1245 results about "Mass spectrum analysis" patented technology

The invention relates to the technical field of industrial control, in particular to an intelligent centerless grinding machine cooling system and method for difficult-to-machine materials, and the system comprises a multi-source sensing unit, an edge calculation module, a decision center module, a precise execution module and a digital immune memory bank; compared with the defects that in the prior art, monitoring depends on a single temperature sensor, thermal field sensing is incomplete, thermal damage early warning lags behind and the like, according to the scheme, grinding power frequency spectrum, acoustic emission signals, triaxial vibration and coolant mass spectrum data are collected in real time through a multi-source sensing unit, and a micron-sized temperature field is reconstructed in combination with an edge end space-time diagram convolutional network; full-dimensional monitoring of the grinding thermal-mechanical coupling effect is achieved, and thermal anomaly recognition sensitivity and early warning timeliness are remarkably improved.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

The invention relates to the technical field of data processing, and discloses an incremental clustering method and system for an atmospheric aerosol particulate matter mass spectrum data set, a terminal and a storage medium, and the method comprises the steps: obtaining the atmospheric aerosol particulate matter mass spectrum data set, and carrying out the clustering initialization processing, and obtaining a plurality of clustering initialization parameters; determining a target cluster selection strategy, and performing first similarity calculation and cluster allocation processing on the plurality of cluster initialization parameters to obtain a plurality of cluster center matrixes; performing merging processing and de-noising processing on the plurality of cluster center matrixes to obtain an initial clustering result, and performing second similarity calculation to obtain an iterative similarity; and performing clustering convergence judgment and iterative loop clustering processing on the initial clustering result, and outputting a clustering result of the atmospheric aerosol particulate mass spectrum data set. According to the method, the inter-cluster mixing condition in the clustering analysis process of the atmospheric aerosol particulate matter mass spectrum data set can be remarkably reduced, and the accuracy of clustering result output is ensured.

The invention discloses a dynamic tracking method for an air pollution source, which relates to the technical field of pollution source tracking and comprises the following steps of: acquiring a pollution concentration raster data set, a UAV sniffing set and a meteorological field data set, reconstructing a wind field and predicting pollution transportation, generating a candidate source hypothesis, matching fingerprints and calculating weights, adaptively scheduling sensors, and performing Bayesian inversion fusion and result output. Unified pollution concentration and meteorological data are generated through multi-source data collection and three-dimensional flow field reconstruction, second-level wind field calculation is achieved through a dimensionality reduction CFD model, candidate source hypotheses are generated in combination with Lagrange backtracking and Euler transport, and a weight matrix is formed through UAV mass spectrum fingerprint bidirectional matching. Secondary cruise of the unmanned aerial vehicle and densification sampling of ground nodes are driven through confidence ranking, and sampling density is adaptively optimized. Bayesian joint likelihood and Markov chain are adopted to fuse multi-source data, dynamic source coordinates and emission rate are output, and a closed-loop iterative air pollution source tracking system is realized.

The invention provides a method for identifying PFAS in an environment based on machine learning pseudo-targeted screening, which comprises the following steps: calling mass spectrum data containing PFAS compounds from a preset mass spectrum database, and performing interference peak elimination processing on the mass spectrum data to obtain a model training data set; extracting a feature data set for model training from the model training data set based on a feature extraction standard; training a plurality of machine learning classification models based on the feature data set, and performing performance evaluation on each machine learning classification model based on a training result to determine an optimal machine learning classification model; and analyzing the optimal machine learning classification model, determining key features when the PFAS is screened and identified, and carrying out PFAS screening identification verification on the optimal machine learning classification model according to the key features based on an actual environment sample. The method has the advantages of saving analysis cost, improving analysis efficiency and improving compound recognition accuracy.

The invention discloses an integrated circuit equipment data optimization monitoring system and method based on big data, and relates to the technical field of integrated circuit manufacturing. The method is used for solving the problems of insufficient multi-physical field monitoring, difficulty in abnormal traceability and lack of closed-loop control in plasma etching. A plasma sheath thickness inversion model and an etching selection ratio model are constructed by collecting radio frequency reflection phase, mass spectrum ion strength and wafer temperature data, and process parameter-plasma state dynamic coupling is established. Interference image distortion features and electron microscope size data are fused, micro-groove geometric parameters are analyzed, morphology instability risk indexes are generated, and nanoscale early warning is achieved. And designing a dual-channel fusion network, embedding a physical constraint attention mechanism, and generating an etching rate optimization instruction. On the basis of incremental learning, model parameters are updated online, a'monitoring-decision-feedback 'closed-loop system is formed, anomaly detection sensitivity and decision reliability are improved, and technical support is provided for intelligence of integrated circuit equipment.

The invention discloses a sewage high-risk pollutant screening and identification method based on fragmentation tree pre-training, and the method comprises the steps: constructing a fragmentation tree pre-training data set according to the second-level mass spectrum data of known high-risk pollutants, so as to carry out the self-supervision pre-training of a graph neural network encoder, and obtaining a fragmentation tree editor; the method comprises the following steps: acquiring secondary mass spectrum data of suspected high-risk pollutant related compounds in a to-be-detected sewage sample, constructing a fragmentation tree set, constructing a high-risk pollutant screening model based on a fragmentation tree encoder, performing high-risk pollutant signal screening on the fragmentation tree set to obtain a high-risk candidate fragmentation tree set, and performing high-risk pollutant signal screening on the high-risk candidate fragmentation tree set. And generating a candidate molecule set of each high-risk candidate fragmentation tree, and identifying a target molecular structure and a matching score of the high-risk candidate fragmentation tree from the candidate molecule set. According to the invention, rapid screening and priority ranking of high-risk pollutants in sewage are realized, and candidate output of structure identification is further provided on the basis of screening.

The invention discloses a scorpion anti-inflammatory polypeptide as well as a screening method and application thereof, and belongs to the technical field of traditional Chinese medicinal material polypeptides. The scorpion anti-inflammatory polypeptide is one of G4, G5 and G17. The method comprises the following steps: detecting a scorpion buthus martensii polypeptide extract through nanoElute 2 nanoliter liquid phase separation, timsTOFPro2 mass spectrometry and BPS Novor search analysis, screening a scorpion buthus martensii polypeptide, and further synthesizing the screened candidate polypeptide by adopting a solid-phase synthesis method; the effect of the synthesized candidate polypeptide on inhibiting microglial cell inflammation is evaluated through a qPCR method, and then the scorpion buthus martensii anti-inflammatory polypeptide is screened out. The Buthus martensii Karsch anti-inflammatory polypeptides G4, G5 and G17 screened by the method, especially G5, can significantly inhibit microglial cell inflammation and present concentration dependence, a theoretical basis is provided for medicinal development of Buthus martensii Karsch polypeptides, and a potential anti-neuroinflammation mechanism of the Buthus martensii Karsch polypeptides is disclosed.

The invention belongs to the technical field of detection, relates to a free radical detection method, particularly provides a water free radical cation detection method, and aims to realize water free radical cation detection which is efficient, rapid, free of sample pretreatment and convenient to analyze. According to the invention, an LTQ-XL linear ion trap mass spectrometer is used as a detection tool, and a dielectric barrier corona discharge method is adopted to provide a water free radical cation detection method. The method comprises the following steps of: filling argon (Ar) between two discharge electrodes as a working gas, applying a certain voltage to two electrodes of a dielectric barrier discharge ionization source (DBDI), detecting water free radical cations to obtain a corresponding mass spectrum to represent the water free radical cations, and optimizing a target signal to obtain corresponding mass spectrum data. The method can be used for efficiently and quickly detecting the water free radical cations without sample pretreatment and the like, is convenient to analyze and apply, and has remarkable innovativeness and wide application prospects.

The invention relates to the technical field of vacuum measurement, in particular to a device for expanding the linear range of mass spectrometry measurement, which comprises a gas transmission pipeline, an electromagnetic valve, a small hole and a mass spectrometer, and is characterized in that the front end of the gas transmission pipeline is used for allowing a sample to be measured to enter, and the rear end of the gas transmission pipeline is connected with the mass spectrometer; the middle part of the gas transmission pipeline is divided into three channels, namely a first gas transmission pipeline, a second gas transmission pipeline and a third gas transmission pipeline; the first electromagnetic valve and the first small hole are sequentially arranged on the first gas transmission pipeline, the second electromagnetic valve and the second small hole are sequentially arranged on the second gas transmission pipeline, and the third electromagnetic valve and the third small hole are sequentially arranged on the third gas transmission pipeline. According to the method, manual dilution or segmented sample injection is not needed, a wide concentration range can be covered by single analysis, more than 60% of time cost is saved, and measurement in a dynamic range of 106 is realized.

The invention relates to a detection method for determining isotope abundance of a 15N or 18O labeled nitrate sample by using an organic mass spectrum. The detection method comprises the following steps: (1) dissolving the 15N or 18O labeled nitrate sample in a methanol aqueous solution; (2) taking the supernatant, filtering with a filter membrane, and diluting with a methanol aqueous solution to obtain a solution to be detected; and (3) detecting the solution to be detected by using a high-resolution liquid chromatograph-mass spectrometer, selecting a mass spectrum in a peak appearance time window, recording relative mass spectrum signal intensities with mass-to-charge ratios of 61.988 and 62.985, and calculating to obtain the 15N isotope abundance. Or recording the relative mass spectrum signal intensities with the mass-to-charge ratios of 61.988, 63.993, 65.997 and 68.001, and calculating to obtain the abundance of the < 18 > O isotope. Compared with the prior art, the method has the advantages that the sample pretreatment time is greatly shortened, the economic cost is low, and the test result is accurate.

The invention discloses a protein mass spectrum coding method based on self-supervised learning, and the method comprises the steps: guaranteeing the consistency of model input through the preprocessing steps of data enhancement, normalization, sequence length standardization and the like; by constructing a self-supervised learning task and utilizing a multi-head attention mechanism of a Transform architecture, efficient extraction of global and local features of mass spectrum data is realized; through an autoregressive encoder training framework, the model can automatically learn the internal structure of mass spectrum data without a large amount of labeled data, and high-dimensional coding representation with robustness and generalization ability is generated. According to the method, the workload of manual feature design is remarkably reduced, and the generated coding representation can be applied to downstream tasks such as mass spectrum data quality evaluation, protein identification and quantification, post-translational modification identification and biomarker discovery, and has wide applicability and high efficiency; and an innovative intelligent solution is provided for proteomics research.

The invention provides an insulating oil gas mass spectrum analysis method and system. The method comprises the following steps: firstly, circulating vacuum degassing for one time, then deoiling and dehydrating the removed gas, introducing the deoiled and dehydrated gas into a mass spectrometry detection unit, carrying out primary detection, calling a self-adaptive algorithm and selecting proper circulating vacuum degassing times based on a primary detection result in combination with degassing temperature and degassing pressure of circulating vacuum degassing, and entering second-stage vacuum degassing; and deoiling and dehydrating the deoiled gas, and introducing the deoiled gas into a mass spectrometric detection unit for secondary detection to obtain a final detection result. According to the method, effective fusion of efficient degassing and rapid detection is realized, and reliable technical support is provided for rapid and accurate online monitoring and real-time fault diagnosis of the insulating oil dissolved gas.

The invention relates to the technical field of monitoring and detection, and discloses an intelligent early warning method and system for gaseous pollutants in the semiconductor industry, and the method comprises the steps: analyzing the molecular weight of the gaseous pollutants according to the mass-to-charge ratio in an original mass spectrum signal, and analyzing the pollutant type and concentration space-time distribution matrix of the gaseous pollutants according to the molecular weight; carrying out pollution source analysis on the gaseous pollutants according to the pollutant types and the concentration space-time distribution matrix; analyzing the concentration contribution rate of the target pollution source by using the pollutant component characteristics, and analyzing the multi-stage dynamic early warning condition of the gaseous pollutants according to the concentration contribution rate and the concentration spatial-temporal distribution matrix; when the pollutant concentration data in the concentration space-time distribution matrix exceeds a multi-stage dynamic early warning condition, triggering a graded early warning signal of gaseous pollutants in the semiconductor industrial environment; and adaptively executing a control strategy corresponding to the gaseous pollutants in the semiconductor industrial environment based on the graded early warning signal. According to the invention, the accuracy of intelligent early warning of gaseous pollutants in the semiconductor industry can be improved.

Known mass spectral data of a library of spectra corresponding to known compounds or known mass spectral data determined from a database of known compounds are compressed using a neural network encoder, producing a group of corresponding compressed known representations of known mass spectral data. Experimental mass spectral data of an experimental mass spectrum is compressed using the neural network encoder, producing a compressed experimental representation of the experimental mass spectral data. The experimental representation is compared to the group of known representations and each comparison is scored. At least one comparison with a score above a predetermined score threshold is selected. A known compound is determined from the selected at least one comparison. The known compound is identified as a compound of the experimental spectrum.

The invention discloses a mass spectrometric analysis-based paint component detection system and method, and the system comprises a data collection and tensor construction module which collects and processes original data to obtain a decomposable tensor; the tensor decomposition module is used for decomposing the sorted tensor through a PARAFAC model; the spectrum prediction model module is used for establishing candidate samples and predicting the candidate samples through a spectrum prediction model to obtain predicted spectrum vectors; the spectrum similarity calculation module is used for comparing the fragment spectrum vectors with the prediction spectrum vectors one by one and obtaining matching scores through calculation; the Bayesian fusion module is used for carrying out probabilistic fusion on the fragment spectrum vector and the prediction spectrum vector, and obtaining the existence probability and the concentration posteriori distribution through calculation; and the detection module is used for labeling and confirming the candidate samples of which the matching confidence is lower than a set threshold value. According to the method, the detection reliability is improved while the accuracy is ensured, and the requirement of being applied to a complex detection scene is met.

The invention relates to a brand-new microbial natural metabolite, namely Fcl-8, which is derived from a fermentation solution of Xenorhabdius budapestensis XBD8, and is characterized in that the Fcl-8 is a new microbial natural metabolite, namely Fcl-8, which is derived from the fermentation solution of the Xenorhabdius budapestensis XBD8. The purified Fcl-8 is a snow white flocculent crystal and is very easy to dissolve in water, and the solid state of the purified Fcl-8 is as shown in Figure 1. The chemical structural formula of the Fcl-8 is as shown in a figure 2, the chemical formula is C57H102N14O12, the molecular weight is 1175.78744, and the typical secondary mass spectrum information of the Fcl-8 is as shown in a figure 3. The Fcl-8 is derived from a microbial natural metabolite, is novel in structure and high in biological activity, and has very high potential to be developed into a novel biopesticide.

The invention discloses a method for determining volatile organic compounds in surface water by purging and trapping and gas chromatography-mass spectrometry, which comprises the following steps: acquiring mass spectrometry data of volatile organic compounds in a surface water sample, and separating and detecting a multi-component mixture in a complex matrix by gas chromatography-mass spectrometry, obtaining an original data set containing retention time and mass spectrum information; according to an output result of the classification model, carrying out qualitative analysis on unknown compounds in a to-be-detected sample, and if the classification probability is greater than a preset threshold value, determining the types of the compounds to obtain an accurate qualitative recognition result; and establishing a concentration standard curve by adopting a corresponding quantitative ion peak area according to a target compound confirmed in the qualitative identification result, and calculating a linear relation parameter between the peak area and the concentration to obtain a regression equation for quantitative analysis.

Systems and methods for determining a molecular structure include obtaining mass spectrum data for an unknown parent ion. The mass spectrum data is recorded at each collision energy of a series of collision energies and includes an abundance of one or more substructures of the unknown parent ion. The molecular structure of the unknown parent ion is determined by composing the mass spectrum data into a fragmentation profile and embedding the fragmentation profile into a reduced dimension profile. The reduced dimension profile is mapped into a fragmentation space. One or more proximate molecules in the fragmentation space are identified using the mapping and a candidate for the parent ion is identified using the one or more proximate molecules.

Disclosed is a method and device for the mass spectrometric quantification of two or more known target ion species, in which a mass spectrometric signal, composed of signal components of the two or more target ion species, which are not mass resolved in the mass spectrum, is weighted with quantification functions and summed, and a quantification parameter is determined for the two or more target ion species from the weighted sums.

The invention discloses a natural gas hydrogen-doped or pure hydrogen verification and calibration loop. The loop comprises a gas supplementing and exhausting device, a filtering device, a circulating fan, a heat exchanger, a temperature control system, a bypass adjusting device, a working-grade standard device, a detected device, a transmission turbine device and a verification control system which are sequentially connected according to a gas flowing sequence to form the loop, the calibration method comprises the following steps: step 1, analyzing an obtained gas component xi by a mass spectrum analyzer; 2, calculating a contrast density function # imgabs0 # and a contrast temperature function # imgabs1 #; 3, assuming the molar density rho value of the gas mixture to obtain the contrast density delta and the reciprocal tau of the contrast temperature of the gas mixture; and 4, calculating to obtain comparison Helmholtz free energy # imgabs2 # binary comparison Helmholtz free energy # imgabs3 # and ideal Helmholtz free energy # imgabs4 #. The loop and the calibration method effectively solve the problems of verification and calibration of the detected flowmeter for trade measurement of natural gas doped hydrogen or pure hydrogen gas.

The invention relates to a continuous ion beam screening and enriching system and method and a mass spectrograph, and the continuous ion beam screening and enriching system comprises a vacuum differential unit, a first ion focusing unit, a mass screening unit, a second ion focusing unit, an enriching and releasing unit and a third ion focusing unit which are sequentially arranged from upstream to downstream. The enrichment and release unit is provided with a shell and a plurality of annular electrode plates which are arranged in parallel and located in a cavity of the shell, a resistor and a capacitor are connected between every two adjacent annular electrode plates to form a gradient electric field, and center holes of the annular electrode plates are concentrically arranged to jointly form an ion channel penetrating between an inlet and an outlet of the enrichment and release unit. According to the invention, continuous ion beam low-energy-consumption enrichment and pulse release can be realized, and strict requirements of scientific and industrial fields on mass spectrum detection and ion control precision are met.

Methods and systems are disclosed for predictive classification of a raw mass spectrum of a sample, the spectrum resulting from mass spectrometry analysis. The predictive classification is performed by a trained large-scale spectral model, including a self-supervised large-scale base spectral model trained with unidentified sample spectra from a plurality of sources and fine-tuned with a task-specific model trained with identified spectra.

The invention provides a method for detecting a genotoxic impurity TG707 in a macassavir raw material medicine and an intermediate TG7, and relates to the technical field of medicine detection, and the method comprises the step of detecting the genotoxic impurity TG707 in the macassavir raw material medicine or the intermediate TG7 sample by adopting a high performance liquid chromatography and mass spectrometry combined method, the mobile phase A is an ammonium acetate solution, and the mobile phase B is methanol. By adopting a liquid chromatography-mass spectrometry method and utilizing the selectivity of mass spectrum multi-channel monitoring, main components and impurities enter mass spectrum at the same time, the interference of high-concentration main components on the mass spectrum detection sensitivity is avoided, good separation on the mass spectrum is realized, and the sensitivity is relatively high; an ammonium acetate solution and methanol are used as mobile phases, so that the peak pattern and sensitivity of TG707 are ensured. The technical problems that in the prior art, the peak of TG707 on a liquid phase is between two peaks of a main component TG7, and baseline separation is difficult to achieve by replacing a chromatographic column and optimizing the fluidity gradient are solved.

The invention discloses a method for determining the content of trace holmium, thulium and tungsten in rare earth erbium and a compound thereof, which comprises the following steps: placing a rare earth erbium and compound sample thereof in a beaker, and performing a blank test along with a sample; adding nitric acid, heating at low temperature until the sample is completely dissolved, and cooling; transferring into a volumetric flask, adding a cesium internal standard solution, diluting with water, and uniformly mixing; the holmium, the thulium and the tungsten are measured by using an oxygen mode of an inductively coupled plasma tandem mass spectrometer, and the contents of the holmium, the thulium and the tungsten are calculated according to a working curve method. According to the method, the inductively coupled plasma tandem mass spectrometry technology is used, and mass spectrum interference of an erbium matrix on holmium, thulium and tungsten is effectively eliminated by selecting a proper reaction mode and optimizing the flow of reaction gas.

According to the embodiment of the invention, leakage characteristic data of a plurality of sensors are collected in real time; performing filtering processing on the leakage characteristic data and then determining an area where a leakage source of the vacuum equipment is located; determining the confidence coefficient of the area where the leakage source is located, determining the confidence coefficient of the position of the leakage source based on the confidence coefficient of the area where the leakage source is located, and giving an alarm when the confidence coefficient of the position of the leakage source reaches a confidence threshold value; and injecting helium to the leakage source position of the vacuum equipment in a preset time period to perform calibration test, calculating a calibration error of the leakage source position of the vacuum equipment, and when the calibration error exceeds an error threshold value, recalculating the confidence coefficient of the leakage source position and resetting the confidence coefficient threshold value. The problems of high cost, low detection precision, slow response speed, dependence on artificial experience and incapability of real-time monitoring due to long-time use of a helium mass spectrum detection method when leakage positioning detection is performed on a leakage source of vacuum equipment in the prior art are solved.

The invention discloses a device and a detection method for online oxidation-enrichment-reduction determination of nanogram-level nitrogen isotopes in a solid, and belongs to the technical field of nitrogen isotope analysis. The device comprises an oxidation system, a nitrogen oxide enrichment system, a nitrogen reduction system, a mass spectrum interface system, a stable isotope ratio mass spectrometer and pipelines for connecting all parts of the device. The device provided by the invention greatly reduces the quantitation limit of the nitrogen isotope in the solid sample, can realize high-precision determination of the nanogram-level nitrogen isotope in the solid sample, and has the advantages of simplicity and convenience in operation, high accuracy and the like.

The invention relates to the technical field of data processing and artificial intelligence, and discloses a Transform-based mass spectrum data noise reduction method, device, equipment and medium, and the method comprises the steps: inputting a data set into a pre-training noise reduction model, and training the pre-training noise reduction model based on a cosine weighted contrast loss function, a cosine weighted comparison loss function is constructed based on cosine similarity and category weight; and carrying out noise reduction processing on the mass spectrum data to be subjected to noise reduction by utilizing the trained noise reduction model. According to the method, the long-distance dependency relationship in the mass spectrum data can be effectively captured, more useful information is reserved to the maximum extent, global noise is effectively removed, and the noise reduction effect is improved; loss calculation is carried out in combination with cosine similarity and class imbalance, so that the training precision of a noise reduction model can be improved, and the accuracy and reliability of mass spectrum detection noise reduction are further improved; the method can be suitable for automatic noise reduction processing of various mass spectrum data and various complex scenes, and manual intervention is greatly reduced.

The invention provides a nuclear magnetic resonance spectrogram analysis method and system, and relates to the technical field of mass spectrometry, and the method comprises the following steps: carrying out Fourier transform on an original FID signal of a to-be-detected solution to obtain a corresponding frequency domain signal; after carrying out phase correction processing on the frequency domain signal, determining positions of a plurality of candidate peaks from the frequency domain signal according to a second derivative; grouping the candidate peaks based on the distance parameter between the adjacent candidate peaks to obtain a plurality of first candidate peak groups; if the difference value between the maximum peak height and the minimum peak height of the candidate peaks is greater than a first preset threshold value, filtering partial candidate peaks in the first candidate peak group; if the difference value between the maximum peak value of the abnormal candidate peak group and the maximum peak value of the first candidate peak groups on the two sides is greater than a second preset threshold value, filtering the abnormal candidate peak group to obtain a target candidate peak group; and performing parameter fitting on the candidate peaks in the target candidate peak group by using a least square method, and determining the solvent peak position and the water peak position.

The invention discloses a distributed cooperative processing method, system and equipment for mass spectrum data analysis, and relates to the field of biological information distributed processing, and the method comprises the steps: receiving original mass spectrum data submitted by a user, and analyzing flow configuration and global parameter configuration; analyzing the analysis process configuration into a directed acyclic graph taking the mass spectrum data processing step as a node; splitting the directed acyclic graph into a plurality of parallel atomic tasks according to the quantity of the original mass spectrum data and the space occupation amount; each atomic task is allocated to a Worker node storing corresponding associated data, and the atomic tasks are executed in parallel; when it is detected that the atomic tasks fail to be executed, the Worker nodes are distributed to the atomic tasks which fail to be executed again; different data storage systems are adopted to store original mass spectrum data, intermediate results and final analysis results. According to the invention, efficient parallel processing of mass spectrum data is realized, and continuity and reliability of an analysis process are guaranteed.