Vehicle body quality inspection method

Abstract

The present application provides a vehicle body quality detection method, which includes: Step S1, determining the minimum frequency fmin of off-line measurement of the measurement point M according to the stability of the on-line measurement of the measurement point M of the target vehicle model; Step S2, the target vehicle model includes m measurement points, and the minimum measurement frequencies of the m measurement points are determined in sequence; Step S3, arranging the minimum measurement frequencies of the m measurement points in descending order, and taking the minimum measurement frequency ranked i as the minimum measurement frequency Fmin of the off-line measurement of the target vehicle model, where i≈m*x%, i is an integer, and 0<x≤100. The present application can improve the rationality of off-line measurement resource allocation.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle production, and in particular to a vehicle body quality detection method. Background Art

[0002] In the vehicle manufacturing industry, vehicles must pass the inspection before they can leave the factory. The inspection of the vehicle body is an important part of vehicle inspection. The methods of vehicle body inspection generally include offline measurement and online measurement.

[0003] Offline measurement uses three-coordinate detection, usually in a special measuring room using high-precision three-coordinate measuring equipment for measurement. The measured parts are transported from the production line to the measuring room, and then transported back to the production line after the measurement is completed. This method has high measurement accuracy. However, the measurement efficiency is low, and it is impossible to achieve real-time dimension monitoring, nor can it timely alarm for batch dimension problems on the production line.

[0004] Online measurement is to install the measuring equipment directly on the production line, usually composed of robot system, laser measurement system, coordinate system calibration system, etc., which can realize real-time measurement. The measurement data is real-time and continuous, and can be used to monitor the stability of the measuring points. However, the measurement accuracy of online measurement is low, and it can only cover some measuring points.

[0005] Combining online measurement with offline measurement can ensure that the dimensions of the vehicle body are more accurate and guarantee the production quality of the vehicle. However, in the prior art, the frequency of offline measurement is usually determined based on experience, resulting in unreasonable allocation of offline measurement resources. Summary of the invention

[0006] The present application provides a vehicle body quality detection method to improve the rationality of offline measurement resource allocation.

[0007] The present application provides a vehicle body quality detection method, which includes:

[0008] Step S1, determining the minimum frequency fmin of offline measurement of the measuring point M according to the stability of online measurement of the measuring point M of the target vehicle model;

[0009] Step S2: The target vehicle model includes m measuring points, and the minimum measuring frequency of the m measuring points is determined in sequence;

[0010] Step S3: Arrange the lowest measurement frequencies of the m measurement points in descending order, and take the lowest measurement frequency ranked i as the lowest measurement frequency Fmin for offline measurement of the target vehicle model, where i≈m*x%, i is an integer, 0 <x≤100。

[0011] Optionally, in step S1, the stability of the online measurement of the measuring point M includes: a fluctuation level parameter σ, a tolerance level IT, and an error percentage.

[0012] Optionally, the vehicle body quality detection method further comprises step S0: formulating a table containing a fluctuation level parameter σ, a tolerance level IT, a measurement frequency f, and an error percentage e;

[0013] The step S1 comprises: according to the acceptable range E of the error percentage e, the fluctuation level parameter σ and the tolerance grade IT, looking up the table of step S0 to determine the minimum measurement frequency fmin of the offline measurement of the measuring point M.

[0014] Optionally, the step S0 includes: formulating a first table;

[0015] The first table includes fluctuation level parameters σ arranged along a first direction, each of the fluctuation level parameters corresponding to a plurality of measurement frequencies f arranged along the first direction;

[0016] The first table includes tolerance levels IT arranged along the second direction, and each tolerance level IT corresponds to an upper limit of an error percentage and a lower limit of an error percentage arranged along the second direction.

[0017] Optionally, the preparing the first table includes:

[0018] Step S01, setting the fluctuation level parameter σ and tolerance level IT of online measurement, and setting the measurement frequency f of offline measurement;

[0019] Step S02, calculating the qualified rate PR(0) of online measurement according to the fluctuation level parameter σ and the tolerance grade IT;

[0020] Step S03, obtaining a set of sample data according to a predetermined measurement duration and measurement frequency f, and calculating the pass rate PR(1) of the set of sample data according to the tolerance level IT;

[0021] Step S04, calculating the error percentage Error of the group of sample data, Error = [PR (1) - PR (0)] / PR (0) * 100%;

[0022] Step S05, sequentially calculating the error percentages of n groups of sample data to obtain an error percentage sequence Error(n), where n is an integer not less than 5000;

[0023] Step S06: According to the error percentage sequence Error(n), according to the 95% confidence interval, the error percentage upper limit and the error percentage lower limit are obtained and filled in the corresponding positions of the first table.

[0024] Optionally, the step S01 further comprises: simulating and generating a normally distributed random sequence with a center of 0 and a fluctuation level of σ, wherein the random sequence is used to simulate the error data of actual measurement;

[0025] The step S02 comprises: calculating the pass rate PR(0) under the tolerance level IT according to the random sequence of step S01;

[0026] The step S03 comprises: randomly acquiring a group of sample data in the random sequence of step S01 according to the measurement frequency f, and calculating the qualified rate PR(1) of the group of sample data according to the tolerance level IT.

[0027] Optionally, step S06 includes: making a normal distribution curve or a normal distribution function of the error percentage according to the error percentage sequence Error(n);

[0028] According to the normal distribution curve or the normal distribution function, according to the 95% confidence interval, the upper limit of the error percentage and the lower limit of the error percentage are obtained and filled in the corresponding positions of the first table.

[0029] Optionally, the step S1 includes: looking up the first table to determine a minimum measurement frequency fmin for offline measurement of the measuring point M.

[0030] Optionally, the step S0 includes: formulating a second table according to the acceptable range E of the error percentage e and the first table;

[0031] The second table includes fluctuation level parameters σ arranged along the first direction and tolerance levels IT arranged along the second direction, and each intersection position of the fluctuation level parameter σ and the tolerance level IT corresponds to a minimum measurement frequency fmin;

[0032] The step S1 includes: looking up the second table to determine the minimum measurement frequency fmin of the offline measurement of the measuring point M.

[0033] Optionally, the vehicle body quality detection method further includes:

[0034] Step S4: Determine the maximum measurement frequency Fmax of the offline measurement of the target vehicle model according to the maximum measurement capability of the offline measurement resource.

[0035] The technical solution provided by this application can achieve the following beneficial effects:

[0036] The vehicle body quality detection method provided by the present application includes: step S1, according to the stability of online measurement of measuring point M of the target vehicle model, determine the minimum frequency fmin of offline measurement of measuring point M; step S2, the target vehicle model includes m measuring points, and the minimum measurement frequency of m measuring points is determined in turn; step S3, the minimum measurement frequencies of m measuring points are arranged in descending order, and the lowest measurement frequency ranked as i is used as the minimum measurement frequency Fmin of offline measurement of the target vehicle model. The present application dynamically adjusts the minimum measurement frequency fmin of offline measurement of measuring point M according to the stability of online measurement of measuring point M, so that more measurements are made for a single measuring point when the fluctuation is large, and less measurements are made for a single measuring point when the fluctuation is small; the overall volatility of online measurement of the target vehicle model can be grasped according to the minimum measurement frequency fmin of all measuring points; according to the actual situation such as offline measurement capability, x% of measuring points with higher fluctuation are sacrificed, so that offline measurement resources are reasonably allocated among multiple different vehicle models. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The process part of the vehicle body quality detection method provided in the first embodiment of the present application;

[0038] Figure 2 A partial flow chart of the vehicle body quality detection method provided in Example 2 of the present application;

[0039] Figure 3 A flowchart for formulating a first table provided in an embodiment of the present application.

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application. DETAILED DESCRIPTION

[0041] 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.

[0042] In the description of this application, unless otherwise clearly specified and limited, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance; unless otherwise specified or explained, the term "plurality" refers to two or more; the terms "connected" and "fixed" should be understood in a broad sense, for example, "connected" can be a fixed connection, a detachable connection, an integral connection, or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0043] In the description of this specification, it should be understood that the orientation terms such as "upper" and "lower" described in the embodiments of this application are described from the angles shown in the drawings, and should not be construed as limitations on the embodiments of this application. In addition, in the context, it should also be understood that when it is mentioned that one element is connected "above" or "below" another element, it can not only be directly connected "above" or "below" another element, but also be indirectly connected "above" or "below" another element through an intermediate element.

[0044] As Figure 1-Figure 3 shown, the embodiments of this application provide a vehicle body quality detection method, which combines online measurement and offline measurement (i.e., offline three-coordinate detection) to detect the vehicle body quality, and the measurement frequency of the offline measurement is F.

[0045] As Figure 1 shown, the vehicle body quality detection method provided by the embodiments of this application includes:

[0046] Step S1: Determine the minimum frequency fmin of the offline measurement of the measurement point M according to the stability of the online measurement of the measurement point M of the target vehicle model. The measurement point M is one of the measurement points for the online measurement of the target vehicle model. According to the stability of the online measurement of the measurement point M, the minimum measurement frequency fmin of the offline measurement of the measurement point M is dynamically adjusted, so as to perform more measurements for a measurement point with large fluctuations and fewer measurements for a measurement point with small fluctuations.

[0047] Among them, when the stability of the online measurement of the measurement point M is low, the fluctuation of the online measurement of the measurement point M is large. Therefore, the minimum frequency fmin of the offline measurement of the measurement point M is large; when the stability of the online measurement of the measurement point M is high, the fluctuation of the online measurement of the measurement point M is small. Therefore, the minimum measurement frequency fmin required for the offline measurement of the measurement point M is small.

[0048] Step S2: The target vehicle model includes m measurement points, and the minimum measurement frequencies of the m measurement points are determined in sequence, so as to master the overall fluctuation of the online measurement of the target vehicle model. Among them, m is reasonably set according to the measurement requirements of the target vehicle model.

[0049] Step S3: Arrange the minimum measurement frequencies of the m measurement points in descending order, and use the minimum measurement frequency ranked i as the minimum measurement frequency Fmin of the offline measurement of the target vehicle model. The measurement frequency F of the offline measurement is ≥Fmin to ensure the detection quality of the target vehicle model.

[0050] Among them, i≈m*x%, i is an integer, and 0<x≤100. That is to say, according to the actual situation such as the offline measurement ability, sacrifice x% of the measurement points with higher fluctuations, so as to reasonably allocate the offline measurement resources among multiple different vehicle models.

[0051] Specifically, the value of x can be reasonably adjusted according to the off-line measurement capability. When the off-line measurement capability is insufficient, increase the value of x to reduce the minimum measurement frequency Fmin of the target vehicle model, which can not only keep the detection quality of each vehicle model consistent but also effectively improve the detection efficiency. When the off-line measurement capability is surplus, decrease the value of x to increase the minimum measurement frequency Fmin of the target vehicle model, which can not only effectively improve the detection quality of the target vehicle model but also improve the utilization rate of off-line measurement resources.

[0052] In one embodiment, 0 < x ≤ 5. For example, the value of x can be 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5 or 5, etc. That is to say, no more than 5% of the measurement points are sacrificed. In probability theory, 5% is usually used as the small probability standard, that is, if the probability of an event occurring is below 5%, it can be considered that the event is almost impossible to occur. Therefore, it can be considered that almost all measurement points of the target vehicle model can meet the minimum measurement frequency requirements, thus ensuring the detection quality of the target vehicle model.

[0053] Step S4: Determine the maximum measurement frequency Fmax of the off-line measurement of the target vehicle model according to the maximum capability of the off-line measurement resources. The measurement frequency F of the off-line measurement satisfies F ≤ Fmax to prevent insufficient measurement resources.

[0054] Specifically, the measurement frequency F of the off-line measurement is selected and adjusted between Fmin and Fmax. When the off-line measurement capability is insufficient (for example, adding vehicle models, etc.), decrease the measurement frequency F; when the off-line measurement capability is surplus (for example, reducing vehicle models, etc.), increase the measurement frequency F.

[0055] Furthermore, in step S1, the stability of the on-line measurement of the measurement point M includes: the fluctuation level parameter σ, the tolerance grade IT, and the error percentage e. That is to say, comprehensively considering the fluctuation level parameter σ of the on-line measurement, the tolerance grade IT of the measurement point M, and the error percentage e of the measurement point M, determine the minimum frequency fmin of the off-line measurement of the measurement point M.

[0056] Specifically, the tolerance grade IT is determined by the production and processing requirements of the measurement point M. The smaller the tolerance grade IT, the lower the stability of the on-line measurement of the measurement point M; the larger the tolerance grade IT, the higher the stability of the on-line measurement of the measurement point M.

[0057] The fluctuation level parameter σ is determined by the performance of the on-line measurement system itself and the tolerance grade IT. The on-line measurement system can directly obtain the process stability parameter Pp of the measurement point M. According to Pp = IT / 6σ, the fluctuation level parameter σ can be calculated. The larger the fluctuation level parameter σ, the lower the stability of the on-line measurement of the measurement point M; the smaller the fluctuation level parameter σ, the higher the stability of the on-line measurement of the measurement point M.

[0058] The error percentage e of the measuring point M is the error percentage of the offline measurement pass rate relative to the online measurement pass rate. The smaller the error percentage e of the measuring point M, the higher the stability of the online measurement of the measuring point M; the larger the error percentage e of the measuring point M, the lower the stability of the online measurement of the measuring point M.

[0059] like Figure 2 As shown, further, the vehicle body quality detection method also includes step S0: formulating a table containing fluctuation level σ, tolerance level IT, measurement frequency f and error percentage e.

[0060] Step S1 includes: according to the acceptable range E of the error percentage e, the fluctuation level parameter σ and the tolerance level IT, looking up the table of step S0 to determine the minimum measurement frequency fmin of the offline measurement of the measuring point M. There is no need to perform a separate calculation for each measuring point, so that the minimum measurement frequency fmin of each measuring point can be determined more efficiently.

[0061] Furthermore, step S0 includes: formulating a first table. A specific implementation of the first table can be referred to Table 1.

[0062] Table 1

[0063]

[0064] The first table includes fluctuation level parameters σ arranged along a first direction (e.g., Y direction, etc.), that is, along the first direction, σ may include multiple different values, thereby representing multiple online measurement systems with different performances. For example, along the first direction, the values ​​of 6σ are 1, 1.5, 2, 2.4, and 3, etc., and correspondingly, the values ​​of σ are 0.167, 0.25, 0.333, 0.4, and 0.5, etc.

[0065] Each fluctuation level parameter σ corresponds to a plurality of measurement frequencies f arranged along the first direction, that is, for each value of σ, f may include a plurality of different values ​​along the first direction, so that each online measurement system corresponds to a plurality of different offline measurement frequencies. For example, along the first direction, corresponding to each σ value, the values ​​of f are 2 / 1D, 3 / 2D, 1 / 1D, 1 / 2D, 1 / 3D, 1 / 5D or 1 / 7D, etc., where D is day, and 2 / 1D means 2 measurements per day.

[0066] The first table includes tolerance levels IT arranged along the second direction (e.g., X direction, etc.), that is, along the second direction, the tolerance level IT may include multiple different values, thereby representing multiple different production and processing requirements. For example, along the second direction, the values ​​of IT are (-0.5, 0.5), (-1, 1), and (-1.5, 1.5), etc.

[0067] Each tolerance grade IT corresponds to an upper limit and a lower limit of the error percentage arranged along the second direction. That is, for each value of the tolerance grade IT, the error percentage e may include two different values ​​along the second direction, thereby determining the range of the error percentage e.

[0068] Further, when step S0 includes preparing a first table, step S1 includes: according to the acceptable range E of the error percentage e, the fluctuation level parameter σ and the tolerance level IT, looking up the first table to determine the minimum measurement frequency fmin of the offline measurement of the measuring point M.

[0069] Specifically, in the first table, a smaller rectangular area is first determined according to the fluctuation level parameter σ and the tolerance level IT; the rectangular area includes multiple measurement frequencies f arranged along the first direction, and each measurement frequency f has an upper error percentage limit and a lower error percentage limit along the second direction. The upper and lower limits of the acceptable range E are compared with the upper error percentage limit and the lower error percentage limit of the same measurement frequency f; when E falls between the upper error percentage limit and the lower error percentage limit, the measurement frequency f can meet the requirements, and f is reduced until the lowest measurement frequency fmin is found; when E falls outside the upper error percentage limit or the lower error percentage limit, the measurement frequency f cannot meet the requirements, and f is increased until the lowest measurement frequency fmin that meets the requirements is found.

[0070] like Figure 3 As shown, further, formulating the first table includes:

[0071] Step S01, setting the fluctuation level parameter σ and tolerance level IT of online measurement, and setting the measurement frequency f of offline measurement. For example, σ=0.167, and the tolerance level IT is (-0.5, 0.5).

[0072] Step S02: Calculate the qualified rate PR(0) of online measurement according to the fluctuation level parameter σ and the tolerance level IT. That is, the online measurement system with the fluctuation level parameter σ is used to measure the product; among all the measurement results, the proportion of the number of products that can fall within the tolerance level IT is PR(0).

[0073] Step S03: Obtain a set of sample data according to the predetermined measurement duration and measurement frequency f, and calculate the qualified rate PR(1) of the set of sample data according to the tolerance level IT. For example, if the measurement duration of the sample data is 30D and the measurement frequency f=1 / 1D, the sample data includes 30 products; among the 30 products, the proportion of the number of products that can fall within the tolerance level IT is PR(1).

[0074] Step S04, calculate the error percentage Error of the group of sample data, Error = [PR (1) - PR (0)] / PR (0) * 100%. Error is the error percentage of the offline measurement pass rate relative to the online measurement pass rate.

[0075] Step S05, calculate the error percentages of n groups of sample data in sequence to obtain an error percentage sequence Error(n), where n is an integer not less than 5000, to eliminate the influence of accidental errors and improve the accuracy of the results.

[0076] Step S06, according to the error percentage sequence Error(n), according to the 95% confidence interval, the upper limit of the error percentage and the lower limit of the error percentage are obtained, and filled in the corresponding positions of the first table. For example, in the first table, σ=0.167, f=1 / 1D, and the tolerance grade IT is (-0.5, 0.5). After the above calculation, it can be obtained that the upper limit of the error percentage is -1.92%, and the lower limit of the error percentage is 1.91%.

[0077] In probability theory, 5% is usually used as the small probability standard, that is, if the probability of an event occurring is below 5%, it can be considered that the event is almost impossible to occur. Therefore, according to the 95% confidence interval, it can be considered that the range includes all possible error percentages.

[0078] In one embodiment, step S01 further includes: simulating and generating a random sequence of normal distribution with a center of 0 and a fluctuation level of σ, and the random sequence is used to simulate the error data of actual measurement. Accordingly, step S02 includes: calculating the pass rate PR(0) under the tolerance level IT according to the random sequence of step S01. Step S03 includes: randomly obtaining a group of sample data in the random sequence of step S01 according to the measurement frequency f, and calculating the pass rate PR(1) of the group of sample data. In other words, the measurement error data of the target product is generated by simulation, without the need for actual online production, thereby saving time and improving the efficiency of formulating the first table.

[0079] In one embodiment, step S06 also includes: making a normal distribution curve or a normal distribution function of the error percentage according to the error percentage sequence Error(n); according to the normal distribution curve or the normal distribution function, according to a 95% confidence interval, obtaining an upper limit and a lower limit of the error percentage, and filling them in corresponding positions of the first table, thereby further improving the accuracy of the calculation results.

[0080] Further, step S0 includes: after formulating the first table, formulating a second table according to the acceptable range E of the error percentage e and the first table. That is, each second table has a fixed E, and a plurality of different second tables can be formulated to meet a variety of different production requirements. A specific implementation of the second table can be referred to Table 2.

[0081] Table 2

[0082]

[0083] The second table includes fluctuation level parameters σ arranged along a first direction (eg, Y direction, etc.), that is, along the first direction, σ may include multiple different values, and the σ values ​​of the second table are consistent with those of the first table.

[0084] The second table includes tolerance levels IT arranged along the second direction, that is, along the second direction, the tolerance level IT may include a plurality of different values, and the IT values ​​of the second table are consistent with the IT values ​​of the first table.

[0085] Each intersection position of the fluctuation level parameter σ and the tolerance grade IT corresponds to a minimum measurement frequency fmin, so that the second table uses σ and IT as independent variables and fmin as the dependent variable, thereby being able to determine the minimum measurement frequency fmin more intuitively and efficiently.

[0086] Further, when step S0 includes preparing a second table, step S1 includes: according to the acceptable range E of the error percentage e, the fluctuation level parameter σ and the tolerance level IT, looking up the second table to determine the minimum measurement frequency fmin of the offline measurement of the measuring point M.

[0087] Specifically, the user first selects a second table corresponding to the acceptable range E of the error percentage e, and then finds the intersection of σ and IT on the second table to obtain the minimum measurement frequency fmin of the measuring point M. This saves the time of looking up the table, and makes it possible to more efficiently determine the minimum measurement frequency fmin of each measuring point.

[0088] Furthermore, formulating the second table includes:

[0089] Determine the acceptable range E of the error percentage e in the second table; in the first table, find the minimum measurement frequency fmin according to the fluctuation level parameter σ, the tolerance grade IT and the acceptable range E; fill fmin into the intersection position of σ and IT in the second table.

[0090] Specifically, in the first table, firstly, a smaller rectangular area is determined according to the fluctuation level parameter σ and the tolerance level IT; the rectangular area includes a plurality of measurement frequencies f arranged along the first direction, and each measurement frequency f has an upper error percentage limit and a lower error percentage limit along the second direction. The upper and lower limits of the acceptable range E are respectively compared with the upper error percentage limit and the lower error percentage limit of the same measurement frequency f; when E falls between the upper error percentage limit and the lower error percentage limit, the measurement frequency f can meet the requirements, and f is reduced until the lowest measurement frequency fmin is found; when E falls outside the upper error percentage limit or the lower error percentage limit, the measurement frequency f cannot meet the requirements, and f is increased until the lowest measurement frequency fmin that meets the requirements is found.

[0091] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A vehicle body quality detection method, characterized in that: include: Step S1, determining the minimum frequency fmin of offline measurement of the measuring point M according to the stability of online measurement of the measuring point M of the target vehicle model; Step S2: The target vehicle model includes m measuring points, and the minimum measuring frequency of the m measuring points is determined in sequence; Step S3: Arrange the lowest measurement frequencies of the m measurement points in descending order, and take the lowest measurement frequency ranked i as the lowest measurement frequency Fmin for offline measurement of the target vehicle model, where i≈m*x%, i is an integer, 0 <x≤100。 2. The vehicle body quality detection method according to claim 1, characterized in that: In the step S1, the stability of the online measurement of the measuring point M includes: the fluctuation level parameter σ, the tolerance level IT and the error percentage.

3. The vehicle body quality detection method according to claim 2, characterized in that: The vehicle body quality detection method further comprises step S0: formulating a table containing a fluctuation level parameter σ, a tolerance level IT, a measurement frequency f, and an error percentage e; The step S1 comprises: according to the acceptable range E of the error percentage e, the fluctuation level parameter σ and the tolerance grade IT, looking up the table of step S0 to determine the minimum measurement frequency fmin of the offline measurement of the measuring point M.

4. The vehicle body quality detection method according to claim 3, characterized in that: The step S0 includes: formulating a first table; The first table includes fluctuation level parameters σ arranged along a first direction, each of the fluctuation level parameters corresponding to a plurality of measurement frequencies f arranged along the first direction; The first table includes tolerance levels IT arranged along the second direction, and each tolerance level IT corresponds to an upper limit of an error percentage and a lower limit of an error percentage arranged along the second direction.

5. The vehicle body quality detection method according to claim 4, characterized in that: The preparing the first table comprises: Step S01, setting the fluctuation level parameter σ and tolerance level IT of online measurement, and setting the measurement frequency f of offline measurement; Step S02, calculating the qualified rate PR(0) of online measurement according to the fluctuation level parameter σ and the tolerance grade IT; Step S03, obtaining a set of sample data according to a predetermined measurement duration and measurement frequency f, and calculating the pass rate PR(1) of the set of sample data according to the tolerance level IT; Step S04, calculating the error percentage Error of the group of sample data, Error = [PR (1) - PR (0)] / PR (0) * 100%; Step S05, sequentially calculating the error percentages of n groups of sample data to obtain an error percentage sequence Error(n), where n is an integer not less than 5000; Step S06: According to the error percentage sequence Error(n), according to the 95% confidence interval, the error percentage upper limit and the error percentage lower limit are obtained and filled in the corresponding positions of the first table.

6. The vehicle body quality detection method according to claim 5, characterized in that: The step S01 further comprises: simulating and generating a normally distributed random sequence with a center of 0 and a fluctuation level of σ, wherein the random sequence is used to simulate the error data of actual measurement; The step S02 comprises: calculating the pass rate PR(0) under the tolerance level IT according to the random sequence of step S01; The step S03 comprises: randomly acquiring a group of sample data in the random sequence of step S01 according to the measurement frequency f, and calculating the qualified rate PR(1) of the group of sample data according to the tolerance level IT.

7. The vehicle body quality detection method according to claim 5, characterized in that: Step S06 includes: making a normal distribution curve or a normal distribution function of the error percentage according to the error percentage sequence Error(n); According to the normal distribution curve or the normal distribution function, according to the 95% confidence interval, the upper limit of the error percentage and the lower limit of the error percentage are obtained and filled in the corresponding positions of the first table.

8. The vehicle body quality detection method according to any one of claims 4 to 7, characterized in that: The step S1 includes: looking up the first table to determine the minimum measurement frequency fmin of the offline measurement of the measuring point M.

9. The vehicle body quality detection method according to any one of claims 4 to 7, characterized in that: The step S0 includes: formulating a second table according to the acceptable range E of the error percentage e and the first table; The second table includes fluctuation level parameters σ arranged along the first direction and tolerance levels IT arranged along the second direction, and each intersection position of the fluctuation level parameter σ and the tolerance level IT corresponds to a minimum measurement frequency fmin; The step S1 includes: looking up the second table to determine the minimum measurement frequency fmin of the offline measurement of the measuring point M.

10. The vehicle body quality detection method according to any one of claims 1 to 7, characterized in that: The vehicle body quality detection method further comprises: Step S4: Determine the maximum measurement frequency Fmax of the offline measurement of the target vehicle model according to the maximum measurement capability of the offline measurement resource.

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