3720 results about "Feature parameter" patented technology

The invention relates to the technical field of servo motor control, and discloses an intelligent servo motor control method and device and computer equipment. The method comprises the following steps: carrying out multi-dimensional data acquisition and preprocessing on a position loop, a speed loop and a current loop of the intelligent servo motor to obtain standard structured parameter data; identifying a working condition characteristic parameter set of the intelligent servo motor; establishing a two-stage optimization architecture comprising a motor parameter layer and a control algorithm layer, and generating a dynamic parameter adjustment mapping table; a depth deterministic strategy gradient network is trained based on the characteristic parameter set and the dynamic parameter adjustment mapping table, and an optimal control strategy model for different working conditions is generated; and the optimal control strategy model is utilized, control parameters are automatically switched according to the working condition types recognized in real time, and smooth transition control of the intelligent servo motor among different working conditions is achieved. Smooth switching among different working conditions is achieved, system oscillation caused by sudden change of control parameters is avoided, and the disturbance influence in the working condition switching process is greatly reduced.

The invention provides an equipment state intelligent early warning method based on danger perception, and relates to the technical field of equipment state intelligent early warning, and the method comprises the steps: obtaining equipment operation data which comprises multi-source heterogeneous data; performing feature extraction on the multi-source heterogeneous data to obtain unified feature representation; according to the unified feature representation, constructing a correlation model between the equipment operation state and the feature parameters; and obtaining to-be-analyzed equipment operation data. According to the method, an evaluation index system is automatically generated, an evidence reasoning network is adopted for comprehensive evaluation, online incremental learning and a self-adaptive threshold adjustment mechanism are introduced, real-time and accurate evaluation of the health state of the equipment is achieved, the method effectively fuses multi-source heterogeneous data, expert knowledge and a deep learning technology, and the method is suitable for large-scale popularization and application. The comprehensiveness, accuracy and real-time performance of equipment health state evaluation are improved, and powerful support is provided for equipment predictive maintenance.

The invention discloses a polyethylene plastic hollow plate defect detection method and system, and relates to the technical field of nondestructive testing, the system is composed of a plurality of functional modules, and the system comprises: a multi-mode sensing module for collecting multi-source data including a hollow plate surface image, three-dimensional morphology data and an ultrasonic reflection signal; the historical database construction module is used for performing space-time alignment and feature level fusion on the multi-source data to generate a composite defect feature matrix; collecting ultrasonic signal waveforms of known defects, extracting characteristic parameters, and establishing a historical waveform library; calculating a characteristic parameter classification range of each type of defects based on a historical waveform library; judging whether the detection waveform parameter falls into a classification range corresponding to the processing noise or not, and if so, rejecting the signal; otherwise, keeping; and the defect area identification module is used for dividing the surface of the hollow plate into uniform grids based on the surface image of the hollow plate, and counting the density of the residual ultrasonic detection sources in each grid.

The invention belongs to the technical field of intelligent machining path control, and discloses an intelligent numerical control machine tool automatic programming path optimization method based on workpiece modeling, which comprises the following steps: acquiring a CAD model, machine tool sensor data, tool wear data and historical machining logs, generating a workpiece characteristic parameter set, and fusing a three-level compensation mechanism to generate a dynamic error parameter set; then, dividing a preliminary risk level of the processing area, and performing secondary risk assessment to generate a comprehensive risk level; extracting a risk level conflict area, and determining a final risk level; constructing a static / dynamic cost matrix to obtain a path priority map; thirdly, generating an initial path, smoothing an optimized path trajectory, and performing multi-objective optimization to generate an optimized path planning table; cutting parameters are adjusted in real time, the path feasibility is verified, and a real-time control instruction set is generated; and finally, constructing a quality-process correlation model, generating a global strategy packet, forming closed-loop iteration, and completing system self-evolution.

The invention relates to an intelligent industrial equipment state monitoring device and method, and the method comprises the steps: installing a vibration sensor, an acoustic sensor and an image sensor at key parts of industrial equipment, and comprehensively collecting the operation data of the equipment. In the data preprocessing stage, vibration and acoustic signals are filtered and denoised, and image data are denoised and enhanced. Extracting multi-dimensional characteristic parameters from three aspects of vibration, acoustics and images; a random forest algorithm is adopted for model training, and the method comprises the steps of data preparation, segmentation, decision tree construction, voting mechanism, majority voting, model verification and optimization, testing and the like. In the model identification stage, the real-time features are input into the trained model, and the equipment state is determined through decision tree prediction and voting summarization. And when an abnormal state is detected, setting a multi-level alarm response according to the probability, and generating a maintenance suggestion. According to the method, the whole-process intelligent monitoring of the industrial equipment is realized, and the fault identification accuracy and the maintenance efficiency are improved.

The invention discloses an AI spraying system control method capable of achieving autonomous 3D scanning, and the method comprises the steps: obtaining three-dimensional surface data of a to-be-coated workpiece through high-precision 3D scanning equipment, constructing a surface model of the workpiece according to the data, and extracting surface feature parameters. Coating parameters set by a user are received, the coating parameters comprise the viscosity, the chemical property and the film thickness requirement of paint, the surface characteristic parameters and the coating parameters are input into the spraying optimization model, and an initial spraying track and technological parameters are generated through calculation in combination with historical data. Environment parameters are collected and associated with the initial process parameters, and the adjusted spraying track and the adjusted process parameters are generated through dynamic environment compensation. And through a robot control system, coating operation is executed according to the adjusted parameters, the spraying effect is optimized in real time, and it is ensured that the surface of the workpiece is evenly coated. Efficient automation of the spraying process is achieved, the coating quality and consistency are remarkably improved, and the automatic spraying device is particularly suitable for coating of workpieces with complex curved surfaces or changeable shapes.

The invention discloses an aluminum profile surface defect automatic detection method and system based on image recognition, and relates to the technical field of image processing and intelligent detection.The method comprises the steps that three-dimensional geometric data of the section of an aluminum profile are obtained through a three-dimensional scanning device, candidate observation angles and geometric feature parameters needed by coverage calculation are extracted, and the three-dimensional geometric data of the section of the aluminum profile are obtained; if the section shape contains a groove or a curved surface structure, marking a space coordinate range corresponding to the area of the shadow region to obtain a section geometric feature vector and a shadow region coordinate set; according to the aluminum profile surface defect automatic detection method and system based on image recognition, all-dimensional dead-corner-free detection of the surface of the aluminum profile is achieved, the method and system can adapt to the production takt of complex section shapes and changes, the accuracy and comprehensiveness of defect detection are improved, and effective technical support is provided for aluminum profile quality control.

The invention relates to a concrete working performance measurement method and system based on a multi-modal visual large model, and solves the problem that rapid detection of concrete working performance parameters is troublesome, and the method comprises the steps: based on the spatial semantic understanding capability of the multi-modal visual large model, combining a multi-view stereoscopic vision and structured light scanning technology, and calculating the working performance of concrete; reconstructing a three-dimensional geometric structure of the concrete slurry, extracting morphological characteristic parameters, and forming characteristic vectors; inputting the feature vectors into a pre-trained multi-task neural network, fusing the spatial-temporal features and combining a rheological algorithm to identify various working performance parameters; integrating identification results for at least three times by adopting integrated learning, and verifying parameters based on a fluid dynamics basic equation through a fluid simulation platform; and based on the verification result, generating a mix proportion optimization suggestion containing the material components. The method has the advantages that non-contact rapid measurement of concrete working performance parameters is achieved, precision and efficiency are improved, and mix proportion optimization suggestions are provided.

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a hydraulic engineering potential safety hazard assessment and prediction system and method based on image recognition. A multi-scale convolutional neural network is combined with a three-dimensional point cloud registration technology to extract surface visual feature parameters, and adaptive time-frequency analysis and a wavelet packet reconstruction algorithm are used to extract physical feature parameters of internal concealment defects; constructing a dual machine learning framework, eliminating environmental interference through a deep residual network, analyzing a causal relationship between features based on a gating cycle unit, and screening a key feature parameter set; a Gaussian process regression model of an adaptive kernel function is used for dynamic risk prediction, risk abrupt change points are identified in combination with multi-scale wavelet transform, and finally a safety state score and a grading early warning signal are generated through a fuzzy comprehensive evaluation algorithm.

The invention relates to the technical field of data processing, and discloses a comprehensive evaluation method and system for the health degree of a distribution network cable. The method comprises the following steps: collecting cable joint multi-source monitoring signals, normalizing the monitoring signals to obtain a degradation degree feature vector, correcting multi-physics field coupling model parameters, obtaining a recessive degradation index through finite element calculation to obtain an enhanced feature vector, and performing time-frequency domain decomposition to extract multi-scale feature parameters to obtain a comprehensive feature matrix; a double attention mechanism calculates a feature weight and a time sequence correlation degree to obtain a deterioration trend prediction value, and fuzzy integral is fused with a multi-classifier output probability to obtain a health degree evaluation grade and an early warning result. According to the invention, the early defect identification accuracy and the degradation trend prediction precision are improved.

The invention relates to an electromechanical fault prediction and diagnosis method and system based on big data, and the method comprises the steps: collecting the operation state data of electromechanical equipment in real time, and synchronously obtaining historical associated data; performing dynamic feature extraction on the operation state data and the historical associated data, constructing a sliding mean value feature matrix, and calculating dynamic weights of feature parameters; generating a fusion weight coefficient according to the dynamic weight and a preset fault threshold interval; extracting a distribution density curve of a historical fault occurrence probability, and calculating a dynamic threshold value; performing weighted reconstruction on the sliding mean feature matrix based on the fusion weight coefficient, and outputting a fault type and an occurrence probability through a pre-trained lightweight residual neural network model; and when the fault occurrence probability exceeds a dynamic threshold value adjusted based on a historical fault occurrence probability distribution density curve, generating an electromechanical fault diagnosis result so as to realize the purposes of real-time monitoring of the operation state of the electromechanical equipment and accurate fault prediction and diagnosis.

The invention provides an intelligent power distribution cabinet safety early warning method, system and device and a storage medium, and relates to the field of electric power. The method comprises the steps of obtaining multi-source heterogeneous operation parameters of the intelligent power distribution cabinet, and performing feature extraction on the multi-source heterogeneous operation parameters to obtain operation feature parameters; performing parameter fusion on the operation characteristic parameters to obtain fused characteristic parameters, and inputting the fused characteristic parameters into a preset fault early warning model to obtain fault early warning data; and inputting the fault early warning data into a preset rule matching engine for rule matching to obtain a matching result, and generating an early warning signal according to the matching result. According to the method, the accuracy and timeliness of safety early warning of the intelligent power distribution cabinet are improved.

The invention relates to the technical field of industrial automatic detection, in particular to an intelligent electromagnetic valve fault detection method and system based on multi-parameter analysis, and the method comprises the following steps: S1, synchronously collecting the dynamic current waveform, vibration spectrum information and environment temperature data; s2, extracting a current characteristic value and vibration energy distribution, and calculating a temperature drift amount; s3, the current characteristic value, the vibration energy distribution and the temperature drift amount are input into a multi-parameter incidence matrix, and a coupling deviation coefficient representing the coupling relation between parameters is generated; s4, calculating a comprehensive health index of the electromagnetic valve based on the coupling deviation coefficient; and S5, matching the comprehensive health index with a preset fault feature library, and outputting a corresponding fault type and a positioning result. According to the method, by fusing the multi-source characteristic parameters and introducing the health index evaluation mechanism, accurate recognition and positioning of the electromagnetic valve faults are achieved, and the detection accuracy and the response efficiency are improved.

The invention discloses a bridge crack identification and automatic evaluation method based on image identification and AI modeling, and the method comprises the following steps: S1, obtaining an original image of a bridge structure surface, and carrying out the image preprocessing; s2, inputting the standardized image into an image recognition model, performing pixel-level segmentation on a crack region in the image, and outputting a crack mask graph; s3, performing feature extraction processing on the crack mask graph, extracting geometric feature parameters of the crack, and constructing a crack feature vector; s4, constructing an evaluation model based on a supervised learning method, and training the evaluation model; and S5, inputting the crack feature vector into an evaluation model, evaluating the structural risk level of the crack, and outputting a structural risk label. According to the method, image recognition and AI modeling are fused, automatic crack recognition and evaluation are achieved, and the method has the advantages of being high in precision, clear in boundary and intelligent in evaluation.

The invention provides a method for diagnosing a structural service behavior based on a high dam accident mode and a failure path, and the method comprises the steps: constructing a high dam accident case database, and extracting accident feature parameters; by applying multi-scale layered adaptive corresponding analysis, through feature weight calculation, risk-out mode layered weighting, physical similarity index establishment and structural difference compensation, the problem of unbalanced feature weight is solved, and a weighted similar project sorting list is generated; constructing a multi-failure mode coupled time sequence adaptive network model, solving the problem that a failure path time sequence evolution mechanism is imperfect, and outputting a time sequence failure path prediction model; and in combination with the similar project sorting list and the time sequence failure path prediction model, performing project analogy analysis, and generating a high dam safety state evaluation report. The technical problems that the operation time of the ultra-high dam is short, the monitoring information is insufficient, the numerical analysis result is difficult to completely reflect the engineering practice and the like are solved, and a new technical path and a new solution are provided for high dam safety assessment and risk management.

The invention relates to the technical field of intelligent sorting, and discloses a multi-mode defect feature fusion intelligent sorting method and system, and the method comprises the steps: obtaining and preprocessing a surface image, infrared thermal imaging and voiceprint vibration data of an object; analyzing and generating multi-modal defect feature parameters, fusing to form a fused feature vector set, and constructing a defect detection reference set; performing dynamic matching verification on the multi-modal data based on the reference set, and analyzing the mismatching state of the surface texture and the thermal distribution by using a space alignment technology; and generating a defect form deviation degree index according to a verification result, and judging whether a sorting action is triggered or not. The system comprises a multi-modal acquisition module, a feature fusion modeling module, a form association verification module and a sorting judgment module which are used for respectively realizing data acquisition preprocessing, feature fusion modeling, cross-modal association analysis and sorting decision. Through multi-modal data fusion and cross-modal quantitative analysis, the comprehensiveness of defect detection and the sorting accuracy are improved.

The invention discloses an appearance visual detection method and device for an electric control part of a new energy automobile. Structural stress distribution data of the electric control component are obtained and subjected to partition processing, and the surface of the electric control component is divided into a stress sensitive area and a conventional area; carrying out multi-wavelength spectrum scanning and polarization state detection on the stress sensitive area by adopting self-adaptive path planning, synchronously acquiring a polarization characteristic matrix, dual-wavelength interference data and depth spectrum data of the determined key attention area, and carrying out time domain synchronization and space registration processing to generate multi-dimensional detection characteristic data; extracting an optical anisotropy coefficient, a microcosmic deformation tensor and an interface reflection feature vector, and determining layering defect feature parameters in combination with multiple threshold judgment; and calculating local curvature distribution, an interface shear stress field and thermal stress distribution, and performing quantitative matching with the material characteristic parameters to generate quantitative evaluation indexes of the layering defects. According to the invention, accurate identification and rapid positioning of microscopic layering defects in the multi-layer composite insulation structure of the electric control component are realized.

The invention discloses a typhoon wave forecasting method based on integrated machine learning. The method comprises the following steps: firstly, integrating historical typhoon wave data, meteorological data and marine environment data; preprocessing the data, including integration, cleaning, vacancy filling and standardization, and performing multi-source data completion by adopting a K-nearest neighbor algorithm and a spline interpolation method; secondly, screening key characteristic parameters through a Pearson's correlation coefficient, and reinforcing nonlinear correlation representation in combination with a mutual information method; then, constructing an integrated prediction model containing an LSTM (Long Short Term Memory), an XGBoost (X Goose Boost) and a Transform; and finally, dividing a training set and a verification set by adopting a dynamic time sequence division strategy, optimizing model hyper-parameters, and completing training and testing of the typhoon wave height prediction model. According to the method, the data sparsity problem is solved through multi-source data fusion and feature selection optimization, the generalization ability is improved through an integrated model architecture, and compared with a traditional single model, the training period is remarkably shortened, and the forecasting precision and timeliness are improved.

The invention relates to the technical field of instrument multi-task optimization, in particular to an intelligent instrument multi-task real-time optimization method and system based on dynamic resource scheduling. The optimization method comprises the following steps: acquiring a target item of each task in real time through a sensor array, constructing a multi-dimensional feature vector, dividing each task into task categories by using a fuzzy clustering algorithm, and presetting an initial priority for the task categories for multi-task feature parameter acquisition and classification modeling. According to the method, the multi-dimensional feature vectors including the task urgency degree, the calculation complexity and the data interaction frequency are constructed, and the fuzzy clustering algorithm of the task dependency constraint is introduced, so that the task categories are accurately divided, the cross-category interaction overhead of the dependency task is effectively reduced, and the compatibility of a scheduling strategy is improved from the source.

The invention relates to the technical field of quality monitoring, and discloses a diamond high-strength micro-powder quality detection method and system based on artificial intelligence. The method comprises the steps of obtaining a two-dimensional projection image sequence of diamond micro-powder particles, calculating a projection matrix based on camera calibration parameters and geometric constraints, obtaining a multi-view image data set of the particles, establishing a pixel-level corresponding relation, extracting three-dimensional space coordinates of the surfaces of the particles, and reconstructing dense point cloud data of the particles. Establishing a local coordinate system based on the dense point cloud data, determining attitude parameters of particles in a three-dimensional space, if the attitude parameters deviate from a normal range, performing attitude compensation processing to obtain standardized point cloud data, and performing three-dimensional grid model construction on the standardized point cloud data; and calculating geometrical characteristic parameters of the particles based on the three-dimensional grid model, performing defect detection on the surfaces of the particles, and generating a crystal integrity evaluation report of the particles. The quality detection accuracy of the diamond high-strength micro-powder particles is improved.

The invention discloses an AI-based bridge construction intelligent monitoring method, particularly relates to the field of data analysis, and comprises the steps of multi-modal sensing network construction, cross-domain characteristic parameter extraction, comprehensive state evaluation modeling and intelligent decision and regulation. According to the method, deformation, corrosion, vibration and other multi-physical field data are synchronously collected through a multi-modal sensing network, a cross-scale feature extraction algorithm is adopted to construct creep-corrosion collaborative factors and other composite indexes, the limitation of traditional single-dimensional parameter analysis is broken through, a dynamic evaluation model is fused with weighted integral and a nonlinear function, and the dynamic evaluation accuracy is improved. Precise early warning of risks such as bridge pier instability and cantilever cracking is achieved, the multi-field coupling effect quantification capacity is remarkably improved, an intelligent joint control mechanism automatically triggers instructions such as jacking reinforcement and damping tuning through a three-level emergency response system, a monitoring-analysis-regulation closed-loop system is constructed, the hysteresis of manual decision is effectively eliminated, and the safety and reliability of the system are improved. And all-weather self-adaptive safety guarantee is provided for bridge construction.

The invention relates to the technical field of data processing, and discloses a multi-granularity extraction and enhancement method, device and equipment for multi-source heterogeneous data. The method comprises the following steps: performing multi-level feature extraction on a multi-modal data set and original feature parameters to obtain a multi-modal feature set; performing correlation analysis on the multi-modal feature set to obtain a feature correlation matrix and optimized feature representation; performing cross-modal knowledge migration processing on the feature incidence matrix and the optimized feature representation to obtain migration feature parameters; performing dynamic fusion processing on the migration feature parameters to obtain target fusion features; and performing multi-task segmentation processing according to the target fusion features to obtain a plurality of sub-task sequences, performing time sequence enhancement and multi-task fusion analysis on each sub-task sequence to obtain target extraction data, and performing visual display on the target extraction data. According to the method and the device, the efficiency and the accuracy of multi-granularity extraction and enhancement for multi-source heterogeneous data are improved.

The invention relates to the technical field of vacuum electric furnace monitoring, and discloses a state monitoring system suitable for a vacuum electric furnace. A multi-source sensor array of the system collects multi-dimensional physical signals such as temperature distribution, pressure change and vacuum degree fluctuation in a furnace in real time; a furnace cavity feature reconstruction module extracts sampling point feature parameters and correlates coordinates to construct a three-dimensional dynamic feature field; the process anomaly analysis module calculates a process deviation degree in combination with a preset reference parameter, and marks an anomaly coordinate area; the state transition evaluation module analyzes an abnormal trend according to historical records and predicts a state transition path and rate; the collaborative regulation and control decision-making module generates a multi-stage vacuum maintenance compensation strategy and a heating power regulation gradient scheme according to the multi-stage vacuum maintenance compensation strategy; and the running log feedback module records a strategy execution process, and associates the three-dimensional feature field data to generate a state tracing log. The system can realize comprehensive monitoring of the state of the vacuum electric furnace, accurate abnormity identification, trend prediction, cooperative regulation and control and state tracing, and helps to improve the operation management level of the vacuum electric furnace.

The invention relates to the technical field of rock mechanics, and discloses a rock three-dimensional reconstruction method and system based on multi-source information fusion, and the method comprises the steps: obtaining and preprocessing data, carrying out the spatial feature learning of a fusion feature vector through a 3D-CNN network, and constructing a three-dimensional voxel model of rock microscopic damage; converting the fused image data into a point cloud model of the underground cavern surrounding rock structure by adopting a three-dimensional reconstruction algorithm based on point cloud, and constructing a digital twin framework of the underground cavern surrounding rock structure based on an implicit surface reconstruction algorithm; feature parameters output by the three-dimensional voxel model and the digital twinning framework are used as input, and the optimal supporting opportunity and supporting parameters are output through an LSTM-CNN fusion model; in the underground engineering construction process, surrounding rock deformation data are collected in real time, and a supporting scheme is adjusted in real time through a depth deterministic strategy gradient algorithm; according to the method, the scientificity and timeliness of support design under complex geological conditions can be improved.

The invention relates to the technical field of aeronautical equipment fault detection, and discloses an aeronautical converter fault feature extraction and diagnosis method based on multi-source information, which is characterized in that time sequence data and spatial distribution data are respectively collected and preprocessed, and the accuracy and the stability of the data are improved. Time feature extraction, spatial feature extraction, frequency spectrum acquisition and feature extraction are respectively carried out on the preprocessed data to realize fusion of multi-source information; information from different types of sensors and different feature domains is integrated, and multi-dimensional information of the equipment operation state is fully excavated, so that the accuracy and reliability of fault detection are improved. And finally, performing envelope detection on the signal to obtain an envelope line of signal amplitude change, and analyzing characteristic parameters of the envelope to detect fault information or pulse type fault characteristics in the modulation signal. And based on a deep convolutional neural network, learning a complex relationship between the envelope features and the fault, and realizing the diagnosis of the aviation airborne converter fault.

The invention relates to a machine room environment intelligent regulation and control method fusing multi-source sensing information, and the method comprises the steps: collecting the temperature and humidity data of different regions in a machine room through a temperature and humidity sensor, and collecting the movement track and stay time data of a person in the machine room through a person positioning device, transmitting the collected machine room environment data and personnel activity data to a data fusion module; in the data fusion module, machine room environment data and personnel activity data are aligned according to a time sequence, wrong data and outliers are eliminated, and a data set in a unified format is obtained; a correlation coefficient matrix among the characteristic parameters of the computer room is analyzed through correlation, the characteristic parameters with correlation higher than a preset correlation threshold value are screened out, and the characteristic parameters comprise temperature, humidity, personnel access frequency and staying duration parameters.

The invention discloses a rotating machine fault diagnosis method, which comprises the following steps of: acquiring vibration, temperature, acoustic emission and current signals at key parts of a rotating machine, and extracting characteristic parameters such as time domain and frequency domain after preprocessing such as filtering and noise reduction; and inputting the characteristic parameters into machine learning models such as a support vector machine, combining deep learning models such as a convolutional neural network and a long-short-term memory network, performing comparative analysis by using a digital twin model, and fusing diagnosis results to output fault types, positions, severity and maintenance suggestions. The method overcomes single diagnosis limitation, multi-source signal complementation, multi-model collaboration, accurate fault diagnosis and diagnosis reliability improvement, provides a scientific basis for equipment maintenance, and is of great significance for guaranteeing safe operation of rotating machinery, reducing maintenance cost and promoting industrial intelligent development.

The invention relates to the technical field of data acquisition and processing, and provides a real-time data acquisition and processing method and system for a distributed control system, and the method comprises the steps: collecting a data flow in real time through a terminal sensor, analyzing the probability distribution of a data change rate through a sliding window, and obtaining a data change rate; and a sensitivity threshold is intelligently generated by combining dynamic factors such as equipment electric quantity and network load. When data mutation exceeds a threshold value, the system automatically extracts a key change section and generates time, period and variable quantity three-dimensional features, and meanwhile, the frequency domain analysis result is fused to enhance the anti-interference performance. And the event emergency degree is calculated based on the time-space correlation of the spectrum energy entropy and the characteristic parameters, and finally a processing queue sorted according to priorities is formed and the bandwidth is dynamically allocated. According to the method, through multi-dimensional data fusion and resource adaptive adjustment, the response speed of the system to emergencies, the data discrimination precision and the stability in a complex environment are improved.

The invention relates to the technical field of power equipment state monitoring, and discloses a power equipment state evaluation and early warning method and system. The method comprises the following steps: collecting multi-source monitoring data of power equipment, and obtaining an equipment state data set by adopting a collaborative preprocessing method; a multi-dimensional feature extraction method is adopted to extract feature parameters reflecting the operation state and the degradation degree of the equipment; constructing an equipment health degree evaluation model, and obtaining the equipment health degree through a multi-time scale evaluation method; predicting a future deterioration trend and state transition time; establishing a grading early warning decision-making mechanism to realize early warning of the state of the power equipment; and identifying factors of equipment state degradation by adopting a root cause analysis method, and generating operation and maintenance decision suggestions according to historical cases. According to the invention, the health state of the power equipment can be accurately evaluated, and degradation trend prediction and fault early warning are realized.

The invention relates to the technical field of data processing, and discloses a load demand prediction method and system for a distributed new energy power generation system, and the method comprises the steps: obtaining historical data corresponding to power generation equipment and a load cluster in the distributed new energy power generation system, extracting feature parameters from the historical data, and carrying out the clustering analysis, dividing the power generation equipment and the load cluster into different subsets; processing the historical data corresponding to the power generation subsets and the load subsets to obtain a power generation output curve and a power utilization load curve; by analyzing the relevance between historical power generation and consumption data, obtaining a relevance index so as to fuse a power generation output curve and a power consumption load curve into a power generation load fusion model; and a parameter identification method is used to optimize the power abrupt change rate and the load change rate corresponding to the historical power generation data and the historical power utilization data, so that the power generation load fusion model is integrated into a generalized load model structure, a fusion equivalent model is obtained, and the load demand of the distributed new energy power generation system is accurately predicted.