67 results about "Model management" patented technology

The application discloses a kind of unified modeling method of morphing aircraft based on multidisciplinary master model, belong to multidisciplinary design optimization field, by constructing by parameterized geometric model, subject characteristic attribute, multidisciplinary coupling relationship, parameter interface representation, requirement function mapping five parts organic integration multidisciplinary master model framework, the morphing parameter of aircraft and each subject design parameter are unified modelization expression and calculation;Five modules are associated and called by unified data dictionary and model management interface, form the single model data source of morphing aircraft, support consistent access, dynamic update and reuse to model in multidisciplinary design optimization process.

The present application provides a kind of geologic stratum lithology regional prediction method, system, terminal equipment and storage medium, it is related to computer system field.The system includes data management module, sample management module, model training module, model management module, lithology prediction module and the like.The present application utilizes geophysical, geochemical, remote sensing and other multi-source data, based on deep learning image segmentation algorithm, forms regional shallow surface stratum lithology identification model, carries out high-precision rapid stratum lithology prediction and regional segmentation to the bedrock exposed area and shallow cover area with lower exploration degree, supports geological mapping and mineral exploration work.

The application discloses an online education data intelligent planning system and method based on big data analysis, and belongs to the technical field of education data analysis. The system comprises a learning text management module, a data acquisition module, an analysis database, a model management module and a test question generation module; the learning text management module is used for collecting different learning texts for user learning and calculating the complexity of each learning text; the data acquisition module is used for respectively acquiring reading behavior data and test question data of different user learning texts; the analysis database is used for storing historical data; the model management module is used for analyzing the influence of different reading behaviors on the understanding degree of user learning texts; and the test question generation module is used for automatically generating a test question set about the user and pushing the test question set to the user; the application automatically generates a test question set about the user by connecting the weak knowledge points of the user, and improves the intelligent service level of the online education platform.

A system and method are provided for artificial intelligence (AI) model selection and loading. The method includes receiving an input data stream for an AI application and analyzing the stream to determine temperature scores based on topic frequency, importance weightage, and access frequency. The method also includes mapping these temperature scores to relevant expert AI models and predicting the temperature and context of the next prompt using historical data patterns through a sliding window that smooths temporary anomalies. The system dynamically selects AI models based on temperature scores and predicted contexts, then pre-loads these models into a memory hierarchy where higher-temperature models are placed in faster memory. The method processes incoming prompts using these pre-loaded models and outputs responses, improving processing efficiency through predictive model management. The system addresses challenges of managing large-scale AI models by implementing intelligent pre-loading mechanisms that anticipate and prepare for upcoming processing needs.

Techniques are disclosed relating to a method that includes receiving, by a particular computer system included in an enterprise computer system, deployment instructions, from a user, for a particular version of a machine-learning model. One or more versions of the machine-learning model may be stored in a database. The particular computer system may select, based on the deployment instructions, a destination within the enterprise computer system for deploying the particular version. The selected destination may provide access to a particular data set. The particular computer system may schedule a deployment of the particular version from the database to the selected destination. The deployed version of the machine-learning model may operate on the particular data set. Performance data associated with operation of the deployed version of the machine-learning model is collected.

This invention discloses a method and system for intelligent prediction and dynamic scheduling of irrigation district water resources based on multi-source model fusion, belonging to the field of water conservancy informatization. The method achieves prediction of inflow floods and various types of water demand through multi-source data acquisition and preprocessing, and multi-model fusion. It generates scheduling schemes through intelligent evaluation and decision-making, completes coordinated control of gates and pumps and engineering safety early warning, and combines visualization display and intelligent safety monitoring. It also conducts full lifecycle management and dynamic optimization of the entire model. The system includes corresponding modules for data processing, multi-source prediction model layer, intelligent evaluation and decision-making model layer, intelligent control and early warning, visualization display, safety monitoring, and model management, with each module operating collaboratively. This invention achieves intelligent scheduling of irrigation district water resources throughout the entire process, improves prediction accuracy and water resource utilization efficiency, has strong drought and flood prevention capabilities, and exhibits good model versatility and scalability, making it suitable for intelligent management of various irrigation districts.

The present application relates to the technical field of energy storage system, and particularly to a BMS simulation verification method and platform based on fault injection closed-loop joint debugging.A BMS simulation verification method and platform based on fault injection closed-loop joint debugging, the platform comprises the following: a model management layer; a scene generation and scheduling layer; a fault injection layer; a closed-loop joint debugging layer; a result collection and evaluation layer; a report generation and backfilling layer.Compared with the prior art, through the combination of the model library, the scene library and the fault injection library, the virtual BMS is quickly verified under different objects, different working conditions and different fault types; through the closed-loop joint debugging between the battery model, the thermal management model, the actuator model and the communication model, the closeness of the verification result to the real system is improved; through a unified coverage, response quality and protection effect evaluation mechanism, the comparability and screening efficiency between different verification tasks are improved.

A communication method and apparatus, and relates to the field of communication technologies. In the method, after indicating a first state of one or more first models to a model management device, a model application device may retain the first state within a first time window, so that the model application device does not frequently report the first state of the one or more first models to the model management device, to reduce information exchange between the model application device and the model management device.

The embodiment of the present disclosure discloses a product BOM structure-based relationship modeling management method, device and system, the method comprises: importing product design data generated from a product design tool, extracting type information of each component in the product design and relationship information between each component; defining related information of a plurality of object models, which is used for structured representation of each component type of the product, contained attributes, and a life cycle of each component type and a life cycle state thereof, and defining an entity storage structure based on the related information of the object models for structured storage of product data; defining related information of a relationship model between the plurality of object models; based on the related information of the object models and the related information of the relationship model, creating a page model using a visual what-you-see-is-what-you-get page modeling tool, the page model being used for showing related information of business data generated by the object models and the relationship model.

Embodiments of the present disclosure provide a model management method and device, a storage medium and a program product, relating to the technical field of communication, which can update the model in the communication device based on the change of the communication environment. The method is applied to a first communication device and includes: updating an initial model to obtain a target model in the case that the initial model currently running does not match the current environment. The target model is switched to be used.

A system and method are provided for artificial intelligence (Al) model selection and loading. The method includes receiving an input data stream for an Al application and analyzing the stream to determine temperature scores based on topic frequency, importance weightage, and access frequency. The method also includes mapping these temperature scores to relevant expert Al models and predicting the temperature and context of the next prompt using historical data patterns through a sliding window that smooths temporary anomalies. The system dynamically selects Al models based on temperature scores and predicted contexts, then pre-loads these models into a memory hierarchy where higher-temperature models are placed in faster memory. The method processes incoming prompts using these pre-loaded models and outputs responses, improving processing efficiency through predictive model management. The system addresses challenges of managing large-scale Al models by implementing intelligent pre-loading mechanisms that anticipate and prepare for upcoming processing needs.

A model management method and apparatus are disclosed, enabling efficient model management and improved resource utilization. In this method, a first node determines a target version from deployed versions of the target model based on version description information of the first version of the target model. It then sends a first unload command to a first control module controlling the deployment of the target version, instructing the unloading of model data corresponding to the target version. Since the version description information of the target version is identical to that of the first version, the newly deployed version can directly replace the target version. This triggers the unloading of related older version model data, preventing older model data from occupying the memory resources of the computing node for extended periods, thereby improving resource utilization.

A data management method, device, equipment, medium and product are disclosed. The method is applied to a data management system, the data management system is used for storing multi-source data, the data includes data associated with a mine, the method comprises: in response to a meta-model management operation on a meta-model management interface, obtaining meta-model management information; based on the meta-model management information, updating the meta-information data of the data management system; in the case that the meta-model management information indicates to create a physical table of the meta-model, creating the physical table based on the meta-model management information. The technical scheme of the embodiment of the present application unifies the description standard of mine disaster data through the meta-model management information, completes the standardized definition of the meta-model, and creates the physical table based on the meta-model management information, so as to quickly manage the physical table when a new data source or data structure is changed, thereby improving the data management efficiency.

The present invention relates to an AI model dynamic update system including an edge device and a cloud server, wherein the edge device includes: a situation recognition unit for recognizing a situation by detecting a surrounding environment; a model management unit that selects a pre-trained AI model suitable for the current situation and loads the pre-trained AI model to an NPU; an inference unit for inferring data acquired by the situation recognition unit as the AI model; and a control unit for controlling, on the basis of a result of the inference, a platform on which the edge device is mounted, the cloud server includes a learning unit that trains the AI model with a GPU by using the received data and provides an updated model to the edge device, the edge device performs inference only with the NPU without including a GPU, and the GPU of the cloud server is dedicated to model training and updating.

This invention discloses an artificial intelligence-based constant temperature and humidity fermentation system for tea, comprising a global perception and data acquisition layer, an AI decision-making and digital twin layer, a precise execution and control layer, and a management and interactive presentation layer. The global perception and data acquisition layer is used to collect multi-dimensional data from the fermentation environment and the tea itself in real time. The AI ​​decision-making and digital twin layer is used to fuse and analyze the collected data, construct a virtual fermentation model, and generate optimized control strategies. The precise execution and control layer is used to distribute the control strategies generated by the decision-making layer to various actuators and drive their actions. The management and interactive presentation layer provides system configuration, process monitoring, model management, and visual interactive functions. This invention relates to the field of intelligent tea processing equipment technology. This system can achieve adjustments from environmental control to quality process control, thereby completing real-time perception, intelligent diagnosis, dynamic optimization, and endpoint determination of the fermentation state.

An anomaly detection device according to the present disclosure comprises a data acquisition unit that acquires data relating to the state of an industrial machine; a feature quantity generation unit that, based on the data relating to the state, generates a feature quantity that indicates a feature of the state of the industrial machine; a feature quantity memory that stores the feature quantity; a feature model memory that stores at least one feature model; an inference calculation unit that uses the feature model to calculate an evaluation value for the state of the industrial machine based on the feature quantity; and an anomaly determination unit that, based on the calculation result from the inference calculation unit, determines that some anomaly has occurred in the state of the industrial machine.a condition diagnosis unit that diagnoses the condition of the industrial machine when the anomaly determination unit has determined that an anomaly has occurred in the industrial machine; and a feature model management unit that, depending on the result of the diagnosis by the condition diagnosis unit, uses the feature quantity stored in the feature quantity memory to generate a feature model and adds the feature model to the feature model memory.

The application relates to the technical field of photovoltaic power station management, and particularly discloses a centralized photovoltaic power station whole life cycle intelligent management system. The system comprises a model management module, a data service module, a decision guidance module and a state tracing module. The digital twin body constructed by the model management module can dynamically reconstruct the model granularity and depth according to the life cycle stage of the power station; the data service module standardizes and encapsulates the whole cycle heterogeneous data according to a predefined data structure template; the decision guidance module generates strategy instructions based on the simulated state of the digital twin body; and the state tracing module records all state changes as a linear event sequence for tracing and auditing. Through the cooperation of the modules, the application realizes the whole process, self-adaptive and traceable integrated management of the photovoltaic power station from planning to decommissioning.

This invention provides a data management system and method, belonging to the field of data management. The system includes: a data source management module, a database management module, a data model management module, a data standard management module, and a data quality management module. The data source management module is used to obtain multiple initial metadata and data types from multiple preset business systems, and construct a database to be processed using these initial metadata and data types. The database management module is used to update the database to obtain an updated database. The data model management module is used to construct a target model based on the updated database. This invention solves the problems of low online data rate, low structured data rate, and multiple data sources caused by the increasing amount of data in business systems. It establishes a unified data asset management standard, laying a solid data management foundation for building intelligent pipelines.

Provided are a wireless communication method, a terminal device and a network device. The method comprises: a terminal device receiving or sending first information, wherein the first information is used for requesting that model information supported by the terminal device is indicated to a first network element, or the first information is used by the first network element to configure a model training task for the terminal device, the first network element being used for managing a model. In the embodiments of the present application, a first network element used for model management is introduced. In this way, a terminal device can interact with the first network element by means of first information, so as to manage a model in a communication system, thereby laying a solid foundation for a model-based communication process.

The application discloses a data leakage detection system and method based on time series data anomaly detection, relates to the technical field of data leakage detection, and solves the problems of a traditional data leakage detection method based on rule matching or static threshold detection, high false positive rate, high false negative rate, poor adaptability and early warning lag when facing zero-day vulnerabilities, malicious theft by internal personnel and advanced persistent threats. The application comprises a data acquisition and analysis module, a user entity behavior portrait construction module, a core detection engine, a model management and optimization module and an alarm aggregation and response arrangement module; the data acquisition and analysis module is used for collecting original security logs and network metadata from heterogeneous data sources in real time and performing standardized analysis. The application can more accurately identify various leakage scenarios from single-point anomalies to coordinated attacks by capturing complex time patterns through "multimodal time series coding" and revealing associated threats through "graph context analysis".

Provided is a proxy existence model management device that makes it possible to easily operate a plurality of proxy existence models.　A proxy existence model management device according to the present disclosure includes: a model pool management unit that associates at least one proxy existence model and model feature information of the proxy existence model with each other and manages the same as a proxy existence model pool; an input information acquisition unit that acquires input information of a user; an input analysis unit that analyzes the input information; a model selection unit that selects at least one proxy existence model simulating a target person from the proxy existence model pool, on the basis of the analysis result of the input information and the model feature information; an information generation unit that gives the input information to the selected proxy existence model to generate information; and an output unit that outputs the information generated by the proxy existence model. Here, the proxy existence model is a model configured to simulate the behavior of a model target person with respect to a large-scale learning model.

1. Name of the product in this design: Graphical User Interface for Generative Artificial Intelligence Evaluation and Verification System for Electronic Devices (3). 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. As is customary, the rear view, left view, right view, top view, and bottom view are omitted. 6. Purpose of the graphical user interface: To display the main content of the evaluation and verification system model management interface. 7. Human-computer interaction method of graphical user interface: The main view is the interface for managing the evaluation model of the generative artificial intelligence evaluation and verification system. Users can enter the main view by logging in and clicking "Model Management" in the left navigation bar. By clicking different function buttons on the model management interface, users can enter the corresponding function operation pages such as import model page, model details page, and edit model information page. 8. Description of the changing states of the graphical user interface: In the interface, users can perform relevant management operations on the models within the generative artificial intelligence evaluation and verification system through interactive operations. The main view is the model management list interface of this design product evaluation and verification system. Users can enter the main view by logging in and clicking "Model Management" in the left navigation bar. In the main view interface, users can click the "Details" button to enter the interface change state. Figure 1 shows the model details page. Figure 2 shows the test pop-up prompt page for different versions of the model in the model details page list. Users can enter the interface change state. Figure 3 shows the test pop-up window for different versions of the model in the model details page list. Users can enter the interface change state.

The application discloses a kind of general line shape calculation methods of railway line, it is related to railway design technical field, including the following steps: constructing line model management link table container;Through the curvature expression of constructing straight line, circular curve, easement curve, create general line shape data structure;Using curvature expression, combined with numerical integration and nonlinear equation solution, calculate line parameter;Interconversion is carried out to two kinds of data organization models of intersection method and unit method;Build mileage conversion system, carry out the conversion of line model mileage and broken chain;Through using model splitting, merging, editing means, realize the interactive modification of line model;According to the design specification and design speed used by line model, carry out line model compliance check;Line model coordinates, line spacing are calculated and output results;The calculation method is novel and unique, can greatly improve the precision and calculation efficiency of railway line line shape calculation, with strong versatility and expandability.

This application provides an intelligent control modeling platform, method, and related equipment. In this embodiment, a data and model management module is connected to the visualization modeling and simulation verification module, and is used to provide the visualization modeling and simulation verification module with processed industrial data and callable models. The visualization modeling and simulation verification module is connected to the online optimization and deployment service module, and is used to transmit verified algorithm models to the online optimization and deployment service module. The online optimization and deployment service module receives the algorithm models from the visualization modeling and simulation verification module and deploys the algorithm models to an industrial controller for operation. It also receives real-time operating data from the industrial controller and performs parameter updates or structural optimizations on the deployed algorithm models based on the real-time operating data.

The application provides a model evaluation method and device, electronic equipment and storage medium. The method comprises the following steps: based on a model management and adaptation tool, migrating a target model from a display card environment to a target chip environment; based on inference services of the target model in the display card environment and the target chip environment respectively, performing correctness evaluation on the target model to obtain a correctness evaluation result; in the case that the correctness evaluation result is passed, based on an inference performance evaluation tool, performing performance evaluation on the target model to obtain a performance evaluation result of the target model. The application realizes standardization and automation of correctness evaluation, obtains reproducible and quantifiable correctness judgment, reduces human error and repetitive labor; through an automatic evaluation mechanism, standardized performance data can be generated, consistency and sedimentability of performance evaluation are realized; and the application can support extension and customization of different chip manufacturers, different models and different test strategies.

The application relates to a method and device for deploying a model of a server, a computer device and a storage medium. The method comprises the following steps: obtaining a model deployment package corresponding to a to-be-deployed model, wherein the model deployment package comprises model configuration information corresponding to the to-be-deployed model; generating a model image corresponding to the to-be-deployed model based on the model deployment package; generating model service configuration information corresponding to the to-be-deployed model based on the model image and the model configuration information; creating a container control component and a service component corresponding to the container control component based on the model service configuration information; wherein the container control component is used for dynamically generating and controlling a model container set corresponding to the to-be-deployed model, and the container control component and the service component are used for cooperating to provide a model processing service corresponding to the to-be-deployed model, and the model processing service is used for distributing a model processing request corresponding to the to-be-deployed model into a model container in the model container set for processing. The method can improve the model management efficiency.

The application relates to the technical field of building engineering safety monitoring, in particular to a deep foundation pit support stress monitoring method and system based on BIM (Building Information Modeling), which comprises a multi-source sensing acquisition module, a communication module and a processing terminal, the processing terminal is provided with a BIM model management module, a coupling analysis module, an intelligent early warning module and a visual interaction module, the coupling analysis module is taken as a core, a dynamic mapping relationship between sensor data and BIM components is established, real-time monitoring data is directly injected into a model attribute set, and a built-in structural mechanics calculation engine is driven to carry out real-time stress simulation. The three-dimensional building information model is changed from a static display platform into a digital twin body which can be autonomously updated, and the structure safety state can be dynamically reconstructed according to real-time data. Meanwhile, the intelligent early warning module actively judges based on simulation results and preset hierarchical early warning rules, so that the BIM system has real-time safety evaluation and early warning driving capabilities.

This invention discloses a method and system for intelligent optimization design of substation lightning protection and grounding based on a geological artificial intelligence large-scale model. It relates to the field of intelligent optimization design technology. The method includes: acquiring multi-source data and processing it to obtain a standardized feature dataset; inputting the geological artificial intelligence large-scale model to derive an initial design scheme; constructing a multi-objective optimization evaluation system to simulate and evaluate the initial scheme; iteratively optimizing based on the evaluation results to output the optimal scheme. The system includes modules for data acquisition and processing, model reasoning, scheme evaluation, iterative optimization, and model management. This invention relies on the strong reasoning capabilities of the geological artificial intelligence large-scale model to achieve intelligent derivation and iterative optimization of the design scheme, fully adapting to the geological and lightning environment of the site selection area, balancing lightning protection reliability, economy, and construction feasibility, significantly improving design efficiency and scheme quality, and possessing outstanding creativity and practicality.

A model management method and apparatus, which are capable of implementing reasonable model management and improving resource utilization. In the method, a first node can, on the basis of version description information of a first version of a target model, determine a target version among deployed versions of the target model; and send a first unloading instruction to a first control module used to control compute nodes on which the target version is deployed, so as to instruct unloading of model data corresponding to the target version. Because version description information of the target version is consistent with the version description information of the first version, a deployed new version can directly replace the target version. Thus, an unloading operation for model data of a related old version is triggered to prevent the model data of the old version from occupying memory resources of the compute nodes for a long time, thereby improving resource utilization.