20 results about "Sequence clustering" patented technology

A complex bait data set construction method based on structure and sequence collaborative redundancy elimination belongs to the field of bioinformatics, and comprises the following steps: screening an initial protein complex structure set, removing entries containing nucleic acids, small molecules or non-protein chains, and selecting binary complexes meeting integrity and resolution requirements; secondly, structure clustering and sequence clustering are carried out based on three-dimensional structure similarity and sequence homology, combined comparison is carried out on the two results, and redundant compound entries which are highly similar in structure and sequence are removed; then, taking each cluster representative compound as a target, generating a plurality of groups of bait structures by using a molecular docking or prediction modeling method, and calculating a quality index; and finally, performing stratified sampling and proportion balance based on the score interval of the quality index, and constructing a high-quality protein complex bait data set with structure and sequence collaborative redundancy elimination and balanced quality distribution. The data set generated by the method has the advantages of low redundancy, high diversity and quality distribution controllability.

The invention relates to the field of agricultural data analysis, in particular to a plough layer capacity expansion regulation and control method fused with an organic material carbon nitrogen conversion model, and the method comprises the steps: obtaining carbon nitrogen mineralization rate time sequence original data; performing coupling evaluation on mineralization energy gravity center moment difference and morphological overlapping characteristics to obtain a phase hysteresis correction factor; performing normalized attenuation mapping on the sequence local oscillation energy functional and the total sample average oscillation energy to obtain a transient excitation smoothing factor; performing optimization factor multiplicative correction on the Euclidean distance to obtain a corrected distance and performing clustering iterative calculation; and performing carbon nitrogen conversion model parameter matching and regulation strategy reverse derivation on a clustering result to obtain a plough layer capacity expansion regulation scheme so as to solve the problems of regulation type region division distortion and model matching precision reduction caused by inoculation hysteresis translation and excitation effect high-frequency oscillation in the existing Euclidean distance-based mineralization rate time sequence clustering.

The invention relates to the technical field of clinical medical treatment, and discloses a disease deterioration risk identification method and system based on depth time sequence clustering enhancement, and the method comprises the steps: 1, obtaining multi-modal clinical sequence data of a patient, including physiological indexes of a time sequence, a laboratory detection result, historical diseases and medication data; 2, performing feature extraction and classification on the patient sequences by adopting a knowledge enhanced sequence clustering method, and grouping the patient sequences according to future outcome distribution of the patient sequences; 3, enabling the model to quickly adapt to a prediction task of a new patient subgroup through a meta-training process; and step 4, based on the trained meta-model, carrying out rapid adaptation on the new patient subtype, and predicting the possibility that the new patient subtype has a deterioration event in a certain time window in the future. The method and the system can effectively learn the disease change mode of the patient under the condition of limited clinical data, improve the prediction accuracy of the new patient subgroup, and are especially suitable for clinical prediction scenes under the condition of small samples.

The invention belongs to the technical field of data management and intelligent analysis, and particularly relates to a data loading and conversion processing method under a lake-warehouse fusion architecture, which comprises the following steps of: after an externally uploaded file is received, performing compatibility verification and content verification on the externally uploaded file in sequence; identifying that the tables and the partitions which are influenced by data loading and need to be synchronously updated are added, executing atomic data loading operation, synchronously updating the tables and the partitions in the table and the partitions, and generating corresponding version numbers for each table and each partition at the same time; for a data block set under the lake-warehouse fusion architecture, performing hierarchical adjustment according to the access popularity of the data block set; income-cost evaluation is carried out on each data block, and the data blocks needing to be subjected to Z-sequence clustering rearrangement are recognized and added into a requeuing column; and performing Z-sequence clustering rearrangement on each data block in the requeuing column.

This invention relates to the field of agricultural data analysis, and particularly to a method for expanding and regulating the topsoil volume by integrating an organic material carbon and nitrogen conversion model. The method includes: acquiring raw time-series data of carbon and nitrogen mineralization rates; obtaining a phase hysteresis correction factor by coupling and evaluating the temporal differences and morphological overlap characteristics of the mineralization energy centroid; obtaining a transient excitation smoothing factor by normalizing and attenuating the local oscillation energy functional of the sequence with the average oscillation energy of the entire sample; obtaining a corrected distance by performing multiplicative optimization of the Euclidean distance and performing iterative clustering calculations; and obtaining a topsoil volume expansion and regulation scheme by matching carbon and nitrogen conversion model parameters and inversely deriving the regulation strategy from the clustering results. This addresses the problems of distortion in the division of regulation type regions and decreased model matching accuracy caused by inoculation hysteresis translation and high-frequency oscillations of excitation effects in existing Euclidean distance-based mineralization rate time-series clustering.

The application discloses a nucleic acid sequence clustering method and device, a computer readable storage medium and a terminal. The terminal constructs a tree structure with multiple branches to search a specified interval of a nucleic acid sequence, thereby avoiding a large amount of time consumed by traditional calculation of an editing distance. In addition, the application adopts a node drift algorithm to resist interference caused by errors in the nucleic acid sequence. Compared with existing nucleic acid clustering algorithms, the method provided by the application can cluster a large number of unidentified nucleic acid sequences, and has the functions of automatically correcting and comparing the clustered nucleic acid sequences. The method can directly output the corrected nucleic acid original sequence, thereby greatly reducing the processing time after sequencing reading.

Described herein are methods synchronizing sequencing primers within a sequencing cluster and methods of generating long-range sequencing reads. The methods can include hybridizing primers to polynucleotide copies within a sequencing cluster; extending the primers through a first region of the polynucleotide copies using labeled nucleotides according to a sequencing flow order; extending the primers through a second region of the polynucleotide copies using one or more re-phasing flow steps that each include at least two different types of nucleotide bases; and extending the primers through a third region of the polynucleotide copies using labeled nucleotides according to the sequencing cycle. The rephasing flow steps may be initiated after a predetermined number of sequencing flow steps, after a measured sequencing signal strength falls below a predetermined sequencing signal strength threshold, or a measured sequencing signal-to-noise ratio falls below a sequencing signal-to-noise ratio threshold.

The invention provides a multi-system data communication and service fusion method and system based on a front-end instruction. According to the method, operation events such as clicking, inputting, skipping and data reading of a user in a plurality of service systems are collected at the front end of a browser, a cross-system operation track is constructed, key action extraction, sequence clustering and structural feature recognition are carried out on the operation track, and a reusable service mode template is automatically generated. Based on natural language service requirements of a user, system execution intention recognition and task element extraction, task requirements are mapped to a service template, structured task description is generated, a front-end instruction sequence and a cross-system dependency relationship are constructed according to the structured task description, and an executable instruction diagram is formed. The instruction graph is dynamically executed in the browser. According to the method, the original system interface does not need to be transformed, automatic communication and data collaboration of multi-system business processes can be realized, the manual operation cost is remarkably reduced, and the business processing efficiency and the data consistency are improved.

The invention discloses a three-phase load prediction method based on time sequence clustering, and the method comprises the steps: firstly collecting the data of an intelligent electric meter of a user in a target transformer area, and carrying out the preprocessing of the collected current load data; clustering the preprocessed load data based on a time sequence clustering algorithm to obtain K clusters of data of different load types; and clustering is completed by iteratively updating the clustering centroid. Constructing a space-time diagram neural network according to the clustering result and the three-phase phase information of the user; the space-time diagram neural network is used for carrying out space-time feature extraction and fusion on the clustered load data and outputting a three-phase load prediction result; and finally, training the constructed space-time diagram neural network by using the training data, and predicting the future three-phase load by using the trained model. According to the method, data with the same load type are classified into one class through a time sequence clustering algorithm, and a space-time diagram neural network is built according to three-phase phase information of a user to carry out three-phase load prediction.

The invention provides an abnormal behavior detection method and system for a communication information flow, and relates to the technical field of network communication security, firstly, an original communication information flow set containing massive communication session recording units is captured in a network communication link, and each recording unit carries information such as source and destination internet protocol address tags; performing communication behavior pattern analysis on the original communication information flow set, and constructing a cross-session associated interaction behavior sequence cluster; secondly, extracting features of an interactive behavior time sequence chain, and processing the features through a semantic embedding layer and a time sequence convolution encoder to generate a joint embedded feature vector; and inputting the vector into an abnormal behavior prototype network for prototype matching to obtain an initial abnormal behavior mark. And finally, tracing the abnormal behavior propagation path according to the initial mark to obtain an abnormal behavior propagation path sequence set. According to the invention, abnormal behaviors in network communication can be effectively detected, and the detection accuracy and reliability are improved.

The embodiment of the invention discloses a data processing method and device, equipment and a medium, and is applied to the technical field of computers. The method comprises the following steps: acquiring a plurality of operation sequence groups to be clustered; performing sequence clustering on the plurality of operation sequence groups based on the operation sequences in the plurality of operation sequence groups to obtain clustering clusters related to the plurality of operation sequence groups; scene matching operation matched with the clustering scene corresponding to the clustering cluster is determined based on service operation represented by the operation sequence contained in the clustering cluster; and generating an element positioning anchor point for performing element positioning on a page element of the scene matching operation, so as to position the page element in a task page of a scene task through the element positioning anchor point when the scene task related to the clustering scene is obtained, and executing the scene matching operation of the scene task through the page element. By adopting the embodiment of the invention, the definition efficiency and flexibility of the business process can be improved.

The invention discloses a method, a system, equipment and a medium for assembling third-generation sequencing data based on clustering and graph construction, and belongs to the technical field of biological sequence processing. The method comprises the following steps: obtaining sequence similarity based on third-generation sequencing data, and clustering sequences by using a clustering algorithm to obtain different clusters; assembling the sequences in the same cluster based on a graph construction method to obtain an intra-group consensus sequence; and combining all intra-group consensus sequences from different clusters, and assembling based on the graph construction method to obtain an inter-group consensus sequence. According to the technical scheme, through the strategy of sequence clustering, intra-group assembly and inter-group assembly, the assembly complexity is effectively reduced, the accuracy and integrity of the consensus sequence are ensured through the optimal overlap graph algorithm and depth pruning, and the method has remarkable technical advantages.

The application discloses a high-throughput sequencing data sequence clustering method and system based on cyclic self-blast: the high-throughput sequencing data is de-duplicated; unique sequences with a number of repetitions lower than a threshold value a are filtered, and the filtered unique sequences are sorted according to the number of repetitions; the sorted unique sequences are divided into subsets and a parent set; the subsets are subjected to blast multiple alignment to obtain a de-redundant subset; the parent set and the de-redundant subset are subjected to cyclic blast alignment to obtain a de-redundant parent set; all representative sequences in a preliminary clustering set are combined, sorted according to the number of repetitions and distributed with identification tags to generate a final OTU clustering result set; in the prior art, when OTU clustering and ASV methods are used to process environmental DNA macro-barcode sequencing data, different OTU or ASV sequences are annotated to the same species, so that a large amount of redundancy still exists in the classified units after clustering, and the reliability of species analysis is improved.

The invention discloses a high-throughput sequencing data sequence clustering method and system based on circulating self-blast. The method comprises the following steps: carrying out duplicate removal processing on high-throughput sequencing data; filtering the unique sequences of which the repetition number is lower than a threshold value a, and sorting the filtered unique sequences according to the repetition number; dividing the sorted unique sequence into a subset and a mother set; performing blast multiple comparison on the subsets to obtain redundancy-removed subsets; performing cyclic blast comparison on the mother set and the redundancy-removed subset to obtain a redundancy-removed mother set; combining all representative sequences in the preliminary clustering set, sorting according to the number of repetitions and distributing identification labels, and generating a final OTU clustering result set; in the prior art, when an OTU clustering method and an ASV method are used for processing environment DNA macro bar code sequencing data, different OTU or ASV sequences are annotated to the same species, so that a large amount of redundancy still exists in a classified unit after clustering, and the reliability of species analysis is improved.

The application discloses an attention mechanism dynamic sparseness and quantization method, system, device and medium, which are corresponding solutions, in the solutions: through a block granularity clustering algorithm, an input vector sequence is rearranged to obtain a vector sequence cluster of block granularity of aggregated semantic information, a clustering center of each cluster is taken as a representative element to calculate an attention score of each cluster, a clustering block with high importance is selected based on the attention score to obtain a block granularity sparse mask, in an attention calculation kernel, a vector sequence block that needs to be calculated is selectively read in according to the mask, then a block-by-block data smoothing operation is performed on the read-in vector sequence block, and symmetric quantization is performed. The above-mentioned solution obtains a hardware-friendly sparse structure beneficial to uniform task division and avoiding waste of calculation and bandwidth resources under the condition of ensuring model accuracy, thereby saving calculation and bandwidth resources, reducing the calculation amount of the attention mechanism module, and further improving the efficiency of video generation model reasoning.

The invention discloses a bathing digital safety management and control system based on an Internet of Things terminal, and the system comprises an event flow obtaining module which is used for obtaining a historical bathing event flow, a sequence construction module which is used for constructing N behavior vector sequences, a sequence cluster clustering module which is used for obtaining K behavior sequence clusters, and a typical sequence obtaining module which is used for obtaining a typical sequence. The typical behavior vector sequence acquisition module is used for acquiring a typical behavior vector sequence, the current sequence acquisition module is used for acquiring a latest behavior vector sequence, the matching degree calculation module is used for calculating the safety matching degree of a current user, and the safety intervention module is used for performing safety intervention; according to the invention, a reasonable behavior can be prevented from being misjudged as a risk; besides, the matching process is carried out based on the first Q behavior events which have occurred currently, the completion of bathing is not needed, intervention can be triggered when the user enters the initial stage of the abnormal state (for example, continuous idling occurs in the second stage), and the response timeliness is remarkably improved.

The invention discloses an intelligent unlocking data analysis system and method based on a television, and relates to the technical field of television management.The intelligent unlocking data analysis method comprises the steps that a user entrance timestamp is obtained through user authorization; acquiring spatial movement behavior data after the user enters the home and a television startup and shutdown event; analyzing behavior characteristics of the user, and integrating the behavior characteristics into an original behavior data stream; extracting typical user behaviors through behavior state discretization and sequence clustering; constructing a behavior state transition diagram with timestamp probability distribution; performing fusion calculation on a real-time watching intention probability by calculating the morphological similarity between a real-time behavior state sequence and a historical typical behavior sequence and combining the transition intensity and time goodness of fit embodied on a state transition diagram; analyzing and generating a predictive wake-up decision; and the system generates a feedback signal according to the subsequent actual active interaction behavior of the user, and optimizes a dynamic threshold value and a historical behavior model. And the time sequence sensitivity and accuracy of intention recognition are improved.

The application provides an abnormal behavior detection method and system for a communication information flow, relates to the technical field of network communication security, and first captures a raw communication information flow set containing a large number of communication session record units in a network communication link, each record unit carrying information such as source and destination internet protocol address labels. Then, the raw communication information flow set is subjected to communication behavior mode analysis, and a cross-session associated interactive behavior sequence cluster is constructed. Then, the features of the interactive behavior time sequence chain are extracted, and a joint embedding feature vector is generated through a semantic embedding layer and a time sequence convolution encoder. The vector is input into an abnormal behavior prototype network for prototype matching to obtain an initial abnormal behavior label. Finally, the abnormal behavior propagation path is traced according to the initial label to obtain a set of abnormal behavior propagation path sequences. The application can effectively detect abnormal behaviors in network communication and improve detection accuracy and reliability.

The application provides a kind of unknown radar signal clustering method of DBscan combination distribution posterior evaluation and optimization, comprising: for the two two-dimensional sequences of signal angle of arrival DOA and signal amplitude AMP two kinds of parameters, according to the performance of reconnaissance system, measurement error and measurement value, define the weighted distance D of two two-dimensional sequences, and define the two parameters Eps and MinPts of Dbscan clustering operation;Wherein, Eps represents neighborhood radius, and MinPts represents density threshold;According to the defined weighted distance D, neighborhood radius Eps and density threshold MinPts, the two two-dimensional sequences are carried out Dbscan clustering operation, and the clustering result is obtained;The obtained clustering result is evaluated, and the parameters of clustering result are updated according to the posterior evaluation result.The application can improve the accuracy and robustness of radar signal sequence clustering.