269 results about "Analysis Dataset" patented technology

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

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 embodiment of the invention discloses a level-to-level management strategy generation method and system for an electronic fence based on a 5G base station iron tower, and the method comprises the steps: collecting a multi-source heterogeneous monitoring data set covering an electronic fence warning region through sensor arrays disposed at different monitoring point positions of the 5G base station iron tower; the data comprise environment sensing data and target motion data; performing space-time correlation fusion processing on the data set to generate a basic analysis data set, determining a space relative relationship between the target motion data and the electronic fence boundary and potential influence of environment perception data on the motion behavior of the target object based on the basic analysis data set, and generating risk assessment information; a hierarchical management strategy is made according to the risk assessment information, strategy execution parameters are adjusted by continuously tracking a behavior evolution process of a target object in an electronic fence warning area to realize optimization iteration, and the accuracy and adaptability of electronic fence management are improved.

The invention discloses a cache data security protection system based on cloud computing, particularly relates to the field of security protection, and comprises a data acquisition module, a cloud storage module, a verification execution module, a security processing module and a protection early warning module. The data acquisition module acquires cache data in the platform in real time, and the cloud storage module adopts a distributed architecture to safely store various types of data, and performs classified preprocessing and encrypted storage; the verification execution module identifies the safety signal, generates an execution signaling, and transmits the execution signaling to the execution node through the encryption channel; the security processing module is used for cleaning, desensitizing and encrypting input and recorded information and converting the input and recorded information into calculation processing data, and the protection early warning module is used for integrating a data source, constructing an analysis data set, detecting abnormal behaviors through an algorithm and a rule engine and generating a security signal; in the security comparison stage, the to-be-approved data is compared in real time, the operation risk is evaluated, the signal data is transmitted through an encryption channel, and a full-link log is stored in the block chain.

The invention discloses a tropical region offshore marine meteorological fusion analysis method. The method comprises the following steps: step 1, data collection and preprocessing: collecting offshore multi-platform collaborative networking observation data of a tropical region; 2, data quality control: checking the data, and analyzing error characteristics of marine meteorological observation data; 3, data fusion processing: establishing a tropical region offshore multi-source data fusion analysis system, performing intelligent fusion on the multi-source observation data subjected to quality control, and constructing a marine meteorological fusion analysis data set; 4, constructing a coupling analysis model: performing coupling analysis on marine meteorological elements by combining a physical model and a numerical simulation technology, and discovering an evolution rule and influence factors of a marine meteorological system by simulating and predicting an interaction process between the ocean and the atmosphere; and 5, dynamic application and evaluation: through real-time analysis of key meteorological elements, potential marine meteorological disaster risk affairs are discovered and early warned.

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 provides a runoff forecasting method and device based on soil humidity calibration, and relates to the technical field of forecasting. The method comprises the steps of generating weather forced data of a WRF mode based on an atmosphere reanalysis data set under the condition that a target area has no runoff observation data; acquiring an observed value of surface soil humidity of a target area in the external meteorological data set; inputting the meteorological forced data into a distributed hydrological model WRF-Hydro model coupled with the WRF mode, and performing simulation to obtain a simulation value of the surface soil humidity of the target area; calibrating a key parameter set of the hydrological model according to the difference between the observed value and the simulated value of the soil humidity and a historical flood event of the target area to obtain a general parameter set of the target area; and using the general parameter set to drive a WRF-Hydro model to carry out runoff forecasting on the target area. Based on the scheme, runoff forecasting can be accurately carried out on the area without runoff observation 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 relates to the technical field of image processing, in particular to an underwater crack segmentation-oriented color correction and texture sharpening double-branch enhancement system, which is characterized in that a data set is used for training, reasoning and analysis, and underwater crack images are stored in the data set; the system comprises an input unit used for receiving an underwater crack image; the color correction network is used for carrying out color correction on the underwater crack image and eliminating the problems of color deviation and low contrast of an underwater environment; the texture enhancement network is used for performing texture enhancement on the image after color correction; and the output unit is used for outputting the image enhanced by the texture enhancement network, and aims to recover the color and texture features of the underwater crack image through the color correction network and the texture enhancement network so as to enhance the perceptibility of the semantic segmentation model to the underwater crack.

The invention discloses a traditional Chinese medicine human body acupuncture point recognition method and system based on virtual reality, and relates to the technical field of acupuncture point recognition. The system can collect action acupuncture point position data and curative effect data of a patient in the massage process in real time through an action tracking and acupuncture point recognition module in combination with VR equipment and a sensor group; and the data are transmitted to a cloud platform through a wireless network to be processed and analyzed. And the data integration processing module carries out visual display and standardized processing on the acquired data, so that the accuracy and consistency of the data are ensured. And the operation error analysis module can calculate an action space error D and evaluate the massage operation precision according to the value. When the output result of the motion space error D exceeds a certain deviation, the system gives a prompt to a physiotherapist through a visual interface to guide the physiotherapist to adjust the massage manipulation, and the integration of the system not only improves the transparency of the treatment process, but also greatly reduces errors caused by human factors.

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.

The invention provides a railway vehicle health degree assessment method and system based on multi-source heterogeneous data. The method comprises the following steps: collecting the multi-source heterogeneous data in the operation of a railway vehicle; performing data connection processing on the multi-source heterogeneous data to generate an analysis data set in a unified format; extracting multi-modal features based on the analysis data set; and inputting the multi-modal features into a pre-established dynamic evaluation model of the health degree of the railway vehicle for feature fusion, and generating a comprehensive scoring result of the health degree of the railway vehicle based on a feature fusion result, and the railway vehicle health degree dynamic evaluation model adjusts the feature fusion weight of each multi-modal feature according to the railway vehicle data collected in real time. According to the method, the multi-source heterogeneous data is processed through data connection, the extracted multi-modal features are fused by constructing the dynamic evaluation model, the health state of the railway vehicle is comprehensively evaluated while the data analysis efficiency is improved, and the evaluation precision and the maintenance decision efficiency are improved.

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.

The invention relates to the technical field of chemical material characteristic analysis, and discloses an AI modeling and data comparison method for chemical material characteristic analysis. Characteristic parameters such as composition, physical form and the like of chemical materials are collected in real time through a multi-source sensing device and input into a preset feature extraction model to extract key feature vectors, and then multi-dimensional fitting is carried out through an AI modeling module according to material category relevance and experimental condition weights to generate a prediction model. And matching and verifying the prediction model and the real-time characteristic parameters by using a data comparison algorithm, and storing to form a dynamic analysis data set. The method further relates to feature extraction model construction, data processing, AI modeling step optimization, data classification and integration, authority control and the like. According to the method, efficient and accurate analysis of chemical material characteristics is realized, data processing and model prediction capabilities are improved, data safety is guaranteed, and the method has wide application prospects in the fields of chemical material research and production.

A computer-implemented method for anomaly detection comprising receiving a set of data parameters; retrieving a dataset corresponding to the set of data parameters from a database; analyzing, using a machine learning model trained in real-time, the dataset to detect one or more anomalies in the dataset; selecting a set of anomaly parameters corresponding to the detected one or more anomalies; filtering an output of the machine learning model according to the set of anomaly parameters; generating a set of instructions for identifying one or more anomalous items based on the set of data parameters, the set of anomaly parameters, and a set of detection pattern parameters; executing the set of instructions for identifying anomalous items to identify one or more anomalous items in real-time within the dataset responsive to updates to the dataset; and transmitting information about the one or more anomalous items to a user device or computing device.

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.