79 results about "Universal model" patented technology

The invention relates to the technical field of incremental training, and particularly discloses a method for large model incremental training, comprising the following steps: S1, obtaining a target data set based on an industry corpus data set, dividing the target data set into sub-data sets, obtaining quality indexes of the sub-data sets, and determining quality scores of the sub-data sets; s2, acquiring a first sequence, and determining a performance score of the universal model from the first sub-data set in the first sequence; calculating a performance score difference value, and determining whether the state is an abnormal state or not according to a performance score difference value threshold value; s3, coordinate points are generated and fitted to obtain a fitted curve, and abnormal sorting positions are obtained according to the fitted curve; and determining a target model according to the abnormal sorting position, and performing incremental training on the target model through the to-be-determined data set. According to the method, the negative influence of low-quality data on the model can be reduced, the adaptability of the model to new domain knowledge is improved, and the problem of performance degradation is avoided.

The invention relates to the technical field of auxiliary lesson preparation, and discloses an auxiliary lesson preparation method and device based on a large language model. Carrying out lesson preparation demand processing on lesson preparation requirements input by the user by adopting a large language model to obtain a lesson preparation demand result; constructing a knowledge base; performing retrieval processing on the knowledge base according to the lesson preparation demand result to obtain a retrieval result; and performing lesson preparation processing on the retrieval result by adopting a large language model to obtain lesson preparation content. According to the auxiliary lesson preparation method and device based on the large language model, the adaptive capacity of the universal large language model in the education field is improved through fine tuning of the universal large language model, the application limitation of the universal model in a teaching scene is solved, and efficient lesson preparation content generation quality is achieved.

The invention belongs to the technical field of crossing of remote sensing and computer vision, and particularly relates to a change general model construction method for multi-task interpretation of a remote sensing multi-temporal scene. Comprising the steps that a CNN encoder and a Vi T encoder are adopted for encoding, and a double-time-phase visual token is generated by an input double-time-phase image; the LoRA technology is adopted to introduce learnable parameters on the basis of the frozen multi-modal large model to achieve visual language alignment in the remote sensing change field, and the multi-modal large model is further used for generating text output containing multi-modal information for input double-time-phase images and text instructions; a visual decoder is added for performing high-dimensional decoding on the obtained double-time-phase visual token and the obtained text output to output a change graph; adding learnable parameters by adopting an Adapter fine tuning mode according to the constructed variable general model, and realizing a multi-task unified processing function of the variable general model; and continuously training based on the text generation loss and the segmentation mask loss, and optimizing the change general model.

The invention relates to a robot action generation method and system combining a universal model and a special model, and the method comprises the steps: constructing the universal model and the special model, carrying out the pre-training of the universal model, carrying out the parameter fine tuning of the pre-trained universal model, and training the special model based on the fine-tuned universal model; obtaining a task instruction and real-time visual information, inputting the task instruction and the real-time visual information into the fine-tuned universal model, and outputting an action sequence and task potential features; and acquiring real-time point cloud sensing data, inputting the real-time point cloud sensing data, the action sequence and the task potential features into the trained special model, and outputting continuous actions of the robot. Compared with the prior art, the robot action generation speed is increased, and the generalization of robot action generation is enhanced.

The present invention belongs to the field of lifting equipment control technology, and specifically relates to a dynamic modeling method and control method for double-bridge lifting equipment. The dynamic equations of a five-degree-of-freedom double-bridge crane are constructed using the Lagrangian method, and the geometric constraint relationships between trolley I and trolley II in the direction parallel to the bridge and the vertical direction are constructed. The dynamic model of the double-bridge crane is constructed by combining the dynamic equations and the geometric constraint relationships, and the dynamic model is transformed into a general model. Based on the general model, a first error function is constructed based on the difference between the actual position and the expected position, and a virtual controller is constructed based on the first error function. A second error function is constructed based on the virtual controller, and an actual controller is constructed based on the first error function, the virtual controller, and the second error function. This technical solution models the system as a whole through the constraint relationship between the two trolleys, taking the mass of the hook into consideration during the modeling process, and solving the singularity problem.

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.

The invention discloses an LNG cold energy gradient utilization general modeling and optimizing method and system for multi-energy micro-grid energy management, and the method comprises the steps: considering the transfer coupling and loss of cold energy based on the gradient utilization purpose and equipment parameters, and building a utilization general model for each stage of the cold energy gradient utilization process; the method comprises the following steps: establishing a multi-energy micro-grid energy optimization model containing cold energy gradient utilization, and carrying out energy management cooperation on a cold energy gradient utilization link and a multi-energy micro-grid based on a multi-energy micro-grid architecture and distributed energy characteristics by taking system economic carbon reduction operation as an optimization target; and converting the energy optimization model into a two-stage stochastic optimization model based on multiple scenes and solving the two-stage stochastic optimization model according to the uncertainty of the source load and the operation characteristics of the multi-energy micro-grid equipment to obtain a multi-energy micro-grid energy management decision of cold energy gradient utilization. According to the invention, integration and collaboration are carried out on a universal model for gradient utilization of cold energy and energy consumption management of the multi-energy micro-grid, and an energy management decision with economic low-carbon property and high energy efficiency property is obtained through optimization.

The invention discloses a three-dimensional molecular text understanding method based on a hybrid expert model and a large language model, and the method comprises the steps: building a new MoE layer of the hybrid expert model, and enabling the MoE layer to comprise a plurality of experts, coding the task information text and the text context through a cross-modal projector, and inputting the coded task information text and text context into a route of a hybrid expert model; and training and processing molecular encoders of different tasks, and integrating the trained molecular encoders into a unified 3D molecular encoder. According to the method, task information is injected into routing input by introducing a text-assisted MoE layer, the capability of processing specific tasks by the model is enhanced, and a universal model capable of simultaneously understanding 2D and 3D molecules and various different molecular properties is provided. By combining a plurality of molecular encoders, the comprehensive understanding of molecular characteristics is improved. By designing a molecule-text mapper, deep fusion between molecular features and texts is promoted, and the understanding ability of the model for three-dimensional molecular texts is improved.

The invention relates to the technical field of glacier monitoring, in particular to a glacier jump prediction method, system and terminal based on an intelligent algorithm, and the method comprises the steps: data collection: collecting multi-source data related to glacier jump through a sensor and a remote sensing technology, the meteorological data comprises temperature, humidity, air pressure and water content in ice, and the glacier movement data comprises flow velocity, ice layer thickness and displacement; according to the method, a simple, convenient and efficient real-time prediction scheme is constructed by adopting a multi-source data and deep learning technology for medium and small-scale glaciers, so that the problem that glacier data cannot be predicted in real time in a multi-scale physical constraint method adopted by existing glacier research is solved, and the prediction precision and timeliness are ensured; meanwhile, a simple and universal model is constructed, and the process and engineering automation deployment are simplified; in addition, disaster early warning is supported in result visualization, and the method is easy to embed into a GIS (Geographic Information System).

The invention provides a universal model heterogeneous federated learning privacy protection method, which comprises the following steps that: a cloud server randomly selects an online client, and obtains a model generation parameter union set of the client through a privacy protection union set protocol; generating heterogeneous models with different sizes and architectures according to the union set, and distributing the models to edge nodes for storage; the client downloads the local model from the corresponding edge node, and disturbs and encrypts the trained model parameters; the edge node receives the encryption model parameters uploaded by the client, and performs secure aggregation through a threshold vector aggregation protocol to obtain an aggregation model meeting the differential privacy requirement; and the cloud server receives the aggregation models from all the edge nodes, and globally updates the aggregation models by adopting a heterogeneous model aggregation method to obtain a new global model. Through an innovative privacy protection protocol and system architecture, the privacy protection problem in a model heterogeneous federated learning scene is effectively solved, and the practicability and security of federated learning are improved.

The invention belongs to the technical field of oral medicine, and relates to a tooth supply universal model design method, system and device in autologous tooth transplantation and a storage medium. The method comprises the following steps: acquiring a plurality of wisdom tooth images as original data; extracting vertex information from the original data, and removing outliers to obtain point cloud data; classifying the point cloud data according to the growth position and the root shape of the wisdom tooth to obtain a plurality of types of data sets; registering any two point cloud data in the same type of data set to obtain a reference coordinate system of multi-point cloud registration; in the registration process, wisdom tooth point cloud data which cannot be registered are removed, and screened point cloud data are obtained; fusing the registered and screened point cloud data to obtain a fused point cloud, calculating the local density of the fused point cloud and filtering, and reserving the dense part of the point cloud as a preliminary model; and carrying out point cloud boundary extraction on the preliminary model, and then carrying out smoothing processing to obtain tooth supply universal models with different sizes in autologous tooth transplantation.

The invention belongs to the technical field of lifting equipment control, and particularly relates to a modeling method and a control method for a dynamic model of double-bridge type lifting equipment, and a dynamic equation of a five-degree-of-freedom double-bridge type crane is constructed through a Lagrange method. Constructing a geometric constraint relationship between the trolley I and the trolley II in the direction parallel to the bridge frame and the vertical direction, constructing a kinetic model of the double-bridge crane by combining the kinetic equation and the geometric constraint relationship, and converting the kinetic model into a universal model; based on the general model, a first error function is constructed according to the difference between the actual position and the expected position, and a virtual controller is constructed based on the first error function; and constructing a second error function based on the virtual controller, and constructing an actual controller based on the first error function, the virtual controller and the second error function. According to the technical scheme, overall modeling is carried out on the system through the constraint relation between the two trolleys, the quality of the lifting hook is considered during modeling, and the singular problem is solved.

The invention provides an Iwan model modeling method and device of a bolted connection structure and electronic equipment, and the method comprises the steps: constructing a dynamic pressure distribution function of the bolted connection structure based on a contact boundary of a contact surface in the bolted connection structure by applying a linear pressure distribution function; solving a density function based on the dynamic pressure distribution function and a friction theory to obtain a density function of yield displacement; and introducing the density function of the yield displacement into a general Iwan model to construct an Iwan model. According to the method provided by the invention, the dynamic pressure distribution function of the bolt connection structure is constructed by selecting the linear pressure distribution function and combining the contact boundary of the contact surface. And density function solving is carried out based on the dynamic pressure distribution function and the friction theory, and a density function of yield displacement is obtained. The density function of the yield displacement is introduced into the universal Iwan model, the Iwan model of the bolt connection structure is obtained, Iwan model construction with few parameters and good representation effect is achieved, and the calculation complexity of the Iwan model is reduced.

The invention discloses a universal model for voice separation and target voice extraction, which relates to the field of voice and comprises a separation network, an EDA (electronic design automation) module and a clue network. The separation network is based on a frequency band division recurrent neural network, namely a BSRNN, and is a frequency domain separation network. The EDA module is introduced into a frequency domain speech separation model BSRNN, so that the BSRNN has the capability of processing mixed speech with unknown and variable speaker numbers; and the RawNet3 is used as the clue network to realize a target voice extraction function. The model can be flexibly switched among different tasks according to different inputs, the'universal 'effect is realized, the flexibility and the universality of the model are greatly improved, the additional overhead caused by using different models for different tasks is avoided, and the cost is reduced.

The invention provides a multi-working-condition simulation method, device, equipment, product and simulation system.The method comprises the steps that simulation parameters of general model elements and simulation parameters of working condition model elements under multiple working conditions are obtained, and the general model elements and the working condition model elements form overall elements of a simulation engineering model; simulation software is called to simulate each working condition based on the comprehensive parameters corresponding to each working condition, simulation result data of each working condition is generated, and the comprehensive parameters corresponding to each working condition comprise the simulation parameters of the general model elements and the simulation parameters of the working condition model elements under the working condition; and outputting simulation result data of each working condition. According to the method, simulation tasks of multiple working conditions can be started in a one-button mode, the working process is simplified through automatic processing, the working efficiency is improved, meanwhile, the consistency and correctness of parameter setting are ensured, and the reliability and credibility of simulation are improved.

The invention discloses a defect detection general model training method, a surface mounting defect detection method, a surface mounting defect detection device, processing equipment, a storage medium and a computer program product, and relates to the technical field of defect detection. In the application, a training method of a defect detection general model is proposed, and a to-be-trained defect detection general model is trained by taking labeled surface-mounted defect sample images as a training set, so that the defect detection general model for surface-mounted defect detection is obtained. Compared with the prior art, the method and the device have the advantages that each surface mounting defect does not need to be independently trained to establish a targeted detection model, the universal model is established by uniformly training on the basis of the obtained data of all production line products, such as the first to fourth types of defect items, and the universal model has the capability of detecting iterative products.

The invention discloses a hot working quality intelligent prediction method based on establishment of coupling structure and deformation condition influences, and belongs to the field of metal material plastic forming processes. According to the method, the coupling influence of the microstructure state and the deformation condition is considered, the metal material hot working quality prediction method suitable for the actual machining process is established, and the problem that the hot working quality under the constant deformation condition and the non-constant deformation condition cannot be accurately predicted through an existing universal model is solved. According to the method, the dislocation density, the grain size, the dynamic recrystallization degree, the strain rate, the deformation temperature and the deformation degree serve as input variables, the power dissipation rate and the deformation instability factor serve as output variables, a BP neural network prediction structure is established, training is conducted according to the experimental result under the constant deformation condition, and the dynamic recrystallization degree is obtained. And a hot working quality intelligent prediction model influenced by the coupling tissue and the deformation condition is obtained. The prediction of the hot working quality of the metal material under the complex deformation condition in the single-pass thermal deformation process is realized, and the prediction accords with the actual engineering processing condition.

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 application discloses an identity recognition and model training method and device. The model training method includes: sampling a private data set to obtain training data; extracting features from the training data using a universal model and a local model to obtain universal features belonging to the universal model and local features belonging to the local model; training the local model based on the universal features, local features, and a preset personalized discrimination loss function, wherein the preset personalized discrimination loss function is used to optimize the local model so that the local model can distinguish local features in the private data set; and generating a pre-trained recognition model when the private data set sampling is completed. Therefore, by adopting the embodiments of the present application, the time consumption of the model can be reduced while improving the localized recognition performance of the model.

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 present disclosure relates to a large-scale model hardware and software integrated machine tuning method and device for rapid deployment, which can improve the overall deployment efficiency of the large-scale model hardware and software integrated machine. The method includes: in response to a user powering on the large-scale model hardware and software integrated machine, obtaining hardware information of each hardware module, and generating a first deployment plan based on the hardware information of each hardware module and preset general model requirements through an artificial intelligence model; in response to a user configuring the business requirements of the large-scale model hardware and software integrated machine in a visual interface, determining a target large model from multiple pre-trained large models built into the large-scale model hardware and software integrated machine through an artificial intelligence model according to the business requirements, and adjusting the first deployment plan according to the hardware conditions required by the target large model to obtain a second deployment plan for the large-scale model hardware and software integrated machine; and deploying the target large model in the large-scale model hardware and software integrated machine based on the second deployment plan.

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.