Digital twinning-oriented discrete manufacturing workshop online simulation method and system

Abstract

The invention discloses a digital twinning-oriented discrete manufacturing workshop online simulation method and system. The system comprises a data connection module, an online simulation operation module and an online simulation visualization module, the data connection module is used for data exchange between the digital twin system and the physical workshop; the online simulation operation module comprises a manufacturing process heterogeneous graph, an equipment finite-state machine, a simulation clock and a simulation event table, and is used for simulating the production process of a discrete manufacturing workshop; the manufacturing process heterogeneous graph describes a manufacturing process by using graph nodes, and is used for generating a simulation event, driving state change of an equipment finite-state machine and recording event predicted execution time; the equipment finite-state machine is used for describing the working state of equipment and driving nodes of the manufacturing process heterogeneous graph to update; and the online simulation visualization module is used for converting the simulation event into a visual simulation animation. According to the method, a simulation event execution mechanism is optimized, and accurate real-time simulation of the digital twinning system of the discrete manufacturing workshop is realized.

Description

Technical Field

[0001] The present invention belongs to the field of digital twin simulation, and mainly relates to an online simulation method and system for a discrete manufacturing workshop oriented to digital twin. Background Art

[0002] As an important part of modern manufacturing, discrete manufacturing workshops are facing an increasingly complex production environment and growing demands for production efficiency, quality, and flexibility. To address these challenges, digital twin technology has gradually become one of the core technologies for the digital transformation of the manufacturing industry.

[0003] Digital twin technology realizes the virtual mapping of a physical workshop by constructing a digital model of a physical entity. The discrete manufacturing workshop technology of digital twin can map physical devices, workpieces, logistics, and processing processes into the digital space. By obtaining real-time workshop operation data and combining modeling and simulation technologies for dynamic simulation and predictive analysis, it provides strong support for the optimization and decision-making of the production process.

[0004] However, as a highly complex and dynamic system, there are complex dynamic interaction behaviors among the various entities in a discrete manufacturing workshop. The existing technology lacks a systematic framework to uniformly describe the diverse production elements and their dynamic interaction behaviors in a discrete manufacturing workshop. It is difficult to efficiently describe the dynamic interaction behaviors such as process flows, equipment state transitions, and scheduling rules in a discrete manufacturing workshop using existing methods, which affects the subsequent optimization and decision-making processes.

[0005] Therefore, there is an urgent need for a discrete manufacturing workshop simulation method oriented to digital twin to solve the problems in the existing technology, achieve efficient modeling and real-time simulation of a discrete manufacturing workshop, and provide stronger technical support for intelligent manufacturing. Summary of the Invention

[0006] Object of the Invention: Aiming at the problems existing in the above background art, the present invention provides an online simulation method and system for a discrete manufacturing workshop oriented to digital twin, optimizes the simulation event execution mechanism, improves the transparency of the production process execution in the simulation engine, and realizes accurate real-time simulation of the digital twin system of a discrete manufacturing workshop.

[0007] Summary of the Invention: An online simulation system for a discrete manufacturing workshop oriented to digital twin according to the present invention includes a data connection module, an online simulation operation module, and an online simulation visualization module; the data connection module is used for data exchange between the digital twin system and the physical workshop; the online simulation visualization module includes a digital twin three-dimensional scene for converting simulation events into visual simulation animations; the online simulation operation module includes a manufacturing process heterogeneous graph, a device finite state machine, a simulation clock, and a simulation event table for simulating the production process of a discrete manufacturing workshop; the manufacturing process heterogeneous graph uses graph nodes to describe the manufacturing process, generates simulation events, drives the state change of the device finite state machine, and records the expected execution time of events; the device finite state machine is used to describe the working state of the device and drive the update of the nodes of the manufacturing process heterogeneous graph; the simulation event table is used to record the occurrence time of events in the workshop and advance the simulation clock.

[0008] Further, the data connection module includes an OPC UA server, an OPC UA client, and an OPC UA information model; the OPC UA server is deployed in the physical workshop and establishes a data connection with the information system; the OPC UA client is integrated in the digital twin system and obtains real-time workshop data for the simulation operation module to call by subscribing to and listening to the server; the OPC UA information model is used to describe the production data of the physical workshop.

[0009] Further, the online simulation visualization module is specifically described as a three-dimensional scene constructed on the Unity platform according to the actual workshop, where the three-dimensional model is produced by SolidWorks, CATIA, and Blender software and imported; the simulation animation is constructed through the C# language and UnityAnimation; the corresponding simulation animation is triggered by the conversion of the state of the device finite state machine in the simulation event table.

[0010] Further, the manufacturing process heterogeneous graph is specifically described as: MPDG = (N, DAM, CAAM, PBL), where N is the graph node for describing the manufacturing process; DAM is an n×m heterogeneous graph weighted matrix, n is the number of graph nodes, and m is the number of workshop devices. Each element dam ij is used to describe the correspondence between the manufacturing process and the device, and its value is the expected completion time of the node under the corresponding device; CAAM is an n×n conjunctive edge adjacency matrix, n is the number of graph nodes, and each element caam ij is used to describe the sequential dependency relationship between processes; PBL is a process bias list for recording the process progress of each processed product at the current simulation clock time.

[0011] Further, the graph node includes a node number, a product number, the estimated completion time of the previous node, the estimated completion time, and an event type; where the node number is used to query the node; the product number is used to update the process offset list; the estimated completion time of the previous node is used to determine whether the node is a node to be processed; the estimated completion time is used to update the simulation event table; and the event type is used to drive the state change of the device finite state machine.

[0012] Further, the device finite state machine is specifically described as: EFSM = (ID, S, E, f, S r ), where ID is the device number, corresponding to the column number of the heterogeneous graph weighted matrix, and is used to query the device finite state machine; S is the state set, used to store all the states of the device finite state machine; E is the event set, used to store all the input events that can be input into the device finite state machine; f is the state transition function, used to judge the executability of the process and change the state of the device finite state machine. The event type output by the node is used as the input of the state transition function, and the changed state S is output in combination with the current state Sr.

[0013] Further, the simulation event table is used to record the time when events occur in the workshop to advance the simulation clock. Specifically: when the simulation runs, the minimum time greater than the simulation clock time is extracted from it by comparing with the simulation clock and assigned to the simulation clock to advance the update of the simulation. The simulation event list is specifically described as: SEL = (TC, T, N, EFSM), where TC is the simulation clock time, used as the trigger time for the visual demonstration animation when the system parses the simulation event table, and T is the estimated completion time of the current node N, used to advance the simulation clock to the next moment.

[0014] An online simulation method for a discrete manufacturing workshop oriented to digital twin according to the present invention includes the following steps:

[0015] S1. Initialize the heterogeneous graph of the manufacturing process, the device finite state machine, the simulation clock, and the simulation event table according to real-time data;

[0016] S2. Extract the node numbers in the process offset list of the heterogeneous graph of the manufacturing process, calculate the difference between the estimated completion time of the previous node in the node characteristics and the current simulation clock time. If the difference is less than or equal to 0, add the node to the list of nodes to be executed. If the list is not empty, execute step S3; otherwise, end the simulation and execute step S13;

[0017] S3. Select and delete the most prioritized node to be executed from the list of nodes to be executed according to the scheduling rule. The node includes a node number;

[0018] S4. Based on the node number in step S3, read the undirected edge adjacency matrix in the heterogeneous graph of the manufacturing process to obtain the list of device finite state machines that can execute the process of this node;

[0019] S5. Select a device that matches the node to be executed according to the scheduling rules;

[0020] S6. Execute the state transition function of the device finite state machine according to the event type in the node characteristics. If the state can be transferred, execute step S7; otherwise, execute step S11;

[0021] S7. Read the estimated completion time of this process from the undirected edge adjacency matrix in the heterogeneous manufacturing process graph according to the node number and the matching device finite state machine number;

[0022] S8. Delete the node number described in step S3 from the process offset list. Read the node number of the next process of this process from the directed edge adjacency matrix in the heterogeneous manufacturing process graph according to the node number. If there is a node number of the next process, insert the node number into the process offset list and execute step S9; otherwise, execute step S10;

[0023] S9. The sum of the estimated completion time obtained in step S7 and the current simulation clock time is the estimated completion time of the previous node of the node characteristics of the next process obtained in step S8;

[0024] S10. The sum of the estimated completion time obtained in step S7 and the current simulation clock time is the estimated completion time of the current node characteristics and insert this time into the simulation event table;

[0025] S11. Check the list of nodes to be executed. If the list of nodes to be executed is not empty, execute step S3; otherwise, execute step S12;

[0026] S12. Extract and delete the minimum time from the simulation event table, assign this time to the simulation clock, and execute step S2;

[0027] S13. The simulation ends, and the simulation results are output for analysis and optimization. The system parses the simulation event table and converts the simulation events into a visual simulation animation in the order of event occurrence time.

[0028] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention optimizes the simulation event execution mechanism, improves the transparency of the production process execution in the simulation engine, and realizes the accurate real-time simulation of the digital twin system of the discrete manufacturing workshop. Description of the Drawings

[0029] Figure 1 is the block diagram of the online simulation system for the discrete manufacturing workshop oriented to digital twin proposed by the present invention;

[0030] Figure 2 is the heterogeneous manufacturing process graph proposed by the present invention;

[0031] Figure 3 It is the block diagram of the digital twin system for a discrete manufacturing workshop provided by the present invention;

[0032] Figure 4 It is the digital twin system for a discrete manufacturing workshop according to an embodiment of the present invention. Specific embodiments

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] As Figure 1 shown, the present invention provides an online simulation system for a discrete manufacturing workshop oriented to digital twin, including a data connection module, an online simulation operation module, and an online simulation visualization module. The data connection module includes an OPC UA information model for data exchange between the digital twin system and the physical workshop. The simulation visualization module includes a digital twin three-dimensional scene for converting simulation events into visual simulation animations. The simulation operation module includes a manufacturing process heterogeneous graph, a device finite state machine, a simulation clock, and a simulation event table for simulating the production process of a discrete manufacturing workshop. The manufacturing process heterogeneous graph uses graph nodes to describe the manufacturing process, for generating simulation events, driving the state change of the device finite state machine, and recording the expected execution time of events; the device finite state machine is used to describe the working state of the device and drive the update of the nodes of the manufacturing process heterogeneous graph; the simulation event table is used to record the event occurrence time in the workshop and advance the simulation clock.

[0035] As Figure 2 shown, the manufacturing process heterogeneous graph uses graph nodes to describe the manufacturing process, for generating simulation events, driving the state change of the device finite state machine, and recording the expected execution time of events. The manufacturing process heterogeneous graph is specifically described as: MPDG = (N, DAM, CAAM, PBL), where N = {N 1 , N 2 ,..., N i} are graph nodes for describing the manufacturing process, and its characteristics include a graph node number for querying nodes, a processed product number to which the node belongs for updating the process bias list, the expected completion time of the previous node for determining whether the node is a node to be processed, the expected completion time for updating the simulation event table, and the event type to which the node belongs for driving the state change of the device finite state machine; DAM is an nΔm heterogeneous graph weighted matrix, where n is the number of graph nodes and m is the number of workshop devices, and each element dam ij is used to describe the correspondence between the manufacturing process and the device, and its value is the expected completion time of the node under the corresponding device; CAAM is an n×n conjunctive edge adjacency matrix, where n is the number of graph nodes, and each element caam ij is used to describe the sequential dependence relationship between processes; PBL is a process bias list for recording the process progress of each processed product at the current simulation clock time.

[0036] The device finite state machine is used to describe the working state of the device and drive the update of the heterogeneous graph nodes in the manufacturing process. The device finite state machine is specifically described as: EFSM = (ID, S, E, f, S r ), where ID is the device number, corresponding to the column number of the heterogeneous graph weight matrix, and is used to query the device finite state machine; S is the state set, which is used to store all the states of the device finite state machine; E is the event set, which is used to store all the input events that can be input into the device finite state machine; f is the state transition function, which is used to judge the executability of the process and change the state of the device finite state machine. The event type output by the node is used as the input of the state transition function, and the changed state S is output in combination with the current state Sr.

[0037] The simulation event table is used to record the occurrence time of events in the workshop and advance the simulation clock. By inserting the estimated completion time of the current node in the simulation run into the simulation event table, the minimum time is extracted and deleted from it and assigned to the simulation clock to advance the update of the simulation. The system parses the simulation event table and converts the simulation events into visual simulation animations in the order of the event occurrence time. Specifically, the events in the simulation event table are parsed. When the simulation clock of the simulation visualization module reaches the simulation clock of the event in the simulation event table, the workpiece and equipment involved in the event are parsed, and the corresponding visual simulation animations are triggered according to the event type and the transformation of the device finite state machine state

[0038] The present invention also proposes an online simulation method for a discrete manufacturing workshop oriented to digital twins, including the following steps:

[0039] S1. Initialize the heterogeneous graph of the manufacturing process, the device finite state machine, the simulation clock, and the simulation event table according to the real-time data.

[0040] S2. Extract the node numbers in the process offset list in the heterogeneous graph of the manufacturing process, and calculate the difference between the estimated completion time of the previous node and the current simulation clock time in the node characteristics. If the difference is not greater than 0, add the node to the list of nodes to be executed. If the list is not empty, execute step S3; otherwise, the simulation ends and step S13 is executed.

[0041] S3. Select and extract and delete the most prioritized node to be executed from the list of nodes to be executed according to the scheduling rules. The node includes the node number.

[0042] S4. Read the undirected edge adjacency matrix in the heterogeneous graph of the manufacturing process based on the node number in step S3 to obtain the list of device finite state machines that can execute the process of this node.

[0043] S5. Select the device that matches the node to be executed according to the scheduling rules.

[0044] S6. Execute the state transition function of the device finite state machine according to the event type in the node characteristics. If the state can be transitioned, execute step S7; otherwise, execute step S11.

[0045] S7. Read the estimated completion time of this process from the undirected edge adjacency matrix in the heterogeneous manufacturing process graph according to the node number and the matched device finite state machine number.

[0046] S8. Delete this node number from the process offset list. Read the node number of the next process of this process from the directed edge adjacency matrix in the heterogeneous manufacturing process graph according to the node number. If there is a node number of the next process, insert this node number into the process offset list and execute step S9; otherwise, execute step S10.

[0047] S9. The sum of the estimated completion time obtained in step S7 and the current simulation clock time is the estimated completion time of the previous node of the node characteristics of the next process obtained in step S8.

[0048] S10. The sum of the estimated completion time obtained in step S7 and the current simulation clock time is the estimated completion time of the current node characteristics, and insert this time into the simulation event table.

[0049] S11. Check the list of nodes to be executed. If the list of nodes to be executed is not empty, execute step S3; otherwise, execute step S12.

[0050] S12. Extract and delete the minimum time from the simulation event table, assign this time to the simulation clock, and execute step S2.

[0051] S13. The simulation ends, and the simulation results are output for analysis and optimization.

[0052] Finally, through this simulation method, accurate real-time simulation of a discrete manufacturing workshop based on real-time data can be realized, improving the transparency of production process execution in the simulation engine, and providing technical support for the intelligent management and optimization of the discrete manufacturing workshop.

[0053] Such as Figure 3As shown in the figure, the digital twin system of the discrete manufacturing workshop in the embodiment of the present invention constructs a three-dimensional scene on the simulation visualization platform according to the actual workshop. Preferably, the Unity platform is used to develop the online simulation system of the digital twin discrete manufacturing workshop, and the three-dimensional models of workshop objects such as machine tools, AGVs, and materials are produced by software such as SolidWorks, CATIA, and Blender and imported; the OPC UA information model describing the manufacturing resources, IoT devices, and information system data of the physical workshop is constructed through KEPServerEX6; the OPC UA client is constructed by combining the C# language with the OPC UA protocol, and subscription and listening are performed through the node attribute data in the server address space to obtain the workshop data in real time for data exchange between the digital twin system and the physical workshop; the simulation operation module based on the manufacturing process heterogeneous graph and the device finite state machine is written through the C# language; the simulation visualization module is constructed through the C# language and UnityAnimation.

[0054] As Figure 4 shown, the digital twin system of the discrete manufacturing workshop in the embodiment of the present invention initializes the manufacturing process heterogeneous graph, the device finite state machine, the simulation clock, and the simulation event table according to the real-time data during operation, drives the system simulation operation through the online simulation method, parses the simulation event table, and converts the simulation events into visual simulation animations in the order of the event occurrence time.

[0055] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An online simulation system for discrete manufacturing workshops for digital twins, characterized in that: It includes a data connection module, an online simulation operation module and an online simulation visualization module; the data connection module is used for data exchange between the digital twin system and the physical workshop; The online simulation visualization module includes a digital twin three-dimensional scene, which is used to convert simulation events into visual simulation animations; the online simulation operation module includes a manufacturing process heterogeneous graph, a device finite state machine, a simulation clock, and a simulation event table, which are used to simulate the production process of a discrete manufacturing workshop; The manufacturing process heterogeneous graph uses graph nodes to describe the manufacturing process, which is used to generate simulation events, drive the state change of the device finite state machine, and record the expected execution time of the event; The device finite state machine is used to describe the working status of the device and drive the update of the nodes in the heterogeneous graph of the manufacturing process; the simulation event table is used to record the time when events occur in the workshop and advance the simulation clock.

2. According to the digital twin-oriented discrete manufacturing workshop online simulation system of claim 1, it is characterized in that: The data connection module includes an OPC UA server, an OPC UA client and an OPC UA information model; the OPC UA server is deployed in the physical workshop and establishes a data connection with the information system; the OPC UA client is integrated in the digital twin system, and obtains real-time workshop data for the simulation operation module to call by subscribing to and listening to the server; the OPC UA information model is used to describe the production data of the physical workshop.

3. According to the digital twin-oriented discrete manufacturing workshop online simulation system of claim 1, it is characterized in that: The online simulation visualization module is specifically described as a three-dimensional scene constructed on the Unity platform based on the actual workshop, wherein the three-dimensional model is produced and imported by SolidWorks, CATIA and Blender software; the simulation animation is constructed by C# language and UnityAnimation; and the corresponding simulation animation is triggered by the transformation of the device finite state machine state in the simulation event table.

4. The discrete manufacturing workshop online simulation system for digital twinning according to claim 1 is characterized in that: The manufacturing process heterogeneous graph is specifically described as: MPDG = (N, DAM, CAAM, PBL), where N is a graph node used to describe the manufacturing process; DAM is an n×m heterogeneous graph weighted matrix, where n is the number of graph nodes, m is the number of workshop equipment, and each element dam ij It is used to describe the corresponding relationship between manufacturing process and equipment. Its value is the estimated completion time of the node under the corresponding equipment. CAAM is an n×n conjunctive edge adjacency matrix, where n is the number of nodes in the graph and each element of caam ij Used to describe the sequential dependencies between processes; PBL is a process bias list, which is used to record the process progress of each processed product at the current simulation clock time.

5. The discrete manufacturing workshop online simulation system for digital twin according to claim 4 is characterized in that: The graph node includes a node number, a product number, an estimated completion time of a previous node, an estimated completion time and an event type; wherein the node number is used to query the node; the product number is used to update the process offset list; the estimated completion time of the previous node is used to determine whether the node is a node to be processed; and the estimated completion time is used to update the simulation event table; Event types are used to drive device finite state machine state changes.

6. The discrete manufacturing workshop online simulation system for digital twinning according to claim 1 is characterized in that: The device finite state machine is specifically described as: EFSM = (ID, S, E, f, S r ), ID is the device number, corresponding to the column number of the heterogeneous graph weight matrix, which is used to query the device finite state machine; S is the state set, which is used to store all states of the device finite state machine; E is the event set, which is used to store all input events of the device finite state machine; f is the state transfer function, which is used to determine the executability of the process and change the state of the device finite state machine. The event type output by the node is used as the input of the state transfer function, and combined with the current state Sr, the changed state S is output.

7. The discrete manufacturing workshop online simulation system for digital twinning according to claim 1 is characterized in that: The simulation event table is used to record the time of occurrence of events in the workshop and to advance the simulation clock specifically as follows: when the simulation is running, the minimum time greater than the simulation clock time is extracted from it by comparison with the simulation clock and assigned to the simulation clock to advance the update of the simulation. The simulation event list is specifically described as: SEL = (TC, T, N, EFSM), TC is the simulation clock time, which serves as the visual demonstration animation trigger time when the system parses the simulation event table, and T is the estimated completion time of the current node N, which is used to advance the simulation clock to the next moment.

8. An online simulation method for a discrete manufacturing workshop oriented to digital twins using the system as claimed in any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Initialize the manufacturing process heterogeneous graph, equipment finite state machine, simulation clock and simulation event table according to real-time data; S2, extract the node number in the process offset list in the manufacturing process heterogeneous graph, calculate the difference between the estimated completion time of the previous node in the node feature and the current simulation clock time, if the difference is less than or equal to 0, add the node to the list of nodes to be executed, if the list is not empty, execute step S3, otherwise, the simulation ends and execute step S13; S3, extracting and deleting the node with the highest priority for execution from the list of nodes to be executed according to the scheduling rule, wherein the node includes a node number; S4, based on the node number of step S3, read the undirected edge adjacency matrix in the manufacturing process heterogeneous graph to obtain a list of device finite state machines that can execute the node process; S5. Select a device that matches the node to be executed according to the scheduling rules; S6, executing the state transfer function of the device finite state machine according to the event type in the node feature, if the state can be transferred, executing step S7, otherwise executing step S11; S7, reading the estimated completion time of the process from the undirected edge adjacency matrix in the manufacturing process heterogeneous graph according to the node number and the matching equipment finite state machine number; S8, deleting the node number described in step S3 from the process offset list, and reading the node number of the next process of the process from the directed edge adjacency matrix in the manufacturing process heterogeneous graph according to the node number, and inserting the node number of the next process into the process offset list if there is one and executing step S9, otherwise executing step S10; S9, the estimated completion time obtained in step S7 plus the current simulation clock time is the estimated completion time of the preceding node of the node feature of the next process obtained in step S8; S10: The estimated completion time obtained in step S7 is added to the current simulation clock time to obtain the estimated completion time of the current node feature, and the time is inserted into the simulation event table; S11, check the list of nodes to be executed, if the list of nodes to be executed is not empty, execute step S3, otherwise execute step S12; S12, extract and delete the minimum time from the simulation event table, assign the time to the simulation clock, and execute step S2; S13, the simulation ends, and the simulation results are output for analysis and optimization. The system parses the simulation event table and converts the simulation events into visual simulation animations in the order of event occurrence.

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