CN114841466A - 3C product assembly line minimum reconstruction optimization method and system with frequent production change - Google Patents
3C product assembly line minimum reconstruction optimization method and system with frequent production change Download PDFInfo
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Abstract
The invention relates to the field of 3C product assembly line reconfiguration optimization, in particular to a 3C product assembly line minimum reconfiguration optimization method and a system with frequent production change, wherein the method comprises the following steps of S1: constructing a two-stage optimization model; step S2: setting related parameters; step S3: receiving different product orders, and adjusting the optimal order sequence in an order scheduling layer; step S4: in each subsystem of the production line reconstruction layer, the line balance and buffer area of the subsystem simultaneously carries out optimization design on each order; step S5: determining a reconstruction scheme of an assembly line with minimum cost, and judging whether evolution algebra is surpassed; step S6: the order scheduling layer readjusts the order sequence, configures a new deviation value, reserves a target value with the minimum reconstruction cost, and then performs the reconstruction design of the production line again, and iterates in the above way; step S7: judging whether the iteration times are larger than a set value or not, if so, returning to the step S4; otherwise, outputting the final reconstruction scheme and finishing the optimization.
Description
Technical Field
The invention relates to the technical field of 3C product assembly line reconstruction optimization, in particular to a 3C product assembly line minimum reconstruction optimization method and system with frequent production change.
Background
The "3C product" is a combination of a Computer (Computer), Communication (Communication), and consumer electronics (ConsumerElectronics), and is also called an "information appliance". With the rapid development of 3C products, market demands require that their assembly lines can flexibly change products, change flow, adapt to emergency orders, and handle sudden interruption problems. Although reconfigurable production lines have been introduced in assembly systems, they are still lacking in the electronic assembly industry and present the following technical problems:
(1) in the face of frequent assembly line reconstruction, the existing design method lacks of simulation integration of software and hardware, so that modules from different sources cannot be integrated together by means of a simulation platform, and the reconstruction configuration, layout and motion conditions of the whole line are simulated in a near-physical mode, so that effective virtual reconstruction and rapid physical reconstruction of a production line during product conversion cannot be realized.
(2) The existing research aims at the single optimization problem of a reconfigurable system, and for a reconfigurable 3C assembly line with frequent replacement, the existing design process takes a lot of time on balancing production line structures, deployment tools and debugging tests, and an effective, real-time and integrated verification method, platform and optimization technology are lacked.
Disclosure of Invention
The invention aims to provide a minimum reconstruction optimization method for a 3C product assembly line with frequent switching, which realizes the coupling optimization of order scheduling, line balancing and buffer area allocation by carrying out virtual trial and error and analysis calculation on possible reconstruction schemes in advance so as to determine the optimal reconstruction scheme and meet the high-efficiency, quick and low-cost reconstruction requirements necessary for high-frequency switching.
The second purpose of the invention is to provide a minimum reconstruction optimization system of a 3C product assembly line with frequent production change, which can support the rapid physical reconstruction of equipment and a production line and can test and optimize a virtual model of the equipment and the production line based on a digital twin technology. Meanwhile, the minimum reconstruction optimization method for the 3C product assembly line with frequent replacement is embedded, so that the problems of coupling optimization of order scheduling, line balancing and buffer area allocation are solved, and the possibility is provided for realizing minimum cost reconstruction.
In order to achieve the purpose, the invention adopts the following technical scheme:
a minimum reconstruction optimization method for a 3C product assembly line with frequent production replacement comprises the following steps:
step S1: constructing a two-stage optimization model, wherein the two-stage optimization model comprises an order scheduling layer at an upper layer and a production line reconstruction layer at a lower layer;
step S2: setting a total target value T 0 Allowable deviation of
And subsystem target value
Setting the iteration times of the upper layer and the evolution algebra of the lower layer at the same time;
step S3: receiving different product orders, and adjusting the optimal order sequence in an order scheduling layer;
step S4: in each subsystem of the production line reconstruction layer, aiming at an order distributed by an upper layer, the line balance and buffer area of the subsystem simultaneously carry out optimization design on each order, and in the design optimization process, the relation among the subsystems at the same level is not considered, and each subsystem carries out independent optimization;
step S5: determining the reconstruction scheme of the assembly line with the minimum cost, judging whether the evolution algebra is transcended, and returning the reconstruction cost if the evolution algebra is transcended
To the upper layer; if not, continuing to perform optimization configuration;
step S6: order scheduling layer responses according to lower layer
Readjusting the order sequence, configuring a new deviation value, keeping a target value with the minimum reconstruction cost, and then distributing the order to a production line reconstruction layer again to perform reconstruction design of a production line, and iterating in the above way;
step S7: judging whether the iteration times are larger than a set value or not, and if so, returning to the step 4; otherwise, outputting the final reconstruction scheme and finishing the optimization.
Preferably, the mathematical model of the order scheduling layer is represented as:
wherein T is 0 For the target value of the original problem, RC 0 The response for the upper system level, i.e. the total reconstruction cost and production cost for all orders,
is the allowable deviation.
Preferably, the mathematical model of the production line reconstruction layer is represented as:
wherein
Representing a target value set to the jth subsystem by the upper system level;
representing the response value of the jth subsystem, namely the calculated reconstruction cost; TC (tungsten carbide) j Representing a latency penalty or storage cost for the jth subsystem; 1,2, …, N j Representing the workstation index within the jth subsystem; c. C h Representing the change cost of the h reconfiguration type in the j subsystem; c. C b Representing the cost of the configuration of the buffer; q. q.s ijh Representing decision variables, such as h reconstruction type of ith workstation of jth subsystem; b is i Indicating the buffer capacity after the ith workstation; y (k, i) denotes decision variables, e.g. assigning the kth assembly task to the ith toolMaking a station; y (a, i) represents a decision variable, such as assigning the a-th assembly task to the i-th workstation; y (b, i) represents a decision variable, such as assigning the b-th assembly task to the i-th workstation; a represents the a-th assembly task; b represents the b-th assembly task; t is t k Represents the set-up time for the kth task; b is max Indicating the maximum buffer capacity.
Preferably, the production line reconstruction layer further comprises a random dynamics sub-model, and the random dynamics sub-model is used for realizing performance evaluation during production line reconstruction, wherein the performance evaluation comprises random disturbance and delivery time;
the stochastic dynamics submodel comprises a state equation and an output equation;
the state equation is:
wherein x (r) ═ x 1 (r),…,x N (r)] T N is the number of all assembly stations, y (r) is the only output, a and B h Is a state matrix determined according to the network structure and time delay data, W (r) ═ w 1 (r),…,w N (r)] T Is the disturbance interruption time detected in real time in a digital twin system;
the output equation is:
where G is the output matrix.
A3C product assembly line minimum reconstruction optimization system with frequent production changing comprises an equipment layer, a control layer and an execution system layer, wherein the equipment layer, the control layer and the execution system layer realize interconnection and intercommunication of data and information through a two-division synchronization technology of instruction downlink and information uplink;
the execution system layer is internally stored with the 3C product assembly line minimum reconstruction optimization method with frequent production replacement.
Preferably, the equipment layer comprises a virtual digital assembly line and a physical entity assembly line, the control layer comprises a control network and a unit control system, the execution system layer comprises a manufacturing execution system, a data analysis center and a monitoring center, and the data analysis center stores the 3C product assembly line minimum reconstruction optimization method with frequent production change.
Preferably, the binary synchronization technique includes: the manufacturing execution system sends an actual production instruction to the unit management system through an industrial Ethernet, the unit management system converts the production instruction into a machine instruction, divides the sent machine instruction into two parts and respectively controls the virtual digital assembly line and the physical entity assembly line, simulation data generated by the virtual digital assembly line and field data generated by the physical entity assembly line are synchronously and upwards transmitted to the data analysis center for analysis, and the data analysis center sends decision support information which can be used for the future of the manufacturing execution system to the manufacturing execution system for decision.
Preferably, in the virtual equipment space, virtual reconstruction is performed by adopting a generalized knowledge encapsulation technology;
the generalized knowledge encapsulation technology comprises the following steps:
step A1: constructing a digital model of the equipment; according to the model selection equipment, a single-machine equipment digital model library covering general processing, storage, transportation and other system control cabinets is established, and in the reconstruction conversion process, encapsulated modules are added through intuitive drag and drop configuration;
step A2: dynamically realizing an assembly line; according to the assembly design scheme, firstly, an action control script is compiled to complete the action realization of the special equipment, and secondly, path parameters required by movement are configured to complete the logistics and movement realization of products in process; wherein the actions of the dedicated equipment include handling movements of the processing and materials;
step A3: controlling network settings; establishing a virtual control network, establishing a synchronous control and sensing channel between a physical entity and a virtual digital model by taking a PLC as a bridge, and realizing real-time communication and action synchronization of a single entity and a digital model thereof;
step A4: the performance module is further packaged; and setting general performance indexes of the equipment units, including average working time, average waiting time and throughput, and realizing quick virtual reconfiguration.
Compared with the prior art, the technical scheme has the following beneficial effects:
(1) the method adopts a framework of a target cascade analysis method, and expresses the minimum reconstruction problem as a two-stage optimization model: an order scheduling layer at an upper level and a production line reconstruction layer at a lower level. The task of the order scheduling layer is to determine the optimal order sequence with the least gross configuration and production costs, and the task of the line reconfiguration layer is to determine configuration parameters, including task allocation and buffer capacity. The method realizes the order scheduling, the line balance and the coupling optimization of the buffer area allocation by carrying out virtual trial and error and analysis calculation on the possible reconstruction scheme in advance, thereby determining the optimal reconstruction scheme to meet the reconstruction requirements of high efficiency, rapidness and low cost necessary for high-frequency production change.
(2) The system disclosed by the invention is based on a digital twin technology, can support rapid physical reconstruction of equipment and a production line, and can test and optimize a virtual model of the equipment and the production line. Meanwhile, the minimum reconstruction optimization method for the 3C product assembly line with frequent replacement is embedded, so that the problems of coupling optimization of order scheduling, line balancing and buffer area allocation are solved, and the possibility is provided for realizing minimum cost reconstruction.
Drawings
FIG. 1 is a schematic diagram of a 3C product assembly line minimum reconfiguration optimization method with frequent retooling according to the present invention;
FIG. 2 is a schematic diagram of the present invention for a 3C product assembly line minimum reconfiguration optimization system with frequent retooling;
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
As shown in fig. 1, a method for optimizing minimum reconstruction of a 3C product assembly line with frequent product replacement comprises the following steps:
step S1: constructing a two-stage optimization model, wherein the two-stage optimization model comprises an order scheduling layer at an upper layer and a production line reconstruction layer at a lower layer;
step S2: setting a total target value T 0 Allowable deviation of
And subsystem target value
Setting the iteration times of the upper layer and the evolution algebra of the lower layer at the same time;
step S3: receiving different product orders, and adjusting the optimal order sequence in an order scheduling layer;
step S4: in each subsystem of the production line reconstruction layer, aiming at an order distributed by an upper layer, the line balance and buffer area of the subsystem simultaneously carry out optimization design on each order, and in the design optimization process, the relation among the subsystems at the same level is not considered, and each subsystem carries out independent optimization;
step S5: determining the reconstruction scheme of the assembly line with the minimum cost, judging whether the evolution algebra is transcended, and returning the reconstruction cost if the evolution algebra is transcended
To the upper layer; if not, continuing to perform optimization configuration;
step S6: order scheduling layer responses according to lower layer
Readjusting the order sequence, configuring a new deviation value, keeping a target value with the minimum reconstruction cost, and then distributing the order to a production line reconstruction layer again to perform reconstruction design of a production line, and iterating in the above way;
step S7: judging whether the iteration times are larger than a set value or not, and if so, returning to the step 4; otherwise, outputting the final reconstruction scheme and finishing the optimization.
To achieve a smooth reconfiguration of an assembly line at minimal cost in a 3C product assembly line, order scheduling, line balancing, and buffer allocation must be optimized simultaneously. Order scheduling is a priori problem for line reconstruction, and line balancing and buffer allocation are two main tasks for line reconstruction. Target transfer and parameter coordination exist between the two layers, so that a framework of a target cascade analysis method is adopted, an assembly line reconstruction method with frequent production change is provided, and the minimum reconstruction problem is expressed as a two-stage optimization model: an order scheduling layer at an upper level and a production line reconstruction layer at a lower level. The task of the order scheduling layer is to determine the optimal order sequence with the least gross configuration and production costs, and the task of the line reconfiguration layer is to determine configuration parameters, including task allocation and buffer capacity. The method realizes the order scheduling, the line balance and the coupling optimization of the buffer area allocation by carrying out virtual trial and error and analysis calculation on the possible reconstruction scheme in advance, thereby determining the optimal reconstruction scheme to meet the reconstruction requirements of high efficiency, rapidness and low cost necessary for high-frequency production change.
To illustrate, the mathematical model of the order scheduling layer is represented as:
wherein T is 0 For the target value of the original problem, RC 0 The response for the upper system level, i.e. the total reconstruction cost and production cost for all orders,
is the allowable deviation. The order scheduling layer is designed as the top layer in a two-level optimization model structure, and the aim of the order scheduling layer is to minimize the residual error between the reconstruction cost and the response of the upper system level under the condition that the constraint condition is met.
To be further described, the mathematical model of the production line reconstruction layer is represented as:
wherein
Representing a target value set to the jth subsystem by the upper system level;
representing the response value of the jth subsystem, namely the calculated reconstruction cost; TC (tungsten carbide) j Representing a latency penalty or storage cost for the jth subsystem; 1,2, …, N j Representing the workstation index within the jth subsystem; c. C h Representing the change cost of the h reconfiguration type in the j subsystem; c. C b Representing the configuration cost of the buffer; q. q.s ijh Representing decision variables, such as h reconstruction type of ith workstation of jth subsystem; b is i Indicating the buffer capacity after the ith workstation; y (k, i) represents a decision variable, such as assigning the kth assembly task to the ith workstation; y (a, i) represents a decision variable, such as assigning the a-th assembly task to the i-th workstation; y (b, i) represents a decision variable, such as assigning the b-th assembly task to the i-th workstation; a represents the a-th assembly task; b represents the b-th assembly task; t is t k Represents the set-up time for the kth task; b is max Indicating the maximum buffer capacity.
The production line reconstruction layer is designed as a bottom layer in a two-level optimization model structure, and aims to minimize the residual error between the subsystem-level response and the subsystem-level target set by the upper system level under the condition that constraint conditions are met.
Wherein among the constraints, TC in the formula (5) j The value of (d) is determined by a performance evaluation of the order delivery time, delayed production may create a delayed penalty, and advanced production may create a storage cost. The response value (reconstruction cost) of a subsystem is thus the delay or storage cost TC of an order j And reconfiguration cost C j And (4) summing. Equation (7) ensures that each assembly task is assigned to only one specific workstation, equation (8) ensures that the priority of assembly tasks on a specific workstation (Pred is a set of priority constraints (a, B) defining that task a must precede B), maximum cycle time CT and maximum buffer capacity B max Is determined by equation (9) and equation (10).
Further, the production line reconfiguration layer further includes a random dynamics sub-model, where the random dynamics sub-model is used to implement performance evaluation during production line reconfiguration, where the performance evaluation includes random disturbance and lead time;
the random dynamics submodel comprises a state equation and an output equation;
the state equation is:
wherein x (r) ═ x 1 (r),…,x N (r)] T N is the number of all assembly stations, y (r) is the only output, a and B h Is a state matrix determined according to the network structure and time delay data, W (r) ═ w 1 (r),…,w N (r)] T Is the disturbance interruption time detected in real time in a digital twin system;
the output equation is:
where G is the output matrix.
The assembly line is subject to different types of interference events during the production process, including external interference (order changes, process changes, etc.) and internal interference (machine malfunctions, material shortages, quality defects, etc.). These destructive events, which occur at different locations, at different times of occurrence, and for different durations, will result in different production losses and risks. Therefore, it is extremely important to consider the dynamic characteristics of the production line reconfiguration design. Therefore, the invention provides a random dynamics sub-model of a production line, and the performance evaluation during the reconstruction of the production line is realized. Based on a digital twin technology, the proposed random dynamics submodel can well evaluate the design scheme of production line reconstruction under the environment of collaborative design of a digital assembly line and a physical assembly line.
As shown in fig. 2, a minimum reconfiguration optimization system for a 3C product assembly line with frequent switching includes an equipment layer, a control layer, and an execution system layer, where the equipment layer, the control layer, and the execution system layer implement interconnection and intercommunication of data and information by a binary synchronization technology of instruction downlink and information uplink;
the execution system layer is internally stored with the 3C product assembly line minimum reconstruction optimization method with frequent production replacement.
The design elements of the production line reconstruction comprise a geometric model, production line layout, machine motion setting, material flow among equipment, control network setting and a scheduling scheme. The reconstruction design not only gives an optimal solution, but also provides implementation measures. If each reconfiguration is reconfigured after the end of the previous batch, the design and balancing of the production line structure, and the deployment and debugging of the workpieces take a significant amount of time. The digital twin system provides a virtual testing and optimizing platform for the production line, so that design planning and preparation activities are modeled in advance in a digital twin virtual mode. The open architecture at the device and system level allows for fast physical reconfiguration during product changeover, saving a significant amount of physical reconfiguration time.
The system of the invention is based on the following preconditions: (1) the system has an open platform capable of performing three-dimensional digital design, can perform virtual equipment of single-machine equipment, can control the action of the equipment or the motion of products under process through scripts, and has a soft PLC function. (2) There are upper layers that make the execution system or its execution engine.
Stated further, the device layer comprises a virtual digital assembly line and a physical entity assembly line, the control layer comprises a control network and a unit control system, the execution system layer comprises a manufacturing execution system, a data analysis center and a monitoring center, and the data analysis center stores a 3C product assembly line minimum reconstruction optimization method of frequent product replacement according to any one of claims 1-4.
The embodiment is based on a digital twin technology, and not only can support rapid physical reconstruction of equipment and a production line, but also can test and optimize a virtual model of the equipment and the production line. Meanwhile, the minimum reconstruction optimization method for the 3C product assembly line with frequent replacement is embedded, so that the problems of order scheduling, line balancing and buffer area distribution coupling optimization are solved, and the possibility is provided for realizing minimum cost reconstruction.
To be further described, the binary synchronization technique includes: the manufacturing execution system sends an actual production instruction to the unit management system through an industrial Ethernet, the unit management system converts the production instruction into a machine instruction, divides the sent machine instruction into two parts and respectively controls the virtual digital assembly line and the physical entity assembly line, simulation data generated by the virtual digital assembly line and field data generated by the physical entity assembly line are synchronously and upwards transmitted to the data analysis center for analysis, and the data analysis center sends decision support information which can be used for the future of the manufacturing execution system to the manufacturing execution system for decision. Since this embodiment involves the management of communication and synchronization between multiple systems, a binary synchronization technique is employed to coordinate and coordinate the various commands and information in the system.
Further, in the virtual device space, a generalized knowledge encapsulation technology is adopted to perform virtual reconstruction;
the generalized knowledge encapsulation technology comprises the following steps:
step A1: constructing a digital model of the equipment; according to the model selection equipment, a single-machine equipment digital model library covering general processing, storage, transportation and other system control cabinets is established, and in the reconstruction conversion process, encapsulated modules are added through intuitive drag and drop configuration;
step A2: dynamically realizing an assembly line; according to the assembly design scheme, firstly, an action control script is compiled to complete the action realization of the special equipment, and secondly, path parameters required by movement are configured to complete the logistics and movement realization of products in process; wherein the actions of the dedicated equipment include handling movements of the processing and materials;
step A3: controlling network settings; establishing a virtual control network, establishing a synchronous control and sensing channel between a physical entity and a virtual digital model by taking a PLC as a bridge, and realizing real-time communication and action synchronization of a single entity and a digital model thereof;
step A4: the performance module is further packaged; and setting general performance indexes of the equipment units, including average working time, average waiting time and throughput, and realizing quick virtual reconstruction.
And performing virtual reconstruction in a virtual equipment space by adopting a generalized knowledge encapsulation technology to realize rapid encapsulation operation, thereby improving the implementation efficiency of the whole system.
The technical principle of the present invention is described above in connection with specific embodiments. The description is made for the purpose of illustrating the principles of the invention and should not be construed in any way as limiting the scope of the invention. Other embodiments of the invention will occur to those skilled in the art without the exercise of inventive faculty based on the explanations herein, and such equivalent modifications or substitutions are intended to be included within the scope of the present invention as defined in the appended claims.
Claims (8)
1. A minimum reconstruction optimization method for a 3C product assembly line with frequent production replacement is characterized by comprising the following steps:
step S1: constructing a two-stage optimization model, wherein the two-stage optimization model comprises an order scheduling layer at an upper layer and a production line reconstruction layer at a lower layer;
step S2: setting a total target value T 0 Allowable deviation of
And subsystem target value
(J ═ 1,2, …, J), and the iteration number of the upper layer and the evolution algebra of the lower layer are set simultaneously;
step S3: receiving different product orders, and adjusting the optimal order sequence in an order scheduling layer;
step S4: in each subsystem of the production line reconstruction layer, aiming at an order distributed by an upper layer, the line balance and buffer area of the subsystem simultaneously carry out optimization design on each order, and in the design optimization process, the relation among the subsystems at the same level is not considered, and each subsystem carries out independent optimization;
step S5: determining the reconstruction scheme of the assembly line with the minimum cost, judging whether the evolution algebra is transcended, and returning the reconstruction cost if the evolution algebra is transcended
To the upper layer; if not, continuing to perform optimization configuration;
step S6: order scheduling layer responses according to lower layer
Readjusting the order sequence, configuring a new deviation value, keeping a target value with the minimum reconstruction cost, and then distributing the order to a production line reconstruction layer again to perform reconstruction design of a production line, and iterating in the above way;
step S7: judging whether the iteration times are larger than a set value or not, if so, returning to the step S4; otherwise, outputting the final reconstruction scheme and finishing the optimization.
2. The frequent-change 3C product assembly line minimum reconstruction optimization method of claim 1, wherein the mathematical model of the order scheduling layer is expressed as:
wherein T is 0 For the target value of the original problem, RC 0 For responses at the upper system level, i.e. allThe total reconstruction cost and the production cost of the order,
is the allowable deviation.
3. The method of claim 2, wherein the mathematical model of the production line reconfiguration layer is represented as:
wherein
Representing a target value set to the jth subsystem by the upper system level;
representing the response value of the jth subsystem, namely the calculated reconstruction cost; TC (tungsten carbide) j Representing a latency penalty or storage cost for the jth subsystem; 1,2, …, N j Representing the workstation index within the jth sub-system; c. C h Representing a change cost of an h reconfiguration type in the j subsystem; c. C b Representing the configuration cost of the buffer; q. q of ijh Representing decision variables, such as h reconstruction type of ith workstation of jth subsystem; b i Indicating the buffer capacity after the ith workstation; y (k, i) represents a decision variable, such as assigning the kth assembly task to the ith workstation; y (a, i) represents a decision variable, such as assigning the a-th assembly task to the i-th workstation; y (b, i) represents a decision variable, such as assigning the b-th assembly task to the i-th workstation; a represents the a-th assembly task; b represents the b-th assembly task; t is t k Represents the set-up time for the kth task; b max Indicating the maximum buffer capacity.
4. The method for optimizing minimum reconstruction of 3C product assembly line with frequent product replacement as claimed in claim 3, wherein the production line reconstruction layer further comprises a random dynamics submodel, the random dynamics submodel is used for realizing performance evaluation during production line reconstruction, wherein the performance evaluation comprises random disturbance and lead time;
the stochastic dynamics submodel comprises a state equation and an output equation;
the state equation is:
wherein x (r) ═ x 1 (r),…,x N (r)] T N is the number of all assembly stations, y (r) is the only output, a and B h Is a state matrix determined according to the network structure and time delay data, W (r) ═ w 1 (r),…,w N (r)] T Is the disturbance interruption time detected in real time in a digital twin system;
the output equation is:
where G is the output matrix.
5. A minimum reconstruction optimization system of a 3C product assembly line with frequent production change is characterized by comprising an equipment layer, a control layer and an execution system layer, wherein the equipment layer, the control layer and the execution system layer realize interconnection and intercommunication of data and information through a binary synchronization technology of instruction downlink and information uplink;
the execution system layer stores a 3C product assembly line minimum reconstruction optimization method for frequent replacement according to any one of claims 1-4.
6. The frequent-replacement 3C product assembly line minimal reconstruction optimization system of claim 5, wherein the equipment layer comprises a virtual digital assembly line and a physical entity assembly line, the control layer comprises a control network and a unit control system, the execution system layer comprises a manufacturing execution system, a data analysis center and a monitoring center, and the data analysis center stores the frequent-replacement 3C product assembly line minimal reconstruction optimization method of any one of claims 1-4.
7. The frequent-replacement 3C product assembly line minimal reconstruction optimization system of claim 6, wherein the binary synchronization technique comprises: the manufacturing execution system issues an actual production instruction to the unit management and control system through an industrial Ethernet, the unit management and control system converts the production instruction into a machine instruction, divides the issued machine instruction into two parts and respectively controls the virtual digital assembly line and the physical entity assembly line, simulation data generated by the virtual digital assembly line and field data generated by the physical entity assembly line are synchronously transmitted upwards to the data analysis center for analysis, and the data analysis center determines according to the obtained future decision support information which can be used for the manufacturing execution system and sends the decision support information to the manufacturing execution system.
8. The frequent-replacement 3C product assembly line minimum reconstruction optimization system of claim 6, wherein in the virtual equipment space, virtual reconstruction is performed by adopting a generalized knowledge encapsulation technology;
the generalized knowledge encapsulation technology comprises the following steps:
step A1: constructing a digital model of the equipment; according to the model selection equipment, a single-machine equipment digital model library covering general processing, storage, transportation and other system control cabinets is established, and in the reconstruction conversion process, encapsulated modules are added through intuitive drag and drop configuration;
step A2: dynamically realizing an assembly line; according to the assembly design scheme, firstly, an action control script is compiled to complete the action realization of the special equipment, and secondly, path parameters required by movement are configured to complete the logistics and movement realization of products in process; wherein the actions of the dedicated equipment include handling movements of the processing and materials;
step A3: controlling network settings; establishing a virtual control network, establishing a synchronous control and sensing channel between a physical entity and a virtual digital model by taking a PLC as a bridge, and realizing real-time communication and action synchronization of a single entity and a digital model thereof;
step A4: the performance module is further packaged; and setting general performance indexes of the equipment units, including average working time, average waiting time and throughput, and realizing quick virtual reconstruction.
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