259 results about "User analysis" patented technology

In various examples, each user of a hosted application may be modeled with a corresponding user-specific resource consumption model that predicts a measure of that user's anticipated (e.g., processing, memory or storage, and / or networking) resource utilization at some future time based on an input representation of one or more features of the user's current session, application (e.g., game) setup, skill or experience level, social media activity, some time series representing feature(s) for a series of interactions, the time of day, and / or other characteristics. As such, user-specific models may be used to predict resource demands at a future time and schedule resource allocations accordingly. The present techniques may be used to manage and reallocate resources for applications such as game streaming applications, remote desktop applications, simulation applications (e.g., an autonomous or semi-autonomous vehicle simulation), virtual reality (VR) and / or augmented reality (AR) streaming applications, and / or other application types.

The invention discloses an AI-based database operating system risk prevention and control system and method, belongs to the technical field of deep learning, and aims to solve the technical problem of how to comprehensively identify, prevent and control database operation risks and improve data management efficiency. Comprising a data processing module for performing data preprocessing on historical operation logs; the risk identification module is used for carrying out grammar and semantic analysis on the SQL statements in the processed operation logs, constructing risk characteristics of the SQL statements, carrying out risk prediction through a trained risk identification model and outputting risk levels; the risk response module is used for carrying out risk warning level by level and carrying out classification and priority ranking on the risk warning based on the Bayesian network; and the user analysis module is used for monitoring user behaviors in real time based on the user portrait and an anomaly detection algorithm, forming alarm information based on a detection result and pushing the alarm information.

A multi-node, multi-container cluster system that generates, aggregates, and manages log files from services and components to be used for audit logs and to debug and perform other serviceability tasks provided by a vendor of the cluster system. Logs are collected from all components of the system and aggregated into a consistent format for user analysis and debugging. Embodiments provide a comprehensive way to parse and index vast numbers of log files that can then be packaged and displayed to a user in a way that facilitates analysis and debugging and / or efficient input to appropriate debugging programs.

The invention relates to a user power consumption behavior portraying method and system based on non-intrusive load monitoring, and belongs to the technical field of residential user power consumption behavior analysis and energy management. According to the technical scheme, a data acquisition module collects user intelligent electric meter data; the user load clustering analysis module adopts a clustering algorithm to classify user load curves; the equipment-level behavior analysis module is used for analyzing the energy consumption ratio, the single-equipment use period preference and the multi-equipment cooperation mode of the equipment type for the user; the user portrait construction module constructs a multi-dimensional user power consumption behavior portrait; and the system application module formulates a personalized energy efficiency optimization scheme. According to the method, user load curve clustering analysis and device-level power consumption data are fused, the types and start-stop time of power consumption devices are identified through the NILM technology, and the total load clustering result is combined, so that fine-grained modeling of user power consumption behaviors is realized, and multi-dimensional mining of the device type energy consumption ratio, the single-device use period preference and the multi-device cooperation mode is carried out.

The invention discloses an agent and large language model evaluation method and device, a medium and electronic equipment, and relates to the technical field of large models and agents, and the evaluation method comprises the steps: creating an evaluation task which is used for evaluating at least one dialogue of a plurality of to-be-evaluated objects in a dialogue process from at least one evaluation dimension, the plurality of to-be-evaluated objects are used for realizing dialogue tasks in the same field; the evaluation task is executed to obtain an evaluation result, and the evaluation result is used for comparing the dialogue ability among the multiple to-be-evaluated objects; displaying the evaluation result on the first interface, and displaying a second interface; in response to the configuration operation for the evaluator of the target type in the second interface, a configured evaluator is obtained, the configured evaluator is used for evaluating the dialogue from the corresponding evaluation dimension, a user can conveniently analyze the building effect of the agents of different configurations, in addition, user-defined configuration can be carried out based on the configuration operation, and the user experience is improved. And thus, evaluators of different evaluation dimensions are obtained.

The invention discloses an AI intelligent proposition method, and belongs to the technical field of intelligent proposition, and the method comprises the steps that a proposition platform end is established by a platform side, and the proposition platform end comprises an industry large model training module, a professional knowledge base construction module and a user analysis module; the platform side establishes a user side for a corresponding user according to the service demand data, wherein the user side comprises a test question generation module, a user professional library and a test question checking module; the user professional library is used for storing industry knowledge information of service industries corresponding to users; the test question generation module is used for generating corresponding first draft test questions according to proposition requirements of a user and a professional library of the user; the test question checking module is used for automatically checking the generated first draft test question; the method comprises the steps of obtaining proposition requirements of a user, analyzing the proposition requirements through a test question generation module, obtaining first draft test questions, checking the first draft test questions through a test question checking module, marking the first draft test questions passing the checking as application test questions, and displaying the application test questions to the user.

A user analysis method and system based on big data, a product and a medium relate to the field of commodity data processing, and the method comprises the following steps: obtaining a specific operation sequence of a current user in a current full-decreasing sales promotion scene, extracting an order-on decision behavior feature in the specific operation sequence according to a preset user behavior analysis rule, and obtaining a decision-on decision behavior feature of the order-on decision behavior feature; the order-on decision behavior feature comprises an abnormal shopping behavior; on the basis of the make-on decision behavior characteristics, calculating a make-on possibility score for each commodity purchased by the user, determining a recommendation weight of each commodity according to the make-on possibility score, and when the make-on possibility score of a target commodity exceeds a preset score threshold value, determining that the target commodity is a recommended commodity; and reducing the recommendation weight of the target commodity in the current full-reduction sales promotion scene to a preset weight. By implementing the method, the accuracy of the e-commerce platform to push commodities to the user can be improved.

The invention discloses a news retrieval question-answering method and system of a big language model based on RAG, relates to the technical field of big language model text generation, and provides the news retrieval question-answering method of the big language model for performing intention understanding on questions of users. Data sources are increased, and the query range is expanded to enhance the comprehensiveness of retrieval results and final answers; the intention of the user is accurately grasped, and the part related to the question of the user in the retrieval result is correctly identified and used to improve the accuracy of answering; questions are answered in a layer-by-layer progressive mode, and an intuitive display mode of event veins is provided to help a user to analyze the development process of events.

The invention belongs to the technical field of data security, and particularly relates to a data security protection system and method based on multi-dimensional factors. Comprising a user analysis module, an environment risk analysis module, a data sensitivity grading module, a behavior analysis module, a risk assessment module and a decision execution module. The method has the beneficial effects that aiming at the defects of a traditional static permission model, a dynamic risk assessment system is constructed by fusing user identity, behavior pattern, access context and environment security four-dimensional features in combination with reinforcement learning weight optimization, and the problem that the permission cannot be adjusted in time when the user behavior is abnormal is solved; according to the method, the problems that multi-dimensional information such as environment, behavior and data sensitivity is not fused, data leakage or unauthorized access risks cannot be recognized in real time and the like are solved, sensitive data exposure risks can be effectively reduced, meanwhile, the problem that a static permission model cannot respond to behavior / environment changes in real time is solved, and enterprise data security is improved.

The invention discloses a user analysis method, device and equipment based on a large model and a storage medium, and relates to the technical field of user analysis, and the method comprises the steps: collecting cross-modal interaction data to construct a representation data set, calculating a cognitive load, extracting an implicit aversion anchor point deviating from dominant feedback, optimizing the large model through incremental adversarial training to generate dynamic user representation, and analyzing the dynamic user representation. And formulating a compensation strategy based on a cognitive disorder theory. The device comprises a representation acquisition module, an anchor point extraction module, a model optimization module and a strategy making module. The device executes the steps of the program implementation method through the processor, and the storage medium stores the corresponding computer program. According to the method, through multi-modal data fusion and dynamic modeling, hidden aversion identification and active compensation are realized, a user experience blind area can be positioned, interface interaction can be optimized, recommendation accuracy can be improved, and the hidden aversion perception capability of a large model can be optimized.

A smart water heating system is disclosed that uses advanced monitoring and control techniques to optimize energy usage and grid stability. Various features include real-time state of charge estimation for water heaters, a "digital twin" representation of each unit, predictive load modelling, and adaptive control algorithms. The system can operate individual heaters as distributed energy resources, or aggregate them into virtual power plants. It employs strategies such as load shifting to align with renewable energy availability, and cooperative multi-agent control to prevent demand spikes. The technology aims to improve energy efficiency, reduce costs, integrate renewable sources, and enhance grid stability while maintaining user comfort. Additional features include user analytics, comparative consumption reporting, and solutions for low-voltage environments using supplementary battery storage.

The invention relates to the technical field of data processing, and discloses a method and system for generating a chart through question and answer dialogues and a storage medium. The method comprises the following steps: carrying out semantic understanding on questions of a user, and extracting keywords and context information; then generating a normalized DSL script based on the semantic understanding result and the DSL specification; analyzing the DSL script to obtain a chart configuration task sequence and a dependency relationship; and finally, carrying out parallel scheduling and resource allocation on the task sequence to generate a visual chart meeting user analysis requirements. According to the task features and the system resource conditions, the execution strategy is dynamically optimized, and the efficiency and quality of chart generation are improved.

The invention provides a data asset display analysis method and system based on data visualization, and belongs to the technical field of data asset display analysis. Then, carrying out visualization dimension mapping processing to obtain a visualization dimension set comprising a structure dimension, an association dimension and a circulation dimension, and constructing a multi-level display template comprising a basic display layer, an association display layer and a dynamic display layer according to the visualization dimension set; according to the method, the multi-level display template is subjected to interaction adaptation processing according to user analysis requirements to generate a visual interaction interface, operation track data of a user in the visual interaction interface is tracked, the multi-level display template is adjusted based on the operation track data, the visual interaction interface is updated, and comprehensive, visual and interactive display analysis of data assets is achieved.

The invention discloses a pressure distribution optimization method for an intelligent mattress with multiple air pumps working cooperatively, and relates to the technical field of mattress pressure optimizing.The method comprises the steps that a plurality of independent air chamber areas in the intelligent mattress are determined, and a neighborhood air chamber pressure interference relation is established; performing pressure data collection on the target user, and performing contact surface analysis according to the pressure collection data to generate a user analysis posture; acquiring a preset spine alignment curve database; inputting the user analysis posture into the preset spine alignment curve database to obtain a target reference spine alignment curve; and carrying out pressure distribution optimization on the plurality of independent air chamber areas, and carrying out pressure distribution control. The technical problem that in the prior art, it is difficult for an intelligent mattress to accurately optimize pressure distribution according to the individual characteristics and the real-time posture of the user is solved, and the technical effects that accurate optimization control is conducted on the pressure distribution of the intelligent mattress according to the individual characteristics and the real-time posture of the user, and the sleep comfort of the user and the mattress adaptability are effectively improved are achieved.

The invention discloses a data analysis method and device, equipment and a storage medium, and relates to the technical field of data analysis, and the method comprises the steps: analyzing a user analysis demand according to a data big model and prompt word information corresponding to a preset grammar rule, and obtaining a demand operation type and a data processing sequence; performing instruction generation according to a preset grammar rule, the demand operation type and the data processing sequence to obtain a domain-specific language; and analyzing the to-be-analyzed data according to the domain-specific language and the data analysis engine to obtain a target data analysis result. Complex data analysis and visualization requirements of a user are expressed in a concise language description through a set grammar rule, a large model only needs to generate a domain-specific language, and then the domain-specific language is analyzed by an analysis engine, so that the use cost of the large model is reduced, and meanwhile, the data analysis efficiency is improved.

The invention discloses a communication trace data compression and storage method and system based on parallel program characteristics. The method comprises the steps that MPI calling is intercepted through a reloading MPI function for a target parallel program, and communication trace data is collected; for each piece of communication trace data, if the data is the communication trace data during first calling, recording the data into a data head by adopting a first data structure body, and if the data is the communication trace data generated during repeated calling, recording the data into a data body by adopting a second data structure body, the numerical field in the second data structure body is less than the numerical field of the first data structure body; and storing the communication trace data recorded in the data head and the data body. The invention aims to realize communication trace data compression and storage, the data scale is obviously reduced on the premise of ensuring the trace data to be complete and rich in content, and low-overhead data support is provided for users to analyze bottleneck problems related to large-scale parallel program communication.

The invention discloses a product recommendation method and device based on a user portrait, a medium and electronic equipment, and relates to the technical field of privacy computing. The method comprises the steps that a user portrait of a target user is acquired, the user portrait at least comprises one of a user interest portrait, a user group portrait and a user attribute portrait, the user interest portrait is the user portrait constructed based on user behavior data, and the user attribute portrait is the user portrait constructed based on user attribute data; the user group portrait is a user portrait constructed based on user behavior data of a plurality of users; and based on the user portrait, determining a recommended product of the target user. The recommendation system based on the user portrait is constructed through multi-dimensional user analysis, the personalized recommended product list is generated for the user, the recommendation accuracy is improved, meanwhile, user experience is considered, excessive recommendation or improper recommendation of products is avoided, and the user satisfaction degree is improved.

The present invention provides an adaptive feedback system and method for Parkinson's disease. The system includes: a central coordinator, a data acquisition system, a stimulation generation system, a stimulation device and a data processing system. The data acquisition system is used to collect data from a target user, obtain user data, and send the user data to the data processing system; the data processing system is used to perform data analysis on the user data to obtain a user analysis result; the stimulation generation system is used to determine stimulation information according to the user analysis result; the central coordinator is used to determine a stimulation signal according to the stimulation information, and control the stimulation device to perform electrical stimulation on the target user according to the stimulation signal. The present invention can automatically determine the stimulation information based on the user analysis result, and can automatically generate a stimulation signal by sending the stimulation information to the central coordinator. Based on the stimulation signal, the stimulation device can automatically control the stimulation device to perform electrical stimulation on the target user, so as to achieve the effect of adaptive feedback for Parkinson's disease.

The invention provides an SMT (Surface Mount Technology) material throwing real-time monitoring system, which comprises a data acquisition unit mounted on a chip mounter and used for acquiring the running state, production quantity and fault information of equipment; the production board is electrically connected with the output end of the data acquisition unit and displays production progress information collected by the data acquisition unit; the early warning and adjusting module judges whether material throwing is abnormal or not according to the data obtained in the data acquisition unit and gives an alarm in time for reminding; the controller drives the audible and visual alarm and the shutdown module. The material throwing data of the current production equipment is monitored in real time, engineers can conveniently master the material throwing condition of the equipment in real time, when the material throwing data reaches a certain level, the controller is driven to carry out sound and light alarm, the function of shutting down the equipment after the alarm value is reached can be achieved by combining with the shutdown module, and the production efficiency is improved. Therefore, the loss caused by material throwing is reduced as much as possible; in addition, the historical data module adopts big data storage, and the purpose of permanently retaining all material throwing data for users to analyze at any time is achieved.

The invention discloses a training teaching intelligent question and answer management system and method based on a large model, relates to the field of training management, solves the problem of low adaptability of a training plan and a target user, and comprises a data acquisition module, a user analysis module, a training plan optimization module and an effectiveness judgment module. The data acquisition module is used for acquiring body characteristic data and historical training item types of a target user; the user analysis module is used for analyzing the physical condition of the target user to obtain a pre-selected training plan of the target user; the training plan optimization module is used for optimizing a pre-selected training plan according to the question and answer text with the target user to obtain an optimized training plan of the target user; the effectiveness judgment module is used for judging the effectiveness of the optimization training plan on the target personnel, continuing training if the optimization training plan is effective, and making corresponding adjustment if the optimization training plan is ineffective; according to the invention, the training plan of the target user is dynamically adjusted.

The invention relates to the field of data analysis, in particular to an information collecting and processing method for heat stroke pathology data integration based on deep learning, which comprises the following steps: determining the data state of user analysis data according to the data richness of the user analysis data and the image conversion completion degree; when the keyword selection mode is associated phrase selection generation, determining the number of the selected keywords according to the phrase influence coefficient, and selecting the keywords according to the sensitivity coefficient from large to small; when the keyword selection mode is image conversion supplementary generation, determining that a keyword generation mode is generated according to a median point or an outlier according to the feature representation degree of the divided paragraph; under the condition that the initial search is completed, optimization analysis is conducted, the search optimization mode is determined to be combined search or sequential search according to the repeated keyword analysis state, and the pathological data integration search efficiency is improved.

The invention discloses an EEG intelligent agent automatic analysis method based on a large language model, which takes the large language model as a strategy engine, autonomously understands user analysis intentions and intelligently decomposes tasks, and further dynamically schedules and collaboratively integrates analysis resources including traditional feature engineering, diversified deep learning models and an external knowledge base. According to the method, end-to-end automatic coordination and execution of complex EEG analysis tasks such as signal preprocessing, feature extraction, event positioning, classification diagnosis, emotion recognition, sleep staging and the like can be realized, the limitation of a single detection or classification task is broken through, and through context perception and flexible space-time analysis capability, the accuracy of the EEG analysis is improved. And multitask and continuous deep reasoning and interpretation can be carried out on the complex EEG data. According to the method, the general planning and reasoning capability of a large language model is deeply fused with a special analysis model in the EEG field, so that the automation level, flexibility and clinical application potential of EEG analysis are remarkably improved.

Systems and methods for on-demand creation, evaluation and / or deployment of machine learning (ML) models for computing the value of a predictive trait. System operations include detecting, at a predictive trait user interface (UI), a selection of a predictive trait of a plurality of predictive traits and a selection of a configuration setting for the predictive trait. System operations further include executing, using an orchestrator, an onboarding flow that retrieves user data, a training workflow that generates a trained predictive trait model, and / or an inference workflow that runs the trained predictive trait model to compute predictive trait values for users in a test set. System operations further include generating and displaying, via the predictive trait UI, explanations associated with the trained predictive trait model, the computed predictive trait values, and the test set. The system further transmits computed predictive values to an audience management service for audience generation or user profiling.

The invention discloses an intelligent display optimization system of a color ink screen, which relates to the technical field of ink screen display optimization and comprises a scene analysis module, a display optimization database and a user analysis module. In order to solve the problems that traditional display equipment usually cannot be optimized in real time according to operation habits of a user and environmental changes, so that the display effect is not ideal under different illumination, temperature and use conditions, eyestrain of the user is possibly caused, power consumption is too high, and optimal visual experience cannot be provided, the invention provides a display device and a control method thereof. If so, recording data by using a color ink screen device through a user analysis module, analyzing the data, and quantifying the difference of operation habits between users; the scene analysis module determines the scene where the user is located by analyzing a complex algorithm and correspondingly adjusts the display mode, it is ensured that the optimal display effect and use experience are provided under different environments and use scenes, and the user satisfaction degree is remarkably improved.

The invention relates to the technical field of e-commerce, in particular to an e-commerce marketing system based on big data analysis, which comprises a user data integration module, a real-time market monitoring module, an advertisement resource optimization module and a purchase behavior prediction module. According to the method, fine insight of user behaviors is realized by analyzing big data from social media, mobile applications and retail platforms, the practicability of the data is improved, and the user portraits are accurately constructed through feature normalization and irrelevant information removal, so that commodity recommendation is more personalized and timely through fine user analysis, and the user experience is improved. The participation degree and purchase behaviors of users are effectively improved, the real-time monitoring function of the system enables commodity matching to be more sensitive, content is adjusted in time to match user requirements and market changes, dynamic optimization of advertisement resources is adjusted according to market feedback, the efficiency of the advertisement resources is maximized, return benefits are improved, and by predicting and analyzing the purchase trend of the users, the user experience is improved. And a scientific basis is provided for commodity inventory and marketing promotion.

The invention relates to the technical field of commodity data management, in particular to a commodity recommendation method based on a multi-agent model. The multi-agent model comprises a main agent, at least one user analysis sub-agent and a plurality of category exclusive commodity analysis sub-agents. The method comprises the following steps: the main agent collects commodity demand information of a user; the main agent analyzes the commodity demand information to determine the category span of the user demand, and judges whether the user demand is a cross-category demand; and if the demand is a cross-category demand, the main agent activates a commodity analysis sub-agent of a corresponding category according to a category span, and sends user demand feature data generated by the user analysis sub-agent to the activated commodity analysis sub-agent. Through a multi-agent model technology of overall planning of the main agent and division and cooperation of the sub-agents, accurate matching of cross-category and single-category demands, efficient utilization of resources and flexible service adaptation are realized, and finally the commodity recommendation accuracy is improved.

The invention discloses a self-adaptive spatio-temporal network behavior pattern evolution analysis method and device and a storage medium, and the method comprises the steps: collecting agent multi-dimensional behavior data through machine learning, the agent multi-dimensional behavior data comprising financial data, social media data and news report data; performing data quality monitoring according to a user analysis target, and updating the multi-dimensional behavior data of the intelligent agent; performing tensor decomposition on the updated agent multi-dimensional behavior data to obtain target behavior data; according to the target behavior data, a self-adaptive space-time network model is constructed, and the type of the self-adaptive space-time network model comprises a directed graph, an undirected graph or a weighted graph; according to the self-adaptive space-time network model, behavior pattern evolution analysis is carried out, and a behavior pattern evolution analysis result is obtained. The behavior pattern evolution analysis is realized, and the analysis accuracy and the analysis depth are improved. The method can be widely applied to the technical field of behavior data analysis.

A system for data aggregation from at least one source for use in data analysis having a graphical user interface that shows multiple data sources for users to analyze data. A database stores and visualizes data from data files captured on a web application, whereby users analyze the data through visual tools, charts, and graphs. The system also automatically transforms the data into evaluation templates. The evaluation templates may be an automated computer readable format. The reports can be customized by users or selected from existing templates. Users have the ability to design their own reports.

The invention discloses an English reading ability training system and method based on artificial intelligence, and aims to solve the problems of insufficient personalization, poor interactivity, feedback lagging, imperfect evaluation system and low resource matching degree in an existing English learning platform. The system comprises a data acquisition module, a user analysis and personalized path planning module, a dynamic content generation and practice design module, a real-time monitoring and feedback adjustment module and a comprehensive evaluation and report generation module. Basic information and real-time feedback of the user are collected, learning characteristics, interests, preferences and ability levels of the user are accurately identified by using a machine learning algorithm, and a personalized learning path is provided for the user; meanwhile, natural language processing, machine learning and text generation technologies are integrated, and reading materials meeting user requirements are dynamically generated; analyzing the comprehensive difficulty of the generated material by utilizing a machine learning algorithm, wherein the comprehensive difficulty is quantified by adopting a text comprehensive difficulty score TCS; reading understanding exercises including vocabularies, grammar, reasoning and critical thinking are provided, and the subsequent exercise difficulty is intelligently adjusted based on user performance data so as to optimize the learning effect. The system also continuously tracks user performance data, analyzes progress and problems through a machine learning algorithm, improves the regular evaluation capability, automatically adjusts a learning path and a material strategy, and periodically generates a multi-dimensional comprehensive evaluation report.

To provide a method for multilaterally evaluating the skill of registered personnel.SOLUTION: In a user evaluation method for evaluating a user, a system executes an input information receiving step of receiving input information including information on a biography of the user, a question output step of creating a question based on the input information using the input information and outputting the question, a response receiving step of receiving response information from the user regarding the question, and a user analysis step of analyzing a feature of the user based on the input information and the response information and obtaining analysis information of the user.SELECTED DRAWING: Figure 2