CN114036842B - Dynamic generation method for full life cycle use strategy of digital twin machine tool - Google Patents
Dynamic generation method for full life cycle use strategy of digital twin machine tool Download PDFInfo
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Abstract
The invention discloses a dynamic generation method for a full life cycle use strategy of a digital twin machine tool, which comprises the following steps: s1) constructing a parallel simulation model DE of the life cycle of the machine tool; s2) constructing a machine tool future scene model S for describing the use condition of the machine tool; s3) constructing an instant use strategy model T of the machine tool; s4) integrating and constructing a machine tool life cycle parallel simulation model DE, a machine tool future scene model S and a machine tool instant use strategy model T into a digital twin machine tool, and running the digital twin machine tool to dynamically generate a full life cycle use strategy. The invention can generate the integral use strategy of the machine tool in the designated period or the full life period, is convenient for users to more comprehensively know the possible use condition and benefit of the machine tool, and is beneficial to the users to make better equipment management decision from the global perspective.
Description
Technical Field
The invention relates to the technical field of digital machine tool control. In particular to a dynamic generation method of a full life cycle use strategy of a digital twin machine tool.
Background
The digital twin technology has been applied to machine tool management, and the current achievements mainly focus on machine tool state tracking display, machine tool operation optimization, preventive maintenance strategy generation and the like based on a three-dimensional model. However, the above results have not been fully satisfactory.
Specifically, firstly, the existing digital twin machine tool model does not have the capability of dynamically generating a full-life-cycle use strategy, the existing strategy model is only a current-step strategy obtained according to the current state, and cannot be used for pushing back the use strategy in a future period of time or even the whole life cycle, and for a user, if the full-life-cycle strategy can be obtained, the arrangement of the whole work is obviously facilitated; secondly, although the machine tool is also shipped with maintenance strategies, these strategies are basically general empirical rules and lack the ability to be personalized and dynamically generated according to actual use.
From the foregoing, it is necessary to design a method for constructing a digital twin machine tool, and in particular, to design a method for constructing a digital twin machine tool that supports dynamic generation of a full life cycle usage strategy.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to provide a dynamic generation method for a full-life-cycle use strategy of a digital twin machine tool, which can generate an overall use strategy of the machine tool in a specified cycle or a full-life cycle, is convenient for a user to more comprehensively know the possible use condition and benefits of the machine tool, and is beneficial for the user to make a better equipment management decision from the global perspective.
In order to solve the technical problems, the invention provides the following technical scheme:
a dynamic generation method for a full life cycle use strategy of a digital twin machine tool comprises the following steps:
s1) constructing a parallel simulation model DE of the life cycle of the machine tool; the machine tool life cycle parallel simulation model DE comprises a parameterized modeling module, a virtual-real synchronization module and a simulation deduction module;
s2) constructing a machine tool future scene model S for describing the use condition of the machine tool; the machine tool future scene model S comprises an event definition module and an event trigger module;
s3) constructing an instant use strategy model T of the machine tool; the machine tool instant use strategy model T comprises a data acquisition module, a strategy deduction module and a strategy output module;
s4) integrating and constructing a machine tool life cycle parallel simulation model DE, a machine tool future scene model S and a machine tool instant use strategy model T into a digital twin machine tool, and running the digital twin machine tool to dynamically generate a full life cycle use strategy.
In step S1), the parameterized modeling module is configured to describe, in a parameterized manner, a use target, a use scenario, and a capability state of the machine tool, where the use target is used as a basis for training and generating an instant strategy of the machine tool in step S3), the use scenario is used for describing a use environment of the machine tool, and the capability state describes typical performance and a current operating condition of the machine tool; the virtual-real synchronous module is used for generating the whole lifeUsage scenario S of synchronous physical machine tool E in life cycletAnd capability status information CtWherein the use scenario S of the physical machine tool EtAnd capability status information CtAs input for subsequent generation of a usage policy; the simulation deduction module is used for using the scene S according to the assumed next stept+1With current usage policy TtCalculating to obtain the next capability state C of the physical machine toolt+1Wherein a usage policy is used to describe at what time what task is performed.
In the above dynamic generation method for the full life cycle usage strategy of the digital twin machine tool, in step S2), the event definition module is configured to define events that may occur in the future, each event is characterized by an event feature quantity, and the event feature quantity at least includes an event type, an event parameter, and an influence effect, where the event type includes events that can or/and may influence the machine tool state and capability, the trigger rule is obtained by defining occurrence probability distribution of the events over a period of time, and the influence effect is used to describe the influence on the machine tool state and capability once an event occurs; the event triggering module is used for generating corresponding events at corresponding moments based on preset triggering rules.
In the dynamic generation method of the full life cycle use strategy of the digital twin machine tool, in step S3), the data acquisition module is used for acquiring the current use scene S in real timetCapability state CtAnd a usage scenario S of the hypothetical next stept+1The strategy deduction module is used for realizing the use scene StAnd capability state CtTo instant policy TtAnd the next step using the scene St+1And next step capability state Ct+1Strategy T of next stept+1The strategy output module is used for outputting the strategy obtained by the strategy deduction module; the mapping relation used by the strategy deduction module is obtained through reinforcement learning algorithm training.
The dynamic generation method of the full life cycle use strategy of the digital twin machine tool adopts the principle of training used in the training of the reinforcement learning algorithm to realize the defined use target A to the maximum extent according to the grasped use scene StAnd status of capabilityCtAnd searching the use target value after using different strategies by the information, and taking the searched use target value as the basis of algorithm convergence.
In the dynamic generation method of the whole life cycle use strategy of the digital twin machine tool, in step S4), a time node t in the whole life cycle of the machine tool is setjJ is 0,1,2, the machine tool full life cycle can be written as { t }0,t1,t2,...,tj,.. }, step S1) is carried out on the machine tool life cycle parallel simulation model and the physical machine tool at a time node tjMaking current usage scenario StWith current capability state information CtAnd (6) synchronizing.
Dynamic generation method of full life cycle use strategy of the digital twin machine tool, t0The selected time nodes are different, and the full life cycle usage policy generated in step S4) is different.
The technical scheme of the invention achieves the following beneficial technical effects:
the invention can generate the integral use strategy of the machine tool in the designated period or the full life period, is beneficial to a user to more comprehensively know the possible use condition and benefit of the machine tool, and thus assists the user to make better equipment management decision from the global perspective.
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FIG. 1 is a flow chart of a dynamic generation method of a full life cycle use strategy of a digital twin machine tool in the invention.
Detailed Description
As shown in FIG. 1, the method for dynamically generating the full life cycle use strategy of the digital twin machine tool comprises the following steps:
s1) constructing a parallel simulation model DE of the life cycle of the machine tool; the machine tool life cycle parallel simulation model DE comprises a parameterized modeling module, a virtual-real synchronization module and a simulation deduction module;
s2) constructing a machine tool future scene model S for describing the use condition of the machine tool; the machine tool future scene model S comprises an event definition module and an event trigger module;
s3) constructing an instant use strategy model T of the machine tool; the machine tool instant use strategy model T comprises a data acquisition module, a strategy deduction module and a strategy output module;
s4) integrating and constructing a machine tool life cycle parallel simulation model DE, a machine tool future scene model S and a machine tool instant use strategy model T into a digital twin machine tool, and running the digital twin machine tool to dynamically generate a full life cycle use strategy.
In the present embodiment, in step S1), the parametric modeling module is used to parametrically describe the usage target, the usage scenario, and the capability state of the machine tool. Wherein, the use target is used as the basis for training and generating the machine tool instant strategy in the step S3), and can be defined by self according to the requirements, such as maximum benefit, longest service life, and the like; the use scene is used for describing the use environment of the machine tool and is described by a group of events which occur currently, such as power failure, execution of temporary emergency tasks and the like; the capability state describes typical performance and current operating conditions of the machine tool, such as the number of machined parts per hour, mean time to failure, spindle noise and the like; the virtual-real synchronization module is used for synchronizing the use scene S of the physical machine tool E in the whole life cycletAnd capability status information CtUse scenario S of physical machine EtAnd capability status information CtAs input for subsequent generation of a usage policy; the simulation deduction module is used for using the scene S according to the assumed next stept+1With current usage policy TtCalculating to obtain the next capability state C of the physical machine toolt+1A policy is used to describe when to perform what task, such as "maintain the machine tool at XX months XX in 20 XX". The executed task has an influence on the capability of the machine tool, the influence can be obtained through calculation, for example, the spindle noise is kept below 80DB after maintenance, and the specific influence effect can be obtained based on empirical knowledge and historical data.
In step S2), the event definition module is configured to define events that may occur in the future, each event being characterized by an event feature quantity, the event feature quantity at least including an event type, an event parameter and an influence effect, wherein the event type includes an event that can or/and may influence the state and capability of the machine tool, such as an external power outage; the triggering rule is obtained by defining the occurrence probability distribution of the event within a period of time, and the external power failure duration is as long as desired; the influence effect is used for describing the influence on the state and the capability of the machine tool once a certain event occurs, and the machine tool is stopped when external power failure occurs as desired; the event triggering module is used for generating corresponding events at corresponding moments based on preset triggering rules. The events and related parameters can be obtained through empirical knowledge and historical data.
In step S3), the data acquisition module is used to acquire the current usage scenario S in real timetCapability state CtAnd a usage scenario S of the hypothetical next stept+1The strategy deduction module is used for realizing the use scene StAnd capability state CtTo instant policy TtAnd the next step using the scene St+1And next step capability state Ct+1Strategy T of next stept+1The strategy output module is used for outputting the strategy obtained by the strategy deduction module; the mapping relation used by the strategy deduction module is obtained through reinforcement learning algorithm training. The training principle used in the reinforcement learning algorithm training is based on the principle of maximally realizing the defined use target A, and according to the grasped use scene StAnd capability state CtAnd searching the use target value after using different strategies by the information, and taking the searched use target value as the basis of algorithm convergence. In this step, the strategy deduction module is based on the current using target A and the current using scene StAnd current capability state CtOutputting the current usage policy TtAnd according to the hypothetical next usage scenario St+1And deducing the obtained next step capability state Ct+1Obtaining the next use strategy Tt+1And by analogy, obtaining the use strategy of the full life cycle { T }t,Tt+1,Tt+2,...}。
In step S4), the machine tool life cycle parallel simulation model DE constructed in step S1) and the physical machine tool E perform the current use scene StWith current capability state information CtSynchronizing, and in operation, the machine tool life cycle parallel simulation model DE constructed in step S1) is based on the assumed next usage scenario St+1With current usage policy TtDeducing the next capability state C of the machine tool life cycle parallel simulation model DE built in the step S1)t+1(ii) a The capability state information C is used for describing the current operation condition of the machine tool; setting time node t in the whole life cycle of the machine tooljJ is 0,1,2, the machine tool full life cycle can be written as { t }0,t1,t2,...,tj,.. }, step S1) of the parallel simulation model DE of the life cycle of the machine tool and the physical machine tool E at a time node tjMaking current usage scenario StWith current capability state information CtIs synchronized when t is0When the selected time nodes are different, the full life cycle usage policy generated in step S4) is also different.
Specifically, in practical application, firstly, a corresponding machine tool life cycle parallel simulation model DE is constructed for a physical machine tool E in the system, and the machine tool life cycle parallel simulation model DE can synchronize the current use scene S of the physical machine tool E within the full life cycletAnd capability status information CtIt is also possible to use the scenario S according to the assumed next stept+1With current usage policy TtDeducing to obtain the next capability state C of the equipmentt+1The usage scenario is used for describing the usage target, the usage environment and other contents of the machine tool, and is often described by a set of parameters, such as a task load degree; the state of capacity is used to describe the current operating conditions of the machine, also often described by a series of parameters, such as spindle noise, etc., using a strategy for describing at what time what task is performed, such as "replace spindle immediately". The system is a machine tool control system or a newly added auxiliary control system. In this embodiment, a series of time nodes are provided for the entire life cycle of the machine tool, and from start of use to end of use, the number is given as { t }0,t1,t2,.. }; the method comprises the following steps that a machine tool use scene is only characterized by a 'task load degree' parameter, and the task load degree is divided into three levels, namely light level, medium level and heavy level; the machine tool capability state is only represented by 'spindle noise' and is expressed by decibels; let the policy denote "what task is performed at what time".
Secondly, a machine tool future scene model S for describing changes of future scenes is constructed in the system, such as unexpected power-off events, 1-time increase of task load and the like, wherein the number of the future scenes contained in the scene model can be multiple. The scene model can be obtained through empirical knowledge and historical data.
Let the future scenario be as shown in the table below.
| Time of day | Future scenario |
| t3 | Unexpected power failure |
| t5 | The task load is changed from light to heavy |
Thirdly, constructing a device instant strategy model T in the system, wherein the model T can not only be used according to the current use target A and the use scene StCapability state CtOutputting the current usage policy TtIt is also possible to use the scene S according to the hypothetical next stept+1And deducing the obtained next step capability state Ct+1Get the next strategy Tt+1. By analogy, the use strategy { T ] in the whole use period can be obtainedt,Tt+1,Tt+2,...}. Wherein, the constructed instant use strategy model T is used to obtain the use strategy Tt+1The specific method comprises the following steps: and feeding back the operation effect of the use strategy based on the use scene of the digital twin machine tool by taking the use target completion degree as a reward, and finally forming an available instant use strategy model T through strategy space search. In this embodiment, the objective is to achieve the best full life cycle benefitBig ", resulting in an instant policy model that can take advantage of the usage scenario StCapability state CtOutputting strategy T of corresponding timet。
Next, the model of the effect of the whole-cycle use of the equipment is designed to include an index of the benefit created in the whole life cycle of the machine tool, and is recorded as P1。
And finally, operating the digital twin machine tool and dynamically generating a full life cycle use strategy.
The operation results are shown in the following table, i.e. at the present time t0And obtaining a use strategy in the whole life cycle, and predicting the benefit created in the whole life cycle of the machine tool.
It is further noted that at t0The whole life cycle strategy obtained at any moment can change along with the change of time, and the same is also expected to be achieved in the created benefit in the whole life cycle.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications are possible which remain within the scope of the appended claims.
Claims (4)
1. A dynamic generation method for a full life cycle use strategy of a digital twin machine tool is characterized by comprising the following steps:
s1) constructing a parallel simulation model DE of the life cycle of the machine tool; the machine tool life cycle parallel simulation model DE comprises a parameterized modeling module, a virtual-real synchronization module and a simulation deduction module; the parameterized modeling module is used for parametrically describing the use target, the use scene and the capability state of the machine tool, wherein the use target is used as the training in the step S3) to generate the immediate strategy of the machine toolThe method is based on the principle that a use scene is used for describing the use environment of the machine tool, and the capability state describes the typical performance and the current operation condition of the machine tool; the virtual-real synchronization module is used for synchronizing the use scene S of the physical machine tool E in the whole life cycletAnd capability status information CtWherein the use scenario S of the physical machine tool EtAnd capability status information CtAs input for subsequent generation of a usage policy; the simulation deduction module is used for using the scene S according to the assumed next stept+1With current usage policy TtCalculating to obtain the next capability state C of the physical machine toolt+1Wherein a usage policy is used to describe what tasks are performed at what times;
s2) constructing a machine tool future scene model S for describing the use condition of the machine tool; the machine tool future scene model S comprises an event definition module and an event trigger module; the event definition module is used for defining events which are likely to occur in the future, each event is characterized by event characteristic quantity, the event characteristic quantity at least comprises an event type, an event parameter and an influence effect, wherein the event type comprises the events which are likely to influence the state and the capability of the machine tool, the trigger rule is obtained by defining occurrence probability distribution of the events within a period of time, and the influence effect is used for describing the influence on the state and the capability of the machine tool once a certain event occurs; the event triggering module is used for generating corresponding events at corresponding moments based on a preset triggering rule;
s3) constructing an instant use strategy model T of the machine tool; the machine tool instant use strategy model T comprises a data acquisition module, a strategy deduction module and a strategy output module; the data acquisition module is used for acquiring a current use scene S in real timetCapability state CtAnd a usage scenario S of the hypothetical next stept+1The strategy deduction module is used for realizing the use scene StAnd capability state CtTo instant policy TtAnd the scene S is used by the next stept+1And next step capability state Ct+1Strategy T of next stept+1The strategy output module is used for outputting the strategy obtained by the strategy deduction module; the mapping relation used by the strategy deduction module is obtained through reinforcement learning algorithm training;
s4) integrating and constructing a machine tool life cycle parallel simulation model DE, a machine tool future scene model S and a machine tool instant use strategy model T into a digital twin machine tool, and running the digital twin machine tool to dynamically generate a full life cycle use strategy.
2. The method according to claim 1, wherein the training principle used in the training of the reinforcement learning algorithm is based on the fact that the maximum achievement of the defined usage goal A is defined, and the method is based on the grasped usage scenario StAnd capability state CtAnd searching the use target value after using different strategies by the information, and taking the searched use target value as the basis of algorithm convergence.
3. The dynamic generation method of the full life cycle use strategy of the digital twin machine tool according to claim 1 or 2, wherein in step S4), a time node t in the full life cycle of the machine tool is setjJ is 0,1,2, the machine tool full life cycle can be written as { t }0,t1,t2,...,tj,.. }, step S1) and the machine tool life cycle parallel simulation model constructed in the step S1) and the physical machine tool at a time node tjMaking current usage scenario StWith current capability state information CtAnd (6) synchronizing.
4. The full lifecycle strategy dynamic generation method of a digital twin machine tool as defined in claim 3, wherein t is0The selected time nodes are different, and the full life cycle usage policy generated in step S4) is different.
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