88 results about "Adaptive interpolation" patented technology

The invention relates to the technical field of data fusion, and discloses an animal scene-oriented adaptive multi-modal data fusion method, which comprises the following steps of: extracting spatio-temporal characteristics from multi-source heterogeneous data such as visual sense, auditory sense and physiological sensing, constructing an animal-environment-group ternary spatio-temporal relation graph, and constructing an animal-environment-group ternary spatio-temporal relation graph; a pilot frequency sampling problem is solved through an adaptive interpolation algorithm, cross-modal projection alignment is completed in a public semantic space, unified space-time representation is output, and confidence coefficient weight is dynamically calculated based on uncertainty measurement of each modal feature. According to the method, accurate alignment of multi-modal data is realized through the cross-modal space-time attention network, the multi-modal feature alignment error is reduced compared with that of a traditional LSTM method, the training data volume of a federated element migration reinforcement learning framework is reduced compared with that of a traditional migration learning method, and the cross-species generalization performance of the model is improved. A multi-level causal inference engine quantitatively reveals causal association between environmental factors and animal diseases, and in combination with a dynamic decision tree visualization technology, the decision recognition degree is improved.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention discloses a space-time sequence interpolation method and device oriented to heterogeneous deletion, and belongs to the technical field of space-time data processing. Aiming at random or continuous loss of the sensor network caused by faults and communication interruption, the method comprises the following steps: setting static space experts, dynamic space experts, short-term experts and long-term period experts in parallel in the same framework, and respectively capturing fixed geographical adjacency, time-varying space correlation, local continuous trend and long-period rules; spatial features are extracted through high-order diffusion diagram convolution and bidirectional gating circulation, time features are extracted through multi-layer space-time attention, a memory attention gating network is introduced to dynamically weight and fuse output of experts according to reconstruction errors, and node-level and time-step-level self-adaptive interpolation is achieved. Experiments show that compared with the prior art, the method has the advantages that under various real data sets and heterogeneous missing scenes, the precision and robustness are remarkably improved, and the method can be widely applied to scenes needing high-integrity spatio-temporal data, such as intelligent transportation, air quality monitoring and energy internet of things.

The invention relates to the technical field of water quality monitoring and environmental protection, and discloses a water quality abnormity real-time early warning method and system based on machine learning, and the method comprises the steps: obtaining a high-quality original water quality time sequence data flow, and carrying out the fault diagnosis, adaptive interpolation and normalization processing; extracting time features, space correlation features and parameter interaction features, and performing feature splicing and PCA dimension reduction; performing isolated forest detection and semi-supervised recognition on the spatial-temporal feature vectors; carrying out LSTM prediction and risk assessment; performing flow velocity estimation and propagation prediction to obtain collaborative early warning information and a model updating result; calculating a dynamic threshold value and a Bayesian decision to obtain a graded early warning result and an early warning execution scheme; performing abnormal attribution analysis and structured early warning generation to obtain structured early warning information and a push result; according to the method, early warning can be given out in advance when the pollutant concentration does not exceed the standard, and the multi-parameter collaborative abnormal mode is accurately recognized.

The invention discloses a lightweight detection method for small defect targets in a complex industrial scene, and relates to the technical field of target detection technology improvement and deep learning computer vision. Spatial shift operation and a layer-by-layer interaction mechanism are introduced into a feature channel through the SS-CMM module, cross-channel and cross-space effective information fusion is achieved, and therefore the relation between local texture details and the global context is enhanced, and the perception ability of fine defects under the complex background is remarkably improved; the C2PSA-Topk sparse attention mechanism is combined, dynamic selection and weighting of important features are achieved on channel and space double domains, redundant background interference is restrained, discriminative response of tiny defects is highlighted, and the recognition precision of the model for small-scale targets and boundary fuzzy defects is effectively improved. The MS-KSU module introduces a multi-scale convolution and adaptive interpolation strategy in an up-sampling stage, so that a global structure and fine-grained details can be considered in a feature recovery process, and the continuity and integrity of a defect region boundary are ensured.

The invention relates to the technical field related to automatic driving, in particular to a vehicle global positioning deviation correction method based on a roadside sensing result, and the method comprises the steps: obtaining the global pose of a vehicle through a 3D target detection network in which roadside equipment is fused with time sequence features, and carrying out the uncertainty quantification; meanwhile, a vehicle end adopts a tight coupling laser radar and IMU data to realize high-frequency local positioning, a time domain self-adaptive interpolation mechanism is designed for compensating time deviation aiming at the problem of roadside sensing time delay, roadside observation is constructed into a roadside sensing factor of self-adaptive covariance, and finally, through factor graph fusion optimization, a road-side sensing model is obtained. According to the present invention, by combining the IMU pre-integration factor, the laser radar odometer factor and the roadside perception factor, the accurate vehicle pose estimation of the accumulative error correction and the global coordinate system alignment is achieved, and the experiment shows that the method significantly improves the positioning precision and the robustness in the complex environment, and effectively solves the positioning drift problem when the GNSS signal is missing.

The invention relates to the technical field of vision, in particular to an industrial intelligent spraying tracing method, which comprises the following steps of: firstly, acquiring an original depth map of a curved surface workpiece by utilizing a depth camera, and obtaining original data containing depth information; next, edge feature enhancement preprocessing is carried out on the original depth image, edge pixels are accurately extracted through morphological gradient operation and a user-defined anisotropic diamond kernel, and an edge mask image is constructed; then, curvature region division and self-adaptive interpolation restoration are carried out, quadratic polynomial curved surface fitting is carried out on a hole pixel neighborhood, a local curvature value is calculated, a curved surface region is divided according to the curvature value, and corresponding interpolation strategies are adopted for different regions; afterwards, edge detail sharpening post-processing is carried out on the repaired depth image, and the edge contrast is enhanced by using a non-sharpening mask algorithm; and the repaired and sharpened depth map is converted into a three-dimensional point cloud, workpiece pose information is determined based on three-dimensional point cloud data, and spraying track planning is carried out.

The invention discloses an I SAR fusion imaging method, system and device based on centralized multi-station networking, and relates to the technical field of radar imaging, and the method comprises the steps: obtaining I SAR frequency domain echo data of each subsystem; performing frequency domain space matching on the frequency domain echo data based on the observation angle difference of each radar; performing frequency domain alignment processing on the matched frequency domain data; and fusion and imaging are carried out. According to the I SAR fusion imaging method, system and device based on the centralized multi-station networking, the imaging resolution is remarkably improved, multi-station frequency domain data are mapped to a unified local frequency space through a coordinate transformation matrix, non-uniform data are subjected to uniform reconstruction in combination with a self-adaptive interpolation algorithm, and the imaging resolution is remarkably improved. The frequency domain integration interval is effectively expanded, compared with a single-station I SAR which only covers a local area of the total rotation angle of the target, the range resolution is further optimized through multi-station bandwidth synthesis in the range direction, and high-fidelity reconstruction of the target in the complex shape is achieved.

The embodiment of the invention discloses a method and device for generating a dam surface settlement cloud picture based on a self-adaptive interpolation model. A specific embodiment of the method comprises the steps of performing feature extraction on multi-dimensional monitoring point data of a preset dam structure to generate a dam body scene portrait; according to the dam body scene portrait, generating an optimal interpolation algorithm; optimizing the optimal interpolation algorithm to generate an optimized interpolation algorithm; generating a non-oscillation cloud picture according to dam body physical attributes of a preset dam structure and an optimization interpolation algorithm; performing preset-size grid block division on the non-oscillation cloud picture to generate a cloud picture grid node set; performing parallel processing on each cloud picture grid node in the cloud picture grid node set to generate an accelerated interpolation result; and performing incremental updating on the accelerated interpolation result to generate an updated interpolation result. According to the implementation mode, the accuracy, efficiency and engineering applicability of dam settlement interpolation are improved.

The invention discloses an ocean terrain digital display method based on three-dimensional modeling. The ocean terrain digital display method comprises the following steps of 1, constructing a multi-source ocean data set; 2, performing space fusion by adopting a self-adaptive interpolation method based on position weight to generate a three-dimensional space density field; 3, performing contour surface extraction by adopting an improved Marching Cubes algorithm to generate an initial three-dimensional terrain model; 4, performing local modeling by adopting a Delaunay triangulation network reconstruction method, and constructing a complete three-dimensional terrain model; 5, obtaining a texture terrain model; step 6, constructing an LOD tree structure and realizing dynamic loading in combination with view cone cutting and a screen space error control strategy; and step 7, based on a dynamic loading result of the LOD tree structure, driving node switching through a time controller, and performing visual display. According to the method, three-dimensional modeling and an LOD tree structure are fused, and high-precision reconstruction and evolution display of the ocean topography are achieved.

The invention provides a ground-based laser radar near-surface wind field modeling method and system based on data fusion, and relates to the technical field of wind field modeling, and the method comprises the steps: firstly carrying out the multiple scanning of a target terrain region through a ground-based laser radar, so as to obtain an observation data set; acquiring land surface temperature data matched with the three-dimensional point cloud data in a space-time manner, and fusing the land surface temperature data with the three-dimensional point cloud data to generate a coupling data set; generating initial wind field feature vectors at different moments based on the coupling data set; the vectors are processed through a long-short-term memory network, and target wind field feature vectors are generated; inputting the target vector into a wind field inversion model constructed based on a high-order Taylor expansion polynomial, and calculating three-dimensional wind field data and turbulence characteristic data; and finally, constructing a near-ground wind field model of the target terrain area according to the data through a self-adaptive interpolation algorithm. According to the invention, the precision and accuracy of complex terrain near-surface wind field modeling are improved.

The invention provides a mine equipment intelligent operation and maintenance system based on digital twin driving, and relates to the technical field of industrial intelligence and equipment operation and maintenance. According to the system, a digital twin model is established through a twin construction module, and virtual sensing nodes are arranged. And the environment perception correction module reconstructs credible signals through multi-source data adaptive interpolation by comparing virtual and real sensing node deviations under a severe working condition. The virtual-real dynamic correction module adjusts model parameters in real time based on state differences, and identifies sudden and slow differences to extract early fault features. And the fault evolution prediction module constructs a time-varying characteristic link in the corrected data space, and predicts a hidden fault evolution path by analyzing a response trend and an energy transfer rule. And the intelligent decision execution module generates a grading response strategy according to the result, realizes maintenance scheduling optimization and operation condition adaptive adjustment, verifies the decision effect through digital twinborn feedback, and significantly improves the reliability and the intelligent level of mine equipment operation and maintenance.

The invention belongs to the technical field of path planning and robot motion planning, and particularly relates to a multi-strategy fusion improved RRT * sampling planning method and device and a storage medium, and the method comprises the steps: constructing a dual-search tree through a starting point and a target point in a three-dimensional working space, and selecting a to-be-expanded search tree through a random mode; halton-Bridge mixed sampling is adopted to generate sampling points, so that the global coverage uniformity is improved, and the narrow channel sampling capability is enhanced; calculating the dynamic step length of the exponential decay based on the local obstacle proportion; adopting a hierarchical guidance expansion strategy, preferentially expanding towards a target, introducing an improved artificial potential field to guide expansion when the expansion fails, and taking the bottom with random expansion; performing RRT * neighborhood father node reselection and reconnection optimization on the new node; greedy pruning, self-adaptive interpolation and cubic B-spline smoothing are carried out on a path after the double trees are connected, and an executable smoothing track is output; according to the scheme, planning efficiency, success rate and path quality are improved.

The invention relates to the technical field of data processing, and discloses an energy consumption loss data verification and correction method and system, and the method comprises the steps: carrying out the data cleaning of original energy consumption data, and obtaining standard energy consumption data; according to data distribution in a normal working condition in the historical energy consumption data set, generating a dynamic verification threshold range matched with the load type in a current time window; collecting data points exceeding a dynamic verification threshold range in the standard energy consumption data into a preliminary abnormal data point set of the standard energy consumption data; determining a target abnormal data segment of the preliminary abnormal data point set according to context information features of the preliminary abnormal data point set; based on the time sequence similarity of the standard energy consumption data, performing adaptive interpolation on the target abnormal data segment to obtain a corrected data set of the target abnormal data segment; outputting the corrected data set passing the integrity verification as a target verification report of the original energy consumption data; according to the invention, the efficiency of verifying and correcting the energy consumption loss data can be improved.

The invention discloses a CBCT metal artifact suppression method and system based on preoperative prior guidance, and the method comprises the steps: taking second CBCT image data, obtained in real time in an operation, of a target tissue as a reference, and carrying out the rigid registration of first CBCT image data, obtained before the operation, of the target tissue; deforming the first CBCT image data to obtain third image data; the relative positions of the tissue structures in the third image data and the second CBCT image data are consistent; based on the third image data, performing coarse segmentation on the metal implant in the second CBCT image data by adopting a digital subtraction technology; segmenting to obtain first mask data and background prior data of the metal implant; performing true and false positive judgment on the first mask data and performing false positive suppression processing to obtain second mask data; carrying out data restoration on the background prior data by adopting a self-adaptive interpolation grid to obtain projection domain data; and reconstructing by using an FDK reconstruction algorithm to obtain a CBCT image of the target tissue without metal artifacts.

The invention discloses a power grid fault-oriented multi-level spatio-temporal data processing and visualization method, comprising spatio-temporal reference unification, including sliding window adaptive interpolation and interpolation result optimization; performing spatial reference normalization, including spatial quantization alignment and power grid-meteorological coupling modeling; data governance, redundancy elimination and missing repair, including dynamic time warping de-duplication, principal component analysis dimension reduction and missing data repair; the visual architecture design comprises a physical power grid layer, an alarm signal layer and a disposal process layer. Through the spatio-temporal data fusion technology, the operation state and fault characteristics of the power grid equipment are accurately captured, and timely and accurate fault diagnosis support is provided; a hierarchical visual architecture is designed, so that a user can go deep into a microscopic equipment level from a macroscopic power grid state layer by layer, fault positioning and analysis are helped to be more accurate and efficient, understanding of power grid dispatchers on fault conditions is enhanced, and rapid response and decision making are achieved.

The invention discloses a loess mechanical parameter spatio-temporal interpolation prediction model construction method based on big data, and relates to the technical field of geotechnical engineering data analys.The loess mechanical parameter spatio-temporal interpolation prediction model construction method comprises the following steps that loess mechanical parameter data, environmental factor data and geologic structure data are collected through a multi-source sensor, and spatio-temporal unified multi-modal data are constructed; designing a collaborative kernel function fusing spatial heterogeneity and time dynamics on the basis of multi-modal data, and quantifying relevance between mechanical parameters and space-time positions and environmental factors; according to the collaborative kernel function, dynamically optimizing a space weight, a time attenuation coefficient and a multi-source data fusion weight, and generating a parameter-adaptive interpolation model; carrying out distributed training and calculation acceleration on the interpolation model by adopting a block matrix approximation and parallel calculation architecture; through the trained model, a mechanical parameter prediction value of a target space-time position is calculated in real time, and the calculation efficiency is improved through a tensor expansion algorithm.

The invention discloses a self-adaptive interpolation restoration device and method for automatic driving continuous time-space data transmission, belongs to the technical field of automatic driving data transmission and processing, and solves the problems of high bandwidth occupation, insufficient restoration precision, poor scene adaptability and the like of an existing transmission scheme. The device comprises a data acquisition module, a vehicle end edge calculation module, a self-adaptive interpolation reduction module and an error verification module. The method comprises the steps that a vehicle end collects multiple types of spatio-temporal data and adds identifiers, dynamic characteristic parameters are extracted to divide low, medium and high dynamic driving scenes, a starting and ending point interval is adaptively configured, data are packaged in a customized mode, a cloud end restores the data according to a scene matching interpolation algorithm, error verification is conducted through original spatio-temporal data cached by the vehicle end, and the data are stored in the cloud end. If the threshold value is exceeded, triggering supplementary transmission and reducing the interval between the starting point and the ending point. According to the method, the data transmission quantity and bandwidth occupation are greatly reduced, the restoration precision and the data reliability are improved, the vehicle end energy consumption is reduced, and the real-time data interaction requirements of all working conditions of automatic driving are met.

The invention belongs to the technical field of intelligent transportation, and provides a truck stop point prediction method based on directed graph spatial-temporal feature fusion, comprising the following steps: S1, preprocessing truck trajectory data through a multi-modal trajectory preprocessing subsystem; s2, constructing a spatial-temporal characteristic matrix with the stay point as a core; s3, encoding the data by using a space-time directed graph encoder, and extracting related features of a stay point; s4, constructing a graph neural network architecture to predict a track and a truck stop point; the step S1 comprises dynamic interruption detection, semantic stay identification and intention analysis, adaptive interpolation and sparsification processing, and stay point determination by using a space-time window function. According to the method, refined staying point classification prediction facing business requirements can be provided. And effective early warning of future key staying behaviors of the truck can be realized.

The present application belongs to the technical field of power grid management and control, and particularly relates to a method for smoothly switching between main and sub scenes based on three-dimensional GIS power grid digital space control. A power grid digital space model is constructed through three-dimensional GIS technology, and main and sub scenes are divided, covering detailed information such as power equipment, to ensure consistency with actual data. An adaptive interpolation algorithm is used, combined with device state and load change to adjust the transition path, to control the smoothness with weighted interpolation, avoiding lag. Hierarchical gradual rendering is introduced, rendering weights are allocated according to device load and importance, and dynamic fuzzy control is used to improve rendering effect. A differentiated loading strategy and dynamic adjustment precision are used to optimize loading speed and synchronization accuracy, reducing response delay. The present application effectively solves the problem of traditional scene switching, improving the visual effect and real-time performance of the power grid.

The invention provides an image amplification method for gradient adaptive interpolation and noise suppression, and belongs to the field of image processing before ink-jet printing. Extracting a sawtooth texture position of the gradient mask image as a noise point through a directional non-extreme suppression operator; performing median filtering on the noise points to generate an intermediate stage gradient image; self-adaptive interpolation processing is carried out according to the edge texture position of the image in the middle gradient stage; the interpolation processing result is corrected through gradient weight and distance weighting, and interference fringes are suppressed; and sending the amplified image block after interference removal into a buffer area. According to the image amplification method based on gradient adaptive interpolation and noise suppression, the processing efficiency is remarkably improved through intelligent blocking and parallel optimization, and sawtooth noise caused by high-power amplification is accurately eliminated by combining a hierarchical amplification strategy and a directional non-extreme suppression operator. And the adaptive interpolation algorithm based on gradient guidance effectively inhibits interference fringes while keeping edge sharpness, and realizes synchronous optimization of operation speed and visual quality.

The invention provides a severe convection identification method and device based on GNSS occultation observation data, and the method comprises the steps: collecting original observation data, carrying out the induction and sorting of the original observation data according to an established real-time data pool, and carrying out the quality control of the sorted original observation data, so as to obtain quality control observation data; performing pattern gridding on the quality control observation data, and interpolating the quality control observation data to numerical pattern grid points through a multi-source constraint adaptive interpolation algorithm to form severe convection single puzzle data; performing data assimilation on the strong convection monomer puzzle data based on a Bayesian-Gaussian mixture assimilation model constrained by observation data to generate an analysis field; and driving a global weather forecasting mode to carry out numerical forecasting hour by hour according to the analysis field so as to identify the severe convection monomers. According to the invention, the precision and efficiency of meteorological data processing, fusion, forecasting and severe convective weather monitoring and early warning are significantly improved, and the precise forecasting capability of severe convective weather is improved.

The present application relates to the field of endoscope imaging, in particular to a single-channel filtering demosaicing method and system for endoscope images; the horizontal direction local gradient intensity and the vertical direction local gradient intensity of green pixels in a color filter array image are calculated, the green pixels are adaptively interpolated according to the difference between the horizontal direction local gradient intensity and the vertical direction local gradient intensity, and an initial green channel is obtained; the pixels in the initial green channel are subjected to bilateral filtering, and a filtered green channel is obtained; based on the filtered green channel, residual interpolation is used to respectively reconstruct a red channel and a blue channel, and a demosaiced image is obtained; the present application reduces the risk of false color and noise amplification, has the characteristics of strong edge preservation ability, high overall color restoration precision, simple algorithm and easy hardware implementation.

This invention belongs to the field of fault prediction and health management technology, specifically involving a machine learning-based method and system for monitoring the operating status of vehicle-mounted brushless fans. The method includes: acquiring multi-source time-series data of the vehicle-mounted brushless fan drive circuit and constructing a non-stationary distortion index; dynamically triggering an adaptive equal-angle synchronous resampling mechanism using the non-stationary distortion index, combining virtual electrical angles and introducing an adaptive interpolation density function to determine the interpolation accuracy at each angle position, thereby mapping the three-phase stator current into a stationary sequence in the angular domain; performing order analysis on the stationary sequence in the angular domain to obtain the order spectrum, introducing the average value of the non-stationary distortion index combined with a logarithmic function for weighting to obtain fault judgment feature values; inputting the fault judgment feature values ​​along with the effective current value into a vector machine classifier to identify and output the operating status label of the vehicle-mounted brushless fan and execute online closed-loop early warning. This invention improves the fault diagnosis accuracy of vehicle-mounted brushless fans under varying operating conditions.

This invention relates to an intelligent processing method for wind profiler data, which solves the problems of existing wind profiler data processing methods, such as reliance on manual intervention for identifying missing height layers and the lack of structural consistency guarantees in interpolation methods. This method achieves automatic structural analysis and intelligent completion of missing height layers in wind profiler data through multi-step collaborative processes. The steps sequentially execute data access and format standardization, height layer structure parsing and index construction, intelligent identification of missing height layers, interpolation condition judgment, adaptive interpolation calculation, data structure reconstruction, and database storage. It automatically identifies missing height layers and adaptively selects interpolation algorithms to generate interpolation results based on data change trends, ensuring the integrity and continuity of the data structure and providing a reliable data foundation for wind field analysis models. This invention can be widely applied to wind profiler radar data platforms, meteorological data operational processing systems, and other scenarios.

The application discloses a kind of chlorophyll relative content prediction method and system based on multispectral image, the method includes: obtaining multi-source data;According to environmental data, determine phenological period category and environmental stress category, and determine comprehensive scene mode accordingly;Environment data and multispectral image are extracted, and vegetation index feature, texture feature and environmental feature are obtained;According to comprehensive scene mode, scene self-adapting interpolation is carried out;According to environmental feature, vegetation index feature correction parameter is generated, and vegetation index feature is dynamically corrected;The corrected feature is constructed as multi-source time series feature sequence, multi-source time series feature sequence is weighted and fused, and input prediction model, and the chlorophyll relative content prediction value is obtained.The system includes data acquisition module, scene mode determination module, feature extraction module, feature processing module and prediction module.The application can be widely applied in the field of agricultural information technology.

This invention provides a time-domain correction method based on local adaptive thin-plate spline interpolation, comprising the following steps: S10, separating the pilot signal from the mixed measurement signal, performing fine phase tracking on the pilot signal, and extracting the phase noise sequence introduced by clock jitter; S20, converting the phase noise sequence into jitter at the sampling time; S30, determining the ideal sampling time, selecting several original sampling points adjacent to each ideal sampling time, obtaining a local interpolation subset, evaluating the jitter intensity of the local interpolation subset, and performing adaptive interpolation calculation accordingly to obtain the signal value at the ideal sampling time; S40, outputting the uniformly sampled signal sequence after interpolation reconstruction; this invention also provides a time-domain correction system based on local adaptive thin-plate spline interpolation. This invention solves the problems of poor real-time performance and weak adaptability of traditional TPS interpolation, achieving high-precision suppression of clock jitter noise and meeting the stringent requirements of phase measurement for space gravitational wave detection.

The application discloses an automatic driving continuous space-time data transmission adaptive interpolation restoration device and method, belongs to the automatic driving data transmission and processing technical field, and solves the problems of high bandwidth occupation, insufficient restoration precision, poor scene adaptability and the like of an existing transmission scheme. The device contains six modules of data acquisition, vehicle end edge calculation, adaptive interpolation restoration, error checking and the like. The method is that multiple types of space-time data are collected at the vehicle end, and an identifier is added, dynamic characteristic parameters are extracted to divide low, medium and high dynamic driving scenes, adaptive configuration of start and end point intervals is carried out, and customized data packaging is carried out, data is restored according to a scene matching interpolation algorithm at the cloud end, error checking is carried out through original space-time data cached at the vehicle end, and if a threshold value is exceeded, supplementary transmission is triggered and the start and end point intervals are reduced. The application greatly reduces data transmission quantity and bandwidth occupation, improves restoration precision and data reliability, reduces vehicle end energy consumption, and adapts to real-time data interaction requirements of automatic driving in all working conditions.

The embodiment of the invention provides an image signal processing method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: carrying out the denoising of an image signal collected by a nine-unit sensor, and generating a denoised image signal; performing wavelet decomposition on the denoised image signal to generate a plurality of sub-band signals; calculating an interpolation weight according to the plurality of sub-band signals, and performing channel interpolation operation to generate a target pixel value; the method comprises the following steps: performing linear combination on target pixel values through a reconstruction matrix to generate a reconstructed image signal, and performing denoising preprocessing, wavelet decomposition and multi-sub-band analysis on the image signal, adaptive interpolation based on sub-bands and linear combination of the reconstruction matrix to obtain a reconstructed image signal. The problems of noise interference, color information loss and poor reconstruction quality in the original image of the nine-unit sensor are solved, and information loss or redundancy in the signal decomposition and reconstruction process is avoided in combination with the multi-scale analysis capability of wavelet transform and the adaptive interpolation strategy.

The application discloses a structure surface grid automatic generation method based on topological filling, and relates to the technical field of aerospace engineering simulation and grid generation. The method comprises the following steps: intelligently dividing a three-dimensional CAD model into parts to obtain parts with semantic labels and geometric contours; sequentially performing ordered topological filling on each part according to a preset order, adjusting the number of intersection edge points of adjacent parts to ensure grid connection, and generating topological lines; projecting the topological lines onto the model surface for calibration; adaptively interpolating the projected topological lines based on model curvature and simulation conditions to generate point cloud data; generating an initial quadrilateral grid based on the point cloud; projecting the initial grid nodes onto the model surface again and optimizing the quality, and outputting the final grid. The application realizes end-to-end automatic generation from a CAD model to a high-quality surface grid, eliminates manual intervention, ensures the natural matching of grids between parts, and significantly improves the grid generation efficiency and quality.