CN115240193A - Surface treatment method and system for motor spindle of electric vehicle - Google Patents
Surface treatment method and system for motor spindle of electric vehicle Download PDFInfo
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Abstract
The invention provides a surface treatment method and a system for a motor spindle of an electric vehicle, which relate to the technical field of data processing, and are characterized in that microscopic image characteristics are obtained by analyzing a microscopic image of a target motor spindle, surface defect identification is carried out according to the microscopic image characteristics, and a surface defect set is output; and when the abnormal probability is greater than or equal to the preset abnormal probability, generating an early warning instruction to remind a target motor spindle of abnormality. The technical problems that in the prior art, the dependence of the detection and elimination of the surface defects of the motor spindle on manual experience is high, and whether the surface defects exist in the motor spindle or not needs technical personnel to judge based on work experience are solved. The technical effects that whether the surface defects are generated during the processing of the motor spindle is rapidly known, and the dependency of a surface defect detection task on the experience of technical personnel is reduced are achieved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a surface processing method and system for a motor spindle of an electric automobile.
Background
The motor spindle is an important part in the motor, is a link for electromechanical energy conversion between the motor and equipment, and plays a role in supporting the rotating part and determining the relative position of the rotating part to the stator.
In the processing process of the motor spindle, process steps such as blanking, turning, infiltration grinding and the like need to be adopted to process a motor blank to generate the motor spindle, when the motor spindle is processed, the turning process causes the temperature change of the motor spindle and the influence of various factors of the cutting speed, so that the surface of the motor spindle generates defects, and in order to avoid the surface defects of the motor spindle, the method is usually realized by adjusting process parameters such as power, lubricating oil adding frequency and the like of the turning and other processing processes and changing the heat dissipation area and the material properties of processing tools.
The technical problems that the dependence of discovering and eliminating the surface defects of the motor spindle on manual experience is high, and whether the surface defects exist in the motor spindle or not needs technical personnel to judge based on working experience exist in the prior art.
Disclosure of Invention
The application provides a surface treatment method and a surface treatment system for a motor spindle of an electric automobile, which are used for solving the technical problems that in the prior art, the dependence on manual experience of discovering and eliminating surface defects of a processed motor spindle is strong, and whether the surface defects exist in the motor spindle needs to be judged by technical personnel based on working experience.
In view of the above problems, the present application provides a surface treatment method and system for a motor spindle of an electric vehicle.
In a first aspect of the present application, there is provided a surface treatment method for a spindle of a motor of an electric vehicle, the method including: according to the optical microscope, microscopic image acquisition is carried out on a main shaft of a target motor, and a microscopic image set is output; connecting a microscopic image management system, and building an image characteristic analysis model; inputting the microscopic image set into the image characteristic analysis model, and outputting microscopic image characteristics according to the image characteristic analysis model; identifying surface defects according to the characteristics of the microscopic image, and outputting a surface defect set; obtaining a surface defect distribution coordinate based on the surface defect set; calculating the defect abnormal probability according to the surface defect distribution coordinate to obtain the abnormal probability; judging whether the abnormal probability is larger than or equal to a preset abnormal probability; and if the abnormal probability is larger than or equal to the preset abnormal probability, generating an early warning instruction, wherein the early warning instruction is used for reminding that the target motor spindle is abnormal.
In a second aspect of the present application, there is provided a surface treatment system for a spindle of a motor of an electric vehicle, the system comprising: the image acquisition execution module is used for acquiring a microscopic image of the main shaft of the target motor according to the optical microscope and outputting a microscopic image set; the analysis model building module is used for connecting the microscopic image management system and building an image characteristic analysis model; the image characteristic generating module is used for inputting the microscopic image set into the image characteristic analysis model and outputting the microscopic image characteristics according to the image characteristic analysis model; the surface defect identification module is used for identifying the surface defects according to the characteristics of the microscopic image and outputting a surface defect set; a defect distribution obtaining module, configured to obtain a surface defect distribution coordinate based on the surface defect set; the abnormal probability calculation module is used for calculating the defect abnormal probability according to the surface layer defect distribution coordinate to obtain the abnormal probability; the abnormal probability comparison module is used for judging whether the abnormal probability is greater than or equal to a preset abnormal probability; and the early warning instruction generating module is used for generating an early warning instruction if the abnormal probability is greater than or equal to the preset abnormal probability, wherein the early warning instruction is used for reminding the target motor spindle of abnormality.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the method provided by the embodiment of the application, microscopic image collection is carried out on a target motor spindle according to an optical microscope, a microscopic image set is output, a high-precision image is provided for subsequent analysis of the surface defect type of the motor spindle, a microscopic image management system is connected, an image characteristic analysis model is built, the microscopic image set is input into the image characteristic analysis model, and microscopic image characteristics are output according to the image characteristic analysis model; identifying surface defects according to the characteristics of the microscopic image, outputting a surface defect set to obtain the specific type of the surface defects of the motor spindle, and obtaining surface defect distribution coordinates based on the surface defect set; and calculating the defect abnormal probability according to the surface layer defect distribution coordinate to obtain the abnormal probability, judging whether the abnormal probability is more than or equal to the preset abnormal probability, avoiding the condition that the small defect without damage and elegance needs to be processed on the motor spindle processing device, and if the abnormal probability is more than or equal to the preset abnormal probability, generating an early warning instruction to remind the target motor spindle of abnormality. The technical effects that whether the surface layer defect needs to be eliminated by adjusting the machining process during the machining of the motor spindle is rapidly known, and the dependency of a surface layer defect detection task on the experience of technical personnel is reduced are achieved.
Drawings
FIG. 1 is a schematic flow chart of a surface treatment method for a motor spindle of an electric vehicle according to the present disclosure;
fig. 2 is a schematic flow chart illustrating the process of obtaining the defect abnormal probability in the surface treatment method for the motor spindle of the electric vehicle provided by the present application;
fig. 3 is a schematic flow chart illustrating adjustment of defect anomaly probability in the surface treatment method for the motor spindle of the electric vehicle according to the present application;
fig. 4 is a schematic structural diagram of a surface treatment system for a motor spindle of an electric vehicle according to the present application.
Description of reference numerals: the system comprises an image acquisition execution module 11, an analysis model construction module 12, an image feature generation module 13, a surface layer defect identification module 14, a defect distribution obtaining module 15, an abnormal probability calculation module 16, an abnormal probability comparison module 17 and an early warning instruction generation module 18.
Detailed Description
The application provides a surface treatment method and a surface treatment system for a motor spindle of an electric automobile, which are used for solving the technical problems that in the prior art, the dependence of the detection and elimination of the surface defects of the processed motor spindle on manual experience is strong, and whether the surface defects exist in the motor spindle needs to be judged by technical personnel based on work experience.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
and constructing an image characteristic analysis model to analyze the acquired microscopic image of the motor spindle of the electric automobile, determining whether the microscopic image has surface defect image characteristics, identifying the microscopic image with the surface defect image characteristics, positioning the distribution coordinates of the defects on the motor spindle body, and judging whether the process to be regulated exists in the machining process by analyzing whether the defect abnormal probability is higher than an abnormal probability threshold value. Whether the surface defects needing to be adjusted in the machining process are generated during machining of the motor spindle is rapidly known, and the dependency of a surface defect detection task on the experience of technicians is reduced.
Example one
As shown in fig. 1, the present application provides a surface treatment method for a motor spindle of an electric vehicle, the method is applied to a surface treatment system for a motor spindle of an electric vehicle, the system is in communication connection with an optical microscope, and the method comprises:
s100, acquiring a microscopic image of a target motor spindle according to the optical microscope, and outputting a microscopic image set;
in particular, the motor spindle is an important part in the motor, is a link for electromechanical energy conversion between the motor and the equipment, and simultaneously plays a role in supporting the rotating parts and determining the relative position of the rotating parts to the stator.
In this embodiment, a spatial rectangular coordinate system is provided in the microscopic image acquisition region, a microscopic image acquisition is performed on the surface of the target motor spindle through an optical microscope, so as to obtain the microscopic image set capable of reflecting the processing condition and the processing process trace of the surface of the target motor spindle, each image in the microscopic image set is marked with an image acquisition coordinate, and whether the processing performed on the target motor spindle causes the defect on the surface of the target motor spindle can be known based on the microscopic image set.
S200, connecting a microscopic image management system and building an image characteristic analysis model;
s300, inputting the microscopic image set into the image characteristic analysis model, and outputting the microscopic image characteristics according to the image characteristic analysis model;
specifically, in this embodiment, it is preferable to analyze the collected microscopic image set by using a method of constructing and training an image feature analysis model, so as to obtain information about defects existing on the surface of the target motor spindle.
The method for constructing and training the image feature analysis model is not limited in any way, and the image feature analysis model can be trained based on a historical acquired microscopic image set and historical defect image features as training data, test data and verification data.
Uploading the acquired microscopic image set to the microscopic image management system, inputting the microscopic image set into the image characteristic analysis model through the microscopic image management system for image characteristic analysis, and obtaining the microscopic image characteristics reflecting whether surface layer defects exist in each microscopic image.
S400, identifying surface defects according to the characteristics of the microscopic image and outputting a surface defect set;
s500, obtaining a surface layer defect distribution coordinate based on the surface layer defect set;
specifically, the target motor spindle has surface defects of types including, but not limited to, inclusions, microcracks, grinding cracks, and impact pits. And identifying the surface defects based on the comparison of the microscopic image characteristics output by the image characteristic analysis model with the surface defect types of the motor spindle to obtain a plurality of surface defect microscopic images to form the surface defect set.
As can be known from the explanation content of the description in step S100, each image in the microscopic image set acquired in this embodiment is marked with an image acquisition coordinate, and the surface layer defect distribution coordinate is obtained based on the image acquisition coordinate marked with the surface layer defect mark acquired by the surface layer defect set through back-stepping.
Meanwhile, image similarity analysis is carried out on a plurality of images with similar surface layer defect distribution coordinates, and the problem that the accuracy of subsequent defect abnormal probability calculation is reduced due to the fact that the same defect is recorded for a plurality of times is avoided.
S600, calculating the defect abnormal probability according to the surface defect distribution coordinates to obtain the abnormal probability;
further, as shown in fig. 2, calculating a defect abnormal probability according to the surface defect distribution coordinates to obtain an abnormal probability, where step S600 of the method provided by the present application further includes:
s610, obtaining the motor main shafts of the same batch of the target motor main shafts;
s620, sampling is carried out on the motor main shafts in the same batch, and a sample motor main shaft is output;
s630, outputting the distribution coordinates of the defects on the surface layer of the sample according to the spindle of the sample motor;
and S640, activating a defect abnormal probability calculation module to obtain the abnormal probability when the similarity between the sample surface defect distribution coordinate and the surface defect distribution coordinate reaches a preset similarity threshold.
Further, activating a defect abnormal probability calculation module to obtain the abnormal probability, where step S640 of the method provided by the present application further includes:
s641, activating the abnormal probability calculation module, performing density analysis on the surface layer defect distribution coordinates according to the abnormal probability calculation module, and obtaining a density analysis result, wherein the density analysis result is an analysis result for identifying the density of the surface layer defects;
and S642, calculating the abnormal probability according to the density analysis result and outputting the abnormal probability.
Specifically, in this embodiment, a sample motor spindle set composed of a plurality of motor spindles is obtained by randomly sampling a plurality of motor spindles produced in the same batch as the target motor spindle, and the sample motor spindles are processed by the motor spindle surface defect collecting method in steps S100 to S500 to obtain a plurality of sample surface defect distribution coordinates of the plurality of sample motor spindles.
And setting the similarity threshold of the sample surface layer defect distribution and the target motor spindle surface layer defect distribution coordinate based on experienced motor spindle processing technicians to obtain the preset similarity threshold.
And comparing the similarity of the sample surface defect distribution coordinate with the surface defect distribution coordinate, wherein when the similarity of the sample surface defect distribution coordinate with the surface defect distribution coordinate reaches a preset similarity threshold, the surface defect existing in the current target motor spindle is a problem in the machining process rather than an accident in single machining.
It should be understood that when the motor spindle is machined, the product defect rate is set, the defect that the surface layer defect does not affect the operation of the motor spindle is required to be adjusted, the production line is suspended in operation and maintenance, and the economic benefit is reduced.
Therefore, in this embodiment, when the similarity between the sample surface defect distribution coordinate and the surface defect distribution coordinate reaches a preset similarity threshold, the defect anomaly probability calculation module is activated, density analysis is performed on the surface defect distribution coordinate to obtain the area proportion of the surface defect of the target motor spindle in a predetermined surface area, a density analysis result is obtained to identify the surface defect density, anomaly probability calculation is performed according to the density analysis result, and the anomaly probability is output.
In the embodiment, the surface layer defect of the target motor spindle is compared with the surface layer defects of other motor spindles in the same batch, the surface layer defect caused by accidental machining errors is eliminated, after the surface layer defect of the target motor spindle is determined to belong to the machining normal state error, the defect abnormal probability is calculated, accurate judgment of the surface layer defect type of the target motor spindle is achieved, accurate judgment of the defect severity is achieved, and the technical effect that production efficiency is reduced due to high-frequency adjustment of a machining device is avoided.
S700, judging whether the abnormal probability is more than or equal to a preset abnormal probability;
and S800, if the abnormal probability is larger than or equal to the preset abnormal probability, generating an early warning instruction, wherein the early warning instruction is used for reminding that the target motor spindle is abnormal.
Specifically, in this embodiment, the preset abnormal probability is consistent with the preset similarity threshold of step S600, and can be set by an experienced motor spindle processing technician. And when the abnormal probability is greater than the preset abnormal probability, indicating that the current processing parameters are adopted to process the motor spindle, and obtaining the severity of the defects on the surface layer of the motor spindle to the extent that the severity of the defects needs to be reduced by adjusting the processing technological parameters.
In this embodiment, a preset abnormal probability is set, whether the abnormal probability is greater than or equal to the preset abnormal probability is judged, and if the abnormal probability is greater than or equal to the preset abnormal probability, an early warning instruction is generated, where the early warning instruction is used to remind that the target motor spindle is abnormal.
According to the embodiment, microscopic images of a target motor spindle are collected according to an optical microscope, a microscopic image set is output, a high-precision image is provided for subsequent analysis of the surface defect type of the motor spindle, a microscopic image management system is connected, an image characteristic analysis model is set up, the microscopic image set is input into the image characteristic analysis model, and microscopic image characteristics are output according to the image characteristic analysis model; identifying surface defects according to the characteristics of the microscopic image, outputting a surface defect set to obtain the specific type of the surface defects of the motor spindle, and obtaining surface defect distribution coordinates based on the surface defect set; and calculating the defect abnormal probability according to the surface layer defect distribution coordinate to obtain the abnormal probability, judging whether the abnormal probability is more than or equal to the preset abnormal probability, avoiding the condition that the small defect without damage and elegance needs to be processed on the motor spindle processing device, and if the abnormal probability is more than or equal to the preset abnormal probability, generating an early warning instruction to remind the target motor spindle of abnormality. The technical effects that whether the surface layer defect needs to be eliminated by adjusting the machining process during the machining of the motor spindle is rapidly known, and the dependency of a surface layer defect detection task on the experience of technical personnel is reduced are achieved.
Further, if the abnormal probability is greater than or equal to the preset abnormal probability, after the early warning instruction is generated, the method provided by the application further comprises the following steps:
s910, connecting a process parameter control system of the target motor spindle according to the early warning instruction to obtain real-time control parameters;
s920, performing correlation analysis according to the surface layer defect set, and outputting a correlation control index;
s930, performing parameter identification from the real-time control parameters by using the related control indexes, and outputting identification parameters;
and S940, the identification parameters are used as parameters to be processed for processing.
Specifically, in this embodiment, when the anomaly probability is greater than or equal to the preset anomaly probability, an early warning instruction is generated to remind that the target motor spindle is anomalous, and the electric vehicle motor spindle surface processing system is connected with the process parameter control system of the target motor spindle to acquire real-time control parameters based on the early warning instruction.
And performing correlation analysis according to various defect states in the surface layer defect set to obtain a processing device control index corresponding to each defect state, for example, the annular crack on the surface of the motor spindle is related to a cutting process. And performing correlation analysis based on the set of surface defects appearing on the target motor spindle to obtain related control indexes having correlation or correspondence with the types of the surface defects, such as lubricating oil adding frequency, processing device output power, heat dissipation instantaneity and the like.
And performing parameter identification on the relevant control indexes from the real-time control parameters, outputting identification parameters, processing the identification parameters serving as parameters to be processed, and adjusting the parameters to be processed.
The embodiment obtains the parameters to be processed for realizing the perfect elimination of the surface defects by parameter adjustment from a plurality of real-time control parameters through analyzing the parameters of the processing device causing the surface defects, thereby realizing the technical effect of providing relatively accurate adjustment reference for the processing operators of the motor spindle and eliminating the production defects of the motor spindle from the processing end by assisting the operators.
Further, performing correlation analysis according to the surface layer defect set, and outputting a correlation control index, where step S920 of the method provided in the present application further includes:
s921, acquiring the surface defect type according to the surface defect set;
s922, respectively carrying out correlation analysis on each defect type according to the surface layer defect type to obtain a correlation control index set;
and S923, outputting the relevant control indexes according to the relevant control index sets, wherein the relevant control indexes are indexes with the maximum number of relations in each set based on the relevant control index sets.
Specifically, in this embodiment, the apparent defect analysis and determination method for the target motor spindle is to classify a plurality of apparent defects in the apparent defect set according to the defect image features, and obtain the apparent defect types, such as cracks, gaps, pits, and bumps. And respectively carrying out correlation analysis on each defect type according to the surface defect type, determining one or more control indexes causing each defect type to occur, and generating the correlation control index set.
And outputting the index with the maximum relation number in each set in the relevant control index set as the relevant control index according to the relevant control index set, and instructing technicians to perform parameter control adjustment to eliminate the surface defects generated in the machining process.
According to the embodiment, the technical effect that the waste of human resources is reduced and the efficiency of maintenance and adjustment of the processing device is improved when the surface defect elimination analysis is performed is achieved by analyzing the surface defect cause and outputting the parameter control index, so that the technical staff can eliminate and guide the surface defects clearly.
Further, as shown in fig. 3, the method provided by the present application further includes:
s650, acquiring material composition information of the target motor spindle;
s660, analyzing the attribute of the main shaft material according to the material component information, and determining the hardness and the surface roughness of the material;
s670, determining a probability error according to the hardness of the material and the surface roughness;
and S680, adjusting the abnormal probability according to the probability error.
Specifically, it should be understood that the material properties of the motor spindle may also cause surface defects in the produced motor spindle to some extent, for example, when the motor spindle is processed, the high heat generated by the cutting process may cause the motor shaft blank made of a partial metal material to crack, and the motor shaft blank made of a non-metal material is prone to have grinding cracks during the cutting process.
In order to eliminate the interference of the characteristics of the metal material processed by the motor spindle on the accuracy of the defect abnormal probability, in the embodiment, the material component information of the target motor spindle is obtained, the spindle material attribute is analyzed according to the material component information, the material hardness and the surface roughness are determined, the interference of the material hardness and the surface roughness on the defect abnormal probability is obtained, the probability error is obtained, and the abnormal probability is adjusted according to the probability error.
In the embodiment, the error degree of the defect abnormal probability caused by the processing characteristics of the material attribute during processing is determined by analyzing the material attribute angle, the obtained defect abnormal probability is corrected, the defect abnormal probability only associated with the processing device is obtained, and the technical effect of improving the accuracy of the defect abnormal probability is achieved.
Example two
Based on the same inventive concept as the surface treatment method of the electric automobile motor spindle in the previous embodiment, as shown in fig. 4, the present application provides a surface treatment system of an electric automobile motor spindle, wherein the system comprises:
the image acquisition execution module 11 is used for acquiring a microscopic image of the target motor spindle according to the optical microscope and outputting a microscopic image set;
the analysis model construction module 12 is used for connecting the microscopic image management system and constructing an image characteristic analysis model;
an image feature generation module 13, configured to input the microscopic image set into the image feature analysis model, and output a microscopic image feature according to the image feature analysis model;
a surface defect identification module 14, configured to perform surface defect identification according to the characteristics of the microscopic image, and output a surface defect set;
a defect distribution obtaining module 15, configured to obtain a surface defect distribution coordinate based on the surface defect set;
the abnormal probability calculation module 16 is used for calculating the defect abnormal probability according to the surface layer defect distribution coordinates to obtain the abnormal probability;
an abnormal probability comparison module 17, configured to determine whether the abnormal probability is greater than or equal to a preset abnormal probability;
and an early warning instruction generating module 18, configured to generate an early warning instruction if the anomaly probability is greater than or equal to the preset anomaly probability, where the early warning instruction is used to remind that the target motor spindle is abnormal.
Further, the system provided by the present application further includes:
the control parameter obtaining unit is used for connecting a process parameter control system of the target motor spindle according to the early warning instruction to obtain real-time control parameters;
the control index output unit is used for carrying out correlation analysis according to the surface layer defect set and outputting a correlation control index;
the parameter identification marking unit is used for carrying out parameter identification on the related control indexes from the real-time control parameters and outputting identification parameters;
and the parameter processing execution unit is used for processing by taking the identification parameter as a parameter to be processed.
Further, the control index output unit further includes:
the defect type determining unit is used for acquiring the surface layer defect type according to the surface layer defect set;
a control index obtaining unit, configured to perform correlation analysis on each defect type according to the surface defect type, to obtain a set of related control indexes;
and the control index output unit is used for outputting the related control indexes according to the related control index sets, wherein the related control indexes are indexes with the largest relation number in each set in the related control index sets.
Further, the anomaly probability calculating module 16 further includes:
a same-batch device obtaining unit for obtaining a same-batch motor spindle of the target motor spindle;
the sample sampling execution unit is used for sampling the motor main shafts in the same batch and outputting sample motor main shafts;
the defect coordinate output unit is used for outputting the defect distribution coordinates of the surface layer of the sample according to the sample motor spindle;
and the abnormal probability calculating unit is used for activating a defect abnormal probability calculating module when the similarity between the sample surface defect distribution coordinate and the surface defect distribution coordinate reaches a preset similarity threshold value to obtain the abnormal probability.
Further, the abnormality probability calculation unit further includes:
the defect density analysis unit is used for activating the abnormal probability calculation module, performing density analysis on the surface layer defect distribution coordinate according to the abnormal probability calculation module and obtaining a density analysis result, wherein the density analysis result is an analysis result for identifying the surface layer defect density;
and the abnormal probability output unit is used for calculating abnormal probability according to the density analysis result and outputting the abnormal probability.
Further, the system provided by the present application further includes:
a material composition obtaining unit for obtaining material composition information of the target motor spindle;
the material attribute analysis unit is used for analyzing the material attribute of the main shaft according to the material component information and determining the hardness and the surface roughness of the material;
a probability error determination unit for determining a probability error according to the material hardness and the surface roughness;
and the abnormal probability adjusting unit is used for adjusting the abnormal probability by the probability error.
Any of the methods or steps described above may be stored as computer instructions or programs in various non-limiting types of computer memory that are recognized by various non-limiting types of computer processors to implement any of the methods or steps described above.
Based on the above embodiments of the present invention, those skilled in the art should make modifications and alterations to the present invention without departing from the principle of the present invention.
Claims (7)
1. A surface treatment method for a motor spindle of an electric automobile is characterized in that the method is applied to a surface treatment system for the motor spindle of the electric automobile, the system is in communication connection with an optical microscope, and the method comprises the following steps:
according to the optical microscope, microscopic image collection is carried out on a main shaft of a target motor, and a microscopic image set is output;
connecting a microscopic image management system, and building an image characteristic analysis model;
inputting the microscopic image set into the image characteristic analysis model, and outputting microscopic image characteristics according to the image characteristic analysis model;
identifying surface defects according to the characteristics of the microscopic image, and outputting a surface defect set;
obtaining a surface defect distribution coordinate based on the surface defect set;
calculating the defect abnormal probability according to the surface defect distribution coordinate to obtain the abnormal probability;
judging whether the abnormal probability is larger than or equal to a preset abnormal probability;
and if the abnormal probability is larger than or equal to the preset abnormal probability, generating an early warning instruction, wherein the early warning instruction is used for reminding that the target motor spindle is abnormal.
2. The method of claim 1, wherein if the anomaly probability is greater than or equal to the predetermined anomaly probability, after generating an early warning command, the method further comprises:
connecting a technological parameter control system of the target motor spindle according to the early warning instruction to obtain real-time control parameters;
performing correlation analysis according to the surface layer defect set, and outputting a correlation control index;
performing parameter identification from the real-time control parameters by using the related control indexes, and outputting identification parameters;
and processing by taking the identification parameter as a parameter to be processed.
3. The method of claim 2, wherein performing a correlation analysis based on the set of skin defects to output a correlation control indicator, the method further comprising:
acquiring a surface defect type according to the surface defect set;
respectively carrying out correlation analysis on each defect type according to the surface defect type to obtain a correlation control index set;
and outputting the relevant control indexes according to the relevant control index sets, wherein the relevant control indexes are indexes with the largest number of relations in each set in the relevant control index sets.
4. The method of claim 1, wherein the defect anomaly probability calculation is performed according to the surface defect distribution coordinates to obtain an anomaly probability, the method further comprising:
obtaining motor spindles in the same batch of the target motor spindles;
sampling is carried out on the motor main shafts in the same batch, and a sample motor main shaft is output;
outputting a sample surface layer defect distribution coordinate according to the sample motor spindle;
and when the similarity between the sample surface defect distribution coordinates and the surface defect distribution coordinates reaches a preset similarity threshold, activating a defect abnormal probability calculation module to obtain the abnormal probability.
5. The method of claim 4, wherein a defect anomaly probability calculation module is activated to obtain the anomaly probability, the method further comprising:
activating the abnormal probability calculation module, performing density analysis on the surface layer defect distribution coordinates according to the abnormal probability calculation module, and obtaining a density analysis result, wherein the density analysis result is an analysis result for identifying the density of the surface layer defects;
and calculating the abnormal probability according to the density analysis result, and outputting the abnormal probability.
6. The method of claim 1, wherein the method further comprises:
acquiring material composition information of the target motor spindle;
analyzing the material attribute of the main shaft according to the material component information, and determining the hardness and the surface roughness of the material;
determining a probability error according to the material hardness and the surface roughness;
and adjusting the abnormal probability according to the probability error.
7. A surface treatment system for a spindle of an electric vehicle motor, the system comprising:
the image acquisition execution module is used for acquiring a microscopic image of the target motor spindle according to the optical microscope and outputting a microscopic image set;
the analysis model building module is used for connecting the microscopic image management system and building an image characteristic analysis model;
the image characteristic generating module is used for inputting the microscopic image set into the image characteristic analysis model and outputting the microscopic image characteristics according to the image characteristic analysis model;
the surface defect identification module is used for identifying the surface defects according to the characteristics of the microscopic image and outputting a surface defect set;
a defect distribution obtaining module, configured to obtain a surface defect distribution coordinate based on the surface defect set;
the abnormal probability calculation module is used for calculating the defect abnormal probability according to the surface layer defect distribution coordinate to obtain the abnormal probability;
the abnormal probability comparison module is used for judging whether the abnormal probability is greater than or equal to a preset abnormal probability;
and the early warning instruction generating module is used for generating an early warning instruction if the abnormal probability is greater than or equal to the preset abnormal probability, wherein the early warning instruction is used for reminding the target motor spindle of having abnormality.
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