553 results about "Motion estimate" patented technology

A method for simultaneous localization and mapping underwater. A robot is equipped with an IMU inertial unit and a sonar unit. When the robot submerges underwater, a buoy connected to the robot floats on the water surface, and the buoy moves in coordination with the movement of the robot. The position and observed velocity of the buoy are obtained by a shore-based lidar. During motion estimation in a SLAM algorithm, when an angular velocity is below a preset angular velocity threshold, the observed velocity of the buoy is decomposed into x-axis velocity and y-axis velocity, and updated as the two-dimensional operating velocity of the robot. The polar coordinates of an obstacle in a map under the coordinate system of the robot are associated with the polar coordinates of sonar data of a current frame by using the Mahalanobis distance. The polar coordinates of a new obstacle are converted to the world coordinate system and added to the map. The present invention has the advantages of not relying on underwater visibility, not requiring pre-installed devices, and possessing good generalization and stability.

The invention discloses a cradle head tracking method, system and device, a storage medium and a program product, and relates to the technical field of cradle heads, and the method comprises the steps: obtaining the target position information of a tracking target in continuous multi-frame image frames collected by an image sensor, and the size of a detection frame, determining a pan-tilt rotation angle of a target pan-tilt carrying an image sensor when multiple image frames are acquired; determining a target position offset between the target position and the center of the image based on the target position information, and mapping the size of the detection frame into a target distance between the tracking target and the image sensor; inputting the target position offset and the holder rotation angle corresponding to the multiple image frames into a motion estimation model to obtain a predicted position; and determining a pan-tilt angle value of the target pan-tilt based on the predicted position and the target distance, and driving the target pan-tilt to rotate based on the pan-tilt angle value. According to the invention, the cradle head keeps stable tracking while quickly responding to target movement, so that the stability and reliability of cradle head tracking control are improved.

Disclosed in the present invention is a LiDAR odometer method using a piecewise linear continuous time trajectory. The present invention comprises the following steps: first, using a time window to divide an initial LiDAR point cloud time sequence so as to obtain LiDAR point cloud sub-sequences under a plurality of time windows, and constructing an initial local point cloud map; and then, on the basis of the initial local point cloud map, optimizing LiDAR poses of the LiDAR point cloud sub-sequences under the time windows to obtain an overall continuous time trajectory and a final local point cloud map. The present invention achieves high-precision motion estimation for LiDARs, has high robustness and real-time performance, is applicable to the fields such as robot navigation, simultaneous localization and mapping, automated driving, and provides a relatively simple and efficient solution that can effectively handle the characteristics of LiDARs as streaming sensors.

The invention discloses a structure large displacement estimation method based on Canny-Hough transformation and KLT optical flow, and relates to the related field of signal processing and vision measurement, and the method comprises the steps: collecting and preprocessing structure vibration data; carrying out integer pixel displacement estimation on the basis of Canny-Hough transformation; reconstructing the video; performing sub-pixel displacement estimation based on the KLT optical flow; and physical displacement. According to the method, the limitation of a single identification method in identifying different pixel levels (pixel level and sub-pixel level) is overcome, the measurement precision of the large displacement of the structure is remarkably improved, and an estimation error caused when an image pyramid is used for large displacement tracking is avoided. The research provides a new target-free large-displacement motion estimation scheme for structural vibration measurement based on computer vision, and has important reference value.

Methods and systems implement a pleno-generation face video compression framework with bandwidth intelligence for generative models and compression. Heterogeneous-granularity facial description regularizes long-term dependencies between video frames and compensates for motion estimation errors caused by compact representations of motion information. A generative decoder reconstructs heterogeneous-granularity visual representations, providing auxiliary visual signals for attention-based recalibration of a GFVC-reconstructed face signal. A coarse-to-fine generation strategy avoids error accumulation. High efficiency for heterogeneous-granularity signal compression is achieved by two different entropy-based signal compression methods: heterogeneous-granularities feature representation from the key-reference frame as hyperpriors to optimize the entropy model for compressing heterogeneous-granularity feature from subsequent inter frames, and a feature difference operation for heterogeneous-granularities feature representation between key-reference and subsequent inter frames, such that the entropy model only compresses heterogeneous-granularities feature residual for redundancy reduction. Mixed-model dataset generation and training and model-specific dataset generation and training are also provided.

The provided is a LiDAR odometry using a piecewise linear continuous-time trajectory. The LiDAR odometry includes the following steps: dividing an initial LiDAR point cloud time sequence through time windows to obtain LiDAR point cloud subsequences in the multiple time windows, and constructing an initial local point cloud map; and optimizing, based on the initial local point cloud map, a LiDAR pose for the LiDAR point cloud subsequence in each time window to obtain an overall continuous-time trajectory and a final local point cloud map. The provided achieves high-precision motion estimation of the LiDAR, with high robustness and real-time performance, and is suitable for fields such as robot navigation, simultaneous localization and mapping, and autonomous driving. The provided gives a simple and efficient solution that effectively utilizes the characteristics of the LiDAR as a streaming sensor.

A method for determining a location of a vehicle includes obtaining, by a processor, a plurality of aerial image segment signatures of segments of a region including the vehicle, and a plurality of sensed image signatures associated with the region including the vehicle. The method matches a selected aerial image segment signature segment signatures to a selected sensed image signature. Based on the matching step, the processor generates probabilistic location information regarding the location of the vehicle. The method also includes generating a movement estimate of the vehicles. The movement estimate is generated based on a vehicle location comparison across a plurality of vehicle sensed images, and the plurality of vehicle sensed images are captured at a plurality of time intervals. The method also includes determining the location of the vehicle by combining the movement estimate of the vehicle and the probabilistic location information.

The invention relates to the technical field of map construction, and provides a mapping method and device based on fusion of a laser radar and an inertial measurement unit. According to the scheme, motion distortion compensation is carried out on original point cloud data through motion estimation based on inertial data, the compensated point cloud is matched with a local map, and the local map is obtained through a filter; carrying out tight coupling fusion on the inertial data and the point cloud matching result, and outputting the pose information of the sensor at the current moment; according to the pose information of the sensor, the compensated point cloud is added into the dynamically managed local map, and the updated local map is periodically published, so that a complete mapping function from data acquisition, processing and fusion to map construction is realized. And through motion distortion compensation, the inertial data and the point cloud matching result are tightly coupled and fused, and the updated local map is periodically published, so that high mapping precision can still be kept when the mapping area is large.

The invention belongs to the technical field of multi-camera cooperative imaging, and discloses a multi-camera cooperative anti-shake control system and method. The method comprises the following steps: generating a global hardware trigger signal by using an FPGA (Field Programmable Gate Array), synchronizing IMU and multi-camera acquisition, and combining motion parameters to obtain a space-time aligned multi-view image; obtaining a global jitter component through feature extraction, motion vector field generation, depth map analysis and the like; fusing the data with IMU data to generate a trajectory, and inputting the trajectory into LSTM to obtain future compensation amount; and cutting based on deep layered transformation to obtain an EIS optimized frame, dynamically electing a main camera for super-resolution reconstruction, and outputting a final anti-shake video stream. Through multi-source information fusion, layered precise compensation and dynamic optimization, the accuracy and stability of motion estimation are remarkably improved, the jitter influence of the finally output video stream is effectively weakened, and the reliability and visual coherence of the image stabilization effect in a complex scene are ensured.

The invention discloses a heart motion feature extraction method based on optical flow estimation, and relates to medical image processing. Preprocessing the input four-dimensional space-time cardiac magnetic resonance imaging data, namely, scaling pixel values; inputting two frames of images which are continuous in time into a feature encoder and a context encoder for feature extraction, wherein the two frames of images are divided into a reference frame and a moving frame; calculating the correlation between the feature maps through a correlation volume calculation module, constructing a correlation pyramid, and extracting the feature maps to provide matching information for subsequent optical flow estimation; the motion feature iteration enhancement module iteratively and continuously refines an optical flow estimation result through a deformable convolution and global motion aggregation (GMA) module; model parameters are optimized based on a weighted sum of luminosity consistency loss, smoothness loss, and gradient consistency loss. Edge features are adaptively captured through deformable convolution, global and local features are fused by using a GMA module, optical flow prediction errors are effectively reduced, motion estimation quality is improved, and key features of a heart edge region are maintained.

The invention provides a medical image denoising and segmentation integrated model trained based on a conductible diffusion method, and belongs to the field of medical image processing, and the model comprises an improved denoising and diffusion module which is used for receiving an initial medical image, predicting an original clean signal of the initial medical image based on an improved denoising and diffusion probability model, and obtaining a final denoised medical image; the joint motion and segmentation module is used for receiving the initial medical image, extracting features through a shared encoder, and performing joint learning through a motion estimation branch and a segmentation branch to obtain segmentation data; the cascade training mechanism carries out end-to-end training on the improved de-noising diffusion module and the joint motion and segmentation module through a derivable connection, gradient back propagation is realized by using a joint loss function, and cascade training is completed. According to the method, the problem that the quality of segmented medical images is reduced due to the fact that denoising and segmentation tasks cannot be collaboratively optimized due to the fact that a traditional denoising method cannot be guided in sampling and is difficult to carry out cascade training with a segmentation model is solved.

The invention provides an imaging spectrometer motion compensation imaging model construction method, an imaging model and a device, and relates to the technical field of space remote sensing. The method comprises the following steps: establishing a satellite orbit model, and defining a plurality of coordinate systems such as a detector coordinate system and a spectrometer imaging coordinate system and a conversion relation thereof; constructing a detector-scanning mirror-lunar surface ideal geometric mapping model based on the load optical parameters and a ray tracing method; determining the relation between the rotation angle of the scanning mirror and imaging spectrum view field positioning by using a reflecting prism conjugation theory; a quantitative relation is established by combining parameters such as satellite orbits and position postures, and finally a motion compensation imaging model is established. According to the method, the influence of the on-orbit error on the compensation imaging model is analyzed, and the error is used as an uncertainty component to be coupled into the model. According to the method, the problems of low model precision, neglect of influence of orbit observation parameters on imaging and the like in the prior art are solved, high-precision motion estimation and compensation can be realized, and the quality of data acquired by an imaging spectrometer is remarkably improved.

There is provided a computer-implemented method for learned video compression, which includes processing a current frame (xt) and previously decoded frame ({circumflex over (x)}t-1) of a video data using a motion estimation model to estimate a motion vector (vt) for every pixel, compressing the motion vector (vt) and reconstructing the motion vector (vt) to a reconstructed motion vector ({circumflex over (v)}t), applying an enhanced context mining (ECM) model to obtain enhanced context ({umlaut over (C)}E) from the reconstructed motion vector ({circumflex over (v)}t) and previously decoded frame feature (x̆t-1), compressing the current frame (xt) with the assistance of the enhanced context ({umlaut over (C)}E) to obtain a reconstructed frame ({circumflex over (x)}′t), and providing the reconstructed frame ({circumflex over (x)}′t) to a post-enhancement backend network to obtain a high-resolution frame ({circumflex over (x)}t).

The invention relates to the technical field of image recognition, in particular to a method and system for early warning below a portal crane, and the method comprises the steps: obtaining site environment sensing data, constructing a space map, and recognizing the position and motion state of equipment in real time; predicting an equipment motion track and a load swing range, and generating a predicted dangerous area; image acquisition devices are installed at a plurality of fixed positions, field images are acquired, an image recognition model is used for detecting a target, and the spatial position and the posture state of the target are obtained; tracking a target trajectory based on a motion estimation model and predicting a future position of the target trajectory; dynamically switching the image acquisition devices according to the motion state of the equipment; and comparing the future position of the target with the predicted dangerous area in real time, when the future position and the predicted dangerous area coincide, carrying out potential interference prediction, calculating predicted interference time, and carrying out grading processing in combination with the attitude state of the target. According to the invention, equipment dynamic perception and personnel behavior prediction are realized, and the intelligent protection capability of the operation area is improved.

The invention discloses an echocardiogram myocardial motion estimation method and system based on a structure perception diffusion model, and relates to the technical field of ultrasonic video data processing. The invention aims to solve the problem of myocardial motion estimation in an ultrasonic video. According to the technical key points, the method comprises the following steps: (1) preprocessing collected echocardiogram video data; (2) establishing an echocardiogram myocardial motion estimation model; (3) constructing a diffusion model motion estimation module of structure perception; (4) constructing a spatial adaptive up-sampling module; (5) training the network by using the existing data to obtain a network model; and (6) estimating the myocardial movement of the echocardiogram video by using the trained model. According to the method, the cardiac structure information and the multi-scale correlation contained in the echocardiogram can be fully utilized to automatically estimate the cardiac muscle movement of the echocardiogram, and the accuracy and the robustness of the algorithm are effectively improved.

A computer-implemented method includes converting by a pose tokenizer, based on a learned codebook, pose parameters of a body into a sequence of discrete pose tokens; randomly masking a portion of the sequence of discrete pose tokens; predicting the randomly masked sequence of discrete pose tokens based on multi-scale features extracted from a monocular image by an image conditioned masked transformer; optimizing the sequence of discrete pose tokens by aligning a re-projected three-dimensional (3D) pose with an estimated two-dimensional (2D) pose; directly regressing, from the multi-scale features, a shape parameter of the body and a weak perspective camera parameter; and generating a 3D mesh reconstruction of the body based on the shape parameter and the weak perspective camera parameter.

The invention discloses a motion carrier self-motion estimation method based on multi-radar fusion, a carrier and a medium, and relates to the technical field of motion carrier positioning. The method comprises the following steps: performing consistency check among radars based on self-motion estimation results of the radars, and judging whether the radars with abnormal self-motion estimation results exist or not according to check results; if not, the self-motion estimation results of all radars are fused, and a final self-motion estimation result is output; if so, identifying an abnormal radar from the radars according to at least one quality index; and discharging the self-motion estimation results of the radars identified as abnormal, and based on the self-motion estimation results of the remaining radars not identified as abnormal, performing fusion or directly outputting the self-motion estimation results as the final self-motion estimation result. According to the method, the abnormal source can be automatically identified and isolated, and the robustness and the function security level of the system in a real complex environment are remarkably improved.

The invention discloses an unmanned aerial vehicle detection and tracking method based on a multi-modal image, and belongs to the field of target detection and tracking. The unmanned aerial vehicle in the air is detected through the combined action of multiple modes, so that the reliability of unmanned aerial vehicle detection in various environments is realized, and identification and classification of the unmanned aerial vehicle are realized; in the tracking stage, a prediction structure in which LSTM and a Kalman filter are fused is introduced, so that the state modeling capability under the conditions of nonlinear motion and shielding of a target is enhanced, and the stability and continuity of tracking are improved; a fusion multi-stage matching strategy is adopted to effectively improve the matching accuracy and robustness in a complex environment; meanwhile, a confidence shunt strategy and a trajectory management mechanism are combined, so that the response speed to a new target and the fault-tolerant capability to a lost target are improved; through combination of global motion estimation and height information judgment, accurate correction and target distinguishing of the track of the unmanned aerial vehicle are realized, aliasing and misjudgment are avoided, and the detection and tracking capability of the unmanned aerial vehicle is remarkably improved.

The invention relates to the field of digital image processing, in particular to a super-resolution imaging method based on focal plane splicing and adaptive fusion. According to the method, sub-pixel offset among nine CCDs is preset through hardware, and nine frames of low-resolution image sequences with accurate displacement are obtained in push-broom. A central image is taken as a reference frame, high-precision mapping is realized based on hardware offset, motion estimation errors are avoided, effective pixels are screened by calculating robustness weight, an anisotropic Gaussian kernel function with a self-adaptive local structure is constructed so as to maintain image edge and detail features, and each frame is accumulated to a high-resolution grid in a weighting mode, so that a high-resolution image is obtained. And a sample compensation mechanism based on cumulative robustness is introduced, a fusion strategy is adaptively adjusted in an information insufficient area, and finally a high-resolution image is generated through normalization. The method significantly improves the imaging quality, suppresses artifacts and noise, and is suitable for the field of satellite remote sensing.

The invention discloses a monocular endoscope real-time depth estimation method and system based on self-supervised learning, and the method comprises the steps: firstly inputting a reference image and a target image, carrying out the preprocessing of the target image, and generating an image block sequence; and inputting the image block sequence into the depth map generation network, and outputting a depth map. And extracting feature graphs of the reference graph and the target graph by using an encoder, and respectively inputting the feature graphs into the appearance flow graph generation network and the optical flow graph generation network to obtain an appearance flow graph and an optical flow graph. Inputting the feature map into a body motion estimation network to obtain relative motion transformation of the endoscope, and inputting the relative motion transformation into a camera internal reference decoder to obtain a camera internal reference matrix; and reconstructing a target map based on the depth map, the relative motion transformation and the internal reference matrix, carrying out brightness calibration in combination with the reference map and the appearance flow map, and supervising and optimizing the depth map generation network through the optical flow map and the visibility mask to realize the real-time depth estimation of the monocular endoscope. According to the invention, depth information can be rapidly output in a medical scene with a high real-time requirement, and accurate three-dimensional visual assistance is provided.

The invention provides a video processing method and a related device, which can be applied to a video conference scene. The method comprises the following steps: acquiring a first video stream, and performing motion estimation and motion compensation processing according to two adjacent frames of images in the first video stream to obtain an intermediate frame of image; the coding formats of the two adjacent frames of images are respectively I frames and P frames, or both frames are P frames, and the middle frame of image is coded in a B frame format. And then inserting the target number of intermediate frame images into the corresponding adjacent frames based on a frame rate required by a receiving end device to obtain a second video stream. According to the method and the device, the middle frame image is obtained through MEMC processing based on the adjacent front and back frames, the middle frame image is coded by adopting the format of the B frame, the MEMC and the B frame are calculated based on the difference value of the front and back frames, and the calculation method is similar, so that only a small number of code streams are added after frame insertion processing.

The invention discloses an ultra-high-definition video stream adaptive coding method based on deep learning visual saliency, and the method comprises the steps: carrying out the five-scale Gaussian filtering processing and image pyramid construction of a video frame, and combining Sobel gradient, Laplacian edge and local binary pattern feature extraction to generate a multi-scale feature map; a pre-training saliency detection network is adopted, and a smooth saliency thermodynamic diagram is generated through processing of a feature adaptation layer, a residual encoder, a self-attention mechanism and a transposed convolution decoder; dividing the video frame into a high region, a middle region and a low region according to the saliency thermodynamic diagram, and establishing a regionalization coding parameter table; performing differentiated prediction modes, motion estimation and quantization strategies on different salient regions; and organizing coded data according to an H.265 / HEVC standard, and embedding the saliency thermodynamic diagram into supplementary enhancement information for transmission. According to the method, the important region concerned by the user can be intelligently identified, a differentiated coding strategy based on content semantics is realized, and the coding efficiency is remarkably improved.

A vehicle motion management, VMM, system for a heavy-duty vehicle has at least one wheel speed sensor to output a wheel speed signal indicative of a rotation speed of a respective wheel on the vehicle, at least one inertial measurement unit, IMU, to output an IMU signal indicative of an acceleration of the vehicle, a motion estimation function to estimate a vehicle motion state comprising vehicle speed over ground, based at least in part on the wheel speed signal and at least in part on the IMU signal, and a motion support device, MSD, coordination function to coordinate actuation of a plurality of MSDs of the heavy-duty vehicle in dependence of a vehicle motion request and the vehicle motion state. The motion estimation function models an error in the estimated vehicle motion state, and to output a free-rolling request to the MSD coordination function in case the modelled error fails to meet an acceptance criterion. The MSD coordination function reduces a wheel slip set-point of one or more wheels of the heavy-duty vehicle in response to receiving the free-rolling request from the motion estimation function.

The invention discloses a multi-view target detection tracking method and device based on adaptive fusion and time sequence association, and relates to the technical field of visual tracking, and the method comprises the steps: synchronously obtaining the input images of the current frames of a plurality of camera devices, and carrying out the multi-scale feature extraction and adaptive multi-scale feature fusion, determining scale fusion features of the input images; mapping each scale fusion feature to a bird's-eye view space, and splicing to generate current bird's-eye view information; inputting the current aerial view information and the historical aerial view information of the adjacent historical frames into an improved aerial view time sequence Transform to output aerial view features; after the aerial view features are decoded, aerial view detection and tracking results are determined through a detection head and a tracking head. Through adaptive multi-scale feature fusion, artifacts are reduced, feature detail expression is enhanced at the same time, a bird's-eye view sequence Transform is adopted to perform pixel-level matching between adjacent frames under a bird's-eye view angle so as to enhance fine-grained motion estimation, and the detection and tracking accuracy can be improved.

Faults in master or slave inertial measurement units in a vehicle are identified using measurements of the master inertial measurement unit as reference values to compensate for measurements of the slave inertial measurement unit by estimating fault model parameters with respect to the master inertial measurement unit in order to recognize a fault based on, in each case, two corresponding sensor signals from the two inertial measurement units by comparison. One of the two inertial measurement units is detected as faulty whose motion estimation calculated in a respective downstream Kalman filter in an object tracking unit leads to an incorrect prediction of the calculated positions of objects in the surroundings of the vehicle.

The invention relates to a surgical navigation positioning method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring target spine image data, and constructing a basic image model; acquiring a navigation marker signal, generating a navigation coordinate system, and combining with the basic image model to obtain a navigation positioning model; acquiring a probe positioning signal, and performing motion estimation in combination with the navigation positioning model to obtain an initial operation positioning scheme; in response to the change of the target body position, obtaining a body position correction factor according to the target body position monitoring data; and based on the body position correction factor, correcting the initial navigation operation scheme to obtain an operation positioning correction scheme. By adopting the method, the high-precision image model can be efficiently constructed, and the operation positioning scheme is corrected in real time according to the probe positioning signal and the target body position change, so that the accuracy of operation navigation positioning is improved, and the operation precision and safety are improved.

The invention discloses a method for preprocessing a video and a related product, and generally relates to the technical field of video coding. The method comprises the following steps: partitioning a filtering frame to obtain a first filtering frame macro block with a first size; partitioning the at least one first filtering frame macro block to obtain a second filtering frame macro block with a second size; determining a matching block matched with the macro block of the second filtered frame based on a reference frame associated with the filtered frame; and updating the second filtering frame macro block according to the matching block. According to the method, motion estimation is carried out between a filtering frame and a reference frame based on variable-size blocks, the time domain filtering effect is improved, and the video coding quality is further improved.

The invention relates to the technical field of steel wire rope state detection, in particular to a steel wire rope diameter measuring method and system based on event compensation correction. According to the technical scheme, the system comprises an imaging module, a boundary initial extraction module, a dynamic motion estimation module, a dynamic correction compensation module and a diameter calculation module. The imaging module comprises a traditional camera, an event camera, an optical component, a synchronous triggering device and a spectroscope. The traditional camera is used for collecting frame images of the steel wire rope. According to the method, on the basis of complementary advantages of an event camera and a traditional camera, systematic measurement deviation under the dynamic working condition is solved from the mechanism through an event-driven dynamic geometric compensation model, and quasi-static imaging dependence is broken through. The method does not need extra constraint or increase of frame rate, control cost and hardware complexity, avoids the problems of reduction of signal-to-noise ratio and the like, realizes high-precision stable measurement under high-speed and vibration working conditions, adapts to online monitoring of multiple industrial scenes, and is remarkable in practicability and application value.

The present invention discloses a key joint motion estimation based method for estimating continuous human postures. A motion estimation block matching algorithm is applied to human key joint tracking, so as to obtain continuous human posture results. Meanwhile, the results are continuously corrected by using a deep neural network based human posture estimator. The present invention may estimate the continuous human postures in a video stream, where the human postures are specifically embodied as coordinate positions of human joints in a video frame. Compared with a posture estimation method completely relying on a deep neural network, the posture estimation method provided by the present invention has the advantages of high frame rate, low hardware requirements, and sequential continuity of recognition results; and compared with a posture estimation method completely relying on a motion estimation algorithm, the present invention may correct a cumulative error, to improve the estimation accuracy.

The invention provides a power grid photovoltaic output short-term prediction method and system fused with a meteorological time sequence, and relates to the technical field of photovoltaic power, and the method comprises the steps: reading multi-source meteorological observation data of a target region; performing cloud field motion estimation, and establishing a cloud motion vector field; activating a temperature corrector, reading environment temperature and photovoltaic module temperature, constructing a temperature-module efficiency dynamic model by utilizing a temperature reading result and wind speed and wind direction data of a weather station, and establishing a power upper limit correction factor; after an illumination disturbance signal is established by using the cloud motion vector field, a short-time scale dynamic output prediction curve is generated; and performing power grid operation constraint mapping adjustment, and outputting a calibration prediction result. According to the method and the device, the technical problem of low photovoltaic output short-term prediction precision caused by instability and unpredictability due to the fact that photovoltaic power generation is easily influenced by weather changes in the prior art is solved, and the photovoltaic output prediction precision is improved and the power grid dispatching and operation are optimized by fusing the meteorological time sequence.