Real-time data management system and method for wafer transfer robotic arms based on digital twins

Abstract

The present invention provides a real-time data management system and method for a wafer transfer robot based on digital twins. The system comprises a data acquisition module, a management and analysis module, a monitoring and management module, a data association module, and a self-test traceability module. By updating and presenting monitoring data such as sensor detection, encoder values, and Cartesian coordinates in real time on a software interface, the present invention can accurately distinguish the data type of the fully automatic robot device and accurately locate the type of abnormal data from the robot device. This improves the efficiency and accuracy of data traceability, facilitates timely status processing and presentation, improves the efficiency of implementation and presentation, and enhances the stability of the device's automatic operation.

Description

Technical Field

[0001] The present invention relates to the field of digital twins of robotic arms, and in particular to a real-time data management system and method for wafer transfer robotic arms based on digital twins. Background Art

[0002] With the advancement of industrial intelligence and automation, robotic arms, as important automation equipment, are increasingly being used in fields such as manufacturing and warehousing and logistics. However, traditional robotic arm design and maintenance present several challenges, such as high costs, design difficulties, and on-site safety risks. The emergence of digital twin technology offers a new solution to these problems.

[0003] Through digital twin technology, the operation process of the robotic arm can be virtually simulated and real-time guidance can be provided. Various sensors and encoders will be used in the process. The operating parameters of the robotic arm are detected and obtained by using various sensors and encoders. Since the robotic arm, encoders, and sensors may malfunction during use and cause data anomalies, it is necessary to trace the source of the fault. However, manual troubleshooting is currently usually used to trace the type of robotic arm fault based on abnormal data. This greatly increases the time for troubleshooting, reduces the efficiency of fault repair, and makes it impossible to query the status of the robotic arm before the anomaly.

[0004] Therefore, the existing technology needs to be further improved and enhanced. Summary of the Invention

[0005] The purpose of the present invention is to provide a real-time data management system and method for wafer transfer robotic arms based on digital twins, so as to accurately and quickly trace the type of robotic arm failure and quickly query the robotic arm status before the abnormality.

[0006] In order to achieve the above objectives, in a first aspect, the present invention provides a real-time data management system for wafer transfer robotic arms based on digital twins, the system comprising:

[0007] A data acquisition module is used to acquire monitoring data of the wafer transfer robot in real time, wherein the monitoring data includes position data and joint data of the robot, Carl coordinate system data, encoder data and sensor data;

[0008] a management and analysis module, configured to classify and label the monitoring data and determine the device type corresponding to the monitoring data, wherein the device type includes a robotic arm, and an encoder and a sensor corresponding to the robotic arm;

[0009] The monitoring management module is used to display the classified and labeled monitoring data in the dynamic data window;

[0010] a data association module, configured to extract and simulate monitoring data within the dynamic data window when the robotic arm is in operation, and obtain a correlation coefficient for each joint of the robotic arm based on the position data and joint data of the robotic arm, as well as changes in the Carr coordinate system data; the correlation coefficient is used to characterize the degree of induced association between any data;

[0011] The self-check traceability tracking module is used to perform self-check on the monitoring data in the dynamic data window, calculate the deviation between the window monitoring data in the dynamic data window and the actual monitoring data under the normal motion state of the robotic arm, and filter out abnormal data from the monitoring data according to the deviation, and obtain the type of equipment that generates the abnormal data based on the correlation coefficient between the abnormal data and each joint of the robotic arm.

[0012] Furthermore, the self-checking traceability module includes:

[0013] A data extraction module is used to extract the window monitoring data from the dynamic data window and obtain the actual monitoring data of the robot arm under normal operating conditions;

[0014] a deviation calculation module, configured to calculate the deviation between the window monitoring data and the actual monitoring data;

[0015] an abnormality analysis module, configured to compare the deviation with a deviation threshold, and if the deviation is less than the deviation threshold, treating the monitoring data corresponding to the deviation as abnormal data;

[0016] The tracing module is used to obtain the corresponding device type based on the abnormal data and use it as the source of the fault.

[0017] Furthermore, the anomaly analysis module is also used to filter out the monitoring data corresponding to the maximum deviation from the deviation, and determine whether the deviations of the monitoring data associated with it are all less than the deviation threshold based on the correlation coefficient. If so, the monitoring data corresponding to the maximum deviation is used as the anomaly data for traceability tracking.

[0018] Furthermore, the system further comprises:

[0019] a path generation module for fitting a movement path in the corresponding digital twin robotic arm in response to the movement of the robotic arm and based on the monitoring data;

[0020] Create an execution module to move the digital twin robotic arm along the fitted movement path.

[0021] Furthermore, the system also includes a database module for storing the monitoring data classified and labeled by the management and analysis module, and the movement path fitted by the path production module.

[0022] Furthermore, the system further comprises:

[0023] The data query module is used to query the monitoring data before the abnormality from the database module based on the abnormal data.

[0024] Furthermore, the monitoring and management module is also used to display the fault source corresponding to the real-time abnormal data based on the abnormal data, and display the corresponding joint position on the digital twin robotic arm.

[0025] In a second aspect, the present invention provides a real-time data management method for a wafer transfer robot based on digital twins, the method comprising:

[0026] Acquire monitoring data of the wafer transfer robot in real time, including position data and joint data of the robot, Carl coordinate system data, encoder data, and sensor data;

[0027] Classifying and labeling the monitoring data to determine a device type corresponding to the monitoring data, the device type including a robotic arm, and an encoder and a sensor corresponding to the robotic arm;

[0028] Display the classified and labeled monitoring data in the dynamic data window;

[0029] Extracting and simulating the monitoring data in the dynamic data window when the robot arm is in operation, and obtaining the correlation coefficient of each joint of the robot arm based on the position data and joint data of the robot arm and the changes in the Carr coordinate system data; the correlation coefficient is used to characterize the degree of induced correlation between any data;

[0030] The monitoring data in the dynamic data window is self-checked, and the deviation between the window monitoring data in the dynamic data window and the actual monitoring data under the normal motion state of the robotic arm is calculated. Abnormal data is filtered out from the monitoring data according to the deviation, and the type of equipment that generates the abnormal data is obtained based on the correlation coefficient between the abnormal data and each joint of the robotic arm.

[0031] Furthermore, the steps of performing self-check on the monitoring data in the dynamic data window, calculating the deviation between the window monitoring data in the dynamic data window and the actual monitoring data under the normal motion state of the robotic arm, screening out abnormal data from the monitoring data according to the deviation, and obtaining the type of device generating the abnormal data based on the correlation coefficient between the abnormal data and each joint of the robotic arm include:

[0032] Extracting the window monitoring data from the dynamic data window and obtaining actual monitoring data of the robot arm under normal operating conditions;

[0033] Calculating the deviation between the window monitoring data and the actual monitoring data;

[0034] Comparing the deviation with a deviation threshold, and if the deviation is less than the deviation threshold, treating the monitoring data corresponding to the deviation as abnormal data;

[0035] According to the abnormal data, the corresponding device type is obtained and used as the source of the fault.

[0036] Furthermore, the step of comparing the deviation with a deviation threshold and treating the monitoring data corresponding to the deviation as abnormal data if the deviation is less than the deviation threshold includes:

[0037] The monitoring data corresponding to the maximum deviation is selected from the deviations, and the deviations of the monitoring data associated with it are determined based on the correlation coefficient to be less than the deviation threshold. If so, the monitoring data corresponding to the maximum deviation is used as abnormal data for traceability.

[0038] The above-mentioned invention application provides a real-time data management system and method for wafer transfer robotic arms based on digital twins. By updating and presenting monitoring data such as sensor detection, encoder values, and Cartesian coordinate systems in real time on the software interface, it can accurately distinguish which type of data the data comes from in the fully automatic robotic arm equipment, accurately locate the type of abnormal data in the robotic arm equipment, improve the efficiency and accuracy of data traceability, facilitate timely processing and presentation of the status, improve the efficiency of implementation and presentation, and improve the stability of automatic operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of an application scenario of a real-time data management system for wafer transfer robotic arms based on digital twins in an embodiment of the present invention;

[0040] Figure 2 1 is a system block diagram of a real-time data management system for wafer transfer robotic arms based on digital twins in an embodiment of the present invention;

[0041] Figure 3a and Figure 3b are schematic diagrams of a group of robotic arms and a digital twin robotic arm in embodiments of the present invention;

[0042] Figure 4 yes Figure 2 System block diagram of the self-inspection traceability module;

[0043] Figure 5 This is a system block diagram of a preferred embodiment of a real-time data management system for wafer transfer robotic arms based on digital twins in an embodiment of the present invention;

[0044] Figure 6 This is a system block diagram of another preferred embodiment of a real-time data management system for wafer transfer robotic arms based on digital twins in an embodiment of the present invention;

[0045] Figure 7 1 is a flow chart of a real-time data management method for a wafer transfer robot based on digital twins in an embodiment of the present invention;

[0046] Figure 8 It is a flow chart of a preferred embodiment of the real-time data management method of a wafer transfer robot based on digital twin in an embodiment of the present invention.

[0047] Figure 9 1 is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and beneficial effects of this application more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] The real-time data management system for wafer transfer robot based on digital twin provided by the present invention can be applied to Figure 1 The terminal or server shown. Among them, the terminal can be but not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server can be implemented as an independent server or a server cluster composed of multiple servers. The server can use the wafer transfer robot arm real-time data management system method provided by the present invention to automatically manage and trace the faults of different robot arms according to actual application needs, and use the traceability results obtained for subsequent robot arm data analysis and research on the server, or transmit them to the terminal for terminal users to view and analyze; the following embodiments will explain the wafer transfer robot arm real-time data management system of the present invention in detail.

[0050] In one embodiment, Figure 2 As shown, an embodiment of the present invention provides a real-time data management system for wafer transfer robotic arms based on digital twins, the system comprising:

[0051] Data acquisition module 1, used to acquire monitoring data of the wafer transfer robot in real time, the monitoring data including position data and joint data of the robot, Carl coordinate system data, encoder data and sensor data;

[0052] Management and analysis module 2, used to classify and label the monitoring data and determine the device type corresponding to the monitoring data, wherein the device type includes a robotic arm and an encoder and a sensor corresponding to the robotic arm;

[0053] Monitoring management module 3, used to display the classified and labeled monitoring data in a dynamic data window;

[0054] The data association module 4 is used to extract and simulate the monitoring data in the dynamic data window when the robot arm is in operation, and obtain the correlation coefficient of each joint of the robot arm based on the position data and joint data of the robot arm and the changes in the Carr coordinate system data; the correlation coefficient is used to represent the degree of association between any data;

[0055] The self-checking traceability tracking module 5 is used to perform self-checking on the monitoring data in the dynamic data window, calculate the deviation between the window monitoring data in the dynamic data window and the actual monitoring data under the normal motion state of the robotic arm, and filter out abnormal data from the monitoring data according to the deviation, and obtain the type of equipment that generates the abnormal data based on the correlation coefficient between the abnormal data and the joints of the robotic arm.

[0056] like Figure 3a and 3b A three-joint robotic arm and a corresponding digital twin robotic arm are shown. This embodiment adopts digital twin technology to synchronously obtain the current absolute position data of the robotic arm in real time. The operating parameters of the robotic arm are detected and obtained by various sensors and encoders. The Cartesian coordinate system of the robotic arm sends the current joint data to the robotic arm real-time data management system in real time through the Ethernet port. The system converts the received data into a spatial coordinate system and updates the position data in real time. By real-time acquisition and update of data from various sensor detections, encoder values, Cartesian coordinate systems, and DH parameters, it can be presented in real time in the dynamic data window (as shown in the figure). Since the management and analysis module classifies and labels the added test data, the management system of the embodiment of the present invention can accurately distinguish which type of data the data comes from the fully automatic robotic arm equipment, realize the root source tracking of the data, improve the accuracy of the data, and facilitate real-time processing based on the data.

[0057] Preferably, if Figure 4 As shown, the self-checking traceability module 5 of the embodiment of the present invention includes:

[0058] A data extraction module 51 is used to extract the window monitoring data from the dynamic data window and obtain the actual monitoring data of the robot arm under normal operating conditions;

[0059] a deviation calculation module 52, configured to calculate the deviation between the window monitoring data and the actual monitoring data;

[0060] An abnormality analysis module 53 is used to compare the deviation with a deviation threshold, and if the deviation is less than the deviation threshold, the monitoring data corresponding to the deviation is regarded as abnormal data;

[0061] The source tracing module 54 is used to obtain the corresponding device type based on the abnormal data and use it as the source of the fault.

[0062] When tracing the source, the embodiment of the present invention adopts a method of comparing and analyzing window monitoring data with actual monitoring data. When the robotic arm is in normal operation, if there is an equipment failure, the corresponding monitoring data will change. Therefore, this embodiment calculates the deviation between the window monitoring data and the actual monitoring data of the robotic arm in normal operation. Through this deviation, it can quickly and effectively reflect whether there is any abnormality in the operation of the robotic arm.

[0063] Preferably, this embodiment sets the deviation threshold to 1. Under this deviation threshold, abnormal situations can be reflected more accurately. Of course, this deviation threshold is only used to explain the example of deviation comparison and is not limited to this. Other deviation thresholds can also be used.

[0064] In order to improve the accuracy of traceability, the abnormality analysis module 53 of the embodiment of the present invention is also used to filter out the monitoring data corresponding to the maximum deviation from the deviation, and judge whether the deviation of the monitoring data associated with it is less than the deviation threshold according to the correlation coefficient. If so, the monitoring data corresponding to the maximum deviation is used as the abnormal data for traceability. That is, the abnormality analysis module 53 of the embodiment of the present invention will filter out all the monitoring data less than the deviation threshold, and then select the monitoring data corresponding to the maximum deviation from them. The maximum deviation means the closer to the source of the fault. Therefore, the embodiment of the present invention then determines the degree of association between any data in the real-time state through the correlation coefficients of each joint obtained in the data association module 4. That is, it can determine whether there are other abnormal data related to it. If the deviation of other monitoring data associated with the monitoring data with the maximum deviation is less than the set deviation threshold, the monitoring data corresponding to the maximum deviation is used as the abnormal data for traceability. If there are other monitoring data that do not meet the above conditions, it means that there is also a fault in the other monitoring data. Therefore, this method can more accurately determine the source of the fault.

[0065] Preferably, in an embodiment of the present invention, Figure 5 As shown, the system also includes:

[0066] a path generation module 6 for fitting a movement path in the corresponding digital twin robotic arm in response to the movement of the robotic arm and based on the monitoring data;

[0067] The execution module 7 is made to move the digital twin robot arm according to the fitted movement path.

[0068] Through the path generation module, the movement path of the digital twin robotic arm can be effectively fitted according to real-time data, and by making an execution module, the digital twin robotic arm can be automatically controlled to move along the same movement path, thereby improving the efficiency of real-time presentation.

[0069] In one embodiment, Figure 6 As shown, the system further includes:

[0070] a database module 8 for storing the monitoring data classified and labeled by the management and analysis module 2 and the movement path fitted by the path production module 6;

[0071] The data query module 9 is used to query the monitoring data before the abnormality from the database module based on the abnormal data.

[0072] As the database of the entire system, the database module can store and update the real-time monitoring data and corresponding classifications. It can also store the corresponding abnormal data and traceability records, etc., to facilitate data calls in the traceability process, and is used for data query to provide data support for system optimization.

[0073] In one embodiment, the monitoring and management module 3 is further configured to display the fault source corresponding to the real-time abnormal data based on the abnormal data, and to display the corresponding joint positions on the digital twin robotic arm. Through the monitoring and management module, embodiments of the present invention can accurately display the fault source and present a dynamic effect.

[0074] The real-time data management system for wafer transfer robotic arms based on digital twins provided by an embodiment of the present invention acquires monitoring data of the wafer transfer robotic arms in real time through a data acquisition module, wherein the monitoring data includes position data and joint data of the robotic arms, Carl coordinate system data, encoder data, and sensor data; the monitoring data is classified and labeled through a management and analysis module to determine the device type corresponding to the monitoring data, wherein the device type includes the robotic arms, and encoders and sensors corresponding to the robotic arms; the classified and labeled monitoring data are displayed in a dynamic data window through a monitoring management module; and the operating status of the robotic arms is extracted and simulated through a data association module. The monitoring data in the dynamic data window is used to obtain the correlation coefficient of each joint of the robotic arm based on the position data and joint data of the robotic arm and the changes in the Carl coordinate system data; the correlation coefficient is used to characterize the degree of association caused between any data; the monitoring data in the dynamic data window is self-checked through the self-checking traceability tracking module, and the deviation between the window monitoring data in the dynamic data window and the actual monitoring data under the normal motion state of the robotic arm is calculated, and abnormal data is screened out from the monitoring data according to the deviation, and the type of equipment that generates the abnormal data is obtained based on the abnormal data and the correlation coefficient of each joint of the robotic arm. It can accurately locate the type of abnormal data of the robotic arm equipment, improve the efficiency and accuracy of data traceability, facilitate timely processing and presentation of the status, improve the efficiency of implementation presentation, and improve the stability of automatic operation of the equipment.

[0075] Based on the above-mentioned digital twin wafer transfer robot real-time data management system, the embodiment of the present invention also provides a wafer transfer robot real-time data management method based on digital twin, such as Figure 7 As shown, the method includes:

[0076] Step S10: Acquire monitoring data of the wafer transfer robot in real time; the monitoring data includes position data and joint data of the robot, Carr coordinate system data, encoder data, and sensor data;

[0077] Step S20: classify and label the monitoring data to determine the device type corresponding to the monitoring data; the device type includes a robotic arm, and an encoder and a sensor corresponding to the robotic arm;

[0078] Step S30: display the classified and labeled monitoring data in a dynamic data window;

[0079] Step S40: extracting and simulating the monitoring data in the dynamic data window when the robot arm is in operation, and obtaining the correlation coefficient of each joint of the robot arm based on the position data and joint data of the robot arm and the changes in the Carr coordinate system data; the correlation coefficient is used to represent the degree of induced correlation between any data;

[0080] Step S50: perform self-inspection on the monitoring data in the dynamic data window, calculate the deviation between the window monitoring data in the dynamic data window and the actual monitoring data under the normal motion state of the robotic arm, and filter out abnormal data from the monitoring data based on the deviation, and obtain the type of equipment that generates the abnormal data based on the correlation coefficient between the abnormal data and each joint of the robotic arm.

[0081] Preferably, if Figure 8 As shown, step S50 further includes:

[0082] Step S501: extracting the window monitoring data from the dynamic data window and obtaining actual monitoring data of the robot arm under normal operating conditions;

[0083] Step S502: Calculate the deviation between the window monitoring data and the actual monitoring data;

[0084] Step S503: compare the deviation with the deviation threshold. If the deviation is less than the deviation threshold, the monitoring data corresponding to the deviation is taken as abnormal data. Specifically, the monitoring data corresponding to the maximum deviation is screened out from the deviations, and it is determined based on the correlation coefficient whether the deviations of the monitoring data associated with it are all less than the deviation threshold. If so, the monitoring data corresponding to the maximum deviation is taken as abnormal data for traceability.

[0085] Step S504: Obtain the corresponding device type based on the abnormal data and use it as the fault source.

[0086] Regarding the specific definition of the wafer transfer robot arm real-time data management method, please refer to the definition of the wafer transfer robot arm real-time data management system above. The corresponding technical effects can also be obtained equivalently, so we will not go into details here. Each module in the above-mentioned wafer transfer robot arm real-time data management system can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0087] Figure 9 FIG. 1 shows an internal structure diagram of a computer device in one embodiment, which may be a terminal or a server. Figure 9As shown, the computer device includes a processor, memory, network interface, display, camera, and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a real-time data management method for a wafer transfer robot arm is implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a key, trackball, or touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse.

[0088] It can be understood by those skilled in the art that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0089] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0090] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0091] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. A real-time data management system for wafer transfer robotic arms based on digital twins, characterized in that: The system comprises: A data acquisition module is used to acquire monitoring data of the wafer transfer robot in real time, wherein the monitoring data includes position data and joint data of the robot, Carl coordinate system data, encoder data and sensor data; a management and analysis module, configured to classify and label the monitoring data and determine the device type corresponding to the monitoring data, wherein the device type includes a robotic arm, and an encoder and a sensor corresponding to the robotic arm; The monitoring management module is used to display the classified and labeled monitoring data in the dynamic data window; a data association module, configured to extract and simulate monitoring data within the dynamic data window when the robotic arm is in operation, and obtain a correlation coefficient for each joint of the robotic arm based on the position data and joint data of the robotic arm, as well as changes in the Carr coordinate system data; the correlation coefficient is used to characterize the degree of induced association between any data; The self-check traceability tracking module is used to perform self-check on the monitoring data in the dynamic data window, calculate the deviation between the window monitoring data in the dynamic data window and the actual monitoring data under the normal motion state of the robotic arm, and filter out abnormal data from the monitoring data according to the deviation, and obtain the type of equipment that generates the abnormal data based on the correlation coefficient between the abnormal data and each joint of the robotic arm.

2. The real-time data management system for wafer transfer robotic arms based on digital twins according to claim 1 is characterized in that: The self-checking traceability module includes: A data extraction module is used to extract the window monitoring data from the dynamic data window and obtain the actual monitoring data of the robot arm under normal operating conditions; a deviation calculation module, configured to calculate the deviation between the window monitoring data and the actual monitoring data; an abnormality analysis module, configured to compare the deviation with a deviation threshold, and if the deviation is less than the deviation threshold, treating the monitoring data corresponding to the deviation as abnormal data; The tracing module is used to obtain the corresponding device type based on the abnormal data and use it as the source of the fault.

3. The real-time data management system for wafer transfer robotic arms based on digital twin according to claim 2 is characterized in that: The anomaly analysis module is also used to filter out the monitoring data corresponding to the maximum deviation from the deviations, and determine whether the deviations of the monitoring data associated with it are all less than the deviation threshold based on the correlation coefficient. If so, the monitoring data corresponding to the maximum deviation is used as the anomaly data for traceability tracking.

4. The real-time data management system for wafer transfer robotic arms based on digital twin according to claim 1 is characterized in that: The system further comprises: a path generation module for fitting a movement path in the corresponding digital twin robotic arm in response to the movement of the robotic arm and based on the monitoring data; Create an execution module to move the digital twin robotic arm along the fitted movement path.

5. The real-time data management system for wafer transfer robotic arms based on digital twin according to claim 4 is characterized in that: The system further includes a database module for storing the monitoring data classified and labeled by the management and analysis module and the moving path fitted by the path production module.

6. The real-time data management system for wafer transfer robotic arms based on digital twins according to claim 5 is characterized in that: The system further comprises: The data query module is used to query the monitoring data before the abnormality from the database module based on the abnormal data.

7. The wafer transfer robot real-time data management system based on digital twin according to claim 1 is characterized in that: The monitoring and management module is also used to display the fault source corresponding to the real-time abnormal data based on the abnormal data, and display the corresponding joint position on the digital twin robotic arm.

8. A real-time data management method for wafer transfer robot based on digital twin, characterized in that: The method comprises: Acquire monitoring data of the wafer transfer robot in real time, including position data and joint data of the robot, Carl coordinate system data, encoder data, and sensor data; Classifying and labeling the monitoring data to determine a device type corresponding to the monitoring data, the device type including a robotic arm, and an encoder and a sensor corresponding to the robotic arm; Display the classified and labeled monitoring data in the dynamic data window; Extracting and simulating the monitoring data in the dynamic data window when the robot arm is in operation, and obtaining the correlation coefficient of each joint of the robot arm based on the position data and joint data of the robot arm and the changes in the Carr coordinate system data; the correlation coefficient is used to characterize the degree of induced correlation between any data; The monitoring data in the dynamic data window is self-checked, and the deviation between the window monitoring data in the dynamic data window and the actual monitoring data under the normal motion state of the robotic arm is calculated. Abnormal data is filtered out from the monitoring data according to the deviation, and the type of equipment that generates the abnormal data is obtained based on the correlation coefficient between the abnormal data and each joint of the robotic arm.

9. The real-time data management method for wafer transfer robot based on digital twin according to claim 8, characterized in that: The steps of performing self-check on the monitoring data in the dynamic data window, calculating the deviation between the window monitoring data in the dynamic data window and the actual monitoring data under the normal motion state of the robotic arm, screening out abnormal data from the monitoring data according to the deviation, and obtaining the type of device generating the abnormal data based on the correlation coefficient between the abnormal data and each joint of the robotic arm, include: Extracting the window monitoring data from the dynamic data window and obtaining actual monitoring data of the robot arm under normal operating conditions; Calculating the deviation between the window monitoring data and the actual monitoring data; Comparing the deviation with a deviation threshold, and if the deviation is less than the deviation threshold, treating the monitoring data corresponding to the deviation as abnormal data; According to the abnormal data, the corresponding device type is obtained and used as the source of the fault.

10. The real-time data management method for wafer transfer robot based on digital twin according to claim 9, characterized in that: The step of comparing the deviation with a deviation threshold and treating the monitoring data corresponding to the deviation as abnormal data if the deviation is less than the deviation threshold includes: The monitoring data corresponding to the maximum deviation is selected from the deviations, and the deviations of the monitoring data associated with it are determined based on the correlation coefficient to be less than the deviation threshold. If so, the monitoring data corresponding to the maximum deviation is used as abnormal data for traceability.

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