129 results about "Action plan" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-level memory collaborative decision-making method, device, equipment and medium, and the method comprises the steps: obtaining environment observation data, and updating a multi-level memory module; retrieving the memory generation candidate communication content; high-level actions are extracted according to the task target, and a to-be-selected action plan is formed; reasoning and analyzing the action plan and the communication content through a language model, and determining whether to send; if so, incorporating into a to-be-executed action set; and generating a final plan based on the action plan and the action set, and decomposing the final plan into basic operation instructions to interact with the environment. According to the method, the environment, history and instructions are processed through the reasoning ability of the language model in combination with multi-level memory, the collaborative plan and the communication strategy of the intelligent agent are optimized in the environment with limited communication and incomplete information, and the decision consistency and the task execution efficiency are improved.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to an agent system based on a large language model and an interaction method, and the system comprises a data collection module which is used for receiving text, voice or other forms of data input by a user in real time; the natural language processing module is used for performing semantic understanding and grammatical analysis on the received data to generate an executable user intention; the knowledge reasoning module is used for reasoning an optimal response result or action scheme according to the requirements of the user and historical interaction records; the method has the beneficial effects that the interaction experience of the user is greatly improved through natural language processing, sentiment analysis and personalized response technologies. The system can perform accurate response according to the emotion and demand of the user, and can provide efficient and intimate services in applications such as intelligent customer service, intelligent assistant, personalized recommendation and the like.

The invention discloses a security reinforcement learning-driven large language model security decision agent, and the decision agent comprises a high-level semantic planner which is used for receiving a target and constraint instruction in a text form, receiving a language or visual observation signal of an environment at the same time, and outputting text formatted security risk information and suggested action planning; the low-layer action actuator is used for receiving low-dimensional observation and semantic codes of the environment, and the semantic codes are output by the high-layer semantic planner after text embedding conversion; the strategy network of the low-layer action actuator outputs a final safety action; the training alignment module is used for optimizing the strategy network and the value network; a high-level semantic planner is fed back and prompted through reward and cost signals collected through environment interaction, and parameters of a strategy network and a value network are trained through a security reinforcement learning algorithm. According to the method, the decision cannot violate the given text security constraint while the decision of the given text target is completed.

The invention relates to the technical field of robots, and discloses a task planning method and system for a robot, and the method comprises the steps: obtaining a user instruction; based on the large language model and the semantic coding model, retrieving in a memory bank to obtain environment perception information related to the user instruction; inputting a first cue word determined based on the user instruction and the environment perception information into the large language model, and generating an overall action sequence corresponding to the user instruction; and for each sub-action in the whole action sequence, determining a second cue word, inputting the second cue word into the large language model, and generating a detailed action plan corresponding to the current sub-action until each sub-action in the whole action sequence is traversed. According to the method, the natural language or unstructured instruction can be received, the intention of the user can be understood, and the executable overall action sequence and the detailed action plan of each sub-action in the sequence are generated, so that the task issued by the user is completed, it is ensured that the robot can execute the task, and the accuracy and efficiency of task execution are improved.

The invention discloses an action management identification method based on earthquake site disaster investigation, and relates to the technical field of data processing, and the method comprises the steps: determining a to-be-acted earthquake site region, and planning a disaster search task corresponding to a plurality of unmanned aerial vehicles in the to-be-acted earthquake site region; issuing the disaster situation search task to multiple unmanned aerial vehicles, controlling the unmanned aerial vehicles to execute the disaster situation search task, and obtaining an actual disaster situation investigation result corresponding to the disaster situation search task; according to an actual disaster situation investigation result, generating disaster situation points on the electronic map corresponding to the earthquake field area to be moved, and obtaining a disaster situation investigation map; and finally, the disaster situation investigation map is fed back to equipment specified by a commander to guide disaster relief actions of the commander, so that the earthquake site disaster situation investigation efficiency is improved, comprehensive disaster situation data can be formed, the commander can comprehensively understand the disaster situation, the commander is assisted to quickly form a global and optimal action scheme, and the disaster relief efficiency of the commander is improved. And the disaster disposal efficiency is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as intelligent, financial science and technology, medical health and the like, and discloses a multi-modal fusion feature driven action control method, device, equipment and medium, and the method comprises the steps: obtaining first modal input information and second modal input information, the method comprises the steps of generating a corresponding first modal feature vector and a corresponding second modal feature vector, fusing the two modal feature vectors to generate a multi-modal fusion feature, generating an action instruction based on the multi-modal fusion feature, generating an initial action plan in combination with the current state of equipment, the current environment information and a task target, and performing motion planning. And generating a global optimal action sequence based on the initial action plan, and controlling the equipment to execute the global optimal action sequence. According to the method, the global optimal action sequence is generated and equipment execution is controlled by combining the action instruction generated by the multi-modal fusion information with the equipment state, the environment information and the task target, so that the decision-making effect and the dynamic adaptive capacity in a complex environment are improved.

The present application discloses a distributed computing power scheduling method and system based on AIGC, which relates to the field of computing power scheduling. First, the GPU indicator stream and request indicator stream are obtained in real time, and time series alignment and feature engineering are performed on them. At the same time, semantic feature extraction is performed on the business intent text, and the above-mentioned processed features are fused into vectorized system states and input into the generative AI scheduling agent to predict future model requirements and output the model requirement prediction JSON. Subsequently, the planner module generates an optimized cache action plan based on this prediction and the current cache status to guide the scheduling executor to pre-load or intelligently replace the model. Finally, when the request router receives a new request, it can query the updated cache status and directly distribute the request to the GPU node that has loaded the target model, thereby significantly reducing the cold start delay and improving the computing power resource utilization efficiency and user experience.

The application discloses a safe decision-making agent of a large language model driven by safe reinforcement learning, and the decision-making agent comprises: a high-level semantic planner, which is used for receiving target and constraint instructions in a text form, receiving language or visual observation signals of an environment, and outputting safe risk information and a suggestion action plan in a text format; a low-level action executor, which is used for receiving low-dimensional observation and semantic encoding of the environment, wherein the semantic encoding is output by the high-level semantic planner after text embedding conversion; a strategy network of the low-level action executor, which outputs a final safe action; a training alignment module, which is used for optimizing the strategy network and a value network; reward and cost signals collected through environment interaction are fed back to the high-level semantic planner, and parameters of the strategy network and the value network are trained through a safe reinforcement learning algorithm. The application is convenient for realizing the decision-making of a given text target while ensuring that the decision-making does not violate a given text safety constraint.

The invention provides an enterprise safety production risk assessment system, which comprises a task library module used for acquiring and screening internal and external operation tasks of an enterprise area. And the step module is used for carrying out step decomposition on each operation task according to an execution function. And the hazard module is used for identifying hazard information in the enterprise operation process. And the risk analysis module is used for identifying the risk possibly caused by the hazard and analyzing the process sequence of converting the risk into the risk and causing the accident. And the risk assessment and grading module is used for carrying out element valuing, calculation and grading processing on the risk. And the control measure module is used for determining a risk response mode and a control measure. And the action plan module is used for generating an action plan and prompting the implementation condition of the risk control measures to the user. According to the system, the whole-process informatization operation of operation risk assessment is realized, the relevance between an accident sequence and a protective measure is particularly deeply analyzed, and the accuracy of risk assessment and the pertinence of a control measure are improved.

The invention relates to the technical field of atlas construction and recognition, in particular to a teenager psychological question recognition and intervention method fusing a teaching atlas and grading, which comprises the following steps: S1, data acquisition is carried out, a family system atlas is constructed, and the family system atlas represents family members and family rules through nodes, representing the relationship strength between the members and the rule execution consistency probability through edges; s2, jointly inputting the home system atlas and user question description input in real time into a situation-risk joint inference model, and outputting an enhanced question identifier fusing a situation type, a risk level and a most fragile relationship path; and S3, taking the most fragile relationship path as a core retrieval key, retrieving an intervention strategy from the strategy knowledge base, and generating a targeted micro-action scheme. According to the method, the family system atlas is constructed, and the most fragile relation path mechanism is introduced, so that the structural modeling and context awareness inference of the family structure and relation dynamics are realized.

Providing technological means to assist human deductive thinking in considering business ideas. [Solution] The business plan consideration support device 1 of the present invention comprises a syntax input unit 111 that inputs sales syntax related to business plan support into a generation AI, a gap measurement unit 112 that measures the gap between the sales target related to the business plan and current sales, and an AI support unit 113 that causes the generation AI to generate an action plan to fill the gap. The syntax input unit 111 inputs a sales syntax (Y=P×N×F×C) that breaks down the sales target (Y) into elements including average customer spending (P), number of customers (N), purchasing frequency (F), and execution ability (C) into the generation AI, and the AI ​​support unit 113 generates an action plan based on the sales syntax, including goals related to average customer spending, number of customers, purchasing frequency, and execution ability.

A large language model (LLM) system includes a personal AI agent, an enterprise AI agent, and a public AI agent. A method for operating the LLM includes: executing a first action plan to obtain a first integrated answer and a first integrated score based on a first query using the personal AI agent; and executing a second action plan to obtain a second integrated answer and a second integrated score based on a second query using the enterprise AI agent. A method for applying the LLM system includes: transforming a bitmap of a block diagram into a scalable vector graphics and integrating a semantic tag into the scalable vector graphics; training the large language model by using the scalable vector graphics integrated with the semantic tag and using a functional specification document; and generating a structured text description of the block diagram using the large language model which is trained.

This invention relates to the field of emergency response simulation technology, specifically disclosing a highly efficient emergency response plan optimization and intelligent reasoning method and system. The method includes: real-time access to data streams from physical sensors, social media, and public databases to construct a panoramic view of the emergency situation; constructing an emergency knowledge graph based on the panoramic view and a pre-defined domain ontology; identifying graph substructures that conform to pre-defined graph features within the emergency knowledge graph, and reading the corresponding emergency action plans to generate at least one comprehensive plan; performing deduction and optimization on the generated comprehensive plan, selecting and applying the optimal action plan, monitoring the application data of the optimal action plan in real time, and recursively updating the deduction process. This invention standardizes the data stream and determines the knowledge graph reflecting the scenario state based on the standardization results, resulting in highly readable data and extremely high efficiency in outputting emergency response plans.

A system is provided.SOLUTION: A system comprising: means for acquiring energy and water usage data in real time from sensors installed in each home or business; means for cleaning the acquired data, removing noise, and converting the data into a format suitable for learning; means for training a model using the cleaned data and generating an algorithm for proposing an optimal resource management method for each home or business; means for generating a specific action plan based on an analysis result and delivering the action plan to a user; and means for collecting feedback from the user and retraining the model.SELECTED DRAWING: Figure 1

A computer-implemented system and method for predicting, visualizing, and applying workplace strengths profiles are disclosed. The system includes a processor executing modules that: (i) map user-identified strengths and external data (e.g., LinkedIn, HR, team benchmarks) to one or more enneagram types; (ii) transform those types into prescriptive guidance, predictive insights, and adaptive developmental plans using machine-learning refinement; and (iii) generate dynamic visualizations of team strengths and “what-if” scenarios through a visualization analytics engine. The system continuously personalizes recommendations and action plans by learning from user data, organizational context, and proprietary prescriptive content.

A system is provided.SOLUTION: The system includes a means for collecting event information across the country and notifying the user according to the user's preference, a means for providing and evaluating a ticket sales schedule and a ticket purchase method, a means for providing transportation means to an event venue and accommodation facility information around the venue and supporting reservations, a means for proposing an action plan based on the user's itinerary and event schedule, and a means for providing a community function that allows users having the same purpose to interact with each other.SELECTED DRAWING: Figure 1

Method for operating a vehicle system (9) of a motor vehicle (8), wherein the vehicle system (9) provides an autonomous driving function for fully automatic control of the motor vehicle (8), wherein, in the event of a deactivation event indicating that the autonomous driving function is to be deactivated, an action plan is executed which includes a request to take over driving to a driver of the motor vehicle (8), where, at least for some of the deactivation events, upon fulfillment of a deactivation event-specific termination condition indicating the elimination of the deactivation reason, the action plan is terminated, particularly within a termination period, while maintaining the autonomous vehicle function.

A method and system and computer program product for subscribing to action plans including a processing device for receiving an encrypted action plan summary transaction message including one or more fields from an analyst node in a blockchain network and generating an encrypted action plan summary transaction in a blockchain including the content of the encrypted action plan summary transaction message. The processing device may transmit the encrypted action plan summary transaction to the blockchain network. The processing device may transmit an encrypted action plan summary notice to the blockchain network alerting the nodes of the blockchain network to the encrypted action plan summary transaction.

Provide a system. 【Solution means】 Means for centrally managing schedule information, Means for proposing an action plan based on the preferences of each household member, Means for automatically collecting and organizing image and video data, Means for sending notifications for specific events or occasions, Means for presenting integrated information using a visual display device, Means for proposing activities based on past data of outdoor activities, Means for making reservations and confirmations for outdoor activities, A system including means for displaying videos and images based on a time series.

A system is provided.SOLUTION: A system comprising: sensor means for measuring environmental information such as soil conditions, moisture levels, light levels, and the like; storage means for storing the environmental information collected from the sensors in a cloud database; analysis means for executing a generative AI model to perform an analysis based on the information stored in the cloud database; action plan generation means for generating an action plan for crop management based on the analysis; and prediction means for providing a prediction of future crop production.SELECTED DRAWING: Figure 1

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal conference summary automatic generation method and system, and the method comprises the steps: fusing multilingual voice transcription and speaker identity alignment information, and constructing a conference speaking directed heterogeneous graph; based on the conference speaking directed heterogeneous graph, generating a task-dependent simple complex with an execution semantic anchor point; based on the task-dependent simple complex, context-aware summary structured rendering and intelligent action plan generation are performed. According to the method, automatic generation of the multi-modal conference summary is realized by constructing the conference speaking directed heterogeneous graph and the task-dependent simple complex, meanwhile, accurate coding of the incidence relation between conference tasks is completed, efficient conversion from conference decision to actual execution is assisted, and the execution efficiency of teams on the conference decision and the project closed-loop management capability are improved.

The invention relates to the technical field of property service, and discloses a cloud computing-based joyful cloud intelligent property service digital platform. A property environment sensing module of the system deploys a distributed sensing network to collect multi-dimensional environment parameters, performs abnormal marking and generates an environment data flow with a mark; the service demand analysis module receives the environment data stream, identifies service demand features through a mode identification engine, constructs demand feature vectors and generates a service demand map; the cloud resource adaptation module queries a cloud resource directory according to the service demand map, calculates a resource adaptation degree through a dynamic matching algorithm, and generates a resource allocation scheme; the service execution planning module converts the resource allocation scheme into a service action plan, formulates a service task sequence and dispatches the service task sequence to a service terminal; and the service efficiency monitoring module tracks a service task execution state in real time, collects feedback data to evaluate a service efficiency index, and feeds back the index to the service demand analysis module to optimize a mode recognition engine.

AI-supported automation system (100) for intelligent computer control, comprising the following: a speech-to-text module configured to receive a user's speech input and convert the input into a text command using an offline speech recognition engine; a real-time screen analyzer configured to capture and analyze the current screen state of a computer device using deep learning-based computer vision models to identify user interface (UI) elements; an agent module configured to: receives the text command and screen data; establishes a task plan based on user intent and UI context; the resolution of ambiguities by querying additional data or clarification by the user; an action command generator configured to dynamically generate executable action modules based on the system state and the plan specified by the agent; an automated action executor configured to execute the aforementioned action modules by interacting with the UI elements or the system backend; an execution confirmation engine configured to verify the successful completion of the executed task by re-evaluating the screen state after execution; an error handling engine configured to analyze failed executions, retry modified action plans, or prompt the user for additional information; an enhancement learning engine configured to improve system performance over time by adjusting future actions based on the success or failure of previous tasks; The entire system operates locally on the user's device without transferring user data to cloud-based services.

Provide a system. 【Solution means】 An information terminal means for inputting user attribute data, A computing device means for receiving and analyzing the attribute data, Means for designing a plurality of career paths for the user based on the generated analysis results, Means for outputting the career path to the user and obtaining opinions, Means for modifying and improving the career path based on the opinions, Means for creating a specific action plan based on the final career path, Means for cooperating with an auxiliary system and providing necessary support, Means for presenting a career improvement path specific to the care field, A system including the above.

The application discloses a disaster early warning system and method based on a large language model multi-agent, which comprises a situation awareness agent, an execution agent, a field expert agent group, a system shared state module and a tool library module, the situation awareness agent is used for monitoring data and triggering a warning workflow signal, the execution agent is used for decomposing and routing tasks, the field expert agent group comprises an emergency management agent and a plurality of expert agents in different fields, and is used for executing professional tasks in different fields and writing back task analysis results in different fields to a unified state, the situation awareness agent, the execution agent and the field expert agent group call corresponding tools in the tool library module and interact information through the system shared state module, and early warning information is automatically generated according to a preset template by the emergency management agent. The application can improve the accuracy and consistency of early warning research and judgment, and shorten the time from risk discovery to forming an executable action plan.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for determining an existence of a perception gap for an interaction event. In particular, in one or more embodiments, the disclosed systems generate a suggested action and provide the suggested action for display via a graphical user interface of an agent device. In some embodiments, the disclosed systems utilize a machine learning model to generate the suggested action for the agent. Furthermore, in one or more embodiments, the disclosed systems generate an agent performance score reflecting an overall performance of an agent. In some embodiments, the disclosed systems utilize a machine learning model to generate the agent performance score.

The invention relates to a technical development for project management for improving the efficiency of an action plan in terms of expected impact on the cognitive abilities of a target audience. The technical development includes a method and system for automating the optimisation of the expected impact. The method consists in evaluating the expected impact, qualitatively and quantitatively, on the basis of a scientific database which is updated according to the specific features of the project, in order to optimise the expected impact when planning and following the action plan. The system automates the analysis of the data in order to evaluate impact indicators and indices and automates the optimisation recommendations from the evaluated indicators and indices. An example of better usage shows how to optimise the expected impact of a communication plan on the cognitive abilities of multiple groups of a target audience. Figures 1, 2 and 3 represent the method, the analysis system and the recommendation system, respectively.

An object of a system according to an embodiment is to efficiently analyze complaint data from a customer and to propose a specific improvement measure.SOLUTION: A system includes a complaint data collection part, a preprocessing part, a text analysis part, an insight generation part, a cause analysis part, and an action plan recommendation part. The complaint data collection unit collects complaint data. The pre-processing section pre-processes the complaint data collected by the complaint data collecting section. The text analysis part performs text analysis of the claim data preprocessed by the preprocessing part. The insight generator generates insights based on the frequency, trend, and relevance of the issues extracted by the text analyzer. A cause analysis part analyzes the cause of the claim based on the insight generated by the insight generation part. The action plan recommendation unit recommends an action plan based on the problem identified by the cause analysis unit.SELECTED DRAWING: Figure 1

To provide a manager, etc. that output information necessary to generate information indicating a lateral motion of a vehicle, to an ADAS application.SOLUTION: A manager mounted on a vehicle includes: a reception unit that receives, from a plurality of ADAS applications, a plurality of action plans including first information that is information indicating a lateral motion of the vehicle; an adjustment unit that makes adjustments to the plurality of action plans; a calculation unit that calculates a motion request, based on an adjustment result by the adjustment unit; a first output unit that distributes the motion request to at least one of a plurality of actuator systems; a second output unit that outputs second information used to generate the first information, to at least one of the plurality of ADAS applications; and a storage unit that stores the second information.SELECTED DRAWING: Figure 1

The system according to this embodiment aims to create concrete action plans for an individual's dreams and goals and to effectively manage their progress. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a visualization unit, and a dialogue unit. The analysis unit analyzes the current situation. The generation unit creates an action plan based on the information analyzed by the analysis unit. The visualization unit visualizes the progress of the action plan created by the generation unit. The dialogue unit performs in-depth analysis and adjustments in a dialogue format based on the progress visualized by the visualization unit.