CN115526482A - Workshop scheduling and dynamic scheduling platform based on digital twin - Google Patents
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Abstract
The invention discloses a workshop scheduling and dynamic scheduling platform based on digital twinning, which consists of seven functional modules, namely an order data processing module, a real-time abnormity monitoring module, a workshop environment configuration module, a material demand processing module, a core algorithm management module, a static scheduling calculation module and a dynamic scheduling adjustment module, and three data modules, namely a production workshop environment basic data module, a core algorithm module and a production state real-time data module. The real-time state and abnormal information of physical equipment in a workshop are obtained based on a digital twin platform, a scheduling plan under an initial task is obtained through static scheduling calculation, and the influence of the abnormal information on normal production is timely processed through dynamic scheduling adjustment calculation. By combining the static scheduling with the dynamic scheduling, the problems that the production instant state is not clear under uncertain emergency in actual production, the production process deviates from the original scheduling plan, and the production efficiency is seriously lagged are solved.
Description
Technical Field
The invention relates to a scheduling technology of a production workshop, in particular to a workshop scheduling and dynamic scheduling platform based on a digital twin, and belongs to the technical field of advanced manufacturing control and scheduling.
Background
In actual production, uncertain emergencies can be generated in installation, positioning, carrying, inspection, processing faults, manual operation and the like, so that an original production plan is invalid, and production management and production efficiency are seriously influenced. Common incident effects include: equipment failure, order changes, man-hour lag, etc.
The equipment failure problem may reduce the probability of equipment failure by periodically maintaining the equipment. The order addition problem has low requirement on completion time, and sufficient buffering time is provided for readjusting the subsequent scheduling plan. However, the emergency events such as urgent ordering, original order change, random lag in working hours, etc. which have immediate influence, will directly lead to the original scheduling plan not being applicable. After the production is finished, the real-time production state is difficult to sense in real time, the base data of the re-planning of the scheduling plan is incomplete, and the calculation period is long. The equipment affected by the emergency stops producing, greatly affects the processing progress of the workpiece related to the equipment and seriously restricts the production efficiency.
The instant production state is not clear under the uncertain emergency in actual production, and the production efficiency is seriously lagged. Therefore, the research on a production scheduling and dynamic scheduling platform based on the digital twin greatly improves the efficiency of production management for processing emergencies, is very meaningful for the scheduling and management in the actual production process, and is also very valuable for the application of the production scheduling technology in the actual manufacturing process.
Disclosure of Invention
The invention provides a workshop scheduling and dynamic scheduling platform based on digital twin, which is used for solving the problems of production lag and low efficiency caused by deviation of a production process from an original scheduling plan due to uncertain emergencies in actual production.
A workshop scheduling and dynamic scheduling platform based on digital twin designs seven functional modules and three data modules for scheduling and scheduling. The functional modules comprise an order data processing module, a real-time abnormity monitoring module, a workshop environment configuration module, a material demand processing module, a core algorithm management module, a static scheduling calculation module and a dynamic scheduling adjustment module. The data module comprises production workshop environment basic data, a core algorithm and production state real-time data.
The order data processing module is used for predicting the order construction period and decomposing order tasks. And calculating the estimated completion period of all orders under the existing production capacity, decomposing and issuing tasks according to the order requirements, and performing scheduling calculation.
The real-time abnormity monitoring module is used for monitoring the actual production process. The module is in real-time communication with a workshop digital twin system, and once the deviation between actual production and a scheduling plan is detected, the influence of abnormal deviation disturbance is analyzed, and a rescheduling triggering scheme is formulated.
And the workshop environment configuration module is used for defining and setting basic data of scheduling under the production environment. And initializing production capacity basic data such as equipment types and equipment quantity of a new workshop, and defining product related basic data such as product types, processing technological processes and estimated working hours.
The material demand processing module is used for processing the material demands under the current scheduling and task in a centralized mode. And (5) counting the material inventory condition, analyzing the material demand under the new scheduling task, and issuing the material ordering demand.
The core algorithm management module is used for managing a scheduling and scheduling algorithm library. The system comprises a genetic algorithm for scheduling production and a reinforcement learning algorithm for dynamic scheduling, and can perform addition, deletion, modification, check and parameter setting on the scheduling and scheduling algorithm.
And the static scheduling calculation module is used for calculating a scheduling plan under the order task. And acquiring the distributed order task requirements, calling a scheduling optimization algorithm to calculate, acquiring a scheduling plan Gantt chart, issuing material requirements and processing tasks of each device.
And the dynamic scheduling adjustment module is used for quickly calculating the scheduling plan after the disturbance of the uncertain event. According to the task characteristics under the influence of uncertain disturbance factors, the real-time material state and production state of the actual production process under monitoring are obtained, a dynamic scheduling algorithm is called, a high-response and fast-feedback dynamic scheduling adjustment scheme is obtained, and the processing tasks of each device after adjustment are issued.
The production workshop environment basic data are used for storing the inherent information of workshop equipment and the basic information of products. The inherent information of the equipment comprises an equipment number, an equipment type, the quantity of the equipment, energy consumption and the like, and the basic information of the product comprises a product number, a product type, a technological process, estimated working hours and the like.
The core algorithm is used for storing all algorithms of static scheduling and dynamic scheduling in a centralized mode. And the packaging algorithm and the corresponding scheduling model thereof provide an input interface and a calling interface of the scheduling and scheduling algorithm.
The production state real-time data is used for storing state information in the actual production process. The method comprises the steps of obtaining and storing the current instant production state including the ongoing order, equipment and workpiece information in real time based on a digital twin system, and providing real-time data guarantee for an abnormality monitoring module, a dynamic scheduling module and the like.
The order data processing module and the real-time abnormity monitoring module are input parts of the platform and respectively trigger production scheduling and dynamic scheduling; the workshop environment configuration module, the material requirement processing module and the core algorithm management module are main execution parts of the platform; the static scheduling calculation module and the dynamic scheduling adjustment module are output parts of the platform and output results of scheduling calculation; the production workshop environment basic data, the core algorithm and the production state real-time data are part of a platform database.
The workshop scheduling and dynamic scheduling platform based on the digital twin is used for scheduling and dynamic scheduling of workshops. The production scheduling process comprises the following steps:
step one, an order data processing module issues order task requirements to a static scheduling calculation module;
secondly, reading basic data of the production environment by a static scheduling calculation module to obtain basic environment configuration data of scheduling and production scheduling problems;
thirdly, calling a production scheduling algorithm by a static scheduling calculation module;
fourthly, the static scheduling calculation module issues material requirements;
and fifthly, outputting a scheduling result by the static scheduling calculation module.
The dynamic scheduling process comprises the following steps:
step one, a real-time abnormity monitoring module monitors abnormal disturbance of a production environment and triggers abnormity to a dynamic scheduling adjustment module;
reading basic data of the production environment by a dynamic scheduling adjustment module to obtain basic environment configuration data of scheduling and production scheduling problems;
step three, acquiring order task requirements by a dynamic scheduling adjustment module;
step three, the dynamic scheduling adjustment module acquires real-time production state data;
step four, the dynamic scheduling adjusting module calls a dynamic scheduling algorithm;
fifthly, the dynamic scheduling adjustment module issues material requirements;
and step six, the dynamic scheduling adjustment module outputs the adjusted scheduling result.
The order data processing module and the real-time abnormity monitoring module are used as input, and the workshop environment configuration module, the material demand processing module and the core algorithm management module are used as execution and static scheduling calculation modules and dynamic scheduling adjustment modules and used as output.
The production scheduling process is used for making a production plan when a production order task is initially issued, and the calculation process consumes more time to obtain a better scheduling plan scheme; and the dynamic scheduling process is used for generating a disturbance event influencing the original scheduling plan in the production process, and adjusting the production scheduling plan in time according to the production state and the demand after disturbance. The timeliness of workshop production state acquisition is improved based on the digital twin platform, and the efficiency of scheduling adjustment calculation is improved based on a dynamic scheduling algorithm. The workshop scheduling and dynamic scheduling platform based on the digital twin can effectively realize production scheduling and dynamic scheduling, solve the problem that a disturbance event influences a normal production plan, greatly save the time cost for processing the disturbance event and improve the production efficiency.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a schematic diagram of functional module composition of a workshop scheduling and dynamic scheduling platform
FIG. 2 is a schematic diagram of a workshop scheduling and dynamic scheduling implementation process based on digital twin
Detailed Description
The invention provides a workshop scheduling and dynamic scheduling platform based on digital twin, which is used for scheduling and dynamic scheduling of workshops, solving the problem that a disturbance event influences a normal production plan, greatly saving the time cost for processing the disturbance event and improving the production efficiency. The invention is further described with reference to the following drawings and detailed description:
as shown in fig. 1, a schematic diagram of functional modules of a workshop scheduling and dynamic scheduling platform is provided, and seven functional modules and three data modules for scheduling and scheduling are designed. The functional modules comprise an order data processing module, a real-time abnormity monitoring module, a workshop environment configuration module, a material demand processing module, a core algorithm management module, a static scheduling calculation module and a dynamic scheduling adjustment module. The data module comprises basic data of the production workshop environment, a core algorithm and real-time production state data.
The order data processing module and the real-time abnormity monitoring module are input parts of the platform and respectively trigger production scheduling and dynamic scheduling; the workshop environment configuration module, the material requirement processing module and the core algorithm management module are main execution parts of the platform; the static scheduling calculation module and the dynamic scheduling adjustment module are output parts of the platform and output results of scheduling and scheduling calculation; the production workshop environment basic data, the core algorithm and the production state real-time data are part of a platform database.
The order data processing module is used for predicting the order construction period and decomposing order tasks. And calculating the estimated completion period of all orders under the existing production capacity, decomposing and issuing tasks according to the order requirements, and performing scheduling calculation.
The real-time abnormity monitoring module is used for monitoring the actual production process. The module is in real-time communication with a workshop digital twin system, and once the deviation between actual production and a scheduling plan is detected, the influence of abnormal deviation disturbance is analyzed, and a rescheduling triggering scheme is formulated.
The workshop environment configuration module is used for defining and setting basic data of scheduling under the production environment. Initializing production capacity basic data such as equipment types and equipment quantity of a new workshop, and defining product related basic data such as product types, processing technological processes and estimated working hours.
The material demand processing module is used for processing the material demands under the current scheduling and task in a centralized mode. And (5) counting the inventory condition of the materials, analyzing the material requirements under a new scheduling task, and issuing the material ordering requirements.
The core algorithm management module is used for managing a scheduling and scheduling algorithm library. The system comprises a genetic algorithm for scheduling production and a reinforcement learning algorithm for dynamic scheduling, and can perform addition, deletion, modification, check and parameter setting on the scheduling and scheduling algorithm.
And the static scheduling calculation module is used for calculating a scheduling plan under the order task. And acquiring the distributed order task requirements, calling a scheduling optimization algorithm to calculate, acquiring a scheduling plan Gantt chart, issuing material requirements and processing tasks of each device.
And the dynamic scheduling adjustment module is used for quickly calculating the scheduling plan after the disturbance of the uncertain event. According to the task characteristics under the influence of uncertain disturbance factors, the real-time material state and production state of the actual production process under monitoring are obtained, a dynamic scheduling algorithm is called, a high-response and fast-feedback dynamic scheduling adjustment scheme is obtained, and the processing tasks of each device after adjustment are issued.
The production workshop environment basic data are used for storing the inherent information of workshop equipment and the basic information of products. The inherent information of the equipment comprises an equipment number, an equipment type, the quantity of the equipment, energy consumption and the like, and the basic information of the product comprises a product number, a product type, a technological process, estimated working hours and the like.
The core algorithm is used for storing all algorithms of static scheduling and dynamic scheduling in a centralized mode. And the packaging algorithm and the corresponding scheduling model thereof provide an input interface and a calling interface of the scheduling and scheduling algorithm.
The production state real-time data is used for storing state information in the actual production process. The method comprises the steps of acquiring and storing the current instant production state including the ongoing order, equipment and workpiece information in real time based on a digital twin system, and providing real-time data guarantee for an abnormality monitoring module, a dynamic scheduling module and the like.
As shown in fig. 2, a schematic diagram of a process for implementing scheduling and dynamic scheduling of a workshop based on a digital twin is shown. The method comprises a production scheduling process and a dynamic scheduling process of a workshop. In the workshop digital twin platform, workshop twin data containing product information, order information and production line information are established, the workshop twin data and workshop physical equipment data are interconnected and communicated, and the real-time state of the workshop physical equipment is obtained. And the production state real-time database acquires twin data of the workshop in real time, so that the real-time state of physical equipment of the workshop is acquired in real time. The production environment base database stores the environment base data according to the workshop twin data. The core algorithm library comprises a production scheduling algorithm mainly based on a genetic algorithm and a dynamic scheduling algorithm mainly based on reinforcement learning.
The specific implementation process of production scheduling is as follows:
step one, an order data processing module acquires production requirements from a production state real-time database, decomposes an order into a plurality of tasks according to order delivery time, and issues order tasks and equipment state information to a static scheduling calculation module;
step two, the static scheduling calculation module acquires basic environment configuration data of scheduling and scheduling problems from the basic data of the production environment, wherein the basic environment configuration data comprises equipment number, equipment type, equipment quantity, equipment energy consumption, product number, product type, technological process, working hour and the like;
step three, calling a genetic algorithm of a core algorithm by a static scheduling calculation module to perform scheduling calculation;
fourthly, the static scheduling calculation module sends the material requirements to the material requirement processing module according to the scheduling result;
and fifthly, the static scheduling calculation module sends the subtasks of each device to the workshop digital twin platform according to scheduling results, and the digital twin platform issues the tasks to the physical devices.
The specific implementation process of dynamic scheduling is as follows:
step one, a real-time abnormity monitoring module monitors abnormal disturbance of a production environment and sends triggering abnormity to a dynamic scheduling adjustment module;
reading basic data of the production environment by a dynamic scheduling adjustment module to obtain basic environment configuration data of scheduling and scheduling problems, wherein the basic environment configuration data comprises equipment number, equipment type, equipment quantity, equipment energy consumption, product number, product type, process, working hour and the like;
step three, the dynamic scheduling adjustment module acquires order task requirements from the order data processing module;
step three, the dynamic scheduling adjustment module acquires real-time production state data from a real-time production state database;
step four, the dynamic scheduling adjustment module calls a reinforcement learning algorithm in the core algorithm to perform dynamic scheduling adjustment calculation;
fifthly, the dynamic scheduling adjustment module sends the material requirement to the material requirement processing module according to the adjustment result;
and step six, the dynamic scheduling adjustment module sends the subtasks of each device to a workshop digital twin platform according to the adjustment result, and the digital twin platform issues the tasks to the physical devices.
Claims (3)
1. A workshop scheduling and dynamic scheduling platform based on digital twin is characterized in that: the system comprises an order data processing module, a real-time abnormity monitoring module, a workshop environment configuration module, a material demand processing module, a core algorithm management module, a static scheduling calculation module, a dynamic scheduling adjustment module, seven functional modules and three data modules of production workshop environment basic data, a core algorithm and production state real-time data; the real-time state and abnormal information of physical equipment in a workshop are obtained based on a digital twin platform, a scheduling plan under an initial task is obtained through static scheduling calculation, and the influence of the abnormal information on normal production is timely processed through dynamic scheduling adjustment calculation;
the order data processing module is used for pre-estimating the order construction period and decomposing order tasks; calculating the estimated completion period of all orders under the existing production capacity, decomposing and issuing tasks according to the order requirements and giving production scheduling calculation;
the real-time abnormity monitoring module is used for monitoring the actual production process; the real-time anomaly monitoring module is in real-time communication with a workshop digital twin system, and once the deviation between actual production and a scheduling plan is detected, the influence of abnormal deviation disturbance is analyzed, and a rescheduling triggering scheme is formulated;
the workshop environment configuration module is used for defining and setting basic data of scheduling under the production environment; initializing and setting basic data of equipment types and equipment quantity production capacity of a new workshop, and defining related basic data of product types, processing technological processes and products in estimated working hours;
the material demand processing module is used for processing the material demands under the current scheduling and task in a centralized manner; counting the material inventory condition, analyzing the material demand under the new scheduling task, and issuing the material ordering demand;
the core algorithm management module is used for managing a scheduling and scheduling algorithm library; the scheduling method comprises a genetic algorithm for scheduling production and a reinforcement learning algorithm for dynamic scheduling, and the genetic algorithm, the reinforcement learning algorithm, the deletion, the modification, the check and the parameter setting are carried out on the scheduling algorithm;
the static scheduling calculation module is used for calculating a scheduling plan under the order task; acquiring the distributed order task requirements, calling a scheduling optimization algorithm to calculate, acquiring a scheduling plan Gantt chart, and issuing material requirements and processing tasks of each device;
the dynamic scheduling adjustment module is used for rapidly calculating a scheduling plan after the disturbance of the uncertain event; according to the task characteristics under the influence of uncertain disturbance factors, acquiring the real-time material state and production state of the actual production process under monitoring, calling a dynamic scheduling algorithm, acquiring a high-response and fast-feedback dynamic scheduling adjustment scheme, and issuing the processing tasks of each device after adjustment;
the production workshop environment basic data is used for storing inherent information of workshop equipment and basic product information; the inherent information of the equipment comprises an equipment number, an equipment type, the quantity of the equipment and energy consumption, and the basic information of the product comprises a product number, a product type, a process and estimated working hours;
the core algorithm is used for storing all algorithms of static scheduling and dynamic scheduling in a centralized manner, encapsulating the algorithms and corresponding scheduling models thereof, and providing an input interface and a calling interface of the scheduling and scheduling algorithms;
the production state real-time data is used for storing state information in the actual production process; the method comprises the steps that a current instant production state including ongoing order, equipment and workpiece information is obtained and stored in real time based on a digital twin system, and real-time data guarantee is provided for a real-time abnormity monitoring module and a dynamic scheduling adjustment module;
the order data processing module and the real-time abnormity monitoring module are input parts of the platform and respectively trigger production scheduling and dynamic scheduling; the workshop environment configuration module, the material requirement processing module and the core algorithm management module are main execution parts of the platform; the static scheduling calculation module and the dynamic scheduling adjustment module are output parts of the platform and output results of scheduling calculation; the production workshop environment basic data, the core algorithm and the production state real-time data are part of a platform database.
2. The platform for scheduling and dynamically scheduling the workshop based on the digital twin as claimed in claim 1, wherein: the workshop scheduling and dynamic scheduling platform based on the digital twin is used for scheduling and dynamically scheduling workshops; the production scheduling process comprises the following steps:
step one, an order data processing module issues order task requirements to a static scheduling calculation module;
secondly, reading basic data of the production environment by a static scheduling calculation module to obtain basic environment configuration data of scheduling and production scheduling problems;
step three, calling a production scheduling algorithm by a static scheduling calculation module;
fourthly, the static scheduling calculation module issues material requirements;
and fifthly, outputting a scheduling result by the static scheduling calculation module.
3. The digital twin-based workshop scheduling and dynamic scheduling platform of claim 2, wherein: the dynamic scheduling process comprises the following steps:
step one, a real-time abnormity monitoring module monitors abnormal disturbance of a production environment and triggers abnormity to a dynamic scheduling adjustment module;
reading basic data of the production environment by a dynamic scheduling adjustment module to obtain basic environment configuration data of scheduling and production scheduling problems;
step three, acquiring order task requirements by a dynamic scheduling adjustment module;
step three, the dynamic scheduling adjustment module acquires real-time production state data;
step four, the dynamic scheduling adjusting module calls a dynamic scheduling algorithm;
fifthly, the dynamic scheduling adjustment module issues material requirements;
and step six, the dynamic scheduling adjustment module outputs the adjusted scheduling result.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115689097A (en) * | 2023-01-04 | 2023-02-03 | 东方合智数据科技(广东)有限责任公司 | Scheduling list generation method, device, storage medium and device |
| CN116300720A (en) * | 2023-02-06 | 2023-06-23 | 广州辰创科技发展有限公司 | Intelligent flexible scheduling advanced planning and scheduling system for production line |
| CN116415803A (en) * | 2023-04-18 | 2023-07-11 | 杰为软件系统(深圳)有限公司 | Discrete manufacturing system integration and scheduling method based on event arrangement |
| CN116859861A (en) * | 2023-08-03 | 2023-10-10 | 广州尚捷智慧云网络科技有限公司 | Flexible processing scheduling system based on ERP and MES |
| CN116934276A (en) * | 2023-09-15 | 2023-10-24 | 深圳市尚泷科技有限公司 | Clothing rapid production management method and management system |
| CN117649211A (en) * | 2024-01-29 | 2024-03-05 | 北京腾华宇航智能制造有限公司 | Enterprise assembly process collaborative management system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115689097A (en) * | 2023-01-04 | 2023-02-03 | 东方合智数据科技(广东)有限责任公司 | Scheduling list generation method, device, storage medium and device |
| CN116300720A (en) * | 2023-02-06 | 2023-06-23 | 广州辰创科技发展有限公司 | Intelligent flexible scheduling advanced planning and scheduling system for production line |
| CN116415803A (en) * | 2023-04-18 | 2023-07-11 | 杰为软件系统(深圳)有限公司 | Discrete manufacturing system integration and scheduling method based on event arrangement |
| CN116859861A (en) * | 2023-08-03 | 2023-10-10 | 广州尚捷智慧云网络科技有限公司 | Flexible processing scheduling system based on ERP and MES |
| CN116934276A (en) * | 2023-09-15 | 2023-10-24 | 深圳市尚泷科技有限公司 | Clothing rapid production management method and management system |
| CN116934276B (en) * | 2023-09-15 | 2023-12-15 | 深圳市尚泷科技有限公司 | Clothing rapid production management method and management system |
| CN117649211A (en) * | 2024-01-29 | 2024-03-05 | 北京腾华宇航智能制造有限公司 | Enterprise assembly process collaborative management system |
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