33 results about "Experimental data analysis" patented technology

The invention relates to the technical field of clinical experiment data processing, and discloses a clinical experiment data analysis method based on reinforcement learning, and the method comprises the steps: obtaining time sequence physiological parameters and treatment scheme execution state parameters; performing hierarchical feature extraction on the time sequence physiological parameters, constructing a dynamic response feature model, and generating an initial treatment response evaluation model in combination with the execution state parameters; loading risk constraints and curative effect indexes to generate a comprehensive evaluation model, and simulating to obtain strategy regulation and control data; constructing and training a reinforcement learning decision model, and generating a treatment path correction model and initial treatment parameters; obtaining dynamic strategy adjustment information, and constructing a multi-stage optimization model for iterative optimization to obtain optimal treatment path parameters; reasoning a curative effect standard reaching probability based on a real-time model, and adjusting a strategy to realize dynamic curative effect matching. According to the method, the clinical data dynamic analysis and treatment scheme optimization capability is improved, and the method is suitable for precise medical scenes.

The invention relates to a partition loading drilling gas extraction dynamic simulation experiment device and method, and belongs to the field of coal mine safety. The device comprises an experiment cylinder body, a loading device and an air supply device. A hole sealing section confining pressure pressurizing device, an extraction section confining pressure pressurizing device and an axial pressurizing device are arranged in the experiment cylinder body, and the influence of coal body elasticoplastic deformation on permeability is truly reflected by accurately distinguishing stress loading of a hole sealing section and stress loading of an extraction section; the method comprises the following steps: analyzing a stress state, formulating an experimental scheme, preparing an experimental sample, simulating a loading experiment and analyzing experimental data. By combining drilling construction simulation and negative pressure extraction testing, gas emission rules and leakage characteristics under different hole sealing materials, processes and extraction parameters are systematically analyzed. The technology can accurately test the gas emission rule and the air leakage characteristic of drill holes with different hole sealing processes and hole sealing parameters under negative pressure extraction, and provides a scientific basis for optimizing the gas extraction process and improving the extraction efficiency.

The invention discloses an intelligent simulation experiment method and device. The method comprises the steps of obtaining task demand information; constructing an intelligent simulation experiment model; and processing the task demand information by using the intelligent simulation experiment model to obtain optimal experiment scheme data. According to the invention, an intelligent simulation experiment model composed of a data input agent, a simulation resource access agent, a basic scenario generation agent, an experiment scenario generation agent, an experiment operation control agent, an experiment data analysis agent and an experiment scheme recommendation agent is constructed; according to the intelligent combat simulation experiment system, software application in the form of an intelligent agent is used for replacing traditional combat simulation experiment software, a combat simulation experiment task AI intelligent agent is used for replacing manual operation, an intelligent combat simulation experiment is achieved, and the problems that a traditional combat simulation experiment is high in personnel requirement, large in energy consumption and few in innovative conclusion are solved.

The invention discloses a metal deposit classification method driven by multi-modal fusion data, and relates to the field of specific calculation models.The metal deposit classification method driven by the multi-modal fusion data comprises the following steps that 1, domestic known metal molybdenum deposit cases are collected, existing molybdenum deposit types are classified, and the molybdenum deposit types are classified; classifying based on different characteristics of the molybdenum deposit; compared with the prior art, the method has the beneficial effects that efficient fitting is carried out on the type of the metal ore deposit, and the cause type of the ore deposit can be rapidly and accurately recognized only through several parameters; geological texts, rock ore images, experimental data analysis and other multi-modal data can be fused, and molybdenum deposit cause type classification can be rapidly completed; the original technical bottleneck is broken through, a more efficient and accurate molybdenum ore type classification prediction method is provided, and powerful support is provided for reasonable development and utilization of Mo multi-metal mineral resources.

The present application relates to a kind of chemical instrument follow-up analysis system, including instrument model construction subsystem, experimental data analysis subsystem and instrument model correction subsystem;According to the type of analysis instrument, an instrument model is constructed, and the corresponding correlation index is established with instrument characteristic data and analysis type data to form the neural network of model, on the one hand, the experimental project information can be analyzed using instrument model, the corresponding adaptable and suggested analysis instrument is analyzed further, and the information of corresponding analysis instrument is presented, on the other hand, according to actual experimental result, instrument characteristic data in instrument model is corrected, instrument model is updated, and instrument characteristic data can be updated according to experimental result to improve the precision of system to provide accurate instrument report information, to assist experimenter to further judge.

The invention discloses a memory alloy part durability experiment data analysis method and system, relates to the technical field of data processing, and aims to solve the problem of poor alloy performance detection effect caused by environmental noise interference and signal scattering of existing memory alloy durability experiment data. A differential signal processing strategy is adopted to carry out noise reduction optimization on multi-dimensional performance response data, characteristic factors representing deformation recovery, residual deformation and energy dissipation are extracted and corrected, and combined logic of the characteristic factors is dynamically judged by combining a recognition rule set based on an application scene so as to determine a specific performance degradation mode. Corresponding residual use cycle prediction models are constructed according to different degradation modes, the credibility range is calculated, finally, a performance optimization strategy is generated through matching, and full-process closed-loop analysis from data preprocessing and feature extraction to degradation mode recognition, service life prediction and strategy generation is achieved. And the performance prediction precision and the reliability evaluation efficiency of the memory alloy part under the complex working condition are remarkably improved.

The invention relates to an autonomous attack and defense method and system, which realize large-scale autonomous attack and defense on an artificial intelligence model based on multi-agent cooperation. The method comprises the following steps: designing an intelligent body check and analyzing technical data by using an attack and defense method, identifying potential attack vectors, defense mechanisms and risk modes from the technical data, and generating an attack and defense blueprint; on the basis of the attack and defense blueprint, software package dependence needed by an experiment is recognized through an experiment deployment agent, a virtualization environment is constructed, and loading and configuration of a model and a data set are completed; and executing an attack and defense experiment in a deployed experiment environment by using an experiment debugging agent, collecting experiment data, analyzing model performance and generating an effect evaluation and feedback report, thereby realizing self-adaptive optimization and sustainable evolution of the system. Compared with the prior art, the method has the advantages that efficient autonomy and knowledge driving of security attack and defense are realized, and security risk mining, alignment evaluation and defense verification can be performed on the model and the system without manual intervention.

The invention discloses an experimental data analysis method, device and equipment and a storage medium, and relates to the field of data processing, and the method comprises the steps: carrying out the demand analysis of an experimental data file and a user analysis demand based on a large language model, and determining an experimental task specification which at least comprises an experimental type; according to the experiment type and the experiment data file, generating a standardized data table and a data portrait; determining an experimental data analysis plan according to the experimental task specifications and the data portraits; executing an experimental data analysis plan, and performing data processing on the standardized data table to obtain cartographic data and a statistical analysis result; based on a decoupling mechanism of drawing code generation and execution, generating experimental chart data according to the drawing data; and generating an experimental data analysis report according to the experimental chart data and the statistical analysis result. According to the scheme, end-to-end experimental data analysis from data input to report generation is realized, and the accuracy of experimental data analysis is improved.

The invention discloses a buckling failure behavior prediction method for a laser welding stiffened plate. The method comprises the following steps: S1, preparing a compression experiment sample; s2, carrying out a compression experiment; s3, analyzing compression experiment data; s4, finite element analysis; s5, establishing a machine learning model and training; according to the method, the compression buckling failure of the laser welding stiffened plate is expanded, an experimental method, numerical simulation, machine learning and software development are combined, a sample made of AlLiS4 and 2099 aluminum-lithium alloy is adopted, structural design is carried out to ensure buckling failure, a compression clamp is used for applying a load in an experiment, strain evolution is monitored through a strain gauge and a fringe projection technology, and the deformation of the laser welding stiffened plate is detected. According to data analysis, Abaqus software is adopted to establish a finite element model, a bilinear isotropic strengthening model is applied to predict a buckling load, post-buckling analysis is carried out, multiple technologies are integrated, an innovative solution is provided for design and analysis of the stiffened plate, and wide application potential is shown.

This invention relates to the field of space application technology, and discloses a method, system, and related equipment for processing scientific experimental data on space payload materials. The method includes: collecting load condition data, experimental image data, and material temperature data from scientific experiments on space payload materials, and performing time-series processing; determining the corresponding laser shutdown time points of the scientific experimental payload in the load condition data, experimental image data, and material temperature data; aligning the load condition data, experimental image data, and material temperature data and normalizing the acquisition frequency based on these time points; and performing joint analysis and processing on the various types of data after data alignment and acquisition frequency normalization. This invention, by determining the laser shutdown time points of the scientific experimental payloads for various types of data and aligning and normalizing the acquisition frequency of the data according to these time points, achieves precise correlation among various types of data, greatly improving the efficiency and accuracy of scientific experimental data analysis.

The application discloses an experimental data analysis method and device, equipment and a storage medium, and relates to the field of data processing.The method comprises the following steps: based on a large language model, performing demand analysis on an experimental data file and a user analysis demand, determining an experimental task specification, and determining that the experimental task specification at least comprises an experimental type; generating a standardized data table and a data portrait according to the experimental type and the experimental data file; determining an experimental data analysis plan according to the experimental task specification and the data portrait; executing the experimental data analysis plan, performing data processing on the standardized data table, obtaining mapping data and statistical analysis results; based on a decoupling mechanism of mapping code generation and execution, generating experimental chart data according to the mapping data; and generating an experimental data analysis report according to the experimental chart data and the statistical analysis results.The application realizes end-to-end experimental data analysis from data input to report generation, and improves the accuracy of experimental data analysis.

The application discloses a cable internal multi-form arc fault model. Experiments show that the cable internal arc generated between the core and the metal shielding layer presents multiple forms. Based on experimental data analysis, the cable internal arc is attributed to three forms of oscillation form, steady state form and semi-steady state form, and a cable internal multi-form arc fault model is proposed accordingly. The core part of the model includes an oscillation arc module constructed by a triangular wave controlled Gaussian random pulse function, a steady state arc module constructed by a 1 / 2 cosine-1 / 4 sine function, and a semi-steady state arc module constructed by a proportion control circuit. Then, a random switching module is used to select the arc form, and a fault timing module is used to control the arc starting time point, and finally a dynamic time-varying arc conductance is output to control the multi-form arc fault process. The application firstly proposes to construct a cable arc fault model in a multi-form manner, discovers the unique waveform characteristics of the cable internal arc fault, enriches the random diversity of the cable arc fault, and provides a new model basis for cable arc fault analysis.

This invention discloses a method for identifying arc grounding faults in distribution networks based on waveform subsequence segmentation and clustering. The method comprises: waveform subsequence segmentation based on an improved long-short-term memory network; combined time series feature analysis and feature dimensionality reduction of three-phase voltage and zero-sequence voltage waveforms; and arc grounding fault identification based on K-means. An improved LSTM algorithm is used to accurately segment different types of fault data within the same recorded waveform, reducing their impact on waveform eigenvalues. A fault identification model is then established that combines time series feature extraction of three-phase voltage and zero-sequence voltage waveforms. Through experimental data analysis, the boundary conditions between arc grounding faults and other faults are determined, effectively improving the shortcomings of traditional feature extraction based on current analysis.

The utility model discloses an organoid culture pore plate convenient to count and position, which belongs to the technical field of cell culture and comprises a culture pore plate, a plurality of pore grooves are arranged on the culture pore plate, scale marks are arranged at the bottoms of the pore grooves, and the scale marks comprise a plurality of transverse lines and a plurality of longitudinal lines which are uniformly distributed outwards by taking the circle centers of the pore grooves as centers. According to the utility model, orientation coordinates in the pore plate are arranged, so that the dynamic growth process of the organoid can be conveniently positioned and observed, regular counting of each organoid in each pore is facilitated, the budding number is calculated, and the accuracy of statistical analysis is improved; by setting the grids, the organoid can be located in a grid with a fixed area, so that the area can be conveniently calculated without being limited by different shooting machines or software, the efficiency of experimental data analysis is improved, and calculation or statistical errors are reduced; the same type of organs are continuously observed through scale line positioning, the efficiency of organoid culture experiment operation and data analysis and the data reliability are improved, and the device has very high practicability and wide application prospects.

A 3D printer based on dynamic extrusion force and precise temperature control and a method for controlling the diameter of a filament, specifically relating to the field of biomanufacturing technology. The present invention is intended to solve the problems of low diameter accuracy and uncontrollable printing structure of existing 3D printing filaments of polymer materials. The 3D printer of the present invention includes a cylinder, a tension and pressure sensor, a nozzle push rod, a printing nozzle and a temperature control device. The cylinder is arranged vertically downward, and the rod end of the cylinder is connected to the top of the nozzle push rod through a tension and pressure sensor. The lower end of the nozzle push rod is inserted into the channel inside the printing nozzle and can be moved up and down. The temperature control device is arranged on the outside of the printing nozzle and can heat the printing nozzle. The method for controlling the diameter of a filament includes: extrusion 3D printing; repeated 3D printing experiments; data analysis; parameter optimization; verification.

The application discloses an intelligent simulation experiment method and device, and the method comprises the following steps: obtaining task demand information; constructing an intelligent simulation experiment model; and processing the task demand information by using the intelligent simulation experiment model to obtain optimized experiment scheme data. The intelligent simulation experiment model is constructed by a data input intelligent agent, a simulation resource access intelligent agent, a basic scenario generation intelligent agent, an experiment scenario generation intelligent agent, an experiment operation control intelligent agent, an experiment data analysis intelligent agent and an experiment scheme recommendation intelligent agent. The software application in the form of the intelligent agent is used to replace the traditional combat simulation experiment software, and the combat simulation experiment task AI intelligent agent is used to replace manual operation, so that the intelligent combat simulation experiment is realized, and the problems of high personnel requirement, great energy consumption and few innovative conclusions in the traditional combat simulation experiment are solved.

The application provides a backlight X-ray image multilayer noise removing method based on deep learning, which comprises the following steps: collecting a spherical shell contour in a backlight X-ray image, and obtaining a noise-free image dataset by computer modeling; simulating image noise and adding the same to the noise-free image dataset to obtain a noisy image dataset; constructing an image noise reduction model with the FR-Unet architecture as the core, inputting the noisy image dataset, performing image enhancement, and outputting a noise-reduced noisy image; comparing the noise-reduced noisy image with the noise-free image to calculate a mean square error loss (MSE), and training multiple times to obtain an optimal image noise reduction model; and applying the optimal image noise reduction model to a real experimental image to verify the noise reduction effect of the model and ensure the image quality and detail retention. The application can effectively remove multilayer noise, realize efficient noise reduction while maintaining high image fidelity, avoid loss of important information, and improve the quality and accuracy of inertial confinement fusion experimental data analysis.

The application discloses a method for predicting the mechanical properties of martensitic heat-resistant steel by using EBSD detection data analysis, adopts area weighted average value to obtain corresponding EBSD data, and overcomes the geometric distortion problem of a traditional equivalent circle diameter method when processing non-equiaxed and slender lath martensite; the concept of martensite lath fitting length-diameter ratio is innovatively proposed, and through a large amount of experimental data analysis, the correlation between the concept and the mechanical properties of the martensitic heat-resistant steel is found; through a large amount of data, a prediction formula between the martensite lath fitting length-diameter ratio and the mechanical properties can be established, so that the mechanical properties of the final product can be accurately predicted by measuring the EBSD data of the sample in the subsequent production process.

The present invention discloses a rolling bearing life prediction method based on the similarity matching optimization theory. First, this method proposes to simulate the bearing degradation signal through an exponential decay function and a double-exponential function model, realizing the expansion of the sample dictionary set. Secondly, based on the Jensen-Renyi divergence health index of the Gaussian mixture model, the extraction of the degradation evolution trend of the rolling bearing vibration signal is realized. Then, by fitting the bearing degradation data with a Gaussian function, noise can be effectively reduced. The similarity is measured by the L2 norm of the function space, and the similarity is obtained based on traversing and searching the dictionary set and corresponding weights are assigned. Finally, the prediction result of the remaining service life of the rolling bearing is obtained. Through the simulation analysis of the bearing full-life cycle signal, the effectiveness of the proposed method is verified. The experimental data analysis results also show that this method can effectively predict the remaining service life of the rolling bearing.

The application discloses a kind of based on no-load uplink experimental data driving's elevator traction and brake performance evaluation method, formulates elevator car no-load uplink experiment, extracts elevator car no-load uplink experimental sample data;Based on experimental sample data, using the time domain analysis method in signal processing technique, in combination with the physical process of starting, braking, the acceleration data of car no-load uplink starting, braking is divided into several characteristic sections, and the characteristic parameters representing traction, braking performance are extracted from the characteristic section, and an elevator state diagnosis model is established;Based on elevator state diagnosis model, the performance of traction and braking is evaluated, and the confidence of the evaluation result is calculated according to the established elevator performance evaluation confidence function.The application can evaluate the performance of elevator traction and braking simultaneously, with high evaluation efficiency.Secondly, the application is an evaluation method based on experimental data analysis, which is closer to engineering practice than theoretical calculation, and can evaluate three indicators of braking performance, with high evaluation accuracy.

The utility model discloses a liquid taking device and a container for containing liquid. The liquid taking device comprises an air inflation part, an air bag, a first liquid taking pipe and a covering assembly, the air inflation part is communicated with the air bag through a first air inlet pipe, the first liquid taking pipe and the first air inlet pipe penetrate through the covering assembly, the first end of the first liquid taking pipe and the air inflation part are arranged on the first side of the covering assembly, and the second end of the first liquid taking pipe and the air inflation part are arranged on the second side of the covering assembly. The second end of the first liquid taking pipe and the air bag are arranged on the second side of the covering assembly. The container for containing the liquid comprises the liquid taking device. The device is simple in structure, low in cost and convenient to use, physical isolation between the taken-out liquid and ambient air is achieved, physical isolation between the liquid remaining in the container and the ambient air is achieved, the risk that the liquid is exposed in the air environment of a laboratory and polluted is avoided, and the device is suitable for popularization and application. The expense caused by waste of the polluted liquid is reduced, and the accuracy of experimental data analysis is guaranteed.

The invention relates to a molecular experiment data analysis method and device and computer equipment. The method comprises the following steps: converting original molecular experiment data uploaded by molecular experiment equipment to obtain standard molecular structure data under a preset data structure; when it is detected that the standard molecular structure data meets a preset data analysis condition, a standard data analysis rule corresponding to the standard molecular structure data is inquired from multiple candidate data analysis rules, and any candidate data analysis rule is obtained through configuration based on a mapping relation between reagent characteristics and equipment attributes; and according to the standard data analysis rule, analyzing the standard molecular structure data to obtain a data analysis result. By adopting the method, the analysis effect of analyzing the molecular experiment data is improved.

The application discloses a zebrafish cardiovascular function intelligent identification and evaluation method, which comprises the following steps: taking an image of a zebrafish under a microscope, inputting the preprocessed image into a trained zebrafish identification and classification model, filtering out zebrafish images that are not conducive to observation, obtaining a segmentation result of cells or tissues through a zebrafish cell and tissue segmentation model, obtaining information data of blood cell flow rate, heart rate and ejection fraction through a cell tracking algorithm and waveform analysis, and visualizing the cardiovascular function. The method can improve the efficiency of zebrafish experimental data analysis and processing, automatically screen appropriate image data from a large amount of images obtained through experiments, analyze and obtain information data of the cardiovascular function with research value, reduce manual operation, has a simple processing procedure, needs relatively few parameters to be adjusted, and provides strong technical support for zebrafish related research and development of complex cardiovascular disease drugs.

The utility model discloses a laboratory data analysis equipment, including electrophoresis appearance main part, set up on electrophoresis appearance main part's plug hole, digital display screen, operating button and electrophoresis tank, electrophoresis appearance main part is provided with the storage mechanism, the storage mechanism includes two wire box that sets from top to bottom, the inside of wire box is provided with transmission wire, coil spring and winding rod. Through setting wire box, coil spring and winding rod, make transmission wire have the tendency of winding in the box after elongation, the storage of transmission wire is convenient, through setting U -shaped board, insert piece and limiting screw, make the storage mechanism and electrophoresis appearance main part quick installation and disassembly convenient, through setting apron, it is convenient to quick maintenance replacement to transmission wire, thereby the experimental data analysis equipment has the effect of the wire of electrophoresis experiment and stores, avoids the mutual entanglement of wire, further reaches the purpose of improving electrophoresis experiment efficiency.

The invention discloses a conditional diffusion model image semantic editing method based on dynamic mask prediction, and belongs to the technical field of computer vision. Comprising the following steps: step 1, early-stage preparation: constructing a model and initializing parameters; step 2, training a mask prediction module; 3, carrying out initialization and editing fusion operation on an image editing framework; (3a) forward noise addition and background information extraction; (3b) de-noising generation and image fusion of editing branches; step 4, feature extraction and dynamic mask prediction; (4a) feature extraction; (4b) vector construction and mask generation; 5, performing loop iteration to obtain a final editing result; according to the method, a reconstruction branch is additionally arranged on the basis of an original diffusion model, a dynamic mask prediction module is designed, and accurate definition and dynamic adaptation of an editing area are achieved. Through experimental data analysis, the method provided by the invention can effectively reduce FID and Local FID values, improve CLIP Score, and obtain a better editing effect.

The present invention discloses a method for evaluating tight beach-bar sand reservoirs. Based on mercury injection experimental data analysis, the pore fractal dimension of a single sample point is calculated. After classifying the target strata as bar sand and beach sand reservoirs, the method uses the correlation coefficients between the experimentally calculated pore fractal dimension of the single sample point and various logging parameters, selecting the logging parameters with the largest correlation coefficients for regression fitting to establish pore fractal dimension logging prediction models for both bar sand and beach sand. The method also predicts the pore fractal dimension values ​​vertically and horizontally for individual wells. The method also establishes a tight beach-bar sand reservoir evaluation standard based on the correlation between the development performance of the produced blocks and the pore fractal dimension, and classifies the tight beach-bar sand according to the reservoir evaluation standard. The method has high prediction accuracy, is simple and easy to operate, and provides comprehensive characterization.

The invention discloses a dynamic experimental analysis method and device based on calibration type flow reaction calorimetry. The method specifically comprises the following steps: firstly, carrying out a flow reaction calorimetric experiment to obtain experimental data, then, carrying out calorimetric analysis to obtain a thermal conversion rate of each section, calculating reaction rate constants of a reaction process under different temperature conditions by utilizing a nonlinear fitting mode, and further, calculating kinetic parameters of the reaction process by utilizing a linear fitting mode. According to the method, additional component detection equipment is not needed, pre-exponential factors and activation energy in the reaction process are obtained through analysis of experimental data under different temperature conditions, dynamic analysis can be carried out while dynamic feeding chemical reaction calorimetric analysis is carried out under the continuous flow process condition, the experimental efficiency is high, and the process is safe.

The invention relates to a chaos engineering experiment data analysis method and device based on an AI large model, and relates to the technical field of data processing, and the method comprises the steps: obtaining target fault data and target system operation data of a chaos engineering platform; the target system operation data is operation data of the application system after the chaotic engineering platform injects the target fault data into the application system; calling an API interface of the large language model according to the target fault data and the target system operation data to obtain a target data analysis result returned by the large language model; providing the target data analysis result to a chaos engineering platform; wherein the target data analysis result is determined in combination with the large language model, analysis and processing by analysts can be avoided, and the data analysis cost is reduced; and the data analysis efficiency can be improved based on the large parameter quantity and accuracy of the large language model.

This invention discloses a method and system for optimizing hot melt adhesive preparation process parameters based on processing feedback, relating to the field of hot melt adhesive preparation technology. The method includes: classifying hot melt adhesive formulations and setting corresponding formulation labels; conducting single-factor group preparation experiments for different formulation labels; plotting process sequence curves and generating preparation trajectory curves based on single-factor experimental data; extracting performance parameters of each process factor and identifying response characteristics; dividing the entire preparation process into multiple stages; constructing a collaborative optimization prediction model for process parameters based on a neural network, using formulation labels, process parameters, performance parameters, and response characteristics as inputs, and outputting preparation time reduction rate and performance achievement rate; and testing the model's predictive reliability for new samples. The advantages of this invention are: by combining formulation classification, single-factor experimental data analysis, and neural network modeling, intelligent optimization of hot melt adhesive preparation process parameters is achieved.

The application discloses a kind of new methods for measuring the diffusion coefficient of substance inclusions in steel, comprising: obtaining experimental material, pretreatment is carried out to experimental material;Wherein, experimental material includes steel sample and inclusion;The high temperature modification experiment of pretreated experimental material is carried out by high temperature confocal equipment, and the composition of inclusion at different time is analyzed according to experimental data;The initial condition of kinetic model is obtained, and the mass transfer coefficient of inclusion is estimated based on initial condition and inclusion composition analysis.The new method for measuring the diffusion coefficient of substance inclusions in steel provided by the application effectively estimates the diffusion coefficient of substance inclusions in steel liquid, thereby providing important basic data for the optimization of steelmaking process.The method of the application has important experimental and production guiding effect and good application and popularization prospect.