CN112287476A - Knowledge-driven automatic generation method for parameterized model of electronic equipment case - Google Patents
Knowledge-driven automatic generation method for parameterized model of electronic equipment case Download PDFInfo
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Abstract
The invention belongs to the technical field of computer aided design, and particularly relates to a knowledge-driven automatic generation method of a parameterized model of an electronic equipment case, which is characterized by comprising the following steps of: establishing a parameterized model representation frame for describing the topological structure of the chassis products; establishing a product knowledge base of functional elements, behavior elements, structural elements and parameter elements; taking design requirement parameters as input, converting the design requirement parameters of the chassis product into product function meta expression through a product knowledge base; the method comprises the following steps of taking a functional parameter flow as input, realizing behavior expansion and decomposition of a case product through design knowledge, solving parameters, iterating and outputting to a structural layer; and taking the behavior parameter flow as input, realizing the structural mapping of the case product through design knowledge, and automatically generating a parameterized geometric model of the product example. The method can comprehensively represent the product model from four aspects of logic expression, graphic expression, geometric expression and semantic expression.
Description
Technical Field
The invention belongs to the technical field of computer aided design, and particularly relates to a knowledge-driven automatic generation method of a parameterized model of an electronic equipment case.
Background
An electronic device enclosure is a structure for housing electronic components, device modules, and mechanical parts, and has the characteristics of high power consumption, high local heat flux density, small size, light weight, and the like, and needs to operate normally under various complex environmental influences and interferences. In order to ensure that each component in the chassis is not affected by a complex environment, the chassis is required to have good environmental adaptability while satisfying electrical performance and mechanical connection performance, which brings difficulties to structural design.
When a designer designs the structure of a chassis product, different frame models need to be constructed manually according to different design requirements and design knowledge and rules, different configurations of parts and features are selected, the operation lacks intelligence, and further improvement of product modeling efficiency is restricted. The parameterization technology (parametrics) is an effective means for realizing the rapid construction of the geometric model of the product parts, and can realize the rapid construction and result reuse of the product model, so that a user can develop a new product on the basis of the original design. The Knowledge-Based Engineering (KBE) technology is combined with the parameterization technology, so that the parameterization technology is further developed, the Knowledge inference level of the parameterization system is improved by realizing the capabilities of Knowledge representation, Knowledge storage and Knowledge reuse of the parameterization system, and the parameterization system is a necessary trend in development of the parameterization system.
The existing parameterization technical means can only realize the rapid construction of a part geometric model in the aspect of geometric relationship and is not suitable for the rapid construction of a complete machine geometric model of a product. When a geometric model of a complete machine of a product is established by adopting a traditional parameterization technology, a designer still needs to perform a large amount of complicated operations, and firstly, the types, the number of examples and the assembly relation of parts forming the product are determined. And secondly, determining the topological structure and the characteristics of the parts. And thirdly, determining design parameters. Each part and the characteristics of the product are described and controlled by a large number of design parameters, complex association relations exist among the parameters, and when some parameters are changed, other parameters are adjusted correspondingly. Complex products often consist of hundreds or thousands of parts, each of which contains a large number of features and design parameters. In the digital modeling process, the number of parts, features and parameters to be maintained is huge, the relationship between the parts, the features and the parameters is complicated, and the parts, the features and the parameters need to be continuously modified and adjusted in the design process, so that the workload of a designer is large, and errors are easy to occur. In the existing knowledge-based model parameterization construction method, various rules (such as constraint conditions, relational expressions, logic operations and the like) in parameterization modeling are usually solidified in a software system in a software coding mode, and a user faces to a closed black box system and cannot expand or modify the closed black box system. Therefore, a parameterized system which cannot be adaptively expanded for products cannot meet the actual requirements of engineering.
Disclosure of Invention
The invention aims to provide a knowledge-driven method for automatically generating a parameterized model of an electronic equipment case, so as to comprehensively represent a product model from four aspects of logic expression, graphic expression, geometric expression and semantic expression.
In order to achieve the above object, the technical scheme of the invention is as follows: a knowledge-driven method for automatically generating a parameterized model of an electronic equipment case is characterized by at least comprising the following steps:
(1) establishing a parameterized model representation frame for describing the topological structure of the chassis products;
(2) establishing a product knowledge base of functional elements, behavior elements, structural elements and parameter elements;
(3) taking the design requirement parameters as input, expanding and decomposing the functions of the design requirement parameters of the case product, solving the parameters and iterating the parameters and then outputting the parameters to a behavior layer;
(4) expanding and decomposing the behavior of the case product by taking the functional parameter flow as input, solving the parameters and iterating, and outputting the parameters to the structural layer;
(1) and taking the behavior parameter flow as input, and mapping the structure of the case product to generate a parameterized geometric model of the product example.
The parameterized model representation framework is as follows: the method is characterized in that geometric elements such as parts and features which are used for forming a model are abstractly expressed by using model representation framework objects, the geometric topological structure of the model is reflected by the incidence relation among the objects, the structure of the representation framework is the abstract representation of a case product, namely, the representation is the common topological structure of the case product, and the general expression of the parameterized model representation framework can be expressed as follows:
PF＝{KBCs，ABSO_Cs，ABSO_Fs，APCs，PARs} (1)
wherein: PF represents a parameterized model representation framework;
KBCs represent a drive constraint description set and are knowledge expressions embedded into a parameterized model representation framework;
ABSO _ Cs represents an abstracted set of part objects;
ABSO _ Fs represents an abstracted feature object set;
APCs represent geometric association between objects;
PARs represents the association of parameter associations between objects.
The product knowledge base for establishing the functional elements, the behavior elements, the structural elements and the parameter elements is to divide the design knowledge of the product into four layers according to the product functions, the product behaviors, the product structures and the product parameters, the distribution is a functional layer, a behavior layer, a structural layer and a parameter layer, and the expression can be expressed as follows:
OFBPS＝∑f(p)∪∑b(p)∪∑s(p) (2)
wherein: o isFBPSRepresenting a "function-behavior-structure-parameter" design element of knowledge;
f (p) represents the functional goals that the product can achieve under the drive of the parameters;
b (p) represents the physical behavior of the product realizing the product function under the drive of the parameter;
s (p) represents the actual physical structure of the product under parametric drive;
and p represents product performance parameters and product principle parameters, geometric structure parameters and the like derived from knowledge of the product performance parameters and is used for driving the functional layer, the behavior layer and the structural layer.
The product knowledge base is an abstract summary of design knowledge required by product construction, and the construction process at least comprises the following steps:
(1) constructing a product function knowledge base;
(2) constructing a product behavior knowledge base;
(3) constructing a product structure knowledge base;
(4) and constructing a product parameter knowledge base.
The product function knowledge base is a set of product function knowledge, and is used for splitting the main functions of the product and decomposing the main functions into independent and single function elements for expression.
The functional element is the minimum component element forming a product functional semantic network and is an abstract representation of a logic unit capable of meeting a certain functional requirement of a product; the general expression for a functional element may be expressed as:
F＝{FAttrs，FParas，FAnnotations} (3)
wherein: FAttrs denotes a set of functional attributes, i.e. sets of various types of attribute information associated with functional elements, such as: function number, function name, function type, creator, application environment, product name, operator, security level, etc.;
FParas represents a functional parameter set, namely a set of various parameter variables related to the functional element;
fanntotations represent functional descriptions, that is, detailed descriptions of functional elements, reflecting the functional implementation objectives of the functional elements. It adopts semantic network diagram for structural representation.
The product behavior knowledge base is a set of product behavior knowledge, is a mapping of product functions, and is an abstract summary of principles, rules and common knowledge of function implementation.
The behavior element is the minimum component element forming the product behavior semantic network, and is used for reading the function element, and the content of the behavior element describes the work executed by the physical structure of the product when the function is realized. A general expression for a behavior element may be expressed as:
B＝{BAttrs，BParas，BAnnotations} (4)
wherein: battrs denotes a behavior attribute set, i.e., a set of various attribute information related to behavior elements, such as: number, name …, etc. The behavior element attribute set plays a role of indexing in the process of reasoning operation related to the behavior element;
BParas denotes a set of behavioral parameters, i.e. a set of parameters related to behavioral elements. The structure is similar to the functional parameter set;
banntotations represent behavioral descriptions, i.e., semantic web graphs of behavioral descriptions. The accurate description of the behavior element reflects the realizable action or operation of the behavior element. Similar to the functional description, it is described using a semantic network graph.
The product structure knowledge base is a set of product structure knowledge and is an expression of a physical structure required by realizing product behaviors.
The structural element is the minimum component element forming the structural semantic network and is used for describing the composition and the relation of a product geometric model; the structural elements are represented by a parameterized model representation framework.
The parameter knowledge base is a set of product parameter knowledge and is an abstract expression of data objects in the product design process.
The parameter element refers to the smallest constituent element constituting a parameter set. The general expression for the parameter element may be expressed as:
P＝{PAttrs，PObjects，POperation，PFunction，PStatus} (5)
wherein: pattrs denotes a set of parameter attributes, such as: number, name …, etc.;
the objects represent parameter-associated object sets, and are used for expressing the objects associated with the parameters, and the expression range comprises function elements, behavior elements and structure elements;
the preference represents the operation of the parameter object to express the operation of the parameter element. The operation range comprises query and value of parameters;
PFunction represents a parameter relation equation for expressing a relation expression contained in a parameter;
PStatus represents the state of the parameter to represent the current solution state of the parameter.
The design requirement parameters refer to performance index parameters of the product determined by designers according to the expected design target of the product, and the performance index parameters can be regarded as an initial parameter set P0 of the product; and the parameters are transmitted into the functional elements as input parameters of the functional elements, the parameters complete solution iteration inside the functional elements, and the parameters are distinguished according to types and are continuously transmitted along with the decomposition process of the functional elements.
General expression for parametric solutions:
Slove(x)＝Search(x)∪Cal(x)∪Check(x)∪Out(x) (6)
wherein: slove (x) denotes a solver;
search (x) represents a query operator for searching and acquiring data required for computation in a dataset;
cal (x) denotes a numerical calculation operator for performing numerical calculation based on predefined design knowledge;
check (x) represents a check operator for checking the state of the current solution object to determine whether the current object needs to be solved;
out (x) represents an output operator for outputting the solution result.
The iterative process of solving the parameters in the functional elements can generate three possible results. One type is determined attribute variables, and the attributes are obtained by searching data sets such as empirical knowledge, experimental data and the like in a database through a query operator; one type is attribute variables which can be directly solved, and the attributes can be numerically solved by using the transmitted parameters through a numerical calculation operator according to preset design knowledge; one type is an attribute variable which cannot be solved, and the attribute is taken as a problem variable to be solved in the design process and is imported into a parameter flow, so that the problem of solving the parameters is refined.
The decomposition and expansion of the functional elements refer to a complex product function and can be formed by combining a plurality of simple functions. In the process of decomposing the function element, the parameters are transmitted to the lower function element by the upper function element.
The principle of the transmission of the parameters in the behavior elements and the decomposition and expansion of the behavior elements is the same as that of the transmission of the parameters in the function elements and the decomposition and expansion of the function elements.
The product structure mapping refers to a process of generating a product instance by using parameters obtained after the functional layer and the behavior layer are solved as parameter input of a structural element and representing a framework through a parameter-driven parameterized model. The general expression of the instantiation process can be expressed as:
KIF(PF，P)＝KIFF(ABSO_Fs，P)∪KICF(ABSO_Cs，P) (7)
wherein: KIF (PF, P) represents a product model instantiation operator;
PF represents a current product parameterized model representation framework;
p represents a design parameter of the front end; this process can also be seen as a process of instantiating abstracted part objects and feature objects in the parameterized model representation framework, i.e. the union of KICF (ABSO _ Cs, P) and KIFF (ABSO _ Fs, P). The general expression for KIFF can be expressed as:
wherein: KIFF (ABSO _ F)iP) is an instantiation operator of the abstract feature object;
Slove(ABSO_Fip) a knowledge solution operation for the feature object;
As(ABSO_Fi) A constraint mapping operation is positioned for the geometry of the model.
The KICF (ABSO _ Cs, P) represents an instantiation of an abstracted component object. The principle and KIFF (ABSO _ F)iAnd P) are identical. And finally generating a parameterized geometric model example of the product through an iterative instantiation process.
The invention has the advantages that:
(1) a parameterized model representation framework is used for explaining a certain product from the aspects of logic expression, graphic expression, geometric expression and semantic expression, and the construction process of a parameterized model can be comprehensively and accurately described.
(2) Design knowledge and product performance parameters are used as driving sources for building the parameterized model, widely applicable modeling rules are formed, and intelligent automatic reasoning and building of the parameterized model of the product based on the design knowledge are realized.
(3) The design knowledge is analyzed from the perspective of functional principle, so that the applicability of the model generation method is greatly improved. By expanding the knowledge base, the parameterized design object can be expanded to various disciplines, and the design requirement of a complex system in engineering application is met.
(4) The design knowledge is utilized to directly realize the automatic detailed parameterized model construction of the product, and the modeling process is flexible and efficient.
Drawings
The following detailed description of embodiments of the invention refers to the accompanying drawings in which:
FIG. 1 is a flow chart illustrating a method for automatically generating a knowledge-driven parameterized model of an electronic device chassis according to the present invention;
FIG. 2 is O according to the present inventionFBPSDesigning a knowledge element structure;
FIG. 3 is a functional semantic network of an electronic device chassis;
FIG. 4 is a schematic diagram of the "requirement-function" parameter passing of the electronic equipment chassis.
FIG. 5 is a "function-behavior" semantic network of an electronics chassis;
fig. 6 is a schematic diagram of the "function-behavior" parameter passing of the electronic device chassis.
FIG. 7 is an electronic device chassis parameterized model representation framework.
Fig. 8 is a schematic diagram of "function-behavior-structure" parameter passing of the electronic device case.
FIG. 9 is an example of a generated electronic device chassis model.
Detailed Description
As shown in fig. 1, a method for automatically generating a knowledge-driven parameterized model of an electronic device chassis is characterized by at least the following steps:
(2) establishing a parameterized model representation frame for describing the topological structure of the chassis product, and carrying out abstract representation on the model of the chassis product;
(3) establishing a product knowledge base of functional elements, behavior elements, structural elements and parameter elements;
(4) the design requirement parameters are used as input, the design requirement parameters of the chassis product are converted into product function expression through design knowledge, function expansion and decomposition are carried out, solution iteration is carried out on the parameters, and then the parameters are output to a behavior layer;
(5) the method comprises the following steps of taking a functional parameter flow as input, realizing behavior expansion and decomposition of a case product through design knowledge, solving parameters, iterating and outputting to a structural layer;
(6) and taking the behavior parameter flow as input, realizing the structural mapping of the case product through design knowledge, and automatically generating a parameterized geometric model of the product example.
The establishing of the parameterized model representation frame for describing the topological structure of the chassis product refers to: the method is characterized in that geometric elements such as parts and features which are used for forming a model are abstractly expressed by using model representation framework objects, the geometric topological structure of the model is reflected by the incidence relation among the objects, the structure of the representation framework is the abstract representation of a case product, namely, the representation is the common topological structure of the case product, and the general expression of the parameterized model representation framework can be expressed as follows:
PF＝{KBCs，ABSO_Cs，ABSO_Fs，APCs，PARs} (1)
wherein: PF represents a parameterized model representation framework;
KBCs represent a drive constraint description set and are knowledge expressions embedded into a parameterized model representation framework;
ABSO _ Cs represents an abstracted set of part objects;
ABSO _ Fs represents an abstracted feature object set;
APCs represent geometric association between objects;
PARs represents the association of parameter associations between objects.
The design knowledge of the box products is divided into four layers according to the product functions, the product behaviors, the product structures and the product parameters, and the design knowledge of the products is distributed into a functional layer, a behavior layer, a structural layer and a parameter layer. The general expression of design knowledge partitioned in "function-behavior-structure-parameter" can be expressed as:
OFBPS＝∑f(p)∪∑b(p)∪∑s(p) (2)
wherein: o isFBPSRepresenting a "function-behavior-structure-parameter" design element of knowledge;
f (p) represents the functional goals that the product can achieve under the drive of the parameters;
b (p) represents the physical behavior of the product realizing the product function under the drive of the parameter;
s (p) represents the actual physical structure of the product under parametric drive;
and p represents product performance parameters and product principle parameters, geometric structure parameters and the like derived from knowledge of the product performance parameters and is used for driving the functional layer, the behavior layer and the structural layer.
The functional element is the minimum component element forming a product functional semantic network and is an abstract representation of a logic unit capable of meeting a certain functional requirement of a product; the general expression for a functional element may be expressed as:
F＝{FAttrs，FParas，FAnnotations} (3)
wherein: FAttrs denotes a set of functional attributes, i.e. sets of various types of attribute information associated with functional elements, such as: function number, function name, function type, creator, application environment, product name, operator, security level, etc.;
FParas represents a functional parameter set, namely a set of various parameter variables related to the functional element;
fanntotations represent functional descriptions, that is, detailed descriptions of functional elements, reflecting the functional implementation objectives of the functional elements. It adopts semantic network diagram for structural representation.
The behavior element is the minimum component element forming the product behavior semantic network, and is used for reading the function element, and the content of the behavior element describes the work executed by the physical structure of the product when the function is realized. A general expression for a behavior element may be expressed as:
B＝{BAttrs，BParas，BAnnotations} (4)
wherein: battrs denotes a behavior attribute set, i.e., a set of various attribute information related to behavior elements, such as: number, name …, etc. The behavior element attribute set plays a role of indexing in the process of reasoning operation related to the behavior element;
BParas denotes a set of behavioral parameters, i.e. a set of parameters related to behavioral elements. The structure is similar to the functional parameter set;
banntotations represent behavioral descriptions, i.e., semantic web graphs of behavioral descriptions. The accurate description of the behavior element reflects the realizable action or operation of the behavior element. Similar to the functional description, it is described using a semantic network graph.
The structural element is the minimum component element forming the structural semantic network and is used for describing the composition and the relation of a product geometric model; the structural elements are represented by a parameterized model representation framework.
The parameter element refers to the smallest constituent element constituting a parameter set. The general expression for the parameter element may be expressed as:
P＝{PAttrs，PObjects，POperation，PFunction，PStatus} (5)
wherein: pattrs denotes a set of parameter attributes, such as: number, name …, etc.;
the objects represent parameter-associated object sets, and are used for expressing the objects associated with the parameters, and the expression range comprises function elements, behavior elements and structure elements;
the preference represents the operation of the parameter object to express the operation of the parameter element. The operation range comprises query and value of parameters;
PFunction represents a parameter relation equation for expressing a relation expression contained in a parameter;
PStatus represents the state of the parameter to represent the current solution state of the parameter.
The design requirement parameters refer to performance index parameters of the product determined by designers according to the expected design target of the product, and the performance index parameters can be regarded as an initial parameter set P0 of the product; and the parameters are transmitted into the functional elements as input parameters of the functional elements, the parameters complete solution iteration inside the functional elements, and the parameters are distinguished according to types and are continuously transmitted along with the decomposition process of the functional elements.
General expression for parametric solutions:
Slove(x)＝Search(x)∪Cal(x)∪Check(x)∪Out(x) (6)
wherein: slove (x) denotes a solver;
search (x) represents a query operator for searching and acquiring data required for computation in a dataset;
cal (x) denotes a numerical calculation operator for performing numerical calculation based on predefined design knowledge;
check (x) represents a check operator for checking the state of the current solution object to determine whether the current object needs to be solved;
out (x) represents an output operator for outputting the solution result.
The iterative process of solving the parameters in the functional elements can generate three possible results. One type is determined attribute variables, and the attributes are obtained by searching data sets such as empirical knowledge, experimental data and the like in a database through a query operator; one type is attribute variables which can be directly solved, and the attributes can be numerically solved by using the transmitted parameters through a numerical calculation operator according to preset design knowledge; one type is an attribute variable which cannot be solved, and the attribute is taken as a problem variable to be solved in the design process and is imported into a parameter flow, so that the problem of solving the parameters is refined.
The decomposition and expansion of the functional elements refer to a complex product function and can be formed by combining a plurality of simple functions. In the process of decomposing the function element, the parameters are transmitted to the lower function element by the upper function element.
The principle of the transmission of the parameters in the behavior elements and the decomposition and expansion of the behavior elements is the same as that of the transmission of the parameters in the function elements and the decomposition and expansion of the function elements.
The product structure mapping refers to a process of generating a product instance by using parameters obtained after the functional layer and the behavior layer are solved as parameter input of a structural element and representing a framework through a parameter-driven parameterized model. The general expression of the instantiation process can be expressed as:
KIF(PF，P)＝KIFF(ABSO_Fs，P)∪KICF(ABSO_Cs，P) (7)
wherein: KIF (PF, P) represents a product model instantiation operator;
PF represents a current product parameterized model representation framework;
p represents a design parameter of the front end; this process can also be seen as a process of instantiating abstracted part objects and feature objects in the parameterized model representation framework, i.e. the union of KICF (ABSO _ Cs, P) and KIFF (ABSO _ Fs, P). The general expression for KIFF can be expressed as:
wherein: KIFF (ABSO _ F)iP) is an instantiation operator of the abstract feature object;
Slove(ABSO_Fip) is specialPerforming knowledge solution operation on the symbolic object;
As(ABSO_Fi) A constraint mapping operation is positioned for the geometry of the model.
The KICF (ABSO _ Cs, P) represents an instantiation of an abstracted component object. The principle and KIFF (ABSO _ F)iAnd P) are identical. And finally generating a parameterized geometric model example of the product through an iterative instantiation process.
FIG. 2 is O according to the present inventionFBPSDesigning a knowledge element structure, and dividing design knowledge into a functional layer, a behavior layer, a parameter layer and a structural layer; the parameter function driving interface is the driving relation between the parameter layer P and the function layer F, and is marked by a symbol IPFRepresents; the interface is used for transmitting parameters and association relation between the parameter layer P and the functional layer F; the parameter behavior driving interface is a driving relation between a parameter layer P and a behavior layer B and is represented by a symbol IPBRepresents; the parameter behavior transmission relation is the parameter and association relation transmitted from the parameter layer P to the behavior layer B; the behavior parameter transmission relation is the parameter and association relation transmitted from the behavior layer B to the parameter layer P; the parameter structure driving interface is the driving relation between the parameter layer P and the structural layer B and is marked by a symbol IPSRepresents; the parameter structure transmission relation is the parameter and association relation transmitted from the parameter layer P to the structure layer S; the I/O driving interface is the driving relation between the parameter layer and the external data and uses the symbol II/ORepresents;
fig. 3 is a functional semantic network structure of an electronic device enclosure according to the present invention, in which among three basic functions of the electronic device enclosure, a connection function is a basis for ensuring normal operation of the enclosure and the module units therein. For the case, the connection function includes electrical connection between the case and other devices, and mechanical connection between the case and the frame and between the structural members of the case. The reliability of the whole electronic system is directly influenced by the reliability of the connection function. The housing, which is a part of the on-board electronic device, may be exposed to various severe working environments, such as various impacts, vibrations, high and low temperatures, high and low air pressures, and so on. This requires the chassis to be able to start from a structural perspective to ensure the normal operation of the internal components under various operating conditions. The structural protection can be divided into protection in terms of temperature and protection in terms of strength, and only the temperature protection function of the chassis structure is discussed herein. Finally, the case is used as a loading device of the module unit, the operation scene of the case is the narrow cabin interior, the simple and easy module installation function is required from the viewpoint of human engineering, and meanwhile, the stable and reliable installation of the module unit is ensured. From these analyses, the electronic device chassis functional semantic network shown in fig. 3 can be obtained.
Fig. 4 is a schematic diagram of data transfer interaction between parameter elements and function elements. According to the data flow direction of the interface, the interface can be divided into parameter-function input interface IFP-InAnd parameter-function output interface IFP-OutTwo types are provided. Wherein IFP-InThe performance index of the product which is expressed quantitatively is converted into parameter variables, the parameters are transmitted into the function element, and the parameters are distinguished according to the type of the parameters and are transmitted continuously along with the decomposition process of the function element. A semantic network of parameter-function interfaces can be represented. At the beginning of product design, the designer determines the expected design target of the product, and the performance index parameter of the product can be regarded as the initial parameter set P0 of the product, P0 ═ P01，P02，...P0nWhere P0n denotes the nth parameter variable subset. After the P0 enters the function element, the P0 is decomposed into temperature protection parameters P01 and the like according to the parameter type and the requirement of the function element on the parameters (namely, the input parameters of the function element), and the parameters are input into the related function element. Taking the temperature protection functional element as an example, P01 is an input parameter of the functional element, the functional element has two inherent attribute parameters, namely a temperature gradient parameter PSgradT and an environment temperature parameter PSTa, and a solution result is a functional element output parameter P01' through a parameter solution function fps (x).
Fig. 5 is a "function-behavior" semantic network of the electronic device case according to the present invention. According to the principle knowledge, the functional elements can be derived in a 'function-behavior' way. Six functional elements of the case are converted into a plurality of behavior elements, for example, a mechanical connection function is converted into two connection behaviors, namely, a connection behavior between structural members and a connection behavior between the case body and the module unit. The temperature protection function is converted into actions related to temperature control, such as environmental temperature rise, temperature check, cooling and the like.
FIG. 6 is a schematic diagram of data interaction between a parameter element and a behavior element. According to the data flow direction of the interface, the interface can be divided into parameter-function input interface IPB-InAnd parameter-function output interface IPB-OutTwo types are provided. The function and action of the parameter-behavior interface is similar to that of the parameter-function interface, except that IPB-InWhich carry the problems to be solved obtained from the parameter-function interfaces, which are to be solved in the decomposition process of the behavior. As shown in the figure, after the functional element of the functional layer and the temperature protection completes the solution, the parameter comprises the problem PR to be solvedTaAnd PRgradT. However, the actual temperature gradient and the actual temperature rise cannot be solved on the functional element level, so that the two problems to be solved are possible to be solved in the subsequent links. The initial parameters also transform from the P0 state to the P1 state. The input and output parameters of each behavior element in the figure are shown in table 1:
table 1: model temperature protection behavior parameter table
Through the expansion of behavior elements and the transmission of parameters, the actual environment temperature PRTaThe final solution is obtained in the 'environmental temperature rise' action element, meanwhile, the 'temperature protection' action element is also deduced to the 'cooling' action element, and the parameter solution result is returned to the parameter element.
FIG. 7 is a parameterized model representation framework of the electronic device chassis of the present invention, the chassis is determined to be composed of 6 panels by onboard box structure knowledge (experience-based knowledge); the number of the module units and HB7704-2001 standards (knowledge of specification classes) are loaded according to requirements, the outer size of the chassis, the number of the internal guide rails and the like are determined; according to the installed power and the related knowledge of thermodynamics (principle knowledge), whether the cabinet needs to be cooled or not and the configuration form of the cooling system are determined. The parameters of the design requirement parameters and the example geometric parameters of the implementation example case are shown in the table 2:
table 2: example part model design features and parameter Table
FIG. 8 is a schematic diagram of data interaction between parameter elements and structural elements. The parameters are directly driven through a parameter-structure interface to generate an actual product three-dimensional model. When the cooling requirement of the product is determined through the parameter solution of the function and behavior layer and the cooling form is determined, the design requirement is taken as the parameter P13' return to parameter element. The design parameters are converted from the P1 state to the P2 state. The parameters of the P2 state are transmitted into the structural element, and the cooling form parameters P2 carried by the structural element1And driving the module framework to determine the corresponding instantiation object. For example: through the action layer, the product is determined to adopt a natural cooling method, so that the model can improve the heat dissipation efficiency by increasing the heat exchange area (the shell is additionally provided with the heat dissipation fins) and increasing the heat exchange (the shell is additionally provided with the ventilation holes); similarly, if the product is determined to adopt air cooling or liquid cooling, the module framework is driven, and instantiation solution is carried out by taking the air cooling or the liquid cooling as a target.
In the implementation process, the design requirements and the design knowledge are solved and iterated, and in the solving and iterating process, the details of the product model are gradually thinned, and finally, the automatic generation of the parameterized model of the electronic equipment case is completed. The results are shown in FIG. 9.
Claims (5)
1. A knowledge-driven electronic equipment cabinet parameterized model automatic generation method is characterized in that:
(1) establishing a parameterized model representation frame for describing the topological structure of the chassis product, and carrying out abstract representation on the model of the chassis product;
(2) establishing a product knowledge base of functional elements, behavior elements, structural elements and parameter elements;
(3) taking design requirement parameters as input, converting the design requirement parameters of the case product into product function element expression through a product knowledge base, expanding and decomposing functions, solving and iterating the parameters, and outputting the parameters to a behavior layer;
(4) the method comprises the following steps of taking a functional parameter flow as input, realizing behavior expansion and decomposition of a case product through design knowledge, solving parameters, iterating and outputting to a structural layer;
(5) and taking the behavior parameter flow as input, realizing the structural mapping of the case product through design knowledge, and automatically generating a parameterized geometric model of the product example.
2. The method of claim 1, wherein the method comprises: the parameterized model representation framework in the step (1) at least comprises the following steps:
the general expression of the parameterized model representation framework can be expressed as:
PF＝{KBCs，ABSO_Cs，ABSO_Fs，APCs，PARs} (1)
wherein: PF represents a parameterized model representation framework;
KBCs represent a drive constraint description set and are knowledge expressions embedded into a parameterized model representation framework;
ABSO _ Cs represents an abstracted set of part objects;
ABSO _ Fs represents an abstracted feature object set;
APCs represent geometric association between objects;
PARs represents the association of parameter associations between objects.
3. The method of claim 1, wherein the method comprises: the step (2) of dividing the design knowledge of the box products by the hierarchy of 'function-behavior-structure-parameter' at least comprises the following characteristics:
1) constructing a product function knowledge base;
2) constructing a product behavior knowledge base;
3) constructing a product structure knowledge base;
4) and constructing a product parameter knowledge base.
The general expression of design knowledge partitioned in "function-behavior-structure-parameter" can be expressed as:
OFBPS＝∑f(p)∪∑b(p)∪∑s(p) (2)
wherein: o isFBPSRepresenting a "function-behavior-structure-parameter" design element of knowledge;
f (p) represents the functional goals that the product can achieve under the drive of the parameters;
b (p) represents the physical behavior of the product realizing the product function under the drive of the parameter;
s (p) represents the actual physical structure of the product under parametric drive;
and p represents product performance parameters and product principle parameters, geometric structure parameters and the like derived from knowledge of the product performance parameters and is used for driving the functional layer, the behavior layer and the structural layer.
The product function knowledge base in the step 1) is a set of product function knowledge, and is used for splitting the main functions of the product and decomposing the main functions into independent and single function elements for expression;
the functional element is the minimum component element forming a product functional semantic network, and is an abstract representation of a logic unit capable of completing a certain functional requirement of a product, and a general expression of the functional element can be represented as follows:
F＝{FAttrs，FParas，FAnnotations} (3)
wherein: FAttrs denotes a set of functional attributes, i.e. sets of various types of attribute information associated with functional elements, such as: function number, function name, function type, creator, application environment, product name, operator, security level, etc.;
FParas represents a functional parameter set, namely a set of various parameter variables related to the functional element;
fanntolites represent function descriptions, namely detailed descriptions of the function elements, and reflect the function realization targets of the function elements;
the product behavior knowledge base in the step 2) is a set of product behavior knowledge, is a mapping of product functions, and is an abstract summary of principles, rules and common knowledge of function implementation;
the behavior element is the minimum component element forming the product behavior semantic network, and is used for reading the function element, and the content of the behavior element describes the work executed by the physical structure of the product when the function is realized. A general expression for a behavior element may be expressed as:
B＝{BAttrs，BParas，BAnnotations} (4)
wherein: battrs denotes a behavior attribute set, i.e., a set of various attribute information related to behavior elements, such as: number, name …, etc. The behavior element attribute set plays a role of indexing in the process of reasoning operation related to the behavior element;
BParas represents a behavior parameter set, i.e. a parameter set related to a behavior element; the structure is similar to the functional parameter set;
banntotations represent behavioral descriptions, i.e., semantic network graphs of behavioral descriptions;
the product structure knowledge base in the step 3) is a set of product structure knowledge and is an expression of a physical structure required for realizing product behaviors;
the structural element is the minimum component element forming the structural semantic network and is used for describing the composition and the relation of a product geometric model; the structural elements are expressed by a parameterized model representation framework;
the parameter knowledge base in the step 4) is a set of product parameter knowledge and is an abstract expression of a data object in the product design process;
the parameter element refers to the smallest constituent element constituting a parameter set. The general expression for the parameter element may be expressed as:
P＝{PAttrs，PObjects，POperation，PFunction，PStatus} (5)
wherein: pattrs denotes a set of parameter attributes, such as: number, name …, etc.;
the objects represent parameter-associated object sets, and are used for expressing the objects associated with the parameters, and the expression range comprises function elements, behavior elements and structure elements;
the preference represents the operation of the parameter object to express the operation of the parameter element. The operation range comprises query and value of parameters;
PFunction represents a parameter relation equation for expressing a relation expression contained in a parameter;
PStatus represents the state of the parameter to represent the current solution state of the parameter.
4. The method of claim 1, wherein the method comprises: the parameter solving iteration of the functional elements, the behavior elements and the structural elements in the steps (3), (4) and (5) at least comprises the following steps:
general expression for parametric solutions:
Slove(x)＝Search(x)∪Cal(x)∪Check(x)∪Out(x) (6)
wherein: slove (x) denotes a solver;
search (x) represents a query operator for searching and acquiring data required for computation in a dataset;
cal (x) denotes a numerical calculation operator for performing numerical calculation based on predefined design knowledge;
check (x) represents a check operator for checking the state of the current solution object to determine whether the current object needs to be solved;
out (x) represents an output operator for outputting the solution result.
5. The method of claim 1, wherein the method comprises: the structure mapping of the case product in the step (5) at least comprises the following steps:
and representing the process of generating the product instance by the parameter-driven parameterized model. The general expression of the instantiation process can be expressed as:
KIF(PF，P)＝KIFF(ABSO_Fs，P)∪KICF(ABSO_Cs，P) (7)
wherein: KIF (PF, P) represents a product model instantiation operator;
PF represents a current product parameterized model representation framework;
p represents a design parameter of the front end; this process can also be seen as a process of instantiating abstracted part objects and feature objects in the parameterized model representation framework, i.e. the union of KICF (ABSO _ Cs, P) and KIFF (ABSO _ Fs, P). The general expression for KIFF can be expressed as:
wherein: KIFF (ABSO _ F)iP) is an instantiation operator of the abstract feature object;
Slove(ABSO_Fip) a knowledge solution operation for the feature object;
As(ABSO_Fi) Locating a constraint mapping operation for the geometry of the model;
the KICF (ABSO _ Cs, P) represents an instantiation of an abstracted component object. The principle and KIFF (ABSO _ F)iAnd P) are identical.
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