Appliance corrosion determination method and device, computer equipment and storage medium

Abstract

The invention relates to an appliance corrosion determination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring an appliance image and an appliance image of a to-be-detected appliance, and determining each rusty part in the to-be-detected appliance according to the appliance image; obtaining rust layer thickness data of each rusty part and pixel position information in the appliance image, and performing correlation fitting on the pixel position information of each rusty part and the corresponding rust layer thickness data to obtain rust layer simulation data of the appliance to be detected; and according to the rust layer simulation data, determining the corrosion condition of the to-be-tested appliance. According to the scheme, the pixel position information of each rusty part and the corresponding rusty layer thickness data are subjected to correlation fitting to obtain the rusty layer simulation data of the to-be-detected appliance, so that the rusty layer development condition of the to-be-detected appliance can be reflected more intuitively and accurately, the rusty condition analysis of the to-be-detected appliance can be more accurately carried out, and the detection accuracy of the to-be-detected appliance is improved. Therefore, the recognition accuracy of the corrosion development condition is improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, computer equipment and storage medium for determining corrosion of an appliance. Background Art

[0002] Identification of rust layers on rust-prone equipment (such as metal equipment) can provide a reference for the operation and maintenance of rust-prone equipment.

[0003] In the related art, the method for determining the rust of rust-prone instruments is mainly to identify the surface morphology of the rust layer and judge the development of the rust layer by the rust layer morphology area.

[0004] However, judging the development of the rust layer by the rust layer morphology and area has the problem of incomplete identification of the rust condition, resulting in low accuracy in identifying the development of the rust. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment and storage medium for determining rust of equipment to address the above-mentioned technical problems, which can improve the accuracy of identifying the development status of rust.

[0006] In a first aspect, the present application provides a method for determining rust of an appliance, comprising:

[0007] acquiring an instrument image of the instrument to be tested;

[0008] Determining various rusted locations in the tool to be tested according to the tool image;

[0009] Acquire the rust layer thickness data of each rusted part and the pixel position information in the tool image;

[0010] Correlation fitting is performed between the pixel position information of each rusted part and the corresponding rust layer thickness data to obtain rust layer simulation data of the device to be tested;

[0011] The rust condition of the device to be tested is determined according to the rust layer simulation data.

[0012] In one embodiment, obtaining the rust layer thickness data of each rusted part includes:

[0013] Determining actual position information of each rusted part according to pixel position information of each rusted part in the tool image;

[0014] According to the actual position information of each rusted part, the rust layer thickness data of each rusted part is obtained.

[0015] In one embodiment, obtaining pixel position information of each rusted part in the tool image includes:

[0016] While acquiring the rust layer thickness data of each rusted part, acquiring a local image of each rusted part;

[0017] According to the tool image and the partial images of each of the rusted parts, pixel position information of each of the rusted parts in the tool image is determined.

[0018] In one embodiment, obtaining the rust layer thickness data of each rusted part includes:

[0019] For each rusted part, a thickness gauge is used to collect rust layer thickness data at multiple collection points of the rusted part at preset distance intervals;

[0020] The rust layer thickness data of the multiple collection points are all used as the rust layer thickness data of the rusted part.

[0021] In one embodiment, the pixel position information of each rusted part and the corresponding rust layer thickness data are correlated and fitted to obtain the rust layer simulation data of the device to be tested, including:

[0022] Determining first position coordinates and second position coordinates of each rusty part according to pixel position information of each rusty part;

[0023] Determining the third position coordinates of each rusted part according to the rust layer thickness data of each rusted part;

[0024] The first position coordinate, the second position coordinate, the third position coordinate and the tool image are correlated and fitted to obtain rust layer simulation data of the tool to be tested.

[0025] In one embodiment, determining the corrosion condition of the device to be tested according to the rust layer simulation data includes:

[0026] According to the candidate rust layer thicknesses and the candidate rust levels, as well as the rust layer simulation data, a target rust level of each rusted portion of the device to be tested is determined.

[0027] In a second aspect, the present application also provides a device for determining rust of an appliance, comprising:

[0028] A first acquisition module, used to acquire an instrument image of the instrument to be tested;

[0029] A first determination module is used to determine the rusted parts of the tool to be tested according to the tool image;

[0030] A second acquisition module is used to acquire the rust layer thickness data of each rusted part and the pixel position information in the tool image;

[0031] A fitting module, used for associating and fitting the pixel position information of each rusted part with the corresponding rust layer thickness data to obtain the rust layer simulation data of the instrument to be tested;

[0032] The second determination module is used to determine the corrosion condition of the device to be tested according to the rust layer simulation data.

[0033] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0034] acquiring an instrument image of the instrument to be tested;

[0035] Determining various rusted locations in the tool to be tested according to the tool image;

[0036] Acquire the rust layer thickness data of each rusted part and the pixel position information in the tool image;

[0037] Correlation fitting is performed between the pixel position information of each rusted part and the corresponding rust layer thickness data to obtain rust layer simulation data of the device to be tested;

[0038] The rust condition of the device to be tested is determined according to the rust layer simulation data.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0040] acquiring an instrument image of the instrument to be tested;

[0041] Determining various rusted locations in the tool to be tested according to the tool image;

[0042] Acquire the rust layer thickness data of each rusted part and the pixel position information in the tool image;

[0043] Correlation fitting is performed between the pixel position information of each rusted part and the corresponding rust layer thickness data to obtain rust layer simulation data of the device to be tested;

[0044] The rust condition of the device to be tested is determined according to the rust layer simulation data.

[0045] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0046] acquiring an instrument image of the instrument to be tested;

[0047] Determining various rusted locations in the tool to be tested according to the tool image;

[0048] Acquire the rust layer thickness data of each rusted part and the pixel position information in the tool image;

[0049] Correlation fitting is performed between the pixel position information of each rusted part and the corresponding rust layer thickness data to obtain rust layer simulation data of the device to be tested;

[0050] The rust condition of the device to be tested is determined according to the rust layer simulation data.

[0051] The above-mentioned instrument corrosion determination method, device, computer equipment and storage medium obtain an instrument image and an instrument image of the instrument to be tested, and determine each rusted part in the instrument to be tested based on the instrument image; then obtain the rust layer thickness data of each rusted part and the pixel position information in the instrument image, associate and fit the pixel position information of each rusted part with the corresponding rust layer thickness data, and obtain the rust layer simulation data of the instrument to be tested; determine the rust condition of the instrument to be tested based on the rust layer simulation data. The above-mentioned scheme associates and fits the pixel position information of each rusted part with the corresponding rust layer thickness data to obtain the rust layer simulation data of the instrument to be tested, which can more intuitively and accurately reflect the rust layer development of the instrument to be tested, facilitate more accurate analysis of the rust condition of the instrument to be tested, and thus improve the accuracy of identifying the rust development condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A schematic diagram of a flow chart of a method for determining rust of an appliance in one embodiment;

[0054] Figure 2 A schematic diagram of a process for obtaining rust layer thickness data and location information of each rusted part in one embodiment;

[0055] Figure 3 A schematic diagram of a process for obtaining rust layer thickness data and location information of each rusted part in another embodiment;

[0056] Figure 4 A schematic diagram of a process for obtaining rust layer thickness data of each rusted part in an image of an appliance in one embodiment;

[0057] Figure 5 A schematic diagram of a process for generating rust layer simulation data of a device to be tested in one embodiment;

[0058] Figure 6 A schematic diagram of rust layer simulation data in one embodiment;

[0059] Figure 7 A schematic diagram of rust layer simulation data in another embodiment;

[0060] Figure 8 is a schematic diagram of an interface of a fitting software in one embodiment;

[0061] Fig. 9 A schematic flow chart of a method for determining rust of an appliance in another embodiment;

[0062] Fig.10 is a structural block diagram of a device for determining rust of an implement in one embodiment;

[0063] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] The method for determining the corrosion of an instrument provided in the embodiment of the present application can be applied to the application scenario of analyzing the corrosion condition of metal instruments, and specifically to the application scenario of analyzing the corrosion condition of transmission towers.

[0066] The method can be executed by a server or by a terminal with a certain computing power. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablets, IoT devices and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc.

[0067] In an exemplary embodiment, Figure 1 As shown, a method for determining rust of an appliance is provided, and the method is applied to a server as an example for explanation, and includes the following steps:

[0068] S101, obtaining an instrument image of the instrument to be tested.

[0069] For example, the device to be tested may be a device that is prone to rust, such as a metal device. In the field of electric power, the device to be tested may be a transmission tower or other equipment.

[0070] The instrument image of the instrument to be tested is used to reflect the structure, position and other information of the instrument to be tested. Optionally, a high-resolution camera device may be used to obtain the instrument image of the instrument to be tested.

[0071] S102, determining rusted locations in the tool to be tested according to the tool image.

[0072] For example, rusted parts usually appear as reddish brown or black spots, patches or layered structures on the surface, and may also be accompanied by loss of gloss and surface roughness. Feature recognition can be performed on the tool image based on the features of the rusted parts to determine the rusted parts in the tool image.

[0073] S103, obtaining rust layer thickness data of each rusted part and pixel position information in the tool image.

[0074] For example, a non-contact device may be used to collect rust thickness data of each rusted part in the tool image. For example, the rusted part of the tool to be tested may be determined based on the tool image of the tool to be tested, and then the non-contact device may be used to collect rust thickness data of the rusted part of the tool to be tested.

[0075] Furthermore, a coordinate system of the tool image may be established, and pixel position information of each rusted portion in the tool image may be determined based on the coordinate system of the tool image.

[0076] For example, taking the test device as a transmission tower, the transmission tower can be divided into three parts: tower body, tower head and tower leg. If these three parts are rusted, non-contact equipment can be used to collect rust thickness data of the tower body, tower head and tower leg.

[0077] Exemplarily, the pixel position information of each rusted part may be the pixel position coordinates of each rusted part in the coordinate system corresponding to the device image of the device to be tested, which is used to reflect the position of the rusted part in the device image of the device to be tested. The pixel position information of each rusted part may be determined when collecting the rust layer thickness data, or may be determined before collecting the rust layer thickness data, or may be determined after collecting the rust layer thickness data. No specific limitation is made here on the timing of obtaining the pixel position information of each rusted part.

[0078] S104, performing correlation fitting on the pixel position information of each rusted part and the corresponding rust layer thickness data to obtain rust layer simulation data of the instrument to be tested.

[0079] Exemplarily, the pixel position of each rusted part in the instrument image of the instrument to be tested can be determined based on the pixel position information of each rusted part; and the thickness of the rust layer in the instrument image of the instrument to be tested can be determined based on the rust layer thickness data; then the pixel position information of each rusted part and the corresponding rust layer thickness data are correlated and fitted to determine the rust layer thickness data at each position in the instrument image of the instrument to be tested, that is, to obtain the rust layer simulation data of the instrument to be tested. Among them, the rust layer simulation data of the instrument to be tested can be two-dimensional data or three-dimensional data, which is used to intuitively reflect the rust condition of the instrument to be tested. For example, the rust layer simulation data can accurately reflect the shape, thickness, distribution and other characteristics of the rust layer.

[0080] S105, determining the corrosion condition of the device to be tested according to the rust layer simulation data.

[0081] Furthermore, the rust condition of the instrument to be tested can be determined based on the rust layer simulation data, wherein the rust condition of the instrument to be tested can be information such as the service life, remaining service life, and rust degree of the instrument to be tested.

[0082] For example, the rust layer simulation data can reflect the rust layer thickness of each part of the instrument to be tested. The corresponding relationship between the rust layer thickness and the remaining service life of the instrument to be tested can be determined in advance based on empirical data. Then, based on the corresponding relationship and the rust layer thickness of each part of the instrument to be tested, the remaining service life of the instrument to be tested can be determined, that is, the rust condition of the instrument to be tested can be determined.

[0083] The above scheme associates and fits the position information of each rusted part with the corresponding rust layer thickness data to obtain the rust layer simulation data of the instrument to be tested, which can more intuitively and accurately reflect the rust layer development of the instrument to be tested, facilitate more accurate analysis of the rust condition of the instrument to be tested, thereby improving the accuracy of identifying the rust development condition.

[0084] In some optional implementations, the position information of each rusted part may be determined before obtaining the rust layer thickness data of each rusted part in the tool image.

[0085] For example, see Figure 2 , Figure 2 A schematic diagram of a process for obtaining rust layer thickness data is provided, which specifically includes the following steps:

[0086] S201, determining actual position information of each rusted portion according to pixel position information of each rusted portion in the tool image.

[0087] Exemplarily, a coordinate system of the tool image may be established in advance, for example, an X-axis, Y-axis, and Z-axis coordinate system may be established. In order to more concisely represent the position coordinates of each part of the tool image, the tool image may be gridded, for example, the tool image may be gridded at intervals of any integer between 1 mm and 5 mm, and the pixel position information of each rusted part in the tool image may be determined based on the tool image after gridding. Furthermore, the actual position information of each rusted part in the tool to be tested may be determined based on the scaling ratio and corresponding positional relationship between the tool image and the tool to be tested.

[0088] S202, acquiring rust layer thickness data of each rusted part according to the actual position information of each rusted part.

[0089] Furthermore, the actual rust position of the instrument under test can be determined according to the actual position information of each rusted position, and the rust thickness data of the position corresponding to the actual position information of each rusted position in the instrument under test can be collected by non-contact equipment.

[0090] In the embodiment of the present application, the actual position information of each rusted part is determined according to the pixel position information of the rusted part, and the rust layer thickness data corresponding to the actual position information of the rusted part in the instrument to be tested is obtained, so that the rust layer thickness data of the rusted part can be obtained more accurately.

[0091] In some optional implementations, it is also possible to obtain the position information of each rusted part while obtaining the rust layer thickness data of each rusted part in the tool image.

[0092] For example, see Figure 3 , Figure 3 A schematic diagram of a process for obtaining pixel position information of each of the parts in the instrument image is provided, which specifically includes the following steps:

[0093] S301, while obtaining the rust layer thickness data of each rusted part in the tool image, obtain the image data of each rusted part.

[0094] Exemplarily, while using a non-contact device to obtain the rust layer thickness data of each rusted part, a non-contact device can be used to obtain a local image of each rusted part. For example, a laser photothermal thickness gauge can be used to collect the rust layer thickness data of each rusted part, and at the same time, a camera function can be started to take a local image corresponding to the collected rust layer thickness data of each rusted part, wherein the local image can reflect the position of each rusted part in the device to be tested. In this way, the rust layer thickness data of each rusted part can be associated with the position information of each rusted part, so as to subsequently generate rust layer simulation data.

[0095] S302: Determine pixel position information of each rusted part in the tool image according to the tool image and the local images of each rusted part.

[0096] Exemplarily, a coordinate system of the instrument image can also be established, for example, an X-axis, Y-axis, and Z-axis coordinate system can be established. In order to more concisely represent the position coordinates of each part of the instrument image, the instrument image can be gridded, for example, the instrument image can be gridded at any integer distance interval between 1 mm and 5 mm, and the position coordinates of each part in the instrument image can be determined based on the instrument image after gridding.

[0097] Furthermore, the corresponding position of each rusted part in the tool image and the coordinates of the corresponding position can be determined according to the coordinate system of the tool image and the local image of each rusted part, that is, the pixel position information of each rusted part can be determined.

[0098] In the embodiment of the present application, while obtaining the rust layer thickness data of each rusted part, a local image of each rusted part is obtained, and the rust layer thickness data and the pixel position information of the rusted part can be associated one by one, so as to more accurately determine the rust layer thickness data of each rusted part in the process of generating rust layer simulation data, so as to improve the accuracy of the generated rust layer simulation data.

[0099] In some optional implementations, in order to more accurately reflect the rust condition of each rusted part, rust layer thickness data of multiple collection points of each rusted part may be collected.

[0100] For example, see Figure 4 , Figure 4 A schematic diagram of a process for obtaining rust thickness data of each rusted part in an image of an instrument is provided, which specifically includes the following steps:

[0101] S401, for each rusted part, using a thickness gauge to collect rust layer thickness data at multiple collection points of the rusted part at preset distance intervals.

[0102] Exemplarily, the preset distance interval may be set according to the interval at which the instrument image is grid-divided. For example, if the instrument image is grid-divided at an interval of 5 mm, the preset distance interval may be set to 5 mm.

[0103] For each rusted part, a thickness gauge can be used to collect rust thickness data at multiple collection points of the rusted parts at preset distance intervals. The thickness gauge can be a laser thermal thickness gauge or an infrared thermal thickness gauge. For example, taking the instrument to be tested as an iron tower, if the rusted part is at a high altitude, the UAV high-stabilization gimbal can be used to drive the thickness gauge to perform multi-point measurement of a single rusted part, and the amount of measurement data can be determined according to the size of the rusted part and the preset distance interval. If the rusted part is at the bottom, a handheld or ground-mounted thickness measuring device can be used to detect the rust thickness data of the rusted part one by one.

[0104] S402, taking the rust layer thickness data of the multiple collection points as the rust layer thickness data of the rusted part.

[0105] For example, the rust thickness data of multiple collection points can be used as the rust thickness data of the rusted part. In the process of generating rust layer simulation data, each rusted part can fit the rust thickness data of each collection point, so that the local corrosion situation is more accurate, and the generated rust layer simulation data is more reliable.

[0106] In some optional implementations, the rust layer simulation data generated for the instrument under test may be three-dimensional simulation data, which may reflect the rusted locations and rust layer thickness of the instrument under test, thereby facilitating analysis of the rust condition of the instrument under test.

[0107] For example, see Figure 5 , Figure 5 A schematic diagram of a process for generating rust layer simulation data of a device to be tested is provided, which specifically includes the following steps:

[0108] S501, determining first position coordinates and second position coordinates of each rusted part according to pixel position information of each rusted part.

[0109] Exemplarily, the pixel position information of each rusted part can be understood as the position coordinates of each rusted part in the coordinate system of the tool image, and thus the first position coordinates and the second position coordinates of each rusted part in the coordinate system of the tool image can be determined according to the pixel position information of each rusted part. The first position coordinates can be the coordinates corresponding to the X-axis, and the second coordinates can be the position coordinates corresponding to the Y-axis.

[0110] S502, determining the third position coordinates of each rusted location according to the rust layer thickness data of each rusted location.

[0111] For example, the rust thickness data of each rusted part can be regarded as the rust thickness of each rusted part on the Z axis, and thus the third position coordinates of each rusted part in the coordinate system of the tool image can be determined according to the rust thickness data of each rusted part. That is, the third position coordinates can be the position coordinates corresponding to the Z axis.

[0112] S503, performing correlation fitting on the first position coordinate, the second position coordinate, the third position coordinate and the tool image of the tool to be tested to obtain rust layer simulation data of the tool to be tested.

[0113] Furthermore, the first position coordinate, the second position coordinate, the third position coordinate and the instrument image of the instrument to be tested can be correlated and fitted to obtain the rust layer simulation data of the instrument to be tested, that is, a three-dimensional view including each rusted part in the instrument image and the corresponding rust layer thickness data can be obtained.

[0114] For example, see Figure 6 , Figure 6 A schematic diagram of rust layer simulation data is provided, where X Lable represents the position coordinate of the X-axis, Y Lable represents the coordinate of the Y-axis, and Depth represents the rust layer thickness data, which can be understood as the coordinate of the Z-axis. Each collection point is represented by a number, and the value in the bracket after the number represents the thickness data in mm; different colors represent different rust layer depths.

[0115] In the embodiment of the present application, by generating three-dimensional rust layer simulation data, the corrosion condition of the instrument to be tested can be more intuitively reflected.

[0116] Optionally, the rust layer simulation data of the test equipment can also be two-dimensional simulation data, see Figure 7 , Figure 7 Another schematic diagram of rust layer simulation data is provided. The rust layer simulation data may include each collection point of each rusted part in the tool image and the rust layer thickness data of each collection point. In this way, it is also convenient to intuitively understand the rust condition of the tool to be tested.

[0117] For example, a fitting software can be developed in advance. Figure 8 , Figure 8 A schematic diagram of the interface of a fitting software is provided. The interface shows an image stitching function module, a stitching result display module and a thickness measurement result display module. Taking the test instrument as a transmission tower as an example, in the image stitching function module, the user can enter the ultra-high voltage line number (e.g. 12), the tower number (e.g. 34), the geographical location of the tower, the detection time and the detection point, etc.; then select a certain picture under the image directory, that is, select the instrument image of the test instrument for rust layer fitting; and can select the rust layer thickness data under the rust layer thickness data path.

[0118] Furthermore, you can choose whether to crop the image and enter the X-axis and Y-axis coordinates of the acquisition point; then click the "Start Stitching" button, and the generated stitching result can be seen in Figure 7 The generated thickness measurement results can be found in Figure 6In this way, rust layer simulation data can be generated, which can more intuitively reflect the rust condition of the instrument under test.

[0119] Optionally, the corrosion condition of the instrument to be tested is determined based on the rust layer simulation data. The corrosion grade may be determined based on the rust layer simulation data, and then the operation and maintenance strategy for the instrument to be tested is determined.

[0120] Exemplarily, a correspondence between candidate rust layer thicknesses and candidate rust grades can be pre-constructed based on empirical data. For example, if the rust layer thickness is less than 1 mm, the rust grade can be determined to be light rust; if the rust layer thickness is greater than or equal to 1 mm and less than 3 mm, the rust grade can be determined to be moderate rust; if the rust layer thickness is greater than or equal to 3 mm, the rust grade can be determined to be heavy rust; thereby, a correspondence between candidate rust layer thicknesses and candidate rust grades can be established.

[0121] Furthermore, the target rust level of each rusted part of the instrument to be tested can be determined based on the candidate rust layer thickness and the candidate rust level, as well as the rust layer simulation data; for example, if the rust layer thickness data of a certain part in the rust layer simulation data is 2 mm, then the target rust level of the rusted part can be determined to be moderate rust.

[0122] The target corrosion level is used to determine the operation and maintenance strategy of the equipment to be tested, which may include continuous observation, repair and replacement.

[0123] The specific operation and maintenance strategy can be determined based on the target rust level and rusted parts. For example, if the target rust level of the main load-bearing parts of the instrument under test is moderate, the operation and maintenance strategy of the instrument under test can be determined as the need to replace the instrument under test. If the target rust level of the main load-bearing parts of the instrument under test is light and the secondary load-bearing parts are moderate, the operation and maintenance strategy of the instrument under test can be determined as the need to repair the instrument under test.

[0124] In the embodiment of the present application, the target rust level of each rusted part of the instrument to be tested can be determined based on the rust layer simulation data, so as to make the correct operation and maintenance strategy in time and avoid safety problems caused by severe rust of the instrument to be tested.

[0125] In some optional implementations, see Fig. 9 , Fig. 9 A schematic flow chart of another method for determining corrosion of an instrument is provided, which specifically includes the following steps:

[0126] S901, obtaining an instrument image of the instrument to be tested.

[0127] S902, determining rusted locations in the tool to be tested according to the tool image.

[0128] S903, obtaining the rust layer thickness data of each rusted part and the pixel position information in the tool image.

[0129] S904, determining first position coordinates and second position coordinates of each rusty part according to the pixel position information of each rusty part.

[0130] S905, determining the third position coordinates of each rusted location according to the rust layer thickness data of each rusted location.

[0131] S906, performing correlation fitting on the first position coordinate, the second position coordinate, the third position coordinate and the tool image to obtain rust layer simulation data of the tool to be tested.

[0132] S907, determining the target rust level of each rusted part of the instrument to be tested according to the candidate rust layer thickness and the candidate rust level, as well as the rust layer simulation data.

[0133] The specific process of S901 to S907 can refer to the description of the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0134] Furthermore, the execution order of the above steps is only an exemplary description and is not used to limit the execution steps. Other execution orders of the steps are within the protection scope of the embodiments of the present application.

[0135] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0136] Based on the same inventive concept, the embodiment of the present application also provides an apparatus for determining rust of an appliance for implementing the apparatus rust determination method involved above. The implementation scheme for solving the problem provided by the apparatus is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the apparatus rust determination apparatus provided below can refer to the limitations of the apparatus rust determination method above, and will not be repeated here.

[0137] In an exemplary embodiment, Fig.10 As shown, a device for determining rust of an appliance is provided, comprising:

[0138] A first acquisition module 10 is used to acquire an image of the device to be tested;

[0139] A first determination module 20, for determining various rusted parts in the tool to be tested according to the tool image;

[0140] A second acquisition module 30 is used to acquire the rust layer thickness data of each rusted part and the pixel position information in the tool image;

[0141] A fitting module 40 is used to associate and fit the pixel position information of each rusted part with the corresponding rust layer thickness data to obtain rust layer simulation data of the device to be tested;

[0142] The second determination module 50 is used to determine the corrosion condition of the device to be tested according to the rust layer simulation data.

[0143] The above-mentioned device for determining rust of an instrument obtains an instrument image and an instrument image of the instrument to be tested, and determines each rusted part in the instrument to be tested based on the instrument image; then obtains the rust thickness data of each rusted part and the pixel position information in the instrument image, associates and fits the pixel position information of each rusted part with the corresponding rust thickness data, and obtains the rust layer simulation data of the instrument to be tested; and determines the rust condition of the instrument to be tested based on the rust layer simulation data. The above-mentioned scheme associates and fits the pixel position information of each rusted part with the corresponding rust thickness data, and obtains the rust layer simulation data of the instrument to be tested, which can more intuitively and accurately reflect the rust layer development of the instrument to be tested, and facilitates more accurate analysis of the rust condition of the instrument to be tested, thereby improving the accuracy of identifying the rust development condition.

[0144] In one embodiment, the second acquisition module 30 is specifically used for:

[0145] According to the pixel position information of each rusted part in the equipment image, the actual position information of each rusted part is determined; according to the actual position information of each rusted part, the rust layer thickness data of each rusted part is obtained.

[0146] In one embodiment, the second acquisition module 30 is specifically used for:

[0147] While obtaining the rust layer thickness data of each rusted part, a local image of each rusted part is obtained; based on the tool image and the local image of each rusted part, the pixel position information of each rusted part in the tool image is determined.

[0148] In one embodiment, the second acquisition module 30 is specifically used for:

[0149] For each rusted part, a thickness gauge is used to collect rust layer thickness data at multiple collection points of the rusted part at preset distance intervals; the rust layer thickness data of the multiple collection points are all used as the rust layer thickness data of the rusted part.

[0150] In one embodiment, the fitting module 40 is specifically used for:

[0151] According to the pixel position information of each rusted part, the first position coordinate and the second position coordinate of each rusted part are determined; according to the rust layer thickness data of each rusted part, the third position coordinate of each rusted part is determined; the first position coordinate, the second position coordinate, the third position coordinate and the instrument image are correlated and fitted to obtain the rust layer simulation data of the instrument to be tested.

[0152] In one embodiment, the second determining module 50 is specifically configured to:

[0153] According to the candidate rust layer thicknesses and candidate rust levels, as well as the rust layer simulation data, the target rust level of each rusted part of the instrument to be tested is determined.

[0154] Each module in the above-mentioned device for determining corrosion of equipment can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0155] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corrosion data of the instrument to be tested. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the corrosion of an instrument is implemented.

[0156] Those skilled in the art will understand that Fig.11The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0157] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0158] Obtaining an instrument image for rust layer fitting of the instrument to be tested, rust layer thickness data of each rusted part in the instrument image, and position information of each rusted part;

[0159] The position information of each rusted part and the corresponding rust layer thickness data are correlated and fitted to obtain the rust layer simulation data of the instrument to be tested;

[0160] According to the rust layer simulation data, the rust condition of the instrument to be tested is determined.

[0161] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0162] Establish a coordinate system of the tool image; determine the position information of each rusted part in the tool image according to the coordinate system of the tool image; obtain the rust layer thickness data of each rusted part in the tool image according to the position information of each rusted part in the tool image.

[0163] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0164] The rust thickness data of each rusted part in the tool image is obtained, and the image data of each rusted part is obtained at the same time; a coordinate system of the tool image is established; and the position information of each rusted part is determined according to the coordinate system of the tool image and the image data of each rusted part.

[0165] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0166] For each rusted part, a thickness gauge is used to collect rust layer thickness data at multiple collection points at preset distance intervals; the rust layer thickness data at the multiple collection points are all used as the rust layer thickness data of the rusted part.

[0167] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0168] According to the position information of each rusted part, the first position coordinate and the second position coordinate of each rusted part in the coordinate system of the instrument to be tested are determined; according to the rust layer thickness data of each rusted part, the third position coordinate of each rusted part in the coordinate system of the instrument to be tested is determined; the first position coordinate, the second position coordinate, the third position coordinate and the instrument image of the instrument to be tested are correlated and fitted to obtain the rust layer simulation data of the instrument to be tested.

[0169] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0170] According to the candidate rust layer thickness and the candidate rust level, as well as the rust layer simulation data, the target rust level of each rusted part of the instrument to be tested is determined; wherein the target rust level is used to determine the operation and maintenance strategy of the instrument to be tested.

[0171] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0172] Obtaining an instrument image for rust layer fitting of the instrument to be tested, rust layer thickness data of each rusted part in the instrument image, and position information of each rusted part;

[0173] The position information of each rusted part and the corresponding rust layer thickness data are correlated and fitted to obtain the rust layer simulation data of the instrument to be tested;

[0174] According to the rust layer simulation data, the rust condition of the instrument to be tested is determined.

[0175] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0176] Establish a coordinate system of the tool image; determine the position information of each rusted part in the tool image according to the coordinate system of the tool image; obtain the rust layer thickness data of each rusted part in the tool image according to the position information of each rusted part in the tool image.

[0177] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0178] The rust thickness data of each rusted part in the tool image is obtained, and the image data of each rusted part is obtained at the same time; a coordinate system of the tool image is established; and the position information of each rusted part is determined according to the coordinate system of the tool image and the image data of each rusted part.

[0179] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0180] For each rusted part, a thickness gauge is used to collect rust layer thickness data at multiple collection points at preset distance intervals; the rust layer thickness data at the multiple collection points are all used as the rust layer thickness data of the rusted part.

[0181] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0182] According to the position information of each rusted part, the first position coordinate and the second position coordinate of each rusted part in the coordinate system of the instrument to be tested are determined; according to the rust layer thickness data of each rusted part, the third position coordinate of each rusted part in the coordinate system of the instrument to be tested is determined; the first position coordinate, the second position coordinate, the third position coordinate and the instrument image of the instrument to be tested are correlated and fitted to obtain the rust layer simulation data of the instrument to be tested.

[0183] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0184] According to the candidate rust layer thickness and the candidate rust level, as well as the rust layer simulation data, the target rust level of each rusted part of the instrument to be tested is determined; wherein the target rust level is used to determine the operation and maintenance strategy of the instrument to be tested.

[0185] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0186] Obtaining an instrument image for rust layer fitting of the instrument to be tested, rust layer thickness data of each rusted part in the instrument image, and position information of each rusted part;

[0187] The position information of each rusted part and the corresponding rust layer thickness data are correlated and fitted to obtain the rust layer simulation data of the instrument to be tested;

[0188] According to the rust layer simulation data, the rust condition of the instrument to be tested is determined.

[0189] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0190] Establish a coordinate system of the tool image; determine the position information of each rusted part in the tool image according to the coordinate system of the tool image; obtain the rust layer thickness data of each rusted part in the tool image according to the position information of each rusted part in the tool image.

[0191] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0192] The rust thickness data of each rusted part in the tool image is obtained, and the image data of each rusted part is obtained at the same time; a coordinate system of the tool image is established; and the position information of each rusted part is determined according to the coordinate system of the tool image and the image data of each rusted part.

[0193] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0194] For each rusted part, a thickness gauge is used to collect rust layer thickness data at multiple collection points at preset distance intervals; the rust layer thickness data at the multiple collection points are all used as the rust layer thickness data of the rusted part.

[0195] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0196] According to the position information of each rusted part, the first position coordinate and the second position coordinate of each rusted part in the coordinate system of the instrument to be tested are determined; according to the rust layer thickness data of each rusted part, the third position coordinate of each rusted part in the coordinate system of the instrument to be tested is determined; the first position coordinate, the second position coordinate, the third position coordinate and the instrument image of the instrument to be tested are correlated and fitted to obtain the rust layer simulation data of the instrument to be tested.

[0197] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0198] According to the candidate rust layer thickness and the candidate rust level, as well as the rust layer simulation data, the target rust level of each rusted part of the instrument to be tested is determined; wherein the target rust level is used to determine the operation and maintenance strategy of the instrument to be tested.

[0199] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0200] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0201] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining rust of an appliance, characterized in that: The method comprises: acquiring an instrument image of the instrument to be tested; Determining various rusted locations in the tool to be tested according to the tool image; Acquire the rust layer thickness data of each rusted part and the pixel position information in the tool image; Correlation fitting is performed between the pixel position information of each rusted part and the corresponding rust layer thickness data to obtain rust layer simulation data of the device to be tested; The rust condition of the device to be tested is determined according to the rust layer simulation data.

2. The method according to claim 1, characterized in that Obtaining the rust layer thickness data of each rusted part, including: Determining actual position information of each rusted part according to pixel position information of each rusted part in the tool image; According to the actual position information of each rusted part, the rust layer thickness data of each rusted part is obtained.

3. The method according to claim 1, characterized in that Obtaining pixel position information of each rusted part in the tool image includes: While acquiring the rust layer thickness data of each rusted part, acquiring a local image of each rusted part; According to the tool image and the partial images of each of the rusted parts, pixel position information of each of the rusted parts in the tool image is determined.

4. The method according to claim 1, characterized in that: Obtaining the rust layer thickness data of each rusted part, including: For each rusted part, a thickness gauge is used to collect rust layer thickness data at multiple collection points of the rusted part at preset distance intervals; The rust layer thickness data of the multiple collection points are all used as the rust layer thickness data of the rusted part.

5. The method according to claim 1, characterized in that The step of correlating and fitting the pixel position information of each rusted part with the corresponding rust layer thickness data to obtain the rust layer simulation data of the device to be tested includes: Determining first position coordinates and second position coordinates of each rusty part according to pixel position information of each rusty part; Determining the third position coordinates of each rusted part according to the rust layer thickness data of each rusted part; The first position coordinate, the second position coordinate, the third position coordinate and the tool image are correlated and fitted to obtain rust layer simulation data of the tool to be tested.

6. The method according to claim 1, characterized in that Determining the corrosion condition of the device to be tested according to the rust layer simulation data includes: According to the candidate rust layer thicknesses and the candidate rust levels, as well as the rust layer simulation data, a target rust level of each rusted portion of the device to be tested is determined.

7. A device for determining rust of an appliance, characterized in that: The device comprises: A first acquisition module, used to acquire an instrument image of the instrument to be tested; A first determination module is used to determine the rusted parts of the tool to be tested according to the tool image; A second acquisition module is used to acquire the rust layer thickness data of each rusted part and the pixel position information in the tool image; A fitting module, used for associating and fitting the pixel position information of each rusted part with the corresponding rust layer thickness data to obtain the rust layer simulation data of the instrument to be tested; The second determination module is used to determine the corrosion condition of the device to be tested according to the rust layer simulation data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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