490 results about "Model configuration" patented technology

The present invention discloses a system and method for performing artificial intelligence (AI) safety red-teaming with integrated policy fuzzing and adversarial prompting to systematically identify, characterize, and mitigate unsafe or non-compliant behaviors in AI models. The disclosed invention automates the process of generating, executing, and analyzing adversarial test cases through coordinated functional units comprising a policy fuzzing unit, an adversarial prompting unit, an execution sandbox, a telemetry processing unit, a scoring and triage processor, and a cryptographic provenance processor. The system applies grammar-driven and reinforcement-based fuzzing techniques to vary policy descriptors, model configuration parameters, and instruction hierarchies, while a learned adversarial prompt generator synthesizes contextually coherent adversarial prompts optimized for maximum policy violation likelihood. The generated prompts and policy vectors are executed in an isolated, instrumented sandbox that records input-output interactions, timing characteristics, and intermediate representations.

The embodiment of the invention provides a medical image aided decision-making method and system, electronic equipment and a storage medium, and belongs to the field of medical treatment. The method comprises the following steps: collecting multi-modal medical data, wherein the multi-modal medical data comprises a medical image and corresponding text data; constructing a multi-modal large language model, and performing pre-training to obtain a target multi-modal large language model; extracting image and text feature vectors corresponding to the medical image and the text data by using the target multi-modal large language model, and fusing the image and text feature vectors to obtain a fused feature vector; and according to different clinical tasks, adopting models corresponding to the clinical tasks, and combining with the fusion feature vector to obtain an auxiliary decision scheme. By fusing detailed clinical information in medical images and text data, the multi-modal large language model can more comprehensively understand the health condition of a patient, so that more accurate and comprehensive diagnosis suggestions are provided, the model configuration can be flexibly adjusted for different clinical tasks, and the diagnosis efficiency is improved. And a targeted auxiliary decision-making scheme is generated in combination with the fusion feature vector.

Methods and apparatus for leveraging transfer learning of one Wireless Transmit / Receive Unit (WTRU) to benefit another WTRU are provided. One method may include the WTRU receiving AI / ML model configuration information indicating one or more AI / ML models available from the network node, a profile associated with the AI / ML models, and a training convergence threshold. Based at least on the profile(s), the WTRU determining that the one or more AI / ML models are not suitable for use by the WTRU, and sending first information indicating that the one or more AI / ML models are not suitable for the WTRU and / or that the WTRU will be training a local AI / ML model. The method may then include training the local AI / ML model according to the convergence threshold, receiving a request to transfer AI / ML model parameters, and sending an indication of the AI / ML model parameters associated with the trained local AI / ML model to the network node.

The invention relates to the technical field of artificial intelligence chips, and discloses a model performance estimation method and device and computer equipment, and the method comprises the steps: determining model configuration data and candidate distributed strategies of a target model; converting the original calculation graph corresponding to the single-card deployment state based on the model configuration data and the strategy configuration data of the candidate distributed strategies to obtain a distributed overhead calculation graph corresponding to the multi-card deployment state; and performing performance estimation on the candidate distributed strategy according to the basic overhead and the additional overhead in the distributed overhead calculation graph to obtain strategy performance data of the target model under the candidate distributed strategy, thereby realizing conversion of a single-card model into a multi-card model. And based on the multi-card model, simulation calculation of the multi-card interconnection mode is realized on the premise of limited hardware resources, so that the influence of a communication operator and a topological structure corresponding to the multi-card interconnection mode in the overall operation of the model is reflected, and the upper limit of the model performance can be accurately evaluated in a simulator verification stage before silicon is applied.

The invention discloses a large language model reasoning method based on an unloading pipeline, and the method comprises the steps: obtaining an optimal unloading reasoning strategy through calculation according to the obtained model structure information and model configuration information of a large language model, and the hardware specification information and system operation load information of reasoning equipment; and then tasks in the optimal unloading reasoning strategy are scheduled through a fine-grained pipeline to output an unloading reasoning result of the large language model. According to the method, reasoning tasks are automatically configured according to input large language model information and a system hardware environment, and hardware resource use and reasoning performance are optimized. For deployment of a large language model on local equipment, transmission optimization is carried out for unloading using a solid state disk, so that the data transmission speed is increased. Meanwhile, fine-grained assembly line task scheduling is carried out for unloading reasoning, and the reasoning concurrency and the model throughput can be remarkably improved through unloading reasoning of an assembly line model.

The invention discloses a method and a device for reasoning a hybrid expert model, which are used for solving the technical problem of low reasoning efficiency caused by the existing reasoning process based on the hybrid expert model. The method comprises the steps of obtaining a pre-running text data set, performing expert quantization on a preset hybrid expert model, and determining a quantized hybrid expert model; adopting a preset torch function to adjust an initial memory environment corresponding to the quantitative hybrid expert model according to the model configuration file of the quantitative hybrid expert model, and determining a target memory environment; in the target memory environment, inputting the pre-running text data set into the quantitative hybrid expert model for pre-running reasoning, and determining the average time length of model calculation; adopting a preset expert prefetching inequality to calculate the average duration according to the model, and calculating the number of prefetchable experts; and in the target memory environment, based on the number of prefetchable experts, reasoning the to-be-reasoned text data by adopting the quantitative mixed expert model, and outputting a model reasoning result.

A method and related systems for dynamically adjusting on-device configurations for models based on detected context switches is disclosed. The system can use an on-device language model to determine context parameters in a first mode and then uses the context parameters to determine prediction models or prediction model parameters to use for inputs provided in a second mode.

The invention discloses an integrated machine learning model pre-labeling method and system. The method comprises the following steps: S1) importing data; s2) model selection and matching; s202) multi-model selection and automatic optimization: comprehensively selecting an optimal model based on a selection strategy of data features and task complexity; s3) automatic configuration of the model; S4) pre-labeling of the model; s5) performing manual auditing and correction; and S6) outputting a labeling result. S7) performing model performance evaluation, and automatically adjusting hyper-parameters; s8) updating a model recommendation strategy through a random forest algorithm; the system comprises a user module, a data import module, an annotation type selection module, a model configuration module, an annotation module, an auditing and adjusting module, an annotation result output module and a model adjusting module. According to the method, the machine learning model is integrated to realize pre-annotation of the data, meanwhile, the recommendation algorithm based on the annotation type is designed, the machine learning model is recommended according to the annotation type selected by the user, and the annotation accuracy and speed are improved.

The invention relates to the technical field of large model data processing, in particular to an agent automatic configuration method and system based on a large model, a knowledge base and a tool, and provides the following scheme: obtaining training data, inputting a natural language into an inference model to generate a query task set, and after optimization, inputting the query task set into an Embedding model to generate a preliminary retrieval result set; the optimization result is input into a reordering model to generate a final retrieval result; and when the training conditions are met, packaging the inference model, the Embedding model and the reordering model into an intelligent agent. The system consists of an inference model configuration module, a retrieval model configuration module and a reordering model configuration module, and cooperatively realizes task analysis, semantic retrieval and result optimization. The method improves the retrieval accuracy and efficiency, and is suitable for knowledge base question answering and complex data processing scenes.

The invention provides a high-strength aluminum alloy selective laser melting forming thermal stress prediction method based on deep learning. The method comprises the steps of 1, finite element model establishment and simulation, wherein a heat transfer model and a thermal coupling model are established; an SLM process is used as a simulation object, a Gaussian model is adopted to define a laser heat source, and thermal stress distribution under different process parameters and different sample sizes is simulated; wherein the process parameters comprise laser power, scanning speed and hatch spacing; step 2, data processing and deep learning model training; comprising the steps of data preprocessing, neural network model architecture determination, adversarial network part generation and model configuration and training. 3, evaluating and optimizing the model; and 4, thermal stress prediction and process optimization. By learning a complex mode in finite element simulation data through a deep learning model, efficient and accurate thermal stress prediction is achieved, and the problems that traditional finite element analysis is complex in calculation and large in resource consumption are solved.

The invention belongs to the technical field of artificial intelligence engineering, and particularly discloses a model training and deploying method and system based on Jenkins and version control, a terminal and a medium, and the method comprises the steps that a submission event of a GitLab code warehouse is monitored through Jenkins, difference data information including a training script, a data set version and model configuration is extracted in combination with DVC metadata, and the submission event of the GitLab code warehouse is obtained; and a training task is dynamically generated, and computing resources are automatically allocated to execute a training process. Through the process, full-process automation and closed-loop management of model training, verification, deployment and monitoring are achieved, development efficiency, deployment safety and model traceability are improved, and the method is suitable for intelligent model development scenes with high-frequency iteration and sensitive performance.

A cloud-based edge-as-as-service (EaaS) system allows edge gateways to be easily configured and deployed on the cloud for collection, contextualization, and egress of industrial data to downstream applications, including analytic applications, work order management systems, or visualization systems. The EaaS system uses predefined device profiles to automatically discover relevant data items on plant floor devices and present these data items to a user. Model configuration interfaces served by the EaaS system allow the user to map selected data items to predefined models for organizing or contextualizing the selected data, and for egressing the contextualized data to the target applications. These model configurations can be deployed on the cloud platform as edge gateways that use the resulting information models to collect and contextualize relevant items of device data during runtime and to export the modeled data to the target application.

The invention discloses an enterprise data analysis system based on natural language interaction, and relates to the field of enterprise data analysis, the system comprises seven core function modules: a dialogue management module supports a user to create a new dialogue and automatically jumps to an initial page; the report interaction module realizes query support and switching functions of a real-time report and a T + 1 report through a floating button; the file import module allows uploading of Word, PDF and JPG format files to supplement question and answer contexts; the model configuration module supports dynamic switching of different AI service interfaces; the interaction auxiliary module provides a quick question button and a dialogue interruption function; the report preview module integrates Excel and PDF plug-ins to realize detail data preview; the session sharing module generates a sharing link and sets an access permission rule; dynamic model configuration optimizes response speed and accuracy; the operation process is simplified by a suspension button and a quick questioning function; the session sharing mechanism supports cross-team cooperation; and the real-time collaborative design with the T + 1 report meets the multi-dimensional data analysis requirement.

The invention provides an automatic script generation method and system based on the Internet, and the method comprises the steps: obtaining a database connection address, a database name and a project name inputted by a user; determining a target database based on the database connection address and the database name; defining in a target database based on the item name, and determining field information of a target model; determining a Redis cache and a message queue plug-in corresponding to the target model based on the field information of the target model; determining model configuration of the target model based on the target task selected by the user and the field information; generating a basic function framework based on the model configuration of the target model, the Redis cache and the message queue plug-in; according to the parameter information configured by the user, generating an automatic script based on the basic function framework; according to the method and the system, the development efficiency can be improved, and the maintainability and the expandability of the project are enhanced.

The invention discloses a joint simulation management platform and a simulation method based on chip packaging, and belongs to the technical field of integrated circuit simulation. The simulation management platform generates a chip packaging physical model configuration table according to a chip packaging design file, creates a packaging material database and a working condition database, solidifies a simulation process of a simulation task, and performs simulation on the chip packaging physical model configuration table according to the chip packaging design file; a user is guided to input simulation parameters in a user interface according to a simulation process, a command control is clicked, interaction with ANSYS is carried out through a PyAnsys library of python, input of the user and pre-generated configuration information are transmitted to the ANSYS, and the functions of simulation pretreatment, simulation material calling, boundary condition setting, solving and post-processing and simulation report generation are achieved. According to the technical scheme, management and sharing of simulation process data and process and standardization of a task execution process are achieved, a sustainable multi-physics field coupling joint simulation management platform is formed, and simulation efficiency and precision are improved.

The invention relates to document generation, in particular to a dynamic document generation method and system based on a large language model LLM, and the method comprises the steps: receiving application form data, carrying out the parameter mapping and data binding of the application form data, converting the application form data into structured data, and storing the structured data into a relational database; converting the submitted document into vectorized data, and storing the vectorized data into a vector database; retrieving vectorized data highly related to the submitted document from a vector database to realize deep understanding and intelligent matching of the submitted document; retrieving model configuration information from the relational database, and dynamically creating a model instance according to the model configuration information; according to the variables defined in the document template and the model instances associated with the variables, integrating contents generated by the model instances into the document template so as to specify that specific contents are generated in the document generation process; according to the technical scheme provided by the invention, the defect that the accuracy, the correlation and the diversity of the generated document are relatively insufficient in the prior art can be effectively overcome.

The invention relates to the technical field of document intelligent review, in particular to a multi-mode digital publishing intelligent review system and method based on a large model, and the system comprises a sample construction module, a multi-mode analysis module, a structure recognition module, a term verification module and an annotation output module. According to the method, a model configuration data set is generated by analyzing a subject-object combination relationship in a text and jointly screening part-of-speech, sentence pattern and semantic structure, the accuracy of semantic recognition and structure judgment is improved, and local features of semantic offset and image-text expression separation are recognized by combining a comparison relationship between an image action path and a text behavior object; a paragraph logic structure and a theme connection mode are analyzed based on semantic offset information, the problems of content dislocation and theme jump between paragraphs are effectively revealed, matching change tracks and part-of-speech continuation fluctuation in term cross-paragraph contexts are recognized, label overlapping and coverage redundancy conditions are analyzed from sentence group hierarchy, and the term cross-paragraph contexts are obtained. And forming a label combination suggestion and constructing an annotation structure record.

The invention discloses an engineering field-oriented AI agent construction and management and control system. The system comprises a data acquisition module, a data preprocessing module, a vector knowledge base module, a large model module, a model configuration strategy module and an agent construction module. The data acquisition module is used for acquiring related data of an engineering project; the data preprocessing module is used for processing original engineering data; the vector knowledge base module is used for performing semantic analysis and feature extraction on the processed data; the large model module is used for deep understanding, semantic analysis and intelligent reasoning of engineering project data; the model configuration strategy module is used for screening out a large model most suitable for a certain task; and the agent construction module is used for constructing an agent. The AI capacity can be managed in a unified mode, model selection and data processing are optimized, repeated construction is effectively avoided, the safety risk is reduced, and the intelligent agent performance and the application effect are improved.

The invention relates to the technical field of model services, and particularly discloses a model reasoning consistency verification method, which comprises the following steps: deploying a model management service and a model publishing service; importing a model into a model center and selecting proper parameter configuration to construct an initial model; configuring training environment information for the initial model, triggering the initial model to execute a prediction task in the training environment according to the basic data set, obtaining a training task execution result, judging whether the initial model normally runs in the training environment, and if yes, reserving the training environment; calling a batch service operator and an online service operator from the initial model of the reasoning environment at the same time, and inputting the same test data to obtain a comparison output result; based on a comparison output result and a model publishing service, publishing a batch service or an online service according to an application scene; the prediction task can be automatically triggered through the model management service, and time and labor cost in the model consistency verification process are saved.

This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for tracking channel variations via a model-based representation of the channel. A first network entity may receive from a second network entity or transmit to the second network entity, a model configuration indicative of a model condition of a channel between the first network entity and the second network entity. The model configuration may be associated with one or more initial parameters. The first network entity may receive a reference signal from the second network entity to measure a condition of the channel between the first network entity and the second network entity, and transmit, to the second network entity, one or more updated parameters for the model configuration based on a measurement of the condition of the channel in comparison to the model condition of the channel based on the model configuration.

The invention relates to the technical field of intelligent medicine, in particular to an autism child early screening and intervention curative effect evaluation method which comprises the following steps: S1, obtaining social reaction scale data of a Chinese version; s2, acquiring and collecting data of preschool autism patients and typical development children; s3, filling missing data by adopting a mean value filling method; s4, dividing the filled data into a test set and a training set, and establishing an XGBoost model; s5, optimizing hyper-parameters in the XGBoost model by using grid search to obtain optimal model configuration; s6, Pearson correlation analysis is adopted, scale entries obtained through optimal model configuration are added to be subjected to correlation analysis with a gold standard scale capable of being used for diagnosing autism, and it is verified that the simplified scale is enough to serve as an effective tool for screening the pre-school-age autism child patients; by combining a machine learning algorithm, the screening process is optimized, and the screening efficiency and the diagnosis accuracy are improved.

Described are systems and methods for implementing a recommendation system that is configured and / or optimized to determine recommended content items based on a long-term objective. The exemplary recommendation system may be generated based on a mapping between content items and the long-term objective. The mapping between the content items and the long-term objective may be determined based on mappings utilizing various interim metrics, which may be determined using trained models configured to predict a respective target variable based on respective inputs. The mapping may also be generated using alignment scores and / or a large language model.

The invention discloses a medical ethical analysis report generation method based on a multi-model game, and the method comprises the steps: analyzing a medical ethical case, and constructing a structured element set and a role model configuration set; implementing a multi-model game, dynamically evolving the demonstration intensity through an excited state attenuation mechanism, and obtaining an evolution demonstration intensity matrix, game track data and a resonance suppression record; analyzing the evolution argumentation intensity matrix and game trajectory data, mining viewpoint fusion and crystallizing structural divergence, and obtaining a fusion viewpoint set, a crystallization divergence set and an ethical tension map; and integrating and generating a medical ethical analysis report. According to the invention, the dynamic authenticity of the medical ethical analysis process and the objectivity of the result are improved.

One or more embodiments of the invention provide a machine learning model construction method and device, a storage medium and a program product. The method comprises the following steps: receiving a modeling demand input by a user and a training data set for generating a training sample, wherein the modeling demand is at least used for describing a task and a performance requirement of a to-be-constructed machine learning model; generating a modeling configuration prompt based on the modeling demand and the training data set, and inputting the modeling configuration prompt into a pre-training language model, so as to generate modeling configuration information used for describing the configuration content of each link in the automatic modeling process by using the pre-training language model; based on the training data set and the modeling configuration information, the automatic machine learning tool is controlled to execute a machine learning model construction task, and the machine learning model construction task comprises the steps of constructing a to-be-trained machine learning model and a training sample, and training the to-be-trained machine learning model by using the training sample, and obtaining a trained machine learning model.

According to various embodiments, a method for configuring a technical system to be configured is described, comprising acquiring reference observations, conditioning reference system models to the reference observations acquired for the reference system, and acquiring, for the technical system to be configured, observations of results of the technical system to be configured for different values ​​of the configuration parameters;Adapting an a priori model for the relationship between the values ​​of the configuration parameters and the results delivered by the technical system to be configured to the observations recorded for the technical system to be configured, wherein the a priori model is formed from a weighted combination of the conditioned reference system models; determining an a posteriori model for the relationship between the values ​​of the configuration parameters and the results delivered by the technical system to be configured by conditioning the adapted a priori model to the observations recorded for the technical system to be configured; and configuring the technical system to be configured using the determined a posteriori model.

The invention provides an integrated learning-based bird buzzing intelligent recognition method and system, and solves the problem that the recognition precision is insufficient when an existing single deep learning model processes high-noise and low-signal-to-noise-ratio audio data. In order to solve the problems that bird species with limited long-tail data distribution or limited data volume are particularly unsatisfactory, the method mainly comprises the following steps: S1, collecting and preprocessing bird buzzing data to construct a standardized bird buzzing database; s2, using a Bootstrap sampling scheme to perform random sampling with playback on audio samples in the standardized twitter database, and generating a plurality of different independent sub-data sets; s3, respectively constructing and training an independent deep learning model for each sub-data set, including model configuration, data preparation, model training and model tuning; and S4, based on a Bagging ensemble learning strategy, fusing output results of the plurality of models to generate a final prediction result.

The invention discloses a differential chlorophyll fluorescence inversion method, system and device and a storage medium, and is applied to the field of vegetation remote sensing and physiological ecological monitoring, and the method comprises the steps: obtaining vegetation state monitoring data of a target region; on the basis of vegetation state monitoring data, asynchronous leaf-changing type evergreen arbors and synchronous deciduous type arbors are identified; carrying out SIF simulation on the asynchronous leaf-changing type evergreen arbors and the synchronous deciduous type arbors by adopting corresponding models respectively; according to the phenological phase function and the stress response function, generating correction factors corresponding to the asynchronous leaf-changing evergreen arbors and the synchronous deciduous arbors; the obtained initial SIF value is optimized through a corresponding correction factor; fusing the optimized SIF values to generate an SIF time sequence product; according to the method, differential model configuration and physical mechanism driven online correction are coupled, so that the problems of model mismatching and physiological mechanism deficiency in heterogeneous vegetation SIF inversion are solved, and an SIF time sequence product which is high in precision and clear in physiological and ecological significance is generated.

The invention provides a deep learning-based turbid underwater image restoration method, which comprises the following steps: establishing a CycleGAN cyclic consistency network model, respectively adding attention modules in an encoder and a decoder to optimize the model so as to enhance the detail extraction ability of the model, the attention modules comprising simAM and MLCA, and training the CycleGAN cyclic consistency network model by using the data set, adjusting the model configuration, adding a loss function and an optimizer, and performing image quality and visual effect evaluation on the processed image of the CycleGAN cyclic consistency network model. By means of the mode, the problems of blurring, distortion and noise in the underwater image can be better solved, the restored image is clearer and more accurate, the underwater image processing efficiency and quality can be improved, and development of the fields of underwater exploration, underwater archaeology, underwater robots and the like can be promoted.

Disclosed in the present application are a time series data anomaly detection method and apparatus, and a non-volatile storage medium. The method comprises: acquiring detection job configuration information, and establishing a detection job on the basis of the detection job configuration information, wherein the job configuration information comprises model configuration information; during the execution of the detection job, acquiring time series data corresponding to the detection job, and determining a training data set on the basis of the time series data; on the basis of the training data set, performing pre-detection on a target data point to be detected; when a pre-detection result indicates an anomaly, training an anomaly detection model on the basis of the model configuration information and the training data set, so as to obtain a trained anomaly detection model; and performing detection on said target data point on the basis of the trained anomaly detection model.

According to the large language model reasoning optimization method and device, the storage medium and the computer equipment provided by the invention, the multi-dimensional monitoring data is acquired, and then the multi-dimensional monitoring data is input into the pre-trained environment decision network to obtain the optimal model configuration. The current resource state is evaluated according to the multi-dimensional monitoring data, and the optimal execution path is determined according to the resource state and the optimal model configuration. And finally, adjusting the target model based on the optimal model configuration, and performing model reasoning according to the optimal execution path in the reasoning process of the target model. In the process, the resource state is evaluated and the optimal model configuration is determined by collecting the multi-dimensional detection data, and then the optimal execution path is dynamically determined, so that the model structure and the calculation strategy can be dynamically adjusted according to the state during actual operation, and the response delay is effectively reduced and the resource utilization efficiency is improved while the reasoning precision is guaranteed.