Data calibration method based on Internet of Things

Abstract

The invention discloses a data calibration method based on the Internet of Things, and relates to the technical field of arm posture calibration, and the method comprises the steps: 1, obtaining feature data of a mechanical arm; step 2, acquiring data information of the mechanical arm; 3, data information of the mechanical arm is obtained; fourthly, data information of the mechanical arm is obtained; fifthly, mechanical arm machining posture information of the same workpiece machining content is obtained; sixthly, workpiece machining monitoring information is obtained; seventhly, when the mechanical arm is actually put into operation, the machining quality of the workpiece is monitored; 8, when the prompt chain of the unqualified workpiece appears, the fault problem is judged in combination with the workpiece machining monitoring information at the same time; and ninthly, after the mechanical arm is put into work. According to the method, the reference point is set through the visual image, data analysis of the operation posture of the mechanical arm can be more visual, long-time shutdown of a production line can be avoided, and normal workpiece machining can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of arm posture calibration, and particularly relates to a data calibration method based on the Internet of Things. Background Art

[0002] With the development of society, the behavior of using robotic arms to participate in industrial processing operations has received increasing attention, improving the efficiency of industrial processing. Since industrial processing operations are carried out by robotic arms, the processing accuracy of robotic arms has always been an issue that needs to be concerned about.

[0003] The prior art has the following deficiencies: During the process of processing workpieces by robotic arms, it is difficult to determine whether the processing posture of the robotic arm is within the control accuracy threshold range. Mostly, regular manual inspections are required to understand the accuracy, which will affect the shutdown of the industrial processing production line and the continuity and processing efficiency of industrial processing. Summary of the Invention

[0004] The purpose of the present invention is to provide a data calibration method based on the Internet of Things to solve the deficiencies in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: A data calibration method based on the Internet of Things, including the following steps: Step 1: Obtain the characteristic data of the robotic arm, set the positioning module according to the robotic arm, and then establish a digital twin of the robotic arm to form the robotic arm data information; Step 2: Obtain the robotic arm data information, set the posture of the robotic arm according to the operation content of the workpiece, and form the robotic arm processing posture information; Step 3: Obtain the robotic arm data information, set the position of the visual image acquisition module, and collect the visual acquisition images under the standard robotic arm processing posture information according to the operation content of the workpiece to form the visual monitoring information; Step 4: Obtain the robotic arm data information, set the sensing module according to the robotic arm moving joints, and collect the data of the robotic arm moving joint sensing module under the standard robotic arm processing posture information according to the operation content of the workpiece to form the moving joint position data monitoring information; Step 5: Obtain the robotic arm processing posture information, visual monitoring information, and moving joint position data monitoring information of the same workpiece processing content to obtain the workpiece processing monitoring information; Step 6: Obtain the workpiece processing monitoring information, test the posture of the robotic arm to form the test stability, and set the calibration period with reference to the test stability; Step 7: When the robotic arm is actually put into operation, monitor the processing quality of the workpiece and form a prompt item; Step 8: When a prompt chain for unqualified workpieces appears, simultaneously combine the workpiece processing monitoring information to determine the fault problem, switch to the standby robotic arm, synchronize data transmission, calibrate the standby robotic arm, and put it into work; Step 9: After the robotic arm is put into work, based on the workpiece processing monitoring information, determine the next-step data in real time. When the data error exceeds the set threshold, it is judged as abnormal, and safety abnormal monitoring information is formed.

[0006] In a preferred embodiment, the method for obtaining robotic arm data is as follows: Obtain the dimensions, external shape characteristics, and joint information of the robotic arm, then construct a robotic arm digital twin. According to the position of the joint, set a positioning module on the robotic arm; Position the robotic arm at zero point, and at the same time record the current position and angle of the positioning module to form positioning module information. At the same time, build the positioning module information into the robotic arm digital twin; Construct a spatial orientation grid in the robotic arm digital twin, monitor the position parameters of the positioning module information in real time, select a reference point at the zero-point position of the robotic arm, mark the position parameters of the reference point in the robotic arm digital twin, and monitor it in real time to form robotic arm data.

[0007] In a preferred embodiment, the method for obtaining the processing posture information of the robotic arm is as follows: Obtain the processing position of the workpiece and the processing sequence of multiple processing positions to form a processing path, and set the motion posture of the robotic arm according to the processing path. The setting method is as follows: The first is to set parameters according to the spatial orientation grid constructed in the robotic arm digital twin; the second is to manually pull the robotic arm to the workpiece processing position and stay to set it as the processing position point, and at the same time record it in the spatial orientation grid constructed in the robotic arm digital twin, and at the same time form spatial orientation grid parameters; Form the robotic arm processing posture information according to the processing of the same kind of workpiece, and store the data.

[0008] In a preferred embodiment, the method for forming visual monitoring information is as follows: Obtain the robotic arm data information and reset the robotic arm to the zero-point position. At the same time, set the position of the visual image acquisition module, and locate the zero-point position of the robotic arm through the visual acquisition module; Obtain the robotic arm processing posture information, operate the robotic arm according to the robotic arm processing posture information, mark and locate the image of the robotic arm during operation through the visual image, select a reference point of the robotic arm in the virtual image, and the number of reference points is manually selected through the management terminal as fixed reference points; Taking the start of the robotic arm operation as the initial time value, the image acquisition frequency is then set, and at the same time, the positions of the selected reference points in the images are marked to form a number of robotic arm pose images as visual monitoring information.

[0009] In a preferred embodiment, the formation method of the moving joint position data monitoring information is as follows: Obtain the robotic arm data information and reset the robotic arm to the zero position, and obtain the data of the robotic arm moving joint setting sensing module; Run the robotic arm according to the robotic arm processing pose information, obtain the information on the change of the sensing module data and the sensing module data at the workpiece processing point position, and the sensing module data at the workpiece processing point position is used as the monitoring point; There are several monitoring points in the robotic arm processing pose information. Store the data corresponding to the workpiece processing at the monitoring points to form the moving joint position data monitoring information as the comparison item of the moving joint data.

[0010] In a preferred embodiment, the acquisition method of the workpiece processing monitoring information is as follows: Establish the workpiece processing information, collect the corresponding robotic arm processing pose information, visual monitoring information and moving joint position data monitoring information of the corresponding workpiece. Using the workpiece name and model as the project name, bind and store the robotic arm processing pose information, visual monitoring information and moving joint position data monitoring information correspondingly to form the workpiece processing monitoring information.

[0011] In a preferred embodiment, the setting method of the calibration period is as follows: Obtain the workpiece processing monitoring information, control the operation of the robotic arm according to the robotic arm processing pose information, and set the accuracy thresholds of the visual monitoring information and the moving joint position data monitoring information; Monitor and obtain the visual image acquisition data and the moving joint position data of the robotic arm operation. Compare the data of the visually acquired images collected according to the frequency with the visual monitoring information to obtain the visual image difference data. The method for obtaining the comparison of the visual object difference data is as follows: Obtain the visual image, compare the positions of the reference points of the images collected at the current frequency with the images collected at the same time frequency in the visual monitoring information, and calculate according to the offset of the reference points; The method for obtaining the offset of the reference point is as follows: Perform pixel filtering on the collected images, overlap the images collected in the current frequency period with the images collected at the same period frequency in the visual monitoring information, obtain the difference straight-line distance between the same reference points, form a position error, and combine the position errors between all reference points in the image to obtain an average value. Then, obtain the offset according to the average value, control the operation of the robotic arm through the repetitive robotic arm processing posture information, obtain the offset change curve, and set the calibration period of the robotic arm according to the offset change curve function and in combination with the accuracy threshold.

[0012] In a preferred embodiment, the formation method of the prompt item is as follows: When the robotic arm actually processes the workpiece after the test is completed, visually monitor the processing quality of the workpiece. When the processing quality of the workpiece is unqualified, give an alarm prompt and continue to work. When there are more than three consecutive defective workpiece chains with unqualified workpiece quality continuously appearing subsequently, stop the robotic arm. When there are no more than three consecutive defective workpiece chains with unqualified workpiece quality continuously appearing, the normal processing runs invisibly, and prompt items are formed according to the information of the processed workpiece; at the same time, collect the offset of the robotic arm during the actual processing of the robotic arm to form the actual operation data of the robotic arm.

[0013] In a preferred embodiment, obtain the prompt item information, and combine it with the actual operation data of the robotic arm to obtain the offset of the robotic arm. Then, based on the data of the initial reference point offset between the images collected by the robotic arm and the visual monitoring information images during the actual processing of the arm in the same period, obtain the starting point of the deviation of the robotic arm, thereby judging the problems of the robotic arm, and providing the analysis data to the management terminal. While judging to stop the robotic arm for processing, switch to the standby robotic arm, and the standby robotic arm stores the workpiece processing monitoring information synchronously.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are as follows: 1. The present invention can understand the stability of the processing accuracy brought by the performance of the robotic arm through testing, and then set the calibration period to implement the maintenance plan of the robotic arm, which can reduce the problem of unqualified workpiece processing caused by accuracy problems. Moreover, the data analysis of the running posture of the robotic arm through visual images to set reference points can be more intuitive, and at the same time, it can also provide data reference for the subsequent reasons for the damaged positions of the robotic arm. 2. The present invention can judge the approximate position of the damage of the robotic arm, provide a reference data for maintenance personnel, and at the same time, it can also avoid the long-term shutdown of the production line and ensure the normal progress of workpiece processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other accompanying drawings can also be obtained based on these drawings.

[0016] Figure 1 It is the method flow chart of the present invention. Specific embodiments

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0018] Embodiment 1. Please refer to Figure 1 As shown, a data calibration method based on the Internet of Things in this embodiment includes the following steps: Step 1: Obtain the characteristic data of the robotic arm, set the positioning module according to the robotic arm, and then establish a digital twin of the robotic arm to form the robotic arm data information; Obtain the size, shape characteristics, and joint information of the robotic arm, then construct a digital twin of the robotic arm, and set the positioning module on the robotic arm according to the position of the joint; Place the robotic arm in the zero position, and at the same time record the current position and angle of the positioning module to form the positioning module information. At the same time, build the positioning module information into the digital twin of the robotic arm; Construct a spatial orientation grid in the digital twin of the robotic arm, real-time monitor the position parameters of the positioning module information, select a reference point at the zero position of the robotic arm, mark the position parameters of the reference point in the digital twin of the robotic arm and perform real-time monitoring to form the robotic arm data; Step 2: Obtain the robotic arm data information, set the posture of the robotic arm according to the operation content of the workpiece to form the robotic arm processing posture information; Obtain the processing position of the workpiece and the processing sequence of multiple processing positions to form a processing path, and set the motion posture of the robotic arm according to the processing path. The setting method is as follows: The first method is to set parameters according to the spatial orientation grid constructed in the robot twin. The second method is to manually pull the robot to the workpiece processing position and stop to set it as the processing position point, and at the same time record and store it in the spatial orientation grid constructed in the robot twin, and form the spatial orientation grid parameters at the same time. According to the processing of the same workpiece, the processing posture information of the robot arm is formed and the data is stored; Step 3: Obtain the data information of the robot arm, set the position of the visual image acquisition module, and collect the visual acquisition image under the standard robot arm processing posture information according to the work content of the workpiece to form visual monitoring information (visual analysis of the robot arm position is compared with the data under standard conditions) (the visual image is collected and stored to determine the fault problem); Acquire the data information of the robot arm and reset the robot arm to the zero position, and at the same time set the position of the visual image acquisition module, and locate the zero position of the robot arm through the visual acquisition module; Obtain the processing posture information of the robot arm, operate the robot arm according to the processing posture information of the robot arm, mark and locate the image of the robot arm operation process through visual images, select the reference points of the robot arm in the virtual image, and the number of reference points is manually selected by the management end as fixed reference points (some reference points must be displayed in the picture collected by the visual image during the operation and change of the robot arm); The time when the robot arm is running is used as the initial time value, and then the frequency of image acquisition is set, and the position of the selected reference point in the image is marked at the same time, forming several robot arm posture images as visual monitoring information; Step 4: Obtain data information of the robot arm, set the sensor module according to the movable joint of the robot arm, collect the data of the movable joint sensor module of the robot arm under the standard robot arm processing posture information according to the operation content of the workpiece, and form the movable joint position data monitoring information (analyze the data position of the movable joint sensor module of the robot arm and compare it with the standard situation); Obtain data information of the robot arm and reset the robot arm to the zero position, and obtain data of the sensor module for setting the movable joint of the robot arm; The robot arm is operated according to the processing posture information of the robot arm, and the sensor module data change information and the sensor module data (angle sensor and torque sensor, the sensor module data of the workpiece processing point position is used as a monitoring point) reaching the workpiece processing point position are obtained. The sensor module data reaching the workpiece processing point position is used as a monitoring point; There are several monitoring points in the processing posture information of the robot arm, and the monitoring points are stored in correspondence with the workpiece processing to form the activity joint position data monitoring information as the control item of the activity joint data; The attitude of the robotic arm can also be understood as being within the accuracy threshold through the monitoring information of the position data of the movable joints; Step 5: Obtain the robotic arm processing attitude information, visual monitoring information, and movable joint position data monitoring information for the processing content of the same type of workpiece to obtain workpiece processing monitoring information; Establish processing workpiece information, collect the corresponding robotic arm processing attitude information, visual monitoring information, and movable joint position data monitoring information for the corresponding workpiece. Using the workpiece name and model as the project name, bind the robotic arm processing attitude information, visual monitoring information, and movable joint position data monitoring information correspondingly and store them to form workpiece processing monitoring information, all of which are data standard reference items; Step 6: Obtain the workpiece processing monitoring information, conduct an attitude test on the robotic arm to form test stability, and set the calibration period with reference to the test stability; Obtain the workpiece processing monitoring information, control the operation of the robotic arm according to the robotic arm processing attitude information, and set the accuracy thresholds for the visual monitoring information and the movable joint position data monitoring information; Monitor and obtain the visual image acquisition data and movable joint position data of the robotic arm operation. Compare the visually acquired images with the visual monitoring information according to the frequency to obtain visual image difference data. The method for obtaining the visual object difference data is as follows: Obtain the visual image, compare the positions of the reference points of the images acquired at the current frequency with the images acquired at the same time frequency in the visual monitoring information, and calculate according to the offset of the reference points; The method for obtaining the offset of the reference point is as follows: Perform pixel filtering on the acquired images, overlap the images acquired at the current frequency period with the images acquired at the same period frequency in the visual monitoring information, obtain the difference straight-line distance between the same reference points to form a position error, and combine the position errors between all reference points in the image to obtain an average value. Then, obtain the offset according to the average value. Control the operation of the robotic arm through the repeated robotic arm processing attitude information to obtain the offset change curve. According to the offset change curve function and combined with the accuracy threshold, set the calibration period of the robotic arm; Obtain the monitoring point information, and judge whether there are differences in the movable joint position data of the robotic arm operation according to the movable joint position data monitoring information, which can be directly obtained from the monitoring data of the movable joint sensing module; It is possible to understand the stability of the machining accuracy brought by the performance of the robotic arm through testing, and then set the calibration period to implement the maintenance plan of the robotic arm, which can reduce the problem of unqualified workpiece machining caused by accuracy problems. Moreover, setting a reference point through visual images for data analysis of the operating posture of the robotic arm can be more intuitive, and at the same time, it can also provide data reference for the reasons for the damaged positions of the robotic arm in the future (collecting through the reference point where the operating posture of the robotic arm first shows a large error, and being able to trace back to the damaged position of the robotic arm based on the position of this reference point); Step 7. When the robotic arm is actually put into operation, monitor the machining quality of the workpiece and form a prompt item; When the robotic arm after the test actually processes the workpiece, visually monitor the machining quality of the workpiece. When the machining quality of the workpiece is unqualified, give an alarm prompt and continue to work. When there is a bad part chain with more than three consecutive unqualified workpieces (the machining accuracy exceeds the set accuracy threshold) continuously in the future, stop the robotic arm; When there is no bad part chain with more than three consecutive unqualified workpieces in the machining quality of the workpiece, operate normally and form a prompt item according to the information of the machined workpiece; At the same time, collect the offset of the robotic arm during the actual machining process of the robotic arm to form the actual operating data of the robotic arm; Step 8. When there is a prompt chain of unqualified workpieces, at the same time, combine the workpiece machining monitoring information to judge the fault problem, switch to the standby robotic arm, synchronize data transmission and calibrate the standby robotic arm, and put it into work; Obtain the prompt item information, and combine the actual operating data of the robotic arm to get the offset of the robotic arm. Furthermore, based on the data of the first reference point offset between the pictures collected by the robotic arm and the pictures of visual monitoring information during the actual machining process of the arm in the same period, obtain the starting point of the deviation of the robotic arm, then judge the problem of the robotic arm, and provide the analysis data to the management end; When judging to stop the robotic arm, switch to the standby robotic arm at the same time. The standby robotic arm stores the workpiece machining monitoring information synchronously (it can be directly put into the machining process of the workpiece); It is possible to judge the approximate position of the damage of the robotic arm, which provides a reference data for maintenance personnel. At the same time, it can also avoid the long-term shutdown of the production line and ensure the normal progress of workpiece machining.

[0019] Embodiment 2, please refer to Figure 1 As shown, after the robotic arm is put into work, judge the next data in real time according to the workpiece machining monitoring information. When the data error exceeds the set threshold, judge it as abnormal and form a safety abnormal monitoring information; Obtain the workpiece processing monitoring information, automatically form the path for the next workpiece processing and the arm posture data according to the working process of the workpiece processing. When the arm posture data fluctuates greatly, it is judged as abnormal, and the safety anomaly monitoring information is formed. The robotic arm immediately stops working and returns to the zero position. The next startup requires confirmation from the management terminal; For example, during the normal operation of the robotic arm, if it accidentally touches a staff member and causes harm to their body, or the robotic arm presses on a staff member, it means a collision or an obstruction to the operation of the robotic arm. During this process, there will be a large fluctuation in the arm posture data of the robotic arm due to external forces, indicating a processing accident. It is necessary to judge that the robotic arm stops running and returns to the zero position; After the personnel judge that the personal situation of the staff has been resolved, the robotic arm can be started again; It can react when an accident causes harm to the staff, avoid continuous harm to the staff's body, and judge the abnormality of the robotic arm posture through the monitoring of the robotic arm posture and the calibration of data, reducing the more serious impact caused by accidental events.

[0020] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A data calibration method based on the Internet of Things, characterized in that: The following steps are involved: Step 1: Obtain the characteristic data of the robotic arm, set the positioning module according to the robotic arm, and then establish the robotic arm twin to form the robotic arm data information; Step 2: Obtain data information of the robot arm, set the posture of the robot arm according to the work content of the workpiece, and form processing posture information of the robot arm; Step 3: Obtain data information of the robot arm, set the position of the visual image acquisition module, and collect visual acquisition images under standard robot arm processing posture information according to the work content of the workpiece to form visual monitoring information; Step 4: Obtain data information of the robot arm, set the sensor module according to the movable joint of the robot arm, collect the sensor module data of the movable joint of the robot arm under the standard robot arm processing posture information according to the operation content of the workpiece, and form the movable joint position data monitoring information; Step 5: Obtain the processing posture information, visual monitoring information and activity joint position data monitoring information of the robot arm for the same workpiece processing content to obtain the workpiece processing monitoring information; Step 6: Obtain workpiece processing monitoring information, perform posture test on the robot arm, form test stability, and set the calibration cycle with reference to the test stability; Step 7: When the robot arm is actually put into operation, the processing quality of the workpiece is monitored and prompt items are formed; Step 8: When a prompt chain of unqualified workpieces appears, the fault problem is determined in combination with the workpiece processing monitoring information, the standby robotic arm is switched, the data is transmitted synchronously and the standby robotic arm is calibrated, and the standby robotic arm is put into operation; Step 9. After the robot arm is put into operation, the data for the next step is determined in real time based on the workpiece processing monitoring information. When the data error exceeds the set threshold, it is judged as an abnormality, and safety abnormality monitoring information is generated.

2. The data calibration method based on the Internet of Things according to claim 1, characterized in that: The method of obtaining the robot arm data is as follows: Obtain the size, shape characteristics and joint information of the robot arm, then build a twin of the robot arm, and set a positioning module on the robot arm according to the position of the joint; The robot arm is positioned at zero point, and the current position and angle of the positioning module are recorded to form positioning module information, and the positioning module information is built into the robot arm twin; Construct a spatial orientation grid in the robotic arm twin, monitor the position parameters of the positioning module information in real time, select the reference point of the robotic arm at the zero point, mark the position parameters of the reference point in the robotic arm twin and monitor it in real time to form the robotic arm data.

3. The data calibration method based on the Internet of Things according to claim 2, characterized in that: The method of obtaining the processing posture information of the robot arm is as follows: Obtain the processing position of the workpiece and the processing order of multiple processing positions to form a processing path, and set the motion posture of the robot arm according to the processing path. The setting method is: The first method is to set parameters according to the spatial orientation grid constructed in the robot twin. The second method is to manually pull the robot to the workpiece processing position and stop to set it as the processing position point, and at the same time record and store it in the spatial orientation grid constructed in the robot twin, and form the spatial orientation grid parameters at the same time. The processing posture information of the robot arm is generated according to the processing of the same workpiece, and the data is stored.

4. The data calibration method based on the Internet of Things according to claim 3 is characterized in that: Visual monitoring information is formed in the following way: Acquire the data information of the robot arm and reset the robot arm to the zero position, and at the same time set the position of the visual image acquisition module, and locate the zero position of the robot arm through the visual acquisition module; Obtain the processing posture information of the robot arm, operate the robot arm according to the processing posture information of the robot arm, mark and locate the operation process image of the robot arm through visual images, select the reference points of the robot arm in the virtual image, and the number of reference points is manually selected by the management end as fixed reference points; The time when the robot arm is turned on is used as the initial time value, and then the frequency of image acquisition is set. At the same time, the position of the selected reference point in the image is marked to form several robot arm posture images as visual monitoring information.

5. The data calibration method based on the Internet of Things according to claim 4, characterized in that: The activity festival location data monitoring information is formed in the following way: Obtain data information of the robot arm and reset the robot arm to the zero position, and obtain data of the sensor module for setting the movable joint of the robot arm; The robot arm is operated according to the processing posture information of the robot arm, and the information of the sensor module data change and the sensor module data reaching the workpiece processing point are obtained, and the sensor module data reaching the workpiece processing point is used as the monitoring point; There are several monitoring points in the processing posture information of the robot arm. The monitoring points are stored in correspondence with the workpiece processing to form the activity joint position data monitoring information as a reference item of the activity joint data.

6. The data calibration method based on the Internet of Things according to claim 5, characterized in that: The way to obtain workpiece processing monitoring information is as follows: Establish the processing workpiece information, collect the corresponding robot arm processing posture information, visual monitoring information and activity joint position data monitoring information of the workpiece, use the workpiece name and model as the project name, bind the robot arm processing posture information, visual monitoring information and activity joint position data monitoring information for storage, and form the workpiece processing monitoring information.

7. The data calibration method based on the Internet of Things according to claim 6, characterized in that: The calibration cycle is set as follows: Obtain workpiece processing monitoring information, control the operation of the robotic arm according to the robotic arm processing posture information, and set the accuracy threshold of the visual monitoring information and the activity joint position data monitoring information; Monitor and obtain the visual image acquisition data and activity joint position data of the robot arm operation, compare the visual image acquired by frequency with the visual monitoring information, and obtain the visual image difference data. The comparison and acquisition method of the visual object difference data is as follows: Obtain visual images to compare the positions of reference points of the images collected at the current frequency with the images collected at the same time frequency in the visual monitoring information, and calculate according to the offset of the reference points; The offset of the reference point is obtained as follows: The collected images are subjected to pixel filtering processing, the images collected at the current frequency period are overlapped with the images collected at the same frequency in the visual monitoring information, the difference straight-line distances between the same reference points are obtained to form a position error, and the position errors between all reference points in the image are combined to obtain the average value, and then the offset is obtained based on the average value. The operation of the robotic arm is controlled through repeated robotic arm processing posture information to obtain the offset change curve, and the calibration period of the robotic arm is set according to the offset change curve function and the accuracy threshold.

8. The data calibration method based on the Internet of Things according to claim 7, characterized in that: The prompt items are formed as follows: After the test, when the robot arm actually processes the workpiece, the processing quality of the workpiece is monitored by visual images. If the workpiece processing quality is unqualified, an alarm prompts and the work continues. If a chain of more than three consecutive workpieces with unqualified quality appears, the robot arm will be shut down. When the workpiece processing quality does not have a chain of more than three unqualified bad parts in succession, normal processing is hidden and prompt items are formed according to the information of the processed workpiece; at the same time, the offset of the robot arm during the actual processing process of the robot arm is collected to form the actual operation data of the robot arm.

9. The data calibration method based on the Internet of Things according to claim 8, characterized in that: Obtain prompt item information, and combine it with the actual operation data of the robot arm to obtain the offset of the robot arm, and then obtain the starting point of the deviation of the robot arm according to the data of the initial reference point offset between the robot arm acquisition picture and the visual monitoring information picture during the actual processing of the arm in the same period, and then judge the problem of the robot arm, and provide the analysis data to the management end; When it is determined that the robotic arm is shutting down, the standby robotic arm is switched, and the standby robotic arm synchronously stores the workpiece processing monitoring information.

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