CN114527714A - Workshop dynamic scheduling method based on digital twin and disturbance monitoring - Google Patents
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Abstract
The invention discloses a workshop dynamic scheduling method based on digital twin and disturbance monitoring. And establishing a workshop multi-objective scheduling model taking completion time and energy consumption as optimization targets, and generating a scheduling scheme by using an NSGA-II algorithm. And establishing a disturbance event monitoring model based on the neural network, carrying out wavelet packet decomposition on the acquired data, and taking the wavelet packet energy vector as the input characteristic quantity of the neural network. And executing a workshop production dynamic scheduling flow based on the digital twin, and when a disturbance event occurs, performing adjustment preparation according to different scheduling strategies to generate a rescheduling scheme. And analyzing the stability of the rescheduling scheme by considering the process deviation and the machine deviation, and evaluating the scheduling scheme by comprehensive evaluation indexes. The invention can respond and adjust the disturbance of the workshop in time, and improve the production efficiency and stability of the workshop.
Description
Technical Field
The invention relates to a workshop scheduling technology, in particular to a workshop dynamic scheduling method based on digital twin and disturbance monitoring, and belongs to the technical field of intelligent manufacturing and scheduling.
Background
With the rapid development of scientific technology, intelligent manufacturing has become an important development direction for manufacturing enterprises. Manufacturing enterprises gradually develop towards intellectualization from production, management to operation and maintenance services, and the intellectualization development becomes an important way for the manufacturing enterprises to improve the competitiveness of the manufacturing enterprises. In manufacturing enterprises, workshop scheduling is a process for guiding workshop production, and effective workshop scheduling can greatly improve the efficiency of task processing and the benefit of enterprises. In the actual production of a workshop, a large number of disturbance events exist, such as emergency insertion, machine faults, delivery date changes, unqualified parts, abnormal processing time and the like, so that a static scheduling scheme without considering disturbance conditions does not meet the actual processing any more, disturbance needs to be considered, the dynamic scheduling aspect is researched, and the scheduling scheme is correspondingly adjusted.
The scheduling results after the disturbance occurs are relatively late, which can affect the production process and efficiency of the workshop. The main reasons for the problem are insufficient informatization, lack of intelligence and advancement. In the context of smart manufacturing and big data, it is necessary to apply intelligent technology and analyze and research a large amount of data. Digital twinning, one of the ten technological advances, is an effective way to address the above-mentioned problems. The digital twin is different from modeling simulation, but real-time mapping interaction between a physical workshop and a virtual workshop exists in the full life cycle of a product, the physical space and the information space are fused and communicated, the virtual workshop can reflect the production and processing conditions of the physical workshop, and a scheduling scheme with more foresight, high efficiency and reasonability is realized by integrating and processing various collected related data, so that the workshop production achieves the maximum benefit.
Disclosure of Invention
The invention aims to provide a workshop dynamic scheduling method, which is based on digital twin and takes a service system, a physical workshop and a virtual workshop as main components to realize dynamic scheduling in the production and manufacturing process. The problems of production stagnation, delay and the like caused by the fact that the disturbance condition cannot be effectively dealt with by scheduling in the traditional manufacturing process are solved.
The method provided by the invention can adjust the disturbance in the workshop production process in time, improve the workshop production efficiency, and select the corresponding scheduling scheme according to different targets, so that enterprises can obtain the maximum benefit. The technical scheme adopted by the invention is a workshop dynamic scheduling method based on digital twinning and disturbance monitoring, and the implementation process of the method is as follows.
Step 1: the digital twin service system monitors the production state of a physical workshop and acquires production task information, such as product information, task quantity, process route, delivery date, selected processing equipment, processing time and the like; workshop resource information, such as material resources, equipment resources, human resources, warehousing resources, logistics resources and the like; manufacturing information, such as operating status information of the processing equipment.
Step 2: and establishing a workshop multi-objective scheduling model taking completion time and energy consumption as optimization targets. The workshop comprises a plurality of workpieces to be processed and a plurality of processing devices with different function types, the completion time and the energy consumption of the workshop are determined according to the resource allocation scheme and the workpiece processing sequence scheduling scheme, and the completion time is shortest and the energy consumption is smallest.
Step 2.1, establishing a workshop energy consumption model;
in the actual production process, the total workshop energy consumption comprises equipment standby energy consumption, equipment processing energy consumption and workshop fixed energy consumption, and the energy consumption of each part is equal to the product of power and time. The total energy consumption calculation formula of the workshop is as follows:
E＝Ework+Eidle+Econstant
wherein E is total energy consumption, EworkEnergy consumption for plant processing, EidleFor standby power consumption of the apparatus, EconstantThe energy consumption is fixed for the workshop.
Step 2.2, establishing a workshop completion time model;
the finish time of a single workpiece is all the time taken from the time when the workpiece starts to be machined until the last process is finished. Thus, the total time to complete the shop is equal to the maximum time to complete all the workpieces, which can be expressed as follows:
C＝max{c1,c2,...ci,...,cN}
in the formula, ciThe machining completion time of the ith workpiece is N workpieces in total, and C is the maximum completion time of all the workpieces.
And 2.3, using an NSGA-II algorithm as a scheduling scheme algorithm, adopting integer coding based on a workpiece procedure, firstly generating an initial population, performing genetic operation, then performing non-dominated sorting and congestion degree calculation, merging parent and child populations, selecting elite individuals according to the level and congestion distance after the non-dominated sorting to form a new parent population, circulating until the maximum iteration number is reached, and finally outputting a scheduling Gantt chart.
And step 3: and establishing a disturbance event monitoring model based on a neural network, carrying out 3-layer wavelet packet decomposition on the acquired data, taking a wavelet packet energy characteristic vector as an LVQ neural input vector, and training and testing by adopting the LVQ neural network.
And 4, executing a dynamic workshop production scheduling process based on the digital twin. And starting the service system to generate an initial scheduling scheme according to the production task information and executing the production and processing of the current scheduling scheme. And simultaneously monitoring a disturbance event, and when the disturbance event is monitored, adjusting and preparing according to different scheduling strategies and regenerating a scheduling scheme. And circularly executing the scheduling flow until the processing task is finished.
And (3) a complete rescheduling strategy: when a disturbance event occurs, the workpieces which are being machined are continuously machined, and the machining scheduling is carried out again on the unmachined processes and the available machines after the moment when the disturbance occurs by considering the disturbance event information, the process information of each workpiece and the machining condition information of the machines.
Transferring a rescheduling strategy: when a disturbance event occurs, the workpiece being machined is continuously machined, and the directly affected processes and the subsequent processes, namely the indirectly affected processes, are found by considering the disturbance event information, the process information of each workpiece and the machining condition information of the machine. The unaffected procedures keep the processing tasks of the original scheduling scheme. The affected processes are transferred to the first idle machine for processing under the constraint conditions.
And 5: and analyzing the stability of the rescheduling scheme, and evaluating the scheduling scheme by using the comprehensive evaluation index.
The process deviation degree PD is the sum of absolute values of difference values of the processing starting time of each process and the initial scheduling scheme in the rescheduling scheme, and the stability of the rescheduling scheme is better when the process deviation degree is smaller.
Wherein j is the jth step of each workpiece, and T is the total number of steps, stijIndicates the starting time, st, of each process of each workpiece in the initial scheduling planij' denotes the start time of each process of each workpiece of the rescheduling scheme.
The machine deviation degree MD represents the number change of processing procedures on each machine in the rescheduling scheme, and the smaller the machine deviation degree is, the better the stability of the rescheduling scheme is.
Wherein l is the first machine, and M machines, splIndicating the number of machining tasks, sp, on each machine in the initial scheduling planl' indicates the number of processing tasks on each machine in the rescheduling scheme.
The comprehensive evaluation index CEI is the completion time C of the normalized scheduling schemenEnergy consumption EnAnd process deviation degree PDnAnd machine deflection MDnIs calculated as a weighted sum of.
CEI＝λ1Cn+λ2En+λ3PDn+λ4MDn
In the formula, λkK is the kth weight coefficient, and the total of W weight coefficients is 1.
Compared with the prior art, the invention has the following advantages:
1. the dynamic scheduling task of workshop production is effectively dealt with.
2. And the digital twin service system monitors the production state of the physical workshop and carries out disturbance monitoring.
3. And when disturbance occurs, adjusting and preparing according to different scheduling strategies.
4. And comprehensively evaluating the rescheduling scheme to enable enterprises to achieve the maximum benefit.
Drawings
FIG. 1 is a dynamic workshop production scheduling flow based on digital twinning.
Fig. 2 is a gantt chart for initial scheduling.
Fig. 3 is a fully rescheduled gantt chart based on digital twinning.
FIG. 4 is a transfer rescheduling Gantt chart based on digital twinning.
Detailed Description
The technical scheme of the invention is described in detail in the following with the accompanying drawings of the specification:
the method comprises the following steps: the service system obtains production task information, 8 processing devices are arranged in a workshop, 6 production tasks are provided, each production task comprises a plurality of processing procedures, and each processing procedure can be completed on at least one candidate device resource. The processing equipment and required time for each process of each workpiece are shown in tables 1 and 2 (time unit: min), and the equipment power is shown in table 3 (power unit: kW).
TABLE 1 Equipment available for each procedure
TABLE 2 processing time of each apparatus
TABLE 3 Power Meter for each device
Step two: the model of the workshop production scheduling problem is as follows: the machine tool comprises N workpieces to be machined and M machining devices with different functions. Each workpiece has a plurality of processes, each process can be processed on more than one machine, and the processing time of each process on different machines is determined. By optimizing resource allocation and process sequencing, the optimal performance index is obtained under the condition of meeting the constraint of equipment capacity. The maximum completion time and the minimum energy consumption are taken as scheduling targets, and can be described as
C＝max{c1,c2,...,cN}
E＝Ework+Eidle+Econstant
Wherein C is the maximum completion time of all workpieces; c. CiThe machining completion time of all the working procedures of the ith workpiece is set; e is total energy consumption; eworkEnergy consumption for equipment processing; eidleEnergy consumption for equipment standby; econstantThe energy consumption is fixed for the workshop.
The following basic assumptions are satisfied:
1) at an initial time, the states of all machine devices are idle and available;
2) the process is not stopped halfway after the process is started except for abnormality such as machine failure;
3) the time for preparing, transporting, installing and the like of the workpiece is contained in the processing time;
4) each working procedure of each workpiece has at least one available device;
5) the processing time of each procedure of each workpiece is related to machine tool equipment;
6) each workpiece has its delivery date known prior to machining;
7) in the processing process, the workpiece can be processed on one device only until the processing is finished;
8) in the processing process, the number of workpieces processed by each machine meets the constraint condition;
9) the machining of the workpiece must be started after the completion of the previous process.
The scheduling scheme algorithm uses an NSGA-II algorithm, adopts integer codes based on a workpiece procedure, firstly generates an initial population, performs genetic operation, then performs non-dominated sorting and congestion degree calculation, merges parent-child populations, selects elite individuals according to the level and congestion distance after the non-dominated sorting to form a new parent population, and circulates until the maximum iteration number is reached, and sets the population size to be 200, the intersection rate to be 0.8, the variation rate to be 0.1 and the maximum iteration number to be 50. Generating the initial scheduling scheme is shown in fig. 2.
Step three: aiming at the disturbance condition of machine tool fault, 3 layers of wavelet packet decomposition are carried out on the collected vibration signals, and the wavelet packet energy of the machine tool mechanical vibration signals is extracted. Each state mode collected 114 sets of data. 80 groups of data are selected from the collected data to train the neural network, the remaining 34 groups of data are used as neural network test data, and part of the training and test data are shown in a table 4, wherein the category 1 represents a normal state, and the category 2 represents an abnormal state.
TABLE 4 neural network training and testing partial data
And training and testing by using the LVQ neural network by taking the collected wavelet packet energy characteristic vector of the machine tool vibration signal as an input vector. The LVQ neural network has 8 neuron nodes in the input layer, corresponding characteristic amount of input data, 20 competition layer nodes, 1 output layer node and learning rate of 0.01. And classifying the test set data by using the trained model. The network output 1 indicates a normal state and 2 indicates an abnormal state. The results of the classification of the LVQ neural network are shown in table 5.
In the production process according to the original scheduling, the digital twin service system obtains the wavelet packet energy of the main shaft vibration signal of the machine 6 at the time T1, the distribution of the wavelet packet energy spectrum is different from that of the wavelet packet energy spectrum in the normal state, the service system tests that the service system is in the abnormal state by using a neural network, judges that the machine 6 is about to break down, and triggers the system rescheduling process.
TABLE 5 LVQ neural network test set classification results
Step four:
a digital twin-based plant production dynamic scheduling flow is performed as shown in fig. 1. And starting the service system to generate an initial scheduling scheme according to the production task information and executing the production and processing of the current scheduling scheme. And simultaneously monitoring a disturbance event, and when the disturbance event is monitored, adjusting and preparing according to different scheduling strategies and regenerating a scheduling scheme. And circularly executing the scheduling flow until the processing task is finished.
The scheduling scheme of the complete rescheduling strategy comprises the following steps: and at the time T1, arranging the unprocessed working procedures on the machine 6 to alternative machines, continuously processing the working procedures which are processed on the non-fault machines, forming a to-be-scheduled set by the residual working procedures on the fault machines and the non-fault machines, and generating a rescheduling scheme by using a scheduling algorithm. The workpiece being machined at the time of T1 is machined, the remaining unprocessed processes on the machine 6 are arranged on other alternative machines, the remaining processes on the faulty machine and the non-faulty machine form a scheduling set, a rescheduling scheme is generated by using a scheduling algorithm, and the obtained scheduling scheme is shown in fig. 3.
The transfer rescheduling strategy scheduling scheme comprises the following steps: finding directly affected and indirectly affected processes, keeping other processes unchanged, and arranging the affected processes to the available machine which is idle at first under the constraint condition to obtain a scheduling scheme as shown in fig. 4.
Step five: table 6 shows the comparison of the scheduling schemes of different scheduling strategies based on digital twin, and the weighting coefficients in the comprehensive evaluation index are the same.
TABLE 6 simulation test results of different scheduling strategies
As can be seen from the table, under the same condition, the process deviation degree and the machine deviation degree of the scheduling scheme based on the digital twin transition rescheduling strategy are smaller than those of the scheduling scheme based on the complete rescheduling strategy, which indicates that the stability of the scheduling scheme based on the transition rescheduling strategy is better. The completion time and energy consumption of the scheduling scheme of the complete rescheduling strategy are smaller than those of the scheduling scheme of the transfer rescheduling strategy. The stability of the scheduling scheme and the completion time and energy consumption are not optimized simultaneously. In the production and processing process, stability pursuit or completion time and energy consumption are selected according to actual conditions so as to achieve the highest benefit.
Claims (4)
1. A workshop dynamic scheduling method based on digital twin and disturbance monitoring is characterized in that: the method comprises the following steps of,
step 1: the digital twin service system monitors the production state of a physical workshop and acquires production task information, workshop resource information and production manufacturing information;
step 2: a workshop multi-objective scheduling model taking completion time and energy consumption as optimization targets is adopted, and an NSGA-II algorithm is used for generating a scheduling scheme;
and step 3: adopting a disturbance event monitoring model based on a neural network, carrying out 3-layer wavelet packet decomposition on the acquired data, and carrying out disturbance monitoring by taking a wavelet packet energy vector as an input characteristic quantity of the neural network;
and 4, step 4: when a disturbance event occurs, executing a workshop production dynamic scheduling flow based on a digital twin, performing adjustment preparation according to different scheduling strategies, and generating a rescheduling scheme;
and 5: and (3) carrying out stability analysis on the rescheduling scheme according to the process deviation and the machine deviation, evaluating the rescheduling scheme by using comprehensive evaluation indexes, simulating in advance in a virtual workshop, transmitting the scheduling scheme to a physical workshop, and producing and processing the physical workshop according to the new scheduling scheme.
2. The dynamic workshop scheduling method based on digital twin and disturbance monitoring as claimed in claim 1, wherein: the step 2 comprises the following steps of,
step 2.1, establishing a workshop energy consumption model;
in the actual production process, the total workshop energy consumption comprises equipment standby energy consumption, equipment processing energy consumption and workshop solid energy consumption, and the energy consumption of each part is equal to the product of power and time; the total energy consumption calculation formula of the workshop is as follows:
E＝Ework+Eidle+Econstant
wherein E is total energy consumption, EworkEnergy consumption for plant processing, EidleFor standby power consumption of the apparatus, EconstantFixing energy consumption for a workshop;
step 2.2, establishing a workshop completion time model;
the finishing time of a single workpiece is all the time spent from the moment when the workpiece starts to be machined until the machining of the last working procedure is finished; thus, the total time to complete the shop is equal to the maximum time to complete all the workpieces, which can be expressed as follows:
C＝max{c1,c2,...,cN}
in the formula, ciThe machining completion time of the ith workpiece is N workpieces in total, and C is the maximum completion time of all the workpieces;
and 2.3, using an NSGA-II algorithm as a scheduling scheme algorithm, adopting integer coding based on a workpiece procedure, firstly generating an initial population, performing genetic operation, then performing non-dominated sorting and congestion degree calculation, merging parent and child populations, selecting elite individuals according to the level and congestion distance after the non-dominated sorting to form a new parent population, circulating until the maximum iteration number is reached, and finally outputting a scheduling Gantt chart.
3. The dynamic workshop scheduling method based on digital twin and disturbance monitoring as claimed in claim 1, wherein: in step 4, a complete rescheduling strategy: when a disturbance event occurs, the workpieces which are being processed are continuously processed, and the scheduling and production scheduling are carried out again on the working procedures which are not processed and the available machines after the disturbance occurrence time by considering disturbance event information, working procedure information of each workpiece and machining condition information of the machines;
transferring a rescheduling strategy: when a disturbance event occurs, the workpieces being processed are continuously processed, and the directly affected processes and the subsequent processes thereof, namely the indirectly affected processes, are found by considering the disturbance event information, the process information of each workpiece and the processing condition information of the machine; the unaffected working procedure keeps the processing task of the original scheduling scheme; the affected process is transferred to the first vacant machine for processing under the constraint condition.
4. The dynamic workshop scheduling method based on digital twin and disturbance monitoring as claimed in claim 1, wherein: the process deviation degree PD is the sum of absolute values of difference values of the processing starting time of each process and the initial scheduling scheme in the rescheduling scheme, and the stability of the rescheduling scheme is better when the process deviation degree is smaller;
wherein j is the jth step of each workpiece, and T is the total number of steps, stijIndicates the starting time, st, of each process of each workpiece in the initial scheduling planij' denotes the start time of each process of each workpiece of the rescheduling scheme.
The machine deviation degree MD represents the number change of processing procedures on each machine in the rescheduling scheme, and the smaller the machine deviation degree is, the better the stability of the rescheduling scheme is.
Wherein l is the first machine, and M machines, splIndicating the number of machining tasks, sp, on each machine in the initial scheduling planl' indicates the number of processing tasks on each machine in the rescheduling scheme.
The comprehensive evaluation index CEI is the completion time C of the normalized scheduling schemenEnergy consumption EnAnd process deviation degree PDnAnd machine deflection MDnIs calculated as a weighted sum of.
CEI＝λ1Cn+λ2En+λ3PDn+λ4MDn
In the formula, λkK is the kth weight coefficient, and the total of W weight coefficients is 1.
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