Die manufacturing and assembly process model research and management system based on digital twinning

Abstract

The application discloses a mold manufacturing and assembly process model research and management system based on digital twinning, relates to the field of digital twinning, and solves the problem of insufficient accuracy of existing mold manufacturing and assembly management, and comprises the following: a data acquisition module that acquires spatial position information of physical equipment; a model construction module that acquires periodic position data, classifies the physical equipment, and constructs a model based on static equipment, dynamic equipment and the periodic position data; a real-time judgment module that acquires real-time data of the static equipment, analyzes the offset condition of the static equipment, acquires real-time data of the dynamic equipment, analyzes the offset condition of the dynamic equipment, and judges abnormalities according to the offset condition of the static equipment and the offset condition of the dynamic equipment; and a model early warning module that visually early warns a data model according to abnormal equipment, so that the management efficiency of mold manufacturing and assembly can be effectively improved, and the operation safety of equipment can be ensured.

Description

Technical Field

[0001] This invention belongs to the field of digital twins, and more specifically relates to mold manufacturing and assembly processes, specifically to a research and management system for mold manufacturing and assembly process models based on digital twins. Background Technology

[0002] The existing research and management of mold manufacturing and assembly process models have the following specific shortcomings:

[0003] 1. Existing model manufacturing and assembly processes mainly rely on product feedback for abnormal situations during manufacturing and assembly. If a large number of abnormalities are found in the product, the manufacturing and assembly process is inspected. This method has a serious lag in feedback on abnormalities, and abnormalities can only be reported after a large amount of losses have occurred, resulting in low economic efficiency.

[0004] 2. Current methods for anomaly analysis during manufacturing and assembly typically rely on manual inspection. This method is highly susceptible to human error, struggles to detect minor anomalies, and has relatively low accuracy in anomaly assessment. Furthermore, it is difficult to conduct timely and effective inspections during the manufacturing process of large models.

[0005] 3. The existing mold manufacturing and assembly process model is mainly constructed by three-dimensional modeling of the overall space. This method requires high computing power for modeling, and the accuracy of the overall modeling method is low, making it impossible to perform high-precision analysis on the model.

[0006] To this end, we propose a research and management system for mold manufacturing and assembly process models based on digital twins. Summary of the Invention

[0007] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a research and management system for mold manufacturing and assembly process models based on digital twins, which aims to improve the management efficiency of mold manufacturing and assembly.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: a research and management system for mold manufacturing and assembly process models based on digital twins, the specific working process of each module is as follows:

[0009] Data acquisition module: Acquires physical equipment in mold manufacturing and assembly, collects spatial location data for each physical device, and obtains spatial location information;

[0010] Model building module: Acquires the behavioral cycle of mold manufacturing and assembly, collects spatial location data in multiple time periods based on the behavioral cycle to obtain periodic location data, classifies physical equipment based on periodic location data to obtain static equipment and dynamic equipment; builds a model based on static equipment, dynamic equipment and periodic location data of equipment to obtain the data model of mold manufacturing and assembly.

[0011] Real-time judgment module: Collects real-time data from static devices and analyzes the positional offset of the static devices in conjunction with the data model;

[0012] Real-time data of dynamic devices is collected and combined with periodic position data to analyze the motion trajectory of the dynamic devices and determine the offset status of the dynamic devices.

[0013] Anomalies in mold manufacturing and assembly are determined by the positional offset of static and dynamic equipment.

[0014] Model early warning module: Based on the judgment results, abnormal equipment is extracted, and the abnormal equipment provides a visual early warning to the data model, providing maintenance personnel with maintenance prompts.

[0015] Furthermore, spatial location data for each physical device is collected, as follows:

[0016] The physical equipment used in mold manufacturing and assembly is acquired, and the number of physical equipment is counted and denoted as ws; the starting equipment used in mold manufacturing and assembly is acquired, and the spatial position data of the starting equipment is collected as the origin data; using the starting equipment as a reference, the spatial position data of each physical equipment is collected to obtain spatial position data, and the spatial position data is denoted as kjw(w) according to the number of physical equipment; the spatial position data is statistically analyzed to obtain spatial position information.

[0017] Further, the model is constructed, as follows:

[0018] The start and end times of mold manufacturing and assembly are obtained. From these times, the behavioral cycle of mold manufacturing and assembly is derived. Based on the behavioral cycle, multiple data acquisitions are performed on the spatial position of the physical equipment to obtain periodic position data. The operating status of the physical equipment is analyzed based on the periodic position data. The physical equipment is classified into static or dynamic equipment based on its operating status. The spatial position information of the static equipment is acquired to construct a data model. The periodic position data of the dynamic equipment is analyzed to obtain the movement range of the dynamic equipment. The movement range of the dynamic equipment and the periodic position data are used to supplement the data model.

[0019] Furthermore, the operational status of the physical equipment is analyzed, as follows:

[0020] Based on the behavior cycle of mold manufacturing and assembly, multiple time nodes are set for data collection. The number of time nodes is counted and denoted as ts. For each time node, the spatial position data of the physical equipment is collected to obtain the temporal position data, denoted as sxw(w,t). The temporal position data of each physical equipment within the behavior cycle is counted to obtain the periodic position data, denoted as zqw(w), zqw(w) = [sxw(w,1), sxw(w,2), ..., sxw(w,ts)].

[0021] Acquire spatial location data kjw(w), combine it with periodic location data to calculate the positional change of the physical equipment, and obtain the operational change ygb(w) of the physical equipment.

[0022] ;

[0023] The operating status of physical equipment is analyzed based on the changes in its operation, and the physical equipment is classified accordingly.

[0024] If ygb(w) = 0, it indicates that the physical device is a static device;

[0025] If ygb(w) ≠ 0, it indicates that the physical device is a dynamic device;

[0026] The spatial location data of static devices is acquired, and data mapping is performed based on the spatial location data of static devices to initially construct a data model;

[0027] The periodic position data of the dynamic device is acquired, and the motion space of the dynamic device is analyzed based on the periodic position data to obtain the extreme values ​​of the dynamic device's changes. The operation model of the dynamic device is constructed from the extreme values ​​of changes, and the data model is improved based on the operation model.

[0028] Furthermore, a dynamic device operation model is constructed, as follows:

[0029] The number of data dimensions for the location data of a dynamic device is obtained and denoted as fs.

[0030] Data extraction is performed based on the periodic position data of the dynamic device and the data dimensions to obtain periodic position data of multiple different dimensions, denoted as dimension periodic data wzq(f); the maximum value of the dimension periodic data is extracted to obtain the upper limit of the dimension wsx(f), and the minimum value of the dimension periodic data is extracted to obtain the lower limit of the dimension wxx(f); the dimension range wfw(f) = [wxx(f), wsx(f)] is obtained based on the upper and lower limits of the dimension; the dimension range of multiple dimensions is statistically analyzed to obtain the spatial range kjf of the dynamic device, kjf = [wfw(1), wfw(2), ..., wfw(fs)];

[0031] Based on the spatial range of the dynamic equipment, an operational model of the dynamic equipment is constructed, spatial location data of the dynamic equipment is obtained, the operational model is initially set, and the operational model is used to fill the data model to obtain the data model for mold manufacturing and assembly.

[0032] Furthermore, anomaly detection is performed on mold manufacturing and assembly, as follows:

[0033] Real-time data from static devices is collected to obtain static real-time data; based on the data model, standard data from static devices is collected to obtain static standard data.

[0034] Based on static real-time data and static standard data, the deviation of static equipment is judged, and the deviation of all equipment is analyzed as a whole to obtain the overall static deviation; the positional offset of static equipment is evaluated by the deviation of static equipment and the overall static deviation.

[0035] Real-time data of dynamic devices within a cycle is collected multiple times to obtain dynamic cycle data. Boundary judgment is performed in combination with the data model. Based on the judgment result and the cycle position data, the position deviation of the dynamic devices is calculated to obtain the dynamic deviation value. The motion trajectory of the dynamic devices within the same cycle is analyzed to obtain the trajectory deviation value. The offset status of the dynamic devices is evaluated based on the dynamic deviation value and the trajectory deviation value.

[0036] The positional offset of static equipment and the offset of dynamic equipment are evaluated as a whole, and anomalies are judged in mold manufacturing and assembly. Based on the anomaly judgment results, mold manufacturing and assembly are controlled.

[0037] Furthermore, the positional offset of the static equipment is evaluated, as follows:

[0038] The number of static devices is counted and denoted as ws1; based on the number of static devices, the static real-time data is denoted as jss(w1); the static standard data is denoted as jbz(w1); based on the static real-time data and the static standard data, the deviation of the static devices is judged, and the position deviation value wpc(w1) is obtained.

[0039] The overall static deviation zpc is obtained by calculating the deviation of all equipment as a whole.

[0040] ;

[0041] The positional offset of static equipment is evaluated by the deviation of static equipment and the overall static deviation. If the overall static deviation is 0, it indicates that the positional deviation is caused by the overall change and there is no abnormality in a single equipment. If the overall static deviation is not 0, the equipment with a positional deviation value wpc(w1) that is not 0 is acquired and marked, indicating that the static equipment has an abnormality.

[0042] Furthermore, the offset of the dynamic device is evaluated, as follows:

[0043] The number of dynamic devices is counted to obtain the number of dynamic devices ws2. Based on the number of dynamic devices, the dynamic periodic data is denoted as dzq(w2). The dynamic periodic data is aligned with the periodic position data, and the time-series dynamic data in the dynamic periodic data is extracted and denoted as sxd(w2,t).

[0044] The time-series dynamic data is checked for out-of-bounds conditions by comparing the time-series dynamic data with the spatial range of the dynamic device. If the time-series dynamic data does not conform to the spatial range of the dynamic device, it indicates that the dynamic device has an out-of-bounds condition, and the device is marked as abnormal. If the time-series dynamic data conforms to the spatial range of the dynamic device, it indicates that there is no out-of-bounds condition, and the time-series dynamic data is analyzed.

[0045] Furthermore, the time-series dynamic data is analyzed in detail as follows:

[0046] Extract the time-series position data from the periodic position data, calculate the deviation between the time-series position data and the time-series dynamic data, and obtain the position deviation wpc(w2) of the dynamic device.

[0047] The trajectory deviation value dgp(w2) of the dynamic device is obtained by calculating the data changes of the temporal position data sxw(w2,t) and the temporal dynamic data sxd(w2,t) within the same period.

[0048] ;

[0049] Where: sxw(w2,t+1) represents the time-series position data of the next time node after sxw(w2,t), and sxd(w2,t+1) represents the time-series dynamic data of the next time node after sxd(w2,t);

[0050] The offset of the dynamic device is evaluated based on the positional deviation wpc(w2) and the trajectory deviation dgp(w2) of the dynamic device.

[0051] If the position deviation wpc(w2) of the dynamic device is 0 and the trajectory deviation value dgp(w2) of the dynamic device is 0, it indicates that the dynamic device is normal; if the position deviation wpc(w2) of the dynamic device is not equal to 0, it indicates that the dynamic device is abnormal in use; if the trajectory deviation value dgp(w2) of the dynamic device is not equal to 0, it indicates that the dynamic device is abnormal in motion.

[0052] Furthermore, visual early warnings will be implemented, as detailed below:

[0053] Abnormal devices are extracted and visualized in a data model. At the same time, abnormal information of abnormal devices is collected and transmitted to maintenance personnel. Maintenance personnel then perform maintenance on abnormal devices based on the data model and abnormal information.

[0054] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0055] 1. This invention constructs a model of mold manufacturing and assembly processes using digital twins. By analyzing the model data, it determines the operating standards of the mold manufacturing and assembly processes. Based on the operating standards, it compares and calculates real-time data to promptly detect abnormalities in the mold manufacturing and assembly processes, ensuring the smooth operation of the mold manufacturing and assembly processes.

[0056] 2. This invention uses a data model to perform data-driven inspection of the mold manufacturing and assembly process. Through data calculation, it reduces the influence of human error in anomaly judgment and avoids human misjudgment. At the same time, it visualizes anomalies through the data model to ensure the accuracy of maintenance personnel's repairs.

[0057] 3. This invention modularizes the equipment in the mold manufacturing and assembly process, improves the modeling accuracy of the data model by analyzing and modeling individual equipment, classifies the equipment according to its motion status, and reduces the computing power required for modeling by extracting static equipment, thereby improving modeling efficiency. Attached Figure Description

[0058] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0059] Figure 1 This is an overall system block diagram of the present invention;

[0060] Figure 2 This is a schematic diagram of anomaly analysis in this invention;

[0061] Figure 3 This is a schematic diagram of dynamic device processing in this invention. Detailed Implementation

[0062] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0063] Example 1

[0064] Please see Figure 1 This invention belongs to the field of digital twins and provides a technical solution: a research and management system for mold manufacturing and assembly process model based on digital twins, including a data acquisition module, a model building module, a real-time judgment module, a model early warning module and a server. The data acquisition module, model building module, real-time judgment module and model early warning module are respectively connected to the server, and the server controls the data acquisition module, model building module, real-time judgment module and model early warning module respectively.

[0065] Digital twin is a technology that creates virtual models of physical entities or systems through digital means, enabling bidirectional interaction between the physical and digital worlds. It provides a full lifecycle digital mapping of physical objects through real-time data synchronization, simulation analysis, and predictive optimization, thereby supporting more efficient decision-making, optimization, and innovation.

[0066] Data acquisition module: Acquires physical equipment in mold manufacturing and assembly, collects spatial location data for each physical device, and obtains spatial location information;

[0067] The specific workflow of the data acquisition module is as follows:

[0068] The physical equipment used in mold manufacturing and assembly is acquired, and the number of physical equipment is counted and denoted as ws; the starting equipment used in mold manufacturing and assembly is acquired, and its spatial position data is collected as the origin data; using the starting equipment as a reference, the spatial position data of each physical equipment is collected to obtain spatial position data, and the spatial position data is denoted as kjw(w) according to the number of physical equipment; kjw(w) represents the spatial position data of the w-th physical equipment; w∈[1, ws]; the spatial position data is statistically analyzed to obtain spatial position information;

[0069] It should be noted that the physical devices in this invention are classified according to degrees of freedom. For example, if a robotic arm has two joints for control, then the area controlled by each joint is a physical device.

[0070] It should be noted that: the starting device refers to the first device to operate during the mold manufacturing and assembly process; by taking the starting device as the origin, the spatial information of the physical devices is unified, which facilitates the subsequent construction of the data model.

[0071] Model building module: Acquires the behavioral cycle of mold manufacturing and assembly, collects spatial location data in multiple time periods based on the behavioral cycle to obtain periodic location data, classifies physical equipment based on periodic location data to obtain static equipment and dynamic equipment; builds a model based on static equipment, dynamic equipment and periodic location data of equipment to obtain the data model of mold manufacturing and assembly.

[0072] It should be noted that by classifying the equipment, we can perform differentiated analysis on different equipment, reduce the analytical computing power of the equipment, and extract dynamic equipment to improve the accuracy of data model construction and prevent the data model from becoming rigid and causing serious errors between the data model and the entity model.

[0073] The specific workflow of the model building module is as follows:

[0074] The start and end times of mold manufacturing and assembly are acquired. From these times, the behavioral cycle of mold manufacturing and assembly is obtained. Based on the behavioral cycle, multiple data acquisitions are performed on the spatial position of the physical equipment to obtain periodic position data. The operating status of the physical equipment is analyzed based on the periodic position data. The physical equipment is classified into static or dynamic equipment based on its operating status. The spatial position information of the static equipment is acquired to construct a data model. The periodic position data of the dynamic equipment is analyzed to obtain the movement range of the dynamic equipment. The movement range of the dynamic equipment and the periodic position data are used to supplement the data model.

[0075] Based on the behavior cycle of mold manufacturing and assembly, multiple time nodes are set for data collection. The number of time nodes is counted and denoted as ts. For each time node, the spatial position data of the physical equipment is collected to obtain the time-series position data, denoted as sxw(w,t), where sxw(w,t) represents the spatial position data of the w-th physical equipment at the t-th time node. The time-series position data of each physical equipment within the behavior cycle is counted to obtain the periodic position data, denoted as zqw(w), where zqw(w) = [sxw(w,1), sxw(w,2), ..., sxw(w,ts)].

[0076] Acquire spatial location data, combine it with periodic location data to calculate the positional changes of physical equipment, and obtain the operational change of physical equipment ygb(w).

[0077] ;

[0078] It should be noted that the overall periodic positional change of the device is calculated by the difference between periodic positional data and spatial positional data. At the same time, the local changes are calculated by the changes in time nodes within the period. By combining the two, the accuracy of positional change judgment is improved and the impact of data errors is reduced.

[0079] The operating status of physical equipment is analyzed based on the changes in its operation, and the physical equipment is classified accordingly.

[0080] If ygb(w) = 0, it indicates that the physical device is a static device;

[0081] If ygb(w) ≠ 0, it indicates that the physical device is a dynamic device.

[0082] The spatial location data of static devices is acquired, and data mapping is performed based on the spatial location data of static devices to initially construct a data model;

[0083] The periodic position data of the dynamic device is acquired, and the motion space of the dynamic device is analyzed based on the periodic position data to obtain the extreme values ​​of the dynamic device's changes. The operation model of the dynamic device is constructed from the extreme values ​​of changes, and the data model is improved based on the operation model.

[0084] The specific process of constructing the operational model of a dynamic device is as follows:

[0085] The number of data dimensions for the location data of a dynamic device is obtained and denoted as fs.

[0086] It should be noted that the location data of the dynamic device of this invention has three data dimensions, which correspond to the dimensions of the physical world;

[0087] Data extraction is performed based on the periodic position data of the dynamic device and the data dimensions to obtain periodic position data of multiple different dimensions, denoted as dimension periodic data wzq(f); the maximum value of the dimension periodic data is extracted to obtain the upper limit of the dimension wsx(f), and the minimum value of the dimension periodic data is extracted to obtain the lower limit of the dimension wxx(f); the dimension range wfw(f) = [wxx(f), wsx(f)] is obtained based on the upper and lower limits of the dimension; the dimension range of multiple dimensions is statistically analyzed to obtain the spatial range kjf of the dynamic device, kjf = [wfw(1), wfw(2), ..., wfw(fs)];

[0088] Based on the spatial range of the dynamic equipment, an operational model of the dynamic equipment is constructed, spatial location data of the dynamic equipment is obtained, the operational model is initially set, and the operational model is used to fill the data model to obtain the data model for mold manufacturing and assembly.

[0089] It should be noted that: by constructing independent models for dynamic devices, the data model construction process is simplified. Furthermore, by analyzing a single model, the anomaly detection results are more accurate, enabling timely location of abnormal parts. The real-time detection module collects real-time data from static devices and, combined with the data model, analyzes the positional offset of the static devices.

[0090] Real-time data of dynamic devices is collected and combined with periodic position data to analyze the motion trajectory of the dynamic devices and determine the offset status of the dynamic devices.

[0091] Anomalies in mold manufacturing and assembly are determined by the positional offset of static and dynamic equipment.

[0092] Please see Figure 2 The specific workflow of the real-time judgment module is as follows:

[0093] Real-time data from static devices is collected to obtain static real-time data; based on the data model, standard data from static devices is collected to obtain static standard data.

[0094] It should be noted that: standard data refers to the spatial location of the device obtained through model building, and the data at the time of model building is used as the standard to evaluate the actual data in subsequent actual use;

[0095] Based on static real-time data and static standard data, the deviation of static equipment is judged, and the deviation of all equipment is analyzed as a whole to obtain the overall static deviation; the positional offset of static equipment is evaluated by the deviation of static equipment and the overall static deviation.

[0096] Real-time data of dynamic devices within a cycle is collected multiple times to obtain dynamic cycle data. Boundary judgment is performed in combination with the data model. Based on the judgment result and the cycle position data, the position deviation of the dynamic devices is calculated to obtain the dynamic deviation value. The motion trajectory of the dynamic devices within the same cycle is analyzed to obtain the trajectory deviation value. The offset status of the dynamic devices is evaluated based on the dynamic deviation value and the trajectory deviation value.

[0097] The positional offset of static equipment and the offset of dynamic equipment are evaluated as a whole, and anomalies are judged in mold manufacturing and assembly. Based on the anomaly judgment results, mold manufacturing and assembly are controlled.

[0098] The specific assessment of the positional offset of static equipment is as follows:

[0099] The number of static devices is counted and denoted as ws1; based on the number of static devices, the static real-time data is denoted as jss(w1); the static standard data is denoted as jbz(w1); based on the static real-time data and the static standard data, the deviation of the static devices is judged, and the position deviation value wpc(w1) is obtained.

[0100] ;

[0101] It should be noted that the positional deviation value is calculated based on the Euclidean distance between two data points;

[0102] The overall static deviation zpc is obtained by calculating the deviation of all equipment as a whole.

[0103] ;

[0104] It should be noted that: by calculating the average deviation of all equipment and the difference between the average deviation and the positional deviation of the equipment, the deviation value is determined as either an overall deviation or an individual deviation. Based on the overall deviation and individual deviation, special situations, such as the overall movement of equipment, are avoided.

[0105] The positional offset of static equipment is evaluated by comparing the deviation of static equipment with the overall static deviation. If the overall static deviation is 0, it indicates that the positional deviation is caused by the overall change and no abnormality exists in a single equipment. If the overall static deviation is not 0, the equipment with a positional deviation value wpc(w1) that is not 0 is acquired and marked, indicating that the static equipment has an abnormality.

[0106] Overall static deviation refers to the statistical calculation of the deviation of all equipment. For example, when the workbench is displaced as a whole, the deviation of each specific static equipment should be consistent. The overall static deviation is calculated. If it is equal to 0, it means that the deviation of the individual equipment is consistent. In this state, it has no impact on the overall operation, that is, there is no abnormality. If it is not equal to 0, it means that there is one or more equipment with abnormal deviation.

[0107] This invention performs separate calculations for static and dynamic devices; the above process is only the anomaly detection process for static devices.

[0108] The specific process for evaluating the offset of dynamic devices is as follows:

[0109] Please see Figure 3 The number of dynamic devices is counted to obtain the number of dynamic devices ws2. The dynamic periodic data is denoted as dzq(w2) based on the number of dynamic devices. The dynamic periodic data is aligned with the periodic position data, and the time-series dynamic data in the dynamic periodic data is extracted and denoted as sxd(w2,t).

[0110] The data acquisition module collects data on the spatial location of each physical device. The dynamic periodic data is denoted as dzq(w2) based on the number of dynamic devices. This refers to the acquisition and naming of the spatial location data of each dynamic device. The dynamic periodic data is equivalent to the real-time data of the device completing the operation.

[0111] Time-series dynamic data refers to the acquisition of the time-series characteristics of dynamic periodic data based on the time-series nature of periodic position data, that is, mapping the starting point in the periodic position data to the starting point of the dynamic periodic data;

[0112] By analyzing the positional deviations at multiple time points within the operating cycle using time-series dynamic data, and by analyzing the movement trajectory of the equipment based on positional changes, the operating status of dynamic equipment can be more accurately judged based on positional data and movement trajectory data.

[0113] The temporal location data and temporal dynamic data are equivalent to standard data and actual data; the temporal location data is a self-established standard, which is established by sampling multiple nodes of dynamic devices within the operating cycle to set judgment criteria. The more nodes established, the higher the judgment accuracy.

[0114] Since the standard is self-established and based on actual data, if the standard cannot make a simple judgment on the operation of the equipment, the actual operation results should show obvious abnormalities; at the same time, the judgment with 0 is only to record the difference, and the specific problems of the equipment should be judged by the operation and maintenance personnel.

[0115] The time-series dynamic data is checked for out-of-bounds conditions by comparing the time-series dynamic data with the spatial range of the dynamic device. If the time-series dynamic data does not conform to the spatial range of the dynamic device, it indicates that the dynamic device has out-of-bounds conditions, and the device is marked as an anomaly. If the time-series dynamic data conforms to the spatial range of the dynamic device, it indicates that there are no out-of-bounds conditions, and the time-series dynamic data is analyzed.

[0116] Extract the time-series position data from the periodic position data, calculate the deviation between the time-series position data and the time-series dynamic data, and obtain the position deviation wpc(w2) of the dynamic device.

[0117] ;

[0118] The trajectory deviation value dgp(w2) of the dynamic device is obtained by calculating the data changes of the temporal position data sxw(w2,t) and the temporal dynamic data sxd(w2,t) within the same period.

[0119] ;

[0120] Where: sxw(w2,t+1) represents the time-series position data of the next time node after sxw(w2,t), and sxd(w2,t+1) represents the time-series dynamic data of the next time node after sxd(w2,t);

[0121] The offset of the dynamic device is evaluated based on the positional deviation wpc(w2) and the trajectory deviation dgp(w2) of the dynamic device.

[0122] If the position deviation wpc(w2) of the dynamic device is 0 and the trajectory deviation value dgp(w2) of the dynamic device is 0, it indicates that the dynamic device is normal; if the position deviation wpc(w2) of the dynamic device is not equal to 0, it indicates that the dynamic device is abnormal in use; if the trajectory deviation value dgp(w2) of the dynamic device is not equal to 0, it indicates that the dynamic device is abnormal in motion.

[0123] The positional offset of static equipment and the offset of dynamic equipment are statistically analyzed to obtain the number of abnormal physical equipment. The number of abnormal equipment and abnormal information are statistically analyzed to establish an abnormal threshold. The abnormal information is matched with the abnormal threshold. When the abnormal information matches the abnormal threshold, the mold manufacturing and assembly are stopped immediately.

[0124] It should be noted that: abnormal threshold information refers to the automatic control of special equipment, such as a robotic arm exceeding its range of motion, which leads to damage to the robotic arm.

[0125] Model early warning module: Extracts abnormal equipment based on the judgment results, and uses the abnormal equipment to provide visual early warnings to the data model, providing maintenance personnel with maintenance prompts;

[0126] The specific workflow of the model early warning module is as follows:

[0127] Abnormal equipment is extracted and visualized in a data model. At the same time, abnormal information of abnormal equipment is collected and transmitted to maintenance personnel. Maintenance personnel then perform maintenance on abnormal equipment based on the data model and abnormal information.

[0128] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A research and management system for mold manufacturing and assembly process models based on digital twins, characterized in that, include: Data acquisition module: Acquires physical equipment in mold manufacturing and assembly, collects spatial location data for each physical device, and obtains spatial location information; Model building module: Obtain the behavior cycle of mold manufacturing and assembly, collect spatial location data in multiple time periods according to the behavior cycle to obtain periodic location data, classify physical equipment based on periodic location data to obtain static equipment and dynamic equipment; Based on static equipment, dynamic equipment, and the periodic position data of the equipment, a data model for mold manufacturing and assembly is obtained by model construction. Model building includes: The start and end times of mold manufacturing and assembly are obtained. From the start and end times, the behavioral cycle of mold manufacturing and assembly is obtained. Based on the behavioral cycle, the spatial position of the physical equipment is collected multiple times to obtain the cycle position data. The operating status of the physical equipment is analyzed based on the cycle position data. The operational status of the physical equipment was analyzed, as follows: Based on the behavior cycle of mold manufacturing and assembly, multiple time nodes are set for data collection. The number of time nodes is counted and denoted as ts. For each time node, the spatial position data of the physical equipment is collected to obtain the temporal position data, denoted as sxw(w,t). The temporal position data of each physical equipment within the behavior cycle is counted to obtain the periodic position data, denoted as zqw(w), zqw(w) = [sxw(w,1), sxw(w,2), ..., sxw(w,ts)]. Acquire spatial location data kjw(w), combine it with periodic location data to calculate the positional change of the physical equipment, and obtain the operational change ygb(w) of the physical equipment. ； The operating status of physical equipment is analyzed based on the changes in its operation, and the physical equipment is classified accordingly. If ygb(w) = 0, it indicates that the physical device is a static device; If ygb(w) ≠ 0, it indicates that the physical device is a dynamic device; The spatial location data of static devices is acquired, and data mapping is performed based on the spatial location data of static devices to initially construct a data model; The periodic position data of the dynamic device is acquired, and the motion space of the dynamic device is analyzed based on the periodic position data to obtain the extreme values ​​of the dynamic device's changes. The operation model of the dynamic device is constructed from the extreme values ​​of changes, and the data model is improved based on the operation model. Spatial location data for each physical device was collected, as follows: The physical equipment used in mold manufacturing and assembly is acquired, and the number of physical equipment is counted and denoted as ws; the starting equipment used in mold manufacturing and assembly is acquired, and the spatial position data of the starting equipment is collected as the origin data; using the starting equipment as a reference, the spatial position data of each physical equipment is collected to obtain spatial position data, and the spatial position data is denoted as kjw(w) according to the number of physical equipment; the spatial position data is statistically analyzed to obtain spatial position information; Real-time judgment module: Collects real-time data from static devices and analyzes the positional offset of the static devices in conjunction with the data model; Real-time data of dynamic devices is collected and combined with periodic position data to analyze the motion trajectory of the dynamic devices and determine the offset status of the dynamic devices. Anomalies in mold manufacturing and assembly are determined by the positional offset of static and dynamic equipment. Model early warning module: Based on the judgment results, abnormal equipment is extracted, and the abnormal equipment provides a visual early warning to the data model, providing maintenance personnel with maintenance prompts.

2. The research and management system for mold manufacturing and assembly process model based on digital twins according to claim 1, characterized in that, Model building also includes: Based on the operating status of the physical equipment, the equipment is divided into static equipment or dynamic equipment. The spatial location information of the static equipment is acquired to construct a data model. The periodic location data of the dynamic equipment is analyzed to obtain the movement range of the dynamic equipment. The movement range and periodic location data of the dynamic equipment are then used to supplement the data model.

3. The research and management system for mold manufacturing and assembly process model based on digital twins according to claim 1, characterized in that, The operational model of the dynamic device is constructed as follows: The number of data dimensions for the location data of a dynamic device is obtained and denoted as fs. Data extraction is performed based on the periodic position data of the dynamic device and the data dimensions to obtain periodic position data of multiple different dimensions, denoted as dimension periodic data wzq(f); the maximum value of the dimension periodic data is extracted to obtain the upper limit of the dimension wsx(f), and the minimum value of the dimension periodic data is extracted to obtain the lower limit of the dimension wxx(f); the dimension range wfw(f) = [wxx(f), wsx(f)] is obtained based on the upper and lower limits of the dimension; the dimension range of multiple dimensions is statistically analyzed to obtain the spatial range kjf of the dynamic device, kjf = [wfw(1), wfw(2), ..., wfw(fs)]; Based on the spatial range of the dynamic equipment, an operational model of the dynamic equipment is constructed, spatial location data of the dynamic equipment is obtained, the operational model is initially set, and the operational model is used to fill the data model to obtain the data model for mold manufacturing and assembly.

4. The research and management system for mold manufacturing and assembly process model based on digital twins according to claim 1, characterized in that, Anomaly detection is performed on mold manufacturing and assembly, as follows: Real-time data from static devices is collected to obtain static real-time data; based on the data model, standard data from static devices is collected to obtain static standard data. Based on static real-time data and static standard data, the deviation of static equipment is judged, and the deviation of all equipment is analyzed as a whole to obtain the overall static deviation. The positional offset of static equipment is evaluated by comparing the deviation of static equipment with the overall static deviation. Real-time data of dynamic devices within a cycle is collected multiple times to obtain dynamic cycle data. Boundary judgment is performed in combination with the data model. Based on the judgment result and the cycle position data, the position deviation of the dynamic devices is calculated to obtain the dynamic deviation value. The motion trajectory of the dynamic devices within the same cycle is analyzed to obtain the trajectory deviation value. The offset status of the dynamic devices is evaluated based on the dynamic deviation value and the trajectory deviation value. The positional offset of static equipment and the offset of dynamic equipment are evaluated as a whole, and anomalies are judged in mold manufacturing and assembly. Based on the anomaly judgment results, mold manufacturing and assembly are controlled.

5. The research and management system for mold manufacturing and assembly process model based on digital twins according to claim 4, characterized in that, The positional offset of static equipment is evaluated as follows: The number of static devices is counted and denoted as ws1; based on the number of static devices, the static real-time data is denoted as jss(w1); the static standard data is denoted as jbz(w1); based on the static real-time data and the static standard data, the deviation of the static devices is judged, and the position deviation value wpc(w1) is obtained. The overall static deviation zpc is obtained by calculating the deviation of all equipment as a whole. The positional offset of static equipment is evaluated by the deviation of static equipment and the overall static deviation. If the overall static deviation is 0, it indicates that the positional deviation is caused by the overall change and there is no abnormality in a single equipment. If the overall static deviation is not 0, the equipment with a positional deviation value wpc(w1) that is not 0 is acquired and marked, indicating that the static equipment has an abnormality.

6. The research and management system for mold manufacturing and assembly process model based on digital twins according to claim 4, characterized in that, The offset of the dynamic device is evaluated as follows: The number of dynamic devices is counted to obtain the number of dynamic devices ws2. Based on the number of dynamic devices, the dynamic periodic data is denoted as dzq(w2). The dynamic periodic data is aligned with the periodic position data, and the time-series dynamic data in the dynamic periodic data is extracted and denoted as sxd(w2,t). The time-series dynamic data is checked for out-of-bounds conditions by comparing the time-series dynamic data with the spatial range of the dynamic device. If the time-series dynamic data does not conform to the spatial range of the dynamic device, it indicates that the dynamic device has an out-of-bounds condition, and the device is marked as abnormal. If the time-series dynamic data conforms to the spatial range of the dynamic device, it indicates that there is no out-of-bounds condition, and the time-series dynamic data is analyzed.

7. The research and management system for mold manufacturing and assembly process model based on digital twins according to claim 6, characterized in that, The time-series dynamic data is analyzed in the following ways: Extract the time-series position data from the periodic position data, calculate the deviation between the time-series position data and the time-series dynamic data, and obtain the position deviation wpc(w2) of the dynamic device. The trajectory deviation value dgp(w2) of the dynamic device is obtained by calculating the data changes of the temporal position data sxw(w2,t) and the temporal dynamic data sxd(w2,t) within the same period. ； Where: sxw(w2,t+1) represents the time-series position data of the next time node after sxw(w2,t), and sxd(w2,t+1) represents the time-series dynamic data of the next time node after sxd(w2,t); The offset of the dynamic device is evaluated based on the positional deviation wpc(w2) and the trajectory deviation dgp(w2) of the dynamic device. If the position deviation wpc(w2) of the dynamic device is 0 and the trajectory deviation value dgp(w2) of the dynamic device is 0, it indicates that the dynamic device is normal; if the position deviation wpc(w2) of the dynamic device is not equal to 0, it indicates that the dynamic device is abnormal in use; if the trajectory deviation value dgp(w2) of the dynamic device is not equal to 0, it indicates that the dynamic device is abnormal in motion.

8. The research and management system for mold manufacturing and assembly process model based on digital twins according to claim 1, characterized in that, Visualized early warnings are provided, as follows: Abnormal devices are extracted and visualized in a data model. At the same time, abnormal information of abnormal devices is collected and transmitted to maintenance personnel. Maintenance personnel then perform maintenance on abnormal devices based on the data model and abnormal information.

Images