733 results about "Multi variable" patented technology

A method for multivariate time-series long-term forecasting based on multi-scale temporal feature enhancements, includes a time-series forcasting model TFEformer. The model utilizes a multi-branch structure and a patch-series attention mechanism to extract global and local time-series features at multiple temporal scales, and designs an adaptive feature fusion mechanism to achieve adaptive fusion of multi-scale temporal features. It employs an variate-wise attention mechanism and a redesigned gated feedforward network to perform feature fusion among multivariate variables and within the time-series, respectively. The time-series forcasting model TFEformer proposed by the present invention significantly improves the prediction of long-term trends in time-series and enhances the fitting ability for short-term local fluctuations, comprehensively increasing prediction accuracy across different prediction time lengths in multivariate time-series forcasting tasks.

The invention discloses an interpretability prediction analysis system for multivariable time series data. According to the method, the prediction precision and the decision support capability of the complex time series data are remarkably improved through multi-module cooperation. Firstly, an adaptive learning optimization module dynamically adjusts model parameters and a prediction strategy, so that the model can quickly adapt to time-varying characteristics of data distribution, for example, when a causal relationship between variables suddenly changes, the weight of latest data is automatically enhanced, and historical noise interference is reduced. The dynamic causal interpretation engine tracks the influence intensity and hysteresis effect of key variables in real time, converts traditional black box prediction into a traceable causal relationship chain, and helps a user to intuitively understand driving factors of a prediction result, for example, it is identified that prediction value sudden increase in a certain period is mainly derived from hysteresis effect accumulation of an upstream variable A.

The invention discloses a multivariable time series prediction method based on an NMF multi-scale lightweight space-time convolutional neural network, and belongs to the technical field of machine learning and deep learning application. The method specifically comprises the following steps: (1) collecting and processing multivariable time sequence data, and unifying the scale of the data; (2) decomposing the time sequence data by using NMF, extracting basis matrixes and coefficient matrixes of different scales, and constructing a multi-scale feature pyramid; (3) applying a space-time convolution layer on the basis of the output of the feature pyramid, introducing a Bayesian optimization algorithm, and adopting a feature fusion layer fused with a multi-scale attention mechanism; (4) dividing a training data set and a test data set, and carrying out model training; and (5) predicting future time sequence data by using the trained model and outputting an expected value. According to the method, a more powerful and more efficient multivariable time sequence prediction model is effectively constructed, and the method is suitable for analysis and prediction of multivariable time sequence data.

The invention relates to the technical field of control, and particularly discloses a multivariable energy efficiency optimization control system for a heating furnace, which is used for solving the problems of local overheating, non-uniform temperature and difficulty in accurate positioning and compensation of heat loss in the operation of the existing cracking heating furnace. Comprising a parameter detection module, a multivariable coupling modeling and simulation module, an optimization control module and an execution and feedback module. According to the method, dynamic digital twinning is constructed through multi-modal online sensing and data assimilation, a Pareto frontier solution is generated based on model prediction control and improved NSGA-II parallel optimization, and the weight is adaptively adjusted; and when the hot spot / cold spot is triggered, a quadric surface fitting compensation strategy is implemented and issued for execution, so that high-precision simulation prediction, precise closed-loop control and real-time online energy efficiency optimization are realized.

An SCR flue gas denitration intelligent control method based on multivariable collaborative optimization specifically comprises the following steps: S1, collecting multi-key variable data of SCR flue gas denitration, processing the multi-key variable data, and storing the processed multi-key variable data into a historical database; s2, on the basis of the processed data, constructing a multivariable correlation model, and predicting operation states of the denitration system under different working conditions; s3, according to the actual operation condition of the system and the equipment performance, determining constraint conditions and optimization targets of SCR flue gas denitration; s4, performing multivariable collaborative optimization calculation in combination with the multivariable correlation model, the optimization target and the constraint condition, and searching an optimal control variable combination; and S5, intelligent control is implemented according to the optimal variable combination, and re-optimization is performed by monitoring feedback deviation in real time. The system can keep stable control precision in different operation scenes, and denitration efficiency fluctuation or parameter adjustment lag caused by sudden change of working conditions is avoided.

The invention discloses a multivariable time sequence prediction method based on GRU and a computer program product. The method comprises the following steps: firstly, introducing the dynamic characteristics of self-adaptive different time sequences of self-defined time sequence decomposition, and splitting an original sequence into trend, seasonal and residual components so as to reduce the data complexity and improve the interpretability; afterwards, a value embedding module is used for unifying feature representation so as to ensure that the model fully captures the time dependency relationship; in the modeling stage, the model adopts a multi-channel recurrent neural network to independently model the three types of components so as to reduce the interference between modes and improve the learning ability. In the prediction stage, a feature splicing strategy is adopted, information of each component is integrated, and richer time sequence representation is provided. In addition, a segmented prediction strategy is designed for the model, the prediction process is divided into multiple time periods, prediction information is combined, error accumulation is reduced, and the stability and robustness of long-sequence prediction are improved. Experimental results show that the prediction precision can be improved on different data sets.

The invention discloses a mapping prediction method and system based on a hardware resource load and system operation relationship. The method comprises the following steps: collecting hardware index data in real time when an operating system operates; the method comprises the following steps: preprocessing hardware index data, performing association analysis on each index data by adopting an association rule mining algorithm, generating association rules, evaluating the association rules, screening out the association rules meeting conditions, and constructing a multivariable association model; based on a correlation analysis result, a load prediction model is constructed and trained, and the trained model is deployed in the system for load prediction; and dynamically adjusting a system hardware resource allocation strategy according to a prediction result, carrying out task migration and load balancing according to a predicted load condition, and automatically adjusting hardware resources through an automatic script or a scheduling tool. Through real-time monitoring and deep correlation analysis of indexes such as the CPU, the memory, the storage I / O and the network bandwidth, the resource utilization rate and the operation efficiency of the system are improved, and the stability and the reliability of the system are improved.

The invention relates to the technical field of multivariable analysis, and discloses a method and a device for improving the strength of flow-state solidified soil based on multivariable analysis, and the method comprises the following steps: collecting a material proportion parameter matrix, a stirring parameter matrix and an environment monitoring data matrix in flow-state solidified soil equipment, and executing feature extraction and feature integration; obtaining a target feature vector; performing intensity prediction of the time sequence on the target feature vector to obtain an intensity prediction value of the flow-state solidified soil; sensitivity analysis is carried out based on the intensity predicted value, and an intensity improvement potential index, a parameter adjustment stability index and an energy consumption influence index are obtained; according to the strength improvement potential index, the parameter adjustment stability index and the energy consumption influence index, adaptive parameter optimization is carried out through discount inverse reinforcement learning, and a target control parameter set is obtained. And the strength and the quality uniformity of the flow-state solidified soil are effectively improved while the operation stability is guaranteed.

The invention discloses a multivariable time series prediction method based on Patching and multi-scale feature extraction, and the method comprises the steps: dividing obtained multivariable time series data into different independent channels for a multivariable long-term time series prediction task, sharing the same converter backbone network, but enabling a forward process to be independent; the method comprises the following steps of: firstly, performing a Patching operation on a long sequence from a time domain angle, segmenting a single variable of each channel into a plurality of short fragments, and transmitting the short fragments into a Transform framework; in the aspect of a frequency domain, a multi-scale convolutional network is adopted to capture features of different frequency ranges, and meanwhile, a scale attention mechanism is introduced to adaptively weight and fuse features of different scales; and carrying out feature fusion on the features extracted from the frequency domain and the time domain so as to realize accurate modeling of periodic and long-term modes. According to the method, experimental analysis is carried out on eight disclosed data sets, and the result shows that compared with existing models, the model provided by the invention has higher accuracy on prediction of future values in a selected scene.

The invention relates to the technical field of ocean data interpolation filling, in particular to a space-time deficiency filling method and system based on context association and physical guidance. The method comprises the following steps: acquiring seawater dissolved oxygen data and context data; multivariable space-time dependence extraction is carried out based on the obtained seawater dissolved oxygen data and context data; gaussian noise diffusion is carried out based on the obtained seawater dissolved oxygen data; noise prediction is carried out based on double-view space-time correlation; and the prediction error is constrained based on the joint loss function. According to the method, a physical consistency constraint mechanism based on a partial differential equation is introduced in a model training process, so that model output better conforms to a physical coupling rule among variables in a marine environment. The constraint effectively inhibits non-physical fluctuation possibly occurring in the interpolation result, enhances the physical credibility and interpretability of the result, and provides a more reliable data basis for subsequent scientific analysis and process modeling.

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

The invention relates to a machine learning-based scouring damage bridge buckling load prediction method and system, and belongs to the technical field of machine learning and structural engineering. The prediction method comprises the steps of obtaining original data, preprocessing the original data, constructing a LightGBM machine learning model, and evaluating and verifying the constructed model by adopting multiple performance evaluation indexes. According to the method, key variables such as bridge structure parameters and foundation soil physical property indexes are comprehensively considered, a large sample database covering a large number of scouring combinations is constructed, and the critical buckling load of the structure is accurately calculated as an output target by using a pier-bearing platform-pile structure system buckling analysis method after scouring damage based on an energy method. A multivariable nonlinear mapping relation is established in combination with an ensemble learning algorithm, traditional complex calculation is replaced, and the method has good engineering adaptability and universality and can be suitable for rapid prediction of the buckling bearing capacity under different parameter bridges, different foundation forms and complex scouring conditions.

The invention belongs to the technical field of power system load prediction, and relates to a long-range multivariable load prediction method and system based on time-frequency domain collaboration, and the system carries out the normalization and stabilization of a multivariate load time sequence through a data preprocessing module; the feature embedding module performs linear embedding on the block sequence to construct high-dimensional feature representation; the state space coding module extracts long-range dependency features and generates depth time sequence representation; the decoding prediction module maps the coding features into a preliminary prediction sequence; the time sequence alignment module identifies a leading-lagging relation among multiple variables and aligns a time sequence; the frequency domain optimization module realizes frequency domain component fusion based on adaptive filtering; and the model training optimization module is used for performing training and optimization through a signal attenuation loss function. The method can effectively improve the precision and robustness of long-range multivariable load prediction, and especially has obvious advantages in the aspects of processing complex dependency relationships and dynamic time delay.

The invention discloses a multivariable underground water level time sequence prediction method and device, equipment and a medium, and relates to the technical field of artificial intelligence. Performing feature variable contribution proportion calculation, feature screening and normalization processing on the processed data based on a random forest regression model by using an average impurity reduction method to obtain target data; constructing a prediction model based on a convolutional neural network and a long-short term memory network, and performing structure setting, cross validation, parameter setting and model training on the prediction model to obtain a target prediction model; the method comprises the following steps: inputting target data into a target prediction model to obtain a predicted value, generating an evaluation index based on the predicted value and an actual measured value, optimizing the target prediction model by using the evaluation index, improving the applicability, generalization ability and training speed of the model, optimizing the convergence of the model, and improving the accuracy and stability of multivariable groundwater level time sequence prediction. And the sustainable utilization efficiency of underground water resources is improved.

The invention relates to the technical field of mine filling control, in particular to a multivariable layered adaptive filling regulation and control method based on reinforcement learning, and the method comprises the steps: constructing a state vector; inputting the state vector into a target reinforcement learning strategy network model to obtain a target action parameter and state value estimation; judging whether an expected standard is met or not based on the state value estimation, and if yes, adjusting a filling control parameter of the filling system based on the target action parameter; in response to completion of adjustment of the filling control parameters of the filling system, state characteristic functions corresponding to the filling system in the current control period are constructed, and a multi-dimensional reward function is constructed based on the state characteristic functions; and judging whether to trigger strategy update based on the control cycle number and the current accumulated reward value, and if so, updating the target reinforcement learning strategy network based on the multi-dimensional reward function. According to the invention, adaptive coordination optimization of a multivariable target can be realized, and the precision and stability of filling control are effectively improved.

The invention provides an automatic data management method and system based on a multi-modal large model, and the method comprises the steps: collecting multi-source heterogeneous industrial data, and carrying out the standardization processing, and forming standardized multivariable time series data; constructing a process knowledge base, and performing semantic embedding coding on a process knowledge text and storing the process knowledge text; constructing and finely tuning a KTSF multi-modal large model, and fusing process knowledge semantics and multivariable time sequence data through a cross-modal attention mechanism to generate joint semantic representation; based on prediction of a KTSF multi-mode large model, outputting a residual error with actual data, and dynamically identifying abnormal data; performing attribution analysis; based on an attribution result, calling a KTSF multi-mode large model to generate a repair value, and performing intelligent correction on the abnormal data; the design quality evaluation and feedback learning module is used for calculating a data quality score and driving incremental updating of the model; and the design rule self-learning module is used for automatically extracting the governance rule through clustering analysis and updating the knowledge base.

The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a manufacturing quality prediction method and system based on a multi-mode sequential network and application, and the method comprises the steps: carrying out the preprocessing of the sequential data of a process manufacturing production line, obtaining a sample set, and carrying out the sequential division into a training set, a verification set and a test set; on the basis of the sample set, key features are enhanced through a frequency domain enhanced channel attention mechanism, a multi-period mode of a time sequence dependence and period sensing module is captured in combination with a multi-layer expansion convolutional network structure, and a multi-mode time sequence network model is constructed; and sequentially carrying out training set training, verification set parameter adjustment optimization and test set performance verification on the multi-modal sequential network model, and outputting a prediction result. According to the method, the deep dynamic association among the multivariable time series data can be mined, the accuracy and robustness of manufacturing quality prediction are improved, and an efficient and reliable technical scheme and an implementation path are provided for process industry quality control and intelligent optimization.

The invention relates to a multivariable multi-step air conditioner load prediction model based on time sequence convolution and a double attention mechanism, and belongs to the technical field of air conditioner load prediction. The model adopts a parallel encoding structure, in an encoder, a time sequence convolution module is responsible for modeling local dependence and long-term trend in a time dimension, and a double attention mechanism module is used for modeling a dynamic dependence structure among multiple variables and a coupling relation between the variables and a target load from a variable dimension. The two structures are respectively subjected to feature extraction from a time dimension and a variable dimension, and are complementary to each other. In a decoder, a decoding module with a memory ability and a teacher mandatory strategy is designed, and continuous prediction from a historical multivariable sequence to a future target load is realized.

The invention belongs to the technical field of information, and discloses an IT performance index early warning method based on time sequence analysis, and the method comprises the steps: S1, inputting historical data; s2, risk detection; and S3, outputting early warning. Through fusion of statistical learning and machine learning technologies, accurate identification and early warning of three scenes of slow degradation of IT performance indexes, mode switching and capacity bottleneck are realized. The method specifically aims to improve the accuracy of performance index trend identification through a variable-point enhanced segmentation regression algorithm, and help an I T manager to more accurately evaluate the influence and trend of performance changes. Designing a cycle-adaptive double-sample hypothesis testing mechanism, and detecting and identifying switching of different load modes, especially significant changes caused by special or emergency events; and constructing a multivariable autoregressive VAR model, establishing a relation model of performance indexes and capacity indexes, evaluating capacity bottleneck conditions, and performing early warning in time.

The invention relates to the technical field of phosphoric acid production control, solves the problem that in the prior art, accurate control over the whole process of phosphoric acid production cannot be achieved, and provides a multivariable model prediction control method and system for the whole process of phosphoric acid production. The method comprises the following steps: acquiring key process data of a plurality of process units in the whole phosphoric acid production process in real time; preprocessing the key process data to obtain real-time standardized data; establishing a soft measurement model according to a pre-collected historical standardized data set; inputting the real-time standardized data into the soft measurement model to obtain a soft measurement predicted value; constructing a multivariable dynamic model according to the historical standardized data set and the soft measurement predicted value; according to the multivariable dynamic model, in combination with a model prediction control algorithm, obtaining target set values of a plurality of preset operation variables; and according to the target set value, realizing control of the whole process of phosphoric acid production. According to the invention, accurate control of the whole process of phosphoric acid production can be realized.

The invention discloses a multivariable predictive control energy consumption adjustment method and system, and belongs to the technical field of automatic control, and the method comprises the steps: collecting a parameter adjustment log in real time through an edge node, carrying out the intervention behavior recognition, and verifying the validity, so as to detect a manual parameter adjustment event; once an artificial parameter adjustment event is detected, multivariable data before and after intervention are extracted, and parameters of the local prediction model are dynamically corrected; performing rehearsal intervention based on the manual intervention parameters, generating space-time coupling constraints, and constructing a hybrid neural network to generate an energy consumption prediction trajectory; dividing types according to operator behavior modes, fusing prediction data to generate a comprehensive state vector, adjusting a reward function, focusing a sensitive variable, and generating a control instruction; and acquiring an actual energy consumption value in real time, comparing the actual energy consumption value with an energy consumption prediction track, calculating an energy consumption deviation, performing secondary optimization, positioning an error root cause through multi-scale decomposition in combination with a semantic tag and a knowledge graph, and performing layered compensation.

The invention provides a multivariable decoupling control method and system for an air inlet system of a high-altitude simulation cabin, and the method comprises the steps: firstly building a precombustion chamber cavity thermodynamic differential equation, and building a state equation in combination with a regulating valve first-order inertia model and an engine model; constructing a decoupling matrix by using a differential flatness theory, and converting a pressure and temperature coupling system into an independent control channel; monitoring a system state in real time by using an extended state observer, uniformly estimating internal and external disturbances as total disturbances, and generating a compensation signal; the model prediction controller performs rolling optimization on future time domain control quantity according to deviation between a set value and an actual value, generates an initial instruction in combination with the compensation signal, and converts the initial instruction into an independent valve control signal through a decoupling matrix; the high / low-temperature airflow mass flow is controlled through the opening degree of the adjusting valve, the engine model outputs actual parameters to form closed-loop feedback, and the controller updates the control quantity in each cycle. The multi-variable strong coupling, the dynamic disturbance and the model uncertainty are effectively overcome, the control precision and the parameter robustness are both achieved, and the complex working condition requirements are met.

The invention discloses a time sequence prediction method and system based on decomposition and a dual-path architecture, and belongs to the field of time sequence analysis and prediction.The method comprises the steps that multivariable time sequence data to be predicted are input, and a decomposition module is used for decomposing a multivariable time sequence into a trend component and a residual component; using a trend processing module to encode the trend component, and efficiently capturing the stationary characteristic of the trend component to obtain a trend characteristic; a residual error processing module is used for coding the residual error component, complex dynamic changes of the residual error component are accurately captured, and residual error features are obtained; fusing the trend feature and the residual feature by using a feature fusion module to obtain a fused feature; and generating a final time sequence prediction result by using a prediction generation module based on the fusion features. According to the method, differentiated and specialized modeling is carried out on the trend component and the residual component, so that the prediction precision and the calculation efficiency are effectively balanced, and the accuracy and the robustness of complex time sequence prediction are improved.

The invention belongs to the technical field of deep learning and time sequence analysis, and particularly relates to a multivariable time sequence prediction method and device based on spatial-temporal feature fusion. The method comprises the following steps: acquiring multivariable traffic time series data, and processing the traffic time series data; dividing the processed traffic time sequence data into overlappable patches, and generating a patch embedding sequence through linear mapping; applying bimodal time attention to the patch embedding sequence to obtain fused attention features; based on the learnable node embedding matrix, generating a time-varying adjacency matrix through dynamic graph construction, executing graph convolution to obtain a time domain graph propagation result, executing fast Fourier transform, multiplying by a learnable scaling factor and then performing inverse transformation to obtain an inverse transformation time frequency result; adding the time domain graph propagation result and the inverse transformation time frequency result to obtain a final spatio-temporal characteristic; and flattening the final spatio-temporal characteristics, and generating predicted values of the variables in a future prediction window through a linear prediction head.

The invention provides a multivariable time series prediction method and device, electronic equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: carrying out the processing of time series data through a period coding function and a seasonal factor coding function, and obtaining preprocessing feature data; performing periodic analysis on the preprocessed feature data through fast Fourier transform to obtain two-dimensional time sequence data; based on a convolutional neural network, obtaining local dependence features according to the two-dimensional time sequence data; on the basis of a self-attention mechanism, global dependency features are obtained according to the preprocessed feature data; obtaining a fused multi-view feature through the local dependency feature and the global dependency feature; and obtaining predicted time series data according to the fused multi-view features. According to the invention, the precision of time sequence prediction is improved.

The invention relates to a heart failure home patient-oriented multivariable trend anomaly detection method, which comprises the following steps of: constructing an initial contour of a multivariable health trend for a patient, and introducing a trend inertia vector: updating trend inertia to form an individual trend trajectory relationship; detecting an inertia breaking point in the trend trajectory diagram; analyzing whether the variables within 12-36 hours before and after the focusing breaking point analysis generate collaborative disturbance or not; performing reprocessing through a disturbance amplification operator to form a potential early warning factor; constructing a multivariable intervention graph by using the disturbance cooperation matrix; monitoring the offset direction and strength of the causal propagation chain on each path; if the plurality of paths are subjected to direction deviation in the same period at the same time, judging that the deviation is pathological trend deviation; converting the causal offset path into a single trend risk factor, spreading a plurality of weak signals in a variable graph, evaluating systematic influence, forming a fuzzy risk scoring curved surface relationship, and outputting an individual trend risk level; the sensitivity and continuity of trend identification are improved, individual differences are adapted, and the false alarm rate is reduced.

The invention relates to the technical field of machine learning, and discloses a load prediction method based on dual feature processing and error correction. The method comprises the following steps: acquiring multi-source time sequence data; decomposing the historical load data into a plurality of modal components by adopting a variational modal decomposition algorithm; classifying each modal component into different frequency levels according to the size of the sample entropy; performing phase-space reconstruction according to the modal component of each frequency level and the corresponding external influence factor data, and generating a multivariable phase-space data set of each frequency level; respectively inputting the multivariable phase space data set of each frequency level into the corresponding load prediction sub-model, generating prediction output results, and superposing the prediction output results; constructing a residual sequence based on the historical load data and the initial load prediction result; inputting the residual error sequence into a residual error prediction model to obtain a load residual error prediction value; and compensating the initial load prediction result through the load residual prediction value. According to the scheme, the load prediction accuracy can be improved.

The invention relates to the technical field of integrated learning deformation prediction, in particular to a double-weight stacking deformation prediction integrated model modeling method based on complex sample guidance, and the method comprises the steps: building five heterogeneous base models: CLAnet, RBF, MLP, CNN and XGBoost; establishing a support vector regression machine as a meta-model; constructing a secondary integration model on the basis of a stacking framework; a complex sample-oriented five-fold cross validation strategy is adopted to optimize training set distribution, and learning of complex samples is dynamically enhanced; by calculating an error index of each base learner, an initial weight is manually allocated to the base model before the meta-model automatically and implicitly allocates the weight, and metadata set distribution is optimized; and a whale optimization algorithm is introduced to adjust hyper-parameters of each model. According to the integrated prediction model, the problems that a single model is insufficient in generalization ability to complex samples and limited in modeling ability of a multivariable coupling relation can be solved by integrating different learning modes of each base model to features and by means of the quadratic fitting ability of the meta-model.

The invention provides an industrial furnace temperature adaptive optimization control method and system based on a multivariable chaotic time sequence, and belongs to the technical field of industrial process control, and the method comprises the steps: collecting operation parameters to form a multivariable time sequence, and carrying out the processing verification of chaotic characteristics, and obtaining an analyzable sequence; extracting features through phase-space reconstruction and quantifying variable coupling strength to obtain multivariable chaotic features; a prediction model and an early warning model are constructed based on the above, and a temperature change trend and early warning information are obtained; and finally, setting and optimizing control parameters, generating strategy execution and combining feedback to form closed-loop control. According to the method, the multivariable chaotic characteristics of the industrial furnace are analyzed, the prediction and early warning model is constructed, and self-adaptive optimization control is carried out, so that accurate regulation and control of the temperature are realized, the product quality stability is improved, and the energy consumption and the production cost are reduced.

The invention relates to the technical field of industrial process automatic control, in particular to an industrial steam flow intelligent adjusting system. According to the method, the continuous change trend of the steam state is subjected to time sequence identification, dynamic classification of the steam state can be realized under multivariate combination, so that a steam state switching path is insighted in advance, an information coupling measurement mechanism between enthalpy change and flow is introduced on the basis, and calculation of mutual information between enthalpy and flow is combined, so that the dynamic classification of the steam state is realized. Abnormal sections with adjustment deviation in actual operation are accurately recognized, the adjustment possibility is further judged according to the deviation quantification result of the target flow and the actual output flow, controllable and uncontrollable sections are effectively divided, and the rollback value domain limitation of the uncontrollable sections is set. Therefore, in the subsequent target value adjusting process, target setting of the remaining sections can be reset in a targeted mode, the priority sequence is constructed according to the adjusting time sequence and the adjusting amplitude, and dynamic sorting output of the adjusting strategy is achieved.