139 results about "Single sample" patented technology

The invention relates to the field of environmental monitoring, and particularly discloses a non-negative matrix factorization and adaptive peak recognition fluorescence feature extraction and traceability system, which comprises a spectral data preprocessing module, a spectral data non-negative matrix factorization module, a component number automatic selection module, an adaptive peak recognition module, a feature library construction module and a similarity comparison module. An improved non-negative matrix factorization model is adopted to decompose the three-dimensional fluorescence spectrum matrix of a single sample, and an optimal component number K is automatically determined through multiplicative update rule iterative optimization; the self-adaptive peak identification module carries out selective filtering, accurately extracts the position and intensity of a fluorescence peak through multiple mechanisms, and carries out peak position calibration in a neighborhood; the Hungary algorithm is adopted to carry out characteristic peak matching to calculate the comprehensive similarity between the samples, and rapid and accurate identification of the pollution source is realized. The method has the advantages of high resolution, strong anti-interference capability, low requirement on the number of samples, automation and the like, and is suitable for water quality fingerprint feature extraction of a water sample in a complex environment and real-time source tracing of sewage.

The invention relates to the technical field of data analysis, and discloses a time series data anomaly detection method and system based on convergence trajectory feature analysis, and the method comprises the steps: collecting and preprocessing multi-dimensional time series data of at least one industrial device belonging to the same type, and obtaining a standardized single device feature sequence of each industrial device; training a baseline auto-encoder model based on the standardized feature tensor to obtain baseline parameters and baseline features of the baseline auto-encoder model; performing disturbance training on the baseline auto-encoder model by taking the standardized single device feature sequence of each industrial device as a single sample based on the baseline parameters to obtain convergence trajectory features of the baseline auto-encoder model, and obtaining a standard digital portrait library based on all industrial devices; and carrying out feature attribution and root cause analysis on the digital portrait. According to the method, the sensitivity and the detection accuracy of weak signals, small samples and complex behavior anomalies are remarkably improved.

The invention discloses a shield tunneling stratum intelligent identification method and system based on strict single sample prototype network and verification set scale calibration, and the method comprises the steps: dividing samples collected from a shield tunneling machine in real time into a training set, a verification set and a test set according to a set time and an engineering interval; carrying out normalized preprocessing on the multi-dimensional mechanism parameters by adopting a training set, and then carrying out supervised pre-training on the lightweight MLP encoder by adopting a cosine interval classifier to obtain a discriminative embedding space; according to the method, a high-precision, reproducible and low-time-delay online recognition function on the shield tunneling stratum under the condition of extremely few labels is achieved, the overall precision and minority class recall can be considered on the premise that the labeling cost is not increased, so that the output probability is more calibrated, the expandability and rapid deployment capacity are achieved, and the method is suitable for popularization and application. And the real-time performance and the reliability of shield tunneling stratum identification are obviously improved.

The invention discloses an artificial leather quality rapid detection method based on spectral analysis, and particularly relates to the field of quality detection.The method comprises the steps that component composition characteristics, surface and microcosmic characteristics and dynamic response characteristics of artificial leather are synchronously obtained through multi-dimensional original data acquisition, and a time-space correlation data set is formed through timestamp alignment and space calibration; constructing component analysis, surface association and dynamic response models, and eliminating space-time migration through a synchronous optimization model; after multi-dimensional features are extracted, optimized features are generated through cross-direction tensor fusion, attention enhancement and gating cycle unit time sequence fusion; and finally, quantizing the quality index based on a multi-task model, and outputting a quality grade and a traceable report in combination with a dynamic threshold value through hierarchical coding, graph neural network association enhancement and evidence fusion. The single sample collection duration is less than 3 minutes, the response time is less than 1 second, and full-dimension, rapid and accurate detection and quality traceability are realized.

The invention belongs to the technical field of image processing, and discloses a rice bud stage drought resistance prediction method, which comprises the following steps: acquiring sample images of a plurality of individuals of a rice variety to be detected under control and stress conditions, performing foreground segmentation and structural region marking on the images, and identifying seed, bud and root regions. And multi-dimensional phenotypic features are extracted for each region to construct a single-sample high-dimensional feature vector. And carrying out group feature extraction on a plurality of single sample vectors under the same processing condition, and carrying out double correction on a sample confidence coefficient weight and a feature steady-state weight to obtain a group feature vector. And constructing a multi-dimensional drought resistance response feature vector according to the difference between the group feature vectors of the stress group and the control group, and inputting the multi-dimensional drought resistance response feature vector into a pre-trained classification model to obtain a drought resistance level prediction result. The method achieves the automation of drought resistance evaluation, effectively inhibits the interference of abnormal samples, and improves the evaluation accuracy.

The application discloses a water quality and soil detection and analysis method and system based on multi-standard automatic evaluation and belongs to the technical field of environmental monitoring data processing. The system comprises a data import module, an intelligent identification module, a batch sample processing module, a multi-standard evaluation module, a comprehensive analysis module, a water chemistry verification module and a result output module. The system automatically analyzes the Excel table structure through an intelligent identification algorithm, extracts index names, units, detection results and background values, supports batch sample format identification, can analyze multiple samples at a time, integrates three environmental quality standards of underground water GB / T 14848-2017, surface water GB 3838-2002 and soil GB 36600-2018, automatically performs quality classification evaluation, calculates single pollution index P i , exceeding multiple, Nemerow comprehensive pollution index P N , performs water chemistry anion and cation balance verification, Shukarev classification, TDS and hardness checking, and supports single sample and batch sample export and summary table generation. The application solves the problems of weak Excel data recognition ability, batch sample processing difficulty, lack of multi-standard automatic switching, single comprehensive evaluation method and insufficient data quality checking in the prior art, and greatly improves the efficiency and accuracy of detection data analysis.

The application discloses a graphite ore grade identification method and device, equipment and medium, and relates to the technical field of graphite ore grade identification. The application introduces Laplacian wavelet convolution instead of a traditional ordinary convolution kernel, realizes parallel extraction of multi-scale features, combines a multi-head attention mechanism to capture scale features of the ore under a global view, constructs a sparse sample relation graph based on an enhanced feature matrix, and uses a Chebyshev graph convolution network to perform multi-order neighborhood information propagation and aggregation on a graph structure to realize grade identification. The process reconstructs the grade identification problem from'single sample independent judgment' to'sample relation learning' under a small sample on the basis of global multi-scale feature extraction, so that the final prediction of each sample node depends not only on its own features, but also on similar feature information of all neighbor samples, thereby realizing high-precision identification of the grade of the graphite ore under a small sample.

The present application relates to the technical field of surface defect detection, and particularly relates to a synthetic leather surface defect detection method based on spatial instance-specific self-attention, which comprises constructing a spatial instance-specific self-attention bottleneck structure, which comprises a main branch and an auxiliary branch; wherein the output of the main branch and the output of the auxiliary branch are fused together with the input of the spatial instance-specific self-attention bottleneck structure, and the fusion result is taken as the output of the spatial instance-specific self-attention bottleneck structure; the spatial instance-specific self-attention bottleneck structure is used to replace the bottleneck structure in the backbone network of the YOLOv8 algorithm to obtain a surface defect detection model; a surface defect sample dataset is obtained to train the surface defect detection model; and the detection data to be detected is input into the trained surface defect detection model to obtain a detection result; the spatial instance-specific self-attention bottleneck structure introduced in the present application is helpful to reduce the false detection and missed detection problems caused by insufficient mining of single sample specificity, and improve the accuracy of target detection.

A system for the simultaneous thermal analysis of a plurality of single samples of, in particular biological, material by means of differential scanning calorimetry, with at least one sample carrier having several sample vessels, wherein a single sensor for measuring an amount of heat emitted or absorbed by the single sample during the thermal analysis is assigned to each sample vessel; a heating and / or cooling unit for the simultaneous temperature application of the single samples included in the sample vessels, with a receptacle for the at least one sample carrier; a measuring instrument, which is connected to the single sensors and which is formed to simultaneously capture a measuring value for the emitted or absorbed amount of heat of the single samples during the thermal analysis, a sample carrier, in particular for use in this system as well as a method for the simultaneous analysis of a plurality of single samples or groups of single samples by means of differential scanning calorimetry.

The invention provides a rapid pollutant detection method based on spectral feature analysis, and relates to the technical field of environmental pollutant spectral detection, and the method comprises the following steps: obtaining an original absorption spectrum of a sample, and preprocessing and reconstructing an intrinsic spectral signal; extracting three types of features of morphology, frequency domain energy distribution and phase disturbance, and splicing to obtain a multi-dimensional joint feature tensor; pollutant classification and confidence judgment are completed through an improved CNN, and concentration is predicted through a linear regression model synchronously; in combination with a Mahalanobis distance verification result, deep residual network secondary feature mining is triggered for a low-confidence sample, a final type is determined according to a fusion result, and the concentration is corrected; and generating a detection report and dynamically updating the spectral feature intrinsic library. The method is adaptive to a portable ultraviolet-visible spectrophotometer, the single sample detection time consumption is less than 10min, the classification accuracy is greater than or equal to 90%, the unknown pollutant judgment accuracy is greater than or equal to 89%, and the method is suitable for on-site rapid detection.

The invention discloses a method suitable for on-site analysis of the valence state of thallium in water. The method comprises the following operation steps: step 1, collecting and separating a water sample; step 2, acidizing treatment; 3, Tl (I) and Tl (III) are separated; 4, total thallium is measured; step 5, Tl (I) determination; the invention relates to the technical field of atomic spectrum metal element analysis, and the method has the beneficial effects that by combining an electrochemical separation technology with a Chelex-100 solid-phase extraction column and cooperating with a portable liquid cathode glow discharge thallium measurement instrument, high-sensitivity field detection of Tl (I) and Tl (III) is realized, the detection limit is lower than 1.0 g / L, and the repeatability is RSDlt; the analysis period of a single sample is less than or equal to 30 minutes, the weight of equipment is only 5kg, battery power supply is supported, and acidification-separation-detection integrated operation can be completed outdoors without professional laboratory conditions.

This invention, entitled "A Method and Application for Target Gene Quantification in qPCR Using a Quantitative Internal Standard and a Comprehensive Correction Coefficient," belongs to the field of nucleic acid detection technology. The technical problem it addresses is that existing qPCR techniques require the establishment of a standard curve using a reference sample of known concentration, increasing reagent costs and experimental complexity, and resulting in poor reproducibility. The key technical solution is to provide a method for quantitative viral load qPCR analysis of a single sample. Specifically, it provides an internal standard with a known copy number, i.e., a quantitative internal standard, and establishes a correction method. Through a systematic error compensation mechanism, it achieves accurate quantification of the target gene. This method ensures both accuracy and reproducibility while simplifying detection design in clinical applications.

The invention discloses a deep coal rock pressure maintaining gas content determination method, device and equipment and a storage medium, and belongs to the technical field of well logging. According to the method, a rock core in a pressure maintaining barrel is segmented according to lithology on the basis of the correlation between the occurrence state of coal rock analytic gas and the pore structure of a coal rock reservoir, then the integral cut weighting coefficient of the pressure maintaining gas is determined, and the content of the pressure maintaining gas in a single sample is further obtained. The testing method can provide more accurate and reliable pressure-maintaining gas content data of the deep single coal rock, is applied to evaluation work of the gas content of the deep coal rock, and provides a basis for accurately calculating the total gas content of the single coal rock, delineating a favorable exploration area and improving the success rate of natural gas exploration and development.

This invention relates to a lightweight adaptive method for testing non-stationary data streams. Its core lies in constructing a two-branch adaptive architecture that decouples observation and execution statistics. A shadow observation branch, independent of the main inference branch, maintains a statistical reference baseline in real time, unaffected by adaptive calibration actions, thus suppressing the closed-loop coupling problem between drift perception and statistical updates at the mechanism level. Based on this decoupled architecture, the shadow branch calculates a composite drift surrogate metric to map and generate dynamic momentum parameters, and introduces relaxed cumulative variables to perform three-level state determination and spatiotemporal misalignment mask generation. Finally, during inference forward propagation, dynamic online calibration of statistics is performed only for the key levels selected by the mask. This invention alleviates the adaptation lag and statistical oscillation risks caused by fixed momentum, suppresses the statistical degradation risk caused by single-sample noise, and is suitable for real-time model optimization in single-sample, non-stationary streaming scenarios.

The invention discloses a neutron activation detection station system for rapidly detecting the element content of a material. The system can be used for rapidly detecting the element content of solid bulk materials in industrial production processes of building materials, coal, metallurgy, mines and the like. The system is composed of an element analyzer, a mechanical arm, a weighing scale, a coding sample barrel, a coding card reader, an electric roller conveyor and a PLC control system. The measurement time of a single sample is 30-300 seconds, the weight of the single sample is 1-10 kg, and a plurality of samples can be continuously and automatically measured. The method expands the application process points of the neutron activation technology, breaks through the traditional material detection mode, and has the advantages of wide application range, convenience in operation, high measurement precision, effective improvement of the detection efficiency and the like.

The invention belongs to the technical field of nuclear reactor physical simulation and safety analysis, and discloses a nuclear reactor passive start-up simulation method based on a Monte Carlo method, which comprises the following steps: calculating a single sample based on a Monte Carlo direct simulation method, modifying a random number seed in an input card to obtain a plurality of calculation results of mutually independent samples, and calculating the single sample according to the calculation results. And finally, obtaining a probability distribution result of the weak neutron field through statistics. The method can accurately describe the physical process of the weak neutron field stage, and is suitable for a three-dimensional complex reactor.

The invention belongs to the technical field of seismic oscillation, and particularly relates to a local terrain seismic oscillation simulation method considering site soil parameter randomness. The method comprises the following steps: S1, based on a random field theory and site soil exploration data, obtaining site soil parameters with soil layer variability; s2, based on the random field soil parameters, solving local terrain seismic oscillation by using an IBEM to obtain seismic response data of a single sample; s3, establishing a CNN model for solving a local terrain earthquake response; random field soil parameters are set as CNN input parameters, an earth surface point acceleration peak value of each group of independent samples is solved by using an IBEM, the earth surface point acceleration peak value is set as CNN output parameters, a data set is constructed based on the input parameters and the output parameters of each independent sample, and model training is carried out; and S4, based on the trained CNN model, obtaining local terrain ground motion considering site soil parameter randomness by using MCS. The method provided by the invention has good accuracy and applicability.

The invention belongs to the technical field of carbon fiber material testing, and discloses a method for detecting and judging the section of a carbon fiber precursor, which comprises the following steps: (1) preparing a precursor section slice sample: fixing a carbon fiber precursor bundle in a sampler, and cutting off the part, exposed out of the sampler, of the bundle to obtain the precursor section slice sample with the thickness of 0.8-1.2 mm; and (2) observing the cross section: placing the protofilament cross section slice sample prepared in the step (1) under an optical microscope, imaging by utilizing a transmission light source to penetrate through the protofilament cross section slice sample, and observing. According to the invention, the principle that the carbon fiber precursor has certain light transmission is utilized, a very thin section slice is cut from the precursor, the transmission light source can be directly utilized under an optical microscope for imaging, the section shape can be observed, the sample preparation process is simple, the detection time of a single sample is shortened, and the working efficiency is improved.

The invention belongs to the technical field of sandy conglomerate oil-gas exploration and development, and discloses a sandy conglomerate impurity group content quantitative characterization method and system based on a CT digital core. According to the method, a three-dimensional digital rock core image is obtained through CT scanning and preprocessed, an intelligent large model and a deep learning standard model are constructed, intelligent segmentation and volume content calculation of a miscellaneous base area are achieved, and the geological sweet spot section is judged with the miscellaneous base content, porosity and permeability as parameters. Compared with a traditional slice method, the method has the advantages that the impurity base segmentation IoU is improved to 0.89, the sweet spot / non-sweet spot identification accuracy rate reaches 88.9%, the single sample analysis time is shortened from 72 hours to 8 hours, the problems of low three-dimensional representation precision and poor efficiency are solved, accurate parameters are provided for oil and gas exploration and development, the efficiency is remarkably improved, and the cost is reduced.

The application discloses a rapid detection method for quality of artificial leather based on spectral analysis, and particularly relates to the field of quality detection, which comprises the following steps: through multi-dimensional original data acquisition, the component composition characteristics, the surface and micro characteristics, and the dynamic response characteristics of the artificial leather are synchronously acquired; time stamp alignment and space calibration are performed to form a space-time correlation data set; then, a component analysis model, a surface correlation model, and a dynamic response model are constructed, and time-space deviation is eliminated through synchronous optimization of the models; after multi-dimensional features are extracted, optimized features are generated through cross-direction tensor fusion, attention enhancement, and gate recurrent unit time sequence fusion; finally, quality indexes are quantified based on a multi-task model, and quality grades and traceable reports are output in combination with a dynamic threshold value through hierarchical encoding, graph neural network correlation enhancement, and evidence fusion; the single sample acquisition time is less than 3 minutes, and the response time is less than 1 second, so that full-dimensional, rapid and accurate detection and quality tracing are realized.

The present application provides a course learning model training method, a course learning method and related devices, the course learning model training method comprises: using the problem data set corresponding to the target course to perform reinforcement learning training on the large language model, in each training process, the current problem data set is filtered using a question classification threshold, and the filtered problem data set is used as the training sample of the next round of iteration training of the large language model, until the course learning model is obtained under the condition that the large language model meets the target iteration condition; the question classification threshold is determined based on at least one of the correctness of the reply data corresponding to different questions and the course category. The method and device of the present application can provide an adaptive course learning scheme with single sample granularity, based on real-time capability feedback and without additional strategy network training, thereby improving the reliability of the course learning result.

This invention belongs to the field of testing and analysis technology, and discloses a method for rapidly extracting and detecting the content of various terpenoids and sterols in rubber grass. The specific steps are as follows: Step 1: Preparation of standard solutions of terpenoids and sterols. Detection is performed using a general-purpose high-performance liquid chromatograph, eliminating the need for large-scale precision mass spectrometry equipment, resulting in lower purchase and maintenance costs. The operation process is simple and easy to learn, meeting the needs of high-throughput rapid detection of batch samples. A single sample test only takes 35-60 minutes, significantly improving detection efficiency. It can simultaneously complete the qualitative identification and content determination of five target components, including lupeol and taraxasterol. The method has good repeatability and high accuracy, with excellent linearity of the standard curve, and stable and reliable detection results. By optimizing the extraction solvent, temperature, time, and isocratic elution chromatographic conditions, the target components are fully extracted and separated effectively, allowing for accurate acquisition of the content data of each component.

The application discloses an infectious disease image data enhancement method and system, which performs single-sample data enhancement on each sample in an infectious disease image data set, and then randomly selects samples for multi-sample data enhancement. In the single-sample data enhancement aspect, multiple data enhancement strategies are summarized, and a random function is used to select the data enhancement strategies, and the possibility of each strategy selection is equal. The samples are enhanced through multiple random layers. The multi-sample data enhancement is enhanced through the single-sample data enhancement part after linear enhancement of multiple samples. The final result summarizes the results of the two kinds of enhancement, and the generalization and robustness of the method are ensured.

The invention discloses a water environment micro-plastic content rapid detection technology based on spectrum identification, and the method comprises the steps: carrying out the filtering and drying pretreatment of a sample, collecting the spectrum of the sample through ATR-FTIR, carrying out the baseline correction, smoothing and SNV transformation of the spectrum, extracting a polymer feature fingerprint spectrum interval, constructing and optimizing a support vector machine classification model, and carrying out the rapid detection of the micro-plastic content in the water environment. And finally, performing automatic qualitative identification on the type of the micro-plastic polymer by using the model, and performing quantitative analysis based on a correction curve. According to the invention, through a machine learning identification model, the whole-process rapid and automatic detection of micro-plastics in a water environment from a sample to an analysis result is realized, the defects of tedious pretreatment, strong subjectivity of artificial identification and inaccurate quantification of a traditional method are overcome, single-sample detection can be completed within one hour, and the method has high-throughput screening capability.

The present application relates to the technical field of space target detection, and specifically provides a progressive active learning weak supervision training method for dim and weak target detection, comprising: obtaining sample data to form a sample set, dividing the sample set into an initial sample set and a to-be-evaluated group according to the signal-to-noise ratio of the target, training a model using the initial sample set to obtain a preliminarily trained model, and starting in-group iteration: selecting the to-be-evaluated group in turn in descending order of signal-to-noise ratio, dividing the target in the to-be-evaluated group into simple samples and difficult samples, training a model using the simple samples to obtain an updated model, re-dividing the difficult samples into simple samples and difficult samples, repeatedly training and dividing until a preset in-group iteration termination condition is met, completing in-group iteration, and selecting a next to-be-evaluated group in turn to repeatedly execute in-group iteration. The present application progressively improves the training difficulty according to the real-time capability of the model, improves the detection precision and training efficiency, and reduces the data labeling cost.

The invention relates to an on-line measuring device and a measuring method for sample abrasion loss under a single variable condition, an impact particle swarm generation unit generates particle swarms with different impact speeds, and a measuring box is used for placing a sample and is provided with a temperature adjusting unit, a gas guide unit and a measuring unit for on-line measurement of sample quality. And the controller controls and cooperates to complete the abrasion loss test of the high-temperature oxidation sample under the variable temperature condition and the corrosion atmosphere. According to the method, the erosion rate data is calculated more accurately by monitoring the sample quality in real time, the prediction logic of the damage period caused by erosion is obtained by mastering the relationship between particle erosion and steel sheet quality change under different working conditions, and a theoretical basis is provided for equipment maintenance; the erosion weight loss of the steel sheet sample under the conditions of different particle components, impact angles and medium flow rates at high temperature can be tested, and repeated erosion experiments are performed on a single sample by testing the mass of the erosion steel sheet in real time, so that the change rule of the surface damage degree and the weight loss of the sample along with time under different working conditions is obtained.

This invention discloses a method and system for determining the temperature / strain rate dependence of metal strength, belonging to the field of micro-nano technology. The method obtains the compression or tension stress-strain curves of micro-nano metal samples, as well as the background noise of the no-load test. Based on the background noise, the maximum displacement rate of the micro-nano mechanical testing instrument is determined and used as a threshold. Based on the threshold, the stress-strain curves are iterated to determine the start and end points of all dislocation avalanches, the total plastic displacement of the dislocation avalanches, and the intensity ratio of the micro-nano metal sample, thereby determining the temperature / strain rate dependence of the metal strength. This method, through testing a single sample at room temperature and calculating the intensity ratio, can quantitatively and accurately confirm whether the strength is temperature-dependent, thus avoiding the limitations of qualitative analysis and the need for multiple multi-sample experiments to save time, manpower, and other experimental costs.

The application provides a single sample learning path construction method for medical image key point detection. The method comprises: feature extraction and sample similarity measurement on images in a data set to obtain a similarity score; according to the similarity score, a single typical sample representative in a key point area structure is screened out from the data set; a pseudo label generator is trained based on the typical sample; pseudo label information of key points of other samples in the data set is generated by using the pseudo label generator; the pseudo label information is used as a supervision signal to train a key point detector, and a detection network of the key point detector adopts a U-Net network as a backbone network and a pre-trained VGG19 encoder as a feature extractor of the network; and the key point detector is used to detect key points of a target image and predict a key point heat map and a coordinate offset map. The method of the application reduces labeling cost, improves the precision and stability of key point detection, and enhances the generalization ability.

This invention relates to an automated sample preparation system and process for aluminum electrolyte testing in the field of sample preparation and testing technology for the aluminum industry. The system includes an equipment cabinet, a loading / unloading tray assembly, a loading / unloading robot, a sorting robot, a grinding system, a tilting and unloading system, a pressing system, a conveying system, a control and management system, a cleaning and drying system, and a dust removal system. Except for manual loading and unloading, all other sample preparation steps are automated, and data management is intelligent. It offers advantages such as safety, high efficiency, low labor costs, improved working environment for workers, stable sample quality, high testing efficiency, and a high degree of automation. It achieves a high degree of hardware and software integration, is simple to operate, and has stable performance. It can automatically read, record, store, share, statistically analyze, and process data. It occupies a small space and can process samples in batches within a closed space, which helps to shorten the sample preparation time for a single sample and ensure the stability of sample quality.

The invention relates to the field of gas analysis and detection, and discloses a helium content and helium isotope detection system and method, a medium and electronic equipment. A quantitative sample injection control unit of the detection system controls a sample to enter an automatic sample pool by controlling a hydraulic rod to press downwards; the automatic continuous sample injection device can realize automatic sample injection and quantification of samples, and can be connected with a plurality of samples to be detected at the same time, so that the tedious step of disassembling a pipeline and connecting a gas cylinder is omitted; the large volume (the quantitative sample injection control unit, the gas purification unit and the helium content determination unit) of the pretreatment cavity is 2000cm < 3 >, so that the pretreatment cavity is more suitable for the characteristics of high concentration, high pollution and difficult extraction of oil gas sample active gas; the constant pipeline volume of each unit and the purification part of each unit are optimized, the balance relation among the vacuum degree, the purification efficiency and the time control is met, the high gas purification rate (the purification rate is greater than 99.9%) is ensured, and meanwhile, the detection efficiency is also greatly improved (the analysis period of a single sample is shortened to 1.5 hours from the original one day).