2082 results about "Feature Dataset" patented technology

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

The invention discloses an intelligent file classification and retrieval method and system, and the method comprises the steps: carrying out the unified format conversion and feature extraction of text, image and audio data according to the original data format of a file through a multi-modal data fusion algorithm, and obtaining a standardized file feature data set; based on a deep learning adaptive classification model, performing classification processing on the standardized archive feature data set to generate multi-level archive classification labels; analyzing the semantic relationship between the archive classification labels through an enhanced semantic network construction technology, generating a semantic association map, and optimizing the semantic association map to obtain an optimized semantic association map; and according to a retrieval request input by a user, performing semantic matching and correlation sorting by utilizing the optimized semantic correlation graph, and outputting archive information most related to the retrieval request. By utilizing the embodiment of the invention, the relevance and accuracy of retrieval can be improved, and modernization and intellectualization of archive management are promoted.

The invention relates to the field of road compactness monitoring, in particular to a road compactness real-time regulation and control method and system based on digital twinning, and the method comprises the steps: collecting vibration acceleration, temperature and position data, and generating a time-space aligned multi-source fusion feature data set after processing; inputting a pre-trained LSTM model to construct a dynamically updated digital twinborn body, and outputting a three-dimensional compaction energy spectrum; dispersing the atlas and calculating parameters, and generating a compaction degree deviation matrix; a regulation and control instruction is generated and issued based on matrix optimization; and according to the measured data and the predicted value residual error, triggering re-optimization and calibrating the sensor. According to the method, the problems of non-uniform compaction quality and over-high energy consumption caused by poor adaptability, decision lag and error coupling of a traditional static model are solved.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention relates to a multimedia equipment control method and system based on adaptive protocol matching. The method comprises the following steps: firstly, collecting communication protocol data and extracting features to form a protocol feature data set; secondly, performing confidence coefficient verification on a data set type identification result, and if the confidence coefficient is lower than a threshold value, extracting a depth feature through a convolutional neural network; and calling a national standard protocol adaptation conversion engine based on a classification result, converting the heterogeneous protocol into data in a unified format, and generating a secure communication data frame through national secret algorithm encryption and bidirectional identity verification. And finally, performing hash check and structured analysis on the data frame, constructing an interaction information packet in combination with user behavior characteristics, and dynamically optimizing a control strategy through real-time feedback data to generate a self-adaptive interaction control scheme. According to the method, through a technical closed loop of intelligent analysis-standard conversion-security reinforcement-closed loop optimization, the high efficiency, the intelligent level and the security performance of multimedia equipment management are remarkably improved.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention discloses a network security threat intelligent identification and defense method based on artificial intelligence, and the method comprises the steps: S1, collecting network flow data, user behavior data and system log data from a network security system, and outputting a finally converged network feature data set; s2, performing global optimization search on the network feature data set by applying a hyper-entropy differential evolution algorithm to generate network feature data optimized by hyper-entropy differential evolution; s3, constructing a network traffic anomaly detection probability model based on variational Bayesian reasoning by using the network feature data after hyper-entropy differential evolution optimization; s4, inputting the network feature data set into the network traffic anomaly detection probability model, and evaluating the risk probability of an abnormal behavior; and S5, generating a self-adaptive defense strategy based on a detection result, and forming defense strategy data. According to the invention, the overall stability and defense capability of the network security system are improved.

The invention relates to the field of industrial robots, and discloses an industrial robot real-time maintenance system and method in combination with edge computing, and the method comprises the steps: obtaining the multi-source operation state data of an industrial robot, and constructing an operation feature data set of key parts of the robot in combination with a state coding mechanism of an edge end and a feature coupling analysis method; carrying out rapid distributed processing on the operation characteristic data set, constructing an equipment health state model based on a lightweight time sequence modeling algorithm, and introducing a multi-dimensional correlation analysis mechanism to carry out incremental learning on the model; judging whether the edge side state recognition result is stable or not based on the change trend of the trigger frequency; according to the corrected state mapping relation, performing response level division on the potential fault trend by applying a multi-scale fault prediction mechanism, and extracting matched maintenance plan parameters; and based on the maintenance scheduling plan, in combination with a preset fault handling knowledge base, performing automatic evaluation and optimization on the current maintenance strategy. The method has the advantage of improving the response speed.

The invention relates to the technical field of inspection monitoring, and discloses an unmanned aerial vehicle intelligent inspection monitoring method and system based on a sensor, and the method comprises the steps: obtaining an initial inspection data set; obtaining a feature data set; generating a unified target feature data set; performing anomaly detection on the target feature data set to obtain an anomaly inspection area data set; performing security level division on the abnormal inspection area to obtain a division result; performing risk degree screening on the safety risk area in the division result to obtain a plurality of high-risk point position types, and when the change threshold value of one high-risk point position reaches a preset threshold value, preliminarily determining a high-risk occurrence zone; the unmanned aerial vehicle is controlled to carry out spot hovering to carry out key monitoring and refined inspection on the high-risk occurrence zone, and a secondary inspection data set is obtained; obtaining an analysis result; and secondarily confirming that the current inspection area is in the high-risk zone, and generating a corresponding emergency response early warning strategy and a corresponding regulation and control strategy, thereby accurately monitoring the inspection area in real time.

The invention belongs to the technical field of intelligent lighting, and discloses an intelligent adjustment method and system for the spectrum of a quantum dot light-emitting lamp. The objective of the invention is to solve the problem that an existing lighting system cannot meet personalized healthy lighting requirements. The method comprises the following steps: firstly, acquiring user biological rhythm, environment spectrum, use scene, user preference and physiological health data through multi-dimensional data acquisition, and constructing a feature data set; then human factor optical feature extraction and cross-situation demand analysis are carried out, and a spectral response model is established; carrying out quantum dot material photoelectric characteristic mapping and spectrum synthesis modeling based on the model, and determining a luminescence parameter space; constructing a spectrum optimization model, performing multi-objective optimization by comprehensively considering visual comfort, energy efficiency and health influence, and generating a dynamic spectrum regulation and control strategy; closed-loop verification and adaptive optimization are carried out based on real-time physiological feedback, and an intelligent spectrum adjustment scheme is formed. According to the invention, the advantage of spectrum adjustability of the quantum dot material is fully exerted, and real personalized intelligent illumination is realized.

The invention provides an intelligent monitoring and early warning method and device for photovoltaic energy storage equipment, and the method comprises the steps: carrying out the multi-time scale sliding window feature extraction of the operation data of a photovoltaic energy storage system, calculating a power coupling degree quantitative index and a system response feature index, and generating a real-time feature data set; based on the real-time feature data set, performing deviation propagation path identification under thermal dynamic constraint to obtain a real-time deviation propagation path diagram marking deviation sensitive nodes; performing multi-level dynamic deduction in combination with the real-time feature data set and the deviation propagation path map to obtain a dynamic deviation accumulation situation map displaying a deviation accumulation risk level and a development trend; and performing adaptive threshold early warning judgment according to the dynamic deviation accumulation situation map, and executing response control according to a hierarchical early warning mechanism to obtain a hierarchical early warning response control instruction. According to the method, the cumulative effect of the power prediction deviation can be effectively identified, and early warning of coordination imbalance of the photovoltaic energy storage system is realized.

The invention relates to the field of new energy charging, in particular to a new energy charging management method for a smart city. The method comprises the steps that a municipal vehicle operation feature data set is acquired, on the basis, municipal vehicles are subjected to type division through a vehicle type classification strategy, and a municipal vehicle driving type set is determined; acquiring a municipal vehicle task set, and on the basis, tracking a dynamic task variable set of a task execution vehicle in a municipal vehicle driving type set in a task execution process; according to the dynamic task variable set, analyzing an association relationship between the dynamic task variable set and the real-time power consumption data through a multi-modal fusion strategy, and determining dynamic power change information; and acquiring a charging station information set, dynamically optimizing a charging decision in combination with the municipal vehicle task set and the dynamic electric quantity change information, and determining and outputting an optimal charging strategy. In the municipal vehicle charging decision-making process, the utilization rate of charging resources is improved, and the continuity of municipal task execution is ensured.

The invention discloses an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention provides an automatic pavement settlement monitoring method and system based on a shield undercrossing airport runway. The dynamic database is constructed by collecting operation parameters such as shield tunneling machine propelling pressure, cutterhead torque and synchronous grouting pressure in real time and combining three-dimensional deformation data obtained through laser scanning. Strain sensing units are arranged in the soil layers to form a layered monitoring network, strain changes of the soil layers of different depths are collected, and spatial correlation is established between the strain changes and the tunneling position. And analyzing a spatial attenuation rule through a dynamic association processing unit, and generating a multi-dimensional feature data set. And establishing a settlement prediction model, fusing the shield dynamic load sequence and the layered strain conduction characteristics, and outputting settlement distribution information. And the system automatically compares the settlement difference value of the adjacent areas with a preset safety threshold value, and generates a graded early warning signal after verification of the three-dimensional deformation data space. According to the technical scheme provided by the invention, real-time three-dimensional prediction and spatial verification type graded early warning of settlement of the airport runway are realized.

The invention discloses an electric power analysis method and system based on artificial intelligence, and relates to the technical field of electric power system intelligent analysis. Establishing a hierarchical depth map neural network model, and setting a cross-layer information interaction channel to connect each sub-network; executing a bidirectional knowledge distillation algorithm, extracting a rule set from neural network output, and forming a parameter-rule bidirectional mapping matrix; deploying a federal reinforcement learning architecture, performing parameter aggregation on the local models on the distributed power nodes, and generating a power system state evaluation result and a risk coefficient matrix; and starting the self-repairing intelligent agent network, calculating a regulation and control parameter set of the multilevel power system, and outputting a regulation and control instruction. According to the invention, through the hierarchical depth map neural network and a cross-layer information interaction mechanism, collaborative optimization among all levels of the power system is realized, and the operation efficiency and stability of the system are improved.

The invention discloses an interactive drawing generation method, system and device based on artificial intelligence, relates to the technical field of artificial intelligence, and realizes the generation of a high-quality image through multi-modal data fusion, semantic alignment, conflict detection and iterative correction. The scheme comprises the following core steps: acquiring multi-modal data such as texts, voices, sketches and the like, and processing the multi-modal data into a formatted feature data set; performing alignment and weighted fusion on the multi-modal features to generate unified semantic space features; detecting semantic inconsistency of the image and text description by using a vision-text attention mechanism, generating a conflict index matrix and triggering an alarm mark; and iteratively correcting the generated image based on the unified semantic space features, the conflict index matrix and the alarm marks. The system is composed of a data acquisition and preprocessing module, a multi-modal feature alignment and fusion module, a conflict detection and alarm module, an image generation and iterative correction module, a system control and scheduling module and the like.

The invention provides an expectoration auxiliary control method and device. The method comprises the steps that audio signals of breathing sounds and cough sounds at multiple positions of the chest of a patient are collected through an audio sensor array, and a standardized audio feature data set is obtained through noise reduction, segmentation and feature extraction; a dynamic weighted graph model is constructed, and a random edge sampling algorithm and a parallel batch processing dynamic algorithm are combined to analyze and obtain a sputum viscosity index and a sputum distribution position map; based on the result, mapping the vibration parameter space into an unweighted disk diagram, and obtaining a personalized vibration treatment scheme by using a shortest path algorithm; in combination with the real-time breathing cycle of the patient, working parameters of the sound wave vibrator and the negative pressure suction device are synchronously controlled, and a coordinated and consistent multi-mode treatment execution instruction is obtained; and collecting real-time feedback data in treatment, and dynamically adjusting working parameters through reinforcement learning to obtain an optimized expectoration adjuvant therapy scheme. Intelligent adjustment can be achieved based on the real-time breathing state and sputum characteristics of the patient, the expectoration efficiency is improved, and discomfort of the patient is reduced.

The invention relates to the technical field of state monitoring, and discloses a tower inclination state monitoring method and system. The method comprises the following steps: performing multi-field digital coupling modeling on an electric power tower to obtain a digital twinborn model and calculating an electrical load-structure response sensitivity matrix; collecting a multi-modal sensing characteristic data set including tower inclination angle time sequence data, insulator chain offset, tower body micro-vibration spectrum and foundation settlement gradient; performing time-delay correlation analysis on the electrical event and the structure response to obtain an electrical-structure mapping matrix; tensor decomposition processing is carried out through a multi-mode inclination feature fusion network, and tower inclination mode fingerprints are obtained; and carrying out abnormal separation processing on the tower foundation area, the connection area and the upper structure area, and outputting structural inclination and non-structural inclination judgment results. According to the invention, the accurate mapping relation between the electrical load and the structure response is realized, the false alarm rate is effectively reduced, and the accuracy and timeliness of early warning are improved.

The invention relates to the technical field of intelligent control, and discloses a dyeing process optimization method and system based on intelligent control. The dyeing process optimization method based on intelligent control comprises the following steps: acquiring key parameters of a dyeing process to obtain an original data set; performing preprocessing and feature extraction on the data set to obtain a feature data set; constructing a deep learning prediction model by using the feature data set, and executing multi-objective optimization through a non-dominated sorting genetic algorithm to obtain an optimal process parameter combination; and performing scheme analysis based on the optimal parameters to obtain an intelligent dyeing scheme. Multi-parameter collaborative optimization of the whole process of the dyeing process is realized, dependence on human experience is reduced, dyeing quality consistency is improved, self-learning and self-adaptive capabilities are achieved, optimization strategies can be automatically adjusted according to different fabric characteristics and production conditions, and production efficiency is improved. Therefore, the problems of large quality fluctuation, high energy consumption, poor adaptability and the like in the traditional dyeing process are solved.

The invention discloses a mine reclamation effect evaluation method based on a multi-temporal remote sensing image, and belongs to the field of mine reclamation effect evaluation, and the method comprises the steps: obtaining and preprocessing multi-source remote sensing data, and obtaining a standardized multi-temporal data set; performing image registration and radiation correction on the standardized multi-temporal data set to obtain a correction data set with consistent space and consistent radiation; constructing a multi-dimensional feature data set based on the correction data set, wherein the multi-dimensional feature data set comprises surface morphological features, vegetation health features, soil temperature features and underground water level change features; extracting a surface form feature vector according to the multi-dimensional feature data set, and generating a vegetation health distribution map, a soil temperature feature map and an underground water level change distribution map; and fusing the surface form feature vector, the vegetation health distribution map, the soil temperature feature map and the underground water level change distribution map, constructing a comprehensive index system, and outputting a mine reclamation effect evaluation result. According to the method, the accuracy and comprehensiveness of reclamation effect evaluation are remarkably improved.

The invention discloses an image processing method applied to printed matter surface color difference detection, which relates to the technical field of image processing, and comprises the following steps: acquiring a digital image of a printed matter to be detected, performing illumination non-uniformity correction, and converting the corrected digital image into a CIELAB color space; performing region segmentation on the digital image based on double constraint conditions of color gradient and texture boundary to generate a detection region graph; feature parameters are extracted based on the detection area graph, and a feature data set is generated; establishing a dynamic reference model by utilizing process parameters and material characteristics of the printed matter; and calculating the distance between the feature data set and the dynamic reference model, identifying color difference regions and generating color difference scores, and screening and grading the color difference regions according to the color difference scores. According to the method, a multi-layer image pyramid and homomorphic filtering combined illumination correction technology is adopted, and an adaptive weight fusion mechanism based on image features is introduced, so that the illumination nonuniformity is effectively eliminated while the definition of printing details is kept.

The invention relates to a power distribution network distributed energy bearing capacity assessment method and device. The method comprises the following steps: carrying out topology analysis on a network structure of a power distribution network to obtain an initial network topology model; obtaining a node dynamic feature data set based on the initial network topology model and the distributed energy access point data of the power distribution network; wherein the node dynamic characteristic data set comprises operation parameters of each node of the power distribution network in different load scenes; generating a parameter incidence matrix according to the node dynamic characteristic data set, and obtaining a bearing capacity reference model of the power distribution network according to the parameter incidence matrix and real-time data of the power distribution network in an operation state; wherein the parameter incidence matrix is used for quantifying the coupling degree between the operation parameters; and obtaining a risk distribution mapping graph according to the bearing capacity reference model, and identifying a potential overload area of the power distribution network based on the risk distribution mapping graph. According to the invention, power distribution network operation risk assessment can be accurately realized.

The invention discloses a visual positioning method based on an indoor fine three-dimensional model. The method comprises the following steps: acquiring an indoor original image through a camera, synchronously acquiring the attitude angular velocity of the camera, constructing a first feature data set, and correcting to generate a second feature data set; aligning the second feature data set with a pre-constructed indoor live-action three-dimensional model to generate first position estimation data under spatial boundary constraint; according to the first position estimation data, positioning a feature point region from the dynamic image data acquired in real time, and tracking a point movement track in movement track offset to generate a first feature sequence and the like; according to the scheme, through the technical means of multi-source data fusion, dynamic environment adaptability, a local updating mechanism, path planning optimization and the like, the precision, stability and efficiency of indoor visual positioning and navigation are remarkably improved, and an efficient and reliable solution is provided for positioning and navigation in a dynamic environment.

The invention belongs to the technical field of monitoring and fault early warning of an intelligent packaging production line, discloses an intelligent packaging production line control system and method, and aims to realize comprehensive monitoring and fault early warning of the intelligent packaging production line. Comprising the steps of collecting electric power parameter data of an electrical component; the power parameter data comprises current, voltage, energy utilization ratio data and a vibration signal of the electrical component; preprocessing the power parameter data to form an electrical characteristic data set; training according to the electrical characteristic data set to obtain a fault state prediction model, and predicting based on the fault state prediction model to obtain a fault probability value of the electrical component; judging whether the intelligent packaging production line has a fault according to the fault probability value of the electrical component; when it is detected that the intelligent packaging production line has a fault, the electrical component having the fault is analyzed, and targeted regulation and control measures are taken, so that efficient fault identification and regulation and control in various special environments are ensured.

The invention discloses a thunder and lightning early warning method based on atmospheric electric field networking monitoring. The method comprises the following steps: collecting multi-point electric field intensity data in a region in real time through a high-density electric field sensor network, and constructing an electric field intensity distribution matrix; for the constructed electric field intensity distribution matrix, space-time evolution characteristics of lightning activities are extracted, and lightning occurrence probability distribution is generated; acquiring temperature, humidity and air pressure parameters from multi-source meteorological data, performing data fusion with the generated thunder and lightning occurrence probability distribution, and constructing a comprehensive environment characteristic data set; and for the constructed comprehensive environment characteristic data set, calculating a comprehensive risk index, and if the index exceeds a preset thunder and lightning triggering threshold, etc. According to the lightning early warning method, the distribution matrix is constructed through the high-density electric field network, the spatial-temporal characteristics are extracted to generate the initial probability, and the physical rationality and the environment false alarm resistance of early risk identification are remarkably improved through fusion with the multi-source meteorological data.

The invention provides a pipeline optical cable laying state monitoring method and system based on artificial intelligence, and the method comprises the steps: obtaining an optical cable vibration signal, and generating a vibration feature data set based on the optical cable vibration signal; constructing a mechanical equilibrium equation based on the vibration characteristics of the vibration characteristic data set, and calculating the stress states of the optical cable in different pipeline environments; obtaining the current laying tension, generating a control strategy, and adjusting the laying tension to obtain an updated tension value; constructing an anomaly detection model of the spatial-temporal characteristics to generate an anomaly score, and identifying whether the optical cable is abnormal or not in real time; and generating an updated laying strategy and a construction quality report based on the corrected tension control signal. The problems of abnormal stress, monitoring lag, lack of intelligent optimization and the like in the prior art can be effectively solved, and the safety, stability and construction efficiency of optical cable laying are improved.

The invention relates to a motion state detection method and device based on a multi-modal sensor, and the method comprises the following steps: carrying out the inertial parameter extraction of an original acceleration signal in the motion process of a human body through a three-axis acceleration sensor, and obtaining a motion acceleration feature vector; the original pulse wave signals in the movement process are monitored in real time, and a cardiovascular physiological feature sequence is obtained; performing time-frequency domain conjoint analysis on the time sequence myoelectricity feature data set, the motion acceleration feature vector and the cardiovascular physiological feature sequence by adopting an adaptive wavelet transform technology to obtain a multi-dimensional feature fusion matrix; and performing dynamic segmentation on the multi-dimensional feature fusion matrix through a hierarchical clustering algorithm to obtain motion state feature subspaces, thereby solving the problem that a traditional motion monitoring method mainly depends on a single type of sensor, although the motion condition of a human body can be reflected to a certain degree, the motion state feature subspaces cannot be monitored. However, the technical problem of lack of comprehensive understanding of complex motion states is solved.

The invention relates to the field of seismic state recognition, and provides a converter station vector seismic state recognition method and system based on distributed optical fibers, and the method comprises the steps: collecting multi-dimensional vibration signal data of a converter station, and obtaining seismic monitoring data through processing; performing phase demodulation processing, time difference analysis and space vector decomposition algorithm processing on the seismic monitoring data to obtain seismic wave propagation vector characteristic data, and performing characteristic integration and optimization to obtain a seismic wave comprehensive state characteristic data set; identifying and classifying the seismic wave comprehensive state feature data set to obtain a seismic wave form classification result; performing analysis based on the seismic wave form classification result to obtain a converter station seismic state evaluation result; and carrying out comparative analysis on the evaluation result and a threshold value, generating earthquake early warning information of the converter station, and providing a corresponding emergency response suggestion according to the earthquake early warning information. According to the invention, accurate identification and classification of seismic waves are realized, and the earthquake safety early warning capability of the converter station is improved.

The invention discloses an intelligent monitoring method and system for the construction progress of a building decoration curtain wall. The method comprises the steps that multi-source construction data of a construction site are collected through Internet of Things equipment and a high-definition camera; constructing a multi-dimensional tensor model and extracting data association features by applying a local reversible mapping algorithm to generate a standardized feature data set; performing parallel computing through shared memory hierarchical process mapping by utilizing the standardized feature data set, and outputting a project progress state evaluation result; constructing a decision diagram based on the project progress state evaluation result, and generating a construction progress deviation report; predicting a construction progress trend by adopting a sample optimal private regression algorithm and generating an optimization scheme; and visually displaying the construction progress prediction report and carrying out early warning. According to the invention, the problems of low data integration efficiency, delayed decision response and weak multi-dimensional data association analysis capability of traditional curtain wall construction monitoring are solved, and intelligent and efficient monitoring of the construction progress of the building decoration curtain wall is realized.

The invention relates to the technical field of wind power generation. The invention provides a yaw static and dynamic error adaptive method based on big data backtesting, which comprises the following steps of: constructing a multi-dimensional feature vector through multi-source data fusion data, and establishing a dynamic feature data set; based on the dynamic characteristic data set, a space-time diagram convolutional network is adopted to establish a wind power plant dynamic error prediction model, and space-time evolution rules of wind shear, turbulent flow and wake flow effects are captured; constructing a variational self-coding reference model based on historical full wind speed section data, calculating a residual error between a current working condition and the variational self-coding reference model in real time, and taking the residual error as a static error prediction value; performing adaptive weight fusion on the static error prediction value and the dynamic error prediction value to obtain a fusion error; and the fusion error is converted into the yaw angle correction amount, and the wind facing action is executed. The problems that an existing yaw error recognition technology is high in data dependence, lack of dynamic analysis, poor in model adaptability and difficult in complex wind field processing are solved.