475 results about "Model management" patented technology

An advanced model management platform for optimizing and securing generative artificial intelligence systems such as large language models (LLMs) and diffusion models. The platform incorporates various techniques to address the limitations of current generative AI systems, such as hallucination, lack of validation, security vulnerabilities, and inadequate model management. The system employs reinforcement learning algorithms for model optimization, retrieval augmented generation (RAG) for hallucination mitigation, domain-specific validation against expert knowledge, model distillation and similarity scoring for security, adversarial training for robustness, and attention mechanism search and model blending for advanced management and neuro symbolic AI routine combinations. By integrating these techniques, the platform significantly improves the performance, reliability, and security of generative AI across a wide range of tasks and domains leveraging the best elements of symbolic and connectionist techniques alongside automated planning and modeling simulation.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A UE transmits information on at least one first artificial intelligence (AI) / machine learning (ML) model, wherein the information on the at least one first AI / ML model includes a list of AI / ML models, and wherein an AI / ML model included in the list of AI / ML models is identified by a first AI / ML model identifier (ID), receives at least one AI / ML model information indicating at least one AI / ML model to be activated, wherein the at least one AI / ML model to be activated is identified by a second AI / ML model ID; and activates the indicated at least one AI / ML model based on the received at least one AI / ML model information.

The invention relates to the technical field of financial risk control, and discloses a telecommunication fraud risk identification method based on a bank card transfer scene. The method comprises the following steps: a basic feature construction stage: acquiring multi-source data of historical victim users, extracting multi-dimensional features, and processing the multi-source data into standardized time sequence data through feature coding and a DTW algorithm; in the multi-modal fusion and adversarial learning stage, a core layer containing a multi-modal analysis engine, a risk reasoning model and an adversarial generation model is constructed, and multi-modal feature fusion, risk probability output under an RLHF framework and simulated fraud feature generation driven by WGAN-GP are achieved; in the strategy output stage, risk scores are mapped through a double-layer scoring system, and three-level interception is triggered; in the model management stage, model iteration is achieved by means of a monitoring instrument panel and a rolling time window, the online effect is guaranteed by combining gray release and A / B testing, the telecommunication fraud in the transfer scene can be accurately recognized, and the risk control efficiency is improved.

The invention discloses a multi-type central air conditioner integrated optimization automatic control system, which relates to the technical field of air conditioner control, and comprises an equipment dynamic modeling module, an MPC optimization control module, a dynamic reference and diagnosis module, an RL driving MPC setting module, a dynamic sliding window processing module and a large model management module, the dynamic sliding window processing module gathers and performs continuous cutting and context association processing on information streams from the modules to generate serialized information fragments adaptive to the input length of the large model management module, and the large model management module serves as a core cognition and decision center; and performing deep fusion analysis on the received serialized information fragments by using an internal attention mechanism to realize advanced operation strategy generation and deep fault reason tracing, and providing decision support and system state interpretation through a natural language interaction interface. And finally, comprehensive cognition, predictive insight and self-adaptive evolutionary management of the global state of the central air conditioning system are achieved.

The invention discloses a Java-based large language model dynamic adaptation method and system, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of overcoming the interface difference of multiple model interfaces, simplifying the development process and providing standardized and high-expansibility large model management. Comprising the following steps: providing a model registration and dynamic loading service, a protocol conversion and parameter standardization service and a load balancing and failover service; a streaming transmission protocol service, a function call dynamic injection service and a global error processing mechanism are provided; when a user provides a document analysis and partitioning service, a vectorization index construction service and an RAG enhanced generation service to ask questions, relevant document blocks in the Pinecone are retrieved through the RAG enhanced generation service to serve as contexts to be injected into cue words of the large language model, and answers generated by the large language model are returned.

The invention discloses a project progress, quality and safety collaborative management and control system and method based on BIM and Internet of Things, and belongs to the technical field of constructional engineering informatization management. In order to improve engineering management efficiency and reliability, the system comprises a data acquisition layer, a data processing layer, an application layer and a security module. The data acquisition layer is connected with the data processing layer, the data processing layer is connected with the application layer, and the security module is respectively connected with the data acquisition layer, the data processing layer and the application layer; the data acquisition layer comprises IoT equipment, an external data interface, data middleware and a BIM model, the IoT equipment acquires data through the external data interface, the IoT equipment is connected with the BIM model through the data middleware, and the data processing layer comprises a multi-source data fusion engine and an intelligent algorithm module; the application layer comprises a model management module, a model display module, an early warning management module, a data management module, a three-dimensional visual monitoring platform, an intelligent decision center and a mobile terminal module. The project management efficiency is improved.

Methods, systems, and devices for wireless communication are described. A radio access network (RAN) node may receive a message including meta-information of a machine learning model for operation by one or more devices within a coverage area of the RAN node (e.g., supported by the RAN node), where the machine learning model may be transparent to the RAN node. The meta-information may include suitability information for control and management of the machine learning model by the RAN node. In some examples, the RAN node may receive meta-information from various logical functions. In some other examples, the RAN node may receive meta-information from one or more devices. In response to receiving the meta-information, the RAN node may send a model management message that instructs a device of the one or more devices to perform a model management operation for the machine learning model.

The invention provides an AI model management method and system based on dynamic NFT, and the method comprises the steps: firstly packaging the core elements of an AI model into on-chain intelligent assets through the dynamic NFT, and achieving the clear definition and credible storage of the ownership of the model; then, a programmable smart contract bound with the dynamic NFT is utilized to support flexible definition of an atomization authorization rule, and traditional fragile API key authorization is replaced; and finally, forcibly recording a father-son relationship in a model iteration process through a dynamic NFT chain derivation mechanism to form a non-tampering version pedigree map, and solving the problems of attribution dispute and technical source tracing of the fine tuning model. According to the invention, property right clarification, authorization control flexibility and version management credibility of AI model digital assets are realized, and the method is suitable for transaction, circulation and governance scenes of AI assets such as large language models and vertical field special models.

A communication method and a related device, in the method, under the condition that different models deployed by different communication devices are used for jointly completing the same AI task, a first communication device can determine whether to adjust the different models based on the condition that the performance of the different models meets (or does not meet) conditions, so that the different models can be adjusted. Therefore, the model management of the model is realized. In some implementation modes, the first communication device can determine third information used for adjusting the first model and / or adjusting the second model based on the first information and the second information, so that the scheme can be applied to a scene of distributed multi-node cooperative processing of the same AI task, and furthermore, the scheme can be applied to a distributed multi-node cooperative processing of the same AI task. According to the method, the node with the fault can be accurately judged based on the condition that the performance of different models meets the condition (or does not meet the condition), the influence on the whole distributed system is avoided, redundant operation caused by adjustment of fault-free equipment is also avoided, and the model management efficiency is improved.

The invention discloses a financial monitoring system based on real-time data processing and intelligent analysis, and relates to the technical field of financial information, and the system comprises a bill collection module which employs a configuration API interface, carries high-concurrency data streams through a distributed message queue, carries out the real-time connection of an upstream business system and a financial system, and receives bill detail data; and the analysis and check task arrangement module manages the task dependency relationship based on a directed acyclic graph model, and arranges a data pulling task, a bill general task and a bill check task according to a preset rule. According to the financial monitoring system provided by the invention, the automation and intelligence level of financial management is remarkably improved by integrating real-time data processing and intelligent analysis technologies, the system adopts a configured API interface and a distributed message queue, efficient butt joint with an upstream business system and a financial system is realized, and the financial management efficiency is improved. The real-time performance and accuracy of data receiving are ensured, and the data processing flow is more flexible and controllable.

The invention discloses a boiler efficiency optimization method based on big data. The method comprises the following steps: S01, carrying out real-time multi-modal data acquisition and verification fusion; s02, performing adaptive data processing and feature engineering on the high-quality data stream, and dynamically adjusting verification depth and frequency; s03, performing dynamic prediction model management and adaptive re-calibration; s04, executing multi-target robust optimization based on dynamic preference, automatically generating preference setting by using a reinforcement learning agent, searching a Pareto optimal solution by using an evolutionary algorithm, and ensuring the security of reinforcement learning; and S05, executing optimization control and fine regulation and control of a specific subsystem. The invention aims to solve the problems of low data quality, poor model adaptability, single optimization target and the like in the prior art, and comprehensively improves the operation efficiency, economical efficiency and intelligent level of the boiler through systematic data processing, self-adaptive modeling, multi-target intelligent decision and fine control.

The model management device includes a communication unit capable of communicating with a plurality of AI devices, each of which continuously performs training of a machine learning model, and a processor configured to manage training of a machine learning model in each of the plurality of AI devices, and acquire accuracy of the machine learning model trained in each of the plurality of AI devices. The processor is configured to change an execution frequency of a process relating to training of the machine learning model having an accuracy within a predetermined range.

The invention relates to the technical field of artificial intelligence and edge computing, in particular to a hybrid expert model distributed computing system based on an edge computing technology. Comprising a central server, an edge device network, a data privacy protection module, an automatic model decomposition module, a performance feedback and adaptive learning module, a fault detection and self-healing module and a task priority scheduling module. The central server comprises a self-adaptive gating network, a task manager and a model manager; the self-adaptive gating network is used for receiving an input task, extracting task characteristics, intelligently identifying task data distribution and routing the task to the expert sub-model matched with the task according to an identification result; according to the method, the tasks are dynamically scheduled according to the computing power and the load condition of the edge device, the model updating and synchronization mechanism is imperfect, the consistency of the model in the distributed environment is ensured, the computing cost is remarkably reduced, and the dependence on special hardware facilities is reduced by utilizing idle edge device computing resources.

The invention provides a model management method for artificial intelligence (AI) / machine learning (ML). The wireless communication device performs the method to perform AI / ML model monitoring and / or perform lifecycle management (LCM) on one or more AI / ML models according to an AI / ML model monitoring configuration.

A model management method and a communication device are provided. The method includes that: a node obtains a model management service provided by a model management module, the model management service supports the node to perform at least one of the following operations: storing one or more models to the model management module; updating one or more models in the model management module; and obtaining one or more models from the model management module.

The invention discloses a multi-dimensional large model test evaluation method and system, belongs to the technical field of artificial intelligence testing, integrates a basic model, platform capability and application performance to realize multi-dimensional large model test evaluation, and comprises the following steps: basic model capability evaluation, including sensitive lexicon + BERT semantic double-engine security detection and context dependence test chain; evaluating the capability of the model platform, including presetting LLM intelligent labels and double-person back-to-back data backflow; application performance evaluation, including character disturbance robustness test and LIME interpretability grading evaluation; large-model full-stack testing is achieved through a RESTful heterogeneous model nanotube interface, and localized compatibility adaptation verification is achieved based on GPU attenuation rate quantification. The method solves the problem of lack of evaluation of platform engineering capability, safety compliance and localization support in the prior art, realizes three-dimensional capability coupling evaluation, solves the problem of localization adaptation, and fills the blank of a large-model full-stack test technology.

The invention discloses a brain disease risk prediction method and system based on big data analysis, and belongs to the technical field of brain disease risk prediction. The method comprises the following steps: carrying out standardized preprocessing and tagged classification on brain disease related big data to generate a feature data set; mining specific disease characteristics and risk factors in the set, and carding an association rule; training a risk prediction sub-model for each disease type based on the data, and building a multi-sub-model hierarchical prediction system; and collecting to-be-predicted object data, matching a disease type, and calling the corresponding sub-model to complete risk assessment. The system comprises multiple modules for collaborative operation, and a full-process closed loop of data storage, feature processing, model management and result output is realized. According to the scheme, the pertinence, the accuracy and the efficiency of risk prediction are improved, the traceability of the whole process and the dynamic optimization of the model are realized, and reliable technical support is provided for early screening and risk early warning of brain diseases.

The invention discloses a development, operation and maintenance platform of a large language model and a model development and deployment method, and the development, operation and maintenance platform adopts a front-end and rear-end separation architecture design, and comprises a front end part and a rear end part, wherein the front end comprises a data set uploading interface, a model training interface, a model evaluation interface, a model deployment interface, a model-adapter merging interface and a model tracing interface; and the rear end comprises a data set management function module, a data set uploading function module, a model training function module, a model evaluation function module, a model deployment function module, a model-adapter merging function module and a model management function module. The data set uploading interface is used for uploading the data set for training and evaluation, the model training interface is used for model training to improve the model performance, the model evaluation interface is used for evaluating the capabilities of the model in different aspects, the model deployment interface is used for providing services for model deployment, and the model development and deployment work is completed.

The subject technology provides for a model management and deployment system for machine learning models. A system includes a model manager configured to schedule execution of one or more machine learning models on one or more electronic devices or servers. The system also includes a model catalog configured to store information associated with the one or more machine learning models. The model manager may access the model catalog to determine scheduling priorities based on the stored information.

The invention relates to the technical field of person and post matching, in particular to a person and post intelligent matching model management method and system based on data analysis, and the method comprises the steps: collecting historical recruitment data, and constructing a job seeker feature vector and a post feature vector; extracting job seeker feature vectors and post feature vectors corresponding to the job application results to form a historical job application feedback data set, and calculating a non-prejudice feature matching degree to construct a prejudice feedback data set; extracting non-prejudice feedback data based on the prejudice feedback data set and the historical job-hunting feedback data set, and performing reconstruction optimization on the historical job-hunting feedback data set; constructing a non-prejudice man-and-post matching data set, and training the man-and-post intelligent matching model by using the non-prejudice man-and-post matching data set; and applying the trained person and post intelligent matching model to post recommendation, and collecting subsequent job application result data to optimize the model. According to the method, the prejudice factors in the historical recruitment data can be effectively eliminated, and the fairness of the matching result is improved.

The invention discloses an assembly type maritime work component management system based on BIM. The system comprises a BIM model management module, a component production management module, a component transportation management module and a component installation management module. The BIM model management module is used for creating, importing, updating and visualizing a BIM model of the assembly type maritime work component; the invention relates to the technical field of maritime engineering management. According to the BIM-based fabricated maritime work component management system, through a BIM model management module, full-process management is performed on fabricated maritime work components from model creation and introduction in a design stage to model updating and visual display in a construction process. All the modules interact through data interfaces, and a cloud computing architecture and a B / S mode are combined, so that all participants of a project can share information such as the size, the material and the progress of components in real time, an information island is broken, the problem of unsmooth information circulation in a traditional management mode is solved, and the information transmission efficiency and accuracy are greatly improved.

A machine learning (ML) model management method for a network data analytics function (NWDAF) device is disclosed. The NWDAF device performs at least one of an analytics logical function (AnLF) for network data and an ML model training logical function (MTLF).

An information processing system, an information processing method, and a program suitable for structure inspection work are provided. [Solution] The information processing device includes a model management device and a terminal device, wherein the model management device stores model data of a structure consisting of multiple components, and the terminal device estimates its own position on the actual structure and corrects the own position using the model data.

The invention relates to a full-process-oriented data asset model management method and device, equipment and a medium, and belongs to the technical field of data processing. The method comprises the following steps: sequentially performing metadata extraction, data asset directory construction and data asset access based on metadata association and directory binding on multiple data sources of a target business system, and constructing and generating a data asset full-flow chart to display a full-flow path of data asset access; predicting and supplementing missing metadata based on the double algorithms of data consanguinity logical reasoning and knowledge graph rule judgment; performing correctable multi-stage multi-round recursive quality evaluation on the data asset full-flow chart after missing metadata prediction and completion; and synchronously executing a three-layer parallel joint retrieval mechanism based on a quality evaluation result to complete joint search of multiple data assets and high-quality retrieval result display. By adopting the method, efficient and accurate management of the data asset model of the target business system can be realized.

The invention relates to an MBSE multi-field-based virtual-real fusion simulation platform and method, and the method comprises the steps: an intelligent matching module is used for receiving a to-be-detected algorithm model according to a user interface module, determining an MBSE multi-field model from a model management module, and achieving the MBSE multi-field model through the MBSE multi-field model; the virtual-real fusion test module is used for determining a plurality of test scenes constructed according to real scene data according to the MBSE multi-field model, performing full-life-cycle verification on the MBSE multi-field model and the to-be-detected algorithm model according to each test scene to obtain a corresponding verification result, and when the verification result meets a preset condition, executing the virtual-real fusion test on the to-be-detected algorithm model. The to-be-detected algorithm model is stored in the model management module, the full-life-cycle automatic verification process is achieved through the virtual-real fusion test module, the problems that virtual simulation and real object test are separated, and evaluation standards are not uniform are solved, and the method has the advantages that the test efficiency is improved, and the virtual-real fusion test capability is enhanced.

The present invention belongs to the field of aerospace technology and relates to a digital twin system for low-orbit giant star constellations and a construction method thereof. The system includes a data management module that comprehensively manages the real-time data, vertical data, horizontal data, simulation data, and fusion data of the giant star constellation system; a model management module that dynamically constructs and comprehensively characterizes the geometric, physical, behavioral, rules, and constraint characteristics of complex physical systems from different dimensions, different spatial scales, and different time scales; an information management module utilizes these multi-scale and multi-level characteristic information to perform local to global network reconstruction of the low-orbit giant star constellation system and restore the complete information network; a collaborative coupling mechanism of data interconnection, information exchange, and model interoperability is adopted to achieve efficient and real-time monitoring and management, providing a solution for digital, visual, and intelligent supervision.

The embodiment of the invention provides a multi-model management method and system, and belongs to the technical field of algorithm models. The method comprises the following steps: in response to a model management request signal, identifying request content corresponding to the request signal; determining a management scheme of a model based on the request content; when the management scheme is a calling scheme, determining a target model type based on the request content, and triggering a corresponding model calling scheme based on the determined target model type; and executing the model calling scheme, and extracting a model mirror image based on the optimal scheme. According to the scheme, the problems that model integration is difficult, scheduling is slow, and a unified platform is lacked are solved, and unified integration and scheduling management of mechanism models and data models of different operation environments and different development languages can be completed.

The invention discloses a metadata version control and automatic structure comparison method and system based on incremental snapshot and intelligent decision, and the method comprises the following steps: managing a metadata version through an incremental snapshot model, and enabling the incremental snapshot model to support the complete backtracking of a historical structure through storing changed incremental information and associating with a version chain; carrying out difference analysis on the metadata structure by adopting a hierarchical progressive comparison algorithm; an intelligent DDL decision engine is adopted, and an executable DDL script is generated according to the difference analysis result; in the process of generating the DDL script, metadata consanguinity information is jointly checked so as to carry out change risk assessment. Through collaborative design of all the modules, double benefits of systematic breakthrough and conventional effect enhancement are brought, and the technical effect is directly associated with collaborative design.

To improve model learning accuracy.SOLUTION: An information processing apparatus comprises: model management means that manages, for each of a plurality of learning models, first information for identifying a dataset used to train the learning model, and second information for identifying an initial model used to train the learning model; data management means that manages third information concerning a plurality of datasets each identified by the first information of each of the plurality of learning models; reception means that receives an instruction of incremental learning using, as an initial model, a predetermined learning model included in the plurality of learning models; and determination means that determines a dataset to be used for the incremental learning from the plurality of datasets. The determination means determines, as a dataset to be used for the incremental learning, a dataset used to train a learning model whose evaluation accuracy is equal to or higher than a predetermined accuracy, the evaluation accuracy being obtained when inputting a predetermined dataset to each of at least one learning model used as the initial model of the predetermined learning model.SELECTED DRAWING: Figure 1