1748 results about "Interface (computing)" patented technology

A server computer system comprises a communications module; at least one processor coupled with the communications module; and a memory coupled to the processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to provide a plug-in to an interface design software application executing on a computing device, the plug-in allowing the server computer system to communicate with the computing device to monitor design of an interface within the interface design software application; generate at least one recommendation for tagging at least one element of the interface; and send, for display on a display screen of the computing device within the interface design software application, the at least one recommendation for tagging the at least one element.

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.

Disclosed are systems and methods that ingest substantial volumes of data from a variety of sources associated with a computing system network to create integrated, intuitive, efficient, and coherent adaptive workspace display interfaces. The ingestion includes event data generated by event source software applications running on end user computing devices as well as call and end user attribute data that is used to determine state, presence, and performance data for the end user computing devices that is formatted for display on the adaptive workspace interfaces and used during performance monitoring and collaboration between system end users. The adaptive workspace interfaces provide functions that facilitate real time collaboration between end users that enhances shared experiences between system end users and customers.

A computing services environment may include a database system storing a plurality of database records for a plurality of client organizations accessing computing services including a conversational chat assistant. The computing services environment may also include an application server may receive user input for the conversational chat assistant, a generative language model interface, an orchestration and planning service configured to identify one or more actions based on the user input, to execute the one or more actions to determine a natural language response message, and to determine a recommended action for selection via a conversational chat interface. The computing services environment may also include a communication interface configured to transmit the natural language response message and a user interface generation instruction executable by the client machine to provide a selection affordance for selecting the recommended action.

The invention provides a modular construction method of a low-code visual interaction system, and relates to the technical field of low-code development. The method comprises the following steps: constructing an edge cognitive topology network, packaging each node into a reconfigurable meta-module, analyzing a user operation track by using an intention perception interface to generate a meta-intention vector, and carrying out context correlation analysis through a semantic behavior chain to generate a potential behavior sequence. And establishing an asymmetric coupling relationship based on an implicit context mapper, forming a dynamic semantic resonance field, triggering distributed intention resonance and activating a matching element module, and generating a collaborative logic cluster under the constraint of edge computing node resources. And dynamically adjusting an interaction rule and a resource allocation strategy of the logic unit. According to the method, automatic construction and high-precision intention adaptation of complex business logic are realized, the elastic expansion capability and the real-time response efficiency of the system in an edge computing scene are improved, and the risk of logic fault and resource scrambling caused by node isomerism is reduced.

Methods and apparatus to determine machine learning (ML) configurations for classifying malware are disclosed. An example server comprises interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to determine a computing parameter associated with a computing device, the computing device communicatively coupled to the server, select a ML model to deploy on the computing device based on the computing parameter, determine a configuration of the ML model based on the computing parameter, deploy the ML model to the computing device, and cause the deployed ML model to classify a sample as clean or malicious, the sample received at the computing device.

The invention provides a method for realizing agent development MCP service through computing power of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the method comprises the following steps: S1, determining interface information of a target application programming interface API, the target API being an interface of a target platform; s2, generating a first coding task based on the interface information through an intelligent agent of the intelligent computing center; s3, generating a first MCP service component corresponding to the first coding task through the intelligent agent; and S4, registering the first MCP service component to the intelligent computing center. According to the invention, the development efficiency of the MCP service of the intelligent computing center can be greatly improved.

The invention discloses a control method for a cooling circulation system of a multi-core processor, and relates to the technical field of computer hardware heat dissipation, and the control method comprises the following steps: the multi-core processor is responsible for executing a calculation task; the sensor module monitors the temperature of each core; the cooling system control module is used for receiving data from the temperature sensor and the load monitoring module, calculating the cooling requirement of each core according to the data and commanding a cooling unit to adjust; the cooling unit is used for adjusting cooling equipment to reduce the temperature of the processor according to an instruction of the control unit; and the debugging interface is used for monitoring the state of the system and displaying the temperature, the load and the cooling resource distribution condition of each core. The method has the beneficial effects that the rotating speed of the fan, the liquid cooling flow and the working state of the heat pipe system can be accurately controlled by combining the accurate analysis of the load change and the temperature data, the heat dissipation efficiency of the processor is improved, and the energy waste is reduced.

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 computing node, a computing system, a control method, a control device, equipment and a medium, and relates to the technical field of computers, a plurality of acceleration cards are interconnected through an Ethernet switching module, and low-order channels of serializer / deserializer interfaces of the acceleration cards are connected with the Ethernet switching module through retimers. A high-order channel of at least one serializer / deserializer interface of each accelerator card is connected with the Ethernet switching module through a retimer, and the working mode of the retimer is configured through a first controller on a computing node according to the signal rate of the accelerator cards, so that a framework that communication interfaces between the accelerator cards are fully interconnected is realized. Any two acceleration cards can be directly connected, the communication bandwidth between the acceleration cards is improved, meanwhile, the number of the acceleration cards can be flexibly expanded, longitudinal expansion and transverse expansion of a computing system can be conveniently achieved, the problem that a traditional acceleration card interconnection scheme is not easy to expand is solved, and the method can adapt to service requirements and flexibly adjust working loads.

A system configured to extract, classify, and order entity insights may include a computing system, a computer readable memory, at least one processor having instruction configured for ingesting data into the system from at least one external application programming interface configured to retrieve data over a network, integrating the data to form a cohesive data set using the at least one processor, storing the cohesive data set into the computer readable memory, processing the cohesive data set by the at least one processor using at least one machine learning model to generate a plurality of entity insights, each of the insights having at least one classification associated therewith, ordering the plurality of the entity insights based on a machine generated prioritization, and generating a presentation comprising the plurality of entity insights in human-readable form.

Methods and apparatus for application aware memory patrol scrubbing techniques. The method may be performed on a computing system including one or more memory devices and running multiple applications with associated processes. The computer system may be implemented in a multi-tenant environment, where virtual instances of physical resources provided by the system are allocated to separate tenants, such as through virtualization schemes employing virtual machines or containers. Quality of Service (QoS) scrubbing logic and novel interfaces are provided to enable memory scrubbing QoS policies to be applied at the tenant, application, and / or process level. This QoS policies may include memory ranges for which specific policies are applied, as well as bandwidth allocations for performing scrubbing operations. A pattern generator is also provided for generating scrubbing patterns based on observed or predicted memory access patterns and / or predefined patterns.

The invention provides a semantic fusion graphical software development management method and system. The method comprises the following steps: acquiring a multi-user interaction behavior data flow containing an operation track sequence and an interface control triggering time sequence; according to operation track sequences of different users and a time-space association mode of an interface control triggering time sequence, constructing a dynamic semantic map; inputting a hierarchical mapping relationship in the dynamic semantic map into a preset edge computing node, and generating a real-time computing power distribution scheme matched with a logic dependency chain in the dynamic semantic map; analyzing a mapping relationship between a control triggering timestamp and a control type in the space-time association mode, and generating an interface control rendering priority queue; the synchronous regulation and control interface control renders the priority queue and an operation instruction response sequence corresponding to the logic dependency chain, and generates a graphical software development process synchronous instruction set; according to the method, the hierarchy and the dependency chain are fused to form the dynamic semantic graph, the hierarchical operation structure and the logic dependency relationship are constructed, and the high efficiency and the consistency of the graphical development process are ensured.

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.

A computing services environment may include a database system may store database records for client organizations accessing computing services including a conversational chat assistant. The computing services environment may also include an application server receiving natural language user input for the conversational chat assistant and a generative language model interface providing access to one or more generative language models. The computing services environment may also include an orchestration and planning service configured to analyze the natural language user input via a generative language model of the one or more generative language models to identify a plurality of actions to execute via the computing services environment to fulfill an intent expressed in the natural language user input. The computing services environment may be configured to execute the plurality of actions to determine a natural language response message.

The invention discloses a low-code platform and Wasm high-performance computing integration system and method, and relates to the technical field of front-end development. In order to solve the problems that an existing low-code platform is limited in calculation performance and poor in security isolation performance, the scheme adopted by the invention comprises five modules: a description and registration module establishes a metadata structure for a Wasm module; the dynamic loading and asynchronous compiling module realizes loading as required and compiling during operation, and supports caching, version verification and hot replacement; the authority sandbox and resource isolation module constructs a security environment based on authority configuration to prevent illegal operation; the parameter binding and safety bridging module encapsulates a uniform interface to realize standardized interaction; the packaging and life cycle management module packages the module into a visual component, and supports full life cycle management. According to the method, high-performance, high-safety and visual integration of the Wasm module in a low-code platform is 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.

The invention belongs to the technical field of cloud computing, and discloses a method for integrating and sharing internal resources of digital media in a cloud environment. The method comprises the following specific steps: S1, multi-modal resource discovery and metadata standardization; s2, distributed resource pool construction and intelligent indexing; s3, a dynamic scheduling algorithm driven by AI; s4, a permission and traceability mechanism endowed by the block chain; s5, opening a containerized micro-service interface; s6, resource optimization driven by user behaviors; and S7, edge-cloud collaborative dynamic caching is carried out. The method has the advantages that on one hand, through multi-objective optimization, delay load regulation and control and reinforcement learning, cost is reduced, response is improved, high-quality tasks are guaranteed, and resource scheduling is optimized; on one hand, a trust foundation is built for resource management and copyright is protected from behavior recording and permission grading to tracking protection by utilizing the characteristics of the block chain; in addition, micro-services are packaged, APIs and SDKs are provided, third-party access and integration are promoted, the development efficiency is improved, and all-around enabling is achieved.

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 relates to a data processing method and device of a distributed computing engine and electronic equipment. The method comprises the steps that under the condition that a structured query statement of a client side is received, the structured query statement is analyzed, a first task and a target task are obtained, the structured query statement comprises a user-defined function, the target task is a task corresponding to the user-defined function, and the first task is a task corresponding to statements except the user-defined function; calling an interface of the target custom function server to enable the target custom function server to execute the target task; target data generated by the target self-defined function server and first data generated by the working node of the distributed computing engine serve as execution results of the structured query statement and are returned to the client side, and the first data are data obtained when the working node of the distributed computing engine executes the first task. According to the method and the device, the technical problem of low processing efficiency of the structured query statement comprising the custom function is solved.

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 provides a method for debugging an intelligent agent through computing power of an intelligent computing center, which comprises the following steps: S1, a debugging intelligent agent receives task information provided by a user through an interactive interface, and the task information is used for indicating to debug a debugged object; the debugged object is a debugged intelligent agent or a function module or a tool function of the debugged intelligent agent; s2, the debugging agent determines a debugging requirement according to the task information; s3, the debugging agent determines a debugging method according to the debugging requirement, deploys a debugging environment according to the debugging method, generates a running instruction and debugs the debugged object; s4, the debugging agent obtains the operation information of the debugged object and analyzes the operation information, and whether the debugging requirement is met or not is evaluated according to the analysis result; s5, if not, the debugging agent checks the code of the debugged object, modifies the code according to the check result, and returns to the step S3 until the condition is met; and S6, the debugging agent feeds back a debugging result to the user through the interactive interface.

The invention relates to the technical field of data information operation and maintenance management, and discloses a data information operation and maintenance management method based on an AI chain trust mechanism, and the method comprises the steps: obtaining multi-source heterogeneous data through a distributed collection terminal composed of an edge calculation node, an Internet of Things sensor and a third-party interface module; and after format conversion, a data trust value is calculated by using a multi-dimensional trust evaluation model containing a source reliable item, a content consistent item and the like, then the data trust value and a historical trust record in a block chain distributed account book are subjected to association verification, and a trusted data identifier is generated through a consensus algorithm. And constructing an on-chain storage index based on the data type, the timestamp and the source node, encapsulating the data by adopting a hierarchical structure of a metadata layer, a content layer and a verification layer, and outputting a storage address. A monitoring layer of the dynamic operation and maintenance control model identifies abnormity in real time, a decision-making layer generates an operation and maintenance strategy in combination with credible data identification and historical records, and an execution layer triggers repairing and updating operation through an intelligent contract, so that on-chain credible operation and maintenance management of data information is realized.

The invention discloses a tensor processing architecture based on reconfigurable calculation, a working mode of the tensor processing architecture and an FPGA (Field Programmable Gate Array) development board. The overall architecture comprises a QSFP interface, a DDR storage module, a memory access control module, a microcode control module, a matrix module, an on-chip shared storage module, a control network, an RISC-V processor, a reconfigurable computing interface module, a reconfigurable computing module, a reconfigurable configuration module and a result return network. The working modes of the overall architecture are divided into two modes: a CPU control mode and a cluster computing mode, wherein the CPU control mode is used for controlling global computing and output through a CPU during single-chip computing; the cluster calculation mode is used for being connected with an upper computer to cooperate with the upper computer to execute calculation tasks such as matrix calculation; the whole architecture is realized through an FPGA (Field Programmable Gate Array) development board, and the FPGA development board adopts Virtex UltraScale + VU13P as a main logic realization platform. Through the dynamic granularity configuration of the on-chip microcode, multiple operation modes can be realized, and the utilization rate of hardware is improved; and hardware realizes multiple configuration strategies, and the universality is high.

The invention discloses a management system of a server and the server, relates to the technical field of computers, and realizes efficient interconnection and centralized management between a plurality of computing nodes and a management module by introducing an interface module between the computing module and the management module. Specifically, each computing node comprises a computing unit and a programmable logic device, a plurality of computing nodes are connected through the programmable logic device in the interface module, and the first management module obtains the operation data of each computing node through the interface module, so that the design that one management module manages multiple nodes is realized, the management units can be reduced, and the management efficiency is improved. And the cost is reduced, so that the expandability and the management efficiency of the system are improved.

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.

A digital assistant can provide support for automated testing of applications. A natural language interface can be provided by which a testing user can specify a request for one or more testing actions. A natural language processing model can recognize intents in the request, and the intents can be used to execute executable code to perform the requested testing actions. Multiple actions per request can be supported. An object repository can be leveraged to determine user interface control identifiers, and a test data container can store values for use during testing. Testing functionality can thus be provided to a wider base of testing users. A real time, scriptless approach can conserve computing resources.