CN113609672B - Digital twin system co-evolution method based on incremental model - Google Patents
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Abstract
The invention discloses a digital twin system co-evolution method based on an incremental model, and belongs to the technical field of intelligent manufacturing. A digital twin system co-evolution method based on an incremental model comprises the following steps: s1, generating digital twin data in the processing process by means of data interaction between a twin model and a physical entity based on a known digital twin system; s2, collecting and analyzing various state variable quantities in the processing process to obtain data related to a decision model in the digital twin system; s3, the digital twin system assists the digital twin system to make decisions according to historical data of the state variable quantity in the S2 by combining an intelligent algorithm; s4, in the processing process, historical data of the same scene are transmitted to the digital twin system through the cloud platform, and the co-evolution of the digital twin system is promoted; the invention better realizes the intellectualization of numerical control processing and effectively improves the precision and efficiency of a machining system in the industrial operation process.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing, in particular to a digital twin system co-evolution method based on an incremental model.
Background
Digital twinning technology provides a great impetus for this as a breakthrough technology, which makes it possible to change the aspects of today's and future manufacturing industries. Digital twinning, a mirror of the real world, provides a means to simulate, predict and optimize physical manufacturing systems and processes. The digital twin and intelligent algorithm can be used for realizing data-driven operation monitoring and optimization, developing innovative products and services, improving the processing efficiency and ensuring the processing quality. Although research has reported potential application prospects of digital twinning in manufacturing industry, the current method for implementing digital twinning in the manufacturing field lacks in-depth knowledge of the concept, framework and development method of digital twinning, which hinders the development of digital twinning for intelligent manufacturing.
As actual industrial processes are in constant change, the system cannot identify new categories that originate from the training sample set that cannot be characterized. Traditional decision models lack a corresponding recognition mechanism, and can only classify new class samples into known classes, resulting in erroneous classification results. When a new class problem occurs, the incremental learning method firstly marks the new class problem by using expert knowledge, and the true class of the new class problem is defined; and then, using the marked sample to update the classification model offline, and then continuously putting the updated model into online use. It is worth noting that many practical industrial processes (especially flow industrial processes) generally need to remain continuously running for a longer period of time, subject to production requirements. Since the offline marking of samples and the offline updating of models takes a long time, incremental learning is not suitable for practical industrial processes with high real-time and long continuity. In other words, if a new type of sample can be identified in an online manner, the method has important significance for process diagnosis of an actual industrial process, and in this regard, we propose a digital twin system co-evolution method based on an incremental model.
Disclosure of Invention
The invention aims to solve the technical problem of providing a digital twin system co-evolution method based on an incremental model, which is an effective realization idea for realizing the intellectualization of a numerical control processing system and improving the system precision of the system.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a digital twin system co-evolution method based on an incremental model specifically comprises the following steps:
s1, generating digital twin data in the processing process by means of data interaction between a twin model and a physical entity based on a known digital twin system;
s2, collecting and analyzing various state variable quantities in the processing process to obtain data related to a decision model in the digital twin system;
s3, the digital twin system assists the digital twin system to make decisions according to historical data of the state variable quantity in the S2 by combining an intelligent algorithm;
and S4, in the processing process, historical data of the same scene are transmitted to the digital twin system through the cloud platform, so that the co-evolution of the digital twin system is promoted.
Preferably, the digital twin system mentioned in S1-S4 includes a digital twin process design unit, a processing monitoring unit and a processing quality inspection unit, where the digital twin process design unit, the processing monitoring unit and the processing quality inspection unit construct corresponding databases through data accumulation during working.
Preferably, as process cases accumulate, the samples are marked off-line to update the classification model, and the updated model is then put on-line again.
Preferably, the co-evolution process includes the following steps of cooperatively updating a model:
a1, defining important parameters in the update mode, including a sharing parameter theta s Task specific parameter θ for each old task o Training data and reality X of new task n And Y is equal to n 。
A2, initializing a model, which comprises the following steps: calculating the output Y of the old task for the new data o ←CNN(X n ，θ s ，θ o ) The method comprises the steps of carrying out a first treatment on the surface of the Random initialization of new parameter θ n ←RANDINIT(|θ n |)；
A3, model training, wherein the model training comprises the steps of defining the output of an old task asDefining new task outputs asThe objective function is:
compared with the prior art, the invention provides a digital twin system co-evolution method based on an incremental model, which has the following beneficial effects:
the invention realizes the real-time sensing, analysis and control of the processing object along with the processing process based on the self-adaptive evolution function of the digital twin system, and forms a real-time data acquisition and analysis system; meanwhile, through real-time analysis of data in the machining process, perception of the change quantity of the machining state is realized, and decision and control precision of the system in the machining process are improved, so that precision and efficiency of the mechanical machining system in the industrial operation process are improved.
Drawings
Fig. 1 is a schematic flow structure diagram of a digital twin system co-evolution method based on an incremental model.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1:
please refer to fig. 1; a digital twin system co-evolution method based on an incremental model specifically comprises the following steps:
s1, generating digital twin data in the processing process by means of data interaction between a twin model and a physical entity based on a known digital twin system;
s2, collecting and analyzing various state variable quantities in the processing process to obtain data related to a decision model in the digital twin system;
s3, the digital twin system assists the digital twin system to make decisions according to historical data of the state variable quantity in the S2 by combining an intelligent algorithm;
and S4, in the processing process, historical data of the same scene are transmitted to the digital twin system through the cloud platform, so that the co-evolution of the digital twin system is promoted.
The digital twin system mentioned in S1-S4 comprises a digital twin process design unit, a processing monitoring unit and a processing quality inspection unit, wherein corresponding databases are built through data accumulation in the working process of the digital twin process design unit, the processing monitoring unit and the processing quality inspection unit.
As the process cases accumulate, the samples are marked off-line to update the classification model, and the updated model is then put on-line again.
The co-evolution process, which co-updates the model, includes the steps of:
a1, defining important parameters in the update mode, including a sharing parameter theta s Task specific parameter θ for each old task o Training data and reality X of new task n And Y is equal to n 。
A2, initializing a model, which comprises the following steps: calculating the output Y of the old task for the new data o ←CNN(X n ，θ s ，θ o ) The method comprises the steps of carrying out a first treatment on the surface of the Random initialization of new parameter θ n ←RANDINIT(|θ n |)；
A3, model training, wherein the model training comprises the steps of defining the output of an old task asDefining new task outputs asThe objective function is：
The invention realizes the real-time sensing, analysis and control of the processing object along with the processing process based on the self-adaptive evolution function of the digital twin system, and forms a real-time data acquisition and analysis system; meanwhile, through real-time analysis of data in the machining process, perception of the change quantity of the machining state is realized, and decision and control precision of the system in the machining process are improved, so that precision and efficiency of the mechanical machining system in the industrial operation process are improved.
Example 2:
referring to fig. 1, based on embodiment 1 but with the difference that,
the digital twin system provided by the invention comprises: the device belongs to a digital twin process design unit, a processing monitoring unit and a processing quality inspection unit which are produced in the processing process; these system elements can build new knowledge base to improve themselves by continuous data accumulation; it includes thinking, decision making, execution, and improvement; namely, the system realizes the perception, understanding and optimization of the whole flow through the accumulation of the processing data along with the processing process, and achieves the process of continuously improving the data performance.
The digital twin system generates digital twin data in the machining process by means of data interaction between the twin model and the physical entity, so that various state variable quantities in the cutting process are collected and analyzed to obtain data related to the decision model in the system.
In the co-evolution process, the digital twin system continuously progresses the decision performance of the digital twin system through historical data of system state change perceived in the processing process and through an intelligent algorithm; meanwhile, the processing process is required to be improved, namely the processing process requires corresponding data support, and the cloud platform is applicable to the transmission of historical data of the same scene for co-evolution.
The digital twin co-evolution method can be used for constructing objects mainly in the machining field, but other fields such as assembly and workshop management can be referred to for use.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (3)
1. The digital twin system co-evolution method based on the incremental model is characterized by comprising the following steps of:
s1, generating digital twin data in the processing process by means of data interaction between a twin model and a physical entity based on a known digital twin system;
s2, collecting and analyzing various state variable quantities in the processing process to obtain data related to a decision model in the digital twin system;
s3, the digital twin system assists the digital twin system to make decisions according to historical data of the state variable quantity in the S2 by combining an intelligent algorithm;
s4, in the processing process, historical data of the same scene are transmitted to the digital twin system through the cloud platform, and the co-evolution of the digital twin system is promoted;
the co-evolution process comprises the following steps of:
a1, defining parameters in the update mode, including sharing parametersθ s Task-specific parameters for each old taskθ o Training data for new tasksX n And the real situationY n ；
A2, initializing a model, which comprises the following steps: calculating the output of an old task for new dataY o ←CNN(X n ,θ s ,θ o ) The method comprises the steps of carrying out a first treatment on the surface of the Random initialization of new parametersθ n ←RANDINIT(|θ n |)；
2. The incremental model-based co-evolution method of a digital twin system of claim 1, wherein: the digital twin system mentioned in S1-S4 comprises a digital twin process design unit, a processing monitoring unit and a processing quality inspection unit, wherein corresponding databases are built through data accumulation in the working process of the digital twin process design unit, the processing monitoring unit and the processing quality inspection unit.
3. The incremental model-based co-evolution method of a digital twin system of claim 1, wherein: as the process cases accumulate, the samples are marked off-line to update the classification model, and the updated model is then put on-line again.
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