89 results about "Model synchronization" patented technology

The invention discloses an intelligent supervision management and control method and system, and the method comprises the steps: generating a violation operation recognition result and an equipment abnormality early warning signal according to construction scene multi-mode data collected by an intelligent camera and real-time monitoring information of an Internet of Things sensor; generating a non-tampering block chain evidence storage data chain based on the violation operation identification result and the equipment abnormity early warning signal; according to the engineering change information in the block chain evidence storage data chain, updating a BIM collaboration platform through an incremental three-dimensional model synchronization technology, and generating a multi-party real-time interaction enhanced three-dimensional model; based on an enhanced three-dimensional model and Internet of Things environment monitoring data, a digital twin mirror image space is constructed, a construction process is simulated through a multi-source data fusion deduction engine, and a risk pre-judgment map and a resource configuration optimization strategy are generated. According to the embodiment of the invention, visual supervision, credible tracing and prospective decision making of the construction process can be realized.

The invention relates to a multi-source monitoring and dynamic regulation and control method and system for a mill, a terminal and a medium, and belongs to the field of mill equipment control. According to the method, multi-source data such as spindle vibration, grinding cavity acoustic emission, gearbox temperature and barrel rotating speed are collected, and multi-dimensional features are extracted by combining first-order difference, energy density spectrum and thermal imaging technologies; and constructing a regression model fusing a linear term and a Gaussian kernel function, and synchronously predicting the operation efficiency, the energy consumption and the grinding particle size. And parameters such as the rotating speed and the feeding amount are dynamically adjusted based on the prediction result, and a three-level early warning mechanism is established. The system comprises a template management module, a data acquisition module, a feature extraction module, a modeling and prediction module, a dynamic control module and an early warning management module, and supports scenarized parameter configuration and adaptive regulation and control. Through multi-source data fusion and an intelligent prediction model, the problems of parameter stiffness and response lag of a traditional mill are solved, the working condition adaptability is remarkably improved, and efficient and safe operation of equipment is guaranteed.

The invention discloses a complete machine variable-dimension simulation method based on a circumferential average flow model, belongs to the technical field of aero-engine numerical simulation and multi-scale modeling, and solves the problem that complete machine simulation efficiency and local flow detail precision are difficult to consider at the same time in the prior art. And traditional full-three-dimensional computing is too high in resource consumption and cannot quickly evaluate the performance of the whole machine. According to the method, a zero-dimensional complete machine and component two-dimensional model of the aero-engine is firstly established, dimensionality reduction is carried out by using a circumferential average N-S equation, the two-dimensional model is embedded into the zero-dimensional model, an equivalent replacement variable reconstruction equation set is solved, synchronous convergence of the complete machine and component models is realized, and simulation precision and efficiency are improved. According to the method, a full coupling mode is adopted, two-dimensional dimension reduction modeling is combined, minute-level convergence is achieved, the simulation error is not larger than 5%, the simulation precision is improved, the calculation efficiency is improved, rich S2 flow field information can be provided, and the research and development cost and period are reduced.

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 relates to a data model construction and simulation method for mathematical data operation processing. The method comprises the steps that self-supervised learning is conducted on multi-modal data of a physical system corresponding to a mathematical data operation processing scene, and a unified embedding space is constructed; based on the unified embedding space, constructing a quantum-neural differential equation hybrid model; based on a digital twin interface, synchronizing a physical system and a simulation environment according to the quantum-neural differential equation hybrid model to obtain a simulation result; and based on a simulation result, dynamically adjusting model parameters of the quantum-neural differential equation hybrid model through reinforcement learning to obtain a corrected quantum-neural differential equation hybrid model. According to the method, precise modeling and efficient simulation of the physical system in a complex mathematical data operation processing scene and dynamic optimization of model parameters are achieved, the simulation precision and adaptability to the physical system are effectively improved, and the problems of multi-modal data fusion processing, complex physical system dynamic evolution simulation and the like can be better solved.

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 a vehicle-mounted system multi-vehicle-type code synchronization method and system based on submitted information, and solves the problems of complexity, accuracy and traceability of multi-warehouse multi-branch code synchronization in vehicle-mounted system development through triple cooperation of submitted information marking, scripted branch management and an automatic assembly line. The core innovation points comprise: 1) submitting an information embedded branch routing technology; 2) dynamic creation and life cycle management of temporary branches; and 3) an automatic generation mechanism of a bidirectional merging request. Experimental data show that according to the scheme, the multi-vehicle-type synchronization efficiency is improved by 80% or above, and the manual error rate is reduced to 2% or below.

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 natural language processing, and particularly discloses a method for performing adaptive fine tuning on LLM based on federated learning, which optimizes a global network model based on federated learning, and enables model design to be highly matched with task requirements by analyzing client performance feedback and adaptively and dynamically adjusting client model configuration by a server. The calculation and communication overhead is obviously reduced, and meanwhile, the personalized adaptation performance is improved; the server performs fine-grained evaluation on client feedback in each iteration, rejects lagged clients which are not trained enough, and supplements a part of missing model layers of the clients by using a historical optimal weight; during model aggregation, a dynamic rotation strategy based on semantic similarity is adopted, and semantic conflicts caused by hierarchy differences are reduced by intelligently adjusting the sequence of client model layers. According to the method, the training efficiency and accuracy of the model are remarkably improved through self-adaptive adjustment of the client model, synchronization of the server-client model and a dynamic rotation strategy.

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 present invention discloses a quasi-minimum volume bounding box optimization method for a three-dimensional point set of an inclined mold, comprising the following steps: S1, based on the principle that a convex multi-deformation in two-dimensional space and a minimum enclosing rectangle must be collinear, the concept of a minimum volume constant coefficient is introduced, and a rotation axis calculation model for a quasi-minimum bounding box of a three-dimensional model is established using a linear space principle; S2, based on vertex area weighting and model detail feature evaluation, a two-dimensional projection vertex weight mathematical model is established; S3, the bounding box accuracy λ is set, a preliminary projection plane YOZ is determined based on the synchronization of the X axis based on the rotation axis calculation model, a covariance matrix model is constructed based on the two-dimensional projection vertex weight mathematical model and the projection vertex set, and then the vertex projection basis is solved, the Y and Z axes are synchronized, and the AABB algorithm is called to calculate the volume of the bounding box; S4, the Y and Z axes are used as the rotation axes to calculate the model synchronization object and S3 is iteratively repeated, thereby obtaining a quasi-minimum volume bounding box; the present invention achieves a reduction in bounding box solution time and an improvement in fitting rate.

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 invention relates to the technical field of engineering management informatization, in particular to a method and system for synchronously updating a P6 progress plan and a BIM model. The method comprises the following steps that S1, an engineering project is decomposed according to levels, a project decomposition table is generated, and the project decomposition table comprises a plurality of tasks; allocating job codes to tasks in the project decomposition table according to coding rules of the job codes; s2, generating a P6 progress plan with job codes according to the project decomposition table; s3, inputting the job code into a BIM component primitive in the BIM model, and establishing an association relationship between the P6 progress plan and the BIM model; and S4, importing the progress information of the BIM model into the P6 progress plan according to the association relationship, so as to update the progress of the engineering project. According to the method disclosed by the invention, the uniqueness of the encoding of the primitive component of the BIM model is ensured, the encoding can be correspondingly associated with the operation task in the P6 construction plan, and the problem that the existing P6 construction plan cannot be synchronously updated according to the completion condition of the component in the BIM model is solved.

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.