199results about How to "Improve analytical performance" patented technology

The invention relates to the technical field of medical data management, in particular to a medical data document classification and marking system which comprises a document structure analysis module, a semantic information identification module, a classification and marking module and a document intelligent label module. According to the method, document visualization and retrieval efficiency is improved by analyzing document content and extracting titles, texts and appendixes, analytic performance and operability of data are enhanced by identifying key information and semantic relations and accurately extracting and labeling medical terms, medicine research data and medical record records, storage is optimized according to priorities in combination with classification and marking, and the method is high in practicability and easy to popularize and use. Clinical values and application scenes of documents are clarified, document management processes are refined, management compatible with clinical data formats is formed through structured storage of marked data and classification label generation, storage normalization and retrieval efficiency are improved, the requirement for accurate information calling in clinical tests is met, document management capacity is improved, and the document management efficiency is improved. And support is provided for improvement of clinical decision-making and research efficiency.

The invention discloses an emergency fire-fighting hidden danger troubleshooting method based on a multi-modal AI large model identification technology, and relates to the field of artificial intelligence. The multi-modal hidden danger knowledge base is constructed by collecting multi-modal data such as images, texts and related laws and regulations actually recorded in the hidden danger checking process, the recognition and analysis ability of hidden danger information is improved based on the deep learning and multi-modal large model technology, the multi-hop reasoning and thinking chain technology is utilized, and the hidden danger checking efficiency is improved. A complex hidden danger positioning problem can be disassembled into a series of interpretable reasoning steps, and a user is helped to understand a hidden danger judgment basis. According to the method, the hidden danger in the image is analyzed from multiple angles and levels, different hypotheses or conditions can be verified step by step, so that the judgment accuracy is improved, the information of each dimension is comprehensively synthesized for comprehensive evaluation, and the comprehensiveness and accuracy of hidden danger investigation are ensured.

The invention discloses a 5G message video monitoring method based on artificial intelligence. The method comprises the following steps: S1, constructing a multi-modal data set; s2, multi-modal fusion features are generated by using a joint convolutional neural network; s3, generating a target behavior trend curve and a target behavior prediction result by using the 3D convolutional neural network; s4, generating a target threshold curve by using an adaptive algorithm based on variable domain search, and obtaining behavior abnormality warning information; s5, generating comprehensive alarm information by using a method of combining fuzzy logic reasoning and a Bayesian network; s6, cross-camera data space-time correlation reasoning is carried out by using the graph neural network; s7, constructing an event severity curve by using a multivariable regression analysis and analytic hierarchy process, and generating a severity level and an event response measure of the event; and S8, pushing alarm information and event response measures to related personnel or equipment through the 5G message platform. The invention provides an efficient video monitoring method in combination with artificial intelligence and a 5G network.

The invention discloses a data analysis method and system and a storage medium, and is applied to the field of natural language processing. The method comprises the steps of obtaining a natural language input by a user; constructing a semantic data model for converting the natural language into a semantic query statement; combining the semantic data model with a user-defined rule to construct a semantic parser; inputting the natural language into the semantic parser for parsing to obtain a semantic query statement; inputting the semantic query statement into a semantic corrector for correction to obtain a target semantic query statement; converting the target semantic query statement into a structured query statement through a semantic translator; and inputting the structured query statement into the database for query to obtain a query result.

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 belongs to the technical field of signal processing, and particularly relates to a multi-source data fusion fault diagnosis method and system based on the DS evidence theory, and the method comprises the steps: firstly collecting the multi-source information of mechanical equipment, including vibration, image and sound data, respectively calculating the corresponding characteristic indexes, carrying out the dimension reduction processing of the indexes through employing a PCA algorithm, and carrying out the fault diagnosis of the multi-source information. And carrying out fault diagnosis by adopting a Bayesian fault diagnosis model, and carrying out fault fusion diagnosis by adopting a DS evidence theory after a diagnosis result of each sensor is obtained. The method can comprehensively consider the characteristics of various data, extract more effective fault indexes, realize more comprehensive fault identification, fuse the diagnosis results of a plurality of sensors, ensure the final diagnosis accuracy, and is suitable for fault diagnosis of various complex mechanical systems.

The invention relates to the field of image enhancement, in particular to an image enhancement method and system of a vehicle-mounted image sensor. The method comprises the following steps: acquiring driving record images of a vehicle at multiple angles; dynamic scene self-adaptive exposure adjustment is carried out on the driving record images of the vehicle at the multiple angles, so that multiple self-adaptive exposure driving images are obtained; carrying out deep network noise identification on the plurality of adaptive exposure driving images, and carrying out dynamic filtering and noise reduction on sub-images one by one so as to generate a plurality of filtering and noise reduction optimized images; carrying out image texture analysis on the plurality of exposure sub-images, and carrying out texture distortion reconstruction to construct a plurality of texture reconstruction images; and road object visual identification is carried out on the plurality of texture reconstruction images, and adaptive contrast enhancement processing is carried out, so that a plurality of detail contrast enhancement images are constructed. According to the invention, a high-definition and stable panoramic vehicle-mounted image is realized.

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 network node classification method based on a multilayer graph neural network model, and the method comprises the steps: constructing a graph according to network nodes, carrying out the preprocessing of initial node features in the graph, and obtaining an initial embedded vector; according to the initial embedding vectors of the node and the neighbor node thereof, updating the node representation by adopting a layered message passing mechanism; according to the initial embedded vectors of the nodes and the neighbor nodes thereof, the graph attention network uses an attention mechanism to calculate weights for the neighbor nodes of each node and then dynamically updates feature vectors of the nodes; a multi-head attention mechanism is adopted to splice and merge the feature vectors of the nodes output by the graph attention network and the updated node representation, and the comprehensive feature vector of each node is obtained; the comprehensive feature vectors are further optimized through graph contrast learning and a node embedding module, and optimized features are obtained; and obtaining predicted node classification according to the optimization features. According to the method, complex features of heterogeneous graph data can be effectively captured, the method has good robustness for an over-smoothing problem, more accurate heterogeneous network representation learning is realized, and the method has advanced performance and robustness in a node classification task.

The invention provides a semiconductor equipment data acquisition method and a semiconductor equipment data acquisition system supporting standardized communication transformation, which are used for realizing data acquisition and process control on different types of semiconductor equipment, formulating corresponding communication rules for equipment conforming to a communication standard, and converting an equipment communication protocol by a cluster system so as to match a standard interface. For equipment which does not completely meet the standard but can realize communication through programming, the trunking system directly acquires required data and converts the required data to meet the standard interface specification. For equipment without communication capability, data is acquired through an equipment interface by adopting an image recognition technology, data input and function operation are realized through simulating keyboard and mouse operation, and finally, protocol conversion from data to a standard interface is completed by a cluster equipment control system. And the equipment data is converted into a message data packet conforming to a standard semiconductor equipment communication interface, so that docking with a semiconductor equipment data analysis and process control system is realized, and the production efficiency and quality of the semiconductor equipment are improved.

The invention discloses an LED lamp strip rhythm flickering control method and system, a storage medium and a program product, and the method comprises the steps: calculating a relative entropy value among a high-frequency band, a middle-frequency band and a low-frequency band; establishing an energy flow diagram, and determining a dominant frequency band according to an energy flow topological structure in the energy flow diagram; a phase tracking function is constructed, and hierarchical level division is carried out on the beat mark points; establishing a recursive segmentation window, calculating a spectrum centroid trajectory, and extracting an inflection point sequence; establishing a corresponding relation between the polar coordinate angle and the color parameter of the LED lamp strip and a corresponding relation between the polar radius and the brightness parameter; a spatial propagation mode of the LED lamp strip is designed based on the topological structure of the energy flow diagram, so that the propagation direction of the light effect along the LED lamp strip is kept consistent with the energy flow direction between the frequency bands, and the propagation speed is adjusted according to the energy flow intensity; and generating a driving control sequence of the LED lamp strip according to the gradient function. The emotional progression and tension change of music are captured, and the LED lamp strip is accurately controlled to flicker according to the emotional progression and the tension change.

The invention relates to a vertical industry document intelligent analysis and question-answering method based on a multi-modal retrieval enhancement generation technology, which can comprehensively process various data in a document and remarkably improve the overall analysis capability of a large model on the document. By deeply analyzing multi-modal data such as texts, tables and charts in the document, the method enables document analysis to be more comprehensive and accurate. According to the method, a vector database and a graph database are combined for efficiently storing and retrieving information. The combination not only improves the ability of the system to understand the context, but also significantly improves the accuracy of retrieval. Meanwhile, the output retrieval result can provide evidence for the result obtained by the user by using the method, so that the output result of the method has acceptable accuracy in various vertical fields.

The invention provides a carbon emission optimization method and system based on green supply chain management, and relates to the technical field of data processing, M key links are positioned based on historical carbon emission data and called to obtain M local carbon emission data, and the M local carbon emission data are adopted to construct a carbon emission prediction model; environment data acquisition is carried out on the M key links, acquisition results are synchronized to a carbon emission prediction model to carry out carbon emission trend prediction, M pieces of carbon emission trend information are obtained, and then a real-time emission reduction strategy is output to execute carbon emission optimization management. The technical problem that in the prior art, targeted emission reduction management cannot be carried out on a supply chain, so that the execution effectiveness of environment management measures is weak is solved. The technical effects of accurately identifying the key carbon emission link in the supply chain and predicting the carbon emission trend of the key carbon emission link with high precision are achieved.

The invention relates to the technical field of dangerous goods storage, and discloses a data twinborn early warning system and method for a dangerous goods storage area. The model establishing module is configured to establish a data twinborn model of the storage area according to the image information; the acquisition module is configured to acquire the environment information and the dangerous goods information of the storage area and display the information on the data twinborn model; the environment evaluation module is configured to obtain an environment evaluation value according to the temperature information and the humidity information and adjust the environment evaluation value according to the change value amplitude; the dangerous goods evaluation module is configured to obtain a dangerous goods evaluation value according to the complete state of the dangerous goods and adjust the dangerous goods evaluation value according to the gas leakage information; and the early warning module is configured to judge the early warning level of the storage area according to the comprehensive evaluation value and perform early warning. Storage of dangerous goods is monitored in multiple aspects, and the safety of storage materials is effectively evaluated.

The invention relates to the technical field of agricultural automation and intelligent control, in particular to a weeding monitoring system and method based on an unmanned aerial vehicle, and the method comprises the steps: collecting spectral reflection data and microwave internal structure data of a target area through a multispectral camera and a microwave sensor carried by the unmanned aerial vehicle; the system generates standardized perception data through a data fusion module, and performs precise classification and recognition of weeds in combination with feature extraction, adaptive fuzzy clustering and multi-model collaborative learning technologies; based on a spatial distribution diagram of weeds, the system predicts a weed diffusion trend through a graph convolutional network, and an optimal weeding strategy is generated in combination with environmental data; and the unmanned aerial vehicle executes the weeding task through the optimized flight path, monitors the weeding effect in real time, continuously optimizes the strategy, and ensures efficient execution of the weeding task.

The invention relates to an intelligent solid state disk quality detection method, system and device and a medium, and the method comprises the steps: obtaining test demand data of a to-be-detected solid state disk, extracting feature point data in the test demand data from the test demand data through a feature extraction algorithm, and obtaining demand feature point data; acquiring environment data, and transmitting the demand feature point data and the environment data to a pre-trained detection model for comparative analysis to obtain a test adjustment scheme; performing a virtual environment test according to the test adjustment scheme to obtain a virtual environment test result; and when the virtual environment test result does not conform to the expectation, obtaining difference information from the virtual environment test result, correspondingly modifying the test adjustment scheme according to the difference information to obtain an optimized adjustment scheme, and adjusting the test process and parameter configuration of the to-be-detected solid state disk according to the optimized adjustment scheme. The method has the effect of improving the accuracy of solid state disk quality detection.

The invention provides an auxiliary pathological analysis method for a microscope system, and belongs to the technical field of pathological analysis, and the method comprises the steps: firstly carrying out the gray normalization, denoising and other preprocessing of an obtained pathological section image, and carrying out the illumination equalization processing through a region adaptive histogram equalization algorithm; then, color features of the image are extracted in an LAB color space, a color representation center is calculated, a feature mapping matrix is established, and meanwhile texture features are extracted and fused with the color features; and carrying out dimension reduction on the fused feature vector by using a principal component analysis algorithm, and reserving important feature information. And then, establishing a convolutional neural network model, inputting the feature vectors after dimension reduction, performing training and cross validation by using a labeling data set, and improving the model performance through iterative optimization. According to the invention, the technical problem that the diagnosis and judgment of a doctor are seriously influenced due to inconsistent color expression caused by the difference of shooting conditions of pathological section images observed by a microscope is solved.

The invention discloses an urban and rural planning management method and system based on artificial intelligence, and belongs to the technical field of urban and rural planning management, and the method comprises the steps of data collection, data preprocessing, urban expansion prediction, air quality prediction and urban and rural planning management. According to the invention, urban and rural planning management is carried out by combining two aspects of urban expansion prediction and air quality prediction, and the planning management dimension is deepened; according to the method, city expansion prediction is carried out by adopting a convolutional long-short-term parallel bidirectional gating circulation network, space and time level features of city expansion are better extracted, the analysis capability of complex data is improved through bidirectional gating circulation, and the overall accuracy of the method is improved; according to the method, air quality prediction is carried out by adopting the time sequence convolution bidirectional long-short-term memory network combined with improved parameter optimization, short-term air change characteristics can be extracted, long-term trend air quality changes can be analyzed, the model performance is optimized by adopting an improved whale optimization search algorithm, and the efficiency and accuracy of urban and rural planning management are improved.

The invention relates to data analysis, in particular to an intelligent dependency graph construction method based on multi-dimensional analysis and self-adaptive optimization, which comprises the following steps: reading module data, and carrying out preliminary analysis on a dependency relationship among modules to construct a preliminary dependency graph; performing multi-dimensional analysis on a dependency relationship between each pair of modules in the preliminary dependency graph, and calculating a comprehensive score of each pair of modules according to a multi-dimensional analysis result; adjusting the preliminary dependency graph according to the comprehensive score of each pair of modules to obtain an adjusted dependency graph; performing cyclic dependency detection on the adjustment dependency graph, and merging static dependency data and dynamic dependency data of each module; self-adaptive optimization is carried out according to the complexity of the adjusted dependency graph, a final dependency graph is generated, and the final dependency graph is stored and output; according to the technical scheme provided by the invention, the defect that the software module dependency graph is difficult to accurately and efficiently analyze and construct in the prior art can be effectively overcome.

The invention discloses a multi-dimensional training method and device for a support vector machine, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the discretization of a training sample data set, and obtaining a discretized data set, the discretized data set comprises a plurality of different attributes, and each attribute corresponds to a plurality of feature vectors; calculating a classification contribution parameter of each attribute feature vector to obtain a plurality of classification contribution parameters; performing data mapping on the plurality of classification contribution degrees by using a kernel function to obtain a target function; and optimizing and training the objective function by using a gradient descent algorithm to obtain a support vector machine model. According to the method, different dimension data are mapped through the kernel function, the data gain weight can be determined, the linear separable effect of the mapped dimension data can be improved, and then the classification and analysis capability of the SVM can be improved.

The invention provides a visual aiding system and method for visually impaired people based on a multi-mode large model, intelligent glasses and application software. The system comprises a multi-mode large model, blind-helping application software and intelligent glasses, enables a visually impaired person to ask questions through simple voice, achieves the recognition of all things such as environment, obstacles, text information, road names / shop names, flowers and plants, and feeds back the questions to the visually impaired person through voice. According to the scheme, the visually impaired person can conveniently and quickly see all information around the person in a hearing mode, then the independence of work and life is greatly improved, and the visual impaired person has wide application prospects and huge social value.

The invention relates to the technical field of flood prediction, in particular to a non-consistent regional flood frequency analysis method based on the Bayesian theory. The method comprises the following steps: acquiring flood sequence data, meteorological data and drainage basin characteristic data of hydrological stations in a drainage basin; performing regional flood dynamic change analysis on the flood sequence data and the meteorological data to generate regional flood time sequence dynamic change data; on the basis of the regional flood time sequence dynamic change data, hydrological station flood characteristic parameters in the drainage basin are extracted from the drainage basin characteristic data, and hydrological station flood characteristic parameters in the region are obtained; and performing flood distribution parameter regression coefficient prior information derivation on the flood characteristic parameters of the hydrological stations in the region to obtain a maximum likelihood estimation value of a single hydrological station distribution parameter. According to the method, the Bayesian theory, the spatial correlation analysis and the spectral analysis are introduced, so that the accuracy and the applicability of flood frequency analysis based on the Bayesian theory are improved.

The invention relates to an unsupervised region representation learning method and system for learning urban region embedding, and belongs to the technical field of urban calculation and intelligent systems. The method specifically comprises the following steps: S1, constructing a regional mixed graph network, and capturing pairing and grouping relationships among regions by using multi-modal data; s2, graph and hypergraph comparative learning is executed, and discriminative representation of region nodes and a high-order relation between regions are learned from graph and hypergraph structures through a parallel comparative learning module; s3, cross-module comparative learning is carried out, information exchange between graph and hypergraph node representation is promoted through a controller assembly, and the learning ability of the model is enhanced; and S4, optimizing model parameters, and generating more effective region embedding through joint optimization of graph loss, hypergraph loss and cross-module loss so as to support various downstream tasks. According to the technical scheme provided by the invention, the accuracy and efficiency of urban area analysis are improved, and the capturing capability of the model on complex interaction between the areas is enhanced.

The invention discloses a printing process quality monitoring management system, and belongs to the technical field of printing supervision. The method is used for solving the technical problems that in an existing scheme, the printing process quality multi-dimensional supervision processing and analysis effect of different printing bodies is poor, and the printing process quality independent sharing recommendation effect in different aspects is poor. Historical printing data corresponding to different printing main bodies in a target area is subjected to multi-dimensional supervision, and a single-time printing supervision data processing result obtained through earlier-stage processing is expanded and utilized, so that different printing types of different printing main bodies are subjected to different-dimensional printing type abnormal state data calculation and digital combination. And performing multi-dimensional processing analysis on the printing process quality of the different printing types according to the main body type abnormity supervision processing data sets corresponding to the different printing main bodies in the target area, and adaptively and respectively performing dynamic recommendation prompt on the printing process quality of the different printing types and the printing process quality of the different printing main bodies.

The invention discloses a ship remote monitoring method and system, a product and a medium, and relates to the field of ship remote monitoring, and the method comprises the steps: receiving real-time sensor data of a target ship, comparing the real-time sensor data with historical sensor data, and obtaining a target fault sensor and a data change time period; acquiring video monitoring data of the target fault sensor area in the data change time period, and comparing the video monitoring data with historical monitoring data to obtain a ship change area; analyzing a time sequence relationship between the sensor change trend of the target fault sensor and the monitoring change trend of the video monitoring data; in a preset fault mode library, judging whether a fault mode matched with the time sequence relationship, the sensor change trend and the monitoring change trend exists or not; and if yes, an early warning instruction containing the fault mode is sent to the target ship client. By implementing the method, the ship fault monitoring accuracy can be improved.

The invention discloses a data processing method for a full-life-cycle resident health record. The method comprises the following steps: S1, integrating data from various devices and systems through a standardized interface; s2, preprocessing the collected health data, and storing the health data in a distributed database and a cloud storage system; s3, performing consistency processing on data from different sources by using data cleaning, duplicate removal and standardization technologies; s4, performing deep analysis on the health data by applying a long-short-term memory network; s5, carrying out data encryption through AES and RSA technologies, and guaranteeing the privacy of the user through role-based access control and a differential privacy technology; and S6, integrating the mobile health application and the intelligent wearable device into a health file management system, and supporting real-time health monitoring and remote medical services. The system has the advantages of comprehensive coverage, real-time processing, efficient storage, intelligent analysis, data security and privacy protection, cross-platform sharing and remote medical integration.

The invention discloses a mine disaster optical fiber intelligent monitoring, early warning and emergency rescue command decision-making system. The system comprises an industrial personal computer, an optical fiber ring network, a distributed optical fiber multi-gas monitoring subsystem, a distributed optical fiber temperature monitoring subsystem, a distributed optical fiber vibration monitoring subsystem and a distributed optical fiber mine pressure monitoring subsystem. The system has high sensitivity, multi-gas monitoring capability, high precision and comprehensive monitoring capability, and can monitor and early warn various parameter changes of a mine in real time, including gas concentration, temperature, vibration and pressure. The system has real-time monitoring and early warning capabilities, can accurately capture tiny changes and timely send out early warning signals, and helps mine monitoring personnel to take safety measures. The system has the functions of rescue command and emergency rescue, can quickly transmit environment information and disaster conditions to a rescue command center, and improves the rescue efficiency. The system also has strong data management and analysis capability, and helps mine monitoring personnel to better understand the environment change and trend of a mine and the change of disaster risks.

The invention discloses an ultrathin electronic glass roller way speed measurement support and a speed measurement method, and the support comprises a support which is connected to a mounting disc for fixing a supporting roller through a bolt, the support is fixedly provided with an encoder, a detection shaft of the encoder is connected with a connecting disc through a coupler, the connecting disc is clamped at the end part of the supporting roller, and a signal of the coupler is connected with a speedometer. And a driving control system and an alarm of the supporting roller are connected through a linear speed meter. The effect of improving the roller way rotating speed detection precision and frequency is achieved.

A transparent visual power grid operation and maintenance system comprises a data acquisition and preprocessing module which is composed of a sensor network and SCADA system docking unit and a data preprocessing unit; the data processing and analysis module is composed of a fault prediction and diagnosis unit, an operation and maintenance strategy optimization unit and a three-dimensional modeling and dynamic simulation unit; the visual display and user interaction module is composed of an AR / VR interface development unit and a user-defined interface and report generation unit; and the system integration and communication module is composed of a standardized API interface and cloud platform unit and a remote access and cooperation unit, and the scheme of the invention solves the problem that the complexity of power grid operation and maintenance system data causes that detection information is difficult to provide visually, thereby affecting the operation and maintenance work quality and efficiency.

The invention relates to an urban distributed energy resource scheduling method and system based on a world model, and the method comprises the steps: collecting the operation data of various distributed energy resources in an urban power system in real time, inputting the operation data into the world model for processing, and outputting a distributed energy resource scheduling scheme, wherein the world model comprises a virtual simulation sub-model, a situation awareness device and an intelligent decision-making device, and the step of outputting the distributed energy resource scheduling scheme comprises the following steps: establishing the virtual simulation sub-model of the urban power system based on real-time collected operation data in combination with historical operation data; based on the virtual simulation sub-model, performing situation awareness and analysis by adopting the situation awareness device; and based on the situation awareness and analysis result, an intelligent decision maker is adopted to automatically generate a distributed energy resource scheduling scheme. Compared with the prior art, the method has the advantages of realizing virtual scheduling simulation of energy resources, quickly and accurately identifying exceptions, timely adjusting strategies and the like.