5532 results about "Physical model" patented technology

The invention provides a building health monitoring and evaluation method and system based on a physical neural network, and the method comprises the steps: collecting the monitoring data of vibration, strain, displacement and the like of a building through a sensor network, and carrying out the data preprocessing based on wavelet transformation and a neural network; inputting the processed data into a physical constraint encoder, mapping the processed data to a physical space by adopting a Hamilton quantity encoding method, and constructing constraint conditions in combination with physical model parameters; performing feature matching by using a physical constraint neural network, and extracting key features; adopting a principal component analysis method to decompose early warning indexes, and constructing parameter mapping through a deep neural network; generating a hierarchical decision rule based on a decision tree algorithm; and carrying out constraint check and cost calculation on the candidate schemes, and finishing scheme sorting through an analytic hierarchy process. Physical knowledge is embedded into a deep learning framework, and the accuracy and reliability of monitoring evaluation are improved.

The invention relates to the technical field of bridge structure health monitoring and early warning, and discloses a bridge support static displacement overrun early warning correction method based on a prediction model, and the method comprises the following steps: S1, simulating the dynamic response of a support and an overall structure under various working conditions through building a coupling finite element model of the bridge support and the overall bridge structure; s2, displacement, vibration and temperature data of the support are collected in real time through a multi-source sensor, and data fusion and noise reduction processing are carried out; s3, establishing a multi-level prediction model based on a physical model and a data driving model, and predicting a future displacement trend of the support; and S4, monitoring a prediction error in real time through an error feedback mechanism. Prediction errors are corrected in real time through recursive Kalman filtering and an error feedback mechanism, an early warning threshold value is dynamically adjusted through Bayesian updating, the precision and reliability of early warning are improved in combination with a double triggering mechanism, the method adapts to changes under complex working conditions, and false alarms and missing alarms are reduced.

The invention provides a power station equipment state real-time monitoring and diagnosing method and system based on cloud edge collaboration, and the method comprises the steps: adjusting a data collection period dynamically determined based on an adaptive sampling frequency adjustment algorithm, and collecting a vibration signal, a temperature signal and a current signal through a multi-source heterogeneous sensor array disposed in a power station equipment body; carrying out preprocessing by utilizing the edge computing node, generating a compressed feature vector, and uploading the compressed feature vector to a cloud end through an MQTT protocol; a multi-modal data fusion analysis module is started through a cloud, a three-dimensional evaluation matrix of the equipment health state is constructed in combination with historical operation data and environmental parameters of the equipment, and a calculation task distribution strategy between an edge calculation node and the cloud is adjusted in real time according to an evaluation result of the three-dimensional evaluation matrix. Abnormal mode recognition based on a deep residual network and fault source tracing double-channel analysis based on a physical model are executed, fault types and fault reasons are diagnosed, and the accuracy and timeliness of fault diagnosis are guaranteed.

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.

A method of constructing a digital twin physical field of a computer numerical control machine tool with double order reduction is provided. A physical field finite element simulation sample data set in a working state sampling space has an order preliminarily reduced to sparse grid node data via an improved K Nearest Neighbor (KNN) algorithm, so that the number of point sets is kept within the real-time rendering capability range of graphic rendering software. A digital twin physical field proxy model is trained using a radial basis function (RBF) to realize reconstruction of the physical field. The proxy model is iteratively optimized using a genetic algorithm to solve the problem of deviation between a proxy physical field model and a real physical model. Finally, the proxy model is deployed to a digital twin system, and the physical field distribution is solved in real time.

The invention discloses an intelligent power distribution network equipment state perception and abnormity diagnosis system, and the system operation process specifically comprises the following steps: collecting the operation state data of power distribution network equipment in real time, carrying out the time-space alignment and feature fusion, and generating an equipment multi-dimensional state vector; inputting a pre-constructed equipment health dynamic baseline model, and outputting a real-time health deviation degree; when the real-time health deviation degree exceeds an early warning deviation threshold value, triggering an abnormal preliminary screening mechanism, and extracting abnormal feature fragments; inputting a multi-stage diagnosis knowledge graph model, and generating an abnormal cause hypothesis set; performing confidence ranking on the abnormal cause hypothesis set, and outputting first # imgabs0 diagnosis results and corresponding confidence weights; and generating an equipment maintenance strategy instruction set according to the diagnosis result. The method has the following advantages and effects: the dynamic baseline is adaptively generated from multi-source data, and a multi-stage diagnosis framework of a physical model, a power grid rule and a historical case is fused, so that the accuracy and timeliness of anomaly diagnosis are finally improved.

The invention relates to the technical field of fault detection control, and discloses an automobile fault detection method and system fusing sensor data. The method comprises the following steps: modeling a multi-system sensor network through an automobile system topology mapping algorithm to obtain a sensor topology incidence matrix; performing synchronous processing on the sensor topological incidence matrix according to a vehicle dynamics constraint equation to obtain a synchronous multi-sensor data set; inputting the synchronous multi-sensor data set into a fault propagation path sensing network for fusion to obtain a vehicle system fault feature vector; detecting the fault feature vector based on an automobile physical model constraint optimizer to obtain a fault state recognition result; and tracing and controlling a fault state identification result according to the fault propagation atlas model to obtain a system adjustment control signal. According to the invention, the source identification accuracy of the complex coupling fault and the pertinence of control and adjustment are improved.

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

The invention provides a frozen soil roadbed thaw collapse prediction method based on multi-source data and deep learning driving, and relates to the field of cold region engineering. The method comprises the steps that mathematical description is conducted on the thermal-mechanical coupling process of the frozen soil roadbed, and a frozen soil roadbed thermal-mechanical coupling physical model is obtained; the method comprises the following steps: acquiring frozen soil roadbed field monitoring data, remote sensing data, indoor test data and engineering environment data, and performing cleaning and normalization processing on multi-source data to obtain a multi-source data set for constructing and training a physical information neural network model; constructing a physical information neural network based on the frozen soil roadbed physical model and the multi-source data set; constructing a physical residual error of the physical information neural network model through a differential equation, and constructing a loss function based on a residual error item; and training the physical information neural network model to obtain a deep learning model for predicting thaw collapse deformation of the frozen soil roadbed. According to the method, the accuracy and reliability of thaw collapse deformation prediction of the frozen soil roadbed can be improved.

The invention provides an energy-saving temperature control optimization method and system based on a central air-conditioning simulation platform, and relates to the field of central air-conditioning simulation platforms, and the method comprises the steps: collecting the operation parameters of a central air-conditioning system in real time through an Internet of Things sensor network, constructing a multi-dimensional dynamic data set in combination with historical data and outdoor meteorological data, and carrying out the calculation of the multi-dimensional dynamic data set; a physical model and machine learning hybrid driven simulation engine is adopted, a dynamic thermodynamic model of the central air-conditioning system is established, and a multi-objective optimization function of a simulation platform model is defined according to user comfort requirements, energy consumption cost constraints and environmental policy indexes; and adopting a hybrid optimization strategy of fusion of reinforcement learning and a genetic algorithm, iteratively optimizing control parameters in the simulation platform model, deploying a digital twin system according to the energy-saving strategy set, predicting potential faults through a virtual sensor, correcting the parameters of the simulation platform model, and realizing closed-loop control and continuous optimization. The method is used for overcoming the defect that a closed-loop feedback mechanism is lacked in the prior art.

The invention provides a complex geothermal system digital twinning online experiment method based on multi-source data deep fusion, and solves the problems of precise construction of geothermal system digital twinning and low-delay visualization of virtual-real interaction. The method comprises the following steps: firstly, building a typical geothermal reservoir multi-source heterogeneous data parallel acquisition system, and carrying out fine inversion, high-precision identification and continuous correction on geothermal reservoir parameters by using a large amount of field experiment test data; then, a complete physical model of the geothermal system is established, online simulation is conducted on an actual system through high-performance scientific calculation, and mutual calibration is conducted on the actual system and test data; and finally, building a geothermal system digital twinning on-line experiment platform based on real-time acquired test data, a reliable mechanism model and an artificial intelligence method, and realizing accurate measurement and on-line monitoring of a geothermal exploitation process and iterative evolution of a geothermal reservoir exploitation scheme. According to the method, multi-source data of the geothermal system are deeply fused, high-precision online simulation and low-time-delay virtual-real interaction in the geothermal experiment process are achieved, and geothermal storage and exploitation details can be comprehensively transparentized. And furthermore, intelligent and rapid inversion is performed on the multi-scale seepage and heat transfer process of the geothermal reservoir by carrying out a virtual online experiment, so that the scientificity and efficiency of full-life-cycle geothermal exploitation are greatly improved. Meanwhile, a multi-agent collaborative optimization regulation and control framework provided by the method has relatively high expandability and flexibility, and an optimal well spacing design scheme and an optimal experiment scheme of the geothermal well group can be automatically excavated through adaptive optimization and iterative evolution of a system regulation and control strategy; and the method has a very strong application prospect in the distributed optimization field of the geothermal storage virtual experiment.

Disclosed is a digital twin-based deduction and optimization method and system for an intelligent reflecting surface (IRS) communication system, including: collecting data from a scenario, including relevant data of an IRS physical model and real channel data; performing real-time data transmission on the collected data; establishing a digital twin three-dimensional (3D) model in a digital twin space based on the data after the real-time data transmission; establishing an IRS reflection mechanism model before fusing with the digital twin 3D model, the generalized Snell Equation being used to simplify a complex system in the IRS reflection mechanism model; deducing and optimizing the IRS communication system to obtain an optimization strategy; and feeding the optimization strategy back to the real world to realize the deduction and optimization of the IRS communication system.

The invention discloses a method for correcting data of abnormal reading of a pressure sensor, and relates to the technical field of sensor equipment. Comprising the following steps: acquiring sensor readings, constructing a multi-sensor cross validation mechanism, establishing an anomaly detection threshold model, performing anomaly detection based on a time sequence mode, constructing a physical constraint feedback mechanism, performing dynamic drift compensation, processing the acquired readings, and performing self-correction feedback optimization. According to the method for correcting the abnormal reading data of the pressure sensor, through a physical constraint feedback mechanism, the output of the sensor is compared with a theoretical physical model for verification, and the recognition capability of nonlinear abnormality is enhanced; through dynamic drift compensation, linear and exponential drifts generated in long-term use of the sensor are separated and corrected, and accurate correction of the drifts is realized. According to the method, the reliability and the stability of pressure sensor data can be remarkably improved, a more accurate measurement basis is provided for a precise measurement scene, and meanwhile, the risk of system misinformation and failure is reduced.

The invention provides a drainage basin flood runoff forecasting system and forecasting method based on water conservancy big data, relates to the technical field of flood runoff forecasting, and constructs the drainage basin flood runoff forecasting system based on the water conservancy big data by integrating the advantages of a physical model and a data driving model. The adopted bidirectional LSTM network is used for abnormal value interpolation, so that the data quality can be effectively improved; the HEC-HMS model is combined with a terrain correction factor to optimize the accuracy of the runoff process; the LSTM network enhances the data driving capability of flow prediction through the fusion of geographic space features and time series data, the model fusion module introduces an attention mechanism and water conservation constraints, the dynamic weight balance of a physical model and a data driving model is realized, the reliability and consistency of a prediction result are comprehensively improved, and the prediction efficiency is improved. And the model verification module realizes continuous comparison and verification of a forecast result and actually measured data through a closed-loop feedback mechanism of a total error and a dynamic threshold value.

The invention discloses a mobile energy storage power supply health state intelligent assessment method and system, and the method comprises the steps: inputting the multi-parameter monitoring data of a power supply into a battery physical model for preliminary assessment, and obtaining a first assessment value; performing feature extraction and feature selection on the obtained monitoring data and environmental data, and inputting an obtained principal component matrix into a health state evaluation model for prediction to obtain a second evaluation value; comparing the first evaluation value with the second evaluation value, and adjusting parameters of the battery physical model by using the health state evaluation model to obtain an optimized battery physical model; obtaining a third evaluation value based on the optimized battery physical model; and obtaining a final battery health state evaluation result based on the third evaluation value and the second evaluation value. By collecting various monitoring data and environmental data in real time, the health state of the battery is comprehensively known, so that the evaluation accuracy is improved; and the established physical model and the health state evaluation model are combined, so that the precision and robustness of the evaluation result are improved.

The invention discloses an unmanned aerial vehicle path planning method and system based on digital twinning. The method comprises the following steps: constructing a three-dimensional environment model based on first target environment data in first target multi-source environment perception data; constructing a digital twin based on the geometric model, the physical model, the control model, the target physical data hybrid dynamic model and the three-dimensional environment model; inputting the second target multi-source environment sensing data into the digital twin, and dynamically simulating the flight behavior of the physical unmanned aerial vehicle based on the second target multi-source environment sensing data to obtain simulated flight environment state data; and based on the simulated flight environment state data, performing path planning on the physical unmanned aerial vehicle in combination with a prediction control algorithm and a path planning algorithm to obtain a target flight path, transmitting the target flight path to the physical unmanned aerial vehicle, and controlling the flight of the physical unmanned aerial vehicle. The method can dynamically simulate the real-time change of the physical unmanned aerial vehicle, plan the optimal flight path and effectively avoid obstacles, and can be widely applied to the technical field of unmanned aerial vehicle path control.

The invention discloses a surgical robot resection navigation method based on multi-modal image registration. The method comprises the following steps: acquiring a preoperative multi-modal medical image of a patient and reconstructing a three-dimensional model containing a focus and a dangerous structure; acquiring an intraoperative real-time image; extracting cross-modal invariant features from the pre-operative and intra-operative images by using a first deep learning network, and establishing an initial corresponding relation; predicting an initial non-rigid deformation field by using a second deep learning network, based on the pre-operative / intra-operative image and the initial corresponding relation and in combination with physical model constraints; tracking a surgical tool in real time, and updating a non-rigid deformation field in real time by adopting an incremental calculation strategy; applying the updated deformation field to the preoperative three-dimensional model to generate a deformed model; and mapping the tool position to the deformed model, and generating a navigation instruction. According to the method, soft tissue deformation can be accurately compensated, multi-modal image information is effectively fused, navigation information is dynamically updated in real time, and the precision and safety of robot surgery are remarkably improved.

The invention provides a multi-stage turbulent dynamic video recovery method based on a physical model, and relates to the technical field of image processing. According to the method, the dynamic benefit index DEI is introduced, the turbulence intensity, the dynamic region ratio and the optical flow diagram are combined, the video dynamic intensity under different turbulence conditions is accurately quantified, the high-dynamic turbulence data set is constructed, and the model training and evaluation pertinence is enhanced. In addition, according to the multi-stage recovery frame PMR provided by the invention, a recovery task is decoupled into three stages of de-inclination, motion segmentation enhancement and deblurring, and step-by-step recovery is carried out, so that complex distortion is more accurately dealt with, and efficient generalization and high-quality recovery are realized. The frame is also combined with a dual-stage registration strategy, and through global motion alignment and pixel-level optical flow correction, camera shake and optical interference in a real scene are reduced, the problem of multi-frame dislocation in a turbulent dynamic scene is effectively solved, and the stability and robustness of recovery are improved.

The invention belongs to the technical field of iron stick yam image recognition detection, and discloses an iron stick yam intelligent system and method based on image recognition, and the method comprises the steps: dynamically adjusting the polarization direction to generate a polarization suppression image, and generating a space mapping relation; based on the polarization suppression image, segmenting the main body area of the iron stick yam, filling and optimizing the hole edge, generating a high-precision contour mask, further extracting the texture difference between wrinkles and cracks, generating a direction sensitive characteristic pattern, and evaluating morphological defects; constructing a comprehensive feature vector, training a Gaussian mixture model to generate reference distribution, calculating a quality deviation index, and judging a risk level; constructing a double-branch feature vector, outputting a preliminary comprehensive quality score through a complementary aggregation model, introducing a planting density-curvature physical model, obtaining a final quality score, dynamically adjusting a quality judgment threshold, dividing quality grades, and triggering a response strategy according to confidence grade to form a closed loop; and comprehensiveness and accuracy of Chinese yam quality evaluation are improved.

The invention discloses a transmission line three-dimensional reconstruction method and system based on a monocular channel monitoring camera, and the method comprises the steps: obtaining compensation image data through adaptive illumination compensation processing, obtaining direction feature data based on frequency domain feature analysis, constructing a texture enhancement model through a graph theory, generating enhanced image data, and carrying out the reconstruction of a transmission line. Outputting feature aggregation data through time sequence feature aggregation; generating three-dimensional contour data in combination with multi-wire topology analysis and geometric constraint optimization; depth probability modeling and multi-scale consistency processing are adopted to obtain optimized depth data; and outputting system state data, three-dimensional point cloud data and deformation parameter data through physical model construction and space-time constraint fusion. According to the method, high-precision three-dimensional reconstruction of the power transmission conductor is realized, and the method has relatively high environmental adaptability and calculation efficiency.

The invention relates to the field of shield tunneling, and discloses a shield tunneling control method and system based on a rock-soil particle motion trail, and the method comprises the steps: fusion deployment of various types of high-precision sensors, intelligent anti-noise data collection and preprocessing, precise physical model construction, efficient numerical simulation optimization, and multi-parameter comprehensive detection and correlation analysis. And real-time data-driven adaptive control model construction, control instruction rapid generation and transmission, closed-loop feedback and continuous optimization are realized. According to the method, multiple links including fusion deployment of multiple types of high-precision sensors, intelligent anti-noise processing, physical model construction and the like are covered, all the links have a synergistic effect, data can be accurately obtained, data quality can be guaranteed, a simulation foundation can be provided, comprehensive evaluation, intelligent control and rapid instruction transmission can be achieved, continuous optimization is achieved through closed-loop feedback, and the method is suitable for large-scale popularization and application. And finally, the construction level in all aspects is improved, and complex engineering requirements are met.

The invention provides a digital twinning method and system based on industrial automation, computing equipment and a storage medium. The method comprises the following steps: acquiring real-time operation parameters from a physical system; based on the real-time operation parameters and a preset physical model, constructing a digital twinborn body of the physical system in a virtual environment; simulating the operation state of the physical system by using the digital twin, and predicting the performance of the physical system under different working conditions to generate a simulation result; comparing the simulation result with the obtained actual operation data of the physical system so as to evaluate the digital twin body and obtain an evaluation result; adjusting the physical model according to the evaluation result so as to update the digital twin; and performing fault prediction and / or optimizing operation parameters of the physical system by using the updated digital twin. According to the technical scheme provided by the invention, the digital twin can be continuously optimized and the physical model can be adjusted.

The embodiment of the invention provides a BIM-based hydraulic engineering digital management method and system. The method comprises the following steps: performing multi-dimensional monitoring system deployment on a to-be-monitored area to obtain multi-dimensional hydrological data; constructing a first BIM based on the topographic data of the to-be-monitored area, the real-time work area image, the hydraulic engineering construction information of the design stage and the equipment deployment information; fusing the multi-dimensional hydrological data, the water area change data, the construction progress data and the operation and maintenance monitoring data into the first BIM, and constructing a second BIM including a full life cycle; and through the equipment characteristic curve and the water conservancy project physical model, in combination with the multi-dimensional hydrological data and the water area change data, predicting a water conservancy project structure change trend and potential risk factors in the second BIM, fusing the water conservancy project structure change trend and the potential risk factors into the second BIM, and displaying an implementation effect and improvement suggestions in real time in the second BIM. A user is assisted to realize hydraulic engineering digital management, and the engineering management efficiency is improved.

The invention provides an intelligent water conservancy digital twinborn simulation system based on multi-source data, and belongs to the technical field of digital monitoring. Minute-level data acquisition and transmission are realized by constructing a space-air-ground three-dimensional sensing network and combining edge intelligent preprocessing, data acquisition, noise reduction and abnormity identification are realized by utilizing sensors such as millimeter wave radar and laser radar, and the system is used for realizing data acquisition and transmission. Dynamic data fusion and intelligent calibration are carried out, and second-level alignment and credible verification of data are realized by means of a space-time calibration algorithm, Kalman filtering and a block chain evidence storage technology; a hybrid simulation and intelligent decision model is established, a physical model, a machine learning architecture and a dynamic threshold decision tree are adopted, flood routing minute-level prediction and emergency response are realized, and the system improves water conservancy monitoring prediction precision and emergency response efficiency.

The invention relates to the technical field of computers, in particular to a temperature control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an estimated temperature of a heat dissipation part based on a pre-established physical model, and obtaining an actual temperature of the heat dissipation part based on the estimated temperature and the actual temperature of the heat dissipation part; judging whether parameters of a pre-established physical model meet preset updating conditions or not; if the parameters of the physical model meet preset updating conditions, the parameters of the pre-established physical model are updated, and based on the updated parameters, regulation and control parameters of a proportional integral differential controller are determined according to a preset objective function, so that the proportional integral differential controller controls the operating temperature of the server based on the regulation and control parameters. Therefore, the problems that in PID regulation and control of the server fan, parameter setting depends on artificial experience, efficiency is low, subjectivity is high, and power consumption, noise and heat dissipation performance cannot be effectively balanced in the prior art are solved, and effective control over the temperature, the power consumption and the noise of the server is achieved.

The invention discloses an urban inland inundation water recession process numerical simulation method and system based on physical-data fusion, and belongs to the technical field of urban flood control and intelligent water affairs. A hydraulic model and a data driving model are fused, a prediction result is dynamically weighted through gating attention, and real-time correction is performed by using Kalman filtering; the method comprises the steps of multi-source data fusion and enhancement, wherein data collection and processing are carried out, space-time alignment is achieved, and feature construction is carried out; mechanism and mathematical double-engine collaborative prediction: collaborative prediction is carried out based on a physical model engine and a data driving engine, and adaptive fusion is realized; online reasoning and dynamic correction, including Kalman filtering dynamic correction and extreme scene emergency processing; and performing model evaluation and iteration. According to the method, the problem of high-precision prediction of the recession time and the ponding range in a complex urban environment is solved, the generalization ability of the model in non-seen scenes such as pipeline blockage and rainstorm extrapolation is improved, and a reliable decision basis is provided for urban inland inundation emergency scheduling and drainage facility optimization.

Provided are a processing system for a three-dimensional model, a method for obtaining a physical model on the basis of three-dimensional printing of a three-dimensional processing system, a system for generating a three-dimensional model from a virtual reality / augmented reality three-dimensional sketch, a diffusion model training method, and a method for editing a three-dimensional model on the basis of a virtual reality / augmented reality three-dimensional sketch. The method for obtaining a physical model on the basis of three-dimensional printing of a three-dimensional processing system comprises: step one, at an input apparatus, a user using a hand or a handle to draw three-dimensional sketch content in the air, wherein a three-dimensional sketch is stored in the form of a three-dimensional point cloud, and when the user draws a trajectory, a corresponding point cloud is generated in the drawn trajectory and is displayed in a three-dimensional virtual space in a highlighted manner, and displacement and deletion operations of the user for the three-dimensional sketch are overall displacement and deletion operations for points in a region corresponding to the point cloud of the three-dimensional sketch; step two, a computing apparatus converting the three-dimensional sketch content into a three-dimensional model; step three, converting the three-dimensional model into a readable file for an execution apparatus, wherein the conversion is implemented by using a slicing algorithm; and step four, a three-dimensional printer executing the readable file to perform a printing operation, so as to obtain a physical model.

An anomaly detection system may receive uniformly spaced digital data from a plurality of sensors connected to the monitored system and physical model parameters specific to components of the monitored system. An orthogonal function processor generates an initial set of orthogonal kernel basis functions to process the digital data to detect signature anomalies. An adaptive analysis is performed based on the number of feature components and the width of each feature component. When the number of feature components is greater than the basis functions, additional basis functions are used for reprocessing. When the feature width is less than the basis function, the resolution of the basis function is increased and reprocessing of digital data is performed. Based on the adaptive analysis of each feature component, signature anomalies are identified and displayed.

Disclosed in the present invention are a railway vehicle-track-bridge system dynamic response prediction method and system, and a medium. The method comprises: inputting vehicle speed samples and track irregularity samples into a vehicle-track-bridge system coupled random distribution physical model to obtain corresponding bridge dynamic responses and effective loads of the vehicle-track-bridge system, and extracting a global stiffness matrix of the vehicle-track-bridge system; constructing a training sample set; constructing a fitness function considering the effective loads, and then on the basis of the training sample set, using a genetic algorithm to optimize parameters of a BP neural network prediction model and training same to obtain a bridge dynamic response prediction model; and using the bridge dynamic response prediction model to carry out bridge dynamic response prediction. By introducing effective loads in a vehicle-track-bridge system into a fitness function in a genetic algorithm, a neural network model and a vehicle-track-bridge physical model are organically combined, thereby improving the prediction precision.

The invention discloses a heating control system based on solar energy-heat pump-building collaborative optimization and a construction method, and belongs to the technical field of renewable energy source heating. Comprising a parameter acquisition module, a dynamic prediction module and an optimization control module. The method comprises the following steps: constructing a building heat transfer dynamic prediction model, a solar energy performance prediction physical model, constructing a ground source heat pump performance prediction model and a solar energy-ground source heat pump system collaborative optimization control model, and predicting and optimizing system operation parameters in the next day. Through cooperative work of the parameter acquisition module, the dynamic prediction module and the optimization control module, intelligentization and high efficiency of building heating are achieved, solar energy and the heat pump efficiency can be utilized to the maximum extent, the ground source heat pump efficiency is improved, and energy consumption is reduced while the indoor comfort degree is guaranteed. In addition, a time-of-use electricity price mechanism is utilized to further optimize system operation parameters, the system operation cost is reduced, and the energy efficiency and the response capability of the system are improved.