26 results about "Marker analysis" patented technology

The invention belongs to the field of big data, and particularly relates to a case detection optimization method based on big data. Acquiring case information for data preprocessing, and summarizing the data to a dynamic database in real time; identifying and associating data in the dynamic database, and establishing an index database with association logic; automatically constructing an evidence chain through an inference model and an index database; logic detection is carried out on the evidence chain, the evidence chain with strong logic association of time, figures and material evidences is reserved as a reference evidence chain, and abnormal points of the reference evidence chain are marked; and performing analysis and prediction based on abnormal point marks of the reference evidence chain, and giving at least one prediction direction to guide case detection. Data summarization is carried out through a big data technology, associated data is rapidly extracted through an index database, multiple evidence chains are automatically constructed for screening, investigation direction expansion is carried out through mark analysis and prediction of abnormal points, and the case solving efficiency is improved through data driving.

The invention belongs to the field of building fire detection, relates to a deep learning and image recognition technology, is used for solving the problem that fire analysis accuracy and fire alarm timeliness cannot be considered at the same time in the prior art, and particularly relates to a building fire early-stage detection method and system based on deep learning image recognition. The detection platform comprises a target marking module, a verification analysis module, a time sequence analysis module and a database. The target marking module is used for performing target marking analysis on the building fire: recording the internal environment of the building through a high-definition camera, decomposing the recorded video into frame-by-frame analysis images, amplifying the analysis images, segmenting the analysis images into a plurality of analysis areas, and extracting fire feature vectors in the analysis areas; according to the method and the system, closed-loop judgment from feature extraction to risk verification is completed in a single-frame image processing period, and due to the fact that data transmission delay between modules is eliminated, the system can respond to potential fire behaviors in a millisecond level.

The invention discloses a methane leakage microbial marker mining method based on multi-source data fusion and related equipment, according to the embodiment of the invention, a multi-dimensional feature space is constructed by fusing microbial gene data, spatial information and environmental geochemical data, and the data dimension and reliability of marker mining are improved; moreover, according to the embodiment of the invention, a mode of combining random forest and regression modeling is adopted, so that quantitative screening of the markers is realized, and manual intervention and subjective deviation are reduced; specifically, according to the embodiment of the invention, the markers are graded on the basis of comprehensive scores of three dimensions of importance, stability and universality, so that priority selection in subsequent practical application is facilitated; the embodiment of the invention is suitable for methane leakage identification of different sea areas and different geological backgrounds, has good generalizability and practicability, and can be widely applied to the technical field of marker analysis.

The invention relates to the technical field of natural language processing, and discloses a power grid fault report automatic analysis system based on natural language processing, and the system comprises a receiving and preprocessing module which is used for receiving an original fault report and preprocessing the text of the original fault report; the vocabulary mark analysis module is used for performing initial primary and secondary marks on the preprocessed text vocabularies and correcting the primary and secondary marks through consistency check and ambiguity analysis; the feature extraction module is used for extracting an effective keyword set according to the corrected primary and secondary marks of the vocabularies, and converting the effective keyword set into a multi-modal feature vector composed of semantic features, structural features and time features; and the fault type determination module is used for determining the fault type. The internal consistency of vocabulary marking is remarkably improved, a high-credibility semantic basis is provided for subsequent feature extraction, and classification deviation caused by marking mismatching is fundamentally reduced.

The invention relates to the technical field of image analysis, and particularly discloses an environment sensing system for a farm, and the system comprises a thermal imaging collection module which is used for collecting a thermal imaging image outside the farm within a first preset time; the image recognition module is used for analyzing the thermal imaging image, recognizing a first preset animal and a second preset animal and marking the first preset animal and the second preset animal; the analysis module is used for extracting activity characteristics of the second preset animal when the first preset animal and the second preset animal are identified at the same time, and constructing a first preset animal identifiability scoring model according to the activity characteristics of the second preset animal; and the processor is also used for extracting the activity characteristics of the current second preset animal when only the second preset animal is identified, inputting the activity characteristics of the current second preset animal into the first preset animal identifiability scoring model, and obtaining the output of the existence probability of the first preset animal. According to the technical scheme of the invention, active identification and early warning of dangerous animals such as snakes can be realized.

Identifying the causes of haematuria requires detailed clinical investigations. Unbalanced patient populations (mostly male) and diverse causes complicate patient classification and biomarker identification. Multi-marker analysis with advanced methods can improve diagnosis even in unbalanced datasets. We used k-means clustering, decision trees, random forest with LIME explainer, and CACTUS algorithm to classify patients as healthy (with no clear cause of haematuria) or sick (with an identified cause of haematuria e.g., bladder cancer, or infection). The CACTUS algorithm was more robust to dataset imbalance, achieving balanced accuracy of 0.747 for both genders, 0.718 for females, and 0.803 for males. Key biomarkers identified included microalbumin, male gender, and tPSA for the whole dataset; age, microalbumin, tPSA, cystatin C, BTA, HAD, and S100A4 for males; and microalbumin, IL-8, pERK, and CXCL16 for females. CACTUS outperformed decision trees and random forest, highlighting potential novel biomarkers for future diagnosis.

This invention discloses a method for testing the corrosion crack propagation path of floating structures based on digital twins, relating to the field of path testing. The method includes the following steps: acquiring basic information about the floating structure, setting up a three-dimensional virtual structure model, and marking key nodes; setting up an intelligent sensor network to acquire real-time monitoring data at each key node; performing labeling and analysis on the historical monitoring data at each node within the three-dimensional virtual structure model to obtain a longitudinal and transverse label dataset; performing longitudinal and transverse analysis on the longitudinal and transverse label dataset to obtain the interaction relationship between seawater corrosion and crack propagation at each key node in three dimensions: time, space, and degree of influence; constructing a monitoring verification model and a crack propagation model based on the analysis results; verifying and analyzing the real-time monitoring data using the monitoring verification model; and performing path analysis on the verification analysis results based on the crack propagation model. This invention improves the accuracy of the crack propagation path testing process.

The invention relates to a spectroscopic imaging and spectrometer system comprising at least two channels, the system being connectable to smartphones. The system is configured to simultaneously determine the optical properties, including reflectance, transmittance, color, and luminescence characteristics, of solids, liquids, and gases. The invention is particularly advantageous for forensic investigations and applications requiring the use of encrypted markers. The system operates by splitting the image of an object into two parts by means of a prism and subsequently employing optical filters and diffraction gratings. Furthermore, the system incorporates software compatible with external devices such as smartphones or computers. Said software enables the cameras of a smartphone to function as a spectrometer, thereby allowing spectra to be acquired through high-resolution imaging.

The application discloses an NCCL migration method based on a resource pool, relates to the technical field of high-performance computing communication optimization, and comprises the following steps: obtaining thread identification records, synchronization timestamp markers and thread state information from the resource pool by scanning the multithread running environment of the NCCL communication program based on the resource pool at a source end; generating a thread state snapshot in combination with data packet transmission progress, constructing a data transmission state file containing transmission interruption recovery points and synchronization signal priorities, and determining an initialized synchronization marker data set; transmitting the thread state snapshot and a data check code value to a target end according to the initialized synchronization marker data set, decoding the synchronization marker by using an inheritance marker analysis rule, generating a control module state cache of the target end, and obtaining a preliminary synchronization marker inheritance result; and the NCCL migration method based on the resource pool significantly improves the migration efficiency, data link continuity and overall running stability of the NCCL communication program in a high-load scene.

The invention relates to the technical field of medical data analysis, in particular to an immune marker analysis system based on machine learning, which comprises an entropy density rheological module, a boundary dynamic labeling module, a collaborative tensor modeling module and a marker discrimination module. According to the method, three-dimensional immune data are processed through kernel density estimation, spatial information entropy is calculated, multi-source heterogeneous medical data spatial features are integrated, a gradient map is generated in combination with a moving average method to capture a marker concentration change trend, and a boundary topology model is constructed based on directional derivative detection and morphological closed operation to recognize morphological boundary features. Three-order tensor modeling is utilized to fuse immune subtypes, time dimensions and marker expression data, a core factor matrix is extracted through dimension adaptive tensor decomposition to improve marker correlation analysis integrity, Manhattan distance is adopted to quantify collaborative expression intensity, and the Hadamard product of a residual matrix is combined to improve the correlation analysis integrity of the marker. And the crossing from single threshold judgment to multi-dimensional cooperative judgment of quantitative analysis of the immune marker is realized.

The invention discloses a selenium-rich solid beverage production whole-process intelligent quality control system and method, and relates to the technical field of food processing and intelligent manufacturing, the selenium-rich solid beverage production whole-process intelligent quality control system comprises a data acquisition module, a process mark analysis module, a feedback control module and a quality control report generation module, the data acquisition module is in communication connection with the process mark analysis module, and the process mark analysis module is in communication connection with the feedback control module; the data acquisition module is used for synchronously acquiring multi-source real-time process parameter data including temperature, humidity, pressure, mixing torque, spraying rotating speed and online selenium content detection value from a plurality of key working sections in the selenium-rich solid beverage production process; the process mark analysis module is used for adding a process flow position label to the acquired data and establishing a process behavior track of the current batch; the system has the advantages of being high in practicability and high in intelligent degree.

The application discloses a kind of predictable phaseolus vulgaris response melatonin regulation SSR molecular markers and its application, it is related to biotechnology field.The application carries out the effectiveness identification of ordinary phaseolus vulgaris natural germplasm resources to melatonin treatment, the efficiency of melatonin treatment-cell wall-salt tolerance in ordinary phaseolus vulgaris germplasm resources is analyzed, and using the reference genome in Esembl plants database, a complete set of SSR molecular markers is developed, and according to transcriptome information, the SSR marker near response gene is mined, through polymorphism analysis and single marker analysis, two SSR markers with significant difference associated with melatonin traits are screened, can be used as the molecular marker for predicting phaseolus vulgaris response melatonin regulation, and provides technical support for the function prediction of melatonin treatment in the sprouting stage of ordinary phaseolus vulgaris.

The present invention relates to a material analysis method, which may include: (a) preparing a test specimen for analyzing its structure; (b) training an artificial intelligence model with training images labeled with the structure of any test specimen; (c) using the trained artificial intelligence model to analyze a plurality of unlabeled analysis images of the test specimen and removing analysis images classified as pre-defined noisy structure patterns; and (d) using the trained artificial intelligence model to analyze the analysis images from which the noisy structure patterns have been removed and classify them into at least one target structure pattern.

The application relates to the technical field of medical data analysis, in particular to an immune marker analysis system based on machine learning, which comprises an entropy density flow change module, a boundary dynamic labeling module, a collaborative tensor modeling module and a marker discrimination module.In the application, three-dimensional immune data is processed through kernel density estimation and spatial information entropy is calculated, multi-source heterogeneous medical data spatial features are integrated, a gradient graph is generated by combining a sliding average method to capture a marker concentration change trend, a boundary topology model is constructed based on directional derivative detection and morphological closing operation to identify morphological boundary features, a three-order tensor modeling is used to fuse immune subtypes, time dimensions and marker expression data, a dimension self-adaptive tensor decomposition is used to extract a core factor matrix to improve the integrity of marker correlation analysis, Manhattan distance is used to quantize collaborative expression intensity and combine residual matrix Hadamard product, and single threshold determination is crossed to multi-dimensional collaborative discrimination in immune marker quantitative analysis.

The application discloses a method for analyzing epigenetic diversity based on a methylation-sensitive amplified polymorphism (MSAP) gene and a biological gene fingerprint table. The method comprises the following steps: obtaining an MSAP amplification electropherogram of a biological sample to be detected, and constructing a 01 matrix of the sample; converting the 01 matrix into an MSAP gene sequence by using a "CCGGN" replacement method; comparing the MSAP gene sequence with a reference genome, and annotating DNA methylation related gene information; based on the annotation result, constructing a gene fingerprint table based on the copy number of the methylation gene and the methylation / hemimethylation mode, and analyzing epigenetic diversity. The method can convert a traditional MSAP band fingerprint map into a gene fingerprint table with annotation function information without performing cloning and sequencing of MSAP differential fragments, can improve MSAP marker analysis to epigenome analysis, solves the problem that MSAP epigenes are difficult to clone, and improves the level of germplasm resource identification and molecular breeding.

This invention discloses a multi-marker analysis method for the classification and origin tracing of peripheral blood target cells, relating to the medical field. This method involves capturing target cells from peripheral blood samples, identifying them as tumor cells using tumor markers, and then further analyzing them using tissue-specific markers to achieve classification and origin tracing. This invention not only traces the origin of peripheral blood tumor cells but also confirms the tissue and organ source of the tumor cells, making the classification and origin tracing of target cells possible and more accurate.

The invention discloses a medical image recognition method and device for a lung tumor marker and a medium, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a to-be-recognized marker based on blood detection; obtaining different markers of the marker to be identified; obtaining heterogeneous mark images; acquiring image recognition features by using a mark analysis method; optimizing the image recognition features, and performing image recognition on the lung tumor markers by using the optimized recognition features; the method is used for solving the problems that in an existing medical image recognition method for the lung tumor markers, a recognition model is relatively conventional, the types of the lung tumor markers cannot be accurately recognized due to the fact that the features of the lung tumor markers in the medical image are not obtained, consequently, much uncertainty exists in the recognition process, and the recognition accuracy is high. And the identification accuracy is low.

To provide a method, a composition and a system for testing a fecal sample containing a small amount of human fecal specimen for a plurality of different marker analytes, for example, a plurality of different human proteins, nucleic acids, and other molecular marker analytes.SOLUTION: There is provided a method for characterizing a human fecal specimen, the method including: (i) providing a dispersed sample containing a small amount of a human fecal specimen dispersed in a predetermined amount of a stabilizing solution; and (ii) analyzing the dispersed sample for various amounts of a plurality of different marker analytes.SELECTED DRAWING: None

The invention discloses a peanut drought-resistant gene associated molecular marker screening optimization system and method, and relates to the technical field of agricultural biology. The system comprises a data acquisition module, a molecular marker analysis module and a data integration and evaluation subsystem, peanut DNA, physiological indexes and environmental data can be acquired, candidate markers are identified through PCR amplification and high-throughput sequencing, and a comprehensive evaluation model is constructed to calculate correlation coefficients. The method comprises the steps of sample preparation and pretreatment, molecular marker preliminary screening, marker verification and optimization and application verification, setting of a drought and normal moisture control environment, elimination of false positive markers through repeated verification, screening of high-correlation markers with a correlation coefficient greater than or equal to 0.6, and application accuracy greater than or equal to 85%. The method solves the problems of low accuracy, poor environmental suitability and the like in the prior art, improves the marker screening accuracy and stability, can efficiently assist in cultivation of new drought-resistant peanut varieties and relieve the restriction of drought on the peanut industry, and has important scientific research and application values.

A monitoring system includes an electrostatic chuck apparatus and a mark detection apparatus. The electrostatic chuck apparatus includes a base and an electrostatic chuck. The electrostatic chuck is disposed on the base, includes a plurality of supporting posts, and is configured to hold a wafer with the supporting posts. The mark detection apparatus includes an image capturing device and a mark analysis device. The image capturing device is configured to capture a wafer image of the wafer, wherein the wafer image includes a plurality of marks, and at least one of the marks is corresponding to at least one of the supporting posts. The mark analysis device is electrically connected with the image capturing device and configured to obtain a total mark-ratio of the number of the marks to the number of the supporting posts.

The invention discloses an unmanned aerial vehicle autonomous landing system and method suitable for an unattended scene, and belongs to the field of unmanned aerial vehicles, and the method comprises the steps: carrying out the staged landmark recognition, which comprises high-altitude global recognition and low-altitude mark tracking; performing dynamic pose estimation, including high-altitude angular point matching and low-altitude mark analysis; environment adaptive flight control, including multi-sensor data fusion and near-earth landing control; and intelligent emergency processing, including abnormity monitoring and dynamic decision making. According to the method, the accuracy of landmark recognition and the precision of pose estimation are improved by adopting a staged landmark recognition and pose estimation method, and the requirement for height change in the autonomous landing process of the unmanned aerial vehicle and the requirement for landing of the unmanned aerial vehicle in an unattended scene are met. The reliability and safety of vertical landing and autonomous landing of the unmanned aerial vehicle are improved. According to the invention, various emergencies can be dealt with in real time, and property losses such as unmanned aerial vehicle damage and cargo damage caused by a landing problem are reduced.

The invention discloses a prostate cancer radical operation nerve real-time positioning method and system based on near infrared fluorescence imaging. Comprising the following steps: intravenously injecting an indocyanine green fluorescence contrast agent before an operation; an operation imaging system provided with an adjustable wavelength excitation light source, a high-sensitivity receiving device and a real-time image processing unit is adopted; the specific development of the vascular nerve bundle is realized by optimizing the excitation / emission wavelength and an image processing algorithm; guiding the surgical operation in real time and monitoring the signal intensity based on the fluorescent navigation image; after an operation, a multi-modal evaluation system such as three-dimensional pathological reconstruction, fluorescent marker analysis and intelligent pathological diagnosis is adopted to verify the operation effect. Through an innovative fluorescent development mechanism, an intelligent image processing technology and a multi-parameter intraoperative navigation system, accurate positioning and protection of a nerve structure in a prostatic cancer operation are realized, and the operation accuracy and the nerve retention rate are remarkably improved.

The embodiment of the application provides an analysis method, a page marking method, a platform, equipment, a medium and a product, relates to the technical field of computers, and the method comprises the following steps: acquiring a recommended marking scheme and a current marking scheme of a target page, the recommended marking scheme comprising at least one marking analysis event and at least one analysis parameter of the marking analysis event; the current marking scheme comprising at least one marking event and at least one marking parameter of the marking event; constructing a first association relationship between the at least one marking analysis event and the at least one marking event, and a second association relationship between the at least one analysis parameter and the at least one marking parameter; and displaying an event analysis result of the marking analysis event, wherein the event analysis result is generated based on the marking event associated with the marking analysis event and the parameter value of the marking parameter associated with the analysis parameter of the marking analysis event. The technical scheme of the embodiment of the application can inject a marking scheme meeting data analysis requirements in the development stage of software application.

The present invention relates to a material analysis method, which may include: (a) preparing a test specimen for analyzing its structure; (b) training an artificial intelligence model with training images labeled with the structure of any test specimen; (c) using the trained artificial intelligence model to analyze a plurality of unlabeled analysis images of the test specimen and removing analysis images classified as pre-defined noisy structure patterns; and (d) using the trained artificial intelligence model to analyze the analysis images from which the noisy structure patterns have been removed and classify them into at least one target structure pattern.

A method for manufacturing battery electrodes with enhanced traceability using fiducial markers. The method includes applying tracing codes and position codes to a substrate, synchronizing measurement profiles with accompanying tracing codes, and dynamically adjusting manufacturing parameters based on marker analysis. The invention improves quality control and traceability in battery electrode production.

The application discloses a methane seepage microbial marker mining method based on multi-source data fusion and related equipment, and the embodiment of the application fuses microbial gene data, spatial information and environmental geochemical data, constructs a multi-dimensional feature space, and improves the data dimension and reliability of marker mining; moreover, the embodiment of the application adopts a combination of random forest and regression modeling, realizes quantitative screening of the marker, and reduces manual intervention and subjective bias; specifically, the embodiment of the application grades the marker based on the comprehensive score of the three dimensions of importance, stability and universality, facilitating priority selection in subsequent practical applications; the embodiment of the application is suitable for methane seepage identification in different sea areas and different geological backgrounds, has good generalizability and practicality, and can be widely applied in the technical field of marker analysis.