49 results about "Homology analysis" patented technology

The invention discloses a TSV packaging defect detection method and system, and the method comprises the steps: carrying out the multi-dimensional excitation scanning of a TSV packaging structure through employing an eddy current pulse thermal imaging device, and generating a three-dimensional eddy current field distribution feature matrix; inputting the three-dimensional eddy current field distribution characteristic matrix into a topological manifold decomposition module for defect characteristic separation, and constructing three characteristic components of a structure deformation topological ring, an interface fracture chain and a thermal stress abnormal curved surface based on a continuous coherence analysis method; performing cross-domain fusion processing of a dynamic heterogeneous neural network on the three feature components, and generating a defect topology fingerprint spectrum through a double-path attention gating mechanism; and inputting the defect topology fingerprint into a multi-scale entropy evaluation module for defect evolution simulation, and outputting a quantitative detection report containing defect geometric parameters, failure probability and reliability threshold. According to the embodiment of the invention, a comprehensive defect detection system can be constructed, and the accuracy and efficiency of defect identification are improved.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention relates to a homology analysis method and device for binary code files and computer equipment. The method comprises the following steps: obtaining and identifying a function call graph corresponding to each binary code file through a security monitoring program; identifying isomorphic sub-graphs among the binary code files through a hybrid search strategy, and performing symbol program slicing processing on respective write function nodes in each isomorphic sub-graph to obtain symbol program slicing codes corresponding to the respective write function nodes of each isomorphic sub-graph; performing escape processing on each symbol program slice code corresponding to the isomorphic sub-graph to obtain each piece of mathematical semantic information corresponding to the isomorphic sub-graph, and performing similarity calculation to obtain a code similarity value between the binary code files; therefore, the homologous binary code file is identified. By adopting the method, the homology analysis accuracy of the highly confused binary code file can be improved.

The invention relates to the technical field of power equipment state monitoring, and discloses an AI-based power distribution switch arc fault prediction system, and the system carries out the persistent coherence analysis of dielectric response data collected by a sensing array through a topological feature extraction unit, and outputs a topological bar code representing an insulation degradation space structure. Further, a dynamic causal graph construction unit performs causal test on the topological characteristic time sequence to construct a dynamic causal graph for revealing the degradation evolution path; and finally, the fault evolution prediction unit adopts a space-time diagram neural network model to process the causal diagram sequence so as to realize accurate prediction of the fault probability and the degradation extension path. The system also integrates a closed-loop mechanism of adaptive excitation control and data verification correction. According to the method, topological data analysis and causal inference are deeply fused, the problems that a traditional method is low in precision and poor in interpretability are solved, and prediction accuracy, reliability and robustness are remarkably improved.

The embodiment of the invention provides a network attack homologous analysis method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a plurality of preset internet protocol addresses and a preset weighted attack behavior graph; dividing a plurality of preset internet protocol addresses in the preset weighted attack behavior graph into a plurality of initial address clusters; for each initial address cluster, determining a connection weight associated with each preset internet protocol address contained in the initial address cluster, and calculating a corresponding modularity according to the connection weight; according to the modularity corresponding to each initial address cluster, a plurality of target address clusters are obtained again, and the feature centroid of each target address cluster is determined; and obtaining a to-be-analyzed internet protocol address, and performing similarity calculation on the target feature vector of the to-be-analyzed internet protocol address and the plurality of feature centroids of the plurality of target address clusters to obtain a network attack homologous analysis result of the to-be-analyzed internet protocol address. Therefore, the efficiency and accuracy of network attack homologous analysis can be improved.

The invention discloses a person information big data screening method and system based on person and certificate recognition, particularly relates to the technical field of high-dimensional vector real-time query, and is used for solving the problem that index maintenance and real-time query are difficult to consider at the same time in a dynamic database environment. Structured personnel identification information is received, updating types are detected, when an index structure is adjusted, space-time continuity fracture characteristics and conflict nodes are analyzed, a parallel index construction mode is selected according to the analysis, the parallel index construction mode and query are cooperatively executed, temporary index space distortion is evaluated, and the index structure is adjusted. On the basis of topology persistent coherence analysis, hotspot / non-hotspot subsets are divided, independent memory nodes are allocated, finally, a verification sample set is generated for double-index consistency confirmation, and millisecond-level response and retrieval precision are guaranteed in a data high-frequency updating scene.

The invention relates to a software homology analysis method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring a target family classification result of to-be-analyzed software by a pre-trained target classification model, and interpreting and analyzing the target classification model based on the target family classification result, so as to acquire the influence degree of each binary code snippet in the to-be-analyzed software on an output result of the target classification model; then, according to a target family classification result, a family gene pool of a target family and a software sample in the target family are obtained from a pre-established family gene resource center, and according to the family gene of the to-be-analyzed software, the family gene pool of the target family and the software sample in the target family, the software sample in the to-be-analyzed software can be analyzed according to the family gene of the to-be-analyzed software, the family gene pool of the target family and the software sample in the target family. According to the method, the homology analysis result of the to-be-analyzed software and the target family is determined, the to-be-analyzed software is traced, the time for tracing the to-be-analyzed software is shortened, and the homology analysis efficiency is improved.

This invention discloses methanogenic lyases with the same protein family annotation as PeiR lyases and their applications. These enzymes are obtained through homology screening based on the known characteristics of PeiR lyases targeting peptide bonds in the methanogenic cell wall, using the Pfam protein family database annotation. PeiR lyase proteins participate in the biological process of hydrolyzing archaea cell walls, effectively killing methanogens and reducing methane production. Proteins homologous to this protein have potential methane-reducing potential. This invention integrates rumen microbial metagenomic sequencing data, uses a series of bioinformatics software to screen methanogenic viral proteins, and combines Pfam functional domain annotation for homology analysis, ultimately identifying a series of lyases. These lyases were successfully expressed in a prokaryotic expression system, and in vitro gas production experiments also showed that the crude enzyme solution significantly reduced methane production.

The invention discloses a topology enhancement prototype and geometric self-adaption few-sample point cloud semantic segmentation method, which belongs to the technical field of three-dimensional vision, and comprises the following steps of: extracting geometric features of point cloud from a support set and a query set by using a feature encoder; a topology guiding prototype optimization module is introduced, persistent coherence analysis is carried out on point clouds of each category in a support set, multi-scale topology features are extracted, topology descriptors are generated, and the topology descriptors are used for guiding structural adjustment of prototype representation; a difficult sample correction module is designed, a difficult sample area in a query set is identified based on a topology instability index, and fine correction is performed on geometric features in combination with local point density and curvature information; and constructing a topology perception optimization module. According to the method, topology priori is systematically introduced into a few-sample point cloud segmentation task for the first time, and a more stable and fine semantic segmentation result is realized under the condition of not depending on a large amount of annotations.

The invention discloses a multi-modal lung cancer risk dynamic assessment system and method, and belongs to the technical field of medical data analys.The multi-modal lung cancer risk dynamic assessment system comprises a data acquisition module, a topological representation module, a self-adaptive attention gating module, a risk assessment module and a feedback optimization module; the method comprises the following steps: constructing a family history topological graph, a gene mutation topological graph and an environmental exposure topological graph, extracting multi-level topological features, generating a continuous bar code and a Betti number sequence based on continuous homology analysis, calculating an interaction coefficient, and triggering a cross-modal risk collaborative amplification mechanism when a gene mutation and environmental toxin coexistence mode is detected. The system is further provided with a three-level collaborative amplification mechanism, a risk decomposition report containing gene risks, environment risks, family risks and collaborative amplification factors is generated, dynamic parameter optimization is achieved by calculating the topological entropy change rate, deep fusion of multi-modal data is achieved, and the risk factor interaction effect is accurately quantified.

This invention provides a method for mining tumor antigens derived from endogenous retroviruses. The method involves bulk RNA-seq sequencing of tumor tissue and adjacent normal tissue samples using a splicing strategy to obtain DNA sequences of human endogenous retrovirus (HERV)-derived transcripts (hervRNA). Subsequently, a specific screening strategy is employed to obtain candidate open reading frames (ORF) sequences on tumor-specific hervRNAs. Finally, homology analysis, prediction of binding energy to human leukocyte antigen class I (HLA-I) molecules, and multi-site analysis are used to predict and mine neoantigens derived from HERV in tumors.

This invention discloses a method for constructing a Brca2-p.Q462Ter genetically engineered mouse and its application. The Brca2 genetically engineered mouse provided by this invention carries a truncated mutation in its Brca2 gene. This mutation is located at exon 10 of the mouse Brca2-201 transcript, changing amino acid position 462 from glutamine Q (Gln) to the stop codon Ter, forming the Brca2-p.Q462Ter mutation. The Brca2-p.Q462Ter genetically engineered mouse is used to simulate the human BRCA2 gene truncated mutation p.Gln472Ter, c.1414C>T. Amino acid homology analysis shows that the mouse BRCA2 protein Q462 site is homologous to the human BRCA2 protein Q472 site; therefore, this mouse mutation site can be used to simulate the corresponding truncated mutation effect in human BRCA2. This genetically engineered mouse can simulate, at the whole animal level, the abnormal DNA damage repair, impaired homologous recombination repair function, decreased genome stability, and susceptibility to related diseases caused by BRCA2 truncated mutations, thus providing a reliable tool for research on BRCA2-related pathogenesis, developmental effects, drug screening, and efficacy evaluation.

The present invention discloses a hybridoma cell line secreting monoclonal antibodies against goose astrovirus cap protein, the monoclonal antibodies, and their preparation methods and applications, belonging to the field of immunotechnology. In the present invention, mice are immunized with goose astrovirus cap protein, and specific hybridoma cell lines 7B2 and 3D4 are isolated from mouse ascites, and monoclonal antibodies are isolated and purified. Further analysis shows that the epitope information recognized by the monoclonal antibody secreted by hybridoma cell line 7B2 is 153 NTAGPESIDT 162 . Homology analysis of this epitope information is carried out among various virus strains, and it is found that this epitope information is highly conserved in goose astrovirus, indicating its potential to specifically inhibit goose astrovirus, and it can be used as a target for the development of therapeutic drugs or vaccines against goose astrovirus.

To provide a vibration-damping rubber member having excellent durability.SOLUTION: There is provided a vibration-damping rubber member comprising a vulcanized body of a rubber composition containing a diene-based rubber and a filler, wherein the ratio (Nn / Nt×100[%]) of the number of birth-death pairs (Nn) to the total number of birth-death pairs (Nt) contained in the persistent diagram information obtained from persistent homology analysis of the image obtained by photographing the vulcanized body is 1.7% or more. Number of birth-death pairs (Ns): the number of birth-death pairs obtained by performing regression analysis using a physical property value indicating the durability of the vulcanized body of a rubber composition containing a diene-based rubber and a filler as a target variable, and persistent diagram information obtained from persistent homology analysis of an image obtained by photographing the vulcanized body as the explanatory variable, showing a positive correlation extracted based on the coefficients of the regression equation obtained from the regression analysis.SELECTED DRAWING: None

The invention discloses a high-order topological brain network analysis method and system based on a high-density electroencephalogram, and belongs to the field of brain network analys.The method comprises the steps that original data of the high-density electroencephalogram are obtained, preprocessing and source positioning are conducted, and a current density time sequence of a brain region of interest is obtained; k-order co-fluctuation is calculated, and multi-brain-region collaborative interaction is quantized; a high-order function connection matrix is constructed based on co-fluctuation, and threshold screening and sparsification are carried out; performing function module identification on the high-order function connection matrix, and extracting brain network topology features, including graph theory, weighted simple complex construction, weight assignment under a symbol remapping rule, illegal triangle detection, persistent coherence analysis, and super-consistency and super-complexity calculation; and carrying out machine learning classification and output. The invention provides a high-order brain network analysis framework under high time resolution, is suitable for the fields of auxiliary diagnosis of neuropsychiatric diseases, cognitive evaluation, brain-computer interfaces and the like, and realizes non-invasive brain function evaluation.

The embodiment of the invention provides an attack homology analysis method and device and a storage medium. According to the method, the attack source IPs can be found from massive security monitoring data, and the attack source atlases can be constructed for the found attack source IPs based on the security monitoring data, so that attack behaviors corresponding to the attack source IPs can be mined through the attack source atlases. On the basis, according to the attack source maps corresponding to the attack source IPs, the behavior similarity relation between the attack source IPs can be analyzed, then a behavior similarity map is constructed, and the behavior similarity map can serve as the basis of attack homologous analysis. Therefore, attack behaviors of different attack source IPs can be mined through the attack source atlas, a behavior similarity atlas can be more efficiently constructed based on the attack source atlas, and then attack homologous analysis can be converted into atlas query operation, so that the efficiency of attack homologous analysis can be effectively improved.

The application discloses a kind of gene data analysis systems based on AI, through multi-omics data matrix;Through 1D-CNN one-dimensional convolutional neural network, local correlation pattern mining is carried out to the multi-omics data matrix, the topological structure of gene network is identified by persistent homology analysis, and dynamic weight is allocated to sequence feature and topological feature matrix using dynamic attention mechanism, and the weighted multi-scale feature vector is output;Based on the architecture of Transformer, a multi-modal fusion model is established to fuse the multi-scale feature vector, and the general genomic features of the pre-training model are fine-tuned to adapt to the disease data set, and the fusion feature vector is output;The fusion feature vector is input into MLP multilayer perceptron for disease risk prediction, and a disease risk prediction index is generated by combining SHAP algorithm, and an auxiliary decision scheme is generated according to the prediction index. The accuracy and generalization ability of disease risk classification are effectively improved.

The present application relates to a kind of biological species homology analysis systems based on protein / gene sequence data, belong to biological species homology analysis technical field, solve the problem of high cost and low efficiency of detection method in prior art.The system of the present application includes: data preprocessing module, for the obtained protein / gene original data is carried out data cleaning, obtain protein / gene sequence data;Data analysis module includes: protein sequence analysis unit, for according to the frequency of each amino acid in protein sequence, position and its physical and chemical properties, generate protein sequence numerical representation vector;Gene sequence analysis unit, for obtaining the optimal matching sequence in two groups of gene sequences;Species homology judging module, for according to protein sequence numerical representation vector / optimal matching sequence, the distance of each two groups of protein / gene sequence data is calculated, and according to the size of distance, the analysis of protein / gene sequence homology nearness.

Provided is a vibration-proof rubber member that exhibits excellent durability.　This vibration-proof rubber member is formed from a vulcanized body of a rubber composition containing natural rubber, silica, and a silane coupling agent. The contained amount of the silica is 5-100 parts by mass with respect to 100 parts by mass of the natural rubber. The ratio (Nn / Nt×100[%]) of the number (Nn) of the following birth-death pairs with respect to the total number (Nt) of birth-death pairs included in persistent diagram information obtained from a persistent homology analysis regarding an image in which the vulcanized body is captured is 1.7% or more.　Number (Nn) of the birth-death pairs: the number of the birth-death pairs that indicate a positive correlation and are extracted on the basis of a regression coefficient obtained from a regression analysis in which a physical property value indicating the durability of the vulcanized body of the rubber composition is set as a criterion variable; and the persistent diagram information obtained from the persistent homology analysis regarding the image in which the vulcanized body is captured is set as an explanatory variable.

The invention discloses an engineering cost anomaly identification and early warning method based on machine learning. The method comprises the following steps: collecting engineering data, and processing the engineering data to generate a state vector sequence; constructing a state diagram, and generating a topological complex; performing continuous homology analysis on the topological complex, calculating topological characteristics, and generating a target engineering topological fingerprint sequence; generating a historical topological fingerprint sequence, performing vectorization embedding, and determining a normal behavior baseline region; carrying out vectorization embedding on the topological fingerprint sequence of the target project, and comparing with a normal behavior baseline region to generate a topological anomaly score; and executing power bifurcation detection processing, and generating a cost early warning signal when a bifurcation sign occurs or the topology anomaly score exceeds an identification threshold. According to the method, topology coherence analysis and power bifurcation identification are fused, accurate early warning of engineering cost evolution abnormity is realized, and the method has the advantages of early identification, high stability and strong applicability.

The application discloses an AhFMO gene for regulating peanut seed size and kernel weight and application thereof, relates to the technical field of bioengineering, and has the technical scheme as follows: 1. An AhFMO gene for regulating peanut seed size and kernel weight and application thereof, wherein the gene sequence is SEQ ID 1; 2. A method for screening candidate genes, wherein the method is realized by combining whole genome association analysis (GWAS) and genetic map positioning linkage analysis (QTL mapping) joint analysis, combining transcriptome analysis gene differential expression, homology analysis and overexpression verification; and 3. Application of the AhFMO gene for regulating peanut seed size and kernel weight, wherein the gene can regulate the size and kernel weight of seeds.

The invention relates to the field of bioinformatics, in particular to a susceptibility gene visualization decision support system and a method thereof.According to the system, a multi-level heterogeneous network of family and gene variation is constructed, topological feature extraction and Laplacian spectrum analysis are combined, a high-risk variation transmission path is recognized, and a high-risk variable transmission path is obtained; multi-scale persistent homology analysis is utilized to reveal risk structure hierarchies, dynamic display of a family-gene association map is realized through interactive visualization, finally, a risk hierarchy tree and a clinical knowledge base are integrated, accurate clinical decision support is provided for users, a complete analysis process from data to decision is realized, and the risk analysis efficiency is improved. A multi-level heterogeneous network is constructed based on a topology invariant theory, and integration expression of family relationship and gene variation data in a unified topology space is realized for the first time, so that doctors can intuitively understand distribution and association modes of variation in families.

The present application relates to a kind of biological species homology analysis methods based on protein sequence data, solve the problem of high cost and low efficiency of detection method.The method of the present application comprises: obtaining the protein sequence of X group biological species, according to the occurrence frequency of various amino acids in each group of protein sequence and the physicochemical properties of each amino acid, generate the amino acid frequency information vector and the average value vector of amino acid physicochemical properties of X group protein sequence;According to the position information of 1~K letter amino acid in protein sequence, generate X group amino acid position probability vector;Dimension reduction is carried out to each group of amino acid position probability vector, and the dimension-reduced amino acid position probability vector is obtained;According to amino acid frequency information vector, amino acid physicochemical properties average value vector and dimension-reduced amino acid position probability vector, the distance of each two groups of protein sequence is calculated, and the distance is analyzed according to the size of protein sequence homology.

The application provides an attack homologous analysis method, system, electronic equipment and storage medium, and relates to the technical field of network security. The method comprises the following steps: obtaining event clues, alarm data and sample data of a to-be-tested attack event; extracting ATT&CK tactics and technology features from the alarm data, extracting sample HASH values and code features from the sample data, and extracting attack industry and country features from the event clues; respectively calculating the similarity between the extracted features and corresponding features in a preset APT organization feature library; calculating a comprehensive similarity score based on each similarity, and determining the homologous APT organization of the to-be-tested attack event according to the comprehensive similarity score. The application integrates multiple-dimensional data sources, breaks through the analysis blind area caused by a single data source, simultaneously fuses static code and dynamic alarm features, effectively overcomes the false alarm problem caused by incomplete confusion code disassembly, and significantly improves the accuracy, comprehensiveness and robustness of attack tracing and homologous identification.

This invention discloses an event aggregation and attribution method based on attack chain homology analysis, comprising the following steps: Step 1, multi-source heterogeneous data fusion and standardization processing; Step 2, dynamic subject-object relationship modeling; Step 3, attack event homology analysis; Step 4, homology attack event aggregation; Step 5, attack path deduction and visualization; Step 6, dynamic threat adaptive optimization. This invention improves the completeness of attack path reconstruction and solves the defect of static modeling in failing to capture attack chain evolution by using a dynamic subject-object relationship model and homology analysis algorithm; it presents the attack process through visualization technology, shortening the analysis time and improving the efficiency of source tracing; it improves the robustness of homology determination and the accuracy of entity relationship identification by using a multi-feature weighted fusion hierarchical clustering mechanism combined with code string features and vulnerability exploitation methods for auxiliary verification; and it enhances the adaptability of dynamic threats and shortens the response window for new threats through a dynamic weight update mechanism.

The application discloses a granular material liquefaction prediction method based on a persistent homology method. First, according to the mutual contact relation between the particles in the granular system, the original granular system is discretized into a contact force network; then, based on the persistent homology method, the contact force threshold value of each homology invariant in the contact force network is identified through multi-scale homology analysis, the corresponding persistence diagram is constructed, the extraction of key homology structure information and the dimension reduction of the contact force network are realized; subsequently, the distance between two adjacent time persistence diagrams is calculated, and the evolution rate of the contact force network with the shearing process is quantified; further, whether the contact force network evolution rate curve has a mutation point is detected, and if the mutation point exists, it indicates that the contact force network has started to collapse at a high speed, which indicates the occurrence of liquefaction. The application provides a new and effective way for quantitatively characterizing the dynamic behavior of the contact force network of the granular material and establishing the macro-micro cross-scale research of the granular material.

The present invention belongs to the technical field of image processing, and particularly relates to a method and system for fine feature extraction of filamentous structure topology perception based on a large model. The method uses a large model for filamentous structure feature extraction to predict the original image of the filamentous structure to obtain an initial likelihood map with the foreground probability as the likelihood value, calibrates the initial likelihood map to obtain a corrected likelihood map, analyzes the corrected likelihood map and constructs a manifold; obtains a stable manifold through persistent homology analysis, and uses the union of all stable manifolds as the topological skeleton map of the filamentous structure; uses an uncertainty quantification model to predict the confidence of each stable manifold in the topological skeleton map to form an uncertainty map; combines the topological skeleton map and the corresponding uncertainty map to optimize the segmented image predicted by the large model for filamentous structure feature extraction. The present invention improves the prediction result by using the methods of topology perception and uncertainty quantification, and enhances the accuracy and reliability of the extraction and segmentation of fine and complex filamentous structure features.

The invention discloses bacteriocin AM12LCIr as well as a preparation method and application of the bacteriocin AM12LCIr. The invention provides a bacteriocin AM12LCIr, the coding sequence of the bacteriocin AM12LCIr is composed of 46 amino acids, the relative molecular weight is 5067.73 Da, homology analysis shows that the bacteriocin AM12LCIr has the highest homology with class II bacteriocin LCI bacteriocin, but the AM12LCIr and LCI have obvious differences in structure and physicochemical properties, and the bacteriocin AM12LCIr belongs to a novel bacteriocin. According to the present invention, the bacteriocin AM12LCIr is subjected to heterologous expression, and the prepared recombinant protein has good inhibition effect on a variety of bacteria, can inhibit Escherichia coli, Listeria monocytogenes, Enterobacter aerogenes, Salmonella paratyphi B, Staphylococcus aureus and Bacillus cereus, has broad-spectrum antibacterial activity, can be used for preparing more antibacterial products, and has broad application prospects. The invention has important significance for clinically preventing and treating a plurality of pathogenic bacteria including escherichia coli.

An embodiment of the present application provides a method, apparatus, computer device, and storage medium for network attack homology analysis. The method includes: obtaining a plurality of preset Internet protocol addresses and a preset weighted attack behavior graph; dividing the plurality of preset Internet protocol addresses in the preset weighted attack behavior graph into a plurality of initial address clusters; for each initial address cluster, determining the connection weight associated with each preset Internet protocol address included, and calculating the corresponding modularity according to the connection weight; according to the modularity corresponding to each initial address cluster, obtaining a plurality of target address clusters again, and determining the characteristic centroid of each target address cluster; obtaining the Internet protocol address to be analyzed, and calculating the similarity between the target feature vector of the Internet protocol address to be analyzed and the plurality of characteristic centroids of the plurality of target address clusters to obtain the network attack homology analysis result of the Internet protocol address to be analyzed. In this way, the efficiency and accuracy of network attack homology analysis can be improved.