98 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.

The invention relates to the technical field of pile foundation construction, in particular to a pile foundation pore-forming quality intelligent management and control method and system based on drilling machine working parameters, and the method comprises the following steps: constructing a data acquisition array, and obtaining key working parameters of a drilling machine and pile foundation pore-forming quality index data based on the data acquisition array; establishing a standard time sequence database; building a dynamic correlation model based on the standard time sequence database, and building a quality judgment standard of the pile foundation pore-forming based on the dynamic correlation model; dynamically predicting the pile foundation formed hole according to the dynamic correlation model to obtain the quality state of the pile foundation formed hole, and obtaining a quality deviation result of the pile foundation formed hole in combination with the quality judgment standard; and a regulation and control strategy of the drilling machine is constructed based on the quality deviation result, the key working parameters are adjusted according to the regulation and control strategy, and intelligent control over the quality of pile foundation hole forming is achieved. Intelligent control over the pile foundation hole forming quality is achieved through driving of the working parameters of the drilling machine.

The invention discloses a real-time data subscription method and device based on a time sequence database and a medium, belongs to the technical field of databases, and aims to solve the technical problems that the real-time performance is poor, the resource consumption is large and the time sequence data required by the current time sequence database in real time acquisition in mass data storage and processing is poor. According to the technical scheme, the method comprises the steps that a data subscription scheduling task is constructed through a predefined SQL statement, in combination with an efficient data change capture module based on a Raft log mechanism, data subscribed by a user is obtained in real time, and the data subscribed by the user is filtered according to a customization rule; then the screened data is automatically transmitted to a preset target sink component in a specified format, so that the whole data processing flow has the characteristics of real-time performance, accuracy and high availability; the method specifically comprises the following steps: constructing a data subscription scheduling task; executing the data subscription scheduling task; and capturing data change based on the raft log.

The invention provides an enzyme modification method based on artificial intelligence. The enzyme modification method comprises the following steps: training an AlphaFold2 model to obtain a glycosyltransferase tertiary structure prediction model; performing multi-sequence comparison on the protein and nucleic acid sequence database and the non-redundant protein feature sequence database based on the active point location recognition model to obtain a comparison result; initializing a matrix of which the length is equal to that of a comparison result, so as to construct a PSSM matrix and screen out potential mutation sites; according to the screened potential mutation sites, constructing an initial mutation library to form a virtual screening mutation library; performing structure reconstruction on the sequence in the virtual screening mutation library based on a glycosyl transferase tertiary structure prediction model so as to predict the glycosyl transferase tertiary structure and perform molecular docking and dynamic simulation on the glycosyl transferase tertiary structure; the method is used for analyzing binding pockets of proteins and ligands and determining dynamic interaction of amino acid residues and ligands in the binding pockets, and the contact number of each amino acid residue and the ligands is used for modifying the tertiary structure of the glycosyltransferase.

The invention provides a guide rail dynamic error real-time compensation system based on multi-sensor fusion, and relates to the technical field of guide rail precision compensation, and the system comprises a calling module which is used for calling and traversing a temperature sequence database, and obtaining a target temperature sequence matched with a target working condition; the prediction module is configured to construct a finite element model of the guide rail, and is combined with the target temperature sequence to obtain a predicted straightness sequence of the guide rail so as to obtain a straightness error sequence; the judgment module is configured to traverse the straightness error sequence so as to take a point location corresponding to the predicted straightness corresponding to the straightness error greater than the preset precision as a to-be-compensated point location; the compensation module is configured to obtain a corresponding thermal deformation compensation amount according to the straightness error corresponding to each to-be-compensated point position, and compensate the straightness error of each to-be-compensated point position by the thermal deformation compensation amount in the operation process of the guide rail under the target working condition through the distributed piezoelectric ceramic array, so as to compensate the straightness error of each to-be-compensated point position. And a more reliable precision guarantee is provided for high-precision machining equipment.

The invention discloses a method for constructing a lung cancer early screening model based on TCR sequencing, which comprises the following steps of: firstly, constructing a TCR-enriched sequence database based on TCR sequencing data from samples of lung cancer patients and healthy people, then performing TCR sequencing on each sample, and acquiring TCR characteristics of each sample; comprising proportion characteristics of V gene and J gene, statistical characteristics of a TCR immune repertoire, convergence sequence characteristics, TCR sequence amino acid proportion characteristics, proportion characteristics of TCR sequences with different lengths, proportion characteristics of TCR sequences with different frequencies and TCR enriched sequence characteristics; screening the TCR features of each sample by using a feature screening method, and constructing a machine learning model in combination with other information of the samples to obtain a lung cancer early screening model; a lung cancer early screening detection device is constructed based on the lung cancer early screening model, and lung cancer early screening can be achieved based on TCR sequencing information of a sample.

The invention discloses an intelligent equipment-oriented time sequence database lightweight API (Application Program Interface), which comprises a data acquisition module used for acquiring time sequence data from a sensor or a data source of intelligent equipment; the local storage module is used for carrying out persistent storage on the data by adopting a lightweight embedded database; the data transmission and synchronization module is used for synchronizing locally stored data to a cloud or a central server; the data query module is used for providing a simplified interface for a developer to query locally stored data; the system management and monitoring module is used for monitoring resource use conditions; and the security and fault-tolerant module is used for ensuring the security, consistency and integrity of the data in the storage and transmission processes. The method has the advantages of lightweight design, real-time performance, high efficiency, adaptability, network optimization, safety and the like.

The invention provides a compatible system and a compatible control method of a cross-time-sequence database, and relates to the technical field of data processing, and the compatible system comprises a service kernel management module which is used for receiving preset source configuration information, creating corresponding kernel service processes according to the preset source configuration information, and controlling the start and stop of the kernel service processes, the kernel service process is used for connecting a corresponding time sequence database; the proxy module is used for acquiring request information of a client; and the data source driving module is used for accessing the time sequence database through the corresponding kernel service according to the request information and receiving a query result of the time sequence database. According to the method, efficient, flexible and compatible data access of the cross-time-sequence database is achieved, a developer only needs to achieve an interface once, the system can automatically process interaction of different types of databases, the requirement for writing specific interface codes for each type of database is reduced, and therefore the development cost of an application program is reduced.

A processor-implemented method for causal representation learning of temporal effects includes receiving, via an artificial neural network (ANN), temporal sequence data for high-dimensional observations. The ANN generates a latent representation based on latent variables for the temporal sequence data. The latent variables of the temporal sequence data are assigned to causal variables. The ANN determines a representation of causal factors for each dimension of the temporal sequence databased on the assignment.

The embodiment of the invention provides a data processing method and device, electronic equipment, a storage medium and a program, and the method comprises the steps: responding to a data query instruction, and obtaining global index information; obtaining target time sequence information from a preset time sequence database according to the data block index; and obtaining target sampling data according to the target data block and the position index information. By creating global index information, index index data capable of being reused by sampled time series data are unified and integrated, and only index data changing in corresponding time needs to be stored in each data block based on local index information, so that when the index data are archived and compressed, the index data written into a disk can be reduced, and the efficiency of archiving the index data is improved. Storage control is saved, index data read from a disk can be reduced during data query, and the purpose of improving query efficiency is achieved.

The invention relates to the technical field of machine vision and industrial quality inspection, in particular to a polishing scheme autonomous optimization method and system for surface defect detection and a medium, and the method comprises the following steps: S1, based on a preset polishing parameter grading rule, collecting detection data to construct a defect-polishing sequence database; s2, segmenting and evaluating the image, and constructing a mapping relation model of each type of defect and a lighting parameter sequence based on a decision tree; s3, optimizing a lighting scheme by adopting an intelligent optimization algorithm based on the mapping relation model of each type of defects and the lighting parameter sequence; and S4, guiding an actual industrial defect detection task based on a lighting scheme corresponding to the light source parameter sequence obtained in the step S3. The problem that an existing lighting scheme depends on manual operation and is low in efficiency is solved, the optimal lighting scheme is automatically determined, the manpower burden is relieved, and the accuracy and stability of defect detection are improved.

The invention relates to the technical field of intelligent dimming, in particular to a lighting scheme autonomous optimization method and system based on a grey wolf optimization algorithm and a medium, and the method comprises the steps: S1, based on a preset lighting parameter grading rule, collecting detection data to construct a defect-lighting sequence database, and S2, constructing a preset standard mask pattern; s3, segmenting and evaluating the image, and constructing a mapping relation model of each type of defect and the lighting parameter sequence based on the optimal quality, S4, optimizing a lighting scheme by adopting an improved grey wolf optimization algorithm based on the mapping relation model of each type of defect and the lighting parameter sequence, and S5, carrying out the optimization of the lighting scheme. And guiding an actual industrial defect detection task based on a lighting scheme corresponding to the light source parameter sequence obtained in the step S4. The light source debugging efficiency and precision are greatly improved, the optimization speed and quality are improved, the optimization efficiency of light source parameters can be greatly improved, and intelligent production development can be further promoted.

The invention relates to a tumor neoantigen prediction method and a tumor neoantigen prediction system. The method comprises the following steps: acquiring a plurality of amino acid sequences in a gene of a person to be tested as a plurality of test peptides to be compared with a plurality of human protein sequences in a protein sequence database so as to find out a plurality of similar peptides matched with the human protein sequences; filtering similar peptides from the test peptides, and respectively inputting the filtered test peptides into a plurality of trained human leukocyte antigen models to obtain a plurality of sequencing results of the test peptides; calculating the weighted sum of the sequencing of each test peptide in the sequencing result as the score of the test peptide; and selecting at least one of the test peptides as a neoantigen suitable for the subject according to the fraction.

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.

A method for treating a mental disorder in a subject, comprising the step of normalizing the expression of both DRD1 and DRD5 belonging to the DRD1-like family of the subject to a preset standard level. The DRD1 / 5 level and expression can be increased or decreased bidirectionally by targeting the upstream promoter sequence of DRD1 / 5. The promoter activity of DRD1 / 5 can be attenuated by using antipsychotic drugs that directly target the DRD1 / 5 promoter. The antipsychotic drugs used are typical and / or atypical antipsychotic drugs such as haloperidol, risperidone, clozapine, and cariprazine. This treatment is also useful for subjects in whom the constitutive expression of the DRD1 / 5 gene is impaired. This method can be used to treat schizophrenia, autism, depression, mania, and bipolar disorder by normalizing the DRD1 / 5 expression level. The promoter gene sequencing of DRD1 and / or DRD5 can also be used for quantitative mental health screening by comparing with a standard sequence database.

The invention relates to a Computer-implemented method for labelling of user action (UAC) data in a user environment (ENV), in particular wherein said user environment (ENV) is a computer (CMP) implemented user interactive (UIA) environment (ENV), characterized in comprising the steps of: a) providing a pre-trained user action (UAC) pattern recognition model (PRM), wherein the pattern recognition model (PRM) is designed in such a way that the pattern recognition model (PRM), from a part-sequence of user actions (UAC) including a user action (UAC) pattern (APT) known to the pattern recognition model (PRM), recognizes the user action (UAC) pattern (APT) known to the pat-tern recognition model (PRM), and outputs the user action (UAC) pattern (APT) or a label associated with the user action (UAC) pattern (APT), b) logging of user actions (UAC), c) analyzing the logged user actions (UAC) by said user action (UAC) pattern recognition model (PRM) for user action (UAC) patterns (APT) known to the pattern recognition model (PRM), d) generating of a user interaction (UIA) in case of not detecting a known user action (UAC) pattern (APT) in a sequence (SQC) of user actions (UAC) as the user interaction (UIA) requesting information about the past sequence (SQC) of user actions (UAC), e) labelling the past sequence (SQC) of user actions (UAC) using the information received from the information request, f) storing the labelled sequence (SQC) of user actions (UAC) in a user action (UAC) sequence (SQC) database (DBS), g) training the pattern recognition model (PRM) using the user action (UAC) sequence (SQC) and corresponding labels of the user action (UAC) sequence (SQC) database (DBS).

A method for predicting DNA binding residues based on a three-segment sliding window is disclosed. Firstly, according to the input protein sequence information P for DNA binding residue prediction, the number of residues is L, the protein sequence database UniRef90 is searched using the PSI-BLAST program to generate a position-specific scoring matrix of size L×20, a solvent accessibility feature matrix of size L×3 is obtained using the SANN program, and a feature matrix of size L×20 is obtained by one-hot encoding. Then, the three feature matrices are concatenated into a super feature matrix of size L×43, that is, each protein residue corresponds to a 1×43 feature vector, and the similarity of the feature vectors between two residues is obtained by cosine similarity, that is, the residue similarity. Secondly, the feature view processing is performed on each residue in the super matrix by using the three-segment sliding window technology, and these feature views are expanded into feature vectors. Thirdly, the corresponding feature vectors and labels are generated by using the protein sequence of known DNA binding residues, and input into the support vector machine for training. Finally, the feature vector generated by the protein sequence to be predicted is input into the trained model to predict whether the residues in the protein sequence are DNA binding residues. The present invention has low calculation cost and high prediction accuracy.

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 invention provides a construction method of a reference sequence for metagenome data compression, which comprises the following steps: constructing a basic reference sequence database according to a sample source of metagenome data; based on the basic reference sequence database, constructing an index of the basic reference sequence database; according to the index of the basic reference sequence database, a first read length sequence is compared with the basic reference sequence database, a comparison result is obtained, and the first read length sequence is a read length sequence of a part of samples randomly selected in metagenome data to be compressed; and according to the comparison result, determining the sequence abundance distribution of the first read length sequence, and constructing the reference sequence for metagenome data compression.

The invention provides a method for comparative evolution analysis based on soybean-related WD40 genes, and relates to the technical field of plant molecular markers. The method for comparative evolution analysis based on the soybean-related WD40 gene specifically comprises the following steps: S1, identifying WD40 gene family members; s2, chromosome distribution characteristics; s4, performing phylogenetic analysis; s5, carrying out Ka / Ks analysis and collinearity analysis; s6, gene structure, Motif and protein structure; and S7, analyzing a cis-regulatory element. The mining and analysis method of the WD40 gene related to the gray mold and the salt stress of the wild soybean and the cultivated soybean is based on a whole genome sequence database, and 497 and 500 WD40 family members are respectively identified by utilizing a bioinformatics method according to the whole genome database of the wild soybean and the cultivated soybean, so that the WD40 gene related to the gray mold and the salt stress of the wild soybean and the cultivated soybean is obtained. Gene characteristics and evolution comparative analysis is carried out on the genes, and the analysis result provides important reference value for improving gray mold resistance and salt tolerance of the cultivated soybeans through genetic engineering means in the future so as to improve the yield of the soybeans and improve the quality of the soybeans.

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.

The invention relates to a Computer-implemented method for labelling of user action (UAC) data in a user environment (ENV), in particular wherein said user environment (ENV) is a computer (CMP) implemented user interactive (UIA) environment (ENV), characterized in comprising the steps of: a) providing a pre-trained user action (UAC) pattern recognition model (PRM), wherein the pattern recognition model (PRM) is designed in such a way that the pat-tern recognition model (PRM), from a part-sequence of user actions (UAC) including a user action (UAC) pattern (APT) known to the pattern recognition model (PRM), recognizes the user action (UAC) pattern (APT) known to the pat-tern recognition model (PRM), and outputs the user action (UAC) pattern (APT) or a label associated with the user action (UAC) pattern (APT), b) logging of user actions (UAC), c) analyzing the logged user actions (UAC) by said user action (UAC) pattern recognition model (PRM) for user action (UAC) patterns (APT) known to the pattern recognition model (PRM), d) generating of a user interaction (UIA) in case of not detecting a known user action (UAC) pattern (APT) in a sequence (SQC) of user actions (UAC) as the user interaction (UIA) requesting information about the past sequence (SQC) of user actions (UAC), e) labelling the past sequence (SQC) of user actions (UAC) using the information received from the information request, f) storing the labelled sequence (SQC) of user actions (UAC) in a user action (UAC) sequence (SQC) database (DBS), g) training the pattern recognition model (PRM) using the user action (UAC) sequence (SQC) and corresponding labels of the user action (UAC) sequence (SQC) database (DBS).

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.

A method of treating a psychiatric disorder in a subject comprises the step of normalizing the expression of DRD1 and DRD5 belonging to DRD1 class receptors in the subject to a preset standard level. By targeting an upstream promoter sequence of DRD1 / 5, the level and expression of DRD1 / 5 can be bidirectionally increased or decreased. The use of antipsychotic agents that directly target the DRD1 / 5 promoter can attenuate the DRD1 / 5 promoter activity. The antipsychotic agents used are typical and / or atypical antipsychotic agents, such as haloperidol, risperidone, clozapine and cariprazine. The treatment is also useful for subjects in which constitutive expression of the DRD1 / 5 gene is impaired. The methods can treat schizophrenia, autism, depression, mania, and bipolar affective disorders by normalizing DRD1 / 5 expression levels. Promoter gene sequencing of DRD1 and / or DRD5 can also be used for quantitative mental health screening by comparison to a standard sequence database.

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.