636results about How to "Improve identification efficiency" patented technology

The invention relates to a method for constructing an electronic evidence. The method specifically comprises the following steps: generating a unique length-fixed digital abstract for electronic data of an applicant, and constructing a Merkle tree of an electronic evidence block chain system; constructing the electronic evidence block chain system with a timestamp; and setting an index number for a block by the electronic evidence block chain system, and returning the index number, the generated digital abstract and the timestamp to the applicant together. According to the method and the system disclosed by the invention, all participating parties can construct jointly, maintain jointly and supervise jointly, thereby meeting the right to know and the supervision right of each party; the identification cost of the electronic evidence is reduced, the identification efficiency is improved, and the objectivity and the reliability of the electronic evidence are enhanced.

The invention discloses a power system load data identification and recovery method. Firstly, according to user historical load data, the number of clusters and initial cluster centers of sample data are determined on the basis of the hill climbing method; secondly, the final cluster center and the characteristic curve of the historical load data are obtained on the basis of the fuzzy C-means clustering algorithm; thirdly, each kind of load characteristic curve is processed, and the feasible region interval where normal data of the load curve is located is obtained; fourthly, according to correlation coefficients with the load characteristic curves, the category to which a to-be-tested load curve belongs is determined; finally, on the basis of the feasible region interval and the to-be-tested load curve whose category is judged, bad data of to-be-tested load data is identified and corrected. According to the method, the fuzzy C-means algorithm serves as the basis, the hill climbing function method is used, the number of clusters and the initial cluster centers are determined at the same time to improve clustering efficiency, and the initial cluster center determination problem and identification effect judgment randomness problem of bad data are solved.

The invention provides a two-stage linear weighted least-square power system state estimation method which is characterized by comprising the following steps of: forming a network model, and calculating a node admittance matrix and a branch-node relationship matrix; transforming a measurement vector and a state vector; forming an accurate linear measurement equation; performing the first stage of linear weighted least-square estimation to obtain the estimated value of the transformed state vector; performing inverse transformation, and performing the second stage of linear weighted least-square estimation to obtain the estimated values of the voltage amplitudes and phase angles of all nodes; and identifying bad data. According to the two-stage linear weighted least-square power system state estimation method provided by the invention, more scientific state estimation result can be obtained, the calculation efficiency is higher, and the engineering application prospect is good.

The invention provides an identity recognition method, device and system based on electrocardiogram signals. The method includes: receiving the electrocardiogram signals of a to-be-recognized user; performing filtering and noise reduction on the electrocardiogram signals of the to-be-recognized user; extracting the primary feature parameters of the electrocardiogram signals subjected to the filtering and noise reduction; using a principal component analysis method to screen the primary feature parameters so as to obtain final feature parameters; using the final feature parameters to construct the electrocardiogram signal feature vectors of the to-be-recognized user; comparing the electrocardiogram signal feature vectors of the to-be-recognized user with a pre-stored feature vector template; judging whether the electrocardiogram signal feature vectors of the to-be-recognized user is consistent with the pre-stored feature vector template or not, and if so, determining that the identity of the to-be-recognized user is successfully recognized. The method has the advantages that the method is especially suitable for implemented on mobile equipment such as smart phones and tablet computers which are relatively weak in computing ability, usability of using the mobile equipment to perform identity recognition based on the electrocardiogram signals is increased, and user satisfaction degree is enhanced.

The invention provides a method and a device for identifying a copying certificate image. The identification method comprises the steps of calculating image features of a given certificate image; and determining whether the given certificate image is a copying certificate image or not by using a trained classification model based on the calculated image features. According to the method and the device provided by the invention for identifying the copying certificate image, the category of the certificate image is judged automatically through the image features, thereby removing a process of manual check and inspection, and improving the identification efficiency of the copying certificate image.

The invention discloses a small-size unmanned rotary wing aircraft dynamic model identification method based on an adaptive genetic algorithm, which relates to flight status data acquisition and optimization, dynamic model building and parameter identification, and parameter optimization validation. Firstly, status data and control data when a small-size unmanned rotary wing aircraft executes standard actions are acquired through a data acquisition system, and smoothing and filtration are conducted to eliminate wild values; then aiming at the operating characteristics of the small-size unmanned rotary wing aircraft at autonomous takeoff and landing stages, a small-size unmanned rotary wing aircraft dynamic model is built through a balance point linearization method and model parameters are identified through the adaptive genetic algorithm; and finally intelligent parameter evaluation indexes are built and the effectiveness of the model parameters is evaluated and judged through a one-step predication method. The small-size unmanned rotary wing aircraft dynamic model identification method based on the adaptive genetic algorithm solves the problem in the dynamic model identificationof the small-size unmanned rotary wing aircraft, can realize the high-accuracy control of the small-size unmanned rotary wing aircraft, and has the advantages of low testing cost, short cycle, simplecalculation, high dynamic model accuracy and weak dependence on initial values.

The invention discloses a hybrid brain-computer interface method based on steady state motion visual evoked potential and default stimulation response. The method includes the steps that 1, a testee wears an electrode cap, a reference electrode, a ground electrode and a testing electrode on the electrode cap make contact with the head of the testee, and the vision and the computer screen are in the eye level through visual inspection; 2, a steady state motion visual evoked potential and default stimulation response mixed normal form program is compiled through MATLAB in advance, the testee selects a stimulation target to stare according to a target prompt, and electroencephalogram signals acquired by the electrode cap are stored in a computer; 3, steady state motion visual evoked potential features and default stimulation response features are subjected to feature extraction respectively, and then the stimulation target is subjected to classified recognition; 4, the computer screen displays the stimulation target recognition result, and visual feedback is conducted on the testee; 5, the steps are repeated, and the next round is conducted till the program is ended. According to the hybrid brain-computer interface method, two types of feature recognition information is adopted, and the method has the advantages that operation is simple, less training time is needed, and less electrodes are needed.

The invention provides an automatic detection system for quality of traditional Chinese medicine and a traditional Chinese medicine processing system and a traditional Chinese medicine extracting system thereof. The automatic detection system for quality of traditional Chinese medicine comprises a variety selection module used for selecting a traditional Chinese medicine variety required to be detected and confirming the traditional Chinese medicine variety required to be detected and quality related parameters thereof as well as the weight coefficients of the parameters in quality, a parameter setting module used for setting characteristic parameters and parameter quality values of the traditional Chinese medicine variety required to be detected, a parameter detecting module used for detecting shape, color, smell and taste parameters of the traditional Chinese medicine, a parameter comparison module used for comparing the detection parameter values with the characteristic parameters and the parameter quality values in the parameter setting module to obtain variety authenticity and relative parameter quality value of the detected traditional Chinese medicine, and a quality calculation module used for calculating according to the relative parameter quality values and the weight coefficients to obtain the quality value. Therefore, automatic and accurate detection for the qualityof the traditional Chinese medicine is realized.

The invention discloses a method for detecting age of yellow rice wine by using a high-speed gas chromatography type electronic nose fingerprint analysis system. The method comprises the following steps: firstly building up a yellow rice wine age detection model; then, shaking up a yellow rice wine sample to be detected; putting 5mL of the yellow rice wine sample to be detected into a 20mL closed sample bottle in the high-speed gas chromatography type electronic nose fingerprint analysis system, acquiring the smell fingerprint information of the yellow rice wine sample to be detected by using the high-speed gas chromatography type electronic nose fingerprint analysis system so as to obtain a chromatography type smell fingerprint map of the yellow rice wine sample to be detected; carrying out principal component analysis on the chromatography type smell fingerprint map of the yellow rice wine sample to be detected to form a characteristic smell fingerprint vector; and finally calling the characteristic smell fingerprint vector in the yellow rice wine age detection model so as to obtain the age of the yellow rice wine sample to be detected. The method for detecting the age of yellow rice wine by using the high-speed gas chromatography type electronic nose fingerprint analysis system has high detection accuracy, is simple to operate and has the advantage of shorter detection time as compared with the traditional sensory evaluation method. The method is a new method for detecting the age of yellow rice wine.

The invention provides an artificial lateral line pressure detection method which comprises the following steps: step 10, selecting a streamlined underwater robot, and determining the pressure trace of the underwater robot by a simulation mode; step 20, arraying pressure sensors along the pressure trace and around an axial line; step 30, putting the underwater robot in different water flow states, acquiring the pressure data of the current state by utilizing each pressure sensor, and uploading the pressure data to an analysis center; step 40, using the analysis center to import the pressure data into a neural network identification tool, and training to obtain a neural network model with an identification ability; and step 50, importing the acquired underwater data into the neural network model so that the flow field / motion state of the position can be determined. According to the invention, a set of artificial lateral line system is developed by utilizing the MEMS technology, and machine learning is carried out through the pressure data acquired by the sensors in the system, thereby realizing development of the ability of the underwater robot for environment identification.

The invention discloses a method of determining thermophysical parameters of rock soil and heat resistance of a vertical ground heat exchanger. The method includes steps: initializing test data and parameters of a drill hole and a buried pipe acquired in a thermal response experiment; initially setting rock soil heat conducting coefficient lambda s, rock soil volume heat capacity Cs and weight coefficient f corresponding to weight average temperature, and setting respective parameter ranges; using a column heat source model and a quasi-three-dimensional heat resistance model to calculate drillhole wall temperature Tb, I, ground heat exchanger inlet-outlet fluid temperature Tin, sim, i, Tout, sim, I and ground heat exchanger heat resistance Rb, I corresponding to each moment in the experiment; using square sum mean value of measured value and simulation value difference of the weight average temperature as a target function fopt, and using a simulated annealing algorithm to perform optimization iteration on lambda s, Cs and f; when fopt is lower than an optimization target value delta Tset, acquiring optimization results of three parameters lambda s, Cs and f, calculating a mean value of the sum of Rb, I at each moment corresponding to the optimization results, and acquiring heat resistance Rb of the ground heat exchanger. By the method, accuracy of recognition results on the thermophysical parameters of the rock soil and the heat resistance of the ground heat exchanger is improved.

The invention provides a microblog rumor recognizing method and system based on propagation structures. The microblog rumor recognizing method includes the steps that according to the machine learning method of a support vector machine, feature data of information features, user features and propagation features of microblogs serves as data, a graph kernel function is calculated with a random walking method, and a support vector machine model is accordingly built in cooperation with a classical radial basis kernel function and used for recognizing rumor; each microblog is represented with a propagation tree and a feature vector, wherein the propagation trees are used for representing the propagation structures in the propagation features, and the dimensions of the feature vectors are the information features, the user features and the propagation features of non-propagation structures; when the microblogs are recognized, the to-be-recognized microblogs are classified through the support vector model built with a model building module, and whether the to-be-recognized microblogs are rumor or not is judged according to the classification result. According to the microblog rumor recognizing method and system, due to comparison of the propagation structures of microblog information, the microblog rumor identifying accuracy and the microblog rumor recalling rate can be greatly increased.

The invention provides an identity uniformity check method and device based on a spectrogram and phoneme retrieval. The method includes the following steps: acquiring a spectrogram corresponding to asample audio file; acquiring the speech feature parameters of the sample audio file; constructing a phoneme recognition model, inputting the speech feature parameters to the phoneme recognition modeland carrying out phoneme retrieval to get qualified phonemes; and marking the qualified phonemes on the spectrogram, checking the uniformity of vowels or vowel combinations with the same identifier, and judging whether a to-be-identified person corresponding to the sample audio file passes identity verification. The technical problem on phoneme searching and finding in actual voiceprint authentication is solved. Phonemes can be displayed visually. The identification efficiency of investigators is improved.

The invention provides a phishing website identification system which comprises a feature extraction module, a feature vector dimensionality reduction module, a classification training module and an identification module. The feature extraction module is used for extracting page information and ranking information of websites and obtaining feature vectors; the feature vector dimensionality reduction module is connected with the feature extraction module and used for performing dimensionality reduction on the feature vectors to obtain the feature vectors undergoing dimensionality reduction; the classification training module is connected with the feature vector dimensionality reduction module and used for performing classification training on feature vectors, undergoing dimensionality reduction, of a known website to obtain classification models; the identification module is respectively connected with the classification training module and the feature vector dimensionality reduction module and used for saving the classification models, the classification models are used for identifying feature vectors, undergoing dimensionality reduction, of an unknown website, and whether the unknown website is a phishing website or not is judged. The phishing website identification system and a phishing website identification method enable website ranking and website content to be used as the feature vectors of the websites at the same time, and the website identification accuracy can be improved.

The invention provides a turbine model parameter identification method based on power response characteristics. The method comprises the following steps that (1) a model is simplified, simplified treatment is performed on a turbine model provided by a calculation procedure of a power system, and therefore the simplified model with the fewer parameters and the more clear structure is obtained; (2) a field test and data processing are performed; (3) parameter identification is performed, and obtained TN values, TCH values and TRH values are identification results. According to the model parameters obtained through the method, the power characteristics of a real turbine can be more accurately reflected, and the identification precision is improved. According to the method, a turbine model provided by the calculation procedure of the power system is simplified, the number of parameters to be identified is reduced, the identification efficiency is improved, and guarantees are provided for analyzing the accuracy of the running condition of the power system.

The invention discloses equipment and a method for identifying the salt tolerance of a plant at the seedling stage, and belongs to the technical field of plant salt tolerance identification. The method comprises the following steps of: 1) breeding plug seedlings of the plant to be identified; 2) preparing a saline solution for identification; 3) combining the plug seedlings with the salt tolerance identification equipment; 4) deeply soaking the plug seedlings into the solution; 5) regularly changing the solution; and 6) performing the identification and evaluation on the seedling-stage salt tolerance of the plant, and the like. As the treatment conditions for the seedlings to be identified and the reference seedlings are the same and the liquid level and concentration of the saline solution are kept stable, the equipment and the method provided by the invention are simple to operate, high in identification efficiency, good in comparison property and low in test error; and therefore, the accuracy and reliability of identification on the seedling-stage salt tolerance of the plant are improved. The equipment and the method provided by the invention can be popularized and applied to agricultural research institutions and seed enterprises.

The invention relates to a method for member identity identification under a similar home environment. Specific steps comprise: first, automatically screening face image quality, preliminarily completing home member register; second, under near distance and good light, identifying identity through faces; third, tracking a face, and obtaining information of height, body type, costume of a same person, and labeling samples according to the face information; fourth, according to information of tracking faces and heights, through a clustering method, obtaining multi-dimensional labeled samples; fifth, using the multi-dimensional labeled samples to train a target environment classifier. The method can track and learn initial no-label data according to environment, and makes up problems that face recognition is low in recognition rate under conditions of small faces, poor light, and excessive face angles, and greatly improves home identity identification practicality.

The invention discloses a method using high-oil inducement series to induce the haploid and the polyembryony body of the corn. The method inducing the haploid and the polyembryony body of the corn provided by the invention is to use high-oil corn haploid inducement series as the father parent to pollinate the female parent, to obtain the first-generation grain and gain the haploid and the polyembryony body of the corn. The method not only has the advantages that the breeding process is simplified, the breeding course is accelerated, the purity of the breed is improved, etc. in the traditional haploid breeding; but also can use the genetic effect of the oil constituent to improve the identification efficiency of the haploid, and at the same can improve the inducement rate of the polyembryony body.

The invention discloses a protein second-level mass spectrum identification method based on peak intensity recognition capability. The method comprises the following steps: firstly, virtualizing enzymatically hydrolyzed protein database sequence, establishing a peptide fragment database and a peptide fragment database index for peptide fragments subjected to enzymatic hydrolysis according to the mass number of the peptide fragments; then, finding out candidate peptide fragments conforming to the requirement from the established peptide fragment database according to the mass number of parent ions without charges in a to-be-analyzed experiment spectrum; then removing an isotopic peak and selecting an effective peak from the to-be-analyzed experiment spectrum so as to generate a theory spectrum of the candidate peptide fragments conforming to the requirement, counting peak intensity information of different ions, calculating the peak intensity recognition capability of different types of ions at different intervals, marking each candidate peptide fragment based on the peak intensity recognition capability, and selecting the peptide fragment with the highest mark as the authentication result of the experiment spectrum; and finally, performing quality control on the authentication result. The number of valid mass spectra and the number of valid protein peptide fragments, which are authenticated by the method, are both higher than those obtained by an existing algorithm; peaks can be selected dynamically; the running speed is high.

The invention discloses a method and a device for detecting a malicious file. One specific embodiment of the method comprises the following steps of obtaining a URL (Uniform Resource Locator) for downloading a to-be-detected file; matching character strings included in the URL of the to-be-detected file and character strings in a preset model; and determining whether the to-be-detected file is the malicious file based on a longest character string matched in a preset model by the URL of the to-be-detected file. According to the embodiment, the efficiency for detecting the malicious file can be improved.

The invention belongs to the technical field of biotype identification, in particular to a method for identifying indica type rice and japonica rice using rice grain by using a rice grain (rice) and inserting or deleting (InDel) a molecular marker. The method comprises the following steps of: extracting DNA from the rice grain, and comparing full-genome DNA sequences of indica type rice 93-11 andjaponica rice Nipponbare to obtain 40 pairs of specific InDel primers; and performing fragment amplification, electrophoretic separation and electrophoresis pattern analysis on the extracted DNA the in rice seed to identify the characteristics of the indica type rice and japonica rice of a rice sample. The method concretely comprises the following steps of: taking the DNA extracted from the rice grain as a template, and analyzing and counting molecular fingerprint patterns obtained on the basis of a polymerase chain reaction and agarose electrophoresis by using combination of the 40 pairs of specific InDel primers; and determining the characteristics of the indica type rice and japonica rice of the tested rice seed (sample) according to gene frequency (Fi or Fj) calculated by 93-11 genotype and japonica rice Nipponbare genotype molecular fingerprint on 40 InDel loci of the sample. The result can be obtained by only detecting the sample of one grain of seed, and the method is convenient and rapid, accurate in identification, and has good popularization and application prospect.

The present invention discloses a distance certification system. The system comprises a server terminal and a client terminal base on a network connection, a real time dynamic audio / video communication and a high definition picture transmission among the client terminals can be realized in the same software and hardware platform through a distance server. The user can take a high definition picture with certification characteristic directed by an expert through the system and send the picture to the expert, a result is fed back from the expert to the user after the certification, and thereby, the certification and the accuracy of the certification are improved. The present invention is particularly suitable for the distance certification of an organism sample (such as an insect, a weed, and a fungus).

The invention discloses a method for a brain-computer interface based on amplitude modulated visual evoked potential. The method comprises the following steps of: firstly, connecting an electroencephalogram signal acquisition system; optimally selecting the most-sensitive frequency of amplitude frequency response of a tested person as carrier frequency; maximizing a signal-noise ratio of a response signal; secondly, providing a selection range of modulation wave frequency, designing an AMVEP (Amplitude Modulated Visual Evoked Potential) normal form and generating a stimulation sequence of an amplitude modulated visual evoked potential brain-computer interface normal form; and finally, realizing the identification of single signal or a small amount of signals of the normal form AMVEP. The invention provides the normal form based on the amplitude modulated visual evoked potential and a method for analysis and characteristic extraction of a modulation signal corresponding to the normal form AMVEP. The identification of single signal or a small amount of signals of the normal form AMVEP based on the amplitude modulated visual evoked potential is realized; and the method disclosed by the invention has the advantages of simpleness in operation, fewer electrodes and more target numbers.

The invention provides a breeding method for a multi-resistance flue-cured tobacco hybrid. The breeding method for the multi-resistance flue-cured tobacco hybrid comprises the steps of firstly using MSG28 as a female parent, using high-quality, widely applicable and blackleg-resistant varieties which are sensitive to TMV and weather fleck and are used for production as recurrent parents, performing eight generations of backcrossing to breed male sterile lines, using fertile materials which are resistant to blackleg and highly resistant to TMV and weather fleck as original resources, performing six generations of selfing purification to breed selfing pure lines with common or better curing characteristics, then using the bred male sterile lines as female parents and the selfing pure lines as male parents to breed hybrid combinations, respectively evaluating resistance to different diseases and economic performance by using land parcels or areas seriously attacked by different diseases and normal land parcels in producing areas, and finally performing 2-3 years of multi-point tests for quality and adaptability evaluation to the hybrid combinations which are simultaneously resistant to different diseases and have good economic performance. By adopting the method, the aggregation of multiple characters such as high quality, simultaneous resistance to foliage diseases and root and stalk diseases and good adaptability can be realized, the breeding efficiency is improved.

The embodiment of the invention discloses an on-line identifying method for a stator resistor. The method comprises the following steps of: judging if a motor is under a stable state, and if yes, obtaining a three-phase stator current Is_abc and a three-phase stator voltage Us_abc; orientating the Is_abc in d direction of a synchronous rotating dq coordinate system and calculating the component Is_d of the d direction, or orientating the Is_abc in q direction of the synchronous rotating dq coordinate system and calculating the component Is_q of the q direction; calculating locking phase angle theta-i and synchronous rotating speed Omega-s; calculating the component Us_d in the d direction and the component Us_q in the q direction of the Us_abc, according to the theta-i; obtaining a stator flux linkage Psi-s and calculating the component Psi-sq of the Psi-s in the q direction or the component Psi-sd in the d direction; and calculating a stator resistance value Rs according to a stator resistance calculation formula. According to the method provided by the invention, the problem of high complexity of the present identifying method is solved. The embodiment of the invention also discloses an on-line identifying device for the stator resistor.

The invention discloses a protein secondary mass spectrum identification method of marker loci based on candidate peptide fragment discrimination, and relates to the field of protein secondary mass spectrum identification. The method comprises the following steps of: establishing a peptide fragment database and a peptide fragment database index; finding out candidate peptide fragments from the peptide fragment database according to parent ions in a to-be-analyzed experiment loci, and generating a theory loci; removing isotopic peaks and selecting effective peaks from the to-be-analyzed experiment loci; generating an experiment marker loci based on candidate peptide fragment discrimination; counting peak intensity discrimination of different sections, theory fragment ions and experiment loci quality error discrimination and fragment ion discrimination of experiment loci peaks and theory loci matching peaks; marking discrimination of each candidate peptide fragment based on the experiment loci matching ions, and selecting the peptide fragment with the highest score as the identification result of the experiment loci. The method is higher in quantity of identified effective mass spectra and quantity of identified protein peptide fragments than the existing algorithm, so that the identification efficiency is also greatly improved.

The invention discloses a tandem mass spectrometric identification method for protein based on matching between characteristic information of a target database and a decoy database. The method mainly carries out statistics of matching between experimental peaks of different types and theoretical peaks of the target database and the decoy database in different error ranges and intensity intervals, then extracts novel characteristic information of tandem spectra and carries out mathematical quantification, and finally, fusing the quantified novel characteristic information into a protein tandem mass spectrometric identification algorithm scoring model. To verify reliability of PepFind algorithm, the PepFind algorithm is tested by using data sets generated by different mass spectrometric platforms; and identification results of the PepFind algorithm are compared with identification results obtained by using widely-applied commercial and related open-source tandem mass spectrometric identification software under the condition that FDR is 1%, and comparison results show that the PepFind algorithm has better identification number and sensitivity to experimental spectra. The tandem mass spectrometric identification method for protein based on matching between characteristic information of the target database and the decoy database can obviously improve the effective mass spectrometric number and the peptide fragment number of protein.

The invention relates to rapid identification of powdery mildew resistance Lotus corniculatus genes utilizing comparative genomics, relates to knowledge of subjects such as plant comparative genomics, genetics and bioinformatics, and belongs to the technical field of plant biotechnology. The rapid identification of powdery mildew resistance Lotus corniculatus genes utilizing the comparative genomics mainly includes the steps of firstly, downloading full genomic sequences of Lotus corniculatus and collecting MLO (mildew resistance locus o) genes; secondly, identifying the MLO genes; thirdly, identifying phylogenetic relationship of the MLO genes; and fourthly comparing the MLO powdery mildew resistance genes. By the rapid identification, mining cycle of the powdery mildew resistance genes is shortened, and the powdery mildew resistance genes can be identified quickly. Corresponding co-separation functional marks (SNP (single nucleotide polymorphism), Scar and the like) can be developed through candidate powdery mildew resistance genes identified, the rapid identification is also available for molecular marker-assisted selection of the powdery mildew resistance genes, and accuracy is high. The rapid identification can also be used with other molecular markers for resistance genes to create multiresistance breeding materials, and accordingly breeding period is shortened, breeding efficiency is improved, and basis for elaborating powdery mildew resistance Lotus corniculatus molecular mechanisms is laid.

The invention discloses a secondary protein mass spectrum identification method based on mass-to-charge ratio error recognition capability. The method comprises the following steps: virtualizing an enzymolysis protein databank sequence, establishing a peptide fragment databank and a peptide fragment databank index for each peptide fragment after being subjected to enzymolysis according to the mass number of the peptide fragment, finding out each candidate peptide fragment which meets the requirement in the established peptide fragment databank according to the mass number after charges of mother ions are removed in an experiment spectrum to be analyzed, performing isotopic peak removal and selecting an effective peak for processing, generating an experiment mark spectrum which meets a candidate peptide fragment theoretic spectrum, counting mass error information of different ions, calculating the mass-to-charge ratio error recognition capabilities of the different ions in different areas, scoring the candidate peptide fragments based on the mass-to-charge ratio error recognition capabilities, selecting the peptide fragment with the highest score as the identification result of the experiment spectrum, and performing the overall mass control on the identification result. The numbers of effective spectrums and peptide fragments identified by using the method are both greater than that of a conventional algorithm, the peak can be dynamically selected, and the operation speed is high.

The invention relates to an online identification and visualization method for power grid disturbances based on multi-resolution wavelet analysis, which belongs to the technical field of wide-area dynamic monitoring and visualization of power systems. The method comprises the following steps: carrying out offline simulation analysis on the features of typical dynamic disturbance accidents in power grid, and selecting a wavelet function Psi and the decomposition layer-number I corresponding to the obtained maximum wavelet coefficient energy as online wavelet multi-resolution analysis tools; carrying out regionalization according to the PMU (power management unit) configuration situation of a target power grid, so that each region has one and only one PMU substation for measurement, and transmitting frequency signals measured by each PMU substation to a WAMS (wide area measurement system) master station; acquiring frequency signals of each PMU substation from the WAMS master station, and carrying out I-layer online wavelet multi-resolution analysis on the frequency signals by using the determined wavelet function so as to obtain wavelet coefficients of an I(th) layer; and discriminating disturbance occurrence time, disturbance occurrence potion and disturbance type according to the wavelet coefficients; and carrying out visualized display through taking a map as the background. The method disclosed by the invention can improve the running reliability of an electrical power grid.