1705 results about "Component analysis" patented technology

The invention discloses a compressed air energy storage system operation state evaluation method based on principal component clustering analysis. The method comprises the steps that system operation data are collected and preprocessed; performing multi-time scale decomposition and feature extraction on the preprocessed data set; performing nonlinear time-varying adaptive principal component analysis on the multi-time scale feature set, and extracting main features of a system operation state; carrying out clustering analysis on the dimension reduction feature space by adopting a PCA-clustering iterative optimization mechanism; establishing an evaluation model of each functional area of the system based on thermodynamic constraints, and calculating performance index values of the functional areas in combination with the principal component transformation matrix and an operation mode clustering result; dynamically adjusting the weight of each evaluation index; and generating a CAES comprehensive operation state evaluation result. According to the method, the problems of poor PCA adaptability, difficulty in multi-time scale data fusion, low abnormal mode detection sensitivity and the like under the nonlinear time-varying characteristic of the CAES system are solved.

A point cloud matching-based welded seam size measurement method, comprising: acquiring first point cloud data of a region to be welded before welding acquired by means of a three-dimensional laser scanner and second point cloud data of a welded region after welding acquired by means of the three-dimensional laser scanner (10); on the basis of the first point cloud data and the region to be welded, determining point cloud data of a region of interest to be welded (20); on the basis of the second point cloud data and the welded region, determining point cloud data of a welded region of interest (30); matching the point cloud data of the welding region of interest and the point cloud data of the welded region of interest, to obtain a matching result (40); according to the matching result, extracting a welded seam point cloud set (50); on the basis of a point cloud principal component analysis method, analyzing the welded seam point cloud set, to obtain welded seam sizes (60). The efficiency and accuracy of rebar welded seam measurement can be improved. Further disclosed are a point cloud matching-based welded seam size detection system, a computer-readable storage medium, and an electronic device.

The invention belongs to the technical field of artificial intelligence. According to the automatic evaluation method and system for the retrieval enhancement generation system, different answer evaluation algorithms are adopted for evaluation for answers generated by the retrieval enhancement generation system and answers in an evaluation data set, and a plurality of algorithm evaluation indexes are obtained; and calculating a plurality of overall evaluation indexes of each retrieval enhancement generation system, performing principal component analysis on all the algorithm evaluation indexes and the overall evaluation indexes, and further evaluating the RAG system. According to the method, the automation problem of document analysis and question and answer data generation is solved, high-quality question and answer pairs are provided, the question and answer quality and retrieval quality of the RAG system are automatically evaluated, and a more accurate automatic evaluation result is obtained by combining multiple evaluation indexes with a principal component analysis method.

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 provides a power transmission and transformation project ecological influence space-time boundary determination method and system based on multi-source remote sensing information deep fusion interpretation, and belongs to the technical field of power transmission and transformation project management. The method comprises the steps of data acquisition, data preprocessing and multi-source data integration, multi-scale space-time analysis, ecological influence index construction, remote sensing ecological index model construction, ecological influence space-time boundary determination, dynamic monitoring and early warning system platform establishment and long-term monitoring and evaluation. By integrating satellite remote sensing, unmanned aerial vehicle aerial photography and ground monitoring data and combining an image fusion algorithm, a principal component analysis method and a random forest algorithm technology, the method is beneficial to promoting accurate evaluation of the influence of the power transmission and transformation project on the ecological environment, visualizes the space-time boundary of the ecological influence, breaks through the limitation of a traditional evaluation method on time and space coverage, and improves the evaluation efficiency of the power transmission and transformation project. And a scientific method system for analyzing and evaluating the ecological influence of the power transmission and transformation project is further improved.

The invention provides a sea level rise influence risk assessment method, medium and system, and belongs to the technical field of risk prediction and assessment, and the method comprises the steps: firstly, analyzing historical sea level data through wavelet decomposition and an ARIMA model, and predicting a future change trend; meanwhile, a digital elevation model is established by using Kriging interpolation, and dynamic change of the terrain is described by combining real-time monitoring data. Secondly, identifying key infrastructures and influence factors by applying an analytic hierarchy process and principal component analysis, and extracting key features through singular value decomposition; and based on a singular value decomposition result, constructing a sensitivity and vulnerability matrix, calculating the exposure degree in combination with Monte Carlo simulation, and comprehensively evaluating the regional vulnerability. Furthermore, asset loss is predicted by applying a random forest algorithm, economic loss is calculated, and an ecological vulnerability index matrix is established to evaluate ecological influence. And finally, realizing quantitative risk assessment by adopting fuzzy comprehensive evaluation of an entropy weight method and a risk prediction model of support vector regression.

The invention relates to the technical field of power distribution system reliability analysis, in particular to a power distribution system reliability analysis method and system. The method comprises the following steps: performing multi-dimensional data acquisition on a power distribution system through a sensor network, and performing multi-dimensional data fusion to obtain multi-dimensional associated data; performing comprehensive operation data set construction on the multi-dimensional associated data to obtain a comprehensive operation data set; performing time sequence data segmentation according to the comprehensive operation data set to obtain a rapid fluctuation characteristic sequence and a slow change long-term trend sequence; performing time sequence decomposition on the rapid fluctuation characteristic sequence and the slow change long-term trend sequence to obtain high-frequency fluctuation characteristic data and long-term trend characteristic data; and performing feature component analysis on the high-frequency fluctuation feature data and the long-term trend feature data to obtain a multi-scale feature set. Through risk factor correlation analysis and reliability network model construction, the reliability analysis precision of the power distribution system 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 provides an SCA and SAST combined vulnerability reachable path detection method and device. The method comprises the steps of obtaining a to-be-detected item; extracting an entry function based on the to-be-detected item; when the to-be-detected item is a source code item or a product item, generating a function call graph based on call graph generation logic corresponding to the source code item or call graph generation logic corresponding to the product item; identifying a vulnerability function in the dependent component of the item to be detected through a software component analysis tool based on preset software bill of material information; taking the vulnerability function as a starting point, performing depth-first search traversal in the function call graph, and detecting whether an entry function is traversed or not; and if yes, determining a path from the entry function to the vulnerability function as a vulnerability reachable path. According to the method and the device, the reachable path of the vulnerability can be automatically detected, so that whether the security vulnerability is triggered or not is determined, invalid checking of the SCA tool in the use process can be avoided, and the reliability of the SCA tool is improved.

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 provides a battery pack fault diagnosis method and system based on multi-domain characteristics, and relates to the technical field of battery management, and the method comprises the steps: obtaining a median voltage curve of a battery pack and a single voltage curve of a single battery in the battery pack; decomposing the monomer voltage curve by taking the median voltage curve as a decomposition reference; optimizing key parameters of a variational mode decomposition algorithm through a grey wolf optimization algorithm by taking the minimum sum of envelope entropies of decomposition components of the single voltage curve obtained through decomposition as a target; decomposing each monomer voltage curve according to the optimized variational mode decomposition algorithm to obtain a high-frequency fault information component; extracting features of the high-frequency fault information components; performing dimension reduction fusion on the time domain features and the frequency domain features through a principal component analysis algorithm to generate a comprehensive fault coefficient; and according to the time domain feature, the frequency domain feature and the comprehensive fault coefficient, a local gravitation algorithm is combined to identify a fault single battery. According to the invention, the fault diagnosis capability can be improved, and early warning is facilitated.

The invention provides a water turbine anomaly detection and fault diagnosis method based on multi-sensor data fusion, and the method comprises the steps: collecting stress data, audio signals, vibration signals and working condition data during the operation of a water turbine, and carrying out the preprocessing of the collected data, including data cleaning, alignment and normalization; extracting time-domain, frequency-domain and time-frequency-domain features of the preprocessed stress data, audio signals and vibration signals, forming a high-dimensional feature vector in combination with the preprocessed working condition data, and performing dimensionality reduction on the high-dimensional feature vector by using principal component analysis; performing anomaly detection on the dimensionality-reduced high-dimensional feature vector according to a clustering algorithm, a local outlier factor algorithm and an anomaly detection algorithm constructed by an auto-encoder neural network, and generating a comprehensive anomaly score in combination with a preset weight to obtain an anomaly detection result; and when the water turbine is in an abnormal state, the fault type is diagnosed through the similarity between the dimensionality-reduced high-dimensional feature vector and the historical fault feature template.

The invention discloses a marine water environment abnormal state secondary and derivation probability modeling and estimating method, which belongs to the field of state modeling estimation, and comprises the following steps: acquiring real-time data flow of marine environment parameters, performing segmentation processing by adopting a sliding window mechanism, and reducing data dimension by utilizing principal component analysis. Then, constructing a graph structure model based on a time sequence, and capturing correlation and a space-time dependency relationship among parameters; and performing nonlinear mapping on the graph structure model by adopting a radial basis function core, and capturing complex nonlinear characteristics among the marine environment parameters. And finally, classifying the abnormal states by using a multi-class support vector machine algorithm, and outputting secondary and derivative probability estimation results of the abnormal states in real time. According to the method, abnormal events in the marine environment can be found in time, dynamic modeling support can be provided for marine environment monitoring, and the accuracy and real-time performance of marine environment monitoring are remarkably improved.

The invention relates to a virtual image expression generation method and system based on a real feeling technology. The method comprises the steps of collecting facial expression data in a multi-angle mode, and performing triangulation and facial region marking through the collected facial expression data; component analysis is carried out on the facial region, main features of facial expression changes are extracted, and a facial model is established through weighted fusion; establishing a mapping relation between the expression features and the facial displacement, and calculating a geometric constraint function of the facial displacement; correspondingly generating different expression models according to the geometric constraint function, optimizing details of a specific area and realizing dynamic face rendering on the virtual image; by introducing multi-view data acquisition, a multi-level feature extraction and fusion strategy, expression displacement regression analysis, geometric constraint and detail optimization, the authenticity and personalized effect of virtual expression generation are greatly improved.

The invention discloses a sensor fault detection and reconstruction method based on multi-sensor fusion, and the method comprises the steps: collecting data in a production process, and obtaining the data information of a detection point; carrying out anomaly detection on the collected data information through an LOF algorithm; classifying the detected abnormal data into normal data and abnormal data; inputting the abnormal data into a CNN-LSTM network sensor fault diagnosis model for principal component analysis dimensionality reduction, and reducing the input abnormal data from high-dimensional data to low-dimensional data by using principal component analysis; inputting sensor abnormal data subjected to principal component analysis dimensionality reduction into a CNN-LSTM-based sensor fault classification model, and determining the type of a sensor fault; and reconstructing the data of the fault sensor.

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 a tunnel section fitting method and device based on a laser radar point cloud, and a medium, and the method comprises the steps: scanning a subway tunnel through employing a laser radar, and obtaining the point cloud data of the subway tunnel; obtaining a tunnel trend by using a principal component analysis method, and performing coordinate transformation on the point cloud; obtaining point cloud slices along the tunnel trend; projecting the slice point cloud to a YOZ plane, constructing a polar coordinate system by taking the mass center of the slice point cloud as a circle center, and converting slice points to polar coordinates; uniformly dividing the section point cloud according to polar coordinate angles; and improving a random sampling consistency algorithm by using angle distribution, and realizing elliptic model fitting of the section point cloud. According to the method, the improved random sampling consistency algorithm based on polar coordinates is introduced, the problems that the section ellipse model is low in fitting efficiency, prone to falling into local optimum and the like are solved, the precision, reliability and efficiency of tunnel section fitting are improved, and optimal fitting of the tunnel section ellipse model is achieved.

The invention relates to the technical field of food component analysis, in particular to a food quality component analysis method and system based on a near infrared spectrum technology, and the method comprises the following steps: obtaining a food sample set, and obtaining original near infrared spectrum data based on the food sample set, combining the original near infrared spectrum data to form an original infrared spectrum matrix; preprocessing the original infrared spectrum matrix to generate a processed spectrum matrix; and extracting the characteristic wavelength of the near infrared spectrum information based on the processed spectrum matrix, constructing a food quality quantitative analysis model according to the characteristic wavelength in combination with a partial least squares regression algorithm, and outputting a component content predicted value according to the food quality quantitative analysis model. And generating a quality component analysis report of the to-be-detected food based on the component content predicted value. The method has the advantages that food quality and ingredients can be quickly, nondestructively and accurately detected, and detection precision and accuracy are improved.

The invention relates to the technical field of intelligent detection and quality grading, in particular to an automatic fish maw detection and grading system which integrates a spectral imaging module, a trace component analysis module, a physical property detection module and the like to realize comprehensive detection of external characteristics and internal components of fish maw. Through a multi-band light source and a high-resolution camera, an image from visible light to near-infrared light is obtained, and key indexes such as collagen, mineral substances and nutritional ingredients are accurately analyzed by utilizing a nondestructive testing technology; the feature extraction and fusion module adopts weighted mutual information and a self-adaptive penalty factor model to optimize features, the intelligent grading decision module provides accurate grading based on a feature intelligent discrimination method, and the self-adaptive control module dynamically adjusts equipment parameters to ensure the process to be stable. The system has the advantages of high efficiency, accuracy and intellectualization, and provides technical support for standardized grading and industrial production of the fish maw.

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.

The invention discloses a predictive maintenance method and system for a wind turbine, and the method comprises the following steps: 1), collecting the multi-source data of the wind turbine, including operation data, environment data and historical maintenance data; 2) preprocessing the collected data, extracting high-frequency features and low-frequency trends of vibration signals by adopting wavelet transform, performing principal component analysis to reduce dimensions and redundant data, and eliminating noise through Kalman filtering; 3) constructing a deep learning prediction model fused with multi-source data, inputting the model including time sequence data, environmental parameters and equipment image data, and outputting health state scores and potential fault categories of equipment; 4) performing fault precursor analysis based on a local anomaly clustering algorithm; according to the method and the system, the aspects of data comprehensiveness, model prediction precision, real-time performance, maintenance automation and the like are remarkably 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.