724 results about "Interface (computing)" patented technology

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

The invention relates to the technical field of distributed computing and computing power scheduling, in particular to a computing power resource fusion method based on a distributed flow pipeline, which comprises the following steps of: disassembling a user computing power request into a flow pipeline unit for packaging computing logic, an input / output interface and a resource demand label; meanwhile, computing power types, real-time load rates, memory occupancy rates, network round-trip delays, geographic positioning and energy consumption data of cloud edge end nodes are collected, a hierarchical topology network framework is constructed based on the collected data, node computing power available values are calculated, an inter-node transmission cost matrix is generated, a fault probability prediction model is constructed, and a fault probability prediction model is constructed. And finally, analyzing a data dependency relationship of the pipeline unit through a four-dimensional joint decision engine, executing dynamic mapping, and preferentially mapping the high-computing-power demand unit to a GPU cluster node, so as to realize non-interruption reconstruction during pipeline topology operation. And the global computing power resource utilization rate, the task operation efficiency and the service stability are improved.

Data platforms described herein are configured to monitor a compute environment and provide a user interface. Such a data platform may direct an application to process, on a user interface computing thread, user input to a graphical user interface view that is rendered based on data received from a data platform, to detect, on the user interface computing thread, a set of instructions associated with a visualization of a logical graph, to determine that the set of instructions satisfies one or more criteria, to execute, based on the set of instructions satisfying the one or more criteria, the set of instructions on a set of additional computing threads, and to render the visualization of the logical graph in the graphical user interface view based on the processing of the user input and the executing of the set of instructions. Corresponding methods, systems, and products are also disclosed.

The invention provides a cross-platform video decoding method and device, computing equipment and a computer readable storage medium, and the method is combined with a factory mode to completely package bottom layer differences of all platform types, and an application layer developer does not need to care about the bottom layer platform differences. The optimal implementation can be automatically selected on different engines and platforms through a uniform API (Application Program Interface); during rendering, a single-frame memory management strategy is used, and only one frame which is currently displayed is reserved in a memory; and in the aspect of performance, the advantages of a multi-thread platform and the parallel computing characteristics of the GPU are fully utilized. According to the embodiment of the invention, through deep fusion of a cross-platform soft decoding architecture, single-frame memory optimization, multi-thread parallel, GPU acceleration and other technologies, perfect balance of cross-platform, high performance and low memory is realized, and the development efficiency of the whole project is improved.

The invention relates to the technical field of digital twinning, in particular to a real-time interactive visualization system and method based on digital twinning, and the system comprises a data collection module which is used for configuring a multi-protocol adapter deployed on a physical equipment layer to collect the operation data of physical equipment, and transmitting the operation data of the physical equipment to an edge computing node. According to the method, the general data adapter is used for driving the three-dimensional rendering engine to execute real-time rendering based on physics, so that the virtual scene can dynamically reflect details such as illumination change and material aging of a physical entity, and high-fidelity visual feedback is provided. According to the method, a closed-loop control mechanism is established in combination with a multi-modal interaction framework, pure data monitoring is converted into operable bidirectional interaction, operation of a user on a visual interface can be converted into feedback instructions in real time to reversely adjust physical equipment, and the response efficiency of remote operation and maintenance is improved.

Techniques for a natural language interface for searching and retrieval of digitally-stored information are disclosed. In one embodiment, a computerized method is disclosed comprising receiving, at a computing device, natural language input from a user; generating, by the computing device, a prompt using the natural language input; submitting, by the computing device, the prompt to a large language model (LLM); receiving, by the computing device, from the LLM, a response to the submitted prompt, the response comprising a logical expression of the natural language input generated by the LLM using the prompt; generating, by the computing device, for a data source, a query using the logical expression; retrieving, by the computing device, a query result set from the data source using the query; and generating, by the computing device, a response to the natural language input using the query result set.

The invention discloses a multi-machine multi-card distributed training optimization system and method oriented to a credential heterogeneous environment, and relates to the technical field of distributed training. In order to solve the defects existing in distributed training in a credential and credential heterogeneous environment, the system comprises an application layer for receiving a training task; the core system layer comprises a self-adaptive task scheduling module, a hybrid communication topological optimization module and a dynamic check point and elastic recovery control module, and the scheduling module generates a performance portrait and an optimal scheme based on a training task and adjusts the scheme according to a node fault; the optimization module executes the scheme, firstly detects node topology, constructs a hierarchical path, loads and initializes an optimal communication rear end for target equipment, and synchronizes the path to the target equipment; the control module periodically stores a complete training context and realizes reliable recovery of a training process; the credential hardware abstraction layer is used for providing a uniform interface for realizing underlying resource access and data acquisition; and the hardware resource layer comprises heterogeneous computing nodes and an RoCE network switch.

The invention discloses a hand-eye cooperative robot control system and method based on dynamic operator arrangement, an upper computer planning layer operates a master control computer to periodically trigger task scheduling, a joint controller of a lower computer execution layer receives an instruction through a redundant bus to perform servo control, hand-eye camera data triggers a visual assembly line through an interrupt event, and a visual assembly line is controlled through a control interface. According to the method, pressure is calculated through visual processing, motion planning and joint control in a load sharing mode, a double-buffering mechanism is adopted to enable current frame visual processing and previous frame motion control to be executed in an overlapping mode, real-time scheduling and parallel computing of tasks are completed, hot data are cached in a memory database, cold data are archived to an HDFS and migrated through an LRU strategy, and the real-time scheduling and parallel computing of the tasks are completed. Data type conversion is automatically derived and performed based on a feature type registry, and conditional branch execution is triggered according to real-time sensor data. According to the control system, cross-hardware plug and play, algorithm flow configurability and data flow real-time sharing are achieved, and the requirement for improving the efficiency of complex operation tasks is met.

Systems and methods for location-based device control are provided. Example systems and methods include determining a location estimate for a computing device and identifying a controllable device based on the location estimate. Some examples also include determining a relative location of the controllable device with respect to the computing device and triggering display, on the computing device, of a user interface for interacting with the controllable device based on the relative location. Another example system and method includes identifying a controllable device within a physical space based on an aiming direction in which a computing device is aimed, determining an angular offset of the controllable device with respect to the aiming direction of the computing device, and triggering display, on the computing device, of a user interface that indicates the determined angular offset.

The invention discloses a cloud edge collaborative information interaction processing device, method and system with an endogenous security architecture. The equipment comprises a main processor, a hardware security chip, a programmable logic unit, a communication unit and an equipment management unit. A trusted execution environment is constructed in the hardware security chip and is in isolated communication with a common execution environment of the main processor through a security bus, a security computing engine, a remote attestation module and a trusted key warehouse are arranged in the trusted execution environment, and the programmable logic unit is directly controlled by the trusted execution environment to realize dynamic hardware acceleration. The communication unit is provided with independent interfaces which are connected with the two execution environments respectively, unification of hardware-level security isolation and efficient cooperative computing is achieved, and the problems of privacy disclosure and untrusted process in cloud edge collaboration are solved.

The invention relates to the technical field of cloud fusion application operation support platforms, and discloses a session flow intelligent scheduling method under a fusion communication architecture, which comprises the following steps: monitoring a session flow logic sequence number of a current node; constructing an asynchronous mirror instance at the target node, and obtaining a computing power comparison parameter; adjusting an asynchronous mirror instance instruction execution rate to execute serial number chasing; after the sequence number difference value enters a synchronous threshold value, processor assembly line branch prediction data is injected, and instruction prefetching sequence filling is driven; according to the instruction level feature injection and execution phase alignment method, hardware execution momentum imbalance in a heterogeneous computing power environment is eliminated through instruction level feature injection and execution phase alignment, and progress connection and logic consistency of session streams at a migration interface are guaranteed.

The invention relates to the technical field of electric power design, and discloses an electric power design agent intelligent processing system based on an AI large model, which comprises an interface interaction module, a professional model module, a dialogue management module and a professional algorithm module. The interface interaction module orderly calls idle modules according to the instruction sequence; the professional model module periodically retrieves multi-source data, constructs a professional data set through dynamic denoising, secondary classification processing and verification, generates a fine tuning data set through various knowledge distillation modes, and optimizes an industry large model by adopting a low-rank adapter; the dialogue management module is connected with input and output and instruction generation; and the professional algorithm module dynamically calls five types of professional models and adapts to computing power resources. According to the system, a traditional tool data island is broken through, the problems of general LLM professional shortages and'illusion 'are solved, automatic closed loop from natural language interaction to professional design tasks is achieved, manual dependence and error risks are reduced, and power design efficiency, quality and system stability are improved.

The present application relates to the technical field of computers. Disclosed are a computing system, a model training method and apparatus, and a device, a medium and a product. The artificial intelligence computing system comprises a plurality of computing nodes. Each computing node comprises a host, a computing device and a network interface card, wherein the computing device comprises a first hardware interface, a first device coherence engine and a first computing core, and is connected to the host by means of the first hardware interface; the network interface card comprises a second hardware interface and a second device coherence engine, and is connected to the host by means of the second hardware interface; and by means of a cache flush operation, the computing device transmits gradient data with a network interface card memory of the network interface card, and the network interface card is used for synchronizing the gradient data with other computing nodes. The present application improves the efficiency of transmitting gradient data between a computing device and a network interface card, thus improving the training efficiency for an artificial intelligence model.

The invention discloses a method for improving data acquisition / writing stability, and relates to the technical field of industrial control systems. The method comprises the following steps: firstly, deploying a real-time operating system kernel on general computing hardware, and carrying out real-time transformation on a hardware interface driving program of the real-time operating system kernel so as to lay a deterministic response foundation; secondly, real-time adaptation is carried out on an engine when the container runs, so that the engine has the capability of calling a kernel real-time interface; meanwhile, a non-real-time data acquisition / write-in program is packaged in a standard container mirror image. And finally, through the adapted container runtime engine, creating and running the container instances on the real-time kernel, thereby ensuring the deterministic execution of key real-time tasks on a unified hardware platform, and flexibly deploying non-real-time tasks through a containerization technology to achieve the comprehensive target of efficient utilization of resources and performance isolation.

A computing system receives text from a vehicle and receives a vehicle state from the vehicle. The computing system composes a prompt including the text, at least a portion of the vehicle state, and an instruction set. The computing system submits the prompt to a machine learning model and receives a response to the prompt from the machine learning model, the response including a selected instruction from the instruction set. The computing system transmits the selected instruction to the vehicle.

A system for generating recommended computing resource configurations for machine learning services is described. The system includes computing resources to host a machine learning model. The system includes a machine learning recommendation service to receive, from a client via an interface, a request to monitor the computing resources. The recommendation service monitors the machine learning model, including recording utilization metrics of the computing resources, recording the different inference requests, and recording the respective inferences. The recommendation service generates a recommended computing resource configuration for the machine learning model based on the utilization metrics and an optimization objective for utilizing computing resources. The recommendation service determines to provide the recommended computing resource configuration based on an accuracy analysis performed for the machine learning model deployed on the recommended computing resource configuration. The recommendation service provides the recommended computing resource configuration for the deployed machine learning model.

A computer-implemented method, computer program product and computing system for accessing interface rules for a remote resource; generating a connector interface based, at least in part, upon the interface rules; and accessing the remote resource via the connector interface

The invention discloses a data processing method and device applied to edge computing equipment. The method comprises the following steps: accessing a plurality of different models and algorithms through model driving middleware; acquiring at least one path of data and caching the data to a pre-processing queue; performing standardization preprocessing on the multi-modal data by calling a standardization interface; according to the configuration information, distributing the preprocessed data to a corresponding model queue, obtaining a reasoning result through model reasoning, and sending the reasoning result to a post-processing queue for caching; or sending the preprocessed data to a post-processing queue for caching; reasoning results or data are taken out from the post-processing queue, and whether alarm needs to be triggered or not is determined through algorithm calculation, so that an alarm message is generated. According to the method, the difference in multiple aspects of each third-party model is shielded, the workload and the debugging period of developing adaptive codes are reduced, the problems of data transmission disorder and format mismatching during multi-model cooperation are solved through a standard data flow model architecture, and the stability of the system architecture is improved.

Apparatuses, systems, and techniques for detecting that a host device is subject to a distributed denial of service (DDOS) attack using a machine learning (ML) detection system are described. A computing system includes a switch with port interfaces, a central processing unit (CPU) that implements a machine learning (ML) detection system, and network monitoring logic. The network monitoring logic can extract features from network data and send the extracted features to the ML detection system. The ML detection system determines whether the host device is subject to a DDOS attack using the extracted features. The ML detection system can send an alert to the host device responsive to a determination that the host device is subject to the DDOS attack.

A computing device includes at least one processor, one or more non-transitory computer-readable storage media, a system for fine-tuning a large language model under low-bit weight-activation quantization. The computing device further comprises a graphics processing unit (GPU), a neural processing unit (NPU), or a tensor processing unit (TPU). The hardware interface module of the system is configured to load a low-bit model representation from the memory module and transmit the model representation to the GPU, NPU, or TPU for inference execution.

The invention provides a quota resource processing method which can be applied to the technical field of big data. The method comprises the following steps: loading batch transaction data files to a distributed file system; performing parallel processing on the transaction data through a distributed calculation engine, and calculating a quota resource value corresponding to each transaction; calling an online interface to execute quota resource updating operation, and obtaining an interface calling result in real time; if the quota resource update fails, writing the failure type identifier, the user identifier, the transaction identifier and the quota resource value into a quota resource update failure record table; in response to the update of the quota resource update failure record table, reading the abnormal type identifier; if it is determined that the exception type is system exception according to the exception type identifier, executing quota resource retransmission operation; if it is determined that the exception type is constraint overrun according to the exception type identifier, monitoring a quota resource quota change event related to the target object; and in response to triggering of the quota resource quota change event, executing quota resource reissuing operation.

Interface and mode selection for digital action execution is provided. For example, a system loads a script library embedded in an electronic resource. The system determines a historic level of engagement between a client computing device and one or more digital assistants. The system selects, based on a first property of the client computing device and the historic level of engagement, a type of digital interface. The system generates, based on the type of digital interface, a digital interface with the call-to-action. The system determines, responsive to an instruction to execute the call-to-action, a mode of execution. The system selects a digital assistant and a second client device to execute the call-to-action. The system transmits the call-to-action to the second client device for execution.

A computing system may receive, via a client interface, a query to trigger a prompt of a set of prompts configured for a large language model (LLM), where the query may be indicative of a set of data from one or more data sources linked to the prompt. The computing system may transmit, to an augmentation service, a request for a set of grounding data associated with the set of data from the data sources linked to the prompt. The computing system may then receive, from the augmentation service, the set of grounding data where the set of grounding data includes hierarchical context data from the data sources. The LLM may then be queried via the prompt using the first set of data and the set of grounding data. The response to the query may then be provided, to the client interface, for display via the client interface.

The invention relates to a performance monitoring driving type heterogeneous computing task hardware scheduling system and method which are applied to an instruction set architecture heterogeneous computing platform supporting a hardware performance monitoring unit. The performance monitoring interface module obtains first performance index data of a first computing unit in the instruction set architecture heterogeneous computing platform and first performance index data and second performance index data corresponding to a plurality of second computing units; the heterogeneous computing unit management module maintains global task topology and task states of the computing units; the hardware task scheduling unit generates a scheduling request signal based on the performance feature vector of the first performance index data and / or the second performance index data, accesses the second performance index data, the global task topology and the task state in parallel, and selects a task calculation unit; the hardware scheduling control bus migrates heterogeneous computing tasks to the task computing unit based on the scheduling request signal. Therefore, low-delay and high-precision scheduling control is realized, and the overall throughput rate and energy efficiency of the system are improved.

A computing system for cross-platform support and log summarization includes a language model, APIs, processors, and memories to generate queries and troubleshooting steps from log files and IR system results. A method involves obtaining log files, generating IR system queries, and creating troubleshooting steps using a language model. A non-transitory computer readable medium contains instructions for obtaining log files, generating queries, and creating troubleshooting steps based on log files and IR results.

The invention discloses a CAE software management system and method, and relates to the technical field of CAE software management, and the method comprises the steps that a graphic rendering virtualization module provides a virtual desktop environment, so that a user interacts with a CAE interface based on the virtual desktop environment to generate a task description file; the job management and monitoring module receives the task description file and performs standardization processing on the task description file to obtain a standard task description file; the computing resource intelligent scheduling module performs resource matching on the standard task description file through a scheduling algorithm to determine a target computing node, and processes the CAE simulation task through the cross-platform scheduler adapter module and the standard task description file according to the target computing node; and when the computing resource intelligent scheduling module processes the CAE simulation tasks, the data management and security control module performs data synchronization through a distributed file system, creates an independent virtual sandbox environment for each CAE simulation task, and binds a sandbox with a target computing node.

The application provides a training method and device of an AI model, a computing device and a storage medium, and belongs to the technical field of artificial intelligence. The method is applied to an AI platform, the AI platform is associated with a computing resource pool, the computing resource pool includes computing nodes for model training, and the method comprises the following steps: providing a training configuration interface to a user, the training configuration interface comprises multiple training modes selected by the user, each training mode represents a distribution strategy of the computing nodes required for training an initial AI model, at least one training task is generated according to the selection of the user on the training configuration interface, the at least one training task is executed to train the initial AI model, an AI model is obtained, and the obtained AI model is downloaded or used by the user. According to the application, distributed training can be more flexibly performed.

Methods and systems for enhanced remote desktop interfaces are described. A computing system may train, using historical or live information, a LAM to execute, within a remote desktop application, textual actions with their parameters if any. A user declarative request (voice or chat) may be interpreted by a LLM to match a specific action (and potentially ask for the corresponding parameters in a conversational way). Subsequently, from the textual action and its parameters, the LAM may execute the action within a remote desktop application and report the result to the user via voice or chat.

The invention relates to a high-performance spaceborne intelligent calculation terminal and a starting method thereof. The terminal comprises a routing unit, a storage unit and an intelligent calculation unit, each of the routing unit and the intelligent calculation unit comprises a hardware physical layer, a hardware driving layer, an embedded operating system, a resource scheduling management layer and an application service layer; the application service layer is used for configuring different application service APPs according to spaceflight application scenes and function requirements, and the application service APPs comprise service plug-ins corresponding to the resource scheduling management layer called by an embedded operating system and universal task distribution and resource management modules in the operating system according to the different application service APPs related to intelligent AI model intelligent calculation. In combination with corresponding hardware drive, computing power and hardware resources required by the intelligent AI model are flexibly configured for multi-user application software; the routing unit is used for providing interface communication with the outside, and realizing optical fiber data receiving and transmitting health management, Ethernet bus communication, RS422 bus communication, data routing management, task scheduling management, remote control and telemetering, centralized management of a system program or an application program or an FPGA configuration program, and a power supply control function; the intelligent calculation unit dynamically loads the on-satellite intelligent AI model and parameters thereof and is used for completing operation of the on-satellite intelligent AI model; and the storage unit is used for storing on-satellite load original data, intermediate data processed by the on-satellite intelligent AI model and processed data.