting the number of first factor sequences in the multiple sub-wall thickness detection factor sequences; extracting one sub-wall thickness detection factor from each of the sub-wall thickness detection factor sequences, and determining the sum of the sub-wall thickness detection factors extracted from all sub-wall thickness detection factor sequences as the first sub-wall thickness detection factor sum; removing all sub-wall thickness detection factor sequences that are less than a preset sub-wall thickness detection factor, and counting the number of second factor sequences in the remaining sub-wall thickness detection factor sequences; extracting one sub-wall thickness detection factor from each of the remaining sub-wall thickness detection factor sequences, and determining the sum of the sub-wall thickness detection factors extracted from the remaining sub-wall thickness detection factor sequences as the second sub-wall thickness detection factor sum; and calculating the comprehensive pipe wall thickness factor of the pipe to be tested based on the number of first factor sequences, the number of second factor sequences, the first sub-wall thickness detection factor sum, and the second sub-wall thickness detection factor sum.

[0016] In some embodiments, calculating the comprehensive pipe wall thickness factor of the pipe to be tested based on the number of first factor sequences, the number of second factor sequences, the sum of the first sub-wall thickness detection factors, and the sum of the second sub-wall thickness detection factors includes: calculating the comprehensive pipe wall thickness factor of the pipe to be tested according to the following formula:

[0017]

[0018] Where H is the comprehensive pipe wall thickness factor of the pipe to be tested, r2 is the number of second factor sequences, r1 is the number of first factor sequences, u1 is the sum of the first sub-wall thickness detection factors, u2 is the sum of the second sub-wall thickness detection factors, and t is the preset sub-wall thickness detection factor.

[0019] In some embodiments, the method further includes: determining whether to issue an alarm based on the relationship between the pipe wall thickness of the pipe to be tested and a preset pipe wall thickness; not issuing an alarm when the pipe wall thickness is greater than or equal to the preset pipe wall thickness; and issuing an alarm when the pipe wall thickness is less than the preset pipe wall thickness.

[0020] Secondly, a device for detecting the wall thickness of an overhead pipeline based on a drone is provided, comprising: a communication unit and a processing unit; the communication unit is used to receive pipeline wall thickness detection commands, analyze the commands, determine the pipeline to be detected, and divide the pipeline into multiple sub-wall thickness detection areas; multiple data detection groups are deployed within each sub-wall thickness detection area, wherein each data detection group includes a first data detection point and a second data detection point, the first data detection point being deployed on the outer surface of the pipeline to be detected, and the second data detection point being deployed on the inner surface of the pipeline to be detected; the pipeline to be detected includes an overhead pipeline; the communication unit is also used to acquire data based on data carried by the drone. The ultrasonic detector sends ultrasonic detection signals to each data detection group, analyzes all acquired ultrasonic detection signals, and calculates the sub-wall thickness detection factor for each sub-wall thickness detection area based on the analysis results. The ultrasonic detection signals include a first ultrasonic detection signal and a second ultrasonic detection signal, with the first ultrasonic detection signal corresponding to a first data detection point and the second ultrasonic detection signal corresponding to a second data detection point. The processing unit is used to calculate the comprehensive pipe wall thickness factor of the pipe to be tested based on all sub-wall thickness detection factors, and determine the pipe wall thickness of the pipe to be tested based on the mapping relationship between the comprehensive pipe wall thickness factor and the pipe wall thickness.

[0021] Thirdly, an overhead pipeline wall thickness detection device based on unmanned aerial vehicles (UAVs) is provided, including a memory and a processor; the memory is used to store computer execution instructions, and the processor is connected to the memory via a bus; when the UAV-based overhead pipeline wall thickness detection device is running, the processor executes the computer execution instructions stored in the memory, so that the UAV-based overhead pipeline wall thickness detection device performs the UAV-based overhead pipeline wall thickness detection method described in the first aspect.

[0022] The UAV-based overhead pipeline wall thickness detection device can be a network device or a component of a network device, such as a chip system within the network device. This chip system supports the network device in implementing the functions involved in the first aspect and any possible implementation thereof, such as acquiring, determining, and transmitting the data and / or information involved in the aforementioned UAV-based overhead pipeline wall thickness detection method. The chip system includes a chip, but may also include other discrete devices or circuit structures.

[0023] Fourthly, a computer-readable storage medium is provided, comprising computer-executable instructions that, when executed on a computer, cause the computer to perform the UAV-based overhead pipeline wall thickness detection method described in the first aspect.

[0024] Fifthly, a computer program product is also provided, which includes computer instructions that, when executed on the UAV-based overhead pipeline wall thickness detection device, cause the UAV-based overhead pipeline wall thickness detection device to perform the UAV-based overhead pipeline wall thickness detection method as described in the first aspect above.

[0025] It should be noted that the aforementioned computer instructions may be stored, in whole or in part, on a computer-readable storage medium. This computer-readable storage medium may be packaged together with the processor of the UAV-based overhead pipeline wall thickness detection device, or it may be packaged separately from the processor of the UAV-based overhead pipeline wall thickness detection device; this application does not limit this.

[0026] The descriptions of the second, third, fourth, and fifth aspects of this application can be referenced to the detailed description of the first aspect.

[0027] In the embodiments of this application, the name of the above-mentioned UAV-based overhead pipeline wall thickness detection device does not limit the equipment or functional module itself. In actual implementation, these equipment or functional modules may appear under other names. For example, the receiving unit may also be called a receiving module, receiver, etc. As long as the function of each equipment or functional module is similar to that of this application, it falls within the scope of the claims of this application and its equivalents. As can be seen from the above, this application detects the pipe wall thickness of the pipeline to be detected by sending ultrasonic detection signals to each data detection group based on the ultrasonic detector carried by the UAV. This not only improves the detection efficiency and safety, but also accurately determines the pipe wall thickness through real-time data transmission and processing, and quickly and accurately assesses the health status of the pipeline to be detected, providing strong support for the operation and maintenance of the pipeline to be detected. Attached Figure Description

[0028] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0029] Figure 1 This is a schematic diagram of the structure of a wall thickness detection system provided in an embodiment of this application;

[0030] Figure 2 A schematic diagram of the hardware structure of a wall thickness detection device provided in an embodiment of this application;

[0031] Figure 3 A schematic flowchart of a wall thickness detection method provided in an embodiment of this application;

[0032] Figure 4 A schematic flowchart illustrating another wall thickness detection method provided in this application embodiment;

[0033] Figure 5 A schematic flowchart illustrating another wall thickness detection method provided in this application embodiment;

[0034] Figure 6 A schematic flowchart illustrating another wall thickness detection method provided in this application embodiment;

[0035] Figure 7 A schematic flowchart illustrating another wall thickness detection method provided in this application embodiment;

[0036] Figure 8 This is a schematic diagram of a wall thickness detection device provided in an embodiment of this application. Detailed Implementation

[0037] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0039] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0040] As described in the background section, traditional pipe wall thickness inspection methods often require manual intervention, with experienced personnel using handheld ultrasonic thickness gauges to measure pipe wall thickness. An ultrasonic thickness gauge is a device that uses the principle of ultrasonic pulse reflection to measure material thickness. When an ultrasonic pulse emitted by the probe of the ultrasonic thickness gauge passes through the object being measured and reaches the material interface, the pulse is reflected back to the probe. By measuring the time it takes for the ultrasonic wave to travel through the material, the thickness of the material can be determined. This traditional method of pipe wall thickness inspection is not only inefficient but also poses certain risks, especially at high altitudes or in inaccessible locations. Furthermore, these methods typically cannot achieve a comprehensive and detailed inspection of the pipe.

[0041] To address the aforementioned issues, this application provides a method for detecting the wall thickness of overhead pipelines based on unmanned aerial vehicles (UAVs). After receiving a pipeline wall thickness detection command, the method analyzes the command to determine the pipeline to be inspected. The pipeline to be inspected is divided into multiple sub-wall thickness detection areas, and each sub-wall thickness detection area deploys multiple data detection groups. Each data detection group includes a first data detection point deployed on the outer surface of the pipeline and a second data detection point deployed on the inner surface of the pipeline. The pipeline to be inspected includes an overhead pipeline.

[0042] Next, ultrasonic detection signals sent to each data detection group by the ultrasonic detector carried by the UAV can be acquired. All acquired ultrasonic detection signals are analyzed, and the sub-wall thickness detection factor for each sub-wall thickness detection region is calculated based on the analysis results. The ultrasonic detection signals include a first ultrasonic detection signal corresponding to the first data detection point and a second ultrasonic detection signal corresponding to the second data detection point.

[0043] Subsequently, the comprehensive pipe wall thickness factor of the pipe to be tested can be calculated based on all the sub-wall thickness detection factors, and the pipe wall thickness of the pipe to be tested can be determined based on the mapping relationship between the comprehensive pipe wall thickness factor and the pipe wall thickness.

[0044] As can be seen from the above, this application detects the pipe wall thickness by sending ultrasonic detection signals to each data detection group based on ultrasonic detectors carried by UAVs. This not only improves detection efficiency and safety, but also accurately determines the pipe wall thickness through real-time data transmission and processing, and quickly and accurately assesses the health status of the pipe to be tested, providing strong support for the operation and maintenance of the pipe to be tested.

[0045] The above-mentioned method for detecting the wall thickness of overhead pipelines based on UAVs can be applied to wall thickness detection systems. Figure 1 A schematic diagram of the wall thickness detection system is shown. Figure 1As shown, the wall thickness detection system includes: a pipe 101 to be tested, a drone 102, a wall thickness detection device 103, and a storage server 104. The drone 102 carries an ultrasonic detector 105.

[0046] Optionally, the pipe 101 to be tested for wall thickness can be of different types, such as natural gas pipes, crude oil pipes, refined oil pipes, etc.

[0047] Optionally, the pipe 101 to be tested for wall thickness can be an overhead pipe. In this way, the wall thickness of the overhead pipe can be tested by a drone 102 carrying an ultrasonic detector 105.

[0048] The ultrasonic sensor 105 is a sensor that converts ultrasonic signals into other energy signals (usually electrical signals) and is typically used to detect parameters such as the thickness of the object being tested.

[0049] Optionally, an ultrasonic sensor typically includes an ultrasonic transmitting module and an ultrasonic receiving module. The ultrasonic transmitting module emits ultrasonic signals. After the ultrasonic signals are refracted and reflected in the pipe sample, the ultrasonic receiving module receives the refracted and reflected signals, i.e., the ultrasonic detection signals.

[0050] In this embodiment, the ultrasonic sensor 105 can emit an ultrasonic detection signal to the pipe 101 to be tested for wall thickness, so that the wall thickness testing device 103 can perform wall thickness testing on the pipe 101 to be tested for wall thickness based on the ultrasonic detection signal emitted by the ultrasonic sensor 105.

[0051] Optionally, the ultrasonic sensor 105 can be any of the following types of ultrasonic sensors: contact ultrasonic sensor, non-contact ultrasonic sensor, clamp-on ultrasonic sensor, etc.

[0052] The wall thickness detection device 103 is used to acquire the ultrasonic detection signal sent by the ultrasonic detector 105 carried by the UAV 102 to the pipe 101 whose wall thickness is to be detected, and to determine the pipe wall thickness of the pipe 101 whose wall thickness is to be detected based on the acquired ultrasonic detection signal.

[0053] Optionally, the physical device of the wall thickness detection device 103 can be a server, a terminal, or other types of electronic equipment, and this application embodiment does not limit it in this way.

[0054] Optionally, the aforementioned terminal may be at least one of the following devices: smartphone, smartwatch, desktop computer, laptop, virtual reality terminal, augmented reality terminal, wireless terminal, and laptop computer.

[0055] Optionally, the server mentioned above can be one of the servers in a server cluster (composed of multiple servers), a chip in the server, a system-on-a-chip in the server, or a virtual machine (VM) deployed on a physical machine. This application embodiment does not limit this.

[0056] The storage server 104 is used to store various types of data in the embodiments of this application, such as the detection results obtained by the wall thickness detection device 103 when performing wall thickness detection on the pipe 101 to be detected.

[0057] The basic hardware structure of the wall thickness detection equipment 103 includes: Figure 2 The components included in the wall thickness detection device shown below. Figure 2 Taking the wall thickness detection device shown as an example, the hardware structure of the wall thickness detection device 103 is introduced.

[0058] like Figure 2 The diagram shown is a hardware structure schematic of a wall thickness detection device provided in an embodiment of this application. The wall thickness detection device includes a processor 21, a memory 22, a communication interface 23, and a bus 24. The processor 21, memory 22, and communication interface 23 are connected via the bus 24.

[0059] Processor 21 is the control center of the wall thickness detection device. It can be a single processor or a collective term for multiple processing elements. For example, processor 21 can be a general-purpose central processing unit (CPU) or other general-purpose processors. Among them, the general-purpose processor can be a microprocessor or any conventional processor.

[0060] As one embodiment, processor 21 may include one or more CPUs, for example Figure 2 CPU 0 and CPU 1 are shown in the diagram.

[0061] The memory 22 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0062] In one possible implementation, the memory 22 can exist independently of the processor 21. The memory 22 can be connected to the processor 21 via a bus 24 and is used to store instructions or program code. When the processor 21 calls and executes the instructions or program code stored in the memory 22, it can implement the wall thickness detection method provided in the following embodiments of this application.

[0063] In this embodiment, the software programs stored in the memory 22 of the wall thickness detection device 103 are different, so the functions implemented by the wall thickness detection device 103 are different. The functions performed by each device will be described with reference to the following flowchart.

[0064] In another possible implementation, the memory 22 can also be integrated with the processor 21.

[0065] Communication interface 23 is used for connecting the wall thickness detection device to other devices via a communication network, such as Ethernet, wireless access network, or wireless local area network (WLAN). Communication interface 23 may include a receiving unit for receiving data and a transmitting unit for transmitting data.

[0066] Bus 24 can be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 2 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0067] It should be pointed out that, Figure 2 The structure shown does not constitute a limitation on the wall thickness detection device, except Figure 2 In addition to the components shown, the wall thickness detection device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0068] The wall thickness detection method provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0069] The wall thickness detection method provided in this application embodiment is applied to... Figure 1 The wall thickness detection device 103 in the wall thickness detection system shown. For example... Figure 3As shown, this embodiment provides a method for detecting the wall thickness of overhead pipelines based on unmanned aerial vehicles (UAVs), including:

[0070] S301. The wall thickness detection equipment receives the pipe wall thickness detection command, analyzes the command, and determines the pipe whose wall thickness needs to be detected.

[0071] In some embodiments, the pipe wall thickness detection command may be sent by a business system or other equipment that needs to determine the pipe wall thickness of the pipe to be detected, or it may be manually input. This application embodiment does not limit this.

[0072] In some embodiments, the pipe wall thickness detection command carries the relevant identifier of the pipe to be detected. The wall thickness detection equipment can analyze the pipe wall thickness detection command, extract the relevant identifier of the pipe to be detected, and determine the pipe to be detected.

[0073] Optionally, the relevant markings of the pipe to be tested for wall thickness may include the pipe number, manufacturing information, and specifications.

[0074] The pipe to be tested for wall thickness is divided into multiple sub-wall thickness testing areas.

[0075] In some embodiments, the surface area of ​​each sub-wall thickness detection area can be preset, and multiple sub-wall thickness detection areas can be divided on the pipe to be tested based on the surface area.

[0076] Optional, combined Figure 1 The surface area of ​​each sub-wall thickness detection area can be pre-stored in a storage server. The wall thickness detection device can obtain the surface area of ​​each sub-wall thickness detection area from the storage server and divide the pipeline to be tested into multiple sub-wall thickness detection areas based on the surface area.

[0077] Optionally, maintenance personnel can manually set the surface area of ​​each sub-wall thickness detection area and divide the pipeline to be tested into multiple sub-wall thickness detection areas based on the surface area. Subsequently, the maintenance personnel can input the divided sub-wall thickness detection areas into the wall thickness detection equipment so that the equipment can perform subsequent wall thickness detection methods.

[0078] Multiple data detection groups are deployed within the sub-wall thickness detection area. Each data detection group includes a first data detection point and a second data detection point. The first data detection point is deployed on the outer surface of the pipe to be tested for wall thickness, and the second data detection point is deployed on the inner surface of the pipe to be tested for wall thickness.

[0079] In this way, by deploying data detection points on the inner and outer surfaces of the pipe to be tested, the ultrasonic detector can detect the data detection points on the inner and outer surfaces of the pipe to be tested, thereby determining the pipe wall thickness, thus laying the foundation for pipe wall thickness detection and providing the prerequisite for detection.

[0080] In some embodiments, the number of data detection groups deployed can be determined based on the extent and shape of the sub-wall thickness detection area, such as deploying 8 or 9 groups. In this case, the multiple data detection groups within the sub-wall thickness detection area can be deployed in the following manner:

[0081] First, the extent and shape of the sub-wall thickness detection area can be determined, as well as the number of data detection groups to be deployed. The number of data detection groups deployed is positively correlated with the extent of the sub-wall thickness detection area, and the distribution of the data detection groups is related to the shape of the sub-wall thickness detection area, ensuring that the data detection groups are evenly deployed within the sub-wall thickness detection area.

[0082] Next, the sub-wall thickness detection region can be divided into multiple sub-detection regions based on the range and shape of the sub-wall thickness detection region, the number of data detection groups deployed, and the Hilbert curve algorithm.

[0083] Each sub-detection area is located on the outer surface of the pipe whose wall thickness needs to be detected.

[0084] The Hilbert curve is a space-filling curve that can map a one-dimensional linear sequence to a two-dimensional or multi-dimensional space. In the sub-wall thickness detection region segmentation, the Hilbert curve can be used to map one-dimensional sub-wall thickness detection data onto a two-dimensional detection region, thereby dividing the sub-wall thickness detection region into multiple sub-detection regions.

[0085] Next, a first data detection point is deployed at the geometric center of each sub-detection area, and a second data detection point is deployed at the corresponding position on the inner surface of the pipe to be tested for wall thickness. Subsequently, multiple data detection groups can be obtained based on all the first data detection points and the corresponding second data detection points.

[0086] In summary, this invention obtains multiple data detection groups based on all the first data detection points and the corresponding second data detection points, which can lay the foundation for pipeline wall thickness detection and provide a prerequisite for detection.

[0087] In some embodiments, the pipe to be tested for wall thickness includes an overhead pipe. An overhead pipe is a pipe erected above the ground or water surface for transporting gas, liquid, or loose solids. Of course, the pipe to be tested for wall thickness may also include other types of pipes, which are not limited in this embodiment.

[0088] S302, The wall thickness detection equipment acquires ultrasonic detection signals sent to each data detection group by an ultrasonic detector carried by a UAV.

[0089] The ultrasonic detection signal includes a first ultrasonic detection signal and a second ultrasonic detection signal, wherein the first ultrasonic detection signal corresponds to a first data detection point and the second ultrasonic detection signal corresponds to a second data detection point.

[0090] Specifically, as described above, when the pipe to be tested for wall thickness is an overhead pipe, especially at high altitudes or in inaccessible locations, manual testing methods are inefficient and pose certain risks. Therefore, this embodiment of the application utilizes ultrasonic testing signals sent to each data testing group by an ultrasonic detector carried by a drone. Subsequently, the wall thickness testing equipment can communicate with the ultrasonic detector to acquire the ultrasonic testing signals sent to each data testing group.

[0091] In some embodiments, the drone may be selected as a multi-rotor or fixed-wing drone suitable for industrial inspection, with the ability to fly stably and carry sensors.

[0092] In this embodiment of the application, when planning the flight path of the UAV, it is necessary to ensure that the ultrasonic detection signal sent by the ultrasonic detector carried by the UAV to each data detection group covers the entire length of the pipe to be tested for wall thickness, and to take into account factors such as wind force and altitude.

[0093] In this way, by using drones to carry ultrasonic detectors, the risk of workers operating in hazardous environments is reduced, and the safety of the inspection is improved.

[0094] S303 The wall thickness detection equipment analyzes all the acquired ultrasonic detection signals and calculates the sub-wall thickness detection factor for each sub-wall thickness detection area based on the analysis results.

[0095] In some embodiments, the analysis of all acquired ultrasonic test signals by the wall thickness detection device may include preprocessing of the ultrasonic test signals, waveform feature recognition, sound velocity calibration, etc.

[0096] Preprocessing is used to ensure that the acquired ultrasonic detection signals are recorded completely and accurately, and it is necessary to check for data loss, abnormal noise interference, etc.

[0097] Specifically, wall thickness testing equipment can filter ultrasonic test signals to remove some high-frequency noise, electromagnetic interference and other background noise. Common digital filtering methods can be used, such as low-pass filtering, high-pass filtering or band-pass filtering, and appropriate filter parameters can be selected according to the effective frequency range of the ultrasonic signal to make the signal waveform clearer and facilitate subsequent analysis.

[0098] Waveform feature recognition refers to observing the waveform of the pre-processed ultrasonic signal and identifying the echo characteristics related to different interfaces of the pipe whose wall thickness is to be measured. For example, in wall thickness measurement, there may be an initial wave (the wave generated by the emitted pulse), a bottom echo (the wave reflected back from the bottom surface of the object by the ultrasonic wave), and possible defect echoes (if there are defects in the wall).

[0099] Sound velocity calibration refers to accurately determining the propagation speed of ultrasonic waves in different media based on the specifications of the pipe whose wall thickness is to be tested, since ultrasonic waves travel at different speeds in different media.

[0100] After analyzing all the ultrasonic detection signals, the sub-wall thickness detection factor for each sub-wall thickness detection region can be calculated based on a general wall thickness detection algorithm.

[0101] S304. The wall thickness testing equipment calculates the comprehensive pipe wall thickness factor of the pipe to be tested based on all sub-wall thickness testing factors.

[0102] In some embodiments, the wall thickness detection device can determine the comprehensive pipe wall thickness factor of the pipe to be detected by the average value of all sub-wall thickness detection factors, or it can determine the comprehensive pipe wall thickness factor of the pipe to be detected by the standard deviation of all sub-wall thickness detection factors, or it can calculate the comprehensive pipe wall thickness factor by screening the same sub-wall thickness detection factors and removing the smaller sub-wall thickness detection factors. The embodiments of this application do not limit this.

[0103] S305. The wall thickness testing equipment determines the pipe wall thickness of the pipe to be tested based on the mapping relationship between the comprehensive pipe wall thickness factor and the pipe wall thickness.

[0104] Specifically, the mapping relationship between the integrated pipe wall thickness factor and the pipe wall thickness can be pre-stored in a preset mapping table. That is, the preset mapping table includes multiple integrated pipe wall thickness factors, and each integrated pipe wall thickness factor corresponds to a pipe wall thickness.

[0105] Optionally, the preset mapping table is calculated in advance based on the pipelines that meet the requirements.

[0106] Optionally, the preset mapping table can be stored in the storage server in advance. When the wall thickness detection device determines the wall thickness of the pipe to be detected, it can retrieve the preset mapping table from the storage server.

[0107] Optionally, the preset mapping table can also be directly configured in the wall thickness detection device.

[0108] As can be seen from the above, by using ultrasonic detection signals sent to each data detection group by ultrasonic detectors carried by UAVs to detect the pipe wall thickness of the pipeline to be tested, and by determining the pipe wall thickness of the pipeline to be tested based on the relationship between the comprehensive pipe wall thickness factor and the preset mapping table, not only is the detection efficiency and safety improved, but also the pipe wall thickness can be accurately determined through real-time data transmission and processing, and the health status of the pipeline can be quickly and accurately assessed, providing strong support for pipeline operation and maintenance.

[0109] In some embodiments of this application, combined with Figure 3 ,like Figure 4 As shown, before receiving and analyzing the pipe wall thickness detection command, the wall thickness detection equipment also includes:

[0110] S401. The wall thickness detection equipment matches the pipe wall thickness detection command with the command category of the previous command and determines whether the pipe wall thickness detection command is the same as the previous command.

[0111] In this embodiment, the previous instructions include: pipeline flow detection instructions, pipeline pressure detection instructions, pipeline surface defect detection instructions, etc.

[0112] Optionally, the previous instruction may be sent by the business system or other equipment that needs to determine the relevant parameters of the pipe to be tested for wall thickness, or it may be manually input. This application embodiment does not limit this.

[0113] Optionally, both the previous instruction and the pipe wall thickness detection instruction include fields related to the instruction category. The wall thickness detection equipment can determine whether the pipe wall thickness detection instruction is the same as the previous instruction based on the relevant fields of the instruction category in the previous instruction and the pipe wall thickness detection instruction.

[0114] S402. If the pipe wall thickness detection command matches the command category of the previous command, the wall thickness detection equipment determines that the pipe wall thickness detection command is the same as the previous command, collects the first time node of sending the pipe wall thickness detection command, and collects the second time node of sending the previous command.

[0115] Specifically, if the pipe wall thickness detection command matches the command type of the previous command, it may be due to operator error or it may indicate that a re-inspection is indeed necessary. Therefore, the wall thickness detection equipment can collect the first time point at which the pipe wall thickness detection command was sent and the second time point at which the previous command was sent. By using the time difference between the first and second time points, it can determine whether the current pipe wall thickness detection command was due to operator error or whether a re-inspection is indeed necessary.

[0116] Optionally, both the previous instruction and the pipe wall thickness detection instruction include fields related to the instruction sending time. The wall thickness detection equipment can obtain the first time node and the second time node based on the relevant fields of the instruction sending time in the previous instruction and the pipe wall thickness detection instruction.

[0117] S403 The wall thickness detection equipment calculates the time node difference between the first time node and the second time node, and determines whether to process the pipe wall thickness detection command based on the relationship between the time node difference and the preset time node difference.

[0118] In this embodiment, the time difference is preferably 720 hours, but it can be set according to the actual situation.

[0119] S404. If the time node difference is greater than or equal to the preset time node difference, the wall thickness detection equipment will process the pipe wall thickness detection command.

[0120] In this embodiment, when the time node difference is greater than or equal to the preset time node difference, it indicates that it is indeed necessary to detect again. Therefore, the wall thickness detection equipment processes the pipe wall thickness detection command.

[0121] S405. If the time node difference is less than the preset time node difference, the wall thickness detection equipment will not process the pipe wall thickness detection command and will generate and output a log reminder.

[0122] In this embodiment, when the time node difference is less than the preset time node difference, it may be due to staff error or the need for re-inspection. Therefore, a log reminder is generated, and the relevant staff decide whether to process the pipe wall thickness inspection instruction.

[0123] S406. If the pipe wall thickness detection command does not match the command type of the previous command, the wall thickness detection equipment determines that the pipe wall thickness detection command is different from the previous command and processes the pipe wall thickness detection command.

[0124] As can be seen from the above, the present invention can not only ensure the normal reception and processing of pipeline wall thickness detection commands, but also avoid erroneous operations by processing personnel.

[0125] In some embodiments of this application, combined with Figure 4 ,like Figure 5 As shown, the method for analyzing all acquired ultrasonic detection signals and calculating the sub-wall thickness detection factor for each sub-wall thickness detection area based on the analysis results using a wall thickness detection device specifically includes:

[0126] S501, the wall thickness detection equipment calculates multiple ultrasonic detection signal differences based on the first ultrasonic detection signal and the second ultrasonic detection signal.

[0127] S502, the wall thickness detection equipment normalizes all ultrasonic test signal differences to obtain the corresponding signal normalization values ​​and constructs a signal normalization sequence.

[0128] Optionally, the above normalization method can be the Min-Max normalization method, which performs a linear transformation on the difference of the ultrasonic detection signal and maps it to the [0,1] interval.

[0129] S503. The wall thickness detection equipment calculates the mean of the signal normalization sequence and extracts all signal normalization values ​​that are greater than or equal to the mean to generate the first signal normalization sequence.

[0130] S504. The wall thickness detection equipment extracts the normalized values ​​of all signals that are less than the mean value and generates a second signal normalization sequence.

[0131] S505, The wall thickness detection equipment calculates the sub-wall thickness detection factor for each sub-wall thickness detection area based on the first signal normalization sequence and the second signal normalization sequence.

[0132] As can be seen from the above, the present invention calculates the sub-wall thickness detection factor of each sub-wall thickness detection region based on the first signal normalization sequence and the second signal normalization sequence, ensuring the calculation accuracy of the sub-wall thickness detection factor, performing multi-point detection, and eliminating thickness detection error.

[0133] In some embodiments of this application, the method for calculating the sub-wall thickness detection factor of each sub-wall thickness detection region based on a first signal normalization sequence and a second signal normalization sequence specifically includes:

[0134] The wall thickness detection equipment calculates the first standard deviation of the first signal normalized sequence and the second standard deviation of the second signal normalized sequence, and calculates the sub-wall thickness detection factor for each sub-wall thickness detection region according to the following formula:

[0135]

[0136] Where W is the sub-wall thickness detection factor of the sub-wall thickness detection region, f1 is the calculated coefficient corresponding to the first signal normalization sequence, p is the mean, e1 is the number of signal normalization values ​​in the first signal normalization sequence, e2 is the number of signal normalization values ​​in the second signal normalization sequence, d1 is the first standard deviation, f2 is the calculated coefficient corresponding to the second signal normalization sequence, and d2 is the second standard deviation.

[0137] In some embodiments of this application, combined with Figure 5 ,like Figure 6 As shown, the method for calculating the comprehensive pipe wall thickness factor of the pipe to be tested based on all sub-wall thickness testing factors using wall thickness testing equipment specifically includes:

[0138] S601, The wall thickness detection device extracts the same sub-wall thickness detection factor from all sub-wall thickness detection factors and obtains multiple sub-wall thickness detection factor sequences.

[0139] For example, assuming the sub-wall thickness detection factor is {0.3,0.3,0.4,0.6,0.7,0.7,0.8,0.8}, then the sub-wall thickness detection factor sequence is {0.3,0.3}, {0.7,0.7}, {0.8,0.8}.

[0140] S602. The wall thickness detection equipment counts the number of the first factor sequence of multiple sub-wall thickness detection factor sequences.

[0141] Based on the example above, the number of first factor sequences is 3.

[0142] S603. The wall thickness detection device extracts one sub-wall thickness detection factor from each of the sub-wall thickness detection factor sequences, and determines the sum of the sub-wall thickness detection factors extracted from all the sub-wall thickness detection factor sequences as the first sub-wall thickness detection factor sum.

[0143] Based on the above example, extract one sub-wall thickness detection factor from each of the sub-wall thickness detection factor sequences, namely 0.3, 0.7, and 0.8. The sum of the first sub-wall thickness detection factors is 0.3 + 0.7 + 0.8.

[0144] S604. The wall thickness detection device removes all sub-wall thickness detection factor sequences that are smaller than the preset sub-wall thickness detection factor, and counts the number of second factor sequences of the remaining sub-wall thickness detection factor sequences.

[0145] Based on the above example, if the preset sub-wall thickness detection factor is 0.5, the remaining sub-wall thickness detection factor sequences are {0.7,0.7} and {0.8,0.8}, and the number of second factor sequences in the remaining sub-wall thickness detection factor sequences is 2.

[0146] S605. The wall thickness detection device extracts one sub-wall thickness detection factor from the remaining sub-wall thickness detection factor sequence, and determines the sum of the sub-wall thickness detection factors extracted from the remaining sub-wall thickness detection factor sequence as the second sub-wall thickness detection factor sum.

[0147] Based on the above example, extract one sub-wall thickness detection factor from the remaining sub-wall thickness detection factor sequence, namely, 0.7, 0.8, and the sum of the second sub-wall thickness detection factors = 0.7 + 0.8.

[0148] S606. The wall thickness detection equipment calculates the comprehensive pipe wall thickness factor of the pipe to be tested based on the number of first factor sequences, the number of second factor sequences, the sum of the first sub-wall thickness detection factors and the sum of the second sub-wall thickness detection factors.

[0149] In some embodiments of this application, the wall thickness detection device can calculate the comprehensive pipe wall thickness factor of the pipe to be detected according to the following formula:

[0150]

[0151] Where H is the comprehensive pipe wall thickness factor of the pipe to be tested, r2 is the number of second factor sequences, r1 is the number of first factor sequences, u1 is the sum of the first sub-wall thickness detection factors, u2 is the sum of the second sub-wall thickness detection factors, and t is the preset sub-wall thickness detection factor.

[0152] Based on the above example, with the first factor sequence number = 3, the second factor sequence number = 2, the sum of the first sub-wall thickness detection factors = 1.8, the sum of the second sub-wall thickness detection factors = 1.5, and the preset sub-wall thickness detection factor = 0.5, substituting these values ​​into the above formula yields the comprehensive pipeline wall thickness factor = (2 / 3). 0.5 +(1.8 / 1.5) 0.5 .

[0153] As can be seen from the above, the present invention calculates the comprehensive pipe wall thickness factor of the pipe to be tested based on the number of the first factor sequence, the number of the second factor sequence, the sum of the first sub-wall thickness detection factor and the sum of the second sub-wall thickness detection factor, thereby providing reliable data support for determining the pipe wall thickness and ensuring the calculation accuracy.

[0154] In some embodiments of this application, combined with Figure 6 ,like Figure 7 As shown, after determining the pipe wall thickness of the pipe to be tested based on the relationship between the comprehensive pipe wall thickness factor and the preset mapping table, the wall thickness testing equipment also includes:

[0155] S701. The wall thickness detection equipment determines whether to issue an alarm based on the relationship between the pipe wall thickness to be detected and the preset pipe wall thickness.

[0156] In this embodiment, the preset pipe wall thickness refers to the safe thickness of the pipe to be tested, which can be set according to specific circumstances.

[0157] S702. When the pipe wall thickness is greater than or equal to the preset pipe wall thickness, the wall thickness detection equipment will not issue an alarm.

[0158] S703. When the pipe wall thickness is less than the preset pipe wall thickness, the wall thickness detection equipment will issue an alarm.

[0159] In some embodiments of this application, after determining that an alarm is issued when the pipe wall thickness is less than a preset pipe wall thickness, the method further includes:

[0160] The wall thickness detection equipment calculates the normalized mean of the second signal normalized sequence and calculates the alarm level factor of the pipe to be tested according to the following formula:

[0161]

[0162] Where G is the alarm level factor of the pipeline whose wall thickness needs to be tested, and y is the normalized mean.

[0163] As can be seen from the above, the present invention calculates the normalized mean of the normalized sequence of the second signal and calculates the alarm level factor of the pipe to be tested for wall thickness. This can provide a basis for maintenance by the staff. When the alarm level factor is larger, a more urgent maintenance plan needs to be initiated. When the alarm level factor is smaller, a conventional maintenance plan needs to be initiated.

[0164] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0165] This application embodiment can divide the wall thickness detection device into functional modules according to the above method example. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0166] like Figure 8 The diagram shown is a structural schematic of a wall thickness detection device provided in an embodiment of this application. Figure 8 The wall thickness detection device shown includes: a communication unit 801 and a processing unit 802;

[0167] The communication unit 801 is used to receive a pipe wall thickness detection command, analyze the command, and determine the pipe to be inspected. The pipe to be inspected is divided into multiple sub-wall thickness detection areas. Multiple data detection groups are deployed within each sub-wall thickness detection area. Each data detection group includes a first data detection point and a second data detection point. The first data detection point is deployed on the outer surface of the pipe to be inspected, and the second data detection point is deployed on the inner surface of the pipe to be inspected. The pipe to be inspected includes an overhead pipe.

[0168] The communication unit 801 is also used to acquire ultrasonic detection signals sent to each data detection group by the ultrasonic detector carried by the UAV, analyze all the acquired ultrasonic detection signals, and calculate the sub-wall thickness detection factor of each sub-wall thickness detection area based on the analysis results. The ultrasonic detection signals include a first ultrasonic detection signal and a second ultrasonic detection signal, and the first ultrasonic detection signal corresponds to the first data detection point, and the second ultrasonic detection signal corresponds to the second data detection point.

[0169] The processing unit 802 is used to calculate the comprehensive pipe wall thickness factor of the pipe to be tested based on all the sub-wall thickness detection factors, and to determine the pipe wall thickness of the pipe to be tested based on the mapping relationship between the comprehensive pipe wall thickness factor and the pipe wall thickness.

[0170] Optionally, the processing unit 802 is further configured to match the pipe wall thickness detection command with the command category of the previous command, and determine whether the pipe wall thickness detection command is the same as the previous command;

[0171] Optionally, the processing unit 802 is further configured to determine that the pipe wall thickness detection instruction is the same as the previous instruction if the instruction category of the pipe wall thickness detection instruction matches the previous instruction, collect the first time node of sending the pipe wall thickness detection instruction, and collect the second time node of sending the previous instruction.

[0172] Optionally, the processing unit 802 is further configured to calculate the time node difference between the first time node and the second time node, and determine whether to process the pipe wall thickness detection command based on the relationship between the time node difference and a preset time node difference.

[0173] Optionally, the processing unit 802 is further configured to process the pipe wall thickness detection command if the time node difference is greater than or equal to the preset time node difference.

[0174] Optionally, the processing unit 802 is further configured to, if the time node difference is less than the preset time node difference, not process the pipe wall thickness detection command, and generate and output a log reminder;

[0175] Optionally, the processing unit 802 is further configured to determine that the pipe wall thickness detection instruction is different from the previous instruction if the instruction type of the pipe wall thickness detection instruction does not match the previous instruction, and to process the pipe wall thickness detection instruction.

[0176] Optionally, the plurality of data detection groups within the sub-wall thickness detection area are deployed in the following manner:

[0177] Determine the extent and shape of the sub-wall thickness detection area, and determine the number of data detection groups to be deployed;

[0178] Based on the range and shape of the sub-wall thickness detection area, the number of data detection groups deployed, and the Hilbert curve algorithm, the sub-wall thickness detection area is divided into multiple sub-detection areas, wherein each sub-detection area is located on the outer surface of the pipe to be detected.

[0179] The first data detection point is deployed at the geometric center of each sub-detection area, and the second data detection point is deployed at the corresponding position of the first data detection point on the inner surface of the pipe to be tested for wall thickness.

[0180] The plurality of data detection groups are obtained based on all the first data detection points and the corresponding second data detection points.

[0181] Optionally, the processing unit 802 is specifically used for:

[0182] Multiple ultrasonic detection signal differences are calculated based on the first ultrasonic detection signal and the second ultrasonic detection signal;

[0183] All ultrasonic detection signal differences are normalized to obtain the corresponding signal normalization values, and a signal normalization sequence is constructed.

[0184] Calculate the mean of the normalized signal sequence, and extract all signal normalized values ​​that are greater than or equal to the mean to generate a first signal normalized sequence.

[0185] Extract all signal normalization values ​​that are less than the mean value to generate a second signal normalization sequence;

[0186] The sub-wall thickness detection factor for each sub-wall thickness detection region is calculated based on the first signal normalization sequence and the second signal normalization sequence.

[0187] Optionally, the processing unit 802 is specifically used for:

[0188] Calculate the first standard deviation of the normalized sequence of the first signal and the second standard deviation of the normalized sequence of the second signal, respectively;

[0189] The sub-wall thickness detection factor for each sub-wall thickness detection region is calculated using the following formula:

[0190]

[0191] Wherein, W is the sub-wall thickness detection factor of the sub-wall thickness detection region, f1 is the calculated coefficient corresponding to the first signal normalization sequence, p is the mean, e1 is the number of signal normalization values ​​in the first signal normalization sequence, e2 is the number of signal normalization values ​​in the second signal normalization sequence, d1 is the first standard deviation, f2 is the calculated coefficient corresponding to the second signal normalization sequence, and d2 is the second standard deviation.

[0192] Optionally, the processing unit 802 is further configured to calculate the normalized mean of the normalized sequence of the second signal;

[0193] Processing unit 802 is further configured to calculate the alarm level factor of the pipe to be tested for wall thickness according to the following formula, wherein the alarm level factor is used to determine the alarm level when an alarm is issued:

[0194]

[0195] Wherein, G is the alarm level factor of the pipe to be tested for wall thickness, and y is the normalized mean.

[0196] Optionally, the processing unit 802 is specifically used for:

[0197] Extract the same sub-wall thickness detection factor from all the sub-wall thickness detection factors to obtain multiple sub-wall thickness detection factor sequences;

[0198] Count the number of the first factor sequences in the multiple sub-wall thickness detection factor sequences;

[0199] Extract one sub-wall thickness detection factor from each of the sub-wall thickness detection factor sequences, and determine the sum of the sub-wall thickness detection factors extracted from all the sub-wall thickness detection factor sequences as the first sub-wall thickness detection factor sum.

[0200] Remove all sub-wall thickness detection factor sequences that are smaller than the preset sub-wall thickness detection factor, and count the number of second factor sequences in the remaining sub-wall thickness detection factor sequences.

[0201] Extract one sub-wall thickness detection factor from the remaining sub-wall thickness detection factor sequence, and determine the sum of the sub-wall thickness detection factors extracted from the remaining sub-wall thickness detection factor sequence as the second sub-wall thickness detection factor sum.

[0202] The comprehensive pipe wall thickness factor of the pipe to be tested is calculated based on the number of the first factor sequence, the number of the second factor sequence, the sum of the first sub-wall thickness detection factors, and the sum of the second sub-wall thickness detection factors.

[0203] Optionally, the processing unit 802 is specifically used for:

[0204] The comprehensive pipe wall thickness factor of the pipe to be tested is calculated according to the following formula:

[0205]

[0206] Wherein, H is the comprehensive pipe wall thickness factor of the pipe to be tested, r2 is the number of the second factor sequence, r1 is the number of the first factor sequence, u1 is the sum of the first sub-wall thickness detection factors, u2 is the sum of the second sub-wall thickness detection factors, and t is the preset sub-wall thickness detection factor.

[0207] Optionally, the processing unit 802 is also used to determine whether to issue an alarm based on the relationship between the pipe wall thickness of the pipe to be tested and the preset pipe wall thickness.

[0208] The processing unit 802 is also configured not to issue an alarm when the pipe wall thickness is greater than or equal to the preset pipe wall thickness;

[0209] The processing unit 802 is also used to issue an alarm when the pipe wall thickness is less than the preset pipe wall thickness.

[0210] This application also provides a computer-readable storage medium, which includes computer-executable instructions. When the computer-executable instructions are executed on a computer, the computer performs the wall thickness detection method as provided in the above embodiments.

[0211] This application also provides a computer program product that can be directly loaded into a memory and contains software code. After being loaded and executed by a computer, the computer program product can implement the wall thickness detection method provided in the above embodiments. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

[0212] The system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0213] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0214] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0215] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0216] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0217] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for detecting the wall thickness of an overhead pipeline based on unmanned aerial vehicles (UAVs), characterized in that, include: The system receives a pipe wall thickness detection command, analyzes the command, and determines the pipe to be inspected. The pipe is divided into multiple sub-wall thickness detection areas. Multiple data detection groups are deployed within each sub-wall thickness detection area. Each data detection group includes a first data detection point and a second data detection point. The first data detection point is deployed on the outer surface of the pipe, and the second data detection point is deployed on the inner surface. The pipe to be inspected includes an overhead pipe. The process involves acquiring ultrasonic detection signals sent to each data detection group by an ultrasonic detector carried by a UAV, analyzing all acquired ultrasonic detection signals, and calculating the sub-wall thickness detection factor for each sub-wall thickness detection region based on the analysis results. This includes: calculating multiple ultrasonic detection signal differences based on a first and a second ultrasonic detection signal; normalizing all ultrasonic detection signal differences to obtain corresponding signal normalization values ​​and constructing a signal normalization sequence; calculating the mean of the signal normalization sequence and extracting all signal normalization values ​​greater than or equal to the mean to generate a first signal normalization sequence; extracting all signal normalization values ​​less than the mean to generate a second signal normalization sequence; calculating the first standard deviation of the first signal normalization sequence and the second standard deviation of the second signal normalization sequence; and calculating the sub-wall thickness detection factor for each sub-wall thickness detection region according to the following formula: Wherein, W is the sub-wall thickness detection factor of the sub-wall thickness detection region, f1 is the calculated coefficient corresponding to the first signal normalization sequence, p is the mean, e1 is the number of signal normalization values ​​in the first signal normalization sequence, e2 is the number of signal normalization values ​​in the second signal normalization sequence, d1 is the first standard deviation, f2 is the calculated coefficient corresponding to the second signal normalization sequence, d2 is the second standard deviation, and the ultrasonic detection signal includes a first ultrasonic detection signal and a second ultrasonic detection signal, wherein the first ultrasonic detection signal corresponds to the first data detection point, and the second ultrasonic detection signal corresponds to the second data detection point; The comprehensive pipe wall thickness factor of the pipe to be tested is calculated based on all sub-wall thickness detection factors, and the pipe wall thickness of the pipe to be tested is determined based on the mapping relationship between the comprehensive pipe wall thickness factor and the pipe wall thickness.

2. The method for detecting the wall thickness of overhead pipelines based on unmanned aerial vehicles according to claim 1, characterized in that, Before receiving the pipe wall thickness detection command and analyzing the pipe wall thickness detection command, the method further includes: The pipe wall thickness detection command is matched with the command category of the previous command, and it is determined whether the pipe wall thickness detection command is the same as the previous command. If the pipe wall thickness detection command matches the command category of the previous command, then it is determined that the pipe wall thickness detection command is the same as the previous command. The first time point of sending the pipe wall thickness detection command is collected, and the second time point of sending the previous command is collected. Calculate the time difference between the first time node and the second time node, and determine whether to process the pipe wall thickness detection command based on the relationship between the time difference and a preset time difference. If the time node difference is greater than or equal to the preset time node difference, then process the pipe wall thickness detection command; If the time node difference is less than the preset time node difference, the pipe wall thickness detection command will not be processed, and a log reminder will be generated and output. If the pipe wall thickness detection command does not match the command type of the previous command, then it is determined that the pipe wall thickness detection command is different from the previous command, and the pipe wall thickness detection command is processed.

3. The method for detecting the wall thickness of overhead pipelines based on unmanned aerial vehicles according to claim 1, characterized in that, The multiple data detection groups within the sub-wall thickness detection area are deployed in the following manner: Determine the extent and shape of the sub-wall thickness detection area, and determine the number of data detection groups to be deployed; Based on the range and shape of the sub-wall thickness detection area, the number of data detection groups deployed, and the Hilbert curve algorithm, the sub-wall thickness detection area is divided into multiple sub-detection areas, wherein each sub-detection area is located on the outer surface of the pipe to be detected. The first data detection point is deployed at the geometric center of each sub-detection area, and the second data detection point is deployed at the corresponding position of the first data detection point on the inner surface of the pipe to be tested for wall thickness. The plurality of data detection groups are obtained based on all the first data detection points and the corresponding second data detection points.

4. The method for detecting the wall thickness of overhead pipelines based on unmanned aerial vehicles according to claim 1, characterized in that, Also includes: Calculate the normalized mean of the normalized sequence of the second signal; The alarm level factor for the pipe to be tested for wall thickness is calculated according to the following formula, and the alarm level factor is used to determine the alarm level when an alarm is issued: Wherein, G is the alarm level factor of the pipe to be tested for wall thickness, and y is the normalized mean.

5. The method for detecting the wall thickness of overhead pipelines based on unmanned aerial vehicles according to claim 1, characterized in that, The calculation of the comprehensive pipe wall thickness factor of the pipe to be tested based on all sub-wall thickness detection factors includes: Extract the same sub-wall thickness detection factor from all the sub-wall thickness detection factors to obtain multiple sub-wall thickness detection factor sequences; Count the number of the first factor sequences in the multiple sub-wall thickness detection factor sequences; Extract one sub-wall thickness detection factor from each of the sub-wall thickness detection factor sequences, and determine the sum of the sub-wall thickness detection factors extracted from all the sub-wall thickness detection factor sequences as the first sub-wall thickness detection factor sum. Remove all sub-wall thickness detection factor sequences that are smaller than the preset sub-wall thickness detection factor, and count the number of second factor sequences in the remaining sub-wall thickness detection factor sequences. Extract one sub-wall thickness detection factor from the remaining sub-wall thickness detection factor sequence, and determine the sum of the sub-wall thickness detection factors extracted from the remaining sub-wall thickness detection factor sequence as the second sub-wall thickness detection factor sum. The comprehensive pipe wall thickness factor of the pipe to be tested is calculated based on the number of the first factor sequence, the number of the second factor sequence, the sum of the first sub-wall thickness detection factors, and the sum of the second sub-wall thickness detection factors.

6. The method for detecting the wall thickness of overhead pipelines based on unmanned aerial vehicles according to claim 5, characterized in that, The step of calculating the comprehensive pipe wall thickness factor of the pipe to be tested based on the number of the first factor sequence, the number of the second factor sequence, the sum of the first sub-wall thickness detection factors, and the sum of the second sub-wall thickness detection factors includes: The comprehensive pipe wall thickness factor of the pipe to be tested is calculated according to the following formula: Wherein, H is the comprehensive pipe wall thickness factor of the pipe to be tested, r2 is the number of the second factor sequence, r1 is the number of the first factor sequence, u1 is the sum of the first sub-wall thickness detection factors, u2 is the sum of the second sub-wall thickness detection factors, and t is the preset sub-wall thickness detection factor.

7. The method for detecting the wall thickness of overhead pipelines based on unmanned aerial vehicles according to claim 1, characterized in that, Also includes: Whether to issue an alarm is determined based on the relationship between the pipe wall thickness to be detected and the preset pipe wall thickness; When the pipe wall thickness is greater than or equal to the preset pipe wall thickness, no alarm will be issued; An alarm is triggered when the pipe wall thickness is less than the preset pipe wall thickness.

8. A wall thickness detection device for an ultrasonic sensor system, characterized in that, include: A processor and a memory; wherein the memory is used to store one or more programs, the one or more programs including computer-executable instructions, wherein when the device is running, the processor executes the computer-executable instructions stored in the memory to cause the device to perform the method of any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, When the computer-executable instructions stored in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is capable of performing the method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes: a computer program or instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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