Overhead pipeline wall thickness detection method and device based on unmanned aerial vehicle, medium and product

Abstract

The invention relates to the technical field of thickness detection, and discloses an unmanned aerial vehicle-based overhead pipeline wall thickness detection method and device, a medium and a product, and the method comprises the steps: receiving a pipeline wall thickness detection instruction, and determining a pipeline to be subjected to wall thickness detection; the pipeline to be subjected to wall thickness detection is divided into a plurality of sub wall thickness detection areas; a plurality of data detection groups are deployed in the sub-wall thickness detection areas; obtaining an ultrasonic detection signal sent to each data detection group based on an ultrasonic detector carried by an unmanned aerial vehicle, analyzing the obtained ultrasonic detection signal, and calculating a sub-wall thickness detection factor of each sub-wall thickness detection area; the 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 the mapping relation between the comprehensive pipeline wall thickness factor and the pipeline wall thickness, so that the detection efficiency and safety are improved, the pipeline wall thickness can be accurately determined through real-time data transmission and processing, and the detection accuracy is improved. And the health condition of the pipeline can be quickly and accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of thickness detection, and in particular to an overhead pipeline wall thickness detection method, equipment, medium and product based on a drone. Background Art

[0002] Oil and gas pipelines are key infrastructure for energy transportation, and their safe operation is crucial to ensuring energy supply and environmental safety. However, due to long-term use, external environmental influences, and material aging, the wall thickness of the pipeline will gradually decrease, increasing the risk of leakage and rupture. Therefore, regular testing of pipeline wall thickness is of great significance to prevent accidents.

[0003] Traditional pipe wall thickness detection methods often require manual intervention, with experienced staff using handheld ultrasonic thickness gauges to perform pipe wall thickness detection. Ultrasonic thickness gauges are devices that use the principle of ultrasonic pulse reflection to measure material thickness. When the ultrasonic pulse emitted by the ultrasonic thickness gauge probe 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 propagate in the material, the thickness of the material being measured can be determined. This traditional pipe wall thickness detection method is not only inefficient, but also dangerous, especially at high altitudes or in difficult-to-reach locations. In addition, these methods usually fail to achieve a comprehensive and detailed inspection of the pipeline. Summary of the invention

[0004] In view of this, the present invention proposes an overhead pipeline wall thickness detection method, equipment, medium and product based on drones, which not only improves the detection efficiency and safety, but also can accurately determine the pipeline wall thickness through real-time data transmission and processing, and quickly and accurately evaluate the health status of the pipeline.

[0005] In the first aspect, the present invention proposes a method for detecting the wall thickness of an overhead pipeline based on a drone, comprising: receiving a pipeline wall thickness detection instruction, analyzing the pipeline wall thickness detection instruction, determining a pipeline to be detected for wall thickness, wherein the pipeline to be detected for wall thickness is divided into a plurality of sub-wall thickness detection areas; a plurality of data detection groups are deployed in the sub-wall thickness detection areas, wherein the data detection group comprises a first data detection point and a second data detection point, wherein the first data detection point is deployed to the outer surface of the pipeline to be detected for wall thickness, and the second data detection point is deployed to the inner surface of the pipeline to be detected for wall thickness; the pipeline to be detected for wall thickness comprises an overhead pipeline; obtaining an ultrasonic detection device based on the drone. The ultrasonic detection signal sent by the device to each data detection group is analyzed, and a sub-wall thickness detection factor of each sub-wall thickness detection area is calculated based on the analysis result, wherein the ultrasonic detection signal includes a first ultrasonic detection signal and a second ultrasonic detection signal, and 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; a comprehensive pipeline wall thickness factor of the pipeline to be detected is calculated according to all sub-wall thickness detection factors, and the pipeline wall thickness of the pipeline to be detected is determined according to a mapping relationship between the comprehensive pipeline wall thickness factor and the pipeline wall thickness.

[0006] In some embodiments, before receiving the pipeline wall thickness detection instruction and analyzing the pipeline wall thickness detection instruction, it also includes: matching the pipeline wall thickness detection instruction with the instruction category of the previous instruction, and judging whether the pipeline wall thickness detection instruction is the same as the previous instruction; if the pipeline wall thickness detection instruction matches the instruction category of the previous instruction, determining that the pipeline wall thickness detection instruction is the same as the previous instruction, collecting the first time node for sending the pipeline wall thickness detection instruction, and collecting the second time node for sending the previous instruction; calculating the time node difference between the first time node and the second time node, and judging whether to process the pipeline wall thickness detection instruction according to 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, processing the pipeline wall thickness detection instruction; if the time node difference is less than the preset time node difference, not processing the pipeline wall thickness detection instruction, and generating and outputting a log reminder; if the pipeline wall thickness detection instruction does not match the instruction category of the previous instruction, determining that the pipeline wall thickness detection instruction is different from the previous instruction, and processing the pipeline wall thickness detection instruction.

[0007] In some embodiments, multiple data detection groups within a sub-wall thickness detection area are deployed in the following manner: determine the range and shape of the sub-wall thickness detection area, and determine the number of deployed data detection groups; divide the sub-wall thickness detection area into multiple sub-detection areas based on the range and shape of the sub-wall thickness detection area, the number of deployed data detection groups, and the Hilbert curve algorithm, wherein each sub-detection area is located on the outer surface of the pipe to be inspected for wall thickness; deploy a first data detection point at the geometric center of each sub-detection area, and deploy a second data detection point at a position corresponding to the first data detection point on the inner surface of the pipe to be inspected for wall thickness; and obtain multiple data detection groups 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 of each sub-wall thickness detection area is calculated based on the analysis results, including: calculating multiple ultrasonic detection signal differences based on the first ultrasonic detection signal and the 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; and calculating the sub-wall thickness detection factor of each sub-wall thickness detection area based on the first signal normalization sequence and the second signal normalization sequence.

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

[0010]

[0011] Wherein, W is the sub-wall thickness detection factor of the sub-wall thickness detection area, f1 is the calculation 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 calculation coefficient corresponding to the second signal normalization sequence, and d2 is the second standard deviation.

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

[0013]

[0014] Among them, G is the alarm level factor of the pipe to be tested for wall thickness, and y is the normalized mean.

[0015] In some embodiments, the comprehensive pipeline wall thickness factor of the pipeline to be inspected for wall thickness is calculated based on all sub-wall thickness detection factors, including: extracting the same sub-wall thickness detection factor from all sub-wall thickness detection factors and obtaining multiple sub-wall thickness detection factor sequences; counting the number of first factor sequences of the multiple sub-wall thickness detection factor sequences; extracting one sub-wall thickness detection factor from all sub-wall thickness detection factor sequences respectively, 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; eliminating 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 of the remaining sub-wall thickness detection factor sequences; extracting one sub-wall thickness detection factor from the remaining sub-wall thickness detection factor sequences respectively, 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 pipeline wall thickness factor of the pipeline to be inspected for wall thickness according to 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.

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

[0017]

[0018] Among them, H is the comprehensive pipe wall thickness factor of the pipe to be tested for wall thickness, 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 factor, u2 is the sum of the second sub-wall thickness detection factor, and t is the preset sub-wall thickness detection factor.

[0019] In some embodiments, it also includes: judging 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; when the pipe wall thickness is greater than or equal to the preset pipe wall thickness, no alarm is issued; when the pipe wall thickness is less than the preset pipe wall thickness, an alarm is issued.

[0020] In a second aspect, an overhead pipeline wall thickness detection device based on a drone is provided, including: a communication unit and a processing unit; the communication unit is used to receive a pipeline wall thickness detection instruction, and analyze the pipeline wall thickness detection instruction to determine a pipeline to be detected by wall thickness, wherein the pipeline to be detected by wall thickness is divided into a plurality of sub-wall thickness detection areas; a plurality of data detection groups are deployed in the sub-wall thickness detection areas, wherein the data detection group includes a first data detection point and a second data detection point, wherein the first data detection point is deployed to the outer surface of the pipeline to be detected by wall thickness, and the second data detection point is deployed to the inner surface of the pipeline to be detected by wall thickness; the pipeline to be detected by wall thickness includes an overhead pipeline; the communication unit is also used to obtain a data signal based on the drone. The ultrasonic detector with an ultrasonic detector sends an ultrasonic detection signal to each data detection group, analyzes all the acquired ultrasonic detection signals, and calculates the sub-wall thickness detection factor of each sub-wall thickness detection area based on the analysis result, wherein the ultrasonic detection signal includes a first ultrasonic detection signal and a second ultrasonic detection signal, and 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; a processing unit is used to calculate the comprehensive pipeline wall thickness factor of the pipeline to be detected according to all the sub-wall thickness detection factors, and determine the pipeline wall thickness of the pipeline to be detected according to the mapping relationship between the comprehensive pipeline wall thickness factor and the pipeline wall thickness.

[0021] In a third aspect, a drone-based overhead pipeline wall thickness detection device is provided, comprising a memory and a processor; the memory is used to store computer execution instructions, and the processor is connected to the memory through a bus; when the drone-based overhead pipeline wall thickness detection device is running, the processor executes the computer execution instructions stored in the memory, so that the drone-based overhead pipeline wall thickness detection device performs the drone-based overhead pipeline wall thickness detection method described in the first aspect.

[0022] The drone-based overhead pipeline wall thickness detection device can be a network device, or a part of a network device, such as a chip system in a network device. The chip system is used to support the network device to implement the functions involved in the first aspect and any possible implementation thereof, for example, to obtain, determine, and send the data and / or information involved in the drone-based overhead pipeline wall thickness detection method. The chip system includes a chip, and may also include other discrete devices or circuit structures.

[0023] In a fourth aspect, a computer-readable storage medium is provided, the computer-readable storage medium comprising computer execution instructions, and when the computer execution instructions are run on a computer, the computer executes the overhead pipeline wall thickness detection method based on a drone as described in the first aspect.

[0024] In a fifth aspect, a computer program product is also provided, which includes computer instructions. When the computer instructions are run on an overhead pipeline wall thickness detection device based on a drone, the overhead pipeline wall thickness detection device based on a drone performs the overhead pipeline wall thickness detection method based on a drone as described in the first aspect above.

[0025] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the drone-based overhead pipeline wall thickness detection device, or may be packaged separately from the processor of the drone-based overhead pipeline wall thickness detection device, which is not limited in the embodiments of the present application.

[0026] The description of the second, third, fourth and fifth aspects of the present application can refer to the detailed description of the first aspect.

[0027] In the embodiments of the present application, the name of the above-mentioned drone-based overhead pipeline wall thickness detection device does not constitute a limitation on the equipment or functional modules themselves. In actual implementation, these equipment or functional modules may appear with other names. For example, the receiving unit may also be called a receiving module, a receiver, etc. As long as the functions of each device or functional module are similar to those of the present application, they belong to the scope of the claims of the present application and their equivalent technologies. From the above, it can be seen that the present application detects the wall thickness of the pipeline to be detected by using an ultrasonic detection signal sent by an ultrasonic detector carried by a drone to each data detection group. This not only improves the detection efficiency and safety, but also can accurately determine the pipeline wall thickness through real-time data transmission and processing, quickly and accurately evaluate the health status of the pipeline to be detected, and provide strong support for the operation and maintenance of the pipeline to be detected. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

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

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

[0031] Figure 3 A schematic diagram of a flow chart of a wall thickness detection method provided in an embodiment of the present application;

[0032] Figure 4 A schematic flow chart of another wall thickness detection method provided in an embodiment of the present application;

[0033] Figure 5 A schematic flow chart of another wall thickness detection method provided in an embodiment of the present application;

[0034] Figure 6 A schematic flow chart of another wall thickness detection method provided in an embodiment of the present application;

[0035] Figure 7 A schematic flow chart of another wall thickness detection method provided in an embodiment of the present application;

[0036] Figure 8 A schematic structural diagram of a wall thickness detection device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0038] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0039] In order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and order of execution.

[0040] As described in the background technology, the traditional pipeline wall thickness detection method often requires manual intervention, and experienced staff use a handheld ultrasonic thickness gauge to detect the pipeline wall thickness. The ultrasonic thickness gauge is a device that uses the principle of ultrasonic pulse reflection to measure the thickness of materials. When the ultrasonic pulse emitted by the probe of the ultrasonic thickness gauge passes through the object to be measured and reaches the material interface, the pulse will be reflected back to the probe. By measuring the time it takes for the ultrasonic wave to propagate in the material, the thickness of the material to be measured can be determined. This traditional pipeline wall thickness detection method is not only inefficient, but also has certain risks, especially in high altitudes or difficult-to-reach locations. In addition, these methods are usually unable to achieve a comprehensive and detailed inspection of the pipeline.

[0041] In view of the above problems, an embodiment of the present application provides an overhead pipeline wall thickness detection method based on a drone, which can analyze the pipeline wall thickness detection instruction after receiving the pipeline wall thickness detection instruction to determine the pipeline to be detected by wall thickness. Among them, the pipeline to be detected by wall thickness is divided into multiple sub-wall thickness detection areas, and multiple data detection groups are deployed in each sub-wall thickness detection area. The data detection group includes a first data detection point deployed on the outer surface of the pipeline to be detected by wall thickness and a second data detection point deployed on the inner surface of the pipeline to be detected by wall thickness. The pipeline to be detected by wall thickness includes an overhead pipeline.

[0042] Next, the ultrasonic detection signal sent by the ultrasonic detector carried by the drone to each data detection group can be obtained, all the acquired ultrasonic detection signals can be analyzed, and the sub-wall thickness detection factor of each sub-wall thickness detection area can be calculated based on the analysis results. The ultrasonic detection signal includes 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, and the first ultrasonic detection signal, the second ultrasonic detection signal.

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

[0044] From the above, it can be seen that the present application detects the wall thickness of the pipeline to be detected by using the ultrasonic detection signal sent by the ultrasonic detector carried by the drone to each data detection group, which not only improves the detection efficiency and safety, but also can accurately determine the pipeline wall thickness through real-time data transmission and processing, and quickly and accurately evaluate the health status of the pipeline to be detected, thereby providing strong support for the operation and maintenance of the pipeline to be detected.

[0045] The above-mentioned UAV-based overhead pipeline wall thickness detection method can be applied to the wall thickness detection system. Figure 1 Figure 2 shows the schematic diagram of the wall thickness detection system. Figure 1As shown, the wall thickness detection system includes: a pipeline 101 to be detected for wall thickness, 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 pipeline 101 to be inspected for wall thickness may be pipelines of different types, such as a natural gas pipeline, a crude oil pipeline, a refined oil pipeline, etc.

[0047] Optionally, the pipeline 101 to be inspected for wall thickness may be an overhead pipeline. In this way, the wall thickness of the overhead pipeline may be inspected by the drone 102 carrying the ultrasonic detector 105 .

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

[0049] Optionally, the ultrasonic sensor generally includes an ultrasonic transmitting module and an ultrasonic receiving module. The ultrasonic transmitting module can transmit an ultrasonic signal. After the ultrasonic signal is refracted and reflected in the pipeline sample, the ultrasonic receiving module can receive the refracted and reflected signal, that is, the ultrasonic detection signal.

[0050] In the embodiment of the present application, the ultrasonic sensor 105 can emit an ultrasonic detection signal to the pipe 101 to be inspected for wall thickness, so that the wall thickness detection device 103 performs wall thickness detection on the pipe 101 to be inspected for wall thickness according to the ultrasonic detection signal emitted by the ultrasonic sensor 105 .

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

[0052] The wall thickness detection device 103 is used to obtain the ultrasonic detection signal sent by the ultrasonic detector 105 carried by the drone 102 to the pipe 101 to be detected for wall thickness, and determine the pipe wall thickness of the pipe 101 to be detected for wall thickness according to the obtained ultrasonic detection signal.

[0053] Optionally, the physical device of the wall thickness detection device 103 may be a server, a terminal, or other types of electronic devices, which is not limited in the embodiments of the present application.

[0054] Optionally, the terminal may be at least one of a smart phone, a smart watch, a desktop computer, a laptop computer, a virtual reality terminal, an augmented reality terminal, a wireless terminal, a laptop computer and the like.

[0055] Optionally, the above-mentioned server can be a server in a server cluster (consisting of multiple servers), or a chip in the server, or a system on a chip in the server, or can be implemented by a virtual machine (VM) deployed on a physical machine, which is not limited in the embodiments of the present application.

[0056] The storage server 104 is used to store various types of data in the embodiment of the present application, such as the detection result 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 wall thickness detection device shown in FIG. 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 , which is a schematic diagram of a hardware structure of a wall thickness detection device provided in an embodiment of the present application. The wall thickness detection device includes a processor 21, a memory 22, a communication interface 23, and a bus 24. The processor 21, the memory 22, and the communication interface 23 can be connected via a bus 24.

[0059] The processor 21 is the control center of the wall thickness detection device, which can be a processor or a general term for multiple processing elements. For example, the 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 an embodiment, the processor 21 may include one or more CPUs, such as Figure 2 CPU 0 and CPU 1 are shown in .

[0061] The memory 22 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0062] In a possible implementation, the memory 22 may exist independently of the processor 21, and the memory 22 may be connected to the processor 21 via a bus 24 to store instructions or program codes. When the processor 21 calls and executes the instructions or program codes stored in the memory 22, the wall thickness detection method provided in the following embodiments of the present application can be implemented.

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

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

[0065] The communication interface 23 is used to connect the wall thickness detection device to other devices through a communication network, and the communication network may be Ethernet, wireless access network, wireless local area network (WLAN), etc. The communication interface 23 may include a receiving unit for receiving data and a sending unit for sending data.

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

[0067] It should be pointed out that Figure 2 The structure shown in the figure does not constitute a limitation on the wall thickness detection device. Figure 2 In addition to the components shown, the wall thickness detection device may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0068] The wall thickness detection method provided in the embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0069] The wall thickness detection method provided in the embodiment of the present application is applied to Figure 1 The wall thickness detection device 103 in the wall thickness detection system shown in FIG. Figure 3As shown, this embodiment provides an overhead pipeline wall thickness detection method based on a drone, comprising:

[0070] S301. The wall thickness detection device receives a pipeline wall thickness detection instruction, analyzes the pipeline wall thickness detection instruction, and determines a pipeline to be detected by wall thickness.

[0071] In some embodiments, the pipe wall thickness detection instruction 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 may be manually input, which is not limited in the embodiments of the present application.

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

[0073] Optionally, the relevant identification of the pipe to be inspected for wall thickness may include the pipe number, factory information, specification parameters, etc. of the pipe to be inspected for wall thickness.

[0074] The pipeline to be inspected for wall thickness is divided into a plurality of sub-wall thickness inspection areas.

[0075] In some embodiments, the surface area of ​​each sub-wall thickness detection region may be preset, and a plurality of sub-wall thickness detection regions may be divided on the pipe to be wall thickness detected according to the surface area.

[0076] Optional, combined Figure 1 The surface area of ​​each sub-wall thickness detection area may be pre-stored in the storage server. The wall thickness detection device may obtain the surface area of ​​each sub-wall thickness detection area from the storage server, and divide the pipe to be wall thickness detected into a plurality of sub-wall thickness detection areas according to the surface area.

[0077] Optionally, the operation and maintenance personnel can manually set the surface area of ​​each sub-wall thickness detection area, and divide the pipe to be wall thickness detected into multiple sub-wall thickness detection areas according to the surface area. Subsequently, the operation and maintenance personnel can input the divided multiple sub-wall thickness detection areas into the wall thickness detection device, so that the wall thickness detection device can perform subsequent wall thickness detection methods.

[0078] Multiple data detection groups are deployed in the sub-wall thickness detection area, wherein the 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 detected for wall thickness, and the second data detection point is deployed on the inner surface of the pipe to be detected for wall thickness.

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

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

[0081] First, the range and shape of the sub-wall thickness detection area can be determined, and the number of deployed data detection groups can be determined. The number of deployed data detection groups is positively correlated with the range of the sub-wall thickness detection area, and the deployment distribution of the data detection groups is related to the shape of the sub-wall thickness detection area, so that the data detection groups can be evenly deployed in the sub-wall thickness detection area.

[0082] Next, the sub-wall thickness detection area may be divided into a plurality of sub-detection areas based on the range and shape of the sub-wall thickness detection area, the number of deployed data detection groups, and the Hilbert curve algorithm.

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

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

[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 a position corresponding to the first data detection point and at the inner surface of the pipe to be inspected 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, the present invention obtains multiple data detection groups according to all the first data detection points and the corresponding second data detection points, which can lay a foundation for pipeline wall thickness detection and provide detection prerequisites.

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

[0088] S302, the wall thickness detection equipment obtains the ultrasonic detection signal sent by the ultrasonic detector carried by the drone to each data detection group.

[0089] The ultrasonic detection signal includes a first ultrasonic detection signal and a second ultrasonic detection signal, and 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, it can be seen from the above that when the pipeline to be tested for wall thickness is an overhead pipeline, especially at a high altitude or inaccessible location, since the manual detection method is inefficient and has certain risks, the embodiment of the present application can be based on the ultrasonic detection signal sent to each data detection group by the ultrasonic detector carried by the drone. After the ultrasonic detector, the wall thickness detection device can communicate with the ultrasonic detector to obtain the ultrasonic detection signal sent to each data detection group.

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

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

[0093] In this way, by carrying ultrasonic detectors by drones, the risk of workers working in dangerous environments is reduced and the safety of detection is improved.

[0094] S303: The wall thickness detection device analyzes all acquired ultrasonic detection signals, and calculates a sub-wall thickness detection factor of each sub-wall thickness detection area based on the analysis result.

[0095] In some embodiments, the wall thickness detection device may analyze all acquired ultrasonic detection signals by preprocessing the ultrasonic detection signals, identifying waveform features, calibrating sound velocity, etc.

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

[0097] Specifically, the wall thickness detection equipment can filter the ultrasonic detection signal to remove some background noise such as high-frequency noise and electromagnetic interference. 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 features related to different interfaces of the pipe to be tested for wall thickness. For example, in wall thickness detection, there will be initial waves (waves generated by the transmitted pulse), bottom echoes (waves reflected by the ultrasonic wave propagating to the bottom surface of the object), and possible defect echoes (if there are defects in the wall).

[0099] Sound velocity calibration means that since the propagation speed of ultrasound waves in different media is different, it is necessary to accurately determine the propagation speed of ultrasound waves in the medium based on the specification parameters of the pipe to be tested for wall thickness.

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

[0101] S304. The wall thickness detection device calculates a comprehensive pipe wall thickness factor of the pipe to be detected based on all sub-wall thickness detection factors.

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

[0103] S305. The wall thickness detection device determines the pipe wall thickness of the pipe to be detected according to the mapping relationship between the comprehensive pipe wall thickness factor and the pipe wall thickness.

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

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

[0106] Optionally, the preset mapping table may be pre-stored in a storage server, and when the wall thickness detection device determines the pipe wall thickness of the pipe to be detected, the preset mapping table may be obtained from the storage server.

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

[0108] From the above, it can be seen that the ultrasonic detection signal carried by the ultrasonic detector carried by the drone is sent to each data detection group to detect the wall thickness of the pipeline to be detected, and the pipeline wall thickness of the pipeline to be detected is determined according to the relationship between the comprehensive pipeline wall thickness factor and the preset mapping table. This not only improves the detection efficiency and safety, but also can accurately determine the pipeline wall thickness through real-time data transmission and processing, quickly and accurately evaluate the health status of the pipeline, and provide strong support for pipeline operation and maintenance.

[0109] In some embodiments of the present application, Figure 3 ,like Figure 4 As shown, before receiving the pipeline wall thickness detection instruction and analyzing the pipeline wall thickness detection instruction, the wall thickness detection device also includes:

[0110] S401. The wall thickness detection device matches the pipeline wall thickness detection instruction with the instruction category of the previous instruction, and determines whether the pipeline wall thickness detection instruction is the same as the previous instruction.

[0111] In this embodiment, the previous instruction includes: a pipeline flow detection instruction, a pipeline pressure detection instruction, a pipeline surface defect detection instruction, and the like.

[0112] Optionally, the previous instruction may be sent by a business system or other equipment that needs to determine relevant parameters of the pipe to be inspected for wall thickness, or may be manually input, which is not limited in this embodiment of the present application.

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

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

[0115] Specifically, if the pipeline wall thickness detection instruction matches the instruction category of the previous instruction, it may be a staff error operation, or it may be necessary to re-test. Therefore, the wall thickness detection device can collect the first time node of sending the pipeline wall thickness detection instruction, and collect the second time node of sending the previous instruction, and determine whether the current pipeline wall thickness detection instruction is a staff error operation, or it is necessary to re-test through the time node difference between the first time node and the second time node.

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

[0117] S403: The wall thickness detection device calculates a time node difference between the first time node and the second time node, and determines whether to process the pipeline wall thickness detection instruction according to a relationship between the time node difference and a preset time node difference.

[0118] In this embodiment, the time node difference is preferably 720 hours, and can be set specifically according to actual conditions.

[0119] S404: If the time node difference is greater than or equal to the preset time node difference, the wall thickness detection device processes the pipeline wall thickness detection instruction.

[0120] In this embodiment, when the time node difference is greater than or equal to the preset time node difference, it means that re-detection is indeed required, so the wall thickness detection device processes the pipeline wall thickness detection instruction.

[0121] S405: If the time node difference is less than the preset time node difference, the wall thickness detection device does not process the pipeline wall thickness detection instruction, and generates and outputs 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 re-inspection may be required. Therefore, a log reminder is generated and the relevant staff decides whether to process the pipeline wall thickness detection instruction.

[0123] S406: If the pipeline wall thickness detection instruction does not match the instruction category of the previous instruction, the wall thickness detection device determines that the pipeline wall thickness detection instruction is different from the previous instruction, and processes the pipeline wall thickness detection instruction.

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

[0125] In some embodiments of the present application, Figure 4 ,like Figure 5 As shown, the wall thickness detection device analyzes all acquired ultrasonic detection signals and calculates the sub-wall thickness detection factor of each sub-wall thickness detection area based on the analysis results, specifically including:

[0126] S501. A wall thickness detection device calculates a plurality of ultrasonic detection signal differences based on a first ultrasonic detection signal and a second ultrasonic detection signal.

[0127] S502: The wall thickness detection device normalizes all ultrasonic detection signal differences to obtain corresponding signal normalization values, and constructs a signal normalization sequence.

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

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

[0130] S504: The wall thickness detection device extracts all signal normalization values ​​that are smaller than the mean value to generate a second signal normalization sequence.

[0131] S505: The wall thickness detection device calculates a sub-wall thickness detection factor of each sub-wall thickness detection area according to 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 area according to the first signal normalization sequence and the second signal normalization sequence, thereby ensuring the calculation accuracy of the sub-wall thickness detection factor, performing multi-point detection, and eliminating thickness detection errors.

[0133] In some embodiments of the present application, a method for a wall thickness detection device to calculate a sub-wall thickness detection factor of each sub-wall thickness detection area according to a first signal normalization sequence and a second signal normalization sequence specifically includes:

[0134] The wall thickness detection device calculates the first standard deviation of the first signal normalization sequence and the second standard deviation of the second signal normalization sequence respectively, and calculates the sub-wall thickness detection factor of each sub-wall thickness detection area according to the following formula:

[0135]

[0136] Wherein, W is the sub-wall thickness detection factor of the sub-wall thickness detection area, f1 is the calculation 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 calculation coefficient corresponding to the second signal normalization sequence, and d2 is the second standard deviation.

[0137] In some embodiments of the present application, 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 detection factors by the wall thickness detection 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 a plurality of sub-wall thickness detection factor sequences.

[0139] Exemplarily, assuming that the sub-wall thickness detection factors are {0.3, 0.3, 0.4, 0.6, 0.7, 0.7, 0.8, 0.8}, 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 device counts the number of first factor sequences of multiple sub-wall thickness detection factor sequences.

[0141] In combination with the above example, the number of the first factor sequence is 3.

[0142] S603: The wall thickness detection device extracts a sub-wall thickness detection factor from all sub-wall thickness detection factor sequences respectively, and determines 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.

[0143] In combination with the above example, a sub-wall thickness detection factor is extracted from all sub-wall thickness detection factor sequences, that is, 0.3, 0.7, 0.8, and the sum of the first sub-wall thickness detection factors = 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 a preset sub-wall thickness detection factor, and counts the number of second factor sequences of the remaining sub-wall thickness detection factor sequences.

[0145] In combination with 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}, {0.8, 0.8}, and the number of second factor sequences of 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 sequences respectively, and determines 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.

[0147] In combination with the above example, a sub-wall thickness detection factor is extracted from the remaining sub-wall thickness detection factor sequences, that is, 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 device calculates the comprehensive pipe wall thickness factor of the pipe to be detected according to the number of first factor sequences, the number of second factor sequences, the sum of the first sub-wall thickness detection factor and the sum of the second sub-wall thickness detection factor.

[0149] In some embodiments of the present 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] Among them, H is the comprehensive pipe wall thickness factor of the pipe to be tested for wall thickness, 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 factor, u2 is the sum of the second sub-wall thickness detection factor, and t is the preset sub-wall thickness detection factor.

[0152] Combined with the above example, the number of first factor sequences = 3, the number of second factor sequences = 2, the sum of the first sub-wall thickness detection factor = 1.8, the sum of the second sub-wall thickness detection factor = 1.5, the preset sub-wall thickness detection factor = 0.5, and the above values ​​are substituted into the above formula to obtain the comprehensive pipe 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 pipeline wall thickness factor of the pipeline to be detected according to the number of first factor sequences, the number of second factor sequences, 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 pipeline wall thickness and ensuring the calculation accuracy.

[0154] In some embodiments of the present application, Figure 6 ,like Figure 7 As shown, after determining the pipe wall thickness of the pipe to be tested according to the relationship between the comprehensive pipe wall thickness factor and the preset mapping table, the wall thickness detection device further includes:

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

[0156] In this embodiment, the preset pipe wall thickness refers to the safe thickness of the pipe to be tested for wall thickness, 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 device does not issue an alarm.

[0158] S703: When the pipe wall thickness is less than the preset pipe wall thickness, the wall thickness detection device sends out an alarm.

[0159] In some embodiments of the present application, when the pipe wall thickness is less than the preset pipe wall thickness, after judging and issuing an alarm, the method further includes:

[0160] The wall thickness detection device calculates the normalized mean of the normalized sequence of the second signal, and calculates the alarm level factor of the pipeline to be wall thickness detected according to the following formula:

[0161]

[0162] Among them, G is the alarm level factor of the pipe to be tested for wall thickness, and y is the normalized mean.

[0163] From the above, it can be seen that the present invention calculates the normalized mean of the normalized sequence of the second signal and calculates the alarm level factor of the pipeline to be detected by wall thickness, which can provide a maintenance basis for the maintenance of 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 above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0165] The embodiment of the present application can divide the functional modules of the wall thickness detection device according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. Optionally, the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0166] like Figure 8 , which is a schematic diagram of the structure of a wall thickness detection device provided in an embodiment of the present 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 pipeline wall thickness detection instruction, analyze the pipeline wall thickness detection instruction, determine a pipeline to be detected by wall thickness, wherein the pipeline to be detected by wall thickness is divided into a plurality of sub-wall thickness detection areas; a plurality of data detection groups are deployed in the sub-wall thickness detection areas, wherein the data detection groups include a first data detection point and a second data detection point, wherein the first data detection point is deployed to the outer surface of the pipeline to be detected by wall thickness, and the second data detection point is deployed to the inner surface of the pipeline to be detected by wall thickness; and the pipeline to be detected by wall thickness includes an overhead pipeline;

[0168] The communication unit 801 is further used to obtain an ultrasonic detection signal sent by an ultrasonic detector carried by the drone to each data detection group, analyze all the obtained ultrasonic detection signals, and calculate the sub-wall thickness detection factor of each sub-wall thickness detection area based on the analysis result, wherein the ultrasonic detection signal includes 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 detected according to all the sub-wall thickness detection factors, and determine the pipe wall thickness of the pipe to be detected according to 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 pipeline wall thickness detection instruction with an instruction category of a previous instruction, and determine whether the pipeline wall thickness detection instruction is the same as the previous instruction;

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

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

[0173] Optionally, the processing unit 802 is further configured to process the pipeline wall thickness detection instruction 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 not process the pipeline wall thickness detection instruction if the time node difference is less than the preset time node difference, and to generate and output a log reminder;

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

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

[0177] Determine the range and shape of the sub-wall thickness detection area, and determine the number of deployment data detection groups;

[0178] Dividing the sub-wall thickness detection area into a plurality of sub-detection areas based on the range and shape of the sub-wall thickness detection area, the number of deployments of the data detection group and the Hilbert curve algorithm, wherein each sub-detection area is located on the outer surface of the pipe to be wall thickness detected;

[0179] Deploy the first data detection point at the geometric center of each sub-detection area, and deploy the second data detection point at a position corresponding to the first data detection point on the inner surface of the pipe to be inspected for wall thickness;

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

[0181] Optionally, the processing unit 802 is specifically configured to:

[0182] Calculate a plurality of ultrasonic detection signal differences according to the first ultrasonic detection signal and the second ultrasonic detection signal;

[0183] Normalize all ultrasonic detection signal differences to obtain corresponding signal normalization values ​​and construct a signal normalization sequence;

[0184] Calculating a 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;

[0185] Extracting all signal normalized values ​​that are smaller than the mean value to generate a second signal normalized sequence;

[0186] A sub-wall thickness detection factor of each sub-wall thickness detection area is calculated according to the first signal normalization sequence and the second signal normalization sequence.

[0187] Optionally, the processing unit 802 is specifically configured to:

[0188] respectively calculating a first standard deviation of the first signal normalized sequence and a second standard deviation of the second signal normalized sequence;

[0189] The sub-wall thickness detection factor of each sub-wall thickness detection area is calculated according to the following formula:

[0190]

[0191] Among them, W is the sub-wall thickness detection factor of the sub-wall thickness detection area, f1 is the calculation 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 calculation 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 a normalized mean of the second signal normalized sequence;

[0193] The processing unit 802 is further used to calculate the alarm level factor of the pipe to be inspected 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 inspected for wall thickness, and y is the normalized mean value.

[0196] Optionally, the processing unit 802 is specifically configured to:

[0197] Extracting the same sub-wall thickness detection factor from all the sub-wall thickness detection factors, and obtaining a plurality of sub-wall thickness detection factor sequences;

[0198] Counting the number of first factor sequences of the plurality of sub-wall thickness detection factor sequences;

[0199] Extracting a 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 the sub-wall thickness detection factor sequences as the first sub-wall thickness detection factor sum;

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

[0201] Extracting a sub-wall thickness detection factor from the remaining sub-wall thickness detection factor sequence respectively, and determining 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 inspected is calculated according to 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 configured to:

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

[0205]

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

[0207] Optionally, the processing unit 802 is further configured to determine whether to issue an alarm based on the relationship between the wall thickness of the pipe to be tested for wall thickness and a preset pipe wall thickness;

[0208] The processing unit 802 is further configured to not 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 further configured to issue an alarm when the pipe wall thickness is less than the preset pipe wall thickness.

[0210] An embodiment of the present application further provides a computer-readable storage medium, which includes computer execution instructions. When the computer execution instructions are executed on a computer, the computer executes the wall thickness detection method provided in the above embodiment.

[0211] The embodiment of the present application also provides a computer program product, which can be directly loaded into a memory and contains software code. After the computer is loaded and executed, the computer program product can implement the wall thickness detection method provided in the above embodiment. Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention is described in detail with reference to the preferred embodiments, ordinary technicians in this field should understand that they can still modify or replace the technical solution of the present invention, and these modifications or equivalent replacements cannot make the modified technical solution deviate from the spirit and scope of the technical solution of the present invention.

[0212] The system provided in the above embodiment 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 decomposed or combined. For example, the modules in the above embodiment can be combined 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 modules or steps, and are not regarded as improper limitations of the present invention.

[0213] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0214] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0215] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0216] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions 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 rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for detecting the wall thickness of an overhead pipeline based on a drone, characterized in that: include: Receive a pipeline wall thickness detection instruction, analyze the pipeline wall thickness detection instruction, determine a pipeline to be detected by wall thickness, the pipeline to be detected by wall thickness is divided into a plurality of sub-wall thickness detection areas; a plurality of data detection groups are deployed in the sub-wall thickness detection areas, wherein the 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 pipeline to be detected by wall thickness, and the second data detection point is deployed on the inner surface of the pipeline to be detected by wall thickness; the pipeline to be detected by wall thickness includes an overhead pipeline; Acquire an ultrasonic detection signal sent by an ultrasonic detector carried by the drone to each data detection group, analyze all acquired ultrasonic detection signals, and calculate a sub-wall thickness detection factor of each sub-wall thickness detection area based on the analysis result, wherein the ultrasonic detection signal includes 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; The comprehensive pipeline wall thickness factor of the pipeline to be detected is calculated according to all the sub-wall thickness detection factors, and the pipeline wall thickness of the pipeline to be detected is determined according to the mapping relationship between the comprehensive pipeline wall thickness factor and the pipeline wall thickness.

2. The method for detecting wall thickness of an overhead pipeline based on a drone according to claim 1 is characterized in that: Before receiving the pipeline wall thickness detection instruction and analyzing the pipeline wall thickness detection instruction, the method further includes: Matching the pipeline wall thickness detection instruction with the instruction category of the previous instruction, and determining whether the pipeline wall thickness detection instruction is the same as the previous instruction; If the pipeline wall thickness detection instruction matches the instruction category of the previous instruction, it is determined that the pipeline wall thickness detection instruction is the same as the previous instruction, and the first time node of sending the pipeline wall thickness detection instruction is collected, and the second time node of sending the previous instruction is collected; Calculating a time node difference between the first time node and the second time node, and judging whether to process the pipeline wall thickness detection instruction according to a relationship between the time node difference and a preset time node difference, If the time node difference is greater than or equal to the preset time node difference, processing the pipeline wall thickness detection instruction; If the time node difference is less than the preset time node difference, the pipeline wall thickness detection instruction is not processed, and a log reminder is generated and output; If the pipeline wall thickness detection instruction does not match the instruction category of the previous instruction, it is determined that the pipeline wall thickness detection instruction is different from the previous instruction, and the pipeline wall thickness detection instruction is processed.

3. The method for detecting wall thickness of an overhead pipeline based on a drone according to claim 1, characterized in that: The multiple data detection groups in the sub-wall thickness detection area are deployed in the following manner: Determine the range and shape of the sub-wall thickness detection area, and determine the number of deployment data detection groups; Dividing the sub-wall thickness detection area into a plurality of sub-detection areas based on the range and shape of the sub-wall thickness detection area, the number of deployments of the data detection group and the Hilbert curve algorithm, wherein each sub-detection area is located on the outer surface of the pipe to be wall thickness detected; Deploy the first data detection point at the geometric center of each sub-detection area, and deploy the second data detection point at a position corresponding to the first data detection point on the inner surface of the pipe to be inspected for wall thickness; The plurality of data detection groups are obtained according to all the first data detection points and the corresponding second data detection points.

4. The method for detecting wall thickness of an overhead pipeline based on a drone according to claim 1, characterized in that: The step of analyzing all acquired ultrasonic detection signals and calculating the sub-wall thickness detection factor of each sub-wall thickness detection area based on the analysis results includes: Calculate a plurality of ultrasonic detection signal differences according to the first ultrasonic detection signal and the second ultrasonic detection signal; Normalize all ultrasonic detection signal differences to obtain corresponding signal normalization values ​​and construct a signal normalization sequence; Calculating a 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 normalized values ​​that are smaller than the mean value to generate a second signal normalized sequence; A sub-wall thickness detection factor of each sub-wall thickness detection area is calculated according to the first signal normalization sequence and the second signal normalization sequence.

5. The method for detecting wall thickness of an overhead pipeline based on a drone according to claim 4 is characterized in that: The calculating the sub-wall thickness detection factor of each sub-wall thickness detection area according to the first signal normalization sequence and the second signal normalization sequence includes: respectively calculating a first standard deviation of the first signal normalized sequence and a second standard deviation of the second signal normalized sequence; The sub-wall thickness detection factor of each sub-wall thickness detection area is calculated according to the following formula: Among them, W is the sub-wall thickness detection factor of the sub-wall thickness detection area, f1 is the calculation 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 calculation coefficient corresponding to the second signal normalization sequence, and d2 is the second standard deviation.

6. The method for detecting wall thickness of an overhead pipeline based on a drone according to claim 5 is characterized in that: Also includes: Calculating a normalized mean of the second signal normalized sequence; The alarm level factor of the pipe to be inspected 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 inspected for wall thickness, and y is the normalized mean value.

7. The method for detecting wall thickness of an overhead pipeline based on a drone according to claim 1, characterized in that: The step of calculating the comprehensive pipe wall thickness factor of the pipe to be inspected based on all the sub-wall thickness inspection factors includes: Extracting the same sub-wall thickness detection factor from all the sub-wall thickness detection factors, and obtaining a plurality of sub-wall thickness detection factor sequences; Counting the number of first factor sequences of the plurality of sub-wall thickness detection factor sequences; Extracting a 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 the sub-wall thickness detection factor sequences as the first sub-wall thickness detection factor sum; Eliminate all sub-wall thickness detection factor sequences that are smaller than a preset sub-wall thickness detection factor, and count the number of second factor sequences of the remaining sub-wall thickness detection factor sequences; Extracting a sub-wall thickness detection factor from the remaining sub-wall thickness detection factor sequence respectively, and determining 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 inspected is calculated according to 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.

8. The method for detecting wall thickness of an overhead pipeline based on a drone according to claim 7, characterized in that: The step of calculating the comprehensive pipe wall thickness factor of the pipe to be inspected according to 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 includes: The comprehensive pipe wall thickness factor of the pipe to be tested for wall thickness is calculated according to the following formula: Among them, H is the comprehensive pipe wall thickness factor of the pipe to be tested for wall thickness, r2 is the second factor sequence number, r1 is the first factor sequence number, u1 is the first sub-wall thickness detection factor and value, u2 is the second sub-wall thickness detection factor and value, and t is the preset sub-wall thickness detection factor.

9. The method for detecting wall thickness of an overhead pipeline based on a drone according to claim 1, characterized in that: Also includes: Determining whether to issue an alarm based on the relationship between the wall thickness of the pipe to be tested and the preset pipe wall thickness; When the pipe wall thickness is greater than or equal to the preset pipe wall thickness, no alarm is issued; When the pipe wall thickness is less than the preset pipe wall thickness, an alarm is issued.

10. 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, and the one or more programs include computer-executable instructions. When the device is running, the processor executes the computer-executable instructions stored in the memory to enable the device to perform the method described in any one of claims 1 to 9.

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

12. A computer program product, characterized in that The computer program product comprises: a computer program or instructions, and when the computer program or instructions are run on a computer, the computer is caused to perform the method according to any one of claims 1 to 9.

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