100 results about "Image fusion algorithm" patented technology

The invention relates to the technical field of photovoltaic power station operation and maintenance, and discloses a photovoltaic power station intelligent operation and maintenance and fault early warning system. A multi-band image fusion algorithm and a temperature gradient enhancement algorithm are applied to identify composite defects; establishing a defect interaction influence model to analyze the synergistic effect of different defects; constructing a defect evolution prediction algorithm to predict a defect development trend; and developing a defect-performance mapping model and a processing priority algorithm to optimize a maintenance decision. According to the method, the defect detection sensitivity and accuracy are improved, accurate identification and evolution prediction of composite defects are realized, a prospective maintenance decision is effectively supported, the operation and maintenance efficiency of a photovoltaic power station is remarkably improved, the service life of the power station is prolonged, and the safety risk is reduced.

The invention relates to the field of image comparison, and discloses a multi-layer PCB alignment deviation detection system and method based on image comparison, and the method comprises the steps: obtaining high-resolution image data before and after lamination of a multi-layer PCB, carrying out the unified registration preprocessing of an original image through the combination of a multi-mode image fusion algorithm and a geometric distortion correction technology, and obtaining a multi-layer PCB alignment deviation detection result; constructing a standardized image registration input set; key alignment feature extraction is carried out on the image registration input set, and a multilayer structure graph model is constructed based on a graph neural network; in combination with the alignment reference map, dynamically adjusting an image comparison window and a search region by adopting a region attention mechanism and a local adaptive matching algorithm, and constructing an alignment deviation mapping map; constructing a deviation evolution model by using a time sequence behavior recognition network according to the constructed alignment deviation mapping graph and historical process deviation data; and performing comprehensive evaluation on the image registration input set based on the deviation evolution model and the generated early warning information. The method has the advantage of improving the accurate detection level.

The invention discloses a semiconductor packaging defect detection system based on machine vision, and relates to the technical field of semiconductor packaging defect detection, and the system comprises a multispectral collection module which uses a multispectral camera and a light source to collect multiband images of a packaging surface from different angles, and carries out the preprocessing of the multiband images; the image fusion module is used for fusing the preprocessed wave band images into a comprehensive image by adopting an image fusion algorithm; the illumination adjusting module is used for carrying out illumination quality analysis on the comprehensive image, adjusting light source parameters based on a quality analysis result and re-collecting a multi-band image; the method has the beneficial effects that through the comprehensiveness of a multispectral image, the detail enhancement capability of image fusion, the stability of dynamic illumination adjustment, the feature extraction capability of deep learning and the anomaly detection capability of an auto-encoder, the robustness of the image fusion is improved; the defects of unstable image quality, insufficient feature extraction capability, inaccurate error judgment and the like in the prior art are comprehensively overcome.

The invention discloses a territorial space dynamic remote sensing monitoring system and method based on spectral analysis, and relates to the technical field of remote sensing monitoring. A characteristic wave band extraction module; the image fusion and ground object monitoring module is used for fusing the feature data of each wave band by using an image fusion algorithm, identifying the fused ground object type and monitoring the change of the ground object type along with time; and a change monitoring and visualization module. According to the method, the remote sensing images of different spectrum sections are acquired and corrected, so that the accuracy and consistency of data are remarkably improved, the accuracy of a monitoring result is ensured, and a solid data basis is provided for subsequent analysis.

The invention discloses a robot automatic grabbing path planning method based on visual identification, and relates to the technical field of intelligent grabbing. The method comprises the following steps: acquiring multi-view visual data of a target scene, and generating a scene three-dimensional compact reconstruction model through a multi-modal image fusion algorithm; performing target detection and feature extraction on the model, and screening an optimal capture point in combination with a visual attention mechanism; constructing a dynamic environment obstacle probability map, updating an obstacle state through time sequence visual tracking, and quantifying an interference weight; an initial grabbing path is planned based on an improved fast expansion random tree algorithm, path smoothness constraints and robot joint movement limit parameters are introduced, and path nodes are optimized through a Bezier curve; visual servo feedback and path deviation prediction are fused, path parameters are corrected in real time, and a continuous movement track is generated. The method effectively adapts to the dynamic environment, gives consideration to path safety, smoothness and mechanical arm motion characteristics, and remarkably improves the grabbing success rate and operation reliability.

The invention discloses a rock and ore microscopic image splicing method and system based on deep learning, and relates to the field of image processing and the technical field of microscopes, and the method comprises the steps: obtaining a local rock and ore microscopic image of a rock and ore slice, carrying out the preprocessing of the local rock and ore microscopic image, and carrying out the overlapping region coarse registration of the preprocessed local rock and ore microscopic image through a phase correlation method; based on an improved image feature detection model, basic features and description features in the local rock and ore microscopic image after coarse registration are extracted, and local image features of the local rock and ore microscopic image are obtained; performing feature matching on the local image features of the two groups of local rock and mineral microscopic images by using an image feature matching model to obtain a matching corresponding relation of the local image features; and based on an image fusion algorithm of a homography matrix and a partial differential equation, splicing and optimizing the local rock and ore microscopic images in combination with a matching corresponding relation of local image features, and generating a large-view-field rock and ore microscopic image. According to the method, the complex transformation between the images can be better processed.

The invention belongs to the field of application of an image processing technology and an automatic detection technology, provides a nodular cast iron pipe white sprayed code integrity detection method and system, and aims to solve the problems of low nodular cast iron pipe white sprayed code recognition accuracy, poor environmental adaptability, low processing speed, image splicing and the like in industrial production. In combination with an optimized image preprocessing technology, an advanced feature point detection and matching technology and an image fusion algorithm are applied to solve the problem of image seamless splicing. By adopting the method provided by the invention, the error and time cost in a manual identification process can be greatly reduced through prediction, and the automation and intelligence level of a production line is improved. The production efficiency can be improved, the product quality can be ensured, and the increasing industrial automation requirement is met. Finally, the system becomes an important tool for improving the product quality and the production efficiency of nodular cast iron pipe production enterprises.

The invention provides a flue-cured tobacco leaf image distortion correction enhancement method, and belongs to the technical field of flue-cured tobaccos. Distortion segmentation point detection is carried out on an obtained flue-cured tobacco leaf image, and an illumination change area caused by unevenness of a tobacco leaf surface is identified by using an illumination homogenization algorithm based on a Poisson equation; secondly, constructing a distortion jump point matrix to mark gray abrupt change positions caused by shadows and light reflection between adjacent areas, then decomposing and reconstructing multi-scale image features by adopting a Laplacian pyramid image fusion algorithm, and eliminating optical interference generated by surface fluctuation through a designed fusion rule; and finishing processing when the accuracy reaches a set range, otherwise, carrying out correction and optimization again, thereby solving the technical problem that the image quality and the subsequent analysis accuracy are influenced by shadow and light reflection generated during image acquisition due to unevenness of the tobacco leaves in the prior art.

The invention is suitable for the field of intelligent jet cleaning, and provides a dirt recognition cleaning method, device and equipment, and the dirt recognition cleaning method comprises the steps: obtaining an original image corresponding to target dirt and a sound reflection signal corresponding to the target dirt; converting the sound reflection signal corresponding to the target dirt into an image representation; obtaining a fused image corresponding to the target dirt according to the original image corresponding to the target dirt, the image representation and a preset multi-channel image fusion algorithm; inputting a fusion image corresponding to the target dirt into the improved deep neural network to obtain segmentation mask data and size data of the target dirt; adjusting the pose of the dirt cleaning equipment according to the segmentation mask data and a preset coordinate conversion algorithm; according to the size data of the target dirt, dirt cleaning parameters are adjusted, and dirt cleaning equipment is controlled to execute cleaning operation. According to the method, the dirt recognition and cleaning precision and efficiency are remarkably improved, and various cleaning requirements and operation environments can be better met.

The invention discloses a power line identification method, device and equipment based on image fusion and a medium, and the method comprises the steps: obtaining an infrared image and a visible light image of a power line of a power distribution network, and carrying out the registration of the infrared image and the visible light image through a preset image registration algorithm, and generating a first image; wherein the infrared image and the visible light image are derived from the same picture; performing feature extraction and feature fusion on the first image and the visible light image through a preset image fusion algorithm to generate a second image; and performing power line identification on the second image through a preset power line identification algorithm. According to the method, the infrared image and the visible light image which are derived from the same power line picture are fused to obtain the fused image which contains more source information and has more obvious power line characteristics, so that power line recognition is performed, the difficulty of power line recognition is greatly reduced, and the accuracy of power line recognition of the power distribution network is further improved.

The invention relates to the technical field of liquid crystal display screen detection, in particular to a liquid crystal display screen defect intelligent detection method and analysis system based on visual perception, and the method comprises the following steps: obtaining multi-angle image data of a liquid crystal display screen; generating a fused image based on the multi-angle image data; extracting defect features according to the fused image; determining a defect category based on the defect features; according to the method, multi-angle image acquisition and an innovative image fusion algorithm are adopted, so that the visibility of the defects and the comprehensiveness of detection are greatly improved. According to the method, different types of defects can be effectively captured, especially small defects which are difficult to observe at a single angle, and the omission ratio is remarkably reduced. By fusing multi-angle information, a more comprehensive and clear defect image can be generated, and high-quality input data is provided for subsequent analysis.

The invention provides a welding spot quality detection method and system, a binocular camera is combined with an image fusion algorithm to form a three-dimensional image of a welding spot, welding spot feature information is obtained through calculation and is automatically compared with technical standard requirements, the quality of the welding spot is judged, and cause feedback of disqualification is given. The method solves the problem of large manual misjudgment caused by the fact that the welding spot cannot be shot by 360 degrees in all directions in the re-inspection operation after AOI misjudgment, the understanding of the geometric shape of the welding spot can be improved by using an image fusion algorithm, and potential defects can be highlighted. And meanwhile, the welding spot characteristics are automatically compared, so that the detection speed, the detection efficiency and the detection accuracy are improved, and subjective errors possibly caused by manual detection are reduced. In addition, due to the reason feedback function, an inspector and the quality control process are greatly facilitated, the inspector is helped to rapidly position problems and take correction measures, and automation and accuracy of welding spot detection are improved.

The invention provides a power transmission line bird damage risk assessment and early warning method and system, and relates to the technical field of intelligent power grid environment perception and risk early warning, and the method comprises the steps: data collection and multi-modal fusion: collecting image data, voiceprint data, meteorological parameters and ecological environment information along a power transmission line to form multi-modal data, the multi-modal data is uniformly coded by synchronizing timestamps and geographic coordinates, an original bird activity sample set is generated, and for the problems that the monitoring means is single and the recognition precision is insufficient, image, voiceprint, weather and ecological information are fused through a multi-modal data acquisition module, so that the recognition precision is improved. The acousto-optic image fusion algorithm and the two-channel convolutional neural network are adopted, all-weather bird type and behavior accurate recognition is achieved, the recognition accuracy is improved, key behaviors such as nesting and dung pulling can be stably captured even at night or in a foggy environment, and potential risk missing detection is remarkably reduced.

The invention relates to a medical image tuning method, and belongs to the technical field of image processing. According to the invention, a medical image tuning device is adopted; a core board fusion algorithm module automatically identifies the number and pixels of connected cameras, automatically matches a built-in image processing algorithm module, and performs preprocessing through an exposure algorithm module, a denoising algorithm module and a contrast enhancement algorithm module to obtain a single or multiple images as a state 1; extracting image features of the state 1, automatically selecting a single-image algorithm module or a multi-image fusion algorithm module, and obtaining a state 2 through the fusion algorithm module; low-delay and high-pixel transmission is met, image information is completely reserved, a camera host can better complete image processing, and the final image quality is guaranteed; the problem of image information loss caused by image processing at the camera module end is reduced, and the problems of module temperature rise, component damage, influence on imaging quality and the like caused by image processing at the camera module end are solved.

The invention relates to the technical field of image fusion, and provides an infrared and visible light image fusion algorithm based on a hybrid expert system, and the infrared and visible light image fusion algorithm comprises the following steps: S1, carrying out the cross feature extraction of an input infrared image and an input visible light image; s2, inputting a cross feature extraction result into a gating module composed of a full connection layer; S3, selecting two environment categories with the maximum probability value, and executing differential fusion processing in an expert model; S31, splitting an infrared image and a visible light image into local windows, then through a wavelet adaptive transformation module, performing multi-band decomposition on each window and applying an extra weight coefficient to generate a preliminary fusion image; s32, performing edge feature extraction, spot feature extraction and feature fusion processing on the preliminary fusion image to obtain a final fusion image; and S4, performing weighted fusion on the final fusion image through the trainable weight coefficient to obtain a final output image.

The invention provides a soil humidity prediction method and device, electronic equipment and a storage medium, and relates to the technical field of soil humidity prediction.The method comprises the steps that current meteorological data and predicted meteorological data are obtained, the predicted meteorological data comprise meteorological data of N days in the future, and N is larger than or equal to 1; acquiring Landsat remote sensing image data and MODIS remote sensing image data; based on a GEE image fusion algorithm, estimating the Landsat remote sensing image data through the MODIS remote sensing image data to obtain fused remote sensing image data; fusing remote sensing image data to obtain a fused spectral index; and inputting the current meteorological data, the predicted meteorological data and the fused spectral index into a hierarchical soil humidity model to obtain a predicted soil humidity data set. According to the invention, the soil humidity prediction precision is improved.

The invention discloses a high-quality sound and picture synchronous speaker generation method based on GAN optimization, and belongs to the field of speaker generation, and the method comprises the following steps: S1, respectively extracting audio features and image features from an audio file and a video file, and carrying out the preprocessing; s2, establishing an image generation model based on a StyleGAN2 framework, and training the image generation model; s3, inputting the extracted audio features and image features into the trained optimal dialogue model to generate a driving image sequence; s4, fusing the generated driving image sequence with the original image by using an image fusion algorithm, and reconstructing a complete frame image; and S5, coding all the frame images into an output video according to a time sequence. According to the GAN optimization-based high-quality sound and picture synchronization speaker generation method, through optimization of data preprocessing, innovation of model architecture and loss function design, speaker video generation with high quality, stable time and accurate sound and picture synchronization is realized.

The embodiment of the invention provides a dike abnormal target object detection method based on infrared and visible light image fusion, which is used for identifying a dike target object needing to be detected in a dike disaster scene, and comprises the following steps: obtaining an infrared image and a visible light image; registering the infrared image and the visible light image by adopting a feature matching algorithm; fusing the registered infrared image and visible light image by adopting an image fusion algorithm to obtain a fused image; and iteratively executing the following segmentation optimization operations until the calculated value of the target function of the mean shift algorithm is minimum to obtain the image segmentation result of the fused image: segmenting the fused image by adopting the mean shift algorithm to obtain the current segmentation result, determining the calculated value of the target function according to the current segmentation result, and obtaining the image segmentation result of the fused image. And optimizing hyper-parameters of the mean shift algorithm according to the calculated value of the target function. According to the scheme, the embankment target object with a small size can be accurately segmented, and the problem of excessive segmentation can be solved.

The invention discloses an intelligent inspection method and system based on night vision digital glasses. According to the method, a thermal infrared image, a visible light image, depth information and ambient light intensity data of an inspection area are collected in real time through night vision digital glasses. Feature level fusion is carried out on the infrared image and the visible light image by using an image fusion algorithm, depth information is corrected and optimized, and imaging parameters are automatically adjusted according to ambient light intensity data to ensure that the image is clear. Then, a deep learning algorithm is used to carry out real-time analysis on fused image data, a target is identified, and three-dimensional positioning and distance measurement are realized. And in combination with a preset risk model and a decision rule, the risk is evaluated, an acousto-optic early warning is sent to the inspection personnel through the glasses display module when an abnormality is found, and the risk information is transmitted to a remote monitoring center. In addition, inspection data are automatically recorded and an inspection report is generated. The method and the system effectively improve the inspection efficiency and accuracy, and are especially suitable for intelligent inspection tasks in complex environments.

The invention discloses a Tibetan antelope behavior analysis system based on multi-modal image fusion and an optimized transmission method, and belongs to the technical field of image fusion. Through synchronously acquiring an infrared image and a visible light image, performing registration, denoising and normalization processing on the infrared image and the visible light image, and extracting a temperature texture collaborative anomaly index and a spatial dynamic transaction index, the optimal transmission of the Tibetan antelope behavior analysis system is realized; constructing a polynomial regression model for identifying a non-target heat source, and outputting a high-temperature non-target object analysis value; the system divides a high-temperature area in the infrared image into a credible target level, an uncertain level and an interference level, and calculates a fusion interference risk value for the uncertain area in combination with the response strength and weight distribution of an image fusion algorithm; finally, Tibetan antelope behavior recognition is executed based on the fused image, the recognition confidence coefficient is dynamically adjusted in combination with the interference risk, and high-precision and anti-interference recognition of Tibetan antelope real behaviors is achieved. According to the method, the behavior recognition accuracy and ecological monitoring intelligence level of multi-modal image fusion in a complex environment are improved.

The invention discloses an image fusion defect detection method and device and a readable storage medium, and the method comprises the steps: collecting the multi-source image data and laser radar data of a power transformation main device through a plurality of sensors, and enabling the multi-source image data to comprise the defect information of the power transformation main device; preprocessing the multi-source image data, and enhancing the defect information; fusing the preprocessed multi-source image data into a multi-source fused image through an image fusion algorithm, wherein the multi-source fused image is obtained by overlapping different images in the multi-source image data based on weights; analyzing the multi-source fusion image through a deep learning model to obtain defect information; and generating a three-dimensional model of the power transformation main equipment based on the multi-source image data and the laser radar data, and superposing the defect information on the three-dimensional model based on augmented reality (AR). The defect detection equipment can comprehensively detect the power transformation main equipment from different dimensions, and it is ensured that no potential defect is missed.

The invention discloses a field operation portable low-light puncture infusion guide system which comprises a multispectral imaging module used for collecting an image containing a plurality of spectral bands by utilizing the penetrating power and reflection characteristic difference of light rays with different wavelengths to human tissues; the near-infrared blood vessel sensor is used for acquiring the contour and the position of a blood vessel; the low-light sensor is used for converting the optical signal into an electric signal based on a photomultiplier or a CMOS (Complementary Metal Oxide Semiconductor) low-illumination imaging technology and generating a low-light image through amplification and noise reduction processing; the image fusion processor is used for carrying out image fusion on the blood vessel image and the low-light-level image based on a feature-level or pixel-level image fusion algorithm; the AR display module is used for superposing a virtual image which is generated by image fusion and contains the position and the direction of the blood vessel into a real scene; the method has the advantages that the multispectral imaging technology and the low-light sensing technology are combined, richer tissue information is obtained, the limitation of a single imaging mode in the low-light environment is solved, and the blood vessel visualization effect is improved.

The invention discloses an intelligent multimode concrete microstructure lossless perspective analyzer which comprises a bottom plate and moving wheels mounted at four corners of the outer wall of the bottom of the bottom plate, and further comprises a lifting assembly mounted on the outer wall of the top of the bottom plate, a box body is mounted at the top of the lifting assembly through bolts, and overturning notches are formed in the outer walls of the two sides of the box body. The intelligent multi-mode concrete microstructure lossless perspective analyzer can clean and polish the surface of a concrete wall before detection, can effectively reduce interference, ensures the accuracy of a detection result, and detects the internal structure of concrete from different angles by fusing X-ray, radar wave and ultrasonic technologies and complementing the advantages of the technologies; comprehensive and accurate detection is achieved, an X-ray image, a radar wave image and an ultrasonic image are fused through an image fusion algorithm, a composite image containing more information is obtained, and observation and analysis of detection personnel are facilitated.

The application discloses an infrared and visible light image fusion algorithm based on a self-encoder, and comprises the following steps: S1, an encoder network is constructed by combining a short cross-layer connection with an ECA attention mechanism; S2, registered infrared and visible light images are input into the encoder network, and the encoder and decoder network are trained to obtain an encoder meeting a condition; S3, feature fusion is performed on the input images by using the constructed fusion network, and the fusion network is trained to obtain a fusion network meeting a condition; and S4, the fused images are decoded and reconstructed by using the decoder.The evaluation index of the fused images is significantly improved, the target is clear, the details are prominent, the outline is obvious, and the images meet the human visual perception.

This invention discloses a visual inspection method and apparatus for glass production quality, relating to the field of glass quality inspection technology. The method includes the following steps: constructing a polarization excitation detection field; acquiring and registering a sequence of original polarization images of the glass based on the polarization excitation detection field to obtain a polarization image sequence; performing image fusion on the polarization image sequence using an image fusion algorithm based on non-subsampled contour wave transform, sparse representation, and guided filters to obtain a defect-enhanced image; inputting the defect-enhanced image into a lightweight glass defect recognition model based on improved self-attention, outputting a glass defect category, acquiring glass defect quality impact data, and classifying glass quality grades based on the glass defect category and the glass defect quality impact data, thereby achieving visual inspection of glass production quality.

The invention relates to the technical field of electrical equipment defect detection, in particular to an electrical equipment defect detection method and system based on a multi-modal image, and solves the problem of information asymmetry in traditional defect detection through cooperative processing of an RGB image and a thermal infrared image. The RGB image provides the surface information of the equipment appearance, the thermal infrared image reflects the temperature distribution of the equipment, and the RGB image and the thermal infrared image are effectively combined through an image fusion algorithm, so that the detection precision and comprehensiveness are improved, and a blind area of a single image mode is avoided; the thermal infrared image is optimized through texture and edge features of the RGB image, so that hot spot positioning is more accurate, and false alarm and missing alarm are reduced; through an efficient image processing algorithm, a large amount of image data can be processed in real time, the detection efficiency is remarkably improved, the detection time is shortened, and online monitoring and real-time early warning of equipment are effectively supported, so that the maintenance cost of the equipment is reduced, and the operation reliability of the equipment is improved.

The present invention discloses a multi-exposure image fusion method based on pyramid convolution, comprising the following steps: Step 1: splicing overexposed and underexposed images by channel and preprocessing them to obtain a system input; Step 2: sending the system input to a decorrelation module to eliminate channel-level and pixel-level correlations in the data; Step 3: performing feature fusion on the output of Step 2 through an encoder and mapping it to a high-dimensional data space; Step 4: performing dimensionality reduction on the fused high-dimensional data through a decoder, reconstructing the fused image, and completing multi-exposure fusion. The multi-exposure image fusion method of the present invention can improve halos and artifacts when fusing extreme lighting scenes, enhance the generalization ability of the multi-exposure image fusion algorithm for extreme scenes, and is widely used in scenes such as underwater and nighttime observation.

The invention provides an infrared and visible light image fusion method based on a second-order attention mixed feature, and the method comprises the steps: inputting the first-order and second-order statistics of an infrared image and a visible light image, and adaptively integrating the global features and local features of the infrared and visible light images, thereby enabling the fused image to obtain a stronger feature expression, and improving the image fusion precision. Pixel intensity and texture details are reserved at the same time, comparable contrast is kept while a more vivid edge is achieved, an output result keeps rich edge and texture information transmitted by an input image, artifacts are few, and visual perception of human beings is better met. Compared with other infrared and visible light image fusion algorithms based on deep learning, the method has a better fusion effect.

The invention relates to the technical field of image acquisition, in particular to a device and a method for acquiring a reflection-removing image on the surface of a high-reflection injection molded part, and the acquisition device comprises a multi-camera assembly, a light source adjusting assembly, a workpiece moving assembly and a control and processing module; the method comprises the steps that the distance between a multi-camera assembly and a workpiece, the distance between a light source adjusting assembly and the workpiece and the angle of a strip-shaped light source in the light source adjusting assembly are adjusted, the situation that strong reflected light is directly received by cameras is reduced, and a workpiece moving assembly is started to drive the workpiece to move; in the moving process of the workpiece, the multi-camera assembly synchronously collects injection molding part surface images at different visual angles, and an image sequence containing complementary information is obtained; according to the invention, a multi-view complementary image is provided for an image fusion algorithm through the multi-camera assembly and the workpiece motion assembly through a strategy of combining a mechanical structure design and a software algorithm, more image details are reserved while a reflective region in the image is removed, and the final image quality is improved.

The application is a kind of vehicle-in-the-loop test method based on visual augmented reality. First, the positioning and motion information acquisition system of the real vehicle is established, and the high-precision motion information of the vehicle can be obtained. Then, the image information and motion information of the vehicle-mounted camera are synchronously collected, and the purpose is to obtain the camera output and vehicle motion information under the same time stamp. Secondly, through the internal parameters and initial external parameter information of the camera, the pose of the camera at each moment is calculated by combining the synchronously collected motion information. The calculated pose is synchronously input into the simulation software to obtain the virtual camera output consistent with the real camera in time and space. The real camera image collected is taken as the background, and the traffic participants provided by the virtual simulation camera are taken as the foreground. Through the image fusion algorithm, the fusion image information of the augmented reality perspective is obtained. Finally, the fusion image is input to the perception layer, the perception result is calculated and input to the decision control layer, and the decision instruction is output to the execution mechanism of the vehicle, so that the vehicle motion information is changed and collected by the high-precision information acquisition system, thereby completing a cycle of information.