477 results about "Sequence analysis" patented technology

The invention provides a green land carbon sink amount estimation method for ecological restoration planning, and the method comprises the steps: precisely collecting the vegetation coverage rate, species distribution, soil type and other data of a green land through integrating remote sensing images, unmanned aerial vehicle monitoring, a geographic information system and other technical means; the method comprises a vegetation carbon reserve model, a soil carbon reserve model and an environmental factor correction module, the vegetation carbon reserve is calculated by adopting an Allometric model, the soil carbon reserve is subjected to multi-layer estimation, and the carbon sink amount is corrected by utilizing meteorological conditions and topographic factors, so that the accuracy and applicability of estimation are improved, and the estimation accuracy is improved. According to the method, dynamic estimation of the carbon sink amount is realized through time sequence analysis and in combination with vegetation growth cycles and seasonal changes, and visual result display and decision support functions are provided. The method is widely applicable to various ecological environments such as urban green land, wet land and mountain land, and scientific basis is provided for ecological restoration planning and carbon sink monitoring.

The invention relates to the technical field of data processing, in particular to a data management method for automatically detecting data abnormal values based on deep learning, which comprises the following steps of: based on an input data set, performing synchronization and missing value filling on a plurality of data sources, and performing standardization and normalization processing on data to obtain a preprocessed data set; according to the method, the data preprocessing is executed, the consistency and quality of data input are optimized, feature extraction and model layer-by-layer training are utilized, the recognition capability of the model for abnormal data is enhanced, the precision and efficiency of data anomaly detection are improved, calculation of residual errors and reconstruction errors is combined, the accuracy of abnormal data scoring is improved, and the accuracy of data anomaly detection is improved. The detection threshold value is dynamically adjusted by utilizing data characteristics, the false alarm rate is reduced, and the reliability and the stability of the enhanced data processing flow are ensured by combining time sequence analysis, evaluating the abnormal development trend, performing comparative analysis on abnormal data and normal data modes and performing data correction.

The invention discloses an equipment monitoring prediction method and system based on industrial middle platform and edge computing, and the system employs a monitoring collection unit to collect the vibration, temperature and current data of equipment in real time, and carries out the analysis through an innovative three-layer prediction architecture: deploying a lightweight threshold rule model at an edge layer to carry out the real-time anomaly detection, an XGBoost model is deployed in the middle layer to execute trend analysis, a time convolution network model is deployed in the cloud layer to realize deep time sequence analysis, and a corresponding grading early warning mechanism is established. The technical problems that an early warning mechanism is not intelligent enough, a prediction model is single, data preprocessing is not perfect enough and the like in traditional equipment monitoring are solved. Through cooperation of three layers of prediction architectures, omnibearing monitoring is realized, system response delay is reduced, and early warning accuracy is improved. Through the combination of a grading early warning mechanism and parameter correlation analysis, the system reliability is enhanced, and the equipment operation and maintenance efficiency is improved; and an edge calculation mode is adopted, so that the network bandwidth requirement is effectively reduced.

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 embodiment of the invention provides an intelligent dynamic load regulation and control method and system for an intelligent power grid. Wherein power parameters of key nodes are collected in real time through time sequence analysis and a deep learning algorithm, future power demand fluctuation is predicted, and a power grid topological structure model is constructed to identify the key nodes and a potential influence range; a genetic algorithm, particle swarm optimization and other multi-objective optimization algorithms are adopted to generate specific operation instructions for different power utilization areas or devices, and optimization objectives comprise minimization of energy cost, maximization of renewable energy utilization rate and guarantee of power supply stability; converting the strategies into guidance suggestions, pushing the guidance suggestions to a terminal user, collecting feedback information, generating a preference model by applying a reinforcement learning algorithm, and selecting an optimal strategy for implementation based on user acceptance and feasibility evaluation; risks and abnormal conditions are detected through the Bayesian network, and the strategy is adjusted in time. According to the technical scheme provided by the embodiment of the invention, the efficient distribution of power resources and the energy utilization efficiency in the smart power grid are improved.

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 relates to the technical field of self-adaptive control, in particular to a tobacco field variable fertilization control system and method, and the system comprises a sensing data collection module which collects soil humidity data through a soil humidity sensor, monitors the growth data of crops at the same time, and carries out the synthesis to obtain real-time soil and growth data; and performing time sequence analysis on the real-time soil and growth data, identifying a data trend and an abnormal point, and generating a trend analysis result. According to the method, the soil humidity and the crop growth data are collected, and the time sequence analysis is combined, so that the trend and the abnormality are quickly identified, and the prediction accuracy is improved. The convolutional neural network is used for carrying out recognition and probability evaluation on diseases and insect pests, so that disease and insect pest management is more accurate, and response is quicker. Through the technologies, the system can dynamically adjust irrigation parameters based on real-time data, optimize water distribution, and ensure that an irrigation strategy is perfectly matched with crop requirements and potential risks.

The invention discloses a large model construction method for intelligent collaboration and decision-making of a power time sequence, and aims to solve the problems of complex time sequence analysis task, high technical threshold and lack of transparency of a prediction result in a power scene. Comprising the following steps: S1, constructing a diversified power time sequence data set and enhancing characteristics thereof; s2, generating a data set with context description; s3, designing a time sequence thinking chain, and finishing alignment of the power time sequence data and the natural language mode; s4, constructing a flexible cue word template, and generating a diversified question and answer training set; and S5, carrying out model adaptation and supervision fine tuning by adopting a low-rank adaptation technology, and constructing a power time sequence large model with intelligent collaboration and decision-making capabilities. According to the method, the technical threshold of power time sequence analysis is remarkably reduced, the accuracy and transparency of a prediction result are improved, the method can be widely applied to the fields of power load prediction, anomaly detection, energy optimization scheduling and the like, and technical support is provided for intelligent transformation of the power industry.

The invention relates to the technical field of building informatization management, in particular to a BIM-based building engineering quality control method and system, and the method comprises the following steps: based on a BIM data model, extracting building material durability and regional climate conditions, carrying out temperature and humidity matching, associating climate factors with durability parameters, and obtaining a building material performance sequence basic value. According to the method, the building informatization technology and the fine execution logic are combined, the building material performance is accurately monitored and predicted, the project management efficiency and quality are improved, the material durability parameters and the regional climate are matched, data are analyzed in details, the aging rate is effectively predicted, and the aging trend is assisted and integrated through time sequence analysis; the material service life is accurately predicted, the replacement demand is marked in time, durability degradation evaluation is matched with BIM inventory data, the timeliness and accuracy of replacement are ensured, the resource allocation efficiency is remarkably improved, the maintenance cost is reduced, and the overall quality control is enhanced.

The invention discloses a combination of different nucleic acid aptamers with known 291 targets, sequences of the nucleic acid aptamers of the combination are subjected to sequencing analysis through a single-cell high-throughput sequencing technology, and a cell map aiming at peripheral blood-derived mononuclear cells of a biliary tract cancer patient can be constructed. The nucleic acid aptamers covered by the combination have high specific affinity to molecular targets of the nucleic acid aptamers, and after the two ends of the nucleic acid aptamer sequences extend to adapt to joints of a commercial sequencing platform, high-throughput sequencing is carried out on the nucleic acid aptamer sequences which are combined with the surfaces of mononuclear cells and are provided with the sequencing joints; high-throughput analysis of the biomarkers on the surface of the peripheral blood-derived mononuclear cell of a biliary tract cancer patient can be realized, so that the cell map of the peripheral blood-derived mononuclear cell of the biliary tract cancer is constructed.

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 relates to the technical field of teaching quality evaluation, and particularly discloses a teaching quality comprehensive evaluation system and method based on a teaching sequence perspective, and the system comprises a data management module, a learning sequence analysis module, a teaching sequence analysis module, a teaching quality evaluation module, a feedback improvement module, and a decision support module. The data management module is used for collecting data generated in an education and teaching process, including learning sequence data of students and teaching sequence data of teachers, and performing arrangement and standardization processing on the collected data; the learning sequence analysis module is used for analyzing learning sequence data of the students through a data mining technology; according to the invention, by integrating the student learning sequence data and the teacher teaching sequence data and combining a data mining technology and a deep learning algorithm, key factors influencing the teaching quality are accurately identified, the teaching quality is objectively evaluated in a data-driven manner, the deviation of manual evaluation is avoided, and the teaching quality is accurately evaluated. And the transparency and fairness of teaching quality evaluation are enhanced.

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 relates to a fault prediction method for a mining high-voltage power distribution device. Temperature, current, voltage and gas leakage sensors are installed in the mining high-voltage power distribution device; integrating the multi-dimensional data by using a multi-sensor data fusion algorithm to form a real-time operation portrait of the power distribution device; extracting time sequence characteristics of temperature and voltage key variables by combining an operation rule of the high-voltage power distribution device; capturing abnormal fluctuation of the key characteristic parameters by using a sequence analysis model, and identifying a specific fault type in the high-voltage environment; constructing a feature mode database based on a time sequence, and matching new data with a historical fault mode; applying a feature selection algorithm to extract representative fault prediction features from the sensor data; establishing a fault prediction risk level model according to the hazard degrees of different fault modes; based on the fault risk level predicted in real time, an optimized self-adaptive maintenance scheme is generated, and the maintenance period and strategy are dynamically adjusted in combination with the actual operation state of the equipment.

A system and methods for adaptive bandwidth-efficient data encoding comprising: a sequence analyzer configured to analyze a received sequence dataset, maintain a count of unique characters, and identify positions where the unique character count increases by a power of two; an adaptive sourceblock optimizer that determines and dynamically adjusts optimal sourceblock sizes based on dataset characteristics; and a data deconstruction engine that deconstructs the dataset into sourceblocks and creates codewords for storage or transmission. The system analyzes sequence complexity, alphabet size, and character frequency distribution to optimize sourceblock sizes, and uses machine learning to improve decision-making over time. This adaptive approach enhances compression efficiency across varied genomic data types, including genome graphs, while maintaining data integrity and security. The system efficiently encodes, stores, and transmits complex genomic and bioinformatic datasets, addressing the growing challenges in data storage and bandwidth limitations.

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.

The invention relates to the technical field of information retrieval, in particular to an animal behavior management system based on an intelligent dog training device, and the animal behavior management system based on the intelligent dog training device comprises a behavior data collection module, a high-dimensional offset calculation module, a behavior boundary recognition module, an abnormal influence degree analysis module and a behavior index updating module. According to the method, the comprehensiveness and accuracy of behavior monitoring are improved by integrating environmental factors and multi-dimensional behavior data, abnormal behaviors are accurately identified and predicted by utilizing time sequence analysis and comprehensive processing of motion data, the accuracy of behavior pattern identification is enhanced, and the accuracy of behavior pattern identification is improved by analyzing the change of motion rhythms and the transfer of behavior paths. The abnormal behaviors are stably recognized, the coping complexity of the system is improved, the behavior index is dynamically updated, the matching degree is optimized, the system can adapt to behavior evolution, and the reasonability and effect of an intervention strategy are improved.

The invention relates to the technical field of risk early warning, and discloses an advanced prediction and early warning system for key risk factors of an oversized deep foundation pit, which realizes advanced prediction and early warning for the key risk factors in the construction process of the oversized deep foundation pit through the following modules which are mutually associated and cooperatively work. According to the advanced prediction and early warning system for the key risk factors of the oversized deep foundation pit, the construction safety management level is remarkably improved by integrating advanced technologies. The system carries out deep analysis and prediction on key risk factors in the construction process by utilizing machine learning and deep learning algorithms and combining multiple models such as time sequence analysis, grey prediction and a neural network model.

The invention relates to the technical field of electric power energy storage planning, and discloses an energy storage planning method and system for an electric power system. The method comprises the steps of extracting a power system operation feature vector according to power grid data; carrying out time sequence analysis and prediction on the load and power generation data; performing probability statistical analysis on multiple scenes based on the prediction data to obtain an energy storage demand; determining capacity configuration parameters through multi-scene optimization calculation to form an energy storage scheme; and a target energy storage scheme including benefit indexes, capacity adjustment and operation optimization is obtained through comprehensive evaluation and dynamic correction. The technical problem of multi-scene collaborative configuration of the energy storage system is solved. Reasonable allocation and dynamic optimization of energy storage resources are realized through feature extraction, time sequence analysis, probability statistics and multi-scene optimization calculation.

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.

The invention relates to the technical field of grinding fluid, in particular to a grinding fluid performance optimization method and system based on data analysis, and the method comprises the following steps: based on collected grinding fluid data, identifying key parameters influencing the performance of the grinding fluid, carrying out time sequence analysis on cutting speed, temperature, viscosity index and pH value data, determining key change nodes of the performance, and calculating the performance of the grinding fluid according to the key change nodes. And performing reanalysis on the node data to obtain a performance key node identifier. According to the method, by applying time sequence analysis and a real-time feedback mechanism, the adjusting process of the grinding fluid is remarkably optimized, key parameters such as the cutting speed, the temperature, the viscosity index and the pH value in the grinding process are allowed to be continuously monitored and analyzed, performance change nodes are accurately recognized, and the grinding efficiency is improved. The proportion and chemical components of the grinding fluid can be adjusted according to real-time data, so that the optimal performance of the grinding fluid is ensured, the cutting efficiency and the machining quality of a workpiece are enhanced, and the performance of the grinding fluid is kept in the optimal state by continuously optimizing the proportion.

The invention relates to a motion capture optimization method of a tactical AR helmet. Based on a motion model and dynamic changes of muscle activities and biological signals; collecting data in real time through a sensor, and establishing a time-dependent model of motion data by using a time sequence analysis method; in combination with an LSTM (Long Short Term Memory) model in deep learning, capturing head motion and time dynamic change of motion, and predicting and correcting an error in motion capture; intelligent processing is carried out based on continuous time sequence data, and short-term errors of capturing data drift are avoided; designing a multi-level sensor correction algorithm, and performing time alignment and error correction on IMU data and visual data; drift phenomena including position errors and rotation errors in motion capture are eliminated; optimizing the helmet capture system using a ground sensor including a gait monitoring device and external vision calibration data; a target-oriented motion trail optimization algorithm is designed by combining a motion target and a tactical task of a user, and a capture strategy is dynamically adjusted according to the change of the task.

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.