79 results about "Dependence analysis" patented technology

The invention discloses a multi-source process parameter mapping supervision system and method based on a big data model, and relates to the technical field of process parameter analysis. Initial process parameters in a process production line are collected, the initial process parameters are processed to obtain standardized process parameters, and the process production line is subjected to process stage division; analyzing a stage product deviation degree of the process stage, calculating correlation between the standardized process parameters and the product deviation degree of the process stage, analyzing the stage product deviation degree of the process stage, predicting the product deviation degree of the process stage in production, and predicting a product reject ratio in production based on the product deviation degree of the process stage. According to the method, the product deviation of each stage and the reject ratio of the whole product are continuously predicted, the production state is judged in real time, a dynamic quality control closed loop is constructed, and the pertinence and effectiveness of supervision are improved.

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 a computer system energy consumption optimization method and system based on machine learning, and the method comprises the steps: collecting energy consumption data of a computer system, carrying out the preprocessing of the energy consumption data, carrying out the correlation analysis of the energy consumption data of all assemblies in the computer system, obtaining the coupling strength between all assemblies, and carrying out the calculation of the coupling strength. Quantizing the energy consumption data by using the coupling strength; inputting the quantized energy consumption data into an energy consumption prediction model to obtain an energy consumption prediction result; the energy consumption prediction model is obtained by training a long-short-term memory network model by using a training set; the training set comprises original energy consumption data; and according to an energy consumption prediction result, triggering a self-adaptive resource scheduling mechanism for optimizing operation parameter configuration of each component in the computer system. According to the method, the time sequence data can be effectively processed, and the time sequence characteristics of the energy consumption data are considered, so that a reliable basis is provided for an adaptive resource scheduling mechanism, and measures are taken in advance to avoid the waste of energy consumption.

The invention provides a correlation analysis method for technological parameters in a wafer processing process and related equipment. The correlation analysis method comprises the following steps: determining target parameters needing correlation analysis in the wafer processing process; according to the target parameter, acquiring all other process parameters belonging to the same wafer batch as the target parameter and corresponding parameter values in the data warehouse, and packaging into a data set; inputting the data set into a machine learning algorithm model, and calculating the correlation between the target parameter and other process parameters through a machine learning algorithm; and determining key process parameters influencing the target parameters according to a correlation calculation result. The correlation between the target parameter and other process parameters is automatically calculated and identified through a machine learning algorithm. In this way, the key process parameters influencing the target parameters can be effectively screened out, objective data support and decision basis are provided for the process defect problem caused by the common influence of multiple factors, and the difficulty of fault analysis and problem positioning is reduced.

The invention belongs to the technical field of intelligent dust removal, and discloses a coal feed-forward dust removal control system and method. The method comprises the following steps: collecting data and carrying out integrity analysis and data cleaning to obtain coal quality feed-forward data X, working condition data Y and equipment data; obtaining a strong correlation subset M and a key electric field identifier F through dynamic Pearson correlation analysis; performing coupling analysis on the X to obtain a coupling index T; analyzing according to T and Y to obtain a theoretical ash deposition amount; carrying out dimensionality reduction on the strong correlation subset, obtaining a strong correlation feature subset in combination with a theoretical ash deposition amount, carrying out normalization processing, and inputting the strong correlation feature subset and F into an LSTM model together to obtain a control parameter; on the basis of an MOPSO algorithm, online rolling optimization is carried out, and optimization control parameters are obtained; controlling the system to perform cooperative execution according to the optimized control parameters; according to the method, the problem of adjustment lag of a traditional dust removal system depending on real-time discharge feedback is effectively solved, and the technical bottleneck that energy consumption and discharge are difficult to take into account under single-target optimization is broken through.

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 safety measure ticket automatic optimization, and discloses a safety measure ticket automatic generation and checking method and system, and the method comprises the steps: obtaining a maintenance plan; generating a work ticket based on the maintenance plan; obtaining topological data and real-time operation data of the power grid; generating safety measure tickets based on the topological data and the real-time operation data of the work ticket and the power grid, wherein the safety measure tickets comprise a mounting safety measure ticket and a dismounting safety measure ticket; performing time sequence dependence analysis on the installation instructions of the safety measure items in the installation safety measure tickets, and generating an optimal operation sequence of each installation safety measure ticket; carrying out association analysis on the safety measure items in each safety measure ticket, and generating a mistaken dismounting verification strategy for dismounting the safety measure ticket; according to the method, the problems of safety measure redundancy, safety measure time sequence conflict and mistaken dismounting risk which often occur in a complex maintenance scene of multi-ticket parallel execution are effectively 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 discloses an abnormal data analysis method for monitoring the energy consumption of a transformer, and relates to the technical field of power monitoring, and the method comprises the analysis steps: S1, collecting the operation data of the transformer in real time based on a sensor, carrying out the real-time collection of the data, carrying out the preprocessing of the collected data, and storing the data; and S2, performing analysis learning on the data based on historical transformer data, extracting indexes capable of reflecting energy consumption characteristics of the transformer, screening the extracted indexes based on a correlation analysis method, reducing data dimensions, and extracting trend characteristics and periodic characteristics of the data based on a time sequence analysis method. A complete system is formed, data acquisition and fault processing are linked, energy consumption abnormity of the transformer can be found and solved in time, operation efficiency is improved, energy consumption and cost are reduced, reliable power supply of the electric power system can be guaranteed, defects of a traditional detection method are avoided, and the method has great value in the aspect of improving the overall economic benefit and stability of the electric power system.

To provide information for specifying a part for dividing a batch system.SOLUTION: A batch system analysis device includes: a time-series analysis part for analyzing an execution order of jobs constituting a batch system based on schedule information for instructing execution of the jobs, and determining execution order information on the jobs; an I / O information analysis part for determining job I / O information indicating input / output of data used in the jobs from a source code of the jobs; a dependence analysis part for determining dependence information between the jobs from the job I / O information; a separation information analysis part for determining separation information which is a part capable of separating a batch system from the execution order information and the dependence information; and an output part for outputting the separation information.SELECTED DRAWING: Figure 1

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.

The invention discloses an XGBoost-based threshing and re-drying process parameter intelligent prediction method, which comprises the following steps: S1, constructing a threshing and re-drying process parameter intelligent prediction module, S2, collecting time sequence process data in a PLC control system, and exporting the data as a table, S3, preprocessing the exported data, using a Python perspective data table, zeroing a negative value, and carrying out the prediction of the parameters in the threshing and re-drying process. The method comprises the following steps: S1, carrying out data preprocessing, S4, carrying out correlation analysis on preprocessed data, and screening out parameters with high correlation, S5, inputting the screened parameters into an XGBoost algorithm, and establishing a machine learning model, S6, carrying out model training, S7, repeating the steps S2-S6, and carrying out iterative training, and S8, carrying out effect testing. The method has the advantages that the parameter prediction precision and the real-time performance in the threshing and redrying process can be remarkably improved, the production process is optimized, and the product quality and the production efficiency are improved.

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 present invention discloses a multi-source process parameter mapping supervision system and method based on a big data model, which relates to the technical field of process parameter analysis, collects initial process parameters in a process production line, processes the initial process parameters to obtain standardized process parameters, divides the process production line into process stages, analyzes the degree of stage product deviation in the process stages, calculates the correlation between the standardized process parameters and the degree of product deviation in the process stages, analyzes the degree of stage product deviation in the process stages, predicts the degree of product deviation in the process stages in production, predicts the product defective rate in production based on the degree of product deviation in the process stages, monitors the predicted product defective rate in real time, adjusts the standardized process parameters, and corrects the standardized process parameters. The present invention constructs a dynamic quality control closed loop by continuously predicting the product deviation in each stage and the overall product defective rate, judging the production status in real time, and improving the pertinence and effectiveness of supervision.

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 relates to the technical field of data processing, and discloses a static false alarm confirmation method and system based on fuzzy testing, a terminal and a storage medium, and the method comprises the steps: obtaining static alarm data corresponding to a target source code, and carrying out the preprocessing, and obtaining a preprocessing result; performing correlation analysis on the preprocessing result to obtain a correlation analysis result, and constructing a minimum compiling code snippet according to the preprocessing result and the correlation analysis result; performing program slicing processing on the minimum compilation code snippet to obtain an execution code snippet, and performing fuzzy test processing on the execution code snippet to obtain a fuzzy test result; and performing alarm classification processing on the fuzzy test result to obtain an alarm classification result. According to the method, the minimum compilation code snippet is generated according to the static alarm data, and program slicing processing, fuzzy test processing and alarm classification processing are performed, so that the alarm classification result is obtained, and the accuracy and efficiency of static alarm automatic confirmation can be effectively improved.

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 discloses a rapid calculation method and device for an energy maximum tracking model, and relates to the technical field of demand analysis, and the method comprises the following steps: dividing an original training set into a training set and a test set, the training set being used for modeling and parameter adjustment, the test set being used for evaluation, and preprocessing the training set; correlation analysis is carried out on training set features, local correlation measurement is used to avoid multiple collinearity, and an energy value F of each feature is calculated; according to the method, the multi-collinearity problem is avoided by calculating the energy value of each feature and selecting the feature with good prediction, redundant features can be identified and removed through local correlation measurement, the accuracy of feature selection and the robustness of the model are improved, orthogonalization processing helps to eliminate the dependency relationship between the features, and the robustness of the model is improved. The introduction of the regularization item is helpful for controlling the complexity of the model, avoids the overfitting caused by the too complex model, and obtains a stable and accurate prediction result.

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.