803 results about "Real time prediction" patented technology

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 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 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 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 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 permanent magnet synchronous motor temperature monitoring method based on digital-analog hybrid drive, and the method specifically comprises the steps: dividing a motor into a plurality of heat source nodes, including a stator winding, a stator tooth part, a rotor core and the like, connecting the nodes through a thermal resistance and thermal capacity network, building a thermal network model of the motor, and carrying out the thermal network model; calculating the initial values of thermal resistance and thermal capacity of each component of the motor, and optimizing parameters in a thermal network model by using a differential evolution algorithm based on the lumped parameter thermal network model and in combination with a data-driven strategy. Furthermore, temperature rise prediction is dynamically adjusted according to motor state data (such as stator current, rotating speed and the like) collected in real time so as to realize real-time temperature estimation. The method is high in temperature estimation precision, does not affect the performance of the motor, is simple in calculation, is good in real-time performance, and has physical interpretability. The temperature rise of the motor can be predicted in real time under different working conditions, early warning is given out, motor faults caused by too high temperature rise are effectively avoided, and the reliability and safety of the motor are improved.

The invention discloses a machine tool thermal error compensation system based on digital twinning and cloud edge collaboration, which belongs to the technical field of precision manufacturing and comprises a physical error control layer, a digital twinning data layer, a virtual error control layer, an edge server and a cloud server. And performing real-time prediction on the acquired temperature and thermal error data through a long-sequence space-time fusion parallel network model deployed in an edge server, and issuing a compensation instruction to the CNC controller. And the cloud server is responsible for training and updating the model. According to the method, high-precision modeling and low-delay real-time compensation of the thermal error are realized through the cloud edge cooperation and digital twinning technology, the thermal error of the main shaft is reduced by 80% at most, the response delay of the system is reduced by about 40%, and the machining precision and stability of a machine tool are remarkably improved.

The invention belongs to the technical field of geological disaster monitoring and early warning, and relates to a rainfall type landslide-debris flow chain disaster intelligent dynamic monitoring and early warning method, which comprises the following steps: data acquisition: generating multi-source data including an optical image, an SAR image, an unmanned aerial vehicle image and ground data; preprocessing the multi-source data; performing space-time registration, data format and organization on the multi-source data after data processing; key characteristic parameters are calculated, and data fusion is carried out; constructing a multi-dimensional feature vector, and generating a multi-dimensional feature image; constructing a risk assessment model by using a deep learning model, and performing real-time prediction; dynamically determining the total risk level of each area according to a preset comprehensive research and judgment rule by integrating the real-time prediction result; and when the total risk level reaches a'high risk 'or'extremely high risk' threshold value, generating early warning information, and performing early warning release. According to the invention, comprehensive, whole-process three-dimensional perception, dynamic and self-adaptive intelligent evaluation and disaster prediction are realized.

The invention discloses a three-dimensional physical field real-time prediction method and system based on geometric deep learning and physical constraint, and relates to the technical field of computer-aided engineering and artificial intelligence. The method comprises the following steps: directly extracting native boundary representation data (B-Rep) of a three-dimensional model from a computer aided design system; constructing a heterogeneous dual graph taking a parameterized curved surface as a graph node, skipping finite element grid division, and aggregating local and global topological features by using a graph neural network; in combination with a physical information driving mechanism, a partial differential equation (PDE) residual error is introduced as a loss function for constraint training, and generalization prediction of a novel geometric structure is realized; and finally, the physical field state quantity is predicted through direct regression and is rendered in real time. An incremental reasoning mechanism based on a local topology subgraph is adopted, millisecond-level physical field real-time feedback under design modification is achieved, and the method is suitable for scheme rapid screening and trend prediction in the initial stage of design.

The invention relates to the technical field of intelligent manufacturing and industrial digital twinning, in particular to a latex paint packaging full-process digital twinning method and system. The method comprises the following steps: collecting multi-source data in real time through a sensor network, and preprocessing to form a standard data stream; a coupled digital twinborn model fusing material characteristics and process parameters is constructed, and real-time prediction is realized by combining physical laws and data driving residual correction and online parameter self-adaption; constructing a multi-scale abnormal index by comparing model output with actual data, identifying an abnormal mode by adopting PCA, calculating local and overall health indexes, and predicting a trend; and generating a preliminary control strategy according to the abnormal mode and the health trend, forming closed-loop adaptive control in combination with health state weighted optimization and look-ahead adjustment, and issuing execution and feeding back an update strategy. According to the invention, the quality control qualification rate and the energy-saving efficiency of the whole process of emulsion paint packaging are improved.

The invention discloses a battery capacity loss real-time prediction method based on Arrhenius-LSTM, and the method comprises the steps: S1, collecting the current, voltage, state of charge and temperature data of a vehicle-mounted battery management system, and generating an original time series data set; s2, performing time alignment and capacity calculation on the original time sequence data set to generate a capacity loss sequence; s3, an Arrhenius Arrhenius model is constructed based on the capacity loss sequence, and a physical baseline prediction sequence is generated through temperature related parameter estimation; s4, calculating a difference value between the capacity loss sequence and the physical baseline prediction sequence, and generating a residual sequence; s5, performing feature extraction and time sequence window construction on the residual error sequence to generate an LSTM training data set; s6, inputting the LSTM training data set into the long short-term memory network for training, and generating a residual prediction model; and S7, adding the physical baseline prediction sequence and the output of the residual prediction model to generate a capacity loss prediction result. And the online monitoring and early warning functions of the health state of the battery are realized.

The invention provides a cardiovascular trend prediction method and system based on time sequence medical health data, and relates to the technical field of medical health data analysis. The method comprises the following steps: collecting time sequence medical health data of a patient through a multi-source sensor, wherein the time sequence medical health data comprises dynamic physiological indexes such as heart rate, blood pressure and oxyhemoglobin saturation; performing time calibration and feature extraction on the acquired multi-source data; constructing a time sequence feature model based on the time sequence features, and fusing the time sequence feature model with the static information of the patient to generate a comprehensive feature vector; using a deep learning model to train historical data, and learning a dynamic change rule of cardiovascular health indexes; predicting the change trend of the cardiovascular health indexes in real time, and evaluating the risk level of cardiovascular diseases; the model parameters are optimized through online learning, the prediction precision is improved, the change trend of cardiovascular health indexes can be predicted in real time, and support is provided for early discovery and personalized medical treatment of cardiovascular diseases.

The invention discloses a stacking-based steel hot-rolled product mechanical property prediction method, and relates to the technical field of steel product quality prediction. The method comprises the following steps: constructing a steel hot rolling actual production data set and carrying out missing value interpolation on the data set; performing normalization processing on the complete data set, and dividing a part of data from the normalized data set as a training set; establishing a stacking ensemble learning model based on a regression chain; taking the yield strength, the tensile strength and the elongation in the training set as training labels, taking other data types as training features, and training the learning model by using the training set to obtain a steel hot-rolled product mechanical property prediction model; and the steel hot-rolled product mechanical property prediction model is practically applied to perform real-time prediction on the mechanical property of the steel hot-rolled product. According to the method, the coupling relation among the multiple target variables can be effectively processed, and the accuracy and stability of mechanical property prediction of the steel hot-rolled product can be effectively improved.

The invention provides a real-time prediction method and system for milling deformation of a thin-wall part and electronic equipment. The method comprises the steps that a partial differential equation set used for reflecting the deflection and stress coupling relation in the milling machining process of the thin-walled workpiece is constructed based on a von Karman control equation set; constructing a deformation prediction model based on a depth operator network; according to the partial differential equation set, constructing a loss function comprising a partial differential equation residual term and a boundary condition term; training the deformation prediction model according to the loss function and two-dimensional coordinate points and load function sampling points used for training the deformation prediction model; and deploying the trained model in a machine tool system, inputting real-time load data and real-time coordinate data of the thin-wall part in the milling process, predicting deformation response of the thin-wall part in the milling process, and obtaining a target prediction result. The method can quickly and accurately predict the deformation response of the thin-wall part under the complex load working condition, and improves the machining quality and efficiency of the thin-wall part.

The invention discloses a hybrid sequential network power consumption prediction method for reinforcement learning dynamic calibration, and the method comprises the steps: collecting multivariable power consumption time sequence data, completing the data preprocessing through resampling, feature engineering, normalization and sliding window technologies, generating a supervised learning sample set, and dividing the supervised learning sample set into a training set, a verification set and a test set; constructing a hybrid prediction model comprising a dynamic capture module, a long-term dependence modeling module, a regression prediction module and a reinforcement learning dynamic fine tuning module; training and optimizing by adopting a staged training strategy to obtain a hybrid prediction model; multivariable power consumption time sequence data are collected in real time and preprocessed, the preprocessed data serve as input, real-time prediction of future total consumption is conducted through the mixed prediction model, and a final prediction result after dynamic fine adjustment is output. According to the method, accurate and efficient prediction of the power grid load can be realized, and a reliable technical solution can be provided for power system scheduling optimization, demand side management, market transaction and other scenes.

The embodiment of the invention provides an energy storage charging and discharging strategy optimization method and device based on day-ahead and real-time electricity price fluctuation, and the method comprises the steps: obtaining a day-ahead electricity price prediction matrix which comprises the day-ahead electricity price prediction value of each time period in an energy storage system, and constructing a day-ahead income objective function through the day-ahead electricity price prediction matrix; generating a day-ahead charging and discharging declaration strategy for reflecting the state of the energy storage system in each time period by using the day-ahead income objective function; after entering the real-time market, acquiring a real-time predicted electricity price matrix containing the real-time predicted electricity value of each time period in the energy storage system, and constructing a real-time income objective function by using the real-time predicted electricity price matrix; a result of maximizing the revenue in the real-time market by considering the conditional value-at-risk, and establishing a final revenue model by taking the sum of the day-ahead revenue and the real-time revenue as a target; and performing optimal energy storage charging and discharging strategy optimization solution on the final income model to obtain an optimal energy storage charging and discharging strategy.

The invention discloses a real-time emergency prevention and control system and method for an electrochemical energy storage system based on digital twinning, and belongs to the field of energy storage safety. The system comprises a multi-source sensing unit, a digital twin deduction unit and a visual emergency decision unit. The sensing unit collects multi-source data to form a state vector; the deduction unit outputs a future thermal runaway evolution path containing a temperature field, a gas concentration field and a fire blast risk probability and uncertainty measurement thereof in real time through a pre-trained Gaussian process regression agent model; and the decision-making unit performs three-dimensional visual rendering, jointly determines a high-confidence risk area based on the risk level and the uncertainty level, and generates an emergency prevention and control instruction with accurate spatial positioning. The problems that in the prior art, disaster evolution paths cannot be predicted in real time, and prevention and control measures are extensive are solved, millisecond-level online deduction and accurate active prevention and control of the thermal runaway process are achieved, and the safety of an energy storage system is remarkably improved.

The invention discloses a marine phytoplankton abundance monitoring method based on data driving and an underwater acoustic network, and the method comprises the steps: building and training a multi-level spatial-temporal feature integration model based on the historical marine phytoplankton abundance and the corresponding marine environment data, and solving the optimal spatial position and monitoring time window of an underwater acoustic network node; constructing a teacher model which comprises a multi-layer cascade structure and a residual module and is used for channel prediction, and compressing the teacher model into a student model by adopting spatio-temporal knowledge distillation; the student model is trained, and channel state real-time prediction is realized at the underwater acoustic network node; on the basis of the optimal spatial position of an underwater acoustic network node and a real-time predicted channel state, adaptive underwater acoustic communication models and seasonal optimal routing strategies are matched, adaptive modulation and coding are executed at a link level, and monitoring of abundance of phytoplankton is completed based on an underwater acoustic network. According to the invention, the data throughput and prediction precision can be improved under strict energy consumption constraints.

The invention discloses a tool face angle real-time prediction method based on ground top drive parameters, and relates to the field of tool face angle real-time prediction, and the method comprises the steps: constructing a model training parameter obtaining experiment bench, and collecting simulation drill string dynamic data under different ground top drive parameter combinations through the bench; calculating an initial tool face angle, and performing unwrapping treatment on the initial tool face angle to obtain real tool face angle time sequence data; constructing a prediction model based on an LSTM neural network by taking the ground top drive parameters and the corresponding real tool face angle time sequence data as training samples; and the RMSE, the MAE, the Rand the prediction accuracy are used as evaluation indexes to verify the reliability of the model prediction performance. The method has the advantages that the top drive parameters located on the ground are adopted to predict the angle of the tool face thousands of meters underground, and data distortion caused by the problems of slow data transmission, large packet loss and the like in the process of transmitting underground signals to the ground is avoided.

The invention provides a coagulant addition control system and method based on multi-scale alumen ustum characteristic analysis, and relates to the technical field of coagulant addition control. Comprising an underwater camera device, an embedded prediction module, an integrated neural network processor NPU, a dosing device, a feature extraction module and a time sequence feature coding module. According to the method, underwater camera devices are arranged in a flocculation area and a settlement area respectively, spatial multi-dimensional feature analysis is carried out on images acquired at double view angles, spatial multi-scale parameters are obtained, and real-time prediction of coagulant dosage is realized through an improved deep learning model; the improved deep learning model is embedded into a controller of an integrated neural network processor NPU, after the controller receives a model prediction result, regulation and control signals of PAC and PAM are generated in combination with the current operation state of the dosing pump, and dosing equipment is driven to execute actions; meanwhile, a regulation and control result is fed back to the feature extraction module in real time, and accurate matching of the coagulant dosage and dynamic evolution of alumen ustum is ensured.

The invention provides a bridge life prediction method and system based on a physical information neural network and fusing a physical degradation mechanism and monitoring data, realizes reliable and real-time prediction of the residual life of a bridge, and relates to the technical field of bridge structure health monitoring. The method comprises the following steps: constructing a bridge real-time feature tensor, forming a bridge real-time feature tensor with uniform space-time alignment, constructing a physical information neural network model by taking a space-time coordinate (x, t) in the formed bridge real-time feature tensor as network input, and outputting endogenous physical field data for representing a degeneration state of a detected bridge; retraining the physical information neural network model; and taking endogenous physical field data output by the retrained physical information neural network model as input, and feeding the endogenous physical field data into a pre-trained Bayesian neural network model to realize uncertainty quantification and prediction of the service life of the tested bridge.

The invention relates to the technical field of electric digital data processing, in particular to a shock absorber data processing method and system based on digital twinning, and the method comprises the steps: obtaining a multi-channel data signal of a shock absorber, and constructing a panoramic vibration data tensor; extracting a discrete digital sequence on a panoramic vibration data tensor time dimension, executing a variational mode decomposition algorithm, and separating and extracting a transient impact feature vector flow; constructing a double-time-axis sliding mapping window, and mapping the transient impact feature vector flow to a future node of a prediction response time axis based on a front and rear wheel preview mechanism of a vehicle wheelbase; operating the time sequence recurrent neural network model, and predicting and generating a target state data matrix of future nodes; and in the numerical constraint space, a prediction type damping adjustment instruction data packet is solved and generated through a rolling optimization algorithm. Through prospective state prediction and optimization solution, the problem of data timeliness caused by calculation delay in digital twinning is solved, and real-time, predictive and safe adjustment of the shock absorber is realized.

The invention discloses a lithium battery thermal runaway prediction and multistage response system based on AI intelligent evaluation, and the system comprises a multi-sensing monitoring module, an AI prediction evaluation module, a thermal runaway risk evaluation module, a safety response control module, and a safety execution module. Through fusion of multi-dimensional sensing data and an artificial intelligence algorithm, real-time prediction and evaluation of the thermal runaway risk of the battery are realized, and graded safety response measures are actively triggered at the early stage of thermal runaway. And aiming at different risk levels, the system automatically executes corresponding multi-level protection actions. The system also has online learning and model adaptive optimization capabilities, and can continuously improve the prediction accuracy and response efficiency along with the change of the state of the battery. And the thermal runaway early warning sensitivity and the response timeliness are remarkably improved, and the method has an intelligent level and engineering practicability and can be widely applied to the field of lithium battery safety management of electric vehicle battery packs, energy storage power stations and the like.

The invention relates to the technical field of robots, in particular to a robot arm grabbing control method and system based on multi-sensor data analysis, and the method comprises the steps: obtaining the spatial position and morphological feature data of a grabbing area and surrounding objects, and the local contact state data of a target object; the internal stress distribution and the local deformation state of the flexible object in the grabbing process are predicted in real time; generating a clamping control instruction based on a prediction result, and dynamically planning a motion path and a grabbing posture in a grabbing process by combining adjacent object disturbance modeling and disturbance risk quantification to form an executable grabbing track and grabbing posture control instruction; and meanwhile, a microcosmic contact state model is constructed, the grabbing process is dynamically adjusted in combination with clamping control and a grabbing track, and real-time updating of local grabbing force and action is achieved. Through multi-sensing data fusion and dynamic modeling, the local stress change of the flexible object can be effectively sensed, and damage and sliding are inhibited.

The invention discloses a digital twinborn model construction and life prediction method for pressure vessel defect data, and belongs to the technical field of pressure vessel safety assessment, and the method comprises the following steps: a defect topological graph construction and feature extraction step: converting defect space distribution into a graph structure, and extracting features by adopting a graph attention network; a crack propagation physical constraint modeling step: predicting a defect propagation behavior by adopting a physical information constraint neural network; an uncertainty quantification and reliability evaluation step: calculating a confidence interval and a failure probability of the residual life by adopting a Monte Carlo Dropout method; the model parameters are dynamically adjusted through an online learning mechanism, high-precision real-time prediction of defect evolution and reliability evaluation of the residual life are achieved, and the calculation efficiency is improved by more than two orders of magnitude compared with a traditional finite element method.

The invention relates to the technical field of linkage control, in particular to a real-time industrial state prediction and linkage control method, which comprises the following steps of: acquiring data of surface sensors of a main pump, a cooler and a steam generator, eliminating an abnormal sampling group, generating a configuration state sequence, extracting a maximum amplification point index, constructing a linkage track set, extracting cross-equipment characteristics and judging sudden change intensity. And generating a pre-triggering section, analyzing trend consistency and response delay, generating a control driving sequence, and outputting a linkage regulation and control instruction set. According to the invention, the method can accurately lock a state change section and recognize a trend pre-triggering segment through sliding a construction window and fusing the sudden change intensity of the features such as current and temperature, and effectively improves the accuracy of the control cooperation judgment through combining the dual constraints of trend consistency and delay time limit. And after the consistency of the control action is judged by combining the change direction and amplitude stability, an instruction is issued, so that the real-time prediction capability of the equipment operation state under the complex working condition can be enhanced, and the response time is shortened.

The invention discloses an amino mixed fuel explosion characteristic visualization and explosion suppression coordinated regulation and control system and method, and the system comprises an explosion suppression device which is disposed in a combustion chamber and is in communication connection with a holographic monitoring device and an explosion overpressure real-time prediction model. A graded explosion suppression scheme is set according to the safety early warning grade, so that active intervention of explosion suppression intensity and mode matched with combustion or explosion development stages with different intensities in the combustion cavity is implemented. Therefore, visualization of the mixed gas combustion and explosion dynamic process under the multi-parameter coupling condition can be achieved, and the comprehensiveness and reliability of combustion and explosion research are remarkably improved; through real-time monitoring and intelligent prediction of the explosion process, the explosion process in the device can be rapidly started, the explosion suppression response can be optimized, the explosion development can be suppressed, the integrity and operation safety of an experiment system can be guaranteed, and the intrinsic safety and the system protection capability of the experiment process can be effectively enhanced.

The invention discloses a wind field yaw wake flow hybrid modeling method and device fusing simulation and deep learning. The method comprises the following steps: firstly, introducing a yaw-corrected actuating disc model, constructing a volume force source item under the yaw action of each fan, performing steady-state computational fluid mechanics simulation on the yaw wake flow of the wind field by combining a Reynolds average Navier Stokes method and a k-epsilon turbulence model, and constructing a yaw wake flow database of the wind field; and then, training the yaw wake flow database by using a deep neural network, and learning a nonlinear mapping relationship between a wind field input wind speed and a multi-fan yaw angle combination and an incoming flow wind speed of each fan, so that a wind field yaw wake flow data driving model is established, and real-time prediction of wake flow characteristics under non-consistent yaw configuration is realized. Compared with an existing yaw wake flow model, the method has the advantages that the technical difficulty of wake flow modeling of multi-fan non-consistent yaw is effectively solved, the prediction speed is remarkably increased while the modeling precision is improved, and the method has high engineering application value.

The invention discloses a solid waste water content prediction system and method based on an infrared thermal imager. The temperature field of the solid waste on the conveyor belt in the natural convection and forced convection moisture evaporation process is monitored and compared in real time through the thermal infrared imager, and the moisture content of the solid waste can be accurately predicted in combination with data processing, environmental parameter correction and prediction of a regression equation. Compared with a traditional sampling detection method, the method has the advantages of being high in real-time performance, non-contact, simple in equipment, good in economical efficiency and the like, can adapt to a complex industrial field environment, and provides a new technical means for intelligent and refined control over waste incineration. The system is simple in structure, high in reliability, good in detection precision and capable of achieving real-time prediction of the water content of the solid waste entering the furnace.