A digital twin model construction method for special vehicle equipment

Abstract

The application discloses a digital twin model construction method for special vehicle equipment, and relates to the technical field of vehicle equipment support, and the method comprises the following steps: acquiring physical environment information of an equipment object based on a sensor network arranged on the equipment object; constructing an equipment digital twin body according to the physical environment information of the equipment object; the equipment digital twin body comprises twin body objects of each component unit in each single machine at each time within a set total service time, and each twin body object comprises a geometric shape model, a mechanism characteristic model and a task behavior model; and the geometric shape, the mechanism characteristic and the task behavior of the equipment object are monitored in real time within the set total service time by using the equipment digital twin body. The application can improve the accuracy and timeliness of the operation state monitoring of the vehicle equipment.

Description

Technical Field

[0001] This application relates to the field of vehicle equipment support technology, and in particular to a method for constructing digital twin models for special vehicle equipment. Background Technology

[0002] With the further development of informatization, vehicles and equipment can play a significant role in rescue and other missions. However, in missions involving large-scale vehicles and equipment, wide spatial and temporal ranges, and rapid information changes, there are often problems such as harsh equipment operating environments and complex support requirements. Currently, the vehicle and equipment support model suffers from issues such as opaque operating status, inaccurate fault tracing, and untimely maintenance support. There is an urgent need to improve the level of intelligence in equipment maintenance support, enhance the precise execution of mission actions, and improve the efficient coordination of support resources. Summary of the Invention

[0003] The purpose of this application is to provide a method for constructing digital twin models for special vehicle equipment, which can improve the accuracy and timeliness of monitoring the operational status of vehicle equipment.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] This application provides a method for constructing digital twin models for special vehicle equipment, including:

[0006] Based on the sensor network deployed on the equipment object, the physical environment information of the equipment object is obtained.

[0007] A digital twin of the equipment is constructed based on the physical environment information of the equipment object; the digital twin of the equipment includes twin objects of each component unit in each single machine at each moment during the set total service time, and each twin object includes a geometric shape model, a mechanism characteristic model and a task behavior model;

[0008] The equipment digital twin is used to monitor the geometric shape, mechanistic characteristics, and mission behavior of the equipment in real time during a set total service life.

[0009] Optionally, the equipment digital twin is represented as:

[0010]

[0011] Among them, DT T The digital twin of the equipment is represented by T, the set total service time is represented by J, the number of component units is represented by I, and the number of individual units is represented by I. This represents the twin object of the j-th component unit in the i-th single machine at time t. and These represent the geometric shape model, mechanistic characteristic model, and task behavior model of the j-th component unit in the i-th single machine at time t, respectively.

[0012] Optionally, the geometric shape model includes the geometric features, performance features, and kinematic pairs of the component unit. The geometric features include holes, slots, and threads. The performance features include material, tolerances, and chamfers. The kinematic pairs include revolute pairs, helical pairs, and prismatic pairs.

[0013] Optionally, the equipment digital twin also includes a multi-body motion model of the equipment digital twin constructed based on the kinematic pairs of each component unit.

[0014] Optionally, a multi-body motion model of the equipment digital twin is constructed based on the kinematic pairs of each component unit, specifically including:

[0015] Based on the geometric and performance characteristics of the equipment, establish the assembly relationships and constraint relationships between the various component units;

[0016] Define and reconstruct each component unit as a rigid body and a flexible body, and define the physical properties of each component unit after reconstruction;

[0017] The assembly relationship, the constraint relationship, and the physical characteristics of each component unit after reconstruction are assigned to the geometric model of the equipment object, and a digital twin geometric model is established in the three-dimensional driving engine.

[0018] Add kinematic or dynamic features to the digital twin geometric model, simulate and verify the motion of the equipment object, and use the digital twin geometric model that passes the simulation verification as the multibody motion model.

[0019] Optionally, the physical properties include stiffness, damping, and coefficient of friction.

[0020] Optionally, the geometric model of the equipment object is a STEP format model.

[0021] Optionally, a digital twin of the equipment is constructed based on the physical environment information of the equipment object, specifically including:

[0022] Failure Mode and Effects Analysis (FMEA) was used to determine the failure modes of each component unit in the digital twin of the equipment.

[0023] Based on the impact of the failure modes of each component unit on the equipment object, the failure phenomena generated by each component unit in the equipment object are determined.

[0024] Based on the fault phenomena generated by each component unit, determine the failure mode generated by each component unit and the corresponding failure mechanism of each failure mode.

[0025] Based on the failure modes, failure phenomena, and failure mechanisms of each component unit, a mechanism characteristic model of each component unit is constructed.

[0026] Optionally, the equipment digital twin also includes the correlation between the failure modes of each individual machine and the failure mechanisms of each component unit in that individual machine.

[0027] Optionally, the task behavior model includes task behavior and dynamic state behavior during the execution of the task behavior. The task behavior is represented by a process sequence, and the dynamic state behavior is used to map the behavior state of the equipment object in real time based on the physical environment information collected by the sensor network.

[0028] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method for constructing a digital twin model for special vehicle equipment. A digital twin of the equipment is constructed based on the physical environment information of the equipment object. The digital twin includes twin objects of each component unit in each single machine at each moment within a set total service time. Each twin object includes a geometric shape model, a mechanistic characteristic model, and a task behavior model. By monitoring the geometric shape, mechanistic characteristics, and task behavior of the equipment object in real time through the digital twin, monitoring of the equipment object in three dimensions—geometric shape, mechanistic characteristics, and task behavior—is achieved, improving the accuracy of equipment status monitoring. Furthermore, the timeliness of equipment status monitoring is improved through the synchronization between the digital twin and the equipment object. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating a method for constructing a digital twin model for special vehicle equipment, provided as an embodiment of this application.

[0031] Figure 2 This is a schematic diagram of the overall architecture of a digital twin model construction method for special vehicle equipment provided in an embodiment of this application.

[0032] Figure 3 This is a schematic diagram illustrating the modeling principle of the geometric shape provided in one embodiment of this application.

[0033] Figure 4 This is a schematic diagram of the modeling process for a geometric shape provided in an embodiment of this application.

[0034] Figure 5 This is a schematic diagram of the mechanism characteristic modeling process provided in an embodiment of this application.

[0035] Figure 6 This is a schematic diagram of the equipment state behavior modeling process provided in an embodiment of this application. Detailed Implementation

[0036] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0037] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] This application provides a method for constructing digital twin models of special vehicle equipment, such as... Figure 1 As shown, the method for constructing a digital twin model for special vehicle equipment includes steps 101-103.

[0039] Step 101: Based on the sensor network deployed on the equipment object, obtain the physical environment information of the equipment object.

[0040] Physical environment information includes information such as the vehicle's overall acceleration and speed, as well as status monitoring information of internal components such as the vehicle's engine and transmission.

[0041] Step 102: Construct a digital twin of the equipment based on the physical environment information of the equipment object; the digital twin of the equipment includes the twin objects of each component unit in each single machine at each moment during the set total service time, and each twin object includes a geometric shape model, a mechanism characteristic model and a task behavior model.

[0042] Step 103: Use the equipment digital twin to monitor the geometric shape, mechanistic characteristics and mission behavior of the equipment object in real time during the set total service time.

[0043] This application, starting from the needs of intelligent equipment support, utilizes digital twin technology to construct a "physical life form" of weaponry and equipment. Modeling is conducted from three dimensions: appearance, characteristics, and behavior, achieving appearance synchronization, characteristic restoration, and mission accompaniment during equipment operation and maintenance. By leveraging the twin to perceive equipment status and identify potential support needs, and combining this with the system's accumulated decision-making knowledge base, various support needs are correlated, matched, and judged. Through the calculation of the correlation between needs and knowledge, the appropriate support decision-making scheme for the corresponding state is determined and then pushed to support personnel for rapid execution of support tasks, providing technical support for equipment mission support.

[0044] The technical solution of this application mainly involves three aspects: the physical entity of the equipment, the digital twin of the equipment, and intelligent operation and maintenance services, such as... Figure 2 As shown.

[0045] (1) The equipment object is the physical entity of the equipment. The physical entity of the equipment is composed of physical objects at various levels. Through direct or indirect interaction, it executes its own control commands and completes its actual tasks and missions. Therefore, the physical entity of the equipment refers to the equipment object and its task process in the physical world. By deploying a sensor network on the equipment object, information about the physical equipment and physical environment is acquired and transmitted to the digital space through the equipment's own communication bus, thereby ensuring that changes in the physical equipment object can be received in the twin environment.

[0046] (2) Equipment digital twin: In order to realize the interaction and mutual support between the digital twin and the physical entity during operation and maintenance, the modeling of equipment digital twin mainly considers three dimensions: "shape", "characteristics" and "behavior".

[0047] "Shape" modeling is used to express the size, structure, position, attitude and motion process of equipment, and can support the visualization of the equipment operation process.

[0048] "Characteristic" modeling is aimed at fault problems in the operation and maintenance process, analyzing their fault modes, clarifying fault mechanisms, and quantitatively describing the inherent mechanisms of twin degradation and failure through fault physical models, providing a basis for intelligent operation and maintenance services in twin environments.

[0049] "Behavior" modeling is aimed at the dynamic mapping and synchronization of the behavior of physical equipment entities by digital twins. The research summarizes the behavior modeling of digital twins into two aspects: from a macro perspective, equipment behavior refers to the task flow of equipment operation and maintenance, such as disassembly and assembly process, replacement process, testing process, etc.; from a micro perspective, behavior modeling refers to the real-time monitorable behavior of components under different working conditions, such as vibration, rotation speed, etc.

[0050] (3) Intelligent operation and maintenance services: Based on the digital twin of equipment operation and maintenance, develop an intelligent operation and maintenance support system for equipment. Based on the capabilities of the digital twin, such as virtual-real mapping, digital-model fusion, and knowledge integration, provide services such as status monitoring, fault diagnosis, strategy push and health assessment for equipment use and operation and maintenance process. It is also packaged into various interconnected and collaborative functional components, application software and mobile terminals to support the formation of intelligent operation and maintenance capabilities for equipment.

[0051] This application integrates the constructed digital twin with the equipment operation and maintenance process. Taking the maintenance and support process of special vehicle equipment as an example, it drives the "form, properties, and operation" status update and iteration of the twin through various equipment operation and maintenance events.

[0052] Digital twins can replicate, simulate, and integrate the attributes, mechanisms, behaviors, and functions of physical systems. The first step is to establish a highly reliable model with consistent attributes across all components. Generally, establishing a highly complete, accurate, and reliable equipment digital twin is an extremely complex systems engineering project. Therefore, this paper addresses the specific needs of intelligent equipment operation and maintenance by establishing an equipment digital twin. It not only considers the overall equipment operation and maintenance status information but also further breaks down the complex structural components of the equipment, focusing on components and units with high failure frequencies or long mean downtime. The equipment digital twin is defined according to the equipment's structural hierarchy, and twin modeling is carried out layer by layer from component level to unit level to equipment level.

[0053] In an exemplary embodiment, suppose a certain equipment object contains I individual units i∈{1,2,...,I}, and each individual unit contains J parts j∈{1,2,...,J}, and the total service time of the equipment twin is T, t∈{1,2,...,T}, then Let DT represent the twin object of the j-th component unit in the i-th single machine at time t, then we have the equipment digital twin DT. T It covers the main operational and maintenance status information of the equipment during its service life.

[0054] The digital twin of the equipment is represented as follows:

[0055] in, It can also include the geometric shape of the unit-level twin object at time t during its service life. Mechanistic characteristics and task behavior

[0056] Among them, DT T The digital twin of the equipment is represented by T, the set total service time is represented by J, the number of component units is represented by I, and the number of individual units is represented by I. This represents the twin object of the j-th component unit in the i-th single machine at time t. and These represent the geometric shape model, mechanistic characteristic model, and task behavior model of the j-th component unit in the i-th single machine at time t, respectively. This represents a conceptual model of an equipment twin for intelligent operation and maintenance.

[0057] Geometric shape modeling principle as follows Figure 3 As shown, the geometric shape model is a visual field representation of the equipment's geometric structure and multi-body motion. Geometric and multi-body motion models are constructed to represent the mechanical assembly structure and motion relationships of the twin model. Following the equipment's assembly structure organization, a bottom-up approach is adopted to construct the geometric shape and multi-body motion models at the component unit level, single-machine level, and equipment level.

[0058] In the twin modeling environment, standard parts such as bearings and bolts are imported from the aerospace general standard parts library, while custom parts are imported from the 3D models provided by the corresponding research and development units, and geometric parameters such as extrusion, rotation, scanning, and arraying are defined. The modeling of component-level shapes mainly considers three aspects: geometric features, performance features, and kinematic pairs. Specifically, the geometric shape model includes the geometric features, performance features, and kinematic pairs of the component unit. The geometric features include holes, slots, and threads; the performance features include material, tolerances, and chamfers; and the kinematic pairs include revolute joints, helical joints, and prismatic joints.

[0059] The geometric shape model of the component unit is represented as follows

[0060] Among them, Ge i,j Mp represents the geometric features of the j-th component unit in the i-th single machine. i,j This represents the performance characteristics of the j-th component unit in the i-th single machine. Let represent the kinematic pair of the j-th component unit in the i-th single machine at time t.

[0061] against The corresponding assembly features (such as surface coincidence, coaxiality, parallelism, distance, tangency, etc.) enable the assembly of component units at the model level, which are then accumulated step by step to form a single-machine-level external model. With equipment-grade shape model Sh tGenerally, the geometric model of the equipment in the detailed design phase is already fully constructed by 3D modeling software. During the twin construction process, the 3D model is loaded into the Unity and UE4 visual driving engine through the STEP model interaction standard. Model reloading is achieved by calling program scripts, and a unified boundary representation at the junction of CSG (Constructive Solid Geometry) is used to describe the boundary details of the object, achieving accurate modeling of complex shapes. Simultaneously, a hybrid approach using constructive solid geometry is used to fuse data structures and models. Data structures are implemented through custom scripts, or model reconstruction is achieved using the engine's scalable data storage mechanism.

[0062] Since a twin needs to map the shape and motion of a physical object, further work is required at the unit level to accurately reflect the equipment's motion characteristics and processes. Features complete the multibody motion modeling and simulation of the digital twin; the digital twin workflow is as follows: Figure 4 As shown.

[0063] The equipment digital twin also includes a multi-body motion model constructed based on the kinematic pairs of each component unit.

[0064] In one exemplary embodiment, such as Figure 4 As shown, the construction of a multibody motion model specifically includes the following steps:

[0065] 1) Based on the geometric and performance characteristics of the equipment, establish the assembly relationships and constraint relationships between the various component units, clarify the relevant entities, joints, and constraint relationships of the geometric shape model of each component unit, and analyze the forces, torques, and loads acting between different units or at connections. The geometric model of the equipment is a STEP format model.

[0066] 2) Construction and connection of rigid-flexible coupled multibody models: Define and reconstruct rigid and flexible body units for each component. Modify and add key components when the model driving settings, motion simulation, and multibody model verification results do not meet the actual situation, and define the physical properties of each component unit after reconstruction. The physical properties include stiffness, damping, and friction coefficient.

[0067] 3) Assign the assembly relationship, the constraint relationship, and the physical characteristics of each component unit after reconstruction to the geometric model of the equipment object, and establish a digital twin geometric model in the 3D driving engine. Specifically, this includes: taking the constructed STEP format model and assembly constraint relationship, and establishing a digital twin geometric model in the 3D driving engine.

[0068] 4) Add kinematic or dynamic features to the digital twin geometric model in the twin modeling environment to realistically reflect the actual connection relationship of each component of the equipment, and select the corresponding solver to solve and simulate various key motion forms of the model, and output time series data such as force, torque, displacement, velocity, acceleration, etc.

[0069] 5) Simulate and verify the motion of the equipment object, and use the digital twin geometric model that has passed the simulation verification as the multibody motion model. Specifically, this includes: for example, to realize the kinematic simulation of a vehicle during the exercise mission, a kinematic model of the vehicle needs to be established, considering parameters such as the vehicle's speed, steering angle, and range. Assuming that the vehicle is traveling on a planar road, the steering motion of the vehicle is described according to the Ackermann steering geometry principle. Based on the specific geometric relationship between the front and rear wheels, straight-line and turning simulations are performed. The process will obtain the position and attitude of the vehicle at each moment over time. The rationality is judged based on the analysis of the changes. When the results do not meet the theoretical calculations, the connection relationships, physical properties, material parameters, and rigid-flexible body settings are modified in the twin modeling environment. When the verification results are met, the construction of the twin multibody motion model is completed.

[0070] Mechanistic characteristic model It represents the inherent mechanisms and characteristics of equipment degradation and failure during use. In equipment operation and maintenance, the intelligent operation and maintenance digital twin can serve as a "container" for accumulated fault modes and mechanisms, solidifying the qualitative analysis of fault modes and the identification and quantification of fault mechanisms within the twin. Combining the understanding of fault problems in the equipment's historical use, a fault physical model is established to achieve a qualitative description of fault modes and a quantitative solution to fault mechanisms, and an explicit expression of the correlation between fault modes and mechanisms is established within the intelligent operation and maintenance twin model.

[0071] In an exemplary embodiment, the construction of the mechanism characteristic model specifically includes: determining the failure modes of each component unit in the equipment digital twin using the Failure Mode and Effects Analysis (FMEA) method; determining the failure phenomena generated by each component unit in the equipment object based on the impact of the failure modes of each component unit on the equipment object; determining the failure modes generated by each component unit and the corresponding failure mechanisms based on the failure phenomena generated by each component unit; and constructing the mechanism characteristic model of each component unit based on the failure modes, failure phenomena, and failure mechanisms of each component unit.

[0072] The mechanism characteristic model is expressed as:

[0073] in, and These are the i-th single machines. jFault phenomena, fault modes, and failure mechanisms of individual component units at time t.

[0074] 1) Qualitative analysis of failure modes.

[0075] Digital twins need to accurately represent the comprehensive impact of various failure modes on the use and function of equipment. Due to the complexity of the equipment's functions, structure, and working principles, Failure Mode and Effects Analysis (FMECA) is generally used for weaponry to determine the possible failure modes of each unit in the system. Then, a bottom-up analysis was conducted to examine its impact on the system and related fault phenomena. This allows for targeted treatment.

[0076] Qualitative analysis process for equipment failure modes, such as Figure 5 As shown. First, relevant information about the weapons and equipment must be collected, and their lifespan, mission profile, and environmental profile must be thoroughly analyzed and clearly defined. Then, based on the agreed-upon levels, functions, and mission types of the weapons and equipment, the failure phenomena of different units must be identified. Failure criteria and severity were defined, and then failure modes were analyzed for the system as a whole, individual units, and components. The analysis includes failure mode and effect analysis, failure mechanism and influencing factor analysis, and finally, the key failure modes and their corresponding failure mechanisms are summarized.

[0077] The equipment digital twin also includes the correlation between the failure modes of each individual machine and the failure mechanisms of each component unit in that individual machine.

[0078] 2) Fault mechanism identification.

[0079] Taking a certain type of equipment system as an example, individual equipment units mainly include three types of products: structural products (such as hulls, wings, rudders, heat shields, etc.), electromechanical products (such as servo motors, etc.), and electronic products (such as inertial navigation systems, computers, etc.). Combining historical failure modes and mechanism identification structures, the constructed equipment mechanism characteristic twin model covers the correlation between failure modes and mechanisms. The correlations between various failure modes and failure mechanisms are shown in Tables 1 to 3. Indicates the first i A single-player machine t Fault modes at any time.

[0080] Table 1. Correlation between structural failure modes and failure mechanisms

[0081]

[0082] Table 2 Correlation between Failure Modes and Failure Mechanisms in Electromechanical Systems

[0083]

[0084]

[0085] Table 3 Correlation between Electronic Failure Modes and Failure Mechanisms

[0086]

[0087] In an exemplary embodiment, the task behavior model includes task behavior and dynamic state behavior during the execution of the task behavior. The task behavior is represented by a process sequence, and the dynamic state behavior is used to map the behavioral state of the equipment object in real time based on the physical environment information collected by the sensor network. The task behavior model, on the one hand, describes the equipment's execution of corresponding operation and maintenance support tasks according to a predetermined process sequence from the perspective of the task process; on the other hand, it describes the working state behavior of different component units of the equipment at different times from the perspective of the equipment's real-time state.

[0088] The task behavior model is represented as follows: Among them, Ta t This refers to the mission actions performed by the equipment, including the specific usage procedures in missions such as equipment training, technical preparation, transportation, and withdrawal. This refers to the dynamic state behavior of equipment components during mission operations.

[0089] 1) Task "behavior".

[0090] For intelligent operation and maintenance, equipment digital twins primarily model task behaviors through process definition, defining the task-type behaviors (Ta) during the equipment operation and maintenance process. t It involves modeling a detailed process sequence, which can be represented as: Ta t = <ta1,ta2,...,ta n >. Among them, ta k Let n represent the k-th task action, where n is the number of task actions, and 1 ≤ k ≤ n.

[0091] Generally speaking, the types of equipment operation and maintenance support tasks mainly include two types: technical preparation support and maintenance and repair support. The specific operation and maintenance tasks have been defined in the equipment supportability design process. Taking the test support process of a certain type of equipment as an example, it mainly goes through the task process of power-on self-test, disconnection and connection, test status preparation, subsystem test, and overall inspection test.

[0092] 2) Status "behavior".

[0093] State "behavior" This refers to the real-time operational status of equipment during maintenance and support tasks. Sensor monitoring data provides the most objective description of the equipment's behavioral status because real-time mapping of status data enables dynamic synchronization between physical equipment and its twin equipment. During task execution, the equipment's status continuously changes over time, and the status changes of different individual units and components are not consistent. A single unit may exhibit different statuses at different times (t), and different individual units (i) and components (parts / units) may exhibit different statuses under different task conditions (ta). k The data structure collected below has typical spatiotemporal characteristics.

[0094] To ensure the consistency of product-dimensional twins, a data organization and modeling technology for equipment state behavior based on a spatiotemporal data model is proposed, according to the division of components, single-unit level, and equipment level.

[0095] Among them, St T To set dynamic state behavior over the total service life.

[0096] A unified data model is used to describe the physical state changes of the equipment, and data updates and synchronization are completed in each node of the constructed twin object, thereby driving the twin's state to update and synchronize with the actual physical state. Figure 6 As shown, Figure 6 It includes three monitoring points: a primary axis monitoring point, a secondary axis monitoring point, and a tertiary axis monitoring point. Each monitoring point monitors four time points: t1, t2, t3, and t4. The monitoring information at each time point of the primary axis monitoring point includes parameters such as temperature, particulate concentration, vibration, and oil pressure of components such as the exhaust manifold, turbocharger, converter, and muffler. The monitoring information at each time point of the secondary axis monitoring point includes parameters such as temperature, pressure, noise, and oil pressure of components such as the cylinder head, cylinder block, combustion chamber, and piston. The monitoring information at each time point of the tertiary axis monitoring point includes parameters such as temperature, hydraulic pressure, and remaining volume of components such as the water jacket, water pump, fan, and water tank.

[0097] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for constructing a digital twin model for special vehicle equipment, characterized in that, The method for constructing digital twin models for special vehicle equipment includes: Based on the sensor network deployed on the equipment object, the physical environment information of the equipment object is obtained. A digital twin of the equipment is constructed based on the physical environment information of the equipment object; the digital twin of the equipment includes twin objects of each component unit in each single machine at each moment during the set total service time, and each twin object includes a geometric shape model, a mechanism characteristic model and a task behavior model; The equipment digital twin is used to monitor the geometric shape, mechanistic characteristics, and mission behavior of the equipment in real time during a set total service life. The digital twin of the equipment is represented as follows: ; ； in, This refers to the digital twin of the equipment, where T represents the set total service time. J Indicates the number of the component units. This indicates the number of individual machines. Indicates the first The first single-player game Each component unit A twin object of time. , and They represent the first The first single-player game Each component unit Geometric shape model, mechanistic characteristic model, and task behavior model at any given moment; The mechanism characteristic model is expressed as: ; in, , and The first The first single-player game Each component unit The fault phenomena, fault modes, and failure mechanisms at any given moment; Correlation between Failure Mode and Failure Mechanism The correlation between various failure modes and failure mechanisms includes the correlation between structural failure modes and failure mechanisms, electromechanical failure modes and failure mechanisms, and electronic failure modes and failure mechanisms. The equipment digital twin also includes the correlation between the failure modes of each individual machine and the failure mechanisms of each component unit in that individual machine; The geometric shape model includes the geometric features, performance features, and kinematic pairs of the component units. The geometric features include holes, slots, and threads. The performance features include material, tolerances, and chamfers. The kinematic pairs include revolute pairs, helical pairs, and prismatic pairs. The equipment digital twin also includes a multi-body motion model constructed based on the kinematic pairs of each component unit.

2. The method for constructing a digital twin model for special vehicle equipment according to claim 1, characterized in that, A multi-body motion model of the equipment's digital twin is constructed based on the kinematic pairs of each component unit, specifically including: Based on the geometric and performance characteristics of the equipment, establish the assembly relationships and constraint relationships between the various component units; Define and reconstruct each component unit as a rigid body and a flexible body, and define the physical properties of each component unit after reconstruction; The assembly relationship, the constraint relationship, and the physical characteristics of each component unit after reconstruction are assigned to the geometric model of the equipment object, and a digital twin geometric model is established in the three-dimensional driving engine. Add kinematic or dynamic features to the digital twin geometric model, simulate and verify the motion of the equipment object, and use the digital twin geometric model that passes the simulation verification as the multibody motion model.

3. The method for constructing a digital twin model for special vehicle equipment according to claim 2, characterized in that, The physical properties include stiffness, damping, and coefficient of friction.

4. The method for constructing a digital twin model for special vehicle equipment according to claim 2, characterized in that, The geometric model of the equipment object is a STEP format model.

5. The method for constructing a digital twin model for special vehicle equipment according to claim 1, characterized in that, Constructing a digital twin of the equipment based on the physical environment information of the equipment object, specifically including: Failure Mode and Effects Analysis (FMEA) was used to determine the failure modes of each component unit in the digital twin of the equipment. Based on the impact of the failure modes of each component unit on the equipment object, the failure phenomena generated by each component unit in the equipment object are determined. Based on the fault phenomena generated by each component unit, determine the failure mode generated by each component unit and the corresponding failure mechanism of each failure mode. Based on the failure modes, failure phenomena, and failure mechanisms of each component unit, a mechanism characteristic model of each component unit is constructed.

6. The method for constructing a digital twin model for special vehicle equipment according to claim 1, characterized in that, The task behavior model includes task behavior and dynamic state behavior during the execution of the task behavior. The task behavior is represented by a process sequence, and the dynamic state behavior is used to map the behavior state of the equipment object in real time based on the physical environment information collected by the sensor network.

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