A method for mapping a logical model to a three-dimensional scene model for virtual testing

Abstract

The present application relates to a kind of virtual test-oriented logic model to three-dimensional scene model mapping method, the method comprises the following steps: step 1: construct virtual entity library based on data dictionary, step 2: based on logical unit screening test equipment, step 3: logic connection to the mapping of spatial relationship.This technical solution realizes the automatic efficient matching of test demand and equipment model, improves the automation and intelligent level of scene assembly and signal path configuration, improves the efficiency and accuracy of virtual test scene building, provides data and process basis for the virtual verification and digital research and development of complex engineering system, expands the application range and engineering applicability of virtual test.

Description

Technical Field

[0001] This invention relates to a mapping method from a logical model to a three-dimensional scene model for virtual experiments, belonging to the field of virtual experiment technology. Background Technology

[0002] As engineering systems become increasingly complex, physical testing faces challenges such as high costs, lengthy cycles, and high manpower consumption, making it difficult to meet the demands of efficient R&D. Virtual testing, which eliminates the need for physical equipment, can significantly reduce costs, shorten verification cycles through parallel simulation, and reduce reliance on highly skilled personnel, thus becoming an important supplement and alternative to engineering verification. However, in existing technologies, the construction of virtual test scenarios typically involves the direct construction of 3D virtual models, lacking the construction, deduction, and verification of complete test logic. The logic model cannot automatically drive the generation of 3D scene models, resulting in test equipment selection, assembly relationships, and signal paths relying solely on manual settings and verification. This process is cumbersome, error-prone, and fails to guarantee consistency between the scenario and test requirements. Therefore, existing solutions struggle to achieve automatic mapping and rapid construction from the test logic model to the 3D scene model, limiting the automation level and engineering applicability of virtual test scenario configuration. Summary of the Invention

[0003] This invention addresses the problems existing in the prior art by providing a mapping method from a logical model to a three-dimensional scene model for virtual experiments. The method includes three main steps: constructing a virtual entity library based on a data dictionary, screening experimental equipment based on logical units, and mapping logical connections to spatial relationships. This achieves efficient matching and automatic configuration of experimental requirements, equipment models, and scene assembly, thereby improving the intelligence and engineering applicability of virtual experimental scene construction.

[0004] The technical solution of the present invention is as follows: a mapping method from a logical model to a three-dimensional scene model for virtual experiments, the method comprising the following steps:

[0005] Step 1: Construct a virtual entity library based on a data dictionary.

[0006] Step 2: Screening test equipment based on logic units.

[0007] Step 3: Logically connect to the mapping of spatial relationships.

[0008] As an improvement to the present invention, step 1 involves constructing a virtual entity library based on a data dictionary, as detailed below:

[0009] Use a quadruple to summarize all the key information about the device:

[0010]

[0011] Here, ID is the unique identifier of the device; Model Name is the name of the device; Technical Parameters includes the technical parameters of the device, including load range, displacement range, frequency range, accuracy requirements, etc., which is usually a subset (e.g., load range, accuracy, etc.). This includes the device's input and output information, describing the types of input signals, data formats, input and output sources, and output formats supported by the device.

[0012] Due to the wide variety of experimental equipment, a multi-level dictionary structure was designed for virtual experiments to avoid the single-level data dictionary being affected by storing a large number of data items, thus slowing down the parsing process. The first-level dictionary is the experimental equipment category dictionary, which includes six categories: sensors, fixtures, test pieces, loading devices, test benches, and test scenarios. This serves two purposes: firstly, it is used to analyze the second-level dictionary required for actual parsing based on the location of the experimental equipment, and secondly, it is used to simply classify the data items contained in the system to meet the principle of appropriateness. The second-level dictionary is the specific equipment dictionary, with each type of equipment having its own independent detailed dictionary.

[0013] The advantages of constructing a virtual entity library based on a data dictionary in Step 1 are as follows: The virtual entity library construction method based on a data dictionary proposed in this invention describes the key information of virtual devices completely and uniformly through a four-tuple structure, and adopts a multi-level dictionary hierarchical management mechanism to classify devices in detail according to categories and specific instances, achieving efficient data storage, retrieval, and parsing. Compared with existing technologies, this method not only standardizes the expression of device information and improves data management and maintenance efficiency, but also enhances the intelligent retrieval and scene adaptation capabilities of virtual devices through fine-grained management of technical parameters and input / output information. In practical applications, this method can improve the efficiency of rapid configuration and flexible integration of devices in virtual testing scenarios, providing strong technical support for the efficient construction, intelligent management, and long-term sustainable development of complex engineering virtual testing platforms.

[0014] As an improvement of the present invention, step 2: screening test equipment based on logic units, specifically as follows: 2-1 Construction of virtual entities based on logic units

[0015] A logical unit is a basic modeling module composed of various logical elements such as attributes, physical quantities, constraints, and physical entities, as well as their relationships, possessing the characteristics of structural integrity and well-defined functions.

[0016] The process of deriving virtual entity classes from logical units is essentially a semantic mapping and functional inheritance process from logical information to entity information. Based on object-oriented programming principles, the inheritance mechanism uses a parent class to represent a logical unit in the virtual experimental system, while the child virtual entity model inherits its attributes according to the parent class's definition and extends it as needed. The logical information of each device (such as functions and performance parameters) is transformed into a concrete representation of the virtual entity model through inheritance. The formulaic expression of the inheritance mechanism is as follows:

[0017]

[0018] Among them, L parent Indicates the parent class of the logical unit; M child Represents a subclass of the virtual entity model; {A parent Q parent C parent I / O parent B parent , ...} represents the attributes, physical quantities, constraints, input / output requirements, behaviors, etc., included in the logic unit; {A child Q child C child I / O child B child , ...} represent attributes, physical quantities, constraints, input / output requirements, behaviors, etc. derived from the virtual entity model.

[0019] Logical units generate corresponding virtual entities based on inheritance and derivation. This process begins with the physical entity of the logical unit, constructing a virtual model based on the various attribute elements, physical quantity elements, constraint relationship elements, information carrier elements, and behavioral elements of the physical entity. By inheriting existing physical characteristics and functional requirements, virtual entities can not only inherit the basic structure and working mode of the equipment, but also further derive sub-entities adapted to different simulation environments and working conditions.

[0020] 2-2 Screening of Experimental Equipment

[0021] In the process of selecting experimental equipment for virtual experiments, the data dictionary-based selection method is an efficient equipment selection mechanism. By parsing the experimental requirements information input by the user, the system, based on the parameters and functional requirements of the equipment, and combining a primary dictionary (such as major equipment categories like test benches, fixtures, and loading devices) and a secondary dictionary (such as specific equipment models), accurately selects equipment that meets the experimental requirements from the virtual entity library constructed in step 1. The following are the steps for selecting experimental equipment based on the data dictionary:

[0022] Step 1. Input test information, including test type, equipment requirements, input / output signal types, working environment, accuracy requirements, etc. The system performs preliminary screening based on the primary dictionary equipment categories (such as test benches, fixtures, loading devices, sensors, etc.) in the data dictionary to determine the required equipment type.

[0023] Step 2. The system enters the secondary dictionary filtering stage, filtering based on information such as the parameter range contained in the virtual entity. The detailed parameters of the equipment (such as load, displacement, accuracy, frequency range, etc.) in the secondary dictionary are compared with the test requirements. Combined with information such as equipment model, function, and materials, the system further determines whether the equipment meets the test requirements. Equipment that does not meet the requirements will be automatically filtered out.

[0024] Step 3. Consult the equipment assembly constraint information in the secondary dictionary, and output all equipment combinations that meet the test requirements and can be assembled in three-dimensional space based on the interrelationships and spatial constraints between the equipment.

[0025] Step 4. Users select from a combination of devices that meet their needs.

[0026] Step 2, based on logic unit-based equipment selection, offers the following advantages: The logic unit-based equipment selection method proposed in this invention achieves a high degree of matching and customized selection to experimental requirements. Compared to traditional single-parameter or simple-condition selection methods, this method employs dynamic mapping and multi-factor interaction mechanisms, significantly improving the accuracy and automation level of equipment selection. This method can efficiently address large-scale, complex, and ever-changing engineering experimental needs, shorten equipment selection time, and improve system adaptability. By organically combining equipment parameters, behaviors, and constraints as a holistic matching basis for equipment selection and adaptation, this integrated matching method more comprehensively and accurately meets the needs of virtual testing platforms in terms of efficiency, intelligence, and continuous optimization, providing a solid technical foundation for the long-term development of the platform.

[0027] As an improvement to the present invention, step 3 is logically connected to the mapping of spatial relationships, as follows:

[0028] The logical connection to the mapping process of spatial relationships realizes the structural implementation and geometric expression of logical information. The mapping process is as follows:

[0029] Step 1: Systematically analyze the logical model to identify the functional dependencies and assembly subordination relationships between subsystems, modules, and components in the virtual experimental system, as well as signal transmission relationships. This information will serve as the basis for constructing the 3D spatial assembly and signal paths, ensuring the continuity and rationality of assembly relationships and signal paths in subsequent mapping processes.

[0030] Step 2: The logical model assembly relationship includes connection methods, target entity anchor point information, and connection constraints. The system constructs a 3D assembly structure based on the logical dependency path, determines the position of each node in the global assembly through a coordinate inheritance mechanism, and completes anchor point docking using attitude adjustment and geometric registration algorithms. This mapping is then expressed as assembly relationships between 3D models, ultimately resulting in a 3D assembly that satisfies logical constraints. The 3D assembly includes the 3D model, assembly relationships, and assembly sequence. The formulaic expression of this mapping is as follows:

[0031]

[0032] Among them, LM parent Represents the parent class of the logical model; TM child Represents a subclass of the experimental model; AR parent The logical model represents the assembly relationships included in the model; 3D Assembly represents the three-dimensional assembly derived from the experimental model.

[0033] Step 3: Map the signal content and target objects for receiving signals in the logical model's signal path to interfaces on solid entities in 3D space for receiving signals. Simultaneously map and verify the input / output interfaces to ensure signal path direction matching, size compatibility, and connection consistency. The formulaic expression for this mapping is as follows:

[0034]

[0035] Among them, LM parent Represents the parent class of the logical model; TM child Represents a subclass of the experimental model; SP parent The logical model represents the signal paths included in the model; the interface represents the signal receiving interface derived from the experimental model.

[0036] The advantages of step 3, mapping logical connections to spatial relationships, are as follows: This invention establishes an automated mapping mechanism from abstract logical relationships to 3D spatial assembly and interface layout. Compared to traditional methods relying on manual experience, this method achieves precise positioning and closed-loop verification of assembly relationships and signal paths in 3D space through coordinate inheritance, attitude adjustment, and multimodal geometric registration algorithms. This mechanism improves the efficiency of 3D modeling and virtual assembly of complex systems, reduces error rates and rework risks, and supports flexible system expansion and sustainable upgrades.

[0037] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the mapping method from a logical model to a three-dimensional scene model for virtual experiments.

[0038] A computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the aforementioned mapping method from a logical model to a three-dimensional scene model for virtual experiments.

[0039] Compared to existing technologies, this invention has the following advantages: This method systematically proposes three core steps for mapping the logical model of a virtual experiment to a 3D scene model, including the construction of a virtual entity library based on a data dictionary, the selection of experimental equipment based on logical units, and the mapping of logical connections to spatial relationships. This technical solution not only achieves automatic and efficient matching between experimental requirements and equipment models, but also improves the automation and intelligence level of scene assembly and signal path configuration. Through the automatic association between the logical model and the 3D virtual scene, the repetition and errors of manual configuration are effectively reduced, improving the efficiency and accuracy of virtual experimental scene construction. This provides a data and process foundation for the virtual verification and digital R&D of complex engineering systems, expanding the application scope and engineering applicability of virtual experiments. Attached Figure Description

[0040] Figure 1 This is a schematic diagram illustrating the construction of a virtual entity based on logic units and the selection of experimental equipment, using a temperature sensor as an example, according to the present invention.

[0041] Figure 2 This is a schematic diagram illustrating the logical connections and spatial relationships of the present invention, using a virtual experiment as an example.

[0042] Figure 3 This is a schematic diagram illustrating the construction of a virtual entity based on logical units and the selection of experimental equipment, using a force loading device as an example, according to the present invention.

[0043] Figure 4 This is a schematic diagram of the logical connections and spatial relationships in the present invention, taking virtual thermal shock test as an example. Detailed Implementation

[0044] To enhance understanding of the present invention, the embodiments will be described in detail below with reference to the accompanying drawings.

[0045] Example 1: See Figure 1 , Figure 2 A mapping method from a logical model to a 3D scene model for virtual experiments includes the following three steps:

[0046] Step 1: Build a virtual entity library based on a data dictionary. The construction process is as follows:

[0047] Taking a thermocouple as an example, a quadruple can be used to summarize all the key information of the device:

[0048]

[0049] Technical Parameters: Measurement temperature range: -55℃~125℃, Sensitivity: 0.1mV / ℃

[0050] Sampling frequency: up to 1.3Hz…}

[0051]

[0052] Wherein, WDCGQ1 is the device ID; thermocouple is the device name; Technical Parameters includes the device's technical parameters such as the measurement temperature range; This includes the device's input and output information, such as information carriers being voltage signals.

[0053] A multi-level dictionary structure was adopted to design a data item dictionary for virtual experiments. The first-level dictionary is the experimental equipment dictionary, which includes six categories: sensors, fixtures, test pieces, loading devices, test benches, and test scenarios. The second-level dictionary is the specific equipment dictionary, such as sensors including the PDL-800-485 photonic precision laser displacement sensor and the IEPE / ICP piezoelectric accelerometer.

[0054] Step 2 involves screening test equipment based on logic units, as detailed below:

[0055] 2-1 Construction of Virtual Entities Based on Logical Units

[0056] Taking the construction of a virtual entity of a temperature sensor through inheritance and derivation mechanism by the logic unit of the temperature sensor as an example,

[0057]

[0058] Among them, L parent Represents the parent class of the temperature sensor logic unit; M child Represents a subclass of the virtual entity model for the temperature sensor; {A parent Q parent C parent I / O parent B parent , ...} represents the attributes, physical quantities, constraints, input / output requirements, behaviors, etc., included in the temperature sensor logic unit; {A child Q child C child I / O child B child , ...} represent the attributes, physical quantities, constraints, input / output requirements, behaviors, etc. derived from the virtual entity model of the temperature sensor. The subsets are as follows:

[0059] A parent ={Type, Material, Size...}

[0060] Q parent ={temperature...}

[0061] C parent ={assembly constraints, signal interfaces, etc.}

[0062] I / O parent ={text...}

[0063] B parent ={Collection behavior...}

[0064] A child ={3D model...}

[0065] C child ={receive interface, send interface...}

[0066] Q child =I / O child =B child ={0}

[0067] The virtual entity of the temperature sensor can not only inherit the basic structure and working method of the device, but also derive sub-entities that are adapted to different simulation environments and working conditions.

[0068] 2-2 Screening of Experimental Equipment

[0069] A virtual test equipment selection method based on a data dictionary analyzes test requirements and uses a combination of a primary dictionary (equipment category) and a secondary dictionary (specific model) in a virtual entity library to efficiently and accurately select suitable test equipment. Taking temperature sensor selection as an example, the steps are as follows:

[0070] Step 1. Input information for a virtual experiment, including the experiment type, equipment requirements, input / output signal types, working environment, and accuracy requirements. The system performs a preliminary screening based on the primary dictionary's equipment categories, determining the required equipment types as sensors, fixtures, test pieces, loading devices, and test benches.

[0071] Step 2. The system enters the secondary dictionary filtering stage, comparing the measurement temperature range contained in the virtual entity of the temperature sensor with the measurement temperature range of the specific device in the secondary dictionary. Similarly, it compares the parameters of other devices with the test requirements one by one to determine whether a temperature sensor meets the test requirements. Devices that do not meet the requirements will be automatically filtered out, such as thermocouples and RTDs. The same method is used to filter out the specific devices such as test specimens and loading devices.

[0072] Step 3. Consult the equipment assembly constraint information in the secondary dictionary. Based on the interrelationships and spatial constraints between the equipment, determine whether the equipment can be assembled reasonably. For example, for test piece P2 and fixture F5 to be assembled reasonably, each needs two grooves that cooperate with the elastic snap ring. Output all equipment combinations that meet the test requirements and can be assembled in three-dimensional space, such as combinations like "temperature sensor S11, magnetic field strength sensor S32, fixture F5, test piece P2, electromagnetic loading device L57, heating device L39, test bench B1, etc."

[0073] Step 4. Users select from a combination of devices that meet their needs.

[0074] Step 3 involves logically connecting to the mapping of spatial relationships, as follows:

[0075] The process of mapping logical connections to spatial relationships enables the structural implementation and geometrical representation of logical information. Taking a virtual experiment as an example, the mapping process is as follows:

[0076] Step 1: Systematically analyze the logic model of a virtual experiment to identify the functional dependencies, assembly subordination relationships, and signal transmission relationships among the subsystems, modules, and components within the virtual experiment system. For example, the magnetic field strength sensor and the fixture form a positioning pin-positioning hole fit assembly relationship, and there is a signal transmission relationship between the electromagnetic loading device and the magnetic field strength sensor. This information will serve as the basis for constructing the 3D spatial assembly and signal paths, ensuring the continuity and rationality of assembly relationships and signal paths in the subsequent mapping process.

[0077] Step 2: The assembly relationship of a virtual experiment's logical model includes connection methods, target entity anchor point information, and connection constraints. The system constructs a 3D assembly structure based on the logical dependency path, determines the position of each node in the global assembly through a coordinate inheritance mechanism, and completes anchor point docking using attitude adjustment and geometric registration algorithms. This mapping represents the assembly relationship between 3D models, ultimately resulting in a 3D assembly that satisfies the logical constraints. The 3D assembly includes the 3D model, assembly relationships, and assembly sequence. The formulaic expression of the mapping is as follows:

[0078]

[0079] Among them, LM parent TM represents the parent class of a certain virtual experiment logic model; child Represents a subclass of a virtual experiment model; AR parentThis represents the assembly relationships included in a certain virtual test logic model. Taking the assembly relationship between the magnetic field strength sensor and the fixture as an example, the assembly relationship in the magnetic field strength sensor logic unit includes: the connection method is a locating pin connection, the target entity (fixture) anchor point information is a locating hole that mates with the locating pin, and the connection constraint is an interference fit; 3D Assembly represents a certain virtual test three-dimensional assembly derived from the test model.

[0080] Step 3: Map the signal content and target objects for receiving signals in the signal path of a virtual experiment logic model to interfaces on entities in three-dimensional space used for signal reception. Take the signal transmission relationship between an electromagnetic loading device and a magnetic field strength sensor as an example:

[0081]

[0082] Among them, LM parent TM represents the parent class of a certain virtual experiment logic model; child Represents a subclass of a virtual experiment model; SP parent This indicates the signal path in the magnetic field strength sensor logic unit, including the signal content: "Loading stops after the magnetic field strength reaches 4.2mT", the target object for receiving the signal: electromagnetic loading device; Interface indicates the signal receiving interface of the electromagnetic loading device derived in the experimental model: http: / / seu / virtualexperiment / electromagneticloadingdevice / receive.

[0083] Example 2: See Figure 3 , Figure 4 Taking virtual thermal shock testing as an example, this paper illustrates the mapping method from the logical model to the three-dimensional scene model for virtual testing:

[0084] Step 1: Construct a virtual entity library based on the data dictionary. The construction process is as follows:

[0085] Taking infrared heating equipment as an example, a quadruple is used to summarize all the key information of the equipment:

[0086]

[0087] Technical Parameters: Operating temperature range: -20℃~800℃, Thermal response time: ≤30 seconds

[0088] Radiation efficiency: ≥65% (under 380V power supply conditions), temperature control accuracy: ±1.5℃…

[0089]

[0090] Wherein, JRSB1 is the device ID; Infrared Heating Equipment is the device name; Technical Parameters includes the device's technical parameters such as operating temperature range; This includes the device's input and output information. The heating start / stop control signal is the input signal type, digital quantity is the data format, and JSON is the transmission format for the output data.

[0091] A multi-level dictionary structure was adopted to design a data item dictionary for virtual experiments. The first-level dictionary is the experimental equipment dictionary, which includes six categories: sensors, fixtures, test pieces, loading devices, test benches, and test scenarios. The second-level dictionary is the specific equipment dictionary, such as the loading devices including the heating and cooling constant temperature test equipment TES-6A60WPEX and the variable frequency triaxial electromagnetic vibration table XLB-5EVT-XYZ.

[0092] Step 2 involves screening test equipment based on logic units, as detailed below:

[0093] 2-1 Construction of Virtual Entities Based on Logical Units

[0094] Taking the construction of a virtual entity of a force-loading device through inheritance and derivation mechanism by the logical unit of the force-loading device as an example,

[0095]

[0096] Among them, L parent M represents the parent class of the force-loading device logic unit; child Represents a subclass of the virtual entity model of a force-loading device; {A parent Q parent C parent I / O parent B parent , ...} represents the attributes, physical quantities, constraints, input / output requirements, behaviors, etc., included in the logic unit of the force loading device; {A child Q child C child I / O child B child , ...} represent the attributes, physical quantities, constraints, input / output requirements, behaviors, etc. derived from the virtual entity model of the force-loading device. The subsets are as follows:

[0097] A parent ={quality, material, size, etc.}

[0098] Q parent ={load...}

[0099] C parent ={assembly constraints, signal interfaces, etc.}

[0100] I / O parent={text...}

[0101] B parent ={loading behavior...}

[0102] A child ={3D model...}

[0103] C child ={receive interface, send interface...}

[0104] B child ={Surface Contact Loading...}

[0105] Q child =I / O child ={0}

[0106] The virtual entity of the force loading device can not only inherit the basic structure and working method of the device, but also derive sub-entities that adapt to different simulation environments and working conditions.

[0107] 2-2 Screening of Experimental Equipment

[0108] The virtual thermal shock test equipment selection method based on a data dictionary analyzes the test requirements and combines a primary dictionary (equipment category) and a secondary dictionary (specific model) in a virtual entity library to efficiently and accurately select the test equipment that meets the requirements. Taking the selection of force loading equipment as an example, the steps are as follows:

[0109] Step 1. Input virtual thermal shock test information, including test type, equipment requirements, input / output signal types, working environment, accuracy requirements, etc. The system performs preliminary screening based on the primary dictionary equipment categories in the data dictionary, determining the required equipment types as sensors, fixtures, test pieces, loading devices, test benches, and test scenarios.

[0110] Step 2. The system enters the secondary dictionary filtering stage, comparing the load range contained in the virtual entity of the force loading equipment with the load range of the specific equipment in the secondary dictionary. Similarly, it compares the parameters of other equipment with the test requirements one by one to determine whether a force loading equipment meets the test requirements. Equipment that does not meet the requirements will be automatically filtered out, such as hydraulic loading equipment and electric loading equipment. The same method is used to filter out specific equipment such as test benches, test pieces, sensors, and fixtures.

[0111] Step 3. Consult the equipment assembly constraint information in the secondary dictionary. Based on the interrelationships and spatial constraints between the equipment, determine whether the equipment can be reasonably assembled. For example, for the test bench TB1 and the heating equipment HE7 to be reasonably assembled, each needs six through holes that match the bolts. Output all equipment combinations that meet the test requirements and can be assembled in three-dimensional space, such as combinations like "test bench TB1, vibration table VT9, force loading equipment FLD3, heating equipment HE7, compartment, tooling compartment, temperature sensor TS4, etc."

[0112] Step 4. Users select from a combination of devices that meet their needs.

[0113] Step 3 involves logically connecting to the mapping of spatial relationships, as follows:

[0114] The process of mapping logical connections to spatial relationships enables the structural realization and geometric expression of logical information. Taking a virtual thermal vibration test as an example, the mapping process is as follows:

[0115] Step 1: Systematically analyze the logical model of the virtual thermal vibration test, identifying the functional dependencies, assembly subordination relationships, and signal transmission relationships among the subsystems, modules, and components in the virtual thermal vibration test system. For example, there is a direct surface contact assembly between the test piece and the temperature sensor, and a signal transmission relationship between the heating equipment and the temperature sensor. This information will serve as the basis for constructing the three-dimensional spatial assembly and signal paths, ensuring the continuity and rationality of the assembly relationships and signal paths in the subsequent mapping process.

[0116] Step 2: The assembly relationship of the virtual thermal vibration test logical model includes connection methods, target entity anchor point information, and connection constraints. The system constructs a 3D assembly structure based on the logical dependency path, determines the position of each node in the global assembly through a coordinate inheritance mechanism, and completes anchor point docking using attitude adjustment and geometric registration algorithms. This mapping is then expressed as the assembly relationship between 3D models, ultimately resulting in a 3D assembly that satisfies the logical constraints. The 3D assembly includes the 3D model, assembly relationships, and assembly sequence. The formulaic expression of the mapping is as follows:

[0117]

[0118] Among them, LM parent Represents the parent class of the virtual thermal vibration logic model; TM child Represents a subclass of virtual thermal shock test model; AR parentThe assembly relationship included in the virtual thermal shock logic model is represented. Taking the assembly relationship between the test piece and the temperature sensor as an example, the assembly relationship in the temperature sensor logic unit includes: the connection method is surface attachment connection, the target entity (test piece) anchor point information has a predetermined pasting area, and the connection constraint is no relative slip constraint; 3D Assembly represents the virtual thermal shock test three-dimensional assembly derived from the test model.

[0119] Step 3: Map the signal content and target objects for receiving signals in the signal path of the virtual thermal vibration test logic model to interfaces on a three-dimensional physical surface used for signal reception. Take the signal transmission relationship between the heating device and the temperature sensor as an example:

[0120]

[0121] Among them, LM parent Represents the parent class of the virtual thermal vibration logic model; TM child Represents a subclass of virtual thermal shock test model; SP parent This indicates the signal path in the temperature sensor logic unit, including the signal content: "Heating stops when 700℃ is reached", and the target object for receiving the signal: heating equipment; Interface indicates the signal receiving interface of the heating equipment derived in the test model: http: / / seu / virtualthermalvibrationtest / heatingequipment / receive.

[0122] In summary, the mapping from the logical model of the virtual thermal shock test to the three-dimensional scene model is realized.

[0123] It should be noted that the above embodiments are not intended to limit the scope of protection of the present invention. Equivalent transformations or substitutions made based on the above technical solutions all fall within the scope of protection of the claims of the present invention.

Claims

1. A mapping method from a logical model to a three-dimensional scene model for virtual experiments, characterized in that, The method includes the following steps: Step 1: Construct a virtual entity library based on a data dictionary. Step 2: Screening test equipment based on logic units. Step 3: Logically connect to the mapping of spatial relationships; Step 3 is as follows: The logical connection to the mapping process of spatial relationships realizes the structural implementation and geometric expression of logical information. The mapping process is as follows: Step 1: Systematically analyze the logical model to identify the functional dependencies and assembly subordination relationships between subsystems, modules, and components in the virtual experimental system, as well as signal transmission relationships. This information will serve as the basis for constructing the 3D spatial assembly and signal paths, ensuring the continuity and rationality of assembly relationships and signal paths in the subsequent mapping process. Step 2: The logical model assembly relationship includes connection methods, target entity anchor point information, and connection constraints. The system constructs a 3D assembly structure based on the logical dependency path, determines the position of each node in the global assembly through a coordinate inheritance mechanism, and completes anchor point docking using attitude adjustment and geometric registration algorithms. This mapping is then transformed into assembly relationships between 3D models, ultimately resulting in a 3D assembly that satisfies the logical constraints. The 3D assembly includes the 3D model, assembly relationships, and assembly sequence. The formulaic expression of the mapping is as follows: in, Represents the parent class of the logical model; Represents a subclass of the experimental model; This represents the assembly relationships included in the logical model; This represents a three-dimensional assembly derived from the experimental model. Step 3: Map the signal content and target object of the signal in the logical model signal path to the interface on the solid in three-dimensional space for receiving signals. Simultaneously map and verify the input and output interfaces to ensure that the signal path direction matches, the size is compatible, and the connection is consistent. The formulaic expression of the mapping is as follows: in, Represents the parent class of the logical model; Represents a subclass of the experimental model; This represents the signal paths included in the logic model; This refers to the receiving signal interface derived from the experimental model.

2. The mapping method from a logical model to a three-dimensional scene model for virtual experiments according to claim 1, characterized in that, Step 1 involves constructing a virtual entity library based on a data dictionary, as detailed below: Use a quadruple to summarize all the key information about the device: in, A unique identifier for the device; The name of the device; The technical parameters of the equipment, including load range, displacement range, frequency range, and accuracy requirements, constitute a subset. This includes the device's input and output information, describing the types of input signals, data formats, input and output sources, and output formats supported by the device.

3. The mapping method from a logical model to a three-dimensional scene model for virtual experiments according to claim 1, characterized in that, Step 2: Screening test equipment based on logic units, as detailed below. 2-1 Construction of Virtual Entities Based on Logical Units A logical unit is a basic modeling module composed of various logical elements such as attributes, physical quantities, constraints, and physical entities, as well as their relationships. It possesses the characteristics of structural integrity and clearly defined functions. The process of deriving virtual entity classes from logical units is essentially a semantic mapping and functional inheritance process from logical information to entity information. This inheritance mechanism is based on object-oriented programming principles. The parent class represents the logical unit in the virtual experimental system, while the child virtual entity model inherits its attributes according to the parent class's definition and extends it as needed. The logical information of each device is transformed into a concrete representation of the virtual entity model through inheritance. The formulaic expression of the inheritance mechanism is as follows: in, Represents the parent class of logical units; Represents a subclass of the virtual entity model; This represents the attributes, physical quantities, constraints, input / output requirements, and behaviors of a logical unit. It represents the attributes, physical quantities, constraints, input / output requirements, and behaviors derived from the virtual entity model; 2-2 Screening of Experimental Equipment In the process of selecting experimental equipment for virtual experiments, the data dictionary-based selection method is an efficient equipment selection mechanism. By parsing the experimental requirements information input by the user, the system accurately selects equipment that meets the experimental requirements based on the parameters and functional requirements of the equipment in the virtual entity library constructed in step 1, combined with the primary and secondary dictionaries. The following are the steps for selecting experimental equipment based on the data dictionary: Step 1. Input test information, including test type, equipment requirements, input / output signal types, operating environment, and accuracy requirements. The system will perform preliminary screening based on the primary dictionary equipment categories to determine the required equipment type. Step 2. The system enters the secondary dictionary filtering stage. Based on the parameter range information contained in the virtual entity, it filters the data, comparing the detailed parameters of the equipment with the test requirements in the secondary dictionary. Combining this with equipment model, function, and material information, it further determines whether the equipment meets the test requirements. Equipment that does not meet the requirements will be automatically filtered out. Step 3. Consult the equipment assembly constraint information in the secondary dictionary. Based on the interrelationships and spatial constraints between the equipment, output all equipment combinations that meet the experimental requirements and can be assembled in three-dimensional space. Step 4. Users select from a combination of devices that meet their needs.

4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the mapping method from a logical model to a three-dimensional scene model for virtual experiments as described in any one of claims 1 to 3.

5. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, the computer instructions implement the mapping method from the logical model to the three-dimensional scene model for virtual experiments as described in any one of claims 1-3.

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