CN115062478A - Dynamic workshop production scheduling method, system and medium based on digital twin - Google Patents
Dynamic workshop production scheduling method, system and medium based on digital twin Download PDFInfo
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Abstract
The invention discloses a dynamic workshop production scheduling method, a system and a medium based on digital twin, wherein the method comprises the following steps: collecting physical workshop information including order information and multi-source production elements; generating a production scheduling plan and establishing a twin model to obtain a twin workshop; performing simulation production and optimizing the scheduling in a twin workshop to obtain an optimal scheduling scheme; uploading the optimal scheduling scheme to a server to issue a production instruction to a physical workshop; the server guides the physical workshop to produce and evaluate, and generates production feedback to dynamically iterate the twin workshop and update the twin space; and after the production is finished, storing the production data in a server database in a classified manner. According to the method, a digital twin method is adopted to map a physical workshop to a twin workshop for verification of a scheduling scheme, and simulation is carried out in the twin workshop in real time according to dynamic changes of production states, so that the efficiency of equipment is exerted to the maximum extent, the production quality and efficiency are improved, and the production cost is reduced.
Description
Technical Field
The invention belongs to the technical field of workshop production scheduling, and particularly relates to a dynamic workshop production scheduling method, system and medium based on a digital twin.
Background
The production scheduling process of the workshop machinable time period is driven by multiple factors, such as order production time, total workshop energy consumption brought by production, production quality qualification rate, equipment utilization rate in the production process and the like, and the factors can dynamically change along with the lapse of the production time; therefore, dynamic scheduling of the shop floor is an important part of the shop floor manufacturing. For a long time, how to coordinate scheduling with sudden dynamic problems in the production process always troubles workshop managers; in the production process, the capacity of the equipment fluctuates along with the operation duration of the equipment, which often causes the production scheduling plan to be separated from the actual production, reduces the decision-making capability of the production scheduling, greatly reduces the production efficiency, and makes the feasibility of the production scheduling plan difficult to estimate. At present, a static production plan is established by establishing a workshop flow line model and a workshop working model at a certain moment by depending on an MES (manufacturing execution system) or an APS (advanced process system) module in commerce; however, in the manufacturing industry, various digital APS models cannot be well combined with physical workshop information (such as real-time load of equipment, engine speed and the like), and in the face of dynamic problems occurring in production, local adjustment is generally performed by means of manual intervention, but because the accuracy rate of manual calculation is far lower than the accuracy rate of computer calculation, the feasibility of production scheduling is often reduced, the production efficiency is greatly reduced, meanwhile, the human brain calculation lacks early warning capability for potential failure problems of the equipment, and the possibility of potential safety hazards is increased to a certain extent.
Under the era background that the production mode of the manufacturing industry is accelerated to digitalization, networking and intellectualization, the digital twin comes from the deep fusion and innovation of the new generation of information technology and the manufacturing industry and gradually becomes the important driving force of digital transformation in a new technological revolution and an industrial revolution. The digital twin provides a multidimensional physical simulation space, on one hand, a physical model of production personnel, production equipment, production materials, a production method and a production environment is established by collecting current production heat data of a physical workshop, on the other hand, historical production cold data of the physical workshop and real data collected by a sensor of the equipment are read, and the two are combined to establish a simulation workshop based on digital twin virtual through digital twin software; the method can simulate the operations of personnel walking, personnel operation, machine conversion and the like in a twin workshop based on digital twin, and can measure multidimensional parameters of an equipment model, wherein the equipment model covers various physical domains such as fluid, electromagnetism, heat, structures and the like. According to the 3D model of the virtual mapping of the physical entity in the twin space, the model truly reproduces attributes such as appearance, geometry, motion structure, geometric association and the like of the physical entity in the twin space, and is established by combining the spatial motion rule of the entity object.
In recent years, the research on digital twins is more and more intensive, the digital twins technology gradually permeates into the field of intelligent manufacturing, and although the digital twins workshop technology is continuously developed, a long way is needed for wide application in the aspect of actual production. At present, analysis optimization applications based on an independent system are gradually increased (such as energy consumption optimization, state alarm and fault location), but the production line level digital twin technology is limited by the development level of the personalized production line modeling technology because the production line level digital twin complex application depends on the precise modeling of the interrelation among multiple links, multiple devices and complex environments. Meanwhile, a great development space exists for the combination of digital twin and production scheduling, for example, virtual-real interaction, man-machine interaction, visual monitoring and the like of a physical workshop and a twin workshop are still to be improved, the existing digital twin workshop technology is still limited in the aspects of digital modeling and data operation, and data output and scheduling decisions are always presented by a two-dimensional report; in the prior art, a dynamic scheduling method based on workshop capacity change generally performs production scheduling based on an old scheduling model and a threshold value set by production experience. In this case, the artificially established threshold is often a fixed value, and the man-machine physical method ring change caused in the workshop production cannot be well considered, so that the workshop production efficiency is low, and potential safety hazards often exist; meanwhile, due to the fact that the dynamic change condition of the workshop productivity changes in a magic manner, the existing digital twin workshop technology does not well interact twin data with the dynamic change of the actual productivity, and a large number of data islands are formed.
Disclosure of Invention
The invention mainly aims to overcome the defects of the prior art and provide a dynamic workshop production schedule scheduling method, a dynamic workshop production schedule scheduling system and a dynamic workshop production schedule scheduling medium based on digital twinning.
In order to achieve the purpose, the invention adopts the following technical scheme:
in one aspect, the invention provides a dynamic workshop production schedule scheduling method based on digital twin, which is characterized by comprising the following steps:
collecting physical workshop information; the physical workshop information comprises order information and multi-source production elements;
generating a production schedule plan according to the order information, establishing a corresponding twin model by adopting a digital twin method according to multi-source production factors, and obtaining a digital twin workshop; the twin model comprises a personnel twin model, an equipment twin model, a material twin model, a method twin model and an environment twin model;
performing simulation production in a digital twin workshop according to a production scheduling plan and optimizing the scheduling to obtain an optimal scheduling scheme;
uploading the optimal scheduling scheme to a server to issue a production instruction to a physical workshop; the server guides the physical workshop to produce and evaluate, generates production feedback to dynamically iterate the digital twin workshop, and adjusts parameters to update the digital twin workshop;
and after the production is finished, storing the production data of the physical workshop, the dynamic mapping relation and the production data of the digital twin workshop into a server database in a classified manner.
As a preferred technical scheme, the collecting physical plant information specifically includes:
acquiring physical workshop information through a sensor element, and informationizing the acquired information to obtain order information and multisource production elements;
the order information comprises an order delivery period, transaction times of both sides of the order and the profit of the order;
the multi-source production elements comprise personnel information, equipment information, material information, production method information and workshop environment information.
As a preferred technical solution, the generating of the production schedule plan according to the order information specifically includes:
analyzing order information, and generating a production scheduling plan by combining and referring to a historical production target;
the analysis order information comprises whether the order delivery period is urgent or not and whether the order delivery period is enough to drive the workshop to carry out overload production or not, whether the transaction times of two parties of the order are enough to drive the workshop to carry out overload production or not and whether the profit of the order is enough to drive the workshop to carry out overload production or not;
the method comprises the following steps of establishing a corresponding twin model by adopting a digital twin method to obtain a digital twin workshop, specifically:
drawing a three-dimensional model by using three-dimensional modeling software according to the multi-source production elements; after the drawing is finished, flexible grid division is carried out on the three-dimensional model, the three-dimensional model is led into three-dimensional animation rendering and manufacturing software for environment rendering and scene building, and a microscopic individual model is constructed; and finally, storing the rendered three-dimensional model as a file with a format matched with the finite element simulation software, importing the file into the finite element simulation software, and carrying out calculation simulation on physical parameters of the model to obtain a digital twin workshop, wherein:
the twin model of the personnel established according to the personnel information in the multi-source production elements is expressed as follows:
H＝{H_Number，H_Name，H_Gender，H_Position，H_Fatigue，H_Type}
wherein H _ Number represents a personnel Number, H _ Name represents a personnel Name, H _ Gender represents a personnel Gender, H _ Position represents a personnel Position, H _ Fatigue represents personnel Fatigue, and H _ Type represents a personnel work Type;
the equipment twin model established according to the equipment information in the multi-source production elements is expressed as follows:
D＝{D_Number，D_Name，D_State，D_Type，D_Position，D_Load，D_Temperature，D_Pressure}
wherein D _ Number represents a device Number, D _ Name represents a device Name, D _ State represents a device State, D _ Type represents a device Type, D _ Position represents a device Position, D _ Load represents a device Load, D _ Temperature represents a device Temperature, and D _ Pressure represents a device Pressure;
the material twinning model established according to the material information in the multi-source production elements is expressed as follows:
S＝{S_Number，S_Name，S_Type，S_Position，S_Inventory}
wherein S _ Number represents a material Number, S _ Name represents a material Name, S _ Type represents a material Type, S _ Position represents a material filling Position, and S _ Inventory represents a material stock;
the method twin model established according to the production method information in the multi-source production elements is expressed as follows:
M＝{M_Number，M_Name，M_Type，M_Information}
wherein M _ Number represents a method Number, M _ Name represents a method Name, M _ Type represents a method Type, and M _ Information represents method specific Information;
the environment twin model established according to the workshop environment information in the multi-source production elements is expressed as follows:
E＝{E_Number，E_Location，E_Type，E_Temperature，E_Pipe，E_Electricity，E_Else}
where E _ Number represents an environment Number, E _ Location represents an environment Location, E _ Type represents an environment Type, E _ Temperature represents an environment Temperature, E _ Pipe represents a Pipe arrangement, E _ electric represents a power supply bus, and E _ electric represents other information.
As a preferred technical solution, the obtaining of the optimal scheduling solution specifically includes:
setting production data indexes, and inputting a production scheduling plan into a digital twin workshop for simulation production;
detecting the digital twin workshop after the simulation production to obtain the production data of the digital twin workshop;
and comparing the production data of the digital twin workshop with the set production data indexes, if the production data indexes are met, carrying out the next step, otherwise, adjusting the set production data indexes to continue the simulation production until a final scheduling scheme appears as an optimal scheduling scheme.
As a preferred technical scheme, the production data indexes comprise production time, total energy consumption of a workshop, qualified rate of production quality and utilization rate of equipment;
the digital twin workshop production data is compared with set production data indexes, and the method specifically comprises the following steps:
comparing the production time of the digital twin workshop with the set production time, and if the production time meets the index, namely the production time of the production scheduling plan can meet the set delivery time target, carrying out next verification; otherwise, adjusting and setting the index value of the production time, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
comparing the total workshop energy consumption of the digital twin workshop with the set total workshop energy consumption, and if the total workshop energy consumption meets the index, namely the total workshop production energy consumption of the production scheduling plan can meet the set energy consumption index, performing next verification; otherwise, adjusting and setting the total energy consumption index value of the workshop, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
comparing the production quality qualified rate of the digital twin workshop with the set production quality qualified rate, and if the production quality qualified rate meets the index, namely the production quality qualified rate of the production scheduling plan can meet the set production quality qualified rate, carrying out next verification; otherwise, adjusting and setting the index value of the production quality qualification rate, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
comparing the equipment utilization rate of the digital twin workshop with a set equipment utilization rate, and if the equipment utilization rate meets an index, taking the equipment utilization rate as an optimal scheduling scheme; otherwise, adjusting and setting the index value of the equipment utilization rate, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
if the production data index cycle verifies for a plurality of times or no scheduling scheme which can meet all indexes appears, the production scheduling plan is modified until a final scheduling scheme appears.
As a preferred technical scheme, the physical workshop information is collected, cleaned and stored in a server database in a set time period;
the server carries out normalization processing on the collected and cleaned information, carries out production state evaluation on equipment information of the physical workshop to obtain an evaluation result, and generates production feedback;
and transmitting the production feedback to the digital twin workshop, adjusting each model parameter in the digital twin workshop, and updating the dynamic mapping of the production of the physical workshop between the digital twin workshops into the digital twin workshop.
As a preferred technical solution, the evaluating the production state of the device information of the physical plant to obtain an evaluation result specifically includes:
acquiring the equipment production state in the physical workshop equipment information; the production state of the equipment is that the physical data of the equipment comprises rotating speed, electric power, magnetism, heat and pressure data;
analyzing the production state of the equipment, and evaluating that the production state of the equipment is good when the production state of the equipment is maintained above a set alarm threshold; when the production state of the equipment is reduced to be below a set alarm threshold value, evaluating that the capacity of the equipment is reduced; when the data disorder occurs to the equipment, the equipment is evaluated to be in fault; and returning the evaluation result to the server.
As a preferred technical scheme, the adjusting of each model parameter in the digital twin workshop and the updating of the digital twin workshop specifically include:
when the evaluation result of some equipment is good in production state, the server transmits the equipment information to the digital twin workshop, and the corresponding model of the equipment in the digital twin workshop executes the operation of modifying the parameters of the equipment to finish self-adaptive updating;
when the evaluation result of some equipment is that the productivity is reduced, the server sends prompt information to the digital twin workshop to prompt that the equipment should be maintained in time, a corresponding model of the equipment in the digital twin workshop executes the operation of modifying the parameters of the equipment, and marks the serial number of the equipment;
and when the evaluation result of a certain device is a fault, the server sends abnormal alarm information to the digital twin workshop to prompt that the device should stop operating, and the corresponding model of the device in the digital twin workshop stops operating to wait for the evaluation result of the next period.
On the other hand, the invention provides a dynamic workshop production scheduling system based on digital twin, which is characterized by comprising a data acquisition module, a workshop construction module, a scheduling optimization module, a workshop updating module and a data storage module;
the data acquisition module is used for acquiring physical workshop information, including order information and multi-source production elements;
the workshop establishing module is used for generating a production scheduling plan according to the order information, establishing a corresponding twin model by adopting a digital twin method according to multi-source production elements and obtaining a digital twin workshop;
the scheduling optimization module is used for performing simulation production in the digital twin workshop according to the production scheduling plan and optimizing the scheduling to obtain an optimal scheduling scheme;
the workshop updating module uploads the optimal scheduling scheme to a server to issue a production instruction to a physical workshop; the server guides the physical workshop to produce and evaluate, generates production feedback to dynamically iterate the digital twin workshop, and adjusts parameters to update the digital twin workshop;
and after the production is finished, the data storage module stores the production data of the physical workshop, the dynamic mapping relation and the production data of the digital twin workshop into the server database in a classified manner.
In still another aspect, the present invention provides a computer-readable storage medium storing a program, wherein the program is executed by a processor to implement the above-mentioned digital twin-based dynamic shop scheduling method.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the existing scheduling method is directed at static scheduling and lacks timeliness; according to the method, the physical workshop is mapped to the twin workshop for the verification of the scheduling scheme through a digital twin technology, and the dynamic simulation of the model is realized in the twin workshop in real time according to the dynamic change of the production state, so that the problem of serious insufficient timeliness of the scheduling scheme in the prior art is solved, the efficiency of equipment is exerted to the maximum extent, the production quality and efficiency are improved, and the production cost is reduced.
(2) The existing scheduling method mostly depends on artificial regulation and manual calculation to ensure the accuracy of scheduling, greatly reduces the efficiency of scheduling, and further influences the production flow of the whole workshop and the delivery time of an order to a great extent, thereby reducing the credibility of enterprises per se; the invention applies a digital twin technology to carry out intelligent simulation on production scheduling, solves the problems of high calculation cost and manual intervention, improves scheduling decision capability, and realizes intelligent scheduling, equipment operation parameter optimization and process optimization, thereby realizing low-cost, high-efficiency and high-quality production and improving product quality and profit margin.
(3) The existing workshop production scheduling method lacks early warning capability for equipment state, and usually the capacity is reduced or even equipment is in failure due to equipment overload, so that the workshop production efficiency is reduced, and meanwhile, the equipment maintenance cost and the potential safety hazard of a workshop are increased. The invention applies a digital twin technology, reads the real-time equipment production state, analyzes the equipment data and uploads the analyzed data to a digital twin workshop, and performs parallel production in the twin workshop according to an established scheduling scheme. After twin production is finished, the equipment states in the twin workshop are scanned one by one, and whether equipment faults occur is judged. The method improves the production early warning capability in the production scheduling, reduces the equipment maintenance cost and greatly reduces the potential safety hazard of a workshop.
(4) The scheduling scheme of the invention is established by utilizing the advantage that a digital twin technology can process and calculate a large amount of data, and a plurality of production data indexes are fully considered, wherein the production data indexes comprise four aspects of order production completion time maximum value, workshop total energy consumption upper limit, quality qualification rate of produced products and equipment utilization rate. And continuously carrying out simulation production in the twin space in a circulating mode to finally obtain a scheduling scheme which can meet the conditions of the maximum value of order production completion time, the upper limit of total energy consumption of a workshop, the quality qualified rate of produced products and the utilization rate of equipment.
(5) The invention establishes a dynamic workshop production scheduling system based on digital twin, which comprises a data acquisition module, a workshop construction module, a scheduling optimization module, a workshop updating module and a data storage module, covers the whole process from workshop production data acquisition to scheduling optimization to data distributed storage of a rear end, and provides an efficient workshop production scheduling solution by combining a dynamic workshop production scheduling method.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a block flow diagram of a digital twin based dynamic shop production scheduling method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a twin workshop construction based on digital twinning according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a comparison between digital twin plant production data and set production data indicators according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating dynamic iteration and parameter adjustment updating for a digital twin plant according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a digital twin-based dynamic workshop production scheduling system according to an embodiment of the present invention;
fig. 6 is a structural diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, 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. It is to be understood that the embodiments described are only a few embodiments of the present application and 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.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, in an embodiment of the present application, a method for scheduling a production schedule of a dynamic workshop based on a digital twin is provided, which includes the following steps:
s1, collecting physical workshop information including order information, multi-source production elements and the like;
s2, generating a production schedule plan according to the order information, establishing a corresponding twin model by adopting a digital twin method according to multi-source production factors, wherein the twin model comprises a personnel twin model, an equipment twin model, a material twin model, a method twin model and an environment twin model, and obtaining a digital twin workshop;
s3, performing simulation production in the digital twin workshop according to the production scheduling plan and optimizing the scheduling to obtain an optimal scheduling scheme;
s4, uploading the optimal scheduling scheme to a server to issue a production instruction to a physical workshop; the server guides the physical workshop to produce and evaluate, generates production feedback to dynamically iterate the digital twin workshop, and adjusts parameters to update the digital twin workshop;
and S5, after the production is finished, storing the production data of the physical workshop, the dynamic mapping relation and the production data classification formula of the digital twin workshop into a server database.
More specifically, step S1 specifically includes:
firstly, acquiring physical workshop information through a sensor element, and then informationizing the acquired information to obtain order information and multisource production elements; the order information comprises an order delivery period, transaction times of both sides of the order, the profit of the order and the like; the multisource production factors comprise personnel information, equipment information, material information, production method information, workshop environment information and the like.
More specifically, step S2 specifically includes:
s21, generating a production scheduling plan according to the order information, including:
analyzing order information such as order delivery time, transaction times of both sides of the order and profit of the order, and generating a production scheduling plan by combining and referring to a historical production target;
when order information is analyzed, the analysis conditions include, but are not limited to: whether the order delivery period is urgent or not and whether the order delivery period is enough to drive the workshop to carry out overload production or not, whether the transaction times of two parties of the order are enough to drive the workshop to carry out overload production or not and whether the profit of the order is enough to drive the workshop to carry out overload production or not; generating a production schedule plan approaching the production limit based on the analysis and by combining and referring to the historical production target; if the past production target is 5 elements in the workshop capacity within 1 hour, a production scheduling plan for producing 10 elements within 1 hour is generated by referring to the production target, so that the aim of seeking a limit production scheduling scheme is achieved by simulating production in a digital twin workshop.
S22, establishing a corresponding twin model by adopting a digital twin method to obtain a digital twin workshop, specifically:
as shown in fig. 2, the man-machine-material method ring is a short for five main factors affecting product quality in the overall quality management theory, wherein people refer to personnel manufacturing products, including physical conditions, technical levels and the like of personnel such as operators, inspectors and technicians; machine refers to equipment used in manufacturing products, including problems that may occur in the production of equipment, such as tool wear, reduced machine tool precision, etc.; the material refers to raw materials used for manufacturing products, including quality conditions of materials for processing and the like; the method refers to a method used for manufacturing products, and comprises a working mode, an operation method, action speed, a program, an installation position, an order and the like; the ring refers to the environment in which the product is manufactured, including circuit layout, lighting, noise, vibration, temperature, etc.; the five production factors are utilized to establish a corresponding twin model to obtain a digital twin workshop, so that the physical workshop can be truly and accurately mapped to the digital twin workshop, the uniformity of the physical workshop and the digital twin workshop is ensured, and the effectiveness of a simulation result is ensured;
drawing a three-dimensional model by using three-dimensional modeling software according to the multi-source production elements; after the drawing is finished, flexible grid division is carried out on the three-dimensional model, the three-dimensional model is led into three-dimensional animation rendering and manufacturing software for environment rendering and scene building, and a microscopic individual model is constructed; finally, the rendered three-dimensional model is stored as a file with a format matched with the finite element simulation software, the file is imported into the finite element simulation software, the physical parameters of the model are calculated and simulated, and a digital twin workshop is established;
the twin personnel model established according to the personnel information in the multi-source production elements is expressed as follows:
H＝{H_Number，H_Name，H_Gender，H_Position，H_Fatigue，H_Type}
wherein H _ Number represents a personnel Number, H _ Name represents a personnel Name, H _ Gender represents a personnel Gender, H _ Position represents a personnel Position, H _ Fatigue represents personnel Fatigue, and H _ Type represents a personnel work Type;
the equipment twin model established according to the equipment information in the multi-source production elements is expressed as follows:
D＝{D_Number，D_Name，D_State，D_Type，D_Position，D-Load，D_Temperature，D_Pressure}
wherein D _ Number represents a device Number, D _ Name represents a device Name, D _ State represents a device State, D _ Type represents a device Type, D-Position represents a device Position, D _ Load represents a device Load, D _ Temperature represents a device Temperature, and D _ Pressure represents a device Pressure;
the material twinning model established according to the material information in the multi-source production elements is expressed as follows:
S＝{S_Number，S_Name，S_Type，S_Position，S_Inventory}
wherein S _ Number represents a material Number, S _ Name represents a material Name, S _ Type represents a material Type, S _ Position represents a material filling Position, and S _ Inventory represents a material stock;
the method twin model established according to the production method information in the multi-source production elements is expressed as follows:
M＝{M_Number，M_Name，M_Type，M_Information}
wherein M _ Number represents a method Number, M _ Name represents a method Name, M _ Type represents a method Type, and M _ Information represents method specific Information;
the environment twin model established according to the workshop environment information in the multi-source production elements is expressed as follows:
E＝{E_Number，E_Location，E_Type，E_Temperature，E_Pipe，E_Electricity，E_Else}
where E _ Number represents an environment Number, E _ Location represents an environment Location, E _ Type represents an environment Type, E _ Temperature represents an environment Temperature, E _ Pipe represents a Pipe arrangement, E _ electric represents a power supply bus cable, and E _ electric represents other information.
More specifically, step S3 specifically includes the following steps:
since the objective of the present application is to accurately obtain an excellent scheduling plan verified by the digital twin plant, but the production scheduling plan mentioned in step S2 is a production plan approaching the production limit, which cannot be completed in the actual production of the plant, the objective of step S3 is to verify whether the plant capacity can meet the capacity requirement of the production scheduling plan in step S2 by the simulated production of the twin plant;
s31, setting production data indexes, and inputting a production schedule plan into a digital twin space for simulation production;
more specifically, the set production data indexes comprise production time, total workshop energy consumption, production quality qualification rate and equipment utilization rate;
s32, detecting the digital twin workshop after simulation production to obtain production data of the digital twin workshop, wherein the production data comprise production completion time, total workshop energy consumption brought by production, production quality qualification rate, equipment utilization rate data and the like;
and S33, comparing the digital twin workshop production data with the set production data index, if the digital twin workshop production data meets the set production data index, carrying out the next step, otherwise, adjusting the set production data index to continue the simulation production until the final scheduling scheme appears as the optimal scheduling scheme.
Further, as shown in fig. 3, the specific process of comparing the digital twin plant production data with the set production data index is as follows:
comparing the production time of the digital twin workshop with the set production time, and if the production time meets the index, namely the production time of the production scheduling plan can meet the set delivery time target, carrying out next verification; otherwise, adjusting and setting the production time index value, regenerating a production scheduling plan and carrying out simulated production in the digital twin workshop again;
comparing the total workshop energy consumption of the digital twin workshop with the set total workshop energy consumption, and if the total workshop energy consumption meets the index, namely the total workshop production energy consumption of the production scheduling plan can meet the set energy consumption index, performing next verification; otherwise, adjusting and setting the total energy consumption index value of the workshop, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
comparing the production quality qualified rate of the digital twin workshop with the set production quality qualified rate, and if the production quality qualified rate meets the index, namely the production quality qualified rate of the production scheduling plan can meet the set production quality qualified rate, carrying out next verification; otherwise, adjusting and setting the index value of the production quality qualification rate, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
comparing the equipment utilization rate of the digital twin workshop with a set equipment utilization rate, and if the equipment utilization rate meets an index, taking the equipment utilization rate as an optimal scheduling scheme; otherwise, adjusting and setting the index value of the equipment utilization rate, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
if the production data index cycle verifies for a plurality of times or no scheduling scheme which can meet all indexes appears, the production scheduling plan is modified until a final scheduling scheme appears.
The optimization of the schedule mentioned in this embodiment can be implemented by various methods, optionally, for example: assuming that the production schedule has a target of 100 elements in 10 hours, but the result of the simulation production verification of the digital twin plant indicates that the solution is not verified, the production schedule can be reduced to 95 elements in 10 hours, and so on.
More specifically, as shown in fig. 4, in step S4, performing dynamic iteration on the digital twin plant, and adjusting parameters to update the digital twin plant, specifically:
s41, collecting, cleaning and storing the physical workshop information to a server database within a set time period (such as one hour);
s42, after normalization processing is carried out on the collected and cleaned information by the server, production state evaluation is carried out on equipment information of the physical workshop to obtain an evaluation result, and production feedback is generated;
s43, transmitting the production feedback to the digital twin workshop, adjusting each model parameter in the digital twin workshop, and updating the production dynamic mapping of the physical workshop between the digital twin workshops into the digital twin workshop;
further, in step S42, the evaluation result obtained by evaluating the production state of the device information of the physical plant specifically includes:
acquiring data of a sensor, and acquiring an equipment production state in the physical workshop equipment information; the production state of the equipment is that the physical data of the equipment comprises data such as rotating speed, electric power, magnetism, heat, pressure and the like;
analyzing the production state of the equipment, and evaluating the production state of the equipment to be good when the production state of the equipment is maintained above a set alarm threshold; when the production state of the equipment is reduced to be below a set alarm threshold value, evaluating that the capacity of the equipment is reduced; when the data disorder occurs to the equipment, the equipment is evaluated to be in fault; and returning the evaluation result to the server.
Further, in step S43, adjusting each model parameter in the digital twin plant, and updating the digital twin plant, specifically:
when the evaluation result of some equipment is good in production state, the server transmits the equipment information to the digital twin workshop, and the corresponding model of the equipment in the digital twin workshop executes the operation of modifying the parameters of the equipment to finish self-adaptive updating;
when the evaluation result of some equipment is that the productivity is reduced, the server sends prompt information to the digital twin workshop to prompt that the equipment should be maintained in time, a corresponding model of the equipment in the digital twin workshop executes the operation of modifying the parameters of the equipment, and marks the serial number of the equipment;
and when the evaluation result of a certain device is a fault, the server sends abnormal alarm information to the digital twin workshop to prompt that the device should stop operating, and the corresponding model of the device in the digital twin workshop stops operating to wait for the evaluation result of the next period.
It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present invention is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present invention.
Based on the same idea as the digital twin-based dynamic workshop production scheduling method in the embodiment, the invention also provides a digital twin-based dynamic workshop production scheduling system, which can be used for executing the digital twin-based dynamic workshop production scheduling method. For convenience of illustration, the structural diagram of the embodiment of the dynamic workshop production scheduling system based on digital twin is only shown in the relevant part of the embodiment of the present invention, and those skilled in the art will understand that the illustrated structure does not constitute a limitation to the device, and may include more or less components than those illustrated, or combine some components, or arrange different components.
Referring to fig. 5, in another embodiment of the present application, a dynamic workshop production schedule scheduling system based on digital twin is provided, which includes a data collection module, a workshop establishment module, a schedule optimization module, a workshop update module, and a data storage module;
the data acquisition module is used for acquiring physical workshop information including order information, multi-source production elements and the like;
the workshop establishing module is used for generating a production scheduling plan according to the order information; according to the multi-source production elements, a corresponding twin model is established by adopting a digital twin method to obtain a digital twin workshop;
the scheduling optimization module is used for performing simulation production in the digital twin workshop according to the production scheduling plan and optimizing the scheduling to obtain an optimal scheduling scheme;
the workshop updating module uploads the optimal scheduling scheme to a server to issue a production instruction to a physical workshop; the server guides the physical workshop to produce and evaluate, generates production feedback to dynamically iterate the digital twin workshop, and adjusts parameters to update the digital twin workshop;
and after the production is finished, the data storage module stores the production data and the dynamic mapping relation of the physical workshop and the production data of the digital twin workshop into the server database in a classified manner.
It should be noted that the digital twin-based dynamic workshop production scheduling system and the digital twin-based dynamic workshop production scheduling method of the present invention correspond to each other one to one, and the technical features and the advantages thereof described in the above embodiment of the digital twin-based dynamic workshop production scheduling method are applicable to the embodiment of the digital twin-based dynamic workshop production scheduling system, and specific contents may refer to the description in the embodiment of the method of the present invention, and are not described herein again, and thus, the present invention is stated.
In addition, in the implementation of the digital twin based dynamic workshop production scheduling system according to the above embodiment, the logical division of each program module is only an example, and in practical applications, the above function allocation may be performed by different program modules according to needs, for example, due to configuration requirements of corresponding hardware or due to convenience of implementation of software, that is, the internal structure of the digital twin based dynamic workshop production scheduling system is divided into different program modules to perform all or part of the above described functions.
Referring to fig. 6, in an embodiment, a computer readable storage medium is provided, which stores a program, and when the program is executed by a processor, the method for scheduling a dynamic workshop production schedule based on digital twin is implemented, specifically:
collecting physical workshop information including order information, multi-source production elements and the like;
generating a production schedule plan according to the order information, establishing a corresponding twin model by adopting a digital twin method according to multi-source production factors, and obtaining a digital twin workshop;
performing simulation production in a digital twin workshop according to a production scheduling plan and optimizing the scheduling to obtain an optimal scheduling scheme;
uploading the optimal scheduling scheme to a server to issue a production instruction to a physical workshop; the server guides the physical workshop to produce and evaluate, generates production feedback to dynamically iterate the digital twin workshop, and adjusts parameters to update the digital twin workshop;
and after the production is finished, storing the production data of the physical workshop, the dynamic mapping relation and the production data of the digital twin workshop into a server database in a classified manner.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such modifications are intended to be included in the scope of the present invention.
Claims (10)
1. The dynamic workshop production scheduling method based on the digital twin is characterized by comprising the following steps of:
collecting physical workshop information; the physical workshop information comprises order information and multi-source production elements;
generating a production schedule plan according to the order information, establishing a corresponding twin model by adopting a digital twin method according to multi-source production factors, and obtaining a digital twin workshop; the twin model comprises a personnel twin model, an equipment twin model, a material twin model, a method twin model and an environment twin model;
carrying out simulation production in the digital twin workshop according to the production scheduling plan and optimizing the scheduling to obtain an optimal scheduling scheme;
uploading the optimal scheduling scheme to a server to issue a production instruction to a physical workshop; the server guides the physical workshop to produce and evaluate, generates production feedback to dynamically iterate the digital twin workshop, and adjusts parameters to update the digital twin workshop;
and after the production is finished, storing the production data of the physical workshop, the dynamic mapping relation and the production data of the digital twin workshop into a server database in a classified manner.
2. The digital twin-based dynamic workshop production schedule scheduling method according to claim 1, wherein the collecting physical workshop information specifically comprises:
acquiring physical workshop information through a sensor element, and informationizing the acquired information to obtain order information and multisource production elements;
the order information comprises an order delivery period, transaction times of both sides of the order and the profit of the order;
the multi-source production elements comprise personnel information, equipment information, material information, production method information and workshop environment information.
3. The dynamic workshop production schedule scheduling method based on the digital twin as set forth in claim 2, wherein the production schedule plan is generated according to order information, specifically:
analyzing order information, and generating a production scheduling plan by combining and referring to a historical production target;
the analysis order information comprises whether the order delivery period is urgent or not and whether the order delivery period is enough to drive the workshop to carry out overload production or not, whether the transaction times of two parties of the order are enough to drive the workshop to carry out overload production or not and whether the profit of the order is enough to drive the workshop to carry out overload production or not;
the method comprises the following steps of establishing a corresponding twin model by adopting a digital twin method to obtain a digital twin workshop, specifically:
drawing a three-dimensional model by using three-dimensional modeling software according to the multi-source production elements; after the drawing is finished, flexible grid division is carried out on the three-dimensional model, the three-dimensional model is led into three-dimensional animation rendering and manufacturing software for environment rendering and scene building, and a microscopic individual model is constructed; and finally, storing the rendered three-dimensional model as a file in a format matched with the finite element simulation software, importing the file into the finite element simulation software, and carrying out calculation simulation on the physical parameters of the model to obtain a digital twin workshop, wherein:
the twin model of the personnel established according to the personnel information in the multi-source production elements is expressed as follows:
H＝{H_Number，H_Name，H_Gender，H_Position，H_Fatigue，H_Type}
wherein H _ Number represents a personnel Number, H _ Name represents a personnel Name, H _ Gender represents a personnel Gender, H _ Position represents a personnel Position, H _ Fatigue represents personnel Fatigue, and H _ Type represents a personnel work Type;
the equipment twin model established according to the equipment information in the multi-source production elements is expressed as follows:
D＝{D_Number，D_Name，D_State，D_Type，D_Position，D_Load，D_Temperature，D_Pressure}
wherein D _ Number represents a device Number, D _ Name represents a device Name, D _ State represents a device State, D _ Type represents a device Type, D _ Position represents a device Position, D _ Load represents a device Load, D _ Temperature represents a device Temperature, and D _ Pressure represents a device Pressure;
the material twinning model established according to the material information in the multi-source production elements is expressed as follows:
S＝{S_Number，S_Name，S_Type，S_Position，S_Inventory}
wherein S _ Number represents a material Number, S _ Name represents a material Name, S _ Type represents a material Type, S _ Position represents a material filling Position, and S _ Inventory represents a material stock;
the method twin model established according to the production method information in the multi-source production elements is expressed as follows:
M＝{M_Number，M_Name，M_Type，M_Information}
wherein M _ Number represents a method Number, M _ Name represents a method Name, M _ Type represents a method Type, and M _ Information represents method specific Information;
the environment twin model established according to the workshop environment information in the multi-source production elements is expressed as follows:
E＝{E_Number，E_Location，E_Type，E_Temperature，E_Pipe，E_Electricity，E_Else}
where E _ Number represents an environment Number, E _ Location represents an environment Location, E _ Type represents an environment Type, E _ Temperature represents an environment Temperature, E _ Pipe represents a Pipe arrangement, E _ electric represents a power supply bus, and E _ electric represents other information.
4. The dynamic workshop production scheduling method based on the digital twin as claimed in claim 3, wherein the obtaining of the optimal scheduling scheme specifically includes:
setting production data indexes, and inputting a production scheduling plan into a digital twin workshop for simulation production;
detecting the digital twin workshop after the simulation production to obtain the production data of the digital twin workshop;
and comparing the production data of the digital twin workshop with the set production data indexes, if the production data indexes are met, carrying out the next step, otherwise, adjusting the set production data indexes to continue the simulation production until a final scheduling scheme appears as an optimal scheduling scheme.
5. The dynamic workshop production schedule scheduling method based on digital twinning as claimed in claim 4, wherein the production data indicators include production time, total energy consumption of the workshop, qualified rate of production quality and equipment utilization rate;
the digital twin workshop production data is compared with set production data indexes, and the method specifically comprises the following steps:
comparing the production time of the digital twin workshop with the set production time, and if the production time meets the index, namely the production time of the production scheduling plan can meet the set delivery time target, carrying out next verification; otherwise, adjusting and setting the index value of the production time, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
comparing the total workshop energy consumption of the digital twin workshop with the set total workshop energy consumption, and if the total workshop energy consumption meets the index, namely the total workshop production energy consumption of the production scheduling plan can meet the set energy consumption index, performing next verification; otherwise, adjusting and setting the total energy consumption index value of the workshop, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
comparing the production quality qualified rate of the digital twin workshop with the set production quality qualified rate, and if the production quality qualified rate meets the index, namely the production quality qualified rate of the production scheduling plan can meet the set production quality qualified rate, carrying out next verification; otherwise, adjusting and setting the index value of the production quality qualification rate, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
comparing the equipment utilization rate of the digital twin workshop with a set equipment utilization rate, and if the equipment utilization rate meets an index, taking the equipment utilization rate as an optimal scheduling scheme; otherwise, adjusting and setting the index value of the equipment utilization rate, regenerating a production scheduling plan and carrying out simulation production again in the digital twin workshop;
if the production data index cycle verifies for a plurality of times or no scheduling scheme which can meet all indexes appears, the production scheduling plan is modified until a final scheduling scheme appears.
6. The method for scheduling production schedule of a digital twin-based dynamic workshop according to claim 4, wherein the dynamic iteration is performed on the digital twin workshop, and parameters are adjusted to update the digital twin workshop, specifically:
collecting, cleaning and storing physical workshop information into a server database in a set time period;
the server carries out normalization processing on the collected and cleaned information, carries out production state evaluation on equipment information of the physical workshop to obtain an evaluation result, and generates production feedback;
and transmitting the production feedback to the digital twin workshop, adjusting each model parameter in the digital twin workshop, and updating the dynamic mapping of the production of the physical workshop between the digital twin workshops into the digital twin workshop.
7. The dynamic workshop production schedule scheduling method based on the digital twin as claimed in claim 6, wherein the evaluation of the production status of the equipment information of the physical workshop to obtain the evaluation result specifically comprises:
acquiring the equipment production state in the physical workshop equipment information; the production state of the equipment is that the physical data of the equipment comprises rotating speed, electric power, magnetism, heat and pressure data;
analyzing the production state of the equipment, and evaluating the production state of the equipment to be good when the production state of the equipment is maintained above a set alarm threshold; when the production state of the equipment is reduced to be below a set alarm threshold value, evaluating that the capacity of the equipment is reduced; when the data disorder occurs to the equipment, the equipment is evaluated to be in fault; and returning the evaluation result to the server.
8. The method for scheduling production schedule of dynamic workshop based on digital twin as claimed in claim 7, wherein the adjusting parameters of each model in the digital twin workshop updates the digital twin workshop, specifically:
when the evaluation result of some equipment is good in production state, the server transmits the equipment information to the digital twin workshop, and the corresponding model of the equipment in the digital twin workshop executes the operation of modifying the parameters of the equipment to finish self-adaptive updating;
when the evaluation result of some equipment is that the productivity is reduced, the server sends prompt information to the digital twin workshop to prompt that the equipment should be maintained in time, a corresponding model of the equipment in the digital twin workshop executes the operation of modifying the parameters of the equipment, and marks the serial number of the equipment;
and when the evaluation result of a certain device is a fault, the server sends abnormal alarm information to the digital twin workshop to prompt that the device should stop operating, and the corresponding model of the device in the digital twin workshop stops operating to wait for the evaluation result of the next period.
9. The dynamic workshop production scheduling system based on the digital twin is characterized by comprising a data acquisition module, a workshop construction module, a scheduling optimization module, a workshop updating module and a data storage module;
the data acquisition module is used for acquiring physical workshop information, including order information and multi-source production elements;
the workshop establishing module is used for generating a production scheduling plan according to the order information, establishing a corresponding twin model by adopting a digital twin method according to multi-source production elements and obtaining a digital twin workshop;
the scheduling optimization module is used for performing simulation production and optimizing scheduling in the digital twin workshop according to the production scheduling plan to obtain an optimal scheduling scheme;
the workshop updating module uploads the optimal scheduling scheme to a server to issue a production instruction to a physical workshop; the server guides the physical workshop to produce and evaluate, generates production feedback to dynamically iterate the digital twin workshop, and adjusts parameters to update the digital twin workshop;
and after the production is finished, the data storage module stores the production data of the physical workshop, the dynamic mapping relation and the production data of the digital twin workshop into the server database in a classified manner.
10. A computer-readable storage medium storing a program, wherein the program, when executed by a processor, implements the digital twin-based dynamic shop scheduling method according to any one of claims 1 to 8.
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