47 results about "Multivariable model" patented technology

The invention discloses an industrial AI assistant cross-modal interaction method based on a dynamic knowledge graph, and the method comprises the following steps: collecting texts, voices, images and multi-source sensor data in an industrial system, carrying out the modal recognition, feature extraction and time alignment, and generating an event feature set and a state feature set with timestamps. Modeling a time dependency relationship between event types by constructing a multivariable Hawkes model, and outputting an event trigger sequence and trigger strength; and in combination with a neural controlled differential equation model, guiding the state to evolve along with time and jump at a specific moment to form a state evolution trajectory. Performing fusion coding on the event and the state, constructing a dynamic knowledge graph with a causal structure and semantic continuity, and generating interactive output based on context reasoning; and after system feedback is received, the triggering strength and the state track are updated, and continuous evolution of the knowledge graph and reverse optimization of model parameters are achieved.

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 provides a waste incineration digital intelligent system and a construction method. A waste warehouse intelligent management module transmits processed and sorted waste digital information to a waste incineration intelligent control module and a smoke intelligent prediction control module; the garbage incineration intelligent control module feeds back garbage combustion state information to the garbage warehouse intelligent management module and the smoke intelligent prediction control module, and the smoke intelligent prediction control module feeds back data information needing to be changed in garbage combustion to the garbage incineration intelligent management module. And the flue gas intelligent prediction control module feeds back flue gas indexes to the garbage warehouse intelligent management module. A multivariable model prediction control technology fusing AI prediction has the advantages that an AI artificial intelligence control technology machine learning technology is introduced, an expert operation mode and a voting operation method are extracted in a prediction model through historical big data learning and production process learning, and a control scheme is innovative and also has practical applicability.

The invention belongs to the technical field of temperature control, and discloses a temperature control method and device of a hot runner system for an inverted mold. The method comprises the following steps: defining a critical melt area in a physical sprue which directly influences the product quality in a sprue of a hot runner system as a virtual sprue; dynamically generating a melt viscosity target track of the virtual gate region according to the type of an injection molding material and an injection molding process stage; based on measurable injection molding process parameters and sprue area temperature, estimating a current melt viscosity value of a virtual sprue area in real time through a state observer; calculating to obtain an optimal power control sequence by adopting a multivariable model prediction controller; and driving a sprue area heater according to the optimal power control sequence, so that the actual change of the melt viscosity of the virtual sprue area tracks a melt viscosity target track. According to the invention, the defects such as salivation and wire drawing can be effectively eliminated, so that the sprue quality stability and the production efficiency are improved.

The invention relates to a chemical pulping, cooking and washing control method based on an ACE technology and a storage medium, the method comprises the following steps: collecting process parameter data of a chemical pulping, cooking and washing process, the process parameter data comprising cooking temperature, cooking pressure, pH value, cooking time, liquid ratio, additive concentration, steam flow and liquid level; preprocessing the collected process parameter data, wherein the preprocessing comprises data screening and data filtering; identifying time delay among the process parameter data in the chemical pulping, cooking and washing process; constructing a high-precision multivariable model, and capturing strong nonlinearity and coupling among process parameter data in the chemical pulping, cooking and washing process; and generating an optimal adjustment strategy of the process parameter data of the required target through the multivariable prediction control model. And the influence of single variable adjustment on other variables is avoided, so that the large loss of the production process in the aspects of yield, quality and energy consumption is reduced.

The embodiment of the invention discloses a dynamic collaborative optimal selection control method for denitration of a garbage incinerator, and relates to the technical field of garbage incineration treatment. The method comprises the steps that operation parameters, collected in real time, of the garbage incinerator are obtained and preprocessed; based on the mechanism of waste incineration and denitration reaction and in combination with historical operation data, a dynamic model of the denitration process of the waste incinerator is established, and the denitration process is described by adopting an autoregressive moving average model; according to the preprocessed operation parameters and the established dynamic model, the working conditions of the garbage incinerator are recognized and classified in real time; multivariable model predictive control is adopted to make a cooperative control strategy; and outputting the formulated cooperative control strategy to execution mechanisms of the denitration system and the incineration system. According to the method, the denitration efficiency can be improved, nitrogen oxide emission can be accurately controlled, and the ammonia escape rate and the denitration cost can be reduced.

The invention discloses a membrane filtration sewage treatment system based on fine control, relates to the technical field of sewage treatment, and solves the technical problems of delayed response to water quality and water quantity fluctuation and extensive biochemical reaction control caused by water inlet load impact in an existing sewage treatment system. The system comprises an inflow water pretreatment module used for predicting inflow water quality in real time by using a water quality prediction model based on a neural network, and carrying out water quantity balance and water quality adjustment on inflow water according to a prediction result to obtain a first water body; the biochemical reaction module is used for cooperatively controlling external carbon source addition, nitrification liquid backflow and aeration intensity in the biological reaction process of the first water body by utilizing a multivariable model predictive control method to obtain a second water body after biochemical reaction; and the deep purification module is used for carrying out solid-liquid separation and deep purification on the second water body to obtain an effluent water body, and triggering cleaning and maintenance operation of the membrane based on a prediction result of the membrane pollution characteristics.

The invention relates to methods for determining the concentration of an analyte in a sample obtained from processing of blood-derived plasma, the method comprising applying a light source in the near-infrared spectrum to a test sample obtained from processing of blood-derived plasma; measuring reflectance, transmission or transflectance of the test sample over a range of near-infrared wavelengths, thereby generating test wavelength spectra, comparing the test wavelength spectra with reference wavelength spectra obtained from reference samples having known concentrations of the analyte, to determine the concentration of the analyte in the sample. The invention also relates to the development of a multivariate model for use in determining the concentration of an analyte, for example, total protein or alcohol (e.g. ethanol).

The invention discloses a multivariable model predictive control method for a photovoltaic inverter grid-connected system, which comprises the following steps of: establishing a mathematical model of an inverter under a rotating coordinate system, and setting a discrete mathematical model on the basis of traditional model predictive control to obtain a predictive value of output current; and taking the predicted value, the current error at the sampling moment and the error of the direct current bus voltage as control factors of the value function, and seeking the minimum solution of the value function to achieve the optimal control effect. According to the multivariable model predictive control method for the photovoltaic inverter grid-connected system, through the steps, the photovoltaic grid-connected response efficiency can be improved, the total harmonic distortion rate can be reduced, the stability and the anti-interference performance of the direct-current bus voltage can be enhanced, the grid-side current can be controlled at a lower total harmonic distortion level, and the effect is obvious.

The invention discloses an ammonia spraying real-time feedback regulation method based on an SCR (Selective Catalytic Reduction) denitration system, which comprises the following steps: dividing a cross section partition on an outlet flue of the SCR denitration system, and synchronously collecting a flue gas sample on the cross section partition; dynamically adjusting the flow of a cooling medium of a gas cold air coil pipe through a self-adaptive fuzzy PID control algorithm, and cooling and dehumidifying each path of collected flue gas sample to obtain a dry flue gas sample; the concentration of each path of dry flue gas sample is analyzed in real time, and the optimal ammonia spraying valve opening strategy of each cross section partition is predicted by combining the ammonia spraying amount of each path, the flue gas flow of the SCR denitration system, the unit load and the ammonia escape concentration and adopting a multivariable model prediction control algorithm; and according to the predicted optimal ammonia injection valve opening strategy of each cross section partition, the ammonia injection valves of the corresponding cross section partitions are adjusted, and real-time feedback adjustment of ammonia injection is achieved.

The embodiment of the present invention discloses a method for dynamic collaborative optimization control of denitrification of a waste incinerator, which relates to the technical field of waste incineration treatment. The method comprises: obtaining the real-time collected operating parameters of the waste incinerator and performing preprocessing; based on the mechanism of waste incineration and denitrification reaction, combined with historical operating data, establishing a dynamic model of the denitrification process of the waste incinerator, wherein an autoregressive sliding average model is used to describe the denitrification process; according to the preprocessed operating parameters and the established dynamic model, the working conditions of the waste incinerator are identified and classified in real time; a multivariable model predictive control is used to formulate a collaborative control strategy; and the formulated collaborative control strategy is output to the actuators of the denitrification system and the incineration system. The method of the present invention can improve the denitrification efficiency, accurately control nitrogen oxide emissions, and reduce the ammonia escape rate and denitrification costs.

The invention discloses a graded blasting construction method for a shallow-buried soft rock tunnel sensitive section. The method comprises the steps that measuring points are arranged in a tunnel chamber, a ground surface non-cavity section and a cavity section, and blasting vibration data are obtained; peak vibration velocity prediction models are established in a partitioned mode, an attenuation model is adopted in a chamber, a correction term is introduced into a non-cavity section of the earth surface to correct the attenuation model, and a multivariable model is established for the cavity section through dimensional analysis by integrating the horizontal distance, the elevation difference, the explosive quantity, the rock mass density and the tensile strength; determining a ground surface vibration velocity safety threshold, and quantifying a cavity vibration velocity amplification coefficient and an influence range thereof; based on the peak vibration velocity probability distribution and a preset safety level, establishing a safe dose prediction model and a safe range model; and dividing control areas according to the model, and implementing graded construction. According to the method, the vibration propagation law can be accurately described, the cavity amplification effect can be quantified, dynamic prediction of the blasting safety explosive quantity and range is achieved, and disturbance of blasting vibration to surrounding rock is effectively controlled through hierarchical management and control.

This invention relates to apparatus and methods for selecting a soil microbiome having increasing plant productivity. The apparatus and methods relate to combining an output from a machine learning model, a generalized linear model, and a distance-based multivariate model to execute a donor forest selection tool that is configured to select from a plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity.

The invention relates to the technical field of data processing, and discloses a material warehouse-in and warehouse-out processing method and system based on big data. The method comprises the steps that material information is collected through RFID and converted into assembly main data in a standardized mode; analyzing the main data to generate material storage mapping; forming a distribution instruction according to the mapping sorting distribution demand; recording component circulation and environment parameters to establish a quality data link; constructing a multivariable model to identify key indexes to form a threshold list; and calibrating inventory parameters by combining historical data to construct a management matrix. The technical problem that intelligent decision support is lacked in material warehouse-in and warehouse-out management in an industrial tablet personal computer production line is solved, and the material quality traceability and the inventory management accuracy are improved.

The invention provides a post-evaluation method, device and equipment for industrial heterogeneous agents and a storage medium, unification of a heterogeneous agent evaluation framework is realized through abstract feature vectors and scene configuration, and the problem of evaluation islands is solved; the method comprises the following steps: acquiring periodic original data, processing the periodic original data according to a mapping rule to obtain a periodic feature vector, inputting the periodic feature vector into a pre-trained multivariable analysis model with inherent interpretability, and receiving a synchronously output comprehensive efficiency score, so that the purpose of outputting the comprehensive efficiency score through a data-driven model is realized; subjective experience judgment is completely replaced, so that evaluation results are consistent, fair and traceable; the contribution degree of each operation factor is analyzed through a multivariable model, the spanning from surface statistics to deep root cause analysis is realized, and the problems of good or bad effects and what good or bad can be accurately answered.

This invention discloses a multi-parameter coupling-based deep peak-shaving full-condition optimization control method for coal-fired power units. Addressing the problems of existing technologies, this invention constructs a multi-parameter coupling index system, calculates the comprehensive coupling strength between each parameter and the load, and selects core parameters. Dynamic clustering is used to finely divide the full-condition operation from 20% to 100% rated load into multiple operating zones. An LSTM-GRU dual-channel deep learning prediction model is established for each operating zone. A three-layer hierarchical control architecture is designed, comprising load optimization scheduling, boiler-turbine coordination control, and combustion optimization execution. The boiler-turbine coordination layer uses multivariate model predictive control and online self-tuning of multi-objective weights through fuzzy inference. The combustion execution layer uses the NSGA-III multi-objective evolutionary algorithm to optimize air distribution. A constraint-adaptive tuning mechanism based on safety margin is established to form a closed-loop iterative optimization across all operating conditions. This invention can significantly improve the load response speed, main parameter stability, and combustion economy of the unit during wide-load operation.

This invention discloses an online assessment system, method, equipment, and medium for visual pollution and transparency of emission plumes based on image recognition. The system includes: an image acquisition module for continuously capturing images of the plume at the chimney outlet; an image preprocessing module for receiving pollutant concentration data from a continuous emission monitoring system; an intelligent assessment module for extracting visual features of the plume from the preprocessed images and outputting plume opacity level and morphodynamic characteristics; and a correlation analysis module for receiving the plume opacity level, morphodynamic characteristics, and pollutant concentration data, performing correlation analysis by calculating correlation coefficients, and determining whether emissions are abnormal based on a multivariate model. This invention achieves real-time online monitoring of emission plumes by continuously capturing images of the plume at the chimney outlet through the image acquisition module, overcoming the limitations of manual visual inspection in terms of time and space.

The invention discloses a model-free self-adaptive thrust keeping combined control method for a self-adaptive cycle engine, which comprises the following steps of: establishing a full-format data strategy on line by utilizing a dynamic linearization method, and providing an improved multivariable model-free self-adaptive combined controller in combination with a proportional-integral controller and an iterative learning control algorithm; constructing a thrust estimator by adopting a stacked self-encoding neural network model, designing an engine instruction management module based on a linear programming algorithm, and designing a limit protection strategy based on instruction adjustment while correcting an instruction according to an engine state; and finally, designing a double-loop multivariable model-free adaptive thrust maintaining combined control system based on a model-free adaptive combined controller and a linear programming instruction management module. According to the method, the problem that aero-engine control excessively depends on an accurate mathematical model can be solved, and when a self-adaptive cycle engine gas circuit component declines, a control system ensures that the key variable of the engine is maintained within a reasonable range while keeping thrust.

The invention provides a dissolver negative pressure control method, system and equipment and a medium, and relates to the field of dissolver negative pressure control, the method according to the application comprises the following steps: based on a preset temperature acquisition mechanism, acquiring dissolver temperature data in real time, and adopting a preset pressure prediction mechanism to predict pressure change in a dissolver according to the temperature data; according to the predicted pressure change, a preset adjuster algorithm is adopted to adjust the pressure of an inlet of a compressed air injection pump of the dissolver so as to adjust the negative pressure of the dissolver, the adjuster algorithm maps negative pressure deviation and an operator manual intervention value into an adjusting valve opening degree instruction based on a fuzzy control rule base, and the adjusting valve opening degree instruction is used for adjusting the negative pressure of the dissolver. And negative pressure closed-loop regulation is realized through multivariable model predictive control. According to the technical scheme provided by the invention, the negative pressure of the dissolver can be automatically adjusted, and the timeliness and safety of operation are improved.

A receiver test and measurement system includes a signal generator to provide a stressed signal. The stressed signal needs to be calibrated as per specification for several parameters. Some parameters including a set of two or more equalization parameters can be calibrated by a test and measurement instrument that captures the stressed signal and executes a calibration operation. A multi-variable model is created establishing a relationship between parameters that are set in the signal generator and the measured values of these parameters in a test and measurement equipment like an oscilloscope. The multi-variable model calculates coefficients that allows us to calculate setting values that need to be set in the signal generator for any desired combination of two or more equalization parameters. This allows receiver tests to be done for any combination of desired equalization parameters in a calibrated manner.

A receiver test and measurement system includes a signal generator for providing a pressure signal. The pressure signal needs to be calibrated in accordance with a specification for several parameters. Some parameters, including a set of two or more equalization parameters, may be calibrated by a test and measurement instrument that captures pressure signals and performs calibration operations. A multivariable model is created to establish a relationship between parameters set in a signal generator and measured values of these parameters in a test and measurement equipment such as an oscilloscope. The multivariable model computes coefficients that allow we to compute setting values that need to be set in the signal generator for any desired combination of two or more equalization parameters. This allows receiver testing to be completed in a calibrated manner for any combination of desired equalization parameters.

To provide techniques for predicting the risk of a subject experiencing a cytokine release syndrome of at least a threshold grade subsequent to receiving a treatment.SOLUTION: A risk may be predicted based on, for example, a set of baseline characteristics, a risk-score generation model, an on-treatment cytokine level, and / or a treatment dosage. The risk may be used to generate an output corresponding to a recommendation as to whether to monitor the subject via in-patient monitoring.SELECTED DRAWING: Figure 1

The application discloses an elevator on-demand maintenance time sequence prediction method and system based on LSTM, which comprises the following steps: collecting various feature data of the elevator and performing pretreatment; selecting key features of the elevator maintenance from the pretreated data, assigning weight values according to the importance of the features, obtaining real values of the features, and constructing a data set; constructing and training a multivariate LSTM model to capture the mutual relationship and influence between the key features; the input of the model is the time sequence data of each feature, and the output is the prediction of the maintenance state of the next time step; real-time collection of the elevator feature data, input of the pretreated data into the trained multivariate LSTM model, output of the time sequence prediction result, and formulation of the elevator maintenance plan according to the prediction result. The application realizes accurate prediction of the elevator maintenance time sequence by collecting and analyzing multiple elevator features and combining the time sequence prediction capability of the multivariate LSTM model.

The invention discloses a spodumene clinker processing collaborative optimization control method and a spodumene clinker processing collaborative optimization control system based on multipoint real-time quality feedback, which relate to the technical field of intelligent control, and execute closed-loop optimization control based on a process control system in combination with key quality data and temperature distribution data, operating parameters of a vertical mill system are dynamically adjusted, and the discharging granularity is stabilized; performing double-closed-loop optimization control on the acidification device, and cooperatively performing sulfuric acid dosage pre-control and acidification rate verification adjustment on the basis of free acid content FA, residual acid content RA data and temperature distribution data; the key quality data comprises vertical mill discharge granularity, free acid content FA and residual acid content RA; and when the double-closed-loop optimization control of the acidification device cannot reach the target, a cooperation request is sent to vertical mill multivariable model prediction control, and the target control range of the discharging granularity is adjusted. The problems of unstable quality, poor continuity, high consumption and low automation level in spodumene clinker processing are solved.

The invention discloses a system and a process method for producing small-particle-size AP products with large circulation volume, the system comprises a charging subsystem, a circulation subsystem, a belt fine grain control subsystem, a cooling subsystem and a crystallization subsystem which are connected through a pipeline in a closed loop mode, the belt fine grain control system is integrated in a traditional large circulation crystallization system, and by adjusting the split ratio of crystal mush returned to a crystallizer, the AP products with the small particle size can be produced. And the content of solid materials serving as seed crystals in the system is actively controlled (namely, the solid materials are provided with fine grains). The industrial problems that the number of crystal nucleuses is insufficient due to low supersaturation degree in a large circulation process, and small-particle-size products cannot be produced easily are solved. The MPC multivariable model prediction controller is used for cooperatively regulating and controlling the circulation amount and the fine grain amount, and an ultrasonic anti-scaling and corrugated pipe external cooler can be selectively arranged, so that the granularity of a small-particle-size AP product is stable and controllable, meanwhile, the spheroidization degree and the surface smoothness of the crystal are remarkably improved, the free-running property, the stacking density and the combustion performance of charge of a user are effectively improved, and the service life of the product is prolonged. The method is suitable for continuous, stable and large-scale production of high-quality AP.

The invention discloses an intelligent control system for an acetic acid production device. The intelligent control system comprises an advanced control server, a switch, an OPC server, a DCS engineer station, a DCS operation station, a DCS control station and a plurality of PID control loops. The temperature transmitters, the pressure transmitters, the flow transmitters and the remote transmission liquid level meters transmit field real-time monitoring data to the DCS control station. The DCS operation station reads field real-time monitoring data and automatically adjusts the remote control valve; the OPC server reads field real-time monitoring data and transmits the field real-time monitoring data to the advanced control server through the switch, the advanced control server monitors key variables and operation data, PID parameter setting multivariable model prediction control, expert strategy control and data analysis optimization are carried out, an obtained control operation result is returned to the DCS control station along an original path, and the DCS control station is connected with the switch. And the remote control valve is automatically adjusted. According to the invention, stable and optimal control of product quality is realized, the stability of device operation is improved, manual operation is reduced, energy is saved, consumption is reduced, and the purpose of improving economic benefits is achieved.

A receiver test and measurement system includes a signal generator that provides a stressed signal. This stressed signal must be calibrated for several parameters according to the specification. Some parameters, including a set of two or more equalization parameters, can be calibrated using a test and measurement instrument that acquires the stressed signal and performs a calibration operation. A multivariable model is created that establishes a relationship between the parameters set in the signal generator and the measured values ​​of these parameters on a test and measurement instrument such as an oscilloscope. The multivariable model calculates coefficients that allow us to determine the settings that must be configured in the signal generator for any desired combination of two or more equalization parameters. In this way, receiver tests can be performed in a calibrated manner for any combination of equalization parameters.

The invention discloses a wind generating set model prediction control method based on a Hammerstein structure, and the method achieves the linear modeling of a system by introducing the Hammerstein structure to approximate the nonlinear static characteristics and linear dynamic behaviors of a wind generating set. On the basis, a multivariable model predictive control framework is established by adjusting the torque and the pitch angle of the generator at the same time. In order to solve the problem of multi-objective optimization between power tracking and fatigue load suppression, a quadratic cost function including a power error term, a control change rate term and a load suppression term is designed, so that smooth power output and structure service life prolonging of the wind turbine unit are realized.

The application relates to the technical field of data processing, and discloses a material warehouse-in and warehouse-out processing method and system based on big data. The method comprises the following steps: collecting material information through RFID and standardizing and converting the material information into component master data; analyzing the master data to generate material storage mapping; distributing demand according to the mapping to form a distribution instruction; recording component circulation and environmental parameters to establish a quality data chain; constructing a multivariate model to identify key indicators to form a threshold list; and combining historical data to calibrate inventory parameters to construct a management matrix. The application solves the technical problem that material warehouse-in and warehouse-out management in an industrial tablet computer production line lacks intelligent decision support, and improves material quality traceability and inventory management accuracy.