CN115373290A - Digital twin-based digital workshop simulation method and system - Google Patents
Digital twin-based digital workshop simulation method and system Download PDFInfo
- Publication number
- CN115373290A CN115373290A CN202211323116.XA CN202211323116A CN115373290A CN 115373290 A CN115373290 A CN 115373290A CN 202211323116 A CN202211323116 A CN 202211323116A CN 115373290 A CN115373290 A CN 115373290A
- Authority
- CN
- China
- Prior art keywords
- digital
- time
- workshop
- actual
- state
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a digital twin-based digital workshop simulation method and system, and belongs to the field of manufacturing system automation. Establishing a synchronous staggered data transmission channel of a virtual system and an actual system, constructing a digital twin model, establishing a virtual-real association relation between digital workshop equipment and the digital twin model, performing real-time analog modulation on the running state of a digital workshop in the digital twin model, and calculating the progress delay in the actual production running process; evaluating various parameter configurations by using a self-adaptive evolution method to realize the construction of a parallel system model; and converting the optimal output value output by the parallel system model, sending the optimal output value to digital workshop equipment in a production instruction form through an interleaved data transmission channel, and executing corresponding actions by the digital workshop equipment according to a given instruction.
Description
Technical Field
The invention belongs to the field of automation of manufacturing systems, and particularly relates to a digital twin-based digital workshop simulation method and system.
Background
Compared with the requirement of intelligent manufacturing, the traditional workshop management system has the following problems that the simulation analysis and autonomous decision making capability of the system is relatively weak in the existing information system architecture; the combination of the workshop information model and the visual model is insufficient, so that the visual degree of the production and manufacturing process is low, and various related state information and production information changes in the workshop production scheduling process cannot be transmitted to an operator in time; meanwhile, under the requirement of intelligent manufacturing, the automation level and the resource complexity of a workshop are continuously improved, and a workshop production scheduling algorithm is more intelligent and has stronger anti-interference capability and self-adjustment capability. However, due to the relative insufficiency of the simulation capability and the visualization degree, the capability of coping with unsteady state abnormality and the decision-making capability of the system in the production scheduling process are low, and a user cannot intuitively acquire various hot spot information in the scheduling process and can only obtain a simple scheduling result. With the continuous development of intelligent manufacturing, the problems can prevent the traditional workshop from transforming into an intelligent workshop.
A digital twin is a virtual association of an actual product or production flow for understanding and predicting the characteristics of the corresponding entity or flow. The method is characterized in that a three-dimensional model is constructed firstly, the model corresponds to production data, in the execution stage of production, hot spot information such as running states of raw materials and equipment can be subjected to unsteady state adjustment, the digital twin body needs to update the changes in a virtual space in real time, the twin body needs to be rapidly updated by combining with unsteady state data of a sensor, and the running abnormity of actual equipment can be monitored and predicted, so that guidance is provided for production decision. Secondly, on the basis of a digital model, the twin body can carry out real-time analog modulation simulation according to real-time processing information and historical information of equipment, and timely correction or production verification is carried out on abnormal results in the production process through simulation results.
In the prior art, for example, patent document CN114967494a discloses a cloud digital twin workshop simulation system based on modular development, which includes an equipment layer, an edge layer, a system layer and an application layer, where the equipment layer includes various production devices in an actual workshop, the edge layer includes a data acquisition module and an equipment control module, and the main functions are to acquire equipment data and receive and send control instructions. The system layer comprises a data processing module, a data storage module, a data analysis module, a data communication module, a logic function module and an event processing module. The application layer comprises an information display module, a digital twin simulation module and a 3D visual interaction module, can receive data information of equipment to realize large-screen display of data, can also perform 3D simulation, and sends a control instruction. However, the technical scheme has a single simulation scene, lacks flexibility and cannot quickly respond to new requirements; the software system occupies more resources, is not light enough and is not easy to expand.
For another example, patent document CN113887016a discloses a digital twin-based ship digital workshop simulation method and system, which includes a digital twin workshop information modeling technology, a workshop information system integration technology, a manufacturing process simulation optimization method, an offline virtual combined design simulation technology, and a digital twin novel operation flow and architecture. The invention provides a digital twin-based ship digital workshop simulation method, which is built according to a novel digital twin operation flow and a novel digital twin operation architecture, data acquisition and modeling are carried out on a ship manufacturing workshop through the digital workshop simulation method, a digital twin simulation system of the ship manufacturing workshop is built, a workshop information integration system is used as an instruction operation medium, production line data are sent to a simulation system, meanwhile, a production plan of a real factory building is scheduled, real production is controlled in real time, the configuration and operation process of the whole production line are optimized, and the production line can be continuously optimized according to a simulation result. However, the technical scheme is not easy to modify, long trial run time is needed to achieve stable production, and the adaptability of a digital workshop is severely limited.
Disclosure of Invention
In order to solve the technical problem, the invention provides a digital twin-based digital workshop simulation method, which comprises the following steps of:
s1, establishing a synchronous staggered data transmission channel of a virtual system and an actual system, and sending a digital twin model to digital workshop equipment through the channel according to a calculation result to realize the synchronization of the virtual system and the actual system;
s2, constructing a digital twin model of the digital workshop equipment in an information space, establishing a virtual-real incidence relation between the digital workshop equipment and the digital twin model, performing real-time analog modulation on the running state of the digital workshop in the digital twin model, and calculating the progress delay in the actual production running process;
s3, based on the virtual scene, evaluating various parameter configurations by using a self-adaptive evolution method to realize the construction of a parallel system model;
and S4, converting the optimal output value output by the parallel system model, sending the optimal output value to digital workshop equipment through a staggered data transmission channel in a production instruction mode, and executing corresponding actions by the digital workshop equipment according to a given instruction.
Further, in the step S3, the actual system S is used R Expressed as:
wherein, the first and the second end of the pipe are connected with each other,
is a real system S R In the state at the time t, the state,
is a real system S R In the state at the time t-1,
is applied to the actual system S at the moment t-1 R The amount of control of (2) is,
is a rule for the migration of the state of the actual system,
indicating that the actual system state is in a rule
The mixture is moved under the action of the water,
is the semantic logical rule "and",
is expressed in observation rules
Lower actual system S R The observed value of the presented one or more of the images,
indicating the actual system S at time t R The observed value of (a);
formula (1) is described in numerical form:
wherein, the first and the second end of the pipe are connected with each other,
in order to be an actual system state transition equation,
is an actual system observation equation.
Further, the control quantity of the virtual system and the actual system is jointly solved
To make the virtual system to the virtual system target from T to T + T
Convergence by minimizing differences in appearance of virtual and real systems
And converging the actual system to a preset virtual system state, namely:
Further, the step S2 specifically includes:
step 21: calculating digital workshop appliance T i Is performed at a rate S U ：
S U ＝min{S T ,S H }；
Wherein S T Is a digital workshop appliance T i Minimum limiting rate in the logistic cycle, S H Is a digital workshop appliance T i A minimum limiting rate within a sufficient stock of material flow;
step 22: will digitalize workshop appliance T i Is performed at a rate S U Sorting is carried out, and the execution task with the maximum execution rate is selected as a focusing task; when the maximum execution time of the tasks is the same, starting the task with early time as a focusing task; when the maximum execution time of the task is the same as the starting time of the task, taking the task with the small subscript as a focusing task;
step 23: calculating digital workshop appliance T i And assigning it to the focus task;
step 24: calculating digital workshop appliance T i And allocating the unsteady idle time to the task with the highest priority of the ready queue according to the unsteady idle time generated online.
Further, S T Calculated from the following formula:
wherein S is RT (i) Is the minimum rate in the set of different logistic cycles, i is an integer; s RT (i) Calculated from the following formula:
wherein e is i Is a digital workshop appliance T i Execution time in worst case, P j Is the period of the material flow, B (T) i ) Is a digital workshop appliance T i The maximum blocking time of the logistics cycle, L is the length of the logistics line body;
S H calculated from the following formula:
wherein n is a digital plant equipment T i Total number of (1), u i Is a digital workshop appliance T i F (n) is the upper utilization bound for n digital plant installations.
Further, in step 23, the steady-state idle time Δ T during the actual production operation is calculated according to the following formula:
in the formula: f j And S j Respectively representing digital plant equipment T i The start time and the end time of the jth process flow in the plan; m represents the total number of the processing technological processes planned to be finished by the workpiece at the current time t; t is M Plan time, FA, representing the total number M of all process flows completed j And SA j Respectively represent digital workshop equipment T acquired by RFID (radio frequency identification) reading device of equipment of internet of things i Actual start time and actual end time of the jth process flow of (1); n represents the total number of the machining process flows actually finished by the workpiece at the current time t; t is N Representing the actual time to complete the total number N of actual process flows.
Further, in the step 24, when the CPU of the digital workshop appliance is in an idle state, if the unsteady idle time at this time is greater than the CPU state switching energy consumption of the digital workshop appliance, the CPU of the digital workshop appliance is switched to the sleep mode until the next task event is started; and if the unsteady state idle time is less than the CPU state switching energy consumption of the digital workshop equipment, the CPU of the digital workshop equipment still keeps an idle state.
The invention also provides a digital twin-based digital workshop simulation system, which is used for realizing the digital workshop simulation method and comprises a physical entity layer, a data protection layer, a digital twin layer, a system development layer and a system application layer;
the physical entity layer is a set of digital workshop equipment which actually exists in the manufacturing type digital workshop;
the data protection layer is used for establishing a staggered data transmission channel for synchronizing the virtual system and the actual system, and the digital twin model is sent to digital workshop equipment through the channel according to a calculation result to realize synchronization of the virtual system and the actual system;
the digital twin layer is used for constructing a digital twin model of the digital workshop equipment in an information space, establishing a virtual-real incidence relation between the digital workshop equipment and the digital twin model, performing real-time analog modulation on the running state of the digital workshop in the digital twin model, and calculating the progress delay in the actual production running process;
the system development layer is used for connecting a system application layer and a physical entity layer, a data protection layer and a digital twin layer;
the system application layer is used for evaluating various parameter configurations by using a self-adaptive evolution method, realizing the construction of a parallel system model, and realizing the target optimization of an actual system through the cooperative evolution, closed-loop feedback and staggered guidance of the actual system and a virtual system.
Compared with the prior art, the invention has the following beneficial technical effects: by establishing a synchronous staggered data transmission channel of the virtual system and the actual system, the digital twin model is sent to the digital workshop appliance through the channel according to the calculation result, so that the virtual system and the actual system are synchronous; establishing a virtual-real incidence relation between digital workshop equipment and a digital twin model, and performing real-time analog modulation on the running state of the digital workshop in the digital twin model to provide corresponding modular functional support for the dispatching management requirement of the actual digital workshop; the intelligent management and control of workshop equipment are realized by using a sensing technology and a data transmission and storage technology, and the working personnel can conveniently acquire the running state information and the real-time production condition of the equipment.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a flow chart of a digital twin based digital shop simulation method of the present invention;
FIG. 2 is a schematic diagram of a digital twin digital workshop simulation system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the drawings of the embodiments of the present invention, in order to better and more clearly describe the operation principle of each element in the system, the connection relationship of each part in the apparatus is shown, only the relative position relationship between each element is clearly distinguished, and the restriction on the signal transmission direction, the connection sequence, and the size, the dimension, and the shape of each part structure within an element or structure is not formed.
Fig. 1 shows a flow chart of a digital twin-based digital workshop simulation method according to the present invention, which includes the following steps:
(1) And establishing a synchronous staggered data transmission channel between the virtual system and the actual system, and sending the digital twin model to digital workshop equipment through the channel according to a calculation result to realize the synchronization of the virtual system and the actual system.
(2) And constructing a digital twin model of the digital workshop equipment in an information space, establishing a virtual-real association relation between the digital workshop equipment and the digital twin model, performing real-time analog modulation on the running state of the digital workshop in the digital twin model, and calculating the progress delay in the actual production running process. Specifically, the method comprises the following steps:
step 1: calculating digital workshop appliance T i Is performed at a rate S U ：
S U ＝min{S T ,S H }；
Wherein S T Is a digital workshop appliance T i Minimum limiting rate in the logistic cycle, S H Is a digital workshop appliance T i A minimum limiting rate within a sufficient stock of material flow; s T Calculated from the following formula:
wherein S is RT (i) Is the minimum rate in the set of different logistic cycles, i is an integer; s RT (i) Calculated from the following formula:
wherein e is i Is a digital workshop appliance T i Execution time in worst case, P j Is the period of the material flow, B (T) i ) Is a digital workshop appliance T i The maximum blocking time of the logistics cycle, L is the logistics line body length.
S H Is a digital workshop appliance T i The minimum limiting rate in the adequate flow reserve, whose value is calculated by the following equation:
wherein n is a digital workshop appliance T i Total number of (1), u i Is a digital workshop appliance T i F (n) is the upper utilization bound in the n pieces of digital plant equipment;
and 2, step: will digitalize workshop appliance T i Is performed at a rate S U Sorting is carried out, and the execution task with the maximum execution rate is selected as a focusing task; when the maximum execution time of the tasks is the same, starting the task with early time as a focusing task; and when the maximum execution time of the task is the same as the starting time of the task, taking the task with the lower index as a focusing task.
And step 3: using double-priority rate strategy to schedule tasks and calculate digital workshop equipment T i And assigning it to the focusing task.
The steady state idle time Δ T during actual production runs is calculated according to the following equation:
in the formula: f j And S j Respectively representing digital plant equipment T i The start time and the end time of the jth process flow in the plan; m represents the total number of the processing technological processes planned to be finished by the workpiece at the current time t; t is M Plan time, FA, representing the total number M of all process flows completed j And SA j Respectively representing digital workshop equipment T acquired by RFID (radio frequency identification) reading device of equipment of Internet of things i Actual start time and actual end time of the jth process flow of (1); n represents the total number of the machining process flows actually finished by the workpiece at the current time t; t is N Representing the actual time to complete the total number N of actual process flows.
And 4, step 4: calculating digital workshop appliance T i And allocating the unsteady idle time to the task with the highest priority of the ready queue according to the unsteady idle time generated online.
When the CPU of the digital workshop equipment is in an idle state, if the unsteady-state idle time is longer than the CPU state switching energy consumption of the digital workshop equipment, switching the CPU of the digital workshop equipment to a sleep mode until the next task event is started; and if the unsteady state idle time is less than the CPU state switching energy consumption of the digital workshop equipment, the CPU of the digital workshop equipment still keeps an idle state. .
And 5: according to the digital workshop appliance T i The available idle time condition determines the final execution rate Si, and the unsteady state power consumption technology determines the state of the CPU of the digital workshop appliance.
In another embodiment, the perception data in the digital twin model driven remote automatic control of the digital plant equipment may also be equipment status data and environmental data.
A machine body position and posture measuring system consisting of a parallel laser direction indicator and a rear camera, an equipment head position and posture measuring system consisting of an infrared LED target and a front camera, an ultrasonic sensor and the like are adopted to obtain state data of digital workshop equipment in real time; and a gas sensor, a dust sensor and the like are adopted to acquire the environmental data of the digital workshop on line.
And (3) building a visual auxiliary system on a terminal computer by using the sensing data to realize real-time online monitoring of the working process of the equipment. Meanwhile, sensing data are sent to a remote control end through a terminal computer, and synchronous action of virtual equipment is achieved according to the driving digital twin model. The remote control end can realize virtual remote control and video monitoring functions.
(3) Based on a virtual scene, various parameter configurations are evaluated by using a self-adaptive evolution method, the construction of a parallel system model is realized, and the target optimization of an actual system is realized through the cooperative evolution, closed-loop feedback and staggered guidance of the actual system and a virtual system.
The parallel system model is a pair of virtual and real system state equations
Describing the parallel system of the real system and the virtual system, the real system S R Expressed as:
wherein, the first and the second end of the pipe are connected with each other,
is a real system S R In the state at the time t, the state,
is a real system S R In the state at the time t-1,
is applied to the actual system S at the moment t-1 R The amount of control of (2) is,
is a rule for the migration of the state of the actual system,
indicating that the actual system state is in a rule
The water-soluble polymer migrates under the action of the water,
is the semantic logical rule "and",
is expressed in observation rules
Lower actual system S R The observed value of the presented one or more of the images,
indicating the actual system S at time t R The observed value of (1).
Formula (1) is described in numerical form, i.e.:
wherein, the first and the second end of the pipe are connected with each other,
in order to be an actual system state transition equation,
is an actual system observation equation.
By jointly solving the control quantities of the virtual system and the actual system
Make the virtual system target from time T to T + T
Convergence while minimizing the difference between the virtual and real system representations
So that the actual system converges to the preset virtual system state, that is:
(4) And converting the optimal output value output by the parallel system model, sending the optimal output value to digital workshop equipment in a production instruction form through an interleaved data transmission channel, and executing corresponding actions by the digital workshop equipment according to a given instruction.
The invention also provides a digital twin-based digital workshop simulation system which comprises a physical entity layer, a data protection layer, a digital twin layer, a system development layer and a system application layer. Fig. 2 is a schematic structural diagram of a digital twin digital workshop simulation system.
Physical entity layer: the physical entity layer refers to a set of digital workshop equipment actually existing in a modeling digital workshop, and is composed of entities such as a production factory building, digital workshop equipment, production resources, sensors, manufacturing execution personnel and the like, and all kinds of entity elements cooperate to jointly complete the production and manufacturing process. The layer is the most basic element in the architecture of the modeling digital workshop scheduling management system, and the research of other layers needs to be spread around the layer.
And the data barrier layer is used for establishing a synchronous staggered data transmission channel of the virtual system and the actual system, and the digital twin model is sent to the digital workshop equipment through the channel according to a calculation result so as to realize the synchronization of the virtual system and the actual system. The data protection layer is composed of three main parts, namely data acquisition, data transmission and data management. The data of other four layers and the interaction among the four layers provide data sources for the system and are updated along with the continuous supplement of real-time data. In order to efficiently and accurately realize data acquisition and management, a data guarantee layer generally uses a sensing technology and a data transmission and storage technology, so that intelligent management and control of workshop equipment are realized, and workers can conveniently acquire equipment running state information and real-time production conditions.
And the digital twin layer is used for constructing a digital twin model of the digital workshop equipment in the information space, establishing a virtual-real incidence relation between the digital workshop equipment and the digital twin model, performing real-time analog modulation on the running state of the digital workshop in the digital twin model, and calculating the progress delay in the actual production running process. The digital twin layer is a digital twin model of a physical entity layer, is a digital mirror image of all data information and equipment states, and is mainly responsible for describing simulation optimization of the actual production process of the digital workshop in a virtual space.
And the system development layer is the connection of the system application layer and other layers and comprises a WEB page framework, a digital twin model, a parallel system model display, a database design and the like. This layer is also the key to the visualization implementation of the underlying technology.
And the system application layer evaluates various parameter configurations by using a self-adaptive evolution method, realizes the construction of a parallel system model, and realizes the target optimization of the actual system through the cooperative evolution, closed-loop feedback and staggered guidance of the actual system and the virtual system. The system application layer provides corresponding modular functional support for scheduling management requirements of an actual digital workshop, and the layer provides service and support for intelligent management tasks of the digital workshop under the support of a drive of a data protection layer and a system development layer. The system application layer provides real-time monitoring, scheduling optimization management and reliable operation and maintenance service functions.
The data protection layer acquires data through devices such as sensors, the data protection layer collects, transmits and stores the data generated by the physical entity layer, so that a bottom support is provided for a digital twin layer and a system development layer, the digital twin layer acquires the data by means of the data protection layer and constructs a virtual model related to the physical entity layer in the system, and after the digital twin model is processed by a functional module designed by the system development layer, an internet-based system application layer platform is constructed, so that rich and efficient management service functions such as device management, model visualization, online scheduling, three-dimensional scheduling simulation and the like are provided.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on or transmitted over a computer-readable storage medium. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A digital twin-based digital workshop simulation method is characterized by comprising the following steps:
s1, establishing a synchronous staggered data transmission channel of a virtual system and an actual system, and sending a digital twin model to digital workshop equipment through the channel according to a calculation result to realize the synchronization of the virtual system and the actual system;
s2, constructing a digital twin model of the digital workshop equipment in an information space, establishing a virtual-real incidence relation between the digital workshop equipment and the digital twin model, performing real-time analog modulation on the running state of the digital workshop in the digital twin model, and calculating the progress delay in the actual production running process;
s3, based on the virtual scene, evaluating various parameter configurations by using a self-adaptive evolution method to realize the construction of a parallel system model;
and S4, converting the optimal output value output by the parallel system model, sending the optimal output value to digital workshop equipment through a staggered data transmission channel in a production instruction mode, and executing corresponding actions by the digital workshop equipment according to a given instruction.
2. The digital plant simulation method according to claim 1, wherein in the step S3, the actual system S is used R Expressed as:
wherein the content of the first and second substances,
is a real system S R In the state at the time t, the state,
is a real system S R In the state at the time t-1,
is applied to the actual system S at the moment t-1 R The amount of control of (a) is,
is a rule for the migration of the state of the actual system,
indicating that the actual system state is in a rule
The water-soluble polymer migrates under the action of the water,
is a semantic logic rule "and",
is expressed in observation rules
Lower actual system S R The observed value of the presented one or more of the images,
indicating the actual system S at time t R The observed value of (a);
formula (1) is described in numerical form:
wherein the content of the first and second substances,
in order to be an actual system state transition equation,
is an actual system observation equation.
3. The digital plant simulation method according to claim 2, wherein the control quantities of the virtual system and the actual system are jointly solved
To make the virtual system target from time T to T + T
Convergence while minimizing the difference between the virtual and real system representations
And converging the actual system to a preset virtual system state, namely:
4. The digital plant simulation method according to claim 1, wherein the step S2 specifically comprises:
step 21: calculating digital workshop appliance T i Is performed at a rate S U ：
S U ＝min{S T ,S H }；
Wherein S T Is a digital workshop appliance T i Minimum limiting rate in the logistic cycle, S H Is a digital workshop appliance T i A minimum limiting rate within a sufficient stock of material flow;
step 22: will digitalize workshop appliance T i Is performed at a rate S U Sorting is carried out, and the execution task with the maximum execution rate is selected as a focusing task; when the maximum execution time of the tasks is the same, starting the task with early time as a focusing task; when the maximum execution time of the task is the same as the starting time of the task, the digital workshop equipment T is used i The task with the lower index of (2) is used as a focusing task;
step 23: calculating digital workshop appliance T i And assigning it to the focus task;
step 24: calculating digital workshop appliance T i Distributing unsteady idle time generated on line to the task with the highest priority of the ready queue;
step (ii) of25: according to digital workshop appliance T i The final execution rate Si is determined by using the steady state idle time condition, and the CPU state of the digital workshop appliance is determined by using the unsteady state idle time.
5. The digital plant simulation method according to claim 4, wherein S is T Calculated from the following formula:
wherein S is RT (i) Is the minimum rate, S, in a set of different logistic cycles RT (i) Calculated from the following formula:
wherein e is i Is a digital workshop appliance T i Execution time in worst case, P j Is the period of the material flow, B (T) i ) Is a digital workshop appliance T i The maximum blocking time of the logistics cycle, L is the length of the logistics line body;
S H calculated from the following formula:
wherein n is a digital workshop appliance T i Total number of (1), u i Is a digital workshop appliance T i F (n) is an upper utilization bound in n digital plant installations.
6. The digital plant simulation method according to claim 4, wherein in the step 23, the steady-state idle time Δ T during the actual production operation is calculated according to the following formula:
in the formula: f j And S j Respectively representing digital plant equipment T i The start time and the end time of the jth process flow in the plan; m represents the total number of the processing technological processes planned to be finished by the workpiece at the current time t; t is M Plan time, FA, representing the total number M of all process flows completed j And SA j Respectively representing digital plant equipment T collected by a reading device i Actual start time and actual end time of the jth process flow of (1); n represents the total number of the machining process flows actually finished by the workpiece at the current time t; t is a unit of N Representing the actual time to complete the total number N of actual process flows.
7. The digital workshop simulation method according to claim 4, wherein in step 24, when the CPU of the digital workshop appliance is in an idle state, if the unsteady idle time at that time is greater than the CPU state switching energy consumption of the digital workshop appliance, the CPU of the digital workshop appliance is switched to a sleep mode until the next task event is started; and if the unsteady state idle time is less than the CPU state switching energy consumption of the digital workshop equipment, the CPU of the digital workshop equipment still keeps an idle state.
8. A digital twin-based digital workshop simulation system for realizing the digital workshop simulation method of any one of claims 1-7 is characterized by comprising a physical entity layer, a data protection layer, a digital twin layer, a system development layer and a system application layer;
the physical entity layer is a set of digital workshop equipment which actually exists in the manufacturing type digital workshop;
the data barrier layer is used for establishing a synchronous staggered data transmission channel of the virtual system and the actual system, and the digital twin model is sent to the digital workshop equipment through the channel according to a calculation result to realize the synchronization of the virtual system and the actual system;
the digital twin layer is used for constructing a digital twin model of the digital workshop equipment in an information space, establishing a virtual-real incidence relation between the digital workshop equipment and the digital twin model, performing real-time analog modulation on the running state of the digital workshop in the digital twin model, and calculating the progress delay in the actual production running process;
the system development layer is used for connecting a system application layer and a physical entity layer, a data protection layer and a digital twin layer;
the system application layer is used for evaluating various parameter configurations by using a self-adaptive evolution method, realizing the construction of a parallel system model, and realizing the target optimization of an actual system through the cooperative evolution, closed-loop feedback and staggered guidance of the actual system and a virtual system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211323116.XA CN115373290B (en) | 2022-10-27 | 2022-10-27 | Digital twin-based digital workshop simulation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211323116.XA CN115373290B (en) | 2022-10-27 | 2022-10-27 | Digital twin-based digital workshop simulation method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115373290A true CN115373290A (en) | 2022-11-22 |
| CN115373290B CN115373290B (en) | 2023-01-24 |
Family
ID=84074132
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211323116.XA Active CN115373290B (en) | 2022-10-27 | 2022-10-27 | Digital twin-based digital workshop simulation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115373290B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108919760A (en) * | 2018-07-05 | 2018-11-30 | 长安大学 | A kind of intelligent workshop autonomy production process dynamic linkage control method twin based on number |
| CN110454290A (en) * | 2019-07-02 | 2019-11-15 | 北京航空航天大学 | A kind of automobile engine management-control method based on the twin technology of number |
| CN110609531A (en) * | 2019-09-27 | 2019-12-24 | 北京航空航天大学 | Workshop scheduling method based on digital twin |
| CN110765589A (en) * | 2019-09-10 | 2020-02-07 | 上海大学 | Intelligent workshop virtual and real synchronous monitoring system and method based on digital twins |
| CN113406968A (en) * | 2021-06-17 | 2021-09-17 | 广东工业大学 | Unmanned aerial vehicle autonomous take-off, landing and cruising method based on digital twinning |
| WO2021227325A1 (en) * | 2020-05-13 | 2021-11-18 | 中国电子科技集团公司第十四研究所 | Digital twin-based production process simulation and optimization method |
| CN113852515A (en) * | 2021-08-26 | 2021-12-28 | 西安电子科技大学广州研究院 | Node state control method and system of digital twin network |
| CN113887016A (en) * | 2021-09-07 | 2022-01-04 | 中船智能科技(上海)有限公司 | Ship digital workshop simulation method and system based on digital twinning |
| CN114035515A (en) * | 2021-04-28 | 2022-02-11 | 南京航空航天大学 | Digital twin system for discrete workshop production process and construction method thereof |
| CN114328672A (en) * | 2021-12-31 | 2022-04-12 | 无锡恺易物联网科技发展有限公司 | Digital farmland scene mapping synchronization device and method based on digital twins |
| US20220164502A1 (en) * | 2020-11-23 | 2022-05-26 | Jiangsu University | Pump machine unit optimized operation regulation system and method based on digital twin |
| CN114707334A (en) * | 2022-04-08 | 2022-07-05 | 西安交通大学 | Twin model operation method of coal mill |
| CN114894507A (en) * | 2022-06-08 | 2022-08-12 | 天津职业技术师范大学(中国职业培训指导教师进修中心) | Fault diagnosis and health state prediction method for digital twin-driven rail vehicle transmission system |
| CN114967494A (en) * | 2022-03-25 | 2022-08-30 | 浙江工业大学 | Cloud digital twin workshop simulation system based on modular development |
-
2022
- 2022-10-27 CN CN202211323116.XA patent/CN115373290B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108919760A (en) * | 2018-07-05 | 2018-11-30 | 长安大学 | A kind of intelligent workshop autonomy production process dynamic linkage control method twin based on number |
| CN110454290A (en) * | 2019-07-02 | 2019-11-15 | 北京航空航天大学 | A kind of automobile engine management-control method based on the twin technology of number |
| CN110765589A (en) * | 2019-09-10 | 2020-02-07 | 上海大学 | Intelligent workshop virtual and real synchronous monitoring system and method based on digital twins |
| CN110609531A (en) * | 2019-09-27 | 2019-12-24 | 北京航空航天大学 | Workshop scheduling method based on digital twin |
| WO2021227325A1 (en) * | 2020-05-13 | 2021-11-18 | 中国电子科技集团公司第十四研究所 | Digital twin-based production process simulation and optimization method |
| US20220164502A1 (en) * | 2020-11-23 | 2022-05-26 | Jiangsu University | Pump machine unit optimized operation regulation system and method based on digital twin |
| CN114035515A (en) * | 2021-04-28 | 2022-02-11 | 南京航空航天大学 | Digital twin system for discrete workshop production process and construction method thereof |
| CN113406968A (en) * | 2021-06-17 | 2021-09-17 | 广东工业大学 | Unmanned aerial vehicle autonomous take-off, landing and cruising method based on digital twinning |
| CN113852515A (en) * | 2021-08-26 | 2021-12-28 | 西安电子科技大学广州研究院 | Node state control method and system of digital twin network |
| CN113887016A (en) * | 2021-09-07 | 2022-01-04 | 中船智能科技(上海)有限公司 | Ship digital workshop simulation method and system based on digital twinning |
| CN114328672A (en) * | 2021-12-31 | 2022-04-12 | 无锡恺易物联网科技发展有限公司 | Digital farmland scene mapping synchronization device and method based on digital twins |
| CN114967494A (en) * | 2022-03-25 | 2022-08-30 | 浙江工业大学 | Cloud digital twin workshop simulation system based on modular development |
| CN114707334A (en) * | 2022-04-08 | 2022-07-05 | 西安交通大学 | Twin model operation method of coal mill |
| CN114894507A (en) * | 2022-06-08 | 2022-08-12 | 天津职业技术师范大学(中国职业培训指导教师进修中心) | Fault diagnosis and health state prediction method for digital twin-driven rail vehicle transmission system |
Non-Patent Citations (3)
| Title |
|---|
| 白浩 等: "基于数字孪生的数字电网展望和思考", 《南方电网技术》 * |
| 陈启鹏: "面向数字孪生的自动化产线制造过程状态监测关键技术研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 陈志鼎 等: "基于数字孪生技术的水轮机虚实交互系统设计", 《水电能源科学》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115373290B (en) | 2023-01-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107861478B (en) | A kind of parallel control method in intelligence workshop and system | |
| Ding et al. | Hidden Markov model-based autonomous manufacturing task orchestration in smart shop floors | |
| US9958861B2 (en) | Production control system and integrated production control system | |
| CN112668237B (en) | Digital twin model based on industrial Internet cloud platform and construction method thereof | |
| CN108009739A (en) | Production-Plan and scheduling integral method between a kind of intelligent machine extra bus | |
| CN109028481A (en) | Unattended machine room control system and method of air conditioning system | |
| CN113112088B (en) | Edge cloud cooperative digital twin intelligent scheduling application operation position adaptation method | |
| Dietmair et al. | Model-based energy consumption optimisation in manufacturing system and machine control | |
| CN102156457A (en) | Industrial control energy object | |
| JP6927557B2 (en) | Management system | |
| Zhou et al. | Fog computing-based cyber-physical machine tool system | |
| Wang et al. | A new data processing architecture for multi-scenario applications in aviation manufacturing | |
| EP2933774A1 (en) | Energy-management server, energy-management method, and program | |
| EP2778807B1 (en) | Extensible energy management architecture | |
| CN114428487A (en) | Automatic control device, system and method | |
| CN112508306A (en) | Self-adaptive method and system for power production configuration | |
| CN109326239A (en) | HVRT Digital Display system | |
| Borangiu et al. | Smart manufacturing control with cloud-embedded digital twins | |
| Zheng et al. | Human-cyber-physical system for production and operation decision optimization in smart steel plants | |
| Wang et al. | Task offloading in cloud-edge collaboration-based cyber physical machine tool | |
| Xing et al. | Honeycomb: An open-source distributed system for smart buildings | |
| David et al. | Towards energy efficient buildings: how ICTs can convert advances? | |
| CN115373290B (en) | Digital twin-based digital workshop simulation method and system | |
| CN117709617A (en) | MES-based intelligent scheduling system for production workshop | |
| Chen | Industry 4.0 for semiconductor manufacturing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |