CN117473871A - Formalized system modeling method based on CATIA (computer aided three-dimensional architecture) Magic - Google Patents
Formalized system modeling method based on CATIA (computer aided three-dimensional architecture) Magic Download PDFInfo
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Abstract
The invention relates to a formalized system modeling method based on CATIA Magic, and belongs to the technical field of formalized system modeling. According to the invention, system demand information is acquired, candidate terms and demand categories are obtained through BiLSTM-BERT-CRF model processing according to the system demand information, a demand view is obtained according to the candidate terms and the demand categories, a behavior model is established according to a state machine diagram and a sequence diagram, a capability model is obtained through a SysML tool according to the demand view, a system function is modeled and analyzed according to a use case model and the capability model, a system architecture is analyzed through a structure model, a function model and the behavior model, a final capability model is obtained through redundancy check model processing capability model, a virtual simulation system is generated according to the final capability model, preset operation parameters are input into the virtual simulation system for simulation verification, a simulation verification result is obtained, a system design scheme is determined according to the simulation verification result, the efficiency and the accuracy of system modeling are improved, and the full life cycle management of a user on the system is realized.
Description
Technical Field
The invention belongs to the technical field of formalized system modeling, and particularly relates to a formalized system modeling method based on CATIA (computer aided three-dimensional architecture) Magic.
Background
With the increase of the complexity of the system, the conventional system modeling method cannot meet the requirements of the system. At present, the traditional system modeling method is generally based on a graphical tool for modeling, can not accurately describe static and dynamic behaviors of a system, and can not support the problems of collaboration and communication among teams across fields and professions.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a formalized system modeling method based on CATIA Magic, which is characterized in that system demand information is acquired, candidate terms and demand categories are obtained through BiLSTM-BERT-CRF model processing according to the system demand information, demand views are obtained according to the candidate terms and the demand categories, a behavior model is established according to a state machine diagram and a sequence diagram, a capability model is obtained through a SysML tool according to the demand views, modeling analysis is performed on system functions according to a use case model and the capability model, a system architecture is analyzed through a structure model, a function model and the behavior model, a final capability model is obtained through redundancy check model processing capability model, a virtual simulation system is generated according to the final capability model, preset operation parameters are input into the virtual simulation system for simulation verification, a simulation verification result is obtained, a system design scheme is determined according to the simulation verification result, the efficiency and the accuracy of the system modeling are improved, and the full life cycle management of a user on the system is realized.
The aim of the invention can be achieved by the following technical scheme:
a formalized system modeling method based on CATIA Magic comprises the following steps:
s1: acquiring system demand information, wherein the system demand information is text description in a custom format;
s2: processing according to the system demand information through a BiLSTM-BERT-CRF model to obtain a candidate term and a demand category, and obtaining a demand view through a CATIA Magic modeling tool according to the candidate term and the demand category;
s3: acquiring a state machine diagram and a sequence diagram, establishing a behavior model according to the state machine diagram and the sequence diagram, obtaining a capacity model according to the demand view through a SysML tool, establishing a use case model according to the demand view, and jointly carrying out modeling analysis on system functions according to the use case model and the capacity model, wherein the capacity model comprises a structure model, a function model and a non-function attribute model;
s4: analyzing a system architecture through the structural model, the functional model and the behavior model;
s5: constructing a redundancy check model, processing the capacity model through the redundancy check model to obtain a final capacity model, generating a virtual simulation system according to the final capacity model, inputting preset operation parameters into the virtual simulation system for simulation verification, and obtaining a simulation verification result;
s6: and generating a parameter adjustment scheme according to the simulation verification result, adjusting according to the parameter adjustment scheme, returning verification passing information, and determining a system design scheme according to the verification passing information.
Preferably, the step S2 specifically includes the following steps:
s201: invoking a term resource library, performing word segmentation processing on the system demand information through a BiLSTM neural network word segmentation model to obtain a system demand corpus, performing corpus labeling on the system demand corpus according to a predefined term type to obtain labeling information, and obtaining the candidate terms through associating the term resource library according to the labeling information;
s202: processing according to the system demand information BiLSTM-BERT-CRF model to obtain a demand information entity and a demand information entity relation, and obtaining the demand category according to the demand information entity and the demand information entity relation, wherein the demand category comprises a structural demand, a functional demand and a non-functional attribute demand;
s203: and respectively obtaining the demand view through a CATIA Magic modeling tool according to the candidate term and the demand category.
Preferably, the step S202 specifically includes performing word segmentation processing on the system demand information through a BiLSTM neural network word segmentation model to obtain a demand entity corpus, processing the demand entity corpus through a BERT model to obtain deep semantic information, processing the deep semantic information through a CRF model to obtain a maximum probability labeling sequence according to the deep semantic information, outputting the demand information entity according to the maximum probability labeling sequence, performing feature extraction on the deep semantic information to obtain a feature vector, performing convolution operation on the feature vector through a convolution neural network to obtain a convolution result, performing maximum pooling on the convolution result to obtain a maximum feature value, and outputting the demand information entity relationship through the convolution neural network.
Preferably, the step S3 specifically includes the following steps:
s301: establishing a device state machine diagram, performing behavior logic analysis on the use case model according to the behavior model to obtain system behavior logic, and obtaining state response information according to the system behavior logic and the device state machine diagram;
s302: and establishing a function tracing association matrix according to the structure model, the function model and the non-function attribute model, and determining the system function integrity according to the state response information and the function tracing association matrix.
Preferably, the step S4 specifically includes the following steps:
s401: determining a subsystem meeting the function, determining input and output information and port information according to the demand view, and establishing a demand traceability relation matrix of the subsystem according to the input and output information and the port information;
s402: and obtaining system signal transmission logic according to the sequence diagram, and determining a system physical architecture according to the demand traceability relation matrix and the system signal transmission logic.
Preferably, the step S402 specifically includes determining a signal interaction relationship between component modules in a system activity through the sequence diagram, generating a system signal transmission logic according to the signal interaction relationship, obtaining a control activity lane diagram according to the structural model, the functional model and the behavior model by respectively combining the system signal transmission logic and the demand traceability relationship matrix, generating a system component scheme according to the control activity lane diagram, and determining a system physical architecture according to the system construction scheme.
Preferably, the step S5 specifically includes the following steps:
s501: obtaining associated redundant information and null element information through the redundant check model processing according to the capability model, generating a corresponding deleting instruction according to the associated redundant information, returning deleting success information, and generating a useless information prompt according to the null element information;
s502: and presetting operation parameters, generating a system virtual simulation system according to the final capacity model, and inputting the preset operation parameters into the virtual simulation system for simulation verification to obtain a simulation verification result.
Preferably, the text description in the custom format includes a device connection mode text description, a system task text description, a system architecture text description, and a system function text description.
The beneficial effects of the invention are as follows:
1. according to system demand information, candidate terms and demand categories are obtained through BiLSTM-BERT-CRF model processing, demand views are obtained through CATIA Magic modeling tools according to the candidate terms and the demand categories, natural language is replaced by unified formal modeling language to eliminate ambiguity, and a quantitative index description system is adopted to facilitate understanding and automatic processing, so that the quality, progress and cost of the whole period of the system are effectively improved;
2. the final capability model is obtained through the redundant check model processing capability model, the virtual simulation system is generated according to the final capability model, the preset operation parameters are input into the virtual simulation system for simulation verification, the simulation verification result is obtained, the predictability and maintainability of the system are improved, the risk and the cost of the system are reduced, and the developer can understand the structure and the behavior of the system conveniently.
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The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a flow chart of the modeling method of the formalized system based on CATIA Magic of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention for achieving the preset aim, the following detailed description is given below of the specific implementation, structure, characteristics and effects according to the invention with reference to the attached drawings and the preferred embodiment.
Referring to fig. 1, a formalized system modeling method based on CATIA logic includes the following steps:
s1: acquiring system demand information, wherein the system demand information is text description in a custom format;
s2: processing according to the system demand information through a BiLSTM-BERT-CRF model to obtain a candidate term and a demand category, and obtaining a demand view through a CATIA Magic modeling tool according to the candidate term and the demand category;
s3: acquiring a state machine diagram and a sequence diagram, establishing a behavior model according to the state machine diagram and the sequence diagram, obtaining a capacity model according to the demand view through a SysML tool, establishing a use case model according to the demand view, and jointly carrying out modeling analysis on system functions according to the use case model and the capacity model, wherein the capacity model comprises a structure model, a function model and a non-function attribute model;
s4: analyzing a system architecture through the structural model, the functional model and the behavior model;
s5: constructing a redundancy check model, processing the capacity model through the redundancy check model to obtain a final capacity model, generating a virtual simulation system according to the final capacity model, inputting preset operation parameters into the virtual simulation system for simulation verification, and obtaining a simulation verification result;
s6: and generating a parameter adjustment scheme according to the simulation verification result, adjusting according to the parameter adjustment scheme, returning verification passing information, and determining a system design scheme according to the verification passing information.
The step S1 specifically includes obtaining system requirement information, where the system requirement information is text description in a custom format, and it should be noted that, the text description in the custom format is that a user uses natural language to perform text description on a system, where the text description of the system includes text description of a device connection mode, text description of a system task, text description of a system architecture, text description of a system function, and the like, and the format of the text description of the present invention supports chinese characters, english letters, or numbers. The system modeling efficiency is improved by acquiring the system demand information, and a data basis is provided for subsequent data processing.
Step S2 specifically comprises the steps of carrying out word segmentation processing on the system demand information through a BiLSTM neural network word segmentation model by calling a term resource library to obtain a system demand corpus, carrying out corpus annotation on the system demand corpus according to a predefined term type to obtain annotation information, and obtaining the candidate terms through associating the term resource library according to the annotation information;
s202: performing word segmentation on the system demand information through a BiLSTM neural network word segmentation model to obtain a demand entity corpus, processing the demand entity corpus through a BERT model to obtain deep semantic information, processing the deep semantic information through a CRF model to obtain a maximum probability labeling sequence, outputting the demand information entity according to the maximum probability labeling sequence, performing feature extraction on the deep semantic information to obtain a feature vector, performing convolution operation on the feature vector through a convolution neural network to obtain a convolution result, performing maximum pooling on the convolution result to obtain a maximum feature value, outputting the demand information entity relation through the convolution neural network, and obtaining the demand category according to the demand information entity relation and the demand information entity relation, wherein the demand category comprises structural demands, functional demands and nonfunctional attribute demands;
s203: and respectively obtaining the demand view through a CATIA Magic modeling tool according to the candidate term and the demand category.
Candidate terms and demand categories are obtained through processing system demand information, a demand view is obtained according to the candidate terms and the demand categories, ambiguity is eliminated by adopting a unified formal modeling language to replace natural language, understanding and automatic processing are facilitated by adopting a quantitative index description system, and the quality, progress and cost of the whole period of the system are effectively improved.
Step S3 specifically comprises the steps of establishing a device state machine diagram, carrying out behavior logic analysis on the use case model according to the behavior model to obtain system behavior logic, and obtaining state response information according to the system behavior logic and the device state machine diagram;
and establishing a function tracing association matrix according to the structure model, the function model and the non-function attribute model, and determining the system function integrity according to the state response information and the function tracing association matrix.
By analyzing the system functions, the system requirements are clarified, the optimization of the system design is facilitated, the system development efficiency is improved, and the system risk is further reduced.
Step S4 specifically comprises determining a subsystem meeting the function, determining input and output information and port information according to the demand view, and establishing a demand traceability relation matrix of the subsystem according to the input and output information and the port information;
determining signal interaction relations among component modules in system activities through the sequence diagram, generating system signal transmission logic according to the signal interaction relations, obtaining a control activity lane diagram through combining the system signal transmission logic and the demand traceability relation matrix respectively according to the structural model, the functional model and the behavior model, generating a system component scheme according to the control activity lane diagram, and determining a system physical architecture according to the system construction scheme.
By analyzing the system architecture, the system architecture analysis can better understand the overall structure and functional requirements of the system, optimize the system design and architecture, improve the expandability and maintainability of the system, and further improve the overall performance and reliability of the system. Meanwhile, by analyzing potential problems in the system architecture, corresponding measures are taken to reduce the risk and cost of the system.
The step S5 specifically comprises the following steps:
s501: obtaining associated redundant information and null element information through the redundant check model processing according to the capability model, generating a corresponding deleting instruction according to the associated redundant information, returning deleting success information, and generating a useless information prompt according to the null element information;
s502: and presetting operation parameters, generating a system virtual simulation system according to the final capacity model, and inputting the preset operation parameters into the virtual simulation system for simulation verification to obtain a simulation verification result.
When redundant association, relationship or connection exists in the model, redundant model checking is performed. The method is helpful to reduce unnecessary complexity and repeatability in the model, ensure that the association in the model is correct and accurate, further verify the functions and performances of the system, discover potential problems and repair and optimize the system, further improve the overall quality and reliability of the system, and the operator can delete the system according to the useless information prompt.
The step S6 specifically comprises the steps of generating a parameter adjustment scheme according to a simulation verification result, adjusting according to the parameter adjustment scheme, returning verification passing information, and determining a system design scheme according to the verification passing information.
The working principle and the using flow of the invention are as follows:
according to the invention, system demand information is acquired, candidate terms and demand categories are obtained through BiLSTM-BERT-CRF model processing according to the system demand information, a demand view is obtained according to the candidate terms and the demand categories, a behavior model is established according to a state machine diagram and a sequence diagram, a capability model is obtained through a SysML tool according to the demand view, a system function is modeled and analyzed according to a use case model and the capability model, a system architecture is analyzed through a structure model, a function model and the behavior model, a final capability model is obtained through redundancy check model processing capability model, a virtual simulation system is generated according to the final capability model, preset operation parameters are input into the virtual simulation system for simulation verification, a simulation verification result is obtained, and a system design scheme is determined according to the simulation verification result.
Program code embodied on a computer readable medium in embodiments of the invention may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations of the present invention 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The present invention is not limited to the above embodiments, but is not limited to the above embodiments, and any technical modifications, equivalents and modifications made to the above embodiments according to the technical principles of the present invention can be made by those skilled in the art without departing from the scope of the invention.
Claims (8)
1. The formalized system modeling method based on CATIA Magic is characterized by comprising the following steps:
s1: acquiring system demand information, wherein the system demand information is text description in a custom format;
s2: processing according to the system demand information through a BiLSTM-BERT-CRF model to obtain a candidate term and a demand category, and obtaining a demand view through a CATIA Magic modeling tool according to the candidate term and the demand category;
s3: acquiring a state machine diagram and a sequence diagram, establishing a behavior model according to the state machine diagram and the sequence diagram, obtaining a capacity model according to the demand view through a SysML tool, establishing a use case model according to the demand view, and jointly carrying out modeling analysis on system functions according to the use case model and the capacity model, wherein the capacity model comprises a structure model, a function model and a non-function attribute model;
s4: analyzing a system architecture through the structural model, the functional model and the behavior model;
s5: constructing a redundancy check model, processing the capacity model through the redundancy check model to obtain a final capacity model, generating a virtual simulation system according to the final capacity model, inputting preset operation parameters into the virtual simulation system for simulation verification, and obtaining a simulation verification result;
s6: and generating a parameter adjustment scheme according to the simulation verification result, adjusting according to the parameter adjustment scheme, returning verification passing information, and determining a system design scheme according to the verification passing information.
2. The modeling method of a formalized system based on CATIA Magic according to claim 1, wherein said step S2 comprises the steps of:
s201: invoking a term resource library, performing word segmentation processing on the system demand information through a BiLSTM neural network word segmentation model to obtain a system demand corpus, performing corpus labeling on the system demand corpus according to a predefined term type to obtain labeling information, and obtaining the candidate terms through associating the term resource library according to the labeling information;
s202: processing according to the system demand information BiLSTM-BERT-CRF model to obtain a demand information entity and a demand information entity relation, and obtaining the demand category according to the demand information entity and the demand information entity relation, wherein the demand category comprises a structural demand, a functional demand and a non-functional attribute demand;
s203: and respectively obtaining the demand view through a CATIA Magic modeling tool according to the candidate term and the demand category.
3. The modeling method of formalized system based on CATIA Magic according to claim 2, wherein step S202 specifically includes performing word segmentation processing on the system demand information through a BiLSTM neural network word segmentation model to obtain a demand entity corpus, processing the demand entity corpus through a BERT model to obtain deep semantic information, processing the deep semantic information through a CRF model to obtain a maximum probability labeling sequence, outputting the demand information entity according to the maximum probability labeling sequence, performing feature extraction on the deep semantic information to obtain a feature vector, performing convolution operation on the feature vector through a convolution neural network to obtain a convolution result, performing maximum pooling on the convolution result to obtain a maximum feature value, and outputting the demand information entity relationship through the convolution neural network.
4. The modeling method of a formalized system based on CATIA Magic according to claim 1, wherein said step S3 comprises the steps of:
s301: establishing a device state machine diagram, performing behavior logic analysis on the use case model according to the behavior model to obtain system behavior logic, and obtaining state response information according to the system behavior logic and the device state machine diagram;
s302: and establishing a function tracing association matrix according to the structure model, the function model and the non-function attribute model, and determining the system function integrity according to the state response information and the function tracing association matrix.
5. The modeling method of a formalized system based on CATIA Magic according to claim 1, wherein said step S4 comprises the steps of:
s401: determining a subsystem meeting the function, determining input and output information and port information according to the demand view, and establishing a demand traceability relation matrix of the subsystem according to the input and output information and the port information;
s402: and obtaining system signal transmission logic according to the sequence diagram, and determining a system physical architecture according to the demand traceability relation matrix and the system signal transmission logic.
6. The formalized system modeling method based on CATIA logic according to claim 5, wherein step S402 specifically includes determining a signal interaction relationship between each component module in a system activity through the sequence diagram, generating a system signal transmission logic according to the signal interaction relationship, obtaining a control activity lane diagram according to the structure model, the function model and the behavior model by combining the system signal transmission logic and the demand traceability relationship matrix respectively, generating a system component scheme according to the control activity lane diagram, and determining a system physical architecture according to the system building scheme.
7. The modeling method of a formalized system based on CATIA Magic according to claim 1, wherein the step S5 specifically comprises the steps of:
s501: obtaining associated redundant information and null element information through the redundant check model processing according to the capability model, generating a corresponding deleting instruction according to the associated redundant information, returning deleting success information, and generating a useless information prompt according to the null element information;
s502: and presetting operation parameters, generating a system virtual simulation system according to the final capacity model, and inputting the preset operation parameters into the virtual simulation system for simulation verification to obtain a simulation verification result.
8. The formalized system modeling method of claim 1, wherein the custom formatted text description includes a device connectivity style text description, a system task text description, a system architecture text description, a system function text description.
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