389 results about "Sequence analysis" patented technology

The invention discloses a dynamic leakage prediction method and system for an old oil and gas pipeline, and the method comprises the steps: collecting pressure, flow and temperature parameters in real time through a multi-source sensor, and recognizing abnormal fluctuation through the combination of time sequence analysis and frequency domain feature extraction; calculating a pipeline state evaluation result based on the material degradation model; establishing a leakage prediction model fusing a wall thickness degradation kinetic equation and an LSTM neural network, calculating a leakage probability by adopting a Monte Carlo method, and generating a diffusion velocity and a concentration gradient through CFD numerical simulation; when the diffusion prediction exceeds a safety threshold value, a control strategy is optimized through fuzzy logic and a genetic algorithm; the verification model is fed back after real-time adjustment, and online learning is carried out through Bayesian optimization; and finally, calibrating the model by using experimental data, and deploying and generating risk early warning. The system comprises a multi-source sensor array, a data processing platform and other modules, and full-chain closed-loop control from sensing to early warning is achieved.

The invention relates to the technical field of big data analysis, in particular to a contact defect detection system and method based on big data, and the system comprises a multi-dimensional data capture module, a time sequence analysis module, a trend evaluation and anomaly recognition module, a future state prediction module, and a fault classification and diagnosis module. According to the invention, by deeply capturing and screening contact operation state data, the accuracy of input data is ensured, abnormal interference of contact resistance is avoided, the data validity is improved, current impact fluctuation points are identified, the sensitivity to operation abrupt change is enhanced, the accuracy of time sequence analysis is ensured, long-term offset is tracked, the wear identification capability is improved, and the future state is accurately predicted. The method is advantaged in that maintenance plan precision is improved, contact pressure reduction trend is analyzed, fault classification is refined, defect identification accuracy is improved, data screening, trend tracking, offset characteristic calculation and fault classification are optimized, accurate detection is realized, and equipment reliability and operation safety are improved.

The invention relates to the technical field of image data processing, in particular to a difficult mountainous area slope three-dimensional modeling method and system based on a remote sensing technology. The method comprises the following steps: obtaining an original remote sensing data set of a target hard mountain area, and carrying out data preprocessing to obtain a standardized remote sensing data set; performing target hard mountainous area surface feature extraction on the standardized remote sensing data set to obtain hard mountainous area surface feature data; constructing a three-dimensional terrain model by using the earth surface feature data of the hard mountain area to obtain preliminary three-dimensional terrain model data; and performing time sequence analysis on the standardized remote sensing data set, and performing slope dynamic change analysis on the initial three-dimensional terrain model data by using a time sequence analysis result to obtain slope displacement change data. Through three-dimensional modeling, dynamic change analysis, stability evaluation and landslide early warning, a set of complete disaster prevention and reduction system for the hard mountainous area is constructed, and the prediction and prevention capability for landslide disasters is effectively improved.

The invention discloses an intelligent data management system, and relates to the field of intelligent data management. The system comprises a data acquisition module for acquiring and preprocessing multi-source data and extracting field information; the decoupling analysis module is used for calculating a mutual information index and an information cementation degree according to the field information; the map generation module is used for constructing an attribute fusion map according to the field information and generating risk mapping skewness and a risk control map; the behavior analysis module is used for collecting action execution logs, extracting field behavior sequences and calculating field behavior interference factors; and the quality evaluation module is used for extracting a key field missing proportion and information entropy based on the field information to calculate an information completeness index, and fusing multiple indexes to generate a comprehensive quality score. Through graph structure construction, behavior sequence analysis and quality scoring fusion, the multi-source data field fusion accuracy and risk identification precision are improved, and intelligent assessment and compression abstract generation of high-quality data are realized.

The invention discloses a steam turbine safe operation health assessment method and system, and belongs to the technical field of steam turbine monitoring. The method comprises the following steps: acquiring running data of a steam turbine in real time through a multi-source sensor network deployed on key components of the steam turbine; the method comprises the following steps: preprocessing original operation data of a steam turbine by adopting edge computing equipment to generate a time domain waveform analysis diagram or a time sequence analysis diagram; transmitting the preprocessed structured feature data to an operation and maintenance management platform, inputting a fault diagnosis model for analysis, pre-training through a historical fault case library, and aligning feature distribution of the historical case library and current equipment data through a domain adaptive algorithm in transfer learning; and inputting a diagnosis result into a self-adaptive dynamic evaluation model based on a reinforcement learning engine, dynamically adjusting a model weight and an alarm threshold according to real-time working condition parameters, outputting a health score and a maintenance decision, and forming a closed-loop process of perception-edge processing-cloud diagnosis-dynamic optimization-decision output.

The invention relates to the technical field of power grid data analysis, in particular to a power consumer anomaly detection and risk assessment method, system and device based on spatial-temporal feature fusion and a storage medium. The method comprises the following steps: acquiring multi-source power consumption data of various power consumers, constructing a space-time correlation feature extraction model, and establishing a user behavior space-time coupling relationship through time sequence analysis and spatial distribution characteristic analysis; constructing a multi-dimensional risk mapping model based on a long short term memory network and a convolutional neural network, establishing a nonlinear mapping relation from feature information to a risk assessment result, and identifying an abnormal power consumption behavior; establishing a distributed node credibility evaluation system, and performing credibility quantitative analysis on the data nodes in combination with a fuzzy comprehensive evaluation method; and based on an abnormal detection result and credibility evaluation, intelligent analysis and dynamic early warning of the electric charge management and control risk are realized. The time-space coupling characteristic of power consumption data can be fully mined, and high-precision identification of abnormal power consumption behaviors and accurate detection of non-technical power loss are realized.

The invention discloses a regional highway road network habitat quality evaluation method and system, and aims to construct a habitat feature library reflecting habitat spatial features and a biological utilization mode by integrating remote sensing image data and biological activity trajectory data. Identifying an ecological fault zone caused by road network cutting by adopting a space fracture analysis technology, quantifying the barrier strength of the road and determining an influence range; analyzing and extracting a key migration gallery by using a species migration network, and evaluating a landscape connectivity condition; a restoring force loss area is identified through ecological restoring force evaluation, and the habitat degradation dynamic state is revealed in combination with time sequence analysis; establishing a multi-scale space evaluation system, and fusing the quality grading sequence and gallery optimization information to generate a habitat quality evaluation matrix; and finally, a complete habitat quality grading scheme is formed through spatial interpolation and natural breakpoint grading, comprehensive evaluation of the habitat quality of the regional highway network is realized, and a scientific basis is provided for ecological protection planning and road construction decision making.

The invention discloses a network abnormal state identification method and device. According to the method, the aging degree quantized value of the network object is generated by combining time sequence analysis, a mining algorithm and a support vector machine algorithm, and the importance quantized value is generated based on the network attribute. And carrying out weighted fusion on the two to generate a comprehensive score, setting an abnormal threshold according to a real-time score distribution curve, and screening out an aged object set. And identifying an aging mode and generating aging description by using a deep learning model combining a long-short-term memory network and a graph neural network. The multi-modal features of the aging object are extracted from multiple dimensions, and a dynamic feature space is constructed. And finally, in combination with the aging description and the dynamic feature space, identifying an abnormal state in the network by adopting a multi-dimensional data association algorithm and a self-adaptive visualization method. According to the process, comprehensive, dynamic and real-time identification of the abnormal state of the network is realized, and the problem that the abnormal state of the network cannot be comprehensively and accurately identified in the prior art is effectively solved.

The invention discloses a diopter detection data fusion processing system based on deep learning, and relates to the technical field of medical detection. The system comprises a multi-modal data acquisition module, a data preprocessing and aligning module, a dynamic sequence analysis module, a deep learning regression core module, a personalized calibration module and a result output and visualization module which are connected in sequence. The system obtains objective detection data through a multi-modal data acquisition module, and multi-modal data is formed after preprocessing and space-time alignment; the dynamic sequence analysis module extracts adjustment response time sequence features; the deep learning regression core module calculates an initial diopter parameter; the personalized calibration module performs parameter optimization in combination with the biological characteristics of the user; and finally, a visual quality simulation result is generated through a result output and visualization module. According to the method, the multi-modal data fusion and deep learning technology is utilized, rapid and accurate diopter automatic detection is realized, and the detection efficiency and the individuation level are remarkably improved.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

Provided herein are technologies for framing and evaluating biological sequence-based analysis tasks in a unified, natural-language-based, text in and text out format. Among other things, methods and systems of the present disclosure provide machine-learning technologies for combining biological sequence data, representing, for example, DNA, RNA, and protein sequences, with natural language, conversational style prompts that set out particular analysis tasks to be performed on the biological sequence data. This approach, for example, allows complex analysis tasks, including, but not limited to, identification of various sequence modifications, genes, and regulatory elements in DNA sequences, and quantification of properties such as degradation propensity of RNA and protein stability, to be input to a machine learning model in a uniform text-based format and for output to be generated in a same, unified, text-based format.

A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline. Additionally, a second integrated circuit is also provided, where each second integrated circuit forming a field programmable gate array (FPGA), the FPGA being configured by firmware to arrange a set of hardwired digital logic circuits that are interconnected by a plurality of physical interconnects to perform a second set of genomic processing steps of the sequence analysis pipeline, the set of hardwired digital logic circuits of each FPGA being arranged as a set of processing engines to perform the second set of genomic processing steps. A shared memory is also provided.

The present invention discloses a combination of different nucleic acid aptamers with 291 known targets. By performing sequencing analysis on the nucleic acid aptamer sequences of this combination through single-cell high-throughput sequencing technology, a cell atlas targeting mononuclear cells derived from the peripheral blood of patients with cholangiocarcinoma can be constructed. The nucleic acid aptamers covered by this combination have high specific affinity for their molecular targets. After extending adapters commercialized for sequencing platforms at both ends of the nucleic acid aptamer sequences respectively, by performing high-throughput sequencing on the nucleic acid aptamer sequences with sequencing adapters bound to the surface of mononuclear cells, high-throughput analysis of biomarkers on the surface of mononuclear cells derived from the peripheral blood of patients with cholangiocarcinoma can be achieved, thereby constructing a cell atlas of mononuclear cells derived from the peripheral blood of cholangiocarcinoma.

The invention discloses a semantic intelligent enhanced DNA storage method for the Internet of Things, and belongs to the crossing field of artificial intelligence and biological information storage, the compression efficiency and robustness of DNA image storage are effectively improved through introduced semantic extraction and a multi-read screening mechanism of design, key information areas in images are extracted through semantic extraction, and the compression efficiency and robustness of DNA image storage are improved. The method has the advantages that redundant backgrounds are eliminated, coding lengths are reduced, high-quality sequences are selected from sequencing copies by the aid of scoring and sequence analysis through a multi-read screening mechanism, fault tolerance is enhanced, the method is applicable to efficient semantic storage in scenes of the internet of things, the problem of low storage efficiency due to excessive redundant information in original images in existing DNA image storage methods is solved, and the method is applicable to high-efficiency semantic storage in scenes of the internet of things. And a high bit error rate exists in the DNA synthesis and sequencing process, so that the image recovery quality is influenced.

The invention discloses an anomaly detection method and system for transaction flow data, and relates to the technical field of big data analysis. The method comprises the following steps: based on transaction flow data, acquiring operation behavior data of a target user in a transaction process and interaction behavior data after the transaction is completed, and sorting according to timestamps to form a user operation behavior sequence and a user interaction behavior sequence; inputting the sequence into a pre-trained abnormal risk prediction model, and outputting an initial risk coefficient, wherein the model integrates an individual behavior baseline and an adaptive weight module; acquiring a group behavior baseline of the similar user group, and correcting the initial risk coefficient in combination with a matching result of the sequence and the group baseline to obtain a final risk coefficient; and performing classification abnormity early warning operation on the final risk coefficient based on a preset risk threshold. According to the method, through whole-process behavior sequence analysis, individual and group baseline dual calibration and dynamic weight adjustment, the accuracy and timeliness of transaction flow data anomaly detection are effectively improved.

The invention relates to the technical field of artificial intelligence application, and discloses an enzyme EC number prediction method, and the method comprises the steps: obtaining the sample sequence characteristics of a to-be-predicted sample containing a substrate SMILES sequence and a product SMILES sequence through a target BERT model; constructing a molecular object and feature coding based on atom mapping, atom truncation and sequence analysis, constructing a reaction graph of a to-be-predicted sample, inputting the reaction graph into a target graph isomorphic neural network, and constructing molecular graph features of the to-be-predicted sample based on a recursive neighborhood aggregation mechanism; and fusing the sample sequence features of the to-be-predicted sample with the molecular map features by using a bidirectional cross attention mechanism to obtain multi-modal features, inputting the multi-modal features into the multi-layer perceptron, and obtaining the prediction probability of the enzyme EC number of the to-be-predicted sample. According to the method, efficient and accurate end-to-end prediction of enzyme EC numbering is realized through the multi-dimensional chemical spatial characteristics of the collaborative modeling reaction.

The invention relates to the technical field of biological detection, in particular to a miniaturized portable fluorescence microplate reader for instant fluorescence detection, and aims to provide a fluorescence detection scheme which is portable, high in sensitivity and quick in response. The detector integrates an MCU microcontroller, a voltage conversion module, a light source driving module, a motor driving module, a photoelectric detection module, an AD sampling module, an automatic temperature control module based on a PID algorithm, a signal processing system and a data processing system. High-sensitivity and high-resolution detection of a fluorescent sample can be realized, the concentration of the sample can be detected in real time, and the change process of the concentration of the sample can be presented through a kinetic curve. Through the optimization of the structure, optics and electronic circuits, the instrument is more miniaturized and has portability. Due to the advantages of portability, high sensitivity, quick response and the like, the kit has wide application prospects in multiple fields of basic research of nucleic acid, gene expression, sequence analysis, transgenic detection and the like.

The invention relates to the technical field of flow calibration, and discloses a multi-pipeline flow collaborative calibration system. The system comprises a flow characteristic acquisition module, a flow category classification module, a sequence analysis module, a trend analysis module, a correction module and a collaborative decision module. The flow characteristic acquisition module is used for acquiring current flow data and real-time characteristic information of multiple pipelines and indexing a historical flow data set; the traffic category classification module classifies the historical characteristic information to obtain a plurality of traffic categories, and extracts related information for processing to obtain a historical calibration score set; the sequence analysis module serializes the historical calibration score set and ranks the historical calibration score set according to time to form a historical traffic sequence; the trend analysis module analyzes the flow change rate and the stability deviation rate according to the historical flow sequence; the correction module matches the real-time characteristic information with the traffic category, and corrects the rate value according to the deviation; and the collaborative decision-making module performs multi-pipeline collaborative calibration decision-making based on the corrected data, generates and executes a flow calibration scheme, and assists the multi-pipeline system in precise flow control.

The present invention discloses a black chicken liver hydrolyzed peptide that can be used to improve ulcerative colitis, and its preparation method and application, which belong to the field of biomedicine technology. The preparation method of the black chicken liver hydrolyzed peptide comprises the following steps: making black chicken liver into minced meat and mixing it with water, heating the obtained liquid material, adding alkaline protease for enzymatic hydrolysis, inactivating the enzyme and collecting the supernatant by centrifugation, and freeze-drying to obtain the black chicken liver hydrolyzed peptide. The present invention adopts formaldehyde titration to detect the degree of hydrolysis, adopts SDS-PAGE electrophoresis and Tricine-SDS-PAGE electrophoresis to detect the molecular weight, performs sequence analysis by LC-MS / MS, uses a database to predict the biological function of peptide segments, detects the antioxidant and blood pressure lowering ability of black chicken liver peptide by in vitro experiments, and detects the effect of black chicken liver peptide in improving colitis by mouse experiments. The results show that the black chicken liver peptide prepared by the present invention has the advantages of antioxidant, blood pressure lowering and improvement of ulcerative colitis.

The invention relates to a privileged account-oriented dynamic authority control and behavior auditing method and system, which integrate operation sequence analysis, environmental threat intelligence and user behavior baselines, construct a behavior DNA model and realize accurate real-time risk scoring. A transient dynamic token is adopted to realize permission minimization, a token fusing mechanism is initiated to realize real-time interruption of high-risk operation, suspicious operation intention verification is performed through a mirror image sandbox, and a virtual pressure test is introduced to actively detect potential threats. A full-link digital twin system is established, it is ensured that auditing records cannot be tampered in combination with a distributed consensus auditing account book, and attack chain visualization and operation scene accurate playback are supported. According to the scheme, the problems of permission flooding, extensive management and control, weak auditing and the like existing in traditional privileged account management are effectively solved, precise management and control, intelligent early warning and credible traceability of privileged behaviors are realized, and comprehensive protection is provided for organizing core assets.

The invention relates to the technical field of physiological signal analysis, in particular to a sleep disorder intelligent diagnosis system based on a multi-modal knowledge graph, which comprises a signal lag analysis module, an abnormal signal identification module, a graph construction module, a graph path generation module and a diagnosis result output module. According to the method, difference comparison and normalized sequence analysis in a time window are performed on multiple types of physiological response data, and derivative trend identification and mutation section calibration are combined, so that collaborative identification of signal delay response and abnormal change positions is realized, and multi-dimensional information of duration, frequency and fluctuation amplitude in abnormal events is further structured; the method comprises the following steps: constructing a traceable symptom expression vector, extracting structural features of the traceable symptom expression vector in a time sequence evolution process through a cross-time window entity evolution path, identifying a growth trend of association strength between symptoms according to an attribute change direction and a path connection mode, and judging a fluctuation mode and an abnormal evolution tendency of a sleep state of a user in a specific time period.

The invention discloses a network security situation awareness method and system based on deep learning, and the method comprises the steps: generating a time-space sequence data set through integrating a multi-source flow log and a behavior record, extracting the abnormal signal intensity, and generating an embedded vector set representing attack multidimensional through employing a graph representation learning method; and when the abnormal signal intensity exceeds a threshold value, mining time sequence relevance through a sequence analysis model, judging a hidden threat evolution path, updating complex attack chain representation in real time by utilizing a dynamic tracking mechanism, generating future threat probability distribution by fusing a risk prediction method, and determining a high-risk threat priority sequence. For high-risk threats, an early warning mechanism is activated through infrastructure influence assessment, a safety guarantee protocol is integrated, a protection layer is applied, and enhanced network defense configuration is generated. According to the embodiment, through integration of spatio-temporal data fusion, dynamic threat tracking and risk prediction, the detection precision and response speed of hidden threats are remarkably improved, and the safety of key infrastructures is guaranteed.

The invention relates to the technical field of high-end equipment, and discloses a real-time micro-defect intelligent control method and system for precision metal parts, and the method comprises the steps: obtaining historical defect detection data, and carrying out the preprocessing of the data to obtain a structured defect data set; determining a defect statistical mode through time sequence analysis and clustering analysis; generating a geometric feature set, a statistical feature set and an associated data set based on the defect statistical mode, and storing by adopting a dynamic retrieval database to form a queriable defect feature library; new defects are detected and matched with the feature library to judge types and potential positions; predicting time points and position coordinates of defects in combination with process parameters; adjusting process parameters according to a prediction result to obtain optimal configuration; optimizing and updating the defect feature library in combination with new defect information; and continuously monitoring and dynamically adjusting process parameters based on the improved feature library, and generating stable production control parameters. According to the method, real-time prediction and dynamic control of microdefects in the precise metal part production process can be achieved, and the product yield is increased.

The invention relates to a nucleic acid aptamer for specifically recognizing tilmicosin as well as screening and application of the nucleic acid aptamer, and belongs to the technical field of food safety detection and biosensing. Aiming at the defects of the existing method for detecting tilmicosin residues in food, the invention provides a high-affinity and high-specificity nucleic acid aptamer obtained by screening based on a graphene oxide index enrichment ligand systematic evolution technology. The aptamer is obtained by multiple rounds of screening from a random single-stranded DNA library through a GO-SELEX technology, and a core sequence Apt-2-1 with the length of 33 nt is finally obtained through sequence analysis and structure optimization. The aptamer shows excellent binding capacity to tilmicosin, the dissociation constant reaches 9.82 nM, and the aptamer has good specificity. The nucleic acid aptamer provided by the invention can be used for constructing a rapid and sensitive tilmicosin residue detection method, and has important application value in monitoring of livestock and poultry products and food safety.

The invention discloses methylobacterium as well as a fermentation method and application thereof, and belongs to the technical field of microorganisms. The methylobacterium YLMb23.1 provided by the invention is subjected to taxonomic identification through 16S rRNA sequence analysis in combination with morphological characteristics, and the preservation number of the methylobacterium YLMb23.1 is CGMCC (China General Microbiological Culture Collection Center) No.32224. The methylobacterium YLMb23.1 has the capability of fixing nitrogen, producing ACC deaminase and IAA (indoleacetic acid), and has good drought tolerance and salt tolerance. A fermentation product of the methylobacterium YLMb23.1 is used for promoting the growth of wheat and corn, can be used as a microbial agent or bacterial fertilizer, and is applied to the agricultural market.

The disclosure provides methods for processing nucleic acid populations containing different forms (e.g., RNA and DNA, single-stranded or double-stranded) and / or extents of modification (e.g., cytosine methylation, association with proteins). These methods accommodate multiple forms and / or modifications of nucleic acid in a sample, such that sequence information can be obtained for multiple forms. The methods also preserve the identity of multiple forms or modified states through processing and analysis, such that analysis of sequence can be combined with epigenetic analysis.

The invention belongs to the technical field of Hu sheep molecular marker-assisted breeding, and particularly relates to application of an AAGAB gene molecular marker g.23443T > C in Hu sheep molecular marker-assisted breeding. According to the invention, Hu sheep is taken as a research object, PCR amplification, direct sequencing of products and sequence analysis are adopted, AAGAB gene polymorphism is analyzed, and correlation between different genotypes of polymorphic sites and different growth traits is comprehensively analyzed. An analysis result shows that g.23443T > C is remarkably related to the chest circumference of Hu sheep and the daily gain of Hu sheep from six-month-old to one-year-old, and the birth weight, weaning weight and six-month-old body weight of CC genotype are remarkably higher than those of TT and TC genotype individuals.

The invention discloses a groundwater level monitoring system and method based on artificial intelligence, and relates to the technical field of groundwater level monitoring. The water level monitoring management center is in communication connection with a multi-source data acquisition module, a feature sequence analysis module, an abnormal water level identification module, an abnormal fluctuation detection module, a fluctuation grading module and an early warning response module; the multi-source data acquisition module is used for integrating various sensors and external data sources and acquiring and preprocessing underground water level related data. By integrating various sensors and external data sources, the system can collect and preprocess underground water level related data, performs deep analysis on water level characteristics in combination with an artificial intelligence algorithm, effectively overcomes the problem of insufficient distribution density of traditional monitoring points, realizes comprehensive reflection of dynamic changes of regional underground water, reduces monitoring blind areas, and improves the monitoring efficiency. And the monitoring precision is improved.

The disclosure provides methods for processing nucleic acid populations containing different forms (e.g., RNA and DNA, single-stranded or double-stranded) and / or extents of modification (e.g., cytosine methylation, association with proteins). These methods accommodate multiple forms and / or modifications of nucleic acid in a sample, such that sequence information can be obtained for multiple forms. The methods also preserve the identity of multiple forms or modified states through processing and analysis, such that analysis of sequence can be combined with epigenetic analysis.

The invention relates to the technical field of data management, in particular to a carbon molecular sieve production automation management method and system based on big data, and the method comprises the following steps: recording an adsorption gas track, dividing path segments and marking direction changes, extracting particle configuration identification interference fragments, and extracting contour tracking structure deformation. The overlapping area is recognized through cross positioning process operation, and an automatic control instruction structure table is generated through sorting reconstruction actions. According to the method, interference areas are extracted by combining particle boundary continuity and path coincidence features, fine-grained expression of structure identification is enhanced, a change track is tracked and collected by using a structure jump trend and a connecting line, dynamic perception of a structure state is realized, and time-space cross mapping is performed on a structure strain area and an operation behavior; a process intervention target is defined, operation sequence analysis and control chain recombination are combined, execution logic and control response are optimized, and a multi-dimensional linkage automatic control strategy is constructed.