90 results about "Dataflow" patented technology

A test question recommendation method based on large language model adaptive multi-level evaluation constructs a multi-level architecture including semantic consistency verification, fine-grained fact alignment and dynamic cognitive evaluation, and comprises the following steps: constructing an initialized data stream and executing dynamic random sampling; calculating discrete semantic entropy by using NLI bidirectional implication clustering so as to quantify and eliminate illusion content with high uncertainty; under the RAG framework, the test questions are deconstructed into atomic statements, and a fact deviation is corrected by calculating a retrieval relevance vector and a logical implication consistency score; analyzing and screening low-quality texts based on multidimensional language features of syntax and logic; vector fusion is carried out on the generated intention and the cognitive portrait of the student, and a dynamic evaluation index system adaptive to a specific teaching scene is constructed in real time by utilizing context learning. The problems of accuracy and adaptability of automatic question setting are effectively solved, and intelligent closed-loop control from test question generation to cognitive alignment is realized.

The invention relates to the technical field of software security, and particularly discloses a real-time vulnerability detection and repair system of an AI code generation model. The system comprises a central coordinator, a real-time monitoring module, a deep analysis module, an intelligent repair module and a feedback learning module. The real-time monitoring module rapidly scans and triggers the generated codes; the deep analysis module performs interprocess stain analysis on the trigger code to generate a diagnosis report containing a precise data flow path; the intelligent repair module generates a repair patch under the function equivalence constraint according to the diagnosis report; and the feedback learning module collects the processing feedback of the developer on the repair patch, and drives the continuous optimization of each module of the system. According to the method, through layered progressive analysis and repair generation, the repair accuracy and the function safety are ensured; through a feedback learning mechanism, the self-adaptive evolutionary ability of the system is realized, the problems of unavailability of repair suggestions and static stiffness of the system in the prior art are solved, and real-time protection is provided for AI auxiliary programming.

A computer-implemented system analyzes program and data flows in a software system comprising code written in multiple programming languages using a generative large language model (LLM) directed by programming-language-specific prompts. The LLM identifies functional components within the code, generating labeled graph nodes that include a node type, a node name, and dependency information. A graph construction computer system processes the labeled graph nodes to generate a directed graph, where nodes represent functional components and directed edges represent dependencies. The system stores the graph in a database and provides a web-based interface for visualization, allowing users to explore, query, and analyze program and data flows across the software system. The system enables automated, language-agnostic dependency mapping, facilitating software analysis, debugging, and modernization.

The invention relates to the technical field of medical information parallel computing, in particular to a multi-thread parallel computing method and system for Brucella spondylitis diagnostic parameters, and the method comprises the following steps: a diagnostic task dependency graph construction step: constructing a directed acyclic graph based on the computing dependency relationship of the diagnostic parameters and determining the task priority; a self-adaptive thread scheduling step: adopting a work stealing strategy and a lock-free concurrent queue to realize dynamic load balancing; a vectorization parallel computing step: performing batch computing on the diagnosis parameters by using a single-instruction multi-data-stream instruction; and a feedback driving optimization step: dynamically adjusting a scheduling strategy according to performance indexes to form closed-loop optimization, and deeply coupling the four core modules to realize synergistic interaction.

The invention discloses a multi-disciplinary simulation task intelligent planning method based on a knowledge graph, and relates to the technical field of intelligent scheduling, and the method comprises the steps: constructing a preliminary knowledge graph comprising multi-disciplinary task description, resource limitation and execution dependence, analyzing historical task data through a pre-trained deep learning model, and obtaining a multi-disciplinary simulation task planning model; dynamically adjusting the preliminary knowledge graph through adaptive learning, and outputting an adaptive knowledge graph; carrying out resource redistribution optimization according to the updated task scheduling strategy, continuing to execute the task, starting monitoring, and collecting task progress and resource data as a new feedback data stream; the resource reallocation and the new feedback data stream are fed back to the adaptive knowledge graph, and multidisciplinary task description, resource limitation and execution dependency are updated through reasoning to generate a final optimization task execution sequence; the synchronous suppression of waiting time and resource contention is realized, the average utilization rate of resources and the time sequence stability are improved, and the adaptive adjustment capability of operation disturbance is formed.

The application relates to an automatic code auditing method and device, computer equipment and a storage medium, comprising the following steps: obtaining the syntax structure, control flow and data flow of a code to be audited; constructing a context graph of the code to be audited according to the syntax structure, control flow and data flow of the code to be audited; obtaining a multi-modal collaborative vulnerability detection method, wherein the multi-modal collaborative vulnerability detection method comprises a static analysis method based on rule matching, a symbolic execution method based on a code path and a large model reasoning method based on semantic understanding, and the weights of the methods; and identifying one or more code vulnerabilities, the vulnerability types of the code vulnerabilities and the confidence according to the context graph of the code to be audited and the multi-modal collaborative vulnerability detection method. The method can improve the code auditing efficiency and process auditing codes of various vulnerability types.

The invention provides an evidence-driven large language model MISRA C rule review method and system for aviation safety. According to the method, formalized feature extraction is carried out from four aspects of grammar structure features, semantic structure features, logic analysis features and preprocessing behavior features for target codes according to various rules of MISRA C forcing class rules, and diagnosis information is diagnosed in combination with an industrial-grade compiler; generating a structured evidence set comprising abstract syntax tree node statistical features, code context structure features, control flow graph features, function internal data flow analysis features, symbol and type table features and macro definition analysis features; and based on the structured evidence set and the large language model, executing evidence tracing, rule matching and logical reasoning according to a preset thinking chain process, and outputting a structured judgment result containing illegal rule numbers, evidence description and code positions. According to the method, the false alarm rate can be remarkably reduced while the high rule coverage rate and the detection accuracy are kept.

Some embodiments are directed to systems and methods that dynamically generate user interfaces according to a user's task, context, or current environment. In one aspect, a computer system includes one or more processors and memory. The computer system obtains a stream of sensor data from one or more sensors. The computer system generates a context attribute based on the stream of sensor data, the context attribute characterizing a condition of a physical environment or a user, The computer system, based on the context attribute, determines a layout configuration of a user interface to be presented to the user. The computer system dynamically renders the user interface for the user based on the layout configuration and displays the user interface on the display of the user interface.

Various embodiments relate generally to data science and data analysis, computer software and systems, and computing architectures and data models configured to facilitate management and performance of enterprise functions, and, more specifically, to an enterprise computing and data processing platform configured to implement applications and computerized tools configured to facilitate implementation of an enterprise data model to generate and manage component data objects representing enterprise tasks and processes, as well as portions thereof, any of which may be used to create and modify an enterprise data flow or a process flow, for example, using an enterprise computerized tool. In some examples, a method includes activating one or more applications as computerized tools to access user account and enterprise function data, identifying permissions, and filtering access to logical components as a function of, for example, persona application data generated using a persona builder application.

The invention discloses a fluid pulsation accelerator architecture for reinforcement learning of a large language model, and belongs to the technical field of artificial intelligence hardware acceleration. The system aims at solving the problems that when a current universal processor executes an RLHF working load, an instruction driving normal form is not matched with data flow calculation, fixed parallel granularity cannot adapt to a dynamic load, and the efficiency is low due to the fact that a framework does not sense data statistical characteristics. The core of the system is a liquid systolic array calculation fabric capable of being dynamically reconstructed, execution is triggered through data flow, and kernel boundaries and instruction overhead are eliminated. According to the system, a global lookup table subsystem is fused, and nonlinear calculation is optimized through merging query and parallel lookup by using data distribution prior; and a dynamic scheduling unit is configured, and elastic parallelism is realized by adopting a resource allocation algorithm supporting work stealing. According to the method, the throughput and the energy efficiency of RLHF training and a large language model reasoning stage can be remarkably improved, and a new design normal form is provided for a next-generation AI special computing architecture.

The application belongs to the technical field of computer architecture design, and discloses a unified accelerator design method and accelerator for diversified graph computing applications, which comprises the following steps: by comparing and analyzing the field-specific execution model of classical graph computing applications, observing the common data flow abstraction of different graph computing applications, establishing a graph computing execution model based on a sparse matrix, and abstracting the common part of the graph computing algorithm; based on the sparse matrix execution model, by analyzing the common hardware features of classical graph computing applications such as graph traversal, graph mining and graph learning, a field general chip architecture for graph computing is constructed, and general and efficient support for complex real-world scenarios is realized; by proposing a graph computing instruction set for a "scalar-vector-graph" heterogeneous core, the programmability of the graph computing chip is improved.

The invention relates to a code error feedback method combining static analysis and dynamic analysis, and mainly solves the problems of insufficient error feedback and low efficiency in an Online Judge system: reconstructing codes of correct and error versions, including alignment and renaming of variables, and rearranging statements on the premise of not influencing semantics; static and dynamic multi-level alignment is carried out, in the static alignment, an editing script with the minimum cost is calculated through a heuristic editing distance algorithm, an alignment block and a difference list are obtained, in the dynamic alignment, compiling execution is carried out on source codes of two versions, test cases which do not pass operation are dynamically printed through gdb, and execution track information of the test cases is dynamically printed through gdb; error positioning is achieved, data flow backtracking analysis and scoring are carried out on error positions, and error feedback is generated. The method has the characteristics of high execution efficiency, high accuracy and easier understanding of generated feedback, automatic feedback of error codes can be realized, and the learning efficiency of students in programming tasks is improved.

The invention discloses an online distributed parameter optimization method and system for intelligent modeling of a complex system, and relates to the technical field of electric digital data processing. The method comprises the following steps that: an end side adopts an adaptive recursive ridge regression algorithm to recursively update the weight of an output layer of a feedforward neural network in real time so as to quickly track the dynamic state of a data stream; when the edge side monitors that the performance continuously declines, node pruning based on the optimal data set is triggered, and node growth is selected and randomly configured; when concept drift is detected, the edge side uploads data and a model state, the cloud side carries out anti-forgetting deep optimization on the feature extraction module, and optimization parameters are downloaded to cooperate with reconstruction and deployment. The method solves the problems of insufficient dynamic working condition adaptability, low computing resource efficiency and easy knowledge forgetting in the prior art, realizes optimal balance of model precision, adaptive speed and resource consumption, and is especially suitable for online modeling of complex dynamic systems such as intelligent control of high-speed trains.

The system flexibly presents additional questions based on the user's responses. [Solution] The computer system outputs output data to present a first question to the user, sequentially receives first stream data from the user including the answer to the first question, performs a speculative execution process for generating question candidates one or more times while receiving the first stream data, and when the input of the answer to the first question is finished, generates a second question based on the question candidates generated by the speculative execution process for generating the first question candidates, and outputs output data to present the second question to the user. In the speculative execution process for generating question candidates, a prompt is generated to cause a natural language processing program to generate question candidates that take into account the answers included in the received stream data, the prompt is input to the natural language processing program, and the question candidates generated by the natural language processing program are stored in a storage medium.

The invention discloses an industrial control instruction stream anomaly detection method and device based on deep learning, and relates to the technical field of energy industry internet security. The method comprises the following steps: restoring to generate an interactive session with a complete context based on a directionally collected industrial control instruction and an equipment state data stream; performing semantic structured processing on the instruction in the interaction session to generate structured semantic information which can be understood by a machine; taking the interaction session and the corresponding structured semantic information as a training sample, and constructing and training an instruction stream semantic model and an instruction stream execution effect prediction model; processing the real-time industrial control instruction stream, and inputting the processed real-time industrial control instruction stream into the instruction stream semantic model and the instruction stream execution effect prediction model to obtain a prediction result; and judging an anomaly detection result of the real-time industrial control instruction flow according to the output prediction result and an anomaly judgment rule.

A method, system, computer program product, application-specific integrated circuit, and cryptographic module are provided in which execution divides an SHA-2 state update into three blocks. A set of operations associated with the three blocks is performed in a pipelined manner, where each block feeds back to itself for computation.

The embodiment of the invention discloses an instruction processing method and system, electronic equipment and a storage medium, relates to the technical field of computer processor design, and can save write port resources of a register file. The method comprises the following steps of: analyzing a read-write dependency relationship of each instruction in a current instruction group on a flag bit register so as to establish a flag bit data stream dependency chain; according to the read-write dependency relationship and a preset elimination rule, judging whether each instruction indicating to be written into the flag bit register in the current instruction group needs to be written into the flag bit register or not; and if the judgment result shows that the instructions indicating to be written into the flag bit register in the current instruction group do not need to be written into the flag bit register, canceling the operation of writing the instructions indicating not to be written into the flag bit register back to the flag bit register. The method and the device are suitable for eliminating invalid flag bit registers.

The invention provides a data processing method, a graphics processor, equipment, a storage medium and a program product. The method comprises the steps that corresponding primitive group data are processed through a geometric processing pipeline, a primitive group processing result is obtained, and the primitive group data are part of data in a geometric data stream; determining coverage block information corresponding to the primitive group processing result through a management module, and applying for a primitive storage space for the primitive group processing result based on the coverage block information; the coverage block information is used for representing the information of the primitive coverage blocks in the primitive group processing result; and partitioning the primitive group processing result through a partitioning device, and storing the partitioned primitive group processing result in a primitive storage space corresponding to the primitive group processing result. By adopting the technical scheme provided by the invention, unnecessary waste of storage resources can be avoided.

The invention provides a method and a system for performing bidirectional data type derivation in graphical programming, and relates to the technical field of graphical programming, and the method comprises the following steps: S1, analyzing graphical programming data into a graph structure and a tree structure; s2, data type derivation from outside to inside is executed, specifically, on the basis of the tree structure, all graph structures are traversed according to the depth priority sequence, internal nodes of the currently traversed graph structure are traversed according to the topological sequence of a directed acyclic graph, and the data type of downstream nodes is derived according to the data type of upstream nodes in the data flow direction; s3, data type derivation from inside to outside is executed, specifically, on the basis of the tree structure, all graph structures are traversed according to a subsequent sequence, for the currently traversed graph structure, the data type of an output boundary of the currently traversed graph structure is determined according to the data type of internal nodes of the currently traversed graph structure, and the data type is reversely propagated to a corresponding node in a parent graph of the currently traversed graph structure; and S4, automatically generating a type conversion code according to a predefined data type processing rule.

A superlattice corresponding to static application security testing (SAST) of source code specifies lattices respectively corresponding to static analyses performable on the source code. Each lattice is specified by all possible lattice elements of the lattice and an operator indicating how two lattice elements of the lattice are combined during the static analysis to which the lattice corresponds. A lattice product of the lattices specified by the superlattice is generated based on all the possible lattice elements of each lattice and the operator of each lattice indicating how two lattice elements are combined. Generalized dataflow analysis executable code is executed on the source code, using the lattice product, to perform the SAST of the source code, including the static analyses respectively corresponding to the lattices.

The invention discloses an AI-combined front-end js code intelligent analysis method and system based on penetration testing, belongs to the technical field of artificial intelligence and penetration testing, and aims to solve the technical problem of how to realize AI-driven front-end penetration testing, improve the penetration testing efficiency, coverage and depth and improve the reliability of the penetration testing. According to the technical scheme, the method comprises the steps of intelligent attack surface surveying and mapping, wherein the structure and the function of a target application are fully perceived through an intelligent crawler, static code analysis and AST; wherein the intelligent crawler is a data acquisition tool combined with an artificial intelligence technology; according to static code analysis, the structure of the intelligent crawler is further analyzed, front-end JavaScript codes are analyzed into AST, the AST is structured representation of the codes, the AI understands the logic structure of the codes through the AST, and data flow tracking is carried out more accurately. Performing reverse analysis on the intelligent front-end code; and performing automatic vulnerability identification and attack simulation.

The invention relates to a method for testing a computer program with a graphical user interface, wherein a test environment is provided for testing the system behavior of the computer program under test by operating at least one control element of the graphical user interface, wherein, in addition to the first image data stream for the display device, a second image data stream is provided to the test environment via the display interface of the data processing device, wherein, by means of optical character recognition, control elements operable by an input device in the graphical user interface provided via the second image data stream are recognized as an object of the graphical user interface.wherein at least the operable controls recognized in the graphical user interface are entered as input data into a large language model connected to the test environment via a data interface, with the stipulation that an action option for operating at least one control element of the graphical user interface is obtained at the data interface, wherein an input signal is generated by the test environment depending on the action option provided at the data interface for operating at least one recognized control element, and wherein the generated input signal is provided at the input interface of the data processing system in order to trigger a simulated user input for testing the computer program.

The invention provides a preschool education system and method based on somatosensory interaction. The method comprises the following steps: acquiring skeleton joint point motion data of a target child in a somatosensory interaction process so as to generate a joint motion track; performing phase compensation on the joint motion trail and an angular velocity change sequence in the skeleton joint point motion data to obtain a motion data stream, and performing space-time coupling on the motion data stream to obtain a space-time dependency matrix between motion joint points; building an embedded feature space of the joint action according to the motion data flow, and applying development perception decoupling constraint to the embedded feature space to obtain space decoupling loss of the joint action; and identifying the interaction action of the target child based on the space-time dependency matrix and the space decoupling loss to obtain an interaction action sequence, and generating an interaction control instruction based on the interaction action sequence. By adopting the scheme of the invention, the interaction action in the somatosensory interaction process can be recognized in combination with the child development characteristics so as to generate the interaction control instruction conforming to the child development level.

The application discloses a processing method and system for an embedded operating system, and relates to the technical field of embedding.Aiming at the problems of task isolated management, rigid strategy configuration, lack of dynamic optimization and the like in the prior art, the application realizes optimization through the following steps: collecting task core attributes, associating data flow correlation between tasks with control flow correlation, and constructing a business chain model; dynamically configuring a shared cycle, a sleep depth and a wake-up window based on the model, and generating a strategy mapping table; coordinating and scheduling tasks according to the strategy, accurately waking up necessary hardware, and differentiating and processing timeout scenarios, and immediately sleeping after the completion of task execution; collecting running index deviation, and dynamically adjusting strategy parameters according to the deviation type.The application realizes business chain level overall scheduling, and improves system running efficiency and stability.

The invention discloses an incremental vulnerability detection method and system based on a symbolized function side effect abstract, and the method comprises the steps: carrying out path sensitive sparse value flow analysis on an initial version, generating a change insensitive side effect abstract in combination with pointer analysis, constructing a sparse value flow graph, mining vulnerability paths such as null pointer dereference, memory leak and the like, and obtaining a sparse value flow graph; a two-layer structure vulnerability search abstract is generated and persisted, in a subsequent version, only bottom-up increment pointer analysis is triggered for a modification function, a cross-function memory object is symbolized and abstracted, and SVFG is updated according to abstract change propagation; and carrying out limited traversal type incremental vulnerability search around a newly added or closed data stream edge. According to the method, through a bottom-to-top increment pointer and vulnerability search mechanism based on abstract change propagation, increment detection of vulnerabilities such as null pointer dereference and memory leak in a software iteration process is realized, repeated analysis is remarkably reduced, and rapid identification of newly introduced vulnerabilities is supported.

The invention is applicable to the technical field of games, and provides an intelligent game assisting method based on multi-modal understanding, which comprises the following steps: acquiring a game state data stream and multi-modal input information of a player, analyzing the game state data stream to generate a game state signal, and analyzing the multi-modal input information to generate a player intention signal; constructing a scene dynamic graph based on the game state signal and the player intention signal; retrieving in a game knowledge base based on the scene dynamic graph to generate an enhanced knowledge set; and based on the scene dynamic graph and the enhanced knowledge set, generating a game guidance scheme, and marking an expected game state change after execution for key steps of the game guidance scheme, thereby realizing multi-modal input understanding to situational problem modeling and knowledge retrieval. And then, an end-to-end automatic process of generating an atomization operation instruction and an expected effect is provided, so that smooth intelligent auxiliary experience is provided.

This application provides an automated code repository generation method, electronic device, and program product, relating to the field of software design and development. The method includes: acquiring functional specification information; generating corresponding functional subtrees based on the functional specification information; clustering nodes in the functional subtrees into multiple functional modules based on the cohesion principle, and generating a modular functional diagram; mapping the functional modules in the modular functional diagram to file structures, generating an enhanced file structure diagram; modeling data flows between functional modules and file sequence dependencies within functional modules based on the enhanced file structure diagram to generate a repository planning diagram; and generating code files using a test-driven development process based on the topological order determined by execution dependencies to form a code repository. This method enhances consistency assurance from high-level functional planning to low-level code implementation, improving the modular quality and maintainability of the code repository generation process.

The invention discloses a hardware simulation-based performance test system, a hardware simulator and a test method, and relates to the technical field of chip design. The system comprises a request generation module, a protocol core module, a data stream sending module and a to-be-tested equipment simulation module, the request generation module is used for generating a test request of the data bus system according to the loaded executable file, and the test request is used for testing the data bus system; the protocol core module is used for converting the test request into a test message and converting the test message into a transmission data stream, and the test message conforms to a bus protocol corresponding to the data bus system; and the data stream sending module is used for sending the transmission data stream to the to-be-tested equipment simulation module, and the to-be-tested equipment simulation module is used for simulating equipment using the data bus system. Real physical equipment is simulated through the performance test system based on hardware simulation so as to test the data bus system, and the test accuracy of the data bus system can be improved.