8results about How to "Realize online identification" patented technology

The application discloses a kind of for tantalum niobium tailings in tin metal recovery's synergistic treatment system, specifically related to metallurgical solid waste resource utilization technical field, acoustic-electricity perception pre-agglomeration module exports pre-agglomeration probability index according to pre-agglomeration threshold value;Heat resistance chromatography module exports the temperature field and fluidized void fraction distribution of suspected agglomeration area;Inhibiting ingredient self-adapting module calculates inhibitor addition rate according to distribution;Wind material decoupling adjustment module exports decoupled wind speed and feed rate set value according to addition rate and bed pressure drop;Chain negative feedback blocking module detects pressure drop deviation to generate feedback correction, superimposed to wind speed set value and reversely adjust feed rate set value, while used to update pre-agglomeration threshold value;The application realizes the online identification of agglomeration precursor, accurate danger area precision inhibiting and the closed-loop cooperation of wind material decoupling, inhibits the nonlinear deterioration of coking loss flow, makes tin recovery rate keep stable when tailings fluctuation, and reduces inhibitor consumption and manual intervention.

This application relates to the field of copper wire drawing technology, and particularly to a method for compensating for the implicit resistance of guide wheels in copper wire drawing machines, and the copper wire drawing machine itself. The method includes: acquiring a tension signal and a set of rotational speed signals; calculating the dynamic friction coefficient of each first-type guide wheel using Fourier transform based on the tension signal, the set of rotational speed signals, and the wrap angle of each first-type guide wheel, obtaining a set of dynamic friction coefficients; determining the smooth friction coefficient of each first-type guide wheel based on the set of dynamic friction coefficients, obtaining a set of smooth friction coefficients; determining that compensation conditions are met, and determining tension compensation based on the set of smooth friction coefficients; determining feedforward compensation based on the tension compensation, and generating a compensation command based on the feedforward compensation; and controlling the output torque of the traction device based on the compensation command. This method achieves online identification and quantification of the friction state of key guide wheels, reduces tension control lag, and improves the real-time performance of tension control.

The invention relates to the technical field of adsorption detection, and discloses an imidazolium resin adsorption kinetic parameter online identification method and system.The method comprises the steps that 1, flow, inlet and outlet water concentration, conductivity, temperature and pH value are synchronously collected, and apparent ion strength is obtained through the conductivity; step 2, performing mass balance based on the flow, the inlet and outlet water concentration and the resin dry basis mass to obtain a real-time track of the average adsorption capacity of the resin; step 3, constructing counter-ion coupling isothermal driving force and screening a quasi-balance section; 4, solving isothermal coupling parameters by combining a quasi-balance relational expression; 5, calculating an overall mass transfer coefficient point by point; step 6, simultaneously solving an outer membrane mass transfer proportionality coefficient and an in-hole mass transfer coefficient under two levels of flow; and step 7, constructing a core parameter group and outputting a consistency judgment result identifier. According to the method, online identification and consistency judgment of the adsorption thermodynamic parameters and the mass transfer kinetic parameters of the imidazolium resin are realized.

The application belongs to the technical field of power distribution network state estimation, and discloses a power distribution network operation mode identification method based on measurement big data analysis, which comprises the following steps: step 1, pre-processing measurement data; step 2, taking the measurement data of each sampling time as a sample, and using an improved t-distributed stochastic neighbor embedding algorithm to reduce the dimension of each sample; step 3, selecting a small number of samples from all samples, manually marking the corresponding power distribution network operation mode of the samples, and then automatically establishing the corresponding relationship between the remaining samples and the power distribution network operation mode through semi-supervised learning; and step 4, pre-processing and reducing the dimension of real-time measurement data, and identifying the real-time operation mode of the power distribution network according to the corresponding relationship obtained through semi-supervised learning. The online identification method provided by the application can quickly and accurately identify the operation mode of the power distribution network according to real-time measurement data, and provides guarantee for real-time observation of the operation mode of an active power distribution network containing a large number of distributed renewable energy sources.

The application discloses a lithium battery operation state evaluation system and method based on data analysis, relates to the technical field of lithium battery operation state evaluation, collects electric excitation response data of a battery under different measurement parameters, generates an impedance spectrum tensor, carries out normalization processing on the impedance spectrum tensor along a temperature dimension, outputs a temperature decoupling impedance tensor, decomposes the temperature decoupling impedance tensor along a rate dimension, outputs a rate separation impedance matrix, extracts static contact impedance from the rate separation impedance matrix, carries out trend deduction along a state of charge dimension, outputs a charge decoupling impedance sequence, decomposes the charge decoupling impedance sequence, outputs a contact impedance fluctuation sequence, carries out filtering processing on the rate separation impedance matrix according to the contact impedance fluctuation sequence, outputs a corrected internal resistance spectrum, calculates a real health state of the battery based on the corrected internal resistance spectrum and carries out determination, and outputs a battery health state signal.

The invention relates to a motor cross coupling effect identification and compensation method for a multi-degree-of-freedom forming robot. The method comprises the following steps: S1, establishing a kinematics model, a mechanical dynamics model, a permanent magnet synchronous motor dynamics model considering the cross coupling effect and an electromechanical coupling dynamics model of the multi-degree-of-freedom forming robot; s2, establishing a motor cross coupling angle identification model of the multi-degree-of-freedom forming robot in combination with an electromechanical coupling dynamic model; and S3, combining the electromechanical coupling dynamics model and the motor cross coupling angle identification model to establish a multi-degree-of-freedom forming robot dynamics feedback control model, wherein the multi-degree-of-freedom forming robot dynamics feedback control model comprises coupling field orientation control, load feedback control and dynamics feedback control. High-precision and high-efficiency forming of the thin-wall high-rib component is achieved, and the forming precision can be guaranteed on the premise that the forming speed is increased.

The application relates to a power grid application layer instruction level detection method fusing control service logic. For power grid application layer instruction level detection, a rule matching-based method is used to detect abnormal messages and attack messages; abnormal message rules are designed according to protocol specifications, and a mature snort network attack rule library is used as an attack message feature library; for the discrete characteristics of service feature values, an abnormal detection technology based on clustering learning is proposed to realize abnormal identification of service instruction frequencies and service feature codes.

The application discloses a heavy mass and equivalent offset state joint identification method based on q-axis current steady-state and dynamic characteristics, belongs to the technical field of gravity energy storage system state perception and motor control, and comprises the following steps: collecting three-phase currents of a permanent magnet synchronous motor and obtaining q-axis currents through coordinate transformation, simultaneously acquiring a reel mechanical angle, a motor angular velocity and a heavy mass running speed; extracting q-axis current steady-state components in a uniform speed steady-state window, combining a motor torque model to estimate the heavy mass and equivalent friction; calling a baseline model to obtain offset related residuals after inertia compensation of the q-axis current, extracting mechanical synchronous order amplitude after conversion to the reel mechanical angle domain, constructing a normalized offset feature, and outputting an equivalent offset or an unbalanced load risk index; and using the identification result for power correction, heavy mass scheduling, unbalanced load early warning or maintenance decision making. The application simultaneously realizes online identification of the heavy mass, equivalent friction and equivalent offset state by using motor current signals, has less dependence on hardware, and has low modification cost.