1262 results about "Interface (computing)" patented technology

The invention discloses a cross-domain computing task processing method, a program product, equipment and a medium, and relates to the technical field of cloud computing. The method comprises the following steps: determining a computing node for executing a cross-domain computing task in a cross-domain collaborative scene, and generating an identity certificate bound with the computing node based on a hardware credible state of the node; collecting security policies of a plurality of management domains for conflict detection and decision, and generating a conflict-free session policy; scheduling the cross-domain computing task to a target computing node meeting a preset credible requirement, and issuing a revocable dynamic session token; and recording the operation information of the cross-domain calculation task in the whole life cycle as an audit event, and generating a chained audit log which is linked by the hash value and is digitally signed through a preset verifiable audit interface. By means of the technical scheme, it can be ensured that the whole life cycle of any computing task meets the closed-loop safety requirements of identity credibility, permission controllability, execution propriability and behavior traceability in the complex distributed computing environment.

The invention discloses a solid state disk management system and a data processing method, and relates to the technical field of solid state disk management. Flexible task scheduling and resource management are provided through a lightweight operating system module, and a computing task program defined by a user is supported to be dynamically loaded; edge computing or machine learning operators are efficiently executed in combination with a programmable hardware processing unit and a DMA channel of the computing acceleration engine module, the data preloading module is utilized to predict and preload data to DDR based on LBA access history, access delay is reduced, an NVMe protocol is expanded by means of the task unloading interface module, host task issuing and result returning are achieved, and the data processing efficiency is improved. And hardware-level memory protection is ensured through the security isolation module, so that the data calculation processing capacity of the solid state disk is remarkably improved, localized calculation tasks such as edge calculation and machine learning are supported, mass data transmission is effectively reduced, and bus and network loads are relieved.

The invention discloses a self-adaptive dynamic interface generation method and system based on a computing power host operation and maintenance platform, and relates to the technical field of operation and maintenance interface dynamic optimization. Aiming at the problem of static stiffness of an operation and maintenance interface in a complex scene, the scheme is adopted as follows: collecting user behaviors, a context environment and equipment data, and fusing into a multi-dimensional state vector; inputting the multi-dimensional state vector into a deep reinforcement learning model, and generating a dynamic interface strategy including layout component dynamic arrangement and function button priority ranking; designing a hierarchical multi-target reward function fusing operation efficiency reward, accuracy reward and cognitive load penalty, and performing strategy optimization; analyzing the optimized dynamic interface strategy through the cross-terminal rendering capability of the WebXR engine, and generating a front-end page adaptive to the current terminal; and continuously feeding back the collected data to the deep reinforcement learning model, and updating the network weight through a strategy gradient algorithm to realize user preference migration. According to the method and the device, cross-terminal self-adaptive dynamic interface generation can be realized.

The invention provides a multimode communication network dynamic scheduling system fused with AI computing power, which belongs to the technical field of communication equipment and artificial intelligence hardware fusion, and is characterized in that based on a four-layer hardware architecture of'core control-computing power support-multimode communication-high-speed interconnection ', an interconnection layer is constructed by adopting high-speed interfaces such as PCIe / SGMII + and the like, and inter-module fault is realized; 10ms low-delay data transmission is realized; the AI computing power module integrated with the 8TOPS NPU is deeply coupled with the multi-mode communication module, and the AI computing power module is quantized and compressed through an LSTM / Transform model and then solidified to an NPU special storage to realize scene self-adaptive scheduling; a'acquisition-identification-decision-feedback 'hardware closed loop is established, the communication interruption rate is reduced in an emergency scene, the overall power consumption is reduced, and AI reasoning is delayed. Through breakthrough hardware architecture design, AI computing power and a multi-mode communication module are deeply integrated, an intelligent communication resource scheduling system is constructed, and dynamic optimal configuration of communication resources in multiple scenes can be realized.

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.

A system is provided for dynamic scheduling and optimisation of diagnostic tasks in a networked computing environment. The system associates issue tickets with a diagnostic task matrix comprising probable causes, diagnostic tasks, probability values, outcome expectations, and resource parameters. A task scheduling controller generates optimised task sequences based on task success likelihoods, cost, technician availability, and evidentiary sufficiency. As tasks are completed, outcomes are used to update the diagnostic model, enabling automatic self-improvement. A statistical learning model, such as aBayesian or neural network, refines diagnostic probabilities using historical data. Integration with calendaring systems allows real-time rescheduling based on personnel availability. A graphical interface supports live drag-and-drop reconfiguration of task associations, with immediate propagation of updates to task probabilities and cost metrics. The system thereby enhances resolution speed, accuracy, and resource efficiency across evolving operational contexts.

The invention discloses an intelligent resource scheduling method and system based on task characteristics and system state perception, and relates to an operating system kernel optimization technology. According to the method, resource demand types, importance labels and time sensitivity of tasks are captured in real time through an eBPF technology, system bottlenecks are recognized by combining / proc and sysfs file system data, a dynamic scheduling strategy is generated by adopting an improved Q-Learning model, and resource allocation is executed through a schemed * function and a cgroup v2 interface. The problems that a traditional scheduler is poor in self-adaptive capacity and resources are mismatched are solved, the actually-measured resource utilization rate is increased to 85%-92%, and the key task delay standard reaching rate gt is achieved; the method is suitable for edge computing, cloud servers and other high-concurrency scenes.

The invention discloses a large model cue word intelligent design and verification system supporting multi-person cooperation, which designs cue words in a manner of cue word project management and multi-person cooperation to realize specialized design of the cue words. The difficulty of cue word design is simplified by using AI aided design; the operation stability and accuracy of cue words in different models and different computing power are verified through single-point testing and batch testing, so that cue word verification and iteration form a closed loop, and the cue word quality is improved; after the cue word is published, directly outputting an interface with an access key for external calling, and storing the interface as a cue word case into a cue word knowledge base; compared with an existing system and technology, the cue word version management difficulty is effectively reduced, the workload of cue word designers is reduced, the cue word quality is improved, and the risk of cue word asset loss is reduced.

The invention discloses an edge real-time analysis-oriented sensing and calculation integrated spectrum camera processor system. Efficient edge calculation is realized through collaborative innovation of hardware, an algorithm and an accelerator. A hardware system adopts a three-level board card framework; a core board integrates a programmable logic unit and a processing system, and processes spectral data acquired by a CMOS board in real time; and the interface board accurately controls the power supply time sequence. In the lightweight classification network, the SSFE branch converts frequency domain filtering into cyclic matrix multiplication; carrying out normalization on the HFViT branch by using a batch normalization substitution layer; and an 8-bit full integer quantization strategy compression model is matched, and input spectrum identification characteristics are reserved. Time overlapping scheduling is designed for the accelerator, and SSFE matrix multiplication is executed in parallel in the HFViT calculation window period; the outer product pulsation architecture improves the matrix operation efficiency; and non-linear kernel multiplexing computing resources are unified. The system supports real-time classification of push-scan spectrum data, results are directly connected with sorting equipment, and the edge end embedded deployment requirement is met.

The invention discloses a direct memory access (DMA) communication device for a computing network convergence computing architecture and a working method of the DMA communication device. The DMA communication device comprises a DMA transaction processing module and a protocol conversion module; the DMA transaction processing module is used for analyzing related DMA read-write requests, realizing processing of the DMA read-write requests and receiving of response data of the DMA read requests and completing processing of interrupt requests at the same time, and the protocol conversion module is used for completing conversion of read-write requests between a DMA interface and an AXI interface and mapping of response states. The invention aims to realize efficient protocol conversion between AXI and DMA interfaces, avoid the problem of DMA read request starvation caused by resource competition and ensure the consistency of DMA data access so as to improve the performance of an accelerator in high-performance calculation and artificial intelligence application, reduce transmission delay and improve data transmission bandwidth and energy efficiency.

The embodiment of the invention provides a data processing method, a data processing device, electronic equipment and a storage medium. The data processing method includes: receiving a signal about an input tensor and an index tensor; target data are read from a source storage space corresponding to the input tensor based on the index tensor through a general tensor operation interface, the target data are written into a target storage space corresponding to the output tensor, and the general tensor operation interface supports unified tensor operation involving at least two data arrangement modes. According to the data processing method, a universal and efficient index operation implementation scheme is provided, different data arrangement modes can be adapted, and complex index access requirements under different hardware architectures are compatible, so that the generalization ability and execution efficiency of index operators are remarkably improved, and the data processing efficiency is improved. High-efficiency and low-latency indexing operation on heterogeneous computing equipment can be supported.

The invention discloses an intelligent video monitoring system and method supporting AI strategy cloud side end cooperative execution. According to the system, unified management of computing power resources and dynamic deployment of an AI algorithm are realized based on an extended standardized protocol through a three-level architecture of a cloud side center platform, an edge computing node and an end side camera. The method comprises the following steps: a cloud platform acquires computing power information of edge equipment; an AI algorithm execution strategy is generated and issued according to service requirements; if the required algorithm is not deployed in the equipment, the equipment is controlled to download and automatically deploy an algorithm module; and finally, the driving equipment executes AI analysis and recovers the structured data. According to the invention, AI computing tasks are reasonably distributed through a cloud side-end cooperative computing architecture, so that the computing power construction cost of a central platform is effectively reduced; through algorithm and hardware decoupling and a standardized protocol interface, ecological binding is broken, on-demand loading and flexible scheduling of the algorithm are realized, and the economical efficiency, the flexibility and the intelligent level of the system in large-scale scenes such as smart cities and the like are remarkably improved.

The invention belongs to the technical field of computer trusted computing, and particularly relates to a method and system for constructing a TPCM trusted root based on a multi-core BMC, and the method comprises the steps: selecting the multi-core BMC, configuring one core as a trusted core, and achieving the physical and logic isolation with other cores; tPCM related codes are integrated in the trusted core, and a TCM module is combined to provide a running environment for the TPCM; when the system is started, the TPCM performs measurement check on the BMC code and the BIOS code, and if the BMC code and the BIOS code are abnormal, a security policy is started In the system operation process, the TPCM monitors hardware, firmware and software of a calculation part in real time, and discovers abnormal behaviors to block and give an alarm; and meanwhile, the management interface is connected with a trusted management center, so that remote configuration and monitoring are realized. A multi-core BMC is used as a protection component, a server host side is used as a calculation component, and a trust chain model of a star-shaped trusted topological structure is constructed. According to the method, the TPCM trusted root is constructed by using the existing resources of the multi-core BMC, so that the hardware cost is reduced, the real-time performance of trusted measurement and the monitoring comprehensiveness are improved, and the running safety and credibility of the server are improved.

The invention provides a component configuration method which can be applied to the technical field of cloud computing and the technical field of big data. The method comprises the steps of obtaining configuration information used for configuring a target interface component from a server side based on an interface request; packaging the interface request into an asynchronous data acquisition object, and storing the asynchronous data acquisition object as a global shared instance; registering a callback processing function based on the global shared instance for the target interface component; and responding to the configuration information returned by the server side and triggering the callback processing function, and executing component rendering on the target interface component based on the configuration information.

The invention provides a multi-modal basic model convolution operation optimization framework method suitable for a GPU / DCU. The method is used for solving the technical problems that the optimization process of an existing convolution operator optimization method is time-consuming and cross-layer operator fusion is difficult to capture. The method comprises the following steps: receiving a convolution operator through a parameterized input interface; collecting shape features of each convolution operator in the structured parameter set by using a shape analyzer to realize convolution feature analysis; judging whether batch normalization and nonlinear activation function fusion calculation is started or not; a heuristic optimizer dynamically selects a calculation graph optimization strategy based on convolution parameter characteristics; a calculation task is adapted to a physical architecture of the GPU / DCU, so that hardware resources are utilized to the maximum extent; a convolution calculation kernel function oriented to DCU / GPU architecture optimization is generated based on the convolution parameter features; kernel function assembly line execution is achieved through a DCU / GPU hardware task queue; gradient synchronization among multiple computing units is realized by adopting atomic operation at an equipment end. According to the method, the execution efficiency of the convolution operation in the multi-modal basic model can be remarkably improved.

It is provided an apparatus comprising interface circuitry, machine-readable instructions, and processing circuitry to execute the machine-readable instructions. The machine-readable instructions include instructions to receive data from a remote entity for handling by a computing system. The machine-readable instructions further include instructions to instantiate a first TEE and a second TEE. The machine-readable instructions further include instructions to generate log data corresponding to predefined activities of the computing system and to generate system record data of a system log of the computing system at predetermined times. The machine-readable instructions further include instructions to generate first audit data by the first TEE and second audit data by the second TEE. The machine-readable instructions further include instructions to transmit the first and the second audit data to a detection system for data aggregation and anomaly detection.

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 embodiment of the invention provides a data vectorization acceleration method and system, and the method comprises the steps: writing an optimizer rule into an optimizer, and carrying out the vectorization acceleration according to the optimizer rule, respectively fusing respective original operators in the first operator rule and the second operator rule with respective matched replacement operators of the first operator rule and the second operator rule to obtain a vectorized fusion operator; and converting the vectorization fusion operator into a vectorization execution operator according to the conversion logic, so that the vectorization execution operator calls a vectorization execution engine through a local interface JNI bridge to accelerate data vectorization. Therefore, through the embodiment of the invention, the problem that the calculation efficiency is relatively low in a large-scale data processing scene due to the fact that an existing Flink framework cannot fully utilize an instruction set and vectorization hardware resources in a batch processing mode can be solved.

A system or method for determining user request intent, and processing the request based upon the determined intent. The system or method includes receiving a user query from an interface, then determining and categorizing the intent of the user based on the query. The query can be reformatted or supplemented. If the intent is information, the query is provided to a Gen AI server—its response is then provided back. If the intent is transaction, the query is also sent to the Gen AI server—however, the response is an instruction set for the system, which executes the instruction set, and provides the subsequent response from the execution point back. If the intent is an action, the system verifies the user has proper access rights to request the action, before executing an instruction set and providing the subsequent response back, like in the instructional intent.

The present application provides a complex programmable logic device (CPLD)-based data processing method and system, and a computing device. The CPLD is arranged on a hard disk backplane of a server, and the hard disk backplane supports a universal backplane management (UBM) protocol; the hard disk backplane comprises an HFC connector for connecting to a RAID controller and a DFC connector for connecting to a hard disk slot, and the CPLD is connected to the HFC connector and the DFC connector, respectively. The method comprises: when a hard disk is inserted into the hard disk slot, determining an interface type of the hard disk according to a signal level transmitted by the DFC; processing interaction data between the RAID controller and the hard disk according to the hard disk interface type, wherein the interaction data comprises data indicating the hard disk interface type, data indicating a hard disk state and read / write operation command data; and transmitting the processed data to the RAID controller or the hard disk so as to achieve hard disk management, wherein the hard disk comprises a hard disk of an SAS / SATA / NVME interface type. In this way, new logic functionality is added to an existing logic chip of the hard disk backplane to achieve the functionality of the UBM protocol.

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 embodiment of the invention provides a large-model-oriented adaptive parameter fine tuning system, method, device and equipment, a storage medium and a program product, and relates to the field of artificial intelligence. The system comprises a user interaction module used for obtaining a fine tuning mode, training data and a basic model input by a user, and determining training parameters according to the fine tuning mode and the basic model; the training execution module is used for determining a computing power demand based on the fine tuning mode and the training data, and allocating computing power resources to the basic model according to the priority of the basic model and the computing power demand; the training module is further used for training the basic model based on training parameters and training data to obtain model weight parameters, the training parameters comprise a learning rate, and the learning rate is adaptively adjusted in the training process; the service deployment module is used for obtaining a target model based on the model weight parameters and the basic model; and the server is also used for obtaining the executable model and generating a service interface of the executable model. According to the system, the large model parameter fine tuning efficiency 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 rail transit multi-system fusion platform, and relates to the technical field of intelligent operation and maintenance of rail transit vehicles, and the platform comprises a fusion platform host, a communication switch, an application system and terminal equipment thereof. The core of the system is that logic functions of all application systems are packaged in an SDK mode, interaction with a fusion platform host is achieved through a unified API interface, computing power sharing of hardware resources such as an AI module and an MPU module in the host is achieved through a 10-gigabit PCIE bus, a SWITCH switch module dispatches data transmission in a unified mode, the independent computing capacity of a subsystem is reserved, and the data transmission efficiency is improved. By means of the framework, the newly-added system does not need to additionally deploy a host, the function can be expanded only by accessing terminal equipment meeting the standard, the limitation that in a distributed framework, hardware needs to be added to the newly-added system is effectively solved, and elastic reuse of the hardware resources is achieved.

One system includes at least one processor configured to receive cybersecurity data using a network connection or interface established with one or more computing systems of at least one entity. The at least one processor can be configured to determine a state of cybersecurity of the at least one entity based on one or more safeguards or configurations implemented in the one or more computing systems in response to receiving the cybersecurity data, and the state of cybersecurity corresponds with a cybersecurity resilience to one or more security incidents. The at least one processor can be configured to generate or update a parameter corresponding with a cybersecurity evaluation, modeling tool, or third-party product based at least in part on the state of cybersecurity corresponding with the one or more safeguards or configurations.

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 an end-to-end zero code AI model automatic training and deployment system and method, and belongs to the technical field of artificial intelligence and machine learning engineering, and the system comprises a user interaction layer which is used for providing a graphical user interface and receiving a model training instruction configured by a user; the business logic layer responds to the instruction, encapsulates the instruction into a structured work order, automatically executes data management, model training and model conversion tasks based on the work order, and generates a model deployment package capable of directly running on at least one target hardware platform; and the resource management layer is used for monitoring and scheduling bottom computing resources to support task execution of the business logic layer. According to the method, a Web-based graphical interface is provided, all operations are completed through clicking, dragging and pull-down selection, dependence on programming and command line operations is thoroughly eliminated, and complete AI workflow zero-code operation from data to deployment in a single system is achieved.

The invention relates to the technical field of computer hardware acceleration and data processing, and discloses a data transmission method and device based on an accelerator card, and the method comprises the steps: collecting an original data stream of external equipment, carrying out the real-time feature analysis of the original data stream through a mode recognition engine, obtaining space-time related features, and carrying out the real-time feature analysis; performing binary coding on the space-time correlation features to obtain feature mark data; according to the instantaneous bandwidth of the M.2 interface and the load state of the AI chip, compressing the feature mark data into optimized compressed data by using a dynamic compression engine; inputting the optimized compressed data into a heterogeneous computing distributor so as to distribute a path selection instruction for the optimized compressed data through the heterogeneous computing distributor according to the feature marking type of the optimized compressed data; and according to the path selection instruction, completing data transmission of the optimized compressed data through the heterogeneous computing distributor to obtain a data transmission result. According to the method, the final data transmission result can meet the space-time consistency requirement of high-precision AI reasoning.