4305 results about "Nerve network" patented technology

The invention relates to the technical field of signal devices, and discloses a traffic signal control method and system based on vehicle-road cloud multi-modal data fusion, and the method comprises the steps: collecting multi-modal traffic data synchronously in real time through a vehicle-end sensor, road-side sensing equipment and a cloud Internet platform; fusing the heterogeneous data by adopting a space-time alignment algorithm, and constructing a standardized space-time feature matrix; traffic flow prediction is carried out based on a multi-layer space-time diagram neural network trained by a federated learning mechanism, and a signal control instruction is generated through reinforcement learning and a multi-objective optimization model; and issuing the green wave parameter, the dynamic timing scheme and the cross-domain coordination strategy to a roadside signal machine through the cloud edge coordination architecture to execute control. The problems that in the prior art, low-delay private network communication cannot be achieved, the data fusion efficiency is low, unmanned driving is not supported, and the deployment cost is high are solved, and the purposes of low-delay communication, high reliability and low risk are achieved.

The invention relates to the technical field of coal mine safety monitoring, in particular to an underground coal mine dust concentration monitoring method and system based on multi-modal data fusion, and the method comprises the steps: synchronously collecting dust concentration time sequence data, dust image data, sound wave signal data and environmental parameters through a multi-modal sensor array deployed in an underground coal mine; carrying out preprocessing and feature extraction on the collected data; predicting the decomposed high-frequency and low-frequency component signals by adopting a long short-term memory neural network and a grey Markov model; when the environment humidity is greater than 80%, carrying out light scattering compensation on the predicted value; inputting various predicted values into an improved D-S evidence theory fusion device, and outputting a fusion dust concentration monitoring value; and when the threshold value is exceeded or the temperature and humidity composite condition is reached, an acousto-optic alarm is triggered and a spraying dust-settling device is started. According to the method, the problems of low precision of a single sensor, multi-source data conflict, high-humidity environment measurement deviation and insufficient time sequence and fusion precision in underground coal mine dust concentration monitoring can be solved.

The invention discloses an intelligent self-monitoring temperature management system for a box-type substation, and relates to the technical field of intelligent power grid equipment monitoring. The problems that an existing system is large in temperature measurement deviation, low in reliability, delayed in early warning, inaccurate in hot spot positioning and extensive in heat dissipation control are solved. According to the scheme, multi-source signals are acquired in parallel through a data acquisition module, and a temperature time sequence is extracted; a boundary calibration module is adopted to fuse data to generate a three-dimensional boundary condition; the multi-physical field solving module obtains an internal temperature / stress field; the physical information prediction module is fused with a heat transfer physical constraint training graph neural network to predict a hotspot migration trend; the hierarchical scheduling module is used for solving a fan and oil pump collaborative optimization instruction in real time based on model predictive control; according to the invention, the accuracy of internal temperature monitoring of the box transformer substation, the reliability of hot spot prediction and the accuracy of heat dissipation control are remarkably improved, the insulation life of equipment is effectively prolonged, and the operation safety and reliability of the system are improved.

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The invention discloses a cable life dynamic evaluation system based on multi-physics field coupling, and particularly relates to the field of industrial automation and control systems, which comprises a multi-physics field sensing module, a coupling analysis engine module, a dynamic life evaluation module, a digital twin interaction module and an environmental interference suppression module, through a distributed optical fiber temperature sensor, a capacitive electric field sensor and a magnetostrictive stress sensor, temperature, electric field, magnetic field and mechanical stress data of a cable are collected in real time, multi-physical field characteristics and a cable defect database are matched in real time by using a cross-scale dynamic association algorithm, a damage state is evaluated, and the cable defect detection accuracy is improved. A time sequence neural network architecture is adopted to predict the remaining life, model self-correction is achieved through digital twin comparison, interference is suppressed in combination with an environment-physical field coupling compensation matrix, sensing and evaluation of the health state of the cable, life prediction and continuous optimization of the model are achieved, and the efficiency of cable life evaluation is improved.

The invention relates to the technical field of safety monitoring, and particularly discloses an embankment danger rapid identification method and system, and the method comprises the steps: collecting multi-modal data in real time through arranging a multi-source sensor network; according to the phase space trajectory, extracting a Lyapunov exponent spectrum, correlating the dimension and the Kolmogorov entropy, and forming a structure response chaos degree index; calculating a hydrogeological coupling coefficient in combination with multi-scale decomposition and mutual information analysis; and fusing the two into a three-dimensional dangerous case feature tensor, inputting the three-dimensional dangerous case feature tensor into a pre-training model based on a deep convolutional neural network and a long-short-term memory network, realizing intelligent discrimination of high, medium and low risk levels, generating an adaptive monitoring instruction for a low-risk working condition, outputting a risk evolution trend map, and supporting closed-loop management and control.

The invention provides a heavy truck battery compartment guiding method and system based on visual perception. The method and system are used for automatic battery replacement operation in a complex industrial environment. According to the system, a multi-camera fusion visual perception platform is constructed, a plurality of industrial cameras arranged on the ground or ceiling of a battery swap station are used for collecting local images of different visual angles of a battery compartment, and a complete visual field image is generated through feature matching and image splicing. A battery compartment is coarsely positioned by adopting a YOLO series model, a bounding box region is extracted, pixel-level contour segmentation is realized by introducing SAM, and the complex background and multi-interference environment recognition capability is enhanced. And after segmentation, calculating a minimum enclosing rectangle of the battery compartment, obtaining a center coordinate and a deviation angle, and transmitting a pose parameter to an upper computer control system. According to the method, the defects of the laser radar are avoided, image processing, the deep neural network and multi-view information are fused, the recognition precision and stability are improved, the battery replacement efficiency and the unmanned level of the electric heavy truck can be remarkably improved, and reliable support is provided for green traffic.

The invention provides a split type direct current charging multi-split group charging and group control control system, and belongs to the technical field of split type direct current charging piles. The split type direct current charging system comprising a main control cabinet, a power distribution unit, a charging pile terminal and other hardware architectures is constructed; a voltage monitoring unit, a current sensor module, a harmonic filter and other multi-dimensional sensor networks are integrated to acquire power grid state data in real time, and a sliding time window method is used to carry out segmented analysis on the data to extract power grid characteristic parameters; establishing a multi-objective optimization model comprising a power balance equation and a power grid stability equation to calculate an optimal power distribution matrix, adopting a neural network to realize intelligent distribution and dynamic balance adjustment of the power of each charging pile terminal, and starting an emergency power redistribution mechanism to ensure stable operation of the system when the power grid is detected to be abnormal. The technical problem of unbalanced power distribution of the multi-pile charging system caused by power grid voltage fluctuation and harmonic pollution is solved.

The invention relates to the technical field of big data, in particular to an e-commerce marketing propaganda system based on behavior analysis, which comprises a global user portrait module, an intelligent recommendation engine, a marketing fatigue management module, a supply chain collaboration module, a lightweight terminal module and a budget and value management module. In the prior art, user portraits are constructed only depending on single channel data such as online clicking or purchase records, so that user interest modeling is incomplete and lagged; according to the method, all-channel behavior data such as APP, Web, offline POS and social media are integrated, a user-commodity-scene heterogeneous graph is constructed by using a graph neural network, and an interest attenuation period (for example, the weight is reduced by 50% after the interest of mother and infant users lasts for 18 months) is dynamically captured through an LSTM model; for example, after a user tries on a certain style of clothes offline, the system associates online behaviors in real time and recommends commodities of the same style, the cross-scene conversion rate is improved by 32%, and the user portrait coverage degree is improved by 60%.

The invention relates to the technical field of intelligent travel control of walking-aided robots, and discloses a multi-sensor-based obstacle avoidance control method and system for a walking-aided robot. The obstacle avoidance control method for the walking-aid robot is applied to walking-aid robot equipment, and specifically comprises the following steps: S101, completing the installation of a multi-sensor module on the walking-aid robot, and collecting multi-source data obtained based on multiple sensors in real time, unifying the multi-source data to a base coordinate system of the walking aid robot by adopting timestamp synchronization and a coordinate system conversion matrix, and generating a fused dynamic obstacle sequence map; and S102, on the basis of the dynamic obstacle sequence map, a Kalman filtering and LSTM neural network fusion model is adopted to predict the motion trajectory of the dynamic obstacle. According to the method, the environment perception and decision-making architecture of the walking-aid robot is reconstructed, a multi-sensor space-time cooperation mechanism is matched through cross-modal data of laser radar and binocular vision, and ultrasonic sensor low obstacle special detection is combined, so that the step recognition rate is greatly improved.

The invention discloses an intelligent management system based on metal powder production, and relates to the technical field of industrial automation intelligent management. Sensor arrays are deployed in an ultrasonic atomizer and a vacuum drying furnace, the dust concentration, the vibration frequency, the pressure, the humidity, the temperature and the oxygen concentration are measured in real time, and the current stage is obtained through a production plan; dynamically and finely adjusting the weight matrix according to the sensor parameters corresponding to different stages, generating a weighted comprehensive index, calculating theoretical control parameters based on a physical model, correcting the theoretical control parameters through an LSTM neural network in combination with real-time environmental data, outputting an equipment control instruction, and extracting the sphericity and the moisture residue of the metal powder after the production of the metal powder is completed, so as to obtain the spherical degree of the metal powder. And the production quality is judged, and different responses are adopted according to the judgment result. The multi-parameter cooperative control precision is further improved, and the system stability is enhanced.

The invention provides an intelligent control method and system based on electro-hydraulic linkage. The method comprises the steps that multi-mode environment parameters are collected, a digital twin environment model is constructed, and data are synchronized with an electro-hydraulic servo system in real time. And establishing a nonlinear model, identifying parameters by using a least square method and a recursive least square method, verifying the precision through a step response test, and updating. And constructing an adaptive LSTM neural network inverse model to output a control voltage signal. And the signal is subjected to weighted fusion with the output of a PID controller, and the PID controller dynamically adjusts a gain parameter according to an error integral term to generate a control instruction to drive the system. According to the invention, the LSTM inverse model provides feed-forward compensation, the PID controller realizes feedback correction, and the LSTM inverse model and the PID controller are complementary. Meanwhile, based on a related nonlinear model and a parameter self-adaptive updating mechanism, the influence of working condition change is accurately described, and high-precision control of the electro-hydraulic servo system under the complex working condition is achieved.

The invention relates to the technical field of computer video processing, and discloses a low-delay video stream real-time processing method and device, and the method comprises the steps: obtaining original video stream data, and processing the original video stream data through employing a lightweight motion prediction method; processing the macro block data set and the predicted coding configuration parameter by adopting multi-thread assembly line coding to obtain a coded data block; establishing a data transmission mechanism to perform data flow control on the unified memory access interface; a heterogeneous task scheduling strategy is adopted to distribute task division results; a lightweight neural network is adopted to carry out parameter adaptive adjustment, and an optimized video stream processing result is obtained; according to the method, a zero-copy data transmission technology is adopted, and optimal configuration and efficient utilization of computing resources are achieved.

The invention provides an unmanned aerial vehicle target detection method based on DC GMA-YOLOv10 infrared and visible light fusion, and relates to the technical field of image detection. The method comprises the following steps: firstly, collecting and manufacturing infrared and visible light unmanned aerial vehicle image data sets of an unmanned aerial vehicle target in a complex environment; then, a DGM-YOLOv10 model is constructed, and the DGM-YOLOv10 model is trained according to the image data set; according to the DGM-YOLOv10 model, a feature extraction network of the YOLOv10 model is changed into two branches, and an improved group mixed attention module DC GMA is introduced to obtain a bimodal feature extraction network; a cross-modal differential perception fusion module CMDAF is introduced between the bimodal feature extraction networks; an information enhancement sampling module MSFS is introduced into the neck network; and on the basis of the trained DGM-YOLOv10 model, paired visible light and infrared unmanned aerial vehicle images are input for detection. According to the invention, based on the recognition of the convolutional neural network model, the robustness and performance of the unmanned aerial vehicle detection system can be improved.

A machine learning model may be configured for training using an associated learning technique. A model configured for end-to-end backpropagation may adapted for associated learning by introducing functions for projecting hidden vectors and labels to a shared representation space and for reconstructing labels from representation vectors. An associated learning loss may be calculated at each layer, with the resulting gradients backpropagated locally through that layer rather than all layers. A reconstruction loss may be calculated using each layer's output including the predicted label. Training by associated learning may be parallelized (e.g., layer by layer) to yield efficiency gains. In addition, associated learning training may be more robust to training label errors. The resulting model may be used to, for example, predict data sequences in an autoregressive manner in which subsequent portions of the output data sequence are predicted in part based on previous predicted portions of the output data sequence.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention discloses a carbon fiber composite material surface modification spraying system and a control method thereof. The system comprises a multi-axis robot, a plasma spray gun, a contact angle measuring probe, a 3D line laser scanner, a hyperspectral imager, an environment sensor and a controller. According to the method, the plasma power and the robot speed are adjusted in real time through contact angle measurement and plasma treatment feedback control, so that the CFRP surface can accurately reach a target value, and the coating adhesive force is improved; 3D line laser scanning and hyperspectral imaging are combined, and the posture, the distance, the wet film thickness and the component uniformity of the spray gun are monitored in real time; based on a prediction model fusing a physical model and a neural network, a self-adaptive fuzzy PID control algorithm is adopted, and the coating flow and the track posture of a spray gun are accurately regulated and controlled in real time. The problems of weak coating binding force, uneven thickness, orange peel, sagging and serious coating waste in traditional spraying are effectively solved, and self-adaptive, high-quality and green spraying of workpieces with complex curved surfaces is achieved.

The invention discloses a power grid fault characteristic determination and positioning method, system, device and storage medium, and relates to the field of power grid fault processing, and the method comprises the steps: synchronously collecting the voltage, current, frequency and other electrical quantities and temperature, humidity and other environmental quantities of a power grid node through a multi-mode sensor, and forming an original time sequence data set; separating electrical quantity frequency domain fault features by using improved variational mode decomposition, and analyzing the correlation between the environmental quantity and the electrical quantity through a grey correlation degree to suppress noise; mapping the dynamic deviation of each order of derivative of the voltage / current into a spatial-temporal characteristic matrix, and constructing a distribution map in combination with power grid topology; based on the dynamic graph neural network, inputting node features and edge features, and establishing a fault-environment association model through feature propagation and a graph attention mechanism; and carrying out fine trimming on a positioning result by combining double-end traveling wave coarse positioning with an environment correlation degree correction coefficient, and verifying connectivity of a fault point and a power grid topology.

The invention relates to the technical field of biomedical engineering, and discloses an acute respiratory distress syndrome mechanical ventilation adaptive adjustment control system, which comprises a data acquisition module, a signal preprocessing module, a physiological parameter calculation module, a prediction module, a decision and control logic module and the like. Wherein the prediction module predicts a dynamic lung compliance change trend by using a long short-term memory neural network model, and the decision and control logic module generates a ventilation parameter adjustment instruction according to a prediction result and a clinical safety rule. By adopting the technical scheme, the system overcomes the hysteresis of traditional feedback regulation, reduces the risk of breathing machine related lung injury, optimizes oxygenation and carbon dioxide removal efficiency, and provides an accurate, safe and individualized mechanical ventilation treatment scheme.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant optimal scheduling system and method, and the system comprises a data obtaining module, an edge calculation module, a prediction module, a scheduling controller, a topology reconstruction module, and an intelligent terminal device cluster. According to the invention, the edge computing module carries out localization processing and prediction on the sensing data, so that rapid generation and issuing of a scheduling scheme are realized, and the problem of response delay caused by network transmission and centralized computing of a traditional centralized architecture is avoided, thereby supporting millisecond scheduling feedback and improving the scheduling efficiency. The real-time response capability under the sudden load fluctuation or fault condition is remarkably improved, a multi-dimensional perception and prediction mechanism is constructed based on an LSTM neural network prediction model, the recognition and trend prediction capability of the system on meteorological disturbance, equipment aging and operation abnormity is enhanced, the intelligent level of the virtual power plant system is improved, and the real-time performance of the virtual power plant system is improved. The system can dynamically generate an optimal scheduling strategy to ensure stable operation of the virtual power plant under various working conditions.

The invention discloses a power grid dynamic scheduling decision-making method and device based on multi-modal prediction, electronic equipment and a storage medium, and belongs to the field of power system regulation and control operation, and the method comprises the steps: obtaining internal state data and external working condition data of each target power grid device, and a future load change curve of a related power transmission and distribution line, and an equipment feature matrix is constructed through space-time alignment. And inputting the feature matrix into a multi-modal neural network, and outputting the health index, the remaining service life and the fault probability. When the equipment health index is lower than a threshold value, a multi-objective optimization model is constructed, a preventive scheduling strategy is generated, and scheduling is executed; and when the equipment fault probability exceeds a set threshold value, updating the power grid line weight based on load prediction, generating a topology reconstruction scheme of the minimum power failure range, and scheduling according to the topology reconstruction scheme. By implementing the method and the device, the problem that the long-term degradation trend and the short-term sudden risk of the equipment cannot be accurately predicted due to single data dimension in the prior art can be solved.

The invention discloses a load decomposition method based on fusion feature data enhancement, and the method comprises the steps: synchronously collecting the low-frequency power data of a bus end of an electrical loop of a building and the low-frequency power data of all electric equipment ends, and generating a confusion power sequence of similar equipment through Beta distribution mixing, so as to enhance the recognition capability of a model for power overlapping features; based on the power time sequence data, extracting a mutation feature, an equipment state feature and a time coding feature to construct a multi-dimensional feature vector; a CNN-BiLSTM double-branch neural network is adopted, spatial-temporal characteristics are fused through a dynamic weight attention mechanism, and equipment state classification and power decomposition tasks are jointly optimized. In practical application, bus end power data is input, and the operation state and power distribution of each electric device are obtained. According to the method, an adversarial training strategy and a gating feature fusion mechanism are innovatively introduced, the load decomposition performance in a complex power utilization scene is remarkably improved, and the method is particularly suitable for identification and power prediction of equipment with similar rated power.

The disclosure generally relates to a method, system and apparatus for processing audio through a neural network contained in a hearing device. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

The invention discloses a military battery operation adjusting system based on extreme environment identification and detection, and relates to the technical field of military power management, the system comprises an environment sensing module, an environment identification and decision module, a thermal management execution module and a battery management module; the environment sensing module is used for collecting environment data such as temperature, humidity, wind speed, dust concentration and solar radiation intensity in real time; the environment recognition and decision module adopts a BP neural network and fuzzy control logic to recognize and classify working condition states, and outputs a control strategy in combination with a PID adjustment algorithm; the heat management execution module realizes heating, heat dissipation and sand prevention operation according to the instruction; and the battery management module dynamically adjusts a charging and discharging strategy according to the identification state. Self-adaptive adjustment of battery temperature and energy management can be realized under extreme working conditions, the system stability is improved, the service life is prolonged, and the system is suitable for battery management in severe environments such as desert, high temperature, low temperature, strong wind and sand, high radiation and the like.

The invention discloses a user security feature recognition method based on behavior pattern analysis, and aims to solve the problems of inaccurate recognition of power utilization security features of power consumers and insufficient robustness in the prior art. The method comprises the following steps: preprocessing and segmenting original power consumption time sequence data; then, a self-supervised learning model based on an expert hybrid architecture is constructed, the architecture integrates five neural networks to construct an expert model, expert weights are dynamically distributed through a gating network, and a power utilization mode deep embedding vector is output through self-supervised training; clustering the embedded vectors by using a clustering algorithm, determining an optimal clustering number in combination with an elbow method and a contour coefficient method, generating a user portrait, and performing visualization and feature analysis; and finally, according to the user portrait data, carrying out transaction behavior pattern recognition on the input to-be-recognized user data, and outputting a security feature recognition result. The method can comprehensively and accurately identify the power utilization safety characteristics of the user, and is suitable for scenes such as intelligent power grid safety monitoring.

The invention relates to an unmanned aerial vehicle path planning method and system based on GNN and high-order security constraints. The method comprises the steps that a navigation scene where an unmanned aerial vehicle is located is represented as a heterogeneous directed graph, message passing and feature updating are conducted through the GNN, a risk-aware attention mechanism is introduced, and an interpretable decision result is output; a differentiable HoCBF-QP optimization layer is introduced, an original control command output by a strategy network is used as input, quadratic programming with high-order control barrier function constraints is solved online, minimum-amplitude safety correction is carried out on the control command, and an actuator control command meeting safety constraints is output; starting a HoCBF safety shield during operation so as to strictly ensure that all safety constraints are met before execution; a calculation task of the whole control cycle is modeled into a directed acyclic graph form, and parallel execution is carried out on heterogeneous multiple cores by utilizing a real-time scheduling strategy. The problem that unmanned aerial vehicle navigation control is not effective and unified in three aspects of structure, safety and scheduling is solved.

The invention belongs to the technical field of drainage pipeline detection, and particularly relates to a voiceprint recognition detection method for internal defects of a drainage pipeline, and the method comprises the following steps: S1, collecting sound signals in the pipeline through an acoustic sensor array; s2, preprocessing the collected sound signals, wherein the preprocessing comprises noise filtering, signal enhancement and segmentation processing; s3, extracting time domain, frequency domain and time-frequency domain features of the sound signals, and constructing voiceprint fingerprints; s4, establishing a defect classification model based on the deep neural network, and identifying different types of pipeline defects; s5, carrying out confidence evaluation on the detection result, and determining the position of the defect in the pipeline; s6, outputting a detection result and carrying out graded alarm; according to the method, various defect forms of cracks, blockage, damage and the like of different degrees of the drainage pipeline can be recognized, the recognition accuracy of the internal defects of the drainage pipeline can reach 95% or above through multi-dimensional voiceprint feature extraction and a deep learning algorithm, and continuous monitoring and real-time alarming can be achieved.

The invention relates to the field of building construction safety, in particular to a risk prediction method and system for building construction. Aiming at the defects of multi-source data isolated analysis, dynamic risk response lagging, insufficient prediction precision and the like in the prior art, a unified analysis base is formed by constructing a space-time fusion data space and integrating multi-dimensional dynamic data such as structure micro-deformation monitoring, environmental parameters, three-dimensional live-action scanning, personnel positioning, a building information model and the like; based on a deep neural network architecture, designing a multi-modal feature extraction mechanism to quantify the coupling risk, and generating a partition risk probability distribution diagram; and in combination with a construction stage characteristic matching security policy library, implementing a three-level early warning mechanism and an automatic avoidance instruction. A closed-loop optimization mechanism is introduced, model parameters and decision threshold values are dynamically adjusted through actual accident feedback, and continuous evolution of a prediction system is achieved. According to the method, the active prevention and control capacity of compound accidents such as collapse and high-altitude falling is remarkably improved, and a self-adaptive intelligent protection system is constructed for a construction site.

The invention belongs to the technical field of hypersonic flight vehicle control, and relates to a rigid-elastic coupling-oriented active and passive integrated control method for a hypersonic flight vehicle. The invention aims to realize stable tracking control of the elastic hypersonic flight vehicle. The method comprises the following steps: constructing a longitudinal dynamic model of the elastic hypersonic aircraft; self-adaptive identification of the elastic vibration frequency is realized through a cascaded self-adaptive filter; an elastic modal filtering estimation method is designed, and high-precision and low-cost elastic modal state quantity is provided for subsequent active feedback controller design; and then rigid-elastic coupling model decomposition is carried out, the control performance is ensured by using active disturbance rejection passive control for a rigid body subsystem, an RBF neural network is introduced for an elastic subsystem, an elastic mode is actively inhibited by using sliding mode control, and stable tracking of a reference instruction is realized. The method is an active and passive integrated control method for the hypersonic flight vehicle oriented to rigid-elastic coupling, and the application prospect is wide.

The invention relates to the technical field of vibration monitoring, in particular to a self-powered transmission line fitting aeolian vibration damage diagnosis system and method, and the system comprises a sensing collection module, a signal decoupling module, a damage identification module, a damage association module and a risk assessment module. According to the method, stress wave velocity and acceleration data are synchronously collected, time alignment is implemented, feature coupling precision is enhanced, wave crest offset and energy density are respectively extracted by using moving average filtering and wavelet transform, effective data segments are dynamically screened, and environmental noise interference is suppressed. A stress wave propagation change rate is quantified based on a path attenuation model, a continuous energy abnormal node is matched to realize damage positioning, a breeze response abnormal region is identified by combining vibration direction change and a signal envelope offset degree, multi-dimensional features are coded and subjected to risk judgment through a neural network, a damage positioning and risk assessment closed-loop framework is formed, and the risk assessment accuracy is improved. And the spatial resolution and evaluation precision of aeolian vibration damage identification under complex working conditions are significantly improved.