CN113361139A - Production line simulation rolling optimization system and method based on digital twin - Google Patents
Production line simulation rolling optimization system and method based on digital twin Download PDFInfo
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Abstract
The invention relates to the technical field of production line simulation, in particular to a production line simulation rolling optimization system and a method based on digital twin, wherein a basic module of the optimization system is provided with a discrete event simulation modeling engine module, a data integration interface module and a process data modeling engine module, a core module of the optimization system is provided with a twin model rolling evolution module, a production line simulation operation visual module and a key performance simulation output module, and the precision of a simulation model is effectively improved by acquiring and inputting real-time data as a basis. The production line scheme is evaluated and analyzed more efficiently and reliably, and workshop production optimization is guided.
Description
Technical Field
The invention relates to the technical field of production line simulation, in particular to a production line simulation rolling optimization system and method based on digital twin.
Background
In a workshop planning stage, compared with a traditional planning method relying on artificial experience and mathematical modeling, the computer simulation planning method comprehensively considers the coupling of a complex dynamic system and the influence of random factors, can describe a physical workshop more intuitively and accurately, and quantitatively evaluates and optimizes a resource allocation scheme, a production logistics strategy and the like through off-line simulation analysis so as to scientifically assist the planning and construction of a factory.
Most of the cognition of the domestic industry to system simulation stays in evaluation and visualization of scheme design stages such as layout and the like, the application is less in a workshop production execution stage, the main reason is that workshop abnormal uncertainty events are various, the dynamic behavior of equipment is greatly influenced by a processing space and a time-varying working condition, the equipment performance, the process quality and the like have time-varying property in the process of product introduction and equipment operation, meanwhile, the simulation needs a large amount of accurate input data as a basis, and the precision of a simulation model cannot be guaranteed in real time.
The digital twin promotes the simulation to the level of highly integrated interaction of a digital model and a physical entity, integrates the characteristics of data fusion, iterative optimization, interactive feedback and the like, takes the simulation model as the basis, the on-line sensing data of the processing state as the drive, and realizes the precision evolution of the digital twin model through the rolling optimization and continuous promotion mechanism of the digital twin model so as to accurately reflect the operation rules of production, logistics and storage systems of a physical workshop in real time. On the premise of a highly digital intelligent workshop, the key technology of the digital twin workshop has great application potential and value in the workshop production execution stage, becomes an important means for improving quality, increasing efficiency and performing digital transformation of enterprises, and related theories and applications become research hotspots at home and abroad.
Disclosure of Invention
In order to solve the problems, the invention provides a production line simulation rolling optimization system and method based on digital twin, and aims to solve the simulation modeling and optimization analysis of the production and logistics processes in a manufacturing workshop and realize the production line simulation with higher precision.
The technical scheme adopted by the invention is as follows:
the utility model provides a production line emulation roll optimization system based on digit twin, includes basic module and core module, the basic module has discrete event simulation modeling engine module, data integration interface module and technological data modeling engine module, the core module has twin model roll evolution module, produces line simulation operation visual module and key performance simulation output module.
Further, the discrete event simulation modeling engine module is used for object modeling of a production line and modeling of production logistics logic; the data integration interface module is connected with a workshop manufacturing execution system and an equipment state monitoring management system, and is integrated and interacted with the workshop manufacturing execution system and the equipment state monitoring management system to acquire product history and real-time production data; the process data modeling engine module is used for reading the data of the integrated interface module and automatically constructing an equipment process data model; the twin model rolling evolution module extracts and iterates the characteristic value of the digital twin production line model in real time through periodic simulation error judgment, product model change and equipment abnormal event triggering; the production line simulation operation visual module defines simulation input and output data and a test scene according to an actual simulation task and visualizes a simulation operation process; and the key performance simulation output module is used for outputting and visualizing the productivity, the temporary storage area queue and the equipment utilization rate data in the simulation process and guiding the optimization of the production line scheme.
Further, an equipment process data model is automatically constructed, and the equipment process data model comprises a process beat analysis model, an equipment fault analysis model and a production quality analysis model.
The production line simulation rolling optimization method based on the digital twin by adopting the optimization system comprises the following steps:
step 3, judging whether a decision condition is met or not to trigger iteration of the digital twin production line model: when a product model change and an equipment abnormal event occur, a model evolution mechanism is directly triggered, a production line simulation model is periodically operated, the deviation of simulation data and actual data of capacity and equipment utilization rate is calculated, and when the deviation is more than 5%, the model evolution mechanism is triggered;
and step 5, defining a simulation test and input and output data according to the simulation task, and operating a digital twin model to realize simulation output and prediction of key performance of the system.
Further, in step 3, the following specific steps are adopted,
step 301, starting a timer thread, circularly extracting production line feeding, capacity and equipment comprehensive efficiency data from a database according to a certain time interval, driving a simulation model to run by a production line feeding record, and counting the ratio of the simulation capacity to the equipment working time;
step 302, calculating the deviation of the actual capacity and the equipment utilization rate acquired in the step 2, and when the deviation exceeds a set threshold value by 5%, determining that the current production line model is no longer credible, triggering a model rolling evolution mechanism, and reestablishing a process equipment data model;
step 303, monitoring product model changing and equipment abnormal events in real time, sending a real-time event instruction to the digital twin platform by the physical production line, analyzing the event instruction by the platform, triggering a model rolling evolution mechanism, and calculating and updating key characteristic values of the process equipment data model.
Further, the rolling evolution of the digital twin model is realized in the step 4, and the specific steps are as follows:
step 401, extracting the work reporting, fault maintenance and quality detection records of each process equipment in the latest period of time through a data interface;
step 402, according to the work reporting record of the process equipment, after extracting and calculating an updated characteristic value, establishing a process beat analysis model, initializing the process beat to obey normal distribution, and calculating the updated characteristic value including an average value, a standard deviation, a maximum value and a minimum value;
step 403, according to the procedure fault maintenance record, after extracting and calculating an updated characteristic value, establishing an equipment fault analysis model, initializing the continuous available time of the equipment to obey the Ellang distribution, calculating the updated characteristic value as the average fault interval time and the average fault maintenance time of the equipment, wherein the equipment maintenance time obeys the negative exponential distribution;
step 404, extracting and calculating an updated characteristic value according to the process quality detection record, establishing a production quality analysis model, initializing quality adverse events to obey binomial distribution, and calculating the updated characteristic value as the product process goodness;
and 405, in the simulation operation process, generating corresponding events through a random number generator based on an inverse transformation method according to the real-time process data model, triggering production, fault and product defect treatment flows of all procedures built in advance by the digital twin production line, and realizing the rolling evolution of the model.
The invention has the following beneficial effects:
1. the optimization system comprises a basic module and a core module, wherein the basic module is provided with a discrete event simulation modeling engine module, a data integration interface module and a process data modeling engine module, and the core module is provided with a twin model rolling evolution module, a production line simulation operation visual module and a key performance simulation output module;
2. the optimization method provided by the invention can be used for visually and accurately describing the dynamic property and randomness of the production line, carrying out digital mapping on real-time data, constructing a digital twin model rolling evolution mechanism, continuously reconstructing a process equipment data model, correcting the simulation model error of the production line and overcoming the problems of poor real-time property, flexibility and precision of the traditional digital simulation model by constructing the digital twin model, is suitable for the production line with higher digital level and has reference significance for operation optimization of similar workshops.
Drawings
Fig. 1 is a schematic diagram of a simulation model of a cell production line according to an embodiment of the present invention;
FIG. 2 is a graph illustrating a difference between production capacity and simulated production capacity according to an embodiment of the present invention;
FIG. 3 is a modeling diagram of winder fault data in an embodiment of the present invention;
FIG. 4 is a graph illustrating the relationship between the whole line throughput and the inter-process WIP capacity according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The production line simulation rolling optimization system based on the digital twin comprises three basic modules, namely a discrete event simulation modeling engine, a data integration interface and a process data modeling engine, and three core modules are used for outputting a twin model rolling evolution, a production line simulation operation visualization and a key performance simulation; the discrete event simulation modeling engine is used for object modeling of a production line and production logistics logic modeling; the data integration interface supports integration interaction with an MES system, an equipment state monitoring system and the like, and acquires product history and real-time production data; the process data modeling engine reads the integrated interface data and automatically constructs an equipment process data model which comprises a process beat analysis model, an equipment fault analysis model and a production quality analysis model; the twin model rolling evolution module extracts and iterates key characteristic values of the model in real time through periodic simulation error judgment and event triggering such as product model changing and equipment abnormity; the production line simulation runs a visual module, simulation input and output data and a test scene are defined according to an actual simulation task, and a simulation running process is visualized; the key performance simulation output module realizes the output and visualization of key performances of simulation process productivity, temporary storage area queues, equipment utilization and the like, and guides the optimization of production line schemes.
The following specific embodiments for optimizing the production line of a certain cell production line
The production line in the embodiment comprises 11 processes from winding to detection, the process devices are connected through an automatic logistics line to realize material transfer, the capacity of the logistics line between the processes is the whole production rate of 3 minutes, the production line is compatible with production of various electric cores, the emptying of all products on the line needs to be completed before the products are switched, and the target production rate of the whole line is 20K/day.
Specifically, the production line simulation optimization is carried out by adopting the following steps:
step 3, judging whether a triggering condition of a model rolling evolution mechanism is met, monitoring product model changing and equipment abnormal event signals in real time, triggering the model evolution mechanism when a corresponding event signal is detected, starting a timer thread, acquiring data such as a nearly one-week feeding record, capacity, equipment utilization rate and the like through a data interface every week, simulating and operating a digital twin model according to a feeding plan, and simulating and outputting the capacity and the equipment utilization rate, wherein when the deviation from actual data is more than 5%, triggering the model evolution mechanism, as shown in fig. 2, the model evolution mechanism is based on a difference curve of the production capacity and the simulated capacity under an offline simulation production line and a digital twin production line, and the prediction deviation of the model evolution mechanism is obviously smaller;
and 4, establishing a real-time equipment process data model, and realizing the rolling evolution of the digital twin production line model. The process beat and the product goodness are directly synchronized with MES data, the embodiment carries out data modeling on equipment fault interval time and equipment maintenance time, fitting is respectively carried out by Ellang distribution and negative exponential distribution in an initialization state, key characteristic values of a data model are directly updated and calculated when an event signal is triggered, when simulation errors are triggered, equipment fault data of nearly one month are taken as a basis, other statistical functions are changed for carrying out data fitting and goodness detection, and a data model with the maximum fitting goodness P value is selected and input into a digital twin model as shown in figure 3;
and 5, performing simulation analysis on whether the target capacity in a real-time state can be met or not on the basis of a digital twin model of rolling evolution, wherein as shown in fig. 4, the relation between the whole-line capacity and the WIP capacity between the working procedures at different times is predicted that the capacity of 20K/day cannot be guaranteed in the state of time 2, the WIP capacity between the working procedures needs to be increased to 3.8min from 3min, and the method can be realized by starting a standby temporary storage area, performing manual intervention and the like.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (6)
1. The production line simulation rolling optimization system based on the digital twin is characterized by comprising a base module and a core module, wherein the base module is provided with a discrete event simulation modeling engine module, a data integration interface module and a process data modeling engine module, and the core module is provided with a twin model rolling evolution module, a production line simulation operation visual module and a key performance simulation output module.
2. The production line simulation rolling optimization system based on the digital twin according to claim 1, wherein the discrete event simulation modeling engine module is used for object modeling of a production line and modeling of production logistics logic; the data integration interface module is connected with the workshop manufacturing execution system and the equipment state monitoring management system, and is integrated and interacted with the workshop manufacturing execution system and the equipment state monitoring management system to acquire product history and real-time production data; the process data modeling engine module is used for reading the data of the integrated interface module and automatically constructing an equipment process data model; the twin model rolling evolution module extracts and iterates the characteristic value of the digital twin production line model in real time through periodic simulation error judgment, product model change and equipment abnormal event triggering; the production line simulation operation visual module defines simulation input and output data and a test scene according to an actual simulation task and visualizes a simulation operation process; and the key performance simulation output module is used for outputting and visualizing the productivity, the temporary storage area queue and the equipment utilization rate data in the simulation process and guiding the optimization of the production line scheme.
3. The production line simulation rolling optimization system based on the digital twin according to claim 1, wherein an equipment process data model is automatically constructed, and comprises a process beat analysis model, an equipment fault analysis model and a production quality analysis model.
4. A production line simulation rolling optimization method based on digital twin adopting the optimization system of any one of the claims 1 to 3, characterized by comprising the following steps:
step 1, establishing a production line simulation model based on a discrete event simulation modeling engine: according to the layout of a production line, the technological process of a product and the rated information of working hours, carrying out simulation object modeling and initialization on equipment, processes, working procedure temporary storage areas, materials, containers and logistics of the production line, defining the rule logic of the production logistics and constructing a simulation model of the production line;
step 2, establishing a digital twin production line model through a data integration interface: the method comprises the steps of obtaining production data and real-time event signals of a production line which operates in the latest period of time through integrated interaction with a workshop manufacturing execution system and an equipment monitoring management system, realizing digital mapping of the production data, and constructing a digital twin production line model, wherein the production data comprises a feeding process, equipment faults, production reporting, quality detection and corresponding capacity and equipment utilization rate;
step 3, judging whether a decision condition is met or not to trigger iteration of the digital twin production line model: when a product model change and an equipment abnormal event occur, a model evolution mechanism is directly triggered, a production line simulation model is periodically operated, the deviation of simulation data and actual data of capacity and equipment utilization rate is calculated, and when the deviation is more than 5%, the model evolution mechanism is triggered;
step 4, establishing a real-time equipment process data model, and realizing the rolling evolution of the digital twin production line model: when the triggering condition in the step 3 is met, reading the data of the integrated interface module, automatically updating and creating an equipment process data model, extracting key characteristic values of the data model, and importing the key characteristic values into the digital twin production line model to realize the rolling evolution of the digital twin production line model;
and step 5, defining a simulation test and input and output data according to the simulation task, and operating a digital twin model to realize simulation output and prediction of key performance of the system.
5. The production line simulation rolling optimization method based on the digital twin as claimed in claim 4, wherein in step 3, the following specific steps are adopted,
step 301, starting a timer thread, circularly extracting production line feeding, capacity and equipment comprehensive efficiency data from a database according to a certain time interval, driving a simulation model to run by a production line feeding record, and counting the ratio of the simulation capacity to the equipment working time;
step 302, calculating the deviation of the actual capacity and the equipment utilization rate acquired in the step 2, and when the deviation exceeds a set threshold value by 5%, determining that the current production line model is no longer credible, triggering a model rolling evolution mechanism, and reestablishing a process equipment data model;
step 303, monitoring product model changing and equipment abnormal events in real time, sending a real-time event instruction to the digital twin platform by the physical production line, analyzing the event instruction by the platform, triggering a model rolling evolution mechanism, and calculating and updating key characteristic values of the process equipment data model.
6. The production line simulation rolling optimization method based on the digital twin as claimed in claim 4, wherein the digital twin model rolling evolution is realized in step 4, and the specific steps are as follows:
step 401, extracting the work reporting, fault maintenance and quality detection records of each process equipment in the latest period of time through a data interface;
step 402, according to the work reporting record of the process equipment, after extracting and calculating an updated characteristic value, establishing a process beat analysis model, initializing the process beat to obey normal distribution, and calculating the updated characteristic value including an average value, a standard deviation, a maximum value and a minimum value;
step 403, according to the procedure fault maintenance record, after extracting and calculating an updated characteristic value, establishing an equipment fault analysis model, initializing the continuous available time of the equipment to obey the Ellang distribution, calculating the updated characteristic value as the average fault interval time and the average fault maintenance time of the equipment, wherein the equipment maintenance time obeys the negative exponential distribution;
step 404, extracting and calculating an updated characteristic value according to the process quality detection record, establishing a production quality analysis model, initializing quality adverse events to obey binomial distribution, and calculating the updated characteristic value as the product process goodness;
and 405, in the simulation operation process, generating corresponding events through a random number generator based on an inverse transformation method according to the real-time process data model, triggering production, fault and product defect treatment flows of all procedures built in advance by the digital twin production line, and realizing the rolling evolution of the model.
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