A quality inspection method for new energy vehicle parts

Abstract

The present invention discloses a quality inspection method for new energy vehicle parts, and specifically relates to the technical field of parts inspection. In order to solve the problem that the inspection method mainly relies on manual visual inspection, this method is easily affected by the operator's subjective judgment and fatigue, resulting in inaccurate inspection results, the present invention collects image information of the inspection parts, constructs a three-dimensional model of the parts after pre-processing the image information, divides the parts into welding areas, painting areas and remaining areas according to their application directions, arranges multiple data points on the surface of each divided area, analyzes the vertical distance between each data point and a reference plane to obtain a flatness value of each divided area, integrates the flatness values of each divided area to obtain a qualified degree value of the inspection part, and the obtained qualified degree value can reflect the quality of the parts, thereby saving manpower and improving the accuracy of inspection.

Description

Technical Field

[0001] The present invention relates to the technical field of parts detection, and in particular to a quality detection method for new energy vehicle parts. Background Art

[0002] As people's quality of life has significantly improved, the demand for automobiles has grown, and automobile manufacturing has also played an increasingly important role in industrial production. During the automobile production process, automobile parts need to be inspected to check whether there are any size or appearance defects after production.

[0003] The following problems exist in the process of testing the quality of automobile parts in the existing technology:

[0004] 1. The detection method mainly relies on manual visual inspection, which is easily affected by the operator's subjective judgment and fatigue, resulting in inconsistent and inaccurate test results;

[0005] 2. After testing the parts, qualified parts are usually transported to the next workshop, and unqualified parts are discarded. Targeted processing cannot be carried out based on the test results of the corresponding parts, affecting the overall efficiency. Summary of the Invention

[0006] In view of the fact that the detection method in the existing technology mainly relies on manual visual inspection, which is easily affected by the operator's subjective judgment and fatigue level, and may lead to inconsistent and inaccurate detection results, the present invention provides a new energy vehicle parts quality detection method.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] The model building module is used to obtain the part number and image information of various directions, build a three-dimensional model of the component based on the obtained image information, and send the built model to the layout analysis module;

[0009] The point distribution analysis module is used to obtain the constructed 3D model and set a reference datum plane based on the constructed model. The surface of the component is divided into multiple point distribution areas according to the application direction. Multiple data points are set in each point distribution area. The vertical distance between the multiple data points in the target area and the reference datum plane is analyzed to obtain the flatness value of the divided area. Specifically:

[0010] Step 1: Mark the flatness value of the target component as J c D, where C is used to represent the number of the component; D is used to represent the number of the divided area, D = 1, 2, 3, and D1, D2 and D3 represent the welding area, coating area and remaining area respectively. Set a weight coefficient for each divided area;

[0011] It should be noted that the weight coefficients corresponding to the welding area, the coating area and the remaining area are marked as Y1, Y2 and Y3 respectively, and Y1>Y2>Y3.

[0012] Step 2: Obtain the vertical distance between each data point in the target area and the parameter reference plane, compare each distance value with the set threshold range one by one, count the number of data points with distance values less than the threshold range and mark them as abnormal number PA1; calculate the difference between the distance value of each abnormal number in the target area and the lower limit of the threshold range and mark it as the convex value, add all the convex values and take the average to obtain the convex mean, count the number of convex values higher or lower than the convex mean and mark them as high value R1 and low value R2 respectively, calculate the sum of the convex values classified as high values and take the average to obtain the high mean, calculate the sum of the convex values classified as low values and take the average to obtain the low mean, set the high mean and low mean to correspond to an influence coefficient K1 and K2 respectively, and K1>K2, multiply the high mean and low mean and the corresponding influence coefficients K1 and K2 to calculate the score. The high impact value P1 and the low impact value P2 are obtained respectively, and the high value R1 and the high impact value P1 are substituted into the formula PA2=R1×ea1+P1×ea2 to obtain the over-threshold value PA2 by calculation, wherein ea1 and ea2 are respectively the preset weight factors of the high value R1 and the high impact value P1, and the low value R2 and the low impact value P2 are substituted into the formula PA3=R2×eb1+P2×eb2 to obtain the over-threshold value PA3 by calculation, wherein eb1 and eb2 are respectively the preset weight factors of the low value R2 and the low impact value P2; the allowed number of abnormalities threshold PA1", the over-threshold value threshold PA2" and the over-threshold value threshold PA3" of the target area are set, and the three abnormalities PA1, the over-threshold value PA2 and the over-threshold value PA3, the corresponding three allowed thresholds and the weight coefficients of the corresponding divided areas are processed to obtain the smoothing value J of the divided area. c D ;

[0013] According to the formula Where sq1, sq2 and sq3 are respectively the preset weighting factors of the ratio of the number of abnormalities PA1 to the abnormality number threshold PA1", the ratio of the over-threshold value PA2 to the allowed over-threshold value threshold PA2", and the ratio of the over-threshold value PA3 to the over-threshold value threshold PA3". Di is the weight coefficient of the corresponding divided area, where i = 1, 2, 3; η is the preset correction factor;

[0014] Set the smoothing value J for each divided area c D The allowable threshold value of each divided area of the target component is J c D And the corresponding allowable threshold is processed to obtain the qualified degree value HG of the target component c And send it to the instruction generation module;

[0015] According to the formula Among them J c D1" 、J c D2" and J c D3" They represent the allowable thresholds of the flatness values of the welding area, the coating area, and the remaining area, respectively; qw1, qw2, and qw3 are the preset weighting factors of the ratios of the flatness values of the welding area, the coating area, and the remaining area to the respective allowable thresholds, respectively; and χ is a preset correction factor;

[0016] The instruction generation module is used to receive the qualification value HG of the target component c and the qualification value HG c Match the corresponding preset range, set three preset ranges, and each preset range corresponds to a processing instruction, and send the generated processing instructions to the processing execution module. The processing instructions include expected standard compliance instructions, secondary processing instructions, and defective product recycling instructions;

[0017] The processing execution module is used to receive the processing instructions of the corresponding components and perform the corresponding operations, specifically:

[0018] When the preset range is matched, the expected standard instruction is generated, and the target component is transported to the assembly workshop for the next step of process processing and assembly;

[0019] When the preset range 2 is matched, a secondary processing instruction is generated, the target component number and the corresponding real-time position on the conveyor belt are obtained, and the work efficiency value GX of each grinding workshop is analyzed, specifically:

[0020] Step 1: Obtain the location of each grinding workshop, calculate the distance difference with the location of the target component to obtain the distance value GB1; obtain the number of qualified parts GB2 of each grinding workshop in the current month, classify the qualified parts according to the first grinding pass and the second grinding pass, obtain the first success rate value by dividing the number of first grinding pass parts by the number of qualified parts, and obtain the second success rate value by dividing the number of second grinding pass parts by the number of qualified parts. Set the influence coefficient of the first success rate value and the second success rate value, multiply the first success rate value and the second success rate value by the corresponding influence coefficient, and then add them to obtain the correction effect value GB3;

[0021] It's important to note that the first-pass and second-pass rates are calculated by calculating the ratio of the number of successfully completed pieces to the total number of pieces processed. These two values can be used to assess the accuracy and consistency of a shop's grinding operations. A high first-pass rate indicates efficient operations and quality control.

[0022] Step 2: Preset the time threshold for secondary grinding of parts, mark the single grinding time less than the time threshold as the advance time value, and mark the single grinding time greater than the time threshold as the delay time value. Count the number of advance time values and mark it as C1, and calculate the time difference between each advance time value and the time threshold to get the advance time. Count the number of delay time values C2, and calculate the time difference between each delay time value and the time threshold to get the delay time. Add all the advance time and delay time and take the average to get the advance mean F1 and delay mean F2. Substitute the advance mean F1, advance number C1, delay mean F2 and delay number C2 into the formula The target grinding workshop's timeliness value GB4 is calculated, where iu1, iu2, iu3, and iu4 are the preset weight factors of the advance mean F1, advance number C1, delay mean F2, and delay number C2, respectively.

[0023] Step 3: Obtain the total number of corrected pieces and the number of corrected pieces of the target grinding workshop, calculate the difference between the total number of corrected pieces and the number of corrected pieces to obtain the number of pieces to be processed GB5, and calculate the difference between the number of corrected pieces and the number of qualified pieces to obtain the number of scrapped pieces GB6; calculate the greater of the distance value GB1 of the target grinding workshop, the number of qualified pieces GB2 of the month, the correction efficiency value GB3, the aging value GB4, the number of pieces to be processed GB5 of the month, and the number of scrapped pieces GB6 of the month to obtain the work efficiency value GX of the target workshop;

[0024] According to the formula Among them, m1, m2, m3, m4, m5 and m6 are the preset weight factors of the distance value GB1, the number of qualified pieces in the month GB2, the correction value GB3, the timeliness value GB4, the number of pieces to be processed in the month GB5 and the number of scrapped pieces in the month GB6 respectively, and μ is the preset correction factor;

[0025] The grinding workshop with the largest work efficiency value GX is selected, and the corresponding part number is sent to the target grinding workshop. After adjusting the conveying direction of the part, the part is conveyed to the grinding workshop, and the total number of corrected parts in the grinding workshop for that month is increased by one.

[0026] When the preset range three is matched, a defective product recycling instruction is generated, the total number of parts to be processed in each recycling workshop is obtained, and the recycling workshop with the smallest total number is marked as the target recycling workshop. The number of the part is sent to the target recycling workshop, and the total number of parts to be processed in the target recycling workshop is increased by one.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The present invention collects image information of the detected parts, pre-processes the image information and then constructs a three-dimensional model of the parts. The parts are divided into welding area, coating area and remaining area according to their application direction. Multiple data points are arranged on the surface of each divided area, and the vertical distance between each data point and the reference plane is analyzed to obtain the flatness value of each divided area. The flatness values of each divided area are integrated to obtain the qualified value of the detected part. The obtained qualified value can reflect the quality of the parts, replacing manual inspection, saving manpower and improving the readiness of inspection.

[0029] 2. The present invention can generate corresponding instructions, including expected compliance instructions, secondary processing instructions, and defective product recycling instructions, by matching the qualification values of parts and components within the corresponding value range. While generating the secondary processing instructions, the various parameters of the polishing workshop in the application scenario are analyzed to obtain the work efficiency value. The work efficiency value can reflect the distance, polishing effect, and polishing speed of the polishing workshop, so that the polishing workshop with the largest work efficiency value can be selected, and the parts can be transported by transportation tools such as conveyor belts, and automatically distributed and processed according to the qualification values of the parts, thereby improving the overall efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0031] Figure 1 Flow chart of the detection method of the present invention;

[0032] Figure 2 This is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0033] In order to help those skilled in the art better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] The following is combined with Figure 1-Figure 2 The present invention is described in further detail:

[0036] First embodiment:

[0037] See also Figure 1-Figure 2 As shown in FIG, a quality inspection method for new energy vehicle parts is implemented through a model building module, a layout analysis module, an instruction generation module, and a processing execution module; specifically:

[0038] The model building module is used to obtain the part number and image information of all directions, and build a three-dimensional model of the component based on the obtained image information, and send the built model to the layout analysis module;

[0039] The point distribution analysis module is used to obtain the constructed 3D model and set a reference datum plane based on the constructed model. The surface of the component is divided into multiple point distribution areas according to the application direction. Multiple data points are set in each point distribution area. The vertical distance between the multiple data points in the target area and the reference datum plane is analyzed to obtain the flatness value of the divided area. Specifically:

[0040] Step 1: Mark the flatness value of the target component as J c D , where C is used to represent the number of the component; D is used to represent the number of the divided area, D = 1, 2, 3, and D1, D2 and D3 represent the welding area, coating area and remaining area respectively. Set a weight coefficient for each divided area;

[0041] It should be noted that the weight coefficients corresponding to the welding area, the coating area and the remaining area are marked as Y1, Y2 and Y3 respectively, and Y1>Y2>Y3.

[0042] Step 2: Obtain the vertical distance between each data point in the target area and the parameter reference plane, compare each distance value with the set threshold range one by one, count the number of data points with distance values less than the threshold range and mark them as abnormal number PA1; calculate the difference between the distance value of each abnormal number in the target area and the lower limit of the threshold range and mark it as the convex value, add all the convex values and take the average to obtain the convex mean, count the number of convex values higher or lower than the convex mean and mark them as high value R1 and low value R2 respectively, calculate the sum of the convex values classified as high values and take the average to obtain the high mean, calculate the sum of the convex values classified as low values and take the average to obtain the low mean, set the high mean and low mean to correspond to an influence coefficient K1 and K2 respectively, and K1>K2, multiply the high mean and low mean and the corresponding influence coefficients K1 and K2 to calculate the score. The high impact value P1 and the low impact value P2 are obtained respectively, and the high value R1 and the high impact value P1 are substituted into the formula PA2=R1×ea1+P1×ea2 to obtain the over-threshold value PA2 by calculation, wherein ea1 and ea2 are respectively the preset weight factors of the high value R1 and the high impact value P1, and the low value R2 and the low impact value P2 are substituted into the formula PA3=R2×eb1+P2×eb2 to obtain the over-threshold value PA3 by calculation, wherein eb1 and eb2 are respectively the preset weight factors of the low value R2 and the low impact value P2; the allowed number of abnormalities threshold PA1", the over-threshold value threshold PA2" and the over-threshold value threshold PA3" of the target area are set, and the three abnormalities PA1, the over-threshold value PA2 and the over-threshold value PA3, the corresponding three allowed thresholds and the weight coefficients of the corresponding divided areas are processed to obtain the smoothing value J of the divided area. c D ;

[0043] According to the formula Where sq1, sq2 and sq3 are respectively the preset weighting factors of the ratio of the number of abnormalities PA1 to the abnormality number threshold PA1", the ratio of the over-threshold value PA2 to the allowed over-threshold value threshold PA2", and the ratio of the over-threshold value PA3 to the over-threshold value threshold PA3". Di is the weight coefficient of the corresponding divided area, where i = 1, 2, 3; η is the preset correction factor;

[0044] Set the smoothing value J for each divided area c D The allowable threshold value of each divided area of the target component is J c D And the corresponding allowable threshold is processed to obtain the qualified degree value HG of the target component c And send it to the instruction generation module;

[0045] According to the formula Among them Jc D1" 、J c D2" and J c D3" They represent the allowable thresholds of the flatness values of the welding area, the coating area, and the remaining area, respectively; qw1, qw2, and qw3 are the preset weighting factors of the ratios of the flatness values of the welding area, the coating area, and the remaining area to the respective allowable thresholds, respectively; and χ is a preset correction factor;

[0046] The instruction generation module is used to receive the qualification value HG of the target component c and the qualification value HG c Match the corresponding preset range, set three preset ranges, and each preset range corresponds to a processing instruction, and send the generated processing instructions to the processing execution module. The processing instructions include expected standard compliance instructions, secondary processing instructions, and defective product recycling instructions;

[0047] The processing execution module is used to receive processing instructions for corresponding components and perform corresponding operations, specifically:

[0048] When the preset range is matched, the expected standard instruction is generated, and the target component is transported to the assembly workshop for the next step of process processing and assembly;

[0049] When the preset range 2 is matched, a secondary processing instruction is generated, the target component number and the corresponding real-time position on the conveyor belt are obtained, and the work efficiency value GX of each grinding workshop is analyzed, specifically:

[0050] Step 1: Obtain the location of each grinding workshop, calculate the distance difference with the location of the target component to obtain the distance value GB1; obtain the number of qualified parts GB2 of each grinding workshop in the current month, classify the qualified parts according to the first grinding pass and the second grinding pass, obtain the first success rate value by dividing the number of first grinding pass parts by the number of qualified parts, and obtain the second success rate value by dividing the number of second grinding pass parts by the number of qualified parts. Set the influence coefficient of the first success rate value and the second success rate value, multiply the first success rate value and the second success rate value by the corresponding influence coefficient, and then add them to obtain the correction effect value GB3;

[0051] It's important to note that the first-pass and second-pass rates are calculated by calculating the ratio of the number of successfully completed pieces to the total number of pieces processed. These two values can be used to assess the accuracy and consistency of a shop's grinding operations. A high first-pass rate indicates efficient operations and quality control.

[0052] Step 2: Preset the time threshold for secondary grinding of parts, mark the single grinding time less than the time threshold as the advance time value, and mark the single grinding time greater than the time threshold as the delay time value. Count the number of advance time values and mark it as C1, and calculate the time difference between each advance time value and the time threshold to get the advance time. Count the number of delay time values C2, and calculate the time difference between each delay time value and the time threshold to get the delay time. Add all the advance time and delay time and take the average to get the advance mean F1 and delay mean F2. Substitute the advance mean F1, advance number C1, delay mean F2 and delay number C2 into the formula The target grinding workshop's timeliness value GB4 is calculated, where iu1, iu2, iu3, and iu4 are the preset weight factors of the advance mean F1, advance number C1, delay mean F2, and delay number C2, respectively.

[0053] Step 3: Obtain the total number of corrected pieces and the number of corrected pieces of the target grinding workshop, calculate the difference between the total number of corrected pieces and the number of corrected pieces to obtain the number of pieces to be processed GB5, and calculate the difference between the number of corrected pieces and the number of qualified pieces to obtain the number of scrapped pieces GB6; calculate the greater of the distance value GB1 of the target grinding workshop, the number of qualified pieces GB2 of the month, the correction efficiency value GB3, the aging value GB4, the number of pieces to be processed GB5 of the month, and the number of scrapped pieces GB6 of the month to obtain the work efficiency value GX of the target workshop;

[0054] According to the formula Among them, m1, m2, m3, m4, m5 and m6 are the preset weight factors of the distance value GB1, the number of qualified pieces in the month GB2, the correction value GB3, the timeliness value GB4, the number of pieces to be processed in the month GB5 and the number of scrapped pieces in the month GB6 respectively, and μ is the preset correction factor;

[0055] The grinding workshop with the largest work efficiency value GX is selected, and the corresponding part number is sent to the target grinding workshop. After adjusting the conveying direction of the part, the part is conveyed to the grinding workshop, and the total number of corrected parts in the grinding workshop for that month is increased by one.

[0056] When the preset range three is matched, a defective product recycling instruction is generated, the total number of parts to be processed in each recycling workshop is obtained, and the recycling workshop with the smallest total number is marked as the target recycling workshop. The number of the part is sent to the target recycling workshop, and the total number of parts to be processed in the target recycling workshop is increased by one.

[0057] When the present invention is used: the number of the corresponding inspection part is obtained, and image information of all directions of the part is collected at the same time. After pre-processing the collected image information, a three-dimensional model of the inspection part is constructed based on the image information. The surface of the part is divided into multiple distribution areas according to the application direction of the part, and multiple data points are set in each distribution area. The vertical distance between the multiple data points in the target area and the reference datum plane is analyzed to obtain the flatness value of the divided area, and the flatness values of each divided area are integrated to obtain the qualified value of the part; the qualified value obtained by the integrated processing reflects the quality of the corresponding numbered part, and three ranges matching the qualified value will be preset in the database at the same time, and each value range corresponds to a processing method. The obtained qualified value is matched with the corresponding range to obtain the corresponding processing method, and then the corresponding part will be transported to the corresponding workshop according to the processing method of the numbered part.

[0058] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A new energy vehicle parts quality inspection method, characterized in that: The following steps are involved: The model building module obtains the component number and image information of all directions, builds a three-dimensional model of the component based on the obtained image information, and sends the constructed model to the layout analysis module; Based on the constructed model, the point distribution analysis module sets a reference datum plane, divides its surface into multiple point distribution areas according to the application direction of the component, and sets multiple data points in each point distribution area. The vertical distance between the multiple data points in the target area and the reference datum plane is analyzed to obtain the flatness value of the divided area. The flatness values of each divided area are integrated to obtain the qualification value of the component and send it to the instruction generation module. The instruction generation module receives the qualification value of the target component and matches the qualification value to the corresponding preset range. Three preset ranges are set, and each preset range corresponds to a processing instruction. The generated processing instructions are sent to the processing execution module. The processing instructions include expected compliance instructions, secondary processing instructions, and defective product recycling instructions. The processing execution module receives the processing instructions of the corresponding components and performs the corresponding operations, specifically: When the preset range is matched, the expected standard instruction is generated, and the target component is transported to the assembly workshop for the next step of process processing and assembly; When the part matches the second preset range, a secondary processing instruction is generated. The target part number and the corresponding real-time position on the conveyor belt are obtained. The work efficiency values of each grinding workshop are analyzed, and the grinding workshop with the highest work efficiency value is selected. The corresponding part number is sent to the target grinding workshop. After adjusting the conveying direction of the part, the part is conveyed to the grinding workshop. At the same time, the total number of corrected parts in the grinding workshop that month is increased by one. When the preset range three is matched, a defective product recycling instruction is generated, the total number of parts to be processed in each recycling workshop is obtained, and the recycling workshop with the smallest total number is marked as the target recycling workshop. The number of the part is sent to the target recycling workshop, and the total number of parts to be processed in the target recycling workshop is increased by one.

2. A new energy vehicle parts quality inspection method according to claim 1, characterized in that: The point distribution analysis module analyzes the vertical distances between multiple data points in the target area and the reference datum plane to obtain the leveling value of the divided area, specifically: Step 1: Mark the flatness value of the target component as J c D , where C is used to represent the number of the component; D is used to represent the number of the divided area, D = 1, 2, 3, and D1, D2 and D3 represent the welding area, coating area and remaining area respectively. Set a weight coefficient for each divided area; Step 2: Obtain the vertical distance between each data point in the target area and the parameter reference plane, compare each distance value with the set threshold range one by one, count the number of data points with distance values less than the threshold range and mark them as abnormal numbers; calculate the difference between the distance value of each abnormal number in the target area and the lower limit of the threshold range and mark it as a convex value, add up all the convex values and take the average to obtain the convex mean, count the number of convex values higher or lower than the convex mean and mark them as high values and low values respectively, calculate the sum of the convex values classified as high values and take the average to obtain the high mean, calculate the sum of the convex values classified as low values and take the average The low mean value is obtained by averaging the values, and an influence coefficient is set to be corresponding to the high mean value and the low mean value respectively. The high influence value and the low influence value are calculated between the high mean value and the low mean value and the corresponding influence coefficient respectively. The over-threshold value is obtained by processing the high value and the high influence value, and the over-threshold value is obtained by processing the low value and the low influence value. The threshold value of the number of allowed abnormalities, the threshold value of the over-threshold value and the threshold value of the over-threshold value of the target area are set, and the flattening value J of the divided area is obtained by processing the three values of the number of abnormalities, the over-threshold value and the over-threshold value, the corresponding threshold value and the weight coefficient of the corresponding divided area. c D ; Step 3: Set the smoothing value J for each divided area c D The allowable threshold value of each divided area of the target component is J c D And the corresponding allowable threshold is processed to obtain the qualification value of the target component.

3. A new energy vehicle parts quality inspection method according to claim 2, characterized in that: The specific process of analyzing the work efficiency value of each grinding workshop and obtaining the work efficiency value is as follows: Step 1: Obtain the location of each grinding workshop, calculate the distance difference with the location of the target parts to obtain the distance value; obtain the number of qualified parts of each grinding workshop in the current month, classify the qualified parts according to the first grinding pass and the second grinding pass, obtain the first success rate value by dividing the number of first grinding pass parts by the number of qualified parts, and obtain the second success rate value by dividing the number of second grinding pass parts by the number of qualified parts. Set the influence coefficient of the first success rate value and the second success rate value, multiply the first success rate value and the second success rate value by the corresponding influence coefficient, and then add them to obtain the correction effect value; Step 2: Preset a time threshold for secondary polishing of parts, mark the single polishing time less than the time threshold as an advance time value, and mark the single polishing time greater than the time threshold as a delay time value, count the number of advance time values, and calculate the time difference between each advance time value and the time threshold to obtain the advance time, count the number of delay time values, and calculate the time difference between each delay time value and the time threshold to obtain the delay time, add all the advance time values and delay time values and take the average to obtain the advance mean value and the delay mean value, process the advance mean value, the number of advance values, the delay mean value and the number of delay values to obtain the timeliness value of the target polishing workshop; Step 3: Obtain the total number of corrected pieces and the number of corrected pieces of the target grinding workshop, calculate the difference between the total number of corrected pieces and the number of corrected pieces to obtain the number of pieces to be processed, and calculate the difference between the number of corrected pieces and the number of qualified pieces to obtain the number of scrapped pieces; process the distance value of the target grinding workshop, the number of qualified pieces of the month, the correction efficiency value, the timeliness value, the number of pieces to be processed of the month, and the number of scrapped pieces of the month, whichever is greater, to obtain the work efficiency value of the target workshop.

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