1430 results about "Real time prediction" patented technology

The invention discloses a safety monitoring system of a liquid cooling over-charging pile, and relates to the technical field of over-charging pile monitoring, the system comprises a data acquisition module, a data processing and analysis module, a dynamic model construction module, a temperature prediction module and a safety early warning module; according to the method, the dynamic model is constructed through the long short-term memory network LSTM, the complex nonlinear relation and the time sequence dependence of the multi-dimensional data are mined by using the gating mechanism of the dynamic model, the accurate characterization of the operation state of the liquid cooling over-charging pile is realized, the actual operation state of the equipment can be accurately described, the temperature data time sequence modeling is performed through the LSTM, and the accuracy of the temperature data time sequence modeling is improved. Parameters such as multi-source temperature and cooling liquid flow are fused, real-time prediction of the temperature change trend is achieved, the defect that a traditional algorithm is insufficient in temperature time sequence dependence capture is overcome, temperature abnormity can be recognized in advance, a safety threshold value is dynamically adjusted through a fuzzy logic algorithm, and self-adaptive threshold value adjustment is achieved in combination with parameters such as charging power. The problem that a traditional fixed threshold value is poor in adaptability is solved, and the early warning accuracy is improved.

The invention relates to the technical field of content pushing, and discloses a personalized content real-time pushing method based on a user portrait. The method comprises the steps of collecting a user behavior data set and historical interaction records, extracting real-time behavior characteristics and generating a behavior dynamic sequence; identifying behavior delay features and converting the behavior delay features into interest feedback lag; determining a dynamic influence weight in combination with an interaction relationship between the static attribute characteristics of the user and the real-time behavior characteristics; predicting the interest tendency of the user in real time by using the hysteresis and the dynamic influence weight, and generating a fluctuation deviation value; and when the deviation value exceeds a preset threshold value, triggering dynamic updating of the user portrait and adjusting a pushing strategy. According to the method, the user behavior dynamic change is captured, the behavior feedback hysteresis is considered, and the feature influence weight is dynamically adjusted, so that the user interest is accurately predicted in real time, the user portrait and the pushing strategy can be flexibly updated, the individuation and timeliness of content pushing are improved, and the real-time requirement of the user is met.

The invention discloses an elevator taking optimization system and method based on artificial intelligence, and relates to an intelligent elevator control technology combining multi-mode perception, prediction model dynamic adjustment and reinforcement learning scheduling strategies. The method comprises the steps that firstly, elevator running states, environment information and passenger behavior data are collected through multiple types of sensors, and multi-modal scene perception vectors are generated; and secondly, elevator loads and floor requirements are predicted in real time through a dynamically-adjusted prediction model, the reward function weight in reinforcement learning is adjusted in a self-adaptive mode on the basis, and accurate response and intelligent scheduling of different scenes are achieved. The system forms a perception-prediction-scheduling closed loop, significantly reduces the waiting time of passengers through multi-objective optimization, reduces the energy consumption, and improves the safety and emergency processing capability. The method is suitable for various high-rise building elevator group control systems, and has high intelligence, flexibility and wide application value.

The invention relates to the technical field of coal mine safety, in particular to a coal mine water disaster prediction system based on a data analysis and machine learning technology, which comprises a multi-source data acquisition module, a dynamic data preprocessing module, a multi-modal feature engineering module, an integrated prediction model construction module and a prediction optimization control module, the multi-source data acquisition module fuses geological and hydrological data, micro-seismic data and equipment working condition data, the dynamic data preprocessing module constructs a noise feature library and realizes noise elimination and data standardization, and the multi-modal feature engineering module extracts dynamic causal feature vectors of a water diversion coefficient change rate and a micro-seismic energy release rate based on convergence cross mapping; the integrated prediction model construction module fuses and outputs a water disaster risk probability value through a meta-learner; and the prediction optimization control module triggers a sampling rate adjustment and disaster response linkage mechanism according to the risk probability value. The method has the advantages of high reliability, high adaptability and timely response, and is suitable for real-time prediction of water disasters in a complex coal mine environment.

The invention discloses a transformer electromagnetic thermal field real-time prediction method based on a physical constraint embedded neural network, and belongs to the technical field of transformer monitoring. The method aims at solving the problems that a traditional finite element method is poor in real-time performance, low in precision and weak in pure data driving model generalization. The method comprises the following steps: selecting a load rate, an environment temperature and a shell convective heat transfer coefficient as key parameters, generating a sample by optimal Latin hypercube sampling, and establishing a three-dimensional electromagnetic-thermal-fluid coupling finite element model to construct a training / testing database; constructing a deep full-connection neural network of which the input is five parameters easy to measure and the output is a winding temperature nephogram, and designing total loss function training containing data / physical loss; and deploying an on-line monitoring system after verification is qualified, and collecting parameters in real time to output a winding temperature cloud picture. The method has the characteristics of high precision, strong generalization and easy deployment, and provides support for intelligent operation and maintenance and digital twinning of the transformer.

The invention relates to a thermal coupling over-reduced order prediction method and system for a multi-component assembled nonlinear system. The method comprises the following steps: collecting multi-scale physical field data; constructing an intrinsic orthogonal decomposition basis function space of a temperature field and a stress field, and establishing a double-field coupling constraint equation; constructing contact thermal resistance parameterized proxy models of a cylinder contact area, a bolt area and a free deformation area by adopting a domain discrete empirical interpolation method; constructing a parametric intrinsic mode tensor network to obtain a decline model which is used for realizing real-time reconstruction of a mode basis function through acquired tensor slices in an online stage; performing dynamic inversion based on a modal basis function reconstruction result, and outputting a predicted transient displacement field, a predicted temperature gradient field and a predicted contact stress field; and obtaining real-time parameters, and calculating a residual error with a corresponding prediction result so as to dynamically update the primary function and interpolation point distribution. Compared with the prior art, the real-time prediction of the transient thermal coupling of the multi-component contact system is realized on the premise of ensuring the precision.

The invention relates to the technical field of water quality prediction, and discloses an industrial sewage water quality real-time prediction and early warning method and system. According to the method, the depth features of the internal treatment process state of each water quality treatment unit are extracted, so that the problem that the prediction precision of a prediction model is limited due to the fact that the internal deep features cannot be excavated in a traditional method is solved; a migration rule and a response relation of pollutants between every two adjacent water quality treatment units are analyzed through real-time water quality parameters, so that a water quality flow association graph with the water quality treatment units as nodes, pollutant migration paths as edges and cross-unit association strength as edge weights is constructed; the driving effect of the water quality change of the upstream water quality treatment unit on the treatment effect of the downstream water quality treatment unit is quantified, the accurate quantification of the cross-unit dynamic linkage effect is realized, and the problem that the linkage effect is caused by neglecting the transfer and conversion of pollutants among the water quality treatment units in the prior art is solved. Therefore, the water quality prediction accuracy is improved.

The invention relates to the technical field of combustion monitoring, and discloses a combustion instability boundary prediction system based on complex environment condition analysis, and the system comprises a multi-source sensing module, a data fusion module, an anti-interference processing module, an instability feature library, an intelligent decision engine and a digital twinborn body. A dynamic environment interference compensation mechanism is constructed, spectrum correction parameters are configured according to different environment working conditions during real-time prediction of a combustion instability boundary, the problem of spectrum distortion caused by environment disturbance on combustion dynamic parameters is eliminated in real time, spectrum interference deviation of a combustion measurement position can be automatically sensed and corrected, and real-time prediction of the combustion instability boundary is realized. The accuracy of combustion instability sensitive feature extraction is guaranteed, the instability early warning error is reduced, a flame pose real-time tracking system is deployed, fuel control parameter self-adaptive adjustment is triggered immediately when it is detected that the pose is abnormal, autonomous correction of the combustion instability feature recognition position is achieved, and it is guaranteed that the feature capture space is accurately positioned.

The invention provides a lithium battery residual life prediction method based on a multi-feature fusion large model, and relates to the field of lithium battery health management and life prediction, and the method comprises the steps: carrying out the segmentation processing of original data through a sliding window technology, constructing a key health index soft measurement module through a KAN, and carrying out the prediction of the residual life of a lithium battery; converting the original data into key indexes representing the health state of the battery; the method comprises the following steps: splicing data and key indexes of a lithium battery to form fusion features, inputting the fusion features into a large language model LLM to construct a fusion feature prediction module, obtaining future fusion features through pre-training word embedding, a multi-head attention mechanism and natural language prefix prompt, inputting the future fused features into a sparse KAN, and obtaining a fusion feature prediction model; and constructing a residual life prediction model. And a regression relation with the residual life of the battery is established, real-time prediction of the residual life of the lithium battery is realized, and the method is suitable for state monitoring and maintenance decision of the lithium battery in scenes of electric vehicles, energy storage systems and the like.

The invention discloses a digital twinning-based heterogeneous body-equipped intelligent equipment collaborative management system and method, and belongs to the technical field of digital twinning and body-equipped intelligent systems. The system comprises a digital twin platform, a communication module, a task scheduling module, a cooperative control module and an edge computing node. The digital twinborn platform constructs a high-fidelity three-dimensional model of a physical environment and digital twinborn bodies of the intelligent devices with the bodies; the communication module is used for realizing bidirectional data interaction between the platform and the intelligent equipment; the task scheduling module performs task allocation and path planning by adopting a reinforcement learning algorithm based on the global dynamic situation map; the cooperative control module models an equipment space-time relationship through a graph neural network, predicts conflicts in real time and generates a dynamic avoidance strategy; and the edge computing node carries out real-time preprocessing on the equipment sensing data. According to the method, global optimization scheduling and multi-device intelligent cooperation are realized, and the overall efficiency, robustness and adaptability of the system are remarkably improved.

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.

The present invention relates to the technical field of algae content monitoring. Disclosed is an algae content information monitoring method based on multi-source remote sensing data. The present invention comprises: acquiring multi-source remote sensing data comprising satellite remote sensing data, unmanned aerial vehicle remote sensing data and ground monitoring data, and preprocessing the data, involving radiation correction, geometric correction and noise elimination; and fusing the multi-source data into a trained algae monitoring model for prediction, and using Kalman filtering to assimilate observation data and model prediction data. The present invention fuses observation data and model prediction data by means of Kalman filtering technology, so as to dynamically adjust the model state, such that the model can more accurately reflect the actual observation situation. The Kalman filtering optimizes the real-time prediction capability of models by balancing the uncertainties between observation data and prediction data, thereby ensuring the accuracy and real-time performance of monitoring results, and also ensuring the reliability and adaptability of models under various environmental conditions.

The invention relates to the technical field of sewage treatment, in particular to a coupling control system and method for deep denitrification of sewage, and the system comprises a real-time water quality monitoring module, a microorganism twinborn modeling module, a real-time prediction module, an intelligent decision module, an execution mechanism module and a prediction regulation and control module. Compared with the prior art that a passive feedback control strategy based on an effluent quality index is generally adopted, the hysteresis quality is high, and violent fluctuation of an inflow load cannot be coped with; according to the method, a digital twinborn body capable of reflecting the functional state of a microbial community in real time is constructed, and a control target is improved from a traditional process parameter set point to direct optimization of microbial ecological functions; according to the invention, the method achieves the fundamental crossing from the control of technological parameters to the regulation and control of microbial ecology, can carry out intervention from the root of the reaction process, enables the system to have the active health management capability, and remarkably improves the stability of the treatment efficiency and the intelligent level of coping with complex working conditions.

The invention discloses a subway depot upper cover building vibration response prediction method based on deep learning, and the method comprises the steps: constructing a feature library containing vibration signals and working condition data, generating enhanced data through a mechanical model, and fusing the enhanced data into a data set; a mixed deep learning model embedded with physical prior is constructed, and training and dual-objective parameter optimization are carried out; a prediction result is output after working conditions of real-time data are recognized through the lightweight model; parameters are finely adjusted through regular incremental learning, and transfer learning adaptation is carried out when working conditions suddenly change; verifying precision and rationality, and adjusting the weight of a regular term or suggesting to add a sensor; according to the method, measured data sparseness is made up by enhancing data fusion; the double-branch architecture overcomes the deep nonlinear mapping problem, and physical constraints are prevented from violating physical rules; the incremental learning reduces the cost, and the transfer learning solves the time-varying vibration capture problem; precision is improved through closed-loop verification, accurate real-time prediction of vibration response is achieved, and safety and comfort of a building are guaranteed.

The invention provides a front-end resource dynamic preloading method and system based on user behavior intention prediction, and relates to the technical field of computers, and the method comprises the steps: collecting continuous behavior data of a user in real time in a client browser, carrying out the feature extraction of the collected behavior data, and generating a behavior feature vector; on the basis of an intention prediction model deployed at a client, according to the behavior feature vector, performing real-time prediction on the click intention probability of each interactive element in the page; when the predicted click intention probability exceeds a preset threshold value, triggering a resource preloading instruction; and according to the resource preloading instruction, dynamically creating a browser preloading label, and starting background downloading of a target resource so as to perform dynamic preloading of a front-end resource. According to the method and the device, the resource utilization rate and the page loading efficiency are improved, so that the waste of a server and network resources is reduced while the user experience is improved.

The invention relates to the technical field of boiler combustion control, and discloses a boiler closed-loop combustion dynamic optimization control system based on big data, and the system comprises an intelligent sensing module which is used for obtaining and outputting time-synchronized monitoring data in real time; the edge calculation module is used for processing multi-source sensor data in real time, realizing data credible processing and combustion state feature coding, and executing local caching of working condition perception; the digital twin modeling module is used for constructing a combustion process dynamic model and realizing real-time prediction of a combustion state; the optimization control module is used for dynamically adjusting combustion parameters and achieving collaborative optimization of the air-coal ratio, the opening degree of a secondary air door and the over-fire air ratio; and the closed-loop execution module is used for executing and feeding back the control instruction and realizing transmission and closed-loop verification of the control instruction. The combustion efficiency can be improved, pollutant emission can be reduced, and the stability and safety of boiler operation can be enhanced.

The invention discloses a charging pile cooling control method and system based on AI prediction reinforcement learning, and relates to the technical field of cooling control. The charging pile cooling control method and system based on AI prediction reinforcement learning comprises the following steps: S1, collecting charging heat dissipation data of a charging pile, and preprocessing the charging heat dissipation data; s2, constructing a time sequence characteristic matrix, inputting the time sequence characteristic matrix into a temperature prediction model, outputting a temperature prediction sequence of a controlled target, evaluating a thermal runaway risk in a prediction stage, and constructing a thermal risk identification sequence; s3, constructing a cooling strategy optimization model, inputting the real-time state vector into the cooling strategy optimization model, outputting an adjustment instruction, and issuing and executing the adjustment instruction; and S4, the execution deviation of the adjustment instruction is evaluated, the cooling strategy is adjusted based on the evaluation result, and a cooling strategy feedback sample is generated. The problems of energy consumption waste and cooling imbalance caused by lack of real-time prediction and self-adaptive regulation and control capabilities in the cooling control process of the existing charging pile are solved.

The invention discloses a mining equipment energy consumption prediction method, and relates to the technical field of mining equipment energy consumption prediction.The mining equipment energy consumption prediction method comprises the steps that through multi-dimensional data collection, operation, process and environment parameters are collected through a sensor cluster; noise reduction is carried out through a generative adversarial network in combination with empirical mode decomposition, and data is restored through a space-time interpolation network; identifying working conditions and extracting features by means of a hidden Markov model in combination with an attention mechanism; a cross-device transfer learning framework is constructed, and a cloud training general model is combined with local data fine tuning; the edge end deploys a lightweight model for real-time prediction, and the cloud end generates a global energy-saving strategy; through digital twinborn visualization, a model and a strategy are automatically corrected based on SHAP value analysis. The equipment idling rate is reduced; the unit energy consumption of the crushing link is reduced; the abnormal response time is shortened; and the prediction precision and the system adaptability are remarkably improved.

The invention discloses a furnace body temperature control system, which belongs to the technical field of industrial automation control, and comprises the steps of monitoring and collecting multi-dimensional data of a physical furnace body in real time, performing preprocessing, generating a state vector, setting a working condition judgment method, classifying various working conditions, outputting working condition labels and characteristic parameters, and establishing a health degree evaluation model; the state of the actuator is monitored in real time, and frequent maintenance and short service life caused by overuse of equipment are prevented; constructing a furnace body virtual model, setting a real-time prediction method, and simulating furnace body temperature distribution so as to predict a future temperature trend, judge whether to start a rehearsal mode, output an optimal scheme, and realize advanced intervention of prediction and decision making; a working condition identification and temperature prediction result is read, a decision engine method is set, and a dynamic control instruction is generated and executed; and comparing data of the physical furnace body with data of the digital twinborn body, setting a self-evolution method, and carrying out online calibration and correction on the virtual model.

The invention discloses an array antenna phase-only real-time beam scanning and side lobe control method based on machine learning. Firstly, array geometry, scanning angles and minor lobe indexes are set; and then, array element excitation phases meeting requirements are rapidly synthesized by using a phase-only fast Fourier FFT (Fast Fourier Transform) algorithm. Constructing a training data set by taking the scanning angle and the minor lobe index as input and the corresponding phase distribution as output; training a neural network by using the data set to obtain a real-time prediction model from initial excitation distribution to accurate excitation distribution; and according to a target expected directional diagram scanning angle and a sidelobe feature, inputting the scanning angle and the sidelobe feature into a neural network to predict and obtain an array element excitation phase so as to realize a real-time phase-only beam and sidelobe control function. Input dimensions are reduced by extracting key features of a directional diagram, data generation is accelerated by means of FFT, real-time performance and precision are both considered, and the method is suitable for fast beam scheduling tasks of various array antennas.

The invention discloses an environmental data processing method and system based on ocean engineering, and relates to the technical field of data processing, and the method comprises the steps: receiving an original observation data flow through a multi-source data preprocessing module, and carrying out the dynamic noise filtering and abnormal value adaptive detection; fusing the multi-source heterogeneous data through a multi-scale data fusion module, and embedding the fused multi-source heterogeneous data into a marine kinetic equation as a soft constraint; non-linear evolution features are extracted from the fusion data through a feature extraction and state representation module, and a high-dimensional environment state vector is constructed; real-time prediction of model parameters is executed through online learning and an inference engine; and performing uncertainty propagation calculation on the processing flow through a confidence evaluation module and generating a final environment state report. According to the method, the adaptive capacity of data preprocessing can be remarkably improved, the physical consistency of multi-source data fusion is improved, the nonlinear evolution law of ocean phenomena is accurately captured, and continuous online optimization and edge side low-delay response of model parameters are achieved.

The invention relates to a clinical psychological treatment effect evaluation method and system based on machine learning, and the method comprises the steps: obtaining biological signals, behavior patterns and psychological state data of a patient during treatment, constructing a three-dimensional tensor structure with aligned timestamps, and carrying out the standardization of the three-dimensional tensor structure to generate a multi-dimensional data matrix; dimensionality reduction, reconstruction and verification are carried out on the matrix through a deep auto-encoder network, and unified feature vector representation is output; utilizing an improved support vector regression algorithm to establish a nonlinear mapping model of the feature vector and the curative effect score; according to the method, the curative effect score is predicted in real time, when the score is abnormal or fluctuation exceeds a threshold value, a personalized treatment scheme optimization mechanism based on the knowledge graph is triggered, deep fusion of multi-source heterogeneous data is achieved, evaluation precision and treatment adaptability are improved through a dynamic optimization mechanism, and intelligent decision support is provided for clinical psychological intervention.

The invention provides an intelligent monitoring and early warning method based on multi-source information fusion, and belongs to the technical field of intelligent diagnosis and early warning, and the method comprises the steps: synchronously collecting the multi-modal data of a voltage transformer, the multi-modal data comprising voltage / current waveform, partial discharge signal, temperature and vibration data; preprocessing the multi-modal data, wherein a preprocessing method comprises waveform segmentation, spectrogram generation and scalar normalization; constructing a dynamic weight distribution mechanism: calculating a weight matrix based on the relevance between the time sequence features and the spatial features, and dynamically adjusting scalar weights by combining the physical coupling relationship between the temperature and the vibration to generate feature fusion weights; iteratively updating the weight matrix according to the real-time prediction error; fusing the time sequence features, the spatial features and the scalar features through the dynamic weight distribution mechanism to generate a comprehensive feature vector; and synchronously outputting a fault type classification result and an equipment health score based on the comprehensive feature vector, and calculating a real-time prediction error.

The invention provides a deep tunnel excavation physical simulation test three-dimensional stress field real-time prediction method, and relates to the technical field of tunnel engineering. Comprising two stages of model construction and training and model use. In the model construction and training stage, a physical constraint reference model is constructed, and training optimization is completed; and in the model use stage, real-time prediction of a stress field of a deep engineering physical simulation test is realized through the trained and optimized model. The method has breakthrough advantages in the aspects of physical law constraint, real-time monitoring data fusion, training loss stability, boundary stress condition accuracy and the like, and is a high-reliability three-dimensional stress field prediction method oriented to a deep complex environment.

The invention, which relates to the technical field of hydropower station dam safety monitoring, discloses a hydropower station dam safety monitoring data acquisition and transmission system comprising a cloud twin brain module and edge neurons. The cloud twin brain module comprises a sequence neural network engine and a reflection kernel generation module, the sequence neural network engine adopts a neural network architecture with parallel and cyclic dual representation, comprises a time mixing module and a channel mixing module, and can learn a normal operation mode of the dam from historical monitoring data; and the reflection nuclear generation module compresses the reference twin model into a lightweight reflection nuclear model and issues the lightweight reflection nuclear model to the edge device. The edge neuron comprises a micro-twinborn prediction module and a hierarchical transmission control module, and the micro-twinborn prediction module predicts a theoretical expected value of a dam state in real time and calculates a reflection deviation with an actual observation value; the hierarchical transmission control module implements a three-level response strategy according to the magnitude of the reflection deviation, transmits abstract information according to an abnormal trend, and uploads an emergency abnormality in time.

The invention discloses a water supply network global water quality prediction method based on a double flow-graph convolutional network, and belongs to the field of urban water supply. According to the method, a pipe network model of a directed graph is constructed, information flows in the upstream direction and the downstream direction are extracted respectively, feature learning is carried out through a parallel graph convolution module, a random mask mechanism is introduced during training to simulate sensor missing, and accurate prediction of the water quality concentration under the sparse monitoring condition is achieved. According to the double flow-graph convolution water quality prediction model provided by the invention, the practicability and coverage capability of the water quality prediction model under the condition of sparse monitoring data are remarkably improved; a double flow-graph convolution structure and a dynamic mask training mechanism are adopted, so that the robustness of the complexity of a pipe network is effectively enhanced; the constructed water quality prediction model has the characteristic of high response speed, can realize real-time prediction of water quality in combination with historical monitoring data and network structure information, is suitable for various scenes such as water quality monitoring, abnormal early warning and intelligent regulation and control, and is beneficial to improving the operation efficiency and management level of a water supply system.

The invention designs an assembly process error modeling method considering heat, and realizes rapid and accurate calculation of thermal deformation of an assembly junction surface affected by heat in the part assembly process. In the part assembling process, heat generated in the assembling process is an important influencing factor influencing the assembling precision. In order to realize rapid calculation of thermal deformation in a part assembling process, a prediction model of thermal deformation of an assembling joint surface in the part assembling process is constructed by utilizing a physical information neural network. According to the method, a full-connection neural network architecture is adopted, and boundary condition constraints are introduced into a loss function item for joint optimization training. Through minimization of a loss function, a driving model learns displacement distribution characteristics of an assembly joint surface under thermal deformation, and a mapping relation between a space coordinate point and a corresponding displacement amount is established. The model finally realizes the real-time prediction capability of the thermal deformation field of the assembly joint surface.

The invention discloses a hoisting posture prediction method and device, electronic equipment and a storage medium, and relates to the technical field of hoisting postures and the like. The hoisting posture prediction method comprises the steps that a three-dimensional model of a target object is constructed based on region division of the target object; extracting a key mode based on the three-dimensional model and constructing an attitude reduced-order model; obtaining a gravity parameter based on the current hoisting path; and solving the attitude reduced-order model based on the gravity parameter and a first constraint condition to obtain a hoisting prediction attitude. According to the hoisting attitude prediction method disclosed by the invention, the three-dimensional model of the target object considering the precision and the calculated amount is adaptively established by dividing the region of the target object, and the attitude in the hoisting process is predicted in real time by solving the attitude reduced-order model, so that real-time risk early warning and active protection are realized.

The invention discloses a dynamic thermal deformation compensation method for a numerical control grinding machine. The dynamic thermal deformation compensation method comprises the following steps that three-dimensional temperature field data of a mechanical head are obtained in real time through a distributed temperature monitoring unit; inputting the three-dimensional temperature field data into a numerical control grinding machine thermal deformation digital twin model, and predicting thermal deformation and spatial distribution of the mechanical head in a future time period through coupling temperature field-stress field-deformation field simulation calculation; and generating a compensation strategy according to the thermal deformation and the spatial distribution, driving the radiator to move to a thermal deformation sensitive area for directional heat dissipation, and adjusting the position and posture of the grinding head through a five-axis linkage system of the numerical control grinding machine to offset the predicted thermal deformation. A three-dimensional temperature field of the mechanical head is reconstructed in real time through the distributed temperature monitoring unit, and accurate real-time prediction of thermal deformation and spatial distribution is achieved; and meanwhile, a five-axis system is cooperated to pre-adjust the position and posture of the grinding head, and the thermal deformation restraining precision and the machining stability are remarkably improved.

The invention discloses a shield tunnel ground subsidence real-time prediction method considering spatio-temporal information, and the method comprises the steps: constructing an input feature system, including classifying model input into geometric information, multi-ring geological condition information, multi-time step shield parameter information and historical subsidence information; a multi-source information fusion model architecture is designed, geometric information, geological condition information, shield operation parameter information and historical settlement information serve as input of the model, and feature extraction is conducted through multiple encoders. Then feature fusion is carried out, nonlinear mapping is carried out by using a residual network (ResNet), and finally a real-time settlement prediction value is output; according to the scheme, the space-time characteristics of ground subsidence induced by tunneling are deeply excavated, and real-time prediction of subsidence of any position in a disturbance range is realized through deep fusion of multi-source characteristics.