419 results about "Nonlinear regression" patented technology

The invention discloses a numerical control machining equipment state monitoring system based on big data analysis, and relates to the technical field of intelligent manufacturing. The method is used for solving the problems of correlation analysis and real-time feedback between tool wear and machining quality in the machining process. A time sequence correlation feature vector is extracted through a vibration signal, an acoustic emission signal and main shaft axial micro-displacement data, a machining quality degradation index is calculated in combination with a nonlinear regression model, and the machining quality is monitored in real time. And on the basis of adaptive feature weight adjustment of the types of the processing materials, feature fusion during processing of different materials is enhanced, and the precision of quality evaluation is improved. And establishing a nonlinear mapping relation between the tool wear and the surface roughness by utilizing a gradient lifting tree model, and realizing accurate prediction of the tool wear and the machining quality. And finally, through a closed-loop dynamic adjustment mechanism, the cutting speed and the feeding amount are automatically adjusted according to the machining quality degradation index and the fault positioning result, and the stability of the machining process and the product quality are ensured.

The invention relates to the technical field of fermentation control, in particular to an intelligent optimization control system of an aroma-enhancing compound leavening agent, which comprises a strain culture selection module, a fermentation process monitoring module, an environment optimization adjustment module and a fermentation effect evaluation module. According to the method, accurate analysis and optimization of microbial reaction in the fermentation process are realized, the selection efficiency of the strain culture combination is improved, the fermentation process can be carried out under the optimal condition, the efficiency of biological reaction is remarkably improved, potential risks and behavior abnormalities can be recognized in time through analysis of time sequence data, and the safety of the fermentation process is improved. The fermentation control process is optimized by dynamically adjusting key parameters, environmental parameters are adjusted through a nonlinear regression model and monitored in real time, the environmental stability is enhanced, it is ensured that the fermentation process is continuous and efficient, data of the whole process are comprehensively analyzed, the fermentation effect is accurately evaluated, multiple performance indexes are quantified, and the quality of a leavening agent is improved.

The invention provides an optical measurement miss distance processing method based on tracks and same-frame picture constraints. The method mainly comprises the steps of same-frame picture angle difference calculation, conversion from a space coordinate system to an observation station coordinate system, motion characteristic parameter representation, track speed constraint model construction and miss distance moment and miss distance calculation. That is to say, according to the method, the same-frame picture and the target motion trail speed constraint are utilized at the same time, multi-device measurement data are comprehensively applied, and key technical contents such as same-frame picture difference calculation, coordinate conversion, target trail motion characteristic representation extraction and miss distance parameterization solving model are elaborated; a data processing problem is converted into parameter estimation of a nonlinear regression model, so that a reliable miss distance result of the meeting section is obtained, and a new thought and a new solution are provided for application development of optical measurement equipment, processing of the miss distance in the air and application of optical measurement data.

The invention discloses a UV illumination control method and system for a reel-to-reel exposure machine. The method comprises the following steps: acquiring a real-time moving speed and a tension change value of a coiled material; when the tension change value is greater than the tension threshold value, predicting the deformation trend of the surface of the coiled material based on a nonlinear regression algorithm in combination with the real-time moving speed to obtain a deformation influence value; according to the deformation influence value, a PID control algorithm is adopted to adjust the UV light source, and a light intensity distribution value is obtained; according to the light intensity distribution value, a fuzzy control algorithm is adopted to calculate the output intensity requirement of the UV light source, and speed adjustment is carried out in combination with the real-time moving speed, so that optimized irradiation parameters are obtained; performing area judgment according to the optimized irradiation parameters to obtain an overexposure area and an underexposure area; and adjusting the intensity and distribution of the UV light source in the overexposure area and the underexposure area until the area exposure quality reaches a preset standard range. The method can realize real-time response regulation and control on tension and speed change of the coiled material, and solves the problem of non-uniform exposure dose.

The invention discloses an extreme low water change attribution and low water resource potential evaluation method, which belongs to the technical field of water resource calculation, and comprises the following steps of: obtaining raster data of runoff and driving factors of the runoff in a research area, performing data preprocessing, deducing the frequency distribution of runoff depth of each grid unit by adopting a non-parametric kernel density estimation method, and calculating the runoff depth of each grid unit; and counting the proportion of the grids meeting the guarantee rate runoff depth threshold in each year in the total grid, and further determining a low water typical year under each guarantee rate. A nonlinear regression simulation model of coupling optimization deep learning is adopted to carry out extreme low-water runoff depth regression simulation research under different driving factor combinations, and a key driving factor combination influencing low-water runoff depth change is mined based on a regression simulation precision result; and analyzing the constraint effect of each key driving factor on the extreme low water and a possible threshold value by adopting a constraint line deduction technology, and evaluating the low water resource potential of a typical year of the extreme low water.

The invention relates to the technical field of ecological environment monitoring, and discloses a regional ecological environment monitoring method and system based on remote sensing image processing, and the method comprises the following steps: collecting remote sensing image data and climate factor data of a target region; performing spatial gridding and standardization processing on the data; establishing a nonlinear regression model to analyze a relation between climate factors and vegetation indexes; extracting spatio-temporal topological characteristics of the climate factor data and identifying a change mode; long-term prediction of regional vegetation productivity is carried out through time sequence model training; the regional ecological environment is analyzed and evaluated based on the prediction result, and a scientific basis is provided for ecological environment monitoring; the system comprises a data acquisition module, a data processing module, a nonlinear regression modeling module, a spatio-temporal topology analysis module, a time sequence prediction module and an ecological evaluation module. According to the method, the problems of small data coverage, low prediction precision and insufficient capture of nonlinear influence of climate change in the existing method are solved.

The invention provides a geopolymer preparation and optimization method and system based on machine learning. The method is applied to the technical field of material science and machine learning. The method comprises the following steps: acquiring geopolymer preparation experimental data and preprocessing the data; performing nonlinear regression modeling on the geopolymer performance based on four machine learning regression algorithms, and constructing a geopolymer performance prediction model; calculating and distributing weights according to the mean square error of each machine learning model on the verification set, and performing weighted fusion to obtain a performance prediction result; receiving target performance parameters input by a user and an initial raw material ratio range, performing performance prediction by using the trained geopolymer performance prediction model, and reversely searching an optimal ratio combination meeting target performance constraints through an optimization algorithm; and preparing a geopolymer according to the optimal ratio combination to prepare the coal gangue-slag-fly ash geopolymer grouting material. According to the method, the prediction precision and the model generalization ability are effectively improved, and intelligent recommendation and accurate performance prediction of the raw material ratio are realized.

A rock mass mechanical parameter prediction method based on an underground powerhouse of a pumped storage power station relates to the technical field of mechanical parameter prediction, and comprises the following steps: identifying an embedding position and distribution characteristics of a sulfur-containing shale interlayer by constructing a three-dimensional geologic structure model and lithologic information of a construction area, and establishing a structural domain partition parameter prediction area system; constructing a nonlinear regression function model taking infrared spectrum characteristic factors and environment control parameters as input and taking elasticity modulus and the like as output by combining infrared spectrum characteristic data and a historical evolution track; the model is embedded into a regional system for dynamic assignment, and a parameter space-time change atlas is constructed; the laser point cloud data and the micro-seismic monitoring data are fused to invert the actual response of the surrounding rock; when the monitoring value deviates from the prediction map, triggering parameter degradation function re-calibration to complete parameter dynamic updating and prediction closed loop; according to the method, the accuracy of rock mass mechanical parameter prediction of the pumped storage power station underground powerhouse can be improved.

The invention relates to an equipment residual life prediction method based on a physical-data model, and the method comprises the steps: building a degradation model based on a physical degradation mechanism and a standard Wiener process model; obtaining fixed parameters in the degradation model by using a maximum likelihood estimation method in combination with a nonlinear regression method; acquiring an equipment state observation value in real time, and updating random parameters in the degradation model by using a weight optimization particle filter algorithm; and utilizing the degradation model, the fixed parameters in the degradation model and the random parameters in the degradation model to obtain a probability density function of the residual life of the equipment under a random failure threshold value, and performing numerical integration on the probability density function of the residual life of the equipment to obtain an expected value of the residual life of the equipment, and outputting the expected value as the residual life of the equipment. Compared with the prior art, high-precision and high-reliability residual life prediction is realized by fusing a physical mechanism and a data driving method.

The invention relates to the technical field of artificial intelligence, and discloses a complex stratum rock mechanics parameter prediction method, which comprises the following steps: collecting multi-source data of a complex stratum, and carrying out data preprocessing on the multi-source data; extracting physical features and statistical features related to the rock mechanical parameters based on the multi-source data, and aligning the physical features, the statistical features and the multi-source data after data preprocessing according to depth to obtain a high-dimensional feature matrix; an integrated machine learning model is constructed, the high-dimensional feature matrix is utilized to train the integrated machine learning model, the integrated machine learning model comprises a bottom layer model and a top layer model, and the bottom layer model comprises a time sequence modeling unit and a nonlinear regression unit; and optimizing model output of the integrated machine learning model based on dynamic weight adjustment and a physical constraint loss function to obtain a rock mechanical parameter prediction result. According to the method, the accuracy of complex stratum rock mechanical parameter prediction can be improved.

The invention discloses a multi-module lithium battery pack system based on cloud computing management, relates to the technical field of cloud computing management, and is used for solving the problem of poor management precision of a multi-module lithium battery pack. Comprising a lithium battery data acquisition module, a battery degradation analysis module, a degradation acceleration point determination module and a strategy management module. According to the invention, data acquisition and preprocessing are carried out on a multi-module lithium battery pack through an Internet of Things sensor and an edge computing device, the data are transmitted to a cloud platform, Fourier transform is used to extract features, a nonlinear regression model is combined to evaluate the health state of the lithium battery, and a degradation speed is determined and a degradation acceleration phenomenon is identified through a segmented nonlinear regression model. Kalman filtering and nonlinear regression are combined, a degradation acceleration point is identified, load distribution is optimized through an intelligent scheduling algorithm after identification, the load of a degradation module is reduced, a scheduling signal is generated by analyzing load adjustment information, a system is guided to perform strategy scheduling management, and a scheduling strategy and energy management efficiency are optimized.

The invention discloses a bridge support column structure reliability design evaluation method, and relates to the technical field of bridge design evaluation, and the method comprises the steps: obtaining a first modeling data set based on a bridge support column structure design drawing, and building a to-be-evaluated three-dimensional model and a finite element model; generating a first simulated traffic flow, performing simulation by applying a load to obtain standard state stress data, and performing screening to obtain a key stress surface; the statically-determinate structure creep force of the key stress surface is calculated, and the evaluated damage life is obtained through calculation based on the statically-determinate structure creep force; generating a second simulation traffic flow, performing simulation to obtain offset state stress data of the key stress surface, and performing calculation based on an offset nonlinear regression equation to obtain anti-offset reliability; establishing an anti-impact reliability performance function, generating a simulation impact combination through a Monte Carlo algorithm, and determining an anti-impact reliability simulation value; and based on the anti-offset reliability and the anti-impact reliability, a fusion robustness value is obtained through calculation, and threshold judgment is carried out to obtain an evaluation result.

The invention discloses a Dst index calculation method and system fusing neutral atom imaging data and machine learning, and the method is realized based on a satellite-borne high-energy neutral atom imager, and comprises the steps: receiving collected neutral atom image data of different energy segments, and carrying out the preprocessing through combining satellite position data; inputting the preprocessed neutral atom image data and satellite position data into a pre-established and trained index prediction model, and outputting a Dst index at the current moment; the index prediction model is combined with a residual network and a multi-layer perceptron, and comprises an image feature extraction module used for extracting image features and capturing distribution information related to a Dst index in a neutral atom image; the feature fusion module is used for performing multi-modal fusion on the image features and the satellite position data; and the regression prediction module is used for performing nonlinear regression on the fused features and outputting a Dst index at the current moment.

The invention relates to the technical field of remote sensing monitoring, and discloses a lake cyanobacterial bloom detection method and system fused with a remote sensing image. The method comprises the following steps: acquiring a multispectral remote sensing image and a synthetic aperture radar image of a target lake; calculating a phycocyanobilin characteristic index and a chlorophyll fluorescence peak index according to the characteristic wave band reflectivity, and screening pixels meeting discrimination conditions to obtain an optical water bloom distribution mask; calculating a radar inversion phycocyanobilin index through a nonlinear regression model according to the dual-polarization backscattering coefficient to obtain a radar water bloom distribution mask; and fusing the double masks to obtain a cyanobacterial bloom monitoring result. The method solves the problems that an existing cyanobacterial bloom detection method cannot realize all-weather monitoring, lacks cyanobacterial specific recognition capability and is insufficient in reliability of a single data source, and improves the timeliness, accuracy and reliability of cyanobacterial bloom detection.

A method and system for performing analysis and calculation of multi-energy flow for integrated energy system (IES), comprising establishing electrical power flow model, hydraulic model and thermal model, to form preliminary model of IES; constructing DHL-LSTM neural network for nonlinear regression of electrical power flow model, construct SHL-LSTM neural network for nonlinear regression of hydraulic model, and finding optimal parameters of two neural networks; training the two models, and adding error compensations into the two models; simplifying the thermal model, to obtain mechanism-driven linear thermal mode; and embedding mechanism-driven linear thermal model into error-compensated hydraulic model and error-compensated electrical power flow model respectively, to form and use final model of the IES to perform analysis and calculation of multi-energy flow. The present invention fully considers the coupling property inside the system, and avoids the situation that the convergence speed is slowed down when solving high nonlinear problems.

The invention discloses a humanoid robot voice-driven facial expression control method and system. The extracted voice audio features are subjected to face corresponding key point prediction through an audio emotion key point prediction model, and the audio emotion key point prediction model is a regression model based on a long short-term memory network and is used for learning a nonlinear mapping relation between an audio feature sequence and face key point coordinates; outputting a predicted key point position difference value or a relative distance; the predicted face key points are input into a steering engine angle mapping model, and the steering engine angle mapping model maps the geometrical relationship of the face key points into angle instruction parameters for controlling a robot face micro steering engine based on a pre-trained nonlinear regression model; and according to the predicted angle instruction parameters, driving a robot face mechanism to make human expression simulating motion synchronous with the voice content. The problem that multi-channel interaction of a traditional robot is not coordinated is solved, and the naturalness and emotional expressive force of man-machine interaction are remarkably improved.

The invention relates to the technical field of three-dimensional real-time wind speed measurement, and discloses a large-span roof three-dimensional ultrasonic anemometer and a measurement method thereof, and the method comprises the steps: building a three-dimensional coordinate system through laser ranging, and analyzing and compensating installation errors; an initial calibration coefficient is generated through a self-inspection and optimization algorithm, and the initial calibration coefficient is compared and analyzed with a standard instrument for initial calibration; ultrasonic pulse emission is controlled, full-path scanning and redundancy measurement of all axes are completed, signal quality is improved through multi-modal signal processing detection, and abnormal values are eliminated through weighted median values; the sound velocity is corrected based on real-time temperature data, the influence of humidity and air pressure on the sound velocity is adjusted through a nonlinear regression model, and a Reynolds stress correction term is introduced to compensate for sound wave path bending in an eddy current field; a three-dimensional turbulence intensity distribution diagram is constructed by fusing ultrasonic and LiDAR data, meteorological parameters are calculated, an extreme weather event is early warned by using a machine learning model, a visual three-dimensional wind speed field is provided through a cloud platform, and a potential risk area is identified.

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.

The invention discloses a dynamic optimization method and system for a boron diffusion process of a photovoltaic cell and electronic equipment. Comprising the following steps: collecting process parameters and sheet resistance data, and associating the process parameters and the sheet resistance data to form a process parameter set; optimizing a process parameter set, removing noise data and incomplete data, and extracting an available feature data set; dividing the feature data set into a training set and a verification set, and training to obtain a nonlinear regression model for predicting resistance data under different feature data; carrying out validity verification on the nonlinear regression model, and constructing an optimization function to reduce a difference value between a predicted value and an actual value; and deploying a nonlinear regression model in the diffusion process, adjusting the process parameters of the diffusion process in real time according to the predicted value, and optimizing the obtained sheet resistance data. The problem that the boron diffusion process is difficult to accurately adjust by a traditional process control method can be solved.

The invention relates to the technical field of geotechnical engineering monitoring, in particular to a soil erosion and particle migration prediction method and system based on acoustic emission monitoring, and the method comprises the following steps: obtaining a soil mass ratio parameter, and preparing a soil sample based on the soil mass ratio parameter to obtain a test soil sample; constructing a multi-field coupling test mechanism to perform a multi-field coupling soil body test on the test soil sample; establishing a soil sample data acquisition device to obtain soil sample monitoring data in the multi-field coupling soil body test process; obtaining acoustic emission characteristic parameters of the test soil body according to the soil sample monitoring data, and establishing a multivariate nonlinear regression model; and constructing a soil body prediction model to realize prediction of soil body erosion and particle migration. Through fusion of multi-field coupling monitoring and machine learning, limitation of a traditional empirical model is broken through, high-precision dynamic prediction of soil erosion and particle migration is realized, a scientific basis is provided for stability evaluation of roadbed engineering, and prediction reliability and engineering applicability are remarkably improved.

The invention provides a laser ceilometer system error compensation method based on regression analysis, and belongs to the technical field of laser ceilometers. A measurement range is divided into four height intervals by constructing a layered height interval step regression equation set, and an independent nonlinear regression equation is established; designing a double-layer game optimization framework to realize collaborative optimization of global error minimization and local fitting precision maximization, executing historical data preprocessing and data set division, and implementing a double-layer game model collaborative optimization algorithm to determine a regression equation coefficient and a neural network parameter; a self-adaptive cloud height error correction model based on a Transform architecture is constructed to realize real-time error compensation, the optimal performance of the model is kept through sliding time window monitoring and automatic retraining, and the technical problem that the measurement precision of the laser ceilometer is affected by atmospheric environment parameters and equipment parameter changes, and consequently system errors are remarkable is solved.

The invention discloses a key phase pulse signal synchronous acquisition and multi-axis centering analysis method, which belongs to the technical field of mechanical monitoring and diagnosis, and comprises the following steps of: configuring a synchronous acquisition system, acquiring a conditioned key phase pulse signal and a conditioned vibration signal, and extracting an axis speed and a load; constructing a time drift monitoring model to compensate the key phase pulse signal in real time, and ensuring the time synchronization precision; the compensation signal is input into a shaft centering correction model based on dynamics and finite element analysis, and radial deviation and axial deviation under the dynamic load are calculated; establishing a nonlinear tolerance threshold model by adopting a nonlinear regression model, and dynamically adjusting an allowable centering deviation threshold in combination with the real-time shaft speed and the load; through closed-loop comparison of the shaft centering deviation and a threshold value, a centering qualified conclusion or a correction suggestion is automatically output, and triggering signal resampling is supported to adapt to working condition changes; the real-time and self-adaptive analysis of the centering deviation of the multi-axis system is realized, and the operation stability and the maintenance efficiency of the rotating machinery are improved.

The invention provides an intelligent scheduling method and device for a multi-hole gate and a server, and relates to the technical field of hydraulic engineering automation and artificial intelligence optimization scheduling, and the method comprises the steps: determining target input characteristics through the historical operation data and simulation data of the multi-hole gate, and improving the target input characteristics through a lightweight gradient lifting tree model; performing nonlinear regression training processing on the target input features, and determining a target traffic prediction model at the current moment; performing hierarchical expansion processing on the gate opening degree combination through a multi-constraint search model to obtain a gate expansion scheduling scheme, and performing batch prediction processing and scoring processing on the gate expansion scheduling scheme by using a target flow prediction model to obtain a gate candidate scheduling scheme set; and performing upper limit detection screening processing and error tolerance screening processing on the gate candidate scheduling scheme set, and determining a target scheduling scheme of the porous gate. The method can significantly improve the prediction precision of the outlet water of the gate and the scheduling efficiency of the multi-hole gate.

The invention relates to the technical field of scene prediction, in particular to a vehicle scene prediction method, and provides the following scheme: a vehicle-mounted end collects vehicle end data and vehicle control data, a cloud end receives and generates a predicted scene video stream, and a cabin end performs color adjustment and image quality evaluation after receiving the predicted scene video stream. And adjusting a display queue according to an evaluation result and generating vehicle control data. In the color adjustment process, motion compensation, color distortion processing and secondary local compensation are performed based on the vehicle control data so as to improve the color consistency and space-time coherence of the prediction frame. Image quality evaluation is based on multi-color space feature extraction and a nonlinear regression network, quality scores are output in real time, and remote control decisions are guided. And the cloud end generates a continuous and high-quality prediction scene video stream by combining light stream prediction with self-motion compensation and shielding repair, so that low-delay and high-stability remote vehicle control is realized.

The invention discloses an industrial chain construction evaluation system based on big data, relates to the technical field of industrial chain construction, and is used for solving the problems of limited guidance value, prolonged industrial chain optimization adjustment period and low resource allocation efficiency of evaluation results during industrial chain optimization strategy formulation. The method comprises the following steps: acquiring industrial chain related economic, technical and policy dimension data from multi-source heterogeneous data, obtaining the actual economic scale, technical level distribution and policy support strength of a marked area according to screening time, obtaining the number of industrial nodes, the association strength among the industrial nodes and node activeness data, and performing classification processing according to a preset rule to obtain a classification result; dividing sub-chain segments and marking high-relevance chain segments, calculating a first relevance coefficient and a second relevance coefficient, constructing a nonlinear regression model to determine a time node, calculating a collaborative efficiency coefficient through a deep learning algorithm, weighting the collaborative efficiency coefficient with a preset weight distribution factor to generate an industrial chain construction comprehensive evaluation index, and sending the industrial chain construction comprehensive evaluation index to a user side. And the evaluation real-time performance and the dynamic tracking capability are improved.

The invention discloses a multi-mode intelligent customer service adaptive dialogue interaction system based on generative AI, and relates to the technical field of intelligent customer service. The working process of the system comprises the following steps: collecting multi-modal data, carrying out sentiment analysis, OCR (Optical Character Recognition) and semantic analysis, extracting potential problem targets, and associating historical dialogue features; constructing a user problem risk field model, quantifying a current theme and related theme risks, dynamically adjusting parameters through historical data, and generating a risk perception index; fitting a user satisfaction function based on nonlinear regression, and setting a four-level response threshold value; historical dialogue resource consumption and progress data are extracted, the trend model is fitted in a segmented mode, and the current dialogue resource surplus and the solving progress are predicted; and monitoring resource abnormality and progress abnormality in real time, and triggering knowledge expansion, AI takeover or manual intervention. According to the system, the service efficiency and the user experience in a complex scene are improved through multi-modal perception, risk-driven decision and an active intervention mechanism.

The invention provides a brain injury prediction method and prediction system based on a multivariate feature fusion framework, and relates to the technical field of deep learning. In order to solve the problem that dynamic characteristics and static characteristics cannot be effectively fused when multivariate data damage prediction is carried out by adopting a nonlinear regression method in the prior art, dynamic characteristic data, static characteristic data, dynamic mechanical effect parameters, static influence effect parameters and brain damage degree data under various working conditions of a vehicle are obtained. A dynamic mechanical effect parameter is used as output to train a dynamic prediction model, accurate extraction of a dynamic feature time sequence rule can be ensured, a static influence effect parameter is used as output to ensure extraction of a static feature structured rule, and finally a dynamic feature vector and a static feature vector are used as input of a fusion prediction model. Fusion of a dynamic time sequence rule and a static structured rule is achieved, it is ensured that the two types of features are fully fused, and the prediction result is more accurate.

The invention provides an intelligent temperature control method for a corrugating machine, and belongs to the technical field of corrugated paper products.The intelligent temperature control method comprises the steps that a steam temperature sensor set and a linear speed detection device are installed on a corrugating machine production line, and a speed-steam temperature dynamic compensation mathematical model based on nonlinear regression analysis is established; a feedforward control system architecture is constructed to realize real-time calculation and transmission of a steam temperature compensation value, a double-layer game optimization model is established to coordinate steam energy consumption minimization and composite quality optimization targets, and a self-adaptive adjustment algorithm is started according to composite strength and appearance quality feedback signals to dynamically adjust model parameters. Steam temperature coordination control is implemented to optimize temperature distribution, a steam temperature control effect evaluation system is established for real-time evaluation, and the technical problem that in the prior art, steam temperature dynamic compensation control cannot be achieved according to the speed change of a production line in the production process of the corrugating machine is solved.

The invention relates to the technical field of risk management and control of expressways and urban expressways, in particular to a real-time prediction method for asphalt pavement friction coefficients in ice and snow weather. Comprising friction coefficient prediction models of an iced road surface, a snowy road surface and a snowy ablation road surface. The influence factor for determining the friction coefficient of the iced road surface is the thickness of the ice layer, and a good exponential function relationship exists between the thickness of the ice layer and the friction coefficient. The friction coefficient of the snowy road surface is obviously influenced by relative sliding in the snow layer, and the thickness of the snow layer is the key for determining sliding between the inner layers of the snow layer. The accumulated snow melting road surface, the ice layer and the snow layer have negative effects on the anti-sliding performance of the road surface, and a good exponential function relation exists between the total ice and snow thickness of the accumulated snow melting road surface and the friction coefficient. According to the method, a friction system prediction equation is established by adopting a nonlinear regression method, real-time prediction can be carried out for different road surface states, and friction coefficient parameters are provided for traffic risk management and control of expressways in severe weather in winter and urban expressways in ice and snow weather.

The invention relates to the technical field of gas turbine combined cycle systems, in particular to a load prediction method and system for a gas turbine combined cycle system based on a nonlinear regression model.The method comprises the steps that historical data of the cycle system are obtained, and the historical data comprise operation parameters, heat efficiency and system loads; based on the thermodynamic equation and the operation parameters, calculating to obtain the theoretical efficiency of the circulating system; using the operation parameters, the thermal efficiency and the theoretical efficiency to form a historical feature set; and predicting the load of the gas turbine combined cycle system. According to the method, a feature set containing real-time state monitoring, actual performance evaluation and physical limit constraint is constructed, irreversible loss in system operation is quantified, physical constraint conditions are provided for prediction, overfitting or working condition extrapolation failure caused by lack of physical significance of a pure data driving model is avoided, and prediction accuracy is improved.