176 results about "Analysis Dataset" patented technology

A method is provided for constructing decision trees. The method includes analyzing a dataset to determine data distribution metrics; calculating feature importance scores for features in the dataset; dynamically adjusting tree depth and node pruning criteria based on the determined data distribution metrics and feature importance scores; initializing a decision tree structure based on the adjusted tree depth and node pruning criteria; and iteratively refining the decision tree by applying heuristic adjustments to improve splits based on updated data distribution metrics and feature importance scores.

The invention discloses a submarine pipeline scour-vibration response analysis method based on a cross-scale coupling framework. The method comprises the following steps: establishing a multi-physics field coupling model according to geographical and structural information of a pipeline layout area and initializing the multi-physics field coupling model; marine environment data are obtained through real-time monitoring, the model is imported for scouring dynamic deduction, and a scouring boundary data set is generated. And reconstructing a multi-physics field solving environment, and forming a real-time physics solving environment package. After the model is updated, pipeline vibration response is analyzed, and a vibration data set is obtained. And performing fatigue damage analysis, deducing a flow guide field disturbance coefficient, updating the model again, performing next time step coupling calculation, and finally obtaining a full-period coupling analysis data set. And evaluating the pipeline operation safety situation based on the data set, and generating and pushing a safety report. Various physical field effects on the submarine pipeline in the marine environment are comprehensively considered, the safety of the submarine pipeline is evaluated more comprehensively, and cross-scale coupling analysis between the scouring effect and the vibration effect is achieved.

Methods, systems, and computer storage media for providing brand-focused security administration in an item listing system are described. Using a brand-focused machine learning model, the brand-focused security administrator engine supports generating different comprehensive reports and analytics on brand-related security metrics, including brand compliance rates, infringement incidents, enforcement actions taken, and overall item listing system integrity. The brand-focused security administrator engine also supports visualizing the brand-focused security analytics results data in a manner that enhances usability and decision-making by presenting complex brand-focused security analytics results data in a clear, actionable format. In operation, brand-focused security data is accessed. The brand-focused security data is analyzed using a brand-focused machine learning model that is trained based on a multi-dimensional authenticity analysis dataset. Brand-focused security analytics results data is generated. The brand-focused security analytics results data is communicated to cause display of one or more visualizations based on the brand-focused security analytics results data.

The invention discloses a network space security intelligent monitoring and analysis system, and relates to the technical field of network security monitoring and analysis, and the system comprises a multi-source data collection module which collects multi-dimensional data in a full-link manner, and the collection frequency is dynamically adjusted along with a network load; the data preprocessing module cleans the fused data and generates a standardized analysis data set; the AI intelligent risk identification module identifies various safety risks in real time through a mixed deep learning model; the real-time response processing module starts differential processing according to a three-level mechanism; the threat traceability analysis module traces an attack link and generates a report; the security situation visualization module displays the security state in multiple dimensions; the data encryption storage module encrypts and protects data and performs double backup; and the system self-optimization module dynamically optimizes the strategy through incremental learning. The method is accurate in risk identification, timely in response processing, reliable in traceability and evidence storage, and efficient in cross-domain cooperation; terminal protection and third-party access control are enhanced, and network space security and stable service operation are comprehensively guaranteed.

The invention provides a user attribution method and system capable of meeting various configurations and conversions, and belongs to the technical field of computer networks, and the method comprises the following steps: according to a unified identity mapping table, ordering behavior records on each device according to a time sequence, integrating the behavior records into a continuous behavior path data set, and determining a full-link view of a user conversion path; carrying out fragmentation storage and parallel analysis by adopting a distributed computing framework through the behavior path data set, and optimizing resource allocation to obtain a real-time processed user conversion path result set; according to a user conversion path result set processed in real time, a cache mechanism is constructed to store high-frequency access path data, and if a new query request is received, data is extracted from a cache preferentially, and a quick-response path analysis result is determined; through a path analysis result, an encryption technology is applied to protect a user identity identifier and behavior data, and in combination with an access control strategy, a safe and compliant attribution analysis data set is obtained. According to the method, the problem of cross-device user behavior tracking is solved, a full-link user conversion path view is provided, the path analysis requirement can be quickly responded, and data support is provided for precise marketing decision making.

The invention discloses a causal perception sentiment analysis method and system based on thinking chain reasoning, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring and reading a multi-modal sentiment analysis data set; extracting video features from the video data in the multi-modal sentiment analysis data set, including voice, text and visual modal features; using the training set and the test set to train and verify the causal perception emotion polarity alignment model; inputting the test set into the trained causal perception emotion polarity alignment model to obtain an emotion state prediction result; video features are input into a causal perception emotion polarity alignment model, and emotion clues are extracted through thinking chain prompt and a self-supervision verification mechanism; then performing causal intervention and anti-factual reasoning on each modal feature by using an emotion clue to obtain a causal-related single-modal feature; and finally, obtaining joint feature representation from the causal-related single-mode features through cross-mode interaction by using a multi-mode representation learning method, and predicting an emotional state.

The invention discloses a storm surge nearshore disaster identification method, device and equipment fused with a typhoon dynamic influence domain, a medium and a product, and relates to the technical field of meteorological disaster monitoring. The method comprises the following steps: acquiring a meteorological ocean reanalysis data set in a historical set age limit of a target area, and preprocessing multi-source data; then background field elimination processing is carried out; determining a dynamic influence radius based on the typhoon intensity grade and the storm surge abnormal data; determining the center position of the typhoon according to the dynamic influence radius, and generating a typhoon dynamic influence domain space mask by adopting a spherical distance calculation method; a near-shore buffer area is determined according to the typhoon dynamic influence domain space mask, and a storm surge disaster area is determined based on a near-shore buffer area constraint and water increase threshold value judgment method; and carrying out storm surge near-shore disaster identification on the basis of the storm surge disaster area by adopting a geographic space visualization method to obtain an identification result. According to the invention, accurate storm surge disaster discrimination can be realized.

The invention provides a method for cooperatively regulating and controlling chlorophyll-a concentration variability through vortex-upwelling, and relates to the technical field of ocean remote sensing and environmental monitoring. A multi-source satellite and reanalysis data set is obtained, and a stationary time sequence is constructed; identifying a dominant variation period in chlorophyll-a concentration, sea surface temperature and sea surface height anomalies, and extracting a monthly abnormal value sequence; performing complex empirical orthogonal function analysis on the sea surface height anomaly, extracting a dominant spatial variation mode of the sea surface height anomaly, and identifying a vortex distribution position and form; classifying and screening target vortexes according to vortex types and seasons, carrying out normalization processing on the chlorophyll-a concentration, aligning to vortex centers, and analyzing a spatial response relationship between the vortexes and the chlorophyll-a concentration; and drawing a density time-depth profile map and a CHL-SLA composite map by combining surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, and analyzing an influence mechanism of vortex-upwelling on chlorophyll-a horizontal distribution.

Proposed is a method and system for displaying data sets on a user interface. The system may retrieve first, second and third datasets from a plurality of databases. The system may then analyze the datasets to determine anomalies and fraud indicators in the datasets. The system may then generate and display an anomaly alert and a fraud alert respectively on first and second areas of a user interface. The system may display a first set of review data on a third area of the user interface. The system may receive a second set of review data and generate a final audit report on a fourth area of the user interface. The system may display an indication of the final audit report on a fourth area of the user interface such that information contained in the first to fourth areas is simultaneously visible via a same display screen.

The invention relates to a digital quality evaluation method, device and equipment for cooperative learning of higher vocational schools and foreign vocational schools, and a medium. The method comprises the following steps: firstly, acquiring multi-source original data of Chinese and foreign education systems, determining a core term definition through a term anchoring technology, and synchronously adopting a noise cleaning algorithm to remove invalid data to form a basic data set; performing format standardization conversion on the basic data set, quantifying a Chinese and foreign education standard deviation value based on an algorithm, establishing a data stream mapping relation, and generating an analysis data set; then, on the basis of the analysis data set, professional core features are screened through a feature vector extraction technology, grouping parameters are dominated by using a clustering algorithm in combination with a clustering boundary right confirmation rule, and a classification data set is obtained; and finally, constructing a dynamic evaluation index system according to the classification data set, obtaining an evaluation result, and outputting an evaluation report. According to the method, standardized and traceable digital technical support is provided for dynamic monitoring, difference analysis and optimization improvement of cooperative learning quality of higher vocational schools and foreign vocational schools.

A method for analyzing datasets for vulnerabilities to adversarial attacks includes storing, by a processing server, a dataset comprising multiple data types in a database; preprocessing the dataset using data preprocessing algorithms based on the data types; performing data featurization to identify dataset features; generating, for each data type, a victim model based on the dataset; determining performance metrics of the victim models; generating attack datasets corresponding to each data type and including the performance metrics and dataset features; executing adversarial attacks, including membership inference, property inference and reconstruction attacks, on the victim models; determining adversarial attack success metrics, including accuracy, precision and F1 score; updating the attack datasets to include the adversarial attack success metrics; and generating, for each updated attack dataset, a vulnerability metric comprising a library of algorithms and models specific to the combination of adversarial attack and data type.

A comprehensive system for tracking, visualizing, and analyzing the lineage of datasets and artificial intelligence (AI) models using blockchain technology. The system attests, registers, and records datasets and AI models on a blockchain-based infrastructure, ensuring they are immutable, discoverable, and searchable. It enables real-time notifications for anomalies and key lineage events, supports integration with machine learning operations (MLOps) pipelines, and enhances data ownership, visibility, and accountability. Through a robust API layer, the system interoperates with hyperscalers and platform providers and includes a consumer-focused analytical and configuration management plane. Equipped with lineage-aware machine learning intelligences, the system delivers intelligent lineage insights that improve confidence and transparency in AI inferences and data usage across enterprise and open-source environments.

The invention discloses a hard rock mining disaster traceability analysis method and system based on big data, and belongs to the technical field of mining data processing, and the method specifically comprises the steps: obtaining multi-source data of a hard rock mining operation area, constructing a mining disaster analysis data set based on the multi-source data, and obtaining a hard rock mining disaster traceability analysis result based on the mining disaster analysis data set. The method comprises the following steps: constructing a fracture disaster-pregnant prior field reflecting a surrounding rock fracture evolution state, determining a disaster suspicious source area, constructing a disaster traceability relation model for the disaster suspicious source area, and carrying out inversion inference on the disaster traceability relation model to obtain a source area position, a trigger factor and a trigger moment of a disaster; according to the method, the source tracing ambiguity caused by inconsistent multi-source data time and unclear spatial reference can be reduced, so that the determination process of the disaster source area, the trigger factor and the trigger moment has a clear data basis and an operation path, and the method is suitable for a hard rock mining scene in which disaster causes are complicated and responsibility boundaries are difficult to define under a deep hard rock mining condition.

The invention discloses a quality-effect index and influence factor correlation analysis model construction method and system, and the method comprises the steps: carrying out the preprocessing of an original analysis data set, and then carrying out the standardization processing, so as to obtain a standard analysis data set which comprises standard quality-effect indexes and standard influence factor data; dimensionality reduction processing is carried out on the standard influence factors to extract key influence factors, the correlation degree between the key influence factors and the standard quality-effect indexes is quantified, and an initial correlation analysis model is constructed based on the correlation degree; and performing iterative training on historical training data according to a machine learning algorithm, and dynamically optimizing model parameters in the initial correlation analysis model to obtain a trained correlation analysis model. The method is used for solving the technical problems of inaccurate quality and efficiency evaluation, fuzzy influence factor relevance and the like in a training scene, systematic analysis and scientific prediction of the training quality and efficiency are realized, and quantitative decision support is provided for training scheme optimization and quality improvement.

The invention discloses a prompt-driven two-stage multi-modal emotion representation learning method. The method comprises the following steps: respectively collecting original video data from a plurality of public multi-modal emotion analysis data sets; preprocessing and feature extraction are carried out to obtain vectorized multi-modal feature representations of vision, audio and texts, and multi-source emotion clues are obtained; in a training stage, a prompt-driven two-stage multi-modal emotion representation learning model is constructed, inter-class separability is enhanced and intra-class intensity features are reserved through an emotion anchor point comparison and alignment stage, including prompt-based emotion anchor point learning and comparison and alignment between joint representation and emotion anchor points; capturing the dynamic change of emotion expression through an emotion intensity offset estimation stage; in the reasoning stage, emotion category prediction and final emotion state prediction are carried out. According to the method, shared emotion features among multiple modes can be stably captured, interference caused by individual differences is effectively suppressed, and the accuracy and robustness of emotion prediction are remarkably improved.

The invention discloses a purchase data analysis method and system based on metadata driving, and relates to the technical field of purchase data management, and the method comprises the steps: obtaining data access configuration and data mapping rules of a plurality of purchase data sources, so as to construct an analysis data set; processing target data in the analysis data set based on a preset business rule to obtain index data; in response to a configuration operation on the visual component, generating a visual billboard, the visual billboard being used for displaying a data state; responding to configuration operation on the business rule and the visual billboard; through a configurable data access mode, flexible business rule setting and a visual billboard construction function, business personnel can autonomously adjust data analysis content and a display mode according to purchase strategy changes and market demands, and program codes do not need to be modified by relying on technicians; the service requirement adapting period of the system is obviously shortened, and the maintenance cost is reduced.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a memory analysis question and answer data construction method and device, equipment and a medium. The method comprises the steps of extracting a fact information set containing entities, relationships and values based on structured message representation, generating candidate question and answer pairs by utilizing a language model, executing multi-source verification to form a target question and answer set, performing semantic extension and question method transformation to generate a diversified question and answer set, and completing credibility screening based on a verification result to obtain a memory analysis question and answer set. And generating a memory analysis data set in combination with the user message sequence. According to the method, through multi-stage extraction, generation, verification and screening, reliability and diversity unification of question and answer content are achieved, the constructed memory analysis data set has the advantages of being consistent in context, accurate in fact, rich in expression and the like, and therefore the credibility of memory ability evaluation of the large language model is improved.

A method is disclosed for analysing a data set to determine a first processes. First messages are provided, the first messages classified into a plurality of different classes with a plurality of different likelihoods, a single first message classified into different classes based on different criteria. From the first messages a first subset of the first messages is retrieved based on a combination of one or more classifications, a likelihood of the one or more classifications, and another classification for messages within the first subset of the first messages. The likelihood of the classifications has more than two (2) potential values.

A method of situation-specific documentation of a traffic situation in which a motor vehicle is located. A control device receives, from a monitoring device, monitoring data which describes a current traffic situation in which the motor vehicle is located, and driving data, from at least one motor vehicle system of the motor vehicle, which describes at least one driving parameter for a current driving behavior of the motor vehicle. The control device determines an extended traffic situation on the basis of the received data and checks whether the extended traffic situation meets a specified danger criterion which specifies a minimum probability of damage occurring to the motor vehicle. Based on the extended traffic situation meets the specified danger criterion, the control device provides an analysis data set which describes the a result of the checking, and causes a documentation device to store the provided analysis data set.

The invention discloses an extension period temperature downscaling method and system based on deep learning in a complex terrain, and belongs to the technical field of temperature prediction. The method comprises the following steps: aiming at the problem of defects in an existing temperature downscaling task, acquiring a back-calculation data set and a reanalysis data set, preprocessing the back-calculation data set and the reanalysis data set to obtain a multivariable training data set, acquiring terrain data, and extracting multi-scale features of a terrain by using a convolutional neural network; constructing an extended-period temperature downscaling model based on deep learning, training the model, fusing topographic data, determining an objective function, and obtaining a trained model; and inputting the low-resolution air temperature forecast data into the trained model to obtain a high-resolution downscaling result. According to the method, complex topographic data and extended period forecast data are adopted, a deep learning method is utilized to extract nonlinear features of air temperature data, respective advantages of multi-scale features are mined and integrated, the forecast air temperature is downscaled, and a high-resolution downscaling result is output.

The invention discloses a three-dimensional design model conformity analysis method and system based on a large language model, and the method comprises the steps: obtaining the related data and design specification constraint conditions of a three-dimensional design model, and building a data interaction channel through a collaborative review platform; calling a semantic geometry collaborative verification algorithm to process the data and extracting feature parameters; generating a rule adaptation weight matrix through a space rule learning adaptation model; constructing a mapping relation between the feature vector and the design specification through a CAD point cloud feature coding mapping model; carrying out multi-dimensional conformity verification on the basis of the result; and outputting the comprehensive analysis data set. The system realizes deep cooperative processing of geometric and semantic data of a three-dimensional design model through multi-unit cooperative linkage and integration of an algorithm and a platform function, improves the accuracy of rule adaptation and feature mapping, comprehensively covers a conformity verification dimension, and meets a high-precision analysis requirement in a complex scene.

The invention relates to a multi-modal sentiment analysis method based on a large language model and quantum computing, and belongs to the technical field of multi-modal feature alignment and fusion. The multi-modal sentiment analysis method comprises the steps of constructing a multi-modal sentiment analysis data set, extracting text features and multi-granularity image sentiment description features by using the large language model and a target detection technology, carrying out multi-level fusion on the image emotion description and the original text to obtain cross-modal potential emotion association information; designing a cross-modal feature fusion mechanism based on a parameterized quantum circuit, and performing efficient fusion on multi-modal features by using quantum superposition and quantum entanglement features to generate unified emotion feature representation; performing sentiment classification based on the sentiment feature representation, and outputting a sentiment analysis result; according to the method, cross-modal potential emotion association can be effectively captured, the fusion efficiency and expression ability of multi-modal features are improved, the method is remarkably superior to the prior art in emotion analysis tasks, and the robustness and accuracy are high.

A system, method, and a computer program product for determining anomalies in a dataset are provided. The dataset, which stores data structures of parameters, is received at an anomaly detection framework. The anomaly detection framework selects multiple models, including time series models, historical models, artificial intelligence models and isolation forest models to analyze the parameters in the dataset and determine anomalies in the data structures.

The invention belongs to the technical field of electric power measurement, and discloses a current transformer error solving method, system, device and medium, and the method comprises the following steps: collecting the operation data of a current transformer, constructing a sample analysis data set, carrying out the component decomposition and feature extraction of the sample data set, and calculating the statistical threshold value of the sample data set; judging the condition of the current transformer, and constructing an in-phase current transformer sample; a calculation step based on a statistical magnitude threshold value: calculating the statistical magnitude of each sample in the same-phase current transformer samples, comparing the statistical magnitude with the statistical magnitude threshold value, obtaining an analysis result of the state of the current transformer, and further obtaining a related solution threshold value; a particle swarm is initialized through a multi-target weighted fitness function model and improved Logistic mapping; based on the fitness of each particle, cooperatively optimizing a particle swarm by combining a grey wolf algorithm, a particle swarm optimization algorithm and a Levy flight disturbance mechanism, and iteratively solving the position of each particle; based on a double-threshold mechanism, whether the current transformer really has a problem is judged.

The application discloses a network coverage quality information determination method and device, equipment and a storage medium, which are applied to the Internet of Vehicles technical field. The method comprises the following steps: acquiring a first analysis data set of N vehicle terminals in a to-be-evaluated area; dividing M pieces of terminal data of each vehicle terminal based on position information corresponding to a time stamp, so as to obtain at least one scene data group; for each scene data group, screening target terminal data based on cell information of an access network service, so as to obtain a second analysis data set; clustering each piece of terminal data in the second analysis data set according to position information of the vehicle terminal; and determining network coverage quality information of a target area according to reference signal receiving power in each piece of target terminal data. In this way, the OTT data of the vehicle terminal, the access cell information and the POI data can be used to identify and check a scene to which terminal data belongs, and the accuracy of the determined network coverage quality information is improved.

The invention relates to the technical field of wheat quality detection, and discloses a wheat quality rapid detection system based on a near infrared spectrum. The system comprises five modules: a spectral feature dynamic acquisition module, an environmental parameter coupling analysis module, a spectral precision evaluation module, a detection state control module and a spectral resource optimization module. The spectral characteristic dynamic acquisition module acquires near infrared spectral reflectivity data of a wheat sample, and divides sample areas with different detection attention degrees; the environment parameter coupling analysis module collects growth environment parameters, and integrates the growth environment parameters with the spectral data of the corresponding region to generate an environment coupling analysis data set; the spectral precision evaluation module calculates the real-time detection accuracy according to the difference between the historical reference of the sample area in the data set and the real-time detection quality index; the detection state control module switches the load state of the spectrum acquisition channel in combination with the accuracy and a preset threshold value; and the spectrum resource optimization module allocates resources according to the load state. The system improves detection pertinence, reliability and resource utilization rate, and meets rapid detection requirements.

The invention provides a digital economy risk assessment method and system based on artificial intelligence, and the method comprises the steps: collecting risk data related to digital economy from a plurality of preset data sources, and fusing the risk data to form a risk analysis data set of the digital economy; performing risk feature extraction on the risk data set in a plurality of preset dimensions to obtain risk features of the digital economy in each preset dimension; obtaining an operation log of the digital economy, training a preset unsupervised learning model by adopting the operation log, and obtaining an abnormal score of the digital economy according to the unsupervised learning model; and obtaining a preset intelligent risk assessment model, and fusing the risk features and the abnormal score by using the preset intelligent risk assessment model to obtain a risk score of the digital economy. According to the technical scheme, the accuracy and reliability of the risk assessment result can be improved.

The invention discloses a teaching objective achievement degree automatic discrimination method based on classroom behavior intelligent analysis, and relates to the technical field of education technology and artificial intelligence, and the method comprises the steps: S1, obtaining multi-dimensional indexes represented by students, carrying out the standardization processing of each index, and obtaining a preliminary achievement degree analysis data set; s2, according to the preliminary achievement degree analysis data set, carrying out weight distribution on each index, determining the contribution degree of each index in the overall achievement degree, and forming a weighted achievement degree comprehensive score; s3, aiming at the weighted achievement degree comprehensive score, generating a contribution explanation report of each index to a final score, and obtaining a traceable scoring basis; according to the teaching objective achievement degree automatic discrimination method based on classroom behavior intelligent analysis, accurate evaluation and targeted improvement of the teaching objective achievement degree are realized, and the teaching quality and effect are effectively improved.

The application discloses a battlefield information processing method based on multi-modal data fusion, comprising the following steps: acquiring multi-modal original data of battlefield information, standardizing different original data to obtain a structured data set; performing time and space alignment on the structured data set to obtain a space-time reference data set; performing feature completion on the space-time reference data set to obtain a complete analysis data set; performing hierarchical encryption on the complete analysis data set to obtain an encrypted transmission data set; performing distributed storage on the encrypted transmission data set to obtain a secure storage data set; performing isolation processing on the secure storage data set to obtain an intermediate result data set; performing real-time detection and protection on the intermediate result data set to obtain a final protection data set; and performing processing detection and credibility judgment on the final protection data set to obtain a final credible information analysis result.

Computer-implemented method for generating a trained combustion property determination machine learning model (24) for determining at least one combustion parameter (18) of a given fuel using analysis data sets which are each characteristic of a composition of a given fuel, comprising the steps - Providing a trainable combustion property determination machine learning model (22) which includes a set of trainable parameters and which, based on an analysis data set acquired for a given fuel as input (2), generates at least one combustion parameter (18) as output (6); - Generating a training dataset comprising a variety of acquired analytical datasets on training fuels, which are used to generate the training dataset, as well as a variety of combustion parameters (18); - Training the combustion property determination machine learning model (22) based on the training data set; - Determine at least one influencing factor (28) by applying a method to explain the trained combustion property determination machine learning model (24).