CN114218745A - Intelligent design method for model-driven autonomous evolution - Google Patents
Intelligent design method for model-driven autonomous evolution Download PDFInfo
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Abstract
The embodiment of the invention discloses a model-driven autonomous evolution intelligent design method and a medium, wherein the method comprises the following steps: s101: describing a physical domain model of a complex product and a working environment thereof by adopting a definition diagram, an internal block diagram, a parameter diagram and a state machine diagram of SysML language; s102: describing an intelligent decision function of the complex product by adopting a rule and a neural network model and inputting the intelligent decision function into a cognitive domain model; s103: generating a physical domain model code based on the description of the definition diagram, the internal block diagram, the parameter diagram and the state machine diagram, generating a cognitive domain model code based on the rule and the description of the neural network model, and generating an interface code for integrating interaction of the physical domain model and the cognitive domain model based on the description of the interaction interface; s104: and describing and deploying the codes based on the simulation training environment operation requirement and the intelligent decision function training method, and constructing a plurality of twin virtual spaces in a CPU and GPU mixed supercomputing environment to perform parallel evolution so as to complete the design of the intelligent decision function.
Description
Technical Field
The present invention relates to the field of computers. And more particularly, to a model-driven autonomous evolution intelligent design method and medium.
Background
With the increasing complexity of customer requirements, working environment, product composition and product technology, product design increasingly depends on the support of intelligent technology. Currently, common intelligent designs mainly include:
(1) intelligent design based on knowledge: the method is mainly characterized in that related knowledge of design reports, models, cases, standards, documents and the like is recommended to designers through intelligent perception of design scenes and inference of knowledge maps.
(2) Intelligent design based on data: the method is mainly characterized in that a candidate data set and a label are selected, and data such as related market demands, appearance schemes, parameter characteristics and the like are recommended to designers based on a data mining method.
The intelligent design is designed by people and machines, and the machines can accurately push proper data and knowledge to people. With the development of artificial intelligence technologies represented by reinforcement learning, machine-based and human-assisted designs have begun to be developed for intelligent product designs represented by robot control systems. The design scheme can be self-learned, self-adaptive and self-evolved based on a virtual space provided by a digital twin, and generally, the problems of 'difficult abstraction' of a high-order multi-dimensional nonlinear model, 'difficult extraction' of a large-scale complex working condition quantization rule and the like can be solved effectively, so that the design scheme represents a new development direction of an intelligent design technology.
At present, self-evolution design based on reinforcement learning needs to prepare two contents, namely an intelligent agent model to be evolved (cognitive domain model) and a simulation training environment (physical domain model) supporting intelligent agent exploration. Usually, the two parts are prepared separately, the intelligent agent model is usually a neural network, and the code development is carried out manually by a human; sometimes, a code is developed by a person in a simulation training environment, and sometimes, an existing simulation platform is selected, but modification and interface packaging are needed; in addition, the design space is huge, the computing power is insufficient, and if the design scheme is completely handed over to the machine for autonomous evolution, the cycle is too long and the situation of non-convergence may occur.
Disclosure of Invention
The invention aims to provide a model-driven autonomous evolution intelligent design method and a medium, so as to solve at least one of the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a model-driven autonomous evolution intelligent design method in a first aspect, which comprises the following steps:
s101: describing a physical domain model of a complex product and a working environment thereof by adopting a definition diagram, an internal block diagram, a parameter diagram and a state machine diagram of SysML language;
s102: describing an intelligent decision function of the complex product by adopting a rule and a neural network model and inputting the intelligent decision function into a cognitive domain model;
s103: generating a physical domain model code based on the description of the definition diagram, the internal block diagram, the parameter diagram and the state machine diagram, generating a cognitive domain model code based on the rule and the description of the neural network model, and generating an interface code for integrating interaction of the physical domain model and the cognitive domain model based on the description of the interaction interface;
s104: and describing and deploying the codes based on the simulation training environment operation requirement and the intelligent decision function training method, and constructing a plurality of twin virtual spaces in a CPU and GPU mixed supercomputing environment to perform parallel evolution so as to complete the design of the intelligent decision function.
Further, the S101 further includes: and reserving modules of the cognitive domain model in the block definition diagram and the internal block diagram, and describing interactive interfaces of the cognitive domain model and the physical domain model in advance.
Further, the interactive interface includes: and the physical domain model inputs the interface of the state variable to the cognitive domain model and the cognitive domain model outputs the interface of the decision action to the physical domain model.
Further, the state variables input to the cognitive domain model by the physical domain model are used as the input of a rule and a neural network, and the output of the rule and the neural network is used as the decision action output to the physical domain model by the cognitive domain model.
The description of the physical domain model requires explicit continuous system state variables and discrete event system state variables.
Further, the state variables are used as the input of a reward function of the intelligent decision function written based on the continuous system state variables and the discrete event system state variables, and the output of the reward function is used as the training feedback of the cognitive domain model and is input to the cognitive domain model.
Further, the simulation training environment operation requirements include: calculating requirements of simulation operation on a CPU and an internal memory based on a physical domain model, and developing parallel evolution requirements on multi-instance simulation operation and multi-scenario simulation scenario setting in a plurality of twin spaces;
the intelligent decision function training method comprises the following steps: the method is based on the requirement of self-learning self-evolution on GPU computing capacity of a cognitive domain model, and the requirement of parallel evolution on a parallel training framework of the cognitive domain model, a training algorithm of the cognitive domain model and hyper-parameter setting is developed in a plurality of twin spaces.
Further, the physical domain model code is used as a simulation training environment for the autonomous evolution of the intelligent decision function.
Further, the cognitive domain model code includes rules, neural networks, and self-learning self-evolutionary related code thereof.
A second aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the model-driven autonomic evolution intelligent design method provided by the first aspect of the present invention.
The invention has the following beneficial effects:
the scheme provided by the application is based on a standard system engineering modeling language SysML, effectively integrates the modeling of a cognitive domain model and a physical domain model, and supports the unified description of a digital twin virtual space for the autonomous evolution of a design scheme; on the other hand, the automatic generation method based on the model driving code realizes the automatic generation of the cognitive domain model and the physical domain model and supports the automatic construction of the digital twin virtual space of the autonomous evolution of the design scheme.
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The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 is a diagram illustrating steps of a model-driven autonomic evolution intelligent design method according to an embodiment of the present invention;
fig. 2 is a logic diagram of a model-driven autonomous evolution intelligent design method according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the present invention, the present invention will be further described with reference to the following examples and the accompanying drawings. Similar parts in the figures are denoted by the same reference numerals. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and is not to be taken as limiting the scope of the invention.
As shown in fig. 1, an embodiment of the present invention provides a model-driven autonomous evolution intelligent design method, including:
s101: describing a physical domain model of a complex product and a working environment thereof by adopting a definition diagram, an internal block diagram, a parameter diagram and a state machine diagram of SysML (systems Modeling language);
s102: describing an intelligent decision function of the complex product by adopting a rule and a neural network model and inputting the intelligent decision function into a cognitive domain model;
s103: generating a physical domain model code based on the description of the definition diagram, the internal block diagram, the parameter diagram and the state machine diagram, generating a cognitive domain model code based on the rule and the description of the neural network model, and generating an interface code for integrating interaction of the physical domain model and the cognitive domain model based on the description of the interaction interface;
s104: and describing and deploying the codes based on the simulation training environment operation requirement and the intelligent decision function training method, and constructing a plurality of twin virtual spaces in a CPU and GPU mixed supercomputing environment to perform parallel evolution so as to complete the design of the intelligent decision function.
In a possible implementation manner, the S101 further includes: reserving a module of the cognitive domain model in the block definition diagram and the internal block diagram, and describing an interaction interface of the cognitive domain model and the physical domain model in advance;
the interactive interface comprises: and the physical domain model inputs the interface of the state variable to the cognitive domain model and the cognitive domain model outputs the interface of the decision action to the physical domain model.
In a specific embodiment, the state variables input by the physical domain model to the cognitive domain model are used as the input of a rule and a neural network, and the output of the rule and the neural network is used as the decision action output by the cognitive domain model to the physical domain model;
the description of the physical domain model requires explicit continuous system state variables and discrete event system state variables.
In a specific embodiment, as shown in fig. 2, the continuous system state variables and the discrete event system state variables are used as the input of the reward function of the intelligent decision function written based on the continuous system state variables and the discrete event system state variables, and the output of the reward function is used as the training feedback of the cognitive domain model and is input to the cognitive domain model. To enable autonomous evolution of the intelligent decision function.
In a specific embodiment, the physical domain model code serves as a simulation training environment for the autonomous drilling of the intelligent decision function; the cognitive domain model code comprises rules, a neural network and a reward function; the invention generates the definition diagram, the internal block diagram, the parameter diagram and the state machine diagram for describing the physical domain and the cognitive domain of the complex product through the SysML language, automatically generates the codes corresponding to the physical domain and the cognitive domain through the diagrams, does not need manual code development, and improves the development efficiency.
In a specific embodiment, the continuous system state variables are state variables of a differential algebraic equation applied to the complex events, and the discrete event system state variables are state variables of the state machine diagram.
In a specific embodiment, the calculation requirements of simulation operation on a CPU and an internal memory are carried out based on a physical domain model, and the parallel evolution requirements on multi-instance simulation operation and multi-scenario simulation scenario setting requirements are carried out in a plurality of twin spaces;
the intelligent decision function training method comprises the following steps: the method is based on the requirement of self-learning self-evolution on GPU computing capacity of a cognitive domain model, and the requirement of parallel evolution on a parallel training framework of the cognitive domain model, a training algorithm of the cognitive domain model and hyper-parameter setting is developed in a plurality of twin spaces.
In a specific embodiment, the physical domain model code is used as a simulation training environment for the autonomous evolution of the intelligent decision function; the cognitive domain model code includes rules, neural networks, and self-learning, self-evolutionary related code thereof.
The method is based on simulation training environment operation requirements and an intelligent decision function training method to describe and deploy the codes, a plurality of twin virtual spaces are constructed in a CPU and GPU mixed supercomputing environment, and the plurality of virtual spaces are subjected to parallel training, so that more model information can be mastered, the model evolution speed is accelerated, and the self-learning, self-adaption and self-evolution of the intelligent decision function in the twin virtual spaces are realized.
A second embodiment of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements:
s101: describing a physical domain model of a complex product and a working environment thereof by adopting a definition diagram, an internal block diagram, a parameter diagram and a state machine diagram of SysML language;
s102: describing an intelligent decision function of the complex product by adopting a rule and a neural network model and inputting the intelligent decision function into a cognitive domain model;
s103: generating a physical domain model code based on the description of the definition diagram, the internal block diagram, the parameter diagram and the state machine diagram, generating a cognitive domain model code based on the rule and the description of the neural network model, and generating an interface code for integrating interaction of the physical domain model and the cognitive domain model based on the description of the interaction interface;
s104: and describing and deploying the codes based on the simulation training environment operation requirement and an intelligent decision function training method, and constructing a plurality of twin virtual spaces in a supercomputing environment mixed by a CPU and a GPU to carry out parallel evolution.
In practice, the computer-readable storage medium may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the description of the present invention, it should be noted that the terms "upper", "lower", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and operate, and thus, should not be construed as limiting the present invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
It is further noted that, in the description of the present invention, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations and modifications can be made on the basis of the above description, and all embodiments cannot be exhaustive, and all obvious variations and modifications belonging to the technical scheme of the present invention are within the protection scope of the present invention.
Claims (10)
1. A model-driven intelligent design method for autonomous evolution is characterized by comprising the following steps:
s101: describing a physical domain model of a complex product and a working environment thereof by adopting a definition diagram, an internal block diagram, a parameter diagram and a state machine diagram of SysML language;
s102: describing an intelligent decision function of the complex product by adopting a rule and a neural network model and inputting the intelligent decision function into a cognitive domain model;
s103: generating a physical domain model code based on the description of the definition diagram, the internal block diagram, the parameter diagram and the state machine diagram, generating a cognitive domain model code based on the rule and the description of the neural network model, and generating an interface code for integrating interaction of the physical domain model and the cognitive domain model based on the description of the interaction interface;
s104: and describing and deploying the codes based on the simulation training environment operation requirement and the intelligent decision function training method, and constructing a plurality of twin virtual spaces in a CPU and GPU mixed supercomputing environment to perform parallel evolution so as to complete the design of the intelligent decision function.
2. The method of claim 1,
the S101 further includes: and reserving modules of the cognitive domain model in the block definition diagram and the internal block diagram, and describing interactive interfaces of the cognitive domain model and the physical domain model in advance.
3. The method of claim 2,
the interactive interface comprises: and the physical domain model inputs the interface of the state variable to the cognitive domain model and the cognitive domain model outputs the interface of the decision action to the physical domain model.
4. The method of claim 1,
and adopting the state variable input from the physical domain model to the cognitive domain model as the input of a rule and a neural network, and taking the output of the rule and the neural network as the decision action output from the cognitive domain model to the physical domain model.
5. The method of claim 4,
the description of the physical domain model requires explicit continuous system state variables and discrete event system state variables.
6. The method of claim 5,
and taking the state variable as the input of a reward function of the intelligent decision function written based on the continuous system state variable and the discrete event system state variable, and taking the output of the reward function as the training feedback of the cognitive domain model to be input to the cognitive domain model.
7. The method of claim 6,
the simulation training environment operation requirements include: calculating requirements of simulation operation on a CPU and an internal memory based on a physical domain model, and developing parallel evolution requirements on multi-instance simulation operation and multi-scenario simulation scenario setting in a plurality of twin spaces;
the intelligent decision function training method comprises the following steps: the method is based on the requirement of self-learning self-evolution on GPU computing capacity of a cognitive domain model, and the requirement of parallel evolution on a parallel training framework of the cognitive domain model, a training algorithm of the cognitive domain model and hyper-parameter setting is developed in a plurality of twin spaces.
8. The method of claim 1,
and taking the physical domain model code as a simulation training environment of the intelligent decision function autonomous evolution.
9. The method of claim 8,
the cognitive domain model code includes rules, neural networks, and self-learning, self-evolutionary related code thereof.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-9 when executing the program.
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