Automobile repair information processing method and system based on artificial intelligence

Abstract

The invention relates to the technical field of data processing, in particular to an automobile repair information processing method and system based on artificial intelligence, and the method comprises the steps: collecting the use duration of a recovered target part and the gray value of the surface of the target part, and solving the remaining life characterization value of the target part; determining the grade of the target part and a corresponding target part processing strategy according to the remaining life characterization value; the processed target parts of the same grade are assembled to a plurality of to-be-repaired automobiles correspondingly, the oscillation frequency of the target parts is collected within the preset running duration of the automobiles, and the stable characteristic values of the parts are obtained; when it is judged that automobile repair does not reach the standard according to the stability characteristic value, whether automobile repair meets the preset standard or not is judged secondarily according to the dominant frequency energy ratio of the target part, or the reason for automobile repair does not reach the standard is determined according to the gap evaluation value of the target part; and the grade of the target part assembled on the automobile which does not reach the standard after being repaired is corrected, so that the repairing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for processing automobile repair information based on artificial intelligence. Background Art

[0002] Automobile repair refers to the process of repairing damaged automobiles and restoring their original functions, appearances, and safety.

[0003] During the automobile repair process, damaged parts often need to be replaced. If these replaced parts are still of use value after inspection, they can be recycled and reused. At the same time, the recycled and reused parts can also provide more options for automobile repair and reduce the repair cost.

[0004] Traditional automobile repair methods often rely on manual experience and intuitive judgment, and there are significant subjectivity and uncertainty in aspects such as the life assessment of automobile parts, the formulation of repair strategies, and the verification of repair effects. This not only affects the efficiency and quality of automobile repair but also may pose a potential threat to driving safety. In terms of the life assessment of automobile parts, traditional assessment methods usually only consider the usage duration of parts, while ignoring factors such as the actual usage environment and wear degree of parts, resulting in inaccurate assessment results. In addition, for recycled parts, there is a lack of scientific classification and processing strategies, causing some parts that still have use value to be wasted, while some parts with potential safety hazards are misused.

[0005] Chinese Patent Publication No.: CN119150082A, discloses a method and device for processing data of an automobile ECU repair instrument. By obtaining the data of the ECU repair instrument and transmitting the data of the ECU repair instrument into a fault detection algorithm for fault detection to obtain a fault detection result, the fault detection algorithm is obtained by optimizing a preset AI algorithm by combining a first algorithm cost and a second algorithm cost. The first algorithm cost is at least one of a data implicit representation cost and a defect detection cost. The data implicit representation cost represents the difference degree between the fault characterization information corresponding to the same in-vehicle machine fault in the data sample library of the ECU repair instrument in the implicit representation of the sample. The defect detection cost represents the difference degree between the fault classification prediction information corresponding to the same in-vehicle machine fault in the data sample library of the ECU repair instrument in the sample classification prediction information.

[0006] It can be seen that the above technical solution only relies on ECU electronic fault data and lacks multi-dimensional state fusion, does not collect the surface gray value and usage duration of parts, cannot identify part wear, and there is a risk of misjudgment; at the same time, the influence of the oscillation frequency of parts on the automobile repair effect is not considered, resulting in low repair efficiency of the automobile. Summary of the Invention

[0007] To this end, the present invention provides a method and system for processing automobile repair information based on artificial intelligence, which are used to overcome the problems in the prior art that only rely on ECU electronic fault data, lack multi-dimensional state fusion, do not collect the surface gray value and service life of parts, cannot identify part wear, and there is a risk of misjudgment; at the same time, the influence of the oscillation frequency of parts on the automobile repair effect is not considered, resulting in low repair efficiency of the automobile.

[0008] To achieve the above object, on the one hand, the present invention provides a method for processing automobile repair information based on artificial intelligence, including:

[0009] Recycling the target part, collecting the service life of the target part and the gray value of the surface of the target part, and obtaining the remaining life characterization value of the target part;

[0010] Determining the grade of the target part and the corresponding target part processing strategy according to the remaining life characterization value;

[0011] Assembling the processed target parts of the same grade into several automobiles to be repaired respectively, collecting the oscillation frequency of the target part within a preset running time of the automobile, and obtaining the stable characteristic value of the part;

[0012] When it is determined that the repair of the automobile does not meet the preset standard according to the stable characteristic value, re-determining whether the repair of the automobile meets the preset standard according to the main frequency energy ratio of the target part, or determining the reason why the repair of the automobile does not meet the preset standard according to the clearance evaluation value of the target part;

[0013] Revising the grade of the target part assembled on the automobile whose repair does not meet the preset standard.

[0014] Further, determining the grade of the target part and the corresponding target part processing strategy according to the remaining life characterization value of the target part, the grade of the target part includes any one of A grade, B grade and C grade, wherein the remaining life grades corresponding to the A grade, the B grade and the C grade decrease in turn.

[0015] Further, the remaining life characterization value is jointly determined by the service life of the target part and the gray value of the surface of the target part.

[0016] Further, it is determined that the repair of the automobile does not meet the preset standard in response to the stable characteristic value of the target part being greater than or equal to the first preset driving stability threshold, wherein the stable characteristic value is determined by the oscillation frequency collected during the driving of the automobile.

[0017] Further, under the condition that the stable characteristic value is greater than or equal to the first preset driving stability threshold and less than the second preset driving stability threshold, re-determining whether the repair of the automobile meets the preset standard according to the main frequency energy ratio;

[0018] And, under the condition that the stable eigenvalue is greater than or equal to the second preset driving stability threshold, determine the reason why the repair of the vehicle does not meet the preset standard according to the clearance evaluation value;

[0019] Furthermore, determine whether the repair of the vehicle meets the preset standard according to the ratio of the main frequency energy of the target part to the total frequency energy, where,

[0020] If the ratio of the main frequency energy is less than the preset ratio of the main frequency energy, it is determined that the repair of the vehicle meets the preset standard;

[0021] If the ratio of the main frequency energy is greater than or equal to the preset ratio of the main frequency energy, it is determined that the repair of the vehicle does not meet the preset standard, and the grade of the target part is corrected;

[0022] The ratio of the main frequency energy is the ratio between the energy of the main frequency band and the energy of the total frequency band.

[0023] Furthermore, determine that the repair of the vehicle does not meet the preset standard according to the clearance evaluation value of the target part, where,

[0024] If the clearance evaluation value is less than the preset clearance evaluation value, it is determined that the reason why the repair of the vehicle does not meet the preset standard is the grading error of the target part and the grade of the target part is corrected;

[0025] If the clearance evaluation value is greater than or equal to the preset clearance evaluation value, it is determined that the reason why the repair of the vehicle does not meet the preset standard is loose assembly and a warning is issued.

[0026] Furthermore, the clearance evaluation value is the ratio of the clearance between the contact part of the target part and the vehicle to the preset clearance.

[0027] Furthermore, the process of correcting the grade of the target part includes:

[0028] Obtain the grade of the target part assembled on the vehicle, denoted as the target grade;

[0029] If the stable eigenvalue of the vehicle equipped with the target part of this grade is greater than or equal to the first preset driving stability threshold and less than the second preset driving stability threshold, then lower the target grade by one level;

[0030] If the stable eigenvalue of the vehicle equipped with the target part of this grade is greater than or equal to the second preset driving stability threshold, then lower the target grade by two levels;

[0031] If the grade after the target grade is lowered exceeds grade C, then scrap the target part.

[0032] On the other hand, the present invention provides a system applicable to an information processing method for vehicle repair based on artificial intelligence, including:

[0033] A data acquisition module, which includes a service life acquisition unit for acquiring the service life of a target part, a surface gray value acquisition unit for acquiring the gray value of the surface of the target part, an oscillation frequency acquisition unit for acquiring the oscillation frequency of the target part, and an energy acquisition unit for acquiring the energy of a frequency band;

[0034] A part rating module, which is connected to the data acquisition module and is used to determine the grade of the target part and the corresponding target part processing strategy according to the remaining life characterization value of the target part;

[0035] An operation detection module, which is connected to the data acquisition module. When it is determined that the repair of the vehicle does not meet the preset standard according to the stable characteristic value, it is used to re-determine whether the repair of the vehicle meets the preset standard according to the main frequency energy ratio of the target part, or to determine the reason why the repair of the vehicle does not meet the preset standard according to the clearance evaluation value of the target part;

[0036] An optimization and adjustment module, which is respectively connected to the part rating module and the operation detection module, and is used to correct the grade of the target part assembled on the vehicle whose repair does not meet the preset standard.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows. The present invention classifies parts according to the service life and surface state of the parts and makes corresponding part processing, reduces the situation of incorrect part scrapping and misusing low-life parts, and reduces the material cost; combines vehicle operation data to monitor the repair quality, and introduces a multi-level determination mechanism to ensure the accuracy of the repair standard. At the same time, it also has an intelligent grade correction function, which can quickly feedback and adjust for non-compliant situations, thereby improving the vehicle repair efficiency.

[0038] Further, the present invention determines the grade of the target part and the corresponding target part processing strategy according to the remaining life characterization value of the target part, classifies the target part into grade A, grade B or grade C, and reduces the repair process and the misuse of low-life parts through the grading strategy, thereby improving the resource utilization efficiency.

[0039] Further, the present invention fuses the service life of the target part and the surface gray value. The service life reflects the historical load accumulation of the part, and the surface gray value detects the material wear condition through image analysis technology, accurately identifies defects, and quantifies the remaining life characterization value, thereby improving the reliability of the evaluation.

[0040] Further, the present invention uses dynamic parameters such as oscillation frequency and main frequency energy ratio to verify the performance of the repaired vehicle, thereby improving the repair efficiency.

[0041] Furthermore, when the stability eigenvalue is greater than or equal to the first preset driving stability threshold, the system preliminarily determines that the repair of the vehicle does not meet the preset standard. Subsequently, the secondary judgment is carried out by using the proportion of the main frequency energy, or the reason for non-compliance is determined according to the clearance evaluation value, thus ensuring the reliability of the repair quality.

[0042] Furthermore, by dynamically adjusting the part level, the system of the present invention can timely reflect the state change according to the actual performance of the part, improving the flexibility and accuracy of part management. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flowchart of the method for processing vehicle repair information based on artificial intelligence according to an embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of module connection of the system applicable to the method for processing vehicle repair information based on artificial intelligence according to an embodiment of the present invention;

[0045] Figure 3 is a flowchart of determining the level of the target part and the corresponding target part processing strategy according to the remaining life characterization value of the target part according to an embodiment of the present invention;

[0046] Figure 4 is a flowchart of secondary determination of whether the repair of the vehicle meets the preset standard according to the proportion of the main frequency energy of the target part according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] The preferred embodiments of the present invention will be described below with reference to the drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present invention and do not limit the protection scope of the present invention.

[0049] It should be noted that the data in this embodiment are obtained through comprehensive analysis and evaluation of the historical detection data and corresponding historical detection results in the three months before the current detection of the present invention. Those skilled in the art can understand that the determination method of the present invention for the above single parameter can be to select the value with the highest proportion according to the data distribution as the preset standard parameter, use weighted summation to obtain the value as the preset standard parameter, substitute each historical data into a specific formula and use the value obtained by this formula as the preset standard parameter or other selection methods, as long as it satisfies that the method of the present invention can clearly define different specific situations in the single-item determination process through the obtained values.

[0050] Please refer to Figure 1 、 Figure 2 、 Figure 3 and Figure 4 as shown, which are respectively the flowchart of the method for processing automobile repair information based on artificial intelligence in an embodiment of the present invention; the schematic diagram of module connection of the system for the method for processing automobile repair information based on artificial intelligence in an embodiment of the present invention; the flowchart of determining the grade of a target part and the corresponding target part processing strategy according to the remaining life characterization value of the target part in an embodiment of the present invention; the flowchart of secondarily determining whether the repair of an automobile meets a preset standard according to the main frequency energy ratio of the target part in an embodiment of the present invention.

[0051] Refer to Figure 1 as shown. On the one hand, an embodiment of the present invention provides a method for processing automobile repair information based on artificial intelligence, including:

[0052] Step S1, recycling the target part, collecting the usage duration of the target part and the gray value on the surface of the target part, and obtaining the remaining life characterization value of the target part;

[0053] Step S2, determining the grade of the target part and the corresponding target part processing strategy according to the remaining life characterization value;

[0054] Step S3, respectively assembling the processed target parts of the same grade to several automobiles to be repaired, collecting the oscillation frequency of the target part within 30 minutes of the preset running duration of the automobile, and obtaining the stable characteristic value of the part;

[0055] Step S4, when it is determined that the repair of the automobile does not meet the preset standard according to the stable characteristic value, secondarily determining whether the repair of the automobile meets the preset standard according to the main frequency energy ratio of the target part, or determining the reason why the repair of the automobile does not meet the preset standard according to the clearance evaluation value of the target part;

[0056] Step S5, correcting the grade of the target part assembled on the automobile whose repair does not meet the preset standard.

[0057] In this embodiment, the target part is selected as a shaft part.

[0058] Refer to Figure 3 as shown. Specifically, determining the grade of the target part and the corresponding target part processing strategy according to the remaining life characterization value of the target part, the grade of the target part includes any one of A grade, B grade and C grade, and the remaining life grades corresponding to the A grade, the B grade and the C grade decrease in turn. Among them,

[0059] if the remaining life characterization value is less than the first preset remaining life characterization value of 0.4, it is determined that the grade of the target part is C grade, and the target part is repaired by welding;

[0060] If the remaining life characterization value is greater than or equal to the first preset remaining life characterization value and less than 0.8 times the second preset remaining life characterization value, determine that the grade of the target part is B, and perform surface cleaning on the target part;

[0061] If the remaining life characterization value is greater than or equal to the second preset remaining life characterization value, determine that the grade of the target part is A, and do not process the target part.

[0062] Specifically, the remaining life characterization value is jointly determined by the usage duration of the target part and the gray value on the surface of the target part.

[0063] The remaining life characterization value is calculated by the following formula:

[0064]

[0065] In the formula, P represents the remaining life characterization value; α represents the first weight, and α is set to 0.56; T represents the usage duration of the target part; T0 represents the design life of the target part; β represents the second weight, and β is set to 0.44; G represents the gray value on the surface of the target part; G0 represents the preset gray value on the surface of the target part, and G0 is set to 180.

[0066] In practice, the generally selected range of the first preset remaining life characterization value is [0.2, 0.5], and the generally selected range of the second preset remaining life characterization value is [0.6, 0.9]. Preferably, the first preset remaining life characterization value is selected as 0.4, and the second preset remaining life characterization value is selected as 0.8.

[0067] Specifically, it is determined whether the repair of the vehicle meets the preset standard according to the stable characteristic value of the target part, where

[0068] If the stable characteristic value is less than the first preset driving stability threshold of 50 Hz, it is determined that the repair of the vehicle meets the preset standard;

[0069] If the stable characteristic value is greater than or equal to the first preset driving stability threshold and less than the second preset driving stability threshold of 70 Hz, it is determined that the repair of the vehicle does not meet the preset standard, and it is further determined whether the repair of the vehicle meets the preset standard according to the main frequency energy ratio;

[0070] If the stable characteristic value is greater than or equal to the second preset driving stability threshold, it is determined that the repair of the vehicle does not meet the preset standard, and the reason why the repair of the vehicle does not meet the preset standard is determined according to the clearance evaluation value.

[0071] The stable characteristic value is determined by the oscillation frequency collected during the vehicle driving process;

[0072] The stable eigenvalue is calculated by the following formula:

[0073]

[0074] In the formula, F represents the stable eigenvalue; f i represents the oscillation frequency of the target part recorded for the i-th time; i = 1, 2, 3... n; n represents the total number of records.

[0075] Refer to Figure 4 as shown. Specifically, it is determined whether the repair of the vehicle meets the preset standard according to the ratio of the main frequency energy of the target part. Among them,

[0076] If the ratio of the main frequency energy is less than the preset ratio of the main frequency energy of 0.65, it is determined that the repair of the vehicle meets the preset standard;

[0077] If the ratio of the main frequency energy is greater than or equal to the preset ratio of the main frequency energy, it is determined that the repair of the vehicle does not meet the preset standard, and the grade of the target part is corrected;

[0078] The ratio of the main frequency energy is the ratio between the energy of the main frequency band and the total frequency band energy.

[0079] In this embodiment, the main frequency band is set to 80 Hz - 120 Hz, but the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0080] Specifically, it is determined that the repair of the vehicle does not meet the preset standard according to the clearance evaluation value of the target part. Among them,

[0081] If the clearance evaluation value is less than the preset clearance evaluation value of 1.2, it is determined that the reason why the repair of the vehicle does not meet the preset standard is the grading error of the target part, and the grade of the target part is corrected;

[0082] If the clearance evaluation value is greater than or equal to the preset clearance evaluation value, it is determined that the reason why the repair of the vehicle does not meet the preset standard is loose assembly and a warning is issued.

[0083] Specifically, the clearance evaluation value is the ratio of the clearance between the contact part of the target part and the vehicle to the preset clearance of 0.78 mm.

[0084] The clearance between the contact part of the target part and the vehicle is obtained by a laser displacement sensor.

[0085] Specifically, the process of correcting the grade of the target part includes:

[0086] Obtain the grade of the target part assembled on the vehicle, denoted as the target grade;

[0087] If the stability characteristic value of the vehicle equipped with the target part of this level is greater than or equal to the first preset driving stability threshold and less than the second preset driving stability threshold, then lower the target level by one level;

[0088] If the stability characteristic value of the vehicle equipped with the target part of this level is greater than or equal to the second preset driving stability threshold, then lower the target level by two levels;

[0089] If the level after the target level is lowered exceeds Class C, then scrap the target part.

[0090] Refer to Figure 2 As shown, on the other hand, the present invention provides a system applicable to an information processing method for vehicle repair based on artificial intelligence, including:

[0091] A data acquisition module, which includes a duration acquisition unit for acquiring the usage duration of the target part, a grayscale acquisition unit for acquiring the grayscale value on the surface of the target part, an oscillation acquisition unit for acquiring the oscillation frequency of the target part, and an energy acquisition unit for acquiring the band energy;

[0092] A part rating module, which is connected to the data acquisition module and is used to determine the level of the target part and the corresponding target part processing strategy according to the remaining life characterization value of the target part;

[0093] An operation detection module, which is connected to the data acquisition module and is used to, when it is determined that the repair of the vehicle does not meet the preset standard according to the stability characteristic value, re-determine whether the repair of the vehicle meets the preset standard according to the main frequency energy ratio of the target part, or determine the reason why the repair of the vehicle does not meet the preset standard according to the clearance evaluation value of the target part;

[0094] An optimization and adjustment module, which is respectively connected to the part rating module and the operation detection module and is used to correct the level of the target part assembled on the vehicle whose repair does not meet the preset standard.

[0095] Specifically, no limitation is imposed on the specific structures of the part rating module, the operation detection module, and the optimization and adjustment module. They and their respective units can be composed of logic components, and the logic components include field programmable components, computers, or microprocessors in a computer.

[0096] Specifically, the grayscale acquisition unit is composed of an industrial camera and the image processing software OpenCV; the oscillation acquisition unit is an acceleration sensor installed on the surface of the target part; the energy acquisition unit is a spectrum analyzer.

[0097] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0098] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An information processing method for vehicle repair based on artificial intelligence, characterized in that, Including: Recycling the target part, collecting the usage duration of the target part and the gray value of the surface of the target part, and obtaining the remaining life characterization value of the target part; Determining the grade of the target part and the corresponding target part processing strategy according to the remaining life characterization value; Assembling the processed target parts of the same grade to several automobiles to be repaired respectively, collecting the oscillation frequency of the target part within a preset running duration of the automobile, and obtaining the stable characteristic value of the part; When it is determined that the repair of the automobile does not meet the preset standard according to the stable characteristic value, re-determining whether the repair of the automobile meets the preset standard according to the main frequency energy ratio of the target part, or determining the reason why the repair of the automobile does not meet the preset standard according to the clearance evaluation value of the target part; Revising the grade of the target part assembled on the automobile whose repair does not meet the preset standard.

2. The method for processing automobile repair information based on artificial intelligence according to claim 1, characterized in that, Determining the grade of the target part and the corresponding target part processing strategy according to the remaining life characterization value of the target part, where the grade of the target part includes any one of grade A, grade B, and grade C, and among them, the remaining life grades corresponding to grade A, grade B, and grade C decrease in sequence.

3. The method for processing vehicle repair information based on artificial intelligence according to claim 2, characterized in that The remaining life characterization value is jointly determined by the usage duration of the target part and the gray value of the surface of the target part.

4. The method for processing automobile repair information based on artificial intelligence according to claim 3, wherein Responding to the stable characteristic value of the target part being greater than or equal to the first preset driving stability threshold, it is determined that the repair of the automobile does not meet the preset standard, where the stable characteristic value is determined by the oscillation frequency collected during the driving of the automobile.

5. The method for processing automobile repair information based on artificial intelligence according to claim 4, wherein Under the condition that the stable characteristic value is greater than or equal to the first preset driving stability threshold and less than the second preset driving stability threshold, re-determining whether the repair of the automobile meets the preset standard according to the main frequency energy ratio; And under the condition that the stable characteristic value is greater than or equal to the second preset driving stability threshold, determining the reason why the repair of the automobile does not meet the preset standard according to the clearance evaluation value.

6. The method for processing automobile repair information based on artificial intelligence according to claim 5, wherein Re-determining whether the repair of the automobile meets the preset standard according to the main frequency energy ratio of the target part, where If the main frequency energy ratio is less than the preset main frequency energy ratio, it is determined that the repair of the automobile meets the preset standard; If the main frequency energy ratio is greater than or equal to the preset main frequency energy ratio, it is determined that the repair of the automobile does not meet the preset standard, and the grade of the target part is revised; The main frequency energy ratio is the ratio between the main frequency band energy and the total band energy.

7. The method for processing automobile repair information based on artificial intelligence according to claim 6, wherein, Determining that the repair of the automobile does not meet the preset standard according to the clearance evaluation value of the target part, where If the clearance evaluation value is less than the preset clearance evaluation value, it is determined that the reason why the repair of the automobile does not meet the preset standard is the grading error of the target part, and the grade of the target part is revised; If the clearance evaluation value is greater than or equal to the preset clearance evaluation value, it is determined that the reason why the repair of the automobile does not meet the preset standard is loose assembly, and a warning is issued.

8. The method for processing automobile repair information based on artificial intelligence according to claim 7, characterized in that, The clearance evaluation value is the ratio of the clearance between the contact part of the target part and the automobile to the preset clearance.

9. The method for processing automobile repair information based on artificial intelligence according to claim 8, characterized in that The process of revising the grade of the target part includes: Obtaining the grade of the target part assembled on the automobile, denoted as the target grade; If the stability characteristic value of the vehicle equipped with the target part of this level is greater than or equal to the first preset driving stability threshold and less than the second preset driving stability threshold, the target level will be lowered by one level; If the stability characteristic value of the vehicle equipped with the target part of this level is greater than or equal to the second preset driving stability threshold, the target level will be lowered by two levels; If the level after the target level is lowered exceeds level C, the target part will be scrapped.

10. A system applicable to the artificial intelligence-based automotive repair information processing method according to any one of claims 1-9, characterized in that, It includes: A data acquisition module, which includes a duration acquisition unit for acquiring the usage duration of the target part, a grayscale acquisition unit for acquiring the grayscale value on the surface of the target part, an oscillation acquisition unit for acquiring the oscillation frequency of the target part, and an energy acquisition unit for acquiring the band energy; A part rating module, which is connected to the data acquisition module, and is used to determine the level of the target part and the corresponding target part processing strategy according to the remaining life characterization value of the target part; An operation detection module, which is connected to the data acquisition module, and is used to determine that when the repair of the vehicle does not meet the preset standard according to the stability characteristic value, re-determine whether the repair of the vehicle meets the preset standard according to the main frequency energy ratio of the target part, or determine the reason why the repair of the vehicle does not meet the preset standard according to the clearance evaluation value of the target part; An optimization adjustment module, which is respectively connected to the part rating module and the operation detection module, and is used to correct the level of the target part assembled on the vehicle whose repair does not meet the preset standard.

Images