42 results about "Sequence database" patented technology

The invention provides a circuit board production yield root cause tracing method, which comprises the following steps of: acquiring process parameters, equipment states, environment variables and quality detection results of a whole production process, and constructing a multi-dimensional time sequence database; extracting a typical manufacturing process modeling unit through a sliding time window and dynamic time warping; establishing a cross-process dynamic causal relationship graph in combination with nonlinear Granger causal test, a structural equation model and a dynamic Bayesian network; an intervention and anti-factual reasoning method is applied, the causal effect and path stability under parameter disturbance of each process are evaluated, and the influence of a key causal path is quantified; according to the method, the accuracy of defect rate root cause positioning can be improved, and powerful support is provided for circuit board production process optimization and quality improvement.

A dynamic cadastral data updating and management method based on a space-time sequence specifically comprises the following steps: S1, collecting dynamic cadastral data, preprocessing the dynamic cadastral data, and outputting a standardized space-time sequence database; s2, performing dynamic modeling on the standardized space-time sequence database, adjusting granularity, and outputting an optimized space-time model; s3, performing space-time sequence comparison by utilizing the optimized space-time model, automatically identifying data change, and outputting a cadastre change incremental data set; s4, constructing a relation chain according to the change incremental data set, writing change data into a current potential library, and archiving historical data into a difference file library; s5, performing space-time consistency verification and efficiency optimization on the current potential library and the difference file library, and outputting a quality inspection passing library; and S6, performing service packaging on the quality inspection passing library, and deploying the quality inspection passing library to a cloud service platform. The method dynamically adapts to change features of different regions, reduces storage cost and update time consumption, improves verification efficiency, and provides dynamically updated spatial basic data support for smart city construction.

The present disclosure relates to the technical field of big data processing, and discloses an account evaluation method and device, computer equipment, a storage medium and a computer program product. The method comprises: obtaining account resource interaction data of a to-be-evaluated account and a preset sequence mode, wherein the preset sequence mode is obtained based on a non-frequent feature sequence database, the non-frequent feature sequence database is determined according to a non-frequent item set obtained by mining a preset non-frequent mode tree, and the preset non-frequent mode tree and an initial feature database have an association relationship; and evaluating the account resource interaction data according to the preset sequence mode to obtain an evaluation result of the to-be-evaluated account. The present method can improve the accuracy of account evaluation and the security of resource interaction.

The application provides an adaptive resource dynamic scheduling method based on multi-modal behavior feature processing, and relates to the technical field of digital data processing.The application collects multi-source data such as network connection behavior, protocol interaction features, time sequence features and system resource states, constructs a visitor behavior time sequence database, and establishes a visitor normal behavior benchmark trajectory model.Based on a behavior evolution deviation index, an intention risk coupling index and a dynamic scheduling drift index, multi-level quantitative evaluation of the access behavior deviation degree, potential risk intention and scheduling safety state is realized.Through threshold value determination, corresponding strategies are triggered, abnormal access behavior is marked, analyzed and dynamically scheduled and controlled, and model parameters are adaptively optimized to realize continuous learning and dynamic evolution.The application can improve the accuracy of access behavior recognition and the refinement level of scheduling control, thereby improving the overall security protection capability and resource scheduling efficiency of the system.

The application relates to a method and system for mining and analyzing the correlation of clinical comorbidity of discharged patients. The method comprises the following steps: constructing an individual diagnosis and treatment narrative graph for each patient according to the discharge medical record text, generating a time sequence transaction sequence corresponding to each patient, and constructing a sequence database; based on the database, the original time sequence frequent pattern set is constructed by analyzing through an improved generalized sequence pattern algorithm; each pattern in the set is classified to obtain multiple classification clusters, and the original time sequence frequent pattern in each classification cluster is processed through multi-sequence alignment to construct a generalized clinical path graph, and the information in the generalized clinical path graph is extracted to generate a natural language abstract. The method improves the time sequence logic, knowledge abstraction degree and clinical interpretability of the clinical comorbidity correlation mining by constructing a diagnosis and treatment narrative graph, mining a time sequence frequent pattern, and constructing a generalized clinical path graph through clustering and multi-sequence alignment.

This invention relates to a method for predicting steel plate temperature during quenching heat treatment based on multi-task learning, belonging to the technical field of steel rolling heat treatment methods in the metallurgical industry. The technical solution of this invention is as follows: Key historical data of the quenching furnace heating process are collected through a standardized steel plate embedded coupler test system to construct a spatiotemporal sequence database for medium-thick plate quenching heat treatment; a spatiotemporal prediction model is constructed using a Transformer multi-task learning architecture, and a high-precision prediction model is finally trained; key historical data of the steel plate quenching furnace heating process are used as model input, and the predicted temperature of the core and tail sections of the medium-thick plate is output in real time. The beneficial effects of this invention are: it can effectively cope with the complex thermal field distribution and dynamic operating condition changes within the quenching furnace, achieving real-time and accurate prediction of the core temperature of medium-thick plates during quenching heat treatment, providing highly reliable and timely decision support and technical guidance for optimizing the quenching heat treatment process in hot-rolled steel plants.

The application discloses a method for collecting active proteins of water leeches, a detection method, an analysis method and application thereof, and relates to the technical field of biological medicines. The application provides the water leech seedlings treated by hunger and the living host treated by emptying, so that the water leech seedlings adsorb and suck the host, and then the active proteins are injected into the body of the host; the sample of the host after being sucked is collected, and then is made into a freeze-dried powder after being frozen and dried, so that the active proteins of the water leeches are obtained; the proteins are extracted from the freeze-dried powder, and then are subjected to enzymolysis treatment to generate peptide segments; the liquid chromatography-mass spectrometry technology is used to detect the peptide segments; and the mass spectrometry data are subjected to database searching analysis based on the protein sequence database of the water leeches and the host, so that the active proteins from the water leeches are identified and the relative content is determined. The application utilizes the characteristic that the active proteins are injected into the body of the host in the sucking process of the water leeches, and realizes efficient enrichment of the target proteins. In combination with the proteomics technology, high-throughput and high-sensitivity protein identification is realized.

The invention discloses a method, a system and a device for identifying the age of a green-head diving duck based on multi-feature fusion. The method comprises the following steps: shooting a high-definition image of a target individual; obtaining one or more of feather state information of an ear feather region, a wing region, a hypochondrium region, a shoulder and back region, a tail region, a head and neck region, a chest and abdomen region, eye iris and beak color change information and chest and tail distance and beak length data information of the target individual in the high-definition image, and performing quantitative processing to obtain feature parameters corresponding to each feature; performing multi-dimensional fusion analysis on the characteristic parameters through an integrated machine learning model to realize accurate matching with corresponding week age / month age data in a feather moulting standard time sequence database of the green-head diving ducklings / young birds; and outputting a week age / month age identification result of the target individual. According to the method, a moulting standard time sequence database covering a young bird stage of the green-head diving ducks for the first time is constructed, high-precision and high-efficiency non-injury age identification is realized through combination of the database and a machine learning model, and the dependence on professionals is reduced.

This invention relates to the fields of molecular biology and forensic identification, specifically to a method for bird species identification based on multi-gene co-amplification and nanopore sequencing. The method involves extracting genomic DNA from avian biological samples; using the extracted DNA as a template, amplification is performed using four independent PCR primer pools that specifically target the avian mitochondrial cytochrome C oxidase subunit I gene, cytochrome B gene, 12S rRNA gene, and 16S rRNA gene; after purification of the amplification products, a nanopore sequencing library is constructed and sequenced; finally, bioinformatics analysis, including read length clustering, draft consensus sequence generation, and polishing, is performed on the raw sequencing data to obtain consensus sequences for each target gene. Species identification is then completed by comparing the sequence with a avian sequence database. This method effectively solves the problem that existing technologies cannot simultaneously meet the practical needs of large-scale, rapid, and high-success-rate identification of avian biological samples.

The application discloses a kind of based on TCR sequencing lung cancer early screening model construction method, first based on the TCR sequencing data of lung cancer patient and healthy person sample is constructed TCR sequence database enrichment, then the TCR sequencing of each sample is carried out and obtains the TCR characteristics of each sample, including V gene and J gene The proportion characteristics of gene, the statistical characteristics of TCR immune group, convergence sequence characteristics, TCR sequence amino acid proportion characteristics, different length TCR sequence proportion characteristics, different frequency TCR sequence proportion characteristics and TCR enrichment sequence characteristics;The TCR characteristics of each sample are screened using feature selection method, and a machine learning model is constructed by combining other information of the sample to obtain a lung cancer early screening model.Based on the lung cancer early screening model, a lung cancer early screening detection device is constructed, which can realize lung cancer early screening based on TCR sequencing information of the sample.

The invention discloses a multi-objective decision-oriented utility sequence pattern mining method and system and application, and the method comprises the following steps: SF1, scanning a DNA sequence database, obtaining a DNA sequence with only a single item as a T1 sequence, and constructing a quantity utility array as a QU array by the T1 sequence; sF2, performing an SQUD pruning strategy on each T1 sequence; calculating a sequence weighted utility value of each T1 sequence based on the QU array, marking the sequence weighted utility value as SWU, constructing a maximum utility number array as MUQA, and deleting the T1 sequence of which the SWU is smaller than the corresponding MUQA value; sF3, an array SPFA of the sequence Pareto front is initialized, and a T1 sequence of the MUQA after non-dominated sorting is recorded; generating a projection database QUPro for the T1 sequence in the SPFA based on a QU array; traversing each T1 sequence in the MUQA and generating an extension mode; and SF4, calculating a utility upper bound by using the PEU, and recursively expanding the current sequence by controlling any elements in the SPFA by the PEU value and quantity. According to the method, by introducing the utility upper bound and the pruning strategy, unnecessary calculation is reduced, and the efficiency of the algorithm is improved.

The invention provides a distributed base station energy storage resource virtual power plant abnormity monitoring method and system, and the method comprises the steps: firstly obtaining the power grid frequency modulation demands, real-time electricity prices, energy storage states, mechanical stresses and radio frequency signal data of all distributed base stations in a virtual power plant; then, aggregating the frequency modulation demand, the electricity price and the energy storage state data to construct an electricity price load response time sequence database, and meanwhile, fusing mechanical stress and a radio frequency signal to generate a stress electromagnetic coupling fingerprint; based on the time sequence database and the physical fingerprints, analyzing and generating charging and discharging competition behavior characteristics of each base station through a Nash equilibrium model; and finally, accurately positioning the abnormal energy storage node by comparing the deviation degree between the actual behavior characteristics and the expected equilibrium state of the model, and realizing credible evidence storage and tracing of the abnormal behavior by using a block chain smart contract. According to the invention, the accuracy and tracing reliability of virtual power plant energy storage abnormity monitoring are improved.

The invention provides a power distribution unit and a management method thereof, and relates to the technical field of power management. Static service priorities and reference electrical parameters of all branches are configured, and real-time electrical parameters are fused to calculate service dynamic priorities; collecting and preprocessing total output current output by a total power supply and current data of each branch according to a fixed sampling window, and constructing a historical load time sequence database; on the basis of historical data, a mixed prediction model combining a seasonal decomposition algorithm and a long-short term memory network is adopted to predict each branch and total output peak current in the future; performing system-level and branch-level pre-overrun judgment and arbitration based on the predicted peak current, and calculating a total current reduction quota and each branch over-standard current; and dynamically generating a regulation and control instruction sequence according to the service dynamic priority, the reduction quota and the overproof current. Accurate prediction and intelligent regulation and control of the power supply load can be realized, and the safety and efficiency of power supply distribution of the production line are improved.

The invention discloses a diffusion model-based protein binding RNA (Ribonucleic Acid) sequence generation method, which comprises the following steps of: in an off-line training stage, carrying out two-stage training on a constructed mixed generation type framework by utilizing a protein binding RNA sequence database, and coupling a variational auto-encoder unit and a conditional potential diffusion model unit by the mixed generation type framework; an online reasoning stage: inputting a to-be-generated protein sequence into the protein language model to extract condition embedding; the conditional potential diffusion model generates a new potential vector through a reverse denoising process taking protein embedding as a guide condition from Gaussian noise in a potential space of the variational auto-encoder, and finally, the potential vector is mapped into the generated protein binding RNA sequence by a decoder of the variational auto-encoder. The invention also discloses a system for realizing the method. According to the invention, a binding sequence can be generated for a protein target except for training data, and excellent biophysical characteristics and functionality are achieved.

Provided are an artificial intelligence-based early cancer diagnosis method and device using a method of inputting information on a cell-free DNA distribution of a tissue-specific regulatory region to an artificial intelligence model learned to early diagnose cancer and analyzing the information, and an information providing device and a storage medium.SOLUTION: A method for providing information for early cancer diagnosis based on artificial intelligence includes extracting a nucleic acid from a biological sample to obtain sequence information, arranging the obtained sequence information in a reference chromosome sequence database, selecting a nucleic acid fragment of a regulatory region based on the arranged sequence information, generating the selected nucleic acid fragment as image data, and inputting the generated image data to an artificial intelligence model learned to distinguish a normal image and a cancer image, analyzing the image data, and comparing the image data with a reference value to determine the presence or absence of cancer.SELECTED DRAWING: Figure 1

The invention relates to a computer-implemented method for tagging user action (UAC) data in a user environment (ENV), in particular wherein the user environment (ENV) is a computer (CMP)-implemented user interaction (UIA) environment (ENV), the method is characterized by comprising the following steps: a) providing a pre-trained user action (UAC) pattern recognition model (PRM), the pattern recognition model (PRM) being designed in such a way that the pattern recognition model (PRM) recognizes a user action (UAC) pattern (APT) known to the pattern recognition model (PRM) from a sequence of user action (UAC) parts comprising the user action (UAC) pattern (APT) known to the pattern recognition model (PRM); and outputting a user action (UAC) mode (APT) or a flag associated with the user action (UAC) mode (APT), the invention relates to a method for identifying user actions (UAC) in a user action (UAC) sequence (SQC), b) recording of user actions (UAC), c) analyzing the recorded user actions (UAC) by a user action (UAC) pattern recognition model (PRM) for a user action (UAC) pattern (APT) known by the pattern recognition model (PRM), d) if a known user action (UAC) pattern (APT) is not detected in the user action (UAC) sequence (SQC), generating a user interaction (UIA) as a user interaction (UIA) requesting information about a previous user action (UAC) sequence (SQC), the present invention relates to a method for training a pattern recognition model (PRM) using a user action (UAC) sequence, e) tagging past user action (UAC) sequences (SQC) using information received from an information request, f) storing the tagged user action (UAC) sequences (SQC) in a user action (UAC) sequence (SQC) database (DBS), g) training the pattern recognition model (PRM) using the user action (UAC) sequences (SQC) and corresponding tags (LBL) of the user action (UAC) sequence (SQC) database (DBS).

An artificial intelligence-based system comprises an input preparation module that accesses a sequence database and generates an input base sequence. The input base sequence comprises a target base sequence with target bases, wherein the target base sequence is flanked by a right base sequence with downstream context bases, and a left base sequence with upstream context bases. A sequence-to-sequence model processes the input base sequence and generates an alternative representation of the input base sequence. An output module processes the alternative representation of the input base sequence and produces at least one per-base output for each of the target bases in the target base sequence. The per-base output specifies, for a corresponding target base, signal levels of a plurality of epigenetic tracks.

The polypeptide design method and sequence of the present application combine mask language modeling and yeast surface display, comprising the following steps: cleaning a protein sequence database, selecting protein sequences meeting the requirements as a training set of a language model, and performing mask language modeling on the protein sequences contained in the training set; designing a set of downstream tasks on the basis of a pre-trained model, and fine-tuning the downstream tasks; randomly masking residues of a selected reference sequence, and predicting the masked residues; performing virtual screening on polypeptide candidates generated by the model; and determining the protein expression level and affinity of the screened polypeptides through yeast display technology. The present application can generate a large number of polypeptide candidates that may have specific properties or functions by transferring natural language processing technology to the polypeptide generation field, and combines artificial intelligence generation, virtual screening and wet experimental characterization to build a relatively complete "dry-wet combination" design process.

This invention discloses a general method for amino acid sequence generation and screening based on machine learning. This method can be used to generate various functional amino acid sequences, such as viral amino acid sequences. It uses a diffusion-based denoising probability model as a deep learning generation framework. Based on the prior distribution of the data, it progressively denoises the diffused data or random noise through a back-diffusion process. Based on existing amino acid sequence databases, it can achieve de novo design of various functional amino acid sequences. For example, when applied to viral amino acid sequence generation and screening, the method includes the following steps: S1, constructing a dataset for training the model from experimental data; S2, training the viral amino acid sequence generation and screening device using the dataset; S3, using the trained viral amino acid sequence generation and screening device to generate and screen viral amino acid sequences; and S4, validating the viral amino acid sequences in the target viral amino acid sequence library through wet experiments. This invention also provides systems and devices related to the general method for amino acid sequence generation and screening.

The invention discloses a host intrusion detection method and device based on system multi-source log ensemble learning and a medium, and the method comprises the steps: S01, collecting normal log data and simulating different attack behavior log data, and forming a multi-source log database; s02, preprocessing the log data in the multi-source log database, and performing log serialization processing after constructing a word list; step S03, inputting the multi-source log sequence database into the MIMO model for training; and step S04, collecting various log sources stored in the tested host system in real time, performing log serialization processing to form a to-be-predicted sample set, and inputting the to-be-predicted sample set into the trained MIMO model to obtain prediction results of the log sources on various attack behaviors. And integrating the prediction results of the log sources on various attack behaviors to obtain a final prediction result. According to the method, the intrusion detection accuracy and robustness can be improved, and meanwhile resource consumption is reduced.

The invention provides a mode mining method, device and equipment for critical path data and a storage medium, and relates to a data mining technology, the method comprises the following steps: obtaining an analysis processing request; and reading a sequence database corresponding to the sequence database identifier according to the analysis processing request. Generating a candidate mode set; determining a candidate mode greater than or equal to a preset minimum support degree threshold in a plurality of candidate modes in the candidate mode set based on the plurality of sequences in the sequence database; taking the candidate modes greater than or equal to a preset minimum support degree threshold as high-support-degree modes, and generating a high-support-degree mode set; and if it is determined that the high-support-degree mode set is null or the second length is greater than the preset maximum mode length, stopping high-support-degree mode mining. According to the method, the technical problem that the accuracy of mode mining of the key path of the processor microstructure correlation diagram is low is solved.

The invention discloses an IGH gene fusion identification method and device, a storage medium and a program product, and the method comprises the following steps: obtaining sequencing data, and preprocessing the obtained sequencing data; the preprocessed data is compared to an IGH library, one or more candidate sequences are obtained, the candidate sequences are sequences containing IGH sections and unknown sections, and the unknown sections are sequences with the length within a preset first length range and are not compared to IGH fragments; acquiring the unknown sections, and comparing the unknown sections to a DNA sequence database to obtain a plurality of comparison results; and analyzing the plurality of comparison results, and identifying the gene corresponding to the optimal comparison result meeting a preset similarity condition as the gene fused with the IGH segment.

The application discloses a kind of based on double cross coupling correlation's unsupervised KPI anomaly detection method, first construct by historical KPI sequence database, preprocessing module, input module, based on double cross coupling correlation's KPI fragment feature extraction model, feature conversion module, feature space distance calculation module, training optimization module and online anomaly detection module constitute based on double cross coupling correlation's unsupervised KPI anomaly detection system.First to the unsupervised KPI anomaly detection system is trained, KPI fragment feature extraction model simultaneously models the coupling correlation of KPI data internal time sequence dimension and the coupling correlation between KPI sequence, extracts the feature vector of KPI data to be detected, feature space distance calculation module calculates the distance of feature vector and feature space center point, and online anomaly detection module determines whether KPI data to be detected is abnormal according to distance.The application can effectively improve the accuracy of unsupervised KPI anomaly detection.

The present application relates to the pile foundation construction technical field, especially is based on the pile foundation hole forming quality intelligent management and control method and system of drilling machine work parameter, the method includes the following steps: the data acquisition array is constructed, the key work parameter and the quality index data of pile foundation hole forming of drilling machine are acquired based on the data acquisition array, and the standard time sequence database is established; the dynamic correlation model is constructed based on the standard time sequence database, and the quality determination standard of the pile foundation hole forming is established based on the dynamic correlation model; the quality state of the pile foundation hole forming is obtained according to the dynamic correlation model to the dynamic prediction of the pile foundation hole forming, and the quality deviation result of the pile foundation hole forming is obtained in combination with the quality determination standard; the control strategy of the drilling machine is constructed based on the quality deviation result, and the key work parameter is adjusted according to the control strategy, so that the quality intelligent management and control of the pile foundation hole forming are realized.The present application realizes the intelligent management and control of the pile foundation hole forming by the drilling machine work parameter driving.

The application discloses an optimal flyby sequence acquisition method based on a parallel architecture and belongs to the field of orbit design and optimization. The application realizes the method as follows: a parallel cluster search algorithm is constructed, and a Lambert problem is solved in parallel; a state of a spacecraft at a starting moment of a task time window is determined, a parallel cluster search is performed to obtain a sub-sequence of target access; then, the sub-sequence is saved to a database, a state and a moment at an end of each sub-sequence in the database are extracted, and the parallel cluster search is performed again from the state; the above process is repeatedly iterated to generate a sub-sequence database; the sub-sequences are spliced to generate a complete target access flyby sequence trajectory; and the total speed increment of the flyby sequence is further reduced by adjusting a transfer time between targets and optimizing a flyby speed. The application can significantly improve flyby sequence planning efficiency, effectively guarantee the optimality of sequence generation for a large-scale target problem, and acquire a high-precision and low-energy-consumption spacecraft flyby sequence.

The invention discloses a data quality evaluation method and system based on a real-sequence database, and the method comprises the steps: collecting factory bit number time sequence data from at least one data source, and storing the time sequence data in a time sequence data storage layer; based on preset multi-dimensional data quality evaluation indexes, the time series data are evaluated, a corresponding quality evaluation result is generated, and the multi-dimensional data quality evaluation indexes at least comprise data integrity, data consistency, data accuracy, data credibility and data stability; according to the quality evaluation result, a factory data quality space is constructed and maintained, and the data quality space is used for centralized management and visual display of the data quality state of the factory position number; and providing an access interface for the data quality space for a user through the client. A statistical analysis technology is utilized to research factory data quality, and a user is helped to better understand and solve a data quality problem.

The invention provides a proteome tandem mass spectrum quality evaluation method based on a machine learning self-attention model encoder, which is used for evaluating the quality of proteome mass spectrum experimental data. The method can be used for identifying high-quality mass spectra (such as search parameters which are not optimized, unspecified post-translational modifications and incomplete sequence databases) which are not identified due to data analysis reasons, can also be used for evaluating the overall data quality of one experiment, provides an overall statistical overview of the proteome mass spectrum experiment data quality, and can be used for analyzing the mass spectrum of the proteome. The method is used for constructing a proteome standard data set with consistent data quality and the like, and the reliability and reproducibility of proteomics analysis conclusions are promoted.

The invention relates to phosphoketolase MaPKT and SzPKT with a function of catalyzing formaldehyde to synthesize acetyl phosphate and application of phosphoketolase MaPKT and SzPKT in CO2 biosynthesis of ethanol, and belongs to the technical field of enzyme mining and biological catalysis application. A phosphoketolase specific protein sequence database and an oligopeptide module sequence library are established through an oligopeptide module recognition (PPR) technology, two phosphoketolase PKTs capable of catalyzing formaldehyde to synthesize acetyl phosphate are efficiently and accurately excavated, further research finds that the formaldehyde tolerance of MaPKT is improved, and the catalytic efficiency of MaPKT on formaldehyde is 8 times that of glycolaldehyde. Based on this, the invention also constructs an efficient condensation approach for synthesizing ethanol by artificially biologically converting CO2, and has potential application prospects.

The invention relates to the technical field of bioinformatics and computational biology, in particular to a multiple PCR amplicon sequencing data detection method based on deep learning. The technical problem that the accuracy of overall detection of complex sequencing data is low in the prior art is solved. The method comprises the following steps: acquiring a to-be-detected sequencing data set; obtaining a reference sequence database; constructing a feature extraction model for extracting amplicon features based on the historical amplification condition information and the multiple groups of historical sequencing data of the target tag; and processing a to-be-detected sequencing data set based on the feature extraction model, and determining a target category corresponding to each sequencing sequence in the sequencing data set. The multi-PCR amplicon sequencing method is used for a multi-PCR amplicon sequencing scene.

The invention discloses a multi-dimensional efficient DNA sequence pattern mining method, and aims to identify a pattern with biological significance in a DNA sequence. According to the method, firstly, an original database containing DNA sequences and dimension information is divided into a DNA sequence database and a dimension database on the basis of a database analysis technology, and secondly, base combinations with remarkable effectiveness under different conditions are extracted from DNA sequence data through an efficient sequence pattern mining algorithm; and finally, generating a multi-dimensional efficient DNA sequence mode in combination with a multi-dimensional data mining technology. According to the method, experimental measurement values or importance of all basic groups are quantified into utility values, deep mining is carried out on multi-dimensional DNA data, and high-utility gene segments under specific species, tissue types and experimental conditions are identified. According to the method disclosed by the invention, the efficiency and depth of DNA sequence analysis are remarkably improved, and a functional sequence mode which is difficult to find by a traditional method and has condition specificity can be systematically revealed, so that the scientific preciseness and the practical application value of research results are improved in multiple aspects of gene function analysis, disease marker discovery, evolution research and the like.