40results about How to "Improve legibility" patented technology

The invention discloses a polarization degree and light intensity image fusion method based on bidirectional cross attention. The method belongs to the technical field of polarization imaging, computational imaging and image processing. The technical problems that in the prior art, dynamic balance between structural detail keeping and polarization saliency enhancing is generally difficult to achieve, and particularly under the condition of a complex background or a low signal-to-noise ratio, texture missing or noise artifacts are prone to occurring in a fusion result are solved. A fusion mechanism capable of establishing a two-way information interaction relationship between a light intensity image and a polarization degree image is provided, and further processing is carried out aiming at a noise problem, so that more stable and more reliable polarization fusion imaging is realized.

The invention discloses a low-sodium myofibrillar protein flavor prediction method and a low-sodium myofibrillar protein flavor prediction device. The method comprises the following steps: firstly, constructing a protein-flavor interaction system containing myofibrillar protein, potassium chloride, kappa-carrageenan and representative aldehyde flavor compounds; then, by means of spectroscopy, rheometer combination, an electronic tongue and the like, protein conformational change, typical aldehyde molecule binding rate and taste features are represented in a multi-dimensional mode. And establishing a random forest prediction model, taking protein conformation parameters as input, and taking a flavor binding rate and effective taste intensity as output. The model can reveal cross correlation between protein conformations and functions and identify key conformation parameters that affect flavor. And in combination with quantification and prediction capabilities of the model, a conformation-flavor relationship can be systematically analyzed, a basis is provided for optimization of a low-sodium formula, flavor is enhanced while salty taste is increased and bitter taste is inhibited, and healthy and sustainable development of low-sodium meat products is promoted.

This invention discloses a method and system for simulating and dynamically updating a seepage field throughout the entire process. The invention divides the entire process into several stages using an engineering event table, updates the geometric state, boundary conditions, and source / sink terms of the seepage numerical calculation model according to each stage, and performs unsteady seepage forward modeling. The current stage uses the seepage field state at the end of the previous stage as the initial condition, and the initial values ​​of the parameters to be inverted are inherited from the optimal parameters calibrated in the previous stage. An objective function is constructed based on head and leakage observation data, and an adaptive trust region quasi-Newton optimization method is used for dynamic parameter calibration. The contribution ratio of each supply source to the leakage is calculated using a source-by-source activation / superposition decomposition method. This invention achieves continuous simulation of the seepage field throughout the entire process, dynamic parameter calibration, and quantitative diagnosis of leakage sources, improving simulation accuracy and interpretability.

A method for assessing protein uptake from a subject's gut includes: a. providing a blood sample from the subject; b. substantially removing proteins exceeding a size limit of 40 kDa from the sample to provide a deproteinized sample; c. determining the level of free amino acids in a subsample of the deproteinized sample CF by colorimetry or fluorescence; d. hydrolyzing the subsample of the deproteinized sample to obtain a hydrolyzed sample, which converts any remaining polypeptides and peptides into free amino acids; e. determining the level of free amino acids in the hydrolyzed sample CT by colorimetry or fluorescence; f. optionally, calculating the level of peptide-derived amino acids in the blood sample CP, where CP = CT - CF; and g. assessing protein uptake from the subject's gut based on the values ​​of CF and CT, and optionally CP. Related applications include diagnostic and therapeutic methods.

The application relates to the technical field of motor assembly quality image detection, and discloses a visual-based motor wire harness connection part abnormality detection method and system, wherein the method comprises the following steps: acquiring a motor wire harness connection part image; constructing a connection part illumination state vector; performing adaptive brightness correction; suppressing reflection and pollution interference; determining an effective wire harness connection part area; and outputting a wire harness connection abnormality detection result. Compared with the method in the prior art which mainly relies on single detection confidence for judgment, especially under complex working conditions such as strong reflection of a metal shell, oil stain adhesion and wire harness shadow superposition, the technical problem that it is difficult to stably distinguish between real structure abnormality and illumination artifact interference is solved. Due to the introduction of adaptive brightness correction and the joint mechanism based on attention enhancement target detection and assembly geometric constraint, the accuracy of motor wire harness connection part abnormality detection is improved.

The application discloses a dark underwater image processing method based on color space conversion, which comprises the following steps: firstly, pre-acquired underwater images are down-sampled to reduce the image size and copied into two images; secondly, color space conversion is performed on the two parts of underwater images respectively; thirdly, an underwater optical imaging image model is established, and the first converted underwater image is processed by using an image deblurring algorithm to obtain a clear image with high contrast; fourthly, an improved white balance algorithm is used to process the deblurred image; fifthly, a nonlinear channel prior is introduced into a traditional image deblurring model to minimize the item to eliminate the motion blur problem caused by water flow fluctuation; sixthly, the obtained accurate blur kernel and potential clear image are iterated with the initial restored image to obtain a final restored image; and finally, the two images are fused, re-sampled and outputted to obtain a final clear underwater image. The application can better realize image restoration, improve the brightness and contrast of the image and improve the recognition degree.

The invention relates to the technical field of industrial robot load identification, in particular to a six-degree-of-freedom robot end load parameter identification method, which comprises the following steps: establishing a six-degree-of-freedom series robot dynamical model, reconstructing a load minimum parameter set, and determining that identification can be completed only by exciting a third joint, a fifth joint and a sixth joint. Designing four groups of symmetric excitation trajectories combined by Fourier series and quintic polynomials, and performing discrete sampling to generate an observation matrix; optimizing a load parameter set and a regression matrix, and reducing the condition number of an observation matrix; a load minimum parameter set is obtained through difference calculation of the no-load identification result and the on-load identification result; and based on the parameter set, solving the mass, the mass center coordinate, the inertia moment and the inertia product of the load. The method solves the ill-conditioned problem of a regression matrix by reconstructing the minimum parameter set, reduces the number of excitation joints, does not need to depend on a robot body kinetic model, and has the advantages of high identification precision, high stability, low experiment complexity and high efficiency.

The present specification provides a point cloud processing method, an electronic device and a program product, acquiring a point cloud collected by a point cloud collection device and an image collected by an image collection device, wherein the collection range of the image matches the collection range of the point cloud. The first pixel position of the target object in the image is obtained, and according to the relative position relationship between the two collection devices, the target point cloud matched with the first pixel position is determined from the point cloud as the point cloud of the target object, thereby completing the process of labeling the target object in the point cloud. Since the image collected by the image collection device is more intuitive than the point cloud data and has higher recognizability, the image with the labeled target object is used to assist the point cloud in labeling the target object, which can effectively reduce the difficulty of direct labeling in the point cloud and improve the labeling accuracy and the accuracy of the high-precision map constructed.

The invention discloses a pipeline abnormal state recognition and positioning method and an inspection robot thereof, relates to the technical field of machine vision recognition, and can solve the problems of low recognition precision and high false detection and omission ratio of structural posture abnormity such as falling, inclination and hanging drooping of an underground coal mine pipeline in the prior art. The pipeline abnormal state identification and positioning method comprises the following steps: S1, constructing a global map of a coal mine tunnel, and establishing a coordinate corresponding relation between an image acquisition position and the global map; s2, acquiring a pipeline image in the roadway and position information corresponding to the pipeline image, and performing noise reduction processing on the pipeline image to obtain a to-be-identified image; s3, judging whether the pipeline is in an abnormal posture state or not, and outputting a pipeline abnormity recognition result; and S4, according to the position information corresponding to the pipeline abnormity identification result, completing the positioning of the abnormal pipeline.

This invention relates to the fields of medical data analysis and artificial intelligence, proposing a dual-latent variable decoupled data analysis method based on plasma Netrin-1 and clinical scale features. First, plasma Netrin-1 test data and clinical characteristics such as UPDRS scale scores and disease duration are acquired from the subjects. Batch effect correction is applied to the raw test values, and an input feature vector is constructed. Then, a variational autoencoder model is established. By setting orthogonal constraints, Netrin-1 regression constraints, and disease duration regression constraints, the latent space is structurally constrained, enabling the model to learn latent variables D representing disease progression and C representing adaptive changes during training. Based on this, an efficiency index η composed of latent variables C and D is calculated, and relevant trend indicator parameters are obtained by combining longitudinal follow-up data. This method can achieve decoupled representation of disease-related features and adaptive change features in multi-source clinical data, providing a new data analysis tool for statistical analysis and research of neurodegenerative disease-related data.

The invention discloses a low-illumination image dynamic noise reduction method for a daytime effect night vision device based on image recognition, and the method comprises the following steps: obtaining a low-illumination image sequence, and determining a current frame as a target frame; performing noise level estimation and image recognition on the target frame to obtain a noise parameter and a recognition confidence map; generating an initial edge set based on the target frame, and determining an initial edge weight; performing semantic boundary constraint reconstruction on the initial edge set, and outputting a reconstructed edge set; performing Felzenszwalb graph segmentation on the reconstructed edge set, sorting the reconstructed edge set according to the reconstructed edge weight, taking edges in sequence according to the sorting sequence, and outputting a final region label graph and a segmented region set according to a merging criterion; determining a region-level noise reduction intensity parameter for each region, and generating a noise reduction control chart; and performing regional dynamic noise reduction processing on the target frame according to the noise reduction control chart to obtain a noise reduction output frame. According to the method, the target edge and detail fidelity is improved, and the excessive smoothing risk of cross-semantic boundaries is reduced.

The application discloses a kind of multi-instrument collaborative synthesis method and related equipment based on MATLAB.The method comprises the following steps: setting standard sampling frequency, and constructing time vector corresponding to note duration based on the standard sampling frequency, establishing the frequency mapping relationship of scale covering the preset sound range, and determining the target fundamental frequency based on the input pitch parameter;For at least two target instruments, respectively call the acoustic parameter configuration corresponding to the target instrument, generate the single-instrument synthesis signal of the target instrument based on the target fundamental frequency;The audio signals of each target instrument are aligned on the multi-track time axis, and the audio signals of each target instrument are mixed based on the alignment result to generate a multi-instrument collaborative synthesis audio signal.Various solutions on the market can solve the problem that the operation threshold is high, lacks a unified integrated framework, and restricts the industry innovation and upgrading.

The application relates to a motorcycle and a muffler thereof, a catalyst deterioration identification method and a storage medium. The muffler of the motorcycle comprises a muffler body, a first catalyst, a front oxygen sensor and a rear oxygen sensor. An air inlet of the muffler body is provided with a pipeline for communicating with an exhaust port of an engine. The first catalyst is arranged on the pipeline. The front oxygen sensor is located upstream of the first catalyst. The rear oxygen sensor is located downstream of the first catalyst. The interval between the central axis of the front oxygen sensor and the central axis of the rear oxygen sensor is S, S is 350mm-480mm; the volume of the first catalyst is B, the displacement of the engine is V, and the volume of the pipeline region corresponding to the central axis of the front oxygen sensor and the central axis of the rear oxygen sensor is C, B is 0.8V-1.4V, and C is 1.1V-2.1V. If the first catalyst does not deteriorate, the rear oxygen electric signal curve is a straight line or substantially a straight line, that is, the rear oxygen electric signal has a small number of flip times. Therefore, the identifiable degree can be improved, and the judgment accuracy is high.

This application provides a 3D Gaussian rendering optimization system and method based on CPU hierarchical splitting and GPU driving, relating to the field of 3D graphics rendering technology. This application divides the 3D Gaussian scene into multiple bounding boxes on the CPU side, determines the view distance level, protection level, and reconstruction cost level of each bounding box, and uses the highest of these three levels as the lower limit of the target level. The scheduling module adjusts the current rendering level frame by frame, determining the summary state, partial unfolding mode, or unfolded state based on screen error and GPU budget. On the GPU side, representative Gaussian points and unfolded 3D Gaussian points are uniformly sorted and rendered, and the frame rate and the total number of activated 3D Gaussian points are fed back. This reduces the amount of data submitted, takes into account the protection of key areas, achieves smooth hierarchical transitions, and forms a rendering feedback loop, improving the frame rate stability and resource utilization of real-time rendering.

The invention discloses a gray cast iron casting surface pore and crack defect multi-angle visual fusion detection method. The method comprises the following steps: synchronously acquiring visible light images and thermal imaging sequences of a casting surface from a plurality of preset angles; performing oil stain area identification and illumination enhancement processing on the visible light image, and extracting a thermal radiation abnormal image from the thermal imaging sequence; respectively processing the heat radiation abnormal image and the enhanced visible light image through a double-branch feature extraction network, and performing adaptive fusion to generate an initial weight image; screening pore candidate areas based on the compactness; and performing three-dimensional shape reconstruction on the initial weight image according to morphological characteristic differences of the pore candidate areas at different angles, positioning a suspected defect area, positioning an independent defect according to a local normal convergence focus of a pixel point, and outputting a defect identification result. According to the method, the limitation of a single view angle is overcome, and the image quality and the identification degree of defect characteristics are remarkably improved, so that the accuracy of defect positioning and the fineness of type judgment are improved.

The invention discloses a preparation method of stable photochromic red luminescent europium ion doped zirconium oxide ceramic and a product thereof, 2-6 mol% yttrium oxide stabilized zirconium oxide (2-6YSZ) is used as a raw material, an Eu (NO3) 3 solution is used as a coloring ion solution, and Eu < 3 + > ions are doped to obtain the stable photochromic red luminescent europium ion doped zirconium oxide ceramic. And the stable photochromic red luminescent zirconia ceramic with a stable single-phase structure is successfully prepared through a high-temperature sintering process. The zirconia ceramic not only can generate a relatively fast photoluminescence phenomenon and has obvious color conversion, but also has relatively strong photoinduced red luminescence performance. The product obtained by the invention has wide application prospects, and can be applied to intelligent terminal equipment, such as polychromatic photochromic luminescent ceramic backboards, information storage, encryption, ultraviolet detection, temperature monitoring and the like.

The invention discloses a preparation method of multicolor ion-doped zirconia ceramic with photochromic characteristic and a product thereof, 2-6 mol% yttria-stabilized zirconia (2-6YSZ) is used as a raw material, one or a combination of Eu (NO3) 3, Er (NO3) 3, Pr (NO3) 3, Nd (NO3) 3, Ce (NO3) 3, Ni (NO3) 2, Co (NO3) 2, Fe (NO3) 3 and Al (NO3) 3 is used as a coloring ion solution, rare earth or transition metal ions are doped, and the multicolor ion-doped zirconia ceramic with photochromic characteristic is obtained. And the multi-color and photochromic zirconia ceramic with a stable single-phase structure is successfully prepared through a high-temperature sintering process. The zirconia ceramic not only can generate a relatively fast photoluminescence phenomenon and has obvious color conversion, but also has relatively strong polychromatic photoluminescence performance. The product obtained by the invention has wide application prospects, and can be applied to intelligent terminal equipment, such as polychromatic photochromic luminescent ceramic backboards, information storage, encryption, ultraviolet detection, temperature monitoring and the like.

The invention discloses a vehicle-mounted display UI interface design method based on equipment state information. The method comprises the following steps: S1, acquiring and classifying equipment data; s2, performing standardized graphic design on different equipment data; s3, designing a display interface template and an interaction mode according to the application scene; s4, displaying a data application on an interface; information is organized by taking the overall operation scene of the train and the operation requirement of a user as the center, and the most relevant and most comprehensive system information is automatically displayed on an interface under different scenes such as normal operation, troubleshooting and testing.

This invention relates to a method for mobile vision detection of construction workers' protective gear. The method uses YOLO11m as the basic architecture to build a detection model. The backbone of the detection model includes an SPFF module, and a reflection-sensitive feature channel is introduced at the end of the backbone. The head of the detection model incorporates a conditional attention mechanism, which activates local attention weights in the region below the safety helmet in the image when a safety helmet is detected. The SPFF module can fuse deep semantics and shallow detail features for small targets at 1K+ resolution through multi-scale dilated convolution and channel attention, overcoming the bottleneck of traditional FPN in losing features of small targets and significantly enhancing the feature representation capability of small targets.

The invention discloses a cryoablation needle target area frosting state identification method based on image processing, and relates to the technical field of cryoablation needles. Constructing a target area original image sequence under the unified space-time coordinate system; outputting corresponding degradation stage information; obtaining a high-fidelity target region clear image sequence; aggregating the signal subspace features through a channel gating mechanism to obtain a signal enhancement feature image set; generating a target area multi-scale semantic feature tensor; outputting an initial frosting probability distribution diagram of the target area; performing form constraint segmentation optimization to obtain a frosted area mask with a consistent form; and a time sequence stable frosting area mask sequence is obtained, a frosting state dynamic evolution model is constructed, and early warning information is displayed in real time. According to the method, the problem of feature submerging caused by mixed noise is effectively inhibited, and the recognizable degree and stability of bottom layer structure information of a target area are remarkably improved in a low-temperature imaging environment.

The invention provides a multi-mode sensing anomaly detection and acousto-optic interaction method and system for process level equipment, and relates to the technical field of industrial automation, energy consumption fluctuation data and wireless communication quality data of a process level equipment cluster are collected, an original operation data set is formed by combining equipment identification, and equipment association identification is marked for the original operation data set; the method comprises the following steps: forming an associated data set, adjusting and verifying an initial filtering gain parameter of a self-adaptive filtering circuit, then determining a target filtering gain parameter, carrying out signal filtering on the associated data set, finally generating an equipment operation link, identifying abnormal equipment with parameters deviating from a normal range in the link, generating an equipment abnormity judgment result, and determining an alarm level. Corresponding alarm audio signals, optical signals and ultrasonic carriers are generated; the characteristic parameters are modulated to ultrasonic carrier waves, and directional acousto-optic prompting of an abnormal area is completed in combination with optical signals, so that accurate identification of implicit anomalies under complex working conditions is realized, and intelligence and precision of anomaly detection and acousto-optic prompting are realized.

The application provides a biomass estimation method and device for aquaculture water and electronic equipment. The method comprises the following steps: acquiring a continuous dynamic sonar image sequence collected by an imaging sonar; performing signal enhancement preprocessing on the sequence; inputting the enhanced sequence into a deep learning detection model to identify and output fish body targets and their positions in each frame; based on the detection results of multiple continuous frames, performing cross-frame association and tracking on the fish body targets, and using a time-weighted fusion strategy for deduplication to obtain time-series fish school data; based on the time-series data, performing regional fish school quantity statistics on the aquaculture water, and fusing and estimating the global fish school quantity; finally, based on the fish school quantity and the fish body individual mass estimation result, the total biomass of the aquaculture water is calculated. The application realizes non-contact and automatic global estimation of the fish school biomass of the aquaculture water, which is beneficial to improving the refinement and decision-making efficiency of aquaculture management.

The invention discloses a rapid direct positioning method and device based on deep structured tensor completion, and the method comprises the steps: carrying out the discretization of a target state space of a to-be-positioned target, and constructing a high-dimensional discrete grid frame corresponding to a high-order target positioning likelihood tensor; performing sparse sampling on the high-dimensional discrete grid frame to obtain a sparse observation tensor; performing structured decomposition on the sparse observation tensor through a preset deep structured tensor complementation network to obtain a main peak structure representing a real state of the target and a background structure representing environmental interference; and determining a target state estimation value of the to-be-positioned target based on the maximum index of the factor vector of the main peak structure. According to the method, full-grid exhaustion search is not needed, and on the premise of keeping DPD physical structure characteristics and positioning precision, the calculation complexity is remarkably reduced, the likelihood value calculation total amount is reduced, and the positioning robustness and generalization ability are improved.

The invention relates to an industrial small target defect detection method and system based on ESA-YOLO, and belongs to the technical field of industrial defect detection. According to the method, structure optimization is carried out on the basis of a YOLOv8n network, and an edge feature interaction module EFIM is designed and constructed to enhance the feature expression ability and improve the perception effect of a model on a defect area; an agency attention mechanism AGAttn is introduced behind an SPPF structure in the backbone network, and feature selection and noise suppression capabilities are enhanced; and a feature enhancement alignment module FEAM is constructed, and multi-scale information fusion is optimized by combining deep and shallow features. Compared with other existing methods, the method has the advantages that the real-time detection speed is guaranteed, meanwhile, the detection effect on the defect small target is improved, the detection precision is improved by 5.6% and 4.6% on a steel surface defect detection data set and a printed circuit board defect data set respectively, and the method is suitable for defect detection application scenes in industrial scenes.

The utility model relates to the technical field of doorplates, in particular to a stand column type light-emitting doorplate which comprises an aluminum back plate, a transformer, a light-emitting diode (LED) lamp strip, black bricks, side door plates, an acrylic main doorplate, a top plate, a bottom plate and an L-shaped connecting plate, the aluminum back plate and a wall body are fixed through a first fastener, and the transformer is installed in the center of the front surface of the aluminum back plate. The LED lamp strip is installed on the front side of the transformer, the black bricks are installed on the two sides of the front surface of the aluminum back plate respectively, the side door plates are fixed to the outer surfaces of the black bricks, and the acrylic main door plate is installed on the front sides of the two black bricks. Compared with the prior art, the LED is arranged inside, and the black bricks are arranged on the two sides, so that lamplight is conveniently gathered and emitted from the direction of the acrylic main doorplate on the front side, and the distinguishing degree of the doorplate at night is improved; a stand column type structure is adopted, stainless steel side door plates are arranged on the two sides, room numbers can be checked from different angles in the daytime, and the distinguishability of the door plates is improved; and the upright post type integral structure is matched with lamplight, so that the decorative effect is improved, and the use in different spaces and scenes is met.

The invention provides a high-pressure gas underwater micropore release experimental device and method for combined measurement of bubble dynamics and gas components, relates to the technical field of underwater two-phase flow visualization and measurement, and is used for combined measurement of bubble dynamics and gas components. The device comprises a high-pressure gas supply system, a transparent experimental water tank, a seven-rod-bundle micropore release assembly, a positioning support, a data acquisition system, a high-speed photography system, an ultrasonic detection system, a gas acquisition analysis system and a PBM local bubble monitoring system, an inverted funnel is arranged below the water surface to form a covering gas cavity, double gas paths are used for realizing online measurement of gas components and cavity pressure maintenance respectively, and sampling disturbance is reduced. The method comprises the following steps: acquiring multi-source data such as void fraction, bubble size and particle size distribution through ultrasound, high-speed photography and a PBM system, fusing the multi-source data with a mass spectrum component volume fraction time sequence, and inverting a molar release rate and an accumulated release amount; tracer gas is introduced to enhance the mechanism identification degree. The scheme is suitable for micropore blow mechanism research and serious accident source item inversion model verification.

The application discloses a water depth inversion method based on SDW-LPT multi-temporal fusion remote sensing image, and particularly relates to the field of water depth inversion. The original image of Sentinel-2 is subjected to radiation correction to eliminate system error, the filter window is adjusted according to the registration error, the filter weight is optimized in combination with the window size and different band characteristics, the image preprocessing is completed, and NDWI is calculated. Based on NDWI, the preprocessing image is subjected to cloud mask and land mask operation, ICESat-2 photon points are projected to the coordinate system consistent with the Sentinel-2 image, isolated noise photons are removed through DBSCAN density clustering, and underwater terrain photons are extracted, the standardized reference water depth aligned with each pixel is obtained through refraction and tide correction, and the training set and the verification set are divided according to the spatial distribution and the water depth range. The SDW-LPT method is used for multi-temporal image fusion, and multiple models are used for water depth inversion.

This invention discloses a loosely coupled sentiment analysis algorithm library based on the Ascend framework. The library includes: algorithm modules, exposed interfaces coupled to the algorithm modules, and a unified calling interface coupled to the exposed interfaces. The algorithm modules are used to build and train the algorithms in the library. Each algorithm module includes a data module, a model module, and a training module. The exposed interfaces provide a calling interface for each algorithm to the unified calling interface. The unified calling interface includes hyperparameter configuration information and calling logic. This invention builds the algorithm library based on the domestic Ascend framework. By preserving the integrity of each algorithm, it reduces the coupling between different algorithms, allowing each algorithm to be self-contained while achieving unified scheduling through an exposed interface. This results in a cross-platform, unified architecture, unified calling interface, and easily extensible algorithm library.

This invention provides a method for suppressing and enhancing infrared image defects and thermal noise, relating to the field of image enhancement. The method aims to construct thermal target protection weights and data fidelity weights by normalizing, performing local statistics, and estimating gradient and fringe principal directions on the original infrared image. The observed image is decomposed into structural components, point-like defective pixel components, structured thermal noise components, and low-frequency thermal bias components. Through a thermal consistency-guided defective pixel weight generation mechanism, direction-aware structured thermal noise separation, background-gated low-frequency thermal bias separation, anisotropic edge-preserving enhancement for thermal target protection, and multi-scale local thermal contrast preservation, a joint optimization solution is achieved, and the enhanced image is output after local thermal contrast remapping. This method effectively suppresses defective pixels, fringe noise, and background thermal bias, reduces the risk of accidental deletion of small thermal targets, maintains the target outline and the thermal difference between the target and the background, and improves the clarity, recognizability, and engineering applicability of infrared images.

This invention discloses a method and system for automatically adjusting the brightness and hue of a vehicle screen. The method includes: acquiring image signals of the external environment and the cockpit environment from cameras on the vehicle; calculating the target instrument panel screen brightness based on the image signals from the cameras; obtaining the three primary color ratios from the image signals of each camera; fusing the three primary color ratios of all cameras to calculate the target three primary color ratio; obtaining the target chromaticity adjustment value based on the target three primary color ratio; and adjusting the instrument panel screen using the target brightness and the target chromaticity adjustment value. This solution utilizes the vehicle's built-in cameras to comprehensively judge the ambient light outside the vehicle and the local lighting inside the vehicle, providing a truly comfortable and eye-protecting display effect, and ensuring that the brightness and hue of the instrument panel screen are synchronized and accurately adjusted.