CN112270508A - Digital twin smart cloud scheduling method meeting personalized customized production - Google Patents
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Abstract
The invention provides a digital twin smart cloud scheduling method meeting personalized customized production, and relates to the technical field of personalized customized production workshop smart scheduling. The cloud pre-scheduling production planning method is used for weighting the personalized orders of the users according to three indexes of user grades, order delivery date priorities and development difficulty, performing prediction analysis on the performance of products based on digital twin modeling and joint simulation of single system or multiple system software, and performing full-digital decision scheduling control on scheduling algorithm information. The equipment information, the material handling information and the warehouse information of the physical workshop can be transmitted to the twin workshop, the reliability and the usability of the product can be effectively improved through the bidirectional information interaction between the twin workshop and the physical workshop entity, the requirement of small-batch individualized production can be met, and meanwhile, the product research and development and manufacturing risks are reduced.
Description
Technical Field
The invention relates to the technical field of intelligent scheduling of personalized customized production workshops, in particular to a digital twin intelligent cloud scheduling method meeting personalized customized production.
Background
Modeling and simulating production planning: the method comprises the steps of modeling and simulating each production unit and the production flow when the production units work together in a coordinated mode through user personalized order demand preference attribute refinement, wherein the modeling and simulation comprises the digital modeling and display of each production unit, and the numerical simulation of the production flows such as material flow, scheduling production scheduling logic, production process logic combing, process constraint establishment, Automatic Guided Vehicle (AGV) control algorithm and the like.
Debugging the production environment of the virtual space: in a network space production environment, process modeling and simulation are carried out on the work flow and efficiency in each production unit, wherein simulation of the automatic operation process of mechanical equipment can be included, and virtual debugging is carried out on a plurality of robot arm control algorithms which work in a coordinated mode, so that the process modeling and simulation is an important step for verifying the overall result and ensuring smooth production. On the other hand, in addition to the automation equipment, the simulation and debugging of man-machine interaction processes in the production unit are also included.
Adjusting and optimizing the digital twin of production based on the actual condition of the production line and the running information feedback; the system is oriented to a user of the product, extracts and analyzes actual characteristics of the specific product based on information such as a physical sensor and the like, realizes functions such as predictive maintenance and the like, and can also guide a design scheme of the product through actual operation information feedback of the product. In the actual execution stage of production or the operation stage of products, system information such as raw materials, equipment, processes, personnel or environmental parameters, operation states and the like can be adjusted and changed at any time, and the digital twin of performance needs to update the changes in a digital space in real time. For this reason, rapid, real-time simulation and prediction in conjunction with data input by physical sensors is an important technique for a digital twin of performance. After the product is put into operation, the real-time simulation calculation can be carried out on important performance parameters which are difficult to measure based on the data input and rapid simulation technology, and the functions of predictive maintenance and the like of the product are realized. Each physical sensor of a production line or a product can generate a large amount of data, and the analysis of the actual data by applying methods such as machine learning and the like is an important technology for realizing digital twin information feedback functions such as active response, accident tracing, predictive maintenance and the like. For example, the digital twin of the production performance can extract data of practical situations such as accidents occurring in the production process, realize reason analysis through modes such as machine learning and numerical simulation verification, and provide a targeted improvement scheme in product design and production flow design for accident reasons to perform rescheduling production of resources. If all the processes in the manufacturing system are accurate, the production can be smoothly carried out. However, in case of unsmooth production progress, the problem is hard to find out quickly when the whole process is very complicated and the problem occurs in the manufacturing process and affects the yield. The simplest approach is to try a completely new production strategy in the production system, but in the face of many different material and equipment choices, it is a difficult task to know clearly which choice will give the best results. Aiming at the situation, the simulation and evaluation can be carried out on different production strategies in the digital twin model, and the working procedure with the blank time can be quickly found out by combining big data analysis and statistical technology. And after the strategy is adjusted, the performance of the whole production system is simulated, the maximization of all resource utilization rates is further optimized, all persons in all processes are ensured to be best, and the maximization of profitability is realized.
In conventional models, physical components must be manufactured after the design is completed to evaluate the quality and manufacturability of the design solution, which means increased cost and risk. By establishing the digital twin model, the finished product quality of any part can be predicted before the part is actually manufactured, and whether design defects exist, such as interference among parts, whether the design meets the specification and the like, are identified. Finding out the reason of generating the design defect, directly modifying the design in the digital twin model, and performing the manufacturing simulation again to check whether the problem is solved. In a manufacturing system, production can be carried out smoothly only when all processes are accurate and correct, a general process verification method is to try out after obtaining configured production equipment and judge whether the equipment operates normally, but the problem is found to be late at this time, so that production delay is possibly caused, and the cost required for solving the problem is far higher than that in the early stage of the processes.
An efficient method of intelligent cloud approach by introducing digital twinning techniques is to build a digital twinning model containing all the manufacturing process details, and verify the manufacturing process in a virtual environment. After the problem is found, only the model needs to be corrected, for example, when the robot interferes, the height of the workbench, the position of the conveying belt, the reverse assembly table and the like are changed, and then the simulation is executed again to ensure that the robot can correctly execute the task.
The performance of the digital twin model is predicted and improved in the product design stage, accurate information is mastered and the manufacturing process is predicted at the initial stage of the manufacturing process, all details are guaranteed to be accurate and correct, and the important significance is undoubtedly achieved because the earlier the user knows how to manufacture excellent products meeting the expectation of the user, the faster the high-quality products can be pushed out to the market, and the prior opportunity is preempted.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a digital twin smart cloud scheduling method meeting personalized customized production, wherein an embedded information transmitter is installed on each device in a personalized production workshop, and a workshop Internet of things network is established in a wireless and wired mode by combining an Internet of things manufacturing technology, so that real-time communication can be carried out between the devices, and each device is established into an intelligent individual.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a digital twin smart cloud scheduling method meeting personalized customized production comprises the following steps:
step 1: a user submits an individualized demand order, wherein the order comprises the number of workpieces, process parameters and product specification information;
step 2: the production planning department carries out process design on the personalized demand order, generates a cloud code and a process constraint parameter corresponding to the process for each workpiece process, and uploads the equipment parameters and the personalized order attribute parameters to a cloud system;
and step 3: the cloud scheduling system carries out pre-scheduling plan according to the order delivery date, the order priority and the manufacturing material inventory information of the personalized order, transmits the order to a workshop, evaluates and calculates the weight of each customized user order according to the user level, the order delivery date and the development difficulty level, sets a user level s, an order delivery date t and a development difficulty level i, and each index corresponds to influence factors C1, C2 and C3 respectively, wherein C1+ C2+ C3 is 1; recording the influence factors into a cloud system code;
and 4, step 4: after receiving a pre-scheduling plan of a cloud scheduling system, a workshop adopts real-time dynamic scheduling of workshop resources to perform resource cooperation to establish a dynamic scheduling model, and at each rescheduling point, a hybrid-driven rescheduling mode based on a decomposed multi-objective optimization algorithm is adopted to update the current attributes of the workshop, including maximum completion time, total pull-off period and processing state energy consumption; in the optimization process, a solution is preset for each emergency in the twin workshop to improve the efficiency of a scheduling scheme; if the interference of the emergency order event does not exist, the scheduling window is updated at intervals by adopting periodic rescheduling;
the resource coordination process comprises the following steps: the intelligent individual equipment information with the capability of completing the order task feeds back self state information and load condition information, the cloud method carries out screening processing on the information, and visual display is carried out on the scheduling result and product modeling;
the dynamic scheduling model is that an information collecting card on each device of a physical workshop uploads data information to a twin workshop in a two-dimensional code scanning mode, and the twin workshop establishes dynamic scheduling models of two dynamic events, namely 'arrival of a new personalized order' and 'insertion of an emergency important customer' through energy consumption, load, fault, detail processing time of a newly inserted order, material information and the like of the devices; on the basis of workshop perception data, a virtual-real mapping frame of a virtual workshop and a physical workshop is established, and as workpiece processing information comprises workpiece procedures, processing equipment, operators and processing time, the ontology of the processing information is formally described as
processinfo＝(jobinfo,Machineinfo,personinfo,operationinfo,Manufacuringinfo)
The processing information body processinfo is composed of 5 sub-bodies, which are workpiece information jobinfo, equipment information Machineinfo, worker information personinfo, process information operationinfo, and processing information Manufacuringinfo, respectively.
The maximum completion time f1(tr) Comprises the following steps:
wherein n (t)r) Representing the total number of workpieces in the workshop at the moment when the rescheduling point is reached; fi(tr) Indicating the time when the workpiece is finished; b isi(tr) Indicating the time when the workpiece starts to be machined after the start of rescheduling; i denotes the ith workpiece
The total drag period f2(tr) Comprises the following steps:
wherein DDi(tr) Is the delivery date of the workpiece;
said process state energy consumption PE (t)r) Comprises the following steps:
wherein CUk(tr) Represents the machining power of machine k; n is a radical ofi(tr) The number of the workpieces which are left to be processed after the workpieces are rescheduled is determined; pijk(tr) Is the processing time; α is a coefficient of a fitting curve, m represents a machine, i represents a workpiece, j represents the number of processes of the workpiece, CUijk(tr) Indicates the machining power of j processes of the ith workpiece
And 5: transferring to the step 4 when the sample product is not qualified, transferring to the step 6 when the sample product is qualified;
step 6: before the machine tool starts to process, reading a workpiece number and a processing sequence constraint from a Radio Frequency Identification (RFID) tag carried by a workpiece, requesting a cloud processing code from a cloud system, searching codes and technological equipment parameters of corresponding procedures in a cloud code library, and returning the codes and the technological equipment parameters to the machine tool, wherein the machine tool processes according to the cloud code;
and 7: in the machining process, the workshop transmits the equipment machining real-time information and the material information visual billboard to the cloud system, a user tracks the order machining progress, and the equipment information and the material information provide reference indexes for pre-scheduling production and are used as historical data to be stored in the cloud system.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
the invention provides a digital twin intelligent cloud scheduling method meeting personalized customized production
The bidirectional information interaction between the digital twin and the physical workshop entity can effectively improve the reliability and the usability of the product and reduce the research, development and manufacturing risks of the product. The requirement of personalized customized production is met.
By the method, the real-time information transparency of the physical workshop and the twin workshop is realized, a user can track real-time information such as processing progress of an order, equipment material energy consumption and the like in real time, machine faults can be eliminated timely, cloud-end of workshop manufacturing information is realized, effective scheduling arrangement is carried out, meanwhile, a processing code base is established at the cloud end, historical processing information and technological equipment parameters are stored, production is guaranteed by the physical vehicle under the condition that personalized order customization and parameters are complex, data flow between a workshop body and the twin model body can be bidirectional, but not only the body can output data to the twin body, and the twin model body can also feed back information to a physical entity of the workshop. The enterprise can take further action and intervention on the physical entity of the workshop according to the information fed back by the twin. The method can support the user to modify the personalized preference of the product in the twin workshop, improve the satisfaction degree of the user and improve the delivery success rate. The bidirectional information interaction between the digital twin and the physical workshop entity can effectively improve the reliability and the usability of the product and reduce the research, development and manufacturing risks of the product.
Drawings
FIG. 1 is a flow chart of a digital twin smart cloud scheduling method according to the present invention;
FIG. 2 is a block diagram of a smart cloud scheduling in an embodiment of the invention;
FIG. 3 is a block diagram of a digital twinning technique according to an embodiment of the present invention;
FIG. 4 is a graph comparing results of the digital twinning technique according to the embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
A digital twin smart cloud scheduling method satisfying personalized customized production, as shown in fig. 1, includes the following steps:
step 1: a user submits an individualized demand order, wherein the order comprises the number of workpieces, process parameters and product specification information;
step 2: the production planning department carries out process design on the personalized order, generates a cloud code and a process constraint parameter corresponding to the process for each workpiece process, and uploads the equipment parameters and the attribute parameters of the personalized order to a cloud system;
and step 3: the cloud scheduling system carries out pre-scheduling plan according to the order delivery date, the order priority and the manufacturing material inventory information of the personalized order, transmits the order to a workshop, evaluates and calculates the weight of each customized user order according to the user level, the order delivery date and the development difficulty level, sets a user level s, an order delivery date t and a development difficulty level i, and each index corresponds to influence factors C1, C2 and C3 respectively, wherein C1+ C2+ C3 is 1; recording the influence factors into the cloud system code, assuming that the influence factors of the user VIP level, the order delivery date and the development difficulty level are respectively 0.5, 0.3 and 0.2, for example, a certain order delivery date is 15 days, the user VIP level is 5 and the development difficulty is 3, then the priority level weight of the order is: 0.5 × 5+1/15 × 0.3+3 × 0.2 ═ 3.12; since the lead time is inversely related to the order weight, the reciprocal lead time is used for calculation.
And 4, step 4: after receiving a pre-scheduling plan of a cloud scheduling system, a workshop adopts real-time dynamic scheduling of workshop resources to perform resource cooperation to establish a dynamic scheduling model, and as shown in fig. 2, at each rescheduling point, a hybrid-driven rescheduling mode based on a decomposed multi-objective optimization algorithm is adopted to update the current attributes of the workshop, including maximum completion time, total pull-off period and energy consumption of a processing state; in the optimization process, a solution is preset for each emergency in the twin workshop to improve the efficiency of a scheduling scheme; if the interference of the emergency order event does not exist, the scheduling window is updated at intervals by adopting periodic rescheduling;
the resource coordination process comprises the following steps: the intelligent individual equipment information with the capability of completing the order task feeds back self state information and load condition information, the cloud method carries out screening processing on the information, and visual display is carried out on the scheduling result and product modeling;
the dynamic scheduling model is that an information collecting card on each device of a physical workshop uploads data information to a twin workshop in a two-dimensional code scanning mode, and the twin workshop establishes dynamic scheduling models of two dynamic events, namely 'arrival of a new personalized order' and 'insertion of an emergency important customer' through energy consumption, load, fault, detail processing time of a newly inserted order, material information and the like of the devices; on the basis of workshop perception data, a virtual-real mapping frame of a virtual workshop and a physical workshop is established, and workpiece processing information comprises ontology formal description of workpiece procedure, processing equipment, operators and processing time processing information
processinfo＝(jobinfo,Machineinfo,personinfo,operationinfo,Manufacuringinfo)
The processing information body processinfo is composed of 5 sub-bodies, which are workpiece information jobinfo, equipment information Machineinfo, worker information personinfo, process information operationinfo, and processing information Manufacuringinfo, respectively.
Each sub-individual contains its own representation of attribute information, such as workpiece information: name, workpiece label, raw material. And establishing formalized expression of each information body and the incidence relation between the information bodies, wherein the virtual space forms virtual mapping expression of the workpiece processing information in the physical space according to real-time data perception, and the physical space perceives the information corresponding to the data and then maps the information to the virtual space by the mapping method, so that the virtual-real one-to-one mapping of the production space is realized.
The maximum completion time f1(tr) Comprises the following steps:
wherein n (t)r) Representing the total number of workpieces in the workshop at the moment when the rescheduling point is reached; fi(tr) Indicating the time when the workpiece is finished; b isi(tr) Indicating the time when the workpiece starts to be machined after the start of rescheduling; i denotes the ith workpiece
The total drag period f2(tr) Comprises the following steps:
wherein DDi(tr) Is the delivery date of the workpiece;
said process state energy consumption PE (t)r) Comprises the following steps:
wherein CUk(tr) Represents the machining power of machine k; n is a radical ofi(tr) The number of the workpieces which are left to be processed after the workpieces are rescheduled is determined; pijk(tr) Is the processing time; α is a coefficient of a fitting curve, m represents a machine, i represents a workpiece, j represents the number of processes of the workpiece, CUijk(tr) Indicates the machining power of j processes of the ith workpiece
And 5: transferring to the step 4 when the sample (demo) product is not qualified, transferring to the step 6 when the processing flow sample (demo) product is qualified;
step 6: before the machine tool starts to process, reading a workpiece number and a processing sequence constraint from a Radio Frequency Identification (RFID) tag carried by a workpiece, requesting a cloud processing code from a cloud system, searching codes and technological equipment parameters of corresponding procedures in a cloud code library, and returning the codes and the technological equipment parameters to the machine tool, wherein the machine tool processes according to the cloud code;
and 7: in the machining process, the workshop transmits the equipment machining real-time information and the material information visual billboard to the cloud system, a user tracks the order machining progress, and the equipment information and the material information provide reference indexes for pre-scheduling production and are used as historical data to be stored in the cloud system.
Because the workshop information not only contains static information, but also contains dynamic information, and is derived from different physical objects of different workshop positions, each link related to production needs to selectively sense information aiming at specific problems; the invention is mainly based on information perception of a workshop scheduling problem. Common information sensing devices and technologies comprise optical fibers, gratings, RFID (radio frequency identification devices), sensors, positioning devices, laser scanners, machine tool PLC (programmable logic controllers) and the like, and the sensing of all element information in a workshop is realized by combining the devices with a workshop field network, wherein the sensing comprises static data sensing based on a manual mode, data sensing of various sensors based on PLC and data sensing based on RFID;
artificial static data perception: static basic attribute information has small change with time, and the inter-vehicle resource information such as basic material information, basic equipment information, basic worker information and the like is directly manually input and stored in a database.
Data sensing of various types of sensors based on plc: PLC sensor data perception: the embedded PLC module in the modern automation equipment has a data acquisition function, and acquires various data information, processing power information, equipment state information, equipment energy consumption information, processing start and end dynamic information and the like in the running process of the equipment through a communication network of a workshop.
RFID-based data sensing: some data cannot be directly collected from equipment, other tools need to be assisted for identification and recording, and an RFID reader-writer and an electronic tag are needed.
A reader-writer is arranged at an inventory entrance and exit to realize real-time perception and acquisition of inventory information; the outer surface of the part is provided with a reader-writer for sensing process information, processing progress, processing starting and finishing time, process equipment position, processing sequence and worker information; the label and the worker number are combined into a whole, and the information perception of staff attendance, staff working state and procedure processing operators is carried out by combining the task arrangement of workers. The method comprises the steps of analyzing workshop production field information through real-time perception of workshop logic complex data of RFID, processing complex events in a CEP (Complex Event processing) technology, carrying out corresponding processing by utilizing an incoming Event stream, marking related complex events, and monitoring abnormal events of workshop workpieces through data perception according to actual needs.
The cloud manufacturing system is applied to a micro factory (hereinafter, referred to as a workshop) in a university laboratory, and the feasibility of the cloud manufacturing system designed herein is verified. The main equipment comprises spraying, packaging, a stereoscopic warehouse, an AGV and a manipulator. And 3 types of workpieces are processed, the schematic diagram of the workpiece types is shown in table 1, and a user can customize the workpiece types at an ordering interface of the cloud manufacturing system.
TABLE 1
The digital twin technical block diagram and the result comparison diagram of the experimental twin technology are shown in fig. 3 and 4, the operation result of the embodiment verifies that the prearranged production effectively solves the problem of unbalanced workshop resource utilization, the system overcomes the defect that real-time information is difficult to obtain in the traditional workshop manufacturing process, and the manufacturing information cloud is realized. Meanwhile, a processing G code base is established according to the characteristic that the personalized order is complex and changeable, and a machine tool can automatically acquire processing G codes and technological equipment parameters of corresponding workpieces from the G code base, so that the processing process is simple. The utilization rate of machine equipment is improved from 70.9% to 87.4%. The delay time is shortened by 14.3min to 7.7 min.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (4)
1. A digital twin smart cloud scheduling method meeting personalized customized production is characterized by comprising the following steps: the method comprises the following steps:
step 1: a user submits an individualized demand order, wherein the order comprises the number of workpieces, process parameters and product specification information;
step 2: the production planning department carries out process design on the personalized demand order, generates a cloud code and a process constraint parameter corresponding to the process for each workpiece process, and uploads the equipment parameters and the personalized order attribute parameters to a cloud system;
and step 3: the cloud scheduling system carries out pre-scheduling plan according to the order delivery date, the order priority and the manufacturing material inventory information of the personalized order, transmits the order to a workshop, evaluates and calculates the weight of each customized user order according to the user level, the order delivery date and the development difficulty level, sets a user level s, an order delivery date t and a development difficulty level i, and inputs the influence factors into a cloud system code, wherein each index corresponds to the influence factors C1, C2 and C3 respectively, and C1+ C2+ C3 is 1;
and 4, step 4: after receiving a pre-scheduling plan of a cloud scheduling system, a workshop adopts real-time dynamic scheduling of workshop resources to perform resource cooperation to establish a dynamic scheduling model, and at each rescheduling point, a hybrid-driven rescheduling mode based on a decomposed multi-objective optimization algorithm is adopted to update the current attributes of the workshop, including maximum completion time, total pull-off period and processing state energy consumption; in the optimization process, a solution is preset for each emergency in the twin workshop to improve the efficiency of a scheduling scheme; if the interference of the emergency order event does not exist, the scheduling window is updated at intervals by adopting periodic rescheduling;
and 5: transferring to the step 4 when the sample product is not qualified, transferring to the step 6 when the sample product is qualified;
step 6: before the machine tool starts to process, reading a workpiece number and a processing sequence constraint from a Radio Frequency Identification (RFID) tag carried by a workpiece, requesting a cloud processing code from a cloud system, searching codes and technological equipment parameters of corresponding procedures in a cloud code library, and returning the codes and the technological equipment parameters to the machine tool, wherein the machine tool processes according to the cloud code;
and 7: in the machining process, the workshop transmits the equipment machining real-time information and the material information visual billboard to the cloud system, a user tracks the order machining progress, and the equipment information and the material information provide reference indexes for pre-scheduling production and are used as historical data to be stored in the cloud system.
2. The method for scheduling digital twin smart cloud for satisfying customized production as claimed in claim 1, wherein the resource coordination process in step 4 comprises: the intelligent individual equipment information with the capability of completing the order task feeds back the self state information and the load condition information, the cloud method carries out screening processing on the information, and visual display is carried out on the scheduling result and the product modeling.
3. The digital twin smart cloud scheduling method meeting the requirement of personalized customized production according to claim 1, wherein the dynamic scheduling model in step 4 is that an information collecting card on each device in a physical workshop uploads data information to a twin workshop in a two-dimensional code scanning manner, and the twin workshop establishes a dynamic scheduling model for two dynamic events, namely 'arrival of a new personalized order' and 'insertion of an emergency important customer' through energy consumption, load, fault, detail processing time of a newly inserted order, material information and the like of the device; on the basis of workshop perception data, a virtual-real mapping frame of a virtual workshop and a physical workshop is established, and as workpiece processing information comprises workpiece procedures, processing equipment, operators and processing time, the ontology of the processing information is formally described as
processinfo＝(jobinfo，Machineinfo，personinfo，operationinfo，Manufacuringinfo)
The processing information body processinfo is composed of 5 sub-bodies, which are workpiece information jobinfo, equipment information Machineinfo, worker information personinfo, process information operationinfo, and processing information Manufacuringinfo, respectively.
4. The method for dispatching the digital twin smart cloud for customized production according to claim 1, wherein the maximum completion time f in step 4 is1(tr) Comprises the following steps:
wherein n (t)r) Representing the total number of workpieces in the workshop at the moment when the rescheduling point is reached; fi(tr) Indicating the time when the workpiece is finished; b isi(tr) Indicating the time when the workpiece starts to be machined after the start of rescheduling; i represents the ith workpiece;
the total drag period f2(tr) Comprises the following steps:
wherein DDi(tr) Is the delivery date of the workpiece;
said process state energy consumption PE (t)r) Comprises the following steps:
wherein CUk(tr) Watch (A)Showing the processing power of the machine k; n is a radical ofi(tr) The number of the workpieces which are left to be processed after the workpieces are rescheduled is determined; pijk(tr) Is the processing time; α is a coefficient of a fitting curve, m represents a machine, i represents a workpiece, j represents the number of processes of the workpiece, CUijk(tr) The machining powers of j steps of the ith workpiece are shown.
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