1130results about How to "Improve analysis efficiency" patented technology

The invention relates to the technical field of medicine quality management, in particular to a medicine quality management method and system based on big data and artificial intelligence, and the method comprises the steps: obtaining the quality influence parameters of a medicine product in the production, transportation and storage processes through a multi-modal data collection device; noise is removed through data cleaning, data dimension reduction is carried out through principal component analysis and an auto-encoder, the analysis efficiency is improved, and the efficiency problem of mass data processing of a traditional method is solved. Time sequence analysis is carried out through the long-short-term memory network, abnormal nodes are recognized in time, manual sampling inspection is replaced, and accurate early warning is achieved. The graph neural network constructs a path tracking model, analyzes the relevance between nodes, traces the quality problem, and reckons the batch of affected drugs. Based on a traceability result, an XGBoost model generates quality risk scores of all links by analyzing historical and real-time data and sets a threshold value, and quality risks are early warned in real time.

The invention discloses an underground cable pipeline abnormity monitoring method and system, and the method comprises the following steps: S1, building a three-dimensional grid based on the regional soil distribution condition, and obtaining a collection point data set based on the three-dimensional grid and underground cable pipeline data; s2, acquiring acquisition distribution corresponding to each to-be-arranged sensor type based on the to-be-arranged sensor type and the acquisition point data set; s3, acquiring underground cable pipeline time sequence data acquired by each sensor based on acquisition distribution, and performing clustering analysis on the underground cable pipeline time sequence data to acquire abnormal data points; and S4, obtaining an abnormal position according to the abnormal data points and the collection distribution, and outputting abnormal information of the underground cable pipeline according to the abnormal position. The method has the beneficial effects that the physical position of the cable pipeline is considered, meanwhile, the soil environment characteristics are considered, the accuracy of sensor arrangement in the pipeline is improved, the abnormal data point analysis efficiency is improved, and the underground cable pipeline abnormity can be conveniently and timely checked.

The invention discloses a multi-source heterogeneous data fusion processing and intelligent analysis method, device and equipment, and relates to the field of data fusion processing.The method comprises the steps that firstly, data in data sources of corresponding types are collected through different collection strategies; performing data preprocessing on the collected data to obtain standard data in a unified format; performing data fusion on the standard data based on the structural features of the data to obtain a fused data set; and determining a target analysis model according to the data analysis target, and performing data analysis on the fused data set. According to the method, various types of data formats can be processed, multi-source heterogeneous data fusion is realized, and through data semantic understanding and a feature-based fusion algorithm, the accuracy and quality of data fusion are improved, and correct data association is established on the semantic level.

The invention relates to an intelligent semantic analysis knowledge graph system for multi-source heterogeneous data. The system comprises a data preprocessing module used for preprocessing to-be-processed multi-source heterogeneous data to obtain format-standardized text data; the semantic analysis module is used for performing natural language processing on the text data to obtain words, and then performing vocabulary layer, syntactic layer and semantic layer analysis on the text data and the words to obtain structured data; the structured data comprises a triple consisting of entities, relationships and events and a network diagram of the entities and the relationships; the model structure mapping module is used for mapping the structured data into a predefined structure of a knowledge graph to obtain structured graph data; and the knowledge graph module analyzes entities and relationships in the graph data, performs reasoning to obtain potential relationships in the graph data by utilizing a logic rule and a machine learning algorithm, realizes cross-data-source and cross-document deep semantic association, and improves the efficiency of large-scale data processing and knowledge management.

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The invention discloses a cloud computer end-to-end fault root cause analysis method and system, and relates to the technical field of operation and maintenance data analysis, and the method comprises the steps: deploying data collectors on a virtual resource pool, a multi-stage cloud management platform and a cloud computer terminal, and monitoring and collecting various operation and maintenance data in real time; a unified operation and maintenance management center is introduced to complete the following work: providing management and configuration services of the data collector; receiving and summarizing operation and maintenance data; various AI models are accessed, operation and maintenance data from different sources are distributed based on the types of the AI models, the AI models perform deep analysis and diagnosis on the operation and maintenance data, and potential fault modes or abnormal behaviors are identified; customizing an alarm strategy, an alarm processing flow and an alarm notification mode; and introducing an AIOps service to generate an alarm and fault analysis report according to the analysis and diagnosis results of the AI model. According to the method, the service quality of the end-to-end full link of the cloud computer is monitored in real time, so that faults can be quickly positioned, early warning can be performed in advance, and the operation and maintenance cost and the fault risk are reduced.

The invention relates to the technical field of fire early warning, in particular to an electrical fire early warning method, device and equipment based on leakage current temperature detection. The method comprises the following steps: acquiring information data of electrical equipment; performing electrical equipment power line position positioning on the electrical equipment information data to generate electrical equipment power line position information data; performing leakage current sensor data acquisition on the electrical equipment power line position information data to obtain standard electrical equipment leakage current monitoring data; and performing maximum fluctuation curve segment confirmation on the leakage current monitoring data of the standard electrical equipment to obtain a maximum fluctuation curve of the leakage current of the electrical equipment. According to the invention, by introducing leakage current fluctuation mode identification, accurate power line positioning, intelligent anomaly detection and heat map analysis, the accuracy, response speed and positioning capability of the electrical fire early warning system are improved.

The invention relates to a replay playback display control method based on event tracing, which is suitable for event analysis and playback control in a simulation deduction process, and comprises the following steps: inputting a key event; event data analysis; key events are dynamically displayed; deducing situation backtracking; performing playback control; subscribing and pushing key events; tracing an evaluation result; and deducing data association analysis. According to the method, the playback experience of the user is improved, the user can subscribe specific key events, the system pushes event results in real time in the deduction process, multi-path analysis is carried out through different conditions, the evaluation results are visually displayed and traced, and the user is helped to comprehensively analyze the influence of the key events on deduction.

The invention provides a fault root cause analysis method and device, electronic equipment and a storage medium, and can be applied to the technical field of computers. The method comprises the following steps: converting a fault description text input by a user into vector representation to obtain a query vector; performing query search in a preset operation and maintenance knowledge vector library according to the query vector, and obtaining an operation and maintenance knowledge vector meeting a similarity condition with the query vector in the operation and maintenance knowledge vector library; wherein the operation and maintenance knowledge vector is a vector representation of the operation and maintenance knowledge text; based on the operation and maintenance knowledge text corresponding to the operation and maintenance knowledge vector and the fault description text, obtaining fault prompt information; and inputting the fault prompt information into the large language model to obtain a fault root cause analysis result. According to the method, by integrating the large language model and the retrieval enhancement technology, fault root cause analysis can be automatically carried out, and the analysis efficiency of fault root causes is effectively improved.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention provides a risk data analysis method based on an LLM large language model, risk data analysis is carried out based on the LLM large language model, data identification, classification and risk feature extraction can be carried out by utilizing the LLM large language model in the whole process, risk feature vector training learning is carried out on the LLM large language model, and the risk data analysis efficiency is improved. The defect that a large number of training samples are needed can be reduced; meanwhile, the LLM big language model cue words on all dimensions are constructed and input on one LLM big language model, the LLM big language model can continue to further carry out risk analysis on the classification and recognition result on all dimensions, and the risk analysis result of the risk data on all dimensions is rapidly and deeply mined and output. Therefore, the risk data can be intelligently processed by the LLM in the whole process, the risk types and all dimensions can be analyzed through one model, and the risk data analysis efficiency is greatly improved.

The invention discloses a user-defined multi-dimensional analysis configuration method, system and equipment based on a constellation model and a medium, belongs to the technical field of data analysis, and aims to solve the technical problem that basic data such as dimensions, indexes and data models in multi-dimensional analysis are complicated to construct. According to the technical scheme, according to the method, the dimensionality, the index and the incidence relation between the fact table and the dimensionality table configured by the model table are imported, the mapping between the dimensionality tables is automatically imported into the correlation table, the attribute and the model relation, the model maintenance cost is reduced, and the model maintenance time is shortened; ordered model construction is realized through dimensions, indexes, fact tables, dimension tables, data models and self-defined multi-dimensional query, multi-fact-table dimensions screen application configuration scenes of reverse mapping of property of the fact tables, required indexes are flexibly selected from the fact tables through dragging operation, and according to a cascade mode of the indexes and the dimensions, the application configuration scenes of reverse mapping of property of the property of the fact tables are selected to be matched with the property of the property of the property of the property of the property of the property of the property of the property of the property of the property of the property of the property. And automatically screening out the generality dimension for display.

The invention provides a code analysis and generation method and system based on a large model. The method comprises the steps of obtaining a generation demand input by a user; based on the generation demand, determining a to-be-called target function module from a code function module set, and determining an execution sequence of the target function module, the code function module set comprising a code analysis module and a large model agent module with various code generation functions. And calling the target function modules in sequence based on the execution sequence to obtain a code corresponding to the generation demand. According to the method, the code snippets conforming to the specifications are automatically generated, so that the coding burden of developers is relieved, the development cost is reduced, and the development efficiency is improved. Meanwhile, a large model agent technology is efficiently utilized, and the analysis accuracy is improved through automatic code analysis. In addition, the system can adapt to different programming languages and development frameworks, and diversified development requirements are met.

The invention relates to an electrochemical impedance acquisition method based on a time sequence model and an electrochemical impedance model. The method comprises the following steps: constructing an experimental test matrix taking a battery material system, temperature, SOC and the like as variables; performing charging and discharging test on the battery according to the experimental test matrix; after the battery stands, current excitation is applied through an electrochemical workstation, real electrochemical impedance spectroscopy data are obtained, parameter identification is carried out, and a model training label is extracted; extracting features of the process data of the charging and discharging test, and constructing an electrochemical impedance data set; based on a time sequence pre-training basic model, training by utilizing the electrochemical impedance data set to obtain an electrochemical impedance model; and predicting the electrochemical impedance spectrum of the battery to be detected by adopting the trained electrochemical impedance model. Compared with the prior art, the method has the advantages that the time sequence large model and the electrochemical impedance model are combined, efficient acquisition and accurate analysis of the electrochemical impedance spectrum are realized, and the method has wide application potential in real-time monitoring and dynamic application environments.

The invention discloses an SEM image granularity analysis method and system based on a visual large model, and relates to the technical field of image recognition. The method comprises the steps that an SEM image is acquired, automatic scale identification is performed on the SEM image, and the actual length and the pixel length of the scale and the actual length of each pixel are obtained; an SEM image set containing particle features is collected, particles in the SEM image set are labeled, and an SEM image training set and an SEM image test set are constructed based on a labeling result; constructing a visual large model, and performing fine tuning on the visual large model by using the SEM image training set; reasoning the SEM image test set by using the fine-tuned visual large model to obtain a particle segmentation result; attributes of each segmented particle are calculated based on the actual length of each pixel for particle size analysis. According to the scheme, the efficiency, precision and consistency of particle analysis are improved, and the efficient and accurate analysis requirements of microscopic particles are met.

The invention discloses an electric power fault diagnosis and prediction system based on big data, particularly relates to the field of intelligent diagnosis of an electric power system, and aims to solve the problems of fault response delay and insufficient diagnosis precision. Localized intelligent diagnosis of power faults is realized, and stable operation and rapid recovery of a power system are guaranteed; a priority classification and sorting uploading mechanism is combined to ensure that key data is transmitted preferentially, diagnosis errors caused by delay are reduced, and the analysis stability of a central dispatching center is enhanced; through comprehensive judgment of a global deviation aggregation index and a trust stability harmonic measurement index, accurate classification of local node diagnosis controllability is realized, more accurate key data is guaranteed to be obtained, finally, the fault response capability of a system and the safety of a power system are improved, and powerful technical support is provided for intelligent diagnosis and maintenance.

A CAE simulation result visualization system supporting multiple interaction modes comprises a multi-resolution rendering module used for obtaining interaction behaviors of a user and dynamically adjusting the resolution of a rendering area according to the interaction behaviors of the user; the key frame and time step screening module is used for analyzing the input simulation data, automatically screening out key frames and detecting the time step change of the simulation data according to a time sequence analysis method; the data compression and simplification module is used for compressing the data by using a feature-preserving compression algorithm, performing incremental storage and sparse representation on the compressed data, and recording data changes; and the dynamic local playing and frame-skipping rendering module is used for acquiring a user local time playing instruction, and processing user local time and non-user local time respectively through a frame-skipping rendering method so as to realize multi-frame rendering and switching. According to the method, the data analysis efficiency is improved, the rendering and data transmission efficiency is improved, and computing resources are saved.

The invention relates to the technical field of operation and maintenance of complete switch equipment, and discloses a DMC material-based operation and maintenance method for complete switch equipment, which comprises the following steps of: determining key performance parameters such as insulating strength, heat conductivity coefficient and arc resistance of a DMC material through an experiment, and constructing an aging characteristic model; calculating a material health index based on running state data collected in real time, and dynamically generating a degradation trend and residual life in combination with a time sequence prediction algorithm; and generating a maintenance plan through an optimization model, and introducing a risk weight coefficient to realize dynamic balance between maintenance cost and equipment reliability. According to the method, through material performance modeling and multi-source data dynamic fusion, the accuracy and response speed of equipment state monitoring are remarkably improved; through multi-dimensional cooperation of trend prediction and maintenance optimization strategies, full-life-cycle closed-loop management is formed, and intelligent upgrading of operation maintenance under complex working conditions is realized.

The invention relates to the technical field of data processing, and discloses a data fusion method and system based on multi-source satellite altimeter data. The method comprises the following steps: performing multi-satellite intersection analysis and orbit error data correction on preprocessed multi-source satellite altimeter data to obtain satellite orbit error correction data; carrying out height subtraction processing on the earth water surface height and the average sea surface height on the sea surface height measured by the correction satellite, obtaining along-orbit sea surface height data, carrying out abnormal height data screening, obtaining coastal height abnormal data, carrying out space-time interpolation processing on the coastal height abnormal data, and obtaining a coastal height data set; the method comprises the steps of obtaining interpolation coastal height abnormal data, performing multi-source fusion to obtain gridded sea surface height abnormal fusion data, and performing abnormal reason analysis on the gridded sea surface height abnormal fusion data to obtain a predicted abnormal reason. According to the invention, the accuracy of data fusion based on the multi-source satellite altimeter data is improved.

The invention discloses a data processing method and device based on a visual database and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring a data processing request of a user; analyzing the data processing request to obtain a corresponding key field and a logic conjunction; according to the key field, performing semantic matching in a preset data model library to obtain a corresponding target data table and a target field; synthesizing the logic conjunction, the target data table and the target field to generate a corresponding database processing statement; executing the database processing statement, and obtaining a result data set generated after the database processing statement is executed; and transmitting the result data set to a visual database terminal, and displaying the result data set on the visual database terminal according to a preset visual template. By implementing the technical scheme provided by the invention, the data query efficiency is improved.

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on digital twinning. The method comprises the following steps: carrying out network topology perception on a target network to obtain a network data transmission topological graph; carrying out real-time traffic first acquisition on the target network to obtain a first real-time traffic data set; performing digital twinborn model construction on the target network according to the first real-time traffic data set based on the network data transmission topological graph to obtain a network digital twinborn model; performing real-time traffic second acquisition on the target network to obtain a second real-time traffic data set; performing equipment system log collection on the target network to obtain an equipment system log set; and performing data synchronous mapping on the second real-time traffic data set, the equipment system log set and the network digital twinborn model to obtain a real-time network digital twinborn model. According to the invention, the network security risk is obviously reduced.

The invention belongs to the technical field of computer vision and artificial intelligence, and discloses a lightweight wisdom classroom learning behavior detection method and system, and the method comprises the steps: extracting student behavior features in a classroom image, and obtaining preliminary multi-scale features; designing a deep convolutional neural network to perform deep spatial-temporal feature representation on the extracted features, and introducing a focusing modulation module to enhance the features; designing a dynamic detection head to fuse and process features of different scales; and calculating the difference between a prediction result and a real label by using the optimized classification loss function and bounding box loss function, optimizing model parameters, and outputting a final student behavior detection result through the model. And finally, the system performs detailed analysis on the detected student behaviors and generates a visual classroom behavior report. According to the method, more accurate student behavior detection in a classroom scene is realized, a complex classroom environment and diversified student behavior modes can be effectively processed, and the method has relatively high practicability.

The invention discloses an overlapped finite element method-based underwater target low and medium frequency sound scattering field forecasting method, which comprises the following steps of: generating a virtual node in an overlapped finite element, distributing a group of quadratic complete shift polynomial basis functions in a local analysis domain of each field node to construct local field approximation, and multiplying the local field approximation by a unit decomposition function of the overlapped finite element, constructing a high-order overlapping finite element approximation function; the method comprises the following steps: cutting off an infinite fluid domain by using an artificial boundary, and introducing a Dirichlet-to-Neumann mapping technology to realize a non-reflection boundary condition. For the problem of underwater target sound scattering in middle and low frequency bands, an overlapped finite element numerical calculation model of elastic target sound scattering is established. According to the method, high-order field variable approximation is realized by using the standard low-order finite element grid, the numerical dispersion error and the numerical anisotropy are effectively inhibited, and the low and medium frequency sound scattering field of the elastic target in the infinite water area can be accurately forecasted.

The invention discloses a method, a device and equipment for testing the uniformity of a light guide film, and the method comprises the steps: carrying out the dynamic temperature adjustment of the light guide film, and obtaining the multi-wavelength dynamic spectrum data of an edge region; nonlinear optical response characteristics are extracted; generating a dynamic optical characteristic diagram and performing three-dimensional structure analysis; calculating a thermo-optical coupling complexity index; generating dynamic change data of the optical performance of the edge region; and executing multi-dimensional evaluation index processing and time sequence analysis to obtain a dynamic accurate evaluation result. According to the technical scheme, the optical performance of the edge area of the light guide film can be accurately evaluated under the dynamic temperature condition, the influence of temperature change on the uniformity of the light guide film is effectively captured, the accuracy and practicability of a test result are improved, and a more reliable basis is provided for design optimization and quality control of the light guide film.

The invention belongs to the technical field of microorganisms, and particularly relates to a microorganism detection culture device and method.The system comprises an intelligent environment control system, and a sensor monitors the temperature, humidity and gas concentration of a culture environment in real time to ensure that environment parameters needed by microorganism growth are kept in the optimal state; the automatic sample processing and operating system avoids pollution or errors caused by manual intervention through a mechanical arm or an automatic transmission device; the real-time image monitoring and analyzing system is used for monitoring the growth state of the microorganisms in the culture dish; the camera can continuously shoot images in the culture process; the rapid detection module rapidly obtains activity information of microorganisms, rapid detection is achieved, and time needed by traditional culture is shortened. According to the invention, accurate control, automatic operation and rapid detection are realized, and an efficient, safe and intelligent solution is provided for microbial culture and detection.

The invention belongs to the field of photovoltaic technology, and discloses a method and system for evaluating different types of roof photovoltaic installation potentials in different climate regions, and the method comprises the steps: shooting different types of aerial images, including orthographic top views and horizontal side views, of buildings with installed roof photovoltaic in different climate regions by using an unmanned aerial vehicle, and combining a roof / photovoltaic identification model to identify the installation potential of different types of roof photovoltaic in different climate regions; and the roof area and the photovoltaic area of each building in the image are counted, and the quotient of the photovoltaic area and the roof area is a roof available coefficient, so that the roof available coefficients of different types of buildings in different climate regions are summarized. The coefficient is closer to an actual value and is more comprehensive and practical compared with 100% utilization or 3D shadow analysis, and in an existing simple coefficient multiplication method, the value of the coefficient is not perfect and scientific. Recognizing the building roof area in the region by combining a roof recognition model; and determining functional attributes of the house building according to the POI data.

The invention discloses a cauliflower phenotype analysis and prediction method based on a field high-throughput phenotype system. According to the method, on the basis of RGB images and hyperspectral images of field cauliflower, through a cauliflower segmentation model, cauliflower, flower ball and leaf areas are automatically extracted, phenotype data and chlorophyll content are extracted, data of different growth periods are sorted according to a time sequence, a growth curve is drawn, and a regression model is constructed, so that the growth states of the cauliflower in different stages are predicted. Compared with the prior art, an effective non-contact scheme is provided for cauliflower phenotype analysis, phenotype parameters of cauliflower can be obtained without manual field on-site measurement, and corresponding culture measurement is combined, so that the cauliflower culture efficiency can be effectively improved, the culture cost can be reduced, and double friendliness of human resources and biological safety is realized.

The invention discloses a log data analysis method and device, equipment and a storage medium, and relates to the technical field of vehicle-mounted interconnection, and the method comprises the steps: obtaining log data, obtaining a key feature and an abnormal score according to the log data through a preset variational auto-encoder model, processing the log data according to the key feature and the abnormal score, and obtaining the log data. The processed log data are output to a visual window for image drawing analysis, and a display image is obtained. And log analysis, feature extraction, abnormity identification and visual display are performed by using the variational auto-encoder, so that the log analysis efficiency is improved.

The invention discloses a time-delay load frequency control stability analysis method considering electric vehicle access, and relates to the technical field of load frequency control stability analysis, and the method comprises the steps: building a nominal load frequency control model of a load frequency control system through the power generation characteristics of an electric vehicle and a distributed power supply through model reconstruction, establishing a determined load frequency control model of the load frequency control system according to the electric vehicle charge state fluctuation; introducing an auxiliary polynomial, constructing two types of improved relaxation matrix functions, combining multiple integrals, and respectively obtaining an asymptotic stability criterion of a nominal load frequency control system and a robust stability criterion of an uncertain load frequency control system by using a Lyapunov stability analysis method and a Bessel-Legendre inequality; time-delay load frequency control stability analysis is completed; the reliability and the accuracy of load frequency control analysis of the power system and the applicability of the analysis method are remarkably improved.

The invention provides a big language model-based intelligent analysis system for a newly created enterprise, and the system comprises a data collection module which is used for collecting data of the newly created enterprise; the data preprocessing module is used for preprocessing the data of the initially created enterprise; the vector database and knowledge graph construction module is used for constructing a vector database and a knowledge graph based on the preprocessed initially created enterprise data; the context information construction module is used for carrying out deep semantic analysis on related information, input by a user, of the to-be-analyzed enterprise, and fusing a semantic analysis result with data of the vector database and the knowledge graph to obtain context information; and the intelligent analysis module is used for carrying out intelligent analysis on the context information based on the initially created enterprise research analysis auxiliary large model, obtaining development state data of the to-be-analyzed enterprise, and completing initially created enterprise intelligent analysis based on the large language model. And comprehensive and efficient retrieval and processing of related data of the initially created enterprise are realized.