A detection system for automobile wheel hub

Abstract

The present invention discloses a detection system for an automobile wheel hub, which relates to the field of wheel hub detection technology and solves the problem that the detection rate cannot be guaranteed and the detection accuracy cannot be guaranteed, and a better detection efficiency cannot be achieved. The present invention determines the change of the corresponding curve of an abnormal automobile wheel hub after the abnormal edge area of ​​the automobile wheel hub is determined, and locks the corresponding regular segment therefrom. Since the automobile wheel hub may have related jumps due to uneven dynamic balance, the curve will be non-standard, thereby causing an assessment error. According to the processing method, such errors can be avoided to determine the most accurate comparison result, thereby achieving a better assessment efficiency, achieving a better automobile wheel hub detection effect, and ensuring the detection-related efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of wheel hub detection, in particular to a detection system for an automobile wheel hub. Background Art

[0002] The wheel hub is the part in the center of the wheel where the axle is installed, which is often called the "wheel rim" or "steel rim"; the wheel hub is easily stained with dirt. If it is not cleaned for a long time, it may be corroded and deformed, causing safety hazards.

[0003] The application with publication number CN113740084B relates to the field of automobile inspection, and in particular to a method for inspecting automobile wheels. It is used in conjunction with an automobile wheel inspection device, which includes a first support plate set up on the ground, a support device is provided on the first support plate, a fixed rotating device is provided at the center of the upper end of the first support plate, and a detection device is provided radially on the upper end of the first support plate. The present invention has the function of marking the position that deviates from the normal value when inspecting the automobile wheel, and the color depth of the mark represents the size of the deviation, which is convenient for subsequent processing, and the automobile wheel with a smaller deviation is sent back for reprocessing, and the automobile wheel with a larger deviation is scrapped, thereby improving the product qualification rate and reducing safety hazards.

[0004] During the inspection of automobile wheels, a three-dimensional model of the corresponding automobile wheel is generally constructed based on the scanning results, and then based on the relevant parameters of the corresponding wheel, it is identified whether the automobile wheel has any related abnormalities and an abnormality judgment is performed. However, this detection method has a relatively cumbersome detection process during the detection process, and its detection rate and detection accuracy cannot be guaranteed, and better detection efficiency cannot be achieved. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a detection system for an automobile wheel hub, which solves the problem that the detection rate cannot be guaranteed and the detection accuracy cannot be guaranteed, and a better detection efficiency cannot be achieved.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A detection system for automobile wheel hubs, comprising:

[0007] The scanning end performs relevant scanning on the automobile wheel hub and generates a relevant model of the automobile wheel hub based on the real-time scanning data;

[0008] At the initial detection end, the generated correlation model is initially detected to identify whether there is an abnormal edge area in the inner circle of the outer edge of the correlation model, and the abnormal edge area is marked. The specific method is as follows:

[0009] Determine the side display diagram of the relevant model, determine the center point of the relevant model, and construct two sets of relevant lines passing through the center point, and the two sets of relevant lines are perpendicular to each other;

[0010] Identify two groups of circles tangent to the corresponding hub support from the relevant model, lock the outer circle, then determine the intersection of the relevant line and the outer circle, connect the adjacent intersections, and determine four groups of relevant calibration lines;

[0011] Make the two groups of related lines rotate in a clockwise direction, and the rotation rate V is a set value. During the rotation process, the length of the corresponding related calibration line is recorded in real time, and the length of the four groups of related calibration lines is compared. When the length of a group of related calibration lines is inconsistent with the length of other related calibration lines, the related calibration lines with inconsistent lengths are marked as abnormal lines. When the abnormal lines return to normal length, they are re-marked as related calibration lines, and the walking areas of the endpoints of the abnormal lines are recorded. The walking area of ​​the front end endpoint of the abnormal line is marked as the abnormal edge area. The so-called front end endpoint is based on the fact that when rotating in the clockwise direction, the endpoint that rotates in front belongs to the front end endpoint;

[0012] The proportion analysis end determines the relevant proportion of the abnormal edge area based on the abnormal edge area marked within the inner circle of the outer edge of the relevant model and the involved length of the abnormal edge area, and identifies whether the relevant model is abnormal based on the determined relevant proportion value. The specific method is as follows:

[0013] Determine the relevant perimeter of the inner circle of the outer edge of the relevant model, and mark the determined relevant perimeter as ZC;

[0014] Then identify the radiation length of the abnormal edge area, which can be determined by the rotation speed V during the rotation test and the duration t1 of the relevant calibration line being calibrated as the abnormal line. The radiation length SC of the abnormal edge area is determined by using SC=V×t1. If there are multiple abnormal edge areas, their radiation lengths SC are confirmed one by one and the total radiation length CC is confirmed by summing them up.

[0015] Use ZB=CC÷SC to confirm the relevant proportion ZB of the abnormal edge area, and identify whether the relevant proportion ZB satisfies: ZB>Y1, where Y1 is a preset value. If it satisfies, it means that the proportion of the abnormal edge area is too large, and then an abnormal signal is directly generated and displayed through the signal end. If it does not satisfy, that is, when ZB≤Y1, the rotation test analysis center is executed to perform subsequent related tests;

[0016] The rotation test analysis center, based on the dynamic test process of the corresponding automobile wheel hub, first dynamically follows the edge point of the automobile wheel hub through the dynamic following module and records its dynamic following curve, and then performs band analysis on the dynamic following curve through the band analysis module to determine the same frequency regular segment, and then identifies the rotation height difference of the automobile wheel hub based on the difference between the highest point and the lowest point in the same frequency regular segment, so as to finally evaluate whether the automobile wheel hub affects normal use;

[0017] Preferably, the specific method of the dynamic following module is:

[0018] Based on the relevant model, a set of characteristic points are locked in the inner circle of the outer edge of the automobile wheel hub, and the characteristic points are in a stationary state. When the automobile wheel hub is rotating, the characteristic points dynamically follow the circumference of the outer circle, and based on the relevant changes of the circumference, the characteristic points jump up and down to construct a set of virtual base surfaces. This virtual base surface moves backward at a rate Vs, where Vs is a preset value. The characteristic points followed in real time are mapped to the virtual base surface according to the process of jumping up and down. As the virtual base surface moves backward, a dynamic following curve of this characteristic point is generated;

[0019] A set of following periods T is determined, wherein T is a preset value, a dynamic following curve of a characteristic point within the following period T is confirmed, and the confirmed dynamic following curve is transmitted to the band analysis module.

[0020] Preferably, the band analysis module identifies the relevant duration of the automobile wheel hub to complete one rotation based on the rotation rate of the automobile wheel hub during the dynamic test, and then performs relevant segmentation on the dynamic following curve based on the relevant duration, and determines a group of control segments, and determines the same frequency regular segment by performing a relevant comparison process; the specific method is:

[0021] The rotation rate during the dynamic test is calibrated as V1, the circumference of the outer ring of the automobile wheel hub is calibrated as ZC, and the relevant time St is determined using ZC÷V1=St;

[0022] Based on the moving speed V2 of the virtual base surface, V2×St=SS is used to determine the lateral distance SS moved by the feature point, and then a set of distance intervals is determined based on SS: [SS-X1, SS+X1], where X1 is a preset value;

[0023] The part of the curve whose lateral distance in the dynamic following curve is before (SS-X1) is calibrated as the related curve, so that the related curve adds a curve of unit length in the current distance interval as an additional segment, determines a set of comparison curves, aligns the starting point of the comparison curve with the end point of the additional segment to compare and analyze with the subsequent curves, and confirms the overlap. After the first set of overlap is confirmed, two curves of unit length are added from the current distance interval as additional segments, determines a set of comparison curves, aligns the starting point of the comparison curve with the end point of the additional segment to compare and analyze with the subsequent curves, and confirms the overlap, and so on, until the end point of the additional segment is (SS+X1), stops, and selects the comparison curve corresponding to the set of overlaps with the largest value from the confirmed several sets of overlaps as the same frequency regular segment;

[0024] Determine the horizontal distance L between the highest point and the lowest point of the characteristic point in the same frequency regular segment, and identify whether the horizontal distance L satisfies: L>Y2, where Y2 is a preset value. If so, an abnormal signal is generated and displayed through the signal end; if not, a normal use signal is generated and displayed through the signal end.

[0025] The present invention provides a detection system for automobile wheel hubs. Compared with the prior art, the system has the following beneficial effects:

[0026] The present invention sets a vertical correlation line and rotates the correlation line to determine the length change of the calibration line. Compared with the original method of confirming the value, the original method is more complicated and the progress is slower. By adopting this method, the area corresponding to the edge abnormality can be quickly locked based on a set of rotation processes, so that the corresponding preliminary detection process can be quickly completed, and the detection and recognition efficiency is guaranteed.

[0027] For abnormal automobile wheels, after the abnormal edge area of ​​the automobile wheel is determined, dynamic following and curve analysis are used to determine the change of the corresponding curve, thereby locking the corresponding regular segment. Since the automobile wheel may have related jumps due to uneven dynamic balance, the curve will be non-standard, resulting in assessment errors. According to this processing method, such errors can be avoided to determine the most accurate comparison results, thereby achieving better assessment efficiency, achieving better automobile wheel detection effects, and ensuring detection-related efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the principle framework of the present invention;

[0029] Figure 2 A schematic diagram of determining the relevant calibration lines of the automobile wheel hub of the present invention;

[0030] Figure 3It is a schematic diagram showing the dynamic following curve of the present invention. DETAILED DESCRIPTION

[0031] 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.

[0032] Embodiment 1

[0033] See also Figure 1 The present application provides a detection system for a vehicle wheel hub, comprising a scanning end, an initial detection end, a ratio analysis end, a rotation test analysis center and a signal end, wherein the scanning end, the initial detection end and the ratio analysis end are all electrically connected from an output node to an input node, and the ratio analysis end is electrically connected to the rotation test analysis center or the signal end input node, and the rotation test analysis center comprises a dynamic following module and a band analysis module, wherein the dynamic following module is electrically connected to the band analysis module input node;

[0034] Among them, the scanning end performs relevant scanning on the automobile wheel hub, and generates the relevant model of the automobile wheel hub based on the real-time scanning data. The scanning device is a special device, which is a corresponding three-dimensional optical scanner. The specific scanning process is performed by the operator. Scanning the relevant device and generating its three-dimensional model is relatively common in the prior art, so it will not be described in detail here.

[0035] Among them, the initial detection end performs preliminary detection on the generated related model, identifies whether there is an abnormal edge area in the inner circle of the outer edge of the related model, marks the existing abnormal edge area, and transmits the marked related model to the proportion analysis end. Specifically, the car wheel hub is detected here to identify whether the car wheel hub is deformed. If the car contour has relevant deformation, it means that the car wheel hub is not suitable for use, and relevant repair measures or relevant deformation measures need to be taken;

[0036] Among them, the specific method of identifying whether there is an abnormal edge area in its related model is:

[0037] Combination Figure 2 , determine the side display diagram of its related model, and determine the center point of its related model (that is, the center of the inner circle), and construct two sets of related lines passing through the center point, and the two sets of related lines are perpendicular to each other;

[0038] From the relevant model, confirm two groups of circles that are tangent to the corresponding hub support parts (the hub support parts can be directly determined from the relevant model, and their positions are as follows Figure 2 The calibrated position) is locked, and its outer circle is locked, and then the intersection of its related line and the outer circle is determined, and the adjacent intersections are connected to determine four groups of related calibration lines (because there are four groups of intersections, and the distribution of the intersections is a circle, the four groups of intersections can be connected in sequence to determine the four groups of related calibration lines);

[0039] Make the two groups of related lines rotate in the clockwise direction (when the related lines rotate, the hub related model does not rotate, and the related lines rotate clockwise based on the center of the circle, rotate on the surface of the related model, and intersect with the inner circle of the related model), and the rotation rate V is the set value, and its specific value is determined by the operator based on experience. During the rotation process, the length of the corresponding related calibration line is recorded in real time, and the lengths of the four groups of related calibration lines are compared. When the length of a group of related calibration lines is inconsistent with the length of other related calibration lines, the related calibration lines with inconsistent lengths are marked as abnormal lines. When the abnormal lines return to normal length, they are re-marked as related calibration lines, and the walking areas of the endpoints of the abnormal lines are recorded. The walking area of ​​the front end endpoint of the abnormal line is marked as the abnormal edge area. The so-called front end endpoint is based on the clockwise rotation, and the endpoint that rotates in front belongs to the front end endpoint;

[0040] Specifically, if the relevant lines are vertical, then based on the vertical lines of the two, the corresponding relevant calibration lines can be determined based on the relevant intersection points. Then, based on the specific rotation process of the two sets of vertical lines, the relevant calibration lines will also move following the rotation of the vertical lines. Then, when there is an abnormality (or deformation) on the inner ring edge of the hub-related model, the specific length of the relevant calibration lines will change. When the change occurs, it must be that the edge is abnormal or uneven, which will cause the length of the relevant calibration lines to change. Then, based on the changed length, the corresponding edge abnormal area can be determined.

[0041] Compared with the original method of numerical confirmation, this method of determining edge anomalies is more complicated and slow in confirmation. In this way, the area corresponding to the edge anomaly can be quickly locked based on a set of rotation processes, thereby quickly completing the corresponding preliminary detection process and ensuring the detection and identification efficiency.

[0042] Among them, the proportion analysis end determines the relevant proportion of the abnormal edge area based on the abnormal edge area marked within the inner circle of the outer edge of the relevant model and the involved length of the abnormal edge area, and identifies whether the relevant model is abnormal based on the determined relevant proportion value. If there is a relevant abnormality, an abnormal signal is generated through the signal end. If there is no relevant abnormality, the rotation test analysis center is executed, wherein the specific method of identification is:

[0043] Determine the relevant circumference of the inner circle of the outer edge of the relevant model (that is, the circumference of the corresponding outer circle), and mark the determined relevant circumference as ZC;

[0044] Then identify the radiation length of the abnormal edge area, which can be determined by the rotation speed V during the rotation test and the duration t1 of the relevant calibration line being calibrated as the abnormal line. The radiation length SC of the abnormal edge area is determined by using SC=V×t1. If there are multiple abnormal edge areas, their radiation lengths SC are confirmed one by one and the total radiation length CC is confirmed by summing them up.

[0045] Use ZB=CC÷SC to confirm the relevant proportion ZB of the abnormal edge area, and identify whether the relevant proportion ZB satisfies: ZB>Y1, where Y1 is a preset value, and its specific value is determined by the operator based on experience. If it satisfies, it means that the proportion of the abnormal edge area is too large, and then an abnormal signal is directly generated and displayed through the signal end, indicating that there is a large area of ​​abnormality in the automobile wheel hub, and manual intervention is required to determine whether there is deformation. If it does not satisfy, that is, when ZB≤Y1, the rotation test analysis center is executed to perform subsequent related tests to identify whether the automobile wheel hub affects subsequent related use;

[0046] Specifically, when the edge area of ​​the car accounts for too large a proportion, it means that the abnormal area of ​​the car wheel is too large. This situation needs to be taken seriously, so an abnormal signal is generated for display for external personnel to view and promptly determine whether the car wheel is excessively abnormal and has related deformation problems. If the abnormal area of ​​the car wheel is not too large, it is uncertain whether the corresponding car wheel will affect subsequent normal use. The second stage of testing is required to perform a rotation test on the car wheel and perform relevant numerical analysis based on the corresponding rotation test analysis center to determine whether the car wheel is normal.

[0047] Among them, the rotation test analysis center, based on the dynamic test process of the corresponding automobile wheel hub, preferentially dynamically follows the edge point of the automobile wheel hub through the dynamic following module, and records its dynamic following curve, and then performs band analysis on the dynamic following curve through the band analysis module to determine the same frequency regular segment, and then identifies the rotation height difference of the automobile wheel hub based on the difference between the highest point and the lowest point in the same frequency regular segment, so as to finally evaluate whether the automobile wheel hub affects normal use. Among them, the so-called dynamic test process of the automobile wheel hub is to place the automobile wheel hub in a rotating mechanism, and the rotating mechanism rotates on behalf of the automobile wheel hub, and during the rotation process, the corresponding high-definition equipment is used to perform feature following on the edge of the automobile wheel hub to determine its related dynamic following curve;

[0048] Among them, the dynamic following module dynamically follows the edge point of the automobile wheel hub in the following specific manner:

[0049] Based on the relevant model, a group of characteristic points are locked in the inner circle of the outer edge of the automobile wheel hub (that is, the corresponding outer circle), and the characteristic points are in a stationary state. When the automobile wheel hub is rotating, the characteristic points dynamically follow the circumference of the outer circle, and based on the relevant changes of the circumference, the characteristic points jump up and down to construct a group of virtual base surfaces. This virtual base surface moves backward at a rate Vs, where Vs is a preset value, and its specific value is determined by the operator based on experience. The characteristic points that are followed in real time are mapped to the virtual base surface according to the process of jumping up and down. Because the virtual base surface moves backward, a dynamic following curve of this characteristic point is generated;

[0050] Determine a set of following cycles T, where T is a preset value, and its specific value is determined by the operator based on experience, confirm the dynamic following curve of its characteristic point within this following cycle T, and transmit the confirmed dynamic following curve to the band analysis module, where when T is set, the wheel hub of the car can rotate several times within this cycle T according to its rotation rate;

[0051] The so-called dynamic following process is to determine a point on the circumference of the outer circle. This point is located on this circumference, but this point is in a stationary state. When the car wheel hub rotates, its circumference will also rotate accordingly. Then the determined point will also move on the circumference. When moving, its feature point will also jump up and down. Then a set of virtual base surfaces are determined on the back of this related model. This virtual base surface moves forward. When its feature points jump up and down, they are mapped on the virtual base surface, which can generate a set of fluctuation curves. The amplitude of the fluctuation is determined by the up and down movement amplitude of the feature point.

[0052] The band analysis module identifies the relevant duration of the automobile wheel hub to complete one rotation based on the rotation rate of the automobile wheel hub during the dynamic test, and then performs relevant segmentation on the dynamic following curve based on the relevant duration, and determines a group of control segments, and determines the same frequency regular segment by performing a relevant comparison process, wherein the specific method of determining is:

[0053] The rotation rate during the dynamic test is calibrated as V1, the circumference of the outer ring of the automobile wheel hub is calibrated as ZC, and the relevant time St is determined using ZC÷V1=St;

[0054] Based on the moving speed V2 of the virtual base surface, V2×St=SS is used to determine the lateral distance SS moved by the feature point, and then a set of distance intervals is determined based on SS: [SS-X1, SS+X1], where X1 is a preset value, and its specific value is determined by the operator based on experience;

[0055] The part of the curve whose lateral distance in the dynamic following curve is before (SS-X1) is calibrated as the related curve, so that the related curve adds a curve of unit length in the current distance interval as an additional segment, determines a set of comparison curves, aligns the starting point of the comparison curve with the end point of the additional segment to compare and analyze with the subsequent curves, and confirms the overlap. After the first set of overlap is confirmed, two curves of unit length are added from the current distance interval as additional segments to determine a set of comparison curves, aligns the starting point of the comparison curve with the end point of the additional segment to compare and analyze with the subsequent curves, and confirms the overlap, and so on, until the end point of the additional segment is (SS+X1), stops, and selects the comparison curve corresponding to the set of overlaps with the largest value from the confirmed several sets of overlaps as the same frequency regular segment, wherein the unit length is prepared in advance by the operator based on experience;

[0056] Determine the horizontal distance L between the highest point and the lowest point of the characteristic point in the same frequency regular segment (that is, the vertical distance between the two, the horizontal distance can be determined by moving the lowest point to just below the highest point), and identify whether the horizontal distance L satisfies: L>Y2, where Y2 is a preset value, and its specific value is determined by the operator based on experience. If it satisfies, an abnormal signal is generated and displayed through the signal end; if it does not satisfy, a normal use signal is generated and displayed through the signal end;

[0057] Specific, combined Figure 3Understand, the proposed SS is 8, where X1 is 0.5, then the corresponding distance range in the figure is: 7.5-8.5, that is, the horizontal length of the two endpoints is 7.5 to 8.5. According to the above content, the first group of line segments with a horizontal length of 7.5 is preferentially determined as the comparison curve, and this comparison curve is compared with the subsequent curves with a horizontal length between 7.5-15 to determine the comparison coincidence. The comparison coincidence is the coincidence value of the two groups of curves, that is, the proportion of the overlapping part in the comparison curve, that is, the corresponding coincidence value;

[0058] The proposed unit length is 0.1, and the second comparison process is performed. The line segment with a length of 7.6 is used as the comparison curve, and is compared with the subsequent curves with a length between 7.6 and 15.2. Then, the curves with lengths of 7.5, 7.8, 7.9, ..., 8.5 are used as comparison curves in sequence, and so on, until all are determined, to determine the final same-frequency regular segment;

[0059] The reason why it is so troublesome to deal with this is that the car wheel hub involves the problem of dynamic balance. If the regular segment is determined according to the original distance SS, there will still be relevant errors. The car wheel hub may have relevant jumps due to uneven dynamic balance, which will cause its curve to be non-standard, thereby causing evaluation errors. Then, according to this processing method, such errors can be avoided to determine the most accurate comparison results, thereby achieving better evaluation efficiency.

[0060] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0061] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A vehicle wheel hub detection system, characterized in that: include: The scanning end performs relevant scanning on the automobile wheel hub and generates a relevant model of the automobile wheel hub based on the real-time scanning data; At the initial detection end, the generated correlation model is initially detected to identify whether there is an abnormal edge area in the inner circle of the outer edge of the correlation model, and the abnormal edge area is marked; The proportion analysis end determines the relevant proportion of the abnormal edge area based on the abnormal edge area marked within the inner circle of the outer edge of the relevant model and the involved length of the abnormal edge area, and identifies whether the relevant model is abnormal based on the determined relevant proportion value; The rotation test analysis center, based on the dynamic test process of the corresponding automobile wheel hub, first dynamically follows the edge point of the automobile wheel hub through the dynamic following module and records its dynamic following curve, and then performs band analysis on the dynamic following curve through the band analysis module to determine the same frequency regular segment, and then identifies the rotation height difference of the automobile wheel hub based on the difference between the highest point and the lowest point in the same frequency regular segment, so as to finally evaluate whether the automobile wheel hub affects normal use; The specific method of the dynamic following module to dynamically follow the edge point of the automobile wheel hub is as follows: Based on the relevant model, a set of characteristic points are locked in the inner circle of the outer edge of the automobile wheel hub, and the characteristic points are in a stationary state. When the automobile wheel hub is rotating, the characteristic points dynamically follow the circumference of the outer circle, and based on the relevant changes of the circumference, the characteristic points jump up and down to construct a set of virtual base surfaces. This virtual base surface moves backward at a rate Vs, where Vs is a preset value. The characteristic points followed in real time are mapped to the virtual base surface according to the process of jumping up and down. As the virtual base surface moves backward, a dynamic following curve of this characteristic point is generated; Determine a set of following periods T, where T is a preset value, confirm the dynamic following curve of the characteristic points within the following period T, and transmit the confirmed dynamic following curve to the band analysis module; The band analysis module identifies the relevant duration of the automobile wheel hub to complete one rotation based on the rotation rate of the automobile wheel hub during the dynamic test, and then performs relevant segmentation on the dynamic following curve based on the relevant duration, and determines a group of control segments, and determines the same frequency regular segment by performing a relevant comparison process, specifically in the following manner: The rotation rate during the dynamic test is calibrated as V1, the circumference of the outer ring of the automobile wheel hub is calibrated as ZC, and the relevant time St is determined using ZC÷V1=St; Based on the moving speed V2 of the virtual base surface, V2×St=SS is used to determine the lateral distance SS moved by the feature point, and then a set of distance intervals is determined based on SS: [SS-X1, SS+X1], where X1 is a preset value; The part of the curve whose lateral distance in the dynamic following curve is before (SS-X1) is calibrated as the related curve, so that the related curve adds a curve of unit length in the current distance interval as an additional segment, determines a set of comparison curves, aligns the starting point of the comparison curve with the end point of the additional segment to compare and analyze with the subsequent curves, and confirms the overlap. After the first set of overlap is confirmed, two curves of unit length are added from the current distance interval as additional segments, determines a set of comparison curves, aligns the starting point of the comparison curve with the end point of the additional segment to compare and analyze with the subsequent curves, and confirms the overlap, and so on, until the end point of the additional segment is (SS+X1), stops, and selects the comparison curve corresponding to the set of overlaps with the largest value from the confirmed several sets of overlaps as the same frequency regularity segment; Determine the horizontal distance L between the highest point and the lowest point of the characteristic point in the same frequency regular segment, and identify whether the horizontal distance L satisfies: L>Y2, where Y2 is a preset value. If so, an abnormal signal is generated and displayed through the signal end; if not, a normal use signal is generated and displayed through the signal end.

2. The automobile wheel hub detection system according to claim 1, characterized in that: The specific method for the initial detection end to identify whether there is an abnormal edge area in its related model is: Determine the side display diagram of the relevant model, determine the center point of the relevant model, and construct two sets of relevant lines passing through the center point, and the two sets of relevant lines are perpendicular to each other; Identify two groups of circles tangent to the corresponding hub support from the relevant model, lock the outer circle, then determine the intersection of the relevant line and the outer circle, connect the adjacent intersections, and determine four groups of relevant calibration lines; Make the two groups of related lines rotate in the clockwise direction, and the rotation rate V is the set value. During the rotation process, the length of the corresponding related calibration line is recorded in real time, and the length of the four groups of related calibration lines is compared. When the length of a group of related calibration lines is inconsistent with the length of other related calibration lines, the related calibration lines with inconsistent lengths are calibrated as abnormal lines. When the abnormal lines return to normal length, they are re-calibrated as related calibration lines, and the walking areas of the endpoints of the abnormal lines are recorded. The walking area of ​​the front end endpoint of the abnormal line is calibrated as the abnormal edge area. The so-called front end endpoint is based on the fact that when rotating in the clockwise direction, the endpoint that rotates in front belongs to the front end endpoint.

3. The automobile wheel hub detection system according to claim 2, characterized in that: The specific method of identifying whether the relevant model is abnormal at the proportion analysis end is: Determine the relevant perimeter of the inner circle of the outer edge of the relevant model, and mark the determined relevant perimeter as ZC; Then identify the radiation length of the abnormal edge area. The radiation length can be determined by the rotation speed V during the rotation test and the duration t1 of the relevant calibration line being calibrated as the abnormal line. The radiation length SC of the abnormal edge area is determined by using SC=V×t1. If there are multiple abnormal edge areas, their radiation lengths SC are confirmed one by one and the total radiation length CC is confirmed by summing them up. Use ZB=CC÷SC to confirm the relevant proportion ZB of the abnormal edge area, and identify whether the relevant proportion ZB satisfies: ZB>Y1, where Y1 is the preset value. If it satisfies, it means that the proportion of this abnormal edge area is too large, and an abnormal signal is directly generated and displayed through the signal end. If it does not satisfy, that is, when ZB≤Y1, the rotation test analysis center is executed to perform subsequent related tests.

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