Undercarriage system scheme design method based on heterogeneous design simulation data management

Abstract

The invention provides an undercarriage system scheme design method based on heterogeneous design simulation data management, which comprises the following steps of: firstly, analyzing, abstracting and standardizing different types of heterogeneous design and simulation data in a development process of an undercarriage system, and constructing a uniform abstract data model; secondly, according to the defined semantic rules, an undercarriage semantic ontology model is constructed, then the semantic rules are loaded through a semantic reasoning engine, and processed data are associated; according to the research and development process of the undercarriage system and the semantic association relationship between the data, data storage and display are realized; after data abstract analysis, semantic association and unified storage are completed, functional information, design information and simulation information are further derived based on an undercarriage semantic ontology and a reasoning result, so that an undercarriage system development scheme driven by requirements is constructed. According to the method, multi-source heterogeneous design and simulation data generated in the research and development process of the undercarriage system can be effectively managed, and a design scheme of the undercarriage system is constructed on the basis of the multi-source heterogeneous design and simulation data.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer-aided system engineering, in particular to a landing gear system scheme design method based on heterogeneous design simulation data management. BACKGROUND

[0002] In the current complex product research and development, especially in the scheme design stage of landing gear system in aerospace equipment, various types of information, simulation and analysis tasks are highly integrated, involving multi-field, multi-stage technical collaboration. As a key subsystem in the aircraft, the scheme design process of the landing gear system needs to fully integrate multiple types of data such as demand parameters, function logic, design parameters, simulation verification, etc. In actual engineering, the data sources involved in this process are highly heterogeneous, including system modeling data (such as SysML model), structure design model (CAD / CAE), system simulation model, test record, design specification document (Word / Excel), etc. The connection between different data is weak, and due to their diverse formats, complex structures and scattered storage, it is difficult to effectively integrate, share and reuse the data, which seriously hinders the efficiency and accuracy of the scheme design.

[0003] The existing technology mainly relies on manual management of various types of data, lacks unified standardized abstraction, semantic modeling and automated processing mechanism, and cannot effectively support the full life cycle management and semantic penetration of heterogeneous data in the scheme design stage of the landing gear system. At the same time, traditional data platforms often focus on general data management and are difficult to adapt to the needs of cross-model, cross-domain data linkage analysis and intelligent recommendation in the design process of the landing gear system. SUMMARY

[0004] In view of the problems existing in the prior art, the present application provides a landing gear system scheme design method based on heterogeneous design simulation data management. This scheme takes "data management" as the core, revolves around the four key stages of "data abstraction-semantic association-dynamic display-intelligent derivation", constructs a unified data semantic model and rule-driven mechanism, realizes the standardized processing, semantic fusion, cross-domain mapping and knowledge reuse of multi-source data in the design simulation process of the landing gear system, and thus supports rapid scheme generation and efficient decision support for the design goal of the landing gear.

[0005] The technical scheme of the present application is as follows:

[0006] A landing gear system scheme design method based on heterogeneous design simulation data management, comprising the following steps:

[0007] Step 1: Analyze, abstract and standardize the heterogeneous design and simulation data of different types in the development process of the landing gear system, and construct a unified abstract data model;

[0008] Step 2: According to the defined semantic rules, a landing gear semantic ontology model is constructed, and then a semantic reasoning engine is used to load the semantic rules to associate the data processed in step 1;

[0009] Step 3: Based on the data parsed and standardized in step 1, and according to the research and development process of the landing gear system and the semantic association relationship between the data, data storage and display are realized;

[0010] Step 4: After completing data abstraction, semantic association and unified storage, further based on the landing gear semantic ontology and reasoning results, functional information, design information and simulation information are derived, thereby constructing a requirement-driven landing gear system development scheme.

[0011] Further, step 1 specifically includes the following steps:

[0012] Step 1.1: Different types of heterogeneous design and simulation data in the landing gear system development process include: SysML model, CAX model, simulation data and requirement document; Abstract, parse and generate corresponding secondary data from different types of heterogeneous design and simulation data;

[0013] Step 1.2: On the basis of step 1.1, the secondary data is standardized to tertiary data in JSON format.

[0014] Further, step 1.1 specifically includes the following steps:

[0015] a) Data source identification and classification: identify the file format, data structure and data source system of the landing gear system heterogeneous data, classify the data into four categories of requirements, design, function and simulation, and each category of data corresponds to structural data, text data, graphical model and simulation results;

[0016] b) Assign data extraction templates: assign data extraction templates to different types of data, and each template contains its corresponding attributes;

[0017] c) Field-level key element extraction: perform field parsing, text parsing and graphical structure extraction on the original data to form unified secondary data.

[0018] Further, step 2 specifically includes the following steps:

[0019] Step 2.1: According to the multi-source heterogeneous data of the landing gear, a unified landing gear semantic ontology model soModel is constructed based on the unified abstract data model obtained in step 1 RFDS ;

[0020] Step 2.2: Based on the construction of the landing gear semantic ontology model, define semantic rules, establish the mapping relationship between requirements, functions, designs, and simulations, and correlate the requirements, functions, designs, and simulation data of the landing gear system;

[0021] Step 2.3: According to the defined landing gear semantic ontology and semantic rules, input the data parsed and abstracted and standardized in step 1 into the reasoning engine, and use the semantic reasoning engine SWRL to generate correlation results based on rules.

[0022] Further, step 2.1 specifically includes the following steps:

[0023] Step 2.1.1: Analyze the composition of heterogeneous data, determine the scope of the ontology, and determine various data sources for building the ontology;

[0024] Step 2.1.2: Define the semantic concepts, attributes, and their relationships of the landing gear system, and use semantic modeling tools to abstract data entities into concepts and add specific attributes to them.

[0025] Further, in step 2.1.1, during the development of the landing gear system, heterogeneous data is generated, including requirements, designs, functions, and simulations; the requirements include number, description, and priority attributes; the functions include module ID, input parameters, and output parameters attributes; the design data includes relational data during the design process; the design model includes geometric features and material attributes; and the simulation data includes boundary conditions, load parameters, and output results attributes.

[0026] Further, in step 2.1.2, the ontology formal definition is a set composed of five modeling meta-symbols: concepts, relationships, functions, axioms, and instances, to build a semantic ontology model, represented by the formula

[0027] Ontology={C,R,F,A,I}

[0028] where C represents the concept set; R represents the relationship set, indicating the interaction between concepts; F represents the function set; A represents the axiom set, which is a restriction on the concept; and I represents the instance set, which is a concretization of the concept;

[0029] According to the definition of the ontology and the characteristics of the landing gear system design process, the landing gear system design semantic ontology model is created based on the ontology; the relationship set and the function set in the formula are combined and represented by C R ; an attribute set P is added to represent the set of concept attributes, describing the relationship between the concept and the data type; a rule set R is added to represent various rules for data correlation reasoning; and finally, the landing gear semantic ontology model soModel RFDS is represented by the formula

[0030]

[0031] are defined, wherein C RFDS covers the requirements, functions, designs, simulations in the development process of landing gear system; C R RFDS is the association between concepts in C RFDS ; P RFDS represents the secondary attributes attached to the concepts in C RFDS ; A RFDS is the limit of C RFDS ; R RFDS is the semantic rule for data association; I RFDS represents the instantiation object of C RFDS .

[0032] Further, step 3 specifically includes the following steps:

[0033] Step 3.1: Store the data after abstracting and standardizing in step 1:

[0034] A distributed database system based on MongoDB is constructed, and is managed according to the dimensions of "requirements", "function modules", "design models", "simulation results" and "design schemes";

[0035] Step 3.2: On the basis of unified abstract data storage in step 3.1, through a graphical visualization interface, various data and their relationships in the development process of landing gear are displayed, and data retrieval and interactive operation are supported.

[0036] Further, step 4 specifically includes the following steps:

[0037] Step 4.1: Semantic path recognition and data element derivation: according to the target node, extract its related path in the semantic association data, and analyze its associated functions, designs and simulation information, and derive the key data elements required by the model, including design parameters, simulation boundaries, structure configuration;

[0038] Step 4.2: Model derivation and scheme construction: based on the derived data elements, generate function modules, design and simulation information; repeat step 4.1 multiple times, and construct the landing gear system development scheme through data reorganization.

[0039] Advantages

[0040] The beneficial effects of the present application are that, compared with the prior research, the present application can effectively manage the multi-source heterogeneous design and simulation data generated in the development process of the landing gear system, and construct the design scheme of the landing gear system based on the data. By constructing a unified abstract data ontology and realizing data semantic association and reasoning based on the same, not only the compatibility and consistency between different data types are ensured, but also the automatic processing capacity of the data is greatly improved.

[0041] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0042] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the accompanying drawings, in which:

[0043] Figure 1 Abstract and standardized processing of heterogeneous data;

[0044] Figure 2 Unified abstract instance of a certain type of format data;

[0045] Figure 3 Unified semantic ontology model and partial semantic relationship based on heterogeneous data;

[0046] Figure 4 Partial semantic rules defined;

[0047] Figure 5 Semantic association reasoning of heterogeneous data;

[0048] Figure 6 Storage design of various types of abstract data;

[0049] Figure 7 Data management effect diagram of the embodiment of the present application;

[0050] Figure 8 Automatic construction process of the scheme based on semantic data. DETAILED DESCRIPTION

[0051] The present invention provides a landing gear system scheme design method based on heterogeneous design simulation data management, which is used to solve the problems existing in the prior art in landing gear system design, such as the difficulty in unified management of multi-source data, complex cross-format semantic associations, difficulty in connecting data relationships, and weak dynamic display and derivation capabilities. The method proposed in the present invention takes "data management" as the core and, centered around the needs of "scheme design", constructs a multi-source heterogeneous data abstract model and a unified semantic system for the landing gear system. Through semantic rules and reasoning mechanisms, it realizes automatic association and cross-domain mapping between different design simulation data. At the same time, it supports efficient retrieval and dynamic visualization of data, and further drives model derivation and scheme construction. The present invention can achieve: standardized processing and unified storage of heterogeneous design and simulation data; semantic association construction and cross-domain linkage between different data sources; efficient retrieval and visualization management of data; and semantic data-driven model derivation and design scheme construction.

[0052] The following describes in detail embodiments of the present invention. The embodiments are exemplary and intended to explain the present invention, but are not to be construed as limiting the present invention.

[0053] This embodiment addresses the requirements for landing gear system design and proposes a landing gear system design method based on heterogeneous design simulation data management. This method implements landing gear system design through the abstraction and standardization of heterogeneous data, data semantic association and processing, unified data storage and display, and the automatic construction of semantically associated data-driven solutions. Specifically, the method includes the following steps:

[0054] Step 1: Analyze, abstract, and standardize different types of heterogeneous design and simulation data during the development of the landing gear system to build a unified abstract data model.

[0055] The development process of the landing gear system involves design and simulation data from multiple fields. This heterogeneous data needs to be parsed, abstracted, and standardized to ensure that all data can be stored and processed in a unified format. This heterogeneous data includes system design models (SysML models, Karma models), 3D design simulation models (CAX models), system simulation models and their simulation data, and text models (Word / Excel). The specific process is as follows:

[0056] Step 1.1: In the development process of a certain aircraft landing gear in this embodiment, the design and simulation data sources involved in multiple fields mainly include: SysML models, CAX models (such as CATIA models, ANAYS models, etc.), simulation data and requirement documents. For these different types of data sources, these data are abstracted and parsed through the matching data system parsing interface to generate corresponding secondary data, such as Figure 1 shown.

[0057] The abstract analysis process is as follows:

[0058] a) Data source identification and classification: The landing gear system heterogeneous data is identified in terms of file format, data structure, and data source system, and is classified into four categories: requirements, design, function, and simulation. Each category of data corresponds to structural data, text data, graphical model, and simulation results, respectively.

[0059] b) Assign data extraction templates: Assign data extraction templates to different data sources. Each template contains its corresponding attributes, and the templates support dynamic loading and customized extension.

[0060] c) Field-level key element extraction: Perform field parsing, text parsing, and graphical structure extraction on the original data to form unified secondary data.

[0061] Specifically:

[0062] For SysML models, the general format is represented in XML format. The data system interface is used to analyze the SysML model data to obtain its requirements, functions, and interfaces. The extracted requirements, functions, and interfaces are defined as SysML model secondary data, which are more suitable for subsequent standardized processing than the original data source.

[0063] For Karma models, the general format is represented in owl format. The data system interface is used to analyze the Karma model data to obtain its requirements and system architecture. The extracted requirements and architecture are defined as Karma model secondary data, which are more suitable for subsequent standardized processing than the original data source.

[0064] For CAX models, the details are represented in CAD models (STEP, IGES) and CAE models (cdb / rst, inp / fil). The data system interface is used to analyze the CAD model data to obtain its assembly structure, topology information, and geometry information, as well as the CAE model data to obtain its mesh information, load information, and result information. These form the CAX model secondary data.

[0065] For simulation models, the model itself has little storage value. The focus is on analyzing and managing simulation data. The data system interface is used to obtain simulation input and output, and the simulation log is used to extract physical quantities such as displacement, stress, and temperature field from the simulation output file to form the simulation model secondary data.

[0066] For text models, the general format is represented as a text file, such as docx, xlsx, txt, etc. The data system interface is used to realize the data analysis of the text model to obtain its associated information and text content. For example, an Excel requirement document is parsed using the POI library in Java to extract key information such as requirement number, description, and priority, and to form the secondary data of the text model.

[0067] Step 1.2: Based on the secondary data generated by the multi-source data abstraction parsing in step 1.1, the secondary data is standardized into tertiary data in JSON format. JSON (JavaScript Object Notation) is a lightweight data exchange format that is easy to parse by machines and read by humans.

[0068] For different data types, data extraction templates are set up by accessing data in formats such as xml, owl, iges, etc. As shown in Figure 2 According to the data type type, the standardized templates of SysML / Karma / CAX / Simulation are matched to lock the core data such as system module name, module description, input / output interface parameters, and parameter data types. According to the template rules, the data is accurately positioned for standardization, thereby building a unified abstract data model.

[0069] In addition, to ensure the accuracy, reusability, and system integration of the multi-source heterogeneous data abstraction parsing process, the following quantitative evaluation index system is established:

[0070] 1) Data extraction accuracy

[0071] a) Definition: The ratio of the number of correctly extracted elements to the total number of elements that should be extracted.

[0072] b) Implementation:

[0073] 2) Data structuring completeness

[0074] a) Definition: The completeness of the structure fields in the abstracted data.

[0075] b) Implementation:

[0076] 3) Multi-source consistency rate

[0077] a) Definition: Whether the same object description from different data sources is consistent.

[0078] b) Implementation: Data sampling inspection.

[0079] 4) Average value of abstraction time

[0080] a) Definition: Average time needed to complete abstract resolution for each data.

[0081] b) Implementation: Detect abstract resolution time for each data source.

[0082] 5) Inference availability rate

[0083] a) Definition: Proportion of data in abstracted data that meets inference rule input requirements.

[0084] b) Implementation: Data structure comparison analysis.

[0085] 6) Traceability chain completeness rate

[0086] a) Definition: Whether the chain from requirement to function to design to simulation / test is complete.

[0087] b) Implementation:

[0088] Step 2: Data semantic association and processing: According to the defined semantic rules, construct the landing gear semantic ontology model, then load the semantic rules using the semantic reasoning engine, and associate the data processed in step 1; Specifically, the following steps are included:

[0089] Step 2.1: Based on the unified abstract data model obtained in step 1, construct a unified landing gear semantic ontology model soModel according to the multi-source heterogeneous data of the landing gear RFDS As shown in Figure 3 , define the relationships between various types of heterogeneous data of the landing gear, including the relationships between requirements and requirements, requirements and functional modules, requirements and design models, requirements and simulations, functions and designs, functions and simulations, etc., providing a basis for semantic reasoning and automated processing of data.

[0090] The specific process of constructing a unified landing gear semantic ontology model is as follows:

[0091] Step 2.1.1: Analyze the composition of heterogeneous data and determine the ontology scope. The goal of this stage is to determine the various data sources for building the ontology. Through data analysis, collect and analyze requirements to clearly define the knowledge domain that the ontology should cover.

[0092] Step 2.1.2: Define the semantic concepts, properties, and associated relationships of the landing gear system, and use the semantic modeling tool protégé to abstract data entities into concepts and add specific properties to them.

[0093] During the development of landing gear system, heterogeneous data is generated, including requirements (constraints or goals of landing gear system proposed by users, tasks, standards or projects), designs (structure, composition, interface parameters and implemented logic of landing gear system), functions (behaviors and capabilities of landing gear system required to meet the requirements of landing gear system), simulations (modeling and running of design model to verify its support for requirements and functions), etc. The requirements include attributes such as number, description and priority. The functions include attributes such as module ID, input parameters and output parameters. The design data includes some relational data in the design process. The design model includes attributes such as geometric features and material properties. The simulation data includes attributes such as boundary conditions, load parameters and output results. In addition, the semantic association relationship between concepts needs to be clarified. For example, the design model generates simulation input, that is, the geometric information of the design model is used to generate simulation parameters. For another example, the requirements are met by the function module, that is, the requirements are implemented by the function module.

[0094] Ontology can be formally defined as a set composed of five modeling elements of concepts, relationships, functions, axioms and instances to construct a semantic ontology model to strictly and accurately describe things, such as formula

[0095] Ontology = {C, R, F, A, I}

[0096] Among them, C represents the concept set, such as landing gear system requirements, functions, etc.; R represents the relationship set, which represents the interaction between concepts, such as the inclusion and refinement relationship between landing gear system requirements; F represents the function set, which is a special relationship, such as hasRequirementId (Requirement, Id) indicating that the landing gear system requirement has a unique Id corresponding to it; A represents the axiom set, which is a restriction on the concept; I represents the instance set, which is the instantiation of the concept, such as “landing gear can be retracted to reduce air resistance during navigation” as an instance of landing gear requirement.

[0097] According to the definition of ontology in the above formula, combined with the characteristics of the landing gear system design process, the landing gear system design semantic ontology model is created based on ontology, which clearly defines the domain concepts and their relationships involved in the landing gear system design process, and provides a model basis for subsequent semantic reasoning and data association. The relationship set and function set in the above formula are combined and represented by C R , because the function is essentially a form of relationship; add the attribute set P to represent the set of concept attributes, which describes the relationship between the concept and the data type, such as the content of the landing gear requirement, which is an attribute of the requirement; add the rule set R to represent various rules for data association reasoning. Therefore, the landing gear semantic ontology model soModel RFDS is represented by the formula

[0098]

[0099] where C RFDS Mainly covers the requirements, functions, design, simulation in the development process of landing gear system; C R RFDS Mainly C RFDS The relationship between several concepts; P RFDS Represented by C RFDS Several concepts attached to the secondary attributes; A RFDS The limit of C RFDS , here belongs to the general description; R RFDS Is used for data association semantic rules, support dynamic configuration; I RFDS Represented by C RFDS The instantiation object is closely related to C RFDS .

[0100] Step 2.2: Based on the construction of landing gear semantic ontology model, define semantic rules, establish the mapping relationship between requirements, functions, design, simulation, and correlate the data of landing gear system requirements, functions, design and simulation.

[0101] The rule is the basis of knowledge reasoning based on semantic ontology model, which is constructed by semantic web rule language (SWRL), as shown in Figure 4 , defined as follows, wherein "?x", "?y", "?z" and the like represent the corresponding variables x, y, z; "∧" represents the logical "and"; "→" represents the inference.

[0102] Specific rules include:

[0103] Rule 1: If the requirement x is traced by the requirement y, and the requirement y derives the requirement z, then the requirement x is traced by the requirement z. Defined as:

[0104] Req(?x)∧Req(?y)∧Req(?z)∧traceReq(?y,?x)∧deriveReq(?y,?z)→traceReq(?z,?x)

[0105] Rule 2: If the use case x contains the scene y, and the scene y captures the requirement z, then the use case x refines the requirement z. Defined as:

[0106] coverSce(?x,?y)∧captureReq(?y,?z)→refineReq(?x,?z)

[0107] Rule 3: If the use case x contains the function y, and the use case x refines the function requirement z, then the function y traces the function requirement z. Defined as:

[0108] FR(?z) A coverFunc(?x,?y) A refineReq(?x,?z) - traceReq(?y,?z)

[0109] Rule 4: If use case x contains function y, and function y is refined by behavior z, then use case x is analyzable by behavior z. Defined as:

[0110] coverFunc(?x,?y) A refineFunc(?y,?z) - analyzeUC(?z,?x)

[0111] Rule 5: If requirement x is refined by use case y, use case y is analyzed by behavior z, and behavior z is allocated to design parameter p, then requirement x is satisfied by design parameter p. Defined as:

[0112] refinedByUC(?x,?y) A analyzeUC(?z,?y) A allocatedToDP(?z,?p) - satisfiedByDP(?x,?p)

[0113] Rule 6: If requirement x is satisfied by design parameter y, and requirement z traces back to design parameter y, then requirement x derives requirement z. Defined as:

[0114] satisfiedByDP(?x,?y) A traceDP(?z,?y) - deriveReq(?x,?z)

[0115] Rule 7: If requirement x is satisfied by design parameter y, and design parameter y generates design solution / model z, then requirement x is satisfied by design solution / model z. Defined as:

[0116] satisfiedByDP(?x,?y) A generateDS(?y,?z) - satisfiedByDS(?x,?z)

[0117] Rule 8: If requirement x is satisfied by design solution y, then the "isSatisfied" property of requirement x is set to true. Defined as:

[0118] satisfiedByDS(?x,?y) - isSatisfied(?x, true)

[0119] Rule 9: If design x is verified by simulation model y, then simulation model y supports design x.

[0120] Design(?x) A SimulationModel(?y) A verifies(?y,?x) - supports(?y,?x)

[0121] Rule 10: If design z is simulated y verified, and design z supports requirement x, then simulation y supports requirement x. Simulation(?y)^Design(?z)^Req(?x)^Verifies(?y,?z)^isSupportedBy(?z,?x)

[0122] →supports(?y,?x)

[0123] Rule 11: If design x has feedback to function y, then function y is adjusted by design x.

[0124] Design(?x)^Function(?y)^FeedbackTo(?x,?y)→isAdjustedBy(?y,?x)

[0125] Rule 12: If multiple requirements map to the same function y, then function y is the common target of these requirements. Req(?x1)^Req(?x2)^Function(?y)^mapsTo(?x1,?y)^mapsTo(?x2,?y)^differentFrom(?x1,?x2)→isCommonTarget(?y,?x1)^isCommonTarget(?y,?x2)

[0126] Rule 13: If simulation y invalidates design z, and design z supports requirement x, then requirement x is marked as not satisfied by simulation y, i.e., the "isSatisfied" property of requirement x is set to true.

[0127] Simulation(?y)^Design(?z)^Req(?x)^invalidates(?y,?z)^isSupportedBy(?z,?x)

[0128] →unsatisfiedBy(?x,?y)^isSatisfied(?x,true)

[0129] Rule 14: If design z supports multiple functions y1, y2, then design z is an aggregate solution.

[0130] Design(?z)^Function(?y1)^Function(?y2)^isSupportedBy(?z,?y1)^isSupportedBy(?z,?y2)^differentFrom(?y1,?y2)→isAggregateSolution(?z)

[0131] The above rules are only a key part of the ontology rule set constructed by the landing gear requirement / function / design / simulation (soModel RFDS ) and can be combined between rules to achieve more complex knowledge reasoning to achieve more complex data association.

[0132] Step 2.3: According to the defined landing gear semantic ontology and semantic rules, the data after step 1 parsing, abstraction and standardization is input into the reasoning engine, and the semantic reasoning engine SWRL is used to generate association results based on rules to realize the association management of landing gear heterogeneous data. As shown in Figure 5 , the landing gear requirement "overload coefficient" is associated with other heterogeneous data through semantic reasoning rules.

[0133] Step 3: Unified data storage and display: Based on the data after step 1 parsing, abstraction and standardization, and according to the landing gear system development process and the semantic association relationship between data, data storage and display are realized. The specific process is:

[0134] Step 3.1: Store the data after step 1 parsing, abstraction and standardization:

[0135] Store the parsed, abstracted and standardized data and its relationship information into the database. Considering that the data volume is relatively large, a distributed database system based on MongoDB is constructed, which is classified and managed according to the "requirement" "function module" "design model" "simulation result" "design scheme" dimensions to ensure the data structured storage for easy retrieval and management. During the storage process, integrity check is performed on all data to ensure that the necessary fields (such as requirement number, function module ID, simulation boundary condition, etc.) are complete and in correct format.

[0136] As shown in Figure 6 , the requirement dimension mainly stores the technical requirements of the landing gear, the function module dimension mainly stores the definition and constraint conditions of the landing gear function module and the interface parameters, the design model dimension stores the landing gear geometric modeling data including file path and material properties, the simulation result dimension stores the landing gear simulation data and its analysis conditions, and the design scheme dimension stores the link scheme of a set of associated data of the landing gear system. The original data file of the abstracted data will be stored in a distributed file system for use by other functions such as data rollback, traceability, etc. Here, only storage management is provided, only data file access interface is provided, and no other use is made.

[0137] Step 3.2: Unified display management of data: Based on the unified abstract data storage in step 3.1, through a graphical visualization interface, various data and their relationships in the landing gear development process are displayed, supporting data retrieval and interactive operation. As shown in Figure 7As shown, the left side shows each data node and its relationship in the landing gear development process based on the data structure, and the right side shows the detailed information of the data, while supporting data filtering, editing, exporting, and dynamic modification of the mapping relationship between data to achieve controllable management of data. For example, when the user inputs the query conditions, first, the condition is parsed, the input condition string is parsed according to the preset syntax rules, and the product model, design stage, data type and other key query elements are identified. Then, according to the parsing result, the system automatically calls the underlying interface to quickly locate the corresponding data according to the storage location, type and other factors of the data.

[0138] Step 4: Semantic-driven model derivation and scheme construction: After completing data abstraction analysis, semantic association and unified storage, further based on the landing gear semantic ontology and reasoning results, functional information, design information and simulation information are derived to construct a demand-driven landing gear system development scheme, realizing intelligent support for the landing gear design process. The specific process is as follows:

[0139] Step 4.1: Semantic path recognition and data element derivation:

[0140] According to the target node (such as a certain requirement of the landing gear), its related path is extracted in the semantic association data, and its associated function, design and simulation information are analyzed to derive the key data elements required by the model, including design parameters, simulation boundaries and structure configurations.

[0141] For example, the user inputs the requirement parameter "the overload coefficient of a certain type of aircraft landing gear is X", which serves as the starting point of the derivation process. Based on the data association relationship constructed by rule reasoning, all valid paths of the target requirement node overload coefficient in the semantic association are identified, and the function elements, design elements and simulation parameters associated with the requirement are automatically extracted using the valid paths.

[0142] Step 4.2: Model derivation and scheme construction: Based on the derived data elements, function modules, design and simulation information are generated; step 4.1 is repeated multiple times to construct a landing gear system development scheme through data reorganization.

[0143] For example, based on the derived function, design and simulation elements, the data extraction template and data system interface are called according to the data type type to complete the preliminary construction of the function, design and simulation parameters. Then, by repeatedly changing the requirement input parameters and repeating step 4.1, multiple function modules, design information and simulation parameters associated with the requirements are generated to construct a relatively complete scheme, including requirement, function, design, simulation and other model information. Then, combined with the design scheme of the historical landing gear system development, a usable design scheme is selected to realize the rapid construction of the model from the requirement and the auxiliary decision-making of the scheme. The technical process is shown in Figure 8 .

[0144] Although the embodiments of the present application have been shown and described above, it is understood that the above-described embodiments are exemplary and are not to be construed as limiting the present application, and that those skilled in the art can make changes, modifications, substitutions and variations to the above-described embodiments without departing from the spirit and scope of the present application.

Claims

1. A landing gear system design method based on heterogeneous design simulation data management, characterized by: The following steps are involved: Step 1: Analyze, abstract, and standardize different types of heterogeneous design and simulation data during the development of the landing gear system to build a unified abstract data model; Step 2: Based on the defined semantic rules, a landing gear semantic ontology model is constructed. Then, the semantic reasoning engine is used to load the semantic rules and associate the data processed in step 1. Step 3: Based on the data parsed, abstracted, and standardized in Step 1, data storage and display are implemented according to the landing gear system R&D process and the semantic relationships between data. Step 4: After completing data abstraction analysis, semantic association, and unified storage, further derive functional information, design information, and simulation information based on the landing gear semantic ontology and reasoning results, thereby constructing a demand-driven landing gear system development plan.

2. The landing gear system design method based on heterogeneous design simulation data management according to claim 1, characterized in that: Step 1 specifically includes the following steps: Step 1.1: Abstract and analyze the different types of heterogeneous design and simulation data during the landing gear system development process, including SysML models, CAX models, simulation data, and requirement documents, to generate corresponding secondary data. Step 1.2: Based on the abstract analysis of multi-source data in step 1.1 and the generation of corresponding secondary data, the secondary data is standardized into tertiary data using the JSON format.

3. The landing gear system design method based on heterogeneous design simulation data management according to claim 2, characterized in that: Step 1.1 specifically includes the following steps: a) Data source identification and classification: The file format, data structure, and data source system of the heterogeneous landing gear system data are identified, and the data is classified into four categories: requirements, design, function, and simulation. Each category of data is represented by structured data, text data, graphical models, and simulation results. b) Assign data extraction templates: Assign data extraction templates to different types of data, and each type of template contains its corresponding attributes; c) Field-level key element extraction: Perform field parsing, text parsing, and graph structure extraction on the original data to form unified secondary data.

4. The landing gear system design method based on heterogeneous design simulation data management according to claim 1, characterized in that: Step 2 specifically includes the following steps: Step 2.1: Based on the multi-source heterogeneous data of landing gear and the unified abstract data model obtained in step 1, a unified landing gear semantic ontology model soModel is constructed. RFDS ; Step 2.2: Based on the construction of the landing gear semantic ontology model, define semantic rules and establish mapping relationships between requirements, functions, designs, and simulations to associate the requirements, functions, designs, and simulation data of the landing gear system. Step 2.3: Based on the defined landing gear semantic ontology and semantic rules, the data parsed, abstracted, and standardized in step 1 is input into the reasoning engine, and the semantic reasoning engine SWRL is used to generate association results based on the rules.

5. The landing gear system design method based on heterogeneous design simulation data management according to claim 4, characterized in that: Step 2.1 specifically includes the following steps: Step 2.1.1: Analyze the composition of heterogeneous data, determine the scope of the ontology, and identify the various data sources for constructing the ontology; Step 2.1.2: Define the semantic concepts, attributes, and their relationships of the landing gear system, use semantic modeling tools to abstract data entities into concepts, and attach specific attributes to them.

6. The landing gear system design method based on heterogeneous design simulation data management according to claim 5, characterized in that: In step 2.1.1, during the development of the landing gear system, the heterogeneous data generated include requirements, designs, functions, and simulations; Requirements include number, description, and priority attributes; functions include module ID, input parameters, and output parameter attributes; Design data includes relational data in the design process; design models include geometric features and material properties; simulation data includes boundary conditions, load parameters, and output result properties.

7. The landing gear system design method based on heterogeneous design simulation data management according to claim 5, characterized in that: In step 2.1.2, ontology is formally defined as a set of five modeling primitives: concepts, relations, functions, axioms, and instances, in order to construct a semantic ontology model. Ontology = {C, R, F, A, I} Indicates, where C represents the concept set; R represents the relationship set, which represents the interaction between concepts; F represents the function set; A represents the axiom set, which is the restriction of the concept; I represents the instance set, which is the embodiment of the concept; According to the definition of ontology and the characteristics of landing gear system design process, a landing gear system design semantic ontology model is created based on ontology; the relationship set and function set in the formula are merged and unified into C R Representation; add attribute set P to represent the set of concept attributes, describing the relationship between concepts and data types; add rule set R to represent various rules for data association reasoning; finally landing gear semantic ontology model soModel RFDS By the formula To define, where C RFDS Covering the requirements, functions, design, and simulation of the landing gear system development process; C R RFDS It is C RFDS The relationship between concepts in P RFDS Representative C RFDS A secondary attribute attached to a concept; RFDS It is C RFDS Restrictions; R RFDS It is a semantic rule for data association; I RFDS Represents C RFDS The instantiated object.

8. The landing gear system design method based on heterogeneous design simulation data management according to claim 1, characterized in that: Step 3 specifically includes the following steps: Step 3.1: Store the data after parsing, abstraction and standardization in step 1: Build a distributed database system based on MongoDB, and manage it by categories based on "requirements", "functional modules", "design models", "simulation results", and "design solutions"; Step 3.2: Based on the unified abstract data storage in step 3.1, various data and their relationships in the landing gear development process are displayed through a graphical visualization interface to support data retrieval and interactive operations.

9. The landing gear system design method based on heterogeneous design simulation data management according to claim 1, characterized in that: Step 4 specifically includes the following steps: Step 4.1: Semantic Path Identification and Data Element Derivation: Based on the target node, extract the relevant path from the semantically associated data, analyze the associated functions, design, and simulation information, and derive the key data elements required for the model, including design parameters, simulation boundaries, and structural configuration. Step 4.2: Model derivation and solution construction: Based on the derived data elements, generate functional modules, design and simulation information; repeat step 4.1 multiple times, and construct the landing gear system development plan through data reorganization.

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