490results about How to "Accurate estimate" patented technology

The invention provides an intelligent fault diagnosis method for a numerical control machine tool, which is applied to a control system of the numerical control machine tool. The method comprises the steps of collecting multi-source sensor data in the operation process of the numerical control machine tool, wherein the multi-source sensor data comprises at least two of a vibration signal, a temperature signal, a current signal and a sound signal; performing preprocessing and feature extraction on the multi-source sensor data to obtain preprocessed data; based on the preprocessed data, constructing and training a machine tool dynamic model to obtain a target machine tool dynamic model; when abnormal operation of the numerical control machine tool is detected, fault state estimation is carried out based on the target machine tool dynamic model and a model prediction control method, and a fault estimation result is obtained; and based on the fault estimation result, constructing a fault evolution path by adopting a Monte Carlo tree search algorithm, and outputting a fault diagnosis result. The accuracy and efficiency of fault diagnosis of the numerical control machine tool can be improved.

The invention discloses a photovoltaic cleaning robot based on automatic path planning and an operation method thereof, relates to the technical field of robots, and solves the problem of cleaning blind spots in an existing cleaning robot and the endless loop risk of a path planning algorithm. According to the invention, the surface information of the photovoltaic panel is accurately captured through the multispectral acquisition module, a high-resolution and high-definition comprehensive image is generated through the data fusion processing module, then dirt is accurately identified on different scales by using a multi-scale pyramid structure and an adaptive feature fusion mechanism, and an attention mechanism and environmental perception data are combined, so that the real-time performance of the system is improved. The recognition threshold is dynamically adjusted, and misrecognition and missing detection are reduced; and finally, enabling the robot to autonomously learn in a complex environment and optimize an obstacle avoidance strategy through a dynamic obstacle avoidance algorithm based on deep reinforcement learning. The intelligent level and the cleaning effect of an existing photovoltaic cleaning robot are greatly improved.

The invention provides a battery health state assessment method and system based on a BMS, and the method comprises the steps: presetting different voltage sampling ranges and resolutions for different load current intervals, judging the current interval to which the load current belongs when the load current changes, and dynamically switching to the corresponding range and resolution; when the current changes violently, the voltage sampling frequency is improved, more data points are obtained, and when the current changes gently, the sampling frequency is reduced, and data redundancy is reduced; filtering and fusing the internal resistance estimated value by adopting a Kalman filtering algorithm, dynamically tracking the change trend of the internal resistance, and adaptively adjusting the parameters of a filter according to the working state and the aging degree of the battery; in combination with working condition parameters including the temperature, the current and the SOC of the battery, a multiple regression model of the internal resistance and the working condition parameters is established, the multiple regression model is trained and optimized through a machine learning method, and online self-adaptive correction of the internal resistance is achieved.

The invention discloses a power distribution network distributed photovoltaic bearing capacity real-time evaluation method, which comprises the steps of determining a mathematical model for power distribution network photovoltaic bearing capacity evaluation, and constructing a Markov decision model for power distribution network photovoltaic bearing capacity dynamic evaluation based on the mathematical model; a soft actor-commentator SAC algorithm is used as a basic framework, graph sampling and aggregation GraphSAGE are used for improving the SAC algorithm, a graph deep reinforcement learning algorithm is constructed, the graph deep reinforcement learning algorithm is used for solving a Markov decision model, and a trained graph strategy network is obtained; carrying out the subsequent real-time evaluation of the distributed photovoltaic bearing capacity of the power distribution network through employing the trained graph strategy network; according to the method, the limitation that precise modeling is needed in a traditional method is effectively overcome, and a more accurate real-time evaluation result is provided.

The invention relates to the technical field of battery state monitoring, and discloses a sightseeing vehicle battery state monitoring method, device and equipment and a storage medium, and the method comprises the steps: collecting the voltage, current and temperature data of a sightseeing vehicle battery, and carrying out the preprocessing, and obtaining a battery operation parameter data set; constructing a battery equivalent circuit model, and performing global and local two-stage optimization to obtain an optimal parameter set of the battery equivalent circuit model; constructing a state equation and an observation equation, and carrying out initialization setting on a covariance matrix of the triple forgetting factor adaptive extended Kalman filter to obtain a battery state observation model; priori state estimation is obtained through time updating operation, posterior state estimation is obtained through measurement updating operation, hierarchical estimation is carried out on the charge state and the polarization voltage of the battery, and the state characteristic quantity of the battery is obtained; according to the method, the reliability and the safety of the battery management system of the sightseeing vehicle are improved.

The invention discloses a high-resolution large-scale forest biomass remote sensing prediction method and system. The method comprises the following steps: acquiring multi-source data in a to-be-predicted geographic range; performing geographical registration and feature variable extraction on the multi-source data in combination with the geometric features and the regional features; the method comprises the steps that a forest biomass prediction model backbone network is input, an image serialization module, a channel prior fused double-branch feature extraction network and a regression network are included, the image serialization module slices an image and converts the image into an image sequence, and then the image sequence is input into the double-branch feature extraction network; the double-branch feature extraction network firstly extracts a feature map subjected to channel and space weighting through a channel prior module, then performs layer-by-layer down-sampling on the weighted feature map in four stages, and performs double-branch feature extraction through a state space feature extraction module. And finally, performing up-sampling regression through a regression network to output a forest biomass prediction result in a prediction geographical range. According to the method, the forest biomass can be predicted.

The invention belongs to the technical field of motor control, and relates to a permanent magnet synchronous motor sensorless control method, system and hardware circuit, angular velocity estimation is designed based on a q-axis current model under a two-phase rotating coordinate system, angular velocity estimation compensation is designed based on a d-axis current model, and a rotating speed correction coefficient is introduced. And dynamic compensation of the estimated angular velocity value is realized. An accurate angular velocity estimated value and an accurate rotor angle estimated value are obtained by constructing a Romberg observer with an angular velocity estimated value and a rotor angle estimated value as outputs based on a dq rotating coordinate system. Taking an angular velocity error obtained by a motor angular velocity given value and a motor angular velocity estimated value as input of an outer ring PI controller, so as to ensure that the angular velocity estimated value tracks the motor angular velocity given value and output a q-axis current reference value; taking the difference value between the q-axis current reference value and the q-axis current sampling value and the difference value between the d-axis current reference value and the d-axis current sampling value as the input of an inner ring PI controller so as to ensure the bandwidth and the influence capability on the load, and outputting a d-axis voltage reference value and a q-axis voltage reference value; and converting the d-axis voltage reference value and the q-axis voltage reference value into voltage reference values under a two-phase static coordinate system based on the rotor angle estimation value so as to divide SVPWM drive circuit sectors, select voltage vectors, calculate action time, control on and off time of a permanent magnet synchronous motor switching device, and control on-off time of the permanent magnet synchronous motor switching device. And the alternating current speed regulation performance of the permanent magnet synchronous motor without the position sensor under the Romberg observer method is ensured.

The invention provides a stabilization control method and system for a fuel filling ship at a zero navigational speed. The method provided by the invention comprises the following steps: acquiring rolling data and wind wave data of a refueling ship under a target sea condition in a fuel refueling process in real time; the rolling data and the wind wave data are processed based on a radial basis function neural network, and the precession angular velocity of the stabilization gyro device under the target sea condition is obtained through calculation; the stabilization gyro device is controlled to rotate at the precession angular speed, the stabilization gyro device generates a gyroscopic effect and generates rolling moment, the rolling moment is converted into stabilizing moment for resisting rolling through precession motion, and the rolling angle of the filling ship is reduced; and the stabilizing moment is opposite to the vibration direction of the rolling. According to the method and system, rolling control of the filling ship at the low speed or the zero navigational speed is achieved, the stabilization torque can be adjusted in real time according to sea wave changes, and the stability and safety of the filling ship in the fuel filling operation process are improved.

The invention discloses a perception fusion method of an unmanned driving system, and relates to the field of unmanned driving, and the method comprises the steps: obtaining a laser point cloud, image data and millimeter wave radar data; a Zhang's calibration method is adopted to obtain internal parameters of a camera, and a first pose transformation relation of a camera coordinate system relative to a laser radar coordinate system is solved; a second pose transformation relation of the millimeter wave radar relative to the laser radar is obtained by using the millimeter wave reflector; a Point RCNN method is adopted to obtain first target information; obtaining target information II by adopting a YOLOv8 method; obtaining target information III by adopting a clustering algorithm; and carrying out coordinate unification on the target information by utilizing a pose transformation relation, fusing the multi-sensor target information under a unified coordinate system by utilizing an unscented Kalman filtering algorithm to obtain target state estimation, and generating an unmanned driving control instruction. The target sensing precision is improved aiming at the low target sensing precision of multiple sensors under the unmanned driving scene of the well industry and mining in the prior art.

The invention discloses a teaching quality diagnosis and evaluation method based on behavior recognition, and relates to the technical field of behavior recognition, and the teaching quality diagnosis and evaluation method based on behavior recognition comprises the steps: 1, collecting a real classroom teaching video, and intercepting a key frame from the video content; 2, a target in the teaching video image is detected, and behavior recognition is achieved based on target detection and a human body posture evaluation algorithm; step 3, using the trained target detection model to detect a target teaching video image data set, and dividing different class types based on detected teacher behaviors; 4, determining student behaviors influencing the teaching quality according to the class type, and calculating a teaching quality scoring index; 5, evaluating the teaching quality according to the teaching quality scoring index, and diagnosing problems affecting the teaching quality; through classroom data acquisition from various sources, the classroom teaching quality is accurately analyzed and evaluated, and accurate teaching of teachers and personalized learning of students are assisted.

The invention relates to a lithium ion battery health state estimation method based on an MSCNN, and belongs to the technical field of lithium ion batteries. The method comprises the following steps: performing an aging cycle test on a lithium ion battery monomer, and establishing a working condition test experiment database; collecting voltage, current and temperature data of the battery in each aging cycle, respectively calculating average values, standard deviations, skewness and kurtosis of the voltage, the current and the temperature, taking the average values, the standard deviations, the skewness and the kurtosis as health factors, and screening out the health factors which are highly related to the health state of the battery; constructing an SOH estimation model, and training the model by taking the screened health factors as input; and the model is used for predicting the health state of the lithium ion battery after being trained. The method can effectively extract features from data of different scales, has higher SOH estimation precision compared with a traditional CNN, can perform accurate SOH estimation on data sets of different working conditions, and has good generalization ability.

The invention discloses a method and system for estimating the two-dimensional attitude of an occlusion scene based on spatio-temporal information, and relates to the field of computer vision, and the method comprises the steps: S1, obtaining a video comprising information of a plurality of human bodies; s2, constructing a two-dimensional attitude estimation model; the model comprises an input module, a visibility prediction and shielding module, a key point reasoning module, a space-time dependency module and a regression layer. S3, inputting the obtained video into a two-dimensional attitude estimation model for training to obtain a trained two-dimensional attitude estimation model; and S4, using the trained two-dimensional attitude estimation model to carry out space-time information-based occlusion scene two-dimensional attitude estimation. According to the two-dimensional attitude estimation model constructed by the method, shielded key points can be accurately reasoned, the smoothness and accuracy of attitude estimation are improved, the interference of high-frequency noise is reduced, and the calculation complexity is reduced while the accuracy of the model is ensured, so that the real-time requirement is met.

The invention provides a three-dimensional gesture estimation method fusing hand and object features, and relates to the technical field of gesture recognition. The method specifically comprises the following steps: acquiring a plurality of different hand and object images containing 3D postures of a hand and an object, constructing an image data set, and dividing a training set from the image data set according to a preset proportion; constructing a hand occlusion estimation joint model fusing the hand object features; inputting the training set into a hand occlusion estimation joint model fused with the hand and object features for training to obtain a trained hand occlusion estimation joint model fused with the hand and object features; and acquiring a hand and object image containing 3D postures of a hand and an object, and inputting the hand and object image into the trained hand occlusion estimation joint model fusing the hand and object features for gesture estimation to obtain a three-dimensional gesture estimation result. According to the method, the hand feature information can be better extracted under the severe shielding condition by designing the hand shielding estimation joint model fusing the hand feature.

A water leakage monitoring method for a digital twinning system of a coal mine shaft comprises the steps that an RFID antenna and a Twinin-Tag sensor are installed on the wall of the shaft, a monitoring terminal is arranged on the ground, and an RFID reader is arranged at an outlet of the shaft; a shaft monitoring signal is collected in real time through a Twindow-Tag sensor, the shaft monitoring signal is read through an RFID reader, and then shaft monitoring data are obtained by analyzing and processing the shaft monitoring signal; collecting RSSI reading and phase data by using a short time window, and performing state identification by using a decision tree; carrying out coarse segmentation and fine segmentation on the leakage signal, and determining the location of a water leakage point in combination with a change point detection algorithm; and the leakage volume and the leakage rate are estimated. According to the method, the water leakage point can be accurately positioned, the volume and flow velocity information of the leaked water in the shaft can be obtained, so that a reliable basis is provided for subsequent maintenance decision making, and the method is suitable for long-term monitoring operation of the coal mine shaft.

The invention relates to a full-route bump feature mining and EDR estimation method based on airborne QAR data. The method comprises the following steps: forming an EDR basic data set; mining and extraction of advanced features are realized; forming an optimized feature set; obtaining a trained neural network model; and calculating the full-route EDR value of the real route. Through combination of discrete wavelet transform and a neural network model, the invention provides an accurate and reliable EDR estimation method, and compared with a traditional single-parameter method, detail description is more obvious and is completely and automatically calculated by a computer without manual reasoning. According to the method, after model training and optimization, the EDR value on a single course can be estimated in real time, and the accuracy and practicability of analyzing an airplane bumping event through flight data in the aviation field are improved.

The invention provides a position prediction method and device based on an ultra wide band (UWB) digital key, and a position prediction system applied to the UWB digital key, the position prediction system comprises a UWB tag and a plurality of UWB anchor points arranged on a vehicle, and the position prediction method comprises the following steps: determining whether UWB positioning information of the UWB tag is invalid; when the UWB positioning information is invalid, acquiring a velocity vector and a historical track set of the UWB tag and orientation information of the vehicle; and inputting the velocity vector, the historical trajectory set and the orientation information into a position prediction calculation model to calculate a predicted position of the UWB tag. The technical problem that when an existing automobile digital key is blocked by an obstacle and the digital key cannot be connected to an automobile UWB anchor point, the function of the digital key is in a failure or invalid state, and the user experience is affected is solved.

The invention discloses an Internet of Things service transmission method for a low-orbit satellite mobile communication system, which relates to the technical field of Internet of Things and comprises the following steps: starting a satellite base station, dividing resources, carrying out initial resource configuration and broadcasting a message by a terminal; the Internet of Things terminal receives the broadcast message, carries out primary connection with the satellite base station, and sends a handshake packet to the satellite base station to carry out intention identification and service parameter configuration; dynamically adjusting a spectrum allocation strategy according to a prediction result, and generating an optimized spectrum allocation scheme and a resource scheduling instruction; performing related state information switching based on the optimized spectrum allocation scheme and the resource scheduling instruction; after receiving the service data uploaded by the terminal, the data center judges whether a confirmation signal needs to be sent to the Internet of Things terminal in combination with the switched state information; according to the invention, the direct communication link is established through the inter-satellite link, so that the data continuity and the low-delay characteristic in the switching process are ensured.

The invention discloses a nuclear power device fault diagnosis method based on feature vectors and Bayesian reasoning, belongs to the technical field of Bayesian reasoning, and particularly relates to nuclear power device fault diagnosis. The method solves the problem that a traditional Bayesian model only uses parameter values as a fault reasoning basis and ignores a large amount of parameter time-frequency feature information, so that the model is difficult to distinguish feature similar faults and realize accurate positioning of the faults, and the fault diagnosis accuracy and the model robustness are affected. The method comprises the following steps: a model learning step: performing structure learning and parameter learning on a Bayesian model according to fault historical operation data to obtain a Bayesian vector reasoning model; the nuclear power device fault diagnosis method based on the feature vector and Bayesian reasoning is suitable for fault diagnosis of a nuclear power device.

The invention discloses a cotton field soil humidity monitoring method, system and device, a medium and a product, and relates to the field of soil humidity monitoring. Sentinel No.1 microwave data and Gaofen-1 optical data of a to-be-monitored cotton field area are acquired, and a cotton microwave characteristic factor and an optical characteristic factor are extracted; estimating the soil humidity of the cotton field area to be monitored by utilizing a cotton field soil humidity estimation model according to the two characteristic factors; the cotton field soil humidity estimation model is obtained by training a pre-trained improved two-dimensional convolutional neural network by using a training data set; the pre-trained improved two-dimensional convolutional neural network is obtained by pre-training the improved two-dimensional convolutional neural network by using a pre-training sample set obtained by simulation of an AIEM-Oh model based on microwave parameters and ground actually measured soil roughness; the improved two-dimensional convolutional neural network comprises a convolutional layer, a depth separable convolutional layer and a full connection layer. The method can accurately estimate the soil humidity of the cotton field area.

The invention discloses a peanut high-oil germplasm resource numerical prediction data model and a construction method and application thereof, and relates to the cross technical field of bioinformatics and peanut breeding. Obtaining peanut germplasm resources and / or peanut germplasm resource combinations with high genetic potential in high oil content characters on the basis of a pre-constructed peanut high oil germplasm resource numerical prediction data model, and further breeding high oil content improved varieties of peanuts. According to the method, early prediction of the oil content character of the peanuts can be realized, and excellent peanut germplasm with high oil content can be screened in the early planting stage of the peanuts, so that the peanut breeding period is shortened, the peanut breeding intensity is improved, and the breeding process of the high-oil-content germplasm of the peanuts is accelerated. The method has dual important values of scientific research and application.

The invention provides a traffic light passing calculation method and system based on the Internet of Things. The method comprises the following steps: receiving a real-time data stream from an Internet of Things intelligent sensor network, and carrying out space-time consistency processing through a high-precision space-time synchronization technology to generate a traffic condition dynamic panorama; a causal inference algorithm is used for inferring a causal relationship, direct and indirect influence paths are calculated, a space-time sequence modeling technology is used for capturing space-time characteristics, a time-varying rule is simulated, and a future traffic state estimation model is generated; an evolutionary strategy optimization algorithm is used to simulate a natural evolutionary process, an optimal traffic light switching strategy is searched, a multi-objective optimization technology is used to consider a mutual exclusion objective, constraint conditions are introduced to carry out comprehensive evaluation, and a traffic light switching strategy evaluation result is generated; a feedback circulation mechanism is introduced, a model operation result is collected in real time, an adaptability improvement report is automatically formed, and a traffic light passing calculation scheme is generated. According to the technical scheme provided by the invention, the traffic light passing calculation efficiency is remarkably improved.

The invention relates to the technical field of wind generating set control, and discloses a three-order integral type wind generating set position and rotating speed estimation method and system. Three-phase power data is converted into rotating two-phase counter electromotive force under rotating coordinates through two times of coordinate conversion and a preset counter electromotive force observer; secondly, calculating error data of the wind generating set, and combining the error data with set operation data to serve as input of a preset third-order integral type position and rotating speed estimator to obtain more accurate target rotating speed data and target position data; the technical problem of how to accurately estimate the position and the rotating speed of the wind generating set is solved.

The invention provides a 6D pose estimation method and device and electronic equipment, and relates to the technical field of computers. The method comprises the steps that a scene graph is obtained, a mask graph corresponding to the scene graph is obtained, the scene graph comprises a target object, and the scene graph comprises a scene RGB graph and a scene depth graph; a plurality of first poses are determined, the first poses are 6D poses, and depth related information in the first poses is determined according to the mask image and the scene depth image; obtaining an image group corresponding to each first pose, wherein the image group comprises a first RGBD image of a target three-dimensional model corresponding to the target object under a visual angle corresponding to the first pose, and a second RGBD image obtained by cutting the scene image according to the first pose; and inputting each image group into a trained pose estimation network to obtain a target 6D pose of the target object, and obtaining the target 6D pose by the pose estimation network based on the matching degree of the images in the image groups. Therefore, the 6D pose of the object can be quickly and accurately estimated.

The invention discloses a response surface method-based composite material starch content inversion model construction method, and relates to the technical field of material processing. The response surface method-based composite material starch content inversion model construction method comprises the following steps of: analyzing a distribution state and a distribution rule of starch content in a composite material by utilizing a spectral measurement technology, and determining main factors influencing the starch content in the composite material; combining a response surface method with a matrix combination algorithm to determine a main factor combination; constructing a coupling model based on a main factor combination and a constraint function; and establishing an inversion model based on the coupling influence degree, and performing inversion prediction on the starch content in the composite material by using the inversion model. The inversion model provided by the invention can process the inversion of the starch content in the complex composite material, has relatively strong applicability, and can provide a global view angle for the overall starch content distribution, so that the change trend of different regions or stages of the composite material can be found.

The invention discloses a dynamic window-based graph optimization GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) integrated navigation method, which improves the adaptability, calculation efficiency and positioning accuracy of a system in a dynamic environment by introducing a dynamic window strategy and a key frame mechanism. According to the method, the size of a sliding window is dynamically adjusted according to a real-time motion state, environment change and computing resources: when the environment is complex or high precision is needed, the window is enlarged to contain more historical information; when the environment is stable or computing resources are limited, windows are reduced to improve computing efficiency. Meanwhile, threshold values of position and posture changes are set, and only when the variable quantity of the current state and the latest key frame state exceeds the threshold values, new key frame nodes are added, key motion information is captured, and redundant calculation is avoided; according to the method, the optimal balance between the positioning precision and the calculation efficiency in a complex dynamic environment is realized, the method is suitable for an embedded platform with limited resources, and the requirement of high-precision real-time navigation is met.

The invention discloses an engine oil temperature estimation method and device, electronic equipment and a storage medium, and is applied to the field of automobiles. According to the engine oil temperature estimation method and device, the current running state of an engine is obtained firstly, the running state comprises the shutdown state, the engine warming state and the engine heating state, then the method for estimating the engine oil temperature is determined based on the running state, the method for estimating the engine oil temperature comprises target factors, and the target factors are determined according to the running state. And finally, estimating the engine oil temperature by using the method. The invention further discloses a device for estimating the engine oil temperature. The accuracy of estimating the engine oil temperature of the engine is improved.

The invention discloses a shellfish carbon sequestration speed quantitative estimation method based on a neural network model, and belongs to the technical field of environmental science and ecology, and the method comprises the steps: obtaining water quality parameter data, building a water quality monitoring data set based on the water quality parameter data, and carrying out the preprocessing; performing data alignment on the shellfish growth data and the preprocessed water quality monitoring data set to obtain a time sequence data set; training a long-short term memory neural network model based on the time sequence data set, obtaining a predicted value of the pH value of the water body in a preset time period through the trained model and monitoring data obtained in real time, and calculating the release rate of carbonate ions, a carbon sequestration speed estimated value is obtained by combining the net absorption amount of carbon dioxide in the shellfish growth process in unit time; and calculating the total carbon sequestration amount of the shellfish in the whole water body based on the carbon sequestration speed estimated value. According to the method, accurate estimation of the carbon sequestration amount of the shellfish culture water body is realized through a method of combining high-precision sensing monitoring and a deep learning model.

The invention discloses a hot upflow positioning method based on data driving. For a long time, unmanned aerial vehicles are always indispensable in the industries of monitoring tasks, geographic surveying and mapping, target reconnaissance and the like. Flight endurance is a key limit for wide use of unmanned aerial vehicles. However, although researchers effort to modify and optimize the aerodynamic shape to reduce glider drag or replace battery reactants, use of these methods to increase flight endurance still has limitations. The invention aims to utilize the energy of the nature by collecting wind energy. According to the method, upflow estimation is considered, and then the position of the wind field is estimated through a sparse recognition algorithm of nonlinear dynamics. The thermal positioning is realized through the SINDy-based method and the characteristic of accurately identifying the mathematical physical specification model, so that the capability of identifying the hot upflow is improved, and the energy of the aircraft is increased.

The invention relates to the technical field of playing frequency response equalization, and discloses a playing frequency response adaptive equalization method and device, and the method comprises the steps: obtaining an original playing signal of a loudspeaker and an audio signal collected by a microphone; designing a room transfer function between the original playing signal and the audio signal by using a preset adaptive filtering algorithm; estimating a frequency response compensation curve according to the room transfer function; and based on the frequency response compensation curve, designing a corresponding IIR filter to carry out frequency response compensation on the original playing signal. The method can achieve the effects of improving the measurement precision of the frequency response, being simple in measurement method, enhancing the robustness, and remarkably improving the frequency response performance of the loudspeaker system.

The invention discloses a color constancy method based on wavelet attention and a VMama network, and belongs to the field of computer vision and image enhancement. According to the method, illumination parameter estimation is carried out by adopting a designed WA-VMama network, and then color correction is carried out by adopting the estimated illumination parameters, so that a corrected image under standard illumination is obtained. According to the method, a designed wavelet attention mechanism is embedded into VMama, and a deep network model (WA-VMama for short) is constructed, so that global information can be effectively extracted by using the network, local information can be considered, the method has higher feature extraction and expression ability, and the accuracy of illumination parameter estimation is improved.