Methods, apparatus and computer equipment for constructing reliability digital twin models

Abstract

This application relates to a method, apparatus, and computer device for constructing a reliability digital twin model. The method includes: acquiring an initial digital prototype model of a product; determining the failure modes of each component in the initial digital prototype model; determining the component reliability model for each component based on the failure modes; associating each component reliability model with the corresponding component in the initial digital prototype model to obtain an initial reliability digital twin model; acquiring actual measurement data obtained from actual measurements of the product; using the initial reliability digital twin model to perform fault analysis on the actual measurement data corresponding to the model input parameters to obtain model fault analysis results; and updating the initial reliability digital twin model by combining the model fault analysis results and the actual measurement fault analysis results to obtain a reliability digital twin model. The reliability digital twin model constructed using this method can evaluate the reliability of a product, meeting the requirements of forward design work.

Description

Technical Field

[0001] This application relates to the field of reliability technology, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for constructing a reliability digital twin model. Background Technology

[0002] With the rapid development of IoT and AI technologies, product manufacturing is showing trends towards systematization, unmanned operation, omnichannel integration, intelligence, precision, and clustering. While the requirements for product manufacturing are becoming increasingly stringent, the corresponding R&D cycles are shortening, driving product development models towards digitalization, modeling, automation, and intelligence. Driven by these trends and engineering needs, a model- and data-driven digital design model has emerged, and digital twins provide an ideal solution for a real-time driven R&D model that integrates equipment lifecycle models and data.

[0003] Digital twins can connect real-world objects. By constructing digital twin models corresponding to physical entities and visualizing, debugging, experiencing, analyzing, and optimizing these models, a comprehensive technical strategy can be used to improve the performance and operational efficiency of physical entities.

[0004] However, among existing methods for constructing product digital twin models, the most mature method is the one based on digital twins for constructing product functional models. The main idea is to propose methods for constructing functional models for each module based on the characteristics of the product's physical entity modules, virtual entity modules, and interaction channel modules, and then construct the overall functional model through the interaction relationships between modules. The resulting digital twin model only represents the interaction relationships between product functions; it cannot analyze and evaluate the product's reliability and therefore cannot meet the requirements of forward design work. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for constructing a digital twin model based on product failure, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a method for constructing a reliability digital twin model, the method comprising:

[0007] Obtain the initial digital prototype model of the product;

[0008] The failure modes of each component in the initial digital prototype model are determined, the component reliability model of each component is determined based on the failure modes, and the component reliability model is associated with the corresponding component in the initial digital prototype model to obtain an initial reliability digital twin model containing the component reliability model.

[0009] Obtain actual measurement data from actual measurements of the product;

[0010] Using the initial reliability digital twin model, fault analysis is performed on the actual measurement data corresponding to the model input parameters in the initial reliability digital twin model to obtain the model fault analysis results;

[0011] The initial reliability digital twin model is updated by combining the model failure analysis results and the measured failure analysis results obtained from the actual measurements, thereby obtaining a reliability digital twin model.

[0012] In one embodiment, the component reliability model includes a fault physical model;

[0013] The process of determining the component reliability model for each component based on the failure mode includes:

[0014] Determine the failure mechanism of each component, and obtain the initial failure mechanism model corresponding to each failure mechanism based on each failure mechanism;

[0015] Based on the historical operating data of each component and the initial fault mechanism model corresponding to the fault mechanism of each component, the physical fault model of each component is determined.

[0016] In one embodiment, the component reliability model includes a fault propagation model;

[0017] The process of determining the component reliability model for each component based on the failure mode includes:

[0018] The failure behaviors of the internal components of each component when they fail are classified to determine the failure type of the failure behavior, wherein the failure type includes component input failure, component self failure and component output failure.

[0019] By logically combining the failure types of the internal components of each component using logic gates, a fault propagation model for each component is generated.

[0020] In one embodiment, the component reliability model includes a failure state behavior model;

[0021] The process of determining the component reliability model for each component based on the failure mode includes:

[0022] Fault analysis is performed on each component to obtain the cause of the fault for each component.

[0023] Based on the causes of the faults, determine the corresponding fault mitigation and control measures for each component;

[0024] Based on the causes of the faults and the fault mitigation and control measures, a failure state behavior model for each component is generated.

[0025] In one embodiment, the component reliability model includes a maintenance support strategy model;

[0026] The process of determining the component reliability model for each component based on the failure mode includes:

[0027] Obtain the maintenance information corresponding to the failure of each component;

[0028] Based on the maintenance information, a maintenance support strategy model for each component is generated.

[0029] In one embodiment, the initial reliability digital twin model is updated by combining the model failure analysis results and the measured failure analysis results obtained from the actual measurements, to obtain a reliability digital twin model, including:

[0030] The model fault analysis results are compared with the measured fault analysis results;

[0031] Based on the comparison results, the deviation of relevant parameters in the initial reliability digital twin model is determined;

[0032] Based on the deviation of the relevant parameters, the initial reliability digital twin model is updated to obtain a reliability digital twin model.

[0033] In one embodiment, the method further includes:

[0034] Obtain detailed product information and product feature information for the next stage of the product;

[0035] The reliability digital twin model for the current stage is updated based on the detailed product information and product feature information to obtain the reliability digital twin model for the next stage.

[0036] Secondly, this application provides a reliable digital twin model construction apparatus, the apparatus comprising:

[0037] The initial digital prototype model acquisition module is used to acquire the initial digital prototype model of the product.

[0038] An initial reliability digital twin model construction module is used to determine the failure mode of each component in the initial digital prototype model, determine the component reliability model of each component based on the failure mode, and associate each component reliability model with the corresponding component in the initial digital prototype model to obtain an initial reliability digital twin model containing the reliability models of each component.

[0039] The product test data acquisition module is used to acquire the actual measurement data obtained by conducting actual tests on the product.

[0040] The model failure analysis result generation module is used to perform failure analysis on the actual measurement data corresponding to the model input parameters in the initial reliability digital twin model using the initial reliability digital twin model, and obtain the model failure analysis result.

[0041] The reliability digital twin model construction module is used to update the initial reliability digital twin model by combining the model failure analysis results and the measured failure analysis results obtained from the actual measurement, so as to obtain a reliability digital twin model.

[0042] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0043] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0044] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for constructing a reliability digital twin model first uses each component in the initial digital prototype model of the product as the basic unit, determines the failure mode of each component, determines the component reliability model of each component based on the failure mode, and associates the component reliability models of each component with the initial digital prototype model of the product, thereby obtaining an initial reliability digital twin model that includes the component reliability models of each component. Subsequently, the initial reliability digital twin model is updated based on the measured data generated from actual product testing to obtain the reliability digital twin model. Since the initial reliability digital twin model is constructed based on the component reliability models of each component of the product, the updated reliability digital twin model, during use, can accurately map all elements of the product with failure as the center, truly reflect the product's reliability characteristics, behavior, and failure formation process, evaluate the product's reliability, and meet the requirements of the product's forward design work. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating a method for constructing a reliability digital twin model in one embodiment;

[0046] Figure 2 This is a flowchart illustrating the steps for determining the component reliability model based on failure modes in one embodiment.

[0047] Figure 3This is a flowchart illustrating the steps for determining the component reliability model based on failure modes in another embodiment.

[0048] Figure 4 This is a flowchart illustrating the steps for determining the component reliability model based on failure modes in another embodiment.

[0049] Figure 5 This is a flowchart illustrating the steps for determining the component reliability model based on failure modes in another embodiment.

[0050] Figure 6 This is a flowchart illustrating a method for constructing a reliability digital twin model in another embodiment;

[0051] Figure 7 This is a structural block diagram of a reliability digital twin model construction device in one embodiment;

[0052] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] In one embodiment, such as Figure 1 As shown, a method for constructing a reliability digital twin model is provided. This method is illustrated using a terminal as an example; however, it can also be applied to servers and systems including both terminals and servers, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0055] Step 102: Obtain the initial digital prototype model of the product.

[0056] The initial digital prototype model of a product is a digital model that reflects the product's geometric features, physical characteristics, and the logical connections between its components. Specifically, researchers, based on the product's functional, logical, or physical characteristics at the current development stage, use appropriate software tools to construct the corresponding digital prototype model according to certain steps, and pre-store the digital prototype model in a database. When a reliability digital twin model of the product needs to be constructed, the corresponding digital prototype model is directly retrieved from the database and used as the product's initial digital prototype model.

[0057] In one embodiment, the initial digital prototype model is constructed in the following ways:

[0058] Construct a geometric feature model of the product based on its three-dimensional geometric parameters.

[0059] Specifically, the product geometric feature model mainly describes the product's three-dimensional geometric parameters, assembly relationships, and structural relationships. The product's three-dimensional geometric parameters include, but are not limited to, its shape, size, and position. Unlike the logical architecture model, the product geometric feature model visually resembles the physical entity more closely, providing a more detailed description of the product's actual composition and connections. Understandably, the product geometric feature model can be obtained using 3D modeling software such as CATIA and SolidWorks.

[0060] Construct a physical property model of the product based on its physical characteristics.

[0061] Specifically, a product physical characteristic model refers to a professional characteristic model covering multiple disciplines and fields. For example, in the mechanical field, this includes characteristics such as mechanical structure, engineering materials, kinematic laws, structural strength, stiffness, fatigue, damage, aerodynamics, and fluid dynamics; in the electronics and electrical field, it includes electromagnetic, heat dissipation, electrical stress, and control characteristics; as well as multidisciplinary and cross-domain characteristics such as thermo-structure interaction, thermo-fluid interaction, and fluid-structure interaction. Understandably, product physical characteristic models can be obtained by modeling using finite element analysis software such as Abaqus, ANSYS, and Hypermesh.

[0062] An initial digital prototype model of the product is generated based on the product's geometric feature model and physical property model.

[0063] Step 104: Determine the failure modes of each component in the initial digital prototype model, determine the component reliability model of each component based on the failure modes, and associate the component reliability model with the corresponding component in the initial digital prototype model to obtain an initial reliability digital twin model containing the reliability models of each component.

[0064] In the initial digital prototype model, each component represents the smallest unit that needs to be analyzed when performing fault analysis on the product. For example, when analyzing a mobile phone, the basic units could be the display screen, information processing module, and power supply module. If the information processing module within the mobile phone is used as the basis for fault analysis, then the basic units could be the signal receiving unit, signal processing unit, and signal transmitting unit. Each component has its corresponding component name and ID code, which serves to identify and locate components by linking their reliability models to the initial digital prototype model later.

[0065] Failure modes are the basic manifestations of component malfunctions. Failure modes include hardware failures and functional failures. Specifically, lower-level components are more prone to failures, while higher-level components can experience both hardware and functional failures simultaneously. For example, the components of a signal processing module—the signal receiving unit, signal processing unit, and signal transmitting unit—are most likely to experience hardware failures. Components in a mobile phone product, such as the display screen, signal processing module, and power supply module, may experience both hardware and functional failures. Understandably, a failure mode where unstable output from a lower-level unit prevents the functioning of a higher-level unit can be considered a functional failure mode. Therefore, when a component's failure mode is determined to be a functional failure, it is only necessary to characterize the logical relationships between the hardware failures of each level of unit.

[0066] The component reliability model is a digital model that can characterize the causes of failures in each component and the series of activities that follow a failure. Understandably, the activities that may occur after a failure include, but are not limited to, fault propagation, fault detection, fault repair and maintenance, and changes in system state.

[0067] Specifically, using each component in the initial digital prototype model as the basic unit, the failure modes of each component are determined. Failure analysis is then performed on each component based on its corresponding failure mode, and the component reliability model is determined based on the failure analysis results. The component reliability models are then linked to the initial digital prototype model of the product according to the component's name and ID code, resulting in an initial reliability digital twin model.

[0068] In one embodiment, the association between each component and the failure mode is pre-stored in a database, and the corresponding failure mode can be retrieved from the database based on the name and ID code of each component.

[0069] In one embodiment, if the component does not have a pre-stored association with the failure mode in the database, the failure mode of the component is determined by analyzing the component's working principle.

[0070] Step 106: Obtain the actual measurement data obtained from the actual measurement of the product.

[0071] Specifically, sensors and data acquisition systems are used to collect relevant environmental parameters, product status, and behavioral data during the operation of the physical product system, thereby obtaining actual measurement data of the product.

[0072] In one embodiment, after collecting actual measurement data of the product during the operation of the physical system using sensors and a data acquisition system, the collected data also needs to be preprocessed. Specifically, preprocessing methods such as data cleaning, data transformation, and data reduction are used to remove abnormal data, correct erroneous data, remove duplicate data, smooth data, normalize data, and find useful data features from the directly collected data.

[0073] Step 108: Using the initial reliability digital twin model, perform fault analysis on the actual measurement data corresponding to the model input parameters in the initial reliability digital twin model to obtain the model fault analysis results.

[0074] Specifically, using physical products, a corresponding test and operation environment is built based on a reliability systems engineering software platform, relevant sensors, and a data acquisition and processing system using an initial reliability digital twin model. A mapping relationship is established between the actual measurement data of the product and the relevant input parameters of the initial reliability digital twin model. The actual measurement data corresponding to the model input parameters in the initial reliability digital twin model are input into the initial reliability digital twin model for fault analysis. The fault analysis results obtained from the virtual simulation analysis are then determined as the model fault analysis results. It is understandable that the input parameters can include the product's task time, the test point locations of the product or components within each time period, the number of maintenance and support stations, and the product's reliability parameters, etc.

[0075] In one embodiment, the reliability systems engineering software platform includes interfaces with MBSE software, product design systems, simulation systems, project management systems, etc.

[0076] Step 110: Update the initial reliability digital twin model by combining the model failure analysis results and the measured failure analysis results obtained from the actual test, and obtain the reliability digital twin model.

[0077] Among them, the actual fault analysis results are obtained by running the product in the built test environment, and represent the actual fault analysis results of the product.

[0078] Specifically, the model failure analysis results are compared with the actual failure analysis results, and the initial reliability digital twin model is updated based on the comparison results to obtain the reliability digital twin model.

[0079] The aforementioned method for constructing a reliability digital twin model first uses each component in the initial digital prototype model of the product as the basic unit, determines the failure mode of each component, determines the component reliability model of each component based on the failure mode, and associates the component reliability models of each component with the initial digital prototype model of the product, thereby obtaining an initial reliability digital twin model that includes the component reliability models of each component. Subsequently, the initial reliability digital twin model is updated based on measured data generated from actual product testing, resulting in the final reliability digital twin model. Since the initial reliability digital twin model is constructed based on the component reliability models of each product component, the updated reliability digital twin model, during use, can accurately map all elements of the product with failure as the central focus, truly reflecting the product's reliability characteristics, behavior, and the formation process of failures, enabling the evaluation of product reliability and meeting the requirements of forward design work.

[0080] In one embodiment, such as Figure 2 As shown, the component reliability model includes a fault physical model; determining the component reliability model for each component based on the fault modes includes the following steps:

[0081] Step 202: Determine the failure mechanism of each component, and obtain the initial failure mechanism model corresponding to each failure mechanism.

[0082] The failure mechanism of a component refers to the physical process that causes failure when the component fails during operation. Each failure mechanism has its corresponding failure mechanism model. Understandably, in this embodiment, each failure mechanism and its corresponding failure mechanism model are pre-stored in the database in an associated form. After determining the failure mechanism of a component, the corresponding failure mechanism model can be directly retrieved from the database as the initial failure mechanism model.

[0083] Specifically, the failure mechanism of each component is determined by analyzing its operational process. Based on this failure mechanism, a pre-stored failure mechanism model is retrieved from the database and used as the initial failure mechanism model. Common failure mechanism models include interconnect thermal fatigue life, low-cycle fatigue model, high-cycle fatigue model, crack propagation model, diffusion model, Arrhenius model, and Eyring model.

[0084] Step 204: Determine the physical model of each component based on the historical data of each component and the initial fault mechanism model corresponding to the fault mechanism of each component.

[0085] The historical data for each component refers to its historical operational data. Specifically, each fault mechanism model has its corresponding model parameters. The specific values ​​of these model parameters are related to the corresponding component and its operational data. Therefore, after obtaining the initial fault mechanism model, it is necessary to determine the model parameters of the corresponding initial fault mechanism model based on the historical data of each component, and finally obtain the fault physical model of each component.

[0086] In one embodiment, if there is no corresponding fault mechanism model for the failure mechanism of a certain component, a corresponding fault mechanism model is constructed using a modeling language based on a large amount of historical failure data of the component, and the fault mechanism model is determined as the physical failure model of the component.

[0087] In this embodiment, the failure mechanisms of each component are analyzed and determined, and an initial failure mechanism model is obtained based on each failure mechanism. Simultaneously, the initial failure mechanism model is updated based on the historical operating data of each component to obtain the corresponding physical failure model for each component. Since the physical failure model is constructed based on the failure mechanisms of each component, it can accurately describe the failure behavior of each component under the influence of mechanical, electronic, thermal, and chemical physical processes. In practical use, it can provide a basis for the improvement, classification, and use of product materials and components, as well as for product reliability assessment, design optimization, and maintenance.

[0088] In one embodiment, such as Figure 3 As shown, the component reliability model includes a fault propagation model; determining the component reliability model for each component based on the fault modes includes the following steps:

[0089] Step 302: Classify the failure behaviors of the internal components of each component when they fail, and determine the failure type of the failure behavior. The failure type includes component input failure, component self failure and component output failure.

[0090] Each component can consist of at least one internal part. When a component malfunctions, the failure type of its internal part differs. Failure types include component input failure, component self-failure, and component output failure. Component input failure refers to the inability of external input to the component; component self-failure refers to the damage to the component's internal parts, rendering it unable to function; component output failure refers to the inability of the component to transmit internal data to the outside.

[0091] Taking a computer's main unit as an example, the main unit contains multiple components, such as a power interface, a display interface, a USB interface, a network port, a processor, and memory. Component input failure refers to abnormal power input, keyboard input, or network data transmission failure to be transmitted to the USB interface or network port. Component failure refers to memory failure, processor failure, power input interface failure, etc. Component output failure refers to the inability to transmit processed information to the display or to send it out through the network port due to the main unit itself.

[0092] Specifically, when each component malfunctions, the failure behavior of its internal parts is analyzed, the failure behavior is classified, and the failure type is determined. The failure type includes component input failure, component self-failure, and component output failure.

[0093] Step 304: The failure categories of the internal components of each component are logically combined using logic gates to generate a fault propagation model for each component.

[0094] Specifically, the failure categories of the internal components of each component are represented by a graphical modeling method using logic gates. The failures are then logically combined using logic gates to generate the corresponding fault propagation model for each component.

[0095] In one embodiment, the logic gates include AND, OR, NOT, selection, and voting logic.

[0096] In the above embodiments, by classifying the failure categories of the internal components of each component and combining them logically using logic gates, a fault propagation model for each component is obtained. Therefore, the fault propagation model can effectively assess the overall impact of a fault when a component fails, meeting the requirements of forward design for products.

[0097] In one embodiment, such as Figure 4 As shown, the component reliability model includes a failure state behavior model; the component reliability model for each component is determined based on the failure modes, including the following steps:

[0098] Step 402: Perform fault analysis on each component to obtain the cause of the fault for each component.

[0099] Specifically, each component has its corresponding cause when it malfunctions. By analyzing the potential malfunctions of each component, we can identify the possible causes. For example, potential malfunctions of a mobile phone display screen include the screen failing to display a page or the display showing distorted colors. Analyzing these potential malfunctions reveals possible causes such as display interface failure, display element failure, or display aging.

[0100] Step 404: Determine the corresponding fault mitigation and control measures for each component based on the cause of the fault.

[0101] Fault mitigation and control measures are the actions taken to eliminate component failures, including but not limited to failure detection, maintenance and assurance activities, and functional reconfiguration. These measures correspond to specific failure causes, with each cause having its own set of mitigation and control measures. For example, if the failure of a display screen is due to a display interface failure, the corresponding mitigation and control measures include, but are not limited to, detecting the display interface, determining the type of failure behavior of the display interface, and specific repair measures for the display interface. Specifically, the fault mitigation and control measures for each component are determined based on the cause of each component failure.

[0102] Step 406: Generate a failure state behavior model for each component based on the causes of the failure and the failure mitigation and control measures.

[0103] Specifically, a failure state behavior model for each component is generated by modeling the causes of failures that may occur in each component and the corresponding failure mitigation and control measures.

[0104] In one embodiment, a state machine diagram method is used to model the failure state behavior model of each component based on the possible causes of failures of each component and the corresponding failure mitigation control measures.

[0105] In the above embodiments, a failure state behavior model for each component is established using the causes of the fault and fault mitigation control measures as input. Therefore, the failure state behavior model can characterize the complete state transition process of a product component due to various reasons, including component failure, failure detection, maintenance and support activities, or functional reconfiguration, ultimately leading to the component's return to normal operation. This includes characterizing component states, state detection relationships, failure propagation relationships, and maintenance and support activities. When a product experiences a fault, the failure state behavior model can provide a complete handling solution from fault to normal operation, enabling a more comprehensive reliability assessment and further meeting the requirements of forward design work.

[0106] In one embodiment, such as Figure 5 As shown, the component reliability model includes a maintenance and support strategy model; the component reliability model for each component is determined based on failure modes, including the following steps:

[0107] Step 502: Obtain the maintenance information corresponding to each component when it malfunctions.

[0108] Among them, maintenance information is information generated by breaking down the maintenance and support work when each component fails into individual tasks or procedures, and storing it in the database in advance.

[0109] Specifically, during the R&D process, considering the failure mechanism and the corresponding maintenance and support strategy based on the real-time operating status, a support plan matching the product's support is formed using product reliability digital twin technology. This involves decomposing maintenance and support work into various sub-works or sub-processes, determining the maintenance, testing, and support resource requirements for each sub-work or sub-process as maintenance information, and obtaining the maintenance information corresponding to the failure of each component.

[0110] Step 504: Generate maintenance support strategy models for each component based on maintenance information.

[0111] Specifically, maintenance information is used as input information to construct maintenance support strategy models for each component.

[0112] In one embodiment, the maintenance and support strategy model for each component is constructed using the SysML specification modeling language.

[0113] In one embodiment, the maintenance support strategy model includes a maintenance test support activity model and a support organization model.

[0114] Specifically, the maintenance and testing support activity model describes the processes of fault detection, fault repair, and resource allocation after a functional failure. Detection activities often employ a built-in test (BIT) design, which can be directly extended from the functional model's activity behavior model. Fault repair and support activities, however, must be performed by external participants and require extension from the external participant's activity behavior model. The support organization model includes maintenance stations, maintenance personnel, and support resource models. It is a component of the equipment system's external interaction system and is primarily modeled using "actors" (meta-model type Actors) in SysML. Maintenance stations consist of maintenance personnel and support resources, possessing attributes such as maintenance level, number of personnel, spare parts type, and spare parts quantity. The specific maintenance organization's relationship with the product's maintenance support is reflected in the maintenance support activity model.

[0115] In the above embodiments, a maintenance support strategy model is generated based on the maintenance information corresponding to the failure of each component. This model can characterize the maintenance testing and support activities and support organization activities after the product experiences functional failure. When the product experiences a failure, a complete solution for how to handle maintenance support can be provided. This allows for a more comprehensive reliability assessment of the product and further meets the requirements of the product's forward design work.

[0116] In one embodiment, the initial reliability digital twin model is updated by combining the model failure analysis results and the measured failure analysis results obtained from actual measurements to obtain a reliability digital twin model, including:

[0117] The model failure analysis results are compared with the measured failure analysis results; based on the comparison results, the deviation of relevant parameters in the initial reliability digital twin model is determined; based on the deviation of relevant parameters, the initial reliability digital twin model is updated to obtain the reliability digital twin model.

[0118] Among them, the relevant parameters are the design parameters when constructing the initial reliability digital twin model, such as the test points of each component and the maintenance sites.

[0119] Specifically, the model failure analysis result is the failure analysis structure obtained by inputting the product's measured data into the constructed initial reliability digital twin model; the measured failure analysis result is the actual failure analysis result of the product obtained after analyzing the failures that actually occur in the constructed operating environment. Comparing the two yields the deviation of relevant parameters in the initial reliability digital twin model. Based on the parameter deviation, the values ​​of relevant parameters in the initial reliability digital twin model are adjusted, and the initial reliability digital twin model is updated to obtain the product's reliability digital twin model.

[0120] In one embodiment, during the product design phase, the model failure analysis results can be compared with the actual failure analysis results to determine whether there are any design weaknesses. If so, the product design scheme can be optimized based on the comparison results.

[0121] In the above embodiments, by comparing the measured fault analysis results with the model fault analysis results, the initial reliability digital twin model is updated based on the fault analysis results of the physical product. This ensures that the final generated reliability digital twin model can accurately map all elements of the product, truly reflect the product's reliability characteristics, behavior, and the formation process of faults, and meet the requirements of forward design work.

[0122] In one embodiment, the reliability digital twin model construction method further includes: obtaining product detailing information and product feature information for the next stage of the product; updating the reliability digital twin model for the current stage based on the product detailing information and product feature information to obtain the reliability digital twin model for the next stage.

[0123] Specifically, in actual production, the product update speed is very rapid as product development progresses. Given that the current stage's product reliability digital twin model has already been built, if the next stage's product is derived from a more detailed version of the current stage's product, the construction of the next stage's reliability digital twin model involves first acquiring the next stage's product detailing information and product feature information. Based on this information, the current stage's reliability digital twin model is then refined and corrected, updating the current stage's reliability digital twin model to obtain the next stage's reliability digital twin model. Understandably, the relevant modeling processes and methods in the next stage's reliability digital twin model are similar to those in the above embodiments, and will not be elaborated upon here.

[0124] Using the method in this embodiment, when the next stage product is derived from the previous stage product through further refinement, the current stage reliability digital twin model can be refined and corrected based on the next stage product refinement information and product feature information, thus quickly obtaining the next stage reliability digital twin model. This greatly accelerates the construction speed of the next stage reliability digital twin model, reduces the workload of R&D personnel, and saves R&D costs in the product development process.

[0125] In one embodiment, such as Figure 6 As shown, a method for constructing a reliability digital twin model is provided. This method is applied to a terminal device equipped with a database that pre-stores the initial model to be used and the relevant data required to construct the reliability digital twin model. The device is equipped with relevant software such as CATIA, SolidWorks, Abaqus, ANSYS, and Hypermesh.

[0126] First, an initial digital prototype model of the product is established. This initial digital prototype model is a digital model that reflects the product's geometric features, physical characteristics, and the logical connections between its components. Specifically, researchers use 3D modeling software such as CATIA and SolidWorks to create a geometric feature model of the product based on its three-dimensional geometric parameters at the current development stage; and use finite element analysis software such as Abaqus, ANSYS, and Hypermesh to create a physical feature model of the product based on its physical feature model. An initial prototype digital model of the product is then established based on both the geometric and physical feature models.

[0127] Subsequently, using the components in the initial prototype digital model as basic units, and focusing on failure, the reliability characteristics and behavioral patterns of the product are analyzed and characterized. The occurrence of product failures and the series of activities they trigger are analyzed from various aspects, including failure propagation, failure detection, failure repair and support, and system state changes. Based on the analysis results, reliability models for each component are established. These component reliability models include fault physics sub-models, fault propagation sub-models, failure state behavior sub-models, and maintenance and support strategy sub-models. The reliability models of each component are then linked to their corresponding components in the initial digital prototype model. Specifically, leveraging an information platform, the system interfaces of the software used to construct the failure sub-models, such as CATIA, Abaqus, ANSYS, and Rhapsody, are integrated. Using systems engineering modeling languages ​​or formal modeling languages, the reliability models of each component are incorporated into the initial digital prototype model, establishing an initial reliability digital twin model of the product that integrates reliability elements. It is understandable that the fault physical model, fault propagation model, state behavior model, and maintenance support strategy model established by the method of this application are all embedded in the product function, logical architecture, or physical characteristic model at the corresponding stage. Therefore, only the initial digital prototype model of the product can be seen on the surface.

[0128] Obtain actual measurement data from product testing, use an initial reliability digital twin model, perform fault analysis on the actual measurement data corresponding to the model input parameters in the initial reliability digital twin model, and obtain model fault analysis results; combine the model fault analysis results and the actual fault analysis results obtained from the actual tests to update the initial reliability digital twin model, and obtain a reliability digital twin model.

[0129] Finally, the reliability digital twin model of the current stage is updated based on the detailed product information and feature information of the next stage to obtain the reliability digital twin model of the next stage.

[0130] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0131] Based on the same inventive concept, this application also provides a reliability digital twin model construction apparatus for implementing the reliability digital twin model construction method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method. Therefore, the specific limitations in one or more embodiments of the reliability digital twin model construction apparatus provided below can be found in the limitations of the reliability digital twin model construction method described above, and will not be repeated here.

[0132] In one embodiment, such as Figure 7 As shown, a reliability digital twin model construction device 700 is provided, including: an initial digital prototype model acquisition module 701, an initial reliability digital twin model construction module 702, a product test data acquisition module 703, a model fault analysis result generation module 704, and a reliability digital twin model construction module 705, wherein:

[0133] The initial digital prototype model acquisition module 701 is used to acquire the initial digital prototype model of the product.

[0134] The initial reliability digital twin model construction module 702 is used to determine the failure modes of each component in the initial digital prototype model, determine the component reliability model of each component based on the failure modes, and associate the reliability model of each component with the corresponding component in the initial digital prototype model to obtain an initial reliability digital twin model containing the reliability models of each component.

[0135] Product measured data acquisition module 703 is used to acquire actual measurement data obtained from actual measurements of the product.

[0136] The model failure analysis result generation module 704 is used to perform failure analysis on the actual measurement data corresponding to the model input parameters in the initial reliability digital twin model using the initial reliability digital twin model, and obtain the model failure analysis results.

[0137] The reliability digital twin model construction module 705 is used to update the digital twin model by combining the model failure analysis results and the actual failure analysis results obtained from the actual test, so as to obtain the reliability digital twin model.

[0138] The aforementioned reliability digital twin model construction device first uses each component in the initial digital prototype model of the product as the basic unit, determines the failure mode of each component, determines the component reliability model of each component based on the failure mode, and associates the component reliability models of each component with the initial digital prototype model of the product, thereby obtaining an initial reliability digital twin model containing the component reliability models of each component. Subsequently, the initial reliability digital twin model is updated based on measured data generated from actual product testing, resulting in a final reliability digital twin model. Since the digital twin model is constructed based on the component reliability models of each product component, the updated reliability digital twin model, during use, can accurately map all elements of the product with failure as the central focus, realistically reflecting the product's reliability characteristics, behavior, and failure formation process, enabling reliability assessment of the product and meeting the requirements of forward design work.

[0139] In one embodiment, the component reliability model includes a fault physical model; the initial reliability digital twin model construction module 702 further includes: determining the fault mechanism of each component, and obtaining the initial fault mechanism model corresponding to each fault mechanism based on each fault mechanism;

[0140] Based on the historical operating data of each component and the initial fault mechanism model corresponding to the fault mechanism of each component, the physical fault model of each component is determined.

[0141] In one embodiment, the component reliability model includes a fault propagation model; the initial reliability digital twin model construction module 702 further includes: classifying the failure behaviors of the internal components of each component when they fail, and determining the failure type of the failure behavior, wherein the failure type includes component input failure, component self-failure and component output failure.

[0142] By logically combining the failure types of the internal components of each component using logic gates, a fault propagation model for each component is generated.

[0143] In one embodiment, the component reliability model includes a failure state behavior model; the initial reliability digital twin model construction module 702 further includes: performing fault analysis on each component to obtain the cause of the fault corresponding to each component;

[0144] Determine the corresponding fault mitigation and control measures for each component based on the cause of the fault.

[0145] Based on the causes of failures and the failure mitigation and control measures, a failure state behavior model for each component is generated.

[0146] In one embodiment, the component reliability model includes a maintenance support strategy model; the initial reliability digital twin model construction module 702 further includes: obtaining maintenance information corresponding to each component when a failure occurs; and generating a maintenance support strategy model for each component based on the maintenance information.

[0147] In one embodiment, the reliability digital twin model construction module 705 further includes: comparing the model failure analysis results with the measured failure analysis results;

[0148] Based on the comparison results, determine the deviation of relevant parameters in the initial reliability digital twin model;

[0149] Based on the deviation of relevant parameters, the initial reliability digital twin model is updated to obtain a reliability digital twin model.

[0150] In one embodiment, the reliability digital twin model building apparatus further includes: a next-stage reliability digital twin model building module, used to obtain detailed product information and product feature information for the next stage of the product;

[0151] The reliability digital twin model for the current stage is updated based on detailed product information and product feature information to obtain the reliability digital twin model for the next stage.

[0152] Each module in the aforementioned reliability digital twin model construction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0153] In one embodiment, a computer device is provided, which may be the terminal device described in this application, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a reliable digital twin model construction method. The display screen can be an LCD screen or an e-ink display screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0154] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0155] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0156] Obtain the initial digital prototype model of the product;

[0157] Determine the failure modes of each component in the initial digital prototype model, determine the component reliability model of each component based on the failure modes, and associate the component reliability model with the corresponding component in the initial digital prototype model to obtain a digital twin model containing the reliability models of each component.

[0158] Obtain actual measurement data from actual testing of the product;

[0159] Using the initial reliability digital twin model, fault analysis is performed on the actual measurement data corresponding to the model input parameters in the initial reliability digital twin model to obtain the model fault analysis results;

[0160] The initial reliability digital twin model is updated by combining the model failure analysis results and the measured failure analysis results obtained from actual measurements, thus obtaining a reliability digital twin model.

[0161] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0162] Component reliability models include physical failure models; component reliability models, which determine the reliability of each component based on failure modes, include:

[0163] Determine the failure mechanism of each component, and obtain the initial failure mechanism model corresponding to each failure mechanism based on each failure mechanism;

[0164] Based on the historical operating data of each component and the initial fault mechanism model corresponding to the fault mechanism of each component, the physical fault model of each component is determined.

[0165] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0166] Component reliability models include fault propagation models; component reliability models that determine the reliability of each component based on fault modes include:

[0167] The failure behaviors of the internal components of each component during failure are classified to determine the failure type. The failure type includes component input failure, component self-failure, and component output failure.

[0168] By logically combining the failure types of the internal components of each component using logic gates, a fault propagation model for each component is generated.

[0169] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0170] Component reliability models include failure state behavior models; component reliability models, based on failure modes, determine the reliability of each component, including:

[0171] Perform fault analysis on each component to determine the cause of each fault.

[0172] Determine the corresponding fault mitigation and control measures for each component based on the cause of the fault.

[0173] Based on the causes of failures and the failure mitigation and control measures, a failure state behavior model for each component is generated.

[0174] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0175] Component reliability models include maintenance and support strategy models; component reliability models that determine the reliability of each component based on failure modes include:

[0176] Obtain maintenance information corresponding to the failure of each component;

[0177] A maintenance support strategy model for each component is generated based on maintenance information.

[0178] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0179] Compare the model fault analysis results with the actual fault analysis results;

[0180] Based on the comparison results, determine the deviation of relevant parameters in the initial reliability digital twin model;

[0181] Based on the deviation of relevant parameters, the initial reliability digital twin model is updated to obtain a reliability digital twin model.

[0182] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0183] Obtain detailed product information and product feature information for the next stage of product development;

[0184] The reliability digital twin model for the current stage is updated based on detailed product information and product feature information to obtain the reliability digital twin model for the next stage.

[0185] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0186] Obtain the initial digital prototype model of the product;

[0187] Determine the failure modes of each component in the initial digital prototype model, determine the component reliability model of each component based on the failure modes, and associate the reliability models of each component with the corresponding components in the initial digital prototype model to obtain an initial reliability digital twin model that includes the reliability models of each component.

[0188] Obtain actual measurement data from actual testing of the product;

[0189] Using the initial reliability digital twin model, fault analysis is performed on the actual measurement data corresponding to the model input parameters in the initial reliability digital twin model to obtain the model fault analysis results;

[0190] The initial reliability digital twin model is updated by combining the model failure analysis results and the measured failure analysis results obtained from actual measurements, thus obtaining a reliability digital twin model.

[0191] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0192] Component reliability models include physical failure models; component reliability models, which determine the reliability of each component based on failure modes, include:

[0193] Determine the failure mechanism of each component, and obtain the initial failure mechanism model corresponding to each failure mechanism based on each failure mechanism;

[0194] Based on the historical operating data of each component and the initial fault mechanism model corresponding to the fault mechanism of each component, the physical fault model of each component is determined.

[0195] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0196] Component reliability models include fault propagation models; component reliability models that determine the reliability of each component based on fault modes include:

[0197] The failure behaviors of the internal components of each component during failure are classified to determine the failure type. The failure type includes component input failure, component self-failure, and component output failure.

[0198] By logically combining the failure types of the internal components of each component using logic gates, a fault propagation model for each component is generated.

[0199] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0200] Component reliability models include failure state behavior models; component reliability models, based on failure modes, determine the reliability of each component, including:

[0201] Perform fault analysis on each component to determine the cause of each fault.

[0202] Determine the corresponding fault mitigation and control measures for each component based on the cause of the fault.

[0203] Based on the causes of failures and the failure mitigation and control measures, a failure state behavior model for each component is generated.

[0204] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0205] Component reliability models include maintenance and support strategy models; component reliability models that determine the reliability of each component based on failure modes include:

[0206] Obtain maintenance information corresponding to the failure of each component;

[0207] A maintenance support strategy model for each component is generated based on maintenance information.

[0208] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0209] Compare the model fault analysis results with the actual fault analysis results;

[0210] Based on the comparison results, determine the deviation of relevant parameters in the initial reliability digital twin model;

[0211] Based on the deviation of relevant parameters, the initial reliability digital twin model is updated to obtain a reliability digital twin model.

[0212] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0213] Obtain detailed product information and product feature information for the next stage of product development;

[0214] The reliability digital twin model for the current stage is updated based on detailed product information and product feature information to obtain the reliability digital twin model for the next stage.

[0215] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0216] Obtain the initial digital prototype model of the product;

[0217] Determine the failure modes of each component in the initial digital prototype model, determine the component reliability model of each component based on the failure modes, and associate the component reliability model with the corresponding component in the initial digital prototype model to obtain a digital twin model containing the reliability models of each component.

[0218] Obtain actual measurement data from actual testing of the product;

[0219] Using the initial reliability digital twin model, fault analysis is performed on the actual measurement data corresponding to the model input parameters in the initial reliability digital twin model to obtain the model fault analysis results;

[0220] The initial reliability digital twin model is updated by combining the model failure analysis results and the measured failure analysis results obtained from actual measurements, thus obtaining a reliability digital twin model.

[0221] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0222] Component reliability models include physical failure models; component reliability models, which determine the reliability of each component based on failure modes, include:

[0223] Determine the failure mechanism of each component, and obtain the initial failure mechanism model corresponding to each failure mechanism based on each failure mechanism;

[0224] Based on the historical operating data of each component and the initial fault mechanism model corresponding to the fault mechanism of each component, the physical fault model of each component is determined.

[0225] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0226] Component reliability models include fault propagation models; component reliability models that determine the reliability of each component based on fault modes include:

[0227] The failure behaviors of the internal components of each component during failure are classified to determine the failure type. The failure type includes component input failure, component self-failure, and component output failure.

[0228] By logically combining the failure types of the internal components of each component using logic gates, a fault propagation model for each component is generated.

[0229] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0230] Component reliability models include failure state behavior models; component reliability models, based on failure modes, determine the reliability of each component, including:

[0231] Perform fault analysis on each component to determine the cause of each fault.

[0232] Determine the corresponding fault mitigation and control measures for each component based on the cause of the fault.

[0233] Based on the causes of failures and the failure mitigation and control measures, a failure state behavior model for each component is generated.

[0234] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0235] Component reliability models include maintenance and support strategy models; component reliability models that determine the reliability of each component based on failure modes include:

[0236] Obtain maintenance information corresponding to the failure of each component;

[0237] A maintenance support strategy model for each component is generated based on maintenance information.

[0238] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0239] Compare the model fault analysis results with the actual fault analysis results;

[0240] Based on the comparison results, determine the deviation of relevant parameters in the initial reliability digital twin model;

[0241] Based on the deviation of relevant parameters, the initial reliability digital twin model is updated to obtain a reliability digital twin model.

[0242] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0243] Obtain detailed product information and product feature information for the next stage of product development;

[0244] The reliability digital twin model for the current stage is updated based on detailed product information and product feature information to obtain the reliability digital twin model for the next stage.

[0245] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0246] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0247] 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.

[0248] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for constructing a reliability digital twin model, characterized in that, The method includes: Obtain the initial digital prototype model of the product; Determine the failure modes of each component in the initial digital prototype model; each component in the initial digital prototype model is the smallest unit that needs to be analyzed when performing failure analysis on the product; Based on the failure modes, failure analysis is performed on each component. Based on the failure analysis results, the component reliability model of each component is determined. The component reliability model is then associated with the corresponding component in the initial digital prototype model and integrated into the initial digital prototype model to obtain an initial reliability digital twin model that includes the component reliability models. The component reliability model is a digital model that represents the causes of failure of each component and the activities caused after the failure. Obtain actual measurement data from actual measurements of the product; Using the initial reliability digital twin model, fault analysis is performed on the actual measurement data corresponding to the model input parameters in the initial reliability digital twin model to obtain the model fault analysis results; The initial reliability digital twin model is updated by combining the model failure analysis results and the measured failure analysis results obtained from the actual measurements, thereby obtaining a reliability digital twin model.

2. The method according to claim 1, characterized in that, The component reliability model includes a fault physical model; The process of determining the component reliability model for each component based on the failure mode includes: Determine the failure mechanism of each component, and obtain the initial failure mechanism model corresponding to each failure mechanism based on each failure mechanism; Based on the historical operating data of each component and the initial fault mechanism model corresponding to the fault mechanism of each component, the physical fault model of each component is determined.

3. The method according to claim 1, characterized in that, The component reliability model includes a fault propagation model; The process of determining the component reliability model for each component based on the failure mode includes: The failure behaviors of the internal components of each component when they fail are classified to determine the failure type of the failure behavior, wherein the failure type includes component input failure, component self failure and component output failure. By logically combining the failure types of the internal components of each component using logic gates, a fault propagation model for each component is generated.

4. The method according to claim 1, characterized in that, The component reliability model includes a failure state behavior model; The process of determining the component reliability model for each component based on the failure mode includes: Fault analysis is performed on each component to obtain the cause of the fault for each component. Based on the causes of the faults, determine the corresponding fault mitigation and control measures for each component; Based on the causes of the faults and the fault mitigation and control measures, a failure state behavior model for each component is generated.

5. The method according to claim 1, characterized in that, The component reliability model includes a maintenance and support strategy model; The process of determining the component reliability model for each component based on the failure mode includes: Obtain the maintenance information corresponding to the failure of each component; Based on the maintenance information, a maintenance support strategy model for each component is generated.

6. The method according to any one of claims 1 to 5, characterized in that, The initial reliability digital twin model is updated by combining the model failure analysis results and the measured failure analysis results obtained from the actual measurements, to obtain a reliability digital twin model, including: The model fault analysis results are compared with the measured fault analysis results; Based on the comparison results, the deviation of relevant parameters in the initial reliability digital twin model is determined; Based on the deviation of the relevant parameters, the initial reliability digital twin model is updated to obtain a reliability digital twin model.

7. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Obtain detailed product information and product feature information for the next stage of the product; The reliability digital twin model for the current stage is updated based on the detailed product information and product feature information to obtain the reliability digital twin model for the next stage.

8. A device for constructing a reliable digital twin model, characterized in that, The device includes: The initial digital prototype model acquisition module is used to acquire the initial digital prototype model of the product; An initial reliability digital twin model construction module is used to determine the failure modes of each component in the initial digital prototype model, where each component is the smallest unit that needs to be analyzed when performing failure analysis on the product. Based on the failure modes, failure analysis is performed on each component; based on the failure analysis results, a component reliability model for each component is determined; and each component reliability model is associated with the corresponding component in the initial digital prototype model and integrated into the initial digital prototype model to obtain an initial reliability digital twin model containing the reliability models of each component. The component reliability model is a digital model that represents the causes of failure in each component and the activities caused after a failure. The product test data acquisition module is used to acquire the actual measurement data obtained by conducting actual tests on the product. The model failure analysis result generation module is used to perform failure analysis on the actual measurement data corresponding to the model input parameters in the initial reliability digital twin model using the initial reliability digital twin model, and obtain the model failure analysis result. The reliability digital twin model construction module is used to update the initial reliability digital twin model by combining the model failure analysis results and the measured failure analysis results obtained from the actual measurement, so as to obtain a reliability digital twin model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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