139 results about "Error correction model" patented technology

The invention belongs to the technical field of ocean surface flow velocity remote sensing measurement, and particularly relates to a Luoga No.2 SAR (Synthetic Aperture Radar) flow velocity correction method based on Bragg orbit velocity, which comprises the following steps of: verifying the physical correctness of an orbit velocity mechanism relative to a phase velocity mechanism; under a DopRIM framework, a physically consistent sea wave error correction model is established; calibrating parameters of the sea wave error correction model; applying the calibrated sea wave error correction model to along-orbit interference SAR observation data of the Luoma No. 2 satellite, and calculating the radial surface flow velocity and sea wave error of each resolution unit; comparing an output result with a matched reference truth value to evaluate the effectiveness of the sea wave error correction model; comparing the sea wave error correction model with a KaDOP model, and respectively counting and analyzing simulated sea wave errors under different methods; the sea wave error simulation result is visually displayed, and the improvement effect is quantitatively evaluated through in-situ observation data and a KaDOP simulation result.

The invention discloses a low-drift laser scanning control method and system based on dynamic error compensation, relates to the technical field of laser scanning control, and aims to solve the drift problem in the laser scanning process and improve the scanning precision. The system comprises a system initialization and calibration module, a data acquisition and storage module, an error modeling module, a route planning and correction module and a real-time compensation and closed-loop optimization module, wherein the error modeling module and the route planning and correction module are key modules. The error modeling module comprises a key route screening unit and a data preprocessing unit, and is responsible for constructing a space-time joint error correction model; the route planning and correction module comprises an initial route generation unit, an error prediction unit and the like, and dynamic correction of a pre-planned route is achieved. According to the system, real-time compensation and closed-loop optimization are achieved through cooperation of all the modules in combination with a dynamic error compensation algorithm, scanning drift is effectively reduced, low-drift and high-precision laser scanning is guaranteed, and meanwhile follow-up scanning precision is optimized.

The invention relates to the field of battery thermal management, in particular to an AUV lithium ion battery thermal state prediction method. Comprising the following steps: constructing an electrothermal coupling reduced-order thermal model, and generating initial temperature estimation with physical consistency; a physical guidance space-time dynamic graph convolutional network PG-STDGCN is constructed as an error correction model, the model constructs a static and dynamic fused adjacency matrix by embedding physical priori such as a battery topological structure and circuit characteristics into dynamic graph learning, and a correction value of initial temperature estimation is output; and adding the initial temperature estimation and the correction value to obtain a final battery thermal state prediction result. According to the method, organic fusion from physical modeling to data-driven correction is realized, interpretability, precision and adaptability are considered under the dynamic working condition of the AUV, and the battery pack-level multi-cell temperature prediction performance is remarkably improved.

The invention relates to a real-time process monitoring method based on data analysis, and the method comprises the following steps: S1, building a three-dimensional component priority evaluation model based on a component operation scene type, a real-time resource occupancy rate and a data dependence degree, and dynamically matching an acquisition strategy; s2, a cleaning rule is adapted according to a data source, a feature extraction dimension is adjusted in combination with process dynamic features, and an improved time sequence decomposition algorithm is used for separating data trends, fluctuations and abnormal residual errors; s3, constructing an exclusive baseline sub-model by using an online learning algorithm according to a scene label, and establishing a scene switching mechanism; s4, in combination with component interaction anomaly features, an anomaly level is judged through a mixed detection model; s5, on the basis of exception processing and user feedback, constructing an error correction model optimization parameter; and S6, generating a report containing an abnormal propagation path, and triggering hierarchical collaborative response of the associated component. The invention aims to solve the problems of single acquisition dimension, no scene adaptability in preprocessing, incomplete abnormal detection and the like in the existing monitoring technology.

The invention discloses a VerilogA code automatic error correction method based on a classification error library and a large language model, and the method comprises the steps: constructing a classification system of the error types of VerilogA codes, starting from an MTJ device, carrying out the statistics of 84 common errors in the VerilogA codes of MTJ based on the system, and developing an automatic training sample generation system according to the 84 common errors; the method comprises the following steps: selecting a DeepSeek-R1-7B model as a basic model through a benchmark test, and carrying out fine tuning training by utilizing a classified error data set generated by scripting to form a special error correction model for the field. The processing process comprises the steps that VerilogA codes to be corrected are received, error types and reasons are positioned through a trained model, correction codes are generated, and verification of eight types of typical circuits is passed. An error classification-model training-simulation verification system is established, verification and error correction of VerilogA codes are achieved, and time and resources in the development process are saved.

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

The invention provides a gearbox variable working condition mechanical loss torque efficiency correction test method based on a neural network. The method comprises the following steps: S1, determining a to-be-tested gearbox and basic parameters; s2, building a gearbox variable working condition test system; s3, acquiring basic operation data of the gearbox; s4, calculating theoretical mechanical loss torque and efficiency of the gear box; s5, the actually-measured mechanical loss torque and efficiency of the gearbox are collected, and an error data set is generated; s6, constructing and training an error correction model fusing the back propagation neural network and support vector regression; s7, performing mechanical loss torque and efficiency correction based on the fusion model; and S8, verification and feedback optimization of a correction result. According to the method, on the basis of all-working-condition data, the defect of a single model is overcome through double-algorithm fusion, precise correction of the mechanical loss torque and efficiency of the gearbox under the variable working conditions is achieved, and reliable data support is provided for gearbox design optimization, working condition matching and energy efficiency evaluation.

The invention discloses a thermal power generating unit coordination control data and mechanism hybrid modeling method and device, computer equipment and a computer readable storage medium, and relates to the technical field of thermal energy engineering and power system modeling and control. Establishing a high-precision dynamic mechanism model of the working medium flow and heat transfer process of the boiler-steam turbine system; then, constructing an error correction model fusing the long-short-term memory network, the convolutional neural network and residual connection; and finally, fusing the mechanism model and the error correction model through parallel computing to form a dynamic hybrid model with physical interpretability and high precision. Through the hybrid modeling strategy, the prediction accuracy of key parameters such as the main steam pressure, the separator outlet steam enthalpy value and the unit load in the wide load range, especially under the dry-state operation working condition is remarkably improved, and a reliable model basis is provided for designing an advanced unit coordination control system.

The invention discloses an on-chip calibration device parasitic parameter determination and traceability method based on an inversion algorithm, and the method comprises the steps: determining eight error terms through TRL calibration which employs three calibration standard components: a straight-through calibration standard component, a reflection calibration standard component and a transmission line calibration standard component; the method comprises the following steps of: acquiring a relation between error terms in TRL calibration and SOLT calibration according to a signal flow graph, acquiring an expression for extracting a true value of a reflection coefficient of a calibration sheet through inversion according to a 12-item error correction model algorithm, substituting scattering parameters acquired by testing into an error term analytical solution of TRL calibration, solving a TRL calibration error term, and calculating the true value of the reflection coefficient of the calibration sheet according to the error term analytical solution of the TRL calibration. The reflection coefficient truth values of the open circuit and short circuit of the calibration piece and the load piece are extracted; according to the method, the extraction process of the parasitic parameters of the calibration piece is simplified, error sources introduced in the extraction process of the parasitic parameters of the calibration piece are reduced, and the extracted parasitic parameters of the calibration piece have a good effect.

The invention discloses a method for forecasting flood in a drainage basin influenced by strong human activities based on a hybrid model. The method comprises the following steps: firstly, constructing a distributed hydrological model combining sub-drainage basin division and a Muskingum method, and performing calibration; using a hydrological model simulation error and multi-step watershed average rainfall based on river channel propagation time as characteristics, and using a Bayesian optimized LSTM (Long Short Term Memory) to construct an error correction model; and finally coupling the two models to realize real-time forecasting. Verification of the Lanxi river basin shows that compared with a traditional model, the Nash efficiency coefficient of the mixed model is remarkably improved, the flood peak relative error is smaller than or equal to 8%, the peak current time difference is smaller than or equal to 2 hours, precision is stable within the 12-hour forecast period, a long series of continuous historical data is not needed to serve as support, and only data in the session flood period are needed; the method is suitable for hour-scale flood forecasting of the watershed lack of data and strong human activity, an efficient and reliable technical means is provided for flood control and disaster reduction, and the method has wide engineering application prospects.

The invention relates to the technical field of smart power grids, in particular to a distributed photovoltaic-oriented smart power grid load prediction method and system, and the method comprises the steps: collecting the total load data, distributed photovoltaic power generation power data and meteorological data of a target region and an adjacent region, and carrying out the preprocessing; extracting spatial correlation characteristics and time sequence evolution characteristics of the photovoltaic power generation power data, and fusing to generate a low-dimension spatial-temporal characteristic matrix; splicing the spatial-temporal feature matrix with the total load data and the meteorological data to form a multi-modal fusion feature vector, and inputting the multi-modal fusion feature vector into a machine learning prediction model to generate an initial load prediction result; and introducing real-time updated ultra-short-term weather forecast data, carrying out dynamic correction on an initial prediction result through a linear regression error correction model, and outputting a final load prediction value. According to the method, the space-time precision and the real-time adaptive capacity of load prediction in a distributed photovoltaic scene can be remarkably improved, and the method has good practicability and engineering generalizability.

The invention discloses a satellite precipitation two-stage error correction method based on an actual measurement site, and the method comprises the steps: extracting drainage basin DEM data, obtaining a mask file of a drainage basin, and extracting the satellite precipitation of the drainage basin; performing inverse distance weighted interpolation on the rainfall data of the actual measurement site to a corresponding resolution to obtain grid data of the actual measurement site; error correction data are obtained through a deep learning model or dynamic quantile mapping; calculating a residual error between the rainfall data after error correction and grid rainfall of the actual measurement site, and learning a nonlinear relationship between the rainfall data and the residual error by using a gradient lifting regression tree model; and adding the precipitation residual error of the nonlinear residual error correction model to the error-corrected satellite precipitation error to obtain a residual error-corrected precipitation product. According to the method, the satellite precipitation product is corrected based on the actual measurement site data, the high-precision precipitation product is obtained, point-to-surface conversion of the precipitation data can be realized, and data support is provided for input of a refined hydrological model.

The invention relates to a hydropower cluster power generation power prediction method based on a residual hybrid model, and the method comprises the following steps: firstly, collecting the historical power data of a hydropower cluster, and carrying out the periodic coding, and forming a time feature set; historical power data is subjected to lagging processing, a lagging power feature set is constructed, the lagging power feature set and a time feature set are combined into a comprehensive feature set, and then the comprehensive feature set is divided into a training set and a test set. Training a basic prediction model by using the comprehensive feature set to obtain basic prediction results of the training set and the test set; and calculating a residual error based on a prediction result of the training set, constructing a residual error data training set, and training a residual error correction model according to the residual error data training set. And finally, performing residual error prediction on the test set through the residual error correction model, and adding a residual error prediction result and a basic prediction result to obtain a final prediction value. According to the method, the prediction precision and robustness are effectively improved through residual hybrid modeling.

The invention provides a text error analysis method and device based on a large model and a medium, and belongs to the technical field of natural language processing. The text error condition analysis method based on the large model comprises the following steps: constructing a training data set covering multiple fields and multiple error types, inputting the training data set into a large language model, and supervising and training the large language model; according to a multi-task collaborative error correction mode, a cue word optimization mode and a multi-error correction model fusion mode, an error correction strategy of the large language model output text data is designed; and screening the text data by using an error correction strategy in combination with the multi-error correction model to obtain a plurality of candidate correction results, and selecting an optimal correction result from the plurality of candidate correction results according to a voting mechanism. The problem that in the prior art, a document error correction mode is insufficient in error correction capability, including the problems of poor error correction accuracy and low error correction efficiency can be solved.

The invention discloses a wiring calibrator output data processing method and system based on artificial intelligence. A wiring topological structure is modeled into a graph structure based on a GNN graph neural network, nodes represent ammeter terminals, edges represent a wiring relation, dynamic data are processed in combination with GCN graph convolution and a GRU neural network, and a hybrid model is constructed; optimizing hyper-parameters of the hybrid model through an improved IWOA whale optimization algorithm to obtain a target G hybrid model; inputting the standard multi-source data into a target mixed model for training, and outputting a wiring calibrator error index; and constructing a nonlinear error correction model based on SVR (Support Vector Regression), inputting a wiring calibrator error index, and outputting a correction coefficient. Compared with a traditional calibration process, the time consumption is shorter, the efficiency is improved, batch calibration is supported, and the calibration and correction tasks of a plurality of ammeters are processed at the same time through the wireless communication module.

The invention provides an inflection point prediction method and an inflection point prediction device applied to the health life of a battery, and a computer storage medium. The inflection point prediction method comprises the following steps: acquiring an original capacity sequence of the battery in a cyclic charging and discharging process; inputting the original capacity sequence into an XLSTM network architecture for training and prediction to obtain a preliminary capacity prediction value; inputting the residual error sequence of the preliminary capacity prediction value and the true value into a multi-output Gaussian process error correction model to correct a prediction result to obtain a residual error correction value; generating a capacity attenuation curve according to the initial capacity prediction value and the residual error correction value; performing high-order differential analysis on the capacity attenuation curve, and determining the curvature of the capacity attenuation curve; and performing fusion identification on a high-order differential analysis result and the curvature by adopting double criteria, and determining a capacity attenuation inflection point of the battery. Through the inflection point prediction method, early, accurate and reliable inflection point prediction is realized.

The invention relates to the field of agricultural monitoring, and discloses a soil moisture content monitoring system which comprises the following steps: performing structure scanning on a soil body in a monitoring area, and extracting response curves and medium parameter characteristics of soil layers with different depths; the method comprises the following steps: constructing a node stability time sequence matrix based on a sensing node installation initial state and historical profile evolution information, and judging whether a monitoring value deviation degree exceeds a threshold by combining a multi-dimensional residual clustering algorithm; according to the microenvironment change record of the position of the node and the synchronization trend of the neighbor node, dynamically generating a health score label; after a risk node identifier is received, parallel processing is carried out on an acquired signal through a dual-channel analog-to-digital conversion mechanism, and conflict data is optimized and compared in combination with an error correction model; and based on a multi-period data reconstruction mechanism in a regional scale, performing reverse derivation on the fault data of the specified node, and outputting corrected soil moisture content data with a space-time weight tag. The method has the advantage that the operation stability is improved.

This invention relates to a method and system for multi-source satellite collaborative remote sensing monitoring of river cross-section water levels, belonging to the field of river cross-section water level remote sensing monitoring. The method includes classifying cross-sections based on their morphological and slope characteristics; constructing error correction models for water levels of different types of cross-sections monitored by satellites at different spatial resolutions; and obtaining time-series water level datasets for different types of cross-sections through multi-source satellite collaborative monitoring, based on these error correction models and the water levels at different time phases of different types of cross-sections monitored by satellites at different spatial resolutions. This completes the multi-source satellite collaborative remote sensing monitoring of cross-section water levels. This invention achieves cross-section classification and then meets the high-frequency monitoring requirements for river section water levels through multi-source satellite collaborative remote sensing monitoring.

The invention belongs to the technical field of geophysical exploration, and particularly relates to an error calibration method and system based on a three-axis fluxgate sensor. The method comprises the following steps: constructing a three-axis fluxgate sensor error model to obtain a measurement error of a to-be-calibrated three-axis fluxgate sensor; constructing an error correction model; iteratively solving the error correction model to obtain a weight vector containing error parameters of the three-axis fluxgate sensor to be calibrated; the method comprises the following steps of: inversely substituting a weight vector which is obtained by iterative solution and contains an error parameter of a to-be-calibrated three-axis fluxgate sensor into a measurement error of the to-be-calibrated three-axis fluxgate sensor to obtain a measurement error of the three-axis fluxgate sensor, and correcting three-axis magnetic field measurement data by using the measurement error of the three-axis fluxgate sensor to obtain a three-axis magnetic field calibration result. And obtaining a corrected three-axis magnetic field value. According to the method, a complex three-dimensional linear calibration problem is converted into a parameter estimation problem which is convenient to optimize and solve, and high-efficiency and high-precision rapid correction and compensation of the inherent error of the three-axis fluxgate sensor are realized.

The invention provides a gas turbine sensor fault diagnosis method and system based on a mathematical model, and the method comprises the steps: determining the parameters of a gas compressor, a combustion chamber, a turbine and a rotor based on the measurement data of a gas turbine sensor, building a gas turbine mathematical model, and building a sensor error correction model; acquiring a measured value of measured data of the gas turbine sensor, and performing error diagnosis on a calculated value output by the error correction model; judging whether the result of the error diagnosis accords with the confidence interval, and if so, judging that the sensor operates normally; and if not, judging that the sensor has a fault, overhauling and debugging, and returning overhauling data to the gas turbine sensor to measure the data. According to the method, the error correction mathematical model is constructed to correct the errors of the sensors of the gas compressor, the combustion chamber, the turbine and the rotor in the gas turbine, and the faulted sensors are diagnosed, so that the problem of measurement errors caused by environmental influence on the sensors is solved.

The invention provides an offshore wind power prediction method and system, and the method comprises the steps: 1, collecting the power time series data of a wind turbine generator or a wind power plant and multi-source external variables, carrying out the time alignment, and defining a prediction object and a causality constraint; step 2, data preprocessing; step 3, performing time domain multi-scale decomposition; step 4, performing frequency domain decomposition and tri-band reconstruction based on self-adaption on the power signal, and constructing two-dimensional time-frequency coupling strength; 5, outputting a main prediction result; step 6, modulating the error correction model through a risk gating weight; and step 7, updating risk gating weight related parameters based on historical prediction errors to obtain final prediction output. According to the method, on the premise of meeting strict causal constraint and online deployment requirements, the precision, stability and long-term adaptive capacity of offshore wind power prediction under the conditions of strong non-stability and sudden disturbance can be remarkably improved.

The invention discloses a large building concrete column radius construction measurement control method, and relates to the technical field of linear dimension metrology, and the method comprises the specific steps: parameter collection, data collection, model construction, real-time correction and strategy adjustment. Corresponding sensing equipment is arranged in a construction area to collect related associated data and form a real-time parameter set, meanwhile, original historical data of projects of the same type are collected and screened and classified to form a historical parameter error set, so that a linear size error correction model is constructed, influence rules of various associated data are determined, and the accuracy of the linear size error correction model is improved. And substituting the real-time parameter set into the linear size error correction model to obtain a radius measurement error compensation value, and dynamically correcting original radius measurement data, so that historical engineering experience and real-time field information are fully fused in the measurement process, multiple types of influence factors are accurately counteracted, and the measurement accuracy is improved. And the measurement result can truly reflect the actual size state of the concrete column body.

This invention discloses a self-correction control method for errors in a three-dimensional lidar device for foundation pit marking in power transmission lines, relating to the field of power transmission line construction technology. The invention is implemented through the following steps: First, based on a Cartesian coordinate system, the distance, horizontal angle, and vertical angle from the three-dimensional lidar device to the target point are defined to determine the initial coordinates of the target point. Second, reference control points are selected, noise and gross errors are removed, vertical offsets are corrected, and point cloud data is refined. Next, errors in the X, Y, and Z axes are defined, and an error correction model is established. Finally, self-correction of the target coordinates is performed based on the observed values ​​and the error correction model, and correction is achieved through an environmental feature-adaptive error weight allocation mechanism. This invention uses a multi-level data verification mechanism for closed-loop iterative optimization to ensure measurement accuracy, improve operational efficiency, enhance environmental adaptability, and is suitable for foundation pit marking operations in complex terrain.

The invention discloses a clothing modeling-oriented fabric shearing property intelligent prediction method and device. The fabric shearing property intelligent prediction device collects an image corresponding to a detected fabric; the image is preprocessed through an intelligent fabric shearing prediction method and then input into a target segmentation model for target segmentation, a threshold value is dynamically adjusted according to the local brightness of the image in the target segmentation model for binaryzation, and the accuracy of target segmentation of the target segmentation model is improved; meanwhile, a feature embedding model is used for extracting embedded features from the segmentation result; carrying out dimensionality reduction on the embedded features by adopting principal component analysis, and fusing the embedded features after dimensionality reduction with the collected three-dimensional features; inputting the fusion result into an error correction model to obtain an error correction value; according to the error correction value and a shear stiffness theoretical value obtained through the shear stiffness mechanism model, a final shear stiffness prediction value is obtained, it is guaranteed that the physical significance of a prediction result can be explained, and the prediction precision of the shear stiffness can be improved.

The invention discloses a data error correction method and device, equipment and a medium, and relates to the technical field of data storage, and the data error correction method comprises the steps: obtaining a state parameter of a target storage block, inputting the state parameter into a prediction model, predicting the error rate of data of the target storage block in a future target time period, and determining the error rate of the data of the target storage block based on a prediction result. And selecting a proper error correction model. Therefore, the number of unnecessary rereading times can be reduced, the error correction efficiency is improved, and then the problem that the performance of the storage device is reduced due to frequent rereading operation in some technologies can be solved.

The invention discloses an air quality prediction method based on a ResLSTM-SAR Hybrid model. The method comprises the following steps: acquiring historical weather data; performing preprocessing operation on the historical weather data; constructing a multi-factor LSTM model according to the preprocessed weather data to obtain a prediction result of the LSTM model; performing residual processing on a prediction result of the LSTM model and original historical weather data, and analyzing to obtain parameters of an SARIMA model; setting an SARIMA model according to the obtained parameters, and inputting the calculated residual sequence into the SARIMA model to output a prediction result; and a prediction result of the LSTM model and a prediction result of the SARIMA model are fused to obtain a final prediction result, and a ResLSTM-SAR Hybrid model is obtained. According to the air quality prediction method based on the ResLSTM-SAR Hybrid model, the LSTM main prediction model and the SARIMA residual error correction model work cooperatively, the LSTM model accurately captures complex association between meteorology and pollutants, and the SARIMA model corrects periodic components in residual errors, so that the accuracy of air quality data prediction is greatly improved, and high fluctuation characteristics are better coped with.

The invention is applicable to the field of jet printing, and discloses a jet printing height measuring and calculating method, which comprises the following steps of: emitting polychromatic light to a measured base material and a nozzle surface from one side, far away from a nozzle, of the measured base material by using a spectral confocal sensor, and receiving a first optical signal reflected from one side, close to the nozzle, of the measured base material and a second optical signal reflected by the nozzle; determining a first focusing wavelength focused on one side, close to the nozzle, of the measured base material, a second focusing wavelength focused on the nozzle and a nozzle incident angle according to the two optical signals, and substituting the first focusing wavelength, the second focusing wavelength and the nozzle incident angle into a pre-constructed refractive index calculation function to calculate the refractive index; substituting the dual wavelengths into a pre-constructed initial thickness calculation function and an error correction model to obtain a theoretical height value and an error compensation amount; if the inclination angle of the measured base material is not 0 degree, the actual height value from the measured base material to the nozzle is calculated according to a pre-constructed first real thickness calculation function, and the method is used for achieving online high-precision monitoring of the jet printing height.

The invention provides a park day-ahead load prediction and management optimization method based on deep learning, and relates to the technical field of load prediction, and the method comprises the steps: collecting historical load data and historical environment data of different buildings; performing time periodic coding feature extraction to obtain feature load data and feature environment data; according to the characteristic load data and the characteristic environment data, a multi-branch load decomposition prediction model is constructed based on Transform; according to the multi-branch load decomposition prediction model, obtaining a load residual error of a corresponding building and an initial day-ahead load prediction value; based on Xgboost, constructing a multi-branch load residual error correction model; according to the multi-branch load residual error correction model, carrying out multiple rounds of residual error processing on a load residual error to obtain a day-ahead load prediction correction value; and obtaining a day-ahead load prediction final value of the corresponding building according to the day-ahead load prediction correction value and the initial day-ahead load prediction value, thereby improving the energy consumption efficiency of the park.

The invention relates to the technical field of computers, in particular to a motion control scene in an artificial intelligence system in the production field. The invention discloses an analog machine actuator cylinder motor power regulation and control method and system, electronic equipment and a storage medium. The method comprises the steps that basic parameters are set, and a multi-dimensional sensor error correction model is established; multi-source data such as a motor, a cabin attitude, a load, an environment and abnormal sound are collected and calibrated in real time; calculating a real-time power demand and a power adaptation degree based on a control action-load-power coupling model; a tensor self-encoder and a gradient boosting tree are fused to carry out power insufficiency risk prejudgment and abnormal sound grading early warning; a multi-mode power regulation mechanism is adopted according to pre-judgment and judgment results, and temperature rise, overload and frequent regulation protection is implemented; and collecting the adjusted data closed-loop correction model and performing incremental updating. According to the scheme, abnormal sound is suppressed, power matching and system stability are improved, and rapid adaptation of different models of analog machines is supported.

The invention aims to provide a method and equipment for correcting regional noun errors. Compared with the prior art, the method and the device have the advantages that the preset number of correct corpora containing the regional nouns are constructed, the regional nouns in each corpus are subjected to erroneous character construction based on the preset erroneous character construction rule, the constructed erroneous character corpus is determined, model training is performed based on the correct corpora and the erroneous character corpus, and the trained error correction model is determined; wherein the error correction model comprises region noun recognition and region noun error correction, and then region noun error correction is carried out on sentences containing region noun based on the trained error correction model. In this way, the effect of correcting regional nouns can be improved, and the error correction accuracy can be improved by assisting the text error correction function through the entity recognition function.