Quality inspection methods, systems, terminal equipment, and media based on integrated thermal insulation pipes

Abstract

This application relates to the technical field of production data processing, and provides a quality inspection method, system, terminal equipment, and medium based on integrated insulation pipes. The method includes first acquiring real-time image information of the target insulation pipe using an industrial camera; then, quickly determining the over-adhesion area information in the real-time image information using a grayscale conversion algorithm; and finally, effectively generating quality inspection results based on the over-adhesion area information. This application enables real-time comprehensive monitoring of the integrated insulation pipe production process, timely identification and elimination of defective products, significantly improving production reliability and providing crucial data support for subsequent quality analysis and improvement.

Description

Technical Field

[0001] This application relates to the technical field of production data processing, and more specifically, to a quality inspection method, system, terminal equipment, and medium based on integrated insulation pipes. Background Technology

[0002] PP-R integrated insulation pipes typically consist of an insulation layer, an outer protective layer, and a PP-R inner tube. They are not easily corroded by chemicals, have good corrosion resistance, and can effectively reduce heat loss, making them suitable for a wide range of applications.

[0003] Currently, the production process of PP-R integrated insulation pipes lacks an effective real-time monitoring mechanism, making it difficult to detect substandard products in a timely manner and resulting in low production reliability, which needs further improvement. Summary of the Invention

[0004] Based on this, the embodiments of this application provide a quality inspection method, system, terminal equipment and medium based on integrated insulation pipe, so as to solve the problem of low production reliability in the prior art.

[0005] In a first aspect, embodiments of this application provide a quality inspection method based on an integrated thermal insulation pipe, the method comprising:

[0006] Based on a pre-set industrial camera, real-time image information of the target insulation pipe is acquired;

[0007] Based on a preset grayscale conversion algorithm, the information of the over-adhesion region in the real-time image information is determined;

[0008] Based on the information about the over-adhesion area, quality inspection result information is generated.

[0009] Compared with the prior art, the beneficial effects are as follows: The quality inspection method based on integrated insulation tube provided in this application embodiment can first acquire real-time image information of the target insulation tube using an industrial camera, then quickly determine the over-adhesion area information in the real-time image information according to the grayscale conversion algorithm, and finally effectively generate quality inspection result information based on the over-adhesion area information. This allows for real-time monitoring of unqualified integrated insulation tubes in mass production, effectively improving production reliability and solving the problem of low production reliability to a certain extent.

[0010] Secondly, embodiments of this application provide a quality inspection system based on an integrated thermal insulation pipe, the system comprising:

[0011] Real-time image information acquisition module: used to acquire real-time image information of the target insulation pipe based on a preset industrial camera;

[0012] Over-adhesion region information determination module: used to determine the over-adhesion region information in the real-time image information according to a preset grayscale conversion algorithm;

[0013] Quality inspection result information generation module: used to generate quality inspection result information based on the information of the over-adhesion area.

[0014] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in the first aspect above.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the first aspect above.

[0016] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0018] Figure 1 This is a schematic flowchart of a quality inspection method provided in an embodiment of this application;

[0019] Figure 2 This is a flowchart illustrating step S200 in a quality inspection method provided in an embodiment of this application;

[0020] Figure 3 This is a flowchart illustrating step S300 in a quality inspection method provided in an embodiment of this application;

[0021] Figure 4 This is a schematic diagram of the first process after step S300 in a quality inspection method provided in an embodiment of this application;

[0022] Figure 5 This is a flowchart illustrating the process after step S430 in a quality inspection method provided in an embodiment of this application;

[0023] Figure 6 This is a schematic diagram of the second process after step S300 in a quality inspection method provided in an embodiment of this application;

[0024] Figure 7 This is a schematic diagram of the third process after step S300 in a quality inspection method provided in an embodiment of this application;

[0025] Figure 8 This is a flowchart illustrating step S700 in a quality inspection method provided in an embodiment of this application;

[0026] Figure 9 A flowchart illustrating step S710 of a quality inspection method provided in an embodiment of this application;

[0027] Figure 10 This is a block diagram of a quality inspection system provided in one embodiment of this application;

[0028] Figure 11 This is a schematic diagram of a terminal device provided in an embodiment of this application. Detailed Implementation

[0029] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0030] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0032] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0033] Please see Figure 1 , Figure 1This is a flowchart illustrating the quality inspection method based on an integrated thermal insulation pipe provided in this embodiment. In this embodiment, the execution subject of the quality inspection method is a terminal device. It is understood that the types of terminal devices include, but are not limited to, mobile phones, tablets, laptops, Ultra-Mobile Personal Computers (UMPCs), netbooks, Personal Digital Assistants (PDAs), etc. This embodiment does not impose any restrictions on the specific type of terminal device.

[0034] Please see Figure 1 The quality inspection method provided in this application includes, but is not limited to, the following steps:

[0035] In the S100, real-time image information of the target insulation pipe is acquired based on a preset industrial camera.

[0036] Specifically, the terminal device can acquire real-time image information of the target insulation pipe based on a preset industrial camera. The real-time image information describes the image obtained by the industrial camera in real-time shooting of the target insulation pipe.

[0037] It should be noted that for substandard PP-R insulated pipes, after the insulation layer in the connection area is peeled off, a large amount of insulation layer will adhere to the inner PP-R pipe, which will seriously affect the subsequent hot-melt connection of the PP-R.

[0038] In S200, based on a preset grayscale conversion algorithm, information on over-adhesion regions in real-time image information is determined.

[0039] Specifically, after the terminal device acquires real-time image information, it can effectively determine the over-adhesion area information in the real-time image information according to the preset grayscale conversion algorithm. The over-adhesion area information is used to describe the area on the target insulation pipe where foaming material is over-adheded.

[0040] In some possible implementations, for efficient determination of over-adhesion region information, please refer to [link / reference]. Figure 2 Step S200 includes, but is not limited to, the following steps:

[0041] In S210, based on a preset grayscale conversion algorithm, the real-time image information is processed to generate grayscale image information.

[0042] Specifically, the terminal device can perform grayscale conversion processing on real-time image information based on a preset grayscale conversion algorithm to generate grayscale image information, wherein the grayscale image information is used to describe the real-time image information after grayscale conversion processing.

[0043] In S220, based on a preset contour extraction algorithm, contour extraction processing is performed on grayscale image information to generate multiple candidate region information.

[0044] Specifically, after the terminal device generates grayscale image information, the terminal device can perform contour extraction processing on the grayscale image information based on a preset contour extraction algorithm to generate multiple candidate region information, wherein the candidate region information is used to describe the region formed by closed contour lines.

[0045] In S230, the average gray value information of each candidate area is determined.

[0046] Specifically, after the terminal device generates multiple candidate area information, the terminal device can determine the average gray value information of each candidate area information. The average gray value information is used to describe the average gray value of the candidate area information.

[0047] In S240, the average gray value of each candidate area is compared with the preset boundary gray value.

[0048] Specifically, after the terminal device determines the average gray value information of the region, the terminal device can compare the average gray value information of each candidate region with the preset boundary gray value information. The specific value of the boundary gray value information can be customized by the inspection personnel. For example, the specific value of the boundary gray value information can be 15.

[0049] In S250, if the average gray value of the candidate area information is greater than the limit gray value information, then the candidate area information is determined to be over-adhesive area information.

[0050] Specifically, if the average gray value of the candidate area is greater than the limit gray value, it indicates that the candidate area is an area with excessive adhesion of foaming material, so the terminal device can determine that the candidate area is an area with excessive adhesion.

[0051] In S300, quality inspection results are generated based on information about the over-adhesion area.

[0052] Specifically, after the terminal device determines the information of the over-adhesion area, the terminal device can accurately generate quality inspection result information based on the over-adhesion area information. The quality inspection result information is either general abnormal adhesion information or severe abnormal adhesion information.

[0053] For some possible implementations, please refer to [link to relevant documentation] for accurate generation of quality inspection results. Figure 3 Step S300 includes, but is not limited to, the following steps:

[0054] In S310, the adhesion region sub-area information of each over-adhesion region and the insulation pipe area information of the target insulation pipe are obtained.

[0055] Specifically, the terminal device can obtain the sub-area information of the adhesion region of each over-adhesion region and the area information of the insulation pipe region of the target insulation pipe based on the grid method. The sub-area information of the adhesion region is used to describe the area of ​​the over-adhesion region information, and the area information of the insulation pipe region is used to describe the area of ​​the target insulation pipe in the grayscale image information.

[0056] In S320, the total area information of the adhesion region is generated based on the sum of the area information of multiple adhesion region sub-areas.

[0057] Specifically, after the terminal device obtains the sub-area information of the adhesion region, the terminal device can generate the total area information of the adhesion region based on the sum of the sub-area information of multiple adhesion regions. The total area information of the adhesion region is used to describe the sum of the sub-area information of multiple adhesion regions.

[0058] In S330, the total area information of the adhesion region is compared with the area information of the insulation pipe region by a specified multiple.

[0059] Specifically, after the terminal device generates the total area information of the adhesion area, the terminal device can compare the total area information of the adhesion area with the area information of the insulation pipe area by a specified multiple. The specified multiple is a natural number between 0.25 and 0.4. When the production requirements are high, the specified multiple is preferably 0.25.

[0060] In S340, if the total area of ​​the adhesion zone is less than a specified multiple of the area of ​​the insulation pipe zone, the quality inspection result is determined to be general abnormal adhesion information; otherwise, the quality inspection result is determined to be severe abnormal adhesion information.

[0061] Specifically, if the total area of ​​the adhesion zone is less than a specified multiple of the area of ​​the insulation pipe zone, the terminal device can determine that the quality inspection result is a general abnormal adhesion; otherwise, the terminal device can determine that the quality inspection result is a serious abnormal adhesion.

[0062] In some possible implementations, to facilitate the analysis of production anomalies by inspection personnel, please refer to [link / reference needed]. Figure 4 After step S300, the method further includes, but is not limited to, the following steps:

[0063] In S400, obtain historical and current abnormal duration information for the insulation pipe production line.

[0064] Specifically, the terminal equipment can obtain historical and current abnormal duration information of the thermal insulation pipe production line. The historical abnormal duration information describes the cumulative duration of the thermal insulation pipe production line from its start-up to the most recent quality inspection result information in history, while the current abnormal duration information describes the cumulative duration of the thermal insulation pipe production line from its start-up to the quality inspection result information in this round of production.

[0065] In S410, duration difference information is generated based on historical and current abnormal duration information.

[0066] Specifically, after the terminal device obtains historical and current abnormal duration information, it can generate duration difference information based on the historical and current abnormal duration information. The duration difference information describes the difference between the historical and current abnormal duration information.

[0067] In S420, it is determined whether the duration difference information is less than the preset limit duration information.

[0068] Specifically, after the terminal device generates the duration difference information, it can determine whether the duration difference information is less than a preset threshold duration information. The specific value of the threshold duration information can be customized by the testing personnel, for example, 15 minutes.

[0069] In S430, if the duration difference information is less than the limit duration information, then the average abnormal duration information is generated based on the historical abnormal duration information and the current abnormal duration information.

[0070] Specifically, if the duration difference information is less than the limit duration information, it indicates that there is a high probability of a production problem occurring at this time point when producing the target insulation pipe of this model. Therefore, the terminal equipment can generate average abnormal duration information based on historical abnormal duration information and current abnormal duration information. The average abnormal duration information is used to describe the average value of the historical abnormal duration information and the current abnormal duration information.

[0071] In some possible implementations, to further facilitate the analysis of production anomalies by testing personnel, please refer to [link / reference]. Figure 5 After step S430, the method further includes, but is not limited to, the following steps:

[0072] In S500, information such as foaming agent formulation, production parameters of the insulation pipe production line, and model information of the target insulation pipe are obtained.

[0073] Specifically, the terminal equipment can obtain information on the foaming agent formula, the production parameters of the insulation pipe production line, and the model information of the target insulation pipe.

[0074] In S510, foaming agent formula information, production parameter information, and model information are uploaded to the cloud database.

[0075] Specifically, after the terminal equipment obtains the foaming agent formula information, the production parameter information of the insulation pipe production line, and the model information of the target insulation pipe, the terminal equipment can upload the foaming agent formula information, production parameter information, and model information to the cloud database, thereby facilitating remote analysis of production anomalies by inspection personnel.

[0076] For analysis of the processing of this integrated insulation pipe, please refer to the following for possible implementation methods. Figure 6 After step S300, the method further includes, but is not limited to, the following steps:

[0077] In S600, the first cumulative processing quantity information of the target insulation pipe and the second cumulative processing quantity information of the insulation pipe with quality defects are obtained.

[0078] Specifically, the terminal device can obtain the first cumulative processing quantity information of the target insulation pipe and the second cumulative processing quantity information of the insulation pipe with quality abnormality. The insulation pipe with quality abnormality is used to describe the target insulation pipe corresponding to general abnormal adhesion information and the target insulation pipe corresponding to severe abnormal adhesion information.

[0079] In S610, processing qualification rate information is generated based on the first cumulative processing quantity information and the second cumulative processing quantity information.

[0080] Specifically, after the terminal device obtains the first cumulative processing quantity information and the second cumulative processing quantity information, the terminal device can calculate the difference between the second cumulative processing quantity information and the first cumulative processing quantity information, and then generate the processing qualification rate information by dividing the difference by the quotient of the first cumulative processing quantity information.

[0081] For analysis of the processing of this integrated insulation pipe, please refer to the following for possible implementation methods. Figure 7 After step S300, the method further includes, but is not limited to, the following steps:

[0082] In S700, the centroid information and weight information of each over-adhesion region are obtained.

[0083] Specifically, the terminal device can first obtain the centroid information and weight information of each over-adhesion region. The centroid information describes the specific location of the centroid of the over-adhesion region information, and the weight information describes the specific weight of the over-adhesion region information.

[0084] For some possible implementations, please refer to [link to relevant documentation] for accurate generation of quality inspection results. Figure 8 Step S700 includes, but is not limited to, the following steps:

[0085] In S701, for each over-adhesion region information: based on a preset integration method, the centroid information of the region corresponding to the over-adhesion region information is obtained.

[0086] Specifically, the terminal device can first perform this processing on the information of each over-adhesion region: based on a preset integration method, specifically obtain the centroid information of the region corresponding to the over-adhesion region information.

[0087] For example, the regional centroid information can be The terminal device can calculate the abscissa using a preset abscissa integration function, which can be:

[0088] ,

[0089] In the formula, For the first The x-coordinate of the centroid of each region. This refers to the sequence number of the centroid information for the region. This refers to the sub-area information of the adhesion region. For any point in the over-adhesion region information, Coordinates on the axis;

[0090] Meanwhile, the terminal device can calculate the abscissa using a preset ordinate integration function, which can be:

[0091] ,

[0092] In the formula, For the first The ordinate of the centroid information of each region For any point in the over-adhesion region information, Coordinates on the axis.

[0093] In S702, the region weight information is generated by dividing the sub-area information of the adhesion region by the total area information of the adhesion region.

[0094] Specifically, after the terminal device obtains the centroid information of the region, it can generate region weight information by dividing the sub-area information of the adhesion region by the total area information of the adhesion region. The value of the region weight information is the quotient of the sub-area information of the adhesion region divided by the total area information of the adhesion region.

[0095] In S710, based on a preset regional clustering calculation function, regional clustering information is generated according to the regional centroid information and regional weight information.

[0096] Specifically, after the terminal device obtains the regional centroid information and regional weight information, it can accurately generate regional aggregation information based on the regional centroid information and regional weight information through a preset regional aggregation calculation function. This allows for accurate and rapid determination of the specific range where excess foaming material is concentrated, which is beneficial for testing personnel to optimize specific process parameters.

[0097] For some possible implementations, please refer to [link to relevant documentation] for accurate generation of quality inspection results. Figure 9 Step S710 includes, but is not limited to, the following steps:

[0098] In S711, key centroid information is determined based on centroid information from multiple regions.

[0099] Specifically, the terminal device can use a preset key centroid calculation function to determine the key centroid information based on the centroid information of multiple regions. The key centroid information is... The terminal device can calculate the x-coordinate using a preset key centroid x-coordinate calculation function. This key centroid x-coordinate calculation function can be:

[0100] ,

[0101] In the formula, The x-coordinate of the key centroid information. This refers to the sequence number of the critical centroid information. This represents the total amount of information about the over-adhesion regions. As the starting value for the summation function, For the first Regional weight information corresponding to the centroid point information of each region;

[0102] Meanwhile, the terminal device can calculate the x-coordinate using a preset key centroid y-coordinate calculation function, which can be:

[0103] ,

[0104] In the formula, The vertical coordinate represents the information of the key centroid.

[0105] In S712, the key centroid information, regional centroid information, and regional weight information are input into the preset regional clustering calculation function to generate regional clustering information.

[0106] Specifically, after determining the key centroid information, the terminal device can input the key centroid information, regional centroid information, and regional weight information into a preset regional clustering calculation function to accurately generate regional clustering information. The regional clustering calculation function can be:

[0107] ,

[0108] In the formula, Information on regional agglomeration; This provides information on the average distance within the region. , For the first In one possible implementation, the shortest distance between the centroid information of a region and the key centroid information is... .

[0109] The implementation principle of the quality inspection method for integrated insulation pipes in this application embodiment is as follows: The terminal device can first acquire real-time image information of the target insulation pipe based on an industrial camera, and then quickly determine the information of the over-adhesion area in the real-time image information according to the grayscale conversion algorithm. Finally, based on the information of the over-adhesion area, the quality inspection result information is effectively generated, thereby realizing the real-time monitoring of unqualified integrated insulation pipes and effectively improving production reliability.

[0110] It should be noted that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0111] Embodiments of this application also provide a quality inspection system based on integrated thermal insulation pipes. For ease of explanation, only the parts relevant to this application are shown, such as... Figure 10 As shown, the system 100 includes:

[0112] Real-time image information acquisition module 101: used to acquire real-time image information of the target insulation pipe based on a preset industrial camera;

[0113] Over-adhesion region information determination module 102: used to determine over-adhesion region information in real-time image information according to a preset grayscale conversion algorithm;

[0114] Quality inspection result information generation module 103: used to generate quality inspection result information based on the information of the over-adhesion area.

[0115] Optionally, the system 100 also includes:

[0116] Regional centroid information acquisition module: used to acquire regional centroid information and regional weight information corresponding to each over-adhesion region;

[0117] Regional clustering information generation module: This module generates regional clustering information based on a preset regional clustering calculation function, using regional centroid information and regional weight information.

[0118] Optionally, the above-mentioned centroid information acquisition module includes:

[0119] Regional centroid information acquisition module: used to acquire regional centroid information corresponding to each over-adhesion region based on a preset integration method;

[0120] The region weight information generation module is used to generate region weight information by dividing the sub-area information of the adhesion region by the total area information of the adhesion region.

[0121] Optionally, the above-mentioned regional clustering information generation module includes:

[0122] Key centroid information determination module: used to determine key centroid information based on centroid information from multiple regions;

[0123] Regional clustering information generation module: This module is used to input key centroid information, regional centroid information, and regional weight information into a preset regional clustering calculation function to generate regional clustering information.

[0124] Optionally, the above-mentioned over-adhesion area information determination module 102 includes:

[0125] Grayscale image information generation submodule: Used to perform grayscale conversion processing on real-time image information based on a preset grayscale conversion algorithm to generate grayscale image information;

[0126] Candidate region information generation submodule: Used to perform contour extraction processing on grayscale image information based on a preset contour extraction algorithm to generate multiple candidate region information;

[0127] The submodule for determining the average gray value of a region is used to determine the average gray value of each candidate region.

[0128] Regional average gray value information comparison submodule: used to compare the regional average gray value information and the preset boundary gray value information of each candidate region.

[0129] The over-adhesion region information determination submodule is used to determine the candidate region information as over-adhesion region information if the average gray value of the candidate region information is greater than the limit gray value information.

[0130] Accordingly, the quality inspection result information is either general abnormal adhesion information or severe abnormal adhesion information; the aforementioned quality inspection result information generation module 103 includes:

[0131] The adhesion region sub-area information acquisition sub-module is used to acquire the adhesion region sub-area information of each excessive adhesion region and the insulation pipe area information of the target insulation pipe.

[0132] The total area information generation submodule for adhesion regions is used to generate the total area information of the adhesion regions based on the sum of the area information of multiple adhesion regions.

[0133] Total Adhesion Area Information Comparison Submodule: Used to compare the total adhesion area information with the insulation pipe area information of a specified multiple, wherein the specified multiple is a natural number between 0.25 and 0.4;

[0134] The General Abnormal Adhesion Information Determination Submodule is used to determine the quality inspection result as general abnormal adhesion information if the total area of ​​the adhesion area is less than a specified multiple of the area of ​​the insulation pipe area; otherwise, it is determined as severe abnormal adhesion information.

[0135] Optionally, the system 100 also includes:

[0136] Historical anomaly duration information acquisition module: used to acquire historical anomaly duration information and current anomaly duration information of the insulation pipe production line. The historical anomaly duration information describes the cumulative duration of the insulation pipe production line from start-up to the most recent quality inspection result information in history. The current anomaly duration information describes the cumulative duration of the insulation pipe production line from start-up to the quality inspection result information in this round of production.

[0137] Duration difference information generation module: used to generate duration difference information based on historical and current abnormal duration information;

[0138] Duration difference information judgment module: used to determine whether the duration difference information is less than the preset limit duration information;

[0139] Average Abnormal Duration Information Generation Module: If the duration difference information is less than the limit duration information, then the module generates average abnormal duration information based on historical abnormal duration information and current abnormal duration information.

[0140] Optionally, the system 100 also includes:

[0141] Foaming agent formula information acquisition module: used to acquire foaming agent formula information, production parameter information of the insulation pipe production line, and model information of the target insulation pipe;

[0142] Foaming agent formula information upload module: used to upload foaming agent formula information, production parameter information and model information to the cloud database.

[0143] Optionally, the system 100 also includes:

[0144] Cumulative processing quantity information acquisition module: used to acquire the first cumulative processing quantity information of the target insulation pipe and the second cumulative processing quantity information of the insulation pipe with quality abnormality. The insulation pipe with quality abnormality is used to describe the target insulation pipe corresponding to general abnormal adhesion information and the target insulation pipe corresponding to severe abnormal adhesion information.

[0145] Processing pass rate information generation module: used to generate processing pass rate information based on the first cumulative processing quantity information and the second cumulative processing quantity information.

[0146] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0147] This application also provides a terminal device, such as... Figure 11 As shown, the terminal device 110 of this embodiment includes: a processor 111, a memory 112, and a computer program 113 stored in the memory 112 and executable on the processor 111. When the processor 111 executes the computer program 113, it implements the steps described in the above-described quality inspection method embodiment, for example... Figure 1 The steps S100 to S300 are shown; or, when the processor 111 executes the computer program 113, it implements the functions of each module in the above-described device, for example... Figure 10 The functions of modules 101 to 103 are shown.

[0148] The terminal device 110 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The terminal device 110 includes, but is not limited to, a processor 111 and a memory 112. Those skilled in the art will understand that... Figure 11 This is merely an example of terminal device 110 and does not constitute a limitation on terminal device 110. It may include more or fewer components than shown, or combine certain components, or different components. For example, terminal device 110 may also include input / output devices, network access devices, buses, etc.

[0149] The processor 111 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.; the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0150] The memory 112 can be an internal storage unit of the terminal device 110, such as the hard disk or memory of the terminal device 110. The memory 112 can also be an external storage device of the terminal device 110, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device 110. Furthermore, the memory 112 can include both internal storage units and external storage devices of the terminal device 110. The memory 112 can also store computer program 113 and other programs and data required by the terminal device 110. The memory 112 can also be used to temporarily store data that has been output or will be output.

[0151] One embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0152] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the methods, principles and structures of this application should be covered within the scope of protection of this application.

Claims

1. A quality detection method based on a heat preservation integrated tube, characterized in that, The method comprises: Based on the preset industrial camera, the real-time image information of the target heat preservation tube is acquired; According to the preset gray scale conversion algorithm, the excessive adhesion area information in the real-time image information is determined; According to the excessive adhesion area information, the quality detection result information is generated; Wherein, after the quality detection result information is generated according to the excessive adhesion area information, the method further comprises: Acquire the area centroid point information and area weight information corresponding to each excessive adhesion area information; Based on the preset area convergence calculation function, the area convergence information is generated according to the area centroid point information and the area weight information; Wherein, the excessive adhesion area information in the real-time image information is determined according to the preset gray scale conversion algorithm, comprising: Based on the preset gray scale conversion algorithm, the gray scale image information is generated by carrying out gray scale conversion processing on the real-time image information; Based on the preset contour extraction algorithm, the multiple candidate area information is generated by carrying out contour extraction processing on the gray scale image information; The area average gray scale value information of each candidate area information is determined; The area average gray scale value information of each candidate area information and the preset limit gray scale value information are compared respectively; If the area average gray scale value information of the candidate area information is greater than the limit gray scale value information, it is determined that the candidate area information is the excessive adhesion area information; Correspondingly, the quality detection result information is general abnormal adhesion information or serious abnormal adhesion information; the quality detection result information is generated according to the excessive adhesion area information, comprising: The adhesion area sub-area information of each excessive adhesion area information and the heat preservation tube area information of the target heat preservation tube are acquired; The adhesion area total area information is generated according to the sum of the multiple adhesion area sub-area information; The adhesion area total area information and the heat preservation tube area information of the specified multiple are compared, wherein the specified multiple is between 0.25 and 0.4; If the adhesion area total area information is less than the heat preservation tube area information of the specified multiple, it is determined that the quality detection result information is general abnormal adhesion information, otherwise it is determined that the quality detection result information is serious abnormal adhesion information.

2. The method of claim 1, wherein, The area centroid point information and the area weight information corresponding to each excessive adhesion area information are acquired, comprising: For each excessive adhesion area information: based on the preset integral method, the area centroid point information corresponding to the excessive adhesion area information is acquired; The area weight information is generated according to the adhesion area sub-area information divided by the adhesion area total area information; Correspondingly, the area convergence information is generated according to the area centroid point information and the area weight information based on the preset area convergence calculation function, comprising: The key centroid point information is determined according to the multiple area centroid point information; The key centroid point information, the area centroid point information and the area weight information are input into the preset area convergence calculation function to generate the area convergence information.

3. The method of claim 1, wherein, After the quality detection result information is generated according to the excessive adhesion area information, the method further comprises: Acquire the historical abnormal duration information and the current abnormal duration information of the heat preservation tube production line, wherein the historical abnormal duration information is used to describe the cumulative duration of the heat preservation tube production line from startup to the latest quality detection result information in history, and the current abnormal duration information is used to describe the cumulative duration of the heat preservation tube production line from startup to the quality detection result information in the current production; Generate duration difference information according to the historical abnormal duration information and the current abnormal duration information; Determine whether the duration difference information is less than the preset boundary duration information; If the duration difference information is less than the boundary duration information, generate average abnormal duration information according to the historical abnormal duration information and the current abnormal duration information.

4. The method of claim 3, wherein, After the above step, the method further comprises: Acquire the foaming agent formula information, the production parameter information of the heat preservation tube production line, and the model information of the target heat preservation tube; Upload the foaming agent formula information, the production parameter information, and the model information to the cloud database.

5. The method of claim 1, wherein, After the above step, the method further comprises: Acquire the first cumulative processing quantity information of the target heat preservation tube and the second cumulative processing quantity information of the quality abnormal heat preservation tube, wherein the quality abnormal heat preservation tube is used to describe the target heat preservation tube corresponding to the general abnormal adhesion information and the target heat preservation tube corresponding to the serious abnormal adhesion information; Generate processing qualification rate information according to the first cumulative processing quantity information and the second cumulative processing quantity information.

6. A quality detection system based on a heat preservation integrated tube, characterized in that, The system comprises: A real-time image information acquisition module for acquiring real-time image information of a target heat preservation tube based on a preset industrial camera; An excessive adhesion area information determination module for determining excessive adhesion area information in the real-time image information according to a preset gray scale conversion algorithm; A quality detection result information generation module for generating quality detection result information according to the excessive adhesion area information; The excessive adhesion area information determination module comprises: A candidate area information generation submodule for performing gray scale conversion processing on real-time image information based on a preset gray scale conversion algorithm to generate gray scale image information; A candidate area information generation submodule for performing contour extraction processing on the gray scale image information based on a preset contour extraction algorithm to generate a plurality of candidate area information; A region average gray scale value information determination submodule for determining region average gray scale value information of each candidate area information; A region average gray scale value information comparison submodule for comparing the region average gray scale value information of each candidate area information with preset boundary gray scale value information respectively; An excessive adhesion area information determination submodule for determining the candidate area information as excessive adhesion area information if the region average gray scale value information of the candidate area information is greater than the boundary gray scale value information; Correspondingly, the quality detection result information is general abnormal adhesion information or serious abnormal adhesion information; and the quality detection result information generation module comprises: The adhesion area sub-area information acquisition submodule is configured to acquire adhesion area sub-area information of each of the excessive adhesion area information and adhesion area information of the target heat preservation tube; The adhesion area total area information generation submodule is configured to generate adhesion area total area information according to a sum of the plurality of adhesion area sub-area information; The adhesion area total area information comparison submodule is configured to compare the adhesion area total area information with the heat preservation tube area information of the specified multiple, wherein the specified multiple is between 0.25 and 0.4; The general abnormal adhesion information determination submodule is configured to determine the quality detection result information as the general abnormal adhesion information if the adhesion area total area information is less than the heat preservation tube area information of the specified multiple, and otherwise determine the quality detection result information as the serious abnormal adhesion information.

7. The system of claim 6, wherein, The system comprises: The cumulative processing quantity information acquisition module is configured to acquire first cumulative processing quantity information of the target heat preservation tube and second cumulative processing quantity information of the quality abnormal heat preservation tube, wherein the quality abnormal heat preservation tube is used to describe the target heat preservation tube corresponding to the general abnormal adhesion information and the target heat preservation tube corresponding to the serious abnormal adhesion information; The processing qualified rate information generation module is configured to generate processing qualified rate information according to the first cumulative processing quantity information and the second cumulative processing quantity information.

8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: The computer program is executed by the processor to realize the steps of the method according to any one of claims 1 to 5.

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