Spaceflight distributor product development method based on digital twinning

Abstract

The invention relates to a spaceflight distributor product development method based on digital twinning, belongs to the technical field of spaceflight electric appliance product development, and solves the problems of insufficient data integration and interaction, lack of historical data constraint and imperfect actual operation data feedback mechanism in the prior art. The method comprises the following steps: analyzing a technical index set of the spaceflight distributor product, and generating a spaceflight distributor product digital prototype model based on a product historical baseline database; product historical process data is injected for reconstruction to form a spaceflight distributor product digital twinborn model V0.0; off-line simulation analysis is executed, and a detailed design scheme is generated based on the technical index set; manufacturing a spaceflight distributor physical model V1.0, and collecting manufacturing process data of the spaceflight distributor physical model V1.0; performing real-time synchronous mapping on the acquired manufacturing process data of the spaceflight distributor physical prototype V1.0 and the spaceflight distributor product digital twinborn model V0.0 to generate a digital twinborn model V1.0; and the spaceflight distributor is produced by taking the digital twin model V1.0 of the spaceflight distributor product as a template.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aerospace electrical product development, and particularly relates to a method for developing an aerospace power distributor based on digital twinning. BACKGROUND

[0002] With the increasing requirements for product development precision and efficiency in the field of aerospace, as a key device for ensuring the power distribution and control of the spacecraft, the development method of the aerospace power distributor is also constantly innovating. The traditional development of the aerospace power distributor usually follows a linear process from design to manufacturing and then to testing, and each link is relatively independent, the information interaction is not smooth, it is difficult to comprehensively evaluate the product performance and reliability in the early stage, and once the product has a problem, the modification cost is high and the cycle is long.

[0003] In the prior art, the aerospace power distributor product is mainly optimized by repeated testing and iteration of the physical prototype, and the design stage mainly relies on the experience of engineers and theoretical calculation, and the performance prediction of the product under actual complex working conditions is not accurate enough, and the data interaction and feedback mechanism between the manufacturing process and the design stage is not perfect, which leads to deviation between the manufactured prototype and the design expectation, and subsequent improvement is difficult. In addition, the use of product historical data is not sufficient, and it is not effectively integrated into the design and optimization of new products.

[0004] The existing development method of the aerospace power distributor lacks effective integration and utilization of product lifecycle data, and the design, manufacturing and testing links are independent of each other, and cannot realize product performance optimization based on complete data chain, so that the potential risks of the product in the face of complex and variable aerospace environment are difficult to predict in advance. Secondly, the product historical baseline database is not introduced in the design stage to constrain the construction of the digital prototype model, which leads to the deviation of the designed product in structure, electricity, thermal control and other aspects from the actual demand, increasing the risk of rework in the manufacturing process. Thirdly, the existing technology does not fully consider how to synchronize the manufacturing process data with the digital model in real time, making it difficult to realize real-time and accurate monitoring and feedback of the product state, and unable to provide reliable guidance for subsequent mass production, affecting the production efficiency and quality stability of the aerospace power distributor. SUMMARY

[0005] In view of the above analysis, the embodiments of the present application aim to provide a method for developing an aerospace power distributor based on digital twinning, to solve the problems of insufficient data integration and interaction, low design reliability, and high modification cost caused by design defects.

[0006] The embodiments of the present application provide a method for developing an aerospace power distributor based on digital twinning, which comprises:

[0007] S1, analyze the technical index set of the space power distribution product, perform conceptual design, build a preliminary geometric structure model, an electrical model, a thermal control model, and a manufacturing process model;

[0008] S2, based on the product historical baseline database, impose constraints on the geometric structure model, the electrical model, the thermal control model, and the manufacturing process model, and generate a space power distribution product digital prototype model;

[0009] S3, inject product historical process data into the space power distribution product digital prototype model, and reconstruct a space power distribution product digital twin model V0.0;

[0010] S4, perform offline simulation analysis on the space power distribution product digital twin model V0.0, optimize the technical index set of the space power distribution product, and generate a detailed design scheme based on the technical index set;

[0011] S5, manufacture a space power distribution physical prototype V1.0 according to the detailed design scheme, and collect manufacturing process data of the space power distribution physical prototype V1.0;

[0012] S6, perform real-time synchronous mapping of the collected manufacturing process data of the space power distribution physical prototype V1.0 and the space power distribution product digital twin model V0.0, and generate a space power distribution product digital twin model V1.0 consistent with the state of the space power distribution physical prototype V1.0;

[0013] S7, mass-produce space power distribution based on the space power distribution product digital twin model V1.0 as a template.

[0014] As a further improvement of the present application, the historical process data includes historical failure data and historical performance data;

[0015] Injecting product historical process data into the space power distribution product digital prototype model to reconstruct a space power distribution product digital twin model V0.0 includes:

[0016] Feature extraction is performed on all historical failure data to obtain failure features;

[0017] Parameter allowable deviation analysis is performed on all historical performance data to determine parameter safety intervals;

[0018] The failure features are applied to the high stress area of the preset digital prototype model to obtain failure mapping results;

[0019] Based on the parameter safety intervals, dynamic tolerance control is performed on the design parameters of the digital prototype to obtain dynamic tolerance;

[0020] Based on the failure mapping results of the high stress area and the dynamic tolerance of the design parameters, a digital twin V0.0 is constructed.

[0021] As a further improvement of the present application, constructing the digital twin V0.0 based on the failure mapping result of the high stress area and the dynamic tolerance of the design parameter includes:

[0022] Constructing the digital twin V0.0 based on the failure mapping result of the high stress area and the dynamic tolerance of the design parameter includes:

[0023] Judging whether the failure risk of the high stress area meets the standard based on the failure mapping result;

[0024] Applying extreme working conditions to the simulation environment of the digital twin V0.0 to verify whether the parameter fluctuation is within the dynamic tolerance range;

[0025] If the failure risk of the high stress area meets the standard and the parameter fluctuation is within the parameter safety range at the same time, generating the digital twin model V0.0 of the aerospace power distribution product;

[0026] Otherwise, return to S2 to regenerate the digital prototype model.

[0027] As a further improvement of the present application, performing offline simulation analysis on the digital twin model V0.0 of the aerospace power distribution product, optimizing the technical index set of the aerospace power distribution product, and generating a detailed design scheme based on the technical index set includes:

[0028] S41, input the parameters in the technical index set into the digital twin model V0.0 of the aerospace power distribution product for simulation processing, obtain the simulation result data corresponding to each parameter in the technical index set, and generate a simulation result set;

[0029] S42, judging whether there is simulation result data in the simulation result set that does not meet the preset simulation condition;

[0030] If so, adjusting the corresponding parameter in the technical index set based on the preset simulation condition corresponding to the simulation result data, updating the technical index set, and returning to step S41;

[0031] If not, output the current technical index set;

[0032] S43, generating a detailed design scheme based on the output technical index set.

[0033] As a further improvement of the present application, collecting the manufacturing process data of the aerospace power distribution product prototype V1.0 includes:

[0034] Verifying the performance of the updated technical index set;

[0035] The parameter with the performance verification value less than or equal to the preset verification threshold is replaced with the corresponding parameter in the backup design parameter library, including: placing the parameter with the performance verification value less than or equal to the preset verification threshold in the corresponding position of the backup design parameter library; placing the corresponding parameter in the backup design parameter library in the corresponding position of the technical index set and adding to the manufacturing index library; the backup design parameter library stores each parameter in the technical index set;

[0036] The parameter with the performance verification value greater than the preset verification threshold is added to the manufacturing index library;

[0037] The manufacturing process data is obtained by inputting the manufacturing process characteristics corresponding to the parameters in the manufacturing index library into the pre-constructed manufacturing simulation model.

[0038] As a further improvement of the present application, based on the product historical baseline database, constraints are imposed on the geometric structure model, the electrical model, the thermal control model and the manufacturing process model to generate a digital prototype model of the aerospace power distribution device, including:

[0039] The constraint reference in the product historical baseline database is extracted, including geometric constraint reference, electrical constraint reference, thermal control constraint reference and manufacturing constraint reference;

[0040] The boundary condition node of the geometric structure model is updated based on the geometric constraint reference to obtain a constrained geometric structure model;

[0041] The electrical constraint reference is input into the parameter checking rule library of the electrical model to obtain a constrained electrical model;

[0042] The thermal control constraint reference is input into the transient solver of the thermal control model to obtain a constrained thermal control model;

[0043] The process chain control information of the manufacturing process model is updated based on the manufacturing constraint reference to obtain a constrained manufacturing process model;

[0044] The constrained geometric structure model, the constrained electrical model, the constrained thermal control model and the constrained manufacturing process model are fused to generate a digital prototype model of the aerospace power distribution device.

[0045] As a further improvement of the present application, before performing real-time synchronous mapping of the collected manufacturing process data of the aerospace power distribution device V1.0 and the aerospace power distribution device digital twin model V0.0, the method further includes:

[0046] Based on the manufacturing process data, determine the unmonitored state parameters in the aerospace power distribution device digital twin model V0.0;

[0047] Perform parameter estimation on each unmonitored state parameter to obtain a parameter estimation value;

[0048] The state parameters of the aerospace power distributor product digital twin model V0.0 are repaired and completed by using the parameter estimation value, and a completed state parameter set of the digital twin model V0.0 is generated.

[0049] As a further improvement of the present application, the aerospace power distributor and its digital twin are batch-produced by taking the aerospace power distributor product digital twin model V1.0 as a template, comprising:

[0050] The aerospace power distributor product digital twin model V1.0 is input into a preset production control model to generate a batch production instruction set of the aerospace power distributor;

[0051] The production line equipment is controlled according to the batch production instruction set to perform batch production operation;

[0052] The corresponding digital twin of each aerospace power distributor is generated synchronously.

[0053] As a further improvement of the present application, the method further comprises: taking the aerospace power distributor product digital twin model V1.0 as a technical baseline to repeat the next development stage, comprising:

[0054] The product historical baseline database is updated based on the aerospace power distributor product digital twin model V1.0;

[0055] The next development stage is completed by repeating S1-S7.

[0056] As a further improvement of the present application, the method further comprises:

[0057] It is determined whether the aerospace power distributor prototype V1.0 meets the preset technical conditions;

[0058] If the aerospace power distributor prototype V1.0 meets the preset technical conditions, the test equipment is controlled to perform the aerospace power distributor prototype V1.0 test operation;

[0059] If the aerospace power distributor prototype V1.0 does not meet the preset technical conditions, the test equipment is controlled to perform the rework prompt operation.

[0060] Compared with the prior art, the present application can achieve at least one of the following beneficial effects:

[0061] 1. The application integrates data of each link such as geometric structure model, electrical model, thermal control model and manufacturing process model by constructing a digital twin model of a space power distribution device product, analyzes a technical index set to construct a preliminary model, synchronously maps manufacturing process data and the digital twin model in real time, realizes data interaction of links such as design, manufacturing and testing, improves the accuracy of product performance prediction, enables designers to comprehensively evaluate product performance and reliability at an early stage, and reduces modification cost and cycle caused by subsequent design defects.

[0062] 2. The application constrains the model by a constraint reference in a product history baseline database, extracts geometric constraint references, electrical constraint references and the like in the history baseline database, updates and constrains geometric structure models, electrical models and the like respectively, generates a digital prototype model, fully utilizes historical data, makes the design more in line with actual demand, reduces rework risk in the manufacturing process, and improves design accuracy and product reliability.

[0063] 3. The application synchronously maps the collected manufacturing process data and the digital twin model in real time, generates a digital twin model consistent with the state of the physical prototype, completes and repairs state parameters of the digital twin model based on the manufacturing process data, realizes real-time monitoring and feedback of the product state, provides guidance for batch production, perfects the feedback mechanism, and can improve production efficiency and quality.

[0064] In the application, the above technical solutions can be combined with each other to realize more preferred combination solutions. Other features and advantages of the application will be described in the subsequent specification, and some advantages will become apparent from the specification or by implementing the application. The purpose and other advantages of the application can be realized and obtained from the contents specifically indicated in the specification and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0065] The accompanying drawings are included to provide a further understanding of the application and are incorporated herein and constitute a part of the application. The same reference numbers in the drawings indicate the same elements throughout the drawings;

[0066] Figure 1 A flowchart of a space power distribution device product development method based on digital twinning provided by an embodiment of the application is shown in the figure.

[0067] Figure 2 A digital prototype schematic diagram of a space power distribution device provided by an embodiment of the application is shown in the figure.

[0068] Figure 3 A relationship diagram of a space power distribution device physical prototype and a digital twin model provided by an embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0069] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0070] like Figure 1 As shown, a method for developing aerospace electrical distribution products based on digital twins includes:

[0071] S1. Analyze the technical specifications of aerospace electrical distribution products, perform conceptual design, and construct preliminary geometric, electrical, thermal control, and manufacturing process models.

[0072] A technical specification set refers to a comprehensive set of parameters including geometric dimensions, electrical parameters, thermal control parameters, and manufacturing process requirements. Specifically, it can be stored in a structured manner using parametric modeling tools such as CATIA and UG. Using the standardized modules of the modeling tools, conceptual design can be carried out based on the technical specification set to construct preliminary geometric structure models, electrical models, thermal control models, and manufacturing process models.

[0073] The geometric structure model is a 3D modeling tool-created model of the external shape and internal structural layout of the aerospace power distribution unit. The electrical model is a circuit connection model built using drawing tools based on the electrical schematic diagram of the aerospace power distribution unit. The thermal control model is constructed using thermal analysis software, establishing an initial thermal control model based on factors such as the heat source distribution of the aerospace power distribution unit. The manufacturing process model is a virtual manufacturing process model built using process planning software, according to the manufacturing process requirements of the aerospace power distribution unit, containing information on each manufacturing step and process parameters. The manufacturing process model defines each step required to manufacture the product, including machining, assembly, inspection, and heat treatment, with each step having clearly defined inputs and outputs. The manufacturing process model sets process parameters for each step, such as speed, temperature, pressure, and time, based on material properties, equipment capabilities, and quality requirements.

[0074] S2. Based on the product historical baseline database, constraints are applied to the geometric structure model, electrical model, thermal control model and manufacturing process model to generate a digital prototype model of aerospace power distribution products.

[0075] Specifically, such as Figure 2 As shown. Based on the product historical baseline database, constraints are applied to the geometric structure model, electrical model, thermal control model, and manufacturing process model to generate a digital prototype model of the aerospace power distribution product, including:

[0076] S21. Extract constraint benchmarks from the product historical baseline database, including geometric constraint benchmarks, electrical constraint benchmarks, thermal control constraint benchmarks, and manufacturing constraint benchmarks.

[0077] The historical baseline database is a database for storing historical constraint benchmarks, and by means of the historical baseline data constraint model parameters, it is ensured that the design of the space power distribution product digital prototype model conforms to the technical specifications. Based on the historical baseline database, constraint benchmarks are provided for the geometric structure model, the electrical model, the thermal control model and the manufacturing process model. Accordingly, the space power distribution product digital prototype model is generated.

[0078] S22, based on the geometric constraint benchmark, the boundary condition nodes of the geometric structure model are updated to obtain a constrained geometric structure model.

[0079] The boundary condition nodes of the geometric structure model are points on the geometric structure model for connection and positioning with other components, and the geometric constraint benchmark provides accurate position and direction information of these nodes when the product is assembled and positioned. Based on the geometric constraint benchmark, the position and direction data of the verified nodes in the historical product are taken as a reference to adjust the position and direction of the corresponding nodes in the current model, so that they are consistent with the geometric constraint benchmark. If the geometric constraint benchmark specifies the specific coordinates and direction of a connection node in three-dimensional space, the position and direction of the node in the current model need to be adjusted through the geometric modeling software to match these benchmark values, thereby ensuring that the geometric structure of the current model is accurate in spatial positioning and size.

[0080] S23, the electrical constraint benchmark is input into the parameter verification rule library of the electrical model to obtain a constrained electrical model.

[0081] The electrical constraint benchmark is the safety threshold range of each electrical parameter in the historical electrical system (such as the maximum allowable voltage, current, etc.); the voltage, current threshold and other constraints in the electrical constraint benchmark are input into the parameter verification rule library of the electrical model, and the combination of each electrical element parameter setting in the electrical model is checked based on the parameter verification rule library. When it is found that a parameter combination exceeds the safety range, the rule library will filter it out, and only the parameter combination that meets the safety requirements is retained, thereby obtaining a constrained electrical model to ensure the safety and reliability of its electrical performance.

[0082] S24, the thermal control constraint benchmark is input into the transient solver of the thermal control model to obtain a constrained thermal control model.

[0083] The thermal control constraint benchmark is the temperature variation range, heat flux density and other standards met by the historical product in terms of thermal environmental adaptability. The transient solver is a tool for analyzing the time-varying behavior of a system and is commonly used in the simulation analysis of thermal control models. In this application, the temperature variation curve in the thermal control constraint benchmark is input into the transient solver of the thermal control model, and the transient solver corrects the thermal conductivity coefficient and other boundary conditions of the thermal control model at different time points according to the temperature variation curve.

[0084] S25, updating the process chain control information of the manufacturing process model based on the manufacturing constraint benchmark, to obtain a constrained manufacturing process model.

[0085] The manufacturing constraint benchmark is a standard and requirement for regulating the product manufacturing process based on historical manufacturing data, including the requirements for equipment processing accuracy and process sequence in the historical manufacturing process. The process chain control information of the manufacturing process model is updated based on the equipment parameters in the manufacturing constraint benchmark, so as to adjust the processing path and process sequence. The process chain control information includes the process of the product process route, the specific process parameters contained in each process, and the process sequence. To update the process chain control information, the equipment parameters and process sequence requirements need to be extracted from the manufacturing constraint benchmark, and then the extracted equipment parameters and process sequence requirements are matched with the process chain of the manufacturing process model to identify the processes and parameters that need to be adjusted. According to the manufacturing constraint benchmark, the identified processes and parameters are adjusted and optimized to determine the processing sequence of each part and its position and structural features in the geometric structure, and the assembly process is matched with the electrical connection points in the electrical model and the heat source components in the thermal control model. At the same time, the process sequence is adjusted, and the process parameters such as processing speed, temperature, and pressure are optimized to meet the equipment capacity and process specifications in the manufacturing constraint benchmark. Finally, the updated process chain control information is verified to ensure that the adjusted process chain control information meets the requirements of the manufacturing constraint benchmark and matches the geometric structure model, the electrical model, and the thermal control model.

[0086] S26, fusing the constrained geometric structure model, the constrained electrical model, the constrained thermal control model, and the constrained manufacturing process model to generate a digital mockup model of the aerospace power distribution product.

[0087] When generating the digital mockup model of the aerospace power distribution product, the constrained geometric structure model, the electrical model, and the thermal control model are first fused to form a preliminary digital mockup model. The product three-dimensional structure of the geometric structure model is matched with the product circuit layout of the electrical model, and the heat source distribution of the thermal control model is matched with the position of the heat generating components in the geometric structure. During the fusion, the parameters of each model need to be coordinated, such as ensuring that the current density distribution in the electrical model is consistent with the heat generating power in the thermal control model, so that the interaction between the physical fields can be accurately reflected in the fused model. It is also necessary to ensure that the electrical connection points in the electrical model are accurately matched with the connection points in the geometric structure model to ensure the accuracy and reliability of the electrical connection. In addition, it is necessary to verify whether the insulation distance and creepage distance in the electrical model meet the safety standards to prevent problems such as arc discharge and insulation breakdown.

[0088] After obtaining the preliminary digital prototype model, the manufacturing process model is embedded into the preliminary digital prototype model. During the embedding process, the process chain control information in the manufacturing process model is extracted and mapped with the structural and functional features in the preliminary digital prototype model. The machining process of each component is determined to correspond to its position and structural features in the geometric structure, and the assembly process is determined to correspond to the electrical connection points in the electrical model and the heat source components in the thermal control model. Then, the process parameters in the manufacturing process model, including machining speed, temperature, pressure, etc., are associated with the material properties, electrical performance parameters, and thermal conductivity coefficients in the preliminary digital prototype model. For example, the influence of processing temperature on the thermal expansion coefficient of the material is considered, which further affects the dimensional stability of the geometric structure and the reliability of electrical connection. Finally, by establishing data mapping and interaction, the manufacturing process model is associated and fused with the preliminary digital prototype model, so that the final aerospace power distributor product digital prototype model not only can display the design structure and performance parameters of the product, but also can trace and simulate the manufacturing process of the product, including the machining steps, assembly sequence, and quality control of each component.

[0089] S3, injecting product history process data into the aerospace power distributor product digital prototype model to reconstruct an aerospace power distributor product digital twin model V0.0.

[0090] The history process data includes historical failure data and historical performance data. In the design, manufacturing, testing or use process of similar aerospace power distributors in the past, the data of events and related parameters about the failure, performance decline or non-compliance of the product or its components are recorded; the historical performance data is the monitoring or recording of the working state, functional performance and model parameters such as voltage, current, temperature, power consumption of the similar products in actual operation or test, which can provide a benchmark for dynamic tolerance control.

[0091] Injecting product history process data into the aerospace power distributor product digital prototype model to reconstruct an aerospace power distributor product digital twin model V0.0, including:

[0092] S31, extracting features from all historical failure data to obtain failure features.

[0093] Extracting features from all historical failure data to obtain failure features. Feature extraction can identify failure features by using statistical analysis, pattern recognition or machine learning methods. Failure features are repeated failure patterns and their associated parameters in the identification. For example, through cluster analysis, the repeated short-circuit failure model is identified, and the associated parameters such as peak current and temperature rise rate corresponding to the short-circuit failure model are extracted.

[0094] S32, performing parameter allowable deviation analysis on all historical performance data to determine parameter safety interval.

[0095] The parameter tolerance analysis on historical performance data includes calculating mean, standard deviation, extreme value, etc. The performance parameters such as output voltage fluctuation, temperature rise speed, insulation resistance value, etc. are investigated in the actual performance range of historical products. The parameter safety interval is determined by combining the product specification requirements. The parameter safety interval is the interval in which the parameter can guarantee reliability and performance requirements.

[0096] S33, applying the fault feature to the high stress area of the preset digital mockup model to obtain a fault mapping result.

[0097] The high stress area is a region in the digital mockup model that is physically subjected to high load and prone to problems, including local regions that are physically subjected to extreme load, thermal load, or electromagnetic interference, such as the vicinity of high-power components, high-frequency signal lines, weak structural connections, and heat dissipation path bottlenecks. When the load borne by the space power distribution product exceeds its design threshold, the fault usually occurs at the high stress area. The high stress area can be determined by identifying the stress concentration parts of the geometric structure model using the finite element analysis method, locating the thermal load overload area with a heat flux density exceeding the threshold as the thermal load high stress area by calculating fluid dynamics (CFD), calculating the parasitic parameters of high-frequency signal lines by electromagnetic field simulation (EMF), and marking the electromagnetic sensitive area as the electromagnetic high stress area. The extracted fault feature is applied to the high stress area of the digital mockup model to verify whether a fault occurs to obtain a fault mapping result.

[0098] S34, based on the parameter safety interval, performing dynamic tolerance control on the design parameters of the digital mockup to obtain a dynamic tolerance.

[0099] The dynamic tolerance is a fluctuation constraint of the design parameters of the digital mockup. The allowed fluctuation range of the design parameters must be completely contained in the parameter safety interval. Based on the determined parameter safety interval, the corresponding design parameters in the digital mockup model are subjected to dynamic tolerance control to ensure that the design parameters remain within the parameter safety interval after fluctuation, such as a nominal 100Ω resistor with a parameter safety interval of 95-105Ω. The parameter can be adjusted to be away from the boundary of the parameter safety interval, leaving sufficient design margin, and the dynamic tolerance is controlled to be no more than ±3%, ensuring that it remains within the parameter safety interval range even under parameter fluctuation.

[0100] S35, based on the fault mapping result of the high stress area and the dynamic tolerance of the design parameters, constructing a digital twin V0.0.

[0101] Specifically, based on the fault mapping result of the high stress area and the dynamic tolerance of the design parameters, the digital twin V0.0 is constructed, including:

[0102] S351, determine whether the fault risk of the high stress area meets the standard based on the fault mapping result;

[0103] The extracted fault features are applied to the corresponding high stress area according to the correspondence between the fault features and the high stress area, and it is verified whether a fault occurs for risk assessment. If all high stress areas do not have fault risk in risk assessment, it is determined that the fault risk meets the standard. If any high stress area has fault risk in risk assessment, it is determined that the fault risk does not meet the standard.

[0104] S352, apply limit conditions to the simulation environment of the digital twin V0.0, and verify whether the parameter fluctuation is within the dynamic tolerance range.

[0105] All design parameters are set to the rated value in the simulation platform, extreme operating conditions are constructed and input to the simulation platform to verify the parameter fluctuation. If the fluctuation range of all monitored parameters under the limit condition is within the dynamic tolerance range, the verification is passed. If any parameter exceeds the dynamic tolerance range, the verification is not passed. The extreme operating conditions can include setting the environmental temperature range to ±20℃ of the standard temperature range, increasing the current to 120% of the rated value, and superimposing 20% of the rated interference noise.

[0106] S353, if the fault risk of the high stress area meets the standard and the parameter fluctuation is within the parameter safety interval, generate the digital twin model V0.0 of the space power distribution device product.

[0107] If the fault risk of the high stress area meets the standard and the parameter fluctuation is within the parameter safety interval, generate the digital twin model V0.0 of the space power distribution device product. If any verification fails, return to S2 to regenerate the digital prototype until the structure deformation variable and the parameter fluctuation meet the requirements, and generate the digital twin model V0.0 of the space power distribution device product.

[0108] S4, perform offline simulation analysis on the digital twin model V0.0 of the space power distribution device product, optimize the technical index set of the space power distribution device product, and generate a detailed design scheme based on the technical index set.

[0109] The technical index set includes electrical parameters, thermal control parameters, or structural parameters of the space power distribution device product. The digital twin model V0.0 can be subjected to multi-physical field coupling simulation analysis using finite element analysis tools, such as establishing coupling equations of electromagnetic field, temperature field, and structural field parameters, solving the model to obtain simulation results, and optimizing the technical index set based on the simulation results.

[0110] Specifically, performing offline simulation analysis on the digital twin model V0.0 of the space power distribution device product, optimizing the technical index set of the space power distribution device product, and generating a detailed design scheme based on the technical index set include:

[0111] S41, input the parameters in the technical index set into the aerospace power distributor product digital twin model V0.0 for simulation processing to obtain simulation result data and generate a simulation result set.

[0112] The technical index set includes an electrical performance parameter set, a thermal control performance parameter set, a structural reliability parameter set, and a process manufacturing parameter set. Specifically, the electrical performance parameter set includes rated output voltage, maximum load current, voltage regulation rate, ripple coefficient, etc. The thermal control performance parameter set includes maximum operating temperature, temperature rise rate, heat dissipation efficiency, etc. The structural reliability parameter set includes mechanical vibration resistance, impact resistance, and connector pre-tightening force, etc. The process manufacturing parameter set includes welding temperature curve and assembly gap, etc.

[0113] The electrical performance parameter set, the thermal control performance parameter set, the structural reliability parameter set, and the process manufacturing parameter set are respectively used as simulation inputs for simulation processing to obtain electrical performance simulation result data, thermal control performance simulation result data, structural reliability simulation result data, and process manufacturing simulation result data, which constitute the simulation result set.

[0114] S42, determine whether there is simulation result data in the simulation result set that does not meet the preset simulation condition; if there is, adjust the corresponding parameters in the technical index set based on the preset simulation condition corresponding to the simulation result data, update the technical index set, and return to step S41; if there is not, output the current technical index set;

[0115] S43, generate a detailed design scheme based on the output technical index set.

[0116] The preset simulation condition can be set through the baseline database. When the simulation result data does not meet the preset simulation condition, the corresponding parameters in the technical index set are adjusted until all simulation result data meets the preset simulation condition.

[0117] Specifically, the preset simulation condition can be set based on industry standards or product research and development requirements. In electrical performance simulation, the preset simulation condition can include output voltage fluctuation range. According to industry standards or product research and development requirements, the output voltage fluctuation range can be set to not more than ±3%. In thermal control performance simulation, the temperature rise of key components can be set to not more than 35℃ to ensure the thermal stability of the product in the working environment. In structural reliability simulation, the maximum deformation of structural components can be set to not more than 10% of the material yield strength to ensure mechanical reliability.

[0118] The output-based technical index set generates a detailed design scheme in the form of design drawings or process documents. For the generation of design drawings, a detailed three-dimensional model and two-dimensional engineering drawing of the product can be drawn using computer-aided design (CAD) software according to the parameters in the technical index set. These drawings will clearly mark the dimensions, tolerances, material properties, and surface treatment requirements of each component, ensuring accuracy during the manufacturing process. Process documents are prepared according to the manufacturing parameters and requirements in the technical index set, detailing the process flow and operating specifications. For example, assembly process documents will specify the assembly sequence, connection method, and required tools and equipment for each component; welding process documents will specify the welding method, welding parameters (such as current, voltage, welding speed, etc.), and post-weld inspection standards. Process documents are used to provide operating guidance for operators on the production site, ensuring that the manufacturing process of the product meets design requirements.

[0119] S5, manufacturing a space power distributor prototype V1.0 according to the detailed design scheme and collecting manufacturing process data of the space power distributor prototype V1.0.

[0120] Specifically, manufacturing a space power distributor prototype V1.0 according to the detailed design scheme and collecting manufacturing process data of the space power distributor prototype V1.0 includes:

[0121] S51, performance verification of the updated technical index set.

[0122] After manufacturing the space power distributor prototype V1.0, the performance of the technical index set is verified again through a preset verification threshold.

[0123] S52, replacing parameters with performance verification values less than or equal to the preset verification threshold with corresponding parameters in a backup design parameter library, including: placing parameters with performance verification values less than or equal to the preset verification threshold in the corresponding positions of the backup design parameter library; placing corresponding parameters in the backup design parameter library in the corresponding positions of the technical index set and adding them to the manufacturing index library; the backup design parameter library stores each parameter in the technical index set.

[0124] The manufacturing index library is a storage of verified parameter sets, which is a parameter set for subsequent product manufacturing. The backup design parameter library is a parameter set used as a backup in emergency situations when the manufacturing index library cannot be normally accessed or temporarily fails to be used. It is a backup solution for the manufacturing index library. All parameters in the manufacturing index library and the backup design parameter library are one-to-one corresponding.

[0125] S53, adding parameters with performance verification values greater than the preset verification threshold to the manufacturing index library.

[0126] If the parameter of the technology index set is greater than the preset verification threshold, it is directly added to the manufacturing index library. If the performance verification value is less than or equal to the preset verification threshold, the parameter is replaced with the corresponding parameter in the standby design parameter library, and is added to the manufacturing index library. After the replacement, all parameters in the standby design parameter library and the manufacturing index library include all parameters used for manufacturing, and are one-to-one corresponding.

[0127] S54, manufacturing a space power distributor physical prototype V1.0 based on the parameters in the manufacturing index library, and collecting manufacturing process data of the space power distributor physical prototype V1.0.

[0128] Based on the finally determined manufacturing index library, the space power distributor physical prototype V1.0 is manufactured, and the manufacturing process data of the space power distributor physical prototype V1.0 is synchronously collected, including process execution data, quality detection data and equipment state data.

[0129] S6, performing real-time synchronous mapping of the collected manufacturing process data of the space power distributor physical prototype V1.0 and the space power distributor product digital twin model V0.0, to generate a space power distributor product digital twin model V1.0 consistent with the state of the space power distributor physical prototype V1.0.

[0130] Real-time synchronous mapping is achieved by acquiring manufacturing parameters of the physical prototype through data acquisition equipment and updating corresponding nodes of the digital model, using an industrial Internet of Things protocol to ensure the consistency of the physical entity and the virtual model.

[0131] As shown in Figure 3 Before performing real-time synchronous mapping of the collected manufacturing process data of the space power distributor physical prototype V1.0 and the space power distributor product digital twin model V0.0, the method further comprises:

[0132] S61, determining unmonitored state variables in the space power distributor product digital twin model V0.0 based on the manufacturing process data.

[0133] Unmonitored state variables refer to physical quantities that are not directly collected by sensors in the digital twin model. The unmonitored state variables can be identified by comparing the difference between the actual measurement parameters in the manufacturing process data and the model preset parameter set.

[0134] S62, performing parameter estimation on each unmonitored state variable to obtain a parameter estimation value.

[0135] The parameter estimation of each unmonitored state variable can derive the value of the unmonitored variable through the known physical correlation between parameters. Specifically, the equation between parameters can be established by multiple regression analysis method, and the known parameters are input into the equation between parameters to solve the unknown parameters, that is, the parameter estimation value of the unmonitored state variable is obtained.

[0136] S63, using the parameter estimation value to complete and repair the state variable of the aerospace power distributor product digital twin model V0.0, and generating a completed state variable set of the digital twin model V0.0.

[0137] The parameter estimation value of the obtained unmonitored state variable is input into the data storage structure of the digital twin model, and the state variable of the aerospace power distributor product digital twin model V0.0 is completed and repaired, so as to obtain a completed state variable set of the digital twin model V0.0.

[0138] S7, using the aerospace power distributor product digital twin model V1.0 as a template to mass-produce aerospace power distributors.

[0139] Specifically, using the aerospace power distributor product digital twin model V1.0 as a template to mass-produce aerospace power distributors includes:

[0140] S71, inputting the aerospace power distributor product digital twin model V1.0 into a preset production control model to generate a set of aerospace power distributor batch production instructions.

[0141] The production control model is an algorithm framework that converts design parameters in the digital twin model into executable production task instructions, which can be implemented based on a rule engine parameter mapping algorithm or a large language model (LLM). The batch production instruction set refers to a structured data set containing device control parameters, process paths, and quality detection standards. When implemented, the aerospace power distributor batch production instruction set is set as a standardized file in XML or JSON format, which can guide the production line equipment to perform operations according to the preset process.

[0142] S72, controlling the production line equipment to perform batch production operations according to the aerospace power distributor batch production instruction set; and synchronously generating a digital twin corresponding to each aerospace power distributor.

[0143] The production line equipment refers to machining centers, test fixtures, and assembly robots with numerical control interfaces. While the physical product is being processed, the process parameters and historical data of the digital twin are dynamically updated by collecting the processing temperature, assembly pressure, and electrical test results recorded by the device sensors, forming a digital mirror corresponding to the physical object.

[0144] Further, the method further comprises: using the aerospace power distributor product digital twin model V1.0 as a technical baseline to repeatedly perform the next development stage, including:

[0145] updating the product historical baseline database based on the aerospace power distributor product digital twin model V1.0; repeating S1-S7 to complete the next development stage.

[0146] The product historical baseline database is updated by importing the verified design parameters, manufacturing process data and physical prototype test results of the digital twin model V1.0 into the historical baseline database. When the next development stage starts, S1-S7 are repeated based on the updated product historical baseline database to perform the next development stage.

[0147] Further, after the aerospace power distributor physical prototype is manufactured, the method further comprises:

[0148] determining whether the aerospace power distributor physical prototype V1.0 meets the preset technical conditions;

[0149] If the aerospace power distributor physical prototype V1.0 meets the preset technical conditions, the test equipment is controlled to perform the aerospace power distributor physical prototype V1.0 test operation; if the aerospace power distributor physical prototype V1.0 does not meet the preset technical conditions, the test equipment is controlled to perform the rework prompting operation.

[0150] The preset technical conditions are preset performance indicators of the aerospace power distributor product, which are used for quality judgment of the aerospace power distributor physical prototype. If the aerospace power distributor physical prototype V1.0 meets the preset technical conditions, the aerospace power distributor physical prototype V1.0 meets the factory standard, and the test equipment is controlled to perform the aerospace power distributor physical prototype V1.0 test operation to test and verify the aerospace power distributor physical prototype V1.0, and verify the performance of the aerospace power distributor physical prototype V1.0 in the real use scenario. If the aerospace power distributor physical prototype V1.0 does not meet the preset technical conditions, the aerospace power distributor physical prototype V1.0 does not meet the factory standard, and needs to be reworked.

[0151] Those skilled in the art can understand that all or part of the processes of the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a computer readable storage medium. The computer readable storage medium includes a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.

[0152] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed by the present application can be easily thought by those skilled in the art, and should be covered within the protection scope of the present application.

Claims

1. A space power distribution product development method based on digital twinning, characterized in that, The method comprises the following steps: S1, analyzing the technical index set of the space power distribution product, performing conceptual design, constructing a preliminary geometric structure model, an electrical model, a thermal control model and a manufacturing process model; S2, based on the product historical baseline database, imposing constraints on the geometric structure model, the electrical model, the thermal control model and the manufacturing process model, and generating a space power distribution product digital prototype model; S3, injecting product historical process data into the space power distribution product digital prototype model to reconstruct a space power distribution product digital twin model V0.0; S4, performing offline simulation analysis on the space power distribution product digital twin model V0.0, optimizing the technical index set of the space power distribution product, and generating a detailed design scheme based on the technical index set; S5, manufacturing a space power distribution physical prototype V1.0 according to the detailed design scheme, and collecting manufacturing process data of the space power distribution physical prototype V1.0; S6, performing real-time synchronous mapping of the collected manufacturing process data of the space power distribution physical prototype V1.0 and the space power distribution product digital twin model V0.0, and generating a space power distribution product digital twin model V1.0 consistent with the state of the space power distribution physical prototype V1.0; S7, mass producing space power distribution according to the space power distribution product digital twin model V1.

0.

2. The method of claim 1, wherein, The historical process data includes historical failure data and historical performance data; Injecting product historical process data into the space power distribution product digital prototype model to reconstruct a space power distribution product digital twin model V0.0 comprises: Extracting features from all historical failure data to obtain failure features; Performing parameter allowable deviation analysis on all historical performance data to determine parameter safety intervals; Applying the failure features to the high stress area of the preset digital prototype model to obtain failure mapping results; Based on the parameter safety intervals, performing dynamic tolerance control on the design parameters of the digital prototype to obtain dynamic tolerances; Based on the failure mapping results of the high stress area and the dynamic tolerances of the design parameters, constructing a digital twin V0.

0.

3. The method of claim 2, wherein, Based on the failure mapping results of the high stress area and the dynamic tolerances of the design parameters, constructing a digital twin V0.0 comprises: Judging whether the failure risk of the high stress area meets the standard based on the failure mapping results; Applying limit working conditions to the simulation environment of the digital twin V0.0 to verify whether the parameter fluctuation is within the dynamic tolerance range; If the failure risk of the high stress area meets the standard and the parameter fluctuation is within the parameter safety interval, a space power distribution product digital twin model V0.0 is generated; Otherwise, return to S2 to regenerate the digital prototype model.

4. The method of claim 1, wherein, Performing offline simulation analysis on the space power distribution product digital twin model V0.0 to optimize the technical index set of the space power distribution product, and generating a detailed design scheme based on the technical index set comprises: S41, inputting the parameters in the technical index set into the space power distribution product digital twin model V0.0 for simulation processing to obtain simulation result data corresponding to each parameter in the technical index set, and generating a simulation result set; S42, judging whether there is simulation result data in the simulation result set that does not meet the preset simulation condition; If the simulation result data exists, the technical index set is updated based on the parameters corresponding to the preset simulation conditions in the simulation result data, and the step S41 is returned; If the simulation result data does not exist, the current technical index set is outputted; S43, generating a detailed design scheme based on the outputted technical index set.

5. The method of claim 4, wherein, The manufacturing of the space power distributor physical prototype V1.0 according to the detailed design scheme, and the collection of the manufacturing process data of the space power distributor physical prototype V1.0 include: Performance verification is performed on the updated technical index set; The parameters with performance verification values less than or equal to the preset verification threshold are replaced with corresponding parameters in the backup design parameter library, including: placing the parameters with performance verification values less than or equal to the preset verification threshold in the corresponding positions of the backup design parameter library; placing the corresponding parameters in the backup design parameter library in the corresponding positions of the technical index set and adding them to the manufacturing index library; the backup design parameter library stores each parameter in the technical index set; The parameters with performance verification values greater than the preset verification threshold are added to the manufacturing index library; The space power distributor physical prototype V1.0 is manufactured based on the parameters in the manufacturing index library, and the manufacturing process data of the space power distributor physical prototype V1.0 is collected.

6. The method of claim 1, wherein, The constraints are imposed on the geometric structure model, the electrical model, the thermal control model and the manufacturing process model based on the product historical baseline database to generate a space power distributor product digital prototype model, including: The constraint references in the product historical baseline database are extracted, including geometric constraint references, electrical constraint references, thermal control constraint references and manufacturing constraint references; The boundary condition nodes of the geometric structure model are updated based on the geometric constraint references to obtain a constrained geometric structure model; The electrical constraint references are inputted into the parameter verification rule library of the electrical model to obtain a constrained electrical model; The thermal control constraint references are inputted into the transient solver of the thermal control model to obtain a constrained thermal control model; The process chain control information of the manufacturing process model is updated based on the manufacturing constraint references to obtain a constrained manufacturing process model; The constrained geometric structure model, the constrained electrical model, the constrained thermal control model and the constrained manufacturing process model are fused to generate a space power distributor product digital prototype model.

7. The method of claim 1, wherein, Before performing real-time synchronous mapping on the collected manufacturing process data of the space power distributor physical prototype V1.0 and the space power distributor product digital twin model V0.0, the method further includes: Based on the manufacturing process data, determine the unmonitored state parameters in the space power distributor product digital twin model V0.0; Perform parameter estimation on each unmonitored state parameter to obtain a parameter estimation value; Use the parameter estimation value to complete and repair the state parameters of the space power distributor product digital twin model V0.0 to generate a complete state parameter set of the digital twin model V0.

0.

8. The method of claim 1, wherein, The space power distributor is batch-produced by taking the space power distributor product digital twin model V1.0 as a template, including: The space power distributor product digital twin model V1.0 is inputted into a preset production control model to generate a space power distributor batch production instruction set; According to the aerospace power distributor batch production instruction set, control the production line equipment to execute batch production operation; Synchronize to generate the digital twin corresponding to each aerospace power distributor.

9. The method of claim 1, wherein, The method further comprises: taking the aerospace power distributor product digital twin model V1.0 as a technical baseline to repeatedly execute the next development stage, including: Based on the aerospace power distributor product digital twin model V1.0, update the product history baseline database; Repeat S1-S7 to complete the next development stage.

10. The method of claim 1, wherein, After the aerospace power distributor physical prototype is manufactured, the method further comprises: Determine whether the aerospace power distributor physical prototype V1.0 meets the preset technical conditions; If the aerospace power distributor physical prototype V1.0 meets the preset technical conditions, control the test equipment to execute the aerospace power distributor physical prototype V1.0 test operation; If the aerospace power distributor physical prototype V1.0 does not meet the preset technical conditions, control the test equipment to execute the rework prompt operation.

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