1392 results about "Static analysis" patented technology

The invention discloses a multi-source heterogeneous data fusion system based on an RAG. According to the method, through deep knowledge fusion and a dynamic cognitive evolution mechanism, the decision-making intelligence level in a complex data environment is remarkably improved, and equipment operation parameters, environment indexes and a domain knowledge base are deeply associated to form a panoramic data view with space-time continuity. A generative enhancement mechanism endows original data with a self-evolution characteristic, industry empirical rules and real-time situation awareness are injected while the fidelity of the original characteristic is maintained, so that a decision model can capture micro data fluctuation and follow macroscopic business logic, accurate balance between risk early warning and resource scheduling is realized, and the risk early warning efficiency is improved. The dynamic adaptation characteristic enables the system to autonomously update a knowledge system and optimize a decision path in a complex and changeable industrial environment, and a post response mode of a traditional static analysis model is converted into an intelligent center with prospective pre-judgment and real-time regulation and control capabilities.

The invention discloses a large model enhanced code security detection method. The method comprises the following steps: dynamically associating CVE vulnerability features with code semantic representation through a knowledge graph construction module; extracting vulnerability features from a CVE vulnerability library by utilizing a knowledge graph construction module, and dynamically associating the vulnerability features with code semantics; based on the CodeQL standard, a query language is generated by adopting a large model and input into CodeQL for AST analysis, and code structure features are extracted; the large model generates a query language meeting the CodeQL standard, codes are analyzed through CodeQL, and key structure features are extracted; integrating a static analysis tool chain to carry out multi-dimensional credible verification on model output; integrating an SAST tool and a symbolic execution tool, and verifying the model output from different dimensions; and summarizing and fusing detection results of the enhancement layers, and determining a final detection result by adopting a decision optimization method. According to the method, through technical fusion and dynamic optimization, the problems of rule stiffness, one-sided detection and low result credibility are systematically solved.

The invention discloses an adapter life prediction system based on digital twinning and a dynamic early warning method. The system comprises a multi-source data acquisition module, a digital twinning model construction module, a data coordination module, a life prediction module and a calibration module. According to the method, the adapter full-life-cycle digital twins are constructed, the limitation of one-way static analysis of a traditional life prediction technology is broken through, and dynamic health assessment under multi-dimensional data driving is achieved; a cross-dimension feature fusion and closed-loop calibration mechanism is innovatively proposed, and the industrial problems that multi-source asynchronous data is weak in relevance and sudden abnormal response lags behind are effectively solved; through the synergistic effect of the generative adversarial network and the attention model, the stability and credibility of a prediction result are remarkably improved under a complex working condition; the technology can be adapted to a harsh use environment of an industrial adapter, and quantifiable and traceable decision support is provided for intelligent operation and maintenance of power electronic equipment.

Some embodiments engineer a prompt for submission to a language model, such as a software development large language model. Some embodiments ascertain a relationship between code development information and potential context. Code development information includes static analysis results, project settings, development tool history or status data, and other software development data which augments training data previously embedded in the language model. Some embodiments compute a prompt inclusion score of the potential context, based on at least the relationship, and use the inclusion score to determine whether to include the potential context in the language model prompt. In some scenarios, an embodiment determines where to place the context in the prompt. Scoring is performed by a formula, statistical scoring model, or machine learning scoring model. Some embodiments reduce context inclusion false positives and false negatives that were based on the use of embedding similarity scores alone.

The invention belongs to the technical field of electrical digital data processing and resource allocation, and provides an agent lightweight deployment method and a computing power elastic allocation method, lightweight deployment realizes one-time development of multi-platform deployment by constructing a plug-in container packaging tool and a multi-architecture compiling engine; static analysis is used for stripping redundant dependence of the model, and the model is dynamically cut in combination with edge resources; developing an intelligent resource description language, integrating a control group and a filtering technology to realize container-level resource monitoring, constructing a 12-dimensional dynamic state space fusing a node state, task characteristics and a network environment through computing power elastic distribution, and introducing a time delay, cost and reliability three-dimensional weighted reward model to quantify distribution earnings; an edge cloud cooperative training framework and an edge execution strategy are designed, experience is collected, a cloud end trains a Q network through federal learning, an optimization decision is played back in combination with priority experience, the agent deployment efficiency and the resource utilization rate are improved, and data privacy is guaranteed.

The invention relates to the technical field of computer processing, in particular to a software architecture code generation method and system based on a large language model, and provides the following scheme: obtaining demand information input by a user, analyzing demands by using a natural language processing technology, and extracting functional elements; performing classification processing on the functional elements through a clustering algorithm to generate a functional module group; mapping the function module group to a system architecture layer, and generating an architecture diagram represented by UML (Unified Modeling Language); and generating a corresponding source code according to the UML graph, and optimizing the source code. The optimization strategy comprises static analysis of format problems and potential vulnerabilities in identification codes and performance optimization of complex functions. Through combination of the large language model and the automatic code generation and optimization technology, the software development efficiency is improved, manual intervention is reduced, and the correctness and maintainability of the generated code are ensured.

The invention provides a security data penetration test method. The method comprises the steps of pre-establishing a multi-thread application program race condition vulnerability feature library, performing static analysis on source codes of a target application program, identifying potential race condition vulnerability points, and obtaining a preliminary vulnerability candidate set; for monitored suspected race condition events, abstracting event context information into constraint conditions, exploring an input combination of suspected static conditions by using a constraint solver, and determining a minimum input set of stable triggering race conditions; aiming at memory models and synchronous primitives of different programming languages, obtaining a race condition inverse mode, and extracting universal utilization primitives covering various race condition vulnerabilities; in the penetration test process, static analysis, dynamic monitoring and symbolic execution are applied to mine race condition vulnerabilities of a target application system, the availability of the vulnerabilities is verified, and evaluation of system security is assisted to be completed.

The invention discloses a power transaction market risk dynamic assessment system, which comprises a multi-modal sensing module used for collecting physical equipment operation data, market transaction data and environmental parameters in real time, and performing space-time alignment processing through edge computing nodes to generate a fusion data stream; the self-adaptive evaluation engine is in data connection with the multi-modal sensing module and is used for analyzing multi-dimensional risk association in the fused data stream; the invention belongs to the technical field of electricity markets, and aims to solve the problems that in the prior art, historical data static analysis is relied on, spatial-temporal scales of multi-source data are not uniform, and data cannot be given in time. The technical effects are that a closed loop is formed through sensor data, a multi-mode sensing module (alignment processing), an adaptive evaluation engine (risk analysis), a digital twin system (simulation strategy) and a dynamic feedback module (optimization system), automatic calibration and learning are realized, and the risk prediction accuracy and the reliability of the scheme are improved.

The invention relates to the technical field of oil and gas field development, and discloses an oil and gas reservoir intelligent dynamic monitoring and optimized mining method based on big data. According to the method, through multi-source data real-time sensing and dynamic coupling modeling, the limitation of traditional static analysis is broken through, and minute-level accurate description of the state of the oil and gas reservoir is achieved; a machine learning prediction model based on physical constraints effectively fuses a data rule and a geomechanics principle, and the early warning reliability of water-driven dynamics and equipment faults is remarkably improved; a multi-objective optimization strategy of digital twin driving comprehensively plans the recovery ratio, the economy and the safety, and supports intelligent decision making under complex working conditions; the edge control and the block chain technology are combined to form a'sensing-optimizing-executing-auditing 'closed loop, so that the high efficiency, the self-adaptability and the data credibility of the mining process are ensured. According to the overall scheme, oil and gas exploitation is promoted to be transformed to intelligentization, refinement and transparency, and technical support is provided for cost reduction, efficiency improvement and sustainable development of an oil field.

The invention discloses a warehouse level code problem solving system based on a large language model, and relates to the technical field of software engineering, a source code file is extracted from a code warehouse, a static analysis tool is utilized to analyze codes, a warehouse level code graph is constructed, and then code entities and dependency relationships thereof are analyzed; based on the warehouse-level code graph, combining historical problem solving data, and constructing a dynamic evolution knowledge graph through continuous learning; through a context retrieval module, according to the context information of the current problem, dynamically retrieving related solution strategies and generating corresponding code patches, complementing or reviewing suggestions; the validity of the generated patch is verified through static analysis, unit testing and integration testing, and a problem solving strategy is optimized according to feedback of a developer. The problem solving efficiency and accuracy can be improved, the development cost is reduced, the solving process is gradually optimized through an intelligent feedback mechanism, a closed-loop feedback mechanism is formed, and therefore the increasingly complex software development requirements are met.

Techniques for optimizing and deploying deep neural network (CNN) machine learning models for inference using static analysis are described. A method includes obtaining a deep neural network (DNN) machine learning (ML) model, generating an intermediate representation for the ML model, the intermediate representation including one or more nodes corresponding to one or more operators utilized by the ML model, identifying, for at least one node of the intermediate representation, an optimized schedule for at least one operator corresponding to the at least one node using a static analysis that is based on a hardware-specific cost model, generating an optimized intermediate representation using the optimized schedule that is optimized for execution on a hardware platform, and generating code corresponding to the ML model based at least in part on the optimized intermediate representation, wherein the code is specific to the hardware platform.

The invention discloses an auxiliary code review method and system based on a large language model, and the method comprises the steps: analyzing a submitted code file based on a static analysis technology and an abstract syntax tree AST extraction tool, and extracting key elements; reviewing the code through a pre-constructed company internal and external knowledge base by relying on a knowledge graph technology to obtain a reviewing result; performing context analysis on the code by adopting a large language model to obtain an analysis result; integrating the review result and the analysis result, and generating a review report through NLP; a containerization technology is adopted to be integrated with plug-ins of mainstream development tools, and private deployment is supported. According to the method, the efficiency, accuracy and safety of code review can be greatly improved, the workload of manual review is reduced, and automation and intelligentization of the code review process are realized.

The invention discloses a steel-concrete hybrid girder bridge operation monitoring and static and dynamic analysis method based on multi-source data fusion, which comprises the steps of 1, structural response data acquisition, 2, multi-source data fusion processing, 3, building a plurality of operation state evaluation models based on parameters acquired by a sensor, and 4, carrying out static and dynamic analysis on the operation state evaluation models. 4, performing static analysis, dynamic analysis and static and dynamic collaborative analysis on the girder bridge based on the comprehensive feature set after multi-source data fusion, and 5, providing data support for the static and dynamic collaborative analysis by the operation state evaluation model through the comprehensive feature set, and meanwhile, evaluating the operation state of the girder bridge. Reversely supplementing the operation state evaluation model with the structural mechanical response characteristics excavated in the static analysis and dynamic analysis processes to form a data closed loop; the method has the advantages that through data sharing and feature interaction, evaluation result cross validation and multi-scale analysis linkage, cooperation of an operation state evaluation model and static and dynamic collaborative analysis is achieved, and a data closed-loop and multi-dimensional evaluation system is formed.

The invention relates to the field of data processing, in particular to an instruction optimization scheduling method based on a large model and a related device. The method comprises the following steps: for a to-be-processed target model, carrying out dynamic and static analysis on the target model to convert the target model into a calculation task graph; determining a scheduling priority of each instruction in the target model based on a calculation task graph; the scheduling priority is associated with the importance and timeliness of the instruction path of each instruction in the calculation task graph; dynamically adjusting an instruction scheduling strategy in the calculation task graph based on the scheduling priority; and dynamically optimizing the instruction scheduling strategy according to the register pressure state monitored in real time, and executing the optimized instruction scheduling strategy. According to the method, instruction scheduling optimization can be achieved, the resource utilization efficiency is improved, the calculation requirement of a large model is met, and the overall model performance is improved.

The invention discloses an automatic code security optimization method based on a large language model, and belongs to the technical field of code security optimization. Acquiring large-scale programming data to establish a programming data set, establishing a primary training model, generating an initial code and establishing an initial code set; the predefined vulnerability mode set scans the initial code set, vulnerabilities in the initial code set are identified and defined as initial vulnerabilities, and the initial vulnerabilities are positioned and classified; static analysis is matched with a semantic discriminator to evaluate the code snippets to obtain a new generation strategy, and the primary generation strategy is adjusted to obtain an optimized training model; capturing potential vulnerabilities as training signals, and optimizing the optimization training model to obtain a continuous optimization training model; and establishing a vulnerability library and a repair strategy library, and optimizing vulnerability time and a repair algorithm by analyzing data of the vulnerability library and the repair strategy library. Potential vulnerabilities in generated codes can be automatically recognized and repaired, a large amount of manual intervention is not needed, and code generation safety is improved.

Systems and methods for efficiently translating program code from a source language to a target language. Input source code is parsed, using a processor device, into an Intermediate Representation (IR). A structural and semantic model of the source code are established by applying static analysis to the IR, and a program skeleton of the target code is constructed from the IR, including generating context-aware placeholders. The IR is transformed into a Single Static Assignment (SSA) form, and a System Dependency Graph (SDG) is built from the SSA form. The SDG is traversed to order translation tasks, and ordered tasks are translated into the target language using a Large Language Model (LLM). A translated program is generated by integrating translated code segments into a coherent program structure in the target language.

The invention discloses a computing power analysis method used in a cloud computing environment, a matching method and a computer device. The computing power analysis method used in the cloud computing environment comprises the steps that historical task operation data of different types of computing power nodes in a cloud computing platform are acquired, associated computing power portraits corresponding to the computing power nodes are generated through preprocessing, and then a target model is trained in combination with the historical task operation data to obtain a task-computing power mapping model; performing multi-dimensional dynamic and static analysis on a to-be-executed user calculation task to obtain task feature information; and analyzing the task feature information based on the task-computing power mapping model to obtain analysis information of each type of computing power nodes. According to the method, the required computing power resources and the analysis information of each type of computing power nodes can be accurately predicted according to the dynamic and static analysis feature information of the task before the task is executed, so that accurate computing power identification and intelligent matching are realized, the reasonability and efficiency of task allocation are improved, the resource waste rate is effectively reduced, and the adaptation accuracy in a multi-tenant environment is improved.

The invention belongs to the technical field of software engineering, and particularly relates to a project dependency library vulnerability step-by-step utilization method based on LLM enhancement. The method comprises the following steps: carrying out lightweight dependency tree and call graph reachability analysis on a project, and identifying all reachability call chains from the project to a vulnerability method; decomposing the fuzz test on the whole call chain into a directional fuzz test on each step of method call in the call chain; in combination with static analysis and prompt engineering technologies, seed generation is assisted by semantic understanding of LLM on a current test function and utilization information generated by a previous test. And performing directional fuzzy testing by utilizing the generated seeds, analyzing execution paths of the seeds through an execution feedback mechanism, guiding selection and variation of the seeds, and finally generating effective input capable of successfully utilizing the project dependent library vulnerabilities. According to the method, developers can be helped to quickly position and timely repair vulnerabilities, so that risks and potential loss of projects caused by dependency on the vulnerabilities are reduced.

The invention focuses on the field of fuzz testing, and particularly relates to intelligent auxiliary fuzz testing drive code generation by using a large language model. The invention provides a convenient, fast and automatic fuzz test driving code generation method for Python library code fuzz test by using a large language model and combining a test function and a sample code. The method comprises the steps that Python library source code analysis and drive code sample information extraction are conducted through a static analysis tool, and a data set of a test function and a sample code is obtained; constructing a prompt word engineering structure comprising a drive sample code, a generation task description, a variation strategy introduction, a test function information set, a drive sample code and a code standardization description, and generating zero-sample, less-sample and thinking chain prompts according to the number of samples; building a large language model agent end, transmitting cue words to open-source and closed-source large language models in an HTTP / HTTPS (Hyper Text Transfer Protocol / Hyper Text Transfer Protocol Secure Socket) message mode, and performing reasoning generation of fuzzy test driving codes; after the drive codes are obtained, the fuzz test drive codes which do not pass the pilot run test are generated and screened for multiple rounds, and finally a fuzz test drive code set to be subjected to vulnerability mining is obtained; and performing vulnerability mining on the test function by utilizing the obtained fuzzy test driving code set, and storing a result to a database for analysis.

The invention discloses a vulnerability hidden danger intelligent detection method based on a large model, and the method comprises the steps: firstly carrying out the global static analysis of a source code set, constructing a complete call graph and a complete data flow graph of a program, and forming a structured code knowledge graph; and then, aiming at the identified candidate vulnerability slices, based on the maps, carrying out accurate context retrieval and enhancement, converting key information such as a call chain and a data traceability path which are strongly related to the vulnerability slices into natural language description which can be understood by a large language model, and injecting the natural language description into cue words, so that missing global context information is provided for the model. And the defect of complex code analysis capability is overcome. In this way, the problem that an attention mechanism loses efficacy in remote code association is solved, and the accuracy and reliability of vulnerability detection are remarkably improved.

A method for virtual fatigue testing includes reading a coarse-grid finite-element-model of a component from one or more libraries with a computer, and identifying static stress locations neighboring at least one of fasteners and non-fastener stress concentration regions in the component by a static analysis of a component intermediate-grid finite-element-model of the component, identifying hot spot locations in the component intermediate-grid finite-element-model by performing a fatigue screening on the component, generating details for the component intermediate-grid finite-element-model based on the hot spot locations, and predicting crack results by a deterministic durability-and-damage-tolerance analysis of the details for a component fine-grid finite-element-model of the component, calculating a crack margins for the component fine-grid finite-element-model by a probabilistic durability-and-damage-tolerance analysis of the crack results, and generating an output file that contains the final crack initiation margin and the final crack growth margin.

The invention relates to the field of intelligent contract vulnerability detection, in particular to an intelligent contract vulnerability detection method based on code graph fusion analysis and a graph matching network. The method comprises the steps of constructing an intelligent contract source code data set; through static analysis, extracting three different code feature information including a control flow diagram, a data flow diagram and an abstract syntax tree from the source code of the smart contract; carrying out information integration on the three extracted code features, and constructing a code graph covering wider features; on the basis of the generated code graph, a GraphCodeBerT model is used for pre-training; vectorizing a code graph which is output by the pre-training model and contains structural and semantic features through a graph embedding network; and learning the feature representation of the contract pair by using a cross-graph attention mechanism of a graph matching network to realize specific vulnerability detection of the smart contract source code. According to the invention, potential vulnerabilities can be accurately identified.

The invention discloses an AI-based localized environment application offline deployment method and device and a medium. The method comprises the steps that a localized environment is pre-detected based on probe service, a system layer configuration list is generated, heterogeneous features are extracted through a dual-channel deep learning model based on the system layer configuration list, and adaptive priority labels of the heterogeneous features are generated; the method comprises the following steps: analyzing an application program through static analysis and dynamic scanning technologies, constructing a dependency list, and constructing a version compatibility atlas based on metadata of a domestic operating system warehouse; generating a minimum-cost replacement path by using a graph attention network, performing hash check on each dependency packet, and generating an incremental update packet through a differential packaging algorithm; the resource occupation condition of the system is monitored in real time through a reinforcement learning model so as to dynamically optimize a deployment sequence and a resource allocation strategy, a deployment log is analyzed based on a natural language processing model, an error type is recognized, a repair operation is triggered, and application offline deployment is achieved.

The invention relates to a malicious script detection method and device, equipment and a storage medium, and the scheme not only can generate a static analysis result through the analysis of a script text, but also can generate a dynamic analysis result according to an operation event of a target script in a running process. And a comprehensive risk assessment result is determined through a machine learning model and a heuristic rule set. Through the mode, malicious behaviors executed by the target script in the running process can be discovered, so that the scheme can effectively detect the novel malicious script; in addition, according to the scheme, the target script is operated through the sandbox container, and the detection efficiency of the malicious script can be improved.

The invention relates to an interactive visualization method, device and equipment for fuzzy test bottleneck breakthrough, and belongs to the field of network security. According to the method, all compiling modules in a tested program are combined into a complete intermediate code, and a global view of the tested program is obtained; generating a unique label for each function by using a hash algorithm; constructing a call graph and a control flow graph, and obtaining function call instruction information by scanning a basic block of each function; carrying out pile insertion on the basic blocks; performing a fuzzy test on the tested program after compiling and instrumentation, and obtaining runtime data in real time; according to the fuzzy test runtime data and the static analysis data, data-driven visualization is carried out, and a call graph, a control flow graph and real-time execution statistical information are displayed in real time; a test engineer is assisted through an interactive chart, and fuzzy test bottleneck constraints are positioned. According to the method, a test engineer can be helped to better understand and intervene the fuzzy test process, and the fuzzy test code coverage rate and the defect detection number are improved.

A system and method for detecting cybersecurity risk on a resource in a computing environment utilizes static analysis of a cloned resource and runtime data from the live resource. The method includes: configuring a resource deployed in a computing environment to deploy thereon a sensor, the sensor configured to detect runtime data; detecting runtime data from the sensor of the resource; generating an inspectable disk based on an original disk of the resource; initiating inspection based on the detected runtime data for a cybersecurity object on the inspectable disk; detecting the cybersecurity object on an inspectable disk; and initiating a mitigation action on the resource.

Targeted dynamic analysis of code is provided by utilizing static analysis and dynamic analysis. A static code analysis system can operate to generate an initial analysis of code. The results of the initial analysis may be used to target a dynamic analysis system to analyze portions of code identified by the static analysis as potentially containing defects. In some cases, a preliminary dynamic analysis may be used to generate code inputs that may be utilized in future analyses to determine the inputs necessary to target particular code paths identified by a static analysis as potentially defective. Future updates to the code base may utilize the generated data to target analysis to code paths affected by the updates.

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

The invention discloses a failure restoration retreatment decision-making method based on dynamic occlusion analysis, and the method comprises the steps: constructing a four-dimensional virtual biomechanical model which comprises a patient anatomical structure and a dynamic function through multi-modal data fusion, and enabling the model to be embedded into a real three-dimensional mandibular movement track of a patient to simulate functional movement, thereby breaking through a static analysis blind area, and achieving the real three-dimensional mandibular movement of the patient. The missed diagnosis rate of dynamic interference is reduced; based on quantitative indexes and thresholds such as occlusal contact displacement, occlusal contact distribution symmetry and adjustment quantity of the maximum tooth tip staggering position and the median relation position, experience judgment of doctors is replaced, objectivity, repeatability and accuracy of failure repair and retreatment decision are improved, and excessive or insufficient adjustment is avoided; the treatment effect rehearsal is realized through virtual presetting, the irreversible operation risk is avoided, and the number of times of re-visit and the trial and error cost of a patient are reduced; and meanwhile, the four-dimensional virtual model can be connected with transition and design and manufacturing of a final restoration, so that the accuracy, safety and efficiency of retreatment of a failed restoration are improved.

A machine learning ensemble receives input data from static analysis and dynamic analysis of binary files to output malicious / benign verdicts for the binary files. The machine learning ensemble comprises a structure aware dynamic compressor (“compressor”). The compressor receives a tree data structure generated based on Application Programming Interface calls of the binary files in various sandbox environments as input. The compressor performs various compression, tokenization, embedding, and reshaping operations to the tree data structure to generate a compressed tensor that preserves structural context from the tree data structure. The machine learning ensemble uses the compressed tensor to generate malicious / benign verdicts for the binary files.