151 results about "Multiple Models" patented technology

This invention provides a method for rapid assembly of a modular tubular beam car body. The car body is divided into several independent modules, which are manufactured in parallel at independent workstations or assembly lines. Corresponding connections between different independent modules are fitted with interlocking mortise and tenon structures for coarse positioning and fastening. The manufactured independent modules are then automatically transferred to an assembly station, where a robot grasps and rapidly assembles them. After assembly, bolts and / or structural adhesive are used to fasten the mortise and tenon joints. Compared to traditional assembly line production processes, this method significantly improves efficiency and economy, effectively reduces floor space, and overcomes the limitation of existing "unpacking processes" that are only suitable for single-model production, thus facilitating flexible manufacturing and rapid iteration across multiple models.

The application discloses a multi-model adaptive low-temperature valve sealing performance testing device and method, and belongs to the technical field of valve detection. The device comprises: a first upper end cover of a low-temperature thermostat, wherein a to-be-tested low-temperature valve is installed on the first upper end cover through a valve support device; the valve support device is in flexible sealing cooperation with a bellows through an adjusting screw, so that the installation height and centering of the to-be-tested low-temperature valve are controlled, and low-temperature valves of different specifications are adapted; the low-temperature thermostat surrounds a first cold screen and a second cold screen; the bottom of the to-be-tested low-temperature valve is connected with one end of a cold-lead bridge through a pipe clamp and is surrounded by the first cold screen; a GM refrigerating machine comprises two-stage cold heads arranged in the low-temperature thermostat; the second cold screen surrounds the two-stage cold heads; the other end of the cold-lead bridge is connected with the two-stage cold heads; three helium supply devices are connected with the inlet and outlet of the to-be-tested low-temperature valve and the low-temperature thermostat respectively; and a vacuum pump unit is connected with the low-temperature thermostat. The application realizes rapid adaptation and accurate testing of low-temperature valves of multiple models.

The utility model belongs to numerical control bending machine field, specifically speaking, it is quick die change and angle calibration device of numerical control bending machine, including support seat, the top of support seat is connected with fixed link, the top of fixed link is connected with top plate, the top of top plate is connected with hydraulic cylinder, the piston rod bottom of hydraulic cylinder is connected with movable plate, the bottom of movable plate is connected with two top supports, through the structural design of support seat, fixed link, top plate, hydraulic cylinder, movable plate, top support, bottom support, top die mounting mechanism and bottom die mounting structure, the function that a plurality of models of moulds were changed has been realized, the problem that single only can replace one model mould has been solved, the efficiency of mould replacement has been improved, can be effectively locked in the structure design of locking mechanism after changing, prevents the problem of accidental rotation, improves the stability when using the mould.

Embodiments of the present application provide a model training method and system, and a storage medium, comprising: obtaining a heterogeneous distribution semi-supervised data set; determining a semi-supervised learning algorithm according to meta features of the heterogeneous distribution semi-supervised data set; obtaining M models and weights corresponding to the M models respectively according to the semi-supervised learning algorithm and the heterogeneous distribution semi-supervised data set, wherein the accuracy of the M models satisfies a preset condition. Through the embodiments of the present application, based on the heterogeneous distribution semi-supervised data set, the semi-supervised learning algorithm is determined according to the meta features of the heterogeneous distribution semi-supervised data set, and M models and weights corresponding to the M models respectively are obtained according to the semi-supervised learning algorithm and the heterogeneous distribution semi-supervised data set. By using this means, multiple models are obtained based on the heterogeneous distribution semi-supervised data set, the accuracy of the model obtained in the heterogeneous distribution semi-supervised data set scenario is improved, and the delivery efficiency and delivery quality are improved.

This invention belongs to the field of aviation maintenance training technology and discloses a teaching and training system suitable for borescope inspection systems of civil aircraft engines. The system includes: a handheld borescope virtual simulation unit, a civil aircraft engine borescope training cabin and virtual simulation main unit cabinet, and a virtual scene and teaching display unit. Through the collaborative construction of these three units, a virtual-real integrated teaching and training system is established, completely recreating the entire process environment of non-destructive borescope inspection of civil aircraft engines. It can simulate various engine models and borescope equipment adaptation scenarios, reproduce typical faults and damages in engine internal components such as blades and turbine disks, and support operating condition simulations under different environmental factors. This allows trainees to conduct repeated practical training without relying on real engines, effectively reducing training costs and safety risks, comprehensively improving trainees' operational proficiency and fault handling capabilities in complex scenarios, and overcoming the core shortcomings of traditional training scenarios, such as limited scenarios and difficulty in reproducing faults.

This application provides a multi-intent prediction model training method and a multi-intent recommendation method, relating to the field of artificial intelligence technology. The multi-intent prediction model training method locates multiple target candidate intents of users to the dimension of target scene, constructs a unified sample for multiple target candidate intents in the target scene, and trains a model using the unified sample. The resulting model can predict the probability of occurrence of multiple target candidate intents in this target scene. It eliminates the need to construct samples separately for each target candidate intent and train the model separately using the constructed samples, thereby avoiding repetitive sampling operations for different intents at the same sampling time, and avoiding multiple model trainings, which can save manpower and resources and reduce costs.

The utility model discloses a kind of hydraulic pressure bearing plate structures of pile foundation static load detection, comprising: bearing plate main body, the upper end of the bearing plate main body is provided with multiple angle plates of uniform distribution, the upper end of the bearing plate main body is contacted with hydraulic jack, the outside of the bearing plate main body is provided with the measuring assembly for measuring the position of hydraulic jack;The outside of the bearing plate main body is provided with the mounting assembly for installing measuring assembly, the utility model is measured by being provided with the measuring assembly for measuring the position of hydraulic jack, measuring assembly adopts modularization design, can be quickly adjusted mounting position according to different specifications bearing plate, adapt multiple models of hydraulic jack, simultaneously avoid the difficulty of manual installation, realize accurate position measurement and ensure the accurate installation positioning of hydraulic jack, avoid the problem of uneven pressure distribution due to installation deviation, provide reliable basis for data acquisition.

This invention belongs to the field of hydraulic structure analysis technology, specifically disclosing a gravity dam coupling analysis method, system, electronic equipment, and storage medium, including: models describing water flow in concrete, the relationship between water pressure and stress, the dissolution reaction process, and the coupling of ion diffusion with water flow. By acquiring influencing variables, parameters of each model are correlated, and a solubility function considering performance parameters is established and dynamically updated. The solubility function is iteratively trained using experimental data to construct a finite element model, and simulations are performed based on the trained function. Early warnings are generated based on the simulation results to predict the concrete deterioration process and optimize maintenance decisions. It has the following advantages: by coupling multiple models such as water flow, chemical dissolution, and hydraulic fracturing, the long-term deterioration process of concrete under the action of water flow and dissolution is accurately simulated. The introduction of a solubility function and dynamic updating of material parameters, considering time-varying deterioration effects, comprehensively improves the accuracy of deterioration prediction and simulation results.

The application discloses a lightning impulse grounding grid frequency characteristic analysis method, system, device and medium, including: obtaining initial sample data; using the sample and a preset test frequency set to construct at least two different structure rational function interpolation models, and calculating the output residual thereof in the test frequency band; determining the target frequency point with the maximum modeling uncertainty according to the residual, calling the moment method to supplement high-precision response samples at the point; based on the updated sample set, constructing a diagonal or near-diagonal form rational interpolation model, and judging whether the prediction error of the model at the target frequency point is lower than a preset convergence threshold; if the convergence condition is not met, returning to the multiple model construction step, reiterating using the current sample set until the error meets the standard, and finally outputting the grounding grid frequency response characteristics covering the lightning impulse frequency band. The application realizes efficient analysis of the grounding grid frequency characteristics under lightning impulse, and effectively reduces the time used for analyzing the grounding grid frequency characteristics under lightning impulse.

The application provides a ship identification model training method, a cloud server and a ship identification system. The method comprises the following steps: inputting sample images of ships of multiple models as a first training set into an initial ship identification model, and determining a first loss value of the initial ship identification model under initial model parameters; inputting the first loss value into the initial ship identification model in reverse, adjusting the initial model parameters and iteratively executing until the first loss value output by the initial ship identification model meets a preset condition, and obtaining a target ship identification model; inputting multiple new sample images of a new ship as a second training set, performing incremental learning and updating of the target ship identification model according to the second training set and a pre-training language model, obtaining an updated ship identification model, and sending the updated ship identification model to each edge node device, so that each edge node device performs ship identification based on the updated ship identification model, and the ship identification precision is improved.

This invention belongs to the field of information detection, specifically referring to a method and system for detecting misleading information based on artificial intelligence. The method includes large language model construction, early hallucination detection, multi-model response fusion graph, and hierarchical conflict detection logic. This solution selects multiple models to generate independent responses, constructs an initial graph, filters a subset of core triples based on confidence levels, mines candidate implicit relationships, and filters effective implicit relationships to complete the graph. It updates the confidence level, classifying triples into consensus, disputed, and isolated facts. The hierarchical conflict detection logic reduces the impact of semantic bias on confidence levels, clarifies conflict judgment criteria, and improves the accuracy of hallucination detection while reducing the false positive rate. By extracting four types of representations from the first word in the model's generated factual response and before the complete response is output, it achieves early hallucination detection, predicting whether hallucinations will occur without waiting for the complete output.

The application provides a multi-source Parkinson's disease database integration method based on LEDD-UPDRS composite index alignment, and is applied to the technical field of data processing. In view of the heterogeneous problems of multi-source Parkinson's disease databases, the application integrates heterogeneous data of PPMI, PDBP and local queues, disassembles characteristics according to four modes of heredity and clinic, introduces a standardized term library and a BERT-BiLSTM-CRF model unified term adjusted in combination with medical corpus, generates a cross-library feature table; a storage architecture is constructed based on FlashROM mode to realize data storage and retrieval; a composite observation system is built with LEDD and UPDRS-III, consistency is verified by multiple methods, data is standardized, missing data is filled in combination with multiple models to generate an integrated data set; a double prediction model is built relying on the data set, and the advantages of the data are realized by a Cohen's d value verification method; finally, cross-library joint analysis is carried out, phenotype-genotype correlation modeling and causal inference are completed, and data support is provided for Parkinson's disease research and clinical prediction.

The application discloses a dynamic prototype multi-model medical image classification method based on feature credibility evaluation, and relates to the technical field of medical image processing.The method effectively solves the problems in traditional medical image subtype classification, such as lack of high-quality labeled data, high uncertainty of pseudo-labels, and difficulty of static prototypes in adapting to dynamic changes of lesions, and the like.Through construction of a multi-structure feature extraction network and completion of hierarchical feature fusion, the method combines feature cross learning, local attention modeling and expanded convolution to supplement context information, and strengthens semantic consistency and structural continuity of lesion region features.Meanwhile, the method constructs a spatial similarity graph through cosine similarity, and models and fuses a spatial uncertainty graph with any and cognitive uncertainty based on a Dirichlet distribution, obtains a reliable evidence graph through exponential fusion, and generates a pseudo-label with sample-level confidence, so that effective supervision information is accurately screened from a feature level, cumulative deviation of false pseudo-labels is greatly reduced, and stability of a semi-supervised learning process is improved.

This invention belongs to the field of seismic exploration technology and relates to a multi-model rapid tomographic imaging method with hard constraints based on physical information. The method includes constructing a PIUFNO network model under hard constraints; using travel time perturbations from different seismic sources and background travel time as network inputs, the network directly outputs the corresponding subsurface velocity structure, establishing an end-to-end nonlinear mapping relationship between input and output; incorporating the physical criteria of seismic wave transmission as a hard constraint condition into the loss function to construct a physical constraint loss function and complete model training; after the network training converges and parameters are optimized, new measured travel time perturbation data and corresponding background travel time data are input into the model to deduce the subsurface medium velocity distribution structure, achieving rapid imaging of the subsurface velocity structure. This invention effectively solves the limitation of traditional tomographic imaging, which can only train a single model; through a single training, it can achieve efficient and accurate imaging of multiple models, applicable to different velocity models.

1. Name of the product in this design: Welding Machine Housing (ARES Mini). 2. Purpose of this design: This design is an accessory for welding machines, which is used to be installed on the outside of the main body of the welding machine. It can be used in various models of welding machines and serves as a protective, support and decorative function. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

1. The name of the design product: electric welding machine shell (ARES-SD). 2. The use of the design product: for installation outside the main body of the electric welding machine, it is a part of the electric welding machine, and can be used for various models of electric welding machines, and plays a protective, supporting and appearance decoration role. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The application relates to a river closure date prediction method based on historical similar year performance driving, which comprises the following steps: using a plurality of feature selection algorithms and leave-one-out cross-validation to screen target predictor sets respectively matched with selected machine learning models and statistical models; based on parameter sensitivity analysis and a Bayesian optimization algorithm, sensitive hyperparameters of the machine learning models are optimized to obtain optimized machine learning models; the statistical models and the optimized machine learning models are configured as candidate river closure date prediction models; a K-neighbor algorithm is used to search a set of similar historical years in a historical observation data set according to current observation data, and a target river closure date prediction model is dynamically optimized according to the comprehensive prediction error of the candidate river closure date prediction models on the set of similar historical years, and a prediction result is output, so that the advantages of multiple models are effectively fused, the generalization limitation of a single model in a complex non-stationary environment is avoided, and the accuracy and robustness of the prediction result are significantly improved.

The application discloses a kind of based on TCN-BiLSTM-FCAM model's aero-engine residual life prediction method and system, it is related to aero-engine prediction and health management (PHM) technical field, the model includes TCN layer, BiLSTM layer, FCAM module and life prediction regression layer.This method is first obtained aero-engine multi-sensor operating data and pre-processes;Local degradation features are extracted using TCN;Global context dependence is captured by BiLSTM;Key time step context vector is obtained by FCAM weighting;Finally, the predicted value is output by the regression layer.The present application integrates the advantages of multiple models, reduces complexity, avoids overfitting and data pollution, and is suitable for aero-engine residual life prediction under different operating conditions and failure modes.

This invention provides a method for generating complex functions on a low-code platform based on large model code interpretation and generation. By capturing and standardizing user input, a structured intent graph containing business entities, operational relationships, parameter constraints, and control flow topology is constructed as a unified semantic anchor. Combined with a pre-defined domain rule base, a semantic alignment validator is designed to achieve multi-dimensional semantic comparison and conflict detection between code and requirements. Code snippets are generated in parallel by multiple models and verified in real time, and semantic conflicts are resolved through local regeneration. By fusing the results of multiple models to construct a joint abstract syntax tree, lightweight symbolic execution is used to simulate the correctness of cross-model code, locate and correct core logical conflicts. This invention significantly improves the consistency, accuracy, and automatic correction capabilities of low-code platform code generation.

A method for maintaining physical assets based on recommendations generated from operational data analysis and a composite model representing multiple models of the physical assets includes: constructing the composite model by a computing device using a port-reduced static condensed reduction primitive approximation with respect to at least a portion of a partial differential equation; the computing device analyzing error indices associated with at least one model within the composite model to determine if the error indices exceed a tolerance level, and accordingly increasing the number of basis functions in the port-reduced static condensed reduction primitive approximation; the computing device receiving first operational data associated with at least one region of the physical asset and updating the composite model; and the computing device providing recommendations for maintaining the physical asset based on the updated composite model.

The present application relates to a real-time artificial intelligence system for production line edge, which comprises a data collection system, a statistical analysis system, an artificial intelligence modeling system and an automatic tuning system. The data collection system is used to preprocess and clean the data. The statistical analysis system analyzes the preprocessed and cleaned data to generate a dataset. The artificial intelligence modeling system is used to run and evaluate the dataset with multiple models to complete the creation and deployment of an AI model. Finally, when the AI model is deployed on a production line, the automatic tuning system is used to automatically tune the AI model according to the latest data of the production line.

This invention provides a method and system for predicting tourism review sentiment based on knowledge graph and multimodal feature fusion, relating to the field of natural language processing. The method includes: collecting multiple tourism reviews from a target region; extracting a comprehensive feature matrix from the multiple tourism reviews based on LDA and TF-IDF; extracting context-enhanced features from the multiple tourism reviews based on a knowledge graph; filtering the comprehensive feature matrix and context-enhanced features of the multiple tourism reviews to select multiple optimal features; and performing sentiment prediction of the reviews in the target region based on multiple optimal features using multiple models. This method has the advantages of improving the accuracy of tourism review sentiment prediction and further providing scientific support for smart tourism services.

This application discloses a method, apparatus, device, and storage medium for multiple suppression, belonging to the field of seismic data processing technology. The method includes: acquiring seismic data and a first multiple model, wherein the multiples represented by the first multiple model deviate from those represented in the seismic data; in a non-stationary state, performing pooling processing on the seismic data and the first multiple model to obtain pooled seismic data and a pooled multiple model, both retaining regular signal characteristics; determining a sparse first prediction filter based on the pooled seismic data and the pooled multiple model; interpolating the first prediction filter to obtain a complete second prediction filter; predicting a second multiple model based on the second prediction filter and the first multiple model; and performing matched subtraction on the seismic data and the second multiple model to obtain the multiple-suppressed seismic data. This scheme improves the computational efficiency of the suppression process.

Entity extraction is performed using a voting ensemble approach to enhance the accuracy and effectiveness of entity extraction processes. Voting ensemble weights are stored. In turn, a prediction value for a first entity is determined from multiple models that are specifically trained for entity extraction. A plurality of first entity prediction values is obtained. The model with the highest prediction value among the plurality of first entity prediction values is then selected.

One embodiment of this disclosure discloses a method for generating a training dataset based on endoscopic images, executed by a computing device including at least one processor. The method includes the following steps: inputting raw data into multiple models trained to infer valid information about endoscopic images of the interior of the body; generating final valid information about the raw data based on the valid information output by each model; and generating a training dataset containing the raw data and the final valid information, wherein the final valid information is information needed to train a model for analyzing the endoscopic images using the training dataset, including labels for the raw data or additional information about the labels.

This invention discloses a method, system, device, and medium for energy efficiency assessment based on digital twins, specifically relating to the fields of digital twins and energy efficiency assessment technology. The method includes: collecting multi-source data from energy-consuming systems, generating time-series datasets, and constructing equipment topology diagrams and operating condition labels; constructing a hybrid twin model pool comprising high-fidelity mechanisms, lightweight data-driven models, and reduced-order models; generating a fusion model and confidence sequence through weighted fusion using a dynamic scheduler; performing root cause diagnosis based on causal graphs and counterfactual reasoning; encapsulating the fusion model into a reinforcement learning environment, offline training a policy network to output the optimal action sequence and predicted energy efficiency gain curve; calculating a comprehensive energy efficiency assessment index to determine whether to issue control commands, and judging whether to update the model based on confidence and prediction errors. This invention achieves high-precision energy efficiency assessment, root cause diagnosis, and autonomous optimization control of energy-consuming systems through dynamic fusion of multiple models and causal inference.

The application discloses a kind of plan optimization systems of distribution network maintenance, it is related to power grid management technical field, including intelligent maintenance dispatching optimization module, equipment monitoring and prediction module, panoramic maintenance plan simulation module, dynamic risk assessment module, inspection robot management module.Intelligent maintenance dispatching optimization module is used to analyze distribution network data, equipment monitoring and prediction module is used to monitor distribution network equipment operating state, and future fault occurrence time and position are predicted.Panoramic maintenance plan simulation module carries out dynamic simulation to maintenance scheme.The application introduces multiple models, effectively overcomes the deficiency of traditional technology in flexibility, real-time and accuracy, so that the system has adaptive learning and dynamic adjustment capability, can quickly respond to failure in complex and changeable working environment, optimize resource scheduling, and significantly reduce maintenance cost and time, the system greatly improves the stability and reliability of distribution network equipment, provides solid technical support for intelligent power operation and maintenance.

This invention provides a method for generating training datasets based on endoscopic images taken by a computing device containing at least one processor. [Solution] The method includes the steps of: inputting raw data into multiple models trained to infer useful information about endoscopic images taken inside the body (S110); generating final useful information for the raw data based on the useful information output from each model (S120); and generating a training dataset including the raw data and the final useful information (S130). The final useful information is information necessary to train a model for analyzing endoscopic images using the training dataset, and includes labels for the raw data or additional information about the labels.

The application discloses an omnidirectional guiding interaction multi-model positioning method and system for a motorized mother ship to recover an underwater robot and a storage medium, the method obtains the state information and distance data of the mother ship by integrating a water acoustic ranging and a water acoustic communication device, constructs an interaction multi-model singular value decomposition unscented Kalman filtering algorithm fusing a uniform straight line and a uniform bow turning model, and realizes real-time estimation of the position of the mother ship under a mouth-shaped maneuvering track. An event-triggered communication mechanism is designed, information is broadcast only when the turning state of the mother ship changes, so as to reduce the communication load and maintain real-time performance; meanwhile, a Markov transition matrix is updated, the turning model weight is enhanced, and the estimation accuracy and model matching are improved. According to the model probability and the estimation result, a LOS guiding law with an adaptive look-ahead distance is constructed, and the autonomous guiding recovery of the underwater robot to the motorized mother ship is realized. The method provided by the application can realize dynamic estimation and guiding based on the measurement data of a single water acoustic device under the conditions of the mother ship maneuvering and communication limitation.