12 results about "Adaptive agents" patented technology

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention relates to a self-adaptive agent decision framework training method with dynamic environment perception capability, which comprises the following steps of: 1, collecting multi-modal sensor data in parallel, and carrying out hardware-level preprocessing on the collected data; step 2, based on the data preprocessed in the step 1, performing dynamic security boundary driven real-time feature extraction to obtain security boundary coordinates; 3, based on the safety boundary coordinates obtained in the step 2, generating an execution action strategy in a decision period; and 4, based on the action strategy generated in the step 3, constructing a feedback closed loop of the distributed execution framework, and realizing real-time closed loop optimization. According to the method, the core requirements of scenes such as mobile robot navigation and intelligent industrial control on autonomous decision-making safety and real-time performance are met.

Embodiments described herein systems and methods for dynamically guiding a generative artificial intelligence model through structured, goal-oriented interactions using machine- generated rubrics for instructing operations of the generative Al model. The system selects, for each conversational turn, a first rubric defining response structure and a second rubric defining evaluation criteria. A generative model formulates prompts based on these rubrics and receives participant responses. An assessment module evaluates the responses against the second rubric to update the interaction context. A rubric orchestration module uses the updated context to select new rubrics for subsequent turns. This iterative process transforms a stateless generative model into a persistent, adaptive agent capable of conducting multi-turn interactions aligned with defined objectives.

The invention particularly relates to a medical artificial intelligence system and method based on situation intelligence and multi-agent cooperation, and relates to the technical field of medical artificial intelligence. Performing context analysis and task planning; selecting and scheduling an expert agent; and performing collaborative execution and result convergence. According to the invention, multiple modules cooperate to greatly optimize the clinical working process, and the diagnosis and treatment efficiency and decision accuracy are improved; the unified context bus can aggregate multi-source heterogeneous data such as patient diagnosis information, allergy history and examination results in real time, and automatically standardizes the data into structured context objects, so that the tedious operation of manually arranging and dispersing data by medical staff is avoided, and a large amount of data preprocessing time is saved; the context awareness and intelligent orchestration engine accurately schedules an adaptive agent by means of the logic reasoning ability of a large language model, and avoids the logic vulnerability of artificial task decomposition.

The application discloses a multi-agent distributed situation interaction device and method based on a multi-modal large model, and relates to the technical field of computer model application. The device comprises the following steps: constructing a double-layer distributed interaction network; setting an environment monitoring time window according to a preset feedback adjustment period, collecting the number of node survival, the available communication bandwidth value and the task urgency level as the environment state parameters; inputting the environment state parameters into a pre-constructed interaction strategy adjustment model to output the adaptive local node interaction strategy and the adaptive agent node interaction strategy in the next feedback adjustment period; and inputting the adaptive local node interaction strategy and the adaptive agent node interaction strategy into the double-layer distributed interaction network in the next feedback adjustment period to control the local consensus node and the agent node to respectively perform distributed situation interaction and output a unified task situation cognition benchmark. The application realizes high reliability, low delay and consistency guarantee of the distributed situation interaction of the unmanned cluster in a complex battlefield or dynamic environment.

The application discloses a service processing method, device, equipment and medium, the method comprises the following steps: in response to a service instruction, determining a target instruction intention; based on the target instruction intention, determining a corresponding task path; based on the task path, determining a target agent, and an execution algorithm and a computing power configuration of the target agent; based on the execution algorithm, the computing power configuration and the task path, calling the target agent for service processing. The method dynamically constructs a task path by identifying an instruction intention, thereby scheduling an adaptive agent and computing power, algorithm resources in real time according to the task path, realizing flexible execution of the service instruction, avoiding resource waste, and improving service processing efficiency and response real-time performance.

The invention discloses a distributed security resource intelligent arrangement and adaptation method. Distributed heterogeneous security resources are efficiently and automatically arranged in combination with capability tagging abstraction, a plug-in type adaptation agent architecture and an efficiency comprehensive optimization algorithm. For distributed security resources, lightweight adaptive agents are deployed at nodes, resource functions are split into standardized capability tags and reported to a registration center, and an arrangement decision module generates an optimal security service chain based on resource capabilities, real-time operation indexes and a multi-objective optimization algorithm according to user requirements analyzed by a strategy management module. And the scheduling distribution module serializes the scheme and issues the scheme to each adaptive agent to call a manufacturer interface to execute configuration, so that efficient, automatic and safe resource arrangement is realized. According to the method, unified management and dynamic arrangement of cross-manufacturer, cross-cloud and cross-network are realized, the method has the advantages of pluggable expansion, efficient automation, real-time online self-adaption and the like, and the arrangement efficiency and protection flexibility of distributed security resources are remarkably improved.

The application discloses a logic defect intelligent detection method and system based on business semantic enhancement, which comprises the following steps: constructing a historical business defect knowledge base by using multi-dimensional information of historical defects; extracting business domain, language, file path and business rules from the code to be detected; generating a code semantic vector according to the code to be detected; performing multi-modal representation learning on the code to be detected, using a multi-modal fusion mechanism to fuse the embedded representations of abstract syntax tree, control flow graph, program dependency graph and business semantic graph, and generating a code graph structure; adopting a hierarchical retrieval strategy to filter similar historical defects from the historical business defect knowledge base; inputting the code to be detected and the similar historical defects into an adaptive Agent reasoning engine, performing static and dynamic analysis on the code to be detected through a natural language instruction driven tool, and comprehensively generating a defect detection result; and verifying the result according to the confidence output by the adaptive Agent reasoning engine and generating a defect report.

The invention provides a contract review-oriented LLM multi-agent adaptive dynamic collaborative design method, which comprises the following steps of: firstly, constructing a specialized agent library containing multiple types of universal bases, special contracts and the like, and covering the professional review capability of multiple fields; secondly, establishing a semantic ontology dynamic communication protocol between intelligent agents, and ensuring that information transmission is accurate and efficient; constructing a workflow adaptive engine through the contract feature vector, and dynamically generating an adjustment review path; after a to-be-examined contract is preprocessed, selecting an adaptive agent to establish a collaboration group and triggering a workflow; the intelligent agent executes review according to the process, a multi-element collaborative decision-making mechanism is adopted to process cross-domain clause conflicts, and a staged structured result is generated; and finally, fusing all node results, and outputting a standardized review report. The method solves the problem that the prior art is difficult to meet the core requirements of complex contract review on specialized division of labor, cross-domain collaboration and whole-process controllability.

The invention discloses a Symbolic Kernel for Neuroadaptive Wearables (SKNW), a real-time embedded operating system enabling symbolic cognition, ethical reasoning, and adaptive agent modulation in wearable AI devices. The system interprets biometric signals including EEG, GSR, HRV, and facial microexpressions, converting them into symbolic cognitive primitives that guide AGI behavior. It comprises a Biometric-Symbolic Compiler, Wearable Arbitration Engine, AGI Intent Modulator, and Neurofeedback Override Channel to establish a closed-loop symbolic feedback pathway allowing users to influence, pause, or override AGI actions based on emotional or ethical states. The kernel provides explainable, consent-aware arbitration and ethical co-regulation between human and agent, ensuring real-time alignment with user volition. Implemented as a POSIX-compliant, RTOS-capable kernel for edge and wearable platforms, the SKNW supports Bluetooth-based data acquisition and on-device symbolic execution, enabling interpretable, ethically responsive, and emotionally adaptive interaction between humans and neuroadaptive AGI assistants.

The invention provides a secondary equipment detection method and system for a transformer substation, and the method comprises the steps: transmitting a minimum authority detection instruction set to detected secondary equipment through standard power constraint based on a security interaction sandbox specification before detection is started, so as to detect the characteristics of the detected secondary equipment; generating an exclusive adaptive agent unit uniquely matched with the detected secondary equipment according to a standardized generation rule, and synchronously constructing a two-way communication link between the standardized detection kernel and the detected secondary equipment; a universal detection task set is output through the standardized detection kernel, and an execution time sequence and a result analysis rule matched with the universal detection task set are synchronously generated through the exclusive adaptation agent unit; and completing full-amount automatic detection of the detected secondary equipment, and after the detection is completed, carrying out standardized filing on the exclusive adaptive proxy unit through a standardized detection kernel according to a detection result based on a bidirectional communication link. The detection efficiency can be remarkably improved.

The invention discloses a domestic operating system-oriented type adaptive agent webpage search method, and belongs to the technical field of interaction of agents and search engines. In the method, after a natural language query of a user is received, a query type is identified as common sense or timeliness and a corresponding information source is matched, then keywords are optimized according to the query type, screening and sorting are carried out according to website authority and the like, contents are captured, a domestic NPU is scheduled to accelerate and filter noise during analysis, and multi-source information is fused to generate answers. And meanwhile, data are encrypted by using a national cryptographic algorithm. According to the end-to-end process, the security and hardware characteristics of a domestic operating system are utilized, and search correlation, accuracy and system security are improved.