71 results about "Model synchronization" patented technology

The invention discloses a vertical take-off and landing airport air traffic collaborative management method and system. The method comprises the following steps: constructing an airport air model integrating a physical environment, an airspace structure, a natural environment, an aircraft state and observation information; receiving observation information in real time to drive model synchronization; performing prospective multi-dimensional conflict prediction based on the synchronous model and eVTOL dynamics; on the basis of the prediction result, generating a cooperative regulation strategy when multiple constraint conditions are met; converting the strategy into an instruction, issuing the instruction through a security data link, and verifying an execution effect based on observation information to realize closed-loop control; according to the method, the air traffic environment of the vertical take-off and landing airport is systematically and digitally reconstructed through the quintuple model, unified representation of the operating environment and the traffic subject is achieved, the problem of heterogeneous information fusion is solved, the airspace utilization efficiency and safety under high-density operation can be remarkably improved, and the method is suitable for large-scale popularization and application. And the method has strong adaptive capacity to dynamic changes such as wind power disturbance and emergency conditions.

The invention relates to the technical field of course analysis management, in particular to a course analysis management system based on deep learning. The method has the advantages that multi-modal data deep analysis realizes full-dimensional analysis of unstructured data by integrating a 3D-CNN model, a Transform architecture and a BERT model and synchronously extracting an attention hot area, a voice emotional state and a text knowledge point association network of a classroom video; nonlinear behavior modeling adopts an LSTM network and time convolutional network fusion model, a knowledge internalization path and forgetting curve prediction are dynamically generated, parameters are optimized in combination with incremental learning, and a transition rule across knowledge points is captured; a teaching scene-evaluation threshold mapping table is constructed based on a reinforcement learning algorithm through dynamic decision and resource collaboration, collaborative optimization under multi-campus data privacy protection is achieved in combination with a federated learning framework, GPU computing nodes are dynamically allocated through a heterogeneous resource scheduling engine, and the analysis efficiency is improved.

The invention provides a BIM (Building Information Modeling) full life cycle splitting and cross-model synchronization method and system. The method comprises the steps that BIM model components are coded based on a full life cycle coding rule, private attributes of all engineering stages and component codes of the engineering stages are bound and stored in component extension attributes, and a BIM master model containing multi-stage attribute information is generated; setting a BIM model splitting rule based on a rule engine; the code of each component in the BIM model is analyzed, the codes are compared with screening conditions in the splitting rules according to levels, and component data corresponding to the successfully matched codes are packaged and stored as sub-models; establishing association mapping between the models; and when the source model is modified, the synchronization engine executes synchronous updating of the target model according to the association mapping between the source model and the target model associated with the source model, so that the cross-stage multi-participant cooperation efficiency is improved, and a solid technical support is provided for digital transformation of the construction industry.

The invention discloses an ANTLR-based SysMLv2.0 text model synchronization method, which comprises the following steps of: S1, constructing a syntactic analysis framework of a SysMLv2.0 language on the basis of the ANTLR, and generating a Leyer and a Parser for analysis; s2, analyzing the original SysMLv2.0 text, and generating an abstract syntax tree AST containing structure, behavior and constraint elements; s3, constructing a semantic model structure based on AST mapping; s4, respectively constructing an original semantic model and a target semantic model, and unifying an identifier, a namespace and a reference path format; s5, comparing semantic model structure differences to generate a difference list; s6, performing insertion, deletion or replacement operation on the original model text according to the difference type, and performing incremental synchronization; s7, deserializing the synchronized semantic model to generate a standard SysMLv2.0 modeling text; and S8, outputting the text file for loading and visual operation of the modeling tool. According to the method, a unified analysis and synchronization system is constructed, and accurate comparison and incremental updating of the SysMLv2.0 text model are realized.

The invention discloses a model optimization method, electronic equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining input information, inputting the input information to a preset main model, and obtaining first answer information; inputting the input information into a preset shadow model to obtain second answer information; under the condition that an intervention behavior aiming at the first answer information is detected, performing reverse optimization on the shadow model according to the intervention behavior and the second answer information to obtain an optimized shadow model; performing performance evaluation on the optimized shadow model and the main model to obtain performance evaluation results; and determining the model with the optimal evaluation result in the performance evaluation results as a main model, and determining the model with the suboptimal evaluation result in the performance evaluation results as a shadow model. According to the method, the shadow model synchronously running with the main model is deployed, and self-improvement is continuously performed according to the user intervention behavior, so that efficient and continuous model optimization is realized.

A system and method is provided for adaptive sharing of cached results in a distributed machine learning environment. The system receives compressed representations of input data from multiple devices and processes them through a model to generate responses. These responses are stored in both local caches and a shared global cache. Each response is evaluated for reuse and classified for privacy, allowing some to be shared widely, some only with select groups, and others to remain private. Usage patterns from different devices are combined to train models that guide which cached responses should be retained or synchronized. Synchronization is managed adaptively, adjusting when and how information is shared depending on network conditions, utility, and privacy budgets. Before sharing, privacy safeguards such as encryption or differential privacy are applied, and entries are distributed through a coordinating system that ensures consistency and avoids duplication across devices.

The invention discloses a control method, and particularly relates to an energy storage power converter self-adaptive control method based on digital twinning, which comprises the following steps: step 1, digital twinning model synchronization and state sensing; step 2, multi-target collaborative optimization based on prediction; and step 3, optimizing instruction issuing and physical system execution. Through real-time prediction and optimization of the digital twinborn model, it is ensured that the power switch tube always works in a zero-voltage switching state, switching loss is effectively reduced, and overall efficiency is improved. Dynamic thermal balance management is realized by combining thermal model prediction, local overheating is avoided, the service life of devices is prolonged, and the reliability of long-term operation of the system is enhanced.

The invention discloses a technological process monitoring method and system for a polyester net, and belongs to the technical field of intelligent control, and the method comprises the steps: obtaining real-time technological parameter data in the production process of the polyester net; inputting the real-time process parameter data into a digital twin model, predicting the mesh forming quality of the next period, and generating predicted quality parameters; collecting a polyester mesh surface image, inputting the polyester mesh surface image into the semantic segmentation neural network, identifying a mesh abnormal region, and generating an actual mesh quality score; on the basis of a deviation value between the predicted quality parameter and the actual mesh quality score and energy consumption per unit yield, constructing a reward signal and inputting the reward signal into a deep reinforcement learning controller; the deep reinforcement learning controller outputs a process parameter adjustment instruction and sends the process parameter adjustment instruction to an execution mechanism; and feeding back the actual mesh quality score to the digital twin model, correcting the error of the prediction quality parameter, and updating the model synchronization precision. The product quality stability is improved.

The invention relates to the technical field of computing resource optimization, and discloses a joint resource allocation and task unloading optimization method and system, and the method comprises the steps: constructing a global resource view to sense the real-time residence state of each computing node cache line in a distributed system; a cache-aware task unloading and data prefetching combined optimization model is established, decision variables of the model include task unloading variables and data prefetching variables at the same time, and cache capacity constraints are constructed in an endogenous mode by means of a task unloading scheme so as to predict a cache line set to be expelled due to task execution; the joint optimization model is solved with the purpose of minimizing the total task completion time, a task unloading decision and a collaborative prefetching decision are synchronously generated, and the prefetching decision is determined according to the predicted expelling set and the task data dependency relationship. According to the method, task unloading and data prefetching are cooperatively decided in a unified optimization framework, so that the problem of mutual interference caused by independent optimization of task unloading and data prefetching is solved, and global active arrangement of calculation and cache resources is realized.

The invention discloses an equipment performance lightweight and reliable integrated evaluation method based on space-time progressive deviation correction, and relates to the technical field of equipment performance evaluation, and the method comprises the steps: firstly obtaining original sensing data in an equipment service process, outputting high-quality data after deviation correction through a weak abnormal sensing characteristic deviation correction model based on space-time progressive deviation correction, and obtaining a weak abnormal sensing characteristic deviation correction model based on space-time progressive deviation correction; the evaluation deviation caused by low-quality data is avoided; inputting the high-quality data into an integrated monitoring model based on a multi-scale depth residual shrinkage network, synchronously generating a decoupling type state vector to complete performance state identification and performance quantification, calculating probability distribution entropy of each category, adaptively adjusting the confidence coefficient during performance quantification prediction according to the probability distribution entropy, and obtaining a prediction result; the evaluation reliability of the equipment in the boundary transition state is improved; and finally, model lightweight is realized through a lightweight strategy, and the model is deployed in industrial equipment to meet the requirements of computing resource limitation and real-time performance. The device performance can be efficiently and reliably evaluated, and powerful support is provided for industrial device performance evaluation.

The invention discloses a complex man-machine cooperation station operation compliance sequential logic verification method and system based on a multi-modal video, and the method comprises the steps: obtaining a standard operation instruction (SOP), analyzing the SOP, and converting the SOP into a formal model with sequential logic constraints; synchronously acquiring a video data stream, an audio data stream and an equipment data stream of the station; performing real-time analysis on the video data stream by using a human-object interaction (HOI) deep learning model to generate structured visual event metadata; taking the visual event metadata as input, and driving the formalized model to carry out state evolution so as to judge the compliance of an operation process; and performing fusion cross validation on the audio data stream and the equipment data stream at a preset key verification node by taking the time window of the occurrence of the visual event as a reference. By fusing fine-grained visual interaction recognition, strict sequential logical reasoning and multi-modal cross validation, high-reliability closed-loop verification can be carried out on operational process compliance and physical result authenticity.

The application discloses a data synchronization method and system with full-link back pressure characteristics, comprising: configuring metadata of a synchronization task; starting incremental synchronization of a binlog log, and selectively performing full synchronization; routing generated add, delete, modify and query events to a distributed parallel Stream pipeline; and distributing the events to an Actor entity in an Actor model shard cluster according to dimension fields configured for each table in an asynchronous request response mode; performing event persistence and state updating, and complex conversion logic based on the current state, and returning the result to the Stream pipeline; performing message batching, sorting and deduplication processing on Response events by the Stream pipeline; writing the result to a Sink data source, and performing an asynchronous ACK mechanism to ensure reliable synchronization. The application can quickly realize complex model synchronization conversion between heterogeneous data sources through highly configurable synchronization tasks, and has the characteristics of short link, few components and efficient synchronization.

The invention discloses a photovoltaic flexible direct current grid-connected system simulation method and device and a storage medium, and relates to the technical field of power system simulation, and the method comprises the steps: building a photovoltaic module, an alternating current system module and a direct current line module on a simulation platform, so as to construct a simulation model; deploying the photovoltaic module on a first device, deploying the AC system module and the DC line module on a second device, and setting each simulation step length; after environment parameters and faults are set, the first device and the second device are operated at the same time based on the simulation step lengths, and data interaction between the first device and the second device is achieved through a data synchronization interface according to a preset data interaction period till a simulation result is output. Compared with the prior art, the method has the advantages that synchronous calculation of the multi-time-scale model can be realized, so that the problems of module calculation failure and simulation result distortion in the prior art are solved.

The invention discloses a software testing method and system based on a data analysis platform, and particularly relates to the technical field of software testing, and the method comprises the following steps: tracking business semantic change by constructing a semantic evolution perception map, and establishing a semantic-driven data label linkage mechanism to maintain the consistency of training samples of a defect recognition model; semantic consistency drifting is monitored, model output is controlled, a map-enhanced test case generation mechanism is introduced to achieve semantic synchronous generation, meanwhile, a semantic regression verification process is set to ensure alignment of a test case and evolution semantics, and a closed-loop test process covering a full path is formed; according to the method, semantic-driven label linkage and model synchronous updating are realized by constructing the semantic evolution perception map, and a map-enhanced test case generation and regression verification process is introduced, so that the test process is ensured to be consistent with the service semantic dynamic state, the defect identification accuracy and the test coverage integrity are improved, and the test efficiency is improved. The method is suitable for software quality guarantee in a continuous delivery environment.

The present application relates to VR auxiliary debugging and bearing fault positioning technical field, specifically, the present application relates to VR auxiliary debugging and fault positioning method for equipment maintenance, aiming at solving the problem that traditional bearing fault detection adopts visual and static detection, and cannot collect full angle torque dynamic characteristics and large fault positioning error, in the bearing press fitting link, the dynamic torque sensor integrated with the angle encoder is used to collect the instantaneous torque of the full angle, the torque curve of the rotation angle is generated and is associated with the coding storage, when the fault occurs, the measured curve and the standard curve of the same type are called, and are bound to the double virtual bearing model of the VR scene, the virtual driving model is synchronously rotated, the signal module generates the resistance signal according to the torque to control the force feedback data glove, the real rotation feeling is restored by superimposing the viscous damping, the comparison module compares the resistance difference point by point and in the sliding window, if the threshold value is exceeded, the abnormal rotation angle interval is locked, the effective fault area is verified and confirmed through the original point, and the positioning result is output, and the fault positioning precision is improved.

This invention discloses a rapid interactive and verification method for mold structures based on wireframe preview, applied to a computer-aided design system. The method includes: receiving and loading a parametric 3D structural model of the target mold; parsing the 3D structural model, extracting and registering the driving design parameters, and generating a parameter registry; constructing and displaying a lightweight wireframe preview model representing the structural outline based on the parameter values ​​in the parameter registry; adjusting the design parameters, updating the parameter values ​​in the parameter registry in real time, and driving the wireframe preview model to update synchronously; after confirming that the updated wireframe preview model meets the design intent, driving the computer-aided design system to generate the final 3D solid structure based on the final parameter registry. This invention significantly improves the efficiency of mold structure design by providing real-time preview and rapid adjustment interaction during the design process.

The invention provides a cooperative control method and device for a driving motor, a vehicle-mounted terminal and a storage medium, and the method comprises the steps: directly mapping a multi-dimensional input signal, a current instruction and target heating power through a target neural network model, on one hand, the current instruction outputted by the model can be accurately converted into a target torque, and on the other hand, the target heating power is outputted; on the one hand, torque requests of a driver or a whole vehicle are strictly responded, timeliness and stability of power output are ensured, on the other hand, the model synchronously outputs target heating power, and it is ensured that the heating power is consistent with a request value; the cooperative control of the two can execute the driving torque required by a driver or the whole vehicle and ensure the dynamic property; and the requested heating power can be accurately generated, and efficient collaboration and decoupling of the two can be realized.

The application discloses a land use type prediction method and system based on multi-source data fusion, and the method comprises the following steps: acquiring historical land use and multi-source driving factor grid data of a target region; generating a shape self-adaptive conversion probability through a neural network coupled with deformable convolution; determining future land demand based on scenario and policy coupling; iteratively simulating by using a dynamic double feedback cellular automaton model, the model synchronously adjusts the competition inertia coefficient and the spatial neighborhood weight, and dynamically integrates spatial constraints such as ecological protection red lines in a three-dimensional lookup table; finally, the prediction result is converted into standardized land cover data through a probabilistic multi-level classification mapping engine, and a spatial uncertainty distribution map and an area confidence interval are synchronously generated. The method realizes the deep internalization of regulatory rules, provides complete decision support information containing uncertainty quantification, and provides a scientific and reliable technical tool for land space planning and management.

This invention belongs to the field of axial flow pump control technology, specifically relating to an intelligent optimization control method and system for axial flow pump operation. The method includes: constructing and fusing a Gaussian process regression surrogate model corresponding to the physical mechanism and operating data; the model synchronously outputting performance prediction values ​​and prediction uncertainties; calculating the state entropy corresponding to the current operating condition based on the surrogate model; performing heuristic pruning by using the surrogate model to estimate the change in state entropy; the system selecting a subset of candidate rules and calling the surrogate model to infer and calculate the comprehensive gain value; the inference depth decreasing as the prediction uncertainty increases; establishing a penalty mechanism that applies penalties to the gain value based on historical failure probabilities and the current state entropy value; and the system determining the optimal control rule and issuing commands based on the adjusted gain value. This invention can reduce the underlying computing power consumption, prevent long-term error accumulation, prevent repeated equipment oscillations, and ensure the long-term efficient operation of the axial flow pump.

This invention relates to the field of software development technology, specifically providing a method and system for code generation and isolated execution based on a multi-level model. The method includes: acquiring business domain data containing business objects, field attributes, object relationships, and semantic metadata, and constructing a business ontology model; generating code based on the model, dividing the code into a model layer, a read-only logic layer, and a data operation layer, and configuring differentiated code regeneration rules; embedding the business domain data into the model layer code in a structured manner, enabling direct runtime access; performing layer validity verification at runtime through an import interception mechanism, prohibiting the import of read-only logic layer code into data operation layer code; repeating the aforementioned operations when the model changes, and ensuring idempotency of the code generation process according to the differentiated rules. This invention can standardize the code layering architecture, achieve mandatory runtime call isolation, balance model synchronization and custom logic retention, and adapt to automated development and deployment processes.

The invention discloses an equipment fault processing method and system based on a double-closed-loop self-evolution intelligent agent. The method comprises the steps of performing iterative optimization on a preset multi-task fault detection model based on a preset training period according to operation data of multiple equipment; when an ampere event is triggered, acquiring an equipment ID, an original fault code and current operation data; inputting the current operation data into the optimized preset multi-task fault detection model, and outputting a fault prediction result of the equipment; extracting corresponding maintenance guidance information from the equipment maintenance knowledge base, and generating a fault maintenance work order; carrying out equipment fault processing according to the fault maintenance work order; and after the equipment fault is processed, carrying out iterative updating on the equipment maintenance knowledge base according to the fault maintenance work order. According to the method, model retraining is triggered through operation data, the maintenance conclusion automatically and reversely supplements the knowledge base, synchronous evolution of knowledge and the model is guaranteed, and then the fault diagnosis accuracy and the fault processing efficiency are improved.

The invention belongs to the technical field of model teaching, and particularly discloses a digital twin-driven multi-model synchronous teaching method and system, a terminal and a medium. Comprising the steps that modeling information of target equipment is acquired, and a three-dimensional geometric model behavior model and a signal model are constructed based on the modeling information; establishing a mapping relationship between corresponding parts in the behavior model and the three-dimensional geometric model, and establishing an association relationship between the signal model and the behavior model; in the teaching process, driving the behavior model to change the state according to an instruction, and synchronously triggering the state updating of the three-dimensional geometric model and the signal model; and determining a signal flow path in the current working state, and mapping the signal flow path to a part position corresponding to the three-dimensional geometric model to realize collaborative teaching output of the equipment. The behavior state serves as a unified control core, and synchronous updating and consistent operation of multiple models in the same working state are achieved. Therefore, the structural form, the working logic and the signal processing process of the equipment can be kept consistent and corresponding in any working state.

The embodiment of the invention relates to the technical field of power system simulation, and discloses a test method of a new energy station controller, electronic equipment and a readable storage medium. The method comprises the following steps: establishing a software test model for performing in-loop test on software functions of the controller; establishing a hardware test model for performing in-loop test on hardware functions of the controller; the hardware test model runs in N simulation circuits; wherein N is an integer greater than or equal to 1; comparing the test result of the software test model with the waveform of the hardware test model; if the comparison result is not in the preset error range, adjusting the software test model until the comparison result is in the preset error range; and synchronizing the software test model and the hardware test model based on the first buffer circuit model. Therefore, the two operation carriers and the models with different simulation step lengths are coupled, so that the two operation carriers and the models can be operated as a unified, dynamically interactive and stable simulation system.

This invention provides a lightweight digital twin migration method and system for vehicle-to-everything (V2X) scenarios, relating to the field of urban V2X digital twins. The invention first constructs a V2X digital twin system, including a communication model, a synchronization delay model, a caching model, and a migration model. Based on the migration model, a digital twin migration utility function is defined. Based on the digital twin migration utility function, the communication model, the synchronization delay model, and the caching model, an optimization problem and its constraints for the V2X digital twin system are established. The optimization problem of the V2X digital twin system is solved to obtain the caching server and the optimal migration strategy. The digital twin is then migrated according to the optimal migration strategy, achieving digital twin migration. This invention utilizes a caching mechanism to effectively address the lag in vehicle twin migration and improve migration flexibility. Simultaneously, the use of Tiny MLP ensures effective feature representation capabilities while adapting to resource-constrained or low-latency scenarios, completing the transformation from environmental observation to action decision-making, and achieving digital twin migration while reducing migration time and energy consumption.

This invention discloses an automated model management method suitable for a 3D collaborative design platform, belonging to the fields of architectural design and software development. The 3D design platform includes a web interface, a Windows application interface, and a Revit interface. The method includes: managing and maintaining a professional template library; project planning and MBS (Model-Based Structure) decomposition; the platform, based on a PDCA (Plan-Do-Check-Act) design task engine, categorizing platform users according to project roles; allowing users to jump to the Revit interface for collaborative model design by clicking on a design task in the Windows application interface; using an automated model management method to compare historical versions of submitted model files, enabling the submitting professional designers to quickly see the differences in modifications and model updates between versions; the platform synchronously uploads submitted models to the server, and after lightweighting the 3D model, it attaches it to the MBS decomposed model structure. This efficiently completes design task allocation and collaborative design work, improving design efficiency and quality.

This invention belongs to the field of intelligent logistics and industrial digital twin technology, specifically disclosing an AGV / AMR digital twin operation and maintenance system that integrates dynamic modeling. The system includes a physical entity perception unit, a visual semantic parsing unit, a multibody dynamics solution unit, a twin model synchronization unit, and a parameter evolution control unit. It uses visual semantic parsing to inversely solve for load distribution and structural deformation as dynamic boundary conditions, and combines this with a recursive Newton-Euler algorithm to calculate forces and deformations in real time, driving the synchronous update of the twin's geometric mesh. Simultaneously, it identifies and corrects mass, friction, and stiffness parameters online based on multi-source observation data, and introduces an aging factor to simulate long-term degradation behavior. Through the above technical solutions, this invention achieves high-fidelity, adaptive, and full-lifecycle digital twin construction, improving predictive control accuracy and operational reliability.

The invention relates to the technical field of enterprise intelligent informatization management, and discloses a material application automatic filling and auditing method and system based on multi-agent collaboration.The method comprises the steps that a user submits a material application request through a text, voice or Web form, the system generates a structured intention according to a material application request and calls a retrieval enhancement RAG model to automatically generate a material application form, then four-dimensional parallel verification is carried out on format, inventory, budget and policy to obtain a comprehensive verification result, and synchronous scoring is carried out through a rule-machine learning double-track auditing model. Three-level risk judgment of low-risk automatic order passing, medium-risk time-limited sampling inspection and high-risk forced manual rechecking is started during conflict, a rechecking result is online used for incremental updating of the model and the rule base, and finally an approval report is generated and written into the block chain. Through the RAG enhancement technology and a multi-agent cooperation mechanism, the whole process, dynamic and intelligent management and control effect of the material application from intention understanding to final auditing is achieved.

The invention relates to an object model dynamic combination method and system based on capability componentization, and belongs to the technical field of the Internet of Things. Equipment functions are disassembled into independent reusable capability units through a capability componentization mechanism, when equipment is online, only a capability list needs to be declarated, and the capability units can be reused; and the system automatically obtains units from the capability warehouse and dynamically combines the units to generate a target object model. Dependence check and conflict detection are introduced in the combination process to ensure that the model is complete and consistent. And meanwhile, versioning management and gray scale upgrading of the capability units and automatic generation of Mock test codes are supported, so that the test efficiency is improved. The system is deployed at the cloud end, the edge end and the equipment end in a distributed mode, and model synchronization consistency is guaranteed through a consensus protocol. According to the method, the object model coupling degree is remarkably reduced, plug-and-play access is achieved, the method is suitable for complex scenes such as the power internet of things, and the equipment management flexibility and the system reliability are improved.

The application provides a power plant boiler fault deduction method, device and system, the method comprises the following steps: constructing an operation architecture, and constructing a first simulation operation model and a second simulation operation model of the power plant boiler; synchronously performing online correction on the simulation operation model, and monitoring the output parameters of the first simulation operation model; under abnormal conditions, calling the second simulation operation model to perform accelerated simulation, and obtaining a predicted accident result by combining multi-layer accident chain analysis. Through the parallel double simulation model, the application solves the operation resource conflict problem of real-time working condition simulation and fault deduction, considers the accuracy of real-time monitoring and the timeliness of fault deduction, combines synchronous online correction, and improves the reliability of the fault deduction result; through calling the independent model to perform accelerated simulation, the fault early warning reference can be provided in time, and through combining the multi-layer accident chain analysis, the modeling specification of accident deduction is unified, and the prediction accuracy of the fault spreading path and the final consequence is improved.

The application discloses a whole machine variable dimension simulation method based on a circumferential average through-flow model, belongs to the technical field of numerical simulation and multi-scale modeling of an aero-engine, and solves the problems that in the prior art, the simulation efficiency and the local flow detail accuracy are difficult to be considered, and the traditional full three-dimensional calculation resource consumption is too large and the whole machine performance cannot be quickly evaluated. The application first establishes a zero-dimensional whole machine and a two-dimensional component model of an aero-engine, reduces the dimension by using a circumferential average N-S equation, embeds the two-dimensional model into the zero-dimensional model, solves the equation group by equivalent replacement of variables and reconstruction, realizes synchronous convergence of the whole machine and the component model, and improves the simulation accuracy and efficiency. The application adopts a full coupling mode, combines two-dimensional dimension reduction modeling, realizes minute-level convergence and simulation error ≯ 5%, improves the simulation accuracy, improves the calculation efficiency, can provide rich S2 flow field information, and reduces the research and development cost and period.