891 results about "Adaptive weighting" patented technology

The invention discloses an adaptive question-answering system based on knowledge distillation and multi-modal dynamic fusion, and the system comprises a knowledge distillation module which is used for migrating knowledge of a teacher model pre-trained on corpora in the communication field to a lightweight student model, achieving model compression through optimizing a distillation loss function, and obtaining a multi-modal dynamic fusion model; the loss function comprises a soft label output by the teacher model and a KL divergence constraint output by the student model; the multi-modal knowledge fusion module comprises a feature extraction unit, a self-adaptive weighting unit and an attention fusion unit; the self-adaptive inference engine comprises a semantic analysis unit; according to the cross-modal reasoning method and system, semantic alignment of equipment parameters, protocol texts and topological graphs is achieved through the multi-modal dynamic fusion technology, and the cross-modal reasoning accuracy is improved; compared with an original model, the lightweight student model has the advantage that the reasoning speed is increased in a protocol analysis task.

The invention discloses an industrial equipment health monitoring and fault early warning system based on edge computing, belongs to the technical field of edge computing, and aims to solve the technical problem of how to efficiently realize health monitoring and fault early warning of equipment in real time and identify different types of faults. Each edge computing node is used for taking local data as input, predicting a fault category and a probability value corresponding to the fault category through a local model, outputting the fault category and the probability value as a prediction result, encrypting the local data and the prediction result through the cloud edge collaboration module and then sending the local data and the prediction result to the central server layer, the fusion model comprises three fault diagnosis models, namely a first fault model constructed based on LSTM, a second fault analysis model constructed based on a rule expert model and a third fault analysis model constructed based on a physical model; and the central server layer is used for fusing prediction results based on a self-adaptive weight mechanism.

The invention provides an access control system based on face recognition and a recognition method, and the system comprises a multi-modal image collection subsystem which integrates a visible light camera, a depth camera and an infrared thermal imaging camera and is used for synchronously collecting a visible light image, a depth image and an infrared thermal imaging image of a face; the image preprocessing unit is used for carrying out corresponding preprocessing on the image; the feature extraction network module is used for extracting image features; the multi-modal feature fusion and alignment module is used for calculating a mutual information matrix by adopting a feature fusion strategy based on mutual information maximization and adaptive weighting, determining an adaptive weight and performing weighted summation, and performing feature alignment by applying a method based on canonical correlation analysis; and the face recognition classification and verification module verifies the image by adopting a multi-level fusion strategy. According to the method, the false recognition rate and the rejection rate are effectively reduced, the strong anti-cheating capability is achieved, and cheating attacks of various fake face means can be accurately recognized and resisted.

The invention relates to the technical field of man-machine interaction, in particular to a robot behavior mode dynamic adjustment method based on multi-mode perception, which comprises the following steps: S1, collecting a visual image, a voice signal and an environment parameter of a target scene in real time; s2, extracting each modal feature; s3, dynamically allocating weights, and generating a fusion feature vector; s4, identifying a current scene type and a user attribute; s5, matching a corresponding interaction mode from a preset strategy library based on the scene type and the user attribute identified in the step S4; and S6, executing the matched interaction mode in the S5, and updating the weight distribution rule according to the feedback data. According to the method, vision, voice and environment characteristics are fused through a multi-mode perception technology, the behavior mode of the robot is dynamically adjusted based on a self-adaptive weighted fusion algorithm, and the interaction strategy is optimized in combination with user feedback, so that the interaction accuracy and the intelligent level of the robot under different scenes and user attributes are improved.

The invention relates to the technical field of aircraft skin defect detection, and discloses an improved YOLOv11-based aircraft skin defect multi-scale detection system and method. Extracting multi-scale features of the input image through HMSB; the HMSB is composed of a plurality of parallel sub-networks, each sub-network independently processes input images with different resolutions, and a lightweight Transform encoder is adopted to extract multi-scale features; the multi-scale features are adaptively fused through DFFM; the DFFM calculates the attention weight of each scale feature through a gating mechanism to realize self-adaptive weighted fusion of the multi-scale features; the defects are classified and positioned through SDH, and a detection result is output; the SDH comprises detection branches customized for various defect types, and each branch adopts a deformable convolution and a Transform decoder to improve the positioning precision of irregular defects. According to the invention, through hierarchical multi-scale feature processing and a dynamic adaptive fusion mechanism, the performance bottleneck of a traditional method in detection of tiny and irregular defects is solved.

The invention provides a transfer learning-based power transmission line fault positioning method and system. Real-time monitoring data of a target power transmission line are collected, meteorological information and satellite remote sensing images are combined, ambient environment changes are analyzed, anomaly detection is performed, an abnormal data set is obtained, and a comprehensive fault feature space is constructed; an adversarial domain adaptation algorithm is adopted to optimize a comprehensive fault feature space, and a long-short-term memory network, an attention mechanism and a graph convolutional network are used for data processing to generate an enhanced fault feature representation; constructing an integrated learning framework based on Bayesian optimization, predicting the target power transmission line by using the plurality of preliminary basic models, integrating all prediction results through an adaptive weighting strategy, and generating initial fault probability distribution; determining fault positioning information by using a geographic information system technology and a topological sorting algorithm; the invention provides an efficient and accurate power transmission line fault positioning method, and provides powerful support for safe operation of a power system.

The invention discloses a multi-scale feature fusion fault prediction method for a high-speed rail bearing. The method comprises the following steps: acquiring a fault vibration signal of the high-speed rail bearing through a vibration signal comparison technology; noise and interference signals of the high-speed rail bearing fault original vibration signals are removed through an adaptive filter; performing multi-level decomposition on the vibration signal by using wavelet packet decomposition, extracting sub-band signals of different frequency bands, calculating instantaneous frequency and energy distribution of the sub-band signals by using Hilbert-Huang transform, and forming a time-frequency-energy three-dimensional feature map; carrying out weight distribution on each feature dimension in the three-dimensional feature map by adopting a self-adaptive weighting algorithm; constructing a training data set through the time-frequency-energy three-dimensional feature map and the fault type label; and training the long short-term memory network LSTM by using the training data set, constructing a fault prediction model, and performing fault prediction on the high-speed rail bearing. According to the invention, accurate prediction of the high-speed rail bearing fault is realized, and the accuracy of fault detection is improved.

The invention provides a real-time deep sea subsurface buoy monitoring system, which belongs to the technical field of deep sea measurement, and comprises a control chip, a multi-parameter sensor array, a data storage device and a power supply, high-precision monitoring is realized through the following steps: recording ocean data acquired by a multi-parameter sensor; correcting the sound wave propagation model and calculating the relative position of the subsurface buoy; constructing a three-dimensional data matrix and realizing data compression; identifying abnormal data by using a matrix disorder index; processing the time sequence data by adopting a sliding window weighted average method and eliminating jump; carrying out distributed preprocessing on the multi-source heterogeneous data and carrying out parallel processing by applying a matrix partitioning technology; accumulated errors are corrected by combining historical data and applying a Kalman filtering algorithm, and the problems of environment interference and sensor drift are solved through a deep sea environment disturbance compensation network model and a self-adaptive weighted error optimization function.

The invention discloses a black pig image segmentation method based on multi-feature fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining a black pig image, and carrying out the contrast enhancement processing; inputting the enhanced image into a residual convolutional network to generate a depth feature map, extracting a local shape feature map through curvature threshold segmentation and a double fitting strategy, and fusing the two feature maps to generate a black pig feature map; establishing a spatial position prior probability graph based on the black pig sample library, calculating regional correlation and performing adaptive weighting to obtain a fusion feature graph; boundary segmentation and iterative optimization are carried out on the fused feature map based on the dynamic behavior pattern map and the attitude constraint rule, and an initial segmentation map is generated; and adopting a group behavior model as an optimization criterion, correcting the boundary of the initial segmentation image, and outputting a final segmentation result. According to the method, the segmented enhancement function based on the double-peak characteristic and the local texture feature self-adaptive adjustment strategy are constructed, so that differential enhancement of image preprocessing is realized.

The invention discloses a multi-scale attention aggregation-based target point cloud segmentation method for a battery replacement robot, and the method comprises the steps: 1, collecting an RGB image, a depth image and three-dimensional point cloud data of a target fastener through a binocular structured light depth camera, and carrying out the fusion to generate a FastSeg3D data set; 2, a two-stage preprocessing method is provided, noise points are removed through radius filtering, and background point clusters far away from a target are removed through DBSCAN density clustering; 3, the network encoder uses a local feature aggregation module to extract geometric features, and the calculation complexity is reduced in combination with a random sampling strategy; 4, embedding a multi-scale attention aggregation module into the jump connection of the encoder and the decoder, fusing the features through a channel and a space attention unit, and achieving the self-adaptive weight weighting of the features of each layer of the encoder; and 5, recovering the resolution of the original point cloud by adopting nearest neighbor interpolation up-sampling, outputting a segmentation semantic tag, and obtaining a high-quality point cloud target. According to the method, the operation time of the battery replacement robot is shortened, and the balance problem of large-scale target point cloud segmentation speed and precision is solved.

The invention discloses an improved YOLOv5 protective equipment detection method combining channel selection and an attention mechanism, and the method comprises the steps: replacing an original spatial pyramid pooling fusion module SPPF of a YOLOv5 backbone network part with a channel selection multi-scale fusion module CSMF, and enabling the channel selection multi-scale fusion module CSMF to be based on a feature adaptive weighted fusion strategy of a multi-channel selection mechanism. According to context information and semantic distribution of input image features, fusion weights of feature channels under different receptive fields are dynamically adjusted, so that more discriminative multi-scale feature integration is realized. According to the invention, the adaptive capacity of the model to multi-scale targets is improved through the CSMF module, and the problem of inaccurate recognition of small targets such as gloves by a traditional model is solved; after the CBAM is adopted to enhance the MBconv module, the model can more effectively distinguish a key area and a background area in an image, and the robustness of target shielding and background interference in a complex scene is enhanced.

The invention belongs to the technical field of model fusion in a marine environment, and provides a reinforcement learning-based sea wave multi-model fusion method, which comprises the following steps of: S1, defining a state, an action and a reward function of a reinforcement learning model; s2, designing and initializing a reinforcement learning agent; s3, collecting data by using the experience playback buffer area; s4, sampling data from the experience playback buffer area to update the network; and S5, dynamically adjusting the fusion weight of the model to realize adaptive weighting. Based on a reinforcement learning algorithm, the weighting coefficient of each basic model is dynamically adjusted through interaction and feedback with the environment, so that the optimal model fusion effect is realized in different marine environments. According to the method, reasonable states, actions and reward functions are defined, so that the model can adaptively learn how to optimally combine with the output of the basic model, and relatively high prediction precision and reliability are always kept in a complex and dynamic marine environment.

The invention discloses an on-load tap-changer vibration fault diagnosis algorithm based on tensor feature and adaptive weighted Stacking integration, relates to the technical field of on-load tap-changer fault diagnosis, and is used for improving the fault diagnosis precision. Comprising the following steps: S1, data acquisition; s2, feature extraction; the method comprises the following steps: extracting multi-scale time-frequency characteristics of an on-load tap-changer vibration signal by using wavelet scattering transform WST, and realizing low-rank decomposition and dimensionality reduction characterization of high-dimensional characteristics by combining a non-negative tensor decomposition model NTF; s3, fault diagnosis; a multi-base learner Stacking integration framework is adopted, and a prediction matrix is generated through K-fold cross validation; through a swarm intelligent optimization algorithm SRA, hyper-parameters and fusion weights of all base learners are adjusted, L2 regularization suppression over-fitting is introduced, and finally fault classification is realized by adopting a logic regression element learner with Softmax cross entropy. According to the invention, through fault diagnosis of multi-model adaptive fusion and optimization, the fault identification precision, stability and on-line monitoring capability are improved.

The invention discloses a heterogeneous sensor fusion method and system for dynamic covariance optimization, and the method comprises the steps: constructing a multi-source heterogeneous sensor network which comprises an underwater inertial navigation unit, a Doppler velocity sensor, an ultra-short baseline positioning system, a submarine topography matching device and a geomagnetic gradient detector; the method comprises the following steps: designing a distributed integrated navigation system by adopting a hierarchical fusion architecture, establishing a sensor confidence coefficient dynamic evaluation model based on online estimated error covariance matrix eigenvalue analysis, carrying out state prediction on a local filter level by applying Kalman filtering, and then carrying out measurement updating by adopting a novel variational Bayesian algorithm; a sensor autonomous switching and adaptive weighted dual-mode adjustment mechanism is developed on the main filter level, and self-healing improvement of navigation precision in a non-stationary environment is realized, so that the problems of dynamic reliability evaluation and fusion efficiency optimization in underwater multi-sensor multi-source navigation are solved.

The invention discloses a fragmentation method, system and device based on information entropy and Transform. The method comprises the following steps: calculating a basic amazing degree index and a corresponding attention distribution entropy value of each word position in a text sequence to be fragmented; dynamically adjusting the basic amazing degree index through the attention distribution entropy to obtain a corrected amazing degree, and obtaining a candidate boundary position set; the KL divergence of each candidate boundary position is calculated, and local entropy increase features are obtained through the information entropy variation; performing feature fusion processing on the corrected amazing degree, the local entropy increasing feature and the KL divergence based on an adaptive weighting mode to obtain a boundary score of each candidate boundary position; constructing an initial boundary position set, and carrying out constraint and optimization adjustment on the candidate boundary position set to obtain an optimized boundary position set; and performing segmentation processing on the to-be-fragmented text sequence to obtain text fragments conforming to semantic logic. The fragmentation method provided by the invention shows good adaptability on corpora in different fields.

Disclosed in the present invention is a physics-informed neural network-based thermo-mechanical coupling analysis method for an inertial microsystem, the method comprising: S1, configuring material parameters and boundary conditions of an inertial microsystem, and establishing a thermo-mechanical coupling analysis model; S2, performing electro-thermal coupling analysis to obtain a temperature distribution of the inertial microsystem; S3, performing thermo-mechanical coupling simulation analysis to obtain a thermal stress distribution of the microsystem; S4, predicting temperature fields of the microsystem by means of a physics-informed neural network; S5, using the temperature fields as boundary conditions for mechanical simulation of the microsystem, obtaining mechanical properties such as stress and strain of the microsystem; and S6, performing electromechanical coupling simulation analysis to analyze the impact of structural deformation on various parameters of electrical performance. The present invention improves the solution accuracy of the neural network by means of an improved adaptive weighting strategy, combines a complete polynomial basis function with the neural network, and introduces an expanded basis function to reduce the state dimensionality, thus reducing computational costs and time, achieving accurate prediction of temperature fields of microsystems at multiple moments, and allowing for computation of the performance of microsystems under electro-thermal-mechanical multi-physics coupling.

The present disclosure discloses a multi-modal brain network calculation method, apparatus, device, and storage medium. The method is configured to train a brain disease prediction model. After the brain region structural feature and the brain region functional feature are separately extracted from magnetic resonance diffusion tensor imaging data and brain functional magnetic resonance data, a graph representation diffusion learning network is used to separate the universal feature and the unique feature in the brain region structural feature and the brain region functional feature. And then, multi-modal universal and unique feature fusion is implemented based on an alignment algorithm and adaptive weighting technology. Thus, complementary information between the multi-modal data is fully mining. The model can learn an effective feature of a related disease in a training process, and a finally obtained brain region disease prediction model has higher precision and better prediction effect.

The invention relates to a multi-modal sparse fusion three-dimensional target detection method in a structured environment, and the method comprises the steps: carrying out the cross-domain self-compensation of point cloud geometric features and image spectral features, and carrying out the fusion of multi-view sparse features; the perception capability and the recognition precision of a long-distance target, a complex shielding target and a small-scale target, and the detection performance and the real-time performance of a long-distance dynamic target are improved; a novel ParScaleNet image backbone network is designed, and a parallel multi-branch structure is introduced to enhance the multi-scale feature representation capability and the channel dependency relationship modeling capability; a staged progressive feature fusion strategy is adopted, hierarchical feature interaction is realized on a fine-grained spatial scale, and a cross-channel adaptive weighting mechanism is combined, so that the network can efficiently extract and focus key features, the feature expression ability of the model is improved, the stability and robustness of detection precision are ensured, and the detection accuracy is improved. And high-efficiency and high-precision target detection in a structured road scene is realized.

The invention discloses a supervised learning-based data recognition method and system, particularly relates to the technical field of data recognition, and is used for solving the defects of an existing supervised learning method in low-frequency feature capture and feature association modeling. Training data are collected from a plurality of data sources and samples which do not meet a quality threshold are filtered; performing information gain measurement on the features through a dimension reduction algorithm, and screening a feature set with high information gain; generating a sparse feature combination set by using an association rule algorithm, and extracting key low-frequency features and an interaction relationship thereof; inputting the low-frequency feature set into the gradient elevator for supervised training, and strengthening the influence of the low-frequency features through an adaptive weighting strategy; in the training process, the gradient updating trend and Shapley value distribution of the low-frequency features are monitored, a weighting strategy is adjusted for abnormal features to correct model weight distribution, the generalization ability and prediction precision of the model are improved, and the method is suitable for feature recognition and analysis of complex data scenes.

The invention discloses an ultrasonic endoscope navigation system and method based on deep learning, and relates to the field of medical image analysis, and the system comprises a parallel encoder module which is used for extracting local features of an ultrasonic endoscope image through a CNN branch, and capturing global context information through a Transform branch; the channel attention fusion module is used for carrying out adaptive weighted fusion on the extracted features; a decoder module that generates an anatomical structure segmentation mask based on the fused feature; the time sequence processing module is used for receiving the frame-by-frame segmentation result output by the decoder module and realizing time sequence coherence analysis of the ultrasonic endoscope video through a bidirectional LSTM network; and the multi-modal fusion module is used for carrying out registration and fusion on the ultrasonic endoscope images. According to the scheme, a high-reliability artificial intelligence auxiliary tool can be provided for early screening of pancreatic cancer, meanwhile, the learning threshold and clinical application cost of the ultrasonic endoscope technology are reduced, and popularization of the ultrasonic endoscope technology in basic medical institutions is promoted.

The invention discloses a multi-base-station AOA cooperative low-altitude target rapid positioning system. The system comprises an AOA measurement base station network, an AOA data preprocessing module, an adaptive weighted intersection positioning module, an extended Kalman filtering state estimation module and a data fusion and system integration module. The system synchronously measures the arrival angle of a target signal through multiple base stations, removes noise in combination with smoothing filtering and an anomaly rejection algorithm, and then solves the initial position of a target by using a self-adaptive weighted intersection algorithm based on measurement quality and geometric distribution. And then, fusing the target motion model and the measurement model by adopting an extended Kalman filtering algorithm to realize dynamic estimation and prediction of the position, the speed and the course. The system can realize high-precision and real-time positioning and continuous tracking of targets such as low-altitude unmanned aerial vehicles, small aircrafts and the like in a complex electromagnetic environment and a sight distance limited scene, and has visual display and regional alarm functions.

The invention relates to the technical field of computer vision, in particular to a high-rise facility operation risk monitoring method based on deep learning and point cloud detection, and the method comprises the steps: collecting a three-dimensional point cloud in real time, and extracting a target point cloud through dynamic threshold denoising and template registration; performing joint coding on space geometry and time sequence motion by using a pre-trained space-time diagram network in combination with an attention mechanism; high-reflectivity beacon points are identified, and the change rate of displacement and angular velocity is calculated; constructing a gating fusion model, dynamically weighting and coupling semantic features and measurement data, and generating risk probability distribution through mutual information consistency check; a fuzzy logic classifier with membership degree optimization is used for mapping to four-level early warning, and grading response is triggered; after early warning, a key frame incremental learning fine tuning model is extracted, and preprocessing parameters are reversely optimized to form a closed loop. According to the method, through multi-source heterogeneous data fusion, dynamic adaptive weighting and a self-evolution mechanism, the real-time performance, accuracy and robustness of risk monitoring in a complex construction environment are remarkably improved.

The invention provides a lightweight video anomaly detection method and system based on information gating compression, and the method comprises the steps: carrying out the feature extraction of a video sequence, and obtaining a high-dimensional semantic feature; calculating an L2 norm, an energy feature vector and a mean feature vector by using the high-dimensional semantic features; enhanced features are obtained through the L2 norm, the energy features, the mean value features and the high-dimensional semantic features; utilizing the enhanced feature vector to obtain a dynamic gating weight; obtaining a value judgment matrix by using an L2 norm; based on the video sequence length, obtaining a dynamic mask through calculation; obtaining a final feature vector through the dynamic mask; obtaining a final abnormal score by using the final feature vector; and obtaining a detection result through the final abnormal score. According to the method, by introducing an attention mechanism and an amplitude-based gating strategy, the non-key feature flow is adaptively weighted or cut off, and the model is guided to focus effective information.

The invention belongs to the field of demand prediction, and particularly relates to a water supply demand prediction and resource allocation method. Comprising the steps of obtaining data from an external data source and performing dynamic multi-stage standardization processing to form an input data set; constructing an adaptive weighted multi-scale time sequence convolutional network model to predict a water supply demand; calculating and distributing the optimal water supply quantity of each region based on the predicted demand quantity and the water resource reserve quantity; and acquiring actual water consumption data regularly, calculating deviation, and feeding back and adjusting parameters of the prediction model. According to the method, the data problem is solved through multi-source data fusion and multi-stage preprocessing, the prediction precision is improved by combining a multi-scale time sequence convolutional network with an adaptive weighting mechanism, the resource allocation is optimized by a dynamic allocation objective function, the long-term stability of a model is ensured by a feedback optimization mechanism, the efficiency of a water supply management system is effectively improved, and the method is suitable for the field of water supply management.

The invention discloses a rolling bearing fault diagnosis method and system based on multi-channel adaptive fusion, and the method comprises the steps: obtaining a one-dimensional vibration acceleration signal, carrying out the normalization, and converting the one-dimensional vibration acceleration signal into a two-dimensional gray-scale map; inputting the two-dimensional grey-scale map into a convolutional neural network model for processing, specifically comprising the following steps: a multi-scale feature extraction module extracts feature information of the two-dimensional grey-scale map, adaptively weights the importance weight of multi-channel information, and outputs a first multi-scale feature map; a double-attention cavity convolution module extracts the spatial position and channel information of the first multi-scale feature map, and outputs a second feature map; and the residual dense module performs deep feature extraction on the second feature map through residual connection and dense connection, and outputs a rolling bearing fault diagnosis classification result. According to the method, the problems of low model fault diagnosis effect and poor generalization ability caused by strong noise interference and large load change of the rolling bearing can be solved.

The invention relates to an MT-InSAR self-adaptive fusion measurement method and device for long-term surface deformation of a coal mining subsidence area, and the method comprises the steps: obtaining surface deformation monitoring data in a plurality of modes, and carrying out the at least one of time-space alignment, noise removal and error correction of the surface deformation monitoring data, so as to obtain surface deformation data; calculating coherence indexes of the earth surface deformation data; generating a space adjustment coefficient by using a double-peak Gaussian function model constructed based on a coal mining subsidence area deformation rule; constructing an adaptive weighting method based on the coherence index and the spatial adjustment coefficient of the earth surface deformation data to calculate the weight of the multi-source InSAR data; and performing weighted fusion on the surface deformation monitoring data by using the weight of the multi-source InSAR data to obtain a final surface deformation result. Therefore, the problems of insufficient monitoring precision, incomplete coverage, poor adaptability and the like due to the fact that a single InSAR technology is difficult to comprehensively reflect spatio-temporal evolution characteristics of long-term surface deformation of the wide-area coal mining subsidence area are solved.

The invention relates to the technical field of image processing, in particular to an intelligent recognition and positioning system for battery support welding defects. Obtaining defect degrees and edge pixel points of the pixel points according to gradient features and neighborhood gray features of the pixel points in the welding image; obtaining change complexity according to the change characteristics of the local edges of the edge pixel points; edge sharpness is obtained according to the local gradient features and the gray level change features of the edge pixel points; obtaining local uniformity according to the local gray level distribution characteristics of the edge pixel points; obtaining a confidence coefficient according to the change complexity, the edge sharpness and the local uniformity; and obtaining an adjustment factor of the edge pixel point according to the confidence coefficient and the defect degree. The method comprises the following steps: adjusting a weighting coefficient in a high-lift filtering algorithm according to an adjustment factor to obtain a self-adaptive weighting coefficient; performing image enhancement and defect identification according to the adaptive weighting coefficient; and the accuracy of image enhancement and defect identification is improved.

The invention discloses an electromagnetic bearing fault mode analysis method for fault diagnosis of a primary helium fan. The method comprises the following steps: establishing an electromagnetic bearing-rotor-rack high-fidelity coupling dynamic model; extracting fault feature frequency and vibration amplitude, preprocessing the monitoring signal, and extracting multi-source heterogeneous fault features; evaluating the sensitivity of each feature to a fault, designing an adaptive weighted D-S evidence theory fusion algorithm, and identifying a bearing fault mode by dynamically adjusting the weight of an information source; building a convolutional neural network model to automatically extract deep fault features corresponding to the working conditions, and performing diagnosis model adaptation among different working conditions by adopting a transfer learning strategy; the GRU network is used for training and learning time sequence monitoring data, the development trend of bearing faults is predicted, and a heuristic algorithm is used for optimizing a fan operation and maintenance strategy. According to the method, the limitation of the existing method can be overcome, accurate diagnosis and trend prediction of the bearing fault are realized, and key technical support is provided for intelligent operation and maintenance of the rotating machinery.

The invention relates to the technical field of industrial mechanism models, in particular to a shock absorber performance optimization control method based on model fusion, which comprises the following steps: extracting a low-frequency disturbance state variable and inputting the variable-topology industrial mechanism model to generate a nominal reference state trajectory; utilizing a depth state observation network fused with energy passivity constraint to calculate a non-linear model mismatch compensation amount and an adaptive weighting parameter; performing dynamic fusion on the nominal reference state trajectory and the compensation amount based on the adaptive weighting parameter to generate a generalized state estimation value; and executing dynamic multi-objective optimization based on the generalized state estimation value, and generating mixed mode control input acting on an execution end. According to the invention, through adaptive fusion of a mechanism model and a data driving method, physical consistency, calculation real-time performance and robustness of a control process are considered.

The invention discloses a high-precision real-time positioning system and method for positioning personnel based on multi-mode fusion. The system comprises a multi-source data acquisition module, an intelligent data processing module, a fusion positioning model construction module and a positioning result application module, provides visual monitoring, trajectory analysis and scene customization functions, and supports flexible expansion through modular design. According to the method, satellite positioning, Bluetooth beacon and inertial navigation technologies are integrated, the weight is dynamically adjusted in combination with an adaptive weighted fusion algorithm, and a fusion positioning model supports outdoor (satellite dominant + EKF calibration, the precision being 2-5 meters) and indoor (beacon dominant + fingerprint matching, the precision being lt); according to the invention, intelligent switching between three modes (inertial navigation + ZUPT correction) and signal loss (1 meter) is realized, data noise is optimized by using Kalman filtering, and high-precision real-time positioning in indoor and outdoor complex scenes is realized. According to the invention, the problems of insufficient precision, poor environmental adaptability and weak expansibility of a single positioning technology are solved, and the comprehensive performance of the positioning system is significantly improved.