Quality monitoring system based on industrial internet

Abstract

The invention relates to a quality monitoring system based on an industrial internet. The quality monitoring system comprises an intelligent manufacturing cloud platform, image acquisition equipment, sensing equipment and a management terminal. The intelligent manufacturing cloud platform comprises a data acquisition module, a data processing module, an error analysis module, an image analysis module, a quality analysis module and a report generation module. The image acquisition equipment is used for periodically performing image acquisition on processing parts on a target production line to obtain a part detection image set; the sensing equipment is used for periodically acquiring sensing data of corresponding production equipment on a target production line to obtain equipment sensing data; and the intelligent manufacturing cloud platform analyzes the part detection image set and the equipment sensing data to obtain the quality standard-reaching rate of all the machined parts in the current period, and generates a multi-dimensional quality monitoring report according to the equipment sensing data, the machining process parameters, the standard process parameters and the quality standard-reaching rate so as to send the multi-dimensional quality monitoring report to a corresponding management terminal.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing, and in particular to a quality monitoring system based on industrial Internet. Background Art

[0002] The Industrial Internet achieves flexible configuration of manufacturing materials, on-demand execution of manufacturing processes, rational optimization of manufacturing processes, and rapid adaptation of manufacturing environments through network interconnection, data intercommunication, and system interoperability of industrial resources, thereby achieving efficient utilization of resources and building a new intelligent industrial system. It helps modern enterprises achieve technological innovation, improve product R&D and design capabilities, optimize product manufacturing processes, improve the utilization rate of manufacturing resources, shorten the design and manufacturing cycle of enterprise products, reduce product R&D and production costs, improve product quality, and speed up product launch.

[0003] With the development of science and technology, the rapid development of production process automation and the widespread application of precision machining, people are paying more and more attention to the accuracy, quality measurement accuracy and processing inspection efficiency of the parts produced and processed. In the existing technology, the quality inspection of the parts after production and processing generally adopts the manual sampling inspection method, which is not only slow in inspection speed, but also has low reliability of inspection results, and it is difficult to timely discover the processed parts whose quality does not meet the standards due to production equipment failure. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a quality monitoring system based on the industrial Internet, which includes an intelligent manufacturing cloud platform, an image acquisition device, a sensor device and a management terminal. The intelligent manufacturing cloud platform is respectively connected to the image acquisition device, the sensor device and the management terminal in communication;

[0005] The intelligent manufacturing cloud platform includes data acquisition module, data processing module, error analysis module, image analysis module, quality analysis module and report generation module;

[0006] The data acquisition module periodically sends image acquisition instructions and equipment monitoring instructions to the image acquisition equipment and the sensor equipment on the target production line to periodically acquire the part detection image set of the processed parts on the target production line and the equipment sensor data of the production equipment;

[0007] The data processing module analyzes the operating status of the production equipment according to the equipment sensor data to obtain the current processing parameters of the target production line, and obtains the standard process parameters of the target production line from the database according to the production line identifier corresponding to the target production line;

[0008] The error analysis module performs parameter analysis on the processing parameters according to the standard process parameters to obtain the current process parameter error of the target production line, and compares the process parameter error with the process error threshold;

[0009] When it is determined that the parameter error is greater than the process error threshold, the preprocessing unit of the image analysis module performs image smoothing and histogram equalization processing on each part detection image in the part detection image set to obtain a detection enhanced image set;

[0010] The region division unit of the image analysis module divides the corresponding detection enhancement image into regions according to the pixel grayscale change information of each detection enhancement image in the detection enhancement image set to obtain a plurality of regions to be detected;

[0011] The feature detection unit of the image analysis module extracts features from all the to-be-detected areas of each detection enhancement image to obtain the global shape features of each to-be-detected area, and determines whether there are processed parts in the corresponding to-be-detected area according to the global shape features of each to-be-detected area;

[0012] When there are processed parts in the corresponding area to be detected, the image recognition unit of the image analysis module extracts feature points of the area to be detected to obtain a number of part feature strings, and obtains the part surface roughness, part surface corrugation depth, part surface defect information and part structure information of each processed part in the detection enhanced image according to the local geometric features and global geometric features constituted by each part feature string;

[0013] The feature fusion unit of the image analysis module performs multi-dimensional fusion based on the part surface roughness, part surface corrugation depth, part surface defect information and part structure information to obtain the part characterization information of each processed part;

[0014] The quality analysis module performs quality analysis based on the part characterization information of all processed parts to obtain the quality compliance rate of all processed parts in the current cycle, and compares the quality compliance rate with the quality compliance threshold;

[0015] When the quality compliance rate is less than the quality compliance threshold, the report generation module generates a multi-dimensional quality monitoring report based on the equipment sensor data, processing parameters, standard process parameters and quality compliance rate to send it to the corresponding management terminal.

[0016] According to a preferred embodiment, the production equipment includes a drilling machine, a milling machine, a boring machine, a lathe, a grinder, a gear processing machine, a thread processing machine, a planer, a sawing machine and a broaching machine.

[0017] According to a preferred embodiment, the equipment sensor data includes tool inclination angle, fixture distance, tool distance, bearing speed, transmission gear speed, equipment load and spindle geometric axis inclination angle when the production equipment is in operation.

[0018] According to a preferred embodiment, the image recognition unit of the image analysis module extracts feature points of the area to be detected and connects them in series to obtain several part feature strings including:

[0019] The image recognition unit extracts feature points from the area to be detected to obtain a number of regional feature points and corresponding position information of each regional feature point;

[0020] The image recognition unit connects the feature points of adjacent regions within a preset distance in series according to the position information of each region feature point to obtain a plurality of part feature strings.

[0021] According to a preferred embodiment, the data processing module analyzes the working status of the production equipment according to the equipment sensor data to obtain the current processing parameters of the target production line, including:

[0022] The data processing module analyzes the working status of the production equipment according to the tool inclination angle, fixture distance, tool distance, bearing speed, transmission gear speed, equipment load and spindle geometric axis inclination of the production equipment in the equipment sensor data to obtain the current processing parameters of the target production line.

[0023] According to a preferred embodiment, the surface defects of the parts include scratch defects, hole defects, fingerprint defects and pit defects.

[0024] According to a preferred embodiment, the region division unit of the image analysis module divides the corresponding detection enhanced image into regions according to the pixel grayscale change information of each detection enhanced image in the detection enhanced image set to obtain a number of regions to be detected, including:

[0025] The region division unit obtains the position information and grayscale value of each pixel in each detection enhanced image, and determines that the pixel is a boundary pixel according to the difference between the grayscale value of each pixel and the grayscale average value of a preset adjacent pixel domain;

[0026] The region division unit performs similarity analysis based on the gray value and position information of each boundary pixel point to obtain a number of boundary pixel point sets;

[0027] The region division unit connects all boundary pixel points belonging to a boundary pixel point set to form a plurality of edge segmentation lines of the detection enhancement image;

[0028] The region division unit divides the corresponding detection enhanced image into regions according to a plurality of edge segmentation lines to obtain a plurality of regions to be detected.

[0029] According to a preferred embodiment, the part structure information includes the surface size, surface geometry and surface relative position of the processed part;

[0030] The surface geometric shapes include cylinders, ellipses, cones, spheres and prisms, the surface dimensions include circular cross-sectional dimensions, elliptical cross-sectional dimensions and cone dimensions, and the surface relative positions include parallelism, verticality, coaxiality and symmetry relative to the line.

[0031] According to a preferred embodiment, the image recognition unit of the image analysis module obtains the part surface roughness, part surface corrugation depth, part surface defect information and part structure information of each processed part in the detection enhanced image according to the local geometric features and global geometric features formed by each part feature string, including:

[0032] The image recognition unit calculates the relative distance and relative angle between each regional feature point in the corresponding part feature string and other regional feature points according to the position information of each regional feature point in each part feature string to obtain a number of local geometric features of each area to be detected;

[0033] The image recognition unit obtains the global geometric features of each area to be detected according to a number of local geometric features of each area to be detected and the position information of the feature points of each area.

[0034] According to a preferred embodiment, the processing parameters and standard process parameters include turning process parameters, milling process parameters, boring process parameters, honing process parameters, hole processing process parameters, stamping process parameters and injection molding process parameters.

[0035] The present invention has the following beneficial effects:

[0036] The present invention detects the operating status of production equipment and promptly performs quality inspection on the processed parts on the target production line when an abnormality is detected in the working status of the production equipment, and generates a multi-dimensional quality monitoring report based on various surface features and geometric structure features of the processed parts, thereby greatly improving the inspection efficiency of the quality of processed parts on the target production line and the accuracy of the inspection results. There is no need to perform quality analysis on the processed parts on the target production line all the time, which saves manpower, material resources and computing resources of related platforms and improves the work efficiency of managers. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a structural block diagram of the quality monitoring system based on industrial Internet of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0039] See also Figure 1 The quality monitoring system based on industrial Internet of the present invention includes an intelligent manufacturing cloud platform, an image acquisition device, a sensor device and a management terminal. The intelligent manufacturing cloud platform is respectively connected to the image acquisition device, the sensor device and the management terminal. The management terminal is a device with computing, storage and communication functions used by management personnel, including: a smart phone, a desktop computer and a laptop computer.

[0040] The intelligent manufacturing cloud platform includes data acquisition module, data processing module, error analysis module, image analysis module, quality analysis module and report generation module.

[0041] The data acquisition module is used to periodically acquire a set of part inspection images of parts processed on a target production line and equipment sensor data of production equipment.

[0042] The data processing module is used to analyze the operating status of the production equipment according to the equipment sensor data to obtain the current processing parameters of the target production line, and obtain the standard process parameters of the target production line from the database according to the production line identifier corresponding to the target production line.

[0043] The error analysis module is used to perform parameter analysis on the processing parameters according to the standard process parameters to obtain the current process parameter error of the target production line, and compare the process parameter error with the process error threshold.

[0044] The image analysis module includes a preprocessing unit, a region division unit, a feature detection unit, an image recognition unit and a feature fusion unit.

[0045] The preprocessing unit is used to perform image smoothing and histogram equalization processing on each part detection image in the part detection image set to obtain a detection enhanced image set.

[0046] The region division unit is used to divide the corresponding detection enhanced image into regions according to the pixel grayscale change information of each detection enhanced image in the detection enhanced image set to obtain a plurality of regions to be detected.

[0047] The feature detection unit is used to extract features from all the areas to be detected in each detection enhancement image to obtain the global shape features of each area to be detected, and determine whether there are processed parts in the corresponding area to be detected based on the global shape features of each area to be detected.

[0048] The image recognition unit is used to extract feature points of the area to be detected to obtain a number of part feature strings, and obtain the part surface roughness, part surface corrugation depth, part surface defect information and part structure information of each processed part in the detection enhanced image based on the local geometric features and global geometric features constituted by each part feature string.

[0049] The feature fusion unit is used to perform multi-dimensional fusion according to the part surface roughness, part surface corrugation depth, part surface defect information and part structure information to obtain the part characterization information of each processed part.

[0050] The quality analysis module is used to perform quality analysis based on the part characterization information of all processed parts to obtain the quality compliance rate of all processed parts in the current cycle, and compare the quality compliance rate with the quality compliance threshold.

[0051] The report generation module is used to generate a multi-dimensional quality monitoring report based on equipment sensor data, processing parameters, standard process parameters and quality compliance rate to send it to the corresponding management terminal.

[0052] Specifically, the processing method of the quality monitoring system based on the industrial Internet includes the following steps:

[0053] S1. The data acquisition module of the intelligent manufacturing cloud platform periodically sends image acquisition instructions and equipment monitoring instructions to the image acquisition equipment and sensor equipment on the target production line to periodically acquire the part inspection image set of the processed parts on the target production line and the equipment sensor data of the production equipment.

[0054] Optionally, the sensing device includes: a static torque sensor, a cantilever sensor, a cylindrical load sensor, a spoke tension pressure sensor, a linear displacement sensor, a wire displacement sensor, and a dynamic pressure sensor.

[0055] Optionally, the production equipment includes drilling machines, milling machines, boring machines, lathes, grinders, gear processing machines, thread processing machines, planers, sawing machines and broaching machines.

[0056] Optionally, the part detection image set is a set of all part detection images acquired by an image acquisition device within a target monitoring period, and the part detection images contain a plurality of processed parts.

[0057] Optionally, the equipment sensing data includes tool inclination angle, fixture distance, tool distance, bearing speed, transmission gear speed, equipment load and spindle geometric axis inclination angle when the production equipment is in operation.

[0058] S2. The data processing module analyzes the operating status of the production equipment according to the equipment sensor data to obtain the current processing parameters of the target production line, and obtains the standard process parameters of the target production line from the database according to the production line identifier corresponding to the target production line.

[0059] Optionally, the production line identifier is used to uniquely identify the production line.

[0060] Optionally, the data processing module analyzes the working status of the production equipment according to the equipment sensor data to obtain the current processing parameters of the target production line, including:

[0061] The data processing module analyzes the working status of the production equipment according to the tool inclination angle, fixture distance, tool distance, bearing speed, transmission gear speed, equipment load and spindle geometric axis inclination of the production equipment in the equipment sensor data to obtain the current processing parameters of the target production line.

[0062] Optionally, the processing technology parameters are actual operating parameters of corresponding production equipment when the target production line is producing and processing under an actual working environment; the standard process parameters are normal operating parameters of corresponding production equipment when the target production line is producing and processing under a normal working environment.

[0063] S3. The error analysis module performs parameter analysis on the processing parameters according to the standard process parameters to obtain the current process parameter error of the target production line, and compares the process parameter error with the process error threshold.

[0064] Optionally, the process error threshold is an error value pre-set by the system according to actual conditions. When the process parameter error is greater than the process error threshold, it indicates that there is a large deviation in the actual operating parameters of the production equipment corresponding to the target production line, which will cause processing defects in the processed parts on the target production line.

[0065] When the process parameter error is less than the process error threshold, it indicates that there is a small deviation or no deviation in the actual operating parameters of the production equipment corresponding to the target production line.

[0066] Optionally, the processing parameters and standard process parameters include turning process parameters, milling process parameters, boring process parameters, honing process parameters, hole processing process parameters, stamping process parameters and injection molding process parameters.

[0067] Optionally, the milling process parameters include fixture height, chisel edge angle, tool position step length, cutting spacing, cutting speed, tool lowering speed, and cutting feed speed.

[0068] S4. When it is determined that the parameter error is greater than the process error threshold, the preprocessing unit of the image analysis module performs image smoothing and histogram equalization processing on each part detection image in the part detection image set to obtain a detection enhanced image set.

[0069] Optionally, the preprocessing unit of the image analysis module performs image smoothing and histogram equalization processing on each part detection image in the part detection image set to obtain the detection enhanced image set, including:

[0070] The preprocessing unit determines a pixel point of a target to be changed from the part detection image as a pixel point to be changed;

[0071] The preprocessing unit presets a sampling window of size 3×3 to sample the part detection image to obtain a first sampling data group, the number of data in the first sampling data group is an odd number, and the area sampled by the sampling window is the neighboring area of ​​the pixel to be changed;

[0072] The preprocessing unit arranges the data in the first sample data group in descending order to obtain a second sample data group;

[0073] The preprocessing unit selects the median data of the second sampling data group as the grayscale value of the pixel to be changed, and completes the transformation of the grayscale value of the current pixel to be changed.

[0074] Optionally, the actual processing parameters on the target production line are analyzed in real time, and when the errors of the actual processing parameters are large, quality analysis of the processed parts on the target production line is performed to save computing resources of the relevant platform.

[0075] S5. The region division unit of the image analysis module divides the corresponding detection enhanced image into regions according to the pixel grayscale change information of each detection enhanced image in the detection enhanced image set to obtain a plurality of regions to be detected.

[0076] Optionally, the pixel grayscale change information is obtained by performing grayscale value statistics on all pixels of the corresponding detection enhanced image by the region division unit.

[0077] Specifically, the region division unit of the image analysis module divides the corresponding detection enhanced image into regions according to the pixel grayscale change information of each detection enhanced image in the detection enhanced image set to obtain a number of regions to be detected, including:

[0078] The region division unit obtains the position information and grayscale value of each pixel in each detection enhanced image, and determines that the pixel is a boundary pixel according to the difference between the grayscale value of each pixel and the grayscale average value of a preset adjacent pixel domain;

[0079] The region division unit performs similarity analysis based on the gray value and position information of each boundary pixel point to obtain a number of boundary pixel point sets;

[0080] The region division unit connects all boundary pixel points belonging to a boundary pixel point set to form a plurality of edge segmentation lines of the detection enhancement image;

[0081] The region division unit divides the corresponding detection enhanced image into regions according to a plurality of edge segmentation lines to obtain a plurality of regions to be detected.

[0082] Optionally, the preset adjacent pixel point domain is composed of a system preset number of adjacent pixel points, and the adjacent pixel points are pixel points whose relative distances to the target analyzed pixel points are within a system preset distance range.

[0083] Optionally, the grayscale average value is the average of the sum of grayscale values ​​of all pixels in a preset adjacent pixel domain.

[0084] S6. The feature detection unit of the image analysis module extracts features from all the areas to be detected in each detection enhancement image to obtain global shape features of each area to be detected, and determines whether there are processed parts in the corresponding area to be detected according to the global shape features of each area to be detected.

[0085] Optionally, the feature detection unit determines whether there is a processed part in the corresponding area to be detected according to the global shape feature of each area to be detected, including:

[0086] The feature detection unit obtains the standard shape features of the parts processed on the target production line from the database according to the production line identifier corresponding to the target production line;

[0087] The feature detection unit compares the standard shape feature with the global shape feature of each area to be detected;

[0088] When the feature similarity between the standard shape feature and the global shape feature is greater than a similarity threshold, it indicates that there are processed parts in the corresponding area to be detected; the similarity threshold is a value pre-set by the system according to actual conditions.

[0089] S7. When there are processed parts in the corresponding area to be detected, the image recognition unit of the image analysis module extracts feature points of the area to be detected to obtain a number of part feature strings, and obtains the part surface roughness, part surface corrugation depth, part surface defect information and part structure information of each processed part in the detection enhanced image based on the local geometric features and global geometric features constituted by each part feature string.

[0090] Specifically, the image recognition unit of the image analysis module obtains the part surface roughness, part surface corrugation depth, part surface defect information and part structure information of each processed part in the detection enhanced image according to the local geometric features and global geometric features formed by each part feature string, including:

[0091] The image recognition unit calculates the relative distance and relative angle between each regional feature point in the corresponding part feature string and other regional feature points according to the position information of each regional feature point in each part feature string to obtain a number of local geometric features of each area to be detected;

[0092] The image recognition unit obtains the global geometric features of each area to be detected according to a number of local geometric features of each area to be detected and the position information of the feature points of each area.

[0093] Specifically, the image recognition unit of the image analysis module extracts feature points of the area to be detected and connects them in series to obtain several part feature strings including:

[0094] The image recognition unit extracts feature points from the area to be detected to obtain a number of regional feature points and corresponding position information of each regional feature point;

[0095] The image recognition unit connects the feature points of adjacent regions within a preset distance in series according to the position information of each region feature point to obtain a plurality of part feature strings.

[0096] Optionally, the preset distance is a distance range pre-set by the system according to actual conditions, and the regional feature points within the distance range are used as neighboring regional feature points of the regional feature points of the target analysis; the neighboring regional feature points are other regional feature points within the distance range except the regional feature points of the target analysis.

[0097] Optionally, the part surface defects include scratch defects, hole defects, fingerprint defects and pit defects.

[0098] Optionally, the part structure information includes surface size, surface geometry and surface relative position of the processed part;

[0099] The surface geometric shapes include cylinders, ellipses, cones, spheres and prisms, the surface dimensions include circular cross-sectional dimensions, elliptical cross-sectional dimensions and cone dimensions, and the surface relative positions include parallelism, verticality, coaxiality and symmetry relative to the line.

[0100] S8. The feature fusion unit of the image analysis module performs multi-dimensional fusion according to the part surface roughness, part surface corrugation depth, part surface defect information and part structure information to obtain part characterization information of each processed part.

[0101] S9. The quality analysis module performs quality analysis based on the part characterization information of all processed parts to obtain the quality compliance rate of all processed parts in the current cycle, and compares the quality compliance rate with the quality compliance threshold.

[0102] Optionally, the quality compliance threshold is a critical value preset by the system for determining the quality compliance rate of all processed parts in the current cycle.

[0103] S10. When the quality compliance rate is less than the quality compliance threshold, the report generation module generates a multi-dimensional quality monitoring report according to the equipment sensor data, processing parameters, standard process parameters and quality compliance rate and sends it to the corresponding management terminal.

[0104] The present invention obtains the current processing parameters of the target production line by periodically analyzing the operating status of the production equipment. When it is determined that there is a large error between the actual processing parameters of the target production line and the standard processing parameters, quality inspections are performed on various surface features and geometric structure features of the processed parts on the target production line to generate a multi-dimensional quality monitoring report, which greatly improves the inspection efficiency of the quality of the processed parts on the target production line and the accuracy of the inspection results, saves manpower, material resources and computing resources of related platforms, and improves the work efficiency of managers.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A quality monitoring system based on industrial Internet, characterized in that: It includes an intelligent manufacturing cloud platform, an image acquisition device, a sensor device and a management terminal, and the intelligent manufacturing cloud platform has communication connections with the image acquisition device, the sensor device and the management terminal respectively; The intelligent manufacturing cloud platform includes data acquisition module, data processing module, error analysis module, image analysis module, quality analysis module and report generation module; The data acquisition module periodically sends image acquisition instructions and equipment monitoring instructions to the image acquisition equipment and the sensor equipment on the target production line to periodically acquire the part detection image set of the processed parts on the target production line and the equipment sensor data of the production equipment; The data processing module analyzes the operating status of the production equipment according to the equipment sensor data to obtain the current processing parameters of the target production line, and obtains the standard process parameters of the target production line from the database according to the production line identifier corresponding to the target production line; The error analysis module performs parameter analysis on the processing parameters according to the standard process parameters to obtain the current process parameter error of the target production line, and compares the process parameter error with the process error threshold; When it is determined that the parameter error is greater than the process error threshold, the preprocessing unit of the image analysis module performs image smoothing and histogram equalization processing on each part detection image in the part detection image set to obtain a detection enhanced image set; The region division unit of the image analysis module divides the corresponding detection enhancement image into regions according to the pixel grayscale change information of each detection enhancement image in the detection enhancement image set to obtain a plurality of regions to be detected; The feature detection unit of the image analysis module extracts features from all the to-be-detected areas of each detection enhancement image to obtain the global shape features of each to-be-detected area, and determines whether there are processed parts in the corresponding to-be-detected area according to the global shape features of each to-be-detected area; When there are processed parts in the corresponding area to be detected, the image recognition unit of the image analysis module extracts feature points of the area to be detected to obtain a number of part feature strings, and obtains the part surface roughness, part surface corrugation depth, part surface defect information and part structure information of each processed part in the detection enhanced image according to the local geometric features and global geometric features constituted by each part feature string; The feature fusion unit of the image analysis module performs multi-dimensional fusion based on the part surface roughness, part surface corrugation depth, part surface defect information and part structure information to obtain the part characterization information of each processed part; The quality analysis module performs quality analysis based on the part characterization information of all processed parts to obtain the quality compliance rate of all processed parts in the current cycle, and compares the quality compliance rate with the quality compliance threshold; When the quality compliance rate is less than the quality compliance threshold, the report generation module generates a multi-dimensional quality monitoring report based on the equipment sensor data, processing parameters, standard process parameters and quality compliance rate to send it to the corresponding management terminal.

2. The system according to claim 1, characterized in that The production equipment includes drilling machines, milling machines, boring machines, lathes, grinders, gear processing machines, thread processing machines, planers, sawing machines and broaching machines.

3. The system according to claim 2, characterized in that The equipment sensing data includes tool inclination angle, fixture distance, tool distance, bearing speed, transmission gear speed, equipment load and spindle geometric axis inclination angle when the production equipment is in operation.

4. The system according to claim 3, characterized in that The image recognition unit of the image analysis module extracts feature points of the area to be detected and connects them in series to obtain several part feature strings including: The image recognition unit extracts feature points from the area to be detected to obtain a number of regional feature points and corresponding position information of each regional feature point; The image recognition unit connects the feature points of adjacent regions within a preset distance in series according to the position information of each region feature point to obtain a plurality of part feature strings.

5. The system according to claim 4, characterized in that The data processing module analyzes the working status of the production equipment according to the equipment sensor data to obtain the current processing parameters of the target production line, including: The data processing module analyzes the working status of the production equipment according to the tool inclination angle, fixture distance, tool distance, bearing speed, transmission gear speed, equipment load and spindle geometric axis inclination of the production equipment in the equipment sensor data to obtain the current processing parameters of the target production line.

6. The system according to claim 5, characterized in that The surface defects of the parts include scratch defects, hole defects, fingerprint defects and pit defects.

7. The system according to claim 6, characterized in that The region division unit of the image analysis module divides the corresponding detection enhancement image into regions according to the pixel grayscale change information of each detection enhancement image in the detection enhancement image set to obtain a number of regions to be detected, including: The region division unit obtains the position information and grayscale value of each pixel in each detection enhanced image, and determines that the pixel is a boundary pixel according to the difference between the grayscale value of each pixel and the grayscale average value of a preset adjacent pixel domain; The area division unit performs similarity analysis according to the gray value and position information of each boundary pixel point to obtain a number of boundary pixel point sets; The region division unit connects all boundary pixel points belonging to a boundary pixel point set to form a plurality of edge segmentation lines of the detection enhancement image; The region division unit divides the corresponding detection enhanced image into regions according to a plurality of edge segmentation lines to obtain a plurality of regions to be detected.

8. The system according to claim 7, characterized in that The part structure information includes the surface size, surface geometry and surface relative position of the processed part; The surface geometric shapes include cylinders, ellipses, cones, spheres and prisms, the surface dimensions include circular cross-sectional dimensions, elliptical cross-sectional dimensions and cone dimensions, and the surface relative positions include parallelism, verticality, coaxiality and symmetry relative to the line.

9. The system according to claim 8, characterized in that The image recognition unit of the image analysis module obtains the part surface roughness, part surface corrugation depth, part surface defect information and part structure information of each processed part in the detection enhancement image according to the local geometric features and global geometric features formed by each part feature string, including: The image recognition unit calculates the relative distance and relative angle between each regional feature point in the corresponding part feature string and other regional feature points according to the position information of each regional feature point in each part feature string to obtain a number of local geometric features of each area to be detected; The image recognition unit obtains the global geometric features of each area to be detected according to a number of local geometric features of each area to be detected and the position information of the feature points of each area.

10. The system according to claim 9, characterized in that The processing parameters and standard process parameters include turning process parameters, milling process parameters, boring process parameters, honing process parameters, hole processing process parameters, stamping process parameters and injection molding process parameters.

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