Pipeline wall thickness data collection method

Abstract

The invention relates to the technical field of pipeline wall thickness collection, and discloses a pipeline wall thickness data collection method, which comprises the following steps: acquiring setting position information of a target pipeline, attribute information of the target pipeline and environment information of the target pipeline; and determining a detection difficulty score of the target pipeline according to the set position information, the attribute information and the environment information. And determining detection equipment according to the detection difficulty score, and correcting the detection equipment according to the detection difficulty score. And according to the connection position, the setting measurement and the setting direction in the position information of the target pipeline, determining a detection path of the detection equipment for detecting the target pipeline. And controlling the detection equipment to detect the target pipeline according to the detection path so as to collect pipeline wall thickness data of the target pipeline. According to the method, the position information, the attribute information and the environment information of the pipeline are comprehensively considered to dynamically evaluate the detection difficulty and optimize the detection equipment, so that the detection accuracy and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline wall thickness collection, and in particular, to a method for collecting pipeline wall thickness data. Background Art

[0002] Pipelines play a crucial role in modern industry and are widely used in fields such as oil, natural gas, chemical industry, and water supply. As the service time of pipelines extends, changes in wall thickness (such as corrosion, wear, etc.) can lead to pipeline failures, thereby triggering safety accidents and environmental pollution. Therefore, regular pipeline wall thickness detection is crucial for ensuring the safety and reliability of pipelines.

[0003] However, traditional methods for collecting pipeline wall thickness data often rely on manual visual inspection or some basic detection equipment, and these methods have exposed many limitations in practical applications. The accuracy of manual inspection is limited by the experience and attention of operators, prone to omissions, and has low efficiency, making it difficult to handle large-scale pipeline detection tasks. Simple detection equipment usually has a single function, cannot provide high-precision measurement results, and in complex or harsh environments, its performance may be severely affected, further reducing the reliability of detection.

[0004] Therefore, traditional methods for collecting pipeline wall thickness data are difficult to dynamically evaluate the detection difficulty and optimize the detection equipment, and the accuracy and efficiency of traditional pipeline wall thickness detection methods are relatively low. Summary of the Invention

[0005] The present invention provides a method for collecting pipeline wall thickness data, aiming to solve the problems in the current technology that the detection efficiency and accuracy of pipeline wall thickness are relatively low, the detection methods are single, and it is impossible to cope with complex pipeline environments.

[0006] In a first aspect, the present invention provides a method for collecting pipeline wall thickness data, including: obtaining the setting position information of the target pipeline, the attribute information of the target pipeline, and the environmental information of the environment where the target pipeline is located; the setting position information includes: connection position, setting measurement, and setting direction; determining the detection difficulty score of the target pipeline according to the setting position information, attribute information, and environmental information; determining the detection equipment according to the detection difficulty score, and calibrating the detection equipment according to the detection difficulty score; determining the detection path of the detection equipment for detecting the target pipeline according to the connection position, setting measurement, and setting direction; controlling the detection equipment to detect the target pipeline according to the detection path to collect the pipeline wall thickness data of the target pipeline.

[0007] Optionally, according to the set position information, attribute information, and environmental information, determine the detection difficulty score of the target pipeline, including: determine the vertical distance between the target pipeline and the pipeline foundation according to the set position information; determine the basic score of the target pipeline according to the vertical distance; when the vertical distance is greater than zero, determine that the basic score is the first basic score; when the vertical distance is less than zero, determine that the basic score is the second basic score; the first basic score is greater than zero, and the second basic score is less than zero; determine the adjustment coefficient of the basic score according to the attribute information and environmental information; adjust the basic score according to the adjustment coefficient to determine the detection difficulty score.

[0008] Optionally, the attribute information includes: the designed wall thickness; determine the adjustment coefficient of the basic score according to the attribute information and environmental information, including: when the designed wall thickness is less than the preset wall thickness, determine that the adjustment coefficient of the basic score is the minimum adjustment coefficient; when the designed wall thickness is greater than or equal to the preset wall thickness, determine the adjustment coefficient according to the wall thickness difference between the designed wall thickness and the preset wall thickness.

[0009] Optionally, determine the adjustment coefficient according to the wall thickness difference between the designed wall thickness and the preset wall thickness, including: when the wall thickness difference is less than the first preset wall thickness difference, determine that the adjustment coefficient is the first adjustment coefficient; when the wall thickness difference is greater than or equal to the first preset wall thickness difference and less than the second preset wall thickness difference, determine that the adjustment coefficient is the second adjustment coefficient; when the wall thickness difference is greater than or equal to the second preset wall thickness difference, determine that the adjustment coefficient is the third adjustment coefficient; the first preset wall thickness difference is less than the second preset wall thickness difference; the minimum adjustment coefficient, the first adjustment coefficient, the second adjustment coefficient, and the third adjustment coefficient increase in sequence.

[0010] Optionally, the environmental information includes: environmental temperature and environmental humidity; the method further includes: obtaining the detection temperature and detection humidity when each detection device has a detection offset, and determining the influence characteristics affecting the detection device according to the detection temperature and detection humidity; determining whether to correct the adjustment coefficient according to the environmental humidity, environmental temperature, and influence characteristics.

[0011] Optionally, the influence characteristics include: characteristic temperature and characteristic humidity; determine whether to correct the adjustment coefficient according to the environmental humidity, environmental temperature, and influence characteristics, including: when the environmental temperature is less than the characteristic temperature and the environmental humidity is less than the characteristic humidity, determine that the adjustment coefficient is not corrected; when the environmental temperature is greater than or equal to the characteristic temperature and the environmental humidity is less than the characteristic humidity, determine that the adjustment coefficient is corrected; when the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is less than the characteristic temperature, determine that the adjustment coefficient is corrected; when the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is greater than or equal to the characteristic temperature, determine that the adjustment coefficient is corrected.

[0012] Optionally, when the ambient temperature is greater than or equal to the characteristic temperature and the ambient humidity is less than the characteristic humidity, it is determined to correct the adjustment coefficient, or, when the ambient humidity is greater than or equal to the characteristic humidity and the ambient temperature is less than the characteristic temperature, after determining to correct the adjustment coefficient, it further includes: determining an environmental characteristic value according to the environmental information and the influencing characteristics; the environmental characteristic value is the difference between the ambient temperature and the characteristic temperature, or the difference between the ambient humidity and the characteristic humidity; when the environmental characteristic value is less than the first preset environmental characteristic value, it is determined that the correction coefficient is the first correction coefficient; when the environmental characteristic value is greater than or equal to the first preset environmental characteristic value and less than the second preset environmental characteristic value, it is determined that the correction coefficient is the second correction coefficient; when the environmental characteristic value is greater than or equal to the second preset environmental characteristic value, it is determined that the correction coefficient is the third correction coefficient; the first preset environmental characteristic value is less than the second preset environmental characteristic value; the first correction coefficient, the second correction coefficient, and the third correction coefficient increase in sequence, and the third correction coefficient is less than 1; correcting the adjustment coefficient according to the correction coefficient.

[0013] Optionally, when the ambient humidity is greater than or equal to the characteristic humidity and the ambient temperature is greater than or equal to the characteristic temperature, after determining to correct the adjustment coefficient, the method further includes: determining the difference between the ambient temperature and the characteristic temperature as the temperature difference; determining the difference between the ambient humidity and the characteristic humidity as the humidity difference; determining a fourth correction coefficient according to the temperature difference and the humidity difference; the fourth correction coefficient satisfies the following formula:

[0014] N = x 1 ×A + x 2 ×S + 1;

[0015] where A is the temperature difference, S is the humidity difference, x 1 、x 2 are weight coefficients, and the sum of x 1 and x 2 is 1; correcting the adjustment coefficient according to the fourth correction coefficient.

[0016] Optionally, determining a detection device according to the detection difficulty score includes: when the detection difficulty score is less than the first preset detection difficulty score, determining the detection device as an electromagnetic detection device; when the detection difficulty score is greater than or equal to the first preset detection difficulty score and less than the second preset detection difficulty score, determining the detection device as an ultrasonic detection device; when the detection difficulty score is greater than or equal to the second preset detection difficulty score, determining the detection device as a laser detection device; the first preset detection difficulty score is equal to 0, and the first preset detection difficulty score is less than the second preset detection difficulty score.

[0017] Optionally, calibrate the detection device according to the detection difficulty score, including: when it is determined that the detection device is an electromagnetic detection device or an ultrasonic detection device, determine the calibration coefficient according to the first score difference, and calibrate the detection device according to the calibration coefficient; the first score difference is the difference between the detection difficulty score and the first preset detection difficulty score; when it is determined that the detection device is a laser detection device, determine the calibration coefficient according to the second score difference, and calibrate the detection device according to the calibration coefficient; the second score difference is the difference between the detection difficulty score and the second preset detection difficulty score.

[0018] In a second aspect, a pipeline wall thickness data collection device is provided, including: a communication unit and a processing unit; the communication unit is configured to obtain the installation position information of the target pipeline, the attribute information of the target pipeline, and the environmental information of the environment where the target pipeline is located; the installation position information includes: the connection position, the installation measurement, and the installation direction; the processing unit is configured to determine the detection difficulty score of the target pipeline according to the installation position information, the attribute information, and the environmental information; the processing unit is further configured to determine the detection device according to the detection difficulty score, and calibrate the detection device according to the detection difficulty score; the processing unit is further configured to determine the detection path for the detection device to detect the target pipeline according to the connection position, the installation measurement, and the installation direction; the processing unit is further configured to control the detection device to detect the target pipeline according to the detection path to collect the pipeline wall thickness data of the target pipeline.

[0019] Optionally, the processing unit is specifically configured to: determine the vertical distance between the target pipeline and the pipeline foundation according to the installation position information; determine the basic score of the target pipeline according to the vertical distance; when the vertical distance is greater than zero, determine that the basic score is the first basic score; when the vertical distance is less than zero, determine that the basic score is the second basic score; the first basic score is greater than zero, and the second basic score is less than zero; determine the adjustment coefficient of the basic score according to the attribute information and the environmental information; adjust the basic score according to the adjustment coefficient to determine the detection difficulty score.

[0020] Optionally, the attribute information includes: the designed wall thickness; the processing unit is specifically configured to: when the designed wall thickness is less than the preset wall thickness, determine that the adjustment coefficient of the basic score is the minimum adjustment coefficient; when the designed wall thickness is greater than or equal to the preset wall thickness, determine the adjustment coefficient according to the wall thickness difference between the designed wall thickness and the preset wall thickness.

[0021] Optionally, the processing unit is specifically configured to: determine the adjustment coefficient as the first adjustment coefficient when the wall thickness difference is less than the first preset wall thickness difference; determine the adjustment coefficient as the second adjustment coefficient when the wall thickness difference is greater than or equal to the first preset wall thickness difference and less than the second preset wall thickness difference; determine the adjustment coefficient as the third adjustment coefficient when the wall thickness difference is greater than or equal to the second preset wall thickness difference; the first preset wall thickness difference is less than the second preset wall thickness difference; the minimum adjustment coefficient, the first adjustment coefficient, the second adjustment coefficient, and the third adjustment coefficient increase in sequence.

[0022] Optionally, the environmental information includes: environmental temperature and environmental humidity; the processing unit is further configured to obtain the detection temperature and detection humidity when each detection device has a detection offset, and determine the influence characteristics affecting the detection device according to the detection temperature and detection humidity; the processing unit is further configured to determine whether to correct the adjustment coefficient according to the environmental humidity, environmental temperature, and influence characteristics.

[0023] Optionally, the influence characteristics include: characteristic temperature and characteristic humidity; the processing unit is specifically configured to: determine not to correct the adjustment coefficient when the environmental temperature is less than the characteristic temperature and the environmental humidity is less than the characteristic humidity; determine to correct the adjustment coefficient when the environmental temperature is greater than or equal to the characteristic temperature and the environmental humidity is less than the characteristic humidity; determine to correct the adjustment coefficient when the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is less than the characteristic temperature; determine to correct the adjustment coefficient when the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is greater than or equal to the characteristic temperature.

[0024] Optionally, when it is determined to correct the adjustment coefficient when the environmental temperature is greater than or equal to the characteristic temperature and the environmental humidity is less than the characteristic humidity, or when it is determined to correct the adjustment coefficient when the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is less than the characteristic temperature, the processing unit is further configured to determine the environmental characteristic value according to the environmental information and the influence characteristics; the environmental characteristic value is the difference between the environmental temperature and the characteristic temperature, or the difference between the environmental humidity and the characteristic humidity; the processing unit is further configured to determine the correction coefficient as the first correction coefficient when the environmental characteristic value is less than the first preset environmental characteristic value; the processing unit is further configured to determine the correction coefficient as the second correction coefficient when the environmental characteristic value is greater than or equal to the first preset environmental characteristic value and less than the second preset environmental characteristic value; the processing unit is further configured to determine the correction coefficient as the third correction coefficient when the environmental characteristic value is greater than or equal to the second preset environmental characteristic value; the first preset environmental characteristic value is less than the second preset environmental characteristic value; the first correction coefficient, the second correction coefficient, and the third correction coefficient increase in sequence, and the third correction coefficient is less than 1; correct the adjustment coefficient according to the correction coefficient.

[0025] Optionally, when the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is greater than or equal to the characteristic temperature, after determining to correct the adjustment coefficient, the processing unit is further configured to determine the difference between the environmental temperature and the characteristic temperature as the temperature difference; the processing unit is further configured to determine the difference between the environmental humidity and the characteristic humidity as the humidity difference; the processing unit is further configured to determine a fourth correction coefficient according to the temperature difference and the humidity difference; the fourth correction coefficient satisfies the following formula:

[0026] N = x 1 ×A + x 2 ×S + 1;

[0027] where A is the temperature difference, S is the humidity difference, and x 1 、x 2 are weight coefficients, and the sum of x 1 and x 2 is 1; the processing unit is further configured to correct the adjustment coefficient according to the correction coefficient, the fourth correction coefficient.

[0028] Optionally, the processing unit is specifically configured to: determine that the detection device is an electromagnetic detection device when the detection difficulty score is less than the first preset detection difficulty score; determine that the detection device is an ultrasonic detection device when the detection difficulty score is greater than or equal to the first preset detection difficulty score and less than the second preset detection difficulty score; determine that the detection device is a laser detection device when the detection difficulty score is greater than or equal to the second preset detection difficulty score; the first preset detection difficulty score is equal to 0 and less than the second preset detection difficulty score.

[0029] Optionally, the processing unit is specifically configured to: when it is determined that the detection device is an electromagnetic detection device or an ultrasonic detection device, determine a correction coefficient according to the first score difference and correct the detection device according to the correction coefficient; the first score difference is the difference between the detection difficulty score and the first preset detection difficulty score; when it is determined that the detection device is a laser detection device, determine a correction coefficient according to the second score difference and correct the detection device according to the correction coefficient; the second score difference is the difference between the detection difficulty score and the second preset detection difficulty score.

[0030] In a third aspect, a pipeline wall thickness data collection device 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 through a bus; when the pipeline wall thickness data collection device runs, the processor executes the computer execution instructions stored in the memory to enable the pipeline wall thickness data collection device to execute the pipeline wall thickness data collection method of the first aspect.

[0031] The pipeline wall thickness data collection device can be an electronic device or a part of the electronic device, such as a chip system in the electronic device. The chip system is used to support the electronic device to implement the functions involved in the first aspect and any possible implementation manner thereof. For example, it acquires and determines the data and / or information involved in the above pipeline wall thickness data collection method. The chip system includes a chip and may also include other discrete devices or circuit structures.

[0032] In a fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium includes computer-executable instructions. When the computer-executable instructions run on a computer, the computer is caused to execute the pipeline wall thickness data collection method described in the first aspect.

[0033] In a fifth aspect, a computer program product is further provided. The computer program product includes a computer program or instructions. When the computer instructions run on the pipeline wall thickness data collection device, the pipeline wall thickness data collection device is caused to execute the pipeline wall thickness data collection method described in the first aspect above.

[0034] It should be noted that the above computer instructions can be stored in whole or in part on the computer-readable storage medium. Among them, the computer-readable storage medium can be packaged together with the processor of the pipeline wall thickness data collection device or separately packaged from the processor of the pipeline wall thickness data collection device. The embodiments of the present application do not limit this.

[0035] The descriptions of the second aspect, the third aspect, the fourth aspect, and the fifth aspect in this application can refer to the detailed description of the first aspect.

[0036] In the embodiments of the present application, the name of the above pipeline wall thickness data collection device does not limit the device or function module itself. In actual implementation, these devices or function modules may appear under 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 function module are similar to those of the present application and fall within the scope of the claims of the present application and equivalent technologies.

[0037] This application can obtain the setting location information of the target pipeline, the attribute information of the target pipeline, and the environmental information of the environment where the target pipeline is located, and determine the detection difficulty score of the target pipeline according to the setting location information, attribute information, and environmental information. In this way, the detection difficulty score can quantify the complexity of pipeline detection. In addition, this application can reasonably select detection equipment according to the detection difficulty score and calibrate the detection equipment according to the detection difficulty score. This makes the detection process more intelligent. This can reduce the risk of incorrect detection by the detection equipment and improve the accuracy of pipeline wall thickness data collection. This application can also clarify the detection path of the detection equipment according to the connection location, setting measurement, and setting direction, so as to control the detection equipment to detect the target pipeline according to the detection path. This can ensure the efficiency and accuracy of the detection equipment when performing tasks. It can not only avoid repeated detection and ineffective detection, but also reduce the detection time and operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0039] Figure 1 Structural schematic diagram of a pipeline wall thickness data collection system provided by an embodiment of this application;

[0040] Figure 2 Hardware structural schematic diagram of a pipeline wall thickness data collection device provided by an embodiment of this application;

[0041] Figure 3 Flow schematic diagram of a pipeline wall thickness data collection method provided by an embodiment of the present invention;

[0042] Figure 4 Structural schematic diagram of a pipeline wall thickness data collection device provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, rather than all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0044] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0045] For the convenience of clearly describing 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 the same items or similar items with basically the same functions and roles. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order.

[0046] As described in the background art, pipelines play a crucial role in modern industry and are widely used in fields such as oil, natural gas, chemical industry and water supply. As the service time of the pipeline extends, changes in wall thickness (such as corrosion, wear, etc.) may lead to pipeline failure, thus triggering safety accidents and environmental pollution. Therefore, regular pipeline wall thickness detection is crucial for ensuring the safety and reliability of pipelines.

[0047] However, traditional methods for collecting pipeline wall thickness data rely on manual visual inspection or some basic detection equipment, and these methods have exposed many limitations in practical applications. First of all, the accuracy of manual inspection is limited by the experience and attention of the operators, prone to omissions, and the efficiency is low, making it difficult to cope with large-scale pipeline detection tasks. Secondly, simple detection equipment usually has a single function, cannot provide high-precision measurement results, and in complex or harsh environments, its performance may be severely affected, further reducing the reliability of detection. In addition, due to factors such as the material, service life, and working environment of different pipelines, the degree of wall thickness wear and corrosion will also vary. This requires the detection method to be adaptively adjusted according to the characteristics and environmental conditions of different pipelines. However, currently in the field of pipeline wall thickness detection, there is a lack of a systematic evaluation and selection standard, making it often impossible to accurately match specific detection requirements when determining and calibrating detection equipment. In this case, the performance of the detection equipment may not be fully utilized, thus affecting the accuracy and reliability of the final detection results.

[0048] Therefore, traditional methods for collecting pipeline wall thickness data are difficult to dynamically evaluate the detection difficulty and optimize the detection equipment, and the accuracy and efficiency of traditional pipeline wall thickness detection methods are relatively low.

[0049] In view of the above technical problems, an embodiment of the present application provides a method for collecting pipeline wall thickness data, including: First, obtain the installation location information of the target pipeline, the attribute information of the target pipeline, and the environmental information of the environment where the target pipeline is located. Among them, the installation location information includes: connection location, installation measurement, and installation direction. Then, determine the detection difficulty score of the target pipeline according to the installation location information, attribute information, and environmental information. Next, determine the detection device according to the detection difficulty score, and calibrate the detection device according to the detection difficulty score. Next, determine the detection path of the detection device for detecting the target pipeline according to the connection location, installation measurement, and installation direction. Next, control the detection device to detect the target pipeline according to the detection path to collect the pipeline wall thickness data of the target pipeline.

[0050] As can be seen from the above, the present application can obtain the installation location information of the target pipeline, the attribute information of the target pipeline, and the environmental information of the environment where the target pipeline is located, and determine the detection difficulty score of the target pipeline according to the installation location information, attribute information, and environmental information. In this way, the detection difficulty score can quantify the complexity of pipeline detection. In addition, the present application can reasonably select the detection device according to the detection difficulty score and calibrate the detection device according to the detection difficulty score. This makes the detection process more intelligent. This can reduce the risk of misdetection by the detection device and improve the accuracy of pipeline wall thickness data collection. The present application can also clarify the detection path of the detection device according to the connection location, installation measurement, and installation direction, so as to control the detection device to detect the target pipeline according to the detection path. This can ensure the efficiency and accuracy of the detection device when performing tasks. It can not only avoid repeated detection and ineffective detection, but also reduce the detection time and operating costs.

[0051] The above method for collecting pipeline wall thickness data can be applied to a pipeline wall thickness data collection system. Figure 1 The structural schematic diagram of the pipeline wall thickness data collection system is shown. As Figure 1 shown, the pipeline wall thickness data collection system includes: a pipeline wall thickness data collection device 101, a data storage device 102, a sensor 103, and a detection device 104.

[0052] Among them, the pipeline wall thickness data collection device 101 is communicatively connected to the data storage device 102 and the sensor 103 respectively.

[0053] The data storage device 102 is communicatively connected to the sensor 103.

[0054] The pipeline wall thickness data collection device 101 is communicatively connected to the detection device 104.

[0055] The sensor 103 includes: a temperature sensor, a humidity sensor, and a pressure sensor.

[0056] Among them, the temperature sensor is used to collect the temperature of the environment where the target pipeline is located.

[0057] The humidity sensor is used to collect the humidity of the environment where the target pipeline is located.

[0058] The pressure sensor is used to collect the pressure on the target pipeline from the external environment.

[0059] The detection device 104 includes: an electromagnetic detection device, an ultrasonic detection device, and a laser detection device.

[0060] The detection device 104 detects the target pipeline in response to the control of the pipeline wall thickness data collection device 101.

[0061] In practical applications, the pipeline wall thickness data collection device 101 can be connected to any number of data storage devices 102 and sensors 103. For the sake of easy understanding, Figure 1 Take an example where one pipeline wall thickness data collection device 101 is connected to one data storage device 102 and one sensor 103 for illustration.

[0062] In the embodiments of the present application, the data storage device 102 is used to provide the pipeline wall thickness data collection device 101 with data for collecting the pipeline wall thickness (for example, the setting position information of the target pipeline, the attribute information of the target pipeline, etc.), and the sensor 103 is used to provide the pipeline wall thickness data collection device 101 with data for collecting the pipeline wall thickness (for example, the environmental information of the environment where the target pipeline is located), so that the pipeline wall thickness data collection device 101 can collect the pipeline wall thickness data according to the data sent by the data storage device 102.

[0063] Optionally, the physical device of the pipeline wall thickness data collection device 101 can be a server, a terminal, or other types of electronic devices, and the embodiments of the present application do not limit this.

[0064] Optionally, the above terminal can be a device that provides voice and / or data connectivity to users, a handheld device with a wireless connection function, or other processing devices connected to a wireless modem. The wireless terminal can communicate with one or more core networks via a radio access network (RAN). The wireless terminal can be a mobile terminal, such as a mobile phone (or called a "cellular" phone) and a computer with a mobile terminal, or a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device, which exchanges language and / or data with the wireless access network. For example, a mobile phone, a tablet computer, a laptop computer, a netbook, a personal digital assistant (PDA).

[0065] Optionally, the above-mentioned server may be one server in a server cluster (composed of multiple servers), or a chip in the server, or a system-on-chip in the server, or may be implemented by a virtual machine (VM) deployed on a physical machine. The embodiments of the present application do not make any limitations in this regard.

[0066] Optionally, the pipeline wall thickness data collection device 101 and the data storage device 102 may be two independently provided devices, or may be integrated in the same device. When the pipeline wall thickness data collection device 101 and the data storage device 102 are integrated in the same device, the data storage device 102 may be a storage module of the pipeline wall thickness data collection device 101.

[0067] It is easy to understand that when the pipeline wall thickness data collection device 101 and the data storage device 102 are integrated in the same device, the communication method between the pipeline wall thickness data collection device 101 and the data storage device 102 is the communication between internal modules of the device. In this case, the communication process between the two is the same as the communication process between the pipeline wall thickness data collection device 101 and the data storage device 102 when they are independent of each other.

[0068] Optionally, the pipeline wall thickness data collection device 101 and the sensor 103 may be two independently provided devices, or may be integrated in the same device. When the pipeline wall thickness data collection device 101 and the sensor 103 are integrated in the same device, the sensor 103 may be a data acquisition module of the pipeline wall thickness data collection device 101.

[0069] It is easy to understand that when the pipeline wall thickness data collection device 101 and the sensor 103 are integrated in the same device, the communication method between the pipeline wall thickness data collection device 101 and the sensor 103 is the communication between internal modules of the device. In this case, the communication process between the two is the same as the communication process between the pipeline wall thickness data collection device 101 and the sensor 103 when they are independent of each other.

[0070] For ease of understanding, the present application takes the pipeline wall thickness data collection device 101, the data storage device 102, and the sensor 103 being independent of each other as an example for description.

[0071] The pipeline wall thickness data collection device 101 includes as Figure 2 the included components. The following takes the Figure 2 shown pipeline wall thickness data collection device as an example to introduce the hardware structure of the pipeline wall thickness data collection device 101.

[0072] As Figure 2As shown in the figure, it is a schematic hardware structure diagram of a pipeline wall thickness data collection device provided by an embodiment of the present application. The pipeline wall thickness data collection 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 through the bus 24.

[0073] The processor 21 is the control center of the pipeline wall thickness data collection device, which can be a single processor or a collective 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, etc.

[0074] As an embodiment, the processor 21 can include one or more CPUs, such as Figure 2 the CPU0 and CPU1 shown in the figure.

[0075] The memory 22 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0076] In a possible implementation, the memory 22 can exist independently of the processor 21. The memory 22 can be connected to the processor 21 through the bus 24 for storing instructions or program codes. When the processor 21 calls and executes the instructions or program codes stored in the memory 22, it can implement the pipeline wall thickness data collection method provided in the following embodiments of the present application.

[0077] In the embodiments of the present application, for the pipeline wall thickness data collection device, the software programs stored in the memory 22 are different, so the functions implemented by the pipeline wall thickness data collection device are different. The functions executed by each device will be described in combination with the following flowcharts.

[0078] In another possible implementation, the memory 22 can also be integrated with the processor 21.

[0079] A communication interface 23 for connecting the pipeline wall thickness data collection device to other devices via a communication network, which can be an Ethernet, a wireless access network, a wireless local area network (WLAN), etc. The communication interface 23 may include a receiving unit for receiving data and a transmitting unit for transmitting data.

[0080] A bus 24, which can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity in representation, Figure 2 it is only represented by a thick line in the figure, but it does not mean that there is only one bus or one type of bus.

[0081] It should be noted that, Figure 2 the structure shown in the figure does not constitute a limitation on the pipeline wall thickness data collection device. Except for Figure 2 the components shown, the pipeline wall thickness data collection device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0082] The pipeline wall thickness data collection method provided by the embodiments of the present application will be introduced in detail below with reference to the accompanying drawings.

[0083] The pipeline wall thickness data collection method provided by the embodiments of the present application is applied to Figure 1 the pipeline wall thickness data collection device 101 in the pipeline wall thickness data collection system shown in the figure. As Figure 3 shown, the pipeline wall thickness data collection method provided by the embodiments of the present application includes:

[0084] S301. Obtain the setting position information of the target pipeline, the attribute information of the target pipeline, and the environmental information of the environment where the target pipeline is located.

[0085] The setting position information includes: connection position, setting measurement, and setting direction.

[0086] The attribute information includes: material, diameter, and thickness.

[0087] The environmental information includes: temperature, humidity, and external pressure.

[0088] In the embodiments of the present application, the pipeline wall thickness data collection device can determine the service life and potential risks of the target pipeline based on the obtained setting position information, attribute information, and environmental information of the target pipeline, so as to provide a basis for determining the detection difficulty score.

[0089] Combined with Figure 1 , the pipeline wall thickness data collection device 101 can obtain the setting position information of the target pipeline and the attribute information of the target pipeline from the data storage device 102.

[0090] The pipeline wall thickness data collection device 101 can obtain the environmental information of the environment where the target pipeline is located from the sensor 103.

[0091] S302. Determine the detection difficulty score of the target pipeline according to the setting position information, attribute information, and environmental information.

[0092] In the embodiments of the present application, the pipeline wall thickness data collection device determines the detection difficulty score of the target pipeline according to the setting position information, attribute information, and environmental information. In this way, the pipeline wall thickness collected data can quantify the complexity of the target pipeline detection. In subsequent applications, the detection difficulty score provides a clear direction for the selection and calibration of detection devices.

[0093] In some embodiments, determining the detection difficulty score of the target pipeline according to the setting position information, attribute information, and environmental information specifically includes:

[0094] S3021. Determine the vertical distance between the target pipeline and the pipeline foundation according to the setting position information.

[0095] S3022. Determine the basic score of the target pipeline according to the vertical distance.

[0096] That is, when the vertical distance is greater than zero, determine that the basic score is the first basic score. The first basic score can be expressed as Li. Wherein, the value of i includes 1, 2, and 3.

[0097] When the vertical distance is less than zero, determine that the basic score is the second basic score. The second basic score can be expressed as Ki. Wherein, the value of i includes 1, 2, and 3.

[0098] The first basic score is greater than zero, and the second basic score is less than zero. That is, Li>0>Ki.

[0099] In an implementable manner, the pipeline wall thickness data collection device can preset a first preset vertical distance and a second preset vertical distance, and the first preset vertical distance < 0 < the second preset vertical distance. When the vertical distance is less than the first vertical distance, the basic score is determined to be K1. When the vertical distance is greater than or equal to the first preset vertical distance and less than 0, the basic score is determined to be K2. When the vertical distance is greater than or equal to 0 and less than the second preset vertical distance, the basic score is determined to be L1. When the vertical distance is greater than the second preset vertical distance, the basic score is determined to be L2. Among them, L2 > L1 > 0 > K2 > K1.

[0100] It can be understood that the change in the vertical distance between the target pipeline and the pipeline foundation not only reflects the physical position of the pipeline but is also closely related to the installation conditions of the target pipeline and the surrounding environment. Therefore, the pipeline wall thickness data collection device can evaluate the detection difficulty by measuring the vertical distance between the target pipeline and the foundation. When the vertical distance is greater than 0, it indicates that the target pipeline is higher than the pipeline foundation, and it may face less soil pressure and external interference. The pipeline wall thickness data collection device can determine that the score at this time is a positive value Li. When the vertical distance is less than 0, the target pipeline is located below the pipeline foundation and may be subject to greater soil pressure or other potential risks, so the basic score is a negative value Ki. This ensures that the basic score can accurately reflect the actual condition and safety risk of the target pipeline, providing a scientific basis for subsequent detection work.

[0101] Secondly, the pipeline wall thickness data collection device can divide the detection difficulty into multiple levels through the preset first and second vertical distances. For example, when the vertical distance is less than the first preset vertical distance, the score is set to the minimum K1, indicating that the pipeline is in a high-risk area and needs to be given priority attention. On the contrary, when the vertical distance is greater than the second preset vertical distance, the score is the maximum L2, indicating that the detection of the pipeline is relatively easy. This multi-level scoring mechanism enables the pipeline detection work under different conditions to be quantified and optimized, ensuring the reasonable allocation of detection resources.

[0102] S3023. Determine the adjustment coefficient of the basic score according to the attribute information and the environmental information.

[0103] In an implementable manner, the pipeline wall thickness data collection device can obtain the designed wall thickness of the target pipeline and determine the adjustment coefficient according to the relationship between the designed wall thickness and the preset wall thickness configured in advance. That is, when the designed wall thickness is less than the preset wall thickness, the adjustment coefficient Mmin is determined. When the designed wall thickness is greater than or equal to the preset wall thickness, the wall thickness difference between the designed wall thickness and the preset wall thickness is obtained to determine the adjustment coefficient.

[0104] In some embodiments, the attribute information includes: the designed wall thickness. Based on the attribute information and the environmental information, the adjustment coefficient of the basic score is determined, specifically including:

[0105] When the designed wall thickness is less than the preset wall thickness, the adjustment coefficient of the basic score is determined as the minimum adjustment coefficient.

[0106] When the designed wall thickness is greater than or equal to the preset wall thickness, the adjustment coefficient is determined according to the wall thickness difference between the designed wall thickness and the preset wall thickness.

[0107] In some embodiments, determining the adjustment coefficient according to the wall thickness difference between the designed wall thickness and the preset wall thickness specifically includes:

[0108] When the wall thickness difference is less than the first preset wall thickness difference, the adjustment coefficient is determined as the first adjustment coefficient. The first adjustment coefficient can be represented by M1.

[0109] When the wall thickness difference is greater than or equal to the first preset wall thickness difference and less than the second preset wall thickness difference, the adjustment coefficient is determined as the second adjustment coefficient. The second adjustment coefficient can be represented by M2.

[0110] When the wall thickness difference is greater than or equal to the second preset wall thickness difference, the adjustment coefficient is determined as the third adjustment coefficient. The third adjustment coefficient can be represented by M3.

[0111] Wherein, the first preset wall thickness difference is less than the second preset wall thickness difference.

[0112] The minimum adjustment coefficient, the first adjustment coefficient, the second adjustment coefficient and the third adjustment coefficient increase in sequence. That is, Mmin < M1 < M2 < M3.

[0113] S3024. Adjust the basic score according to the adjustment coefficient to determine the detection difficulty score.

[0114] It can be understood that after the pipeline wall thickness data collection device determines the adjustment coefficient of the basic score, it can adjust the basic score according to the adjustment coefficient to determine the detection difficulty score.

[0115] In some embodiments, the environmental information includes: the environmental temperature and the environmental humidity. The pipeline wall thickness data collection method further includes: obtaining the detection temperature and the detection humidity when each detection device has a detection deviation, and determining the influence characteristics of the detection device according to the detection temperature and the detection humidity. According to the environmental humidity, the environmental temperature and the influence characteristics, it is determined whether to correct the adjustment coefficient.

[0116] Specifically, when determining the adjustment coefficient, first, the pipeline wall thickness data collection device can obtain the detection temperature and detection humidity when each detection device has a detection offset, and establish an influence relationship formula for each detection device. Then, the pipeline wall thickness data collection device can, based on the distance metric between each influence relationship formula (which can also be referred to as the similarity of the influence relationships between detection devices), establish a distance matrix according to the distance metric. Next, the pipeline wall thickness data collection device can recursively merge each influence relationship formula based on the distance matrix to obtain a merged influence relationship formula. Next, the pipeline wall thickness data collection device can obtain the detection temperature and detection humidity corresponding to the recursively and merged influence relationship formula, and determine the detection temperature and detection humidity corresponding to the merged influence relationship formula as influence characteristics. Next, the pipeline wall thickness data collection device obtains the ambient temperature and ambient humidity of the target pipeline, and determines whether to correct the adjustment coefficient according to the relationship between the ambient temperature and ambient humidity and the influence characteristics.

[0117] Among them, the influence relationship formula of the detection device satisfies the following formula:

[0118] f i (T, H) = a i T + b i H + c i ;

[0119] Among them, f i (T, H) is the influence relationship formula of the i-th detection device, T is the temperature, H is the humidity, a i is the temperature influence coefficient, b i is the humidity influence coefficient, c i is the constant term (which can also be referred to as the baseline influence).

[0120] The distance metric satisfies the following formula:

[0121]

[0122] Among them, D ij is the distance between the i-th detection device and the j-th detection device, a i is the temperature influence coefficient in the i-th detection device, b i is the humidity influence coefficient in the i-th detection device, c i is the constant term of the i-th detection device, a j is the temperature influence coefficient in the j-th detection device, b j is the humidity influence coefficient in the j-th detection device, c j is the constant term of the j-th detection device.

[0123] The distance matrix satisfies the following formula:

[0124]

[0125] The combined influence relationship satisfies the following formula:

[0126]

[0127] where k is the number of combined detection devices, w i is the combined weight of the i-th detection device, and f i (T, H) is the influence relationship of the i-th detection device.

[0128] It can be understood that during the process of detecting the target pipeline, environmental temperature and humidity are key factors affecting the performance of detection devices. Different detection devices have different sensitivities to environmental temperature and humidity, and even the detection results of the same detection device may vary significantly under different environmental conditions. Therefore, the present application can systematically analyze the influence of environmental factors on detection devices, i.e., detection offset, by obtaining the actual working data (i.e., detection temperature and detection humidity) of each detection device under different conditions. The present application takes into account the influence of environmental fluctuations on the stability of detection devices, which can lay a solid data foundation for subsequent analysis.

[0129] Secondly, the present application can also construct corresponding mathematical models for each detection device by collecting the performance data of detection devices under different temperature and humidity conditions. This mathematical model can not only intuitively show the influence of environmental temperature and humidity on device performance, but also provide theoretical support for subsequent data analysis and detection device calibration. In this way, the present application can predict the performance of detection devices under different environmental conditions and formulate corresponding adjustment strategies for detection devices.

[0130] Thirdly, various methods can be adopted for the distance metric of the present application, for example, Euclidean distance and Manhattan distance. The distance metric can quantify the similarity of influence relationships. The present application can identify the similarity and difference in the performance of detection devices under similar environmental conditions, that is, the present application can determine which detection devices have similar performance and which detection devices have significant differences. Further, the present application can construct a systematic distance matrix by calculating the distance metric between each influence relationship, which can provide structured data support for recursive merging, thereby simplifying the processing process of complex data in recursive merging and reducing redundant information.

[0131] Finally, by recursively merging the influence relationships, the present application can effectively extract the main influence features, reduce the data dimension, and make subsequent analysis more efficient. This not only improves the efficiency of data processing but also enhances the readability of the analysis results. The merged influence relationship can better reflect the overall performance of the detection device under specific environmental conditions, reduce the detection deviation caused by individual extreme values, and make the above data more representative and applicable. In this way, the present application ensures the centralization and integration of information, providing a clear direction for optimizing the detection process.

[0132] In some embodiments, the influence features include: characteristic temperature (which can also be referred to as the preset environmental temperature) and characteristic humidity (which can also be referred to as the preset environmental humidity); determining whether to correct the adjustment coefficient according to the environmental humidity, environmental temperature, and influence features includes:

[0133] When the environmental temperature is less than the characteristic temperature and the environmental humidity is less than the characteristic humidity, it is determined not to correct the adjustment coefficient. When the environmental temperature is greater than or equal to the characteristic temperature and the environmental humidity is less than the characteristic humidity, it is determined to correct the adjustment coefficient. When the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is less than the characteristic temperature, it is determined to correct the adjustment coefficient. When the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is greater than or equal to the characteristic temperature, it is determined to correct the adjustment coefficient.

[0134] Specifically, the collection of pipeline wall thickness data can determine whether to correct the adjustment coefficient Mi according to the relationships between the environmental temperature and the characteristic temperature, and between the environmental humidity and the characteristic humidity. That is, when the environmental temperature is less than the characteristic temperature and the environmental humidity is less than the characteristic humidity, it is determined not to correct the adjustment coefficient Mi. When the environmental temperature is greater than or equal to the characteristic temperature and the environmental humidity is less than the characteristic humidity, the correction coefficient is determined according to the temperature difference between the environmental temperature and the characteristic temperature, and the adjustment coefficient Mi is corrected according to the correction coefficient. When the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is less than the characteristic temperature, the correction coefficient is determined according to the humidity difference between the environmental humidity and the characteristic humidity, and the adjustment coefficient Mi is corrected according to the correction coefficient. When the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is greater than or equal to the characteristic temperature, the correction coefficient is determined according to the temperature difference and the humidity difference, and the adjustment coefficient Mi is corrected according to the correction coefficient.

[0135] In some embodiments, when the ambient temperature is greater than or equal to the characteristic temperature and the ambient humidity is less than the characteristic humidity, it is determined to correct the adjustment coefficient. Alternatively, after determining to correct the adjustment coefficient when the ambient humidity is greater than or equal to the characteristic humidity and the ambient temperature is less than the characteristic temperature, it further includes: determining an environmental characteristic value according to the environmental information and the influencing characteristics. The environmental characteristic value is the difference between the ambient temperature and the characteristic temperature, or the difference between the ambient humidity and the characteristic humidity. When the environmental characteristic value is less than the first preset environmental characteristic value, it is determined that the correction coefficient is the first correction coefficient N1. When the environmental characteristic value is greater than or equal to the first preset environmental characteristic value and less than the second preset environmental characteristic value, it is determined that the correction coefficient is the second correction coefficient N2. When the environmental characteristic value is greater than or equal to the second preset environmental characteristic value, it is determined that the correction coefficient is the third correction coefficient N3. The first preset environmental characteristic value is less than the second preset environmental characteristic value; the first correction coefficient, the second correction coefficient, and the third correction coefficient increase in sequence, and the third correction coefficient is less than 1. That is, N1 < N2 < N3 < 1. The adjustment coefficient is corrected according to the correction coefficient.

[0136] In an implementable manner, that is, when the ambient temperature is greater than or equal to the characteristic temperature and the ambient humidity is less than the characteristic humidity, the environmental characteristic value is the difference between the ambient temperature and the characteristic temperature. The pipeline wall thickness data collection device can determine the correction coefficient according to the relationship between the environmental characteristic value, the first preset environmental characteristic value, and the second preset environmental characteristic value. Further, the pipeline wall thickness data collection device can correct the adjustment coefficient according to the correction coefficient.

[0137] In another implementable manner, that is, when the ambient humidity is greater than or equal to the characteristic humidity and the ambient temperature is less than the characteristic temperature, the environmental characteristic value is the difference between the ambient humidity and the characteristic humidity. The pipeline wall thickness data collection device can determine the correction coefficient according to the relationship between the environmental characteristic value, the first preset environmental characteristic value, and the second preset environmental characteristic value. Further, the pipeline wall thickness data collection device can correct the adjustment coefficient according to the correction coefficient.

[0138] In some embodiments, after determining to correct the adjustment coefficient when the ambient humidity is greater than or equal to the characteristic humidity and the ambient temperature is greater than or equal to the characteristic temperature, the method further includes: determining the difference between the ambient temperature and the characteristic temperature as the temperature difference. Determining the difference between the ambient humidity and the characteristic humidity as the humidity difference. Determining a fourth correction coefficient according to the temperature difference and the humidity difference; the fourth correction coefficient satisfies the following formula:

[0139] N = x 1 ×A + x 2 ×S + 1;

[0140] Where A is the temperature difference, S is the humidity difference, and x1 , x 2 is a weight coefficient, and the sum of x 1 and x 2 is 1.

[0141] According to the fourth correction coefficient of the correction coefficient, correct the adjustment coefficient.

[0142] It can be understood that the set position information of the target pipeline directly affects its surrounding environment and detection conditions. For example, the vertical distance between the pipeline and the foundation stone not only reflects the physical position of the pipeline, but is also closely related to factors such as soil pressure and ground settlement. When the pipeline position is higher than the foundation stone, the basic score is set to a positive value, indicating that the detection is relatively easy. On the contrary, it is evaluated as a negative value, suggesting an increase in potential risks. In this way, the present application can provide a scientific basis for subsequent detection work and ensure the safety and effectiveness during the detection process.

[0143] Secondly, in the process of determining the detection difficulty score in the present application, the comparison between the designed wall thickness and the preset wall thickness is crucial. The change of the designed wall thickness directly affects the bearing capacity and service life of the target pipeline. The present application can dynamically adjust the detection difficulty score by comparing the designed wall thickness with the preset value. For example, when the designed wall thickness is less than the preset value, the detection difficulty score will be automatically reduced to prompt more cautious measures in subsequent detection work.

[0144] In addition, the ambient temperature and ambient humidity have a significant impact on the performance of the detection equipment. During the actual detection process, changes in environmental conditions may lead to instability in the performance of the detection equipment, thereby affecting the accuracy of the detection results. Therefore, the present application can obtain the equipment detection data under different environmental conditions, establish the influence relationship formula of the equipment, and systematically analyze the specific influence of the ambient temperature and ambient humidity on the performance of the detection equipment. This not only provides theoretical support for subsequent equipment calibration, but also can form a targeted adjustment strategy in practical applications. In this way, the present application can ensure that even under complex environmental conditions, the detection equipment can still maintain a good working state and improve the reliability of the detection results.

[0145] Finally, the present application can use the distance matrix and clustering algorithm to summarize and integrate the performance of multiple detection equipment. In this way, the present application can quantify the similarity and difference between different detection equipment, enabling efficient analysis and decision-making in the face of a large amount of complex data. Through the recursive merging influence relationship formula, the present application can not only simplify the data processing process, but also reduce redundant information and improve the efficiency of the detection process. The obtained merged influence relationship formula of the present application can better reflect the performance of the detection equipment under specific environmental conditions and provide a clear direction for optimizing the detection process.

[0146] S303. Determine the detection equipment according to the detection difficulty score, and calibrate the detection equipment according to the detection difficulty score.

[0147] The detection equipment includes: electromagnetic detection equipment, ultrasonic detection equipment, and laser detection equipment.

[0148] It can be understood that different detection equipment can be selected to detect the target pipeline according to different detection difficulties. In the underground environment, the detection difficulty of the target pipeline is usually affected by the setting position information of the target pipeline (i.e., the vertical distance between the target pipeline and the pipeline foundation stone (which can also be called the burial depth)), and the environmental information of the environment where the target pipeline is located (including: soil characteristics, groundwater level, soil conductivity, soil density, and environmental humidity). Therefore, the pipeline wall thickness collection equipment can evaluate the underground environment detection difficulty score by measuring the vertical distance between the target pipeline and the pipeline foundation stone. Since the pipeline setting position information and environmental information increase the difficulty of the detection equipment to obtain signals. Therefore, if the burial depth of the target pipeline is large or the surrounding soil humidity is high, the detection difficulty score is high.

[0149] Since the setting position information and environmental information of the target pipeline will affect the detection performance of the electromagnetic detection equipment, in this environment, it is usually preferred to select equipment suitable for underground conditions, such as electromagnetic detection equipment.

[0150] In this way, the pipeline wall thickness collection equipment can determine the detection difficulty score based on the underground distance, and can more accurately evaluate the equipment performance required to detect the target pipeline.

[0151] Secondly, in the ground environment, the detection difficulty of the target pipeline is also affected by ground obstacles, buildings, and surrounding facilities. Compared with the underground environment, the ground environment usually has a richer physical structure, and these physical structures will cause different degrees of signal attenuation and interference. Therefore, for the ground environment, the detection equipment needs to have good anti-interference ability and adaptability. The pipeline wall thickness collection equipment can help evaluate the clarity and reliability of signal transmission by measuring the distance between different obstacles and the detection equipment.

[0152] Since the ultrasonic detection equipment has a longer wavelength and can effectively penetrate smaller obstacles, it performs excellently in the ground environment. Therefore, in the ground environment, ultrasonic detection equipment is usually selected.

[0153] In this way, the pipeline wall thickness collection equipment can optimize the detection equipment selection and deployment strategy through distance analysis to ensure the accuracy and effectiveness of detection.

[0154] Finally, the difficulty of detecting the aerial environment is also affected by factors such as climate conditions, wind speed, and air humidity. In the aerial environment, the distance factor includes not only the physical distance between the detection device and the target pipeline, but also the influence of atmospheric conditions on signal propagation. For example, high humidity or haze weather may cause attenuation of the laser signal, thereby reducing the detection accuracy. The pipeline wall thickness collection device determines the inspection difficulty score based on the distance influence of the aerial environment, selects the laser device and performs corresponding calibration to ensure high detection performance under various meteorological conditions. Therefore, in the aerial environment, a laser detection device (i.e., a detection device for high-altitude measurement or remote sensing monitoring) is usually selected.

[0155] As can be seen from the above, the present application comprehensively considers the distance factors in the underground, ground, and aerial environments to scientifically determine the detection difficulty score. This can not only improve the accuracy of detection device selection, but also enhance the adaptability and reliability of the detection device under different environmental conditions through a dynamic calibration mechanism. This multi-level analysis method based on distance ensures the comprehensiveness and effectiveness of pipeline detection, laying a solid foundation for subsequent detection work.

[0156] In some embodiments, determining the detection device according to the detection difficulty score specifically includes: when the detection difficulty score is less than the first preset detection difficulty score, determining the detection device as an electromagnetic detection device; when the detection difficulty score is greater than or equal to the first preset detection difficulty score and less than the second preset detection difficulty score, determining the detection device as an ultrasonic detection device; when the detection difficulty score is greater than or equal to the second preset detection difficulty score, determining the detection device as a laser detection device. The first preset detection difficulty score is equal to 0, and the first preset detection difficulty score is less than the second preset detection difficulty score.

[0157] In some embodiments, calibrating the detection device according to the detection difficulty score includes: when the detection device is determined to be an electromagnetic detection device or an ultrasonic detection device, determining the calibration coefficient according to the first score difference, and calibrating the detection device according to the calibration coefficient. Wherein, the first score difference is the difference between the detection difficulty score and the first preset detection difficulty score; when the detection device is determined to be a laser detection device, determining the calibration coefficient according to the second score difference, and calibrating the detection device according to the calibration coefficient. Wherein, the second score difference is the difference between the detection difficulty score and the second preset detection difficulty score.

[0158] S304. Determine the detection path of the detection device for the target pipeline according to the connection position, setting measurement, and setting direction.

[0159] In the embodiments of the present application, the pipeline wall thickness collection device can determine the detection path of the detection device for the target pipeline according to the connection position, set measurement, and set direction. In this way, through reasonable path design, the present application ensures the efficiency and accuracy of the detection device when performing tasks (i.e., detecting the target pipeline). Repeated detection and ineffective detection can be avoided, reducing the detection time and operating costs. At the same time, the path planning of the present application reduces the need for manual intervention, improves the automation level, and reduces the workload of operators.

[0160] S305. Control the detection device to detect the target pipeline according to the detection path to collect the pipeline wall thickness data of the target pipeline. In the embodiments of the present application, the pipeline wall thickness collection device can control the detection device to detect the target pipeline according to the detection path to collect the pipeline wall thickness data of the target pipeline.

[0161] In the above embodiments, the pipeline wall thickness data collection device constructs a multi-dimensional data basis by obtaining the set position information, attribute information, and environmental information of the target pipeline. This comprehensive information collection makes the detection scheme more scientific and reasonable, and can effectively reflect various challenges that the target pipeline may face during actual operation. The pipeline wall thickness data collection device accurately judges the service life and potential risks of the pipeline according to the attribute information of the target pipeline and the environmental information of the environment where it is located, thereby providing an important basis for the subsequent detection scheme design.

[0162] Secondly, the pipeline wall thickness data collection device determines the detection difficulty score according to the set position information, attribute information, and environmental information to quantify the complexity of pipeline detection. The detection difficulty score determined by the pipeline wall thickness data collection device not only helps engineers quickly identify the pipeline segments that need to be prioritized, but also provides a clear direction for the selection and calibration of the detection device. It can ensure the pertinence of the detection work, enable resources to be concentrated where they are most needed, and improve the detection efficiency.

[0163] In addition, the pipeline wall thickness data collection device reasonably selects and calibrates the detection device according to the detection difficulty score, making the entire detection process more intelligent. The selection of the device not only depends on its technical parameters, but also takes into account the pipeline attributes and environmental information of the specific target pipeline, so as to maximize the performance of the detection device. This personalized device configuration reduces the risk of false detection and improves the accuracy of data collection. The calibration process of the detection device ensures that the detection device operates in the best state, reducing errors caused by the mismatch of the detection device.

[0164] Finally, the pipeline wall thickness data collection device can determine the detection path of the detection device for the target pipeline according to the connection position, set measurement, and set direction. The clear detection path planning ensures the efficiency and accuracy of the detection device when performing tasks. A reasonable detection path design can avoid repeated and ineffective detections, reducing the detection time and operating costs. At the same time, the pipeline wall thickness data collection device determining the detection path can reduce the need for human intervention, improve the automation level, and reduce the workload of operators. The above mainly introduces the solution provided by the embodiments of the present application from the perspective of methods. To implement the above functions, it includes the corresponding hardware structure and / or software module for each function. Those skilled in the art should easily realize that, combining 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 certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians 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 embodiments of the present application can divide the functional modules of the pipeline wall thickness data collection device according to the above method examples. For example, each functional module can be divided corresponding 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 a software functional module. Optionally, the division of modules in the embodiments of the present application is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0166] As Figure 4 shown, it is a schematic structural diagram of a pipeline wall thickness data collection device provided by the embodiments of the present application. Figure 4 The pipeline wall thickness data collection device shown includes: a communication unit 401 and a processing unit 402; the communication unit 401 is used to obtain the set position information of the target pipeline, the attribute information of the target pipeline, and the environmental information of the environment where the target pipeline is located; the set position information includes: connection position, set measurement, and set direction; the processing unit 402 is used to determine the detection difficulty score of the target pipeline according to the set position information, attribute information, and environmental information; the processing unit 402 is further used to determine the detection device according to the detection difficulty score and calibrate the detection device according to the detection difficulty score; the processing unit 402 is further used to determine the detection path of the detection device for the target pipeline according to the connection position, set measurement, and set direction; the processing unit 402 is further used to control the detection device to detect the target pipeline according to the detection path to collect the pipeline wall thickness data of the target pipeline.

[0167] Optionally, the processing unit 402 is specifically configured to: determine the vertical distance between the target pipeline and the pipeline foundation according to the set position information; determine the basic score of the target pipeline according to the vertical distance; when the vertical distance is greater than zero, determine that the basic score is the first basic score; when the vertical distance is less than zero, determine that the basic score is the second basic score; the first basic score is greater than zero and the second basic score is less than zero; determine the adjustment coefficient of the basic score according to the attribute information and the environmental information; adjust the basic score according to the adjustment coefficient to determine the detection difficulty score.

[0168] Optionally, the attribute information includes: the designed wall thickness; the processing unit 402 is specifically configured to: when the designed wall thickness is less than the preset wall thickness, determine that the adjustment coefficient of the basic score is the minimum adjustment coefficient; when the designed wall thickness is greater than or equal to the preset wall thickness, determine the adjustment coefficient according to the wall thickness difference between the designed wall thickness and the preset wall thickness.

[0169] Optionally, the processing unit 402 is specifically configured to: when the wall thickness difference is less than the first preset wall thickness difference, determine that the adjustment coefficient is the first adjustment coefficient; when the wall thickness difference is greater than or equal to the first preset wall thickness difference and less than the second preset wall thickness difference, determine that the adjustment coefficient is the second adjustment coefficient; when the wall thickness difference is greater than or equal to the second preset wall thickness difference, determine that the adjustment coefficient is the third adjustment coefficient; the first preset wall thickness difference is less than the second preset wall thickness difference; the minimum adjustment coefficient, the first adjustment coefficient, the second adjustment coefficient, and the third adjustment coefficient increase in sequence.

[0170] Optionally, the environmental information includes: the environmental temperature and the environmental humidity; the processing unit 402 is further configured to obtain the detection temperature and the detection humidity when each detection device has a detection offset, and determine the influence characteristics on the detection device according to the detection temperature and the detection humidity; the processing unit 402 is further configured to determine whether to correct the adjustment coefficient according to the environmental humidity, the environmental temperature, and the influence characteristics.

[0171] Optionally, the influence characteristics include: the characteristic temperature and the characteristic humidity; the processing unit 402 is specifically configured to: when the environmental temperature is less than the characteristic temperature and the environmental humidity is less than the characteristic humidity, determine not to correct the adjustment coefficient; when the environmental temperature is greater than or equal to the characteristic temperature and the environmental humidity is less than the characteristic humidity, determine to correct the adjustment coefficient; when the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is less than the characteristic temperature, determine to correct the adjustment coefficient; when the environmental humidity is greater than or equal to the characteristic humidity and the environmental temperature is greater than or equal to the characteristic temperature, determine to correct the adjustment coefficient.

[0172] Optionally, when the ambient temperature is greater than or equal to the characteristic temperature and the ambient humidity is less than the characteristic humidity, or when the ambient humidity is greater than or equal to the characteristic humidity and the ambient temperature is less than the characteristic temperature, after determining to correct the adjustment coefficient, the processing unit 402 is further configured to determine an environmental characteristic value according to the environmental information and the influencing characteristics; the environmental characteristic value is the difference between the ambient temperature and the characteristic temperature, or the difference between the ambient humidity and the characteristic humidity; the processing unit 402 is further configured to determine that the correction coefficient is the first correction coefficient when the environmental characteristic value is less than the first preset environmental characteristic value; the processing unit 402 is further configured to determine that the correction coefficient is the second correction coefficient when the environmental characteristic value is greater than or equal to the first preset environmental characteristic value and less than the second preset environmental characteristic value; the processing unit 402 is further configured to determine that the correction coefficient is the third correction coefficient when the environmental characteristic value is greater than or equal to the second preset environmental characteristic value; the first preset environmental characteristic value is less than the second preset environmental characteristic value; the first correction coefficient, the second correction coefficient, and the third correction coefficient increase in sequence, and the third correction coefficient is less than 1; the adjustment coefficient is corrected according to the correction coefficient.

[0173] Optionally, when the ambient humidity is greater than or equal to the characteristic humidity and the ambient temperature is greater than or equal to the characteristic temperature, after determining to correct the adjustment coefficient, the processing unit 402 is further configured to determine the difference between the ambient temperature and the characteristic temperature as the temperature difference; the processing unit 402 is further configured to determine the difference between the ambient humidity and the characteristic humidity as the humidity difference; the processing unit 402 is further configured to determine a fourth correction coefficient according to the temperature difference and the humidity difference; the fourth correction coefficient satisfies the following formula:

[0174] N = x 1 ×A + x 2 ×S + 1;

[0175] where A is the temperature difference, S is the humidity difference, x 1 、x 2 are weight coefficients, and the sum of x 1 and x 2 is 1; the processing unit 402 is further configured to correct the adjustment coefficient according to the correction coefficient, the fourth correction coefficient.

[0176] Optionally, the processing unit 402 is specifically configured to: determine that the detection device is an electromagnetic detection device when the detected difficulty score is less than the first preset detected difficulty score; determine that the detection device is an ultrasonic detection device when the detected difficulty score is greater than or equal to the first preset detected difficulty score and less than the second preset detected difficulty score; determine that the detection device is a laser detection device when the detected difficulty score is greater than or equal to the second preset detected difficulty score; the first preset detected difficulty score is equal to 0 and less than the second preset detected difficulty score.

[0177] Optionally, the processing unit 402 is specifically configured to: when it is determined that the detection device is an electromagnetic detection device or an ultrasonic detection device, determine a correction coefficient according to a first score difference, and correct the detection device according to the correction coefficient; the first score difference is the difference between the detected difficulty score and the first preset detected difficulty score; when it is determined that the detection device is a laser detection device, determine a correction coefficient according to a second score difference, and correct the detection device according to the correction coefficient; the second score difference is the difference between the detected difficulty score and the second preset detected difficulty score.

[0178] The embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium includes computer-executable instructions, and when the computer-executable instructions run on a computer, the computer is enabled to execute the pipeline wall thickness data collection method provided in the above embodiment.

[0179] The embodiment of the present application further provides a computer program, which can be directly loaded into a memory and contains software codes. After being loaded and executed by a computer, the computer program can implement the pipeline wall thickness data collection method provided in the above embodiment.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions of the present invention or make equivalent replacements, and these modifications or equivalent replacements do not make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

[0181] For the system provided by the above embodiments, only the division of the above functional modules is used as an example for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, 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 combined into one module, or can be further split into multiple sub-modules to complete all or part of the functions described above. For the names of the modules and steps involved in the embodiments of the present invention, they are only used to distinguish each module or step, and are not regarded as an improper limitation of the present invention.

[0182] Those skilled in the art should be able to realize that in one or more of the above examples, the functions described in this application can be implemented by hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. A computer-readable medium includes a computer-readable storage medium and a communication medium, where the communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0183] Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0184] In several embodiments provided in this application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated. The components displayed as units can be one physical unit or multiple physical units, that is, they can be located in one place, or can be distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0185] In addition, in each embodiment of the present application, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the general technology, or all or part of the technical solution, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0186] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for collecting pipeline wall thickness data, characterized in that: include: Acquire setting location information of a target pipeline, attribute information of the target pipeline, and environment information of an environment in which the target pipeline is located; The setting location information includes: connection location, setting measurement and setting direction; Determining a detection difficulty score of the target pipeline according to the setting location information, the attribute information and the environment information; Determining a detection device according to the detection difficulty score, and calibrating the detection device according to the detection difficulty score; Determining a detection path for the detection device to detect the target pipeline according to the connection position, the setting measurement, and the setting direction; The detection device is controlled to detect the target pipeline according to the detection path to collect pipeline wall thickness data of the target pipeline.

2. The pipeline wall thickness data collection method according to claim 1, characterized in that: The step of determining the detection difficulty score of the target pipeline according to the setting location information, the attribute information, and the environment information includes: Determine the vertical distance between the target pipeline and the pipeline foundation according to the setting position information; Determine the basic score of the target pipeline according to the vertical distance; when the vertical distance is greater than zero, determine the basic score to be a first basic score; when the vertical distance is less than zero, determine the basic score to be a second basic score; the first basic score is greater than zero, and the second basic score is less than zero; Determining an adjustment coefficient of the basic score according to the attribute information and the environmental information; The basic score is adjusted according to the adjustment coefficient to determine the detection difficulty score.

3. The pipeline wall thickness data collection method according to claim 2, characterized in that: The attribute information includes: designed wall thickness; the adjustment coefficient of the basic score is determined according to the attribute information and the environmental information, including: In the case where the designed wall thickness is less than the preset wall thickness, determining the adjustment coefficient of the basic score to be the minimum adjustment coefficient; In the case where the designed wall thickness is greater than or equal to the preset wall thickness, the adjustment coefficient is determined according to a wall thickness difference between the designed wall thickness and the preset wall thickness.

4. The pipeline wall thickness data collection method according to claim 3, characterized in that: The step of determining the adjustment coefficient according to the wall thickness difference between the designed wall thickness and the preset wall thickness includes: When the wall thickness difference is less than the first preset wall thickness difference, determining the adjustment coefficient to be the first adjustment coefficient; When the wall thickness difference is greater than or equal to the first preset wall thickness difference, and the wall thickness difference is less than the second preset wall thickness difference, determining the adjustment coefficient to be the second adjustment coefficient; When the wall thickness difference is greater than or equal to the second preset wall thickness difference, the adjustment coefficient is determined to be the third adjustment coefficient; the first preset wall thickness difference is less than the second preset wall thickness difference; the minimum adjustment coefficient, the first adjustment coefficient, the second adjustment coefficient and the third adjustment coefficient increase in sequence.

5. The pipeline wall thickness data collection method according to claim 4, characterized in that: The environmental information includes: environmental temperature and environmental humidity; the method also includes: Acquire the detection temperature and the detection humidity of each detection device when the detection offset occurs, and determine the influencing characteristics that affect the detection device according to the detection temperature and the detection humidity; Determine whether to correct the adjustment coefficient according to the ambient humidity, the ambient temperature and the influencing characteristics.

6. The pipeline wall thickness data collection method according to claim 5, characterized in that: The influencing characteristics include: characteristic temperature and characteristic humidity; and determining whether to correct the adjustment coefficient according to the ambient humidity, the ambient temperature and the influencing characteristics includes: When the ambient temperature is lower than the characteristic temperature and the ambient humidity is lower than the characteristic humidity, determining not to correct the adjustment coefficient; When the ambient temperature is greater than or equal to the characteristic temperature and the ambient humidity is less than the characteristic humidity, determining to correct the adjustment coefficient; When the ambient humidity is greater than or equal to the characteristic humidity and the ambient temperature is less than the characteristic temperature, determining to correct the adjustment coefficient; When the ambient humidity is greater than or equal to the characteristic humidity, and the ambient temperature is greater than or equal to the characteristic temperature, it is determined to correct the adjustment coefficient.

7. The pipeline wall thickness data collection method according to claim 6, characterized in that: After determining to correct the adjustment coefficient when the ambient temperature is greater than or equal to the characteristic temperature and the ambient humidity is less than the characteristic humidity, or determining to correct the adjustment coefficient when the ambient humidity is greater than or equal to the characteristic humidity and the ambient temperature is less than the characteristic temperature, the method further includes: Determine an environmental characteristic value according to the environmental information and the influencing characteristics; the environmental characteristic value is a difference between the environmental temperature and the characteristic temperature, or a difference between the environmental humidity and the characteristic humidity; In the case where the environmental characteristic value is less than the first preset environmental characteristic value, determining the correction coefficient to be the first correction coefficient; When the environmental characteristic value is greater than or equal to the first preset environmental characteristic value, and the environmental characteristic value is less than the second preset environmental characteristic value, determining the correction coefficient to be a second correction coefficient; In the case where the environmental characteristic value is greater than or equal to the second preset environmental characteristic value, determining that the correction coefficient is a third correction coefficient; the first preset environmental characteristic value is less than the second preset environmental characteristic value; the first correction coefficient, the second correction coefficient and the third correction coefficient increase in sequence, and the third correction coefficient is less than 1; The adjustment coefficient is corrected according to the correction coefficient.

8. The pipeline wall thickness data collection method according to claim 6, characterized in that: After determining to correct the adjustment coefficient when the ambient humidity is greater than or equal to the characteristic humidity and the ambient temperature is greater than or equal to the characteristic temperature, the method further includes: Determine the difference between the ambient temperature and the characteristic temperature as a temperature difference; Determine the difference between the ambient humidity and the characteristic humidity as a humidity difference; A fourth correction coefficient is determined according to the temperature difference and the humidity difference; the fourth correction coefficient satisfies the following formula: N = x1 × A + x2 × S + 1; Among them, A is the temperature difference, S is the humidity difference, x1 and x2 are weight coefficients, and the sum of x1 and x2 is 1; The adjustment coefficient is corrected according to the fourth correction coefficient.

9. The pipeline wall thickness data collection method according to claim 1, characterized in that: Determining the detection equipment according to the detection difficulty score includes: In a case where the detection difficulty score is less than a first preset detection difficulty score, determining that the detection device is an electromagnetic detection device; When the detection difficulty score is greater than or equal to the first preset detection difficulty score, and the detection difficulty score is less than a second preset detection difficulty score, determining that the detection device is an ultrasonic detection device; When the detection difficulty score is greater than or equal to the second preset detection difficulty score, the detection device is determined to be a laser detection device; the first preset detection difficulty score is equal to 0, and the first preset detection difficulty score is less than the second preset detection difficulty score.

10. The pipeline wall thickness data collection method according to claim 9, characterized in that: The calibrating the detection device according to the detection difficulty score includes: In the case where it is determined that the detection device is the electromagnetic detection device or the ultrasonic detection device, a correction coefficient is determined according to a first score difference, and the detection device is calibrated according to the correction coefficient; the first score difference is the difference between the detection difficulty score and the first preset detection difficulty score; When it is determined that the detection device is the laser detection device, a correction coefficient is determined according to a second score difference, and the detection device is calibrated according to the correction coefficient; the second score difference is the difference between the detection difficulty score and the second preset detection difficulty score.

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