188 results about "Space time domain" patented technology

The invention discloses a dynamic planning method for an air route of an urban low-altitude unmanned aerial vehicle. The method comprises the following steps: firstly, constructing an urban low-altitude environment model based on an airspace rasterization technology, fusing multi-dimensional constraint factors such as geographic data, meteorological conditions and airspace control information through multi-source environment information, and accurately calibrating the position of a take-off and landing point; secondly, a dynamic grid availability evaluation model is established in combination with environmental constraints and a real-time airspace state, and the navigation feasibility of each grid unit is quantitatively analyzed; then, an improved A * algorithm is combined with a grid availability evaluation result to generate a global optimal initial route; finally, a rolling time domain optimization strategy is introduced, and uncertain factors such as sudden obstacles and airspace dynamic limitation are responded in real time through periodic non-flight route re-planning after the unmanned aerial vehicle takes off. Through collaborative fusion of static environment modeling and a dynamic optimization mechanism, the problem of real-time planning of the air route of the unmanned aerial vehicle in the urban low-altitude complex environment is effectively solved, and the environmental adaptability and task reliability of the system are remarkably improved.

The invention relates to a quick river flood forecasting method based on a physical information neural network, and belongs to the field of river flood forecasting. The method comprises the following steps: on the basis of a traditional physical information neural network (PINN), introducing a boundary condition parameter as an input variable, and enabling the PINN to learn a flood wave propagation rule under different boundary conditions. Furthermore, on this basis, a physical information neural network flood fast forecasting framework (RFF-PINN) integrated with a hydrodynamic method is provided, a numerical solution based on grid discretization is reconstructed into a continuous function in a time-space domain through a piecewise polynomial interpolation method, residual error loss between network output and a hydrodynamic model simulation value is constructed, and therefore, a flood fast forecasting result is obtained. The network parameters are optimized in cooperation with the PDE loss, and the problem that the network parameter optimization effect is reduced due to the fact that the PDE loss of the complex flow state area is difficult to converge is solved. The method has the beneficial effect that the water depth change process of each section of the river channel under any boundary condition can be accurately and quickly predicted.

The invention relates to the technical field of geotechnical mechanics and engineering, and particularly discloses a soft soil foundation settlement monitoring method and system based on multi-field multi-source information, and the method comprises the steps: collecting multi-source data of a target soft soil area; performing constitutive parameter inversion, data standardization and discrete Fourier transform frequency domain conversion on the data to obtain a standardized frequency domain multi-field multi-source data set; based on the data set, a soft soil constitutive parameter library and a soil mechanics physical equation, constructing an FD-PINN frequency domain physical information neural network, embedding the physical equation as a prior constraint, and training the model by adopting an alternating optimization strategy; inputting the real-time frequency domain data flow into the model, and outputting the current settlement amount, the settlement rate and the multi-physical field frequency domain distribution; and in combination with a pre-established large scale model test result, through IDFT inverse transformation, a time-space domain settlement field is reconstructed, multi-stage early warning is triggered, a targeted reinforcement scheme is recommended, the soft soil foundation settlement monitoring precision and the engineering practicability are remarkably improved, and the method is suitable for construction, operation and maintenance of infrastructures such as high-speed rails and highways.

The invention discloses a space-time speckle projection three-dimensional imaging method based on multi-frame optical flow alignment. Firstly, a projector based on DLP is used for projecting a space-time speckle pattern to a measured scene, and a binocular camera synchronously collects a three-dimensional space-time speckle image. The calibration parameters of the binocular camera are used to carry out stereo correction on an acquired original speckle image, and a parallax image is generated frame by frame in combination with a coarse-to-fine single-frame speckle matching strategy. And estimating a two-dimensional inter-frame displacement field between continuous disparity maps by using an optical flow method by taking an intermediate frame disparity map as a reference, compensating motion artifacts in a space-time speckle image, and ensuring strict space-time registration of a dynamic target. And based on the speckle image after motion correction, a speckle matching strategy is expanded to a time-space domain, and high-precision multi-frame three-dimensional measurement of a complex dynamic scene is realized. The method is suitable for performing rapid and high-precision three-dimensional modeling on a moving target in an unstructured environment, and can perform accurate three-dimensional measurement on a high-speed dynamic target undergoing any translation or rotation motion.

The invention discloses a cable accessory nondestructive testing method based on image recognition, and relates to the technical field of data encryption, and the method comprises the steps: extracting a photon signal from an anti-noise coding control template, generating an anti-interference partial discharge light spot image in combination with a compressed sensing reconstruction algorithm, and carrying out the alignment fusion of the anti-interference partial discharge light spot image and LiDAR point cloud data of a cable accessory, generating spatial registration feature data; based on the neural radiation field network, constructing a three-dimensional radiation field model, inputting spatial registration feature data, generating time-space domain deformation field data and partial discharge hot spot distribution data, and generating a defect candidate region by using a spatial weighted fusion algorithm; a deep residual network is used as a framework, a transfer learning strategy is combined, a cable accessory defect model is constructed, a defect candidate area is input, and a cable accessory health state report is generated. According to the method, the three-dimensional radiation field model is constructed by using the neural radiation field network as a framework, accurate positioning and characterization of defects are realized, the detection reliability is improved, and the method is suitable for micro-defect identification.

The invention belongs to the technical field of robot path planning, and provides a path planning method for an electric power inspection mobile robot, which comprises the following steps of: firstly, constructing an environment grid map, establishing a corner directional expansion safety boundary, and performing safety expansion on an outer right angle of an obstacle; secondly, establishing an intelligent neighborhood selection mechanism based on fuzzy control, and performing adaptive adjustment neighborhood search; then, a multi-target heuristic function fusing the local obstacle density, the safety distance and the traffic difficulty is established, path search is carried out, and an initial path is obtained; then, constructing a path optimization strategy, and carrying out redundant point deletion and B spline curve smoothing processing on the initial path until a target executable path is obtained; and finally, optimizing an executable track in the target executable path in a time-space domain through an improved time-domain elastic band algorithm to obtain an optimal path. According to the invention, accurate obstacle avoidance of complex geometric obstacles can be realized.

The invention relates to an unmanned aerial vehicle track optimization method, system and device in an urban high-density airspace and a medium, and the method comprises the steps: dynamically dividing the urban airspace into space-time four-dimensional grid units of different scales according to the vertical height and the time axis based on the real-time airspace traffic density and environmental conditions; establishing a guide vector field pointing to a target position in each four-dimensional grid unit, and when an obstacle and / or a flight conflict is detected, generating a local disturbance vector and dynamically correcting the guide vector field in real time so as to autonomously generate a candidate flight trajectory; and calculating the energy consumption of the candidate flight path based on an unmanned aerial vehicle dynamics model and an environment model, and selecting a path with the lowest total energy consumption as a final flight path under the condition of meeting safety constraints and task time requirements. Compared with the prior art, in a complex dynamic environment of an urban high-density airspace, unification of real-time efficient planning of the flight path of the unmanned aerial vehicle, safe obstacle avoidance and low-energy-consumption optimization is realized.

The invention discloses a multi-scene micro garbage real-time detection method and a multi-scene micro garbage real-time detection system, and aims to solve the defects of an existing garbage detection method in the aspects of small target identification, environmental adaptability and system integration. According to the method, on the basis of an improved YOLOv8 architecture, a Transform module and an efficient channel attention mechanism are fused, and the tiny target feature extraction and multi-scale information fusion capability is enhanced. The system adopts dynamic resolution reasoning, INT8 quantitative compression and time-space domain post-processing, so that the detection precision and the reasoning efficiency are remarkably improved. Experiments prove that the system effectively improves the small target recall rate and environmental adaptability in a garbage detection task, comprises eight corresponding modules, realizes full-process automatic detection, has relatively high real-time performance and automation level, is suitable for various application scenes such as smart city environment monitoring, and has important practical application value.

The invention, which belongs to the technical field of remote sensing image processing and geological disaster monitoring, discloses a high-risk slope multi-temporal remote sensing image deformation monitoring and early warning system comprising a multi-platform remote sensing data acquisition module, a deep neural network registration module, a sub-pixel-level deformation detection module, a meteorological data fusion module and a dynamic early warning module. The centimeter-level registration precision is realized by introducing an adaptive feature registration mechanism based on a graph attention network, the deformation estimation precision of a texture sparse region is improved by adopting a time-space domain combined sub-pixel-level deformation field estimation algorithm, and the early warning timeliness is improved by establishing a dynamic early warning threshold self-adjustment mechanism fused with meteorological data. The system supports multi-scale multi-platform cooperative monitoring, the early warning advance period is prolonged by 7-15 days, and the system is successfully applied to high-risk slope monitoring projects along the expressway.

The invention provides a video coding and compression transmission method for intelligent equipment, which belongs to the technical field of video coding and compression transmission, realizes advanced channel quality estimation by establishing a channel prediction model enhanced by a spatial transformation network, configures layered coding parameters according to a prediction result and optimizes motion search in combination with inertial measurement unit data. An optimal coding mode is selected by an application rate distortion optimization algorithm, enhancement layer sending is adjusted according to real-time feedback by adopting a dynamic transmission scheduling strategy, and differential forward error correction protection and a time-space domain error concealment mechanism are matched; the technical problem that video transmission is unstable due to violent fluctuation of channel quality when intelligent equipment moves and works in complex electromagnetic environments such as a transformer substation is solved.

The invention relates to the technical field of building detection, and discloses a building retard-bonded prestress intelligent detection method based on the Internet of Things. The method comprises the steps of collecting and preprocessing real-time monitoring data of the retard-bonded prestressed structure through an Internet of Things platform, and filtering interference signals to improve data quality; classifying the preprocessed data according to a preset stress state interval, and extracting time-space domain features of each interval to generate a stress feature set; detecting the change frequency of the stress state interval, and starting a dynamic adjustment mechanism to mark an abnormal interval if the change frequency exceeds a threshold value; according to the matching degree of the historical damage mode of the structure and the current feature, a non-abnormal interval stress feature set is decomposed to generate a damage feature subset, multi-parameter collaborative correction is carried out in combination with the incidence relation of material mechanics and environmental action parameters, and finally the position of a potential damage source is reversely traced along a damage evolution path. And determining a maintenance priority and generating a maintenance instruction.

The invention discloses a gas leakage infrared detection algorithm for self-adaptive environmental noise suppression. The gas leakage infrared detection algorithm comprises the following steps: S1, preprocessing input data; s2, adaptive filtering is carried out; s3, carrying out multi-frame fusion, carrying out time-space domain analysis on continuous infrared image frames, separating high-frequency noise and low-frequency gas signals by utilizing wavelet transform, calculating a difference image of adjacent frames by combining an inter-frame difference method, enhancing gas diffusion morphological characteristics, distributing a weight for each frame according to a signal-to-noise ratio, and synthesizing a final image; and S4, post-treatment: concentration quantification and leakage area marking treatment are respectively carried out. According to the gas leakage infrared detection algorithm for self-adaptive environmental noise suppression disclosed by the invention, high sensitivity is kept through a self-adaptive environmental noise suppression algorithm, meanwhile, environmental noise interference is effectively eliminated, and the detection stability and accuracy in a complex scene are improved.

The invention discloses a human body posture sensing method and device based on channel state information. The method comprises the following steps: firstly, acquiring the channel state information of a communication signal for human body posture sensing; secondly, preprocessing the channel state information to respectively obtain a time-space domain tensor, a frequency domain tensor and a wavelet domain tensor; and inputting the time-space domain tensor, the frequency domain tensor and the wavelet domain tensor into a pre-trained target network model, performing feature fusion on the time-space domain tensor, the frequency domain tensor and the wavelet domain tensor by the target network model, performing human body posture prediction based on the fused features, and outputting human body posture information. According to the method, the fusion features of the time-space domain tensor, the frequency domain tensor and the wavelet domain tensor are adopted for human body posture prediction, joint information of the time-space domain, the frequency domain and the wavelet domain of communication signals can be fully mined, the expression ability and the utilization efficiency of signal features are improved, and therefore the human body posture prediction accuracy of the model is improved, and the human body posture prediction efficiency is improved. And the sensing precision of the human body posture is improved.

The invention relates to a figure operation planning method and device based on satellite remote sensing, equipment and a medium. The method comprises the steps that structured processing is conducted on multi-source satellite remote sensing data, an integrated data packet is obtained, and the integrated data packet comprises a cloud catalysis potential matrix, a terrain slope matrix, an obstacle distribution matrix and a real-time airspace occupation state; analyzing the cloud catalysis potential matrix in the integrated data packet, generating an executable task set, performing task division on the task set, obtaining decomposed tasks, performing resource verification on the decomposed tasks, and generating a verified task set; and obtaining a historical risk coefficient matrix, and constructing an executable operation instruction based on the verified task set, the terrain gradient matrix, the obstacle distribution matrix and the historical risk coefficient matrix. According to the method, by means of multi-source data integration, multi-constraint task generation, instruction construction based on historical risks and real-time environments and the like, the efficiency and accuracy of figure operation planning and the safety and reliability of operation execution can be improved.

The invention discloses a local motion and frequency domain knowledge-guided motion infrared small target detection method, which comprises the following steps of: firstly, constructing a motion infrared small target detection network model, inputting a continuous infrared image, respectively carrying out feature extraction on a spatial domain and a local time-space domain through a fast multi-scale backbone network and an efficient local motion Mama module, and carrying out feature extraction on the continuous infrared image; and the multi-directional adaptive frequency domain enhancement module effectively reduces the sampling information loss of the target information in the fast multi-scale backbone network, and the efficient local motion Mama module fully utilizes the local multi-frame information to carry out time sequence modeling, and finally a key frame target prediction image is obtained, and the motion infrared small target detection is completed. The method can effectively improve the efficiency of feature extraction, reduces false alarms generated by background shaking, and guarantees the real-time performance of multi-frame target detection by adopting an efficient module and a multi-position lightweight design.

The invention discloses a nuclear safety simulation verification method and device based on digital twinning and a medium, and relates to the technical field of nuclear engineering and digital twinning, and the method comprises the steps: training a physical information neural network based on a digital twinning initial model in combination with a nuclide transport equation and a thermal hydraulic equation, and outputting a nuclear safety multi-field simulation result; based on a nuclear safety multi-field simulation result, establishing a space-time diagram structure, extracting dynamic evolution characteristics by using a space-time diagram convolutional network, and outputting a nuclear accident propagation path and a risk distribution diagram; according to the method, the space-time diagram structure is constructed, the space-time diagram convolutional network is utilized to extract the dynamic evolution characteristics, the nuclear accident propagation process is modeled as cascade response in the space-time domain, and the reliability of the nuclear accident propagation process is improved. And the accuracy, the foresight and the intelligent level of nuclear security simulation verification are improved.

The invention relates to the technical field of satellite navigation and positioning, in particular to a Beidou signal anti-interference method based on space-time state prediction.When the Beidou signal anti-interference method is used, time-space domain parameters, including key information such as directions and phase differences of direct and multi-path signals, of Beidou signals are extracted, and multi-path special suppression measures are combined, so that the anti-interference performance of the Beidou signals is improved; a multi-path signal spectrum peak is subjected to graded attenuation, a positioning result is calibrated in cooperation with a scene correction coefficient, finally, the urban canyon scene positioning precision is achieved, high-precision scene application such as automatic driving and precise surveying and mapping can be stably supported, meanwhile, a prediction model integrating LSTM and Kalman filtering is constructed, a multi-path signal prediction result is output in advance, and the accuracy of urban canyon scene positioning is improved. In cooperation with the rapid matching function of the interference feature library, the method can adapt to the dynamic change of multi-path signals, ensures that interference suppression measures take effect in time, and avoids signal receiving interruption or positioning deviation sudden increase.

The invention discloses a remote sensing time series data analysis method and system considering geographic object space-time correlation, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring and preprocessing a multi-source multi-temporal remote sensing image and auxiliary data to generate a consistent time sequence data stack; dividing the image into voxels in a three-dimensional time-space domain, carrying out adjacent tracking, identifying a geographic process object with an evolution behavior, and recording the attribute of the geographic process object; judging a space-time topological relation between geographic process objects, and generating a unified space-time relation table through composite reasoning; constructing a geographic process object space-time diagram model by taking the geographic process object as a node and the space-time relationship as an edge, and extracting and standardizing attribute features of the node and the edge; a graph convolutional network and a topology dynamic mechanism are used for joint modeling, and the importance index of each node is calculated. According to the method, closed-loop analysis from data processing, relation modeling and importance evaluation to decision support is realized, and the interpretability of remote sensing time sequence change detection and decision effectiveness are improved.

The invention discloses a swimming skeleton point coordinate data denoising method and device based on spatio-temporal topological structure learning, and belongs to the field of data processing. The method comprises the following steps: acquiring and splicing a swimming video of a target object; detecting a target object in the video, and obtaining skeleton point coordinates and corresponding confidence scores of the target object by adopting a posture estimation model; preprocessing the coordinates to obtain feature vectors; adjacency information of each skeleton point is obtained based on the human body skeleton topology, the feature vectors of all the skeleton points are input into a spatial domain noise removal network, the adjacency information of the skeleton points is aggregated, multi-scale pooling is carried out, and denoised spatial domain features are obtained; sampling the high-dimensional spatial-temporal characteristics by adopting a deformable time convolutional network; and carrying out deformable convolution operation and decoding operation on the sampling features to obtain skeleton point coordinates after time-space domain denoising. Linear and nonlinear noise in data is processed in a time-space domain in a cooperative manner, so that the limitation of a traditional low-pass filter method and an existing ST-GCN method is effectively overcome.

According to one aspect, local path planning may include classifying an object within an operating environment as an upper bound object, a lower bound object, or infeasible based on a distance between a bounding box associated with the object and an upper environment feature, a distance between the bounding box associated with the object and a lower environment feature, and a cost function, generating a boundary associated with the object and the upper environment feature or the lower environment feature based on the classification of the object and boundary points of the bounding box associated with the object, and generating a local path planning trajectory for a vehicle based on the classification of the object, the boundary associated with the object, and transforming a kinematic model from a space-time domain to a space-only domain.

The invention discloses a battery charging and discharging memory degree prediction method and system based on time-space domain conjoint analysis, a medium and a processor, and belongs to the technical field of battery charging and discharging memory degree prediction. The method comprises the following steps: extracting a time domain initial feature Z and a space domain initial feature S of a battery charging and discharging process through a residual network; screening time domain key nodes by using a full convolutional network and extracting corresponding spatial domain information; enhancing time-space domain features through a bidirectional long-short-term memory network and a position self-attention mechanism; the features are fused through a one-way interactive attention network and then input into a regression network to predict a memory degree score. The system includes extraction, analysis, enhancement and prediction modules. A computer readable storage medium and a processor may execute the method. According to the method, through spatio-temporal feature joint modeling and key information screening, the battery memory degree prediction precision is improved, effective support is provided for battery health management and charge and discharge strategy optimization, and the method has high engineering application value.

The invention provides a multi-view three-dimensional virtual-real fusion rendering method and related equipment, and relates to the field of computer vision, a target scene is divided into a space-time flow line or local space-time blocks by constructing a geometric field, an appearance field and a space-time flow field on a four-dimensional time-space domain, fragmentation caused by pure frame-by-frame rendering and scene specific rules is avoided, and the rendering efficiency is improved. The problem of multi-scene splitting is solved, and space-time importance distribution at each moment and under each virtual view angle is calculated through the updated feature vector and the attention weight corresponding to the updated feature vector so as to render a geometric field and an appearance field on a four-dimensional time-space domain. And the rendering picture quality is improved on the premise that the total calculation overhead is not obviously increased.

The invention discloses an axle coupling vibration response prediction method based on PIKAN, and the method comprises the following steps: carrying out the discretization of the time-space domain of a bridge and a vehicle according to the structure of the bridge and the running speed of the vehicle on a bridge floor; constructing an axle coupling control equation according to a discrete result; selecting proper internal and external univariate functions, and respectively constructing a KAN1 module used for bridge vertical displacement response prediction and a KAN2 module used for vehicle vertical displacement response prediction; and constructing a residual loss function based on an axle coupling control equation, conditional constraints and real observation data to train and optimize the KAN1 module and the KAN2 module. The method has the beneficial effects that the method provided by the invention has a relatively high calculation rate, has relatively good calculation precision on the premise of considering the calculation efficiency, and can be used as a substitute model for axle coupling finite element numerical simulation. And the bridge vibration response of a heavy-load vehicle not getting on the bridge can be calculated in real time, and the safety risk of the bridge structure is rapidly evaluated.

The invention provides a frequency-wavenumber domain-based matrix array full-matrix capture three-dimensional ultrasonic imaging method and system, and belongs to the technical field of ultrasonic imaging. Five-dimensional time-space domain radio frequency data are collected through a two-dimensional full-sampling matrix array and are converted into a frequency-wave number domain through five-dimensional fast Fourier transform, angular frequency is mapped into axial wave number through Stolt interpolation, data resampling is achieved, then three-dimensional image frequency spectrum data are synthesized by defining spatial wave number coordinates of an image and conducting coherence stacking, and the image frequency spectrum data are obtained. And finally, reconstructing a high-quality three-dimensional ultrasonic image through three-dimensional inverse fast Fourier transform. Compared with a traditional delay summation beam forming method, the method has the advantages that the calculation efficiency is remarkably improved, the speed is increased by several times to dozens of times, the spatial resolution, the contrast noise ratio and the spot signal-to-noise ratio are remarkably improved, and the method is suitable for high-resolution real-time three-dimensional and four-dimensional ultrasonic imaging application.

The invention provides a gas leakage three-dimensional reconstruction method and system based on thermo-optical fusion, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: extracting multi-scale features from a thermal radiation and visible light image sequence, constructing a space-time cause and effect graph based on mutual information fusion, and carrying out the directed message transmission to obtain enhanced node features; and after mapping to a continuous time-space domain, carrying out Fourier neural operator transformation to obtain a characteristic field as a condition to guide iterative denoising, and finally generating a three-dimensional space distribution model of gas leakage. According to the invention, the three-dimensional space distribution of gas leakage can be accurately reconstructed, and the precision and reliability of leakage monitoring are improved.

The invention relates to the technical field of information security, in particular to an AI video detection method based on multi-feature branch fusion, and the method comprises the following steps: extracting a frame extraction color image from a video, and obtaining a standardized frame sequence after preprocessing; calculating an inter-frame differential volume; obtaining a time sequence spectrum volume according to the standardized frame sequence; performing bilateral filtering decomposition on the standardized frame sequence to obtain an illumination consistency volume; respectively inputting the three types of volume features into a deep convolutional neural network, and after feature extraction and fusion, outputting an AI forgery probability through a classifier; and comparing the AI forgery probability with a decision threshold to generate a video category label, and calculating an index. According to the method, the multi-dimensional features of the time-space domain, the frequency domain and the physical illumination domain are fused, the potential traces of the deeply-forged video are effectively captured, the stable detection performance is kept under various video quality conditions, the accuracy is improved, and reliable technical guarantee is provided for media information security.

The invention discloses a self-adaptive optimization method of a time-space domain intelligent sensing collaborative compression algorithm. According to the method, the adaptive coupling matrix can dynamically adjust the time and space feature weights according to the fed-back game conflict, and when the demand conflict occurs between the time compression strategy and the space compression strategy, the matrix can enhance the proportion of key features in a targeted manner, so that information imbalance caused by unilateral optimization is avoided; therefore, the retention capability of the core spatio-temporal information in the compression process is enhanced, the redundancy loss of non-key details is reduced, the compression strategy is enabled to be more fit with the internal law of data, and the adaptive adjustment capability of the algorithm on dynamic scene and resource constraints is enhanced by the linkage mechanism. Game optimization takes the fused feature vector as a decision basis, and the coupling matrix is continuously optimized through conflict feedback, so that the algorithm can respond to scene demand changes in real time, the compression effect stability under different application scenes is effectively improved, the utilization efficiency of system resources is enhanced, and the problem of resource waste or overload is avoided.

The invention provides a light-weight pedestrian motion prediction method based on a mask, and is suitable for the technical field of human-computer interaction. According to the method, human body 3D skeleton point data is processed through space and time masks, key features are extracted by using a local perceptron constructed by a lightweight multilayer perceptron module and a cross-frame fusion device, the features are fused by using 1 * 1 convolution and splicing technologies, then a frequency-space domain pedestrian prediction action sequence is generated through a prediction module, and the frequency-space domain pedestrian prediction action sequence is obtained. And converting into a time-space domain sequence through an inverse discrete cosine converter. The method has the advantages of light model, quick response and suitability for real-time interaction. In addition, the strategy of gradually increasing the number of training frames improves the accuracy and stability of prediction.

The invention discloses an image processing method based on deep learning and Fourier time-space domain transformation, and the method comprises the specific steps: image processing: collecting an original image frame from a video, inputting the original image frame into a convolutional neural network, carrying out the convolution of horizontal and vertical latitudes on an image, outputting an operation result through a convolution kernel, and carrying out the image processing; a modified image is obtained; and carrying out feature extraction through BN operator operation: carrying out normalization processing on a result output by the deep convolution operation, unifying data distribution of different network layers, carrying out data transformation and reconstruction after normalization, and recovering feature information of original data. The BN operator operation is carried out on the result output by the deep convolution operation, the BN operator operation enables the distribution of input data of each layer in the network to be relatively stable, the learning speed of the model is increased, the regularization effect on the model is achieved to a certain extent, and the analyzed result is more accurate.

This invention provides a method and system for vehicular data transmission based on spatiotemporal autonomous load balancing routing, belonging to the field of intelligent network routing technology. The method includes: sending vehicle driving data to a non-congested network relay device; filtering the vehicle driving data by the non-congested network relay device to obtain a reference trajectory path; sensing the location and storage information of neighboring network relay devices and outputting a set of candidate relay devices; calculating the spatiotemporal parameter set of the candidate relay device set, and obtaining the comprehensive data transmission capability of the candidate relay device set based on weights and the spatiotemporal parameter set; updating network status data and the congestion status of network relay devices; and arranging the data transmission order in ascending order according to the remaining lifetime, transmitting the vehicle driving data to the data transmission destination. This invention employs a weighted and spatiotemporal autonomous load balancing routing method to comprehensively evaluate the data transmission capability of network relay devices and select the optimal next-hop network relay device for the data.