50 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 relates to the field of bimetal composite materials, in particular to a method for researching the influence of an intermediate layer on bimetal interface assembly and performance, which comprises the following steps: electrochemically depositing a FeCoNiCr-M high-entropy alloy coating; a micro-textured high-entropy alloy coating is constructed in a laser controllable manner; according to Al / HEA / Fe composite material liquid-solid forming process optimization and structure property characterization, the Al / HEA / Fe composite material liquid-solid forming process optimization and structure property characterization method has the following beneficial effects that according to the requirement for Al / Fe bimetal high interface bonding strength, the microtextured high-entropy alloy coating is prepared through the electrochemical and femtosecond laser composite technology, the microtextured high-entropy alloy coating serves as a middle layer between a solid steel matrix and a liquid aluminum alloy melt, and the high-strength Al / HEA / Fe composite material is obtained; the Al / HEA / Fe composite material is obtained through a liquid-solid forming technology, and the Al / HEA / Fe composite material is obtained through high-entropy alloy component / content optimization, coating surface structure design, technological parameter regulation and control and the like on the basis of thermal and dynamic calculation and experimental data analysis. And research on the aspects of a high-entropy alloy coating solid solution phase forming mechanism, a composite material interface structure evolution rule, an interface strengthening mechanism and the like is carried out.

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 relates to the technical field of cell data processing and analysis, in particular to a stem cell experimental data analysis method and system. The method comprises the following steps: acquiring an original experimental data set of stem cells; performing standardization processing on the stem cell original experiment data set to obtain stem cell experiment data; performing multi-dimensional feature recognition on the stem cell experimental data to obtain stem cell multi-dimensional feature data; performing stem cell multi-dimensional differentiation information extraction on the stem cell multi-dimensional feature data to obtain stem cell multi-dimensional differentiation information; and performing differentiation marker identification on the multi-dimensional differentiation information of the stem cells to obtain differentiation marker characteristics. Through a data processing technology, a pattern recognition technology and a machine learning technology, the differentiation process gene expression state comprehensive analysis of the stem cell multi-dimensional feature information is realized, so that the gene expression condition of the stem cell in a specific period can be completely presented.

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 invention discloses an alloy high-temperature tensile and creep deformation parameter identification and prediction method and device based on a physical mechanism and a storage medium, and relates to the field of alloy high-temperature tensile and creep plasticity constitutive parameter identification and prediction.The alloy high-temperature tensile and creep deformation parameter identification and prediction method comprises the steps that a sample is subjected to a tensile test and a creep test to obtain experimental data; the nominal yield stress and the steady-state creep rate are obtained through analysis; measuring and analyzing the sample by using a backscattered electron diffraction analysis technology to obtain data, establishing a finite element model according to the data, and performing tensile finite element simulation and creep finite element simulation to obtain simulation data; constructing a crystal plastic constitutive model, and obtaining primary target parameters of an improved plastic flow rule through a least square method; a true stress strain value and a creep strain time value which are obtained by comparing experimental data and simulation data are compared to obtain matched secondary target parameters, and then high-temperature mechanical response, deformation behaviors and failure behaviors of the sample in different states are predicted.

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 present invention relates to the technical field of crystal data processing, and in particular to a method and system for analyzing crystal experimental data. The method comprises the steps of: obtaining a preset neighboring segment of the data point with a data point in a crystal XRD data sequence as the center, obtaining the local symmetry of the data point by the intensity change in the neighboring segment of the data point; calculating the possible degree of the diffraction peak of the data point; obtaining the noise degree of the data point by the mean value of the intensity difference between the adjacent data point and the previous group of adjacent data points in the neighboring segment of the data point; calculating the polynomial order adjustment coefficient of the sliding window; weighting the preset polynomial order by the polynomial order adjustment coefficient of the sliding window to obtain the polynomial order of each sliding window; using the polynomial order of the sliding window in the S-G filtering algorithm to filter the XRD data sequence to realize crystal experimental data analysis, and effectively improve the accuracy of crystal experimental data analysis.

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 invention discloses a semi-immersive penetration experiment system for a water filter pipe, and particularly relates to the technical field of data analysis, and the semi-immersive penetration experiment system comprises a detection environment determination module, an experiment data acquisition module, an experiment data preprocessing module, an experiment data analysis module, a data comprehensive analysis module, a judgment module and a man-machine interaction module. The detection area determination module is used for determining a target detection environment and dividing water level environments at different heights, and the experimental data acquisition module is used for acquiring parameters of a water filter pipe in a semi-immersion penetration experiment process; the experimental data preprocessing module and the experimental data analysis module are used for analyzing and processing data acquired by the experimental data acquisition module, and the comprehensive data analysis module is used for calculating a comprehensive evaluation index of a penetration experiment; the judgment module is used for establishing a penetration experiment comprehensive evaluation standard value and judging whether the semi-immersion type penetration experiment of the water filter pipe reaches the standard or not, the man-machine interaction module generates a detection report, and the penetration performance of the target water filter pipe is comprehensively and effectively fed back.

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 invention discloses oral liquid for treating cognitive impairment and a preparation system and a detection method thereof.The oral liquid comprises a decocting device, a filtering device, a concentrating device, an alcohol precipitation device, a uniform stirring device, a storage device and a sterilization device, the storage device comprises a filling mechanism and a detection mechanism, the filling mechanism is used for filling the oral liquid, and the detection mechanism is used for detecting the oral liquid; the detection mechanism is used for detecting whether the filled bottles are sealed or not; the detection mechanism comprises a conveying assembly used for conveying the bottles, a vacuum assembly used for detecting whether the bottles are sealed or not in a vacuum mode and an identification assembly used for identifying whether the bottles leak liquid or not. The conveying assembly comprises a conveying frame, a rotating part arranged on the conveying frame and a conveying part used for conveying the conveying frame, and the rotating part is used for achieving simultaneous rotation of the multiple bottles; the conveying frame is provided with a plurality of inserting holes used for inserting bottles. The bottle cap sealing device has the effect of sealing a bottle and a bottle cap, and the effect that the product can obviously improve cognitive impairment is obviously obtained through experimental data analysis.

The invention provides a backlight X-ray image multi-layer noise removal method based on deep learning, and the method comprises the steps: collecting a spherical shell contour in a backlight X-ray image, and carrying out the modeling through a computer, and obtaining a noise-free image data set; simulating image noise, and adding the image noise into the noiseless image data set to obtain a noisy image data set; constructing an image noise reduction model taking the FR-Unet architecture as a core, performing image enhancement by taking the noisy image data set as input, and outputting a noisy image after noise reduction; comparing the image with a noiseless image, calculating mean square error loss (MSE), and training for multiple times to obtain an optimal image noise reduction model; and applying the optimal image noise reduction model to a real experimental image, verifying the noise reduction effect of the model, and ensuring image quality and detail retention. According to the method, multi-layer noise can be effectively removed, high-efficiency noise reduction is realized while high image fidelity is kept, loss of important information is avoided, and the quality and accuracy of inertial confinement fusion experimental data analysis are improved.

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 present invention provides an experimental data analysis method, device and electronic device based on OCR, which obtains experimental data by performing OCR recognition on a target experimental report, and obtains a complete experimental detection data set corresponding to the target device; performs data pruning operation on the complete experimental detection data set through multiple data pruning strategies to obtain multiple partial experimental detection data sets; for each partial experimental detection data set, selects a target inference neural network in a neural network queue to process the partial experimental detection data set to obtain the inference analysis result corresponding to the partial experimental detection data set; the neural network queue includes multiple inference neural networks, and the multiple inference neural networks are obtained by debugging multiple debugging learning sample libraries under multiple debugging learning sample libraries; performs a fusion operation on the inference analysis results corresponding to the multiple partial experimental detection data sets to obtain the target inference analysis result. The present invention can increase the accuracy of the inference analysis result.

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.