53 results about "Dependence analysis" patented technology

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 relates to the technical field of data analysis, in particular to an ultra-low temperature refrigerator based on operation data analysis, which comprises the steps of forming a fluctuation event sequence under multiple time periods by regarding a time period corresponding to temperature fluctuation as a fluctuation event; dividing the fluctuation event sequence into event types corresponding to fluctuation events according to values in an operation period, and calculating a fluctuation duration proportion under each time segment; defining temperature state transition of a plurality of fluctuation events under each fluctuation duration proportion, combining the fluctuation events according to a plurality of time periods, and solving a fluctuation event combination probability; performing correlation analysis on the fluctuation events by taking the event attributes and the temperature characteristics of the fluctuation events as input to obtain related factors of the combined fluctuation events; carrying out multi-objective solution according to related factors of the fluctuation event, solving an optimal control parameter combination, and taking the optimal control parameter combination as an output control strategy; the control efficiency of the refrigerator and the accuracy of temperature regulation and control are achieved.

The invention relates to the technical field of software testing, and discloses a large-model-based REST-Like API dependency analysis and test case generation method, which comprises the following steps of: obtaining API interface description information, performing semantic analysis on the API interface description information by utilizing a large language model, and generating structured semantic representation so as to identify an operation type, dependency resources and operation resources of each API; an API-resource dependency graph is constructed based on the representation, the dependency graph comprises resource nodes and API nodes, and the operation and dependency relationship between the API and resources is represented through two kinds of directed edges of a dependency edge and an operation edge; on the basis of the graph, a test case is automatically generated through resource initialization template pre-generation and recursive dependency analysis, and the core of the test case is an API calling sequence used for testing the target API. According to the method, through semantic understanding of a large model, the problem that a traditional test tool loses efficacy in dependence analysis on a non-standard REST-Like API is solved.

The invention relates to the technical field of simulation modeling, in particular to a BIM-based building embedded pipeline simulation system, which comprises a pipeline path optimization module, a space conflict detection module, a construction sequence planning module, a dynamic adjustment generation module and a resource utilization rate evaluation module. According to the method, by automatically extracting the spatial distribution information of the pipelines and analyzing the geometrical characteristics and the connection relation of the pipelines, accurate marking of potential conflict positions can be achieved, the comprehensiveness and the accuracy of spatial conflict recognition are effectively improved, and further based on extraction of the construction sequence relation and logic dependency analysis, the construction efficiency is improved. The method can achieve the intelligent judgment of the construction conflict state, achieves the dynamic adjustment of a layout path and a time range, assists in forming a targeted wiring strategy in a conflict region, optimizes the resource space matching degree, automatically recognizes a low-efficiency time period, carries out the real-time identification, improves the response capability of a construction process to the resource utilization efficiency, and improves the construction efficiency. And the precision of path adjustment and the coordination of construction organizations are improved.

The invention relates to a blast furnace temperature prediction method, and belongs to the field of intelligent metallurgy. The method comprises the following steps: firstly, acquiring historical production data of a blast furnace, processing missing values by adopting an adaptive hybrid interpolation method, screening effective characteristics in combination with Pearson correlation analysis, a maximum information coefficient and a grey correlation degree, and compressing redundant variables based on a metallurgical mechanism; then a lag sequence of the target parameter molten iron temperature or silicon content is created, and after dimension expansion is conducted through a time window, the lag sequence and the operation parameters are spliced into a data sample; constructing a long sequence dependence attention mechanism model based on a bidirectional gating circulation unit, introducing a time attention and space time combined attention mechanism to focus key process nodes, and training the model step by step by adopting a staged learning strategy; and finally inputting a test sample and outputting a furnace temperature prediction result. According to the method, the prediction accuracy and real-time performance are remarkably improved, the robustness of the model to complex working conditions is enhanced, the furnace temperature abnormity is pre-warned in advance so as to reduce the safety accident risk, and the smelting period and the energy consumption efficiency are optimized.

The invention provides a system and method for upgrading without service interruption, and belongs to the technical field of cloud computing, and the system comprises a service dependence analysis module which is used for generating an initial upgrading sequence topological graph; the visual process arrangement module is used for providing an interactive interface to display and allow adjustment of the initial upgrading sequence topological graph, generating a target upgrading sequence topological graph and compiling the target upgrading sequence topological graph into an executable upgrading task sequence; the state machine engine is used for instantiating the upgrading task sequence into a state machine model with multi-stage states and persistent context, and responding to an external instruction or an internal decision event; the decision engine is used for collecting real-time performance indexes at a preset state transition monitoring node and generating a decision instruction; and the operation execution module is used for executing the atomic upgrading operation scheduled by the state machine engine and providing packaging and management support for the upgrading operation. Through the scheme, the technical effect of improving the upgrading success rate and efficiency is achieved.

A resource event sensing method and system based on computing network fusion relates to the technical field of computing network fusion resource sensing, and comprises the following steps: decomposing an application into a computing task and a connection task to generate application information; generating task information including a task ID, a task type, a resource type, a corresponding resource ID and resource context information for each task; constructing a unified computing network resource information topological graph based on the task information, taking computing resources corresponding to the computing tasks as nodes, and taking connection resources corresponding to the connection tasks as edges; a unified clock source is adopted to ensure the time consistency of sensing data, and the resource state is sensed; and carrying out aggregation and correlation analysis on the sensing events according to the resource dependency chain. Through the unified event model, the computing network topology and the resource association mechanism, the problems of sensing fragmentation, fault positioning difficulty and low resource scheduling efficiency caused by sensing separation of the computing power domain and the network domain are solved, a precise end-to-end resource view is provided for AI application, and the operation and maintenance efficiency of the AI cluster is remarkably improved.

The invention belongs to the technical field of power system security protection, and discloses a binary program vulnerability detection method and related device.The binary program vulnerability detection method comprises the steps that a VEX instruction of a to-be-detected binary program is obtained, and a program dependency graph is generated according to the VEX instruction; disassembling the to-be-detected binary program to obtain an assembly instruction, traversing the program dependency graph based on a preset sensitive operation instruction, and performing operand granularity slicing of the assembly instruction to obtain assembly instruction slices; a pre-trained encoder model is adopted to obtain feature vectors of the assembly instruction slices; and according to the feature vector of the assembly instruction slice, based on a pre-trained vulnerability detection model, obtaining a vulnerability detection result of the assembly instruction slice. According to the method, accurate dependency analysis is carried out around operands, a large number of irrelevant instruction interferences in binary program codes are effectively filtered out, most sensitive operations generating vulnerabilities can be covered based on preset sensitive operation instructions, the noise influence of redundant information on a model is reduced, and the robustness and generalization ability of detection are improved.

The invention discloses a power station boiler NOx emission prediction method and system based on XGBoost. The method comprises the steps of S1, obtaining boiler operation data; s2, preprocessing the operation data; s3, performing time delay correlation analysis on each variable in the normalized data set and the NOx concentration at the outlet of the SNCR region by adopting an MIC correlation coefficient method, determining the time delay time of each variable relative to the NOx concentration at the outlet of the SNCR region, and performing time translation and reconstruction on each variable to form a training data set with aligned time delay; s4, establishing a NOx emission prediction model on the training set by using an XGBoost algorithm, and adjusting and optimizing model hyper-parameters through a grid search method to obtain an optimized NOx emission prediction model; and S5, predicting the test set by using the optimized NOx emission prediction model, and outputting a predicted value of the NOx concentration at the outlet of the SNCR region. The method has the advantages of high prediction precision and the like.

This application relates to the field of computer technology, specifically to a method, processor, device, and storage medium for identifying the operating conditions of oil and gas pipelines. The method includes: acquiring first operating data of the pipeline in real time within a first preset time period according to a preset acquisition frequency; detecting the first operating data using a change point detection algorithm to determine the state change times in the first operating data; segmenting the first preset time period according to the state change times to obtain multiple operating condition time periods, and determining multiple feature values ​​corresponding to the first operating data within each operating condition time period; analyzing the first operating data through correlation analysis to determine a first correlation weight matrix between various operating features in the first operating data; embedding the first correlation weight matrix into an operating condition identification model, and inputting the multiple feature values ​​corresponding to each operating condition time period into the operating condition identification model to output the operating condition corresponding to each operating condition time period.

The embodiment of the invention discloses a job checking method, device and equipment and a storage medium, and relates to the technical field of data processing, big data and financial science and technology, and the method comprises the steps: obtaining job scheduling configuration information of a target checking job; performing automatic check on job scheduling configuration information of the target check job according to a self-defined check rule to obtain a first job check result; determining multi-dimensional dependency analysis data of the target check job according to job scheduling configuration information of the target check job; and performing automatic check on the job scheduling configuration information of the target check job according to the multi-dimensional dependency analysis data of the target check job to obtain a second job check result. According to the technical scheme of the embodiment of the invention, the technical effect of improving the efficiency, comprehensiveness and accuracy of operation checking can be achieved.

The invention discloses a multi-dimensional data intelligent processing and analysis method, which relates to the technical field of data processing, and comprises the following steps of: performing static dependency analysis on a parallel task set, and constructing a directed resource dependency graph containing a semaphore occupancy relationship, a calling hierarchy and a recursive path; depth-first loop detection is performed based on the directed resource dependency graph, a potential deadlock closed loop is identified and divided into a plurality of edge loop-free sub-graphs, and a sequenced execution plan is generated for each sub-graph. According to the method, by introducing static dependency analysis, resource dependency graph construction, a token lease mechanism and a multi-level semaphore pool, active avoidance of task deadlock risks and automatic recovery of resource anomalies in the engineering machinery multi-dimensional data processing process are achieved. Through sequential scheduling, adaptive regulation and control and self-healing monitoring strategies, the task scheduling stability and the resource utilization efficiency in a high-concurrency scene are effectively improved, and closed-loop optimization of task scheduling is realized in cooperation with process traceability and evolutionary knowledge base feedback.

The invention provides a water flow real-time forecasting method based on LSTM (Long Short Term Memory) time sequence prediction and residual analysis, which comprises the following steps: collecting historical multi-source hydrometeorological data for preprocessing, and constructing a standardized feature data set; training a basic LSTM prediction model based on the data set; predicting by using the basic model to obtain a historical basic prediction sequence, calculating a historical residual sequence of the historical basic prediction sequence and a measured value, and performing stationarity verification, time sequence dependency analysis, positioning and classification on the sequence; for different types of residual errors, adaptively fusing GRU, LSTM and GARCH models to construct and train a residual error prediction model; and collecting data in real time, predicting a basic prediction sequence and a residual prediction sequence in a future time period by using the trained basic model and residual model, and generating a final water flow prediction sequence through a dynamic fusion strategy. According to the method, through a collaborative architecture of'basic prediction-residual modeling-fusion correction ', the real-time performance is ensured, and meanwhile, the forecasting precision and the extreme flood peak capturing capability are remarkably improved.

The application discloses an industrial control system configuration cloud compiling method and system, relates to the cross technical field of cloud computing, and comprises the following steps: carrying out deep-first traversal on a logic flow graph, counting the occurrence frequency of instruction operation codes, and calculating the information-intensive degree evaluation value of program codes; based on the information-intensive evaluation value, analyzing the distribution characteristics of the occurrence frequency of operation code types and the weight of operation codes, combining the comparison result of the instruction entropy threshold value, identifying high-frequency redundant instruction operation codes, compressing and merging the high-frequency redundant instruction operation codes, optimizing the conditional jump structure of the logic flow graph, and generating intermediate optimized code units; based on the intermediate optimized code units, carrying out topological dependence analysis and implementing local incremental compiling verification, and generating executable machine code. The application can accurately identify high-frequency redundant operation codes and carry out semantic-level compression and merging by calculating the information-intensive degree evaluation value of program codes, and can generate concise and reliable control program codes under the condition of maintaining industrial hard real-time constraints.

The invention discloses a trauma hierarchical mapping method and system fusing multi-source physiological parameters, and relates to the technical field of data processing, and the method comprises the steps: inputting the trauma condition description semantics of a target user, carrying out entity recognition, building an entity relationship to construct a trauma mechanism knowledge graph, carrying out the correlation analysis of trauma physiological parameters, and outputting a trauma physiological parameter template; defining a wound grading mapping model; identifying a scoring physiological parameter set of each scoring item in the plurality of parallel scoring items, and constructing a mapping relationship between the plurality of parallel scoring items and the plurality of scoring physiological parameter sets; performing score item analysis on the trauma physiological parameter template, and calculating an adaptive learning weight based on a plurality of parallel score items; and performing hierarchical mapping on a plurality of parallel scoring indexes obtained by calculation of the plurality of parallel scoring items, and outputting a comprehensive scoring result. The technical problem that the accuracy of the grading result is insufficient in the prior art is solved, and the technical effect of improving the comprehensiveness and accuracy of the wound grading result is achieved.

The application discloses a kind of based on driving engine's semiconductor manufacturing business document automation management method and system, to solve the problem of low efficiency caused by order whole process manual dependence.The method obtains customer production demand by driving engine, and is automatically converted into customer order, service order and manufacturing order according to preset mapping rule and is issued to manufacturing execution system;Driving engine receives execution result and automatically updates service order and customer order state until closed loop.Driving engine uses exponential backoff retry strategy and task state machine to manage abnormal conditions, support topology dependence analysis of parallel / serial order.The application realizes order whole process automation promotion and real-time monitoring, reduces manual intervention, and improves production efficiency.

The invention discloses an OSGi (Open Service Gateway Initiative) framework-oriented cross-module call graph construction method, which comprises the following steps of: S1, processing a to-be-detected target program and a related configuration file to obtain a result IR for static analysis; s2, performing pointing analysis on the IR generated in the step S1, and generating a pointing flow graph and a preliminary calling graph in a single software module; s3, in combination with an intermediate code IR related to module interaction in the target program, establishing calling edges among the modules through API semantics of interaction among the modules provided by the modeling OSGi framework; and S4, on the basis of analysis results of the steps S2 and S3, constructing a complete cross-module call graph. Compared with a traditional Java application-oriented call graph construction method, by modeling a module interaction mechanism of an OSGi framework, analysis of a cross-module interaction path is realized, a complete cross-module call graph is effectively constructed, and basic support is provided for tasks such as dependency analysis and security detection of the OSGi application.

The present application relates to a kind of blast furnace key furnace condition parameter optimization method based on industrial big data, its method includes: the full process data in the obtained blast furnace smelting process is preprocessed operation;Based on the data after being preprocessed, the selected blast furnace key furnace condition parameter is carried out feature screening by correlation analysis to obtain influence parameter;Based on blast furnace key furnace condition parameter and influence parameter, the multivariate linear fitting function of target and constraint function is structured, and optimization operation solution set is obtained by solving;Optimization operation solution set is classified and sorted by using preset case library to carry out multistage matching and combined with feasibility analysis, and the optimal operation solution is obtained.The present application collects, analyzes and processes blast furnace parameter by big data technology, obtains optimization operation solution by optimization algorithm based on machine learning and integrated learning fusion metallurgical theory and expert experience, and finally selects the solution of minimum operation deviation and lowest economic cost by multistage matching analysis and feasibility analysis, to be used to guide blast furnace production.

The invention is applicable to the technical field of computers, and provides a DXE driver scheduling method and system, electronic equipment and a program product, and the method comprises the following steps: taking a plurality of DXE drivers as nodes, and constructing directed edges representing dependency directions among the DXE drivers among the nodes based on dependency expressions of the plurality of DXE drivers, obtaining a directed acyclic graph comprising the plurality of nodes and the directed edges; performing topological sorting on the directed acyclic graph, and sequentially screening out target nodes which do not depend on other nodes from the plurality of nodes; and distributing the target DXE drive corresponding to the target node to an execution processor for drive execution. According to the scheme, the dependency analysis efficiency can be improved, and then the starting speed of the server is improved.

The invention discloses a block chain data processing method and a security guarantee system, and the method comprises the following steps: calculating an optimal operation precision value through a multi-objective optimization algorithm according to an operation request of a protocol layer and to-be-processed data in combination with a precision strategy template library; according to the optimal operation precision value, an arithmetic logic unit data path is recombined through a multiplexer tree and a precision switching controller, and a hardware ready signal and a precision switching delay index are generated; according to the hardware ready signal and the current precision, performing analog-to-digital operation on the to-be-processed data to obtain an encrypted intermediate result, and calculating a security-efficiency product of the encrypted intermediate result; according to the safety efficiency product index and the operation side channel trace, the leakage risk is judged through correlation analysis, and the safety risk level is output; and according to the security risk level and historical data, obtaining global security configuration through variable coefficient and compatibility intersection analysis. According to the method, the security of the block chain in a quantum computing environment is enhanced by adopting an analog-to-digital operation technology with dynamic adjustable precision.

The invention discloses a method and system for partitioning the engineering influence degree of ground loading on a tunnel below. The method comprises the steps that dependent variables and independent variables of the tunnel below influenced by the ground loading are determined; selecting a basic dimension from the plurality of physical and mechanical parameters, and performing dimensionless processing on independent variables and dependent variables except the basic dimension to obtain dimensionless parameters; carrying out combinatorial operation by utilizing the dimensionless parameters to form new dimensionless parameters; performing correlation analysis on the data of the dependent variables and the dimensionless parameters, and selecting the dimensionless parameters meeting target requirements as control variables; drawing the obtained values of the dependent variable and the control variable into a contour map, adjusting the level and the numerical value of a contour line, and carrying out detail amplification on the local area to obtain a detail contour map; and optimizing the detail contour map to form a straight and smooth partition division map. According to the invention, fine quantitative partitioning of ground loading influence can be realized, so that the tunnel safety assessment and risk management and control precision is improved.

This application relates to the field of aviation digital analysis technology, specifically to a data processing method for digital quality management in the aviation industry. The method includes: constructing a dependency graph based on the data source information included in the metadata of data processing units within a first data processing unit set; determining the correlation between data processing units based on data extracted from instantiated data processing units to obtain an updated dependency graph; and removing redundant items from the original data source after grouping to obtain a second data processing unit set and thus the data processing result. This method manages the first data processing unit set in the form of a dependency graph through metadata and correlation analysis, and allows access to the original data source to be implemented by the second data processing unit set, thereby reducing redundant data acquisition, optimizing data access patterns, and solving the data redundancy problem in digital quality management in the aviation industry.

The application relates to the technical field of computer software, can be applied to the field of science and technology finance / digital medical treatment, and discloses an intelligent analysis method for mobile application third-party component dependence and related equipment. The method comprises the following steps: obtaining engineering information of a mobile application to be analyzed, analyzing basic dependence data of a third-party component based on engineering construction configuration, filtering the basic dependence data according to a preset dependence filtering rule to form a target dependence list, extracting basic attributes such as the name, version and source type of each dependence component and generating structured dependence description data, inputting a large model analysis engine by combining the dependence description data with a preset prompt word configuration, generating extended attributes associated with the dependence component, writing third-party component dependence information files according to a data structure specification, and outputting to a specified path or a target server according to a preset configuration. The method improves the accuracy and automation degree of third-party component dependence analysis in a mobile application.

The invention provides a dynamic adaptive multi-parameter trimming optimization method and system, and the method comprises the steps: obtaining a high-correlation parameter combination through correlation analysis; carrying out combined trimming to obtain a combined trimming parameter group; setting a searched initial vertex according to the combined trimming parameter group, and carrying out initial trimming code combination; the initial trimming code combination is subjected to process deviation function calculation, vertex process deviation values are solved and sorted, and a sorting result is obtained; designing an adaptive search algorithm, based on data driving, fitting a quadratic deviation function, dynamically adjusting a search starting point, searching an optimal trimming parameter set, and obtaining a dynamic centroid; and according to the sorting result and the dynamic centroid, performing reflection, expansion and contraction operations, and iteratively searching an applicable trimming parameter combination. The technical problems that in the trimming process, parameters interfere with one another, the trimming process is tedious, time consumption is large, a globally optimal solution is difficult to find quickly, and dynamic adaptability is lacked are solved.

The application relates to the technical field of data analysis, in particular to an ultralow-temperature refrigerator based on operation data analysis, which comprises the following steps: regarding a time period corresponding to temperature fluctuation as a fluctuation event, forming a fluctuation event sequence under multiple time periods; dividing the fluctuation event sequence according to the value in the operation period, dividing the event type corresponding to the fluctuation event, calculating the fluctuation time length proportion under each time section; defining the temperature state transition of multiple fluctuation events under each fluctuation time length proportion, combining the fluctuation events according to the multiple time periods, and solving the fluctuation event combination probability; taking the event attribute and temperature characteristics of the fluctuation event as input, performing correlation analysis on the fluctuation event, and obtaining the correlation factors of the combined fluctuation event; performing multi-target solving according to the correlation factors of the fluctuation event, solving the optimal control parameter combination, and taking the optimal control parameter combination as the output control strategy; and the efficiency of the refrigerator control and the accuracy of the temperature regulation are realized.

The application provides a multi-device cooperative task execution management method and system for power distribution scenarios, and relates to the technical field of distributed control. The method comprises the following steps: loading power distribution scenario demand targets to a task rule library, and decomposing to obtain a plurality of atomic sub-tasks; performing task parallel dependence analysis, and constructing a task sequential execution chain; performing device reuse analysis, and obtaining P reuse time chains of P reuse devices and W task execution time windows of W non-reuse devices; performing task execution control fitting, and outputting a plurality of task execution control parameters; obtaining a task cooperative control chain; and driving the P reuse devices and the W non-reuse devices to perform device space-time cooperative control management. The application solves the technical problem that the multi-device task scheduling method of the prior art usually ignores the space-time coordination problem between devices, resource contention and time conflicts occur when devices are scheduled, the scheduling efficiency of the devices is reduced, and the task execution efficiency in the power distribution scenario is low.

The invention discloses a plate shape quality defect analysis method, system and device for a medium plate and a medium, and relates to the technical field of computers, and the method comprises the steps: obtaining a plurality of technological parameters and corresponding plate shape quality data in the production process of the medium plate; analyzing each process parameter and the corresponding plate shape quality data based on a correlation analysis algorithm, and determining an influence degree value corresponding to the process parameter; under the condition that the influence degree value is greater than a preset correlation threshold value, constructing a key parameter data set based on the process parameters corresponding to the influence degree value; performing feature extraction on each process parameter in the key parameter data set to obtain a statistical feature value; the statistical characteristic value is compared with a corresponding standard value in a plate shape control knowledge base, the cause of the plate shape quality defect is determined based on the comparison result, and the plate shape control knowledge base is constructed based on data with the judgment result being qualified after the historical statistical characteristic value and the corresponding plate shape quality detection result are judged in the historical production process.

The invention discloses a fault parameter data prediction method, and relates to a VMD-PXLSTM-based aero-engine fan fault parameter data prediction method. Pearson correlation analysis is carried out on the input and / or output parameters related to the engine fan, the input and / or output parameters with too large correlation or too low correlation in the parameters are eliminated, and the input and / or output parameters with proper correlation are obtained; performing fault processing on the input and / or output parameters with proper correlation to obtain a fault simulation data set; dividing the fault simulation data set to obtain a training set and a test set; carrying out normalization processing on the data of the divided data set; establishing an engine parameter prediction model based on a VMD-PXLSTM deep learning model, performing variational mode decomposition on the input and / or output parameters after normalization processing, and decomposing the input and / or output parameters into a plurality of intrinsic mode components IMF; and the generalization ability and stability of the model on different data sets are enhanced.