58 results about "Differential equation models" patented technology

The invention provides a hydropower station AI supervision system and method based on a multi-modal large model, and relates to the technical field of intelligent hydropower. The system comprises a multi-modal data acquisition module, a cross-modal space-time alignment module, a multi-modal feature extraction module, a multi-modal large model processing module and an intelligent reasoning and decision module. A neural differential equation model is introduced to carry out space-time alignment on asynchronous sensing data, networks such as Vision Transformer, MelCNN, TCN and the like are utilized to extract multi-modal features, cross-modal fusion analysis is realized by combining a local and global attention mechanism and dynamic weight distribution, and equipment abnormality is further reasoned based on a reconstruction error, a mahalanobis distance and a knowledge graph and a maintenance strategy is generated. According to the method, high-precision anomaly detection, fault root cause positioning and dynamic maintenance optimization of key equipment of the hydropower station are realized, diagnosis errors caused by traditional manual inspection and data splitting are avoided, and the operation and maintenance intelligence level and the equipment operation reliability are improved.

The invention discloses an industrial AI assistant cross-modal interaction method based on a dynamic knowledge graph, and the method comprises the following steps: collecting texts, voices, images and multi-source sensor data in an industrial system, carrying out the modal recognition, feature extraction and time alignment, and generating an event feature set and a state feature set with timestamps. Modeling a time dependency relationship between event types by constructing a multivariable Hawkes model, and outputting an event trigger sequence and trigger strength; and in combination with a neural controlled differential equation model, guiding the state to evolve along with time and jump at a specific moment to form a state evolution trajectory. Performing fusion coding on the event and the state, constructing a dynamic knowledge graph with a causal structure and semantic continuity, and generating interactive output based on context reasoning; and after system feedback is received, the triggering strength and the state track are updated, and continuous evolution of the knowledge graph and reverse optimization of model parameters are achieved.

The invention relates to the technical field of detection and identification, and discloses a mobile ship dynamic tracking and locking method and system based on target detection and identification, and the method comprises the steps: constructing a manifold embedded network, and mapping the multi-modal features of a ship to a Riemannian manifold space; constructing a continuous evolution modeler of the Shenchang differential equation model, and predicting a continuous evolution trajectory of the features under time, view angle and scale changes; a multi-scale bridging network is constructed, and bidirectional conversion between long and short distance feature representations is realized; constructing a differential geometric attitude encoder, and representing a ship attitude in an SO (3) Lie group space; a feature memory and reconstruction mechanism is realized, and the problem of short-time disappearance target recovery is solved; the components are integrated to construct a unified tracking and locking system. According to the method, the problems of discretization processing limitation, feature representation fragmentation, cross-condition consistency deficiency and the like in the prior art are solved, remarkable technical effects are achieved in the aspects of cross-condition recognition capability, continuous feature evolution capability, short-time disappearance target recovery capability and the like, and the method is suitable for the scenes of Yangtze River shipping monitoring, maritime affair safety supervision and the like.

The present disclosure provides a computer-implemented system for automated augmentation of differential equation models. The system stores differential equations representing mechanistic behavior of physical or computational processes and constructs a hybrid computational solver by embedding a trainable universal approximator with adjustable parameters into the differential equations, where approximator outputs augment time derivatives during numerical integration. An iterative training process adjusts parameters through numerical integration, monitors integration failures, assigns infinite penalty values to loss functions when failures occur, and computes gradients using automatic differentiation otherwise. The system computes sensitivity metrics via Jacobian matrix evaluation, classifies input / output subsets as significant based on threshold-exceeding sensitivity metrics, generates a reduced approximator operating on classified subsets, and replaces the universal approximator with the reduced version to create an optimized solver.

The invention discloses a lower limb vein thrombus formation risk prediction method based on machine learning. The method comprises the following steps: step 1, constructing a triaxial thrombus evolution dynamic container space; 2, generating a pre-thrombus micro-state orbital chain; 3, determining a thrombus formation critical mutation window; 4, introducing a causal stem budget at the thrombus formation critical mutation window, and constructing a natural evolution path and a controlled path; step 5, obtaining an orbit offset difference value; 6, calculating a dynamic orbit stability index; 7, inputting the pre-thrombus micro-state orbit chain and the dynamic orbit stability index into the improved PatchTST model, and introducing a flow continuity regulation operator into a reconfiguration module to obtain a corrected orbit stability index; and 8, outputting a thrombus formation risk prediction result. According to the method, the thrombus formation risk prediction is realized by constructing a triaxial thrombus evolution dynamic container space and combining a mental structure differential equation model and an improved PatchTST model.

The invention relates to the technical field of boiler control, in particular to a multivariable coupling thermal process regulation and control system and method for carbon pollution governing, and the method comprises the steps: collecting multi-source data such as acoustic emission, temperature, humidity and spectrum, and constructing a feature sequence through time mark alignment and wavelet packet enhancement; a heat value characterization quantity is predicted by using a Shenchang differential equation model fused with dynamic gating, a partition equivalent thermal network model is driven on this basis, and accurate prediction of a future time domain temperature field is realized by dynamically correcting thermal resistance and thermal capacity; based on the prediction result, a control instruction is solved through multi-objective optimization under the condition that the active temperature constraint is met; and in combination with heat flow density feedback, a layered reinforcement learning controller is adopted for online compensation of a pre-feedback instruction, and stable and efficient regulation and control of the boiler are achieved.

The invention relates to the technical field of multi-modal ophthalmology image analysis, and particularly discloses a multi-modal ophthalmology image analysis method, model and system based on optimal transmission image diffusion and a medium thereof.The method at least comprises the steps that S100, the distance between fundus color photo and OCT image modals is explicitly calculated through an optimal transmission algorithm, and the distance between fundus color photo and OCT image modals is calculated; carrying out collaborative evolution on the features in a continuous time domain in combination with a graph neural network and a Sheng differential equation model; and step S200, applying self-attention and cross attention in parallel, reserving modal specificity with low-layer fine granularity, and progressively fusing the color photo texture and the OCT depth structure in a high layer to realize cross-layer long-range dependent depth semantic fusion. The method not only promotes the leading-edge development of multi-modal medical image analysis, but also provides important scientific basis and technical support for constructing a high-precision and high-robustness intelligent ophthalmology diagnosis system.

The invention relates to the technical field of geothermal pipe network monitoring, in particular to a geothermal pipe network intelligent monitoring method and system based on edge calculation. The method comprises the following steps: acquiring a sound pressure signal, a vibration signal and a pressure signal in real time, and performing first-order difference calculation based on the pressure signal to obtain a pressure difference change sequence; inputting the sound pressure signal and the vibration signal into a micro-seismic intelligent identification model, outputting a micro-seismic risk quantitative scoring index, generating an execution regulation and control instruction, and recording execution feedback data to form a micro-seismic scoring feedback data set; performing time sequence reconstruction on the pressure difference change sequence by using a neural differential equation model, identifying an abnormal propagation source point and a shortest propagation path, and constructing a water hammer propagation response structural body; and the microseismic score feedback data set and the water hammer propagation response structural body are transmitted to a cloud platform, and the state of the geothermal pipe network is monitored and early warned in real time. According to the invention, intelligent monitoring and tracking of the micro-seismic abnormal state and the water hammer effect of the geothermal pipe network are realized.

The invention discloses a multi-dimensional network intrusion behavior intelligent identification method based on deep learning. The method comprises the following steps: collecting network multi-dimensional data and generating a standardized network event set and a network control path input set; establishing a neural controlled differential equation model, and generating a continuous time context representation set through hidden state evolution; establishing a neuro-hox process identification model, performing intensity function modeling, and generating an event intensity prediction sequence set and an identification intermediate representation set; forming an intrusion behavior decision rule set and a reasoning configuration set through joint training; new network multi-dimensional data are collected, the reasoning configuration set operation model is loaded, and a new event intensity prediction sequence set and a new candidate trigger time set are output; and generating a network intrusion behavior recognition result set in combination with the intrusion behavior decision rule set. According to the method, time modeling and logical reasoning are fused, and high-precision intrusion identification is realized.

The invention discloses a carbon footprint real-time evaluation system based on the Internet of Things, and the system comprises the steps: collecting the high-frequency energy consumption and carbon emission activity data of industrial production equipment and environment monitoring equipment in real time, and forming a data sequence with a timestamp; performing anomaly detection, denoising and interpolation preprocessing on the data sequence; extracting high-dimensional continuous time features of the data sequence; predicting a carbon emission sequence based on a neural controlled differential equation model; time-varying carbon emission factors corresponding to the enterprise area and the power grid nodes are obtained in real time; and coupling the carbon emission sequence and the time-varying carbon emission factor sequence in a continuous time domain, calculating a real-time carbon footprint, and performing dynamic monitoring and visual display. According to the invention, high-precision real-time prediction and monitoring of the carbon footprint data are realized, and the real-time performance and accuracy of evaluation are improved.

This invention proposes a method for predicting the health status of lithium batteries based on multidimensional features and neural network constant differential equations. The method includes the following steps: S1, preprocessing the capacity data and charging stage operation data collected during lithium battery operation, and constructing features from historical health status data; S2, constructing multidimensional feature inputs for health status prediction based on the charging stage operation data; S3, inputting the multidimensional features into a gated recurrent unit network to fuse and encode the historical health status sequence and constant current charging stage features to obtain a potential feature representation characterizing the battery degradation state; S4, comparing the predicted health status value output by the neural network constant differential equation model with the corresponding actual health status value, calculating the prediction error, and evaluating the prediction accuracy. This application achieves high-precision prediction of lithium battery health status by integrating a multidimensional feature screening mechanism and a continuous-time state evolution modeling method.

The invention relates to the technical field of carbon emission prediction and early warning, and provides a multi-scale neural distribution prediction and hierarchical migration early warning method, which comprises the following steps: acquiring historical carbon emission data, respectively inputting a historical sequence and a to-be-predicted sequence into an energy consumption stochastic differential equation model and a carbon factor stochastic differential equation model, generating a multi-scale carbon emission path sample set through an independent random disturbance term; calculating a path-level suitability score of each path sample based on the standard-exceeding risk integral, the first standard-exceeding moment and the path fluctuation variance; layering the calibration data set into a plurality of working condition layers according to working condition labels, sharing distribution shape parameters among the working condition layers through a hierarchical Bayesian method, and regularizing quantiles of small sample working condition layers to obtain an early warning threshold value of each working condition layer; and selecting a corresponding early warning threshold value according to the current working condition label to compare and trigger early warning. According to the method, the accuracy of carbon emission distribution prediction and the robustness of an early warning system are improved, and the problem that the early warning threshold value is unstable under the small sample working condition is relieved.

The invention relates to the technical field of data processing, in particular to a drink sweetness preference dynamic prediction model and a construction method thereof, the dynamic prediction model establishes a coupling differential equation model of sweetness preference and multi-dimensional state factors, replaces a traditional single temperature factor method, and is more comprehensive in prediction dimension and higher in capability of coping with a complex environment. Meanwhile, by introducing a saturation effect mechanism, the model is prevented from outputting a prediction result of anti-physiological common sense under extreme conditions, so that the prediction result is more in line with human labor-saving and psychological response laws, thereby being more consistent with real behaviors of a user. The invention further provides a method for predicting the sweetness preference of the user beverage, the method can respond to changes of various environment and product factors dynamically and quantitatively in real time, accurate prediction of the sweetness preference of the user is achieved, data support is provided for personalized product recommendation or automatic production scheduling, and the method has good practical value.

The invention discloses a tourist emotion real-time perception and intervention method based on a multi-modal large model, and particularly relates to the technical field of computer perception. The method comprises the following steps: acquiring a facial micro-expression image sequence, a gait posture sequence, a phrase-level voice stream and a body surface infrared heat map of a tourist at a scenic spot sightseeing path node, constructing a current state feature vector set F, calculating an emotion weight coefficient based on a cross-modal attention mechanism, and generating an emotion fusion representation vector E; judging the current emotional risk level R of the tourist according to E, predicting an emotional fluctuation trend delta E in a future time period based on a Shenchang differential equation model in combination with a historical path trajectory and an environment state parameter, and judging whether a critical emotional transition risk exists or not; if the risk exists, an adaptive intervention strategy is matched and executed in real time; updating the emotional state vector E'again according to the tourist intervention response data; according to the invention, high-precision and dynamic tourist emotion management can be realized, and the intelligent level of scenic spot service and the tourist safety guarantee capability are effectively improved.

The application relates to the technical field of intelligent robot inspection, and discloses a somatic intelligent AI inspection method based on digital twinning, wherein the somatic intelligent AI inspection method based on digital twinning comprises the following steps: a dynamic attention multi-layer feature extraction network is used to analyze multispectral images collected by a robot-mounted sensor to generate a real-time distribution diagram of industrial facility abnormal coverage; a space-time sequence contrast learning model is used to analyze historical abnormal coverage data to generate an abnormal coverage trend prediction; a physically constrained neural differential equation model is used to analyze an abnormal coverage evolution process to generate an accurate abnormal evolution prediction result; a multi-objective optimization algorithm is used to generate an optimal inspection path; a digital twinning system is dynamically updated, and an industrial facility maintenance optimization suggestion is generated; and through the dynamic attention multi-layer feature extraction network and a light condition self-adaptive enhancement algorithm, the detection difficulty in a strong light and high reflection environment is effectively solved.

The invention discloses a sea rocket launching platform wave compensation control method based on deep reinforcement learning. The sea rocket launching platform wave compensation control method comprises the following steps: S1, collecting real-time motion state data; s2, inputting the motion state data into an improved Shenchang differential equation model; s3, constructing a strategy network according to a SoftActor-Critic algorithm, and generating a corresponding continuous control action signal; s4, the control action signal is input into a propeller control system and a ballast adjusting system, and combined compensation control over the posture and the position of the platform is achieved; s5, in a simulation environment, carrying out end-to-end joint training on the ShentActor-Critic strategy network and the ShentActor-Critic strategy network; and S6, realizing wave compensation of the offshore rocket launching platform in the wave disturbance environment in the platform control system. According to the invention, accurate prediction and adaptive compensation of platform stability control under complex disturbance are realized, and the control precision and reliability are significantly improved.

The invention discloses a software quality dynamic measurement and uncertainty quantification method based on a Bayesian-KAN-ODE series architecture, and belongs to the technical field of software quality metrics, and the method comprises the following steps: carrying out dimensionless processing on a quality index of software to be evaluated, and determining an initial weight; inputting the processed data into a Kolmogorov-Arnold network, extracting quality features by using a learnable spline function of the Kolmogorov-Arnold network, and deducing the uncertainty of a quantization parameter based on variation; inputting the initial weight and the extracted features into an ordinary differential equation model, and solving an ordinary differential equation to realize continuous time dynamic evolution of the weight; and finally, constructing a series architecture, and generating software quality prediction distribution containing a prediction mean value and a confidence interval through Monte Carlo sampling. According to the method, the defects of a traditional method in coping with parameter uncertainty, time evolution uncertainty and a complex nonlinear relation are overcome, and the dynamic property, robustness and interpretability of measurement are improved.

The application discloses a kind of mouse brain ischemic lesion automatic quantification method and system based on deep learning, belong to medical image processing and artificial intelligence technical field.The method includes: standardization pretreatment is carried out to T1WI, T2WI, DTI and ASL and other multi-modal MRI data, and enhanced feature tensor is constructed;Lesion intelligent preliminary segmentation is carried out using BA-U-Net network guided by physical prior, and biophysical consistency loss is introduced;Boundary refining is carried out to segmentation result using graph convolution network;Based on LDDMM registration and neural ordinary differential equation model, dynamic evolution modeling is carried out to multi-time point lesion, and severity score is calculated;Finally, comprehensive pathophysiological feature vector is constructed, XGBoost model is used for prediction, and interactive report containing 3D visual model is generated by SHAP explanation.The system includes corresponding functional module.The application realizes high-precision, reproducible automatic quantification to fuzzy lesion, improves the robustness of pathological pattern recognition, and provides dynamic biomarker for drug evaluation.

The invention discloses an electric vehicle charging out-of-tolerance measurement modeling method, system and device and a medium, and the method comprises the steps: obtaining vehicle end operation data, pile end operation data and cloud end operation data, and obtaining a measurement error prediction result of an electric vehicle charging facility through constructing a charging environment topological structure and analyzing an energy conservation relation of a charging area; the method comprises the following steps: obtaining a voltage estimation error and calculating measurement uncertainty in combination with a statistical theory to obtain a quantitative index set; the method comprises the following steps: inputting a quantitative index set as a feature into a Sheng differential equation model, and learning a differential rule of the input feature along with time evolution by using the Sheng differential equation model to obtain a matching result of a nonlinear dynamic characteristic of a metering error in a charging process; and inputting real-time operation data of the electric vehicle charging facility into the trained model to obtain an out-of-tolerance measurement result of the electric vehicle charging facility. According to the method, the metering accuracy and the error response rate under the complex working condition are remarkably improved.

This invention discloses an AI-based modeling method for assessing the health status of integrated power supply boxes, comprising the following steps: S1, constructing a multivariate time series; S2, modeling the multivariate time series trajectory using a neural control differential equation model to obtain the latent space trajectory; S3, performing dynamic modal decomposition on the latent space trajectory to construct a modal health space; S4, generating an enhanced training sample set using a manifold interpolation hybrid method in the modal health space; S5, inputting the hybrid modal vectors from the enhanced training sample set into a health classification module; S6, inputting the latent space trajectory into a temporal semantic playback module to generate the final predicted state trajectory; S7, comparing the final predicted state trajectory with the actual historical state trajectory, and generating an anomaly marker and performing a state rollback operation when the semantic difference exceeds a set tolerance threshold. This invention integrates neural differential modeling, interpolation enhancement, and temporal semantic playback to achieve accurate assessment of the health status of integrated power supply boxes.

The invention provides a building electrical system fault diagnosis method and system, belongs to the field of building electrical system monitoring and fault diagnosis, and is used for solving the problems that the diagnosis dimension is single, a fault dynamic propagation and causal mechanism is difficult to model, and the model self-adaption and generalization ability is insufficient in the related technology. According to the method, multi-modal time sequence data is mapped to Riemannian manifold, a unified manifold control differential equation model is utilized, node state evolution dynamics and geometric causal interaction between nodes are modeled internally and synchronously, and accurate fault detection and positioning are realized based on Lie derivative difference and geometric divergence difference. The system correspondingly comprises a data acquisition module, a unified model module and a fault judgment module. Through combination of meta-learning rapid adaptation, antagonistic robust training and geometric federated learning, the scheme realizes high-precision and explainable diagnosis of the composite fault, and has strong adaptive ability and coevolution potential.

The invention discloses a parameter self-tuning method for a short-circuit breaking test loop of a high-voltage alternating-current circuit breaker, which belongs to the technical field of high-voltage electrical equipment test of a power system, and is characterized in that a three-phase direct test loop is equivalent to a single-phase model, and voltage and current standard values and time angle domain conversion are introduced; establishing a digital twinning differential equation model under dimensionless parameters; secondly, taking dimensionless parameters as decision variables of an improved particle swarm optimization algorithm, dynamically adjusting an inertia weight through a state sensing mechanism to balance global exploration and local development capabilities, and giving a targeted flight direction to particles in combination with a multi-source error compensation strategy; in the design of a value function, a layered adaptive penalty mechanism is adopted to preferentially guarantee the satisfaction of hard constraints such as an amplitude coefficient and the like, and transient recovery voltage waveform characteristic parameters are automatically extracted through a coordinate conversion method for simulation verification. According to the invention, high-efficiency, accurate and automatic setting of high-voltage AC circuit breaker short-circuit breaking test loop parameters is finally realized.

The invention discloses a digital native model energy prediction method and system of a physically-driven computing power center. The method comprises the steps of obtaining time sequence mass flow, temperature data and geometric physical parameters required by operation of the cold storage tank, and setting prior information such as an initial to-be-identified parameter set and a flow direction to obtain input data and parameters; on the basis of energy conservation, constructing a layered ordinary differential equation model for describing the temperature change of each layer by using the input data and the parameters; using an adaptive step ODE solver to predict temperature distribution of each layer at each time point based on the hierarchical ordinary differential equation model according to input data and parameters so as to obtain predicted temperature; constructing a loss function by comparing the predicted temperature with an actual measurement value; a gradient is calculated based on the loss function, and parameters are adjusted by an optimization algorithm to minimize the loss function. By implementing the method provided by the invention, the model dimension can be reduced and the calculation can be simplified while the main physical mechanism is reserved.

The application discloses a kind of equipment asset depreciation prediction methods based on machine learning, comprising the following steps: S1, acquisition asset original value, depreciation code, life, starting date and historical net value, normalization generates the asset state input sequence in uniform time domain;S2, the normalized asset state input neural differential equation model, constructs the differential system of asset state evolution with time;S3, identify the parameter change during the depreciation of assets, extract change time and amplitude, construct disturbance event sequence;S4, according to the disturbance event sequence constructs time gate function and disturbance coupling function;S5, inject the disturbance coupling function into the differential system, and obtain the depreciation evolution path by solving;S6, the predicted path is mapped to actual time interval, and the depreciation trend prediction result is output.The present application realizes the fine prediction of equipment depreciation trend and the dynamic response of strategy change, and improves the intelligent level of asset management.

The invention discloses a transformer temperature rise parameter identification method based on a physical information deep neural network, and relates to the technical field of transformer temperature rise parameter identification, and the method comprises the steps: obtaining the historical operation data and environment data of a transformer, and forming a training set; embedding the temperature rise differential equation model into a PINN model based on a physical information deep neural network; training the PINN model by using the sample at each moment in the training set, and updating the initialized physical parameters in the transformer based on the output of the PINN model and the temperature rise differential equation model in the training process; obtaining a transformer temperature rise parameter through the PINN model meeting the training ending condition and the physical parameters determined after the training ending condition is met; according to the method, physical consistency and data self-adaption are considered, and key parameters such as thermal resistance / thermal capacity can be updated online, so that operation boundary evaluation, dynamic current-carrying capacity decision and operation and maintenance strategies based on temperature rise are directly supported, and the method has clear engineering value.

This invention discloses a power system node inertia time constant assessment strategy based on continuous evolution feature learning. The steps include: First, collecting frequency response data of the power system during active power disturbances, extracting statistical and time-frequency features from the frequency response data, constructing a feature vector, and obtaining coded features through a multilayer perceptron encoding module; Second, gating and weighting the coded features through a hybrid expert module to form a fused feature vector, which is then input as a state variable into a neural frequent differential equation model. In the state space adaptively adjusted by the hybrid expert module, a numerical step-by-step solution algorithm is used to continuously evolve the state variable, obtaining a dynamic feature vector; Finally, the dynamic feature vector is input into a decoding module based on a multilayer perceptron, outputting the power system node inertia time constant assessment result. This invention achieves accurate assessment of the power system node inertia time constant through encoding, continuous evolution, and decoding processes.

A method for predicting drug pathways that modulate cellular phenotypes is provided. The method includes obtaining a logic-based differential equation model of a signaling network relating to a cellular phenotype, comprising nodes representing proteins or mRNAs and edges representing interactions. The method further includes identifying candidate compounds affecting the cellular phenotype, determining protein targets using a drug-target database, and mapping pathways from protein targets to network nodes by searching a protein interaction database and computing ranked pathways by minimizing a cost function based on edge weights. The method includes expanding the model by incorporating mapped pathways, simulating expanded model variants to predict compound effects on the cellular phenotype, and outputting predictions of how candidate compounds modulate the cellular phenotype through identified pathways.

This invention relates to the field of battery health management and prediction technology, specifically a method and system for predicting the capacity degradation trend of lithium batteries. The method includes: generating a deeply fused feature by combining data-driven features and electrochemical features through a dual calibration mechanism; inputting this feature sequence into an electrochemical process sensing model, encoding it as a potential state vector characterizing personalized degradation; then inputting this vector as an initial condition into a neural differential equation model, learning the degradation dynamics and solving it through integration to ultimately generate a continuous health state degradation trajectory and obtain the prediction result. This invention overcomes the weakness of traditional "black box" models in generalization ability by deeply integrating physical mechanisms with a data-driven model. It can achieve long-term, high-precision, and continuous prediction of the degradation trend throughout the entire battery lifecycle using only weak early-stage signals, exhibiting high reliability and practical value.

This invention belongs to the field of intelligent transportation, specifically relating to a road network comprehensive resilience assessment method based on a dual-component neural rough differential equation. The method includes the following steps: Step 1: Constructing high- and low-frequency dual-component feature information based on historical traffic speed data and topology data of the urban road network; Step 2: Constructing a dual-component neural rough differential equation model, solving for high-frequency and low-frequency spatiotemporal feature representations, fusing the high- and low-frequency dual-component spatiotemporal features, and outputting speed prediction results; Step 3: Constructing a road network comprehensive resilience index; Step 4: Road network comprehensive resilience assessment. Experimental results show that, compared with other prediction methods, this invention not only has superior traffic speed prediction performance but also provides more comprehensive, dynamic, and reliable road network resilience assessment results, providing effective technical support for management decisions and resilience enhancement of urban transportation systems under extreme weather conditions.

The invention discloses a noise reduction processing method for a dynamic change signal, and the method comprises the following steps: carrying out the preprocessing of a collected dynamic signal, and converting the collected dynamic signal into a discrete time sequence; constructing a multi-dimensional feature space, and mapping the discrete time sequence into the feature space; in the multi-dimensional feature space, a high-order differential equation model for noise suppression is established; solving the high-order differential equation model in combination with a random process theory; and a result obtained by solving is converted back to an original signal space through inverse mapping, and a dynamic signal after noise reduction is obtained. The method has the advantages that the dynamic change signals can be efficiently processed, noise interference is remarkably suppressed by combining the multi-dimensional feature mapping and the high-order differential equation model with the stochastic process theory, the signal quality and accuracy are improved, and the method is suitable for various real-time or offline dynamic signal processing applications.