Comprehensive service platform for industrial doctors and enterprises

Abstract

The invention discloses an industrial doctor and enterprise comprehensive service platform, which comprises a data acquisition module, a data preprocessing and storage module, an abnormality diagnosis module, an early warning transmission module and a user visualization and touch type management interface, and relates to the technical field of management service platforms. According to the industrial doctor and enterprise comprehensive service platform, the abnormality diagnosis model is established, the data is introduced to analyze whether the data of each sensor on the production line or the production line equipment is abnormal or not, the tracing and positioning operation of specific problems is performed according to the abnormality, and the diagnosis result is generated for data transmission, so that the real-time monitoring of each process is realized; according to the system, the fault is quickly diagnosed, the shutdown time is shortened, the production efficiency is further improved, unified instruction management and automatic abnormality diagnosis are completed by using the comprehensive service platform, and the traceability positioning operation of fault equipment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of management service platforms, and in particular to an industrial doctor enterprise comprehensive service platform. Background Art

[0002] At present, the operation status monitoring of production line equipment can usually only rely on manual visual inspection or the display screen of the equipment. The use of manual online management places high demands on on-site personnel, requiring personnel to monitor the on-site situation in a timely and accurate manner, especially during night shift production, which is generally neglected in management, causing great losses to production and safety. Personnel cannot be reasonably allocated, and the operation of each equipment and process status of the enterprise production line cannot be monitored online in real time.

[0003] The reference patent name is: An online operation and maintenance management system for an enterprise production line (patent publication number: CN116562500A, patent publication date: 2023-08-08), including: a data acquisition unit, used to acquire enterprise production line data; a data transmission module, used to transmit enterprise production line data to a data analysis module in real time through wireless communication technology; a data analysis module, used to analyze enterprise production line data in real time to obtain the operating status of the enterprise production line, and the operating status includes: normal state and abnormal state; an abnormality handling module, used to process the enterprise production line data corresponding to the abnormal state based on the abnormal state, and restore the normal operating state of the enterprise production line. It can monitor the operation of each equipment and process status of the enterprise production line in real time online. If an abnormal condition occurs in the enterprise production line, it can handle the abnormal condition in time, and at the same time, it can grasp the abnormal position of the entire production line at the first time, and accurately determine the specific location of the abnormal operation condition.

[0004] Based on the description in the above-mentioned documents, in the existing industrial production line processes, the industrial production line equipment or sensors often have abnormalities. In the current monitoring and management operations, although it is known that there are abnormalities in the operation process, the specific location of the abnormality is unknown, and the specific abnormal equipment cannot be diagnosed, so it is difficult to achieve real-time monitoring and rapid diagnosis operations, resulting in the problem that equipment failures cannot be handled in time. For this reason, the present invention provides an industrial doctor enterprise comprehensive service platform. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an industrial doctor enterprise comprehensive service platform, which solves the problem that in the existing industrial production line process, the industrial production line equipment or sensors often have abnormalities. In the current monitoring and management operations, although it is known that there are abnormalities in the operation process, the specific location of the abnormality is unknown, and the specific abnormal equipment cannot be diagnosed, making it difficult to achieve real-time monitoring and rapid diagnosis operations, resulting in the problem that equipment failures cannot be handled in a timely manner.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: Industrial Doctor Enterprise Comprehensive Service Platform, including:

[0007] The data acquisition module uses multiple sensors and combines IoT transmission to collect the operating status data of industrial production line equipment in real time;

[0008] The data preprocessing and storage module receives the collected data and stores it in the database, and simultaneously implements the classification and screening operations of the data to form the required data set;

[0009] The abnormality diagnosis module establishes an abnormality diagnosis model and introduces data analysis to determine whether there are abnormalities in the data of each sensor or equipment on the production line. It also traces and locates specific problems based on the abnormalities and generates diagnostic results for data transmission.

[0010] The early warning transmission module sends abnormal instructions to the nearest device terminal held by personnel and marks the abnormal location with a flashing red light;

[0011] The user visualization and touch management interface provides users with intuitive data display, transmits instructions according to on-site conditions, and performs integrated management and control operations.

[0012] Preferably, the operations for implementing data classification and screening in the data preprocessing and storage module are:

[0013] A1. Process the collected video data into frames to form the required image data;

[0014] A2. Set the required data template, extract the corresponding image category name from the data template, and classify the image data name according to the corresponding image category name;

[0015] A3. Classify the image data corresponding to the sensors in the process sequence of the industrial production line to form an initial image data set labeled J.

[0016] Preferably, the frame processing operation of the video data collected in A1 is:

[0017] a11. Take a section of video data and set the processing frame cycle according to the running rate of the production line, and then segment the video data from the beginning according to the frame cycle, and the images separated by the corresponding frame cycle form the sequential image data of the video data;

[0018] a12. Compare the sequential image data. If the adjacent image data in the sequential image data are the same, remove the preceding image data and retain the subsequent image data. If the adjacent image data in the sequential image data are different, both the preceding and subsequent image data are retained.

[0019] Preferably, the abnormality diagnosis model is established by:

[0020] B1. Based on the actual equipment operation data, a simulation model similar to the actual industrial production line equipment is introduced and constructed;

[0021] B2. Extract the historical data of industrial production line equipment from the database based on the product image data at the end of each process, and summarize them into a result image data set labeled K;

[0022] B3. Introduce the result image data set K into the simulation model for data combination to establish the required abnormality diagnosis model.

[0023] Preferably, the abnormality diagnosis module analyzes whether there is an abnormality in the data of each sensor or production line equipment on the production line:

[0024] C1. Introduce the classified initial image data set J into the abnormal diagnosis model, and divide the image data corresponding to the sensor in the initial image data set J into image data sets corresponding to each different process in the industrial production line, labeled as j m (j1,j2,…,j m ), first implement the image data set j m The traversal operation;

[0025] C2, through the image data set j m (j1,j2,…,j m ) performs feature comparison operation with the result image dataset K in the abnormal diagnosis model;

[0026] C3. If there is a difference in the feature data of the corresponding image data, the currently detected image data is abnormal and a mark extraction is performed. Conversely, if the feature data of the corresponding image data remains consistent, the currently detected image data is not abnormal.

[0027] Preferably, the C1 pairs of image data sets j m The traversal operation is:

[0028] c11, realize the image data group j m The number of sensors is checked and the image data set j is m The number of sensors is compared with the number of actual sensors. If the two numbers are consistent, then the image data set j m The number of is normal. If the two numbers are inconsistent, then the image data set j m Some image data is missing and some sensor devices have problems recognizing;

[0029] c12. Realize image data set j mThe product features are checked in the process, and the image data are identified in turn. If there are product features that are not present in the image data, the device that generated the image data is directly traced back to determine if there is a problem with the production line equipment here.

[0030] Preferably, the specific steps of the feature comparison operation in C2 are:

[0031] c21. Select image data set j m (j1,j2,…,j m ) in which the last image data in a single process is taken as the image to be compared;

[0032] c22, then performing pixel conversion on the selected image data to form the same size as the image data corresponding to the result image data set K;

[0033] Grayscale the selected image data and the result image data to achieve the segmentation operation of the product features and the background features, set reference points P1 and P2 for the product features of the selected image data, and set reference points Q1 and Q2 at the same positions corresponding to the product features of the result image data;

[0034] According to the direction of the background features, the corner points of the two image data are aligned to realize the overlay operation of the image data, so as to realize the comparison of the positions of the corresponding reference points on the two image data;

[0035] c23. And extract and mark the parts where the feature data are different in the comparison.

[0036] Preferably, the operation of comparing the positions of corresponding reference points on the two image data in c22 is:

[0037] D1. Establish a judgment coordinate axis about XY based on the lower left corner point of the image data as the initial point, establish the X axis from the initial point to the bottom edge of the image data, and establish the Y axis from the initial point to the side edge of the image data, and the X axis and the Y axis intersect at right angles at the initial point;

[0038] D2. According to the determination coordinate axis, the coordinates of the reference points P1 and P2 in the image data are (x1, y1) and (x2, y2), respectively, and the coordinates of the reference points Q1 and Q2 are (x3, y3) and (x4, y4), respectively.

[0039] D3. Calculate the deviation value between reference point P1 and reference point P2 and reference point Q2. If the value is equal to 0, the product feature is normal, and the corresponding equipment and sensor are also normal. Otherwise, if the value is not equal to 0, the product feature position is abnormal, and the corresponding equipment is abnormal.

[0040] Preferably, the formula for calculating the deviation value in D3 is:

[0041] F=∑[|x1-x3|+|y1-y3|+|x2-x4|+|y2-y4|];

[0042] F represents the calculated deviation value, |x1-x3| is the absolute value of the deviation between the reference point P1 and the reference point Q1 on the X-axis, |y1-y3| is the absolute value of the deviation between the reference point P1 and the reference point Q1 on the Y-axis, |x2-x4| is the absolute value of the deviation between the reference point P2 and the reference point Q2 on the X-axis, |y2-y4| is the absolute value of the deviation between the reference point P2 and the reference point Q2 on the Y-axis;

[0043] If F=0, the corresponding device and sensor are normal; if F>0, the corresponding device is abnormal.

[0044] Preferably, the abnormality diagnosis module performs the following steps to trace and locate specific problems:

[0045] E1. After discovering an abnormality, it is diagnosed that the fault is caused by production line equipment or sensor failure;

[0046] E2. Based on the sorting and positioning of the image data, trace the corresponding device or sensor location where the abnormal image data appears, and perform maintenance and repair operations through personnel;

[0047] The early warning transmission module will use the corresponding device or sensor location found as the center point and send out a signal. The signal will be sent first to the person holding the device terminal closest to the center point, informing the person to carry out timely processing instructions.

[0048] The present invention provides an industrial doctor enterprise comprehensive service platform. Compared with the prior art, it has the following beneficial effects:

[0049] (1) The Industrial Doctor Enterprise Comprehensive Service Platform establishes an abnormality diagnosis model and introduces data analysis to determine whether there are abnormalities in the data of each sensor or equipment on the production line. It then traces and locates the specific problem based on the abnormality, generates diagnostic results for data transmission, and realizes real-time monitoring of each process while completing rapid fault diagnosis operations, reducing downtime and further improving production efficiency. It also uses the comprehensive service platform to complete unified instruction management and automated abnormality diagnosis, and realizes the traceability and location of faulty equipment.

[0050] (2) The Industrial Doctor Enterprise Comprehensive Service Platform introduces the classified initial image data set into the abnormal diagnosis model, and divides the image data corresponding to the sensor in the initial image data set to form an image data group corresponding to each different process in the industrial production line, thereby realizing the traversal operation of the image data group. In this way, the number of image data groups corresponding to the number of processes can be known in advance, so as to identify the abnormality of the sensor. At the same time, it checks whether there are product features in the image data to determine the problem of the production line equipment, so as to avoid subsequent losses caused by not knowing the problem in time after it occurs, and avoid the problem of long-term downtime for maintenance.

[0051] (3) The Industrial Doctor Enterprise Comprehensive Service Platform realizes the segmentation operation of product features and background features by gray-scaling the selected image data and the result image data, sets reference points for the product features of the selected image data and the result image data, and aligns the corner points of the two image data according to the direction of the background features to realize the overlay operation of the image data, and compares the positions of the corresponding reference points on the two image data, thereby diagnosing equipment abnormalities based on the abnormalities of product features, and can trace the source to inform the nearest person holding the equipment terminal, complete fast and effective processing operations, and improve the efficiency of service management. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a principle block diagram of the integrated service platform of the present invention;

[0053] Figure 2 It is an operation flow chart of the data preprocessing and storage module of the present invention;

[0054] Figure 3 An operation flow chart established for the abnormality diagnosis model of the present invention;

[0055] Figure 4 This is an operational flow chart of the abnormality diagnosis module of the present invention for determining abnormalities. DETAILED DESCRIPTION

[0056] 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.

[0057] See also Figure 1-Figure 4 , the present invention provides two technical solutions:

[0058] Embodiment 1: Industrial Doctor Enterprise Comprehensive Service Platform, including:

[0059] The data acquisition module uses multiple sensors and combines IoT transmission to collect the operating status data of industrial production line equipment in real time;

[0060] The sensors are used to collect the operation data of the industrial production line and control it through video. The video data parameters collected by the sensors are consistent, and multiple sensors are located directly above the transmission route of each process.

[0061] The data preprocessing and storage module receives the collected data and stores it in the database, and simultaneously implements the classification and screening operations of the data to form the required data set;

[0062] The database can not only store newly collected data, but also compress and store historical data to facilitate subsequent comparison and data tracing;

[0063] The abnormality diagnosis module establishes an abnormality diagnosis model and introduces data analysis to determine whether there are abnormalities in the data of each sensor or equipment on the production line. It also traces and locates specific problems based on the abnormalities and generates diagnostic results for data transmission.

[0064] The early warning transmission module sends abnormal instructions to the nearest device terminal held by personnel and marks the abnormal location with a flashing red light;

[0065] The user visualization and touch-screen management interface provides users with intuitive data display in the form of charts and text, and transmits instructions according to on-site conditions for integrated management and control operations.

[0066] Among them, by establishing an abnormal diagnosis model and introducing data analysis to determine whether there are abnormalities in the data of each sensor or production line equipment on the production line, and tracing and locating the specific problem based on the abnormality, the diagnosis results are generated for data transmission, and real-time monitoring of each process is achieved while completing rapid diagnosis operations of faults, reducing downtime, and further improving production efficiency. In addition, the integrated service platform is used to complete unified instruction management and automated abnormal diagnosis, and realize the tracing and locating of faulty equipment.

[0067] In the embodiment of the present invention, the operations for implementing data classification and screening in the data preprocessing and storage module are:

[0068] A1. Process the collected video data into frames to form the required image data;

[0069] A2. Set the required data template, extract the corresponding image category name from the data template, and classify the image data name according to the corresponding image category name;

[0070] A3. Classify the image data corresponding to the sensors in the process sequence of the industrial production line to form an initial image data set labeled J.

[0071] In the embodiment of the present invention, the frame processing operation of the video data collected in A1 is as follows:

[0072] a11. Take a section of video data and set the processing frame cycle according to the running rate of the production line, and then segment the video data from the beginning according to the frame cycle, and the images separated by the corresponding frame cycle form the sequential image data of the video data;

[0073] a12. Compare the sequential image data. If the adjacent image data in the sequential image data are the same, remove the preceding image data and retain the subsequent image data. If the adjacent image data in the sequential image data are different, both the preceding and subsequent image data are retained.

[0074] In the embodiment of the present invention, the operation of establishing the abnormality diagnosis model is:

[0075] B1. Based on the actual equipment operation data, a simulation model similar to the actual industrial production line equipment is introduced and constructed;

[0076] B2. Extract the historical data of industrial production line equipment from the database based on the product image data at the end of each process, and summarize them into a result image data set labeled K;

[0077] B3. Introduce the result image data set K into the simulation model for data combination to establish the required abnormality diagnosis model.

[0078] In the embodiment of the present invention, the abnormality diagnosis module analyzes whether the data of each sensor or production line equipment on the production line is abnormal in the following steps:

[0079] C1. Introduce the classified initial image data set J into the abnormal diagnosis model, and divide the image data corresponding to the sensor in the initial image data set J into image data sets corresponding to each different process in the industrial production line, labeled as j m (j1,j2,…,j m ), first implement the image data set j m The traversal operation;

[0080] C2, through the image data set j m (j1,j2,…,j m ) performs feature comparison operation with the result image dataset K in the abnormal diagnosis model;

[0081] C3. If there is a difference in the feature data of the corresponding image data, the currently detected image data is abnormal and a mark extraction is performed. Conversely, if the feature data of the corresponding image data remains consistent, the currently detected image data is not abnormal.

[0082] In the embodiment of the present invention, C1 is used for the image data set j m The traversal operation is:

[0083] c11, realize the image data group j m The number of sensors is checked and the image data set j is m The number of sensors is compared with the number of actual sensors. If the two numbers are consistent, then the image data set j m The number of is normal. If the two numbers are inconsistent, then the image data set j m Some image data is missing and some sensor devices have problems recognizing;

[0084] c12. Realize image data set j m The product features are checked in the process, and the image data are identified in turn. If there are product features that are not present in the image data, the device that generated the image data is directly traced back to determine if there is a problem with the production line equipment here.

[0085] Among them, by introducing the classified initial image data set into the abnormality diagnosis model, and dividing the image data corresponding to the sensor in the initial image data set, an image data group corresponding to each different process in the industrial production line is formed, and the traversal operation of the image data group is realized, so as to know in advance the number of image data groups corresponding to the number of processes, so as to identify the abnormality of the sensor, and at the same time check whether there are product features in the image data to determine the problem of the production line equipment, so as to avoid subsequent losses caused by not knowing the problem in time after it occurs, and at the same time avoid the problem of long-term downtime for maintenance.

[0086] In the embodiment of the present invention, the specific steps of the feature comparison operation in C2 are:

[0087] c21. Select image data set j m (j1,j2,…,j m ) in which the last image data in a single process is taken as the image to be compared;

[0088] c22, then performing pixel conversion on the selected image data to form the same size as the image data corresponding to the result image data set K;

[0089] Grayscale the selected image data and the result image data to achieve the segmentation operation of the product features and the background features, set reference points P1 and P2 for the product features of the selected image data, and set reference points Q1 and Q2 at the same positions corresponding to the product features of the result image data;

[0090] According to the direction of the background features, the corner points of the two image data are aligned to realize the overlay operation of the image data, so as to realize the comparison of the positions of the corresponding reference points on the two image data;

[0091] c23. And extract and mark the parts where the feature data are different in the comparison.

[0092] In the embodiment of the present invention, the operation of comparing the positions of corresponding reference points on the two image data in c22 is:

[0093] D1. Establish a judgment coordinate axis about XY based on the lower left corner point of the image data as the initial point, establish the X axis from the initial point to the bottom edge of the image data, and establish the Y axis from the initial point to the side edge of the image data, and the X axis and the Y axis intersect at right angles at the initial point;

[0094] D2. According to the determination coordinate axis, the coordinates of the reference points P1 and P2 in the image data are (x1, y1) and (x2, y2), respectively, and the coordinates of the reference points Q1 and Q2 are (x3, y3) and (x4, y4), respectively.

[0095] D3. Calculate the deviation value between reference point P1 and reference point P2 and reference point Q2. If the value is equal to 0, the product feature is normal, and the corresponding equipment and sensor are also normal. Otherwise, if the value is not equal to 0, the product feature position is abnormal, and the corresponding equipment is abnormal.

[0096] In the embodiment of the present invention, the formula for calculating the deviation value in D3 is:

[0097] F=∑[|x1-x3|+|y1-y3|+|x2-x4|+|y2-y4|];

[0098] F represents the calculated deviation value, |x1-x3| is the absolute value of the deviation between the reference point P1 and the reference point Q1 on the X-axis, |y1-y3| is the absolute value of the deviation between the reference point P1 and the reference point Q1 on the Y-axis, |x2-x4| is the absolute value of the deviation between the reference point P2 and the reference point Q2 on the X-axis, |y2-y4| is the absolute value of the deviation between the reference point P2 and the reference point Q2 on the Y-axis;

[0099] If F=0, the corresponding device and sensor are normal; if F>0, the corresponding device is abnormal.

[0100] In the embodiment of the present invention, the abnormal diagnosis module performs the following steps to trace and locate a specific problem:

[0101] E1. After discovering an abnormality, it is diagnosed that the fault is caused by the production line equipment or the sensor;

[0102] E2. Based on the sorting and positioning of the image data, trace the corresponding device or sensor location where the abnormal image data appears, and perform maintenance and repair operations through personnel;

[0103] The early warning transmission module will use the corresponding device or sensor location found as the center point and send out a signal. The signal will be sent first to the person holding the device terminal closest to the center point, informing the person to carry out timely processing instructions.

[0104] Among them, the segmentation operation of product features and background features is realized by graying the selected image data and the result image data, and reference points are set for the product features of the selected image data and the result image data. According to the direction of the background features, the corner points of the two image data are aligned to realize the overlay operation of the image data, and the positions of the corresponding reference points on the two image data are compared. In this way, the equipment abnormality is diagnosed based on the abnormality of the product features, and the source can be traced to inform the nearest person holding the equipment terminal, so as to complete the fast and effective processing operation.

[0105] Embodiment 2: The difference from Embodiment 1 is that the present invention also designs a comparative experiment, in which the existing industrial production line management and the industrial doctor enterprise comprehensive service platform of the present invention are applied to multiple production lines of a certain industry, and the number of problems generated by industrial equipment during the monitoring and management of the cycle time, as well as the time for problem handling and the time for production line downtime are recorded. The specific results are shown in Table 1:

[0106] Table 1 Result record table

[0107]

[0108] Comprehensive service platform

[0109] To sum up, when the Industrial Doctor Enterprise Comprehensive Service Platform of the present invention is applied, more problems can be discovered during the monitoring process within the cycle time, and the average time required to solve the problems is shorter, and the production line downtime required is shorter, so it can be better applied to actual operations.

[0110] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0111] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0112] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The Industrial Doctor Enterprise Comprehensive Service Platform is characterized by: include: The data acquisition module uses multiple sensors and combines IoT transmission to collect the operating status data of industrial production line equipment in real time; The data preprocessing and storage module receives the collected data and stores it in the database, and simultaneously implements the classification and screening operations of the data to form the required data set; The abnormality diagnosis module establishes an abnormality diagnosis model and introduces data analysis to determine whether there are abnormalities in the data of each sensor or equipment on the production line. It also traces and locates specific problems based on the abnormalities and generates diagnostic results for data transmission. The early warning transmission module sends abnormal instructions to the nearest device terminal held by personnel and marks the abnormal location with a flashing red light; The user visualization and touch management interface provides users with intuitive data display, transmits instructions according to on-site conditions, and performs integrated management and control operations.

2. The industrial doctor enterprise comprehensive service platform according to claim 1 is characterized by: The operations for implementing data classification and screening in the data preprocessing and storage module are: A1. Process the collected video data into frames to form the required image data; A2. Set the required data template, extract the corresponding image category name from the data template, and classify the image data name according to the corresponding image category name; A3. Classify the image data corresponding to the sensors in the process sequence of the industrial production line to form an initial image data set labeled J.

3. The industrial doctor enterprise comprehensive service platform according to claim 2 is characterized by: The frame processing operation of the video data collected in A1 is as follows: a11. Take a section of video data and set the processing frame cycle according to the running rate of the production line, and then segment the video data from the beginning according to the frame cycle, and the images separated by the corresponding frame cycle form the sequential image data of the video data; a12. Compare the sequential image data. If the adjacent image data in the sequential image data are the same, remove the preceding image data and retain the subsequent image data. If the adjacent image data in the sequential image data are different, both the preceding and subsequent image data are retained.

4. The industrial doctor enterprise comprehensive service platform according to claim 2 is characterized by: The establishment operation of the abnormal diagnosis model is: B1. Based on the actual equipment operation data, a simulation model similar to the actual industrial production line equipment is introduced and constructed; B2. Extract the historical data of industrial production line equipment from the database based on the product image data at the end of each process, and summarize them into a result image data set labeled K; B3. Introduce the result image data set K into the simulation model for data combination to establish the required abnormality diagnosis model.

5. The industrial doctor enterprise comprehensive service platform according to claim 4 is characterized by: The steps of the abnormality diagnosis module analyzing whether the data of each sensor or production line equipment on the production line is abnormal are as follows: C1. Introduce the classified initial image dataset J into the abnormal diagnosis model, and divide the image data corresponding to the sensor in the initial image dataset J into image data sets corresponding to each different process in the industrial production line, labeled as j m (j1,j2,…,j m ), first implement the image data set j m The traversal operation; C2, through the image data set j m (j1,j2,…,j m ) performs feature comparison operation with the result image dataset K in the abnormal diagnosis model; C3. If there is a difference in the feature data of the corresponding image data, the currently detected image data is abnormal and a mark extraction is performed. Conversely, if the feature data of the corresponding image data remains consistent, the currently detected image data is not abnormal.

6. The industrial doctor enterprise comprehensive service platform according to claim 5 is characterized by: The C1 is for the image data set j m The traversal operation is: c11, realize the image data group j m The number of inspection operations is performed to group the image data into groups j according to the actual number of sensors. m The number of sensors is compared with the number of actual sensors. If the two numbers are consistent, then the image data set j m The number of is normal. If the two numbers are inconsistent, then the image data set j m Some image data is missing and some sensor devices have problems recognizing; c12. Realize image data set j m The product features are checked in the process, and the image data are identified in turn. If there are product features that are not present in the image data, the device that generated the image data is directly traced back to determine if there is a problem with the production line equipment here.

7. The industrial doctor enterprise comprehensive service platform according to claim 5 is characterized by: The specific steps of the feature comparison operation in C2 are: c21. Select image data set j m (j1,j2,…,j m ) in which the last image data in a single process is taken as the image to be compared; c22, then performing pixel conversion on the selected image data to form the same size as the image data corresponding to the result image data set K; Grayscale the selected image data and the result image data to achieve the segmentation operation of the product features and the background features, set reference points P1 and P2 for the product features of the selected image data, and set reference points Q1 and Q2 at the same positions corresponding to the product features of the result image data; According to the direction of the background features, the corner points of the two image data are aligned to realize the overlay operation of the image data, so as to realize the comparison of the positions of the corresponding reference points on the two image data; c23. And extract and mark the parts where the feature data are different in the comparison.

8. The industrial doctor enterprise comprehensive service platform according to claim 7 is characterized by: The operation of comparing the positions of the corresponding reference points on the two image data in c22 is: D1. Establish a judgment coordinate axis about XY based on the lower left corner point of the image data as the initial point, establish the X axis from the initial point to the bottom edge of the image data, and establish the Y axis from the initial point to the side edge of the image data, and the X axis and the Y axis intersect at right angles at the initial point; D2. According to the determination coordinate axis, the coordinates of the reference points P1 and P2 in the image data are (x1, y1) and (x2, y2), respectively, and the coordinates of the reference points Q1 and Q2 are (x3, y3) and (x4, y4), respectively. D3. Calculate the deviation value between reference point P1 and reference point P2 and reference point Q2. If the value is equal to 0, the product feature is normal, and the corresponding equipment and sensor are also normal. Otherwise, if the value is not equal to 0, the product feature position is abnormal, and the corresponding equipment is abnormal.

9. The industrial doctor enterprise comprehensive service platform according to claim 8 is characterized by: The formula for calculating the deviation value in D3 is: F=∑[|x1-x3|+|y1-y3|+|x2-X4|+|y2-y4|]; F represents the calculated deviation value, |x1-x3| is the absolute value of the deviation between the reference point P1 and the reference point Q1 on the X-axis, |y1-y3| is the absolute value of the deviation between the reference point P1 and the reference point Q1 on the Y-axis, |x2-x4| is the absolute value of the deviation between the reference point P2 and the reference point Q2 on the X-axis, |y2-y4| is the absolute value of the deviation between the reference point P2 and the reference point Q2 on the Y-axis; If F=0, the corresponding device and sensor are normal; if F>0, the corresponding device is abnormal.

10. The industrial doctor enterprise comprehensive service platform according to claim 1 is characterized by: The abnormal diagnosis module performs the following steps to trace and locate specific problems: E1. After discovering an abnormality, it is diagnosed that the fault is caused by production line equipment or sensor failure; E2. Based on the sorting and positioning of the image data, trace the corresponding device or sensor location where the abnormal image data appears, and perform maintenance and repair operations through personnel; The early warning transmission module will use the corresponding device or sensor location found as the center point and send out a signal. The signal will be sent first to the person holding the device terminal closest to the center point, informing the person to carry out timely processing instructions.

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