26 results about "Universal model" patented technology

The invention relates to the technical field of computers, and discloses an application development method based on a large model technology, and the method comprises the steps: 1, carrying out the demand analysis, and converting a fuzzy business description into an executable technical scheme through natural language understanding and a structured modeling technology; 2, carrying out architecture design and planning, and constructing a code-free and low-code dual-mode collaborative architecture; 3, establishing a visual arrangement engine, and establishing a dual-mode development interface of a code-free development mode and a low-code development mode; step 4, aiming at different requirements of a code-free scene and a low-code scene, a dynamic adaptation strategy is adopted, and differentiated resource management of lightweight adaptation and elastic expansion is carried out; and step 5, performing field adaptation, and obtaining the industry exclusive capability according to the general model and the field knowledge through code-free rapid adaptation and low-code deep customization. By means of the scheme, the development threshold can be lowered, the efficiency can be improved, and the accuracy of cross-industry application is improved.

A universal model-based modeling and detection method and system for defect detection in panel production lines, a device, and a storage medium. The method comprises: step 1, collecting panel production line sample data and performing defect annotation to obtain a sample data set; step 2, using the data set for modeling and training to obtain a localization model; step 3, on the basis of the data set, cropping images each containing a defect and classifying the images to form defect image data sets; step 4, using the defect image data sets for modeling and training to obtain an image classifier, and cascading the image classifier with the localization model; and step 5, on the basis of factory attributes and manufacturing process attributes, mapping defects to specific defect names by means of the image classifier and the localization model. In the method, accurate defect name mapping can be performed according to specific production environments and requirements. The overall solution reduces communication and checking time, increases sales, and also improves the applicability and accuracy of detection results.

This invention discloses a method for maintaining the service performance of machine tool feed systems based on sensitivity analysis using a twin model and a particle swarm optimization algorithm. The overall method encompasses a five-layer framework for digital twin modeling, electromechanical wear mechanism fusion and encapsulation, time-varying element sensitivity analysis, and multi-objective maintenance decision optimization. The modeling part employs an object-oriented five-layer architecture, deeply encapsulating physical mechanisms such as electromechanical coupling wear evolution based on spatial location distribution into a quasi-physical model layer, achieving accurate description and real-time updates of implicit time-varying elements such as localized guideway wear. The decision-making part uses the Sobol sensitivity analysis method to quantify the impact weight of each element on service performance, constructs a full-effect exponential-driven adaptive tightening mechanism for early warning thresholds, and combines it with a particle swarm optimization algorithm (PI-PSO) that introduces degradation inertia and physical boundary penalties to iteratively calculate the optimal maintenance scheme with the goal of minimizing maintenance costs, downtime losses, and performance degradation. This method not only solves the problem that general models are difficult to describe the underlying electromechanical-dynamic coupling mechanism but also achieves deep integration of optimization algorithms and physical degradation laws, significantly improving the service reliability and economic benefits of machine tool feed systems.

The application provides a UVM verification platform reference model hardware architecture supporting multistage dynamic configuration, belongs to the technical field of UVM verification platforms, and comprises the following steps: S1, a hierarchical reference model construction step, which comprises a model splitting link and a cascaded connection link; S2, a dynamic configuration mechanism implementation step, which comprises a centralized configuration link, a tag distribution link and a real-time reconfiguration link; and S3, a hierarchical independence processing step, which comprises a universal model template link and a differential injection link. The traditional verification platform is generally a single-stage reference model, and for some hardware designs with multistage similar structures, directly integrating all the structures leads to the problem of low efficiency. The application constructs a verification platform with multistage reference models through a multistage hardware design structure, and solves the problems of code reusability and low efficiency of the traditional platform.

The application discloses an online roll temperature and thermal crown prediction method based on finite difference and polynomial regression, aiming at the technical pain point that the accuracy of the traditional analytical model is insufficient and numerical simulation cannot meet the online real-time calculation demand, a plurality of groups of working conditions are designed through an orthogonal experiment method, and a finite difference method is used to calculate roll steady-state temperature field data to construct a training data set, based on the data set, a polynomial regression algorithm with elastic network regularization is used to fit an axial temperature distribution general model with the roll temperature field as an input variable, after obtaining the online surface axial temperature distribution of the roll, the general model is substituted into the internal temperature field to restore the internal temperature field and calculate the radial thermal expansion amount at each position, so that compensation information is provided for a shape control system of a rolling mill. The application simplifies complex physical field simulation into lightweight algebraic formula operation, retains the high analytical accuracy of the finite difference method, realizes real-time prediction, and can significantly improve the rolling quality of silicon steel products.

The invention discloses a construction method of an oil and gas production large model platform, and relates to the technical field of oil and gas production intellectualization, and the method comprises the following steps: pre-constructing an oil and gas field knowledge graph, employing a BERT-BiLSTM-CRF model to carry out the automatic extraction of entities and attributes of massive unstructured engineering documents in oil and gas production, the extraction result is used for filling and perfecting the knowledge graph; a WellGPT model focusing on professional knowledge reasoning and a WellGPT-VL model focusing on view fusion understanding are constructed respectively. According to the method, on the basis of Qwen2.5 double bases and the dynamic weight hybrid LoRA technology based on intention recognition, efficient fine adjustment of a universal base model is achieved through precise classification of an intention recognition scheduling center and special training of an independent LoRA adapter, the generalization ability of the universal model is reserved, professional knowledge of the oil and gas industry is deeply injected, and the method has the advantages of being high in universality, high in practicability and high in practicability. And professional terms, physical laws and multi-modal data characteristics in the field can be accurately understood, and the professional adaptability and output reliability of the model in an oil and gas production scene are remarkably improved.

The invention belongs to the technical field of wheat plant modeling, and discloses a wheat plant tillering geometric model general modeling method based on a discrete element method.The wheat plant tillering geometric model general modeling method comprises the steps that statistical analysis is conducted on shape parameters of wheat tillering plants, and single tillering modeling parameters and root system modeling parameters are obtained; constructing a single tillering model framework which comprises a stalk model and a wheat ear model; calculating expressions of spherical coordinates of the stalk model and the wheat ear model according to the single tillering modeling parameters to obtain a single tillering general model; taking one single-tillering general model as a main stem model, taking a plurality of single-tillering general models as tillering plant models, and combining to obtain a plant general model; constructing a root system model framework which comprises a tillering joint model, a primary rooting model and a plurality of secondary rooting models; calculating expressions of spherical coordinates of the tillering joint model, the primary rooting model and the secondary rooting model according to the root system modeling parameters to obtain a root system universal model; and combining the root system general model with the plant general model to obtain a wheat plant tillering geometric general model.

The invention discloses a large language model equipment relation extraction method based on two-way retrieval and context learning, and relates to the technical field of relation extraction in natural language processing. The large language model equipment relation extraction method based on two-way retrieval and context learning comprises the following steps: S1, fine adjustment of an embedded model; s2, constructing a retrieval example library; s3, performing double-path retrieval and correlation sorting; and S4, prompt word construction and relation extraction. According to the large language model equipment relation extraction method based on two-way retrieval and context learning, global semantics and local instance relations are captured at the same time through two-way retrieval, three types of overlapping relations of Normal, SEO and EPO in the equipment field can be effectively recognized, and an F1 value is remarkably improved compared with an existing optimal baseline model GPT-RE; through comparison learning and a BGE-M3 model optimized by a domain adaptation layer, semantic information of technical terms and technical description in the field of equipment can be accurately captured, and the problem of insufficient domain adaptation of a general model is solved.

The invention provides a model reasoning method, device and system based on a storage and calculation integrated hybrid calculation architecture, and the method comprises the steps: carrying out the prediction of to-be-processed target domain data on a simulation storage and calculation integrated unit based on a deployed universal model, and determining a prediction result; under the condition that the prediction result does not meet the preset condition, retrieving the to-be-processed target domain data based on a deployed database on the digital storage and calculation integrated unit, and determining a retrieval result; and determining a reasoning result corresponding to the to-be-processed target domain data according to the prediction result and the retrieval result. According to the method, hybrid reasoning of the prediction result of the universal model and the retrieval result of the database can be realized, the reasoning precision of the universal model for personalized data of the user is improved, storage and calculation are integrated, and the reasoning speed and energy efficiency are improved by reducing the data carrying frequency.

The invention provides a transformer substation GIM model intelligent retrieval and BIM general format conversion method, relates to the technical field of transformer substation data processing, and solves the problem that an existing transformer substation GIM model is difficult to realize cross-modal accurate retrieval, high-fidelity BIM format conversion and dynamic standard knowledge fusion. The method comprises the steps of firstly constructing a power field knowledge graph and extracting key features in user multi-modal query intention data, then understanding the key features by a multi-modal retrieval model through a mode of combining a graph neural network and a vision-language pre-training model, and carrying out joint reasoning in the power field knowledge graph; a target GIM model is retrieved; the geometric topology of the target GIM model is reconstructed, professional attributes of the target GIM model are mapped, then a generative adversarial network is adopted to convert material codes into PBR materials, and a corresponding general BIM model is generated together; and after compliance verification and optimization of the general BIM model are completed, the model can be output to realize business application.

The invention relates to the technical field of artificial intelligence model servitization, and particularly discloses a universal model reasoning service framework supporting multi-type heterogeneous algorithm packaging. According to the framework, by designing a unified algorithm adaptation layer, a standardized model management module, a universal inference engine and a standardized interface interaction module, unified packaging and management of different types of heterogeneous algorithms such as Sciit-learning (sklearning for short), PyTorch, YOLO and the like are achieved. An external system can call any model reasoning service in the framework through a standardized API interface without paying attention to implementation details of an underlying algorithm, so that the development cost of multi-algorithm collaborative application is greatly reduced, and the efficiency and flexibility of model servitization deployment are improved. The method is suitable for various scenes requiring integration of multiple artificial intelligence algorithms, and has a wide application prospect.

The invention discloses a gravity compensation method and equipment of force feedback equipment, a medium and a product, and belongs to the technical field of robots. According to the gravity compensation method of the force feedback equipment, a universal model skeleton diagram of the force feedback equipment is constructed, standardized joint characteristic parameters are collected, an accurate dynamic model is established according to the parameters, and the force feedback equipment is obtained by combining set universality constraint conditions. According to the method, the optimal spring parameters adaptive to master manipulators of different structure types can be automatically calculated by utilizing an iterative optimization algorithm, so that smooth and stable gravity balance is realized in the whole working space of various force feedback equipment, and the operation portability and control precision of the force feedback equipment are remarkably improved.

The invention relates to the technical field of micro-energy collection, particularly discloses a surface-mounted magnetic field energy collection system modeling method considering magnetic core structures and materials, and aims to solve the problems that an existing model is insufficient in universality and applicability, and particularly, system design under different magnetic core structures and materials is lack of theoretical guidance. General modeling is realized through four core steps: establishing an equivalent circuit model of a system; constructing a general mutual inductance model considering the structure and the material of the magnetic core based on a magnetic core shape equivalent method; establishing a system loss model including coil loss, magnetic core loss and diode conduction voltage drop loss; and combining the models to form a complete magnetoelectric conversion general model. According to the method, shape coefficient correction mutual inductance calculation is introduced, material characteristics and circuit loss are comprehensively considered, the adaptability and accuracy of the model in different application scenes are remarkably improved, and a reliable theoretical basis and design guidance are provided for optimization design of the surface-mounted magnetic field energy collection system.

Systems, methods, apparatuses and non-transitory computer executable media configured to unify preprocessing, configuration, training, monitoring, and evaluation of multiple neural network based object detection algorithms under a singular development environment / platform (i.e., a “unified training platform”). The unified training platform may include a neural network agnostic model training environment that addresses the deficiencies described above and may allow for unified data annotation formatting. In addition to incorporating a wide variety of state-of-the-art neural networks into the unified training platform, the unified training platform may also provide full accessibility to available network optimizations. The present disclosure may also include a universal model converter.

The invention relates to the technical field of medical instruments, in particular to an osteotomy distraction stabilizer distraction angle calibration system based on medical big data, and the system analyzes the skeleton creep law of a current patient based on the data of the current patient through a universal model generation module, and generates a digital twin universal model; a practical model generation module is used for carrying out data assimilation digital twinning general model calibration, a personalized digital twinning practical model capable of simulating the skeleton creep behavior of the current patient is generated, the unique biological dynamic characteristics of the skeleton of the patient can be accurately reproduced, and then the accurate comparison of the individual conditions of the current patient and historical patients is realized; finally, the deviation between the distraction angle of the current patient and the optimal distraction angle interval is evaluated through accurate comparison, the correction angle is dynamically calculated, then self-adaptive closed-loop control over the distraction angle is achieved, and system stability is maintained.

This invention relates to the field of multi-axis CNC machining and industrial robot machining technology, specifically disclosing an asymmetric five-axis toolpath smoothing method based on path weighted synthesis. The method includes: constructing a universal corner smoothing model based on path weighting; constructing an analytical expression for a high-order continuous weighted function; Cartesian space smoothing of the tool tip position path; unit spherical space smoothing of the tool axis direction path; synchronous adjustment of C3 continuous parameters for position and direction paths; interference verification and asymmetric adjustment of adjacent corner transition intervals; continuous speed planning and real-time interpolation; and motion control of the machining equipment. This invention overcomes the shortcomings of existing five-axis path transition smoothing technologies, such as poor geometric versatility, incoordination between tool tip and tool axis movements, easy interference in short path segments, and inconsistent bidirectional machining trajectories. It improves geometric versatility and processing efficiency, achieves high-order continuity and consistent machining surfaces, and can be ported across platforms and extended to higher orders.

An aircraft electrical structure comprises equipment items that are interconnected by cables. A processing system constructs (400) a digital representation of the electrical structure in the form of nested graphs. A first graph represents the electrical structure by first arcs representing the cables and first nodes representing the equipment items. A second graph represents an internal interconnection structure between at least one input and at least one output of each equipment item which has such an internal interconnection structure, second arcs representing interconnections between inputs and outputs, second nodes representing the inputs and the outputs concerned. The first graph is obtained from contextual data (411) describing the electrical structure and each second graph is obtained from a generic model (412) for each type of equipment item concerned. An analysis is carried out by performing a search (404) for equipotentials by browsing through the nested graphs. Thus, the analysis is facilitated.

The invention discloses an electrocardiosignal personalized classification method based on quantum features and meta learning and a medium. The electrocardiosignal personalized classification method comprises the steps that S1, electrocardiosignals are preprocessed; s2, extracting electrocardiosignals by adopting quantum global features to obtain a first feature vector; meanwhile, extracting local waveform features of the electrocardiosignal to obtain a second feature vector; s3, channel splicing is carried out on the first feature vector and the second feature vector, normalization processing is carried out, and unified double-flow fusion feature representation is formed; s4, on the basis of the model-independent element learning framework, performing element training on the electrocardiogram analysis model by using the double-current fusion features to obtain an electrocardiogram analysis universal model; s5, acquiring electrocardiogram data of the target user to obtain personalized double-current fusion features of the target user; and inputting the personalized double-current fusion features into the electrocardio analysis general model, and finally generating a personalized classification model. According to the method, the problems of low adaptation speed and large individual difference of the electrocardio model under the small sample condition are solved, and the accuracy and the individuation level are improved.

Systems, methods, apparatuses and non-transitory computer executable media configured to unify preprocessing, configuration, training, monitoring, and evaluation of multiple neural network based object detection algorithms under a singular development environment / platform (i.e., a “unified training platform”). The unified training platform may include a neural network agnostic model training environment that may allow for unified data annotation formatting. In addition to incorporating a wide variety of state-of-the-art neural networks into the unified training platform, the unified training platform may also provide full accessibility to available network optimizations. The unified training platform may also include a universal model converter.

The invention discloses a rationality judgment method of an intrusion detection scheme based on a large model, and relates to the technical field of network security, and the method comprises the steps: collecting multi-dimensional data, and carrying out the cleaning, labeling and format conversion of the collected multi-dimensional data to form a high-quality data set; based on the high-quality data set, selecting a security pre-training large model or a universal model to carry out training and parameter optimization; constructing an interpretable threat mode and rule association graph; according to the invention, static rule verification is upgraded to a dynamic intelligent evaluation system through rationality judgment of an intrusion detection scheme of a large model, and through comprehensive coverage of a data layer, field adaptation of a model layer, multi-dimensional quantification of an evaluation layer and closed-loop feedback of an optimization layer, the rationality of the scheme can be judged more accurately; the method can more actively cope with unknown threats, simplify the rule optimization process, control the risk of false alarm and missing alarm, and provide more adaptive guarantee for a network security system.

The invention relates to the technical field of fatigue strength evaluation of mechanical structures, and provides a universal model and method for predicting the fatigue life of a structure with defects, finite element models with defects are respectively established according to different defect information, and the fatigue life of the structure with the defects is solved based on fatigue analysis software; in combination with a Z parameter model, obtaining a Z parameter under each defect according to the established finite element model containing the defects; establishing linear correlation between the obtained fatigue life and a Z parameter, and fitting all obtained data points to obtain a fatigue life prediction formula; and calculating a Z parameter value according to actually detected defect information, and substituting the Z parameter value into a fatigue life prediction formula to complete fatigue life prediction. By expanding the applicability of the Z parameter to the whole service cycle of short service life and long service life and perfecting related parameters in the Z parameter, a universal model and method are provided for predicting the fatigue life of a structure containing defects.

The invention belongs to the technical field of intelligent algorithm test verification, and particularly relates to an intelligent algorithm metamorphic test verification method and system based on model driving, and the efficiency and accuracy of algorithm metamorphic test are improved through a model driving method. Comprising the steps of 1, analyzing an intelligent algorithm code, and extracting basic structure information of an intelligent algorithm; 2, adopting a model-based mode to realize universal modeling packaging of the intelligent algorithm, and establishing a universal model object of the intelligent algorithm; 3, establishing a metamorphic test scene of the intelligent algorithm model; 4, generating an intelligent algorithm metamorphic test case; and step 5, executing verification of the intelligent algorithm test case.

This invention relates to a method for constructing blade stiffness and linear density based on finite element analysis, belonging to the field of wind turbine blade technology. It solves one of the problems of lacking a universal model, which is detrimental to the early prediction and rapid evaluation of test bench schemes. The invention includes the following steps: Step S1: Calculate the linear density and stiffness of the wind turbine blade; Step S2: Normalize and preprocess the stiffness and linear density to obtain a discrete point data set; Step S3: Fit a double exponential function with 15 different initial values, use the Levenberg-Marquardt algorithm to solve for the optimal parameters, and select R... 2 The highest fitting result is taken as the initial optimal solution; Step S4: Adaptive outlier detection and removal, comparing the R-values ​​of several methods. 2 For the degree of improvement, select R. 2 The method that maximizes the improvement is taken as the final result; Step S5: Use the double exponential function model for subsequent prediction of the linear density and stiffness of ultra-long blades. This invention is used to improve the accuracy of blade physical performance curve fitting and provides a model basis for predicting the physical performance of ultra-long blades.

The invention belongs to the field of binary code analysis, and particularly relates to an embedded device-oriented Mama-based binary function identification method, which comprises the following steps of: carrying out disassembly and fine-grained marking on a solid binary system; the method comprises the following steps: pre-training a Mamba model by utilizing massive unlabeled solid instruction sequences to obtain a pre-trained universal model; and performing fine adjustment on the general model by using the new data set with the function boundary labeling information so as to judge whether the boundary is the boundary of the binary function or not. The key improvement of the method is that a Mama architecture with linear calculation complexity is adopted to replace a traditional Transform, the recognition accuracy is ensured, the speed of processing a long instruction sequence is remarkably increased, and the memory overhead is reduced, so that automatic and real-time security audit and vulnerability analysis on massive embedded solids become possible.

This invention discloses a modular universal model and simulation method for simulating laparoscopic breast surgery, comprising a thoracic base and a detachable chest skin module covering the thoracic base; the thoracic base includes a chamber with an opening at the top, on which the chest skin module covers; a breast module is detachably disposed within the chamber; the breast module includes a receiving part for placing / replacing pathological modules or ex vivo animal tissue; the outer surface of the chest skin module has several operating holes for inserting a laparoscope to perform simulated surgical operations; the breast module has a side opening communicating with the operating holes; this invention features a flexible design, a high degree of modularity, and individual parts can be replaced. It also provides personalized training programs, and the training difficulty can be adjusted as needed to meet the training needs of doctors at different levels; furthermore, the individual replacement and maintenance of each part not only greatly reduces maintenance costs but also extends the model's lifespan, resulting in a significant decrease in maintenance costs.