24060 results about "Non linearite" patented technology

Ship structural ultimate bearing analysis and design research is involved. A method for implementing ultimate strength analysis of plate frame structure based on isogeometric analysis includes the following steps: according to the plate frame structure, constructing an IGA model, wherein the IGA model comprises several plane shell elements; carrying out a coordinate transformation on the plane shell elements; carrying out a weak coupling on boundaries of transformed plane shell elements by Nitsche method and carrying out a mesh refinement; setting material properties, loads and boundary conditions of refined plane shell elements, and constructing a nonlinear equation according to setting results; and solving the nonlinear equation by Newton-Raphson iteration method, and carrying out a simulation analysis by NX software to obtain an ultimate strength of the plate frame structure.

The invention discloses a dam safety monitoring system and method based on digital twinning, and particularly relates to the technical field of digital twinning. The method comprises the following steps: acquiring stress, deformation, displacement, inclination and environmental change data of a dam structure in real time, regularly carrying out high-precision calibration on a sensor, recording a time drift error, analyzing nonlinear sensitivity of a digital twin model under an extreme working condition, simulating the influence of the sensor error and the model sensitivity on dam evaluation, predicting a deviation degree, and calculating the evaluation accuracy of the dam. Based on a prediction result, a dynamic drift compensation algorithm is adopted to correct sensor data in real time, the sensitivity of the model is dynamically adjusted, the optimized data and model are integrated, the structural state and potential risk of the dam under normal and extreme working conditions are accurately evaluated, accurate early warning information and decision scheme are generated, and the accuracy of early warning is improved. The accuracy and reliability of dam safety monitoring and risk assessment are remarkably improved, decision delay or resource waste caused by misjudgment or missed judgment is reduced, and the dam safety guarantee capacity is improved.

The invention relates to the field of temperature control, in particular to an automatic control method and system for a constant-temperature test room. The method comprises the following steps: acquiring real-time indoor operation monitoring parameters and external environment multi-dimensional data of a constant-temperature test room; performing indoor operation state analysis on the real-time indoor operation monitoring parameters, and constructing a laboratory dynamic state space model; carrying out multi-time-point environment temperature fluctuation calculation on the external environment multi-dimensional data, and carrying out nonlinear correlation learning to generate indoor state-environment nonlinear correlation characteristics; carrying out dynamic temperature evolution simulation on the laboratory dynamic state space model based on the indoor state-environment nonlinear correlation characteristics, and carrying out dynamic temperature evolution prediction to obtain temperature prediction values of a plurality of regions; constant-temperature deviation compensation calculation is carried out on the temperature prediction values of the multiple areas, then intelligent temperature control compensation is carried out, and an intelligent temperature control strategy is constructed; according to the invention, efficient and accurate constant-temperature automatic control is realized.

The invention relates to the technical field of computer programming languages, and particularly discloses a coal-fired power plant safety monitoring system and method. The system comprises a multi-modal data fusion platform, a federal learning agent network and a digital twinborn simulation engine. The edge computing node carries out unified acquisition and feature extraction on multi-source heterogeneous data through multi-protocol conversion, quantum noise suppression and a preprocessing chipset; the multi-modal data fusion platform realizes semantic mapping based on a knowledge graph, and fuses time-space correlation characteristics of data such as an infrared image and gas concentration by adopting a time-space encoder and a cross-modal attention mechanism. According to the method, the problems of early warning delay and high false alarm rate caused by low multi-source data decentralized processing efficiency and insufficient nonlinear correlation analysis in a traditional scheme are solved, efficient data fusion, complex risk accurate prediction and automatic safety response are realized, and the real-time performance and reliability of a coal-fired power plant monitoring system are remarkably improved.

The invention discloses an electromechanical system fault pre-diagnosis method and system based on digital twinning. The method comprises the following steps of obtaining multi-source data in an electromechanical system operation process; preprocessing the acquired multi-source data, wherein the preprocessing comprises data cleaning, normalization processing and feature extraction; and on the basis of the preprocessed multi-source data, an electromechanical system design drawing, a three-dimensional geometric model, material attributes and a kinetic equation are fused, and a digital twin model is constructed. According to the invention, through a digital twin model dynamic calibration and prediction algorithm, early abnormity of the equipment is identified in advance, the fault probability and the residual life are output, and non-planned shutdown is reduced; by constructing a cross-physical domain fault feature system and fusing model simulation and actual measurement data, the potential fault identification accuracy is improved, and the missed diagnosis rate is reduced; by calibrating parameters of the digital twin model in real time, the method adapts to nonlinear changes of equipment, ensures high-fidelity mapping of the model, and improves fault prediction precision.

The invention relates to the field of power equipment control data processing, in particular to a boiler combustion optimization control method based on data driving, which comprises the following steps of: acquiring multi-source data such as temperature field distribution, air and smoke pressure, smoke components and coal quality characteristics, and eliminating noise interference by adopting sliding window mean filtering; generating a standardized feature matrix in combination with principal component analysis and a dynamic time warping algorithm; constructing a dynamic coupling model fusing a gradient boosting decision tree and a long short-term memory network, analyzing a nonlinear relationship between pulverized coal particle size distribution and a wind-coal ratio, and predicting combustion efficiency, pollutant concentration and temperature field uniformity; and model parameter self-correction and weight dynamic adjustment are triggered through actual combustion data feedback, and a closed-loop control link is formed. According to the method, accurate modeling of the multi-physical field coupling characteristic of the combustion system is achieved, the time sequence generalization ability under the dynamic working condition is improved, and the purposes of heat efficiency improvement and pollutant emission reduction are effectively balanced.

The invention provides a demand side response-based distributed power supply optimal scheduling method and system, and relates to the technical field of power grids, and the method comprises the steps: building a distributed power supply prediction model based on a deep neural network, and predicting the photovoltaic power generation power, the energy storage charge state and the user load. Then, a robust optimization model is constructed, prediction errors and energy storage nonlinear characteristics are considered, an optimal time-phased load scheduling scheme and an energy storage charging and discharging strategy are generated, the scheme comprises an interruptible load execution time sequence, and the strategy comprises an energy storage charging and discharging power curve; and finally, establishing a hierarchical optimization execution system based on model predictive control, monitoring the state of the system in real time, and correcting a control instruction according to the deviation to realize optimal scheduling of the distributed power supply. The method can improve the consumption capability of the distributed power supply, reduce the power consumption cost, and enhance the stability of the power grid.

The invention relates to the technical field of information data processing, in particular to a mountain area engineering slope real-time monitoring and early warning method and system based on digital twinning. The method comprises the following steps: acquiring original slope real-time monitoring data, and carrying out standardization processing to obtain standardized slope real-time monitoring data; the method comprises the following steps: constructing a digital twinborn model by using standardized slope real-time monitoring data to obtain a preliminary slope digital twinborn model; and dynamically updating the preliminary side slope digital twinborn model by using the standardized side slope real-time monitoring data and the side slope historical multi-source monitoring data to obtain a real-time updated side slope digital twinborn model. According to the method, the digital twinborn model is updated in real time, multi-physics coupling analysis and nonlinear stability prediction are combined, it is ensured that dynamic monitoring, stability evaluation, risk prediction and emergency response of the slope can reflect the actual condition in time, and therefore the scientificity and timeliness of disaster prevention and control are improved.

The invention discloses a time sequence data modeling method for an agent with a body based on frequency domain learning, and the method comprises the steps: carrying out the self-adaptive partitioning of an original time sequence data sequence, extracting features through multi-scale convolution, and generating a position code; fourier transform is carried out on the feature block to extract frequency domain information, and nonlinear transform and residual compensation are carried out; calculating an attention relationship between the features and carrying out feature fusion; constructing a time sequence dependency relationship graph, and extracting time sequence aggregation features and dynamic system features; and constructing a probability prediction model, and calculating the uncertainty of a prediction result. According to the method, the modeling precision is improved through self-adaptive blocking and frequency domain feature extraction, the feature expression ability is enhanced through an attention mechanism and graph structure modeling, the prediction reliability is improved through uncertainty quantification, and the method is suitable for time series data modeling of an agent with a body in a complex environment.

The invention discloses a power transmission and transformation equipment fault early warning system based on online monitoring, and the system comprises the following modules: a multi-parameter sensor module which is used for collecting data in real time; the wireless sensor network module is used for transmitting the data to the data processing center for processing; the data fusion module is used for generating a comprehensive health score of the equipment by adopting a multi-modal data fusion algorithm based on a self-evolution nested network; the trend prediction module introduces an adaptive residual module to carry out trend prediction based on a nonlinear time sequence analysis technology of a generative adversarial adaptive residual network; the early warning threshold value adjusting module is used for dynamically adjusting an early warning threshold value by adopting a self-adaptive evolutionary algorithm of meta reinforcement learning; the maintenance suggestion generation module is used for generating maintenance suggestions; and the power grid dispatching linkage module is used for automatically linking a power grid dispatching system and detecting and adjusting an equipment load. According to the invention, a multi-parameter fusion and generative adversarial network method is adopted, and an adaptive residual module is introduced, so that accurate early warning of faults of power transmission and transformation equipment is realized.

The invention relates to the technical field of operation and maintenance scenes, in particular to a fault diagnosis prediction method and system based on an operation and maintenance scene, and the method comprises the steps: collecting equipment data, and constructing a scene fault diagnosis model; performing nonlinear fusion on the collected operation and maintenance data and historical maintenance records to obtain a real-time data fusion result; constructing an adaptive prediction algorithm based on interaction of a scenarized fault diagnosis model and real-time data fusion; and establishing a multi-level warning system according to a prediction result. According to the method, through multi-dimensional data acquisition, nonlinear data fusion, adaptive algorithm prediction and a multi-level warning system, accurate prediction and effective prevention of equipment faults are realized. Experimental results show that compared with a traditional method and a general machine learning method, the method has remarkable advantages in the aspects of early warning accuracy, false alarm rate, early warning time in advance and the like, meanwhile, the maintenance cost is effectively reduced, the downtime of equipment is shortened, and the method has good application prospects.

The invention discloses a knowledge graph construction method and system based on traditional Chinese medicine classics, and relates to the technical field of knowledge graph construction, and the method comprises the steps: collecting multi-source heterogeneous data of traditional Chinese medicine classics, carrying out the standardization processing, and generating a cross-time aligned standardized corpus through optical character recognition and multi-modal feature extraction; based on a language model and a causal reasoning engine, extracting an entity relationship and a dialectical rule from the standardized corpus, and generating a dynamic dialectical reasoning model supporting nonlinear reasoning; obtaining an output result of the dynamic dialectical reasoning model, and constructing a redundancy-removed and dynamically-calibrated traditional Chinese medicine knowledge base through knowledge fusion and conflict resolution of various traditional Chinese medicine classics; based on edge calculation and privacy protection technologies, real-time iteration and clinical curative effect feedback optimization of the knowledge graph are realized. According to the method, the problems of trans-era evolution, regional expression difference, multi-modal feature splitting and the like in the traditional Chinese medicine classics digitalization process are solved.

The invention discloses a sewage treatment dynamic process simulation prediction method and system based on digital twinning, and relates to the field of digital twinning sewage treatment. By introducing a digital twinborn technology and a simulation prediction method based on advanced data analysis, complex dynamic changes in the sewage treatment process can be reflected in real time, and the defects of a traditional simulation technology in the aspects of treatment of nonlinear disturbance, frequent fluctuation and sudden pollution sources are overcome. Through multi-source data acquisition and time sequence alignment, in combination with nonlinear frequency domain disturbance modeling, noise and disturbance in the data can be effectively suppressed, and the quality and reliability of the data are improved, so that a more accurate basis is provided for subsequent modeling and prediction.

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

This invention relates to a gait emotion recognition method, system, storage medium, and computer device based on spatiotemporal graph convolution. The method includes the following steps: S1, data augmentation by reversing the temporal direction of gait; S2, obtaining deep emotion features and prior emotion features respectively through a spatiotemporal graph convolutional network and prior feature statistical methods; S3, performing nonlinear mapping on the prior emotion features using a feature mapping layer; S4, inputting the fused features of the deep emotion features and prior emotion features into an emotion classifier to obtain the emotion category. The feature mapping layer of this invention achieves more effective feature fusion by performing nonlinear mapping on prior features; it also introduces causal temporal convolution to replace general temporal convolution, effectively extracting fine-grained temporal features by enhancing temporal correlation and cross-period feature fusion. Furthermore, a walking direction recognition auxiliary task is designed to accelerate the training and convergence speed of the model, enhancing the ability to extract temporal-dependent features and the performance of emotion recognition.

The invention relates to the technical field of wind power prediction. The invention provides a wind power prediction method and system. The method comprises the following steps: acquiring multi-dimensional meteorological time series data, three-dimensional elevation data and unit operation data of a target wind power plant; constructing a spatial-temporal feature fusion network, extracting time sequence dynamic features, and performing weighted fusion on the spatial correlation features and the time sequence dynamic features to obtain a fusion feature vector; establishing a hybrid prediction model, and taking the fusion feature vector as input to obtain a wind power initial prediction result; introducing a terrain correction factor, constructing a turbulence intensity compensation function, and performing micro-terrain disturbance correction on the wind power initial prediction result; and outputting a final power prediction curve and a confidence interval. The problems that in an existing wind power prediction method, a physical model is insufficient in complex terrain microclimate modeling precision, high in calculation complexity and difficult to meet the real-time requirement, a statistical learning method is limited in high-dimensional nonlinear time sequence feature expression capacity, and prediction errors are remarkably increased under the abnormal working condition are solved.

The disclosure provides a method and system for trajectory tracking control of a vehicle-manipulator coupling system with finite time prescribed performance. Specifically, the trajectory tracking error of the vehicle-manipulator coupling system is obtained. A finite time performance function is designed to constrain the trajectory tracking error. In a case the constraint conditions corresponding to the finite time performance function are satisfied, the trajectory tracking error is converted to obtain a transformed error. The sliding mode surface is designed based on the transformed error to control the transformed error to converge in a finite time, and the external disturbance of the vehicle-manipulator coupling system is observed based on non-linear disturbance observer. The control input of the vehicle-manipulator coupling system is designed based on the sliding mode surface and the non-linear disturbance observer output. This ensures that the vehicle-manipulator coupling system can be controlled to operate precisely along the desired trajectory.

The invention discloses a big data auxiliary key generation method and system in communication data encryption transmission, and relates to the technical field of big data analysis and processing. The dynamic entropy source processing module is used for generating a high-randomness entropy pool by combining an information entropy quantification model and adopting a Shannon entropy and minimum entropy fusion algorithm; the anti-quantum key generation module is used for generating a dynamic variable-length key seed based on an entropy pool driven post-quantum cryptographic algorithm; the hierarchical key negotiation module adopts a clustering Diffie-Hellman protocol, dynamically divides negotiation according to network topology, and precomputes and reduces the load of a core network through edge nodes; and a lightweight verification and update module. According to the method, high-entropy sources such as environmental noise, user behaviors and equipment hardware fingerprints are fused with low-entropy sources such as network messages and sensor data, and an intelligent acquisition strategy and a nonlinear decorrelation technology are combined, so that the anti-quantum dynamic entropy pool is generated, and the randomness of a secret key and the reliability of the entropy sources are improved.

The invention relates to the technical field of agricultural information perception and decision making, in particular to a multi-source perception fused agricultural monitoring method and system. The method comprises the following steps: converting a multi-source heterogeneous agricultural sensing signal into a space-time tensor, constructing a semantic resonance field to simulate nonlinear coupling between modals, and driving multi-modal data to adaptively aggregate by using a gravitational evolution mechanism to form a fused semantic field; calculating non-linear response to generate an agricultural state emergence index, and according to the index, identifying a potential risk area and constructing a binary risk map; for the risk area, semantic disturbance is mapped into an agricultural variable disturbance vector through a modal decoupling matrix, a minimum intervention strategy is generated in combination with sparse optimization of an operation response matrix, and feasible operation suggestions are output after verification of an agricultural knowledge graph; a drift potential energy function is constructed based on strategy execution feedback, strategy parameters are dynamically updated through gradient descent, and closed-loop self-evolution optimization is achieved in combination with trend prediction. According to the invention, full-link adaptive optimization from multi-source sensing to regulation and control decision is realized.

The invention provides a method and a system for evaluating the packaging tightness of an integrated circuit in a high-temperature environment, and relates to the technical field of circuit testing, and the method comprises the steps: obtaining the temperature, tracer gas concentration and gas pressure data of the integrated circuit, and extracting temperature nonlinear features; non-local weighted adaptive anisotropic diffusion filtering and mixed tensor decomposition are utilized to obtain concentration multi-dimensional distribution features, air pressure frequency domain features are extracted, and feature fusion and space-time dependency analysis are performed through a quantum depth auto-encoder, a multi-head cross attention network and a heterogeneous space-time diagram neural network. Time sequence features are extracted through waveform dynamic time warping and a bidirectional gating circulation attention network, sealing defect diagnosis is performed based on knowledge distillation, a failure trend is predicted by using a causal long-short term memory network, and a failure mode is matched in combination with a dynamic heterogeneous knowledge graph. And a multi-target Pareto differential evolution algorithm is adopted to evaluate the packaging reliability and generate an evaluation report.

The invention provides an electromagnetic-assisted nickel-based alloy laser melting deposition crack inhibition method and system, relates to the technical field of automatic control, constructs an intelligent control system, carries out deep fusion and real-time prediction on multi-source heterogeneous sensing data, generates a prediction result, sets a reward function and an action-value network, and improves the performance of the nickel-based alloy laser melting deposition crack inhibition system. Solving to obtain an optimization control strategy and analyzing and outputting the optimization control strategy; and constructing a three-dimensional electromagnetic field distribution matrix. In combination with real-time feedback of the shape of the molten pool, the molten pool is stimulated to flow directionally and oscillate non-linearly according to the resonance effect, oscillation parameters are obtained, thermodynamic behaviors corresponding to the molten pool are predicted according to the oscillation parameters, and a thermodynamic prediction result is fed back to the central control unit; the method comprises the following steps: detecting the surface appearance of a part, extracting defect characteristic parameter vectors, inverting defect influence factors, generating a dynamic regulation and control curve, dispersing the dynamic regulation and control curve into multi-dimensional control instruction vectors, updating a preset regulation and control model, outputting optimized regulation and control model parameters, and carrying out crack suppression operation again.

The invention discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

The invention discloses a fusion optimization method based on Kalman filtering and LSTM cascade. The fusion optimization method comprises three steps of dynamic state estimation, time sequence error modeling and closed-loop fusion optimization. IMU (Inertial Measurement Unit) data is used as input, dynamic state estimation is realized through Kalman filtering, modeling system errors are separated, an LSTM (Long Short Term Memory) network is used for carrying out time sequence modeling on a residual error sequence to capture nonlinear errors, and finally, a corrected state quantity is fed back to a Kalman filtering updating link through a closed-loop feedback mechanism. And collaborative optimization of error compensation and state estimation is realized. According to the method, IMU error accumulation is effectively inhibited through a dynamic-data dual-drive mechanism, the navigation precision and robustness in a complex scene are remarkably improved, and the requirements of high-dynamic applications such as intelligent driving and unmanned aerial vehicle navigation can be met. Finally, the optimized IMU data and the GNSS observation value are fused for integrated navigation, high-precision navigation solution is achieved through Kalman filtering, and indoor and outdoor seamless positioning and the all-attitude control requirement of a high-dynamic carrier are met.

The invention provides a twin power station collision visual simulation method and system based on physical simulation, and relates to the technical field of data processing, and the method comprises the steps: obtaining a three-dimensional geometric model of a twin power station, carrying out space division, extracting geometric parameters, obtaining material data, constructing a material correlation model and a mechanical model, and carrying out fusion to obtain a physical simulation model; acquiring initial collision condition parameters as boundary conditions, performing local grid encryption, constructing a nonlinear kinetic equation of a collision process, performing time domain solution, determining equivalent stress distribution and equivalent plastic strain distribution, and generating a collision result; judging whether plastic deformation or fracture failure occurs, generating full collision process physical field data, generating a collision animation frame sequence, generating a stress distribution cloud picture and a plastic strain cloud picture, constructing a fracture special effect, and comprehensively generating a collision visual scene.

The invention discloses a target echo signal simulation method and system with unmanned aerial vehicle rotor frequency spectrum characteristics, and the method comprises the steps: obtaining an original radar echo signal, carrying out the preprocessing and feature enhancement, and obtaining an enhanced rotor feature signal; sequentially performing dynamic component decomposition, nonlinear phase coupling analysis, adaptive parameter estimation and trajectory prediction to generate probability distribution of a feature trajectory; probability feature space mapping and dynamic feature fusion are carried out to obtain a fusion feature vector; constructing a nonlinear dynamic model, and performing adaptive signal synthesis to generate a synthesized signal; performing quality evaluation on the composite signal to generate a quality evaluation report and an optimization parameter set; based on the quality evaluation report and the optimization parameter set, performing multi-target parameter optimization and monitoring system performance to obtain a performance evaluation result; system parameters are updated, stability analysis is carried out, and a system stability report and regulation and control suggestions are generated. According to the invention, the calculation efficiency and the simulation precision are both improved.

The invention discloses a mechanical arm positioning and grabbing method based on machine vision, and relates to the technical field of machine vision and mechanical arm control, the method comprises the following steps: synchronously acquiring RGB-D images of a target scene through a multi-view camera array, and generating three-dimensional point cloud data through data fusion; an improved LSD algorithm and a PnP algorithm are adopted to calculate the initial pose of the target object, illumination distortion is eliminated in combination with the generative adversarial network, and three-dimensional coordinates are output; a mechanical arm motion error transfer model is constructed based on Monte Carlo simulation, and a candidate grabbing scheme set is generated through reinforcement learning; and an optimal grabbing scheme is screened through a preset priority evaluation rule, and a mechanical arm joint movement track and a control instruction set are generated. Through multi-modal data fusion and a nonlinear optimization algorithm, the technical problems of large target positioning deviation and sensitive illumination interference in a complex environment are solved, and the grabbing precision and robustness of the mechanical arm are improved.

The invention provides a ground-based radar landslide monitoring and early warning method and system, and relates to the technical field of landslide monitoring, and the method comprises the steps: collecting a monitoring signal, and carrying out the filtering processing; extracting an instantaneous phase and performing wavelet packet decomposition; enhanced features are obtained through morphological filtering; constructing a monitoring feature vector; carrying out nonlinear dimension reduction on the characteristic time sequence matrix; obtaining an evolution trajectory by using a fractional order adaptive neural network; constructing a hierarchical risk reasoning framework; a risk metric value is obtained through multi-source information fusion; and carrying out dynamic evaluation by adopting a fuzzy evolution algorithm and determining an early warning level. The method can improve the accuracy and real-time performance of landslide monitoring and early warning.

The invention relates to the technical field of structural health monitoring and intelligent diagnosis, in particular to a structural fatigue damage identification method based on acoustic emission and deep learning, and the method comprises the steps: collecting a structural response signal under a fatigue load through an acoustic emission sensor array, inputting the structural response signal to a CNN-BiLSTM-Attention mixed deep learning model, and carrying out the recognition of the structural fatigue damage through the CNN-BiLSTM-Attention mixed deep learning model; the model extracts local time domain features through a dynamic adaptive convolution kernel, captures long time sequence dependence by using a bidirectional long-short-term memory network, focuses key damage features through a bimodal space-time attention mechanism, divides damage stages based on a nonlinear dynamic threshold algorithm of fracture opening amount, constructs a training data set of physical-data fusion, and performs dynamic time domain feature extraction. The learning rate is optimized by adopting a gradient sensitive cosine annealing algorithm, and the robustness of the model is improved in combination with an anti-noise and anti-loss function. The method integrates physical characteristics and an intelligent algorithm, and has the advantages of adaptive noise suppression, strong cross-domain generalization ability, high real-time performance and the like.

The embodiment of the invention provides an environment monitoring method and system based on 5G. According to the method, a dynamic coupling sensing field is constructed in a 5G network coverage domain, an adaptive topology with nonlinear interaction of parameters is established, and when parameter abnormity is detected, differential geometric reconstruction of a 5G network slice control surface is triggered based on Riemannian curvature change. And constructing a space-time manifold space at the 5G edge computing node, decomposing and extracting a diffusion principal curvature direction, and iteratively solving a propagation rate of an abnormal parameter in combination with a geodesic equation to generate a diffusion leading edge envelope surface. And according to the main curvature distribution characteristics of the diffusion leading edge envelope surface, constructing a dynamic sampling optimizer, generating an exponential decay sampling function, and deploying a directional sensing wavefront. And carrying out conformal tightening processing on the sampling function and the geographic grid to generate an interactive holographic manifold, establishing an aging judgment criterion of an abnormal level based on curvature 2-form characteristics, and triggering a graded early warning pulse wave packet. According to the technical scheme provided by the embodiment of the invention, the real-time performance and precision of environment monitoring are improved.

The invention relates to the technical field of data encryption, and discloses a data encryption protection system and method for intelligent controller hardware, and the method comprises the steps: generating a random confusion factor based on a real-time operation state through constructing a dynamic confusion algorithm, and carrying out the nonlinear transformation of original data; implementing a fragment encryption strategy, dividing fragments according to data sensitivity levels, and adopting a heterogeneous encryption algorithm; generating a dynamic binding key in combination with the environment parameters and the hardware unique identifier; hardware-level integrity check is executed, and data and hardware are verified through cyclic redundancy check codes and hardware fingerprints; establishing a self-adaptive key updating mechanism; performing multi-layer defense linkage; and implementing anti-side channel protection, and constructing a security audit log. According to the method, the hardware data security of the intelligent controller is comprehensively guaranteed, various security threats are effectively resisted, and the data confidentiality, integrity and availability are improved.