3746 results about "Nerve network" patented technology

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.

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 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 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.

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 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 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 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 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.

The invention relates to the technical field of backhaul monitoring, in particular to a backhaul link monitoring method and system applied to a 5G base station, and the method comprises the steps: collecting physical signal statistical data, backhaul service data and network topology data of a backhaul link in real time; according to physical signal statistical data, a physical layer entropy value is calculated through a Shannon entropy formula, and recessive degradation characteristics of a physical layer are quantified; a pulse neural network space-time coding mechanism is used to generate business layer anomaly prediction parameters, and cross-layer anomaly prediction is realized; according to service types and priorities, constructing a time-varying service awareness reference model by adopting a chaos kinetic equation to serve as a dynamic judgment reference; when the return state is abnormal, combining the physical layer entropy and the network topology data to construct a feedback correction loop, and generating a self-adaptive protection strategy; according to the method, advanced prediction and accurate positioning of recessive degradation of the backhaul link are realized, and the self-healing capability and the service quality guarantee level of the backhaul link in a multi-service scene are improved.

The invention belongs to the technical field of sensor error correction, and discloses a data synchronization and error correction method based on multi-source gas detection, and the method comprises the steps: firstly achieving the time-space synchronization of a multi-source sensor through the combination of GPS / Beidou hardware time service and a gas diffusion transmission model; non-negative matrix factorization (NMF) is adopted to decouple sensor cross interference, and an LSTM neural network is utilized to predict a sensor drift amount caused by environmental temperature and humidity changes; and finally, carrying out dynamic weight fusion based on the real-time confidence of each sensor, and outputting a high-precision gas concentration value. The problem of data distortion caused by time sequence dislocation, cross sensitivity and environment drift in a multi-sensor system is effectively solved, the accuracy of detection data and the reliability of the system are remarkably improved, and the method is particularly suitable for multi-component gas detection scenes in the fields of chemical engineering, environment monitoring and the like.

The invention provides an artificial intelligence network model-based early warning and monitoring method for the occurrence amount of agricultural pests, and particularly relates to a time sequence modeling method by combining a variable structure bus module VSB with a bidirectional long short-term memory network BiLSTM, which is used for predicting the occurrence dynamic state of important pests in a field and an orchard with high precision. Comprising the following steps: selecting three monitoring sites in a main crop producing area; and establishing a time sequence data set of the corresponding relationship between the average daily temperature, the rainfall and the effective accumulated temperature and the number of pests in ten days. According to the method, a hybrid neural network prediction model is constructed, the model comprises five function modules, and the model can accurately early warn annual dynamic changes of main crop main pest populations and judge peak values, and helps farmers establish efficient pest prevention and control measures.

The invention discloses a multi-scale SAR (Synthetic Aperture Radar) image ship detection method and system based on edge enhancement and diffusion denoising, and mainly solves the problems that the existing SAR ship detection method is sensitive to noise and poor in small target feature extraction capability. According to the implementation scheme, the method comprises the following steps: obtaining an SAR image, carrying out standardized preprocessing, inputting the SAR image into a deep convolutional neural network, extracting a multi-scale feature map, and carrying out dynamic channel fusion enhancement on a low-layer feature map in the multi-scale feature map to obtain a fused high-quality feature map; performing differential edge enhancement on middle and high-level feature maps in the multi-scale feature map, and inputting the enhanced feature map and the fused feature map into a diffusion model detection head for training; and inputting a to-be-detected SAR image into the trained diffusion model detection head, outputting a preliminary target bounding box and a category confidence coefficient, performing score screening and non-maximum suppression operation on the preliminary target bounding box and the category confidence coefficient, and generating a final ship target detection result. According to the method, the precision and robustness of SAR image ship detection are remarkably improved, and the method can be used for ocean monitoring and military reconnaissance.

The invention discloses an automatic driving track generation method and device, equipment and a medium. The method comprises the steps that target state information of a vehicle is obtained, and the target state information comprises vehicle body state information, obstacle information, road structure information and traffic state information; the target state information is input into a trajectory planning model for trajectory generation, a plurality of candidate driving trajectories and trajectory evaluation results corresponding to the candidate driving trajectories are determined, and the trajectory planning model is constructed based on a deep neural network; and determining a target driving trajectory based on the plurality of candidate driving trajectories and the trajectory evaluation result corresponding to each candidate driving trajectory. According to the method, the automatic driving track of the vehicle can be automatically and accurately generated, emergencies in dynamic traffic are covered, the generalization ability during cross-scene migration is improved, the track generation flexibility is improved, and therefore the track precision and safety in complex scenes are guaranteed.

The invention provides an osteoporosis automatic diagnosis method and system based on position specific attention, and the method comprises the steps: carrying out the dual-channel enhancement preprocessing of a lumbar CT image, and generating an enhanced image taking the bone mineral density and the bone trabecula structure characteristics into consideration; inputting the enhanced image into a deep convolutional neural network for multi-scale feature extraction to obtain a feature map containing spatial semantic information; on the basis of the anatomical position index information, generating attention weighted features which highlight the key diagnosis area of the vertebral body; global pooling is carried out on the attention weighted features, and then comprehensive diagnosis features containing anatomical priori knowledge are constructed; and inputting the comprehensive diagnosis features into a classifier to realize centrum-level accurate diagnosis. According to the invention, the workload of radiologists can be effectively relieved, and the efficiency and coverage of osteoporosis screening can be improved. Especially under the condition that primary medical institutions are lack of experienced radiologists, the primary medical service level can be improved.

The invention relates to the technical field of audio processing, in particular to a self-adaptive scene sound effect adjusting method and device and a storage medium, and the method comprises the steps: collecting a mixed audio stream and an original playing audio, and extracting an acoustic fingerprint vector through a pre-trained convolutional neural network; performing acoustic feature deconstruction on the acoustic fingerprint vector to obtain a plurality of acoustic feature components, performing scene recognition judgment according to a preset scene judgment rule based on the plurality of acoustic feature components, and determining an audio playing scene; constructing a time-frequency mask according to the audio playing scene, generating a noise reduction gain matrix based on the time-frequency mask, and performing scene sound field enhancement on the audio playing scene to obtain a sound field optimization matrix; and performing noise reduction and sound effect adjustment on the original playing audio according to the noise reduction gain matrix and the sound field optimization matrix, and outputting the optimized playing audio. According to the invention, sound effect adjustment can be dynamically adapted according to different scenes, and the noise reduction accuracy and the multi-scene sound quality adaptability are improved.

The invention discloses a juicy peach yield prediction method based on comprehensive data analysis, and particularly relates to the technical field of agricultural intelligent perception. The method comprises the following steps: collecting multi-source data of an orchard, and constructing a data set containing meteorological, physiological, remote sensing and soil information; fruit tree physiological response parameters are extracted, and a bimodal diagram structure fusing the spatial adjacency relation and the physiological state similarity is established in combination with the dynamic climate anomaly index; inputting the graph structure into a graph neural network model, extracting spatial-temporal characteristics, dynamically adjusting an edge weight and a propagation layer number, and constructing an adaptive model; performing region division and weighted summarization according to a model output result, and finally obtaining a predicted value of the total yield of the orchard; the method improves the prediction accuracy under the conditions of complex climate and unstable data, and is suitable for refined orchard management.

The invention belongs to the technical field of radiotherapy dose simulation measurement, and provides a heavy ion fixed machine head radiotherapy dose simulation measurement method and system. The method comprises the following steps: firstly, in a solid water model, according to a layering and key area encryption sampling principle, carrying out finite point and / or cutting layer dose measurement to obtain sparse dose data and a sampling position; inputting the three-dimensional dose distribution and a sampling position mask into a coding-decoding structure and a mask perception neural network with an attention mechanism in a decoding stage, and outputting complete three-dimensional dose distribution; and finally, outputting a simulation result containing dose distribution and uncertainty distribution. The method improves dose reconstruction precision and clinical quality control efficiency, and is suitable for dose verification before heavy ion radiotherapy.

The invention provides an integrated doctor-patient cooperation and health management platform and a data processing method thereof, and relates to the technical field of health management, and the integrated doctor-patient cooperation and health management platform comprises a multi-source data access layer, a credibility evaluation and correction engine, a semantic fusion and knowledge graph layer, a patient digital twin module, a cooperation interaction module, a privacy protection and joint training module and a contract and audit layer. Dynamic modeling of health data is realized through a space-time diagram neural network, a risk prediction and intervention scheme with a confidence interval is generated in combination with Bayesian reasoning and Monte Carlo simulation, and then digital twins of a patient are constructed and personalized simulation is performed; and meanwhile, the safety and performance of cross-mechanism joint modeling are guaranteed by adopting hybrid synchronous-asynchronous federal learning and a dynamic privacy budget mechanism, and the traceability and compliance of the whole process are realized through a block chain contract. The accuracy and transparency of medical data processing can be remarkably improved, close cooperation between doctors and patients is promoted, and comprehensive health management of the patients is achieved.

The invention relates to the technical field of traffic detection, in particular to a traffic abnormal event cooperative detection method and system based on vehicle-road cooperation, and the method comprises the steps: collecting vehicle end data and road end data from a vehicle end and a road end respectively, and carrying out the timestamp alignment, coordinate transformation, noise filtering and missing value supplementation of the vehicle end data and the road end data; establishing target state estimation based on a state space motion model, performing recursive estimation on a target, and identifying abnormal candidates based on observation residual errors; constructing a space-time diagram based on the vehicle end data and the road end data, reconstructing node features by adopting a time sequence diagram neural network, generating an anomaly score according to a reconstruction error, and outputting an anomaly candidate; and according to the state space motion model and the anomaly candidates of the time sequence diagram neural network, confidence fusion is carried out according to confidence, and whether an anomaly alarm is triggered and whether an anomaly type and positioning information are output are judged based on a fusion result. And through confidence fusion, false alarms triggered by isolated noise can be effectively suppressed.

The invention relates to the technical field of optical communication, and discloses an optical fiber loss evaluation method and system. The method comprises the steps that an optical fiber sensor collects real-time optical signal data and generates a dispersion velocity data sequence; extracting a fluctuation feature vector by adopting an adaptive filtering algorithm, and determining a dispersion velocity change trend; inputting the change trend and the transmission distance into a neural network model, predicting a loss fluctuation amplitude and generating a prediction loss sequence; calculating a coupling effect correlation coefficient, judging abnormal coupling and outputting an evaluation result; adjusting signal compensation parameters according to an evaluation result, optimizing nonlinear influence and determining a compensated dispersion velocity value; calculating a signal attenuation factor according to the compensated dispersion velocity value and the transmission distance, and generating an optimized transmission scheme; and updating optical communication network parameters according to the scheme, outputting stable signal quality and generating an optical fiber loss evaluation result. According to the invention, the problem of inaccurate evaluation caused by nonlinear fluctuation and signal coupling is solved, and the stability of optical fiber transmission and the signal quality are improved.

The invention discloses a multi-source data-based extreme weather economic influence spatio-temporal dynamic evaluation method, which comprises the following steps: collecting a multi-source heterogeneous data set, preprocessing the multi-source heterogeneous data set, and constructing a standardized data set, the multi-source heterogeneous data set comprising meteorological data, economic data, geographic information data and social data; performing dynamic weight fusion on the multi-source heterogeneous data set based on a dynamic weight fusion algorithm to generate a multi-source feature matrix; constructing a space-time coupling model through the graph neural network and the long-short-term memory network, and generating a space-time dynamic evaluation result based on the space-time coupling model in combination with the multi-source feature matrix; and visualizing a spatio-temporal dynamic evaluation result and determining a risk early warning level, generating an optimal adaptation strategy through a multi-objective decision optimization method, executing the optimal adaptation strategy, and dynamically optimizing dynamic weight distribution and model parameters through new data. According to the invention, the accuracy of time-space dynamic evaluation of the extreme weather economic influence and the early warning timeliness can be remarkably improved.

The invention discloses a high-pier large-span extra-large bridge suspended pouring beam hanging basket construction method, and belongs to the field of bridge engineering construction.The method comprises the following steps that hanging basket rails are installed, the precision is controlled, and a hanging basket comprising a double-channel-steel main truss, a manganese steel sling, an arc-shaped flow guide plate and a honeycomb formwork is assembled; grading pre-pressing is carried out according to the specified multiple of the design load, and the pre-camber of the template is corrected by adopting a BP neural network to ensure that the finished bridge linear error meets the requirement; a mechanical-hydraulic-electrical integrated intelligent control system is deployed, and the traveling speed and synchronism of the hanging basket are controlled through double-oil-cylinder driving, a 5G digital twin platform and an edge computing technology, so that rapid alarm is realized when the load is abnormal; segmental circulation construction is carried out, and when the environment wind speed reaches a set value, a hydraulic rail clamping device is automatically locked; a model is established by using ANSYS software, and simulation verification is performed on multiple types of key working conditions to ensure that the safety coefficient of the structure meets the specification requirement. The construction precision and safety are guaranteed, and the method adapts to complex environments.

The invention provides a highway pavement structure settlement deformation monitoring and identification method and system based on digital twinning, and belongs to the field of fiber bragg grating sensing structure health monitoring. According to the method, the vibration mode characteristics of the pavement structure are obtained through the vibration pickup, the modal information is calculated by matching numerical values, and the pavement deformation is reconstructed by adopting a modal vibration mode matrix and monitoring strain data; sensor layout optimization is carried out based on an actual test road section, theoretical calculation deviation existing under the road surface strong constraint condition is corrected by means of a neural network model, and the road surface deformation type disease development trend is predicted. The method can effectively monitor pavement structure settlement evolution development under unfavorable geological conditions, early warns sudden collapse risks in advance, and provides an important basis for intelligent monitoring of road settlement. A digital model is built based on the multi-source data, and real-time diagnosis of the state of the monitored road section and early warning and treatment of settlement risks are achieved.