Industrial digital twin system and method

Abstract

The invention discloses an industrial digital twinning system and method, and relates to the industrial technical field, the industrial digital twinning system comprises an industrial assembly process simulation module, an industrial assembly process evaluation module, a Web set display end and a web data warehouse, and the industrial digital twinning system comprises the following steps: determining a part interference group label, a part interference environment group label and a part interference execution group label; a foundation is laid for follow-up industrial assembly process simulation; the industrial assembly process is simulated based on the part interference group label, the part interference environment group label and the part interference execution group label in combination with the three-dimensional live-action data of industrial assembly, the training difficulty of the three-dimensional model is reduced, the training accuracy and training efficiency of the three-dimensional model are improved, and the training efficiency of the three-dimensional model is improved. According to the method, the optimization type of the industrial assembly is determined through the simulation data of the industrial assembly, the accuracy of the optimization type of the industrial assembly is guaranteed, and the value of industrial assembly process simulation is improved.

Description

Technical Field

[0001] The present invention relates to the field of industrial technology, and in particular to an industrial digital twin system and method. Background Art

[0002] The application of industrial digital twins is becoming more and more extensive, and industrial assembly is an indispensable process in industrial production. The importance of the industrial assembly process is self-evident, and simulation training is gradually becoming a secret weapon for enterprises to enhance their competitiveness. With the rapid development of science and technology and the continuous changes in market demand, traditional assembly training methods can no longer meet the requirements of efficient and precise production. Therefore, it is extremely necessary to combine industrial assembly with simulation training. Simulation training of the industrial assembly process can not only improve production efficiency and reduce costs, but also ensure product quality, laying a solid foundation for the sustainable development of enterprises.

[0003] Prior art, such as the invention application patent with announcement number: CN114260893B, discloses a method for constructing a digital twin model of an industrial robot assembly pick-and-place process, comprising the following steps: constructing a digital twin virtual model of an industrial robot. Specifying data interaction instructions to realize data interaction between the digital twin virtual model of the industrial robot and the industrial robot to complete the actual assembly work. Collecting the actions of the industrial robot under each data interaction instruction, constructing a digital twin model of the industrial robot assembly pick-and-place process, and simulating the assembly process. This invention constructs a digital twin virtual model of the industrial robot assembly pick-and-place process, which can be used to simulate the real industrial robot assembly process, helps to simulate the real industrial robot assembly pick-and-place process with high fidelity in a virtual environment, and provides support for the efficient design of the industrial robot assembly process.

[0004] The prior art, such as the invention application patent announced as CN115052258B, discloses an industrial monitoring system based on big data and digital twins, including a digital twin module, an Internet of Things module and a big data processing module. The digital twin module is used to generate a three-dimensional model of a production workshop. The Internet of Things module is used to obtain environmental data in the production workshop. The big data processing module is used to calibrate the environmental data and obtain calibrated environmental data. The digital twin module is also used to import the calibrated environmental data into the three-dimensional model for display. When monitoring industrial production, this invention calibrates the environmental data and then performs industrial monitoring based on the calibrated environmental data, thereby effectively improving the accuracy of using digital twin technology to monitor industrial production.

[0005] Referring to the above scheme, it is found that there are still some deficiencies in the existing technology, which are specifically reflected in: most of the existing technologies directly simulate the three-dimensional model, and there is little prior production data for industrial assembly to confirm the interference data of industrial assembly. The prior production data of industrial assembly reflects the interference probability and interference reasons of parts to a certain extent. The neglect of this level in the existing technology makes it difficult to provide support for the subsequent optimization of industrial assembly, increases the difficulty of training the three-dimensional model, and reduces the training accuracy and training efficiency of the three-dimensional model. At the same time, there is a lack of verification of the training results, which can ensure the accuracy of the optimization type of industrial assembly, resulting in a reduction in the value of industrial assembly process simulation. Summary of the invention

[0006] The purpose of the present invention is to provide an industrial digital twin system and method to solve the problems existing in the background technology.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: The first aspect of the present invention provides an industrial digital twin system, including: an industrial assembly process simulation module, which is used to simulate the industrial assembly process and generate simulation data of the industrial assembly.

[0008] The industrial assembly process simulation module includes an industrial assembly real scene acquisition unit, an industrial assembly production data acquisition unit, an industrial assembly interference data confirmation unit, and an industrial assembly simulation unit.

[0009] The industrial assembly real scene acquisition unit is used to acquire three-dimensional real scene data of industrial assembly.

[0010] The industrial assembly production data acquisition unit is used to monitor the priori production data of the industrial assembly through an online monitoring platform.

[0011] The industrial assembly interference data confirmation unit is used to obtain the test volume of the industrial assembly and confirm the interference data of the industrial assembly based on the prior production data and three-dimensional real scene data of the industrial assembly.

[0012] The industrial assembly simulation unit is used to simulate the industrial assembly process based on the interference data of the industrial assembly and generate industrial assembly simulation data.

[0013] The industrial assembly process evaluation module is used to generate the optimized type of industrial assembly based on the simulation data of industrial assembly.

[0014] Web integrated display terminal, used to display the optimized type of industrial assembly.

[0015] The second aspect of the present invention provides a method for executing the industrial digital twin system described in the present invention, including: ST1, collecting three-dimensional real-scene data of industrial assembly, and monitoring the prior production data of the industrial assembly through an online monitoring platform.

[0016] ST2. Obtain the test volume of the industrial assembly, and confirm the interference data of the industrial assembly based on the prior production data and three-dimensional real scene data of the industrial assembly.

[0017] ST3. Based on the interference data of industrial assembly, the industrial assembly process is simulated and industrial assembly simulation data is generated.

[0018] ST4. Generate optimized types of industrial assemblies based on simulation data of industrial assemblies.

[0019] ST5. Display the optimization type of industrial assembly.

[0020] The beneficial effects of the present invention are as follows: (1) The present invention extracts assembly data sets, environmental data sets and execution data sets from prior production data of industrial assembly, firstly determines component interference group labels, that is, the interference categories and interference risks that components are prone to generate, then determines component interference environment group labels, that is, the example environment data sets of various interference categories at various levels of risk, and determines component interference execution group labels, that is, the example execution data sets of various interference categories at various levels of risk, thus laying a foundation for the subsequent simulation of the industrial assembly process.

[0021] (2) The present invention simulates the industrial assembly process based on component interference group labels, component interference environment group labels and component interference execution group labels, combined with three-dimensional real-scene data of industrial assembly, thereby reducing the difficulty of training the three-dimensional model and improving the training accuracy and efficiency of the three-dimensional model.

[0022] (3) The present invention determines the optimization type of industrial assembly through the simulation data of industrial assembly, ensures the accuracy of the optimization type of industrial assembly, and improves the value of industrial assembly process simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 It is a schematic diagram of the system structure connection of the present invention.

[0025] Figure 2 The present invention is a schematic flow chart of the steps for implementing the method. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Reference Figure 1 As shown, the first aspect of the present invention provides an industrial digital twin system, including: an industrial assembly process simulation module, an industrial assembly process evaluation module, a Web integrated display terminal and a web data warehouse.

[0028] It should be noted that the industrial assembly process simulation module is connected to the industrial assembly process evaluation module, the industrial assembly process evaluation module is connected to the Web integrated display terminal, and the web data warehouse is respectively connected to the industrial assembly process simulation module and the industrial assembly process evaluation module.

[0029] The industrial assembly process simulation module is used to simulate the industrial assembly process and generate simulation data of the industrial assembly.

[0030] The industrial assembly process simulation module includes an industrial assembly real scene acquisition unit, an industrial assembly production data acquisition unit, an industrial assembly interference data confirmation unit, and an industrial assembly simulation unit.

[0031] The industrial assembly real scene acquisition unit is used to acquire three-dimensional real scene data of industrial assembly.

[0032] It should be noted that the method for collecting the three-dimensional real scene data includes but is not limited to using laser radar scanning, oblique photography scanning and structured light scanning, etc., and is relatively mature in the existing technology and will not be elaborated here.

[0033] The industrial assembly production data acquisition unit is used to monitor the priori production data of the industrial assembly through an online monitoring platform.

[0034] It should be noted that the online monitoring platform is equipped with a monitoring system, an environmental data acquisition system and an end-effector data acquisition system, wherein the monitoring system is collected by video acquisition equipment, the environmental data acquisition system includes but is not limited to infrared cameras, humidity sensors, wind speed sensors and other environmental monitoring sensors for collection, and the end-effector data acquisition system includes but is not limited to positioners, speed sensors and other end-effector monitoring sensors for collection.

[0035] As a preferred solution, the priori production data includes: an assembly data set, an environment data set, and an execution data set.

[0036] The assembly data set includes assembly dynamic images of all parts.

[0037] The environmental data set includes an assembly environmental data set of all components.

[0038] The execution data set includes an end-effector execution data set of all components.

[0039] The industrial assembly interference data confirmation unit is used to obtain the test volume of the industrial assembly and confirm the interference data of the industrial assembly based on the prior production data and three-dimensional real scene data of the industrial assembly.

[0040] It should be noted that the test volume of the industrial assembly is specifically uploaded by the staff.

[0041] The present invention extracts assembly data sets, environmental data sets and execution data sets from the prior production data of industrial assembly, firstly determines the component interference group labels, that is, the interference categories and interference risks that the components are prone to generate, and secondly determines the component interference environment group labels, that is, the example environment data sets of various interference categories at various levels of risk, and determines the component interference execution group labels, that is, the actual execution data sets of various interference categories at various levels of risk, which lays the foundation for the subsequent simulation of the industrial assembly process.

[0042] As a preferred solution, the interference data of industrial assembly is confirmed by the following specific confirmation method: extracting assembly data sets from prior production data of industrial assembly, obtaining assembly dynamic images of all parts, and identifying interference types and interference risk characteristic parameters of several parts through motion recognition technology. The interference types include part interference, end effector interference and other interferences. The interference risk characteristic parameter is a value of 0-1, which reflects the degree of interference of parts. The greater the interference degree, the greater the interference risk characteristic parameter, thereby determining the part interference group label.

[0043] Specifically, the component interference refers to the mutual interference between components, the end effector interference refers to the mutual interference between components and end effectors, and other interference refers to the mutual interference between components and other devices.

[0044] It should be noted that the specific method for obtaining the interference risk characteristic parameter is: the embedded length of several components identified by motion recognition technology is compared with the overall length of several components to obtain the embedding ratio of several components, which is used as the interference risk characteristic parameter of several components.

[0045] The assembly environment data set of all parts is extracted from the prior production data of industrial assembly, and the component interference environment group labels are determined in combination with the component interference group labels.

[0046] The end-effector execution data set of all parts is extracted from the prior production data of industrial assembly, and the component interference execution group labels are determined in combination with the component interference group labels.

[0047] The component interference group labels, component interference environment group labels, and component interference execution group labels are summarized to obtain the interference data of industrial assembly.

[0048] As a preferred solution, the component interference group label is determined by mapping the components corresponding to each interference type and their corresponding interference risk characteristic parameters based on the interference types and interference risk characteristic parameters of the components. ,in is the number of each interference type, .

[0049] like , then the component of this interference type is recorded as a first-level risk component. is the first-level risk convergence value in the web data warehouse.

[0050] like , then the component of this interference type is recorded as a secondary risk component. is the secondary risk convergence value in the web data warehouse.

[0051] It should be noted that the first-level risk convergence value and the second-level risk convergence value are the basis for the risk classification of parts and components, and are specifically set by staff. For example, in order to reduce the interference risk of parts and components, the first-level risk convergence value and the second-level risk convergence value are set to 0.3 and 0.5 respectively.

[0052] like , then the component of this interference type is recorded as a level 3 risk component.

[0053] Summarize several first-level risk components whose interference type is component interference to generate a first-level sub-label of the component first category interference group label; summarize several second-level risk components whose interference type is component interference to generate a second-level sub-label of the component first category interference group label; summarize several third-level risk components whose interference type is component interference to generate a third-level sub-label of the component first category interference group label; and so on, generate the first-level, second-level, and third-level sub-labels of the component second category interference group label and the first-level, second-level, and third-level sub-labels of the component third category interference group label.

[0054] Specifically, the component type II interference group label is reflected as the interference type being end-effector interference, and the component type III interference group label is reflected as the interference type being other interference.

[0055] As a preferred solution, the component interference environment group label is determined by the following method: based on the assembly environment data set of all components, combined with the component interference group label, the assembly environment data set of each component of the first-level sub-label of the component type interference group label is extracted, and the environment judgment value of each component is obtained through data processing. The assembly environment data set includes characteristic parameters of several environmental data, and the environment judgment value includes values ​​of -1 and 1. When the environment judgment value is -1, it indicates that the assembly environment of the component is not suitable. Therefore, the assembly environment data set of the component can be used as an example. When the environment judgment value is 1, it indicates that the assembly environment of the component is suitable.

[0056] It should be noted that the characteristic parameters of the several environmental data include but are not limited to temperature distribution diagram, humidity distribution diagram, wind speed distribution diagram, etc. The temperature distribution diagram refers to the temperature distribution diagram within the assembly range of the parts during the assembly process. The humidity distribution diagram and wind speed distribution diagram are the same as above. The assembly range of the parts specifically refers to the assembly range generated with the center point of the part as the origin and a set diameter. The set diameter is specifically set by the staff. For example, in order to improve the accuracy of the environmental assessment of the component assembly, the set diameter is set slightly larger.

[0057] It should also be noted that the environmental judgment value of each component is obtained through data processing, and its specific processing method is: importing the temperature distribution map of each component and the suitable temperature distribution map stored in the web data warehouse into the graphic analysis tool, outputting the similarity between the temperature distribution map of each component and the suitable temperature distribution map, recorded as the first similarity of each component, outputting the similarity between the humidity distribution map of each component and the suitable humidity distribution map, recorded as the second similarity of each component, and similarly, obtaining several similarities of each component.

[0058] The similarities of each component are compared with the similarity threshold in the web data warehouse. If there is a similarity less than the similarity threshold, the environmental judgment value of the component is recorded as -1. If there is no similarity less than the similarity threshold, the environmental judgment value of the component is recorded as 1.

[0059] The graphic analysis tools include professional image processing software such as Adobe Photoshop and ImageMagick, scientific computing and data analysis software such as MATLAB and Python, and online image processing platforms such as Pixlr and Online Image Comparer, which are relatively mature in the prior art and will not be elaborated here.

[0060] If the environmental judgment value of a component is -1, the assembly environment data of the component is aggregated as a sample environment data set, and several sample environment data sets are aggregated to obtain the component interference environment group label.

[0061] It should be noted that the component interference environment group label is obtained through aggregation processing, and its specific processing method is: aggregate several example environment data sets of the first-level sub-labels of the first-category interference group label of the component, generate the environment label of the first-level sub-label of the first-category interference group label of the component, and so on, to obtain the environment labels of the second-level and third-level sub-labels of the first-category interference group label of the component, the environment labels of the first-level, second-level and third-level sub-labels of the second-category interference group label of the component, and the environment labels of the first-level, second-level and third-level sub-labels of the third-category interference group label of the component.

[0062] As a preferred solution, the component interference execution group label is determined by the following method: based on the end effector execution data set of all components and in combination with the component interference group label, the end effector execution data set of each component of the first-level sub-label of the component type interference group label is extracted, and the execution judgment value of each component is obtained through data processing. The end effector execution data set includes characteristic parameters of several execution data, and the execution judgment value includes values ​​of -1 and 1. When the execution judgment value is -1, it indicates that the execution of the component is not suitable. Therefore, the end effector execution data set of the component can be used as an example. When the execution judgment value is 1, it indicates that the execution of the component is suitable.

[0063] It should be noted that the characteristic parameters of the execution data include position change graph, posture change graph, speed change graph and other relevant data reflecting the execution accuracy of the end effector. The position can be monitored using a locator, and the speed can be monitored using a speed sensor.

[0064] It should be noted that the posture change diagram specifically refers to the change diagram of the Euler angle of the end effector. The Euler angle is used to describe the rotation direction and angle of the end effector. The position change and speed change, the monitoring of the Euler angle can be monitored using an inertial measurement model (IMU), a visual sensor, an encoder, etc., and it belongs to the existing technology and is not specifically limited here.

[0065] It should also be noted that, based on the reference characteristic parameters of several execution data of each component in the web data warehouse, the reference characteristic parameters of several execution data include position change graphs, posture change graphs, speed change graphs, etc. The end effector generally performs its work according to a preset operating program, and the reference characteristic parameters of several execution data can be obtained through the preset operating program.

[0066] It should be noted again that the execution judgment value of each component is obtained through data processing, and the processing method thereof is consistent with the environmental judgment value of each component.

[0067] If the execution judgment value of a component is -1, the end-effector execution data set of the component is collected as a sample execution data set, and several sample execution data sets are obtained through aggregation. The component interference execution group label is obtained through aggregation.

[0068] It should be noted that the specific processing method of the component interference execution group label obtained through aggregation processing is: aggregating several example execution data sets of the first-level sub-labels of the first-level interference group label of the component, generating the execution label of the first-level sub-label of the first-level interference group label of the component, and so on, obtaining the execution labels of the second-level and third-level sub-labels of the first-level interference group label of the component, the execution labels of the first-level, second-level and third-level sub-labels of the second-level interference group label of the component, and the execution labels of the first-level, second-level and third-level sub-labels of the third-level interference group label of the component.

[0069] The industrial assembly simulation unit is used to simulate the industrial assembly process based on the interference data of the industrial assembly and generate industrial assembly simulation data.

[0070] The present invention is based on component interference group labels, component interference environment group labels and component interference execution group labels, combined with three-dimensional real-scene data of industrial assembly, to simulate the industrial assembly process, reduce the training difficulty of the three-dimensional model, and improve the training accuracy and training efficiency of the three-dimensional model.

[0071] As a preferred solution, the industrial assembly process is simulated, and its specific method is: based on the test volume of industrial assembly, the number of components of the first-level, second-level, and third-level sub-labels of each type of interference group label of the components is counted from the component interference group label, and the number of components is divided by the total number of components respectively, and then multiplied by the test volume of industrial assembly to obtain the training times of the first-level, second-level, and third-level sub-labels of each type of interference group label of the components.

[0072] Based on the training times of the first-level, second-level and third-level sub-labels of various interference group labels of parts, a corresponding number of assembly environment data sets and end-effector execution data sets are randomly extracted from the corresponding environmental labels and execution labels, and two by two are used as the training data of the first-level, second-level and third-level sub-labels of various interference group labels of parts. Based on the three-dimensional real-life data of industrial assembly, a three-dimensional model of industrial assembly is constructed, and the industrial assembly process is simulated by combining all parts of the first-level, second-level and third-level sub-labels of various interference group labels of parts.

[0073] It should be noted that there is a one-to-one correspondence between the assembly environment data set and the end-effector execution data set, that is, one assembly environment data set is grouped with only one end-effector execution data set.

[0074] It should also be noted that the current three-dimensional simulation technology has been widely used. It can create accurate three-dimensional models based on the specific information in the environmental data set and the end-effector set to simulate the assembly process. The existing technology is relatively mature.

[0075] It is necessary to explain again that all components of the first-level, second-level, and third-level sub-labels of various interference group labels of components are placed in the corresponding training data to simulate the assembly of components.

[0076] As a preferred solution, the simulation data includes the interference types of each training of the primary, secondary and tertiary sub-labels of each type of interference group label.

[0077] It should be noted that, based on the interference types of all components in each training of the first-level, second-level, and third-level sub-labels of each interference group label, all components of each interference type in each training are mapped, the number of components is counted, and the interference type with the largest number of components is recorded as the interference type of this training. Then, the interference types of each training of the first-level, second-level, and third-level sub-labels of each interference group label are obtained. If the number of components is tied for the largest number, the corresponding interference types will all be recorded as the interference types of this training.

[0078] The industrial assembly process evaluation module is used to generate an optimized type of industrial assembly based on the simulation data of the industrial assembly.

[0079] The present invention determines the optimization type of industrial assembly through simulation data of industrial assembly, ensures the accuracy of the optimization type of industrial assembly, and improves the value of industrial assembly process simulation.

[0080] As a preferred solution, the optimization type of industrial assembly is generated by: based on various interference group labels, determining the reference type of various interference group labels, comparing the interference type of each training of the first, second, and third sub-labels of each interference group label with the reference type, and determining the accurate value of each training through comparison. , the exact value includes values ​​of -1, 0 and 1. When the exact value is 1, it indicates that the interference type is the same as the reference type. When the exact value is -1, it indicates that there is no interference type. When the exact value is 0, it indicates that the interference type is different from the reference type. is the number of each type of intervention group label, , is the number of each level of sub-label, , is the number of each training session, , is an integer greater than 2.

[0081] It should be noted that the reference type of the first type of interference group label is component interference, the reference type of the second type of interference group label is end-effector interference, and the reference type of the third type of interference group label is other interference.

[0082] The interference relationship is obtained through numerical processing to determine the accuracy ,in , They are respectively the weight influence factor of the h-th level sub-label in the web data warehouse and the accurate threshold of the m-th type of interference group label.

[0083] It should be noted that the weight influencing factors of the sub-tags at each level specifically reflect the importance of the determination accuracy of the sub-tags at each level. Specifically, the interference risk of the tertiary sub-tag is greater than the interference risk of the secondary sub-tag, then the importance of the determination accuracy of the tertiary sub-tag is greater than the determination accuracy of the secondary sub-tag, and the interference risk of the determination accuracy of the secondary sub-tag is greater than the interference risk of the primary sub-tag, then the determination accuracy of the secondary sub-tag is greater than the determination accuracy of the primary sub-tag. It is specifically set by the staff, and the accurate thresholds of the various interference group labels are also specifically set by the staff as a basis for judging whether the simulation accuracy of various interference group labels meets the requirements.

[0084] The interference relationship determination accuracy is compared with the interference relationship determination accuracy interval corresponding to each optimization type in the web data warehouse, and the optimization type of industrial assembly is screened, which is environment + end-effector optimization, training optimization and production optimization.

[0085] It should be noted that the interference relationship determination accuracy interval corresponding to each optimization type is specifically set by the staff and stored in the web data warehouse. For example, the interference relationship determination accuracy interval corresponding to each optimization type is set to (-1, -0.9), (-0.9, 0.5), (0.5, 1) respectively.

[0086] It should also be noted that the training optimization is specifically to optimize the industrial assembly process simulation model.

[0087] The Web integrated display terminal is used to display the optimized type of industrial assembly.

[0088] Reference Figure 2As shown, the second aspect of the present invention provides a method for executing the industrial digital twin system described in the present invention, including: ST1, collecting three-dimensional real-scene data of industrial assembly, and monitoring the prior production data of industrial assembly through an online monitoring platform.

[0089] ST2. Obtain the test volume of the industrial assembly, and confirm the interference data of the industrial assembly based on the prior production data and three-dimensional real scene data of the industrial assembly.

[0090] ST3. Based on the interference data of industrial assembly, the industrial assembly process is simulated and industrial assembly simulation data is generated.

[0091] ST4. Generate optimized types of industrial assemblies based on simulation data of industrial assemblies.

[0092] ST5. Display the optimization type of industrial assembly.

[0093] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An industrial digital twin system, characterized in that: include: Industrial assembly process simulation module, used to simulate the industrial assembly process and generate industrial assembly simulation data; The industrial assembly process simulation module includes an industrial assembly real scene acquisition unit, an industrial assembly production data acquisition unit, an industrial assembly interference data confirmation unit, and an industrial assembly simulation unit; The industrial assembly real scene acquisition unit is used to acquire three-dimensional real scene data of industrial assembly; The industrial assembly production data acquisition unit is used to monitor the priori production data of the industrial assembly through the online monitoring platform; The industrial assembly interference data confirmation unit is used to obtain the test volume of the industrial assembly and confirm the interference data of the industrial assembly based on the prior production data and three-dimensional real scene data of the industrial assembly; The industrial assembly simulation unit is used to simulate the industrial assembly process based on the interference data of the industrial assembly and generate industrial assembly simulation data; An industrial assembly process evaluation module is used to generate an optimized type of industrial assembly based on the simulation data of industrial assembly; Web integrated display terminal, used to display the optimized type of industrial assembly.

2. An industrial digital twin system according to claim 1, characterized in that: The prior production data includes: an assembly data set, an environment data set, and an execution data set; The assembly data set includes assembly dynamic images of all parts; The environmental data set includes an assembly environmental data set of all parts; The execution data set includes an end-effector execution data set of all components.

3. An industrial digital twin system according to claim 2, characterized in that: The specific confirmation method of the interference data of industrial assembly is as follows: Extract assembly data sets from prior production data of industrial assembly, obtain assembly dynamic images of all parts, and identify interference types and interference risk characteristic parameters of several parts through motion recognition technology. The interference types include part interference, end effector interference and other interferences. The interference risk characteristic parameter is a value of 0-1, which reflects the degree of interference of parts. The greater the interference degree, the greater the interference risk characteristic parameter, thereby determining the component interference group label; Extract the assembly environment data set of all parts from the prior production data of industrial assembly, and determine the component interference environment group label by combining it with the component interference group label; Extract the end-effector execution data set of all parts from the prior production data of industrial assembly, and determine the part interference execution group label by combining the part interference group label; The component interference group labels, component interference environment group labels, and component interference execution group labels are summarized to obtain the interference data of industrial assembly.

4. An industrial digital twin system according to claim 3, characterized in that: The method for determining the component interference group label is as follows: Based on the interference types and interference risk characteristic parameters of several parts, several parts corresponding to each interference type and their corresponding interference risk characteristic parameters are mapped. ,in is the number of each interference type, ; like , then the component of this interference type is recorded as a first-level risk component. is the first-level risk convergence value in the web data warehouse; like , then the component of this interference type is recorded as a secondary risk component. is the secondary risk convergence value in the web data warehouse; like , then the component of this interference type is recorded as a level 3 risk component; Summarize several first-level risk components whose interference type is component interference to generate a first-level sub-label of the component first category interference group label; summarize several second-level risk components whose interference type is component interference to generate a second-level sub-label of the component first category interference group label; summarize several third-level risk components whose interference type is component interference to generate a third-level sub-label of the component first category interference group label; and so on, generate the first-level, second-level, and third-level sub-labels of the component second category interference group label and the first-level, second-level, and third-level sub-labels of the component third category interference group label.

5. An industrial digital twin system according to claim 4, characterized in that: The method for determining the component interference environment group label is as follows: Based on the assembly environment data set of all parts and components, combined with the component interference group label, the assembly environment data set of each component of the first-level sub-label of the component type interference group label is extracted, and the environment judgment value of each component is obtained through data processing. The assembly environment data set includes characteristic parameters of several environmental data, and the environment judgment value includes values ​​of -1 and 1. When the environment judgment value is -1, it indicates that the assembly environment of the component is not suitable. Therefore, the assembly environment data set of the component can be used as an example. When the environment judgment value is 1, it indicates that the assembly environment of the component is suitable. If the environmental judgment value of a component is -1, the assembly environment data of the component is aggregated as a sample environment data set, and several sample environment data sets are aggregated to obtain the component interference environment group label.

6. An industrial digital twin system according to claim 4, characterized in that: The determination method of the component interference execution group label is as follows: Based on the end-effector execution data set of all parts and components, combined with the component interference group label, the end-effector execution data set of each component of the first-level sub-label of the component first-class interference group label is extracted, and the execution judgment value of each component is obtained through data processing. The end-effector execution data set includes characteristic parameters of several execution data, and the execution judgment value includes values ​​of -1 and 1. When the execution judgment value is -1, it indicates that the execution of the component is not suitable. Therefore, the end-effector execution data set of the component can be used as an example. When the execution judgment value is 1, it indicates that the execution of the component is suitable. If the execution judgment value of a component is -1, the end-effector execution data set of the component is collected as a sample execution data set, and several sample execution data sets are obtained through aggregation. The component interference execution group label is obtained through aggregation.

7. An industrial digital twin system according to claim 1, characterized in that: The specific method for simulating the industrial assembly process is as follows: Based on the test volume of industrial assembly, the number of components of the first-level, second-level, and third-level sub-labels of various interference group labels of components is counted from the component interference group labels, and the number is divided by the total number of components, and then multiplied by the test volume of industrial assembly to obtain the training times of the first-level, second-level, and third-level sub-labels of various interference group labels of components; Based on the training times of the first-level, second-level and third-level sub-labels of various interference group labels of parts, a corresponding number of assembly environment data sets and end-effector execution data sets are randomly extracted from the corresponding environmental labels and execution labels, and two by two are used as the training data of the first-level, second-level and third-level sub-labels of various interference group labels of parts. Based on the three-dimensional real-life data of industrial assembly, a three-dimensional model of industrial assembly is constructed, and the industrial assembly process is simulated by combining all parts of the first-level, second-level and third-level sub-labels of various interference group labels of parts.

8. An industrial digital twin system according to claim 7, characterized in that: The simulation data includes the interference types of each training of the primary, secondary and tertiary sub-labels of each type of interference group label.

9. An industrial digital twin system according to claim 8, characterized in that: The specific generation method of the optimization type of generating industrial assembly is as follows: Based on various interference group labels, determine the reference type of each interference group label, compare the interference type of each training of the first, second, and third level sub-labels of each interference group label with the reference type, and determine the accurate value of each training after comparison , the exact value includes values ​​of -1, 0 and 1. When the exact value is 1, it indicates that the interference type is the same as the reference type. When the exact value is -1, it indicates that there is no interference type. When the exact value is 0, it indicates that the interference type is different from the reference type. is the number of each type of intervention group label, , is the number of each level of sub-label, , is the number of each training session, , is an integer greater than 2; The interference relationship is obtained through numerical processing to determine the accuracy ,in , are the weight influence factor of the h-th level sub-label in the web data warehouse and the accurate threshold of the m-th type of interference group label; The interference relationship determination accuracy is compared with the interference relationship determination accuracy interval corresponding to each optimization type in the web data warehouse, and the optimization type of industrial assembly is screened, which is environment + end-effector optimization, training optimization and production optimization.

10. A method for executing the industrial digital twin system according to any one of claims 1 to 9, characterized in that: include: ST1. Collect 3D real-life data of industrial assembly and monitor the prior production data of industrial assembly through the online monitoring platform; ST2. Obtain the test volume of the industrial assembly, and confirm the interference data of the industrial assembly based on the prior production data and three-dimensional real scene data of the industrial assembly; ST3. Based on the interference data of industrial assembly, simulate the industrial assembly process and generate industrial assembly simulation data; ST4. Generate optimized types of industrial assemblies based on simulation data of industrial assemblies; ST5. Display the optimization type of industrial assembly.

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