Quality evaluation method, system and equipment for special-shaped pipes based on industrial Internet of Things

Abstract

The invention discloses a method, system and equipment for evaluating the quality of a special-shaped pipe based on the industrial Internet of Things, and relates to the technical field of pipe quality monitoring, and comprises the following steps: respectively obtaining end face images of two ends of a corresponding target special-shaped pipe; wherein the cross section of the target special-shaped pipe is elliptical, and the outer wall of the target special-shaped pipe is provided with a strip groove along the axial direction of the target special-shaped pipe, and the outer wall of the target special-shaped pipe is provided with a plurality of blind holes along the axial direction of the target special-shaped pipe; according to two end face images, the bottom surface inclination of the strip groove is obtained; according to any end face image, the radial deformation of the target special-shaped pipe is obtained; the pressure data corresponding to the plurality of blind holes is obtained, and the depth non-compliance rate of the blind holes is obtained according to the plurality of pressure data; according to the bottom surface inclination, the radial deformation and the depth non-compliance rate, the quality evaluation score of the target special-shaped pipe is obtained; according to the quality evaluation score, whether the target special-shaped pipe is qualified is judged. The invention has the advantage of improving the accuracy of special-shaped steel quality detection.

Description

Technical Field

[0001] The present invention relates to the technical field of pipe quality monitoring, and in particular to a method, system and equipment for evaluating the quality of special-shaped pipes based on industrial Internet of Things. Background Art

[0002] Special-shaped pipes are seamless steel pipes made of various special shapes by cold drawing. Special-shaped pipes are generally distinguished by the cross-section. Special-shaped seamless steel pipes are the general term for seamless steel pipes with other cross-sectional shapes except round pipes. According to the different cross-sectional shapes and sizes of steel pipes, they can be divided into equal wall thickness special-shaped seamless steel pipes, unequal wall thickness special-shaped seamless steel pipes, and variable diameter special-shaped seamless steel pipes. Special-shaped seamless steel pipes can be widely used in various structural parts, tools and mechanical parts.

[0003] Due to the special structure of special-shaped tubes, it is necessary to conduct quality inspection on the easily deformed parts of special-shaped tubes during the production process, such as Figure 2 and Figure 3 As shown, there is a special-shaped tube with an overall elliptical cross-section. The outer wall of the special-shaped tube is provided with a plurality of blind holes along its axial direction, and the special-shaped tube is also provided with strip grooves along its axial direction. For the special-shaped tube of this structure, currently most of them are inspected visually or with tools, which not only has low inspection efficiency, but also has large inspection errors, making it difficult to accurately evaluate the quality of the special-shaped tube of this structure. Summary of the invention

[0004] The main purpose of the present invention is to provide a method, system and equipment for evaluating the quality of special-shaped pipes based on the industrial Internet of Things, aiming to solve the technical problem of low accuracy of existing special-shaped steel quality detection methods.

[0005] To achieve the above object, the present invention provides a method for evaluating the quality of special-shaped pipes based on industrial Internet of Things, comprising the following steps:

[0006] The end surface images of the two ends of the corresponding target special-shaped tube are respectively obtained; wherein the cross section of the target special-shaped tube is elliptical, the outer wall of the target special-shaped tube is provided with a strip groove along the axial direction of the target special-shaped tube, and the outer wall of the target special-shaped tube is provided with a plurality of blind holes along the axial direction of the target special-shaped tube;

[0007] According to the two end surface images, the bottom surface inclination of the strip groove is obtained;

[0008] According to any end face image, the radial deformation degree of the target special-shaped tube is obtained;

[0009] Obtain pressure data corresponding to a plurality of blind holes, and obtain a depth failure rate of the blind holes according to the plurality of pressure data;

[0010] The quality evaluation score of the target special-shaped pipe is obtained according to the bottom surface inclination, radial deformation and depth non-compliance rate;

[0011] Based on the quality evaluation score, it is determined whether the target special-shaped tube is qualified.

[0012] Optionally, acquiring the bottom surface inclination of the strip-shaped groove according to the two end surface images includes:

[0013] Respectively identify the target contour features of the strip grooves corresponding to the two end surface images;

[0014] Respectively identify target reference lines in two target contour features; wherein the target reference line is a contour line formed by the bottom surface of the strip groove at the corresponding end of the target special-shaped tube;

[0015] The distances d1 and d2 between the two target reference lines and the target sharp points are respectively obtained; wherein the target sharp points are the common points of the outer wall of the target special-shaped tube and the notch of the strip groove;

[0016] According to the spacing d1 and the spacing d2, the bottom surface inclination θ of the strip groove is obtained, and the expression of θ is:

[0017] θ = arctan (|d1-d2| / H);

[0018] Where H is the axial length of the target special-shaped tube.

[0019] Optionally, obtaining the radial deformation degree of the target special-shaped tube according to any end face image includes:

[0020] According to the end face image, the outer edge contour of the target special-shaped tube is extracted;

[0021] Fit the outer edge contour to the notch of the strip groove to obtain a fitting ellipse;

[0022] The fitted ellipse and the standard ellipse are superimposed based on the same center point to obtain the intersection area S after the fitted ellipse and the standard ellipse are superimposed;

[0023] According to the cross area S, the radial deformation degree Q of the target special-shaped tube is obtained. The expression of Q is:

[0024] Q = ε1*S;

[0025] Where ε1 is the proportionality coefficient.

[0026] Optionally, the obtaining of pressure data corresponding to the plurality of blind holes, and obtaining the depth non-compliance rate of the blind holes according to the plurality of pressure data, includes:

[0027] Acquire corresponding pressure data detected when the pressure sensing device extends into each blind hole;

[0028] Determine whether the multiple pressure data are all within the range of the pressure threshold. If so, the depth non-compliance rate of the blind hole is 0. If not, filter out the pressure data outside the range of the pressure threshold.

[0029] The average value of the filtered pressure data is obtained, and the depth failure rate of the blind hole is obtained based on the average value.

[0030] Optionally, obtaining an average value of the filtered pressure data and obtaining the blind hole depth failure rate according to the average value includes:

[0031] Grouping the filtered pressure data into a first pressure data group and a second pressure data group; wherein the first pressure data group is a set of pressure data less than a pressure threshold, and the second pressure data group is a set of pressure data greater than the pressure threshold;

[0032] Obtain a first average value X1 corresponding to the first pressure data group, and obtain a second average value X2 corresponding to the second pressure data group;

[0033] According to the first average value X1 and the second average value X2, the depth non-compliance rate η of the blind hole is obtained, and the expression of η is:

[0034] η=(X-X1)*ε2+(X2-X)*ε3;

[0035] Wherein, X is the pressure threshold, ε2 is the second proportional coefficient, and ε3 is the third proportional coefficient.

[0036] Optionally, the pressure sensing device includes a base, on which a plurality of detection components are arranged, the detection component includes a movable sleeve connected to the base, a pressure sensor is arranged at the bottom of the movable sleeve, a compression spring is arranged on the pressure sensor, a detection rod is connected to the top of the compression spring, one section of the detection rod is slidably connected to the movable sleeve, and the other section of the detection rod is used to extend into the corresponding blind hole.

[0037] Optionally, assuming the quality evaluation score E of the target special-shaped tube, the expression of E is:

[0038] E=E'-(θ*K1+Q*K2+η*K3);

[0039] Wherein, E' is the total evaluation score, θ is the bottom surface inclination, Q is the radial deformation, η is the depth non-compliance rate, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

[0040] To achieve the above object, the present invention also provides a special-shaped pipe quality evaluation system based on industrial Internet of Things, including a user platform, a service platform, a management platform, a sensor network platform and an object platform which are sequentially connected in communication, and the object platform includes:

[0041] An image acquisition module is used to respectively acquire end surface images of both ends of the corresponding target special-shaped tube; wherein the cross section of the target special-shaped tube is elliptical, the outer wall of the target special-shaped tube is provided with a strip groove along the axial direction of the target special-shaped tube, and the outer wall of the target special-shaped tube is provided with a plurality of blind holes along the axial direction of the target special-shaped tube;

[0042] An inclination detection module is used to obtain the bottom surface inclination of the strip groove according to two end surface images;

[0043] A deformation detection module is used to obtain the radial deformation of the target special-shaped tube according to any end face image;

[0044] A depth detection module is used to obtain pressure data corresponding to a plurality of blind holes, and obtain a depth non-compliance rate of the blind holes according to the plurality of pressure data;

[0045] A quality evaluation module is used to obtain the quality evaluation score of the target special-shaped pipe according to the bottom surface inclination, radial deformation and depth non-compliance rate;

[0046] The judgment module is used to judge whether the target special-shaped tube is qualified according to the quality evaluation score.

[0047] To achieve the above object, the present invention further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.

[0048] To achieve the above object, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above method.

[0049] The beneficial effects that can be achieved by the present invention are as follows:

[0050] Since the detection object of the present invention is a target special-shaped tube with a strip groove, a blind hole and an elliptical cross-section, it is necessary to comprehensively consider the common influence of the three parameters on the quality of the target special-shaped tube. Therefore, the present invention obtains the end face images of the two ends of the corresponding target special-shaped tube, and the contour features of the strip groove can be compared and analyzed through the two end face images, so as to calculate the bottom surface inclination of the strip groove. At the same time, according to any end face image, the radial deformation of the target special-shaped tube can be evaluated and calculated. When calculating the depth of the blind hole, the pressure data corresponding to multiple blind holes can be obtained. The pressure data generated by blind holes with different depths are also different, so it can be The blind hole depth is characterized according to a plurality of pressure data, so as to evaluate and calculate the depth non-compliance rate of the blind hole. Finally, the quality evaluation score of the target special-shaped tube is jointly evaluated and calculated according to the bottom surface inclination, radial deformation and depth non-compliance rate. Whether the target special-shaped tube is qualified can be judged according to the quality evaluation score. Therefore, the present invention quantitatively evaluates the bottom surface inclination of the strip groove, the depth non-compliance rate of the blind hole and the radial deformation of the elliptical target special-shaped tube respectively, and comprehensively obtains the overall quality evaluation score of the target special-shaped tube, which is more reference and guiding, thereby improving the accuracy of the quality inspection and evaluation of special-shaped steel. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0052] Figure 1 It is a flow chart of a method for evaluating the quality of special-shaped pipes based on the industrial Internet of Things in an embodiment of the present invention;

[0053] Figure 2 It is a schematic diagram of the radial cross-sectional structure of the target special-shaped tube in the embodiment of the present invention;

[0054] Figure 3 for Figure 2 A schematic diagram of the structure in top view;

[0055] Figure 4 is a schematic diagram of one of the end surface images in an embodiment of the present invention;

[0056] Figure 5 is a schematic diagram of another end surface image in an embodiment of the present invention;

[0057] Figure 6 It is a schematic diagram of the axial cross-sectional structure of the target special-shaped tube in the embodiment of the present invention;

[0058] Figure 7Schematic diagram of the principle of calculating the intersection area S of the fitted ellipse and the standard ellipse in an embodiment of the present invention;

[0059] Figure 8 is a schematic structural diagram of a pressure sensing device in an embodiment of the present invention;

[0060] Fig. 9 Schematic diagram of the framework of the Internet of Things system involved in the embodiments of the present invention.

[0061] Reference numerals:

[0062] 110 - blind hole, 120 - strip groove, 130 - target reference line, 140 - target sharp point, 150 - outer edge contour, 160 - fitting ellipse, 170 - standard ellipse, 180 - pressure sensing device, 181 - base, 182 - movable sleeve, 183 - pressure sensor, 184 - compression spring, 185 - detection rod.

[0063] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] If there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0066] Example 1

[0067] Reference Figure 1-Figure 8 This embodiment provides a method for evaluating the quality of special-shaped pipes based on the industrial Internet of Things, comprising the following steps:

[0068] The end surface images of the two ends of the corresponding target special-shaped tube are respectively obtained; wherein the cross section of the target special-shaped tube is elliptical, the outer wall of the target special-shaped tube is provided with a strip groove 120 along the axial direction of the target special-shaped tube, and the outer wall of the target special-shaped tube is provided with a plurality of blind holes 110 along the axial direction of the target special-shaped tube;

[0069] According to the two end surface images, the bottom surface inclination of the strip groove 120 is obtained;

[0070] According to any end face image, the radial deformation degree of the target special-shaped tube is obtained;

[0071] Obtain pressure data corresponding to the plurality of blind holes 110, and obtain a depth failure rate of the blind holes 110 according to the plurality of pressure data;

[0072] The quality evaluation score of the target special-shaped pipe is obtained according to the bottom surface inclination, radial deformation and depth non-compliance rate;

[0073] Based on the quality evaluation score, it is determined whether the target special-shaped tube is qualified.

[0074] In the present embodiment, since the detection object of the present embodiment is a target special-shaped tube having a strip groove 120, a blind hole 110 and an elliptical cross-section, the strip groove 120 is prone to deformation resulting in a bottom surface tilt defect, and multiple blind holes 110 are prone to have a defect of non-uniform depth after processing, and the elliptical target special-shaped tube is prone to overall radial deformation. Therefore, it is necessary to comprehensively consider the joint influence of the three parameters on the quality of the target special-shaped tube. Therefore, the present embodiment obtains end face images at both ends of the corresponding target special-shaped tube, and the contour features of the strip groove 120 can be compared and analyzed through the two end face images, so as to calculate the bottom surface inclination of the strip groove 120. At the same time, according to any end face image, the radial deformation of the target special-shaped tube can be evaluated and calculated, and when calculating the depth of the blind hole 110, it can be calculated by By acquiring pressure data corresponding to a plurality of blind holes 110, the pressure data generated by blind holes 110 of different depths are also different. Therefore, the depth of the blind hole 110 can be characterized according to the plurality of pressure data, so as to evaluate and calculate the depth non-compliance rate of the blind hole 110. Finally, according to the bottom surface inclination, radial deformation and depth non-compliance rate, the quality evaluation score of the target special-shaped tube is jointly evaluated and calculated, and whether the target special-shaped tube is qualified can be judged according to the quality evaluation score. Therefore, in this embodiment, the bottom surface inclination of the strip groove 120, the depth non-compliance rate of the blind hole 110 and the radial deformation of the elliptical target special-shaped tube are quantitatively assessed respectively, and the overall quality evaluation score of the target special-shaped tube is comprehensively obtained, which is more reference and guiding, thereby improving the accuracy of the quality inspection and evaluation of the special-shaped steel.

[0075] It should be noted that a qualified threshold can be set here, and the final quality evaluation score is compared with the qualified threshold. If the quality evaluation score is greater than the qualified threshold, the current target special-shaped tube is marked as a qualified product. Otherwise, the current target special-shaped tube is marked as a defective product. At the same time, the bottom surface inclination, radial deformation and depth non-compliance rate parameters can be output respectively, so that the staff can carry out targeted rework. At the same time, for each parameter, if the bottom surface inclination, radial deformation and depth non-compliance rate exceed their respective maximum thresholds, that is, the relevant defects are difficult to be reworked, the target special-shaped tube can be directly marked as scrapped.

[0076] As an optional implementation, the step of obtaining the bottom surface inclination of the strip groove 120 according to the two end surface images includes:

[0077] Respectively identifying target contour features of the strip-shaped groove 120 corresponding to the two end surface images;

[0078] Respectively identify the target reference lines 130 in the two target contour features; wherein the target reference line 130 is a contour line formed by the bottom surface of the strip groove 120 at the corresponding end of the target special-shaped tube;

[0079] The distances d1 and d2 between the two target reference lines 130 and the target sharp point 140 are respectively obtained; wherein the target sharp point 140 is the common point between the outer wall of the target special-shaped tube and the notch of the strip groove 120;

[0080] According to the spacing d1 and the spacing d2, the bottom surface inclination θ of the strip groove 120 is obtained, and the expression of θ is:

[0081] θ = arctan (|d1-d2| / H);

[0082] Where H is the axial length of the target special-shaped tube.

[0083] In this embodiment, since the end face profiles of the two end faces of the target special-shaped tube are different when the bottom face of the strip groove 120 is tilted, the target contour features of the strip groove 120 corresponding to the two end face images are respectively identified, and then the target reference line 130 in the two target contour features is identified. The target reference line is the contour line formed by the bottom face of the strip groove 120 at the corresponding end of the target special-shaped tube. At the same time, the common point of the outer wall of the target special-shaped tube and the notch of the strip groove 120 is used as a reference point (i.e., the target sharp point 140), and the target reference line 130 can be calculated respectively. The spacing d1 and spacing d2 between the target sharp point 140, and based on the above expression of θ, when d1=d2, it means that the bottom surface of the strip groove 120 is not inclined, that is, θ=0, and when d1≠d2, it means that the bottom surface of the strip groove 120 is inclined, resulting in the inconsistency of the relative spacing between the identified target reference line 130 and the target sharp point 140. At this time, the ratio of the difference between the two and the axial length H of the target special-shaped tube can be used in combination with the inverse trigonometric function to accurately calculate the bottom surface inclination θ. This embodiment combines machine vision recognition technology to accurately and efficiently identify the bottom surface inclination θ.

[0084] As an optional implementation, obtaining the radial deformation degree of the target special-shaped tube according to any end face image includes:

[0085] Extracting the outer edge contour of the target special-shaped tube 150 according to the end face image;

[0086] Fitting the outer edge contour 150 to the notch of the strip-shaped groove 120 to obtain a fitting ellipse 160;

[0087] The fitting ellipse 160 and the standard ellipse 170 are superimposed based on the same center point to obtain the intersection area S after the fitting ellipse 160 and the standard ellipse 170 are superimposed;

[0088] According to the cross area S, the radial deformation degree Q of the target special-shaped tube is obtained. The expression of Q is:

[0089] Q = ε1*S;

[0090] Where ε1 is the proportionality coefficient.

[0091] In this embodiment, the outer edge contour 150 of the target special-shaped tube can be extracted according to the end face image. When the target special-shaped tube is deformed, the corresponding outer edge contour 150 is deformed randomly. Therefore, the outer edge contour 150 is first fitted to the gap of the strip groove 120 to obtain a fitting ellipse 160, and then the fitting ellipse 160 and the standard ellipse 170 are superimposed based on the same center point. The cross area S (i.e., the cross area S) after the fitting ellipse 160 and the standard ellipse 170 are superimposed is calculated. Figure 7The area of ​​the shaded part in the middle), which can characterize the deformation degree of the outer edge contour 150 of the target special-shaped tube, based on the above expression of Q, ε1 can represent the correlation degree between the size of the cross area S and the radial deformation degree Q. When the fitting ellipse 160 and the standard ellipse 170 can completely overlap, that is, the cross area S=0, Q=0, that is, there is no radial deformation. When they cannot completely overlap, the cross area S>0 will be calculated. Therefore, the larger the cross area S, the larger Q, which represents the greater the radial deformation degree. Therefore, this embodiment can indirectly characterize the size of the radial deformation degree Q based on the cross area S, which reduces the measurement difficulty, and the measurement result is accurate and reliable.

[0092] As an optional implementation manner, the obtaining of pressure data corresponding to the plurality of blind holes 110 and obtaining the depth non-compliance rate of the blind holes 110 according to the plurality of pressure data includes:

[0093] Acquire corresponding pressure data detected when the pressure sensing device 180 extends into each blind hole 110;

[0094] Determine whether the multiple pressure data are all within the range of the pressure threshold. If so, the depth non-compliance rate of the blind hole 110 is 0. If not, filter out the pressure data outside the range of the pressure threshold.

[0095] The average value of the filtered pressure data is obtained, and the depth failure rate of the blind hole 110 is obtained according to the average value.

[0096] In this embodiment, the pressure sensing device 180 can measure the corresponding pressure data when extending into each blind hole 110. Since the pressure data measured varies with the depth of the blind hole 110, a pressure threshold range is set here, which can characterize the pressure range corresponding to the depth of the blind hole 110 within the error range. If all the measured pressure data are within the range of the pressure threshold, it means that the depth of the blind hole 110 is within the qualified range, that is, the depth non-compliance rate is 0. If there is pressure data that is not within the range of the pressure threshold, such pressure data will be screened out and the average value will be calculated. The depth non-compliance rate of the blind hole 110 can be evaluated and calculated based on the average value.

[0097] It should be noted that this embodiment uses pressure data to indirectly characterize the depth of the blind hole 110. Compared with direct manual depth measurement, the detection is more convenient and quick, and the detection efficiency is improved. Compared with using depth images to detect the depth of the blind hole 110, the detection difficulty is lower, and the detection results are intuitive and reliable, with higher accuracy.

[0098] As an optional implementation manner, the step of obtaining an average value of the filtered pressure data and obtaining the non-standard depth rate of the blind hole 110 according to the average value includes:

[0099] Grouping the filtered pressure data into a first pressure data group and a second pressure data group; wherein the first pressure data group is a set of pressure data less than a pressure threshold, and the second pressure data group is a set of pressure data greater than the pressure threshold;

[0100] Obtain a first average value X1 corresponding to the first pressure data group, and obtain a second average value X2 corresponding to the second pressure data group;

[0101] According to the first average value X1 and the second average value X2, the depth non-standard rate η of the blind hole 110 is obtained, and the expression of η is:

[0102] η=(X-X1)*ε2+(X2-X)*ε3;

[0103] Wherein, X is the pressure threshold, ε2 is the second proportional coefficient, and ε3 is the third proportional coefficient.

[0104] In this embodiment, since the screened pressure data may include data greater than or less than the pressure threshold, directly calculating the average value may easily lead to data distortion. Therefore, the pressure data is grouped here, and the pressure data less than the pressure threshold is grouped into a first pressure data group, and the pressure data greater than the pressure threshold is grouped into a second pressure data group. The average values ​​of the two data groups are calculated respectively, namely the first average value X1 and the second average value X2. At the same time, based on the above formula, (X-X1) and (X2-X) can respectively characterize the degree of difference between the first pressure data group and the second pressure data group and the pressure threshold. The higher the degree of difference, the greater the difference between the depth of the blind hole 110 and the qualified depth. At the same time, based on the second proportional coefficient ε2 and the third proportional coefficient ε3, the correlation degree between the pressure difference data and the depth non-compliance rate η can be respectively characterized, so that the depth non-compliance rate η can be accurately calculated.

[0105] It should be noted that since the depth of the blind hole 110 should be inversely proportional to the pressure data, that is, the deeper the depth of the blind hole 110, the smaller the corresponding pressure data. If the depth of the blind hole 110 exceeds its qualified range, it is a defect that is difficult to rework, and a first pressure data group will be generated. If the depth of the blind hole 110 is less than its qualified range, the hole processing depth can be further increased to reach the qualified range. It is a defect that is easy to rework, and a second pressure data group will be generated. Therefore, the first pressure data group has a greater impact on the depth non-compliance rate η than the second pressure data group. Therefore, ε2>ε3 can be set here, and the specific value can be set according to the respective degree of influence, thereby characterizing the different degrees of influence of the first pressure data group and the second pressure data group on the depth non-compliance rate η.

[0106] As an optional embodiment, the pressure sensing device 180 includes a base 181, on which a plurality of detection components are arranged, the detection components including a movable sleeve 182 connected to the base 181, a pressure sensor 183 is arranged at the bottom of the movable sleeve 182, a compression spring 184 is arranged on the pressure sensor 183, a detection rod 185 is connected to the top of the compression spring 184, one section of the detection rod 185 is slidably connected to the movable sleeve 182, and the other section of the detection rod 185 is used to extend into the corresponding blind hole 110.

[0107] In this embodiment, during measurement, multiple detection rods 185 are simultaneously extended into the corresponding blind holes 110. When the detection rod 185 hits the bottom of the blind hole 110, the detection rod 185 generates pressure on the compression spring 184 to compress it, thereby causing the pressure sensor 183 to generate corresponding pressure data. When all pressure sensors 183 generate pressure data, it means that all detection rods 185 hit the bottom of the blind hole 110, and the corresponding pressure data of all blind holes 110 can be measured at one time, thereby improving the detection efficiency. At the same time, based on the same detection conditions, the accuracy of the detection data is guaranteed, and data errors that may be caused by separate measurements are avoided. Therefore, based on the pressure sensing device 180, the higher the depth of the blind hole 110, the smaller the measured pressure data should be, and the lower the depth of the blind hole 110, the larger the measured pressure data should be.

[0108] As an optional implementation, assume that the quality evaluation score E of the target special-shaped tube is expressed as:

[0109] E=E'-(θ*K1+Q*K2+η*K3);

[0110] Wherein, E' is the total evaluation score, θ is the bottom surface inclination, Q is the radial deformation, η is the depth non-compliance rate, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

[0111] In this embodiment, based on the above formula, a total evaluation score E' is set. The larger the bottom surface inclination θ, radial deformation Q or depth non-compliance rate η, the smaller the quality evaluation score E will be. At the same time, since the bottom surface inclination θ, radial deformation Q and depth non-compliance rate η are parameters of different units, they are converted by the first adjustment coefficient K1, the second adjustment coefficient K2 and the third adjustment coefficient K3 respectively, so that each parameter can be superimposed equally. At the same time, each adjustment coefficient can be assigned a weight value to characterize the degree of influence of different parameters on the quality evaluation score E.

[0112] Example 2

[0113] Reference Figure 1-Figure 9Based on the same inventive idea as the above embodiment, this embodiment further provides a special-shaped pipe quality evaluation system based on industrial Internet of Things, including a user platform, a service platform, a management platform, a sensor network platform and an object platform which are sequentially connected in communication, and the object platform includes:

[0114] An image acquisition module is used to respectively acquire end surface images of both ends of the corresponding target special-shaped tube; wherein the cross section of the target special-shaped tube is elliptical, the outer wall of the target special-shaped tube is provided with a strip groove 120 along the axial direction of the target special-shaped tube, and the outer wall of the target special-shaped tube is provided with a plurality of blind holes 110 along the axial direction of the target special-shaped tube;

[0115] An inclination detection module, used to obtain the bottom surface inclination of the strip groove 120 according to the two end surface images;

[0116] A deformation detection module is used to obtain the radial deformation of the target special-shaped tube according to any end face image;

[0117] A depth detection module, used to obtain pressure data corresponding to the plurality of blind holes 110, and obtain a depth non-compliance rate of the blind holes 110 according to the plurality of pressure data;

[0118] A quality evaluation module is used to obtain the quality evaluation score of the target special-shaped pipe according to the bottom surface inclination, radial deformation and depth non-compliance rate;

[0119] The judgment module is used to judge whether the target special-shaped tube is qualified according to the quality evaluation score.

[0120] It should be noted that the special-shaped pipe quality evaluation system based on the industrial Internet of Things in this embodiment is a standard five-platform structure of the Internet of Things, such as Fig. 9 As shown. Among them, the physical entity of the user platform includes various user terminals, such as mobile phones, computers, special terminals, etc., which realize user-side services by combining with user information system software. The service platform is a functional platform for realizing service communication. The management platform is a functional platform for realizing the operation and management of the Internet of Things system. The sensor network platform is a functional platform for realizing sensor communication. The object platform is a functional platform for realizing perception control. In some embodiments, the object platform may include multiple object sub-platforms, which are respectively connected to the above-mentioned sensor network platforms, and each object sub-platform respectively includes an image acquisition module, a tilt detection module, a deformation detection module, a depth detection module, a quality evaluation module and a judgment module.

[0121] The relevant explanations and examples of each module in the device of this embodiment can refer to the method of the aforementioned embodiment, and will not be repeated here.

[0122] Example 3

[0123] Based on the same inventive concept as the above-mentioned embodiment, this embodiment provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned method.

[0124] Example 4

[0125] Based on the same inventive concept as the aforementioned embodiment, this embodiment provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the aforementioned method.

[0126] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. The quality evaluation method of special-shaped pipes based on industrial Internet of Things is characterized by: The following steps are involved: The end surface images of the two ends of the corresponding target special-shaped tube are respectively obtained; wherein the cross section of the target special-shaped tube is elliptical, the outer wall of the target special-shaped tube is provided with a strip groove along the axial direction of the target special-shaped tube, and the outer wall of the target special-shaped tube is provided with a plurality of blind holes along the axial direction of the target special-shaped tube; According to two end surface images, the bottom surface inclination of the strip groove is obtained; including: respectively identifying the target contour features of the strip groove corresponding to the two end surface images; respectively identifying the target reference lines in the two target contour features; wherein the target reference line is the contour line formed by the bottom surface of the strip groove at the corresponding end of the target special-shaped tube; respectively obtaining the spacing d1 and the spacing d2 between the two target reference lines and the target cusp; wherein the target cusp is the common point of the outer wall of the target special-shaped tube and the notch of the strip groove; according to the spacing d1 and the spacing d2, the bottom surface inclination θ of the strip groove is obtained, and the expression of θ is: θ = arctan (|d1-d2| / H); Where H is the axial length of the target special-shaped tube; According to any end face image, the radial deformation degree of the target special-shaped tube is obtained; Obtain pressure data corresponding to a plurality of blind holes, and obtain a depth failure rate of the blind holes according to the plurality of pressure data; The quality evaluation score of the target special-shaped pipe is obtained according to the bottom surface inclination, radial deformation and depth non-compliance rate; Based on the quality evaluation score, it is determined whether the target special-shaped tube is qualified.

2. The method for evaluating the quality of special-shaped pipes based on the industrial Internet of Things according to claim 1, characterized in that: The step of obtaining the radial deformation degree of the target special-shaped tube according to any end face image comprises: According to the end face image, the outer edge contour of the target special-shaped tube is extracted; Fit the outer edge contour to the notch of the strip groove to obtain a fitting ellipse; The fitted ellipse and the standard ellipse are superimposed based on the same center point to obtain the intersection area S after the fitted ellipse and the standard ellipse are superimposed; According to the cross area S, the radial deformation degree Q of the target special-shaped tube is obtained. The expression of Q is: Q = ε1*S; Where ε1 is the proportionality coefficient.

3. The method for evaluating the quality of special-shaped pipes based on the industrial Internet of Things according to claim 1, characterized in that: The step of obtaining pressure data corresponding to the plurality of blind holes and obtaining the depth non-compliance rate of the blind holes according to the plurality of pressure data includes: Acquire corresponding pressure data detected when the pressure sensing device extends into each blind hole; Determine whether the multiple pressure data are all within the range of the pressure threshold. If so, the depth non-compliance rate of the blind hole is 0. If not, filter out the pressure data outside the range of the pressure threshold. The average value of the filtered pressure data is obtained, and the depth failure rate of the blind hole is obtained based on the average value.

4. The method for evaluating the quality of special-shaped pipes based on the industrial Internet of Things according to claim 3 is characterized in that: The step of obtaining an average value of the filtered pressure data and obtaining the non-standard depth rate of the blind hole according to the average value includes: Grouping the filtered pressure data into a first pressure data group and a second pressure data group; wherein the first pressure data group is a set of pressure data less than a pressure threshold, and the second pressure data group is a set of pressure data greater than the pressure threshold; Obtain a first average value X1 corresponding to the first pressure data group, and obtain a second average value X2 corresponding to the second pressure data group; According to the first average value X1 and the second average value X2, the depth non-compliance rate η of the blind hole is obtained, and the expression of η is: η=(X-X1)*ε2+(X2-X)*ε3; Wherein, X is the pressure threshold, ε2 is the second proportional coefficient, and ε3 is the third proportional coefficient.

5. The method for evaluating the quality of special-shaped pipes based on the industrial Internet of Things according to claim 3 is characterized in that: The pressure sensing device includes a base, on which a plurality of detection components are arranged, the detection components include a movable sleeve connected to the base, a pressure sensor is arranged at the bottom of the movable sleeve, a compression spring is arranged on the pressure sensor, a detection rod is connected to the top of the compression spring, one section of the detection rod is slidably connected to the movable sleeve, and the other section of the detection rod is used to extend into the corresponding blind hole.

6. The method for evaluating the quality of special-shaped pipes based on the industrial Internet of Things according to any one of claims 1 to 5, characterized in that: Assume the quality evaluation score E of the target special-shaped tube, and the expression of E is: E=E'-(θ*K1+Q*K2+η*K3); Wherein, E' is the total evaluation score, θ is the bottom surface inclination, Q is the radial deformation, η is the depth non-compliance rate, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

7. The quality evaluation system of special-shaped pipes based on industrial Internet of Things is characterized by: It includes a user platform, a service platform, a management platform, a sensor network platform and an object platform which are sequentially connected in communication, and the object platform includes: An image acquisition module is used to respectively acquire end surface images of both ends of the corresponding target special-shaped tube; wherein the cross section of the target special-shaped tube is elliptical, the outer wall of the target special-shaped tube is provided with a strip groove along the axial direction of the target special-shaped tube, and the outer wall of the target special-shaped tube is provided with a plurality of blind holes along the axial direction of the target special-shaped tube; The inclination detection module is used to obtain the bottom surface inclination of the strip groove according to two end surface images; including: respectively identifying the target contour features of the strip groove corresponding to the two end surface images; respectively identifying the target reference lines in the two target contour features; wherein the target reference line is the contour line formed by the bottom surface of the strip groove at the corresponding end of the target special-shaped tube; respectively obtaining the spacing d1 and spacing d2 between the two target reference lines and the target cusp; wherein the target cusp is the common point of the outer wall of the target special-shaped tube and the notch of the strip groove; according to the spacing d1 and the spacing d2, obtaining the bottom surface inclination θ of the strip groove, and the expression of θ is: θ = arctan (|d1-d2| / H); Where H is the axial length of the target special-shaped tube; A deformation detection module is used to obtain the radial deformation of the target special-shaped tube according to any end face image; A depth detection module is used to obtain pressure data corresponding to a plurality of blind holes, and obtain a depth non-compliance rate of the blind holes according to the plurality of pressure data; A quality evaluation module is used to obtain the quality evaluation score of the target special-shaped pipe according to the bottom surface inclination, radial deformation and depth non-compliance rate; The judgment module is used to judge whether the target special-shaped tube is qualified according to the quality evaluation score.

8. A computer device, characterized in that: The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the special-shaped pipe quality evaluation method based on the industrial Internet of Things as described in any one of claims 1 to 6.

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