848 results about "Static analysis" patented technology

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 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 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.

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.

The invention discloses an engineering safety early warning method and system based on artificial intelligence real-time risk identification, and relates to the technical field of engineering safety, and the method comprises the steps: collecting scattered engineering safety data from each engineering platform in batches, and carrying out the preprocessing of the data, and constructing a dynamic database; according to the engineering safety data collected in batches, an engineering safety knowledge graph is constructed, and different risk levels are preset according to engineering safety standards. According to the method, multi-source engineering safety data are integrated, dynamically changing risk characteristics are analyzed in real time by using an AI risk identification model, the hysteresis of traditional manual inspection and static analysis is overcome, a nonlinear relationship among the risk characteristics is captured by using a random forest model through integrated learning of multiple decision trees, and the risk characteristics are analyzed in real time. And the probability values of high, medium and low risk levels are output in combination with Softmax probability normalization, so that the evaluation precision is remarkably improved, the risk features are positioned, the scientificity of risk traceability is ensured, and data-driven decision support is provided for engineering safety management.

The invention belongs to the technical field of artificial intelligence and high-performance computing, and particularly relates to a neural network large model efficient reasoning method based on multiple GPGPUs. The method aims to solve the problems of high communication overhead, non-uniform load, low resource utilization rate, high data transmission delay and the like among multiple processors. Dividing a calculation task into a plurality of sub-graphs through static analysis and mixed granularity partitioning of a model calculation graph; distributing the sub-graphs to the optimal GPGPU based on a weighted cost function in combination with heterogeneous resource perception and a dynamic mapping strategy; a global pipeline scheduling plan is constructed by using communication topology perception, and calculation and communication overlap are maximized; data are loaded in advance through a host side hierarchical caching and asynchronous prefetching mechanism, and transmission delay is hidden; multi-stream concurrent execution and event-based lightweight synchronization are adopted on each GPGPU, so that waiting overhead is reduced. According to the method, the reasoning delay can be remarkably reduced, the throughput and the hardware utilization rate are improved, and the method has good adaptivity and expandability.

The invention provides a reinforced concrete structure performance degradation intelligent prediction and durability limit state evaluation method. The method comprises the following steps: establishing a random variable probability model of environment and structure parameters; a convolutional neural network is fused to construct a chloride ion diffusion intelligent prediction model, and Monte Carlo sampling is adopted to analyze probability distribution of the initial corrosion time of the steel bar, so that prediction of the steel bar de-blunt time is realized; establishing a steel bar time-varying corrosion model to obtain the change condition of the steel bar corrosion loss rate along with time; further, an incremental static analysis method is adopted, and a multi-scale finite element model of the reinforced concrete structure under different corrosion rate conditions is established through random sampling; obtaining a critical load value in a limit state, and obtaining a failure probability curve under different corrosion degrees; finally, the bearing capacity failure time of the reinforced concrete structure is obtained by defining a bearing capacity reduction coefficient, and evaluation of the durability limit state of the reinforced concrete structure in the corrosion state is achieved.

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 discloses a code generation and restoration method and device based on multi-round feedback of a large language model, and the method comprises the following steps: 1, converting problem description into a structured tetrad (target, input, output and constraint) through a two-stage task demand deconstruction mechanism, and generating a pseudo-code frame according to the structured tetrad; 2, generating a plurality of candidate code schemes in parallel based on different implementation strategies; 3, selecting an optimal code implementation scheme through multi-dimensional evaluation; 4, generating an extended test case through boundary condition analysis and public test reasoning, and expanding a verification range; 5, classifying error types in combination with static analysis and dynamic execution information, and applying a special repair strategy; 6, providing detailed repair reasons and ideas by an interpretable repair mechanism; and 7, establishing a multi-round feedback repair verification iteration mechanism composed of testing, analysis, repair and verification. According to the method, the performance of an existing large language model in processing a complex programming task is effectively improved.

The invention discloses a multi-source data fusion wind power plant micro-siting dynamic evaluation method, relates to the technical field of wind power plant siting, and solves the problem that a mainstream method still depends on single anemometer tower data or static GIS analysis during wind power plant siting in the prior art. The method comprises the following steps: firstly, acquiring wind measurement data of multiple sources, unifying the wind measurement data of different sampling frequencies into a set time window, and realizing spatial scale alignment through an interpolation method; then obtaining a wind energy score according to the annual average wind speed and the main wind direction frequency, obtaining a suitability score according to the distance to the residential area or the main road, obtaining an environmental impact factor according to the noise and the ecological protection area, and constructing a target function based on the wind energy score, the suitability score and the environmental impact factor; and finally, combining a genetic algorithm and a real-time optimization model of a wake flow interference model, and dynamically adjusting the evaluation tendency according to the evaluation score of the fan layout inversion site, so that the evaluation result is more comprehensive, scientific and reasonable.

The invention discloses a large model multi-agent-based quantum software defect detection method and a related device, and relates to the technical field of computer software tests.The method comprises the steps that efficient quantum software defect detection is achieved through multi-agent cooperation based on large language model driving, and first, initial defect judgment, abstract syntax tree and framework classification agents are conducted; extracting auxiliary information from the to-be-detected quantum software code to generate multi-dimensional auxiliary data; a static analysis integration agent is driven by the large language model, a corresponding quantum software static analysis tool is dynamically called according to the frame type, and structured alarm information related to the defect is extracted; and finally, based on the multi-dimensional auxiliary data and the structured alarm information, chain reasoning is carried out through the quantum field, defect prediction, defect verification and a defect evaluation agent, and a structured defect report is generated. The method effectively solves the problems that a traditional static tool is single in adaptation, large language model quantum knowledge is insufficient, and multi-source information is isolated, and guarantees defect judgment accuracy and report interpretability.

The invention discloses an aviation airborne software test case intelligent generation and closed-loop optimization method, which comprises the following steps of: knowledge graph construction: analyzing a DO-178C standard document and a related field document, extracting entities and relationships defined in the DO-178C standard document and the related field document, and constructing a field knowledge graph fused with DO-178C standard knowledge; initial test case generation: based on the domain knowledge graph, combining a static analysis result of the source code of the tested airborne software, and utilizing a large language model to drive and generate an initial test case set; and closed-loop iterative optimization: executing the test case, evaluating whether the structural coverage rate reaches the standard or not, automatically identifying uncovered codes when the structural coverage rate does not reach the standard, generating a supplementary test case for iterative optimization, and outputting a final test case set until a coverage rate target corresponding to the software security level is met. According to the method, the test quality and efficiency of the aviation airborne software can be improved.

The invention provides a finite element analysis method for a competition-level site cross-country motorcycle frame, and belongs to the technical field of crossing of computer-aided engineering and vehicle structure design. The method comprises the following steps: performing parametric modeling on a frame main bearing structure based on CATIA software, and simplifying non-functional characteristics; importing the model into ANSYS Workbench, generating a high-precision finite element model by adopting a grid division strategy of mixing tetrahedron and hexahedron units, and considering the calculation efficiency and precision; defining material characteristics; a limit load working condition is simulated through statics analysis, a displacement cloud picture and a stress cloud picture are combined to be compared with the yield strength of a material, and topological optimization is conducted on a weak area; meanwhile, the inherent frequency of the frame is extracted through modal analysis, and the resonance risk is avoided. According to the method, statics analysis and modal analysis are carried out on the frame through cross-platform co-simulation, the structural reliability is evaluated, and the method is suitable for efficient development and performance optimization of competition-level cross-country motorcycle frames.

The invention discloses a test generation and defect prediction method and system based on a heterogeneous program diagram, and the method comprises the steps: constructing a heterogeneous dynamic program dependency diagram: extracting an abstract syntax tree, a control flow diagram and a program dependency diagram of a source code through static analysis, capturing an execution track during operation in combination with dynamic instrumentation, and integrating version evolution information, forming a heterogeneous dynamic program dependency graph containing various types of nodes and edges; heterogeneous graph neural network modeling: designing a four-layer eight-header heterogeneous graph neural network model based on the heterogeneous dynamic program dependency graph, and outputting a function level defect probability and a test case sequence by adopting a type awareness attention mechanism and time coding injection; and multi-task joint training and reasoning: adopting an end-to-end training strategy, jointly optimizing defect prediction loss and test generation loss, preferentially screening high-risk functions during reasoning, and generating a coverage test case. According to the method, the defect detection accuracy is remarkably improved, the test coverage rate is increased, and the method is suitable for enterprise-level complex software systems.

The invention belongs to the technical field of vulnerability detection, and particularly relates to a source code vulnerability detection method and system based on semantic comparative learning. Comprising the steps of obtaining source codes, preprocessing the source codes, and generating positive and negative samples paired with the source codes; improving the CodeBERT model to obtain an encoder, and building a momentum contrast learning model comprising a vulnerability classification branch, a representation consistency branch and a contrast learning branch based on the obtained encoder; training the momentum contrast learning model comprising the three branches by adopting a contrast learning method; and based on the trained momentum contrast learning model, performing vulnerability detection on a to-be-analyzed code. According to the method, a semantic contrast learning-driven source code vulnerability detection framework is designed and realized, and the problems of limitation of traditional static analysis and insufficient generalization ability of an existing deep learning model are solved by learning representation which is robust to code semantic changes and sensitive to vulnerability modes.

The invention relates to an open source component multi-modal dependency risk tracing method and device, and the method comprises the steps: employing a mode of combining static analysis and dynamic analysis to analyze a component dependency relationship of software, and combining AST analysis and construction script analysis; function-level features are extracted, a binary fingerprint database is constructed, the similarity between different versions is analyzed through LSH, behavior patterns in binary codes are analyzed, and hidden dependencies or malicious code injection is detected; the version updating history of the dependent component is analyzed and monitored by using a time sequence, and the vulnerability security of the component is evaluated in combination with attack graph analysis; high-risk components on the path are calculated, potential supply chain attack points are identified, and risk points are subjected to cross validation; a time sequence diagram database is used for recording the component dependency relationship, and time backtracking query is supported. According to the method, a multi-mode dependency analysis method is adopted, potential dependency risks are rapidly identified, and potential supply chain attack risks are timely warned.

The present invention relates to a system and method for deterministically generating reproducible evaluative scores for a subject of analysis (e.g., a security). The system comprises a processor and memory storing instructions to: receive verified data describing the subject; store this data in a fixed and version-controlled corpus to define a static analytical context; execute a large-language model (LLM) under a structured prompt framework that directs a controlled scratch-pad reasoning process for preliminary interpretations and evidence extraction; perform a multi-pass deterministic analysis of the fixed corpus to produce structured, synthesized statements as reproducible evidentiary outputs; and finally, apply a rubric-based scoring engine that converts these statements into calibrated alignment scores and aggregates them to generate a composite deterministic score. This architecture ensures reproducibility, transparency, and auditability by anchoring the flexible analysis of the LLM and the final scoring logic to a known, unchanging evidence corpus.

The invention discloses a power grid overhaul cost factor identification method and system based on multi-dimensional feature analysis, and belongs to the field of power system analysis and management, and the method comprises the steps: obtaining multi-source data and historical cost data of a power grid overhaul project, and carrying out the preprocessing of the cost data; the method comprises the following steps: constructing a multi-dimensional entity and an associated knowledge graph, carrying out dynamic weight calculation on influence factors through a graph neural network, establishing a factor quantification model, modeling a risk conduction path, identifying high-order association among key influence factors, establishing a cost reference library and a compliance verification rule system, and detecting abnormal cost items in project budget. And generating root cause analysis and optimization suggestions. The method breaks through the limitation of a traditional static analysis model, significantly improves the accuracy and evaluation efficiency of cost recognition, reduces the cost waste caused by human deviation, provides intelligent decision support for an overhaul project under a novel power system, and assists the lean management of assets in the whole life cycle.

The invention relates to the technical field of data acquisition, in particular to an AOP-based user behavior data acquisition method. The method comprises the following steps: firstly, identifying user behavior related code features by statically analyzing Java source codes of an application program, and constructing a feature template library; during a compiling period, according to a template library identification target method, embedding a point burying instruction by utilizing a byte code enhancement technology, and constructing a monitoring section framework; and during running, dynamically adjusting an execution strategy according to the running state of the application program by virtue of a dynamic rule engine. Meanwhile, an annular buffer area is adopted to asynchronously process user behavior data streams, cache data are managed in a sub-generation mode, and asynchronous processing is triggered when a threshold value is exceeded. The method also constructs an adaptive thread pool model based on a hierarchical thread pool architecture, distributes tasks according to data value weights, monitors the task backlog rate of a high-priority sub-pool, adjusts the sampling frequency or acquisition dimension when the task backlog rate exceeds a threshold value, forms adaptive closed-loop control, and effectively acquires and processes user behavior data.

The invention discloses a privacy protection effect evaluation method and system, and the method comprises the steps: carrying out the hierarchical analysis of a privacy protection effect evaluation index system, defining the definition and calculation boundaries of four first-level dimensions, i.e., data features, compliance, availability and safety, and subordinate second-level indexes, carrying out the adaptation of a calculation algorithm for different types of indexes, and carrying out the weighted integration of the second-level indexes through employing an analytic hierarchy process, thereby achieving the evaluation of the privacy protection effect. Multi-dimensional quantitative evaluation is realized; an index association rule base and a matrix are constructed through static analysis and dynamic mining, and linkage analysis is achieved; determining an index fluctuation reference according to scene characteristics and historical data, converting user demands into a variation amplitude of a fluctuation reference unit, and realizing index dynamic linkage updating based on an incidence matrix; and finally, the optimization rule is verified through simulation testing and historical data backtracking, and a dynamically adaptive and self-iterative optimization relation rule system is formed, so that the limitation of traditional evaluation is broken through, a scientific basis is provided for privacy protection strategy optimization, and the privacy governance efficiency is improved.

The invention discloses a software source code security vulnerability detection method based on an artificial intelligence large model, and belongs to the technical field of information security and source code detection, and the method comprises the following steps: carrying out user registration and login, uploading a file, judging whether static analysis is carried out or not, judging whether dynamic analysis is carried out or not, introducing the AI large model, and generating a detection report; according to the method, through multi-level technology integration and a self-evolution mechanism, normal form innovation of vulnerability detection from a single view angle to global perspective is realized. According to the system, on the basis of unified intermediate representation, a closed-loop architecture of static rule screening, dynamic behavior verification and AI semantic reasoning is constructed, and the core contradiction of high false alarm rate, insufficient path coverage and semantic understanding deficiency of a traditional method is effectively solved.

The invention discloses a knowledge extraction and envelope coverage-oriented software vulnerability test method and related equipment, and the method comprises the steps: obtaining vulnerability key information of target software, and obtaining sensitive function features through defect dependence analysis and construction based on the vulnerability key information; performing semantic analysis on the analysis report and the code abstract associated with the sensitive function characteristics, screening to obtain a target sensitive function, performing path envelope reverse tracking on the target sensitive function, and constructing to obtain a target path envelope; and obtaining coverage information enveloped by the target path, and carrying out fuzzy testing on the basis of the coverage information in combination with the iteratively optimized variation sample. According to the method, through intelligent closed loop of analysis-positioning-testing-feedback-optimization, static analysis provides accurate guidance for dynamic testing, and the static analysis strategy is inversely optimized by the result of the dynamic testing, so that the maximum improvement of the testing efficiency and the vulnerability discovery accuracy is realized in limited testing resources, and the testing efficiency and the vulnerability discovery accuracy are improved. The method can be widely applied to the technical field of software security.

The invention discloses a multi-dimensional binary software component analysis system and method, and the system comprises a static enhanced feature extraction module which is used for extracting multi-dimensional features of a target binary system through a disassembling engine, carrying out the fusion of the multi-dimensional features to form an enhanced feature representation model, and carrying out the preliminary matching through the combination of a feature hash library; the component verification module is used for recording runtime behaviors and analyzing the behavior log to verify a static analysis result; the intelligent knowledge base linkage module is used for realizing automatic traceability and risk assessment; the false alarm filtering and confidence evaluation module is used for optimizing credibility grading of a final result, constructing a false alarm filtering mechanism based on Bayesian reasoning, integrating results of multiple analysis stages, calculating the confidence of an identification result of each component, outputting a grading report and supporting a user to screen high-credibility results as required; and comprehensiveness, accuracy and intelligence of binary software component identification are realized.

The invention provides a Web application-based slow denial of service attack path acquisition method and device and a medium. The method comprises the following steps of: constructing a program analysis chart of a target Web application program; extracting all program execution paths from an entry node to an exit node based on the graph; calculating the weight of the path by analyzing the key characteristics of the path, and screening candidate paths of which the weight is higher than a threshold value; and further through path state value evaluation, data flow continuity detection and type compatibility detection, finally determining a target path which may have a slow denial of service attack risk. According to the method, a potential attack path is efficiently identified through a static analysis technology, accurate positioning is provided for Web application security protection, and the risk that a system is attacked is reduced.

The invention relates to the technical field of coal mine test equipment, in particular to a one-stop automatic test integration platform and a test method for coal mine application software. The one-stop automatic test integration platform comprises an executable test case construction module, an automatic test execution module and a test result management module. According to the automatic test platform and the test method, the functions of test management, system safety penetration, software automatic test, source code static analysis, system performance test, localization adaptation test and the like are integrated; the problem that in the prior art, it is difficult to comprehensively cover all function combinations and abnormal conditions by manually compiling test cases is solved, and the test omission ratio is remarkably reduced; centralized management and correlation analysis of data in each link in the testing process are realized through the data centralized management module, so that the testing efficiency is improved, and the problem positioning is more accurate and rapid; a standardized testing process is integrally formed, and software quality and compliance are guaranteed through all-around testing means.

The invention discloses an automatic compiling method and device for a test case, electronic equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: carrying out the structural analysis of a demand document for creative and creative transformation, and extracting the demand information of the demand document; the code warehouse is connected to obtain the source code and performs static analysis to generate code logic; establishing a semantic association mapping table of the demand information and the code logic, generating a test scene library according to the semantic association mapping table, and automatically generating a test case according to the parameter type and the constraint rule; and performing multi-dimensional verification on the generated test case to generate a verification result, and adjusting the priority of the test case according to the verification result. The method has the technical effects of improving marginal scene coverage depth and breadth, improving test case writing efficiency and accuracy, reducing manual dependence, and optimizing test case quality and execution pertinence.

The invention discloses a software variation testing system and method based on structure-semantic fusion, and the system comprises a document analysis module, a structure diagram construction module, an agent module, a defect injection module and an influence domain verification module.The method comprises the steps that a function document or natural language description serves as an entry, and after a code structure diagram (such as a function call diagram) is constructed through static analysis, the influence domain is verified; an agent autonomously explores semantic information of a code under the guidance of a structure diagram, precise positioning from function description to a target code position is completed, and defect injection and influence domain verification are carried out.

The invention discloses a cloud native security configuration system and method based on container analysis and LLM, and the system comprises a mirror image static analysis module which is used for compiling a Linux kernel and a standard library source code to extract intermediate representation, and tracking and constructing a double-layer mapping relation library from the system call to the container capability; the dynamic binary extraction module is used for executing the container mirror image and extracting a binary file of a core function; the mirror image static and dynamic association module is used for executing two-stage static binary file analysis and extracting function requirements required by system calling and mirror image for loading during container running; and the cue word construction and model fine adjustment module is used for constructing cue words, performing fine adjustment on the LLM model and generating a minimum privilege function limitation configuration file of the container. According to the method, static analysis and runtime analysis are combined, the container mirror image is thoroughly checked, the function, which is specified by a user and exceeds the original definition of the mirror image, of the LLM model is finely adjusted through the cue word, and privileged function security configuration is generated according to specific requirements.

The invention discloses a teaching information management method based on big data, and particularly relates to the field of teaching, comprising data acquisition, behavior characteristic analysis, knowledge mastering evaluation, dynamic feedback adjustment, comprehensive evaluation and management strategy. The teaching behavior analysis depth is improved through multi-dimensional data fusion and non-linear feature analysis, real-time adjustment of an evaluation model along with knowledge mastery is achieved through a dynamic feedback mechanism, limitation of traditional static analysis is broken through, theoretical test and practice project data are integrated through an innovatively constructed three-dimensional evaluation system, and the teaching behavior evaluation efficiency is improved. A comprehensive evaluation model covering knowledge application and innovation ability development is formed, a dynamic path selection mechanism synchronously supports basic ability enhancement and high-order skill development requirements, a resource allocation strategy has instant intervention and long-term optimization characteristics, and adaptability and decision scientificity of a teaching management system are remarkably enhanced.