216 results about "Abstraction layer" patented technology

Various embodiments of the technology described herein cause an LLM to intelligently process data based on a user query and a schema determined for a data set. Certain embodiments programmatically leverage an LLM and utilize its output based on a user query. In this manner, data is processed without the LLM having to access an entire data set, and instead only utilizes information associated with the user query and the schema. The schema comprises a textual description, such as a string of alphanumeric characters, that describes the data, data types, and / or data structure of the data set. Embodiments of the technology described herein are performed by an LLM interface layer separate from a user device layer and an LLM layer. The LLM interface layer is positioned between an LLM abstraction layer and an application layer by which a user can interface with the LLM interface layer.

The invention discloses a quantum classical hybrid computing task dynamic scheduling system and method based on SLURM. The system comprises a quantum task feature extraction module, a dynamic priority evaluation module, a dependency analysis module, a quantum perception backfilling module and a uniform resource abstraction layer module. The method comprises the following steps: extracting quantum features of a to-be-processed task and a computing resource to form a quantum feature set and caching the quantum feature set; the feature set is obtained in real time, task priorities are output through multi-dimensional evaluation, and a real-time priority sequence is generated; based on the task type and the feature set, forming a dependency relationship between the classic task and the quantum task, and converting the dependency relationship into a dependency constraint and / or resource reservation instruction; according to the task priority, the resource reservation instruction and the real-time resource state, a future idle period is predicted, and a short-time quantum task is inserted for backfilling; computing resources are distributed according to the task priority and the dependency constraint, and the resource utilization state is fed back to the backfill and priority evaluation module in real time. According to the method, efficient scheduling of hybrid computing tasks can be realized, and the overall performance of the system is remarkably improved.

Technologies for creating an optimized and accelerated network pipeline using an emulated network pipeline abstraction layer (NPAL) of an emulated data processing unit (DPU), including an emulated processing device and an emulated acceleration hardware engine, are described. The emulated NPAL supports multiple network protocols and network functions in an emulated network pipeline. The emulated network pipeline includes a set of tables and logic organized in a specific order to be accelerated by the emulated acceleration hardware engine. The emulated acceleration hardware engine can process network traffic data using the emulated network pipeline.

Technologies for creating an optimized and accelerated network pipeline using a network pipeline abstraction layer (NPAL) for policy-based routing (PBR) over Service Function Chaining (SFC) are described. A DPU includes acceleration hardware engine to provide a single accelerated data plane. A processing device can generate a first virtual bridge and a second virtual bridge, the first virtual bridge to be controlled by a first network service hosted on the DPU and having a set of one or more network rules, and the second virtual bridge having a policy-based routing policy (PBR policy). The processing device can add the virtual port between the first virtual bridge and the second virtual bridge. The acceleration hardware engine, in the single accelerated data plane, can route network traffic data using the PBR policy and process the network traffic data using the set of one or more network rules.

The invention discloses a cloud server physical GPU acceleration capability release method and system, and belongs to the technical field of computer GPU capability release, and the method specifically comprises the steps: collecting GPU operation parameters in real time, carrying out the preprocessing, storing the GPU operation parameters in a shared state database, constructing a lightweight virtualization abstraction layer on a physical GPU, dividing the physical GPU into a plurality of logic units, and storing the logic units in a shared state database; the method comprises the following steps: establishing a physical GPU, constructing an isolation layer, utilizing a three-dimensional resource segmentation strategy, adopting a priority queue, a jump mechanism and a delay tolerance mechanism in a scheduling process, dynamically allocating logic unit resources of the physical GPU, accelerating release of GPU resources, and realizing unification and real-time updating of physical GPU state information among nodes of a cloud server through a distributed state synchronization mechanism; through dynamic task scheduling, a jumping mechanism, a delay tolerance mechanism and distributed state synchronization, efficient GPU resource management is realized, and GPU capability release is accelerated.

A system and method for real-time or near real-time multilingual language transformation in telecommunications environments using distributed artificial intelligence (Al). The system includes at least one processor configured to instantiate a plurality of Al agents, each corresponding to a user in a communication session, and to manage their operation via an Al-agent runtime adapter comprising a microkernel agent manager and a virtual machine layer. A language model abstraction layer enables access to language models and Al algorithm libraries through publish-subscribe interfaces. The system supports contextual transformation of speech based on user profile data such as sentiment, emotional tone, and cultural background. Execution can occur on telecommunication-class switches, edge devices, or general-purpose computing hardware. Al agents operate asynchronously, access shared or private memory, and produce personalized, bidirectional linguistic output. The architecture supports secure, scalable deployment across heterogeneous devices and networks.

Technologies for creating an optimized and accelerated network pipeline using a network pipeline abstraction layer (NPAL) are described. A DPU includes DPU hardware and memory that stores DPU software with the NPAL that supports multiple network protocols and network functions in a network pipeline. The network pipeline includes a set of tables and logic organized in a specific order to be accelerated by an acceleration hardware engine of the DPU. The acceleration hardware engine processes network traffic data using the network pipeline.

The present application provides a large language model-based query statement generation method, apparatus, and device, and a medium. The large language model comprises a plurality of encoders. The method comprises: acquiring a query text; performing vectorization processing on the query text to obtain a target vector representation corresponding to the query text; inputting the target vector representation into the plurality of encoders, to perform encoding processing on the target vector representation by means of weight matrices of the encoders, so as to obtain context vectors corresponding to the encoders, wherein different encoders have different weight matrices; and generating a target query statement on the basis of the context vectors corresponding to the encoders. A model is allowed to focus on different information features in different attention heads, independently capture different aspects of a query text, and process information at multiple abstraction levels, thereby better capturing complex and abstract semantic relationships, effectively distinguishing the importance of information when processing complex and fuzzy questions, improving the accuracy of query statement generation.

The invention relates to code verification, in particular to a code and document consistency reasoning verification system based on a cross-modal logic atlas, which comprises an input layer for receiving source codes and software documents; the feature extraction layer is used for performing feature extraction on the source code and the software document; the abstraction layer is used for converting the extracted features into comparable intermediate representations; the unified modeling layer is used for mapping code logic and document semantics into a unified atlas structure and performing formalized representation by using description logic to obtain a cross-modal logic atlas; the reasoning verification layer is used for performing reasoning verification on the consistency of the code and the document according to the cross-modal logic graph; the result processing layer is used for judging a verification result according to the reasoning verification condition, starting a contradiction positioning mechanism when inconsistency is found, and carrying out contradiction positioning and report generation to obtain an interpretable report; according to the technical scheme provided by the invention, the defects that the consistency between the code and the document is difficult to accurately verify, and accurate contradiction positioning and an interpretable report are difficult to provide can be effectively overcome.

The invention discloses an architecture system applied to embedded system development, and relates to the field of embedded system development, and the architecture system comprises an application layer which is used for executing business logic in an embedded system and managing the configuration of the embedded system, and the application layer is developed by adopting a Lua script; the hardware abstraction layer provides a unified hardware access interface; and the virtualization interface layer is used for providing a uniform interface for the application layer, so that the application layer can access the hardware resources of the hardware abstraction layer and call the system service in the embedded system. According to the method and the device, the problem that cross-platform transplantation is difficult due to the fact that a C language direct programming mode is adopted in an embedded system in the related technology is solved.

The invention relates to the technical field of business process management, in particular to a data processing method, system and device based on business process management and a medium, and the method comprises the steps: constructing a business process model driven by an abstraction layer, and configuring a multi-protocol standardized interface; according to the business process model driven by the abstraction layer and the multi-protocol standardized interface, a process instance matched with the business scene is generated, and a task is distributed to a target client according to a task demand; managing form state circulation and node jump logic according to a task control engine; an event monitor is deployed through a process key node to monitor the process, and when the process reaches a specified node, external service calling operation is triggered to form an end-to-end automatic closed loop. Therefore, the problems of code redundancy, high maintenance cost, difficulty in quickly adapting to diversified customer requirements and the like due to the fact that business process management generally needs to be subjected to customized development for different subsystems in related technologies are solved.

The invention relates to the technical field of deep learning, in particular to a PyTorch framework-based compatibility expansion system, which comprises a cross-framework model converter, a heterogeneous hardware abstraction layer, a hybrid computational graph execution engine, an intelligent distributed trainer and a self-adaptive optimizer, the cross-frame model converter converts input model formats of different frames into PyTorch executable formats, the heterogeneous hardware abstraction layer supports rear ends of various hardware and automatically selects an optimal calculation path, and the hybrid calculation graph execution engine fuses dynamic graph flexibility and static subgraph optimization and supports dynamic control function execution flow. An intelligent distributed trainer automatically selects a parallel scheme and is compatible with multi-protocol communication, a self-adaptive optimizer performs dynamic optimization based on hardware characteristics and a PyTorch tensor, a cross-frame model converter supports model conversion of multiple deep learning frames, the cost of migration among different frames by a user is reduced, and the universality of the PyTorch frame is improved.

The invention discloses an intelligent routing and unified adaptation method for a large language model, and the method comprises the steps: defining all access details of the model through a declarative configuration file, and achieving the zero-code access of the large language model without writing any code for a newly-added model; a completely consistent calling interface is provided for an upstream application, the isomerism of all downstream large language models is shielded, and a unified request and response abstraction layer is constructed; through a strategy engine and a JSON path technology, complex streaming response including content thinking is precisely processed, and intelligent analysis and content extraction are carried out; dynamic configuration and intelligent strategy hot update are supported; intelligent routing of the model is realized, and an optimal large language model instance is dynamically selected; and meanwhile, enterprise-level governance capability is provided, governance functions such as fusing, degradation, current limiting and monitoring are integrated, and stability guarantee is provided for model calling. Therefore, the maintainability, the expandability and the user experience consistency of the system are comprehensively improved.

A cloud computing platform provides zero trust network access as a service to customers that maintain applications on-premises. In this context, the cloud computing platform may associate customers and / or applications with specific service proxies, and add an abstraction layer for network access that maps an alias domain for each customer and / or application to a network load balancer associated with the specific service proxies associated with the corresponding application(s). This approach advantageously simplifies the configuration of service proxies at the cloud computing platform by permitting dedicated relationships among network load balancers, specific service proxies, and specific applications, while concurrently reducing or avoiding the administrative burden on customers of updating network pointers when the clusters of service proxies are periodically reconfigured to adjust to varying user traffic.

According to embodiments of the present disclosure, a system includes: a microkernel having a low-level application programming interface (API) and providing memory protection domains to user-level processes; and an abstraction layer running on top of the microkernel and comprising a plurality of service extensions to the microkernel and configured to provide a high-level operating system (OS) API for use by one or more application processes running in user space, wherein different ones of the service extensions are configured to run within different ones of the memory protection domains provided by the microkernel.

A system and method of decoding a bitstream to produce tensors for use by a neural network second portion. The method comprises decoding a network abstraction layer (NAL) unit from the bitstream having a predetermined length, wherein the NAL unit of the predetermined length indicates a network abstraction layer (NAL) unit format of one inner codec of a plurality of inner codecs, each other inner codec having NAL unit lengths different to the predetermined length; selecting an inner codec from the plurality of inner codecs based on the decoded NAL unit of the predetermined length; and decoding the bitstream using the selected inner codec to produce the tensors.

The invention relates to the technical field of data recovery, and discloses a data recovery method and system based on a multi-cloud platform, and the method comprises the following steps: S1, constructing an intelligent data acquisition layer: carrying out the dynamic adaption of four data collection channels based on a multi-modal data recognition engine; s2, constructing an adaptive transmission protocol stack: dynamically selecting based on a network quality real-time evaluation model; s3, heterogeneous system architecture recovery: realizing architecture recovery through a virtual environment abstraction layer; s4, establishing a space-time dimension data management model, which is realized based on a cloud native storage architecture; and S5, automatically recovering an arrangement engine through a workflow engine. According to the method, the reliability and the stability of the system are improved while the labor cost is saved, high-frequency copy synchronization is supported, the resource utilization rate is improved, and the data reading efficiency can be improved through block-level data synchronization.

A computing system can provide an interface for users of a messaging service to exchange messages with other users of the messaging service. The system can associate each exchange of messages with an underlying document, and structure the underlying document of each message exchange so that each message as a comment that is embedded or linked to the document.

The invention discloses a hierarchical abstraction method and system for computing resources, and relates to the technical field of resource abstraction. The hierarchical abstraction method for the computing resources comprises the following steps: constructing a three-level abstraction architecture system; wherein the three-level abstract architecture system comprises a service interface layer, an equipment function abstraction layer and a hardware adaptation layer; horizontal function decoupling is adopted in the equipment function abstraction layer, hardware operation is abstracted into a plurality of independent function modules, and each function module provides a unified abstraction interface for the service interface layer in a dynamic link library form; and defining a device description structural body in the device function abstraction layer. The invention solves the following technical problems in the existing service system: interface isomerism, non-uniform drive interface specifications of hardware platforms, and need to develop a special adaptive code for each hardware; the resource coupling degree is high, business logic and hardware operation codes are deeply coupled, and business layer codes need to be modified during hardware switching.

The invention relates to the technical field of Internet of Things, in particular to an Internet of Things equipment collaborative management system based on an intelligent edge, which is composed of an equipment abstraction layer, a converged communication layer, a service logic layer and a firmware upgrading layer, the equipment abstraction layer realizes standardized access and full life cycle management of equipment through modeling, interface abstraction and resource pool management; the converged communication layer comprises protocol adaptation, intelligent routing and QoS guarantee to ensure smooth multi-protocol communication; the business logic layer adopts a micro-service architecture, and flexible business processing is realized through flow arrangement, intelligent decision making and event driving; the firmware upgrading layer comprises state perception, intelligent analysis, intelligent execution and firmware management so as to adaptively select an optimal upgrading strategy according to network state prediction and evaluation. According to the invention, modules at all levels cooperate, intelligent and collaborative management of Internet of Things equipment is realized, and the equipment management efficiency and the operation stability are improved.

The invention discloses a construction method for dynamically reconstructing a multi-modal edge computing hardware architecture and a cross-industry application method, and relates to the field of edge computing hardware architectures. A five-level heterogeneous architecture is adopted to construct a field self-adaptive dynamic reconstruction calculation unit, and an equipment abstraction layer is constructed to realize module hot plugging and calculation power distribution; a multi-protocol ubiquitous interconnection interface engine is established, a hardware-level analysis architecture is integrated, and multi-protocol analysis and rapid switching are supported; an environment self-adaptive energy efficiency regulation and control system is deployed, and wide-temperature operation is realized by adopting a bionic composite heat dissipation design; an edge-region-cloud three-layer elastic scheduling network is constructed, cross-domain computing power arrangement is realized through federal learning, and the method is adaptive to multi-industry scenes. According to the method, the hardware reuse rate and the wide-temperature environment adaptability are improved, the multi-protocol processing delay is reduced, the computing power scheduling efficiency is optimized, the limitation of traditional equipment is broken through, and the efficient and stable operation requirements of cross-industry scenes such as electric power and medical treatment are met.

The invention relates to the technical field of computer server resource management and network virtualization, and particularly discloses a server hybrid deployment and management system based on a virtualization technology, which comprises a uniform resource abstraction layer, a global resource sensing and modeling unit, a strategy-driven intelligent arrangement engine and a heterogeneous resource execution adapter. The uniform resource abstraction layer performs standardized abstraction on a physical server, a virtual machine and a container; the global resource sensing and modeling unit integrates the resource data and constructs a global resource model; a strategy-driven intelligent arrangement engine performs multi-objective optimization solution based on strategies and constraints to generate a scheduling scheme; the heterogeneous resource execution adapter translates the scheme into bottom executable atomic operations. According to the invention, unified management and intelligent cooperative scheduling of heterogeneous computing resources are realized, and the resource utilization efficiency and the system automation level are improved.

The embodiment of the invention provides an operating system and a signal system cloud platform, and the operating system comprises an abstraction layer which is used for packaging the architecture-level drive of a computing node, architecture-level virtualization and the core of a CPU, and providing a packaging interface irrelevant to a hardware system for other layers; the microkernel layer is used for constructing various components of the microkernel by using the interface provided by the abstraction layer; and the application layer comprises a plurality of virtual machines running on a micro kernel, the virtual machines comprise real-time virtual machines and non-real-time virtual machines, and each real-time virtual machine is configured with resources isolated from other virtual machines. According to the technical scheme of the embodiment of the invention, the computing node hardware is abstracted, and the microkernel constructed based on the abstraction supports real-time and non-real-time virtual machines, so that applications with different security levels run in parallel on the same physical computer, and the utilization efficiency of hardware resources is improved.

The invention discloses a data layout optimization method and device applied to NPU code compilation and a medium. The method comprises the steps that an intermediate representation IR input by an upper layer is split into an operation type OP and an operand, and the operand input by the upper layer is divided into logic data and a logic mask; converting the logic data of the upper layer into data vector representation of the abstraction layer, and converting the logic mask of the upper layer into mask vector representation of the abstraction layer; obtaining a corresponding bottom hardware instruction capability according to an operation type OP input by an upper layer, and performing legalization operation on the operation type OP; and performing legalization processing on the converted abstract data vector type representation and mask vector representation according to the target hardware capability, performing instruction mapping on the processed abstract instruction according to the underlying hardware pair operation type OP, and generating a target LLVM instruction compatible with the underlying hardware. Register layout abstraction and conversion can be carried out on the NPU code under mixed precision input, and the utilization efficiency of the NPU bottom layer register is improved.

Device for the intelligent encapsulation of legacy data information systems with the integration of artificial intelligence, comprising: a collection unit for legacy interfaces, configured to intercept communication data from existing platforms; a semantic interpretation unit designed to analyze and map data structures and process semantics; a contextual abstraction layer designed to convert interpreted information into standardized, interoperable formats; a policy-driven management module that ensures secure access, Data transformation and regulatory compliance are ensured; and a learning and inference unit for adaptive optimization of data mappings and operational logic.

The invention discloses a natural resource asset checking management system and method based on a cross-platform architecture, and relates to the technical field of resource management, and the system comprises a cross-platform adaptive architecture module, a real-time data synchronization and verification module and an intelligent analysis and multi-dimensional visualization module. The cross-platform adaptive architecture module carries out fragmentation preprocessing on collected data through a uniform interface abstraction layer, a dynamic adaptation layer, a cross-platform rendering engine and a platform difference compensation mechanism and the real-time data synchronization and verification module, and the transmission efficiency is optimized through multi-link transmission and dynamic path selection. And the timeliness and accuracy of data synchronization are ensured in combination with block chain hash check and conflict detection. According to the system, through cross-platform adaptation, efficient data synchronization and intelligent visual analysis, the efficiency and scientificity of natural resource asset checking management are remarkably improved.

The invention discloses a method and system for packaging a webpage embedded sign language translation JS library, and belongs to the field of electrical digital data processing.The method comprises the steps that by constructing an ECS core architecture with perception feedback, a bidirectional data flow between modules is established to achieve dynamic collaboration of rendering and grammar; predictive streaming resource management is adopted, and resources are intelligently preloaded based on gesture sequence contexts; multi-instance physical isolation is realized by utilizing a sandboxed container and a ShadowDOM (Document Object Model); implementing multi-level adaptive rendering, and dynamically switching a rendering path and a frame rate according to equipment performance; and finally, shielding the bottom layer difference through cross-end unified rendering of the abstraction layer. According to the method, the problems of animation and semantic dislocation, resource loading conflict and poor equipment compatibility in a multi-user concurrent scene are effectively solved, and high-fidelity, low-delay and cross-end consistent experience of sign language translation animation is realized.

The invention relates to the technical field of automatic data processing of integrated circuit design, in particular to an automatic parasitic parameter result analysis system based on an FUZZ test. The fuzzy test engine module adopts syntax tree variation to generate a test case library comprising illegal characters, extreme values and logic loops, the exception response module captures illegal operations in an analysis and simulation stage through the probe detection unit, and the correction decision unit executes data restoration or generates an exception report. And the incremental updating unit synchronizes the abnormal features to a model self-optimization module rule base. And the dynamic adaptation module converts the multi-process parasitic parameters into a standardized simulation model through the process parameter abstraction layer. All the modules form a closed-loop system for test case generation, anomaly detection and repair and rule dynamic optimization, automatic processing of an unexpected input scene is achieved, and robustness and multi-process compatibility of parasitic parameter analysis are improved.

The invention relates to a template-based AADL platform irrelevant code generation method, and belongs to the field of automatic code generation. According to the method, an analyzer reads an AADL model and extracts model elements described in the AADL model, the model elements comprise system architecture, component information, inter-component connection and related attributes, and necessary data support is provided for subsequent mapping steps; defining a mapping relation between the AADL model and the platform-independent abstraction layer according to structures and requirements of the AADL model and the platform-independent abstraction layer; the method specifically comprises the steps that component types, connection relations and related attributes involved in the AADL model are matched with corresponding elements in an abstraction layer, so that it is ensured that the abstraction layer can completely describe the function and structure of the AADL model; according to the mapping rule formulated in the second step, the analyzed AADL model elements are converted into corresponding elements in the abstraction layer one by one, and abstraction layer template codes irrelevant to a platform are generated. According to the invention, the expansibility and flexibility of code generation are improved.

The invention provides a server delivery and operation and maintenance automation system oriented to multi-platform collaboration, and aims to solve the problems of split delivery process, lagging expansion response, high operation and maintenance complexity and the like in a heterogeneous infrastructure environment. According to the system, by constructing a multi-platform adaptive abstraction layer, bottom layer differences of private cloud, job scheduling platforms and the like are shielded, and unified modeling and standardization of delivery and operation and maintenance processes are achieved; on the basis, key links such as installation file generation, expansion file generation, configuration injection and deployment verification are integrated, an end-to-end one-key delivery assembly line is built, and rapid and consistent deployment of a cross-platform environment is supported; meanwhile, an automatic operation and maintenance closed loop is constructed, and operation and maintenance operations such as health examination, fault warning, configuration repair, service restart, node replacement and elastic capacity expansion and contraction are automatically executed by collecting operation states, logs and performance indexes of the server and the service in real time and combining a preset strategy or an intelligent analysis model. By means of the method, efficient delivery and intelligent capacity expansion of server resources are achieved, and the autonomous operation and maintenance capacity, the high availability and the service continuity guarantee level of the system in a complex multi-platform mixed deployment scene are remarkably improved.