546 results about "Reduced model" patented technology

The embodiment of the invention provides a method for reducing model output illusion based on knowledge retrieval. The method comprises the following steps: constructing a knowledge base for storing knowledge entries; receiving a question text input by a user, converting the question text into vector representation, performing matching retrieval on the vector representation in the knowledge base, and retrieving and outputting candidate knowledge entries; obtaining an output answer text of the question text generated by the large model, comparing the candidate knowledge entries with the output answer text, and judging whether the candidate knowledge entries violate the output answer text or not; and performing illusion detection on the output answer text, correcting the output answer text according to an illusion detection result, and outputting an answer corresponding to the question input by the user. According to the method, real-time or near-real-time verification and intervention can be carried out on model output, and illusion is inhibited from the source or the early stage.

The positioning error of a feeding system obviously influences the machining precision, especially under the high-speed and high-load working condition. An existing positioning error compensation system often faces the problems of high calculation cost and complex sensor configuration and is difficult to be widely applied to the industry. Therefore, a simplified model needs to be established and an economical and efficient sensor integration and real-time compensation scheme needs to be developed urgently. Therefore, the invention discloses a dynamic positioning error compensation system of a numerical control machine tool feed driving system, and provides an error suppression system fusing a mechanism driving positioning error prediction model and a real-time compensation strategy. The positioning error prediction model is constructed as a hyperbolic cosine function of temperature and nut position, and the residual error-based dynamic compensation model can effectively process unmodeled errors and system dynamic changes. The dynamic positioning error compensation system shows excellent adaptability and precision under the dynamic working condition, and an extensible practical solution is provided for positioning error suppression in the high-precision manufacturing fields of aerospace, automobiles, electronics and the like.

The invention relates to the technical field of edge computing, distributed artificial intelligence and multi-agent collaboration, in particular to a cross-device multi-agent edge collaborative reasoning system and method, and the system comprises a cloud coordination module which is responsible for maintaining main model parameters, grading based on importance scores, and receiving edge device data to optimize a model; the edge management module communicates with the cloud, receives parameter grading data, allocates parameter subsets according to equipment resources, integrates inference experience and returns the inference experience to the cloud; the equipment execution module receives the parameter subset, executes lightweight reasoning, records experience and sends a cooperation request; the distributed knowledge distillation module realizes multi-level heterogeneous distillation, differential parameter selection and adaptive parameter compression; the collaborative reasoning module constructs a dynamic collaborative topology, allocates subtasks and integrates multi-device results; according to the system, the edge resource demand is obviously reduced, the model volume is reduced by 75-95%, 91% reasoning precision is kept, and efficient collaborative reasoning is realized.

The invention discloses a transmission tower vulnerability analysis method and system based on seismic simulation, and belongs to the technical field of electric power facility disaster prevention, and the method comprises the steps: correcting bedrock seismic oscillation parameters based on equivalent shear wave velocity, and generating a high-precision earth surface PGA distribution diagram; automatically positioning a key node layer through eigenvalue decomposition, reducing the order of the three-dimensional model of the power transmission tower into a two-dimensional series multi-degree-of-freedom model, and outputting a layer mass vector, a condensation stiffness matrix and rod piece bending stiffness; performing time-history analysis by using the corrected PGA-driven reduced-order model to generate an earthquake damage probability; the multi-source remote sensing data and the PGA are fused, the landslide space probability is output through a deep learning model, and the impact strength and the landslide kinetic energy are calculated to judge the landslide damage probability; and based on the dual-threshold condition triggering probability union set, calculating a comprehensive damage probability, and generating a three-dimensional vulnerable curved surface. According to the method, the problems of high missing report rate and low calculation efficiency of single disaster assessment are solved, and minute-level accurate early warning of the composite disaster risk of the power transmission tower is realized.

The invention discloses a sudden drought identification method and system based on a space-time double-branch fusion model. The method comprises the steps that meteorological data are acquired and preprocessed; calculating a composite sudden drought index according to the obtained data; performing data dimension reduction on the obtained data through singular value decomposition (SVD); a deep learning model is adopted to construct a time branch model, a graph attention network GAT is adopted to construct a space branch model, and the time branch model and the space branch model are dynamically fused through a cross attention mechanism to construct a space-time double-branch fusion model; according to the method, data dimensions are compressed and model complexity is reduced by fusing multi-source variables and combining singular value decomposition (SVD), meanwhile, geographic neighborhood weights are dynamically learned by adopting Transforme and based on a graph attention network (GAT), dynamic fusion of spatial-temporal characteristics is finally realized through a cross attention mechanism, a sudden drought recognition result is generated, and the method has the advantages of being high in robustness, high in accuracy and high in reliability. The limitation of a traditional method on nonlinear feature capture, space-time modeling splitting and generalization ability is broken through.

The invention is suitable for the technical field of computer vision and deep learning, and provides a multi-scale feature fusion concrete defect detection method based on improved SAM, and the method comprises the steps: obtaining a concrete defect image data set; preprocessing the image data set and dividing the image data set into a training set, a verification set and a test set; the YOLOv9 is trained to automatically detect concrete defects; constructing an improved SAM model, replacing a prompt encoder of the SAM with YOLOv9 and edge detection, and modifying a mask encoder of the SAM; the training effect of the model is evaluated through the four indexes of the accuracy rate, the recall rate, the F1 value and the intersection-to-union ratio, and the improved SAM model is optimized according to the training effect. By improving the structure of the SAM, the detection capability of the model on small defects can be enhanced, and the detection accuracy and efficiency are improved. In addition, according to the method, the complexity of the model can be reduced, and the training efficiency and generalization ability of the model are improved.

The invention discloses a high-precision electrical calibration method and system for a wedge flowmeter. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, dynamic working condition identification; 3, updating the self-adaptive model; 4, multi-source data fusion calibration is carried out; and 5, performing closed-loop verification and compensation. According to the invention, multiple types of signals are collected in real time through the multi-dimensional sensor array and de-noised to construct a data set, so that comprehensive and accurate data collection is realized, and a good basis is provided for calibration; carrying out dynamic working condition identification by utilizing a support vector machine, triggering model updating, and endowing calibration with dynamic self-adaptive capability; model parameters are updated by adopting a recursive least square method, compensation items are established, and the characterization capability of the model is enhanced; a calibration coefficient is generated by means of a data fusion algorithm, and the calibration precision is improved; through verification and compensation of a digital twinborn model, traditional defects are effectively overcome, model transplantation errors are reduced, the measurement precision of the wedge flowmeter is improved under extreme working conditions, and production stability and product quality are guaranteed.

The invention requests to protect an unmanned aerial vehicle light-weight small target detection method based on a YOLO-GLL neural network, and the method comprises the following main steps: S1, constructing a YOLO-GLL target detection model based on YOLOv8s, and inputting a training set image to complete model training; s2, extracting different scale feature maps of the image through a backbone network containing a partially grouped multi-scale convolution module; s3, performing cross-level fusion on the feature map by using the lightweight multi-scale fusion feature pyramid neck network; s4, executing target classification and bounding box regression by adopting a lightweight shared detail enhancement detection head to obtain a lightweight high-precision model; and S5, inputting a to-be-detected unmanned aerial vehicle image, and realizing small target detection and identification in combination with the Soft-NMS and Shape-IoU post-processing strategies. The method has the advantages that (1) the detection precision is improved; (2) the parameter quantity of the model is reduced, and the deployment of resource-constrained equipment is adapted; and (3) a post-processing strategy is improved, the problem of missing detection of small targets in a dense shielding scene is relieved, and the robustness of a complex scene is enhanced.

The invention discloses a retrieval method and device based on a hybrid vector model and a readable storage medium, and relates to the field of artificial intelligence, and the method comprises the steps: firstly responding to a natural language query, and processing to generate a query vector; recalling candidate results from the index library based on the query vector, and screening to obtain a refined related information set; based on the refined information set, generating and executing an SQL, obtaining result data and generating a natural language answer; and outputting answers and result basis descriptions. According to the method, the retrieval accuracy is improved through mixed vector recall, the model reasoning complexity is reduced through multi-stage processing, the robustness is guaranteed in combination with a reflection mechanism, interpretable output meets the compliance requirement, the problems that retrieval is inaccurate, generation is unreliable and a result cannot be interpreted are effectively solved, and efficient automation of complex Text2SQL query is achieved.

The invention relates to the technical field of concrete structure damage detection, in particular to a post-fire RC structure burst damage three-dimensional segmentation method, which comprises the following steps: firstly, determining the type and three-dimensional characteristics of post-fire RC structure member surface concrete burst damage, and constructing a corresponding three-dimensional point cloud data set for network training and verification; and then based on a KP-FCNN network structure, a KPConv layer is improved and optimized, so that the detection and segmentation precision is improved, the model size is reduced, the reasoning time is remarkably shortened, the optimal segmentation precision of 82.3% is achieved under the working conditions of different damage degrees, automatic damage segmentation of the burst damage of the concrete structure after the fire disaster is realized, and the automatic damage segmentation of the burst damage of the concrete structure after the fire disaster is realized. And technical support is provided for subsequent damage three-dimensional quantification and deployment to an unmanned aerial vehicle system.

The invention discloses a multi-target prediction method, system and device for a financial time sequence and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining financial time sequence data and auxiliary information, and carrying out the vector conversion and fusion to form a unified input representation; extracting features through a shared encoder, and generating a global feature vector; and inputting the data to a multi-task prediction output head to realize parallel prediction of the prediction tasks. According to the scheme, cross-task feature sharing is realized by using the shared encoder, the modeling collaboration is improved, and resource waste and prediction conflicts are avoided; through data fusion, the depiction capability of complex financial dynamics is enhanced; the generalization and robustness of the model are improved by adopting a lightweight output head, the method is suitable for diversified financial prediction scenes, and the prediction efficiency and accuracy are improved while the model complexity is reduced.

The invention discloses a bedrock discrete fracture network model generation method based on multi-source drilling data. The bedrock discrete fracture network model generation method comprises the following steps: step 1, preprocessing multi-source data; step 2, recognizing a drilling fracture image, and clustering trace line segments recognized in a drilling image area into continuous fracture trace lines; step 3, fracture parameter statistics; step 3.1, calculating and determining geometric parameters of the fracture; step 3.2, performing statistics on a rock mass fracture network distribution model and parameters; 4, generating semi-determined fracture networks in batches; 5, multi-source data iterative analysis is carried out, and an optimization model is selected. Modeling is carried out through multi-source data cross comparison without depending on a single data source, and the accuracy of the model is improved. Automatic analysis is achieved through a digital method, manual operation is reduced, dependence on engineering experience is reduced, and large-scale application in the engineering scale can be achieved; and meanwhile, the determined crack of the drilling area and the uncertain crack of the drilling adjacent area are considered, so that the model uncertainty of grouting engineering modeling is greatly reduced.

The invention relates to the technical field of tunnel monitoring, in particular to a TBM tunnel weak broken zone jamming risk prediction and active reinforcement decision-making method. According to the method, the TBM shield pressure prediction sub-model and the multiple TBM feature data are used for prediction, Dropout operation is introduced, and model overfitting is reduced; according to the method, the deviation degree is utilized for risk grading, the risk grades can be rapidly and effectively obtained, different supporting modes are selected according to the different risk grades, and the problem that a reinforcement treatment method and the surrounding rock condition cannot be effectively matched under the condition that the machine jamming risk exists is solved; the system is simple in structure and high in applicability, a proper active reinforcement decision is made, resource waste is reduced, workers are assisted in completing tunnel construction, TBM equipment is prevented from being damaged, and the occurrence rate of engineering accidents is reduced.

The application provides a simplified modeling method, device and computer program product of a complex mixed fuel combustion reaction kinetics model. The simplified modeling method comprises the following steps: model coupling, one-time simplification of the model by using an error transmission-based directed relationship diagram method and a species sensitivity analysis method, optimization of the one-time simplified model by taking the optimal pre-exponential factor, temperature index and activation energy of the three-parameter modified Arrhenius equation as optimization objects, and re-simplification of the optimized model. The application adopts a method for directly simplifying a complex model, does not need to artificially disassemble and construct the model, does not need to perform in-depth reaction path analysis, does not need to perform program writing, is suitable for combustion simulation engineering practitioners without a combustion reaction kinetics research foundation, is used to quickly produce a fuel mechanism model, and is reasonably verified.

The invention discloses a welding simulation heat source parameter calibration method and system based on a hybrid agent model, and belongs to the technical field of welding numerical simulation and optimization, and the method comprises the following steps: S1, problem definition and experiment design; s2, analyzing a product structure and constructing a joint model; s3, parameter space sampling; s4, performing simulation calculation; s5, constructing a mixed agent model; s6, proxy model optimization based on a genetic algorithm; s7, parameter verification and model updating; and S8, optimizing and outputting optimal parameters. According to the method, most time-consuming full-order finite element calculation is replaced by the proxy model and the simplified joint model, the parameter calibration process is shortened from several days or weeks to several hours, and the optimization efficiency is improved; a simplified model and a complete model are used for optimization, firstly, the simplified model is used for rapidly obtaining an optimal parameter approximate value, and the complete model is used for obtaining accurate parameters.

The invention discloses a real-time detection system and method for coating surface defects based on deep learning, and aims to improve cross-product-line detection adaptability, reduce model training cost and enhance detection stability under complex illumination through multi-scale feature extraction, feature decoupling and small sample adaptation technologies. The system obtains a coating surface image through an image acquisition module, a multi-scale feature extraction sub-module extracts low-level, middle-level and high-level features, the features are decomposed into domain invariant defect features and domain specific features through a feature decoupling sub-module, and interference features are suppressed through adversarial training. The small sample adaptation sub-module can realize rapid parameter adjustment and data enhancement by means of a small number of labeled samples, and the feedback control module triggers a production line repair instruction in real time and generates a quality report, so that the problem of false detection caused by cross-domain detection performance degradation, poor small sample adaptability and illumination interference is solved, the false detection rate of cross-product line detection is reduced, and the detection efficiency is improved. The sample demand is reduced, and the detection accuracy is improved.

The invention discloses a multi-variable measurement sensor state lightweight evaluation method based on knowledge distillation, and belongs to the technical field of electric digital data processing and multi-sensor data fusion. The method comprises the following steps: firstly, carrying out time synchronization and physical consistency constraint modeling on original data of multiple sensors, and extracting feature representation; a high-precision teacher model is trained at the cloud end, and the high-dimensional mapping relation of the sensor state is learned; intermediate features, soft output and uncertainty information of a teacher model are extracted to serve as distillation knowledge, lightweight student model training is guided, and effective migration of discrimination knowledge is achieved in combination with soft label constraint, feature alignment and an uncertainty guiding mechanism; and finally, compressing, quantifying and optimizing the student model, and deploying the student model to a vehicle-mounted end to realize real-time evaluation and dynamic updating of the states of multiple sensors. According to the method, the model complexity is greatly reduced while the evaluation precision is ensured through a knowledge distillation framework, and efficient and reliable state perception and fault-tolerant control support is provided for an intelligent driving system.

The invention discloses a flywheel system bearing assembly multi-dimensional rule reduction health state testing method, and relates to the technical field of flywheel system bearing assembly health state testing. Acquiring an actual measurement data set of the flywheel system bearing assembly; determining a confidence coefficient corresponding to the reference value set and the result, and constructing an initial health state test model based on a confidence rule base; constructing a multi-dimensional rule evaluation framework; performing composite activation on the reduced model; optimizing parameters in the model by adopting an optimization algorithm; and reasoning the model by using an evidence reasoning analysis algorithm to obtain a health test result of the flywheel system bearing assembly, and realizing an accurate health state test through the evidence reasoning algorithm.

The invention discloses an air inlet casing full-process simulation closed-loop optimization method, which relates to the technical field of mechanical manufacturing and processing, and comprises the following steps: simplifying an air inlet casing model; selecting a plurality of key procedures from the whole procedures; according to process parameters and boundary conditions of the key process, grid division is carried out on the simplified model, and a corresponding finite element simulation model is constructed; sequentially simulating the finite element simulation model according to a key process sequence, correcting a simulation result according to deformation data of actual processing, transmitting the corrected simulation result to a next key process for model simulation until simulation and correction of all key processes are completed, and constructing a full-process simulation prediction model; performing iterative optimization on multiple parameters in the whole-process simulation prediction model to obtain an optimal parameter combination; and correcting the simulation model according to the deviation between the actual processing data and the simulation result under the control of the optimal parameter combination. According to the method, high-precision prediction of various complex manufacturing processes can be realized, and the manufacturing precision is improved.

According to the KAN nuclear reactor LOCA accident deduction and diagnosis method based on Bayesian optimization, the problems of parameter deduction and fault diagnosis in the LOCA accident working condition process can be solved, and KAN nuclear reactor LOCA working condition accident deduction and diagnosis based on Bayesian optimization are based on multi-pipeline sensor measurement signals; according to the method, modeling is carried out by using a two-fluid six-equation theory and a B-PIKAN network is combined to carry out pipeline core physical quantity prediction and progressive transfer learning. The whole process starts from acquisition of measurement signals of a multi-pipeline sensor, and through steps of physical two-fluid six-equation modeling, B-PIKAN model training, output and progressive transfer learning, deduction change values of core physical parameters of other pipelines along with time are finally obtained. The method is suitable for a complex LOCA pipeline environment, and the fault judgment accuracy is improved; according to the method, generalization and robustness are enhanced, and the modeling cost is reduced; according to the method, the reliability and robustness of the model are improved, and the interpretability is enhanced.

The invention relates to an air-ground integrated three-dimensional scene automatic modeling method, system and device and a storage medium, and the method comprises the steps: carrying out the point cloud fusion and coordinate registration of a preprocessed air original point cloud and a preprocessed ground original point cloud, and obtaining an air-ground fusion point cloud image; performing semantic segmentation on the air-ground fusion point cloud image through a pre-trained point cloud semantic segmentation network to obtain a composite point cloud cluster containing multiple semantic categories; extracting a building outer contour of the building type point cloud cluster, simplifying the building outer contour according to a preset simplification rule, and constructing a building three-dimensional simplified model according to the simplified outer contour; extracting category feature parameters of the non-building type point cloud clusters, searching a standard model conforming to the category feature parameters from the preset model library, and replacing the corresponding point cloud clusters with the standard model to obtain a non-building three-dimensional simplified model; and performing archiving according to the semantic layer to form a structured three-dimensional model of the to-be-modeled urban area.

The present invention discloses a smart bio-inspired material design platform to satisfy multi-objective material design featuring complex microstructure for the future. the platform sets mechanical properties of a simulative material element via establishing a reduced model. A distribution of the simulative material element is simulated so as to output a material simulative parameter. A deep learning framework is combined in the platform for computing and evaluating an optimal material design that meets a target material parameter. Specifically, the reduced model can be based on data provided by any test of material mechanical properties, and the deep learning framework evaluates whether a biomimetic material design meets demand of the optimal target material parameter according to a standardized reward function model. The platform is applicable to multi-objective simulative material design, and is greatly potential for futuristic applications.

The invention relates to the field of ecological environment and digital simulation, in particular to a phosphorus pollution risk evaluation method and system based on a migration and transformation mechanism. A phosphorus pollution risk evaluation method based on a migration and transformation mechanism comprises the following steps that S10, at a preset sampling point position of a preset drainage basin, various data are collected according to preset parameter types, and the parameter types comprise various parameter types needed by a preset mechanism model and a preset output coefficient model; sorting the collected data according to a collection time sequence, and establishing a space-time matching database; and S20: extracting parameter types in the database, defining a cross-medium migration relationship of phosphorus based on physical parameters, constructing a conversion relationship of a corresponding form in combination with parameters of chemical and biological types, and dividing factor types. According to the scheme, the problem of low evaluation accuracy caused by insufficient sensitivity of analysis results of a static evaluation method and a dynamic evaluation method based on a simplified model in phosphorus pollution risk evaluation is solved.

The invention discloses a dense topology model lightweight method, and belongs to the technical field of three-dimensional model processing. The problems that an existing scheme is low in efficiency, large in structural damage, poor in mapping quality and the like are solved. The method comprises the following steps: importing a dense grid model containing high-precision geometry and texture; selecting an automatic (adaptive lattice clustering algorithm) or manual mode to generate a target simplified topology; constructing a simplified model fitting the original model through a multi-direction projection technology; uV expansion is realized through automatic blocking, LSCM algorithm optimization and manual adjustment, and textures are baked in combination with RendertoTexture and SSAA technologies; and after performance optimization, outputting a lightweight model compatible with a mainstream format and an engine. According to the invention, high efficiency, stability and light weight are realized, key features are accurately reserved, visual consistency is guaranteed, and multi-scene application is adapted.

The invention discloses a large model parameter protection method based on singular value decomposition in a trusted execution environment. Positioning a key layer of the large model through gradient sensitivity and interlayer correlation measurement in a large model fine tuning training process, and decomposing a weight matrix of the key layer by using singular value decomposition to obtain sensitive parameters and non-sensitive parameters; dividing a large model operation environment into a rich execution environment and a trusted execution environment, and generating a reversible key matrix in the trusted execution environment; when input data pass through the key layer for the first time, the rich execution environment uses non-sensitive parameter reasoning and then transmits the input data to the trusted execution environment for sensitive parameter reasoning, then the data are encrypted by using the key matrix, the encrypted data are transmitted back to the rich execution environment for reasoning, and when the input data pass through the key layer again, the rich execution environment conducts reasoning and then transmits the input data to the trusted execution environment. And the final data is output after decryption and reasoning. According to the method, the side channel attack risk is reduced, the data transmission security and the calculation efficiency are improved, and the model reasoning delay is reduced.

The invention discloses a hierarchical simulation modeling system for a new energy station cluster and a real-time verification method, belongs to the technical field of new energy power generation simulation, and solves the problems of high simulation calculation complexity, difficulty in real-time operation and closed-loop interaction verification with an external control system of a large-scale new energy station cluster. According to the technical scheme, the method comprises the steps that a digital dynamic real-time simulation platform configured with a multi-thread parallel computing architecture is adopted, and a new energy station detailed model comprising a wind power generation unit model, a photovoltaic power generation unit model, an energy storage power generation unit model and a current collection system model is deployed on the platform; generating an independent wind speed sequence for each fan through a wind resource distribution model; and constructing a large-base station cluster model composed of a plurality of station simplified models through aggregation equivalence. The system is mainly used for real-time simulation testing of a large-scale new energy base and closed-loop verification of a control system.

The invention relates to the technical field of building information model optimization, in particular to a large-volume BIM model optimization system based on intelligent loading, which comprises a model analysis unit, a function identification unit, a simplified execution unit and a model recombination unit, and is characterized in that the model analysis unit reads an IFC file and separates component data; the function identification unit outputs a decoration component (first class) set, a non-bearing structure (second class) set and a bearing component (third class) set through geometric and semantic dual-channel analysis, the simplified execution unit performs edge folding on the first class, performs vertex clustering on the second class and skips simplification on the third class, and the model recombination unit binds metadata and constructs a dynamic octree index. The problems that a traditional simplification strategy is single and core information is lost are solved, the model loading efficiency and application precision are improved, and the method is suitable for complex building scenes.

The invention relates to the field of natural language processing, in particular to a low-rank adaptation system capable of differential polarization rank search, which comprises an initialization module, a warm-up training module, a joint optimization module, a sparse pruning module and a final fine tuning module. According to the method, dynamic rank allocation and continuous search are realized by introducing trainable rank architecture parameters, and model parameter waste and calculation overhead are remarkably reduced by combining polarization regularization and L2 regularization strategies. According to the method, the performance of the large model in the downstream task can be effectively improved, meanwhile, the method is suitable for scenes with multi-task concurrence, resource-constrained device deployment and high real-time requirements, and a technical solution is provided for efficient adaptation of the large-scale pre-training model.

The invention discloses an equivalent dielectric parameter calculation method for a truss type structure, and belongs to the technical field of electronic science. The method solves the problem that an existing equivalent medium theory cannot directly perform equivalent calculation on a truss structure model, constructs a general expression from the equivalent medium theory, can improve the calculation universality and portability, considers the influence of material composition and structure on equivalent dielectric parameters, and improves the calculation accuracy. A precise tool is provided for analyzing electromagnetic behaviors, undetermined indexes are discretized in an interval range at equal intervals, processing and analysis can be facilitated, a scattering result data set of an equivalent model corresponding to each discrete index can be calculated, comparison data can be accumulated, a typical actual model can be established, and a scattering result is calculated by adopting an area integral equation method. According to the method, data can be ensured to be close to reality and reliable, accurate parameters are determined through fitting, equivalent conversion from a complex truss structure to a simple homogeneous medium model is achieved, and main electromagnetic characteristics of an original structure can be reserved while the complexity of the model is simplified.