20 results about "User modeling" patented technology

The present application relates to the field of dimension marking, especially to a multi-dimensional dynamic dimension marking system for wood components. Dynamic dimension marking is a tool that can dynamically display the dimensions of a modeling object according to user operations. However, the mainstream dynamic dimension marking system can only actually realize the dynamic display of the dimensions of the modeling object when the structure of the modeling object is relatively simple. The present application comprises an information storage module for storing the parameters of the wood component and updating the parameters of the wood component when the parameters of the wood component change. The three-dimensional wood component formed by modeling is simplified into a series of point positions and functional relationships between the point positions. Through deep learning, the change of the functional relationship between the point positions is estimated according to the change of the point positions, and then the change of the shape and size of the wood component is quickly estimated, which greatly reduces the computational load and improves the generation speed of the dynamic dimension marking.

The embodiment of the invention provides an information recommendation method and device, a storage medium and a program product. In the embodiment of the invention, the target geographic object associated with the user and the attribute data of the target geographic object in the target library are introduced, and the geographic semantic feature, the distance feature and the geographic context feature are incorporated into the user modeling process, so that the data normal form which only depends on established behavior data and static portraits is broken through; on this basis, according to attribute data and historical behavior data of the target geographic object, steady-state demand potential information and dynamic interest intention information of the user are predicted, and user demand features are described from two dimensions of long-term steady-state demand and short-term dynamic interest; furthermore, the steady-state demand potential information and the dynamic interest intention information are combined with candidate information objects in the target application scene to perform joint information recommendation, so that the recommendation result can not only fit the current interest of the user, but also keep the consistency with the long-term demand of the user, and the recommendation effect is greatly improved.

The invention discloses an AI-based multi-modal digital content intelligent recommendation method and system, relates to the technical field of artificial intelligence and education recommendation, and is used for solving the problems of inaccurate matching of multi-modal learning resources and user intentions, false feature interference and unclear semantic links in a weak network scene. Firstly, intention analysis and user modeling are carried out on user multi-modal interaction behaviors through a large model, and user feature vectors with unified dimensions and stable calibers are formed; and the resource side extracts semantic and time sequence features, and introduces preview elf chart recognition in a weak network scene. In order to suppress false features, a minimum evidence account book and a three-point co-location check chain are provided, and accounting management is carried out on evidence lease; and main channel writing is released only when the real frame backbone is consistent with the subtitle key points. Then combining modal gating fusion, knowledge network anchoring and path construction, and finally implementing hierarchical reordering according to the reference semantic vector and the incidence matrix to generate a traceable, verifiable and robust personalized recommendation result.

The invention belongs to the technical field of computer collaborative modeling, and discloses a system modeling language online collaborative modeling method and system in a network environment. The method comprises the steps that user modeling operation is packaged into an editing event through a browser side and sent to a server side, the server side conducts concurrency control through a multi-granularity locking mechanism, a system modeling language graph serves as the main locking granularity, and model elements serve as the auxiliary locking granularity; the browser end maintains a command execution queue, and multi-end state synchronization is achieved through a version field; the system supports a design intention interaction mechanism, allows a non-lock holder to request operation through an intention event, and allows a server to forward the operation to a lock holder and execute corresponding operation according to feedback, thereby realizing state consistency, concurrency control and intention communication in a collaborative modeling process.

The invention relates to a method and a system for automatically generating a SysML v2 visual analysis model of system engineering, belongs to the technical field of program analysis, and solves the problem that a traditional system engineering document cannot be effectively and automatically modeled in the prior art. The method comprises the following steps: converting an input initial system engineering design document into a document structured representation model; extracting related document contents from the document structured representation model according to user modeling requirements, extracting related knowledge entries from a pre-configured domain knowledge base, and extracting related case entries from a pre-configured modeling case library; and inputting the extracted document content, knowledge entries and case entries as context information into a large language model, and generating a SysML v2 visual analysis model of the system engineering by using the large language model according to the modeling requirement of a user. Based on the document structured representation model, the field knowledge base and the modeling case base, automatic analysis and modeling of the system engineering document are achieved.

This application relates to the field of healthcare informatics technology, providing a drug recommendation method and related equipment based on drug representation and dynamic user modeling. User features are generated based on the acquired user's historical health records and current health status. Diagnostic features and procedural features are sequentially input into a GRU network and a Transformer network, respectively, to generate user representations through dynamic modeling. Drug features are input into a pre-constructed graph attention network to construct a heterogeneous graph between drug attributes and molecular motifs. Drug representations are generated by message propagation and stacking on this heterogeneous graph. User and drug representations are input into a pre-constructed feedforward neural network, outputting fused features between the drug and the user. The fused features are used to generate probabilities through an activation function, and recommendation information is generated based on the target drugs corresponding to these probabilities. This method can accurately match user health needs and provide personalized and effective drug recommendations.

The invention belongs to the technical field of vision training, and particularly relates to a children personalized vision training system and method based on multi-parameter self-adaption, and the method comprises the following steps: S1, obtaining clinical baseline data of a user, the information at least comprising basic physiological parameters of the user and an initial visual ability evaluation target; guiding a user to complete a standardized baseline visual evaluation task, and synchronously acquiring multi-modal dynamic response data of the user; and S2, inputting the clinical baseline data and the multi-modal dynamic response data into a fusion analysis model constructed based on an information bottleneck theory. According to the invention, personalized, dynamic, safe and controllable visual function rehabilitation training in a real sense can be realized through multi-modal data fusion analysis, user modeling based on an information bottleneck theory, a dual-time scale adaptive decision engine and a man-machine cooperation calibration mechanism; and the scientificity and effectiveness of the training scheme are guaranteed through a continuous learning mechanism.

This invention discloses a dynamic user profile update method based on multimodal data, relating to the field of user modeling technology. The method includes: acquiring the collaborative analysis software of each software in the user's device; obtaining the association weight based on the association filtering value of the collaborative analysis software; acquiring status update words and updating the user's profile based on the collaborative analysis software of the updated reference software and the association weight of the collaborative analysis software. This invention addresses the problem in existing dynamic user profile update methods that lack analysis of multimodal data generated by users based on user software usage data. This results in the inability to effectively acquire dynamic profile data related to the user's immediate interests and needs, leading to inaccurate user profiles and a loss of dynamism after data processing.

The invention relates to a recommendation system (100) and method (200) where personalized recommendation clusters are computed by querying streaming data for flow-based recommendation systems used in the machinery and power systems sector, and these recommendations are presented to the user.

According to the message pushing method based on user behavior analysis provided by the invention, the fused user portrait is generated by obtaining the multi-source heterogeneous data stream and carrying out space-time alignment and cross-modal feature fusion, so that the comprehensiveness and accuracy of the user portrait are ensured. And performing real-time user intention reasoning through an edge end lightweight time sequence prediction model based on the fused user portrait, and combining with a federated learning framework to aggregate encryption model parameters to update a global recommendation model so as to generate a target global recommendation model. And based on the target global recommendation model and the target push candidate set, generating a target push strategy by integrating causal reinforcement learning of anti-factual reasoning. And performing attribution analysis based on user behavior feedback data corresponding to the target push strategy, and optimizing system model parameters to form closed-loop iteration. By constructing a complete technical link, the technical problems of difficulty in multi-modal data collaboration, complexity in real-time computing power scheduling and insufficient privacy security protection caused by unidirectional user modeling of an existing push system are solved.

The invention discloses a Modelica intelligent modeling optimization method based on AI-Agent. The Modelica intelligent modeling optimization method is used for modeling an electrical-thermal coupling system of a high-dynamic and high-reliability energy system and an energy battery. The method comprises the following steps: acquiring device parameters, an equivalent circuit and an electro-thermal coupling dynamic equation set of a target energy system or a battery unit to be researched, and inputting the data and user modeling requirements into AI-Agent; the AI-Agent triggers a physical knowledge retrieval module to retrieve related physical knowledge from a knowledge base, then calls a mathematical equation processing module to process an equation set of the physical knowledge, outputs an optimized electric-thermal coupling equation set, and generates an initial Modelica model of the target based on the optimized equation set; and carrying out model code verification and error correction, and outputting a final optimized model. The model obtained through the method is more accurate and higher in robustness, the model development period is greatly shortened, and the model simulation speed is greatly increased.

The invention provides a large model enhancement recommendation method and system based on multi-view nonlinear modeling, and relates to the technical field of computer personalized recommendation, and the method comprises the steps: constructing a recommendation project library; obtaining a first historical interaction record of browsing the recommended project library by at least one user; constructing a multi-head Fourier recommendation model, wherein the multi-head Fourier recommendation model comprises a user history mining module, a Fourier-kernel adaptive LLM encoder and a user modeling module; performing end-to-end optimization training on the multi-head Fourier recommendation model to obtain a trained multi-head Fourier recommendation model; and obtaining a historical interaction record of a target user, calculating the click probability of each item in the recommended item library clicked by the user based on the trained multi-head Fourier recommendation model, and judging a user click result. By constructing the Fourier-kernel adaptive LLM encoder, the limitation that a nonlinear relationship in a high-dimensional space is neglected in an existing method is solved, and the expression ability and recommendation precision of high-dimensional sparse features are remarkably improved.

This application provides a modular simulation modeling system, method, electronic device, and storage medium, comprising: a model development device for responding to user modeling requirements and generating a model matching the modeling requirements; a component modeling device for responding to user component modeling operations, determining a target component general template corresponding to the component modeling operation from a component general template, and generating a target component based on the target component general template and the model; an entity assembly device for responding to user entity modeling operations, determining a target entity general template corresponding to the entity modeling operation from an entity general template, and generating a target entity based on the target entity general template and the target component; and a distributed deployment device for deploying different models on servers with different computing power according to the model computing power requirements of multiple target models. This application can improve the flexibility and convenience of modeling in different application scenarios and can improve the resource utilization rate of simulation after modeling.

A system and method for machine learning platform management includes a unified architecture for developing and deploying machine learning models at scale. The platform integrates feature generation, model training, and inference services through a centralized interface. The system processes source data through a feature platform to generate training datasets and real-time features. A multi-stage training pipeline enables automated model experimentation through configurable workflows combining core frameworks and user modeling code. The platform implements specialized inference services optimized for high-throughput ranking and recommendation use cases, with distributed feature stores and local caching for efficient feature serving. A comprehensive monitoring system tracks model performance, feature distributions, and prediction quality through automated anomaly detection. The platform enables rapid experimentation while maintaining production reliability through automated deployment orchestration, optimized inference engines, and continuous feedback loops for model improvement.

The invention discloses a tourist low-altitude flight personalized recommendation system based on generative artificial intelligence, relates to the technical field of low-altitude tourism, and solves the problems of insufficient recommendation precision, response delay, single user experience and the like in the prior art. The system comprises a user modeling module which is used for fusing image-text audio data of a user to construct a multi-modal user portrait; the scenic spot and route data fusion module is used for fusing multi-source information such as scenic spots, routes and airspaces; the generative recommendation module is used for generating a personalized flight recommendation path by using the fine-tuned large language model; the feedback adjustment module is used for dynamically adjusting the personalized flight recommendation path according to user feedback; the safety and airspace management module is used for verifying the safety of the adjusted personalized flight recommendation path; the recommendation display module is used for displaying the 3D flight path; according to the method, personalized preference modeling, flight path generation, safety scheduling and interactive experience of the low-altitude tourism users are systematically fused, and the method has high practical application value.

The invention relates to the technical field of social public opinion guidance, and discloses an LLM-based multi-agent social public opinion simulation and active guidance system. According to the system, real cognition and behavior characteristics of different types of users are accurately simulated through an LLM-driven multi-agent cognition architecture in combination with layered user modeling and a large five-personality tag system; through a four-stage event life cycle model and dynamic negative emotion monitoring, a complete evolution rhythm of a public opinion incubation period, an outbreak period, a peak period and a decline period is duplicated; a solid fact support is provided for a common guiding agent and an authoritative guiding agent to make guiding decisions by relying on an RAG authoritative knowledge base; through a public opinion simulation function of the system, the influence of different intervention strategies on public opinion evolution is simulated, a preview platform is provided for a public opinion governance department, the governance department is assisted to quickly respond to hot events, clarify rumors and relieve social anxiety, scientific decision support is provided for public opinion governance, and the scientific and intelligent level of public opinion governance is improved.

Provided are systems and methods for performing self-supervised learning (SSL) of user sequence representations. In particular, an example method can include obtaining sequences of user feature data, applying various augmentation techniques such as random masking or permutation, and then processing these through a user sequence model to generate embeddings. These embeddings can be further transformed by a projection network, and a correlation-based loss function, such as the Barlow Twins loss, can be used to refine the model parameters.

The invention belongs to the technical field of data analysis and user modeling, and discloses a multi-dimensional user behavior data real-time portrait construction method and system for the rare earth industry. Aiming at the problems that traditional static color distribution cannot be matched with real appeals of users and abnormal operation is difficult to perceive, multi-dimensional feature vectors such as operation habits, access preferences and work collaboration are extracted by gathering operation track data of the users on multiple platforms; dynamically refreshing a user portrait by adopting a stream-oriented computing framework, and supporting a time sliding window and aging attenuation; and value-added services such as permission optimization suggestions, abnormal operation identification and interface personality adaptation are provided based on the portraits. According to the method, the permission configuration goodness of fit is improved by four percent, the abnormal operation recognition accuracy is over 90 percent, and the method is suitable for user management and safety audit scenes of a rare earth enterprise informatization platform.

The invention provides an optical fiber communication system modeling and simulation method based on graph native modeling programming. According to the method, a DSL standard and specification system with a graph structure as a core is constructed, and devices, signal paths and transmission relations in the system are abstracted into the graph structure in a unified mode. The DSL comprises a data structure type, an entity and function classification mechanism, a parameter configuration mode and a modular expansion interface. The system operation architecture comprises a front-end analysis module and a rear-end execution module. The front-end analysis module analyzes a user modeling script into a graph intermediate representation. And the rear-end module performs optimization, byte code compilation and virtual machine execution on the graph intermediate representation to obtain a simulation result. In the implementation level, a function mapping modeling method is adopted, input, output and configuration parameters of equipment are in mapping association with an optical fiber communication assembly, and a hierarchical simulation model from a device to a system is constructed through function combination and nesting. The method has the intuition of graph structure modeling and the flexibility of a programming interface, and can efficiently support the simulation requirements of multi-level users.

The application relates to the technical field of artificial intelligence education and discloses an individualized online education system and method based on artificial intelligence, which comprises a data acquisition and perception module, a knowledge processing and construction module, a user modeling module and an intelligent decision and intervention module.The data acquisition and perception module is used for collecting dynamic behavior data and multi-modal perception data of learners.The knowledge processing and construction module is used for processing teaching content to construct a structured knowledge graph.The user modeling module is used for constructing a dynamic learner portrait including a knowledge state model and a cognitive-emotional state model for learners based on the collected data and the constructed knowledge graph.The intelligent decision and intervention module is used for generating and executing individualized learning intervention based on the dynamic learner portrait.The application realizes accurate, timely and individualized intervention on learners and intelligent guidance of optimal learning strategies by fusing multi-modal cognitive state perception, reinforcement learning intervention decision and a knowledge graph dynamic evolution mechanism.