3287 results about "Physical model" patented technology

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 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 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 discloses an industrial equipment fault prediction system based on federated learning and a privacy protection method, and relates to the field of industrial equipment fault prediction. The data acquisition preprocessing module extracts fault features through compressed sensing downsampling, screens and uploads the fault features; the federal learning training module adopts a layered architecture and a dynamic algorithm to schedule a learning rate; the fault prediction and diagnosis module constructs a space-time diagram neural network and fuses a physical model to improve generalization; the privacy protection security communication module performs homomorphic encryption storage and zero-knowledge proof verification update; the knowledge graph construction reasoning module constructs a dynamic graph, locates a fault root cause through causal reasoning, and supports cross-device knowledge migration. By adopting the quantum and federated learning technology, the industrial equipment fault diagnosis accuracy is high, the attack resistance is high, the encryption efficiency is greatly improved, the model training time is shortened, cross-equipment knowledge migration is realized, the operation and maintenance cost is reduced, and the intelligent operation and maintenance development of the industrial equipment is promoted.

The invention provides a sewage system traceability analysis and intelligent monitoring method, system and device based on a graph neural network, and a storage medium, and belongs to the technical field of environment monitoring and artificial intelligence. The invention aims to solve the technical problems of low efficiency, low precision, difficulty in processing multi-source data, poor monitoring network and the like of the existing sewage system pollution tracing method. The method comprises the following steps: constructing a sewage system knowledge graph fusing multi-source heterogeneous data such as water quality and water volume; adopting a multi-scale graph neural network model to learn pollution propagation characteristics based on the knowledge graph; after a pollution event occurs, pollution path backtracking is carried out in combination with physical models such as flow conservation so as to identify a pollution source; bayesian inference is introduced to carry out uncertainty quantification on a traceability result so as to assess the credibility of the traceability result; and finally, dynamically optimizing the layout of the monitoring points based on information gain and other criteria. According to the invention, rapid and accurate positioning of the pollution source can be realized, and the method is suitable for intelligent supervision of an urban sewage system.

The invention discloses a Bayesian learning and piezoelectric ceramic driving numerical control machine tool thermal error compensation system and method. According to the system, a distributed temperature sensor array is arranged in heat sensitive areas such as a machine tool spindle, a ball screw, a guide rail and a bearing seat, whole-field temperature information is collected in combination with a thermal infrared imager, and multi-source thermal field sensing is achieved; meanwhile, a laser interferometer and a capacitive displacement sensor are used for constructing a dynamic pose monitoring network. The intelligent decision-making unit integrates a Bayesian online learning engine, fuses a physical model and a data driving model, dynamically predicts a thermal error and generates a compensation instruction. And the piezoelectric execution mechanism carries out nonlinear pre-compensation on a driving signal through a three-section Prantl-Ishlinskii hysteresis inverse model according to the instruction, so that high-precision pose adjustment is realized. The thermal error compensation precision is remarkably improved, the adaptability of the system to complex working conditions is enhanced, the service life of equipment is prolonged, and the method is suitable for various numerical control machine tools.

The invention relates to the technical field of equipment monitoring, in particular to an equipment corrosion evaluation and life prediction method and application, and the method comprises the following steps: deploying a sensor network in an easily-corroded area of coal chemical equipment, and collecting multi-dimensional data; carrying out abnormal value elimination, data compression, time synchronization and space-time alignment preprocessing on the collected multi-source data; image features are extracted through a convolutional neural network, processed data are analyzed through an LSTM-attention model, and a fuzzy comprehensive evaluation matrix is established to evaluate the corrosion level; a physical model based on the Faraday electrolysis law and a data driving model based on the Transform network are constructed, and the residual life is predicted through Bayesian network fusion output and Monte Carlo simulation. Through fusion of multi-source data and an intelligent algorithm, accurate evaluation of the corrosion state of the equipment and accurate prediction of the residual life are realized, and safe and efficient operation of the coal chemical equipment is guaranteed.

The invention relates to the technical field of state detection and fault diagnosis, in particular to an intelligent early warning and fault diagnosis system for a thermal power plant, which comprises a multi-source data acquisition module for acquiring data in real time; the edge computing node is used for performing noise filtering and abnormal value correction on the acquired data; the digital twin modeling unit is used for constructing a dynamic simulation model of the equipment based on a physical model and historical data; the hybrid analysis engine is used for positioning early abnormal detection and fault sources; the visual early warning interface is used for dynamically displaying the health state and the fault probability of the equipment and generating a graded alarm signal; according to the invention, the multi-source data acquisition module acquires equipment multi-dimensional signals in real time, after edge computing node filtering and denoising, a digital twin modeling unit constructs a precise simulation model, a hybrid analysis engine fuses LSTM and a Bayesian algorithm, fault features are deeply mined, data weights are optimized, and the fault detection accuracy is improved. According to the system, the accuracy and timeliness of diagnosis are remarkably improved.

The invention belongs to the technical field of tunnel construction geological disaster prevention and control, and discloses a multi-source data fused refined treatment decision-making method for a complex stratum disaster source, which comprises the following steps: collecting and fusing multi-source geological data, and constructing a three-dimensional geological model; generating a disaster source risk dynamic assessment and treatment scheme; based on a fluid-solid coupling similarity theory, verifying the preliminary treatment scheme by adopting a physical model test, and determining an optimal treatment scheme; the optimal treatment scheme is executed, and the treatment process is dynamically regulated and controlled; after treatment, the treatment effect is evaluated through posterior data, and the effect data is fed back to the three-dimensional geologic model and the knowledge base, so that the dynamic updating of the model and the self-learning of the decision-making system are realized. By the adoption of the treatment decision method, the problems that a traditional method depends on experience, information is one-sided, and treatment is extensive are solved, advanced accurate forecasting and refined and personalized treatment of complex stratum disaster sources are achieved, and the safety and efficiency of tunnel construction are remarkably improved.

The invention provides a multi-dimensional grid-connected test system for a photovoltaic string inverter, and the system comprises a power grid simulation module which is used for constructing a power grid simulation environment based on a multi-dimensional coupling mechanism of voltage fluctuation, frequency deviation and harmonic distortion; the photovoltaic array simulation module is used for constructing IV characteristic dynamic deduction models of different irradiance and temperature gradient effects based on a photovoltaic array equivalent circuit physical model to realize a natural simulation environment; the loading module is used for loading the power grid simulation environment and the natural simulation environment to the photovoltaic string inverter to be tested; the data acquisition module is used for monitoring running state data of the photovoltaic string type inverter in real time and constructing a running state data set containing multi-physical-quantity coupling characteristics; and the diagnosis module is used for inputting the running state data set into the trained LSTM neural network model to obtain a test result of the photovoltaic string inverter. According to the invention, the efficiency and convenience of the grid-connected test of the inverter can be improved, and the accuracy and reliability of the grid-connected performance of the inverter are ensured.

The present disclosure discloses a seismic inversion method based on joint constraint of a physical model and priori information. The method includes: extracting seismic wavelets based on seismic data, and determining an amplitude scaling factor of the wavelets; counting priori information of impedance parameters; establishing an initial impedance parameter model by using seismic structural interpretation information and logging data; obtaining a simplified approximate equation based on an interface weak elasticity difference hypothesis, forward modeling a seismic gather by using the simplified equation, and calculating an inversion residual; rewriting an objective function into a function related to the impedance parameters by using a generalized linear inversion idea, solving the impedance parameters by using an iterative reweighted least squares algorithm, and updating the impedance parameters; and repeating the above steps until the inversion residual reaches the requirements or reaches the maximum number of iterations, and outputting a final processing result.

The invention discloses a bus duct full life cycle health management system based on digital twinning, and relates to the technical field of health management. The multi-source sensor collects operation parameters and static information, the digital twin modeling and mapping module constructs a physical model and associates real-time data, and a thermal-electric coupling equation is used for simulating temperature; the state monitoring and fault diagnosis module compares data to judge states and diagnoses faults by means of methods such as a fault tree, the health assessment and decision making module constructs an index system to assess health and makes a maintenance decision, the data management and interaction module stores data and realizes visual interaction and system integration, and the intelligent optimization module performs intelligent optimization based on operation and maintenance data. And optimizing model parameters and a decision strategy. According to the invention, intelligent health management of the bus duct is realized, multi-source data acquisition is accurate and comprehensive, fault diagnosis is more accurate and prediction is more timely through combination of digital twinning and an algorithm, and intelligent assessment assists scientific maintenance decision; and the operation and maintenance efficiency and the power transmission stability are improved through system integration and edge calculation.

The invention discloses a relay state prediction and fault early warning method and system based on deep learning, and the method comprises the steps: S1, building a constraint condition of a generative adversarial network based on a relay physical model, and forming an enhanced fault waveform signal according with a physical rule through adversarial training; s2, receiving a real-time current and voltage signal and a mechanical vibration signal, and extracting an electric signal feature vector by using a time sequence convolutional network; s3, inputting the combined feature tensor into the lightweight assessment model, and outputting a health degree scoring signal; s4, responding to the meta-learning activation instruction, loading historical data of equipment to construct a parameter optimization set, performing online fine tuning on the early warning model based on a meta-learning framework, and generating a fault determination parameter; and S5, analyzing real-time signal characteristics according to the fine-tuned judgment parameters, and outputting graded early warning signals to a monitoring terminal. According to the method, the problems of early state prediction and accurate early warning of the relay under small sample fault data can be solved.

The invention discloses a dam measurement system and method based on multi-modal data processing, and relates to the technical field of dam measurement system data processing, a physical model is adopted to extract dam physical form change features in visual data, dam physical change associated sound features in audio data and micro-displacement features in radar point cloud data; according to the method, a three-dimensional monitoring network from macroscopic deformation to microcosmic displacement is constructed through cooperation of visual, audio and radar three-mode data, a monitoring blind area of a single sensor is broken through, and correlation analysis of dam body surface deformation and internal stress change is achieved. Feature fusion driven by physical constraints is carried out, finite element model constraints are embedded in a feature level fusion stage, radar point cloud displacement vectors need to conform to geological structure direction constraints, audio and voiceprint features need to be in space-time synchronization with visual crack expansion rates, and environmental noise interference is eliminated; the crack propagation early warning time is greatly shortened; the cavitation damage positioning is more accurate, and the abnormal working condition recognition rate is greatly improved.

A physical AI-based magnetic field environment modeling method and a physical AI-based magnetic field environment modeling system are disclosed, the physical AI-based magnetic field environment modeling system comprises a data layer, a preprocessing layer, a core modeling layer and a verification and application layer, and the core modeling layer comprises a physical knowledge base and stores physical laws, material constitutive relationships and boundary conditions related to a target magnetic field environment; the model construction module is responsible for designing and constructing a main body structure of a physical AI model according to problem characteristics and a physical knowledge base; the physical AI engine represents a constructed or trained physical AI model instance, receives input and quickly outputs a predicted magnetic field value; and the model training module is responsible for training the neural network model generated by the model building module by using the preprocessed data and physical constraints in the physical knowledge base. The core innovation point of the invention lies in that a physical artificial intelligence normal form is systematically introduced and applied to the modeling process of a complex magnetic field environment, and an efficient, accurate, robust and physically consistent magnetic field modeling system is created through deep fusion of physical laws and data driven learning.

The invention relates to the field of high-voltage cable fault diagnosis, and discloses a high-voltage cable insulation fault intelligent diagnosis method and system based on multi-information fusion, and the method comprises the following steps: S1, obtaining the operation multi-source data of a high-voltage cable, and constructing a multi-source original data matrix; s2, preprocessing to obtain a time-space aligned standardized data matrix; s3, performing feature extraction to obtain a multi-dimensional feature vector, and learning internal association between features by using a multi-modal deep network to obtain a joint multi-modal feature; s4, constructing a fault type identification model based on Bayesian reasoning and Monte Carlo sampling, and obtaining a fault type classification result; s5, in combination with deep learning and a physical model, obtaining a fault occurrence interval, positioning information and a fault level; and S6, based on a fault type classification result, a fault occurrence interval and positioning information, obtaining a fault level, and carrying out early warning pushing on a generated diagnosis report. According to the invention, high-precision identification, positioning and risk assessment of high-voltage cable insulation faults are realized.

The invention discloses a bridge management and maintenance decision-making system and method based on multi-agent collaborative optimization, and the system comprises a monitoring agent which is deployed in a cloud server and is used for obtaining abnormal data in a bridge structure and environment data; the diagnosis agent is used for evaluating the health state of the bridge by utilizing a built-in knowledge base, a built-in machine learning model and a built-in physical model based on the abnormal data; the decision-making agent is used for generating a plurality of candidate maintenance schemes based on an evaluation result, and screening out an optimal scheme by integrating the comprehensive utility of each scheme and the resource matching degree score of the resource scheduling agent on each scheme; the resource scheduling agent generates a construction plan according to the optimal scheme; the coordination / communication agent is used for ensuring efficient cooperation among the agents through a communication protocol and a negotiation mechanism among the agents; the diagnosis report, the optimal scheme and the construction plan are integrated and presented to a bridge manager through a human-computer interaction interface; and collaborative optimization of bridge management and maintenance decisions is realized through continuous feedback and self-learning.

The invention discloses a radar lifting control method and system based on meteorological monitoring, and relates to the technical field of radar lifting control, and the method comprises the steps: completing the switching of a power supply and communication after a radar is powered on, initializing a controller, collecting the data of a meteorological station, and generating a future fusion wind speed in real time through a Kalman filtering physical model and a residual neural network; future fused wind speed is converted into wind pressure for evaluation, the risk degree is judged according to the evaluation result, early warning is given out, and the controller is preheated to enter a lifting preparation state. The input stability is improved through meteorological data sliding window smoothing and feature extraction, wind speed dynamic prediction and uncertainty quantification are achieved through XGBoost prediction and residual variance estimation, the time sequence consistency and robustness are enhanced through remote API interpolation correction and adaptive extended Kalman filtering, residual correction is conducted through a neural network, the prediction precision is improved, and the prediction accuracy is improved. And a reliable decision basis is provided for radar lifting control.

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

The invention discloses a high-low voltage switch cabinet feeder fault positioning method and system based on transient traveling waves, and belongs to the technical field of power system fault detection and positioning, and the method comprises the steps: synchronously collecting electric and acoustic multi-mode signals, and generating a weighted transient synchronization feature matrix; performing time-frequency transformation and feedback optimization on the matrix, and outputting a multi-scale time-frequency feature set; analyzing the feature set by using an integrated learning network, and outputting a layered preliminary positioning result; convergence to an accurate fault section is carried out through iteration calibration; and finally, fusing multi-model calculation and outputting a comprehensive fault positioning report. According to the method, a technical path of combining multi-modal signal fusion and a physical model is adopted, convergence from fuzzy region division to an accurate position can be realized in stages, and the accuracy, the speed and the anti-interference capability of switch cabinet feeder line fault positioning are remarkably improved.

The invention discloses a flood disaster monitoring and early warning system and a flood disaster monitoring and early warning method. A cloud, rain, water and I integrated sensing network is constructed through a full-chain monitoring capability; a hybrid prediction model coupled with HEC-HMS and SWMM physical models and an LSTM-Transformer deep learning architecture is established, parameter deviation is dynamically corrected through NSGA-II and a symbolic regression multi-objective optimization algorithm, the flood prediction period is prolonged to 10 days (the precision of the southern watershed is larger than or equal to 90%, and the precision of the northern watershed is larger than or equal to 70%), the flood peak time error is compressed to be within 30 minutes, and compared with a scheme based on a static flood risk model, the method has the advantage that the flood prediction efficiency is greatly improved. The false alarm rate is reduced from 20% to 5% through the dynamic threshold calibration technology; hierarchical response and survivability communication are adopted, Beidou satellite and NB-IoT dual-channel redundant transmission is deployed, and a Mesh ad hoc network and frequency modulation subcarrier technology are combined, so that the direct rate of early warning information in extreme weather is ensured to be greater than or equal to 99%; and three-dimensional GIS platform dynamic rendering is supported, and collaborative visualization of a submerging thermodynamic diagram, a material scheduling path and ecological flow monitoring is realized.

The invention provides a wind power plant unit state monitoring and fault early warning system and method based on deep learning, and belongs to the field of wind power generation and artificial intelligence. According to the system, a cloud edge collaborative architecture is adopted, an edge computing terminal operates a data-driven space-time prediction model and a physical digital twinborn model in parallel, and abnormity is preliminarily screened by calculating a double-track residual error and comparing the double-track residual error with a dynamic early warning threshold value. And when an exception occurs, the cloud platform receives multi-modal data including a sensor, a model state and an operation and maintenance text, performs deep root cause analysis by using a diagnosis model fused with a wind power fault knowledge graph, and generates an interpretable diagnosis report. According to the method, deep fusion of data and a physical model is realized, and the accuracy of fault monitoring, the interpretability of diagnosis and the intelligent level of operation and maintenance decision are remarkably improved through a data-physical double-track driving mode.

The invention relates to the technical field of data processing, in particular to a new energy uncertainty multi-type energy storage staged capacity optimization configuration method, which comprises the following steps: acquiring energy storage parameters, new energy output and user load data through edge equipment, and verifying photovoltaic conversion efficiency and lithium battery electrochemical behaviors through a physical model to generate a high-confidence data set; the federal cooperative system fuses distributed node data to output a health degree distribution diagram, and the memory network analyzes the temperature and the charge-discharge coupling effect to generate a dynamic compensation coefficient alpha; an optimization engine receives the alpha value and wind and light prediction, generates a staged scheme through a parallel algorithm, and inputs the staged scheme into a simulation system for verification; and the edge node executes the scheme and collects response data, and twin compares a simulation value with an actual deviation to update a collection strategy. The method eliminates data distortion through physical rule verification, corrects aging unit state deviation through a dynamic compensation mechanism, optimizes decision binding real-time health degree, and effectively defends charging and discharging analysis errors of a virtual power plant dispatching desk.

The invention relates to a small sample fault prediction method based on physical information guidance and multi-source adaptive fusion. The method comprises the following steps: collecting real operation data of preprocessing target equipment; according to the physical model or domain knowledge of the target equipment, generating simulation sensor data conforming to a physical rule under various fault modes of different degrees; constructing diversified training samples in combination with real data and simulation data; for different types of sensor data, designing corresponding feature extraction branches, mining potential fault features in the data, and dynamically adjusting the weight of each data source fault feature for fusion based on an output result of a physical model and data-driven feature correlation analysis; inputting the obtained fusion features into a fault prediction model based on a small sample learning framework for training; comprehensively considering the fault prediction result, the real operation data and the analysis result of the physical model, and carrying out quantitative evaluation on the overall health state of the target equipment; and causal diagnosis and visual interpretation are carried out.

The invention discloses a photovoltaic array life prediction method based on hybrid driving of a physical model and data. The method comprises the following steps: defining failure time and residual life of a photovoltaic array; preprocessing data; degeneration trend extraction: separating a trend term, a seasonal term and a residual term of output power through block median filtering and seasonal effect correction, and eliminating the influence of environmental fluctuation on degeneration analysis; residual life prediction based on data driving; parameter prediction based on a physical model: identifying degradation parameters of the double-diode model; a degradation parameter trajectory model is established, an output power attenuation curve is inverted, and the remaining life is predicted; and result fusion: comparing data driving and physical model prediction results, and outputting a final residual life value through weighted average or confidence interval fusion. According to the invention, a set of complete photovoltaic array residual life prediction system is constructed, a whole-process closed loop from data acquisition, mechanism analysis to life prediction is realized, and an efficient and accurate solution is provided for reliability evaluation of a photovoltaic system.

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

The invention belongs to the technical field of laser processing, and particularly relates to a laser processing control method based on a neural network, and the method specifically comprises the following steps: a target definition and input stage; a physical model and database stage: establishing a basic model library; establishing a mapping database; in the AI core engine stage, a training model learns a complex nonlinear relation among laser parameters, material response and a final processing result; searching an optimal laser parameter combination by using an optimization algorithm based on the prediction model and a target set by a user; a laser parameter automatic adjustment and execution stage; a real-time monitoring and feedback stage; and an iterative learning and system improvement stage. The invention further discloses a control system, electronic equipment and a computer storage medium. According to the method, submicron precision control is achieved, thermal damage can approach to zero, the development period is shortened, online real-time regulation and control are achieved, energy consumption is reduced, the material utilization rate is increased, and the method has an interpretable decision-making mechanism and cross-material generalization ability.

The invention provides a heat storage heat pump system control method based on a physical information neural network, and belongs to the technical field of heat storage pump system intelligent control. Aiming at the problems that in the prior art, an algorithm is difficult to adapt to dynamic energy consumption requirements, engineering application of a model is difficult due to building space heterogeneity, high-order RC model prediction credibility is weak, engineering feasibility is poor and the like, a solution combining a physical information sequence to sequence neural network technology and a finite-state machine control strategy is provided. On the model level, a 2R2C resistance-capacitance RC model of building temperature change is established, and then a PI-Seq2seq prediction model is proposed based on the physical model. On the control flow optimization level, on the basis of an industrial and commercial time-of-use electricity price policy, an FSM control model is designed, a system state set is defined, parameters and a transfer function are input, and a control rule is constructed in combination with the working period of a building heat pump and the characteristics of a heat storage tank. And finally, energy consumption cost optimization and indoor temperature stabilization under the peak-valley electricity price are realized.

The invention discloses an intelligent life prediction and optimization system and method for a steam turbine rotor welded joint. The system comprises a multi-source data acquisition module, a digital twin modeling module, a health state evaluation and life prediction module, a risk early warning module and an operation collaborative optimization module. The multi-source data acquisition module acquires the multi-dimensional physical quantity of the rotor welding joint in real time. The digital twin modeling module establishes a high-fidelity virtual model and realizes real-time synchronization and correction of a physical entity and a digital model. And the health state evaluation and life prediction module is used for calculating a damage accumulation rate and a health index based on fusion of a physical model and an LSTM neural network so as to realize residual life estimation. And the risk early warning module performs graded early warning according to the dynamic trend of the health state. And the operation collaborative optimization module adaptively adjusts operation parameters and optimizes the unit efficiency based on a reinforcement learning algorithm. All the modules are interconnected through an industrial network to form a closed loop, and real-time monitoring, intelligent evaluation and active service life management of the rotor welding joint are achieved.

The invention belongs to the technical field of tunnel engineering, and provides a multi-physics field real-time assimilation simulation and regulation method and system in a tunnel grouting process in order to solve the problem that real-time dynamic simulation and automatic regulation are lacked in existing tunnel construction, and the real-time assimilation simulation and regulation method and system in the tunnel grouting process are provided by utilizing ensemble Kalman filtering and combining real-time monitoring data in the tunnel grouting process. Dynamically correcting parameters of the multi-physical model; time correlation in the slurry condensation process is considered, a time-varying condensation model depicting physical property changes of slurry evolving along with time is integrated, the time-varying condensation model serves as an external function in the time step length to be embedded into the multi-physical field model in correction, and the slurry flowing state is adjusted in a self-adaptive mode through numerical simulation; and generating control parameters of tunnel grouting according to a dynamic simulation result, and realizing closed-loop regulation and control of tunnel grouting. Synchronous linkage of numerical simulation and on-site working conditions is realized.