1086 results about "Component analysis" patented technology

The invention provides a mechanical arm autonomous grabbing method and system based on image instance segmentation, relates to the field of computer vision and mechanical arm grabbing, and solves the limitation problems of poor generalization and stability and the like in traditional mechanical arm visual grabbing. The method comprises the following steps: performing multi-angle image acquisition on a target object, segmenting and outputting object mask information, adjusting a mechanical arm to an observation position, and obtaining depth point cloud data of the target object; performing principal component analysis on the depth point cloud data, extracting a principal direction vector representing spatial distribution of a target object, and constructing a principal direction coordinate system; generating a candidate grabbing pose set based on the depth point cloud data, and screening out an optimal grabbing pose meeting a preset direction constraint; and based on the optimal grabbing pose, the mechanical arm is driven to execute the grabbing action. The method has the advantages of being small in calculation amount and insensitive to environmental changes, the mechanical arm can autonomously move to the optimal position where the target object is observed and conduct grabbing, and the grabbing accuracy is improved.

The invention belongs to the technical field of atomized liquid component analysis, and discloses an intelligent analysis system and method based on atomized liquid components, and the method comprises the following steps: carrying out multi-parameter sampling on atomized liquid to obtain pyrolysis data; performing temperature gradient analysis to generate a pyrolysis spectrogram; identifying trace markers to generate a feature library; analyzing the component evolution track to generate a dynamic evolution spectrum; carrying out partition demarcation processing to generate an atomization characteristic spectrum; performing thermodynamic analysis to generate a reaction map; analyzing component diffusion to generate quantized data; constructing a sensory response prediction engine; performing intelligent analysis to generate response feature data; evaluating and generating a quality report; and performing optimization processing to generate formula suggestions and executing accurate blending. According to the method, accurate analysis and evaluation of atomized liquid components are achieved through multi-dimensional analysis, a scientific basis is provided for product optimization, and safety and taste experience are effectively balanced.

The invention discloses a class case recommendation method based on deep understanding, and the method comprises the following steps: semantic extraction: carrying out the preprocessing of a case text, and carrying out the semantic feature extraction of the preprocessed case text through an encoder; the semantic feature extraction comprises initial crime name prediction and legal entity identification; performing structure extraction, converting nonlinear legal provisions, judicial interpretation and judgment rules into a legal provision map database, performing essential component analysis, and performing entity-essential component matching on a legal entity recognition result and essential components; and performing class case retrieval, performing dynamic fusion on the preliminary crime name prediction result and the entity-essential element matching result to obtain a case feature fusion vector, performing similarity calculation according to the case feature fusion vector, and performing class case recommendation. The technical problems that an existing method is low in recognition accuracy in long legal texts and insufficient in precise semantic boundary recognition of legal terms are solved.

The invention relates to the technical field of medical image processing, and particularly discloses a lung focus recognition method and system based on image deep learning, and the method comprises the steps: carrying out the enhancement and registration of an input lung image, and constructing a marking data set; constructing a convolutional neural network model with a multi-scale receptive field, introducing an attention mechanism to focus the model on a lesion area, coarsely and finely identifying the lesion position and estimating the size of the lesion position; further purifying a focus area and removing artifacts and noise by applying morphological filtering and connected domain analysis; and carrying out uncertainty evaluation on the identification result to obtain a focus identification result. According to the method, local details and global structure information in the lung image can be comprehensively captured, the model can be more accurately focused on the lesion area in combination with an attention mechanism, and the lesion recognition accuracy is improved. According to the invention, confidence information of an identification result is provided for doctors. And for a low-confidence result, measures such as manual judgment or re-collected data identification can be taken, so that the diagnosis reliability is improved.

The invention discloses a charger shell screw hole rapid positioning method based on three-dimensional point cloud identification, and the method comprises the steps: collecting a polarization structured light three-dimensional point cloud, carrying out the preprocessing, building a workpiece coordinate system, and obtaining a steady point cloud; calculating a homology bar chart in the neighborhood of the point cloud, screening candidate areas according to a threshold value, and generating candidate masks and topology confidence; performing principal component analysis and polar coordinate projection on the candidate region, and outputting a local point cloud; projecting and correcting a local point cloud to obtain a complemented point cloud and implicit field features; solving a hole axis by adopting random sampling consistency, and carrying out Gaussian mixture fitting on an output center and a hole diameter; fusing topology and geometric scores as joint confidence, and judging whether to enter execution or not; and mapping the center and the axis to a robot coordinate system, and compensating and updating a parameter output result during assembly. According to the invention, through three-dimensional point cloud identification and multi-stage geometric topology analysis, rapid and accurate positioning and assembly adaptive correction of the charger shell screw holes are realized.

The invention discloses a distributed photovoltaic power prediction method, system and device based on a Gaussian mixture model and a medium, and belongs to the technical field of photovoltaic power prediction.The distributed photovoltaic power prediction method comprises the steps that a time sequence vector is collected, principal component analysis is carried out on the time sequence vector, low-dimensional feature representation is obtained, and a power feature vector of each photovoltaic power station is formed; performing clustering analysis based on a probability model on the power feature vector to generate a plurality of photovoltaic power station clusters; for each cluster, acquiring meteorological input data through a set data source priority rule and a completion mechanism; and constructing a neural network power prediction model based on the accumulated power data in the cluster and the corresponding meteorological features, and outputting a future power generation power prediction value of the photovoltaic power station in the corresponding cluster. According to the invention, N photovoltaic power stations in a region are divided into M clusters through a GMM clustering method, so that the design is simplified; and the power prediction of the whole area is realized.

The invention relates to the technical field of retrieval enhancement generation, in particular to a method for automatically adjusting retrieval enhancement generation parameters based on content feature modeling. The method comprises the following steps: receiving an original query text of a user, performing component analysis, identifying terminologies, general vocabularies and question entities, and quantifying to form query fingerprints; acquiring a historical behavior sequence of the user, and constructing a score reflecting the level of the user by combining the query fingerprints and adopting a time decay weighting algorithm; the user level score is converted into specific retrieval parameter configuration, and a retrieval strategy blueprint is formed; guiding document library retrieval according to the retrieval strategy blueprint, and screening out a candidate knowledge set which is most matched with the professional level of the user; and according to the user level score, a preset instruction template is intelligently filled, and a situational generation instruction is constructed. According to the method, the problem of non-uniform cognitive load caused by a traditional system is solved through a retrieval enhancement generation technology, and the technical knowledge transmission efficiency and the user satisfaction are remarkably improved.

The invention discloses an intelligent industrial wastewater treatment method based on edge calculation and multi-modal data fusion, which comprises the following steps: synchronously acquiring various data of an industrial wastewater treatment site through edge nodes, automatically adjusting and aligning the generation time of each data, and ensuring that the time error of different data is less than a set threshold value; organically fusing the image features, the chemical parameters and the component analysis report to generate pollutant description features; constructing a simplified simulation model on the edge equipment, and predicting the diffusion trend of the wastewater pollutants by using the simulation model based on the pollutant description characteristics; and when the network is interrupted, the edge equipment is automatically switched to a local mode, and the operation is continued by using the stored model and the wastewater data acquired in real time. According to the method, the simulation model is deployed through the edge equipment, so that the calculation amount can be greatly reduced, the diffusion trend of wastewater pollutants can be accurately predicted, effective treatment measures can be taken in time, and the stability and reliability of an industrial wastewater treatment system are improved.

A method and system for AI based insight extraction from format-bound financial transaction data includes transforming a structured dataset in ISO format into a transformed dataset having metadata interpretable by a LLM Metadata includes descriptions of field names, expected values, entity relationships, and business rules. The transformed dataset is analyzed using machine learning models such as regression analysis, principal component analysis, predictive modelling, or anomaly detection. An intent and content of a natural language query are determined using an NLP model. Based on the intent, context, and metadata, the LLM generates a database query, which is executed on the structured dataset to retrieve relevant data. Insights are generated by combining the retrieved data with machine learning results.

The invention relates to the technical field of multi-source data fusion, in particular to a multi-source geological data processing method and system of a three-dimensional geological model.The method comprises the following steps that mountain landform and river valley images are obtained, gray frequency characteristics are extracted, a frequency energy gradient layer is constructed, a frequency continuous response area is screened to generate a structure boundary set, and a structure boundary set is constructed; the method comprises the following steps of: extracting a boundary normal vector by utilizing principal component analysis, identifying boundary sections with consistent directions, estimating a physical property parameter gradient direction, judging an included angle screening blocking region, generating a space attribute limiting layer, carrying out space alignment analysis on an overlapping region vector included angle, updating a boundary label, and generating an available attribute path structure set in three-dimensional geological modeling through a Dijkstra algorithm. According to the method, a conduction model is constructed through frequency domain decomposition and logarithmic transformation enhanced recognition, frequency window analysis noise reduction, principal component extraction vector analysis direction and center difference estimation, dynamic matching is promoted through alignment, a Dijkstra algorithm optimizes a path, and the geological model bedding characterization and conduction simulation precision is improved through cooperation of a multi-dimensional technology.

The invention discloses a wire harness product quality prediction system based on big data. An initial multi-source data set is acquired; core features in the initial multi-source data set are extracted based on the crimping height, the insulation resistance value and the environment temperature and humidity of the wire harness quality, high-weight features in the core features are screened through a PCA principal component analysis method, and wire harness feature data are obtained; processing time series data based on a long-short-term memory network, performing feature selection by using an extreme gradient boosting tree, and establishing a hybrid prediction model; using an improved IWOA whale optimization algorithm to optimize hyper-parameters of the hybrid prediction model; and inputting the wire harness characteristic data into the target hybrid prediction model for prediction, outputting a quality risk grade index, and if the quality risk grade index exceeds a set threshold, triggering an early warning signal. The limitation of traditional single data or simple model prediction is changed, so that quality prediction better fits an actual production scene, and the accuracy and reliability of prediction are remarkably improved.

The invention belongs to the technical field of radar signal processing, and discloses a radar scattering rapid test method based on polarization decomposition. Comprising the following steps: scanning a target 3D model, simulating scattered field distribution in different incident angles and polarization modes, calculating polarization contrast, and identifying a polarization sensitive area; establishing a dynamic coordinate system conversion model, fusing RTK data, IMU data and laser tracking data, and correcting the position of the trolley and the model in combination with a Doppler frequency shift model and a least square method; calculating an optimal test path of the trolley according to the sensitive area by adopting a natural heuristic optimization algorithm; establishing a geometric environment model, obtaining a back scattering signal through beam forming, analyzing a polarization coherence matrix, obtaining a high-entropy region, and decomposing scattering components; according to the method, closed-loop optimization is formed from a data acquisition source to a scattering component analysis tail end, and the test efficiency and the real-time performance are remarkably improved.

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

The invention relates to a carbon emission comprehensive efficiency evaluation method, system and device and a storage medium. According to the method, after carbon emission data is obtained, based on the carbon emission data and a pre-constructed carbon emission comprehensive efficiency evaluation index model, at least one first-level evaluation index value and the index weight of each first-level evaluation index are obtained through a principal component analysis method and an analytic hierarchy process; the carbon emission comprehensive efficiency evaluation index model comprises a plurality of first-level evaluation indexes, and finally, based on first-level evaluation index values and corresponding index weights, obtaining a carbon emission comprehensive efficiency evaluation result. Compared with the prior art, the method has the advantages that the accuracy and objectivity of the evaluation result of the comprehensive efficiency of the carbon emission can be ensured under the condition that the original data is sufficient or insufficient, and the like.

The invention relates to the technical field of intelligent control, in particular to an inspection system and method for a heating and ventilation system based on a knowledge graph, and the system comprises a feature extraction module, a graph construction module, an anomaly tracing module, a causal analysis module and a priority module. According to the method, the inlet and outlet water temperature and flow data of the water chilling unit are collected, deviation characteristics are extracted in combination with principal component analysis, the operation state is quantified, an embedded map participates in state judgment, the anomaly detection capacity is improved, initial anomaly and upstream nodes are focused, the analysis range is narrowed, and causal tracing is carried out based on the state influence between devices; by means of a Bayesian network, a state evolution chain is constructed, a time sequence and an inter-structure fault are bridged, task priority is evaluated according to a link starting point and length, equipment dynamic sorting and state updating are carried out, inspection scheduling accuracy and efficiency are improved, and a closed-loop process which is high in recognition accuracy, small in analysis boundary, clear in path and excellent in resource allocation is formed.

The invention belongs to the technical field of crop growth prediction, and particularly relates to a crop growth prediction method and system based on multispectral unmanned aerial vehicle monitoring. The method comprises the following steps: acquiring multi-dimensional data of real crops in different growth stages under different soil water contents and disease and insect pest states, determining the contribution degree of each vegetation index to crop growth through a factor analysis algorithm, carrying out dimension reduction on hyperspectral data by using a principal component analysis algorithm, fusing with the multi-spectral data, constructing a multi-spectral resolution characteristic space, and carrying out multi-spectral analysis on the hyperspectral data. The method comprises the following steps: firstly, obtaining a real plant height growth fitting function of crops through analogue simulation by combining LiDAR data and vegetation indexes, thirdly, calculating a real growth vegetation index space and obtaining a real growth state space of a standard staged growth period, and finally, inputting the calculated space and function into a model constructed by a reinforcement learning algorithm for training, and accurate prediction of the crop growth state is realized.

The invention discloses a two-dimensional principal component analysis method for reinforcement learning of a multi-section airfoil optimization strategy, and belongs to the technical field of aircrafts, and the method comprises the following steps: S1, defining a multi-section airfoil optimization problem; s2, establishing a pneumatic data sample library by using Latin hypercube sampling; s3, performing dimension reduction on the velocity field matrix by adopting a two-dimensional principal component analysis method; s4, establishing a reinforcement learning model of the optimization strategy; and S5, training the reinforcement learning model to obtain an optimal optimization strategy with direct migration capability. According to the method, while the effectiveness of the reinforcement learning agent in observing the environment state is ensured, the dimensionality of the state is effectively reduced, the number of layers of the neural network and the number of parameters required by optimization strategy learning are reduced, and the trained optimization strategy is suitable for various design working conditions and multi-section airfoil profiles.

The invention relates to the technical field of flight ability evaluation, in particular to a pilot emergency processing ability evaluation method and system fused with physiological signals. Initial ability indexes are extracted based on literature research and physical examination standards, and physiological data of a pilot in a normal state and a simulated emergency state are collected; the method comprises the following steps: extracting potential ability indexes through dimensionality reduction and denoising of a component analysis algorithm and factor analysis (FA), and screening remarkably changing physiological indexes (plt; 0.05), and performing correlation combination with the initial index (a correlation coefficient gt; 0.8) constructing an emergency capability evaluation index system including situation awareness, decision making, communication, pressure management, team cooperation, reaction time, strain and flexibility; the pilot emergency ability assessment method and system are constructed through multi-source physiological signal fusion and dynamic modeling, accurate quantitative analysis and personalized training guidance are achieved, and civil aviation safety specifications are adapted.

The invention relates to the technical field of intelligent risk analysis, in particular to an intelligent risk analysis system of a financial system. The system comprises a data flow analysis module, a relational graph construction module, an abnormal mode detection module, a security boundary monitoring module, a staged strategy planning module, a scene simulation evaluation module, a decision support module and a comprehensive risk management module. A graph database and a graph convolutional network are utilized to deeply analyze a financial entity relationship, reveal a risk association network, improve abnormal behavior recognition through a K-mean value and an isolated forest algorithm, enhance security boundary monitoring through a random forest algorithm, enable risk threshold adjustment to be more dynamic, combine a decision tree with a genetic algorithm, optimize a risk management strategy, and improve risk management efficiency. The system dynamics and proxy model technology in scene simulation evaluation strengthens risk prediction and increases foresight, principal component analysis and risk matrix evaluation are applied in a comprehensive risk management module, a quantitative analysis tool is provided, and more systematic and comprehensive risk management is realized.

The invention discloses a rapid local repair method for a self-intersecting triangular mesh. According to the method, local processing and rational number calculation are combined, aiming at an input low-quality triangular mesh, firstly, the mesh quality is optimized through a preprocessing step, intersection areas are positioned, then the mesh is partitioned based on octree division and connected component analysis, and the intersection areas and non-intersection areas are separated. In each intersection area, a shape-preserving boundary three-dimensional grid optimization method based on rational numbers is adopted to ensure the robustness of intersection calculation, and surface grids are extracted after successful conversion into floating-point number representation. And finally, seamlessly splicing the repaired local grid and the non-intersecting grid, and improving the overall grid quality through an edge re-gridding method based on curvature self-adaption. According to the method, the non-intersecting high-quality grid can be quickly generated, the method is suitable for downstream applications such as finite element analysis, meanwhile, the calculation overhead is remarkably reduced, and the algorithm robustness is improved.

The invention discloses a load demand interval probability prediction method considering abnormal meteorological conditions, and relates to the field of power load prediction. Obtaining multi-dimensional data and carrying out data fusion processing to obtain a multi-dimensional feature set; performing dimension reduction on the multi-dimensional feature set by using a principal component analysis method to extract all meteorological factors influencing load demand changes in historical meteorological data under an abnormal meteorological condition; analyzing the combined features of all meteorological factors to obtain all extreme meteorological scenes under abnormal meteorological conditions; all the extreme meteorological scenes are clustered to obtain different types of extreme meteorological scenes, and key factors influencing load demand changes under the different types of extreme meteorological scenes are identified; constructing time sequence models corresponding to different types of extreme meteorological scenes based on the key factors; and combining quantile regression with a time sequence model to construct an interval load prediction model. According to the invention, the prediction precision and stability of the load demand under the abnormal condition are improved.

The invention discloses a healthy drink recommendation system based on artificial intelligence, and relates to the technical field of health preservation and health, and the healthy drink recommendation system based on artificial intelligence comprises a central control module, a data acquisition module, a data processing module, a recommendation generation module and a user feedback module. According to the system, the body data, the beverage preference data and the environment data of the user are obtained through the data acquisition module, the extracted data are processed through the data processing module, the beverage features are matched with the user requirements through the recommendation generation module, the recommendation list is generated, the recommendation result is displayed through an interaction interface in the user feedback module, and the user experience is improved. According to the method, the user can conveniently select proper beverage supplies according to the body health condition of the user, the problems that in the prior art, functional component analysis in a beverage recommendation scene is insufficient, and drinking time and drinking frequency are not fully considered are solved, and the accuracy and adaptability of a recommendation result are enhanced.

The invention discloses a deep learning-based coal mine foreign matter visual detection system, which is characterized in that a coal mine image is acquired through a multispectral imaging technology, a static compensation image is obtained through registration, enhancement and motion compensation processing, multidimensional features are extracted, dimensionality reduction is carried out by utilizing principal component analysis to generate an optimized feature set, an improved YOLOv12 model is trained, and the visual detection of foreign matters in a coal mine is realized. A lightweight foreign matter detection model is obtained through parameter simplification and network optimization, and foreign matter real-time recognition and detection report generation are achieved. A time sequence analysis model is constructed by associating historical detection data, and a detection model is iteratively updated in combination with an incremental learning mechanism. Multispectral imaging and deep learning are combined, the foreign matter detection precision of the complex underground environment is improved, industrial site deployment is adapted through model lightweight design, the learning ability of the system for the foreign matter distribution rule is enhanced through a time sequence analysis mechanism, and efficient detection and early warning of the coal mine foreign matter are achieved.

The invention discloses an operation state and characterization state parameter method of a GIS isolation switch, and relates to the field of GIS isolation switch operation. Multi-source heterogeneous sensors including vibration, temperature, strain gauges, fiber bragg gratings, acoustic cameras and the like are deployed at key parts, and data are collected in a self-adaptive mode according to working conditions; processing data by using a deep learning noise reduction auto-encoder, adaptive weighted fusion and DBSCAN; deeply mining features by using empirical wavelet transform, a heat conduction model and the like; constructing a composite state parameter by means of a deep belief network and principal component analysis in combination with particle swarm optimization; the state is evaluated through a convolutional neural network of transfer learning, LSTM prediction and early warning are carried out, and a fault tree and a Bayesian network are combined to locate a fault. According to the invention, data is comprehensively collected, intelligent processing and deep mining are carried out, scientific state parameters are constructed, and accurate state evaluation and early warning are realized in combination with transfer learning; the adaptability is improved through self-learning and multi-modal fusion, the operation and maintenance efficiency is improved through a VR / AR visual platform, the risk of the power system is reduced, and benefits are remarkable.

The invention provides a software component analysis method oriented to C / C + + source codes, relates to the technical field of software component analysis, and provides a flexible and efficient data collection mechanism, comprehensively constructs a TPL feature library to improve the quality of the feature library, preliminarily filters non-specific TPL functions in combination with a directory structure, reduces noise interference and improves the software component analysis efficiency. The method comprises the following steps: firstly, classifying similar TPLs into the same family by using a clustering algorithm, sharing a certain number of common functions in each family, and meanwhile, including a unique marker function of each TPL, further analyzing a multiplexing relationship between the TPLs sharing the common functions, and removing the common functions which do not belong to a specific TPL range, thereby overcoming the defects related to the birth time of the functions; according to the method, the multiplexing proportion of the target code at the function level and the file level is further comprehensively analyzed, the TPL multiplexing situation of the code is accurately judged, the target code is divided into TPL modules, the dependency relationship between the modules is analyzed through include and extran statements, and therefore more accurate component recognition and dependency analysis are achieved.

The invention discloses a Mars transverse wind ridging few-sample remote sensing interpretation method and system based on an SAM model. According to the method, contrast stretching preprocessing is carried out on an original Mars image, a dual-branch feature fusion framework based on VIT and CNN is constructed to extract and fuse image features, a position coding generator is introduced to support input of any size, a fine-tuning SAM strategy of a selective freezing encoder and a trainable mask decoder is adopted, and LoRA low-rank adaptation optimization calculation is combined, so that the Mars image is obtained. And a double-branch prompt generation module is used for fusing labeled and unlabeled data to generate a high-precision prompt, and finally, an interpretation result is output through a mask decoder and connected component analysis is carried out, so that instance-level labeling is realized. The system comprises an image preprocessing module, a double-branch feature extraction and fusion module, an image embedding generation module, a model fine tuning module, a prompt embedding generation module and an interpretation module. According to the method, the recognition precision and robustness of the mars transverse wind ridge formation under the condition of few samples are effectively improved.

The invention relates to the technical field of modeling, in particular to an aero-engine main bearing kinetic model correction method based on sensitive feature fusion, which comprises the following steps: constructing an aero-engine main bearing kinetic model; simulation data are generated according to the collected actual measurement data, time-frequency domain features of the actual measurement data and the simulation data are extracted, and an initial feature set is constructed; based on a binary improved horse swarm optimization algorithm, sensitive features are screened out from the initial feature set, a sensitive feature set is constructed, a component matrix coefficient of the sensitive feature set is calculated in combination with a principal component analysis method, and a fused sensitive feature expression is obtained; calculating fusion sensitive features of the measured data and the simulation data by using the fusion feature expression, and taking a difference calculation formula of the two as a target function for model correction; and performing optimization based on a horse group optimization algorithm and the target function, updating model parameters of the aeroengine main bearing dynamic model, and realizing correction of the aeroengine main bearing dynamic model.

The invention discloses a cardiovascular disease risk assessment system based on big data analysis, which relates to the technical field of medical big data and quantum and comprises a data acquisition module, a preprocessing module, a feature extraction module, a model training module, a risk assessment module and a result display module. Data acquisition integrates multi-source heterogeneous data, quantum encryption is used to guarantee security, and preprocessing is carried out by deep reinforcement learning cleaning and adaptive normalization; feature extraction is combined with quantum principal component analysis and an auto-encoder, and a quantum attention mechanism is introduced; the model training adopts quantum neural network ensemble learning of quantum annealing optimization; introducing fuzzy logic reasoning to correct probability and layering in risk assessment; results are displayed through virtual reality and augmented reality technologies, and report suggestions are automatically generated. The method has the advantages of prominent advantages, comprehensive multi-modal data acquisition, accurate advanced technology preprocessing and feature extraction, efficient and accurate quantum optimization model training, and combination of real-time monitoring and a knowledge graph, provides a basis for prevention and treatment of cardiovascular diseases, and promotes medical intellectualization and precision.

The invention relates to the technical field of component analysis, in particular to an intermediate infrared spectrometer sensor verification system which comprises the following steps: acquiring spatial distribution information of a target sample through an automatic sampling module, and generating a corresponding first feature group based on the spatial distribution information; adjusting an emission wave band and a modulation strategy corresponding to a mid-infrared light source, converting mid-infrared photons into visible light signals, and generating a corresponding second feature group; performing down-sampling processing on the original spectral data to generate a corresponding third feature group; inputting the sparse spectral coefficient into a pre-trained deep learning reconstruction model, and dynamically correcting the reconstruction process in combination with an environment temperature compensation parameter to generate reconstructed spectral data corresponding to high resolution; and analyzing the reconstructed spectrum data, matching a preset substance spectrum database, extracting a characteristic absorption peak position and an intensity ratio of the target substance, and generating a corresponding final analysis result. According to the invention, the intelligence of the sensor verification system can be improved.

The present invention discloses a method, device, and medium for identifying software component analysis, which relates to the technical field of software technology. Specifically, it includes: obtaining all files in the software to be identified to obtain a first data set; extracting meta-file data to obtain a second data set, and obtaining a first open-source file set through first feature matching; performing deletion processing on the first data set to obtain a third data set, calculating the minimum hash signature through the program abstract syntax tree and the corresponding sub-syntax tree to generate a second feature set, and obtaining a second open-source file set through third feature matching; performing deletion processing on the third data set to obtain a fourth data set, obtaining an updated syntax tree based on the abstract syntax tree and the fourth data set and constructing a control flow diagram, and obtaining a third open-source file set through fourth feature matching between the third feature set and a preset reference database; merging the first open-source file set, the second open-source file set, and the second open-source file set to obtain the total open-source component set in the software to be identified.