1265 results about "Data integration" patented technology

The invention relates to the field of environment monitoring, discloses an ecological protection red line holographic dynamic monitoring and early warning method and system, and aims to solve the defects of an existing monitoring and early warning mechanism in the aspects of timeliness, data integration and intelligent analysis. According to the method and the system, multi-source heterogeneous data are acquired and fused, a space bottom line digital model is constructed, space-time situation awareness analysis and change abnormity identification are performed, risk assessment early warning triggering is performed, and early warning issuing and processing feedback are managed. According to the invention, high-precision holographic acquisition and fusion of multi-source data can be realized, the accuracy and timeliness of change abnormity identification are improved, passive early warning is changed into active early warning, a monitoring disposal closed loop is formed, supervision intelligence and treatment efficiency are improved, and ecological safety is guaranteed.

The embodiment of the invention provides an equipment fault prediction management method and device based on a large model, and the method and device achieve the accurate evaluation of a state through the innovative design of a multi-modal feature fusion model, data integration and feature learning. A fault diagnosis system is constructed, and a reliable root cause analysis mechanism is established in combination with semantic analysis and case retrieval. Maintenance guidance is introduced, and the feasibility of a maintenance scheme is ensured through experience precipitation and priority evaluation. According to the method, the defects of the traditional technology in the aspects of feature extraction, fault diagnosis, maintenance guidance and the like are effectively overcome, and technical guarantee is provided for equipment management.

The invention relates to the technical field of power plant operation monitoring, in particular to an intelligent power plant operation data monitoring method based on artificial intelligence assistance. The method comprises the following steps: extracting multi-source power plant operation data, detecting an abnormal value, obtaining optimized power plant operation characteristics through interpolation optimization processing, analyzing the multi-source data by using an artificial intelligence technology, predicting aging state characteristics of equipment, carrying out difference comparison, identifying potential hardware operation faults, and carrying out fault diagnosis. The method comprises the following steps: identifying a cooperative connection path of equipment through operation data, predicting a cooperative fault risk node, mapping a risk physical connection relationship of the equipment, performing fault transfer simulation based on the risk physical connection relationship, generating hardware fault transfer data, and determining hidden danger data when each hardware has an operation fault; and integrating the hardware operation fault information and the corresponding data into operation fault monitoring data, and carrying out fault alarm. According to the invention, intelligent and systematized power plant operation monitoring is realized, and construction and development of an intelligent power plant are promoted.

The invention belongs to the field of artificial intelligence, particularly relates to a medical resource scheduling optimal configuration method for data analysis, and aims to solve the problems of non-uniform resource allocation, response lag, difficulty in data integration decision and the like in existing medical resource scheduling. According to the method, multi-source heterogeneous medical data (such as patient diagnosis and treatment, medical care, equipment states and department parameters) are uniformly collected, preprocessed and deeply analyzed, and real-time resource state characterization is constructed; based on the characterization, the demand is accurately predicted, supply is evaluated, an optimization algorithm is used for dynamic scheduling configuration, and an optimal scheme is generated. Then, scheduling is executed according to the scheme, and iterative optimization is fed back in real time; the medical service efficiency and quality can be remarkably improved, the patient experience is optimized, the operation cost is reduced, emergencies are effectively dealt with, and the resource management toughness of medical institutions is enhanced.

The invention relates to the technical field of medical data management, and discloses a medical data classification management system based on big data. A data integration and classification module of the system receives a historical medical data set and a real-time medical data stream, performs feature fusion and classification prediction through a deep learning model, and generates a theoretical classification result. And the deviation quantitative analysis module performs multi-dimensional deviation calculation on the theoretical classification result and the clinical actual classification result to generate a patient-level multi-dimensional deviation tensor. And the graph network anomaly positioning module inputs the multi-dimensional deviation tensor into a graph neural network for anomaly propagation simulation based on a medical institution topological structure, and generates an anomaly probability distribution diagram to identify a physical region where an anomaly occurs. And the adaptive decision control module dynamically allocates monitoring resources according to the abnormal probability distribution diagram, including starting high-frequency data acquisition for a high-probability abnormal region and executing an intervention test on adjacent nodes.

The invention belongs to the technical field of engineering progress tracking and early warning, and particularly discloses an AI-based engineering progress tracking and early warning method and system. According to the method, the problem of information islands in multi-modal data integration of a traditional method is solved by constructing the engineering space-time knowledge graph, and the comprehensiveness of construction site state sensing is improved; an intelligent mechanism of deviation analysis and trend deduction is established, and accurate prediction of the engineering state evolution trend is realized through multi-dimensional comprehensive evaluation of geometric morphology, construction process topology, resource in-place time sequence and quality compliance attributes; through structured early warning generation and multi-level risk conduction path modeling, the risk source and the influence range thereof are clarified, and a subsequent construction scheme is optimized in combination with a dynamic adjustment strategy, so that the scientificity and execution efficiency of project progress management are improved.

The invention relates to the technical field of computers, discloses a multi-system data fusion predictive maintenance method and system, and aims to solve the problems that multi-source heterogeneous data integration is difficult, health state perception is one-sided, prediction model adaptability is poor and maintenance decision closed loops are missing. The method comprises the following steps: acquiring mechanical vibration, thermodynamics and electrical signals through a multi-source sensing access module, and performing time alignment; and in combination with operation behavior information input by the operation registration terminal, a data fusion engine executes standardized mapping and missing value compensation to generate a structured input matrix. According to the scheme, deep fusion of multi-system data and continuous quantitative evaluation of the health state are achieved, the fault early warning advance time is shortened, the evaluation accuracy is improved, adaptive regulation and control and closed-loop maintenance decision are supported, the service life of key components is prolonged, and the method is suitable for various high-end manufacturing scenes.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The invention relates to the technical field of pediatric medical treatment, and discloses a pediatric neonatal jaundice clinical phototherapy management system, which integrates illumination data in an equipment lamp box through a data integration module, extracts the illumination data acquired by the data integration module through a fusion analysis module, and fuses the operation duration of a lamp tube to obtain a phototherapy result. An equipment health state basic model is established, the current equipment health state is predicted through an equipment prediction module on the basis of the established health state basic model, a prediction result is fed back, a patient information system is connected through a personality support module, and basic physiotherapy data of a patient is acquired. And outputting recommended illumination intensity and recommended treatment time based on the basic physiotherapy data, and monitoring a plurality of pieces of monitoring information and feedback output results through a remote monitoring module. The system accurately solves the core problems of dynamic fluctuation of phototherapy intensity, calibration lag and lack of personalized adaptation, and finally achieves dual guarantee of phototherapy effect and safety.

The invention relates to the technical field of geological engineering, and discloses a geological safety risk dynamic assessment method based on multi-source data fusion, which comprises the following specific steps: step 1, collecting and standardizing multi-source geological data; 2, performing semantic fusion and conflict resolution on the geologic features; 3, constructing a dynamic risk assessment model; 4, risk situation real-time updating and early warning are carried out; the satellite remote sensing system in the first step adopts the synthetic aperture radar interference measurement technology, the spatial resolution is better than 3 meters, the revisit period is shorter than 7 days, and the earth surface deformation monitoring precision reaches the millimeter level. Through standardized processing and semantic fusion of the multi-source geological data, the problem of heterogeneous data integration is effectively solved, the data basic quality of risk assessment is improved, a space-time coupling neural network model is adopted, nonlinear features and space-time correlation characteristics of geological risk evolution are accurately captured, and prediction precision and timeliness are improved.

The invention relates to the field of emergency management, in particular to an emergency cooperative scheduling strategy generation method based on swarm intelligence. The method comprises the following steps: acquiring multi-source data of each agent, preprocessing the multi-source data, and feeding back the preprocessed multi-source data to the corresponding agent; the intelligent agent generates a preliminary scheduling strategy according to the received multi-source data, and updates and maintains a strategy distribution snapshot of an edge node in the preliminary scheduling strategy; and obtaining an evolution path of the secondary disaster, inputting the evolution path of the secondary disaster, the preprocessed multi-source data and the strategy distribution snapshot of the edge node into the federal depth Q network model, and generating a collaborative scheduling strategy. In this way, the technical problems that structural obstacles exist in data integration and sharing facing emergency scenes, allocation and scheduling of computing resources are difficult to meet dynamic and high-timeliness requirements of emergency responses, and the overall toughness and cooperative capacity of a system are insufficient are solved.

The invention discloses a winter wheat yield forecasting method based on a random forest regression model, and relates to the technical field of winter wheat yield prediction.The winter wheat yield forecasting method comprises the steps of multi-source data integration, dynamic feature screening, random forest model optimization and yield prediction and verified.The multi-source data integration provides input data for dynamic feature screening; high-quality features screened by the dynamic features are used as training features of a random forest model optimization module, and a result of random forest model optimization is used as final yield prediction and verification of yield prediction and verification. According to the method, by designing multi-source data fusion and automatic model optimization, the problems that a traditional method is single in data source, difficult to capture complex agronomic relations and insufficient in model generalization ability are solved, high-precision and high-stability winter wheat yield forecasting is achieved, and powerful support is provided for agricultural production decision making.

The invention provides a pressure vessel full life cycle stress fatigue assessment and predictive maintenance system based on digital twinning. The system comprises a model construction module used for constructing a three-dimensional digital twinning model corresponding to a solid pressure vessel; the data synchronization module is used for integrating historical test data and actual operation load data of the solid pressure vessel into the three-dimensional digital twinborn model and simulating dynamic stress distribution of the solid pressure vessel in an actual operation load process; the evaluation and maintenance module is used for evaluating the accumulated fatigue damage of the solid pressure vessel and the residual fatigue life in the current state based on the dynamic stress distribution, and determining a potential risk area and a risk time window of the solid pressure vessel based on an evaluation result; and on-demand inspection plans and predictive maintenance strategies of the entity pressure vessels are generated based on the potential risk areas and the risk time windows. Stress fatigue evaluation of the whole life cycle is achieved, and the accuracy and reliability of life prediction are remarkably improved.

The invention relates to the technical field of land reserve, in particular to a multi-source integration and dynamic updating method and system for land reserve data, and the method comprises the steps: obtaining multi-source land data, constructing a unified space-time database through hierarchical coordinate fusion, and achieving the data integration. And then, land parcel ownership characteristics are extracted, compliance evaluation is completed, and policy change data are synchronously analyzed to generate planning constraints. Idle risk assessment is carried out based on spatio-temporal data and policy constraints, land value fluctuation is predicted, and accurate valuation data is obtained through idle risk weighted correction. And finally, according to risk assessment and policy constraints, a low-efficiency land taking strategy is generated, all data and strategies are uploaded to a management platform in real time, value abnormal block marking and land reserve dynamic optimization tasks are executed respectively, and full-process closed-loop management from data integration to intelligent decision making is realized.

The invention discloses a load prediction method, system and device based on the situation of a power distribution network and a storage medium, and the method comprises the steps: collecting historical load data and real-time monitoring data of the power distribution network, and carrying out the ultra-short-term load prediction; calculating a key load index of the power distribution network based on the real-time monitoring data, evaluating the safety state of the key load index, and generating a preliminary safety situation evaluation result; and fusing the preliminary security situation assessment result and the ultra-short-term load prediction result, and predicting the final regional power distribution network security situation. And optimizing operation parameters of the power distribution network according to the final load prediction result. According to the method, the defect that the non-linear characteristic processing capacity of an error correction technology is insufficient is overcome, and the dynamic correlation analysis and time sequence modeling capacity and the multi-dimensional data integration and application capacity of an existing situation assessment technology are improved.

The invention provides an intelligent progress management and control method and system for information engineering supervision, and relates to the technical field of information, and the method comprises the steps: pushing early warning information to a first-party system and a second-party system through message middleware, collecting a feedback state to complete closed-loop cooperative processing, and updating a processing result to a central database; based on the central database, combining historical project data, performing pattern recognition analysis to extract a progress deviation rule, and dynamically optimizing a plan node threshold and buffer time of a subsequent stage according to the progress deviation rule to obtain an optimization strategy; and on the basis of the optimization strategy, constructing a configurable progress management and control template library, so as to quickly adapt and generate a corresponding progress management and control node and an early warning threshold value in the new-type project. According to the invention, a full-process closed-loop management system from data integration, intelligent optimization to dynamic adaptation is constructed, and the fundamental transformation of project progress management and control from passive response to active prediction and from experience driving to data driving is realized.

The invention discloses a multi-source heterogeneous data integration analysis treatment method, system and device and a medium, and the method comprises the steps: obtaining multi-source heterogeneous original data, carrying out the semantic mapping through a power grid business ontology library, and carrying out the coordinate dynamic normalization through a self-adaptive conversion matrix, and obtaining standardized spatial data; carrying out spatial query, dynamic buffer area calculation and protection area grading fusion through a spatial superposition optimization algorithm, and carrying out topological relation identification and regularization processing on the power line and an external linear ground feature to obtain a spatial conflict analysis result; through a three-level conflict resolution mechanism, recognition and cooperative resolution operation of geometry, attribute and temporal conflicts are carried out, and a resolved compliance space data set is obtained; and performing governance flow driving and rule adaptation through a dynamic business rule engine to obtain a spatial data governance report. According to the method, efficient integration and intelligent treatment of spatial data in the power transmission line project are realized, the data processing timeliness is remarkably improved, and spatial conflicts are eliminated.

A method includes obtaining video data of a dental patient. The method further includes obtaining an indication of selection criteria in association with the video data. The selection criteria includes conditions related to a target dental treatment of the dental patient. The method further includes performing an analysis procedure on the video data. Performing the analysis procedure includes determining a first score for each frame of the video data based on the selection criteria. Performing the analysis procedure further includes determining that a frame satisfies a first threshold condition based on the first score. The method further includes providing the first frame as output of the analysis procedure.

An autonomous warehouse robotics system integrates a multi-sensor platform, adaptive payload handling, dynamic task reallocation, advanced navigation, energy management, and comprehensive safety features into one mobile robot chassis. The system utilizes LiDAR, stereo vision, ultrasonic sensors, mmWave radar, thermal cameras, and event cameras to perform complete environmental sensing and obstacle detection. Sensor fusion combines adaptive weighting, multi-modal data integration, and statistical filtering to create high-confidence maps for reactive path planning and collision avoidance. The robot's payload system features machine vision for item recognition, telescopic lifts, variable-width grippers, and real-time toolhead verification to handle a variety of goods. Fleet management is achieved through dynamic task reallocation that considers robot location, battery level, and operational delays, all coordinated by a cross-platform middleware architecture that ensures standardized communication, remote monitoring, and over-the-air updates among diverse robot brands. Energy management optimizes power usage via predictive routing, autonomous return-to-charge, and auction-based scheduling. Safety is maintained through proximity detection, behavior-based intervention, and human-robot cohabitation protocols while advanced localization is enhanced by fusing ultra-wideband positioning with visual landmark alignment, inertial sensing, and machine learning to deliver high accuracy in non-line-of-sight conditions. An onboard edge AI module further refines navigation and task prioritization through neural network inference, ensuring robust, adaptive operation in dynamic, unstructured warehouse environments.

The invention relates to the technical field of deformation detection, in particular to a method for measuring the deformation of a rotary body part, which comprises the following steps of: arranging measuring points on a target rotary body part, and performing dotting measurement by using a three-coordinate measuring instrument to obtain dotting data which are three-dimensional coordinates; based on the dotting data, calculation is carried out, and deformation information of the target rotary body part is obtained, the calculation comprises the steps of determining a data source, normalizing coordinates, converting a coordinate form, sequencing path measuring points, compensating a measuring head, calculating a position-error function, calculating all-directional axial deformation and radial deformation, speculating all-position deformation, and performing any combination calculation in multi-coordinate system data integration. According to the invention, the deformation of the rotary part can be measured conveniently, quickly and efficiently.

The invention discloses a regional grid safety evaluation method and device based on smart gas and a medium, belongs to the technical field of gas safety supervision, and aims to solve the technical problem of how to overcome the defects of insufficient matching between grid division and gas facility characteristics and lagging leakage risk prediction in the prior art. In order to realize full-process intelligence from risk identification to closed-loop management and improve supervision efficiency and early warning precision, the technical scheme adopted by the invention is as follows: dividing a pressure sensitive grid: performing optimization division on a regional grid based on a gas pipeline pressure grade and facility distribution characteristics, and realizing accurate division of the regional grid where a pipe network is located by combining a GIS and an improved DBSCAN algorithm; acquiring three-level Internet of Things data: integrating sensor data of an unmanned aerial vehicle, a pipe network pressure device and a leakage monitoring device, and covering a full-link data system of a terminal sensing layer, an edge computing layer and a cloud storage layer; constructing a double-engine evaluation model; carrying out digital twinborn visualization; and scheduling the capability matrix.

The invention discloses a new energy station operation index multi-dimensional comparative analysis display method and system, and belongs to the field of new energy station operation management and data analysis. The method comprises the following steps: fusing and standardizing multi-source heterogeneous data, collecting and cleaning multi-station data, and classifying and storing the multi-station data as a standardized data pool; rasterized multi-dimensional indexes are calculated, grids are divided, data are mapped, and grid-level and hierarchical multi-dimensional indexes are generated; performing dynamic weight and TOPSIS evaluation, constructing an evaluation model to calculate the score of a single station, extracting a core index intersection, and transversely comparing the ranking of the stations; the evaluation result is subjected to instantiation storage and hierarchical storage to guarantee data query and tracing; and performing multi-dimensional visual display, displaying the data by multiple terminals, and generating a decision report. The system comprises corresponding function modules. According to the method, the problems of data fragmentation, evaluation system deficiency, non-visual display and the like are solved, multi-station data integration, quantitative evaluation and visual decision making are realized, and the operation management efficiency is improved.

The invention relates to the technical field of data analysis, in particular to a dialogue type data analysis method, system and device based on a large language model and a medium, and the method comprises the steps that a natural language query request input by a user is acquired, semantic analysis is conducted on the natural language query request through the large language model, and key query elements are extracted; generating a structured query instruction according to the key query elements; calling a data integration engine based on the structured query instruction, and obtaining target data from the plurality of heterogeneous data sources; performing processing analysis on the target data, wherein the processing analysis comprises drilling-down analysis, attribution analysis and anomaly detection; and generating an analysis report and a visual chart based on the processing analysis result, and feeding back the analysis report and the visual chart to the user. According to the method, high-precision intention analysis and multi-source data integration can be realized, deep analysis and interactive visualization are supported, and the accuracy, efficiency and user experience of data analysis are remarkably improved.

The invention belongs to the technical field of tumor clustering analysis, and particularly relates to a tumor clustering analysis system and method based on big data. Through deep fusion and space-time alignment processing of multi-dimensional biological data, the limitation of single-dimension or simple data integration in a traditional method is overcome, complex biological characteristics and space-time evolution laws in the tumor can be captured more comprehensively, and the tumor can be rapidly and accurately captured by constructing a low-dimensional association and independent characteristic space and combining a nonlinear dimension reduction technology. According to the method, low-dimensional feature vectors with biomarker features are effectively extracted, a high-quality data basis is provided for subsequent clustering analysis, meanwhile, based on sliding window density estimation and determination of a dynamic neighborhood radius, accurate spatial adjacency relation construction of the low-dimensional feature vectors is achieved, and the accuracy of the spatial adjacency relation is improved. And the potential clustering center of the tumor subtype is extracted through the multi-scale characteristic spectrum, so that the generated classification label can more accurately reflect the real distribution of the tumor subtype.

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

The invention relates to the field of fan spindle state monitoring, and aims at synchronously acquiring sound and vibration signals through multiple channels, realizing nanosecond time alignment by adopting a precision time protocol, and performing denoising and normalization preprocessing on the signals to improve data integration and signal fidelity. Furthermore, short-time Fourier transform and continuous wavelet transform are combined to extract multi-scale time-frequency features, and a high-dimensional combined feature vector is generated in combination with cross-correlation analysis. And mapping the feature vectors to a low-dimensional manifold space through a local linear embedding algorithm, and constructing a dynamic mode reference template. Indexes such as curvature, track length and direction entropy are monitored in real time, whether the spindle has an abnormal evolution trend or not is judged through a self-adaptive curvature threshold, and abnormity judgment is achieved in combination with track backtracking verification. According to the scheme, the abnormal starting boundary of the spindle state can be caught in a refined mode, and the early warning and operation and maintenance response capacity of fan operation is effectively improved.