20 results about "Scenario based" patented technology

Techniques for assigning scenario-based tests to test assets are described. In an example, a scenario-based test operable to test a key performance indicator (KPI) of a System Under Test (SUT), a component behavior exhibited by a first component of the SUT, and a scenario characteristic are received. Based on the component behavior and the scenario characteristic, a first plurality of behavior models associated with the component behavior are identified. Based on the scenario characteristic a characteristic value is extracted from the scenario-based test. Each behavior model of the first plurality of behavior models is executed using the characteristic value to generate a first plurality of predicted behavior outcomes. Based on the first plurality of predicted behavior outcomes, a first test asset type from a plurality of test asset types is selected and the scenario-based test is transmitted to a test asset of the first test asset type.

The application discloses a kind of based on cabin high delay scene volume control method, system and vehicle, method steps include: real-time monitoring user in preset time volume adjustment frequency, when volume exceeds the safety volume threshold, start volume anti-explosion strategy, detect audio signal, determine the normal fluctuation range of audio signal, the abnormal data points are removed to audio signal is intelligently analyzed and filtered, and verification is carried out using filter or machine learning model;Based on adaptive adjustment algorithm, volume setting is adjusted according to user usage habit and environmental noise, control volume smooth reduction, the adaptive adjustment algorithm has user volume preference model built-in.The application can effectively prevent the problem of too large or too small volume caused by improper operation by implementing anti-explosion strategy in the process of optimizing volume setting, so as to ensure the accuracy and stability of volume setting and improve the volume display effect.

To provide players with an engaging and enjoyable game. [Solution] The game system 1 includes a scenario acquisition unit 212 that acquires scenarios generated by a large-scale language model using creation instruction information, and a game provision unit 211 that provides a game progressing using the scenario to the player's terminal device. The creation instruction information includes outline information that shows the outline of the scenario, and a part of the outline information differs depending on the scenario rank. The scenario rank is determined by the player's actions, and the game progress result based on a scenario generated when the scenario rank is a first value is more advantageous to the player than the game progress result based on a scenario generated when the scenario rank is a second value lower than the first value.

The invention relates to the technical field of spacecraft orbit control and game decision, in particular to a reinforcement learning-based information waiting strategy forming method in an orbit pursuit scene, which comprises the following steps of: establishing a pulse round system game environment for describing relative movement of an orbit based on an orbit mechanical equation; based on the relative distance between the chasing party and the target, calculating the real-time observation confidence of the chasing party for observing the authenticity of the target, and updating the observation belief of the chasing party in real time by adopting Beta distribution; designing an expert strategy, and generating a state action database containing states and expert actions in a simulation environment; constructing a strategy network with an observation belief feature embedding layer, and pre-training the strategy network by using a state action database; and defining a reward function which aims at minimizing the capture time and contains distance penalty when the capture is not carried out, refining by maximizing a soft Q value of the sum of the reward and the strategy entropy, and finally outputting an optimal chasing party information waiting strategy with the high-entropy characteristic.

A system (100) for performance enhancement of professionals and participants using artificial intelligence is provided An automated transcription module (130) records and transcribes video and audio data. An analysis module (135) performs real-time analysis using a large language model and a graph retrieval-augmented generation training model to identify performance gaps and areas for improvement. A performance metrics evaluation module (140) extracts a plurality of metrics to evaluate the performance. The personalized training module (145) generates customized training programs with rule-based prompts. A collaboration module (150) aggregates best practices and fosters knowledge sharing among professionals, while the performance tracking module (155) monitors their participation in development activities and tracks progress. A machine learning and adaptive learning module (160) predicts potential challenges and adapts training programs. A rewards module (165) enhances engagement through badges, leaderboards, and progress tracking, and a simulations and scenario-based learning module (170) for interactive practice in virtual environments.

This invention discloses a kill net generation method and system based on offline reinforcement learning, relating to the fields of simulation and reinforcement learning. To address the problems of slow kill net generation speed, poor adaptability, reliance on manual intervention, and lack of end-to-end coordination in existing technologies, this invention performs scene modeling based on the tasks, resources, and dynamic factors involved in the simulation scenario, including state space, action space, reward function, and state transition rules. Based on the scene modeling, reinforcement learning interactive simulation is performed to obtain offline data containing simulation trajectories. The offline data is divided into multi-task segments to obtain a multi-task offline dataset, and a meta-reinforcement learning training is performed on the generative trajectory decision network. The trained generative trajectory decision network is then used to generate the target kill net. This invention can efficiently generate highly adaptable and automated kill nets, quickly respond to changing battlefield environments, reduce manual intervention, and improve generation efficiency.

This invention relates to the interdisciplinary field of artificial intelligence and intelligent education, and provides a method for adaptively constructing virtual simulation training scenarios based on generative AI. It aims to address the problems of insufficient personalization and poor dynamic adaptability in existing training technologies due to their reliance on static pre-built scenario libraries. The method dynamically constructs and updates the cognitive state graph of trainees to achieve accurate state perception, and generates optimal scenario strategies through multi-agent collaborative inference and multi-objective game evaluation based on this. The optimal scenario strategies are then integrated with a static domain knowledge graph to generate structured instructions that drive a multimodal generative AI model to synthesize specific training scenario content. Finally, the virtual simulation training engine is driven to update the training scenario and collect feedback data, thereby constructing a closed loop of "perception-decision-generation-execution." This allows for the dynamic generation of personalized training content based on the trainees' real-time state, significantly improving the timeliness, adaptability, relevance, and personalization of the training.

The invention discloses a central control screen instruction set processing method and system based on a scene mode, and relates to the technical field of instruction set processing, and the method comprises the steps: carrying out the real-time fusion of multi-dimensional information flows from voice, a driver monitoring system, a vehicle bus and an external environment, and forming the depth perception of the current state and abstract intention of a driver; based on this, a large-scale language model is introduced as a decision core, the extracted high-order intention is taken as a target, and a preset vehicle function atomic operation library is taken as an available resource to carry out dynamic logic arrangement and combination. And finally, the initial instruction sequence generated by the model is combined with the real-time vehicle state to carry out instant verification and safety optimization, and an accurate and executable composite macro instruction is output. Therefore, a decision closed loop from multi-modal scene perception to adaptive task execution can be realized, so that the active service capability and seamless interaction experience of the intelligent cabin are improved.

A system is provided.SOLUTION: A system, comprising: means for collecting sales data, market data, store data, and salesperson data; means for pre-processing the collected data; means for analyzing the pre-processed data and extracting features; means for generating a role play scenario based on the extracted features; means for distributing the generated scenario to a terminal of each salesperson; means for performing role play by the salesperson based on the scenario and collecting feedback; and means for updating a machine learning model based on the collected feedback.SELECTED DRAWING: Figure 1

The present disclosure is a system and method to provide an interactive AI-driven soft skills training system that simulates realistic scenarios based on a five-factor personality model. It offers personalized coaching, real-time feedback, and a rewind and retry feature for iterative learning. Additionally, the system includes a meta-prompt functionality to generate industry-specific simulations by leveraging large language models to search and integrate relevant data, providing a comprehensive and adaptive training environment.

Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining a symmetric scenario for a scenario; training a first machine learning model for the scenario based on first training data generated from second training data for the symmetric scenario; and generating a prediction for the scenario based on the first machine learning model.

The present application relates to the technical field of safety training and data analysis, and specifically discloses a safety training scenario construction method based on behavior tracing, which comprises the following steps: firstly, collecting the behavior time series data of the individual and the collaborative instruction time series data of the central control system in parallel; secondly, performing time stamp alignment and cross-modal fusion coding on the two types of data to generate joint representation data; thirdly, identifying and labeling the system-induced behavior nodes induced by the collaborative instruction logic through multi-scale sliding window analysis and bidirectional guidance intensity calculation; fourthly, synchronously reconstructing the behavior and instruction processes in a virtual simulation environment and constructing an interactive training scenario through high-correlation presentation of dynamic visual connectors; and finally, setting branch choices in the scenario, driving multi-path deduction and comparison, and generating a traceability analysis report containing individual decision evaluation and collaborative instruction quantitative optimization suggestions; the present application improves the accuracy and systematic improvement value of training.

An object of the system according to the embodiment is to create a comic that effectively conveys service content without expert knowledge.SOLUTION: A system according to an embodiment includes a service content acquisition section, a scenario generation section, and a comic creation section. The service content acquisition unit acquires service content from a user. The scenario generation unit generates a scenario based on the service content acquired by the service content acquisition unit. The comic creation section creates a comic on the basis of the scenario generated by the scenario generation section.SELECTED DRAWING: Figure 1

The system according to this embodiment aims to allow users to view past, present, and future events at a glance and to provide dialogue scenarios tailored to specific events. [Solution] The system according to the embodiment comprises a timeline view unit, a diary unit, a future prediction scenario unit, a special event scenario unit, and an emotion analysis and feedback unit. The timeline view unit allows users to view past, present, and future events at a glance. The diary unit records daily events. The future prediction scenario unit generates future scenarios based on the events recorded by the diary unit. The special event scenario unit provides special dialogue scenarios tailored to specific events. The emotion analysis and feedback unit analyzes the interaction with the user, understands their emotions, and provides appropriate feedback.

A system is provided.SOLUTION: A system comprising: user registration means; means for displaying a plurality of scenario data; generation means for generating a conversation scenario based on scenario data selected by a user; acquisition means for acquiring answer data from the user; analysis means for analyzing the acquired answer data and generating feedback in real time; providing means for providing the generated feedback to the user; and means for storing and analyzing progress data of the user.SELECTED DRAWING: Figure 1

A system is provided.SOLUTION: A system comprising: data input means for customizing based on data corresponding to a user; scenario generation means for generating a role playing scenario based on the input data; interaction means for interacting with the user according to the role playing scenario; response generation means for generating a response based on an interaction of the user; feedback generation means for generating feedback after the interaction; and storage means for storing a result of the interaction and the feedback.SELECTED DRAWING: Figure 1

This invention provides a scenario-based sandplay game-based language teaching method for early childhood, belonging to the field of early childhood language education technology. This method uses sandplay games as a medium and constructs a personalized language teaching system through a four-step core process: "theme scenario generation – age-appropriate design – interactive guided output – closed-loop evaluation and optimization." The core innovation lies in generating thematic sandplay scenarios such as characters, transportation, and animals according to the language development patterns of children aged 3-6, adapting to the age-appropriate goals of "vocabulary accumulation" in lower grades, "sentence structure application" in middle grades, and "story creation" in upper grades. Practical verification shows that this method can increase children's frequency of spontaneous speech by 60%, increase the percentage of children who tell complete stories from 25% to 72%, and reduce social conflicts by 40%. It also enhances teachers' ability to develop school-based curriculum, forming a new "gamified, contextualized, and personalized" early childhood language teaching model, applicable to kindergarten group teaching, group activities, and home-school collaboration scenarios, with significant promotional value.

The system according to this embodiment aims to automate the entire content creation process. [Solution] The system according to the embodiment comprises a reception unit, a scenario creation unit, an actor generation unit, and a filming location generation unit. The reception unit receives user input. The scenario creation unit generates a scenario based on the input received by the reception unit. The actor generation unit generates actors based on the scenario generated by the scenario creation unit. The filming location generation unit generates filming locations based on the actors generated by the actor generation unit.

Disclosed are a system and a method for adaptive performance evaluation of a trainee through scenario-based communication simulation. The system includes a persona configurator module hosting a virtual persona agent configured by a first large language model (LLM) to engage the trainee in dialog, and a feedback module hosting an evaluation agent configured by a second LLM to analyze the dialog. An orchestrator module extracts persona parameters, scenario parameters and evaluator parameters from a configuration command and generates separate configuration commands for the persona LLM and the evaluation LLM. The persona agent replicates professional identity and behavioral traits under a scenario context, while the evaluation agent applies skill-testing attributes and evaluation rubrics to assess the trainee's performance. The evaluation output includes scores, strengths, weaknesses and improvement recommendations. The system prevents circular bias, ensures objective evaluation, and enables holistic training of communication and professional skills.

This invention discloses an information recommendation method for crisis scenarios based on multiple behaviors and multiple views. The specific steps of the method are as follows: (1) Preprocess the data according to the characteristics of the crisis scenario and obtain a recommendation candidate pool; (2) Use user vectors, item vectors, and item text content as input to the model; (3) Obtain the initial embedding of the user and the initial embedding of the item through the embedding layer; (4) Use the multiple views to learn the representation of the user's multiple behaviors through the candidate generation layer; (5) Update the model parameters according to the results and loss, and continuously train until the convergence stopping condition is reached; (6) Obtain the TOP N recommendation list through the ranking layer; (7) Save the model, load the model into the new data, and obtain the recommendation list. This invention improves the accuracy of the information recommendation model in crisis scenarios by fully combining multiple behaviors, multiple views, etc. with the characteristics of crisis scenarios, thereby improving the recommendation task effect in this special scenario.