MES-based production process visual monitoring management system and method

Abstract

The invention relates to the technical field of production supervision, and particularly discloses an MES-based production process visual monitoring management system and method.Firstly, in combination with operation state data of a camera and part image data, the system comprises an image acquisition module comprising a camera shooting unit and a recognition unit, the camera shooting unit comprises the camera, and the recognition unit comprises a recognition module; the image processing unit is used for performing image shooting on each processed part, the image quality of the processed part shot by the image shooting unit is analyzed, the image quality can be judged, the quality of part image data is ensured, then feature extraction is performed based on the high-quality image data, and the image quality of the processed part is improved. And whether the quality of the machined parts is qualified or not is analyzed, so that the accuracy of part quality analysis can be improved, the part machining stability is analyzed by combining the quality conditions of all the machined parts in the same batch subsequently, and the accuracy of a supervision result in the part production process can be further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production supervision, and in particular to a production process visualization monitoring management system and method based on MES. Background Art

[0002] The production process visualization monitoring and management system is a software and hardware system that integrates monitoring, control and visualization functions. It is based on information technology and realizes real-time monitoring and analysis of various indicators in the production process through data collection, processing and visualization display.

[0003] Traditional monitoring and management systems monitor the production process of metal parts by photographing the metal parts after processing, identifying representative features in the images, such as thickness, damage, and size, and comparing and analyzing qualified data to supervise the processing quality of metal parts to ensure the efficiency and quality of parts processing.

[0004] When the traditional monitoring and management system in the prior art is used, it is necessary to photograph the metal parts after processing. During this process, the captured images will be affected by the external environment and its own operating status, resulting in low image quality. In this case, when identifying the features in the image, the recognition results may be less accurate, which will affect the subsequent evaluation and analysis of the quality of metal parts, resulting in less accurate supervision results of the parts production process. Summary of the invention

[0005] The purpose of the present invention is to provide a production process visualization monitoring management system and method based on MES to solve the following technical problems: How to improve the accuracy of metal parts quality assessment results.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A production process visualization monitoring management system and method based on MES, the system comprising: An image acquisition module, including a camera unit and a recognition unit, wherein the camera unit includes a camera for taking images of each processed part; The recognition unit is used to recognize the part image and extract the data features that need to be monitored; A data acquisition and recording module is used to collect the operating status data of the camera in the camera unit and the image data of the parts; The image evaluation module is used to analyze the quality of the image of the processed parts taken by the camera unit in combination with the data from the data acquisition and recording module, and judge whether the operating status of the camera is good or not according to the analysis results; The quality monitoring module is used to analyze whether the quality of the parts after processing is qualified in combination with the data features identified by the recognition unit when judging that the camera is in good operating condition; The data analysis module is used to analyze the stability of part processing during this period of time based on the quality of all parts processed in the same batch, and further monitor and manage the status of processing equipment; The data visualization module is used to display the camera's operating status data, part quality data after processing, part processing stability data, and processing equipment status data in the form of charts on a large visualization screen for back-end management personnel to monitor.

[0007] Furthermore, the evaluation process of the image evaluation module includes: By formula Calculate the influence coefficient of the camera's operating status when shooting for the i-th time ; Among them, i is any shot of the processed parts by the camera. is any time point since the start of production on that day, b is the total number of time points from the start of production to the i-th shooting on that day, is the voltage value of the camera at the ath time point since the start of production on that day, For all The standard value of is the blurred area in the image taken by the camera for the i-th time, is the preset fuzzy area, is the fuzzy area influence coefficient, which is set based on empirical fitting. is the brightness of the image captured by the camera for the i-th time, is the preset brightness, is the number of days since the camera was last calibrated, is the preset calibration day interval, for The standard value of is the influence coefficient of the camera’s service life, which is set based on empirical fitting. and is the weight coefficient, To define a function, if , then let , otherwise, let .

[0008] Furthermore, the evaluation process of the image evaluation module also includes: By calculating the influence coefficient of the camera's operating status during the i-th shooting The preset operating status impact coefficient threshold Make a comparison; like ,The system judges that the camera is in good operating condition during the i-th shooting, the quality of the part image is qualified, and the image shooting operation can continue; like ,The system judges that the operating status of the camera during the i-th shooting is slightly poor, and the quality of the parts picture is unqualified, so the camera needs to be calibrated and debugged in time.

[0009] Furthermore, the analysis process of the quality monitoring module includes: By formula Calculate the quality influence coefficient of the corresponding processed workpiece when the camera takes the i-th shot ; in, is the total area of ​​the workpiece processed when the camera takes the i-th shot, is the preset total area of ​​the workpiece, for The standard value of is the damaged area of ​​the corresponding processed workpiece when the camera takes the i-th shot, is the preset damaged area, is the crack area of ​​the corresponding processed workpiece when the camera takes the i-th shot, is the preset crack area, is the error influence coefficient, which is set according to empirical fitting.

[0010] Furthermore, the analysis process of the quality monitoring module also includes: By calculating the quality influence coefficient of the corresponding processed workpiece at the time of the i-th shooting The preset quality impact coefficient threshold Make a comparison; like , the system determines that the quality of the processed workpiece is affected during the i-th shooting, which means that the quality of the processed workpiece is unqualified; like The system determines that the quality of the processed workpiece is not affected during the i-th shooting, which means that the quality of the processed workpiece is qualified, and analyzes the stability of part processing in combination with the quality of all processed parts in the same batch.

[0011] Furthermore, the analysis process of the data analysis module includes: The data acquisition and recording module collects the interval time between all two adjacent shots of the camera during the processing of a batch of parts, and establishes the processing time change curve of the batch of parts. By formula Calculate the dispersion coefficient of the quality influence coefficient of a batch of parts after processing ; Where n is the total number of times the camera takes pictures of any processed part, and since the camera is used to take pictures of each processed part, n is also equal to the total number of parts in a batch. For all The average value of is the starting time for processing this batch of parts. The end time of processing this batch of parts.

[0012] Furthermore, the analysis process of the data analysis module also includes: By calculating the dispersion coefficient of the quality influence coefficient after processing a batch of parts The preset coefficient of dispersion threshold Make a comparison; like , the system judges that the quality of the batch of parts after processing is good, indicating that the stability of parts processing is high and the operating status of the processing equipment is good; like ,The system judges that the quality of this batch of parts after processing fluctuates greatly, indicating that the stability of parts processing is low and the operating status of the processing equipment is abnormal.

[0013] A production process visualization monitoring and management method based on MES, the method comprising: S1: The camera unit in the image acquisition module takes an image of each processed part, and the recognition unit recognizes the part image to extract the data features that need to be monitored; S2: Collecting the operating status data of the camera in the camera unit and the part image data through the data acquisition and recording module; S3: Analyze the quality of the image of the processed part taken by the camera unit through the image evaluation module combined with the data of the data acquisition and recording module, and judge whether the operating status of the camera is good according to the analysis result; S4: When it is determined that the camera is in good operating condition, the quality monitoring module is combined with the data features identified by the identification unit to analyze whether the quality of the parts after processing is qualified; S5: The data analysis module combines the quality of all parts after processing in the same batch to analyze the stability of part processing during this period, and further monitors and manages the status of processing equipment; S6: Through the data visualization module, the camera's operating status data, the part quality data after processing, the part processing stability data and the processing equipment status data are displayed in the form of charts on the visualization large screen for back-end management personnel to monitor and manage.

[0014] Beneficial effects of the present invention: (1) The present invention first analyzes the quality of the image of the processed parts taken by the camera unit in combination with the operating status data of the camera and the part image data, thereby judging the image quality and ensuring the quality of the part image data. Feature extraction is then performed based on the high-quality image data, and the quality of the processed parts is analyzed to see whether they are qualified, thereby improving the accuracy of the part quality analysis. Subsequently, the quality of all processed parts in the same batch is combined to analyze the stability of part processing, thereby further improving the accuracy of the results of the part production process supervision.

[0015] (2) The present invention calculates the operating state influence coefficient of the camera at the time of the i-th shooting The preset operating status impact coefficient threshold Through this comparison method, it is possible to make an accurate judgment on whether the operating status of the camera during the i-th shooting is good, and based on the judgment result, it is possible to analyze whether the image quality of the part is qualified, thereby ensuring the quality of the part image data, and ensuring that the subsequent analysis of the part quality is based on high-quality image data for feature extraction, thereby ensuring the accuracy of the part quality analysis.

[0016] (3) The present invention calculates the quality influence coefficient of the corresponding processed workpiece during the i-th shooting. The preset quality impact coefficient threshold For comparison, since the feature parameters in the data are extracted based on high-quality image data and combined with the operating status influence coefficient of the camera at the i-th shooting The calculation results are corrected, so that an accurate judgment can be made on whether the quality of the completed workpiece is affected during the i-th shooting, and the accuracy and reliability of judging whether the quality of the workpiece after processing is qualified can be improved, thereby avoiding the situation where the accuracy of the supervision results of the parts production process is low.

[0017] (4) The present invention calculates the dispersion coefficient of the quality influence coefficient after a batch of parts are processed. The preset coefficient of dispersion threshold For comparison, since the data is based on the quality influence coefficient of the corresponding finished workpieces at the time of all shooting The calculation result is obtained, so the accuracy of the calculation result is relative to the result. Therefore, through this comparison method, the quality of the batch of parts after processing can be analyzed, thereby analyzing the stability of the parts processing, and the stability of the parts processing can reflect whether the operating status of the processing equipment is good, thereby realizing the monitoring and management of the parts production process, and accurate data can also ensure the accuracy of the monitoring and management results.

[0018] (5) The present invention combines the operating status data of the camera and the part image data, and analyzes the quality of the image of the processed part taken by the camera unit, thereby ensuring the quality of the image taken by the camera, thereby ensuring that when the part quality, part processing stability and the operating status of the processing equipment are subsequently evaluated and analyzed, feature extraction is performed based on high-quality image data, thereby ensuring the accuracy of the analysis. The evaluation results are then displayed in the form of charts on a large visualization screen through a data visualization module for monitoring and management by back-end management personnel, thereby realizing visualized monitoring and management of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings.

[0020] Figure 1 It is a schematic block diagram of a production process visualization monitoring and management system based on MES in the present invention; Figure 2 It is a flow chart of a production process visualization monitoring and management method based on MES in the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 As shown, in one embodiment, the present application provides a production process visualization monitoring management system and method based on MES, the system comprising: An image acquisition module, including a camera unit and a recognition unit, wherein the camera unit includes a camera for taking images of each processed part; The recognition unit is used to recognize the part image and extract the data features that need to be monitored; A data acquisition and recording module is used to collect the operating status data of the camera in the camera unit and the image data of the parts; The image evaluation module is used to analyze the quality of the image of the processed parts taken by the camera unit in combination with the data from the data acquisition and recording module, and judge whether the operating status of the camera is good or not according to the analysis results; The quality monitoring module is used to analyze whether the quality of the parts after processing is qualified in combination with the data features identified by the recognition unit when judging that the camera is in good operating condition; The data analysis module is used to analyze the stability of part processing during this period of time based on the quality of all parts processed in the same batch, and further monitor and manage the status of processing equipment; The data visualization module is used to display the camera's operating status data, the quality data of the parts after processing, the stability data of the parts processing, and the status data of the processing equipment in the form of charts on the visualization screen for monitoring by backstage managers; Through the above technical scheme, this embodiment provides an image acquisition module. When monitoring and managing the production process of metal parts, first, the camera unit in the image acquisition module is used to capture images of the parts after processing, and the recognition unit is used to recognize the part images, and the data features to be monitored are extracted. Then, the data acquisition and recording module is used to collect the operating status data of the camera in the camera unit and the part image data, and the image evaluation module is used to analyze the quality of the image of the parts after processing taken by the camera unit in combination with the data, and judge whether the operating status of the camera is good according to the analysis results. Then, when it is judged that the operating status of the camera is good, the quality monitoring module is used to analyze whether the quality of the parts after processing is qualified in combination with the data features recognized by the recognition unit, and the data analysis module is used to analyze the quality of all the parts after processing in the same batch in combination, and the processing stability of the parts within this period of time is analyzed, and the processing equipment status is further monitored and managed. Finally, the operating status data of the camera, the quality data of the parts after processing, the processing stability data of the parts, and the status data of the processing equipment can be displayed in the form of charts on the visualization large screen through the data visualization module for monitoring by the backstage management personnel. Through such an arrangement, when supervising the production process of metal parts, by first combining the camera's operating status data and part image data, the quality of the part image taken by the camera unit after processing is analyzed, the image quality can be judged to ensure the quality of the part image data, and then feature extraction is performed based on the high-quality image data, and the quality of the parts after processing is analyzed to see whether it is qualified, thereby improving the accuracy of the part quality analysis, and subsequently combining the quality of all parts after processing in the same batch to analyze the stability of part processing, thereby further improving the accuracy of the parts production process supervision results.

[0023] The evaluation process of the image evaluation module includes: By formula Calculate the influence coefficient of the camera's operating status when shooting for the i-th time ; Among them, i is any shot of the processed parts by the camera. is any time point since the start of production on that day, b is the total number of time points from the start of production to the i-th shooting on that day, is the voltage value of the camera at the ath time point since the start of production on that day, For all The standard value can be selected and set according to the allowable error in the empirical data. is the blurred area in the image taken by the camera for the i-th time, is the preset fuzzy area, is the fuzzy area influence coefficient, which is set based on empirical fitting. is the brightness of the image captured by the camera for the i-th time, is the preset brightness, is the number of days since the camera was last calibrated, is the preset calibration day interval, for The standard value can be selected and set according to the allowable error in the empirical data. is the influence coefficient of the camera’s service life, which is set based on empirical fitting. and is the weight coefficient, which is set according to empirical fitting. To define a function, if , then let , otherwise, let ; Through the above technical solution, this embodiment provides the operating state influence coefficient of the camera when shooting for the i-th time: , can be obtained by formula Calculated, where the formula The voltage fluctuation value of the camera when in use can be calculated. Therefore, it is obvious that when the voltage fluctuation value of the camera when in use is greater than the blurred area in the i-th captured image, the image brightness is lower, and the number of days since the last calibration of the camera is greater than the preset calibration day interval, then the operating status influence coefficient of the camera at the i-th shooting The larger the value, the worse the camera's operating status is. It needs to be calibrated or the camera equipment needs to be changed. Otherwise, the image quality will be poor, which will affect the subsequent feature extraction. On the contrary, when the voltage fluctuation value of the camera during use is smaller than the blur area in the i-th image, the image brightness is higher, and the number of days since the last calibration of the camera is less than the preset calibration day interval, then the camera's operating status influence coefficient for the i-th shooting is The smaller the value is, the better the camera is. There is no need to calibrate or change the camera equipment. The image quality is relatively good and will not affect the subsequent feature extraction. Through this calculation method, the camera's operating status data, part image data and calibration data are combined to calculate the camera's operating status impact coefficient. This data can reflect the image quality level of the camera during current shooting, thereby providing accurate data for whether it can continue to perform shooting operations, ensuring the accuracy of the data when the part quality is subsequently monitored through image data, and thus ensuring the accuracy of the judgment results.

[0024] The evaluation process of the image evaluation module also includes: By calculating the influence coefficient of the camera's operating status during the i-th shooting The preset operating status impact coefficient threshold Make a comparison; like ,The system judges that the camera is in good operating condition during the i-th shooting, the quality of the part image is qualified, and the image shooting operation can continue; like ,The system judges that the operating status of the camera during the i-th shooting is slightly poor, and the quality of the parts picture is unqualified, so the camera needs to be calibrated and debugged in time; Through the above technical solution, this embodiment calculates the operating state influence coefficient of the camera at the i-th shooting The preset operating status impact coefficient threshold Through this comparison method, it is possible to make an accurate judgment on whether the operating status of the camera during the i-th shooting is good, and based on the judgment result, it is possible to analyze whether the image quality of the part is qualified, thereby ensuring the quality of the part image data, and ensuring that the subsequent analysis of the part quality is based on high-quality image data for feature extraction, thereby ensuring the accuracy of the part quality analysis.

[0025] The analysis process of the quality monitoring module includes: By formula Calculate the quality influence coefficient of the corresponding processed workpiece when the camera takes the i-th shot ; in, is the total area of ​​the workpiece processed when the camera takes the i-th shot, is the preset total area of ​​the workpiece, for The standard value can be selected and set according to the allowable error in the empirical data. is the damaged area of ​​the corresponding processed workpiece when the camera takes the i-th shot, is the preset damaged area, is the crack area of ​​the corresponding processed workpiece when the camera takes the i-th shot, is the preset crack area, is the error influence coefficient, which is set according to empirical fitting; Through the above technical solution, this embodiment provides the quality influence coefficient of the corresponding processed workpiece when the camera takes the i-th shot: , can be obtained by formula It is obvious that when the camera takes the i-th shot, the larger the damaged area and crack area of ​​the corresponding processed workpiece are, and the greater the difference between the total area and the preset total area is, the quality influence coefficient of the corresponding processed workpiece when the camera takes the i-th shot is The larger the difference, the worse the quality of the part. On the contrary, when the camera takes the i-th shot, the smaller the damaged area and crack area of ​​the corresponding finished workpiece are, and the smaller the difference between the total area and the preset total area is, the quality influence coefficient of the corresponding finished workpiece when the camera takes the i-th shot is The smaller it is, the higher the quality of the part; Since the characteristic parameters in the calculation process are extracted based on high-quality image data, and combined with the operating state influence coefficient of the camera at the i-th shooting Correcting the calculation results can improve the accuracy of the calculation results, thereby improving the accuracy of subsequent evaluation results of part quality.

[0026] The analysis process of the quality monitoring module also includes: By calculating the quality influence coefficient of the corresponding processed workpiece at the time of the i-th shooting The preset quality impact coefficient threshold Make a comparison; like , the system determines that the quality of the processed workpiece is affected during the i-th shooting, which means that the quality of the processed workpiece is unqualified; like , the system determines that the quality of the finished workpiece is not affected during the i-th shooting, which means that the quality of the finished workpiece is qualified, and analyzes the stability of part processing in combination with the quality of all finished parts in the same batch; Through the above technical solution, this embodiment calculates the quality influence coefficient of the corresponding processed workpiece during the i-th shooting The preset quality impact coefficient threshold For comparison, since the feature parameters in the data are extracted based on high-quality image data and combined with the operating status influence coefficient of the camera at the i-th shooting The calculation results are corrected, so that an accurate judgment can be made on whether the quality of the completed workpiece is affected during the i-th shooting, and the accuracy and reliability of judging whether the quality of the workpiece after processing is qualified can be improved, thereby avoiding the situation where the accuracy of the supervision results of the parts production process is low.

[0027] The analysis process of the data analysis module includes: The data acquisition and recording module collects the interval time between all two adjacent shots of the camera during the processing of a batch of parts, and establishes the processing time change curve of the batch of parts. By formula Calculate the dispersion coefficient of the quality influence coefficient of a batch of parts after processing ; Where n is the total number of times the camera takes pictures of any processed part, and since the camera is used to take pictures of each processed part, n is also equal to the total number of parts in a batch. For all The average value of is the starting time for processing this batch of parts. The end time of processing this batch of parts; Through the above technical solution, this embodiment provides a batch of discrete coefficients of the quality influence coefficient after processing of parts. , can be obtained by formula Obtained by calculation, the discrete coefficient of the quality influence coefficient of a batch of parts after processing is obtained by calculation , the stability of the processing quality of the same batch of parts can be analyzed, thereby mapping out whether the operating status of the processing equipment is good, and then realizing the monitoring and management of the parts production process.

[0028] The analysis process of the data analysis module also includes: By calculating the dispersion coefficient of the quality influence coefficient after processing a batch of parts The preset coefficient of dispersion threshold Make a comparison; like , the system judges that the quality of the batch of parts after processing is good, indicating that the stability of parts processing is high and the operating status of the processing equipment is good; like ,The system judges that the quality of the batch of parts after processing fluctuates greatly, indicating that the stability of part processing is low and the operating status of the processing equipment is abnormal; Through the above technical solution, this embodiment calculates the discrete coefficient of the quality influence coefficient after a batch of parts are processed. The preset coefficient of dispersion threshold For comparison, since the data is based on the quality influence coefficient of the corresponding finished workpieces at the time of all shooting The calculation result is obtained, so the accuracy of the calculation result is relative to the result. Therefore, through this comparison method, the quality of the batch of parts after processing can be analyzed, thereby analyzing the stability of the parts processing, and the stability of the parts processing can reflect whether the operating status of the processing equipment is good, thereby realizing the monitoring and management of the parts production process, and accurate data can also ensure the accuracy of the monitoring and management results.

[0029] See also Figure 2 As shown, a production process visualization monitoring and management method based on MES, the method includes: S1: The camera unit in the image acquisition module takes an image of each processed part, and the recognition unit recognizes the part image to extract the data features that need to be monitored; S2: Collecting the operating status data of the camera in the camera unit and the part image data through the data acquisition and recording module; S3: Analyze the quality of the image of the processed part taken by the camera unit through the image evaluation module combined with the data of the data acquisition and recording module, and judge whether the operating status of the camera is good according to the analysis result; S4: When it is determined that the camera is in good operating condition, the quality monitoring module is combined with the data features identified by the identification unit to analyze whether the quality of the parts after processing is qualified; S5: The data analysis module combines the quality of all parts after processing in the same batch to analyze the stability of part processing during this period, and further monitors and manages the status of processing equipment; S6: Through the data visualization module, the camera's operating status data, the part quality data after processing, the part processing stability data and the processing equipment status data are displayed in the form of charts on the visualization large screen for back-end management personnel to monitor and manage.

[0030] Through the above technical scheme, this embodiment provides a production process visualization monitoring management method based on MES. When supervising the production process of parts, firstly, the camera unit in the image acquisition module takes an image of each part after processing, and the recognition unit recognizes the part image to extract the data features to be monitored. Then, the data acquisition and recording module collects the operation status data of the camera in the camera unit and the part image data, and the image evaluation module combines the data of the data acquisition and recording module to analyze the quality of the image of the part after processing taken by the camera unit, and judges whether the operation status of the camera is good according to the analysis result. When it is judged that the operation status of the camera is good, the quality monitoring module combines the data features recognized by the recognition unit to analyze whether the quality of the parts after processing is qualified, and the data analysis module combines the quality of all the parts after processing in the same batch to analyze the processing stability of the parts within this period of time, and further monitors and manages the processing equipment status. Finally, the operation status data of the camera, the quality data of the parts after processing, the processing stability data of the parts and the status data of the processing equipment can be displayed in the form of charts on the visualization large screen through the data visualization module for monitoring and management by the backstage management personnel. Through such a setting, by combining the camera's operating status data and part image data, and analyzing the quality of the part images after processing taken by the camera unit, the quality of the camera images can be guaranteed, thereby ensuring that the subsequent evaluation and analysis of part quality, part processing stability and the operating status of the processing equipment is based on high-quality image data for feature extraction, thereby ensuring the accuracy of the analysis. The evaluation results are then displayed in the form of charts on the visualization large screen through the data visualization module for back-end management personnel to monitor and manage, thereby realizing visualized monitoring and management of the production process.

[0031] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A visual monitoring and management system for production process based on MES, characterized in that: The system comprises: An image acquisition module, including a camera unit and a recognition unit, wherein the camera unit includes a camera for taking images of each processed part; The recognition unit is used to recognize the part image and extract the data features that need to be monitored; A data acquisition and recording module is used to collect the operating status data of the camera in the camera unit and the image data of the parts; The image evaluation module is used to analyze the quality of the image of the processed parts taken by the camera unit in combination with the data from the data acquisition and recording module, and judge whether the operating status of the camera is good or not according to the analysis results; The quality monitoring module is used to analyze whether the quality of the parts after processing is qualified in combination with the data features identified by the recognition unit when judging that the camera is in good operating condition; The data analysis module is used to analyze the stability of part processing during this period of time based on the quality of all parts processed in the same batch, and further monitor and manage the status of processing equipment; The data visualization module is used to display the camera's operating status data, part quality data after processing, part processing stability data, and processing equipment status data in the form of charts on a large visualization screen for back-end management personnel to monitor.

2. According to claim 1, a MES-based production process visual monitoring and management system is characterized in that: The evaluation process of the image evaluation module includes: By formula Calculate the influence coefficient t of the camera's operating state when shooting for the i-th time i ; Where i is any time the camera takes a picture of a finished part, d is any time point after the start of production on that day, b is the total number of time points from the start of production to the i-th time of shooting on that day, and dy is a is the voltage value of the camera at the ath time point since the start of production on that day, For all a Standard value, moh i is the blurred area in the image taken by the camera for the i-th time, moh y is the preset fuzzy area, θ is the fuzzy area influence coefficient, which is set according to empirical fitting, and ld i is the brightness of the image captured by the camera for the i-th time, ld y is the preset brightness, jx is the number of days since the camera was last calibrated, y is the preset calibration day interval, jx b is the standard value of jx, σ is the influence coefficient of the camera’s service life, which is set based on empirical fitting, x1 and x2 are weight coefficients, and f z To define a function, if f z (x)≥x, then let f z (x) = x, otherwise, let f z (x)=1.

3. The MES-based production process visualization monitoring and management system according to claim 2 is characterized in that: The evaluation process of the image evaluation module also includes: By calculating the operating state influence coefficient t of the camera at the i-th shooting i The preset operating status influence coefficient threshold t 01 Make a comparison; If i ≤t 01 ,The system judges that the camera is in good operating condition during the i-th shooting, the quality of the part image is qualified, and the image shooting operation can continue; If i >t 01 ,The system judges that the operating status of the camera during the i-th shooting is slightly poor, and the quality of the parts picture is unqualified, so the camera needs to be calibrated and debugged in time.

4. The MES-based production process visualization monitoring and management system according to claim 3 is characterized in that: The analysis process of the quality monitoring module includes: By formula Calculate the quality influence coefficient r of the corresponding processed workpiece when the camera takes the i-th shot i ; Among them, mj i is the total area of ​​the workpiece processed when the camera takes the i-th shot, mj y is the preset total area of ​​the workpiece, mj b for mj i The standard value, ps i is the damaged area of ​​the corresponding processed workpiece when the camera takes the i-th shot, ps y is the preset damaged area, lf i is the crack area of ​​the corresponding processed workpiece when the camera takes the i-th shot, lf y is the preset crack area, δ is the error influence coefficient, which is set according to empirical fitting.

5. The MES-based production process visualization monitoring and management system according to claim 4 is characterized in that: The analysis process of the quality monitoring module also includes: By calculating the quality influence coefficient r of the corresponding processed workpiece at the time of the i-th shooting i The preset quality influence coefficient threshold r 01 Make a comparison; If r i ≥r 01 , the system determines that the quality of the processed workpiece is affected during the i-th shooting, which means that the quality of the processed workpiece is unqualified; If r i <r 01 The system determines that the quality of the processed workpiece is not affected during the i-th shooting, which means that the quality of the processed workpiece is qualified, and analyzes the stability of part processing in combination with the quality of all processed parts in the same batch.

6. The MES-based production process visualization monitoring and management system according to claim 1 is characterized in that: The analysis process of the data analysis module includes: The data acquisition and recording module collects the interval time between all two adjacent shots of the camera during the processing of a batch of parts, and establishes the processing time variation curve ω(t) of the batch of parts; By formula Calculate the dispersion coefficient of the quality influence coefficient of a batch of parts after processing Where n is the total number of times the camera takes pictures of any processed part, and since the camera is used to take pictures of each processed part, n is also equal to the total number of parts in a batch. For all r i The average value of , t1 is the starting time point of processing this batch of parts, and t2 is the ending time point of processing this batch of parts.

7. The MES-based production process visualization monitoring and management system according to claim 6 is characterized in that: The analysis process of the data analysis module also includes: By calculating the dispersion coefficient of the quality influence coefficient after processing a batch of parts The preset coefficient of dispersion threshold Make a comparison; like The system judges that the quality of the parts after processing is good, which means that the stability of parts processing is high and the operating status of the processing equipment is good; like The system determines that the quality of this batch of parts after processing fluctuates greatly, indicating that the stability of parts processing is low and the operating status of the processing equipment is abnormal.

8. A method for visual monitoring and management of production process based on MES, the method adopts a method for visual monitoring and management of production process based on MES as claimed in claims 1-7, characterized in that: The method comprises: S1: The camera unit in the image acquisition module takes an image of each processed part, and the recognition unit recognizes the part image to extract the data features that need to be monitored; S2: Collecting the operating status data of the camera in the camera unit and the part image data through the data acquisition and recording module; S3: Analyze the quality of the image of the processed part taken by the camera unit through the image evaluation module combined with the data of the data acquisition and recording module, and judge whether the operating status of the camera is good according to the analysis result; S4: When it is determined that the camera is in good operating condition, the quality monitoring module is combined with the data features identified by the identification unit to analyze whether the quality of the parts after processing is qualified; S5: The data analysis module combines the quality of all parts after processing in the same batch to analyze the stability of part processing during this period, and further monitors and manages the status of processing equipment; S6: Through the data visualization module, the camera's operating status data, the part quality data after processing, the part processing stability data and the processing equipment status data are displayed in the form of charts on the visualization large screen for back-end management personnel to monitor and manage.

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