201 results about "Dataflow" patented technology

The invention discloses an unmanned aerial vehicle flight control system vulnerability detection method based on data flow analysis and LLM, and belongs to the technical field of intelligent software testing. Comprising the following steps: extracting a code function module associated with user operation in an unmanned aerial vehicle flight control system through a data flow analysis method, and establishing an operation-code mapping relation library; generating a structured natural language semantic description for each function module code by adopting a large language model LLM, and forming a multi-dimensional semantic feature vector; based on correlation analysis of multi-module semantic features, a combined test scene is constructed, and a natural language test case is generated; the natural language test case is converted into an executable test code through reverse semantic mapping, and coding reconstruction of test logic is completed; and executing a test code and capturing a runtime log in an unmanned aerial vehicle simulation environment, and performing vulnerability feature extraction and root cause positioning by using a large language model. According to the method, the efficiency is improved, and meanwhile, the deep coverage test of a complex interaction scene is supported.

The invention discloses a variation test method and device based on an intelligent automation script. The method comprises the following steps: extracting a statement execution sequence and a branch dependency relationship in an original source code by using a context-aware syntax tree, and generating a control flow diagram and a data flow diagram; performing code semantic analysis on the multi-dimensional code features by using a large language model, identifying potential defect types and variation rules, predicting the test efficiency of variants in combination with a deep Q learning model, and generating to-be-processed variants; performing grammar check and equivalence analysis on the to-be-processed variants to obtain effective variants, and testing the effective variants; variation test indexes are calculated, code weak points are positioned, and test blind areas are identified according to survival variants, so that a visual analysis report is generated; and iteratively generating a new test case according to the current survival variant and the visual analysis report, and testing the current survival variant by using the new test case. According to the invention, the variation test efficiency can be improved.

The invention discloses a self-adaptive teaching strategy adjustment method based on sentiment analysis and a computer device. The method comprises the following steps: obtaining a target emotion feature data stream according to multi-modal student emotion data, and then extracting visual deep features, audio deep features and physiological deep features from the target emotion feature data stream; processing the visual attention weight, the audio attention weight, the physiological attention weight, the visual deep feature, the audio deep feature and the physiological deep feature according to a preset fusion strategy to obtain a multi-dimensional fusion feature, and analyzing the feature to obtain basic emotion recognition information of the individual student; analyzing the basic emotion recognition information through a built personalized emotion model of each student individual to obtain corresponding emotion state evaluation information; and on the basis of the constructed teaching knowledge graph and the emotional state evaluation information, generating a personalized teaching adjustment strategy so as to dynamically adjust an actual teaching scheme. According to the method, dynamic adjustment can be realized, and personalized teaching requirements are met.

The invention discloses a code reconstruction method, system and device based on function recognition and a medium, and the method specifically comprises the steps: scanning a project file topological structure, and constructing a knowledge graph based on a code import relation and a technology stack feature; performing context enhancement by analyzing the code function description and combining with the knowledge graph to generate a code modification instruction set; based on the code modification instruction set, in combination with data stream sensitivity marking and performance portrait analysis, evaluating the target code to obtain an evaluation result; based on an evaluation result, matching a historical optimal practice mode through a graph neural network, and generating an optimization scheme which retains an original design style; by comparing abstract syntax tree differences between original codes and verified optimization schemes, only functional logic nodes are replaced, and legal code style features are reserved. According to the method, the efficiency and quality of code reconstruction and optimization are improved, and a more convenient, efficient and intelligent code processing tool is provided for software developers.

The invention relates to an automatic code auditing method and device, computer equipment and a storage medium. The automatic code auditing method comprises the steps of obtaining a grammar structure, a control flow and a data flow of a to-be-audited code; constructing a context graph of the to-be-audited code according to the grammatical structure, the control flow and the data flow of the to-be-audited code; 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, a large model reasoning method based on semantic understanding and weights of the methods; and identifying one or more code vulnerabilities, the vulnerability type of each code vulnerability and the confidence coefficient according to the context graph of the to-be-audited code and the multi-modal collaborative vulnerability detection method. According to the method, the audit codes of various vulnerability types can be processed while the code audit efficiency can be improved.

The invention provides a syntactic structure tree-based code change influence intelligent analysis method and system, and the method comprises the steps: extracting a control flow, a data flow and a call flow according to an AST generated through the analysis of an original code and a changed code, and constructing a static dependency graph SDG containing a multi-dimensional dependency relationship; the method comprises the steps that a base layer is used for comparing AST differences before and after code change and generating grammatical structure change feature vectors; the service layer matches a corresponding service label for the changed AST node by using the service domain template; the change labeling layer constructs a cross-version dependency chain for the change AST nodes and generates historical feature vectors; and calculating a risk value of changing the AST node by utilizing a feature calculation result and combining with a multi-dimensional influence diffusion algorithm, and automatically generating a decision suggestion. According to the method, multiple dimensions of a control flow, a data flow and a call flow are deepened, risk prediction is optimized by utilizing natural language processing and historical change data, and the influence brought by code change is assisted to be better understood and managed.

The invention provides a multi-rule static detection and large-model dynamic repair method for code defects. The method comprises the steps that code defect types are summarized and abstracted into a unified defect mode rule set; constructing an abstract syntax tree, a control flow diagram and a data flow diagram of the source code; performing static analysis based on a multi-rule engine to identify potential defects; inputting the defect context into a large language model to generate a repair suggestion; performing semantic consistency verification and integration on the repair suggestions; and executing automatic testing and secondary static analysis to verify a repair result. According to the method, the traditional static analysis technology and the modern large language model capability are combined, accurate recognition and intelligent repair of code defects are achieved, a closed-loop defect processing flow is formed, the defect detection accuracy is improved, intelligent repair is achieved, closed-loop verification is formed, the development efficiency is improved, and man-machine cooperation is supported.

The invention relates to the field of artificial intelligence, in particular to an AI accelerator construction method and a related device. The method comprises the following steps: importing machine learning models of different AI frames through a multi-frame adaptation interface, and converting the machine learning models into linear algebraic dialect representation by adopting an MMIR dialect conversion chain; through target-independent optimization processing, core calculation semantics in linear algebraic dialects are reserved, and linear algebraic dialect representation is reconstructed into optimization intermediate representation with an efficient data flow structure; identifying target hardware characteristics of the target hardware; adaptively executing hardware optimization processing matched with target hardware characteristics on the optimization intermediate representation; and converting the target intermediate representation into a hardware operation unit executable by the target hardware, loading the hardware operation unit into the target hardware to obtain an AI accelerator, thereby running the hardware operation unit on the target hardware through the AI accelerator, scheduling corresponding computing resources in the target hardware, and completing hardware acceleration of the machine learning model.

The invention provides a single instruction stream multi-fiber programming model and a construction method thereof. The method comprises the following steps: constructing a task, thread and fiber three-level concurrent unit; different code logic units are configured for each task, and one task is mapped to a plurality of threads; mapping the threads and processor cores one by one, and mapping one thread to a plurality of threads; the threads within the thread are caused to share all registers on the processor core. According to the single-instruction-stream multi-fiber-thread programming model and the construction method thereof, the programming model is constructed based on the fiber threads, concurrent execution of asynchronous data streams is achieved, the resource utilization rate is effectively increased, and redundant calculation is eliminated.

The invention relates to the technical field of chemical reaction experiment simulation, and discloses an AI-based chemical reaction experiment simulation system and method, the system comprises a reaction mechanism model and a dynamic simulation model, the reaction mechanism model carries out integration processing on experiment initial data, and the dynamic simulation model carries out dynamic simulation on the experiment initial data; and the dynamic disturbance suppression layer performs dynamic disturbance suppression on the real-time reaction data flow, inputs a path generation layer simulation reaction track and generates a state instruction. The path generation layer comprises a data preprocessing module and a dynamic path generation module, and reaction key node recognition, compensation field data generation and state instruction optimization are achieved through energy level association processing, dynamic matching modeling, multi-dimensional parameter fusion and the like. According to the method, multi-dimensional data integration, dynamic disturbance processing and trajectory simulation precision are improved, accurate regulation and control and high-risk early warning of the reaction state are realized, the experiment cost and risk are reduced, and the method is suitable for reaction analogue simulation in chemical research and industrial production.

The invention provides a virtual classroom interaction method and system based on knowledge representation and reasoning, and the method comprises the steps: obtaining a student interaction data flow in a power system training virtual classroom, carrying out the knowledge granularity decomposition of the student interaction data flow, and generating a knowledge tuple set containing concept nodes and relation edges, and inputting the knowledge tuple set into an inference engine to execute multi-dimensional inference processing, generating a student knowledge mastering state vector and a learning demand prediction vector, executing interaction content generation processing based on the knowledge mastering state vector and the learning demand prediction vector, generating classroom interaction response data with adaptability, and sending the classroom interaction response data to the student. And feeding back the classroom interaction response data to a virtual classroom interaction interface to form a dynamic interaction process of the student learning state and classroom feedback. According to the invention, the pertinence and practicability of virtual classroom interaction are effectively enhanced, so that the learning effect of students in power system training is improved.

The invention provides an automatic test generation platform and method for an operating system kernel derived function. The method comprises the following steps: (1) intelligent symbol extraction and analysis; (2) generating an LLM-driven test code; (3) performing automatic compiling and virtualization testing; and (4) intelligent error diagnosis and repair. According to the method, the test coverage degree is greatly improved, through the multi-level symbol context extraction technology, single function information is extracted, the dependency relationship of related functions in the same element is analyzed, a function call graph and data flow analysis are constructed, rich context information is provided for LLM, and the problem that traditional manual test case writing is incomplete in coverage is solved.

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 provides a binary translation system applied to an X86 program, which is used for translating a source program following X86 semantics into a target program following other semantics, and comprises a data acquisition module used for acquiring the source program; the disassembling module is used for dividing a source program into a plurality of basic blocks and analyzing subsequent basic blocks corresponding to each basic block; the backward data flow analysis module is used for sequentially analyzing each instruction in each basic block from back to front so as to obtain a target definition set and a target subsequent use set corresponding to each instruction in the source program; and the translation module is used for eliminating redundant instructions of high-order zero clearing or high-order retention of the general register generated in translation. According to the technical scheme, the register state of the general register corresponding to each instruction of the source program is analyzed through the backward data flow analysis module so as to eliminate redundant instructions generated in the translation process.

The invention discloses a multi-language code generation method based on self-supervised pre-training, which comprises the following steps: acquiring and cleaning multi-language code data to form a training corpus; the method comprises the following steps: representing code data as an abstract syntax tree, extracting a control flow diagram and a data flow diagram of the code data, and obtaining unified semantic representation through combination of a diagram encoder and a sequence encoder; designing a self-supervised pre-training task, and pre-training the semantic representation based on the training corpus; constructing a multi-language pre-training model based on the structure-improved recurrent neural tensor network and the multi-language embedding matrix; when a user inputs a natural language, generating a target language code by using the multi-language pre-training model; and target language code correction is carried out through conventional function testing and grammar checking. According to the method, multi-channel recursive combination and a hierarchical recursive expansion mechanism are combined with self-supervised pre-training, so that accurate generation and performability improvement of cross-language codes are realized.

A system includes processor(s) configured to: receive natural language text describing software requirements for software program; analyze natural language text describing software requirements to identify relationships between different software requirements at least in part by: analyzing how data flows between different software requirements; analyzing how different software requirements influence path and decision points to achieve functionality identified by software requirements; and identifying dependencies between different software requirements; establish sequence for different software requirements based on relationships identified between different software requirements; group plurality of different software requirements together into logical group(s) of software requirements based on sequence for different software requirements and relationships between different software requirements; generate test cases based on logical group(s) of software requirements; execute software program using test cases; and analyze results of execution of software program using test cases to identify any defects in software program.

The invention relates to the field of nonlinear multi-agent control, in particular to an intelligent design teaching resource recommendation system based on AI and a knowledge graph, which comprises a student data analysis module, a knowledge graph construction module, a resource recommendation module, an intelligent cooperation module, a teacher aid decision module and an AI recommendation algorithm. An overall data flow formed by the student data analysis module, the knowledge graph construction module, the resource recommendation module, the intelligent cooperation module, the teacher aid decision-making module and the AI recommendation algorithm is composed of an input layer, a processing layer and an output layer, and knowledge graphs of related majors are constructed according to the knowledge graph construction module; the student learning data is collected by the student data analysis module, the collected student data is transmitted to the knowledge graph construction module to form associated knowledge points, the associated knowledge points flow to the resource recommendation module to form a recommendation result, and then the intelligent cooperation module provides cooperation support by using the recommendation result. And the teacher auxiliary decision-making module provides teaching optimization by using the recommendation result.

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

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.

A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning (ML) pipeline, is represented in a data flow graph (DFG). The represented pipeline is subject to annotations, with the annotations including pipeline nodes and object references. The pre-processed pipeline is subject to execution or processing with the annotated object references capturing object lineage. Output from the executed pipeline is constructed and a corresponding control signal is formatted to dynamically and selectively control an operatively coupled physical hardware device or software.

A method, apparatus, and system are disclosed. The method includes constructing a static single assignment (SSA) form and a static single use (SSU) form for a program; setting a single-use disqualifying property locally for each SSU version, and propagating the single-use disqualifying property both forward and backward on an SSU graph uniquely formed from the constructed SSU form so it becomes a global property; transferring results from the SSU form to the SSA form to set a single-use property locally for each SSA version based on an occurrence of any use being associated with a disqualifying SSU version; performing data flow analysis on an SSA graph uniquely formed from the constructed SSA form so the single-use property becomes a global property to identify one or more definitions as dynamic single-use for variables in the program; and generating computer-readable instructions for executing the program based on the one or more definitions identified as dynamic single use for the variables in the program, wherein the one or more definitions identified as dynamic single-use has a defined value used exactly one time during execution of the program.

The invention relates to the technical field of gas power generation, and particularly discloses a gas generator set performance optimization method and system based on deep learning, and the method comprises the steps: collecting the initial state data flow of a set in real time after receiving a starting instruction, and introducing a deep learning algorithm to carry out the deep feature extraction of the initial state data flow of the set, according to the method, a nonlinear coupling effect among multi-dimensional state parameters in the transient starting process of a gas generator set is captured, gas starting initial condition characterization is constructed, and then the current gas starting initial condition characterization is inquired and matched with a historical successful starting case library, so that the successful starting condition characterization of the gas generator set is obtained. And the control strategy of the extracted historical case most similar to the current condition is used as a starting strategy, and the starting process of the gas generator set is guided. According to the method, the state characteristics of the gas generator set in the initial starting stage can be effectively revealed, and the new starting process is guided by fully utilizing the historical accumulated success experience, so that the intelligent control of the starting process is realized, and the starting success rate is improved.

The invention provides an instruction-level parallel scheduling method and device in a deep learning compiler, and the method comprises the steps: decomposing a calculation graph of a deep learning task, decomposing a task represented by each calculation graph node on the calculation graph into a hardware calculation instruction according to a current task decomposition scheme, and obtaining an instruction set of the calculation graph; according to the data flow dependency relationship between the nodes on the calculation graph, obtaining a dependency relationship graph between the instructions in the instruction set; dividing the hardware calculation instructions in the instruction set into each sub-core in the processor; according to the dependency graph, determining a directed acyclic graph containing the dependency among the hardware calculation instructions on the sub-cores in the processor, and according to an emission model of the processor and the reciprocal throughput rate of the hardware calculation instructions, determining the execution sequence of the hardware calculation instructions divided to the sub-cores on the sub-cores, and the processor executes the instruction scheduling to obtain an execution result of the deep learning task.

The invention relates to the technical field of software development, and discloses a software development system and a software development method. The software development method comprises the following steps: constructing a multi-dimensional technical debt quantitative model, and calculating a technical debt score based on factors such as code complexity, change frequency and defect association degree; a code semantic multi-level representation model is constructed, and multi-level representation of codes is constructed through combined analysis of an abstract syntax tree, a data flow diagram and a control flow diagram; a self-supervised learning model is applied to train code representation, and development intentions and business concepts contained in codes are recognized; generating a context-dependent reconstruction suggestion; and optimizing the reconstruction path planning. Through objective quantification of the technical debt and deep understanding of code semantics, the technical problems that in the prior art, technical debt management is difficult to quantify and reconstruction decision-making lacks scientific basis are solved, code maintenance cost is remarkably reduced, and development efficiency is greatly improved.

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 method for allocating resources for a machine learning model is disclosed. A machine learning model to be executed on a special purpose machine learning model processor is received. A computational data graph is generated from the machine learning model. The computational dataflow graph represents the machine learning model which includes nodes, connector directed edges, and parameter directed edges. The operations of the computational dataflow graph is scheduled and then compiled using a deterministic instruction set architecture that specifies functionality of a special purpose machine learning model processor. An amount of resources required to execute the computational dataflow graph is determined. Resources are allocated based on the determined amounts of resources required to execute the machine learning model represented by the computational dataflow graph.

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 provides a speed reducer digital twinning model construction method and system for processing precision optimization, and relates to the technical field of digital twinning, and the method comprises the steps: predicting a plurality of optimization thinking paths which may be generated when a user carries out the processing precision optimization through employing a digital twinning body of a speed reducer; building an active auxiliary module of each optimization thinking path; wherein when the operation behavior of the user is matched to any optimization thinking path, the corresponding active auxiliary module is activated in real time, and an optimization decision data stream is generated through a simulation engine of the digital twin; and embedding the active auxiliary module of each optimized thinking path into an interactive logic layer of a digital twinborn body, and constructing a speed reducer digital twinborn model. According to the invention, dynamic and all-around active assistance of the user processing precision optimization thinking path is realized. The processing precision optimization efficiency is obviously improved, and the humanization and intelligence level of the digital twin model of the speed reducer is greatly enhanced at the same time.

Type definitions of user-defined types in application program code for which definitions are absent (“unknown types”) are inferred. A static analyzer implements two passes of a fixed-point type inference algorithm. Each pass encompasses a plurality of traversals of the application's control flow to build inferred definitions of unknown types until the inferred definitions are maximally built. To build an inferred definition, based on inferring a variable is an unknown type, the static analyzer infers member variables / functions of the unknown type based on contextual information associated with the variable. Type information of unknown types is propagated along control flow paths. After the first pass terminates, unknown types can be assigned known types based on matching of inferred definitions. Inferred definitions of remaining unknown types are incorporated into the application program code. A second pass of type inferencing and data flow analysis are then performed with the inferred definitions incorporated therein.

A system and method is provided for designing and optimizing hardware accelerators for neural networks. During a pre-design phase, rules are extracted from compilation patterns that describe conversion between neural network operators, coarse-grained operators, and fine-grained dataflow. A fast mapper for converting neural network models to coarse-grained operator descriptions and a dataflow mapper are generated. A coarse-grained design phase employs an architecture optimizer to generate plural provisional hardware accelerator designs The coarse-grained operator descriptions are simulated using a coarse-grained simulator to obtain performance metrics of each provisional accelerator design. A fine-grained design phase employs a dataflow mapper and fine-grained simulator to finalize provisional hardware accelerator designs. A hardware accelerator is generated from a finalized hardware accelerator design and a corresponding software toolchain is created including a compiler and software development kit (SDK) for programming, debugging, and deploying the hardware accelerator design.