502 results about "Linear model" patented technology

The invention discloses an indoor temperature real-time regulation and control method of a heat distribution pipeline and a control system of the indoor temperature real-time regulation and control method, relates to the technical field of dynamic control of a heat distribution pipe network, and solves the problems of hydraulic oscillation and temperature control hysteresis caused by the fact that local valve regulation neglects whole-network coupling and a first-order linear model is difficult to describe multi-order thermal inertia and large heat capacity in the prior art. According to the scheme, on the basis of pipe network distributed PDE / lumped parameter hybrid modeling and in combination with extended Kalman filtering and unscented Kalman filtering on-line identification, a feedforward decoupling compensation item is generated through spectral decomposition, a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed, a control instruction is issued according to a pump-first and valve-second serialization strategy, and a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed. Meanwhile, the model weight and the prediction time domain are dynamically adjusted; according to the method, the global balance capability and the temperature tracking precision of heat distribution pipeline regulation and control are remarkably improved.

Systems, methods, and instrumentalities are disclosed for template based cross component linear model / multimode linear model (CCLM / MMLM) adjustment. In an example, a device, such as a video decoding device, or a video encoding device, may obtain a prediction model for predicting a coding block. The device may select an adjustment model, from multiple adjustment models, for adjusting the prediction model. The device may adjust the prediction model based on the selected adjustment model. The device may process (e.g., encode and / or decode) the coding block based on the adjusted prediction model.

The invention relates to a time sequence prediction method based on Transform, and belongs to the field of artificial intelligence time sequence prediction. The method comprises the following steps: preprocessing multivariable time series data, and performing season trend decomposition to obtain a season term and a trend term; predicting the trend term by adopting a linear model; a multi-layer perceptron (MLP) and a graph convolutional network (GCN) are adopted to model correlation among seasonal item variables; using convolution kernels of different scales to extract multi-scale features of seasonal items; constructing a position code and a timestamp code; a Transform-based encoder is constructed, and a sparse attention mechanism is adopted to capture a global dependency relationship of the time sequence; capturing a short-term dependency relationship of the time sequence by adopting a local attention mechanism; initializing a decoder input; and a decoder based on a Transform is constructed to realize prediction.

The invention relates to the technical field of industrial mechanism models, in particular to a shock absorber performance optimization control method based on model fusion, which comprises the following steps: extracting a low-frequency disturbance state variable and inputting the variable-topology industrial mechanism model to generate a nominal reference state trajectory; utilizing a depth state observation network fused with energy passivity constraint to calculate a non-linear model mismatch compensation amount and an adaptive weighting parameter; performing dynamic fusion on the nominal reference state trajectory and the compensation amount based on the adaptive weighting parameter to generate a generalized state estimation value; and executing dynamic multi-objective optimization based on the generalized state estimation value, and generating mixed mode control input acting on an execution end. According to the invention, through adaptive fusion of a mechanism model and a data driving method, physical consistency, calculation real-time performance and robustness of a control process are considered.

The invention discloses a hydraulic engineering operation and management cooperative control system based on digital twinning, and the system comprises the following steps: a data assimilation module which collects observation station, remote sensing and meteorological data and assimilates the data to obtain an assimilation estimator; the dictionary and lifting module is used for generating a lifting variable sequence according to hydrodynamic force prior; the structure identification and uncertainty module is used for identifying a lifting linear model and recursively predicting uncertainty under the constraints of conservation, monotonicity, dissipation and spectral radius; the terminal security module constructs a positive invariant terminal security set according to a support function; the opportunity constraint module is used for setting a total out-of-limit probability budget and generating an opportunity constraint substitution set; the Koopman rolling optimization module is used for solving the control sequence and executing the first control quantity; and the online updating and correcting and deformation and verification module is used for executing residual error triggering low-rank correction, projection return and support surface parameter online deformation. According to the invention, risk-controllable and stable and efficient collaborative scheduling is realized.

The ELM-based frequency safety constraint linearization optimization scheduling method comprises the following steps: establishing a fire-wind-light multi-energy complementary frequency dynamic response model, and deducing a time domain analytical expression of a frequency safety index by taking a frequency maximum change rate and a frequency maximum deviation as the frequency safety index of a system; considering a frequency lowest point security constraint and a frequency maximum change rate security constraint, and establishing a frequency security constraint-containing optimization scheduling model; a maximum bearable disturbance power linearization mapping method based on ELM is provided, nonlinear frequency security constraints are converted into a series of linear constraint sets, and linearization processing of the frequency security constraints is achieved; and piecewise linearization is carried out on quadratic terms in the established objective function containing the frequency security constraint optimization scheduling model, the overall model is converted into a mixed integer linear model, and a Gurobi solver is used to solve the linearly processed model. The scheduling method can ensure that the system frequency is safe and stable, so that the established optimal scheduling model gives consideration to both economy and frequency safety.

The invention relates to the technical field of prediction models, in particular to a battery energy storage life prediction method and system based on multiple time scales, and the method comprises the following steps: deploying three groups of short-term, medium-term and long-term parallel data windows, obtaining battery capacity and internal resistance parameters, sliding window initialization parameters, synchronous baseline calibration, and Kalman filtering correction parameters, and extracting a variation amplitude and rate, an output capacity mean value and an internal resistance variance, calculating a residual sequence, performing three-time linkage on the capacity residual exceeding a threshold to trigger an abnormity, performing weighted fusion after the abnormity, and outputting the residual life. According to the invention, through multi-time scale parallel data window dynamic monitoring, sliding window synchronous calibration base line, Kalman filtering dynamic correction parameters, capacity and internal resistance residual error linkage determination mechanism establishment, dual-attenuation coupling model fusion multi-dimensional attenuation characteristics and multi-scale collaborative analysis of battery aging, the limitation of a linear model is broken through; the anomaly detection sensitivity and robustness are enhanced, the life prediction accuracy and timeliness are improved, and the misjudgment risk is reduced.

The invention discloses a plant extraction method and system based on image processing, and relates to the technical field of image processing. The method comprises the following steps: acquiring and standardizing a plant raw material image; extracting feature vectors of the images by using a neural network convolutional layer algorithm, comparing the feature vectors with a plant raw material standard feature database, and screening out qualified images of the plant raw materials; qualified images are processed through multispectral image analysis and an image segmentation algorithm, and an effective component spatial distribution diagram is obtained; constructing a partial least squares regression linear model, extracting spectral feature vectors of the effective components, inputting the spectral feature vectors into the model to obtain concentration values, and calculating quantitative data of the effective components; establishing an extraction process rule base, selecting a process parameter combination according to a spatial distribution diagram and quantitative data, and extracting to obtain a primary extracting solution and plant residues; and calculating an effective component residual error rate by matching and mapping to a qualified image coordinate, and if the effective component residual error rate is greater than a preset threshold, performing secondary extraction to obtain a secondary extracting solution. And quantitative basis is provided for extracting process parameters through the spatial distribution diagram of the effective components.

The invention relates to an electric vehicle wireless charging system LPV-Hammerstein model identification method based on an alternating least square iteration strategy, and belongs to the technical field of power electronic system modeling and parameter identification. The method comprises the following steps: firstly, constructing an LPV-Hammerstein model structure based on a WPT system circuit topology and a working mechanism, and then collecting discrete data of a system input control signal U, an output DC voltage Vo and a scheduling variable Req; then, alternately fixing other parameters by adopting an alternate least square iterative algorithm, and converting a to-be-estimated parameter problem into a linear least square sub-problem to be solved; and when a convergence condition is satisfied, a final LPV-Hammerstein model parameter estimation result is obtained. According to the method, the common nonlinear and parameter change coupling characteristics in the wireless charging system of the electric vehicle can be effectively processed, a systematized and efficient way is provided for obtaining a high-precision system dynamic model, and the limitation that the system cannot be accurately described by a traditional linear model or a simple nonlinear model is overcome.

The invention belongs to the field of near-inertia internal wave modeling, and particularly relates to a near-inertia internal wave modeling method based on a reverse echo observation array, which comprises the following steps: arranging a reverse echo measurement device array in an observation area, and acquiring echo signals from the seabed to the sea surface; performing data cleaning and band-pass filtering on the signals, and extracting near-inertia internal wave signals; calculating the depth and the relative vorticity of a mixed layer by combining historical temperature-salinity data; calculating a wind input energy flux by using the wind stress data; calculating a net level energy flux by adopting a boundary integration method; judging whether the boundary energy flux influence is smaller than a threshold value or not, and if not, adjusting array layout; and finally establishing a multivariable linear model containing wind energy input, mixed layer depth, relative vorticity and energy flux. According to the method, the temporal-spatial resolution and prediction precision of near-inertia internal wave modeling are effectively improved, the problems that in a traditional method, the boundary effect is remarkable, and multi-factor comprehensive modeling is insufficient are solved, and the method is suitable for long-term efficient monitoring of a large-range marine environment.

The application discloses a curved surface structure zero state form finding method, comprising the following steps: (1) a model is established based on a design configuration, and deformation data is obtained by simulating the whole construction process; (2) a zero state configuration is deduced reversely: key node deformation values are extracted, arches are started, a simulation is re-performed after a linear model is fitted and adjusted, and iteration correction is performed until deformation error meets the requirement, and a zero state installation configuration is obtained; and (3) construction data is generated by extracting component line shapes and node coordinates. The application innovatively adopts an integral curvature adjustment strategy to replace a traditional node coordinate local adjustment mode, and through reverse deduction of 'construction simulation-control point arching-curved surface fitting-grid mapping', the problem of curved surface discontinuity caused by the traditional reverse superposition method is solved, construction precision and efficiency are significantly improved, and the application is suitable for intelligent construction of complex curved surface space grid structures.

The invention discloses an air-ground cooperative system model prediction formation control method in an underground pipe gallery environment, and the method comprises the steps: constructing an air-ground cooperative system nonlinear model, and constructing an underground pipe gallery model; designing a high-order interference observer to estimate current unknown external disturbance, and establishing an interference prediction model to estimate future unknown external disturbance; a rolling optimization control strategy based on state prediction is designed, control signals of all followers are optimized according to the current state of the system and the predicted future state in each control period, and the tracking precision is ensured while the tracking precision is improved. Collision between followers, collision between the followers and the wall of the pipe gallery and obstacle avoidance between the followers and static and dynamic obstacles in the pipe gallery are achieved; and solving is carried out based on a distributed model prediction formation control algorithm combined with a Lyapunov stability theory, so that each follower keeps an expected formation configuration while tracking a leader trajectory. And the driving safety of the formation in the underground pipe gallery environment is ensured.

The invention discloses an NUMA (Non Uniform Memory Access) architecture perceived high-scalability learning index construction method and system and an operation method. According to the construction method, a mixed node tree structure is adopted to organize data, the structure comprises internal nodes accurately searched by a linear model and leaf nodes for storing data, the leaf nodes are composed of ordered gap nodes and segment nodes, and the segment nodes support dynamic evolution from an ordered stage to a semi-ordered stage based on data density. Conflict data is managed using hierarchical benchmark buckets. According to the system, a self-adaptive node evolution mechanism based on an operation cost model is realized, index performance is optimized through node reconstruction triggered by foreground write operation and background hot and cold node compression, a multi-thread concurrency control strategy is adopted, and a memory management and thread scheduling framework perceived by NUMA is adopted. The operation method provides an efficient point query and point insertion operation process. The data layout can be dynamically optimized, the operation cost is reduced, and the method is suitable for high-performance database and memory management scenes.

A method and apparatus for video coding are disclosed. According to the method for the decoder side, encoded data associated with a current block comprising a first-colour block and a second-colour block are received. An inherited model parameter set is determined from a previously coded block coded in a first CCLM related mode, wherein the inherited model parameter set comprises a first scaling parameter associated with the first CCLM related mode. A final inherited model parameter set is derived if an update value for the inherited model parameter set is determined, where the final inherited model parameter set is determined based on the first scaling parameter and the update value. Then, the encoded data associated with the second-colour block are decoded using prediction data based on an updated CCLM related model associated with the final inherited model parameter set. A method and apparatus for the encoder side are also disclosed.

The invention relates to a time sequence prediction method based on time-frequency double-domain decomposition and multi-cycle feature fusion, and the method comprises the following steps: carrying out the normalization operation of input historical data, and carrying out different feature extraction operations for different data sets. Specifically, a trend term is firstly extracted using a moving average in a time domain. Then, a trend term is extracted by applying a selection mode of an adaptive spectrum rarefaction mechanism in a frequency domain, and meanwhile, noise is isolated into a residual term; for a multi-period data set, trend terms are decomposed in a recursive manner, and period modes are separated from short to long time scales. And for each decomposed feature item, learning and predicting by using a prediction module based on a linear model or a multi-layer perceptron, and finally fusing prediction components of each feature and performing inverse normalization operation to obtain a prediction result. According to the method, novel characteristic decomposition processing is carried out on the input data, more details are provided for future prediction, and a better prediction effect is achieved.

The invention relates to the technical field of equipment calibration, in particular to a multi-channel pressure sensor calibration method and system, and the method comprises the steps: constructing a flexible pressure response platform, carrying out the channel calibration according to the flexible pressure response platform, and carrying out the preliminary deviation modeling; generating a nonlinear response correction factor according to a result output by the preliminary deviation modeling, and performing local regression adjustment on the channel according to the nonlinear response correction factor; in response to completion of local regression adjustment of each channel, performing unified response surface fusion processing; and in response to completion of unified response surface fusion processing, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function. According to the method, the limitation that a traditional method depends on a linear model and static single-point calibration is overcome, and the problem of error accumulation caused by inter-channel interaction interference and delay response is solved.

The invention provides a self-adaptive deep learning prediction control method and device suitable for the process industry. Comprising the following steps: constructing an all-condition dynamic model of a target control variable based on a deep learning technology according to historical control information of the process industry, and performing segmentation processing on a change gain curve of the all-condition dynamic model based on a self-adaptive segmentation algorithm of a threshold value to obtain a segmented linear gain model; a piecewise linear variable constraint of the piecewise linear gain model is determined, and an optimization target of a target control variable is determined based on the piecewise linear variable constraint; and obtaining a step response sequence of the target control variable at the current working condition point based on the piecewise linear gain model, and predicting a future control execution sequence of the target control variable according to the step response sequence and the optimization target. The problem that an existing predictive control method generally constructs a mapping relation between a control variable and a controlled variable based on a linear model, the nonlinear characteristic of the control variable cannot be accurately reflected, and then the predictive control accuracy is low is solved.

The invention discloses a rapid progression type nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and relates to the field of medical informatics crossing. The invention provides a rapid progressive nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and aims to solve the problem that a rapid progressive nasopharyngeal carcinoma patient is difficult to recognize in time by depending on TNM staging and experience judgment in the prior art. According to the method, historical case data collection, missing value filling and standardization preprocessing, core feature determination through feature screening, class imbalance correction, feature coding and feature matrix construction are sequentially carried out, an artificial neural network model is trained and optimized under a cross validation framework, and performance and threshold values are determined on a validation set. During clinical application, patient features are input, and the model outputs a rapid progress risk probability and a risk level. Compared with a conventional staging or linear model, the method can improve the prediction accuracy, and achieves the early recognition and individualized treatment of a high-risk patient.

A video coding mechanism is disclosed. The mechanism includes receiving a video signal partitioned into a chroma block and a first neighboring luma block. The mechanism also includes encoding prediction information for chroma samples of the chroma block into a bitstream. The prediction information for the chroma samples is determined via intra-prediction based on down-sampled neighboring luma samples. The down-sampled neighboring luma samples are generated from a single row of luma samples from the first neighboring luma block. A bitstream including the prediction information for the chroma samples is transmitted to support decoding and display of the chroma samples.

The invention belongs to the technical field of primary frequency modulation control of doubly-fed variable-speed pumped storage units, and particularly discloses a method for evaluating frequency response performance of a doubly-fed variable-speed pumped storage unit. Establishing a linear model of the doubly-fed variable-speed pumped storage unit participating in primary frequency regulation, and further constructing a system frequency response full-order model containing the doubly-fed variable-speed pumped storage unit; performing order reduction on the system frequency response full-order model containing the doubly-fed variable-speed pumped storage unit to obtain a system equivalent second-order frequency response equation containing the doubly-fed variable-speed pumped storage unit, and performing analysis to obtain a time domain function containing the doubly-fed variable-speed pumped storage unit system frequency response and a typical frequency modulation dynamic performance index; and further analyzing key parameters and influence rules which influence the frequency response performance of the doubly-fed variable-speed pumped storage system. The method solves the problem that the accuracy of the frequency modulation model and the analytic and quantitative performance of the frequency modulation mechanism cannot be considered in the prior art.

The invention provides a navigation satellite short-term clock error forecasting method considering forecasting residual error periods, and relates to the technical field of satellite navigation. The method comprises the following steps: acquiring satellite real-time clock error data, and determining a modeling time window length and a forecast time length; performing epoch-by-epoch real-time sliding forecast on the satellite clock error by using a linear model, and subtracting the forecast clock error from the real-time clock error to obtain an error sequence; performing spectral analysis on the error sequence, and identifying main periodic terms; setting a scaling parameter related to the forecast time length, and establishing a periodic model in combination with the main periodic term fitting error sequence; in the subsequent sliding forecasting, the linear model is used for forecasting the clock error main trend, meanwhile, the periodic model is used for forecasting the residual periodic error, and the two results are added to obtain a final forecasting value. According to the method, by capturing the periodic error in the clock error, the short-term clock error forecasting precision is remarkably improved, the problems of real-time clock error service delay and data loss can be solved, and support is provided for real-time precise single-point positioning.

A batch process two-dimensional model-free signal compensation control method with network packet loss and unmodeled dynamics belongs to the technical field of industrial process control, and comprises the following specific steps: step 1, describing a state-space equation of a batch process in a packet loss environment, and expanding the state-space equation into a linear model and unmodeled dynamics; 2, constructing a packet loss model in a network environment, and introducing a two-dimensional Smith predictor with packet loss compensation; 3, expanding the optimal Q function into a quadratic form, and deriving the optimal Q function to obtain an optimal value; 4, designing a control algorithm with signal compensation; 5, integrating packet loss compensation into a control algorithm, and solving an optimal control law; according to the method, the problem of network packet loss existing in the production process is solved, a two-dimensional Smith predictor is introduced, data packet loss compensation is effectively performed, unmodeled dynamics are solved and compensated through a method based on reinforcement learning, the influence of the unmodeled dynamics on the control performance is reduced, and the control effect is greatly improved.

A video signal decoding device comprises a processor, wherein the processor predicts a sample of a chroma component corresponding to a sample of a luma component of a current block on the basis of the sample of the luma component, and predicts the current block on the basis of a predicted value of the sample of the chroma component. The predicted value of the sample of the chroma component is obtained using a linear equation, and the linear equation may include a term for a gradient value of the sample of the luma component.

The invention provides a three-coordinate measuring machine adaptive dynamic error compensation method, and relates to the field of three-coordinate measuring machines, and the method comprises the steps: obtaining multi-source state data of a three-coordinate measuring machine, and calculating a real-time dynamic error, the state data comprising a motion state, a dynamic response and environment disturbance data; based on the real-time dynamic error, using a recurrent neural network to construct a virtual measuring machine model, and performing offline training to obtain an initial error prediction model; and acquiring real-time error feedback data, and performing fine adjustment on the initial error prediction model by using an incremental learning algorithm and a sliding time window mechanism to obtain a prediction model capable of dynamically evolving and aging adaptive parameters. The method is used for overcoming the defect that in the prior art, a linear model or a fixed compensation parameter is difficult to accurately describe and compensate all dynamic errors sometimes.

The invention relates to the field of nonlinear system modeling, and particularly provides a global linear modeling method and system for a nonlinear system based on Lie derivative and sparse recognition, a terminal and a medium, and the method comprises the steps: obtaining an explicit nonlinear kinetic equation of a target system, calculating the Lie derivative of a state variable or an output variable of the system based on the kinetic equation, constructing a candidate observation function library directly associated with the physical mechanism of the system; performing screening and dimension reduction on the candidate observation function library by adopting a sparse recognition method to obtain a low-dimensional observation function set; and on the basis of the screened observation function and system operation data, a finite-dimensional global linear model of the target system in the dimension raising observation space is obtained through identification by using a data driving algorithm. According to the method, the online solving efficiency and the control real-time performance are improved, and efficient and reliable multi-target collaborative optimization control of systems such as a wind driven generator is realized.

The invention relates to the technical field of process control, in particular to an injection molding process optimization method and device, computer equipment and a storage medium, and the method comprises the following steps: obtaining stress signals of four tie bars of an injection molding machine, calculating a total mold locking force, and preprocessing the stress signals through temperature compensation and moving average filtering; real-time mold cavity pressure is calculated based on a mold filling stage linear model, a pressure maintaining stage quadratic function model and a cooling stage exponential attenuation model triggered by an injection molding machine control signal; generating a standard curve template with a confidence interval according to the characteristic parameters of the qualified sample, and dynamically updating the standard curve template; and comparing the current pressure curve with a standard template output quality judgment result in real time, and triggering automatic parameter adjustment to form closed-loop control. The device and the method have the effect of solving the problems of high cost, poor compatibility and difficulty in maintenance when the pressure of the die cavity is directly measured.

The invention relates to the technical field of power system modeling and simulation, in particular to a load model identification error analysis method, device and equipment and a storage medium, and the method comprises the steps: obtaining the prediction output and the measurement output of a preset load model, and building a target function based on the error between the prediction output and the measurement output; decomposing the measurement output to obtain an actual output and a noise component of a preset load model; and based on the objective function and the actual output, establishing a linear model between the identification error of the preset load model and the noise component through a first-order approximation method, and performing linear regression according to the linear model to obtain an analysis result of the identification error. Therefore, by constructing the theoretical model of the load model identification error, the problems that the identification error is difficult to predict, the data processing strategy selection lacks theoretical guidance and the like in related technologies are solved, and a theoretical basis is provided for selecting an optimal load modeling data processing strategy.

The invention relates to a time sequence prediction method based on an adaptive multi-scale Transform, and belongs to the field of time sequence prediction. The method comprises the following steps: carrying out preprocessing and seasonal trend decomposition on multivariable time series data; predicting a trend term based on a linear model; splitting a seasonal item into a univariate sequence, performing fast Fourier transform, and extracting main periodic components; carrying out multi-scale fragment division on each variable time sequence, and distributing a corresponding global Token; on the basis of a cross attention mechanism, correlation among variables is calculated; on the basis of a self-attention mechanism, calculating the correlation of the multi-scale sequence fragments of the variables; constructing a clustering distributor based on the global Token; and performing weighted fusion on the multi-scale output of each variable, and combining with a trend term prediction result to generate a complete prediction result.

The invention discloses a geophysical method for predicting coal rock thickness, particularly relates to the technical field of coalbed methane exploration, and has the core innovation that a wedge-shaped geologic model containing nine kinds of roof and floor lithology combinations is constructed, and seismic attributes sensitive to a thin coal seam are optimized through principal component analysis; converting the complex relationship between the seismic attributes and the coal thickness into a linear separable problem by using the specific high-dimensional nonlinear mapping capability of the radial basis function neural network; a three-level closed-loop mechanism of attribute optimization-network training-dynamic verification is established, and model self-optimization is realized through spider diagram analysis, over-fitting monitoring and new well triggering iteration. According to the method, the tuning distortion problem of a traditional linear model in thin coal seam prediction is solved, the reliability of well-free area prediction is remarkably improved, and a key technical support is provided for coal seam gas dessert identification and development decision making.

The invention provides a photoelectric volume pulse wave identity recognition method and system, and relates to the technical field of identity recognition, and the method comprises the steps: obtaining a to-be-recognized PPG signal; and inputting the PPG signals into a trained identification model, firstly extracting linear features and nonlinear features, then projecting the features fused by the two features into a feature space by using a learned discriminant projection matrix to obtain multi-view features, and finally classifying the PPG signals by using the multi-view features to obtain an identity identification result. According to the method, manifold regularization and inter-class-error double sparse constraints are combined, the problems of intra-class discretization and inter-class overlapping of a linear model are solved, a graph structure learning method for adaptive local density adjustment is provided, and the manifold modeling precision of non-stationary PPG signals is improved.